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title: Obese asthma phenotypes display distinct plasma biomarker profiles
authors:
- Sophia Björkander
- Susanna Klevebro
- Natalia Hernandez‐Pacheco
- Maura Kere
- Sandra Ekström
- Maria Sparreman Mikus
- Marianne van Hage
- Anna James
- Inger Kull
- Anna Bergström
- Jenny Mjösberg
- Christopher Andrew Tibbitt
- Erik Melén
journal: Clinical and Translational Allergy
year: 2023
pmcid: PMC10032201
doi: 10.1002/clt2.12238
license: CC BY 4.0
---
# Obese asthma phenotypes display distinct plasma biomarker profiles
## Abstract
### Background
Obese asthma is a complex phenotype and further characterization of the pathophysiology is needed. This study aimed to explore inflammation‐related plasma biomarkers in lean and overweight/obese asthmatics.
### Methods
We elucidated levels of inflammation‐related plasma proteins in obese asthma phenotypes in the population‐based cohort BAMSE (Swedish: Children, Allergy, Milieu, Stockholm, Epidemiology) using data from 2069 24‐26‐year‐olds. Subjects were divided into lean asthma ($$n = 166$$), lean controls ($$n = 1440$$), overweight/obese asthma ($$n = 73$$) and overweight/obese controls ($$n = 390$$). Protein levels ($$n = 92$$) were analysed using the Olink Proseek Multiplex Inflammation panel.
### Results
Of the 92 included proteins, 41 were associated with lean and/or overweight/obese asthma. The majority of proteins associated with overweight/obese asthma also associated with overweight/obesity among non‐asthmatics. Beta‐nerve growth factor (BetaNGF), interleukin 10 (IL‐10), and matrix metalloproteinase 10 (MMP10) were associated only with lean asthma while C‐C motif chemokine 20 (CCL20), fibroblast growth factor 19 (FGF19), interleukin 5 (IL‐5), leukemia inhibitory factor (LIF), tumor necrosis factor ligand superfamily member 9 (TNFRSF9), and urokinase‐type plasminogen activator (uPA) were associated only with overweight/obese asthma. Overweight/obesity modified the association between asthma and 3 of the proteins: fibroblast growth factor 21 (FGF21), interleukin 4 (IL‐4), and urokinase‐type plasminogen activator (uPA). In the overweight/obese group, interleukin‐6 (IL‐6) was associated with non‐allergic asthma but not allergic asthma.
### Conclusion
These data indicate distinct plasma protein phenotypes in lean and overweight/obese asthmatics which, in turn, can impact upon therapeutic approaches.
## INTRODUCTION
Asthma is a major non‐communicable disease related to reduced quality of life and high health‐care costs. The obese asthma syndrome is associated with female sex, more severe symptoms and poorer disease control compared to lean asthma. It is a complex phenotype related to both type‐2 and non‐type‐2 inflammation. 1, 2 Adipose tissue is important in regulation of inflammation and obesity could affect the inflammation homeostasis. 3 Changes in cytokine levels as well as altered immune responses have been suggested as potential mechanisms relating obesity to asthma. 1, 4 Adipocytes and adipose tissue macrophages produce pro‐inflammatory cytokines such as IL‐6 which has been found in increased levels in asthmatics and has been related to low lung function. 5, 6 Type 2 innate lymphoid cells have important functions in adipose tissue regulation of energy expenditure and metabolic homeostasis but are also related to asthma and asthma severity. 4 Results from animal models suggest that systemic inflammation induced by obesity stimulate migration of innate lymphoid cells to the lungs where these cells could exhibit tissue dependant actions related to asthma. 7 Adipose tissue has also been demonstrated in the outer wall of the large airways, where it correlated positively with BMI, wall thickness and granulocytes, highlighting a possible connection between obesity and asthma pathology. 8 Biological drugs that target type‐2 pathways are of great interest in asthma treatment. However, targeting non‐type‐2 mechanisms in asthma patients is challenging and there is a need to identify novel, easily measurable biomarkers beyond classical type‐2 markers. 9 Additionally, in obese asthmatics the predictive value of conventional biomarkers such as sputum eosinophils, serum eosinophils and fractional exhaled nitric oxide (FeNO) is poor. 10 Research on immune cell phenotype and functionality is undoubtedly important to understand underlying mechanisms in asthma subtypes. Still, cell‐based assays are not feasible to routinely perform in clinical settings. There is a need to identify novel protein biomarkers in easily accessible tissues like blood plasma that could further inform us about involved pathways and disease mechanisms and help guide tailored treatment in asthma obesity phenotypes. The objective of this study was to explore plasma biomarkers related to lean and overweight/obese asthma in young adults. Since disease mechanisms and prevalence of overweight/obesity differ between allergic and non‐allergic asthma, association with biomarkers was also analysed in these sub‐phenotypes.
## METHODS
The study population includes 2069 subjects born in 1994–1996 who completed a questionnaire and clinical examination at the 24‐year follow‐up of the ongoing population‐based Swedish cohort BAMSE (Barn/Child, Allergy, Milieu, Stockholm, Epidemiology). 11 Individuals with asthma ($$n = 239$$) had a doctor's diagnosis ever of asthma in combination with symptoms of breathing difficulties and/or asthma medication use in the last 12 months. Individuals without asthma ($$n = 1830$$) are referred to as “controls”. All individuals were subdivided into “lean” (body mass index (BMI) < 25.0 kg/m2) or “overweight/obese” (BMI ≥ 25.0 kg/m2). Overweight/obese subjects with asthma were further subdivided into “allergic asthma” or “non‐allergic asthma” based on co‐incidental IgE‐sensitization (Figure S1).
## Clinical variables
To assess IgE‐sensitization, sera were analysed for allergen‐specific IgE antibodies towards common airborne (birch, timothy, mugwort, house dust mite, cat, dog, horse, and mold) and food (egg, milk, cod, wheat, peanut, and soy) allergens by Phadiatop and fx5, respectively, using the ImmunoCAP System and a cut‐off of IgE ≥0.35 kUA/L (Thermo Fisher Scientific, Uppsala, Sweden). 12 The Asthma Control Test (ACT) was used to assess the level of asthma control. 13 An eosinophil blood concentration of ≥0.3 × 109/L was used to define eosinophilic asthma according to the European Respiratory Society guidelines. 14 Rhinitis was defined as symptoms from eye or nose because of furred animals or pollen (without having a cold) in the last 12 months prior to the questionnaire. 15 Eczema was defined as any itchy skin rash in the last year in combination with 3 out of 4 following criteria: (i) dry skin in the last year, (ii) eczema onset <2 years of age, (iii) history of flexural eczema, (iv) history of asthma and/or rhinitis. 16 Smoking and snuff use were defined as no, occasionally, and daily based on answers in the questionnaire. Weight and body fat percentage were measured using a Tanita MC 780 body composition monitor. FeNO measurements were performed using the Exhalyzer® D (EcoMedics Ltd) with the Air Safety Eco Slimline filter, cat No $\frac{4222}{01}$ (Air Safety LTD), and the Spiroware 3.6.1 software. Lung function testing was performed through spirometry according to ERS/ATS criteria using the Jaeger spirometry apparatus and SentrySuite 2.17. 17 The forced expiratory flow during 1 s (FEV1) and forced vital capacity (FVC) were not allowed to differ more than 150 mL or $5\%$ from the previous value. The subjects received 4 × 0.1 mg Airomir inhalation (beta‐2 agonist) and re‐did the test after 15 min to investigate the degree of lung function reversibility. FEV1/FVC are presented as z‐scores using reference values from the Global Lung Initiative. 18
## OlinkTM multiplex protein assay
Venous blood was collected in EDTA tubes and plasma was obtained by centrifugation, aliquoted, and stored at −80° Celsius. Samples were collected during the clinical examination without specific requirements of prior fasting or time of sampling. The expression of 92 protein biomarkers in plasma was analysed by the Proseek Multiplex Inflammation Panel (Olink Biosciences, Uppsala, Sweden) as described in 19. Data are expressed as normalized protein expression (NPX) on a log2 scale calculated from normalized Ct values. Protein abbreviations are used in tables throughout the manuscript (full names in Table S1).
## Statistical analysis
The chi‐square, Fisher's exact, Mann‐Whitney U or Kruskal‐Wallis tests were used to analyse differences in baseline and clinical variables. The expression levels of inflammation‐related proteins in the lean asthma and overweight/obese groups were investigated by a crude or sex‐adjusted multinomial logistic regression model with the lean control group as the reference. To entangle if the association between plasma proteins and asthma differed depending on BMI status, an interaction term (protein*BMI‐group) was included in a binary logistic regression model with asthma as the outcome. To further explore the difference in biomarkers related to allergic and non‐allergic asthma a multinomial regression model with the overweight/obese control group as the reference was used to analyse these sub‐phenotypes. Sex, BMI (continuous) and body fat percentage were included as covariates in the adjusted model.
## Ethics statement
The study was approved by the Regional Ethics Committee in Stockholm (DNR $\frac{2016}{1380}$‐$\frac{31}{2}$) and conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent.
## RESULTS
Baseline and clinical variables are shown in Table 1. The study subjects were divided as described into lean asthma ($$n = 166$$), lean controls ($$n = 1440$$), overweight/obese asthma ($$n = 73$$) and overweight/obese controls ($$n = 390$$). Subjects with asthma were more often female ($62\%$), IgE‐sensitized to airborne and food allergens, had rhinitis and eczema, experienced more respiratory infections, had higher BMI, body fat percentage, FeNO, and poorer lung function (Table 1).
**TABLE 1**
| Unnamed: 0 | Unnamed: 1 | LEAN (BMI < 25.0) | LEAN (BMI < 25.0).1 | LEAN (BMI < 25.0).2 | LEAN (BMI < 25.0).3 | LEAN (BMI < 25.0).4 | OVERWEIGHT/OBESE (BMI ≥ 25.0) | OVERWEIGHT/OBESE (BMI ≥ 25.0).1 | OVERWEIGHT/OBESE (BMI ≥ 25.0).2 | OVERWEIGHT/OBESE (BMI ≥ 25.0).3 | OVERWEIGHT/OBESE (BMI ≥ 25.0).4 | Unnamed: 12 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | Control (n = 1440) | Control (n = 1440) | Asthma (=166) | Asthma (=166) | p | Control (n = 390) | Control (n = 390) | Asthma (n = 73) | Asthma (n = 73) | p | p overall |
| | | n | % | n | % | Fisher's/Chi2 | n | % | n | % | Fisher's/Chi2 | Chi2 |
| Sex | Female | 816 | 56.7 | 106 | 63.9 | 0.082 | 181 | 46.4 | 42 | 57.5 | 0.097 | <0.001 |
| Eosinophilic asthma a | Yes | | | 34 | 20.5 | | | | 18 | 25.0 | | ns b |
| Sensitization, any | Yes | 560 | 38.9 | 127 | 76.5 | <0.001 | 176 | 45.1 | 52 | 71.2 | <0.001 | <0.001 |
| Sensitization to airborne allergens | Yes | 542 | 37.6 | 125 | 75.3 | <0.001 | 172 | 44.5 | 51 | 69.9 | <0.001 | <0.001 |
| Sensitization to food allergens | Yes | 82 | 5.7 | 49 | 29.5 | <0.001 | 25 | 6.4 | 20 | 27.4 | <0.001 | <0.001 |
| Rhinitis | Yes | 382 | 26.7 | 111 | 66.9 | <0.001 | 108 | 28.4 | 45 | 62.5 | <0.001 | <0.001 |
| Eczema | Yes | 214 | 14.9 | 66 | 39.8 | <0.001 | 70 | 18.1 | 28 | 38.4 | <0.001 | <0.001 |
| Respiratory infections c | Never | 183 | 12.8 | 14 | 8.5 | <0.001 | 50 | 13.0 | 7 | 9.7 | 0.033 | <0.001 |
| | 1–3 times | 944 | 66.1 | 86 | 52.1 | | 251 | 65.2 | 39 | 54.2 | | |
| | ≥4 times | 183 | 21.1 | 65 | 39.4 | | 84 | 21.8 | 26 | 36.1 | | |
| Smoking | No | 1157 | 80.4 | 127 | 76.5 | ns | 297 | 76.2 | 61 | 83.6 | ns | 0.001 |
| | Occasionally | 197 | 13.7 | 24 | 14.5 | | 43 | 11.0 | 7 | 9.6 | | |
| | Daily | 85 | 5.9 | 15 | 9.0 | | 50 | 12.8 | 5 | 6.9 | | |
| Snuff | No | 1251 | 86.9 | 145 | 87.4 | ns | 326 | 83.6 | 64 | 87.7 | ns | ns |
| | Occasionally | 63 | 4.4 | 6 | 3.6 | | 18 | 4.6 | 4 | 5.5 | | |
| | Daily | 126 | 8.8 | 15 | 9.0 | | 46 | 11.8 | 5 | 6.9 | | |
## Association with biomarkers in lean and overweight/obese asthma
In the multinomial regression, levels of 41 proteins were associated with either lean or overweight/obese asthma, of which Beta‐nerve growth factor (BetaNGF), interleukin 10 (IL‐10), and matrix metalloproteinase 10 (MMP10) were associated only with lean asthma while C‐C motif chemokine 20 (CCL20), fibroblast growth factor 19 (FGF19), interleukin 5 (IL‐5), leukemia inhibitory factor (LIF), tumor necrosis factor ligand superfamily member 9 (TNFRSF9), and urokinase‐type plasminogen activator (uPA) were associated only with the overweight/obese asthma phenotype. The remaining 32 proteins also associated with the overweight/obese control phenotype (Table 2).
**TABLE 2**
| Unnamed: 0 | Lean asthma (n = 166) | Lean asthma (n = 166).1 | Lean asthma (n = 166).2 | Lean asthma (n = 166).3 | Overweight/obese asthma (n = 73) | Overweight/obese asthma (n = 73).1 | Overweight/obese asthma (n = 73).2 | Overweight/obese asthma (n = 73).3 | Overweight/obese control a (n = 390) | Overweight/obese control a (n = 390).1 | Overweight/obese control a (n = 390).2 | Overweight/obese control a (n = 390).3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Protein | RRR (95% CI) | p | RRR (95% CI)adj | p adj | RRR (95% CI) | p | RRR (95% CI) adj | p adj | RRR (95% CI) | p | RRR (95% CI)adj | p adj |
| ADA | 1.0 (0.7,1.5) | 0.908 | 1.1 (0.8,1.7) | 0.534 | 1.8 (1.1,3.0) | 0.029 | 1.9 (1.1,3.1) | 0.021 | 2.1 (1.6,2.7) | <0.001 | 2.0 (1.5,2.5) | <0.001 |
| AXIN‐1 | 1.0 (0.9,1.2) | 0.913 | 1.1 (0.9,1.3) | 0.522 | 1.3 (1.0,1.7) | 0.061 | 1.3 (1.0,1.7) | 0.045 | 1.5 (1.3,1.7) | <0.001 | 1.4 (1.3,1.6) | <0.001 |
| BetaNGF | 1.9 (1.1,3.3) | 0.033 | 1.9 (1.1,3.4) | 0.025 | 0.6 (0.1,2.8) | 0.486 | 0.6 (0.1,2.9) | 0.494 | 0.9 (0.5,1.7) | 0.830 | 0.9 (0.5,1.6) | 0.653 |
| CCL3 | 1.3 (0.9,1.9) | 0.139 | 1.4 (1.0,2.0) | 0.086 | 2.6 (1.8,3.9) | <0.001 | 2.6 (1.8,3.8) | <0.001 | 2.3 (1.8,2.9) | <0.001 | 2.2 (1.8,2.8) | <0.001 |
| CCL4 | 1.1 (0.8,1.5) | 0.621 | 1.1 (0.8,1.6) | 0.378 | 1.9 (1.3,2.7) | 0.002 | 1.9 (1.3,2.8) | 0.001 | 1.9 (1.5,2.3) | <0.001 | 1.8 (1.5,2.2) | <0.001 |
| CCL11 | 0.6 (0.4,1.0) | 0.034 | 0.7 (0.5,1.1) | 0.089 | 1.2 (0.7,2.0) | 0.625 | 1.2 (0.7,2.1) | 0.589 | 1.0 (0.7,1.3) | 0.739 | 0.8 (0.6,1.1) | 0.186 |
| CCL19 | 1.2 (0.9,1.6) | 0.122 | 1.2 (1.0,1.6) | 0.118 | 1.9 (1.4,2.6) | <0.001 | 1.9 (1.4,2.6) | <0.001 | 1.4 (1.2,1.7) | <0.001 | 1.5 (1.2,1.7) | <0.001 |
| CCL20 | 1.0 (0.9,1.3) | 0.654 | 1.1 (0.9,1.3) | 0.594 | 1.3 (1.0,1.7) | 0.031 | 1.3 (1.0,1.7) | 0.031 | 1.1 (1.0,1.2) | 0.203 | 1.1 (0.9,1.2) | 0.272 |
| CD5 | 0.9 (0.6,1.3) | 0.660 | 0.9 (0.6,1.4) | 0.722 | 1.6 (1.0,2.6) | 0.037 | 1.6 (1.0,2.6) | 0.036 | 1.7 (1.3,2.1) | <0.001 | 1.7 (1.3,2.1) | <0.001 |
| CD40 | 1.0 (0.7,1.5) | 0.814 | 1.1 (0.8,1.6) | 0.540 | 1.8 (1.1,3.1) | 0.029 | 1.9 (1.1,3.2) | 0.023 | 2.1 (1.6,2.7) | <0.001 | 2.0 (1.5,2.6) | <0.001 |
| CDCP1 | 1.8 (1.1,2.8) | 0.012 | 1.8 (1.1,2.8) | 0.014 | 8.7 (5.4,13.9) | <0.001 | 8.7 (5.4,13.9) | <0.001 | 4.5 (3.3,6.1) | <0.001 | 4.6 (3.4,6.2) | <0.001 |
| CSF1 | 2.0 (1.0,4.1) | 0.055 | 1.8 (0.9,3.6) | 0.126 | 3.7 (1.3,10.3) | 0.011 | 4 (1.4,11.5) | 0.011 | 2.3 (1.4,3.8) | 0.001 | 3.2 (1.9,5.5) | <0.001 |
| CST5 | 0.8 (0.5,1.2) | 0.226 | 0.8 (0.5,1.2) | 0.282 | 0.4 (0.2,0.8) | 0.009 | 0.4 (0.2,0.8) | 0.010 | 0.6 (0.4,0.8) | <0.001 | 0.5 (0.4,0.7) | <0.001 |
| CXCL5 | 1.0 (0.9,1.2) | 0.814 | 1.0 (0.9,1.2) | 0.838 | 1.3 (1.0,1.7) | 0.039 | 1.3 (1.0,1.7) | 0.040 | 1.2 (1.1,1.4) | 0.001 | 1.2 (1.1,1.4) | 0.001 |
| ENRAGE | 1.0 (0.7,1.3) | 0.774 | 1.0 (0.8,1.4) | 0.772 | 1.6 (1.1,2.2) | 0.009 | 1.6 (1.2,2.3) | 0.006 | 1.5 (1.2,1.7) | <0.001 | 1.4 (1.1,1.6) | 0.001 |
| FGF19 | 1.0 (0.8,1.2) | 0.916 | 1.0 (0.8,1.2) | 0.931 | 0.7 (0.5,0.9) | 0.003 | 0.7 (0.5,0.9) | 0.003 | 0.9 (0.8,1.1) | 0.325 | 0.9 (0.8,1.1) | 0.341 |
| FGF21 | 0.9 (0.8,1.1) | 0.238 | 0.9 (0.8,1.1) | 0.219 | 1.5 (1.3,1.8) | <0.001 | 1.5 (1.3,1.8) | <0.001 | 1.2 (1.1,1.4) | <0.001 | 1.2 (1.1,1.4) | <0.001 |
| FGF23 | 1.0 (0.7,1.4) | 0.906 | 0.9 (0.6,1.4) | 0.780 | 1.7 (1.2,2.5) | 0.007 | 1.7 (1.2,2.6) | 0.007 | 1.5 (1.2,1.9) | <0.001 | 1.6 (1.3,2.0) | <0.001 |
| HGF | 0.9 (0.6,1.4) | 0.808 | 1.1 (0.7,1.6) | 0.747 | 4.7 (2.8,8.0) | <0.001 | 5.1 (3.0,8.7) | <0.001 | 3.4 (2.6,4.5) | <0.001 | 3.3 (2.4,4.3) | <0.001 |
| IL‐5 | 1.0 (0.9,1.2) | 0.976 | 1.0 (0.9,1.2) | 0.974 | 1.3 (1.1,1.5) | 0.001 | 1.3 (1.1,1.5) | 0.001 | 1.0 (0.9,1.1) | 0.641 | 1.0 (0.9,1.1) | 0.550 |
| IL‐6 | 0.9 (0.7,1.2) | 0.569 | 0.9 (0.7,1.2) | 0.533 | 2.9 (2.2,3.9) | <0.001 | 2.9 (2.2,4.0) | <0.001 | 2.2 (1.9,2.7) | <0.001 | 2.2 (1.9,2.7) | <0.001 |
| IL‐7 | 1.3 (1.0,1.7) | 0.059 | 1.3 (1.0,1.7) | 0.043 | 1.6 (1.1,2.3) | 0.010 | 1.6 (1.1,2.3) | 0.009 | 1.5 (1.2,1.7) | <0.001 | 1.4 (1.2,1.7) | <0.001 |
| IL‐10 | 1.3 (1.0,1.5) | 0.020 | 1.3 (1.0,1.5) | 0.020 | 1.1 (0.7,1.5) | 0.710 | 1.1 (0.7,1.5) | 0.707 | 1.0 (0.9,1.2) | 0.775 | 1.0 (0.8,1.2) | 0.883 |
| IL‐10RB | 1.5 (0.8,2.8) | 0.228 | 1.5 (0.8,2.9) | 0.210 | 5.5 (2.4,12.7) | <0.001 | 5.5 (2.4,12.7) | <0.001 | 3.0 (1.9,4.7) | <0.001 | 3.0 (1.9,4.6) | <0.001 |
| IL‐12B | 0.8 (0.6,1.1) | 0.180 | 0.8 (0.6,1.1) | 0.113 | 1.7 (1.1,2.6) | 0.015 | 1.7 (1.1,2.6) | 0.015 | 1.3 (1.1,1.6) | 0.007 | 1.4 (1.1,1.7) | 0.001 |
| IL‐18 | 1.3 (0.9,1.8) | 0.138 | 1.3 (1.0,1.8) | 0.086 | 2.6 (1.7,4.0) | <0.001 | 2.6 (1.7,4.1) | <0.001 | 1.9 (1.5,2.4) | <0.001 | 1.8 (1.5,2.3) | <0.001 |
| IL‐18R1 | 1.1 (0.7,1.8) | 0.534 | 1.2 (0.8,1.8) | 0.427 | 5.9 (3.2,11.1) | <0.001 | 6 (3.2,11.2) | <0.001 | 4.1 (3.0,5.7) | <0.001 | 4.0 (2.9,5.5) | <0.001 |
| LAPTGFβ1 | 1.1 (0.7,1.6) | 0.794 | 1.1 (0.7,1.7) | 0.613 | 1.9 (1.1,3.2) | 0.024 | 1.9 (1.1,3.3) | 0.022 | 1.8 (1.4,2.3) | <0.001 | 1.7 (1.3,2.2) | <0.001 |
| LIF | 0.9 (0.6,1.3) | 0.547 | 0.9 (0.6,1.3) | 0.578 | 1.5 (1.1,2.0) | 0.007 | 1.5 (1.1,1.9) | 0.008 | 1.2 (0.9,1.4) | 0.154 | 1.2 (0.9,1.4) | 0.169 |
| MCP1 | 0.8 (0.5,1.2) | 0.274 | 0.9 (0.6,1.3) | 0.457 | 2.3 (1.4,3.6) | 0.001 | 2.3 (1.4,3.6) | <0.001 | 1.8 (1.4,2.3) | <0.001 | 1.7 (1.3,2.2) | <0.001 |
| MCP3 | 1.4 (1.0,2.0) | 0.058 | 1.4 (1.0,2.0) | 0.048 | 3.2 (2.3,4.5) | <0.001 | 3.2 (2.3,4.5) | <0.001 | 2.3 (1.9,2.9) | <0.001 | 2.3 (1.9,2.9) | <0.001 |
| MCP4 | 1.0 (0.8,1.2) | 0.835 | 1.0 (0.8,1.3) | 0.882 | 1.8 (1.3,2.4) | 0.001 | 1.8 (1.3,2.5) | <0.001 | 1.5 (1.3,1.7) | <0.001 | 1.4 (1.2,1.7) | <0.001 |
| MMP10 | 1.4 (1.1,1.8) | 0.002 | 1.4 (1.1,1.8) | 0.002 | 0.9 (0.6,1.3) | 0.601 | 0.9 (0.6,1.3) | 0.603 | 0.8 (0.7,1.0) | 0.036 | 0.8 (0.7,1.0) | 0.030 |
| OSM | 1.1 (0.9,1.2) | 0.526 | 1.1 (0.9,1.3) | 0.397 | 1.3 (1.0,1.6) | 0.021 | 1.3 (1.0,1.7) | 0.020 | 1.4 (1.2,1.6) | <0.001 | 1.4 (1.2,1.5) | <0.001 |
| PDL1 | 1.0 (0.6,1.5) | 0.876 | 1.1 (0.7,1.6) | 0.762 | 1.7 (1.0,2.8) | 0.056 | 1.7 (1.0,2.8) | 0.046 | 1.7 (1.3,2.2) | <0.001 | 1.6 (1.2,2.0) | 0.002 |
| TNFRSF9 | 1.1 (0.7,1.8) | 0.608 | 1.2 (0.8,2.0) | 0.376 | 2.5 (1.3,5.0) | 0.008 | 2.6 (1.3,5.2) | 0.007 | 1.5 (1.0,2.0) | 0.025 | 1.3 (0.9,1.8) | 0.120 |
| TNFSF14 | 1.1 (0.9,1.5) | 0.352 | 1.2 (0.9,1.6) | 0.179 | 1.8 (1.3,2.6) | 0.001 | 1.9 (1.3,2.7) | 0.001 | 1.9 (1.6,2.3) | <0.001 | 1.8 (1.5,2.2) | <0.001 |
| TRAIL | 1.2 (0.7,1.9) | 0.562 | 1.4 (0.9,2.4) | 0.178 | 4.0 (2.1,7.6) | <0.001 | 4.5 (2.3,8.8) | <0.001 | 3.7 (2.6,5.2) | <0.001 | 3.5 (2.4,5.0) | <0.001 |
| TRANCE | 0.9 (0.7,1.2) | 0.402 | 1.0 (0.7,1.2) | 0.723 | 1.8 (1.2,2.6) | 0.004 | 1.9 (1.2,2.7) | 0.002 | 2.0 (1.6,2.4) | <0.001 | 1.9 (1.6,2.3) | <0.001 |
| uPA | 1.2 (0.7,1.9) | 0.513 | 1.3 (0.8,2.1) | 0.312 | 2.7 (1.5,5.1) | 0.001 | 2.8 (1.5,5.2) | 0.001 | 1.0 (0.7,1.4) | 0.941 | 0.9 (0.6,1.2) | 0.398 |
| VEGFA | 1.4 (0.9,2.4) | 0.164 | 1.5 (0.9,2.5) | 0.158 | 5.9 (3.1,11.4) | <0.001 | 5.9 (3.1,11.4) | <0.001 | 4.2 (3.0,6.0) | <0.001 | 4.2 (3.0,5.9) | <0.001 |
## Effect modification of overweight/obesity on the association between biomarker and asthma
We identified 14 proteins associated with asthma in the lean and/or overweight/obese BMI‐groups in the logistic model of asthma. Effect modification by overweight/obesity was significant in 3 of the proteins in the sex‐adjusted model: fibroblast growth factor 21 (FGF21), interleukin 4 (IL‐4), and uPA (Table 3, Figure 1).
## Biomarkers related to allergic and non‐allergic asthma among overweight/obese study subjects
Overweight/obese non‐allergic asthma ($$n = 21$$) was characterized by a higher proportion of females ($81\%$ compared to $48\%$, $$p \leq 0.009$$), lower prevalence of rhinitis ($25\%$ compared to $77\%$, $p \leq 0.001$) and eczema ($19\%$ compared to $46\%$, $p \leq 0.001$), and lower median FeNO (9 ppb compared to 20 ppb, $p \leq 0.001$) compared to overweight/obese allergic asthma ($$n = 52$$). Median BMI was 29.3 kg/m2 and 28.5 kg/m2 in the two groups ($$p \leq 0.300$$) and median body fat percentage was $36\%$ in subjects with non‐allergic asthma compared to $31\%$ in subjects with allergic asthma ($$p \leq 0.069$$). Differences in protein levels were examined in a multinomial logistic regression model with the overweight/obese control group as the reference. The proteins FGF19, interleukin 2 (IL‐2), IL‐4, and monocyte chemotactic protein 3 (MCP3) were associated with allergic asthma, whereas IL‐5, interleukin 6 (IL‐6), silent information regulator‐2‐like protein 2 (SIRT2), signalling lymphocytic activation molecule (SLAMF1), STAM‐binding protein (STAMBP), and uPA associated with non‐allergic asthma in the model adjusted for sex, BMI and body fat percentage (Table 4, Figure 2).
## DISCUSSION
In this study, investigating young adults from a population‐based cohort with mild/moderate asthma, several plasma proteins were related to a lean and/or overweight/obese asthma phenotype. IL‐6 was associated with overweight/obese non‐allergic asthma, but not lean asthma or overweight/obese allergic asthma. Since IL‐6 is strongly associated with both sex and body composition measurements, 19 it is important to note that our results remained significant after adjusting for sex, BMI, and body fat percentage. Levels of IL‐6 were reduced by a combined dietary and exercise intervention in a randomized trial. 20 IL‐6 has also been linked to a severe asthma phenotype with worse lung function and more frequent exacerbations, independently of BMI. 5 IL‐6 has a complex role in adipose tissue 21 and is likely to have a complex role also in asthma pathogenesis that needs to be further elucidated.
FGF19 had a negative association with asthma among overweight/obese subjects whereas FGF21 had a positive association. FGF19 and FGF21 are known to be involved in energy homeostasis and obese subjects have lower levels of FGF19 and higher levels of FGF21. 22 Our finding could indicate specific metabolic changes that are associated with asthma risk related to obesity. A recent study demonstrated that, in a model of severe steroid‐resistant asthma, inhibition of the FGF receptor prevented neutrophilic inflammation suggesting FGFs as potential therapeutic targets in asthma. 23 uPA correlated with asthma among overweight/obese subjects and most notably in non‐allergic asthma. Reduced levels of the soluble uPA‐receptor (uPAR) have been shown 1 year after bariatric surgery 24 as well as after a combined intervention of diet and exercise 25 and uPAR also correlates with severe non‐allergic asthma and bronchial hyperresponsiveness. 26, 27, 28 These results suggest importance of the uPA‐uPAR signalling pathway primarily in a non‐allergic asthma obesity phenotype.
Members of the matrix metalloproteinases have been linked to airway remodelling in several lung diseases and identified as potential therapeutic targets. 29 In our study, MMP10 was one of few proteins associated only to lean asthma. We have recently shown that levels of MMP10 were associated with eosinophilic asthma. 30 MMP10 has been related to airway remodelling and bronchial inflammation in asthma and regulates macrophage activity and subsequently the extent of inflammatory injury on the airways. 31, 32 CUB domain‐containing protein 1 (CDCP1) has previously been suggested as a serum biomarker differentiating between poorly and well‐controlled asthma. 33 In our study, CDCP1 associated with both lean and overweight/obese asthma and with overweight/obese allergic and non‐allergic asthma, however not after adjusting for sex, BMI and body fat percentage. These results suggest a potential role in asthma, however further studies to elucidate the link to different asthma phenotypes are needed.
An additional complexity of the obesity asthma phenotype is the co‐existence of other morbidities, such as obstructive sleep apnoea (OSA), which could potentially modify airway inflammation. 34 Elevated levels of IL‐6 have been shown in the airways of patients with OSA 35 and elevated levels of MMP9 were found in sputum of difficult‐to‐treat asthmatics with OAS. 36 Strengths of our study include the well‐characterized cohort of young adults and the robustness of the Olink assay. Limitations are the inclusion of few subjects with severe asthma and non‐feasibility to analyse obese asthma separately from overweight asthma. Additionally, the cross‐sectional design of this study prevents conclusions regarding causation. The biomarkers identified in our results need to be further studied in interventional studies aimed at specific obesity asthma phenotypes.
In conclusion, this study highlights the importance of considering overweight/obesity as well as type‐2 and non‐type‐2 mechanisms when identifying potential new therapeutic targets in asthma treatment.
## AUTHOR CONTRIBUTIONS
Sophia Bjorkander: Conceptualization (equal); *Formal analysis* (lead); Methodology (equal); Visualization (lead); Writing—original draft (lead); Writing—review & editing (equal). Susanna Klevebro: Conceptualization (equal); *Formal analysis* (lead); Methodology (equal); Writing—original draft (lead); Writing—review & editing (lead). Natalia Hernandez‐Pacheco: Conceptualization (equal); Methodology (equal); Writing—review & editing (equal). Maura Kere: Conceptualization (equal); Visualization (equal); Writing—review & editing (equal). Sandra Ekstrom: Conceptualization (equal); Data curation (equal); Project administration (equal); Writing—review & editing (equal). Maria Sparreman Mikus: Writing—review & editing (equal). Marianne van Hage: Funding acquisition (supporting); Investigation (supporting); Writing—review & editing (equal). Anna James: Writing—review & editing (equal). Inger Kull: Data curation (equal); Funding acquisition (supporting); Investigation (equal); Project administration (equal); Resources (supporting); Writing—review & editing (equal). Anna Bergstrom: Data curation (equal); Funding acquisition (supporting); Investigation (equal); Project administration (equal); Resources (supporting); Writing—review & editing (equal). Jenny Mjosberg: Conceptualization (equal); Investigation (equal); Writing—review & editing (equal). Christopher Andrew Tibbitt: Conceptualization (equal); Investigation (equal); Writing—review & editing (equal). Erik Melen: Conceptualization (equal); Data curation (equal); Funding acquisition (lead); Investigation (equal); Methodology (equal); Project administration (equal); Resources (lead); Supervision (lead); Writing—original draft (supporting); Writing—review & editing (equal).
## CONFLICT OF INTEREST STATEMENT
EM reports lecture, consulting or advisory boards fees from AstraZeneca, Chiesi, Novartis and Sanofi outside the submitted work. SK reports lecture or advisory boards fees from Novartis and AstraZeneca outside the submitted work. MvH has received lecture fee from Thermo Fisher Scientific outside the submitted work. The other authors declare no conflicts of interest.
## DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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|
---
title: 'Rasd2 Mediates Acute Fasting-Induced Antidepressant-Like Effects via Dopamine
D2 Receptor Activation in Ovariectomized Mice'
authors:
- Ziqian Cheng
- Chaohe Zhang
- Fangyi Zhao
- Jingjing Piao
- Ranji Cui
- Bingjin Li
journal: International Journal of Neuropsychopharmacology
year: 2023
pmcid: PMC10032308
doi: 10.1093/ijnp/pyac082
license: CC BY 4.0
---
# Rasd2 Mediates Acute Fasting-Induced Antidepressant-Like Effects via Dopamine
D2 Receptor Activation in Ovariectomized Mice
## Abstract
### Background
Previous studies have shown that estrogen and acute fasting for 9 hours have antidepressant-like effects by reducing immobility time in the forced swimming test. Estrogen and acute fasting share a common regulatory gene, Rasd2. RASD2 regulates dopamine D2 receptor (DRD2) transmission, but the role of Rasd2 in the DRD2-mediated antidepressant-like effect of acute fasting has not been examined.
### Methods
In this study, open field test, forced swimming test, tail suspension test and sucrose preference test were used for behavioral assessments. RNA-seq, western blot, enzyme-linked immunosorbent assay, and co-immunoprecipitation were used to explore the role of Rasd2 in a depression model induced by ovariectomy and the antidepressant-like effects of 9-hour fasting.
### Results
The RNA seq results showed that acute fasting induced a significant change in *Rasd2* gene expression. Depression-like behaviors induced by ovariectomy were associated with decreased RASD2 and DRD2 protein levels in the hippocampus, and Rasd2 overexpression in the hippocampus alleviated depression-like behaviors and increased DRD2 expression. Nine-hour fasting had antidepressant-like effects in ovariectomized mice by upregulating the protein levels of RASD2, DRD2, CREB-BDNF, Akt, and estrogen receptor beta, and these effects can be blocked by DRD2 antagonists.
### Conclusions
Our results suggest that Rasd2 and DRD2 play pivotal roles in depression-like behavior induced by ovariectomy. Rasd2 regulates DRD2-mediated antidepressant-like effects of acute fasting in ovariectomized mice. Rasd2 can therefore be postulated to be a potential therapeutic target for depression and perhaps also a potential predictive marker for depression.
## INTRODUCTION
Depression is a mental disease characterized by low mood, psychomotor retardation, and cognitive impairment, which severely reduce quality of life (Nestler et al., 2002). Currently, depression is one of the leading causes of disability and a major contributor to the overall global burden of disease (Lancet, 2022). It is worth noting that a meta-analysis has shown that the heritability for major depression is approximately $37\%$ (Flint and Kendler, 2014), and the prevalence of depression in women is almost twice that of men worldwide (Martin et al., 2013). In particular, women are at high risk of depression during hormonal transition phases (peripartum, perimenopause, etc.) ( Freeman et al., 2014; Borgsted et al., 2022). However, preclinical study of depression in females remains understudied (Jiang et al., 2022; Lima et al., 2022).
Calorie restriction has been shown to extend the life span of several species over the past few decades (Fontana and Partridge, 2015) and has positive effects on neurological diseases, including Alzheimer’s disease and Parkinson’s disease (Zhang et al., 2021b; Ezzati and Pak, 2023; Govic et al., 2022). In our previous studies, mice treated with 9-hour fasting significantly shortened the immobility time of the forced swimming test (FST), whereas mice fasted for 3 hours and 18 hours had no significant changes (Li et al., 2014). Further studies revealed that acute fasting produces antidepressant-like effects through the activation of the cyclic adenosine monophosphate (cAMP)-response element binding protein (CREB)-brain-derived neurotrophic factor (BDNF) signaling pathway in the prefrontal cortex (PFC) and hippocampus (HP) (Lutter et al., 2008; Li et al., 2014; Cui et al., 2018; Wang et al., 2019). Additionally, caloric restriction upregulates estrogen receptor expression but has no effects on androgen receptor (Słuczanowska-Głąbowska et al., 2015). Fasting produces estrogenic effects in ovariectomized mice (Bigsby et al., 1997), and estrogen enhances the antidepressant-like effects of acute fasting via the activation of the CREB-BDNF signaling pathway in the PFC and HP (Wang et al., 2019). Therefore, fasting might be used as an adjunct to estrogen replacement therapy for depression.
RNA-seq data suggest that estrogen and acute fasting exert antidepressant-like effects through a common gene, Rasd2 (Wang et al., 2019). Whether Rasd2 participates in the antidepressant-like effects of fasting has not been directly examined yet, to our knowledge. RASD2 is a GTP binding protein that is highly enriched in the striatum and found at lower levels of expression in the HP, cerebral cortex, olfactory bulb, etc. ( Vargiu et al., 2004). Rasd2 negatively regulates G protein–coupled receptor-mediated cAMP production, and the targeted deletion of Rasd2 in mice can significantly activate the cAMP/protein kinase A signaling pathway in the striatum (Vargiu et al., 2004; Errico et al., 2008; Ghiglieri et al., 2015). In addition, recent research indicates that Rasd2 regulates the phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt)/mechanistic target of rapamycin signaling pathway and consequently has a role in several neurological and psychiatric diseases, such as schizophrenia and Huntington’s disease (Emamian et al., 2004; Subramaniam et al., 2011; Lee et al., 2015). However, the role of Rasd2 in depression remains unclear.
Rasd2 function is closely tied to dopamine function. Depleting the striatum of dopaminergic input decreases Rasd2 mRNA expression in the striatum (Harrison and LaHoste, 2006). In addition, activation of dopamine D2 receptors (DRD2) produces exaggerated stereotypy in Rasd2 knockout mice (Quintero et al., 2008). Sciamanna et al. [ 2015] found that Rasd2 deficiency produces aberrant DRD2-dependent activity through an abnormal Ca2+-dependent modulation of PI3K/Akt signaling. Rasd2 mRNA has been located in dopamine D1 receptor-medium spiny neurons, DRD2-medium spiny neurons, and cholinergic interneurons (Sciamanna et al., 2015). Rasd2 regulates dopamine-dependent neurotransmission by affecting the survival of nigrostriatal dopaminergic neurons (Sciamanna et al., 2015; Pinna et al., 2016). These findings suggest that Rasd2 effects on other aspects of dopamine signaling may be involved in depression. In addition to DRD2 signaling pathways, dopamine supersensitivity in response to antidepressant treatment is mediated by the activation of the CREB-BDNF signaling pathways in the nucleus accumbens (Guillin et al., 2001; Gershon et al., 2007).
In this study, we used RNA-seq, behavioral tests, western blot (WB), enzyme-linked immunosorbent assay, and co-immunoprecipitation (Co-IP) to comprehensively investigate the role of transcription factor RASD2 in 9-hour fasting on the improvement of depression-like behavior induced by ovariectomy and whether this effect is regulated by DRD2. Considering the significant effects of fasting and estrogen on the BDNF-CREB signaling pathway in the HP and PFC of mice and the fact that RASD2 is enriched in the striatum while interacting with DRD2, in this study, we aimed to investigate the molecular mechanisms involved in the HP, PFC, and striatum.
## Animals
Female ICR mice (6–10 weeks, 25 ± 2 g) were purchased from Jilin University (Changchun, China). The mice were kept in plastic cages (25.5 × 15 × 14 cm) under standard laboratory conditions: room temperature 23°C ± 1°C, a 12-hour-light/-dark cycle (7:00 am-7:00 pm light period). Food and water were available ad libitum. Before experiments, mice were randomly assigned to each group. Five mice were housed in 1 cage before surgery and were housed in a single cage after surgery to prevent the mice from biting each other. Transparent cages were used to allow the mice to see each other, and toys were placed in the cages throughout the single-cage rearing period. All experiments were conducted according to the standards set forth in the Laboratory Animal-Guideline for ethical review of animal welfare (GB/T 35892-2018) and under protocols approved by the Institutional Animal Care and Use Committee of Jilin University.
## Experimental Design
The experimental design and timeline are shown in Figure 1. To investigate the effect of acute fasting on gene expression in mouse brain, mice were killed after 9-hour fasting or normal diet, and brain tissues (PFC) were dissected and processed for RNA-seq (Figure 1A). To investigate the effect of ovarian removal on depression-like behavior and related changes in protein expression, behavioral tests (FST, $$n = 13$$ each group; open field test [OFT; $$n = 13$$ each group], tail suspension test [TST; $$n = 6$$–7 each group], and sucrose preference test [SPT; $$n = 8$$ each group]) and serum ($$n = 8$$ each group) and brain tissue (PFC and HP, $$n = 3$$–6 each group) extraction were performed 7 days after ovariectomy (Figure 1B).
**Figure 1.:** *Schematic of experimental design and timeline. Co-IP, co-immunoprecipitation; ELISA,
enzyme-linked immunosorbent assay; FST, forced swimming test; HP, hippocampus; OFT,
open field test; PFC, prefrontal cortex; SPT, sucrose preference test; TST, tail
suspension test.*
To explore the effect of Rasd2 overexpression in the HP on ovariectomy-induced depression, ovariectomy was performed 14 days after injection of the virus (control or Rasd2-overexpression), behavioral tests [FST ($$n = 11$$–15 each group), OFT ($$n = 11$$–15 each group), TST ($$n = 7$$–9 each group) and SPT ($$n = 7$$–9 each group)] and brain tissue (HP, $$n = 4$$–6 each group) dissection was performed 7 days after ovariectomy (Figure 1C).
To investigate the effect of sulpiride (a DRD2 antagonist) on the antidepressant-like effect of 9-hour fasting, sulpiride (50 mg/kg, i.p.; Sigma Aldrich, S8010; dissolved initially in 0.1 M HCl) (Cunha et al., 2012; Donato et al., 2013) was administered after 8-hour fasting. Behavioral tests (FST [$$n = 11$$–15 each group], OFT [$$n = 12$$–15 each group], TST [$$n = 7$$–8 each group], and SPT [$$n = 8$$–9 each group]) and brain tissue (PFC, HP, and striatum, $$n = 3$$–5 each group) dissection were performed 1 hour after administration (Figure 1D). In all experiments, fasting started at 12:00 am and ended at 9:00 am.
## Surgery
All animals were adapted to the laboratory environment for 3 days before undergoing ovariectomy. The surgical procedure for ovariectomy followed the same procedure described in our previous report (Liu et al., 2012). Briefly, mice were anesthetized with pentobarbital sodium (65 mg/kg, i.p., Dingguo Changsheng Biotechnology, Beijing, China), and the mice were kept in a lateral position. Hair was removed 1 cm horizontally from both sides of the spine, and the skin was disinfected with betadine. A small incision was made parallel to the spine at the intersection of the upper thigh and the lateral spine of the mice, and then the ovaries were removed bilaterally. A week was allowed for recovery before further testing. Sham-operated animals only had incisions without removing the ovaries.
To overexpress Rasd2 in the HP, a lentiviral expression vector was synthesized by Obio Technology (Shanghai, P.R. China). Viral titers were 4.77*108 particles/mL for pLenti-Ubc-EGFP-2A-3FLAG-Rasd2 and 1.55*109 particles/mL for pLenti-Ubc-EGFP-3FLAG-control. After anesthesia with pentobarbital sodium, mice were placed on the stereotactic frame and the scalp and connective tissue were cut to fully expose the skull. After holes were drilled at the appropriate locations, the virus was microinjected bilaterally into the HP (–1.8 mm anterior-posterior, ±1.6 mm medial-lateral, and –1.5 mm dorsal-ventral from bregma; Figure 1C) at a speed of 0.2 μL/min.
## RNA Isolation, Sequencing, and Bioinformatic Analysis
The animals were decapitated, and the PFC was quickly removed, placed on ice, labeled, and stored in a refrigerator at –80°C for later processing and analysis. Tissue was processed following the instructions of the Trizol kit to extract total RNA and then using RNase-free DNase I to remove genomic DNA. RNA purity and concentration were determined using a Nano Photometer spectrophotometer (IMPLEN, Westlake Village, CA, USA) and a Qubit 2.0 kit. High-quality RNA samples were transported on dry ice to Sangon Biotech (Shanghai, China) for sequencing and testing. The sequencing of the established library was performed with the Illumina HiSeq XTen platform (Illumina, San Diego, CA, USA), and paired-end reads at 150 bp were obtained. The Bioconductor software package was used to correct for multiple testing (false discovery rate cutoff <0.1) and to identify differentially expressed transcripts based on counts per million values. $P \leq .05$ was considered statistically significant.
## Open Field Test
Mice were placed in the center of an acrylic apparatus (48.8-cm diameter, 16 cm high) (Liu et al., 2012). The floor of the apparatus was divided into 16 equal squares. The test lasted 6 minutes and was recorded with a video camera (DCR-SX83E, Sony, Shanghai, China). Horizontal locomotor activity (the number of grid lines crossing traversed by all 4 paws of the mouse) and vertical locomotor activity (number of times the mouse stood with both forepaws off the ground) were counted by an observer blind to the treatment conditions.
## Forced Swimming Test
Each mouse was individually placed in a cylindrical container (11 cm diameter × 25 cm high), filled with water (12 cm depth), with the water temperature maintained at 25°C ± 1°C (Liu et al., 2012). The test lasted 6 minutes and was recorded with a video camera (DCR-SX83E, Sony). The first 2 minutes of the test were considered adaptation time, and behavior was recorded for the only final 4 minutes of the test. Duration of immobility, swimming, and climbing as well as defecation (number of fecal boli) were determined by an observer blinded to the experimental conditions. Specific discrimination of behavior in the FST (immobility, swimming, and climbing) was according to the criteria previously reported (Cryan et al., 2002). Immobility was defined as having no additional movement other than that necessary to keep the head above the water. Swimming was defined as swimming with the body parallel to the wall. Climbing was characterized by pawing movements oriented at the side of the chamber with the animal oriented perpendicularly to the wall (Cryan et al., 2002).
## Tail Suspension Test
The TST was referred to in previously published articles (Kim et al., 2021; Zhang et al., 2021a). Tape was attached 2 cm from the mouse tail-tip, and the mouse was held in an inverted state with the head approximately 20 cm above the ground with tape. The behavior of the mice within 5 minutes was recorded by a video camera (DCR-SX83E, Sony). The cumulative immobility time (the body of mice was vertically inverted and immobile) during the last 4 minutes was recorded by an observer blind to the treatment conditions.
## Sucrose Preference Test
Mice were trained to acclimate to $1\%$ sucrose solution (two $1\%$ sucrose water bottles per cage) 2 days before the formal test. Mice were water deprived for 12 hours, then 2 weighed water bottles (one $1\%$ sucrose solution and one pure water) were placed in each cage. After 1 hour, all bottles were weighed to calculate sucrose solution and water consumption (Zhang et al., 2021a). Sucrose preference = sucrose solution consumption / (sucrose solution consumption + pure water consumption) * $100\%$.
## Enzyme-Linked Immunosorbent Assay
Mice were anesthetized and their whiskers were clipped. Blood was collected by retro-orbital bleeding and placed at room temperature for 1 hour, followed by centrifugation at 2000 rpm for 10 minutes. The plasma supernatant was collected and stored at –80°C until use. Assay was performed by recommended protocol of kit (Feiya Biotechnology Co., Ltd, Jiangsu, China). To the wells were added standard or samples and added sample diluent. We then added horseradish peroxidase (HRP)-conjugate reagent to each well and incubated for 60 minutes at 37°C. After washing, chromogen A and B were added to each well and incubated for 15 minutes at 37°C. Finally, stop solution was added to each well. We read optical density at 450 nm by using a microtiter plate reader (Variosbon Flsh, Thermo Scientific, Waltham, MA, USA) within 15 minutes.
## Western Blot
The mice were decapitated, the brain removed, and the PFC, HP, and striatum dissected. Samples from the PFC, HP, and striatum tissue were homogenized in radio immunoprecipitation assay (RIPA) buffer (R0020, Solarbio, Beijing, China) with $1\%$ phenylmethylsulfonyl fluoride (PMSF) solution. The homogenate was centrifuged at 12 000 rpm at 4°C for 20 minutes, and the precipitate was discarded for the removal of insoluble proteins. After mixing with the loading buffer, the samples were placed in boiling water for 5 minutes. Proteins were separated by constant pressure electrophoresis (Bio-Rad, CA, USA) at 110 constant voltages on $10\%$ sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) gels. Then the target protein was transferred to polyvinylidene difluoride membranes (100 constant voltages, 1 hour) prior to blocking with $5\%$ skim-milk (dissolved in Tris buffered saline [TBS]) for 2 hours. The membrane was incubated with the primary antibody overnight at 4°C: RASD2 (1:800, rabbit polyclonal; Abcam, Cambridge, UK, #ab67277); BDNF (1:1000, rabbit polyclonal; ABclonal, Wuhan, China, #A16229); CREB (1:1000, rabbit polyclonal; Abcam, #ab32515); p-CREB (1:1000, rabbit polyclonal; CST, Danvers, MA, USA, #9198); Akt (1:1000, rabbit monoclonal; CST, #9272S); DRD2 (1:2000, rabbit polyclonal; Abcam, #ab99446); estrogen receptor alpha (ERα) (1:1000, rabbit polyclonal; Affinity, Danvers, MA, USA, #AF6058); estrogen receptor beta (ERβ) (1:1000, rabbit polyclonal; Affinity, #AF6469); and β-actin (1:2000, mouse monoclonal; Transgen Biotech, Beijing, China, #HC201). After TBST (TBS containing $0.1\%$ Tween-20) washing, the membranes were incubated with secondary antibody (anti-rabbit: 1:1500; ZSBG-Bio, Beijing, China, #ZB2301; anti-mouse: 1:6000; ZSBG-Bio #ZB2305). Then, after incubation for 1 hour, the membranes were washed 3 times with TBST. The target protein signal was detected using enhanced chemiluminescence (ECL) reagent and analyzed with Image J software, version 1.52.
## Co-immunoprecipitation
Co-IP was performed by the recommended protocol of kit manufacturer (Abs955, Absin, Shanghai, China). Firstly, RIPA buffer (R0020, Solarbio) with $1\%$ PMSF solution was added to the collected tissue, and the tissue was homogenized by homogenizer. Then, the samples were centrifuged at 12 000 rpm for 20 minutes at 4°C, and the supernatant was removed for use. Primary antibody (RASD2, RHES-101AP, Fabgennix, Frisco, TX, USA) was added to the samples, while homologous antibodies (Rabbit IgG, abs20035, Absin) from nonspecific immunization were used as controls and the samples were incubated overnight at 4°C. Protein A and G were added to the samples and gently mixed overnight at 4°C then centrifuged at 12 000 rpm for 1 minute to retain the precipitate. Precipitate was washed by wash buffer 3 times. 1*SDS sample buffer was added to resuspend the precipitate, and the sample was held at 95°C–100°C for 5 minutes. All samples were subsequently analyzed by WB.
## Statistical Analysis
All data values are expressed as mean ± SEM and were analyzed by GraphPad Prism Software (version 8.0.1). Student’s t test was used to compare means between 2 groups (sham vs ovariectomy; control vs Rasd2 overexpression). Two-way ANOVA was used to compare the effects of factorial designs (factor 1: fasting; factor 2: sulpiride). When a significant difference was obtained in an ANOVA, post hoc comparisons were performed between means using Tukey’s honestly significant difference test (Tukey’s HSD). $P \leq .05$ was considered statistically significant. The Shapiro-Wilk test was used to evaluate the normality of the data by SPSS (version 23). Effect size was assessed calculating η2 or Cohen’s d as needed by SPSS. Following Cohen [1988], we interpreted estimated η2 and d values as follows: η2 = 0.01 small, 0.06 medium, 0.14 large; $d = 0.2$ small, 0.5 medium, 0.8 large.
## Effect of Acute Fasting on Brain Gene Expression Changes
Gene expression significantly changed in the PFC as a result of 9-hour fasting. Bioinformatic analysis of the pattern of significantly altered genes is shown in Figure 2 for biological processes (A), cellular components (B), molecular functions (C), and overall functions (D). Figure 2A identified the first 20 biological processes related to differential expressed genes. Among them, dopaminergic synaptic transmission, as well as several biological functions involving dopaminergic neurotransmission, were altered. Figure 2B shows the top 20 cellular components showing altered gene expression. Figure 2C shows the top 20 molecular functions related to differentially expressed genes. Among them, neuropeptide hormone activity, dopamine binding, and syntaxin binding are obviously related to central nervous system functions. Figure 2D shows the top 20 overall results from the analysis of GO enrichment in which the neuronal cell body, neuropeptide signaling pathway, myelin sheath, synaptic transmission (dopaminergic), and neuropeptide hormone activity are related to central nervous system function. The above data suggest that the changes in differentially expressed genes induced by 9-hour fasting may be involved in nerve cell growth and development, hormone regulation, and signal transmission as well as other processes. *Further* gene function analysis was carried out from the biological process of synaptic transmission (dopaminergic), and genes with significant differences were screened out as shown in Figure 2E: Adenosine A2a receptor (Adora2a), Drd2, Drd1, Tyrosine hydroxylase (TH), and Rasd2. In the enrichment analysis of the KEGG pathway in the PFC after fasting (Figure 2F–H), the PI3K-Akt pathway has the largest number of differentially expressed genes (16 differentially expressed genes), although the analysis again identified several gene sets related to dopaminergic function (e.g., cocaine addiction, Parkinson’s disease, and dopaminergic synapse), shown in Figure 2H. Ribosomes, Parkinson’s disease, and herpes simplex infection ranked next in this analysis with 11 differentially expressed genes, followed by the cAMP signaling pathway with 10 differentially expressed genes.
**Figure 2.:** *Effects of acute fasting on gene expression in the prefrontal cortex (PFC). (A-D)
Differential gene expression in the PFC of mice after 9 hours of acute fasting. Red
bars indicate upregulated genes, and blue bars indicate downregulated genes. P
< .05 was used as the standard to judge whether there is a significant
difference in genes. Fold change ≥1.7 is considered to be an upregulated gene, and
fold change ≤0.6 is considered to be a downregulated gene. (A) Analysis of the
biological process of GO enrichment in the PFC after fasting. (B) Analysis of the
composition of GO-enriched cells in the PFC after fasting. (C) Molecular function
analysis of GO enrichment in the PFC after fasting. (D) Total analysis of GO
enrichment in the PFC after fasting. (E) Differentially expressed genes involved in
dopaminergic synaptic transmission. (F–G) KEGG pathway enrichment analysis of genes
differentially expressed in the PFC after fasting. (H) Venn diagram of differential
gene expression of nervous system-related KEGG pathways in the PFC. The red line
represents P = .05 and the number at the top of each column
represents the number of differentially expressed genes.*
## Ovariectomy Induces Depression-Like Behavior and Decreases the Expression of RASD2
and DRD2 in the HP
In the OFT, ovarian removal had no effect on either locomotor activity or rearing (Figure 3F and G). Compared with the sham group, immobility time in the FST and TST significantly increased in ovariectomized mice (FST: t[24] = 2.378, $$P \leq .0257$$, $d = 0.191$; TST: t[11] = 2.217, $$P \leq .0486$$, $d = 0.309$), and swimming time (t[24] = 2.727, $$P \leq .0118$$, $d = 0.236$) and sucrose consumption (t[14] = 2.270, $$P \leq .0396$$, $d = 0.269$) significantly decreased (Figure 3A–E and H). In addition, serum estrogen levels were decreased in the ovariectomized mice (t[14] = 2.472, $$P \leq .0269$$; Figure 3J). The results of WB showed that ovarian removal reduced RASD2 (t[5] = 3.090, $$P \leq .0271$$; Figure 3K) and DRD2 (t[9] = 2.390, $$P \leq .0406$$; Figure 3M) expression in the HP but not the PFC (Figure 3L and N). Co-IP showed that RASD2 interacted with DRD2 in the HP of the ovariectomized mice (Figure 3I).
**Figure 3.:** *Effects of ovariectomy on depression-like behavior and the expression of RASD Family
Member 2 (RASD2) and dopamine D2 receptor (DRD2). Immobility time (A), swimming time
(B), climbing time (C), and defecation (D) in the forced swimming test (FST). (E)
Immobility time in the tail suspension test (TST). Locomotor behavior (F) and rearing
(G) in the open field test (OFT). (H) Sucrose consumption in the sucrose preference
test (SPT). (I) A representative image of co-immunoprecipitation in the hippocampus
(HP) of ovariectomized mice. (J) Estrogen level in serum. (K–L) The effect of ovarian
removal on the expression of RASD2 in the prefrontal cortex (PFC) and HP. (M–N) The
effect of ovariectomy on the expression of DRD2 in the PFC and HP. The data are
expressed as mean ± SEM. Student’s t test, *P <
.05 vs sham. OV, ovariectomy.*
## Overexpression of Rasd2 in the HP Produced Antidepressant-Like Effects and Increased
the Expression of DRD2 in Ovariectomized Mice
As shown in Figure 4e and F, Rasd2 overexpression in the HP decreased immobility time (t[22] = 3.249, $$P \leq .0037$$, $d = 0.324$) and increased the swimming time (t[23] = 2.749, $$P \leq .0114$$, $d = 0.247$) of ovariectomized mice in the FST while having no effects on other behavioral measures in the FST or OFT (Figure 4B, C, G, and H). In addition, Rasd2 overexpression increased sucrose consumption (t[14] = 2.252, $$P \leq .0409$$, $d = 0.266$; Figure 4D) and reduced the immobility time in the TST (t[14] = 2.832, $$P \leq .0133$$, $d = 0.364$; Figure 4I). Figure 4J–L show that overexpression of Rasd2 increased RASD2 (t[8] = 4.174, $$P \leq .0031$$), DRD2 (t[8] = 2.803, $$P \leq .0231$$), and BDNF (t[8] = 2.796, $$P \leq .0234$$) expression in the HP.
**Figure 4.:** *Effects of Rasd2 overexpression on depression-like behavior and the expression of
RASD Family Member 2 (RASD2) and dopamine D2 receptor (DRD2). (A) A representative
fluorescent image showing green fluorescent protein (GFP) expression in the
hippocampus (HP) of virus-injected mouse at 3 weeks after viral delivery. Locomotor
behavior (B) and rearing (C) in the open field test (OFT). (D) Sucrose consumption in
the sucrose preference test (SPT). Immobility time (E), swimming time (F), climbing
time (G), and defecation (H) in the forced swimming test (FST). (I) Immobility time in
the tail suspension test (TST). Figures represent changes in the protein expression of
RASD2 (J), DRD2 (K), and brain-derived neurotrophic factor (BDNF) (L) in the HP of
mice. The data are expressed as mean ± SEM. Student’s t test,
*P < .05, **P < .01.*
## Sulpiride Reversed the Alleviating Effects of Acute Fasting on Depression-Like
Behaviors
As shown in Figure 5A, fasting decreased immobility time in the FST in vehicle-treated ovariectomized mice (Fsulpiride[1,42] = 27.60, $P \leq .0001$, η² = 0.396; Ffasting[1,42] = 3.351, $$P \leq .0743$$, η² = 0.074; Fsulpiride×fasting[1,42] = 3.543, $$P \leq .0667$$, η² = 0.078). Thus, there was a significant post hoc Tukey’s HSD comparison between the fasting and non-fasting vehicle-treated groups ($$P \leq .0495$$). Sulpiride reversed the effect of fasting ($P \leq .0001$). As shown in Figure 5B and C, fasting increased swimming time (Fsulpiride[1,53] = 4.748, $$P \leq .0338$$, η² = 0.082; Ffasting[1,53] = 3.419, $$P \leq .0700$$, η² = 0.060; Fsulpiride×fasting[1,53] = 4.856, $$P \leq .0319$$, η² = 0.084; Tukey’s HSD: $$P \leq .0355$$) and climbing time (Fsulpiride[1,50] = 9.633, $$P \leq .0031$$, η² = 0.162; Ffasting[1,50] = 2.639, $$P \leq .1106$$, η² = 0.050; Fsulpiride×fasting[1,50] = 16.99 $$P \leq .0001$$, η² = 0.254; Tukey’s HSD: $$P \leq .0009$$) in the vehicle-treated mice. Sulpiride reduced swimming time ($$P \leq .0173$$) and climbing time ($P \leq .0001$) in the fasted mice.
**Figure 5.:** *Effects of sulpiride on the antidepression-like effect of fasting. Immobility time
(A), swimming time (B), climbing time (C), and defecation (D) in the forced swimming
test (FST). (E) Sucrose consumption in the sucrose preference test (SPT). (F)
Immobility time in the tail suspension test (TST). Locomotor behavior (G) and rearing
(H) in the open field test (OFT). The data are expressed as mean ± SEM. Two-way ANOVA
with Tukey’s honestly significant difference (HSD), *P < .05 vs
vehicle (VEH):non-fasting; #P < .05,
###P < .001 vs VEH:fasting. SUL, sulpiride.*
As shown in Figure 5E, fasting increased sucrose consumption in the vehicle-treated mice (Fsulpiride[1,29] = 4.837, $$P \leq .0360$$, η² = 0.143; Ffasting[1,29] = 2.214, $$P \leq .1476$$, η² = 0.071; Fsulpiride×fasting[1,29] = 6.540, $$P \leq .0160$$, η² = 0.184; Tukey’s HSD: $$P \leq .0403$$), and sulpiride treatment eliminated the effects of acute fasting on sucrose consumption ($$P \leq .0124$$). As shown in Figure 5F, sulpiride reduced immobility time in the TST in the fasted mice (Fsulpiride[1,26] = 9.889, $$P \leq .3292$$, η² = 0.037; Ffasting[1,26] = 0.2075, $$P \leq .6525$$, η² = 0.008; Fsulpiride×fasting[1,26] = 9.209, $$P \leq .0054$$, η² = 0.262). Tukey’s HSD showed that sulpiride treatment eliminated the effects of fasting on immobility time ($$P \leq .0397$$).
Figure 5G and H show that sulpiride decreased locomotor activity (Fsulpiride[1,51] = 27.64, $P \leq .0001$, η² = 0.066; Ffasting[1,51] = 2.690, $$P \leq .1071$$, η² = 0.363; Fsulpiride×fasting[1,51] = 0.6708, $$P \leq .4166$$, η² = 0.023) or rearing (Fsulpiride[1,49] = 25.90, $P \leq .0001$, η² = 0.346; Ffasting[1,49] = 3.962 $$P \leq .0521$$, η² = 0.075; Fsulpiride×fasting[1,49] = 1.690, $$P \leq .1997$$, η² = 0.033) in both fasting- and non-fasting–treated mice. Tukey’s HSD showed that there were significant differences after sulpiride treatment in non-fasting–treated mice ($$P \leq .0112$$) and in fasting-treated mice ($$P \leq .0006$$) on locomotor activity. And there were significant differences after sulpiride treatment in non-fasting–treated mice ($$P \leq .0445$$) and in fasting-treated mice ($$P \leq .0003$$) on rearing. It should be noted that sulpiride reduced locomotor activity and rearing in both fasted- and non-fasted mice, which may be an indication of general motor impairing effects.
## Sulpiride Reversed the Fasting-Induced Increase in RASD2, ER
β , Activation of DRD2-Linked, and CREB-BDNF Signaling
Pathway
The effects of sulpiride on fasting-induced changes in protein expression in the HP are shown in Figure 6A (A–H). ANOVA showed that fasting increased RASD2 in vehicle-treated mice but not sulpiride-treated mice (Fsulpiride[1,13] = 14.50, $$P \leq .0022$$; Ffasting[1,13] = 7.522, $$P \leq .0168$$; Fsulpiride×fasting[1,13] = 9.363, $$P \leq .0091$$). Tukey’s HSD confirmed that there was a significant effect of fasting in vehicle-treated mice ($$P \leq .0072$$), and the effect of fasting was reversed by sulpiride ($$P \leq .0012$$). A similar pattern was seen for BDNF (Fsulpiride[1,11] = 8.140, $$P \leq .0157$$; Ffasting[1,11] = 15.95, $$P \leq .0021$$; Fsulpiride×fasting[1,11] = 1.615, $$P \leq .2301$$), CREB (Fsulpiride[1,12] = 9.909, $$P \leq .0084$$; Ffasting[1,12] = 11.40, $$P \leq .0055$$; Fsulpiride×fasting[1,12] = 1.660, $$P \leq .2219$$), and p-CREB (Fsulpiride[1,10] = 6.707, $$P \leq .0270$$; Ffasting[1,10] = 2.060, $$P \leq .1818$$; Fsulpiride×fasting[1,10] = 8.168, $$P \leq .0170$$) in the ANOVA. And Tukey’s HSD showed that there was a significant effect of fasting in vehicle-treated mice (BDNF: $$P \leq .0118$$; CREB: $$P \leq .0256$$; p-CREB: $$P \leq .0353$$). Again, the effect of fasting was reversed by sulpiride (BDNF: $$P \leq .0479$$; CREB: $$P \leq .0252$$; p-CREB: $$P \leq .0142$$). A similar pattern was also seen for Akt (Fsulpiride[1,10] = 16.12, $$P \leq .0025$$; Ffasting[1,10] = 12.74, $$P \leq .0051$$; Fsulpiride×fasting[1,10] = 1.257, $$P \leq .2884$$) and DRD2 (Fsulpiride[1,11] = 20.74, $$P \leq .0008$$; Ffasting[1,11] = 0.6827, $$P \leq .4262$$; Fsulpiride×fasting[1,11] = 2.253, $$P \leq .1615$$) as shown in the 2-way ANOVA. Tukey’s HSD showed that there was a significant effect of fasting in vehicle-treated mice (Akt: $$P \leq .0458$$), and this effect was normalized by sulpiride (Akt: $$P \leq .0273$$; DRD2: $$P \leq .0078$$). There were no significant effects of fasting or sulpiride on ERα expression. However, a similar pattern was observed for ERβ as for some of the other measures (Fsulpiride[1,8] = 0.1456, $$P \leq .7127$$; Ffasting[1,8] = 1.178, $$P \leq .3093$$; Fsulpiride×fasting[1,8] = 17.81, $$P \leq .0029$$). Tukey’s HSD showed that there was a significant effect of fasting in vehicle-treated mice ($$P \leq .0233$$). Sulpiride reduced ERβ in fasted mice ($$P \leq .0465$$) but not in unfasted mice.
**Figure 6.:** *Effect of sulpiride (SUL) on fasting-induced changes in protein expression. Figures
represent the changes in the protein expression of RASD Family Member 2 (RASD2),
brain-derived neurotrophic factor (BDNF), cAMP-response element binding protein
(CREB), phospho-CREB (p-CREB), protein kinase B (Akt), dopamine D2 receptor (DRD2),
estrogen receptor α (ERα), and estrogen receptor β (ERβ) in the hippocampus (HP) (A,
a–h), prefrontal cortex (PFC) (B, i–p), and striatum (C, q–x) in ovariectomized mice.
The data are expressed as mean ± SEM (n = 3–5). Two-way ANOVA with Tukey’s honestly
significant difference (HSD), *P < .05 vs
ovariectomy (OV), **P < .01 vs vehicle
(VEH):non-fasting; #P < .05,
##P < .01 vs VEH:fasting.*
WB results for the PFC are shown in Figure 6B (I–P). Acute fasting increased RASD2 expression in the PFC, and this effect was reversed by sulpiride (Fsulpiride[1,12] = 7.826, $$P \leq .0161$$; Ffasting[1,12] = 2.554, $$P \leq .1360$$; Fsulpiride×fasting[1,12] = 9.802, $$P \leq .0087$$). Tukey’s HSD showed that there was a significant effect of fasting in vehicle-treated mice ($$P \leq .0260$$). Sulpiride decreased RASD2 expression in fasted mice ($$P \leq .0059$$) but not unfasted mice. A similar pattern was seen for BDNF (Fsulpiride[1,12] = 5.896, $$P \leq .0318$$; Ffasting[1,12] = 4.782, $$P \leq .0493$$; Fsulpiride×fasting[1,12] = 3.273, $$P \leq .0955$$) and CREB (Fsulpiride[1,14] = 1.278, $$P \leq .2773$$; Ffasting[1,14] = 3.412, $$P \leq .0860$$; Fsulpiride×fasting[1,14] = 11.73, $$P \leq .0041$$) in the 2-way ANOVA. Tukey’s HSD showed that there was a significant effect of fasting in vehicle-treated mice (CREB: $$P \leq .0070$$) and a significant effect of sulpiride treatment in fasted mice (BDNF: $$P \leq .0477$$; CREB: $$P \leq .0279$$). DRD2 expression in the PFC was also increased by fasting, and this effect was normalized by sulpiride (Fsulpiride[1,12] = 11.54, $$P \leq .0053$$; Ffasting[1,12] = 0.4593, $$P \leq .5108$$; Fsulpiride×fasting[1,12] = 2.068, $$P \leq .1759$$). Tukey’s HSD showed that there was significant effect of sulpiride in fasted mice ($$P \leq .0228$$) in the PFC. ANOVA did not find significant effects of sulpiride treatment or fasting on the expression of p-CREB, Akt, ERα, or ERβ.
WB results for the striatum are shown in Figure 6C (Q–X). Fasting increased expression of RASD2 in the striatum, but this effect was not reversed by sulpiride (Fsulpiride[1,8] = 2.465, $$P \leq .1551$$; Ffasting[1,8] = 2.847, $$P \leq .1300$$; Fsulpiride×fasting[1,8] = 11.22, $$P \leq .0101$$). Tukey’s HSD showed that there was a significant effect of fasting in the vehicle-treated groups ($$P \leq .0303$$). Expression of BDNF (Fsulpiride[1,13] = 3.417, $$P \leq .0874$$; Ffasting[1,13] = 2.824, $$P \leq .1167$$; Fsulpiride×fasting[1,13] = 11.46, $$P \leq .0049$$) and CREB (Fsulpiride[1,11] = 0.7837, $$P \leq .3949$$; Ffasting[1,11] = 0.5026, $$P \leq .4931$$; Fsulpiride×fasting[1,11] = 14.80, $$P \leq .0027$$) in the striatum were also increased by fasting, and this effect was eliminated by sulpiride. Tukey’s HSD showed that there was a significant effect of fasting in vehicle-treated mice (BDNF: $$P \leq .0129$$) but not sulpiride-treated mice. Sulpiride administration decreased the expression of BDNF in fasted mice (BDNF: $$P \leq .0148$$; CREB: $$P \leq .0226$$). Expression of Akt (Fsulpiride[1,11] = 14.41, $$P \leq .0030$$; Ffasting[1,11] = 13.38, $$P \leq .0038$$; Fsulpiride×fasting[1,11] = 0.6597, $$P \leq .4339$$) and DRD2 (Fsulpiride[1,10] = 13.54, $$P \leq .0042$$; Ffasting[1,10] = 10.81, $$P \leq .0082$$; Fsulpiride×fasting[1,10] = 4.116, $$P \leq .0700$$) in the striatum was also increased by fasting and normalized by sulpiride. Tukey’s HSD found significant effects of fasting in vehicle-treated mice (Akt: $$P \leq .0470$$; DRD2: $$P \leq .0165$$), and sulpiride decreased Akt ($$P \leq .0264$$) and DRD2 ($$P \leq .0065$$) expression in fasted mice. Fasting also increased ERβ expression in the striatum, and this effect was reversed by sulpiride treatment (Fsulpiride[1,10] = 0.3373, $$P \leq .5743$$; Ffasting[1,10] = 0.8156, $$P \leq .3877$$; Fsulpiride×fasting[1,10] = 30.44, $$P \leq .0003$$). Tukey’s HSD showed that fasting increased ERβ expression in the striatum in vehicle-treated mice ($$P \leq .0362$$), and sulpiride reduced ERβ expression only in fasted mice ($$P \leq .0042$$). In non-fasted mice, sulpiride increased the expression of ERβ ($$P \leq .0360$$). There were no significant effects of sulpiride treatment or fasting on p-CREB or ERα levels in the striatum.
## DISCUSSION
In the present study, we found that 9-hour fasting altered differential gene expression in the PFC of ovariectomized mice. The results of GO enrichment analysis and KEGG pathway enrichment analysis of differentially expressed genes showed that fasting affected genes related with dopaminergic signaling, including Drd2, Drd1, TH, and Rasd2. A study also found that calorie restriction causes dopaminergic dysregulation in female mice (Carlin et al., 2016). In addition, calorie restriction delays the age-related or diabetes-related loss of DRD2 in rat brain (Roth et al., 1984; Thanos et al., 2008; de Leeuw van Weenen et al., 2011). Our results are consistent with the above studies showing that the molecular mechanisms of fasting on depression may be closely linked to dopamine.
To further explore the molecular mechanisms underlying the effects of fasting in ovariectomized mice, Drd2 and Rasd2 were selected from the RNA-seq study for further study. Interestingly, in our studies, RASD2 protein was decreased in ovariectomized mice in the HP but not in the PFC. These results indicate that the depression model established by ovariectomy induces the downregulation of RASD2 in the HP (but not in the PFC), and the downregulation of RASD2 expression in the HP is one of the pathogeneses of depression. Although Rasd2 has been reported to be a common regulator of fasting and estrogen in the PFC (Wang et al., 2019), there may be other regulators involved in ovariectomized mice. It has been reported that short-term fasting increases autophagy in cortical neurons (Alirezaei et al., 2010), and overexpression of Rasd2 can also activate autophagy (Mealer et al., 2014). However, whether short-term fasting further increases autophagy through Rasd2 is still unknown, and this should be investigated in further studies. In addition, ovariectomy also reduced DRD2 expression in the HP.
Considering that there is a high density of DRD2 binding site in dorsal HP (Charuchinda et al., 1987; Edelmann and Lessmann, 2018), lentivirus vectors was microinjected into the dorsal HP to achieve Rasd2 overexpression in the HP of ovariectomized mice. Rasd2 overexpression in ovariectomized mice significantly decreased immobility in the FST and TST and increased swimming time and sucrose consumption, indicative of antidepressant effects. No effects were observed in the OFT, showing that the effects of Rasd2 overexpression were behaviorally specific and not just the result of elevated spontaneous activity. These results indicate that Rasd2 and DRD2 are fundamentally involved in ovariectomy-induced depression. Previous studies have shown that antidepressants work on catecholaminergic systems selectively increase climbing behavior, whereas antidepressants targeting serotonergic systems selectively increase swimming behavior (Cryan et al., 2005; Slattery and Cryan, 2012). In addition, emotional animals defecate more than non-emotional animals (Broadhurst, 1957; Craft et al., 2010). In our study, Rasd2 overexpression primarily increased swimming, with only modest effects on climbing and defecating behavior. Whether these effects involve dopamine or dopamine interactions with other monoaminergic systems is uncertain. In addition, Rasd2 overexpression in the HP of ovariectomized mice significantly increased DRD2 expression in the HP. Studies have found that Rasd2 affects DRD2-dependent activity (Sciamanna et al., 2015) and also regulates striatal-dependent behaviors in a gender-specific manner (Ghiglieri et al., 2015). These results indicate that Rasd2 and DRD2 are likely to be involved in the molecular mechanisms underlying depressive-like symptoms induced by ovariectomy.
The role of RASD2 in DRD2-mediated antidepressant-like effects of 9-hour fasting based on ovariectomized mice was then examined. Immobility time in the FST was increased and sucrose consumption decreased, indicative of a depressive profile, and fasting significantly reversed the changes of depression-like behaviors. Moreover, DRD2 antagonists blocked the antidepressant-like effects of fasting. Antidepressant-like effects of fasting on climbing and swimming in the FST may involve dopamine or its interaction with other monoaminergic systems. This finding is consistent with previous studies showing that sulpiride antagonizes antidepressant effects on immobility (Borsini et al., 1988; Donato et al., 2013). Importantly, the present study suggests that DRD2 mediates the reduction in immobility time induced by fasting as well. Nonetheless, RASD2 and DRD2 appear to be intimately related in the effects of ovariectomy and fasting. Firstly, the antidepressant-like effect of 9-hour fasting involving DRD2 was shown to be closely related to RASD2 expression. Fasting increased the expression of RASD2 in the PFC, HP, and striatum, and DRD2 antagonists reversed this increase in RASD2 levels induced by fasting. Similarly, fasting increased DRD2 expression in the PFC and HP. These results suggest that the reduction in immobility caused by fasting is likely to be caused by regulating the expression of DRD2 and RASD2.
Studies have found that Rasd2 deficiency leads to abnormal excitatory responses of cholinergic interneurons to activation of DRD2 receptors. Furthermore, PI3K inhibitors rescue the abnormal DRD2 response in Rasd2 knockout mice, and it has been found RASD2 acts as a bridge between PI3K and Akt signaling pathways (Subramaniam et al., 2011; Bang et al., 2012; Harrison et al., 2013; Lee et al., 2015). Fasting was shown to activate the PI3K-Akt pathway in the KEGG pathway analysis and lead to a decrease in Akt expression, and sulpiride treatment reverses the fasting-induced increases in Akt expression. These results indicate that changes in Akt expression may also participate in the reduction of immobility time mediated by DRD2.
The PI3K-Akt pathway is a DRD2-linked signaling pathways that has been linked to the pathogenesis of mood disorders and BDNF-mediated neuroprotection (Cao et al., 2019; Huang et al., 2021). Consistent with previous studies (Cui et al., 2018; Wang et al., 2019), in our studies, fasting increased CREB and BDNF expression in the PFC and HP of ovariectomized mice. Moreover, DRD2 antagonist antagonized fasting-induced increases in CREB and BDNF expression, indicating that the CREB-BDNF signaling pathway is likely to play a role in the antidepressant-like effects of fasting mediated by DRD2. Therefore, RASD2 may produce antidepressant-like effects by regulating the expression of Akt and further affecting the CREB-BDNF signaling pathway.
It has been reported that the mouse plasma levels of estrone, estradiol, and estriol were reduced 1 week after ovariectomy, and estradiol and estriol levels in plasma were similar between 1 week and 3 months post-ovariectomy and 17β-estradiol treatment (Zhang et al., 2019). In the present study, ovariectomy induced an increase in immobility in the FST at 1 week after ovariectomy, similar to previous findings (Estrada-Camarena et al., 2011). In previous studies, caloric restriction produced estrogen-like effects (Bigsby et al., 1997) and increased estrogen levels; moreover, there was an additive antidepressant-like effect of fasting and estrogen in ovariectomized mice (Wang et al., 2019). Fasting increased ERβ expression in the HP and striatum, an effect antagonized by sulpiride. 17 β-Estradiol has no effect on immobility in ERβ knockout mice, but not ERα knockout mice in the FST (Rocha et al., 2005). These studies indicate that the increase in immobility time of ovariectomized mice is mainly related to ERβ. Moreover, the present study suggests that ERβ is involved in the effects mediated by DRD2 on the antidepressant-like effects of fasting, although the connection between RASD2, DRD2, and ERβ remains to be fully elucidated.
## CONCLUSION
In summary, Rasd2 plays a role in depression-like behavior induced by ovariectomy, and this role is related to the regulation of DRD2. Nine-hour fasting has antidepressant-like effects in ovariectomized mice and upregulates the expression of RASD2, DRD2, CREB-BDNF, Akt, and ERβ (Figure 7). Moreover, these effects are blocked by DRD2 antagonists. Rasd2 can therefore be postulated to be a potential therapeutic target for depression and perhaps also a potential predictive marker for depression. Finally, dopamine receptor-mediated gene regulation in antidepressant-like effects of acute fasting also provides new ideas for the treatment of depression. However, whether fasting has similar therapeutic effects on patients with depression and the conditions for implementing fasting (such as the specific time point and duration of fasting) need to be further explored in clinical studies.
**Figure 7.:** *A schematic diagram of the effect of RASD Family Member 2 (RASD2) in dopamine D2
receptor (DRD2)-mediated antidepressant-like effects produced by acute fasting in the
hippocampus (HP). Akt, protein kinase B; BDNF, brain-derived neurotrophic factor; CREB,
cAMP-response element binding protein; ERβ, estrogen receptor β; Gi, inhibitory
adenylate cyclase g protein.*
## Author Contributions
B.J.L. contributed conception and design of the study; Z.Q.C., C.H.Z., F.Y.Z., and J.J.P. performed the research; Z.Q.C. wrote the paper; and B.J.L. and R.J.C. provided the critical revisions. All authors read and approved the final manuscript.
## Conflict of Interests
The authors declare that they have no conflict of interests.
## Data Availability
The data underlying this article will be shared on reasonable request to the corresponding author.
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|
---
title: Fetal anomalies in gestational diabetes mellitus and risk of fetal anomalies
in relation to pre-conceptional blood sugar and glycosylated hemoglobin
authors:
- Rami M. M. Al-Shwyiat
- Ahmed M. Radwan
journal: Journal of Mother and Child
year: 2023
pmcid: PMC10032312
doi: 10.34763/jmotherandchild.20222601.d-22-00040
license: CC BY 4.0
---
# Fetal anomalies in gestational diabetes mellitus and risk of fetal anomalies in relation to pre-conceptional blood sugar and glycosylated hemoglobin
## Abstract
### Background
The risk of fetal anomalies (FAs) is increased in infants of diabetic mothers. FAs are closely related to the glycosylated hemoglobin (HbA1c) level in pregnancy.
### Objectives
To detect the prevalence of FAs in women with gestational diabetes mellitus (GDM).
### Material and methods
157 pregnant women with GDM were included in this study, and data from 151 women were analyzed. Beyond the regular antenatal check-up, the HbA1c was checked monthly during the antenatal follow-up. Collected data after delivery were analyzed to detect the prevalence of FAs in women with GDM and the risk of FAs in relation to the pre-conceptional blood sugar and HbA1c.
### Results
The FAs were recorded in $8.6\%$ [13] of the 151 women with GDM. The recorded FAs were cardiovascular [$2.6\%$ [4]], musculoskeletal [$1.3\%$ [2]], urogenital [$1.3\%$ [2]], gastrointestinal [$1.3\%$ [2]], facial [$0.7\%$ [1]], central nervous system [$0.7\%$ [1]], and multiple FAs [$0.7\%$ [1]]. The uncontrolled pre-conceptional blood sugar significantly increased RR [RR 2.2 ($95\%$CI: 1.7-2.9); $P \leq 0.001$], and odds of FAs [OR 17.05 ($95\%$CI: 2.2-134.9); $$P \leq 0.007$$] in women with GDM. In addition, the HbA1c ≥6.5 significantly increased RR [RR 2.8 ($95\%$ CI: 2.1-3.8); $P \leq 0.001$], and odds of FAs [OR 24.8 ($95\%$ CI: 3.1-196.7); $$P \leq 0.002$$] in women with GDM.
### Conclusion
In this study, the prevalence of FAs in women with GDM was $8.6\%$. Uncontrolled pre-conceptional blood sugar and HbA1c ≥6.5 in the first trimester significantly increased the relative risk and the odds of FAs.
## Introduction
The incidence of gestational diabetes mellitus (GDM) is 2-$9\%$ [1, 2]. Diabetes during pregnancy is associated with increased risk of fetal anomalies (FAs) [3, 4].
An association between DM and FAs has been recorded since the 19th century, and caudal regression syndrome has a strong association with DM. Studies of infants of diabetic mothers showed increased risk of cardiovascular system (CVS), genitourinary, and musculoskeletal FAs in infants of women with uncontrolled diabetes during pregnancy [5, 6].
Diabetic embryopathy is a spectrum of FAs or disruptions caused by maternal DM [7]. The risk of FAs, particularly open neural tube defect (ONTD) anomalies [8], is markedly increased in infants of diabetic mothers.
FAs are closely related to the glycosylated hemoglobin (HbA1c) level in pregnancy [9, 10]. Therefore, this prospective observational study was designed to detect the prevalence of FAs in women with GDM (primary outcome), and the risk of FAs in relation to the pre-conceptional blood sugar and HbA1c (secondary outcome).
## Patients and Methods
This prospective observational study was conducted over two years (August 2020 until August 2021) after approval of the Obstetrics and Gynecology department’s ethical committee of United Doctors Hospital, Jeddah, KSA.
Women discovered to have GDM during the antenatal screening (between 24-28 weeks’ gestation), with regular antenatal follow-up at least twice a month, were included in this study after informed consent in accordance with the Declaration of Helsinki.
Women with irregular antenatal care or incomplete antenatal records, and those who refused to participate, were excluded from this study.
157 pregnant women with GDM regularly attending the antenatal clinic were included in this study, and the data of 151 women were eventually analyzed (with 6 women being excluded because of incomplete antenatal records and absence of delivery outcome).
The American Diabetes Association recommends screening of all pregnant women at 24-28 weeks’ gestation for GDM using the OGTT (oral glucose tolerance test) [11].
GDM is a group of glucose intolerances discovered for the first-time during pregnancy and diagnosed according to the American Diabetes *Association criteria* [11].
In women with GDM, the diabetic diet regimen started to control blood glucose level. If the diabetic diet regimen gave unsatisfactory blood glucose control (pre-prandial >5.3 mmol/l, and 2-hours post-prandial >6.7 mmol/l), subcutaneous insulin was prescribed to control the blood glucose level (mainly insulin Monotard and insulin Actrapid as bolus injections).
During the antenatal follow-ups, HbA1c levels were checked monthly (normal HbA1c <$5.7\%$) beside the regular antenatal check, which included urine sugar, urine albumin, blood pressure, fetal assessment, and basic laboratory investigations according to the hospital’s protocol.
Collected data after delivery were analyzed to detect the prevalence of FAs in women GDM (primary outcome), and the risk of FAs in relation to the pre-conceptional blood sugar and HbA1c (secondary outcome).
## Sample Size
The required sample size was calculated using G Power software version 3.1.9.4 for sample size calculation, setting α-error probability at 0.05, power (1- β error probability) at $0.95\%$, and effective sample size (w) at 0.5. An effective sample including ≥100 was needed to produce a statistically acceptable figure.
## Statistical Analysis
Collected data were statistically analyzed using the Statistical Package for Social Sciences (SPSS): computer software version 20 (Chicago, IL, USA). Numerical variables were presented as mean and standard deviation (±SD), while categorical variables were presented as number (n) and percentage (%). The odds ratio (OR) and relative risk (RR) of FAs in relation to the pre-conceptional blood sugar and HbA1c were calculated. P-value <0.05 was considered significant.
## Results
157 pregnant women with GDM attending the antenatal clinic were included in this study, and data of 151 [$96.2\%$] were finally analyzed. The mean age of the studied women, history of medical disorders, HbA1c, daily insulin dose, and the obstetrics outcome are presented in Table 1.
**Table 1**
| Variables | Studied population Number 151 |
| --- | --- |
| Maternal characteristics | |
| Age (years) | 26.9 ± 3.28 |
| History of hypertension | 11.9% (18/151) |
| Family history of diabetes | 63.6% (96/151) |
| HbA1c (g%) | 5.57 ± 0.91 |
| Daily insulin dose | 25.7 ± 19.03 |
| Obstetrics Outcome | |
| Past history of Miscarriage | 73.5% (111/151) |
| Cesarean section (CS) | 30.5% (46/151) |
| Gestational age at delivery (weeks) | 37.8 ± 0.6 |
| Birth weight (kg) | 3.26 ± 0.2 |
FAs were recorded in $8.6\%$ ($\frac{13}{151}$) of the studied women with GDM. The recorded FAs were cardiovascular [transposition of great arteries (TGA), ventricular septal defect (VSD), and atrial septal defect (ASD)] in $2.6\%$ [4]; musculoskeletal [caudal dysgenesis, and limb reduction] in $1.3\%$ [2]; urogenital [renal agenesis, and hydronephrosis] in $1.3\%$ [2]; gastrointestinal [pyloric stenosis and duodenal atresia] in $1.3\%$ [2]; facial [cleft lip and palate] in $0.7\%$ [1]; central nervous system [(ONTD) spina bifida] in $0.7\%$ [1]; and multiple FAs in $0.7\%$ [1] [Table 2].
**Table 2**
| Fetal anomalies (FAs) | Number and (%) |
| --- | --- |
| Cardiovascular system (TGA, VSD, ASD) | (4/151) 2.6% |
| Musculoskeletal system (caudal dysgenesis and limb reduction) | (2/151) 1.3% |
| Urogenital anomalies (renal agenesis and hydronephrosis) | (2/151) 1.3% |
| Gastrointestinal system (pyloric stenosis and duodenal atresia) | (2/151) 13% |
| Facial anomalies (cleft lip and palate) | (1/151) 0.7% |
| Central nervous system (ONTD (spina bifida)) | (1/151) 0.7% |
| Multiple FA anomalies | (1/151) 0.7% |
| Total | (13/150) 8.6% |
The uncontrolled pre-conceptional blood sugar significantly increased RR [RR 2.2 ($95\%$CI: 1.7-2.9); $P \leq 0.001$], and odds of FAs [OR 17.05 ($95\%$CI: 2.2-134.9); $$P \leq 0.007$$], in women with GDM.
In addition, the HbA1c ≥6.5 significantly increased RR [RR 2.8 ($95\%$ CI: 2.1-3.8); $P \leq 0.001$], and odds of FAs [OR 24.8 ($95\%$ CI: 3.1-196.7); $$P \leq 0.002$$], in women with GDM [Table 3].
**Table 3**
| Studied women with GDM (N = 151) | FAs group Exposed group (N = 13) | Non-FAs group Controls (N = 138) | RR (95%CI) P-value | OR (95%CI) P-value |
| --- | --- | --- | --- | --- |
| Pre-conceptional blood sugar | | | 2.2 | 17.05 |
| Controlled (N = 82) | 1.0 | 81.0 | (1.7-2.9) | (2.2-134.9) |
| Uncontrolled (N = 69) | 12.0 | 57.0 | <0.001* | 0.007* |
| HbA1c in first trimester | | | 2.8 | 24.8 |
| <6.5 (N = 94) | 1.0 | 93.0 | (2.1 – 3.8) | (3.1-196.7) |
| ≥6.5 (N = 57) | 12.0 | 45.0 | <0.001* | 0.002* |
## Discussion
157 pregnant women with GDM attending the antenatal clinic were included in this study, and data of 151 ($96.2\%$) were finally analyzed to detect the prevalence of FAs in women with GDM (primary outcome), and the risk of FAs in relation to the pre-conceptional blood sugar and HbA1c (secondary outcome).
In this study, $63.6\%$ [96] of the studied women had a positive family history of DM, and $30.5\%$ [46] infants were delivered by caesarean section.
Allen et al. concluded that a careful history obtained from women with GDM can identify other risks, such as family history of DM or advanced maternal age, that may further increase the risk of chromosomal abnormalities or FAs [5].
Blackwell et al. concluded that the overall cesarean delivery rates for women with GDM Class A2, B, C, D-F pregnancies were $20.3\%$, $40\%$, $37\%$, and $57.1\%$, respectively [12].
FAs were recorded in $8.6\%$ [13] of the studied women with GDM. The recorded FAs were cardiovascular [$2.6\%$ [4]], musculoskeletal [$1.3\%$ [2]], urogenital [$1.3\%$ [2]], gastrointestinal [$1.3\%$ [2]], facial [$0.7\%$ [1]], central nervous system [$0.7\%$ [1]], and multiple FAs [$0.7\%$ [1]].
Similarly, Aberg et al. found that the FAs associated with diabetic pregnancies included cardiovascular (TGA, VSD, situs inversus, and hypoplastic left ventricle); central nervous system (anencephaly, encephalocele, and spina bifida); caudal regression syndrome; renal agenesis; multicystic dysplasia; and gastrointestinal (anal/rectal atresia and small left colon) anomalies [13].
An association between DM and FAs has been recorded since the 19th century. Caudal regression syndrome has had a strong association with DM (200 times more frequently in diabetic mothers than controls), and $4\%$ of fetuses of diabetic women had at least one major FA (most commonly, CVS or musculoskeletal anomalies) [14].
Studies of infants of diabetic mothers showed increased risk of cardiovascular, genitourinary, musculoskeletal, and other FAs in women with uncontrolled diabetes during pregnancy [5, 6]. FAs are closely related to the HbA1c level in pregnancy [9, 10].
In this study, the uncontrolled pre-conceptional blood sugar significantly increased RR (RR 2.2; $P \leq 0.001$), and odds of FAs (OR 17.05; $$P \leq 0.007$$) in women with GDM. In addition, HbA1c ≥6.5 significantly increased RR (RR 2.8; $P \leq 0.001$), and odds of FAs (OR 24.8; $$P \leq 0.002$$) in women with GDM.
Miller et al. found the FAs rate was $22.4\%$ in diabetic pregnant women when the maternal first trimester HbA1c was >8.5 [15]. Allen et al. concluded that pregnancy in diabetic women should be planned with proper pre-pregnancy counseling and proper glycemic control [5]. Previous studies and meta-analysis concluded that pre-conceptional counseling for diabetic women with optimum glycemic control can significantly decrease the risk of FAs [16, 17].
Baptiste-Roberts et al. demonstrated that women without diabetes before pregnancy are not at risk of FAs, and that an elevated HbA1c in the first trimester of pregnancy is a fair indicator of hyperglycemia during organogenesis [18]. In addition, the aneuploidy and chromosomal anomalies that occur with GDM are likely associated with increased maternal age [19, 20].
The Canadian Diabetes Association also concluded that type 1 or type 2 pregnant diabetic women should maintain the preconception HbA1c ≤$7\%$ to decrease the risk of FAs [8].
Miller et al. concluded that the HbA1c level before the 14th week of gestation reflects the diabetic state during organogenesis and strongly correlated with the rate of FAs [15]. The rate of FAs was $3.4\%$ when the maternal first trimester HbA1c was ≤8.5, and it was $22.4\%$ when the maternal first trimester HbA1c was >8.5 [15].
Failure to study the chromosomal anomalies associated with the detected FAs, and absence of a control group (i.e. non-diabetic pregnant women), were the limitations of this study.
To the best of our knowledge, this study was the first prospective cohort study conducted in our region to detect the prevalence of FAs in GDM and the risk of FAs in relation to the pre-conceptional blood sugar and HbA1c. Future comparative studies, including the neonatal outcome of diabetic pregnant women compared to non-diabetic pregnant controls, are needed.
## Conclusion
In this study, the prevalence of FAs in women with GDM was $8.6\%$. Uncontrolled pre-conceptional blood sugar and HbA1c ≥6.5 in the first trimester significantly increased the relative risk and the odds of FAs.
## References
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|
---
title: Gestational weight gain and blood pressure control in physiological pregnancy
and pregnancy complicated by hypertension
authors:
- Tomasz Mikołaj Maciejewski
- Ewa Szczerba
- Agnieszka Zajkowska
- Katarzyna Pankiewicz
- Anna Bochowicz
- Grzegorz Szewczyk
- Grzegorz Opolski
- Maciej Małecki
- Anna Fijałkowska
journal: Journal of Mother and Child
year: 2022
pmcid: PMC10032322
doi: 10.34763/jmotherandchild.20222601.d-22-00020
license: CC BY 4.0
---
# Gestational weight gain and blood pressure control in physiological pregnancy and pregnancy complicated by hypertension
## Abstract
### Background
Obesity is a widely recognised risk factor for chronic and gestational hypertension. Influence of gestational weight gain on blood pressure control throughout the pregnancy is not well characterised.
### Material and methods
Women in the third trimester of a singleton pregnancy were recruited to the study. Medical records were analysed and a special survey was conducted to obtain history on hypertensive disorders in pregnancy and weight changes during pregnancy. Blood pressure measurements were taken during the office visit in line with international guidelines. Relationships between gestational weight gain and maximal and office values of systolic and diastolic blood pressure values were analysed.
### Results
Data of 90 women in normal pregnancy, 40 with gestational hypertension and 21 with chronic hypertension were analysed. Gestational weight gain was 11.9 ± 4.6 kg in the normal pregnancy group, 13.0 ± 5 kg in the gestational hypertension group and 10.6 ± 3.4 kg in the chronic hypertension group. Gestational weight gain positively correlated with both office ($r = 0.48$; $p \leq 0.001$) and maximal blood pressure values ($r = 0.34$; $$p \leq 0.004$$) in normal pregnancy and with maximal blood pressure values ($r = 0.57$; $$p \leq 0.02$$) in women with chronic hypertension. No correlation was observed between gestational weight gain and blood pressure values among women with gestational hypertension.
### Conclusion
In normal pregnancy and in women with chronic hypertension greater gestational weight gain is related to higher blood pressure values in the third trimester.
## Introduction
Hypertensive disorders in pregnancy (HDP) including pre-existing/chronic hypertension (CH), gestational hypertension (GH), pre-existing hypertension with superimposed gestational hypertension with proteinuria and antenatally unclassifiable hypertension are associated with different risk factors and complication rates in pregnancy [1]. Although the rate of GH in Europe and the USA is declining slightly, it still affects 4–$8\%$ of pregnancies [2]. Understanding the pathophysiology and identifying risk factors, especially modifiable ones, for rises in blood pressure during pregnancy is essential in prevention of some types of HDP and minimisation of complication rates [3].
With every 1 kg/m2 of prepregnancy body mass index (BMI), the risk for GH rises by $6\%$ [4]. Gestational hypertension increases the risk for future hypertension, coronary heart disease, and stroke [5]. Overweight and obesity are one of the modifiable risk factors for HDP [6].
There is emerging evidence on the role of gestational weight gain (GWG) on short- and long-term maternal and neonatal health outcomes. Additionally, GWG has been linked with increased risk of obesity in the mother [7] and cardio-metabolic outcomes in children [8].
During normal pregnancy blood pressure decreases in the first and second trimester and begins to rise from the mid-third trimester, achieving prepregnancy values [9]. The factors influencing blood pressure profile throughout the pregnancy are not fully described. There seems to be a complex relationship between prepregnancy weight and BMI, gestational weight gain and blood pressure profile during pregnancy and in later life. So far, few studies have assessed the relationship between GWG and blood pressure profile in pregnant women. There is little data on factors influencing blood pressure control in CH women during pregnancy. Women with CH were often excluded from the analysis [10]. Data comparing GWG in different subpopulations of women with HDP and its impact on blood pressure control during pregnancy is lacking. The question whether pregnancy blood pressure profile itself could also provide information on stratification of cardiovascular risk remains to be uncovered and therefore it is important to identify factors influencing changes in blood pressure during pregnancy. We aimed at describing the effects of GWG on blood pressure values during the third trimester of pregnancy in GH and CH as well as in normal pregnancy. Our hypothesis was that GWG will have different impact on blood pressure control in those groups.
## Study population and protocol
Women in the third trimester of a singleton pregnancy were recruited for the study. This was a single-center prospective trial carried out between October 2014 and June 2017. Data regarding anthropometric and demographic factors – such as height, pregestational weight, gestational weight at the time of the examination, the history of hypertensive disease – were obtained by a questionnaire and compared, when available, with obstetric records to ensure accuracy. The analysed information regarding hypertension includes the time of diagnosis, highest values of systolic and diastolic blood pressure during pregnancy, treatment, and number of ambulatory blood pressure measurement tests. Chronic hypertension was defined as hypertension diagnosed before or during the first 20 weeks of pregnancy. Women with secondary hypertension were excluded from the study. Hypertension diagnosed after the twentieth week of pregnancy constituted gestational hypertension. As no data on postpartum resolution of blood pressure abnormalities were collected, this criterion was not a part of the used definition of gestational hypertension. There were no women with pre-existing hypertension with superimposed gestational hypertension with proteinuria in the studied group. This is a substudy of a project financed by the National Science Centre, Kraków, Poland (NCN $\frac{2013}{11}$/N/NZ$\frac{5}{03388}$). The project was approved by the local Ethical Committee (opinion number $\frac{17}{2013}$). Women were included in the study after providing a written informed consent.
## Blood pressure measurement
Each women had an office blood pressure measurement obtained according to recommended guidelines with an Omron device certified for use during pregnancy [11]. Measurements were obtained on both arms.
## Statistical analysis
We analysed the results for women in normal pregnancy and for women with hypertensive disorders of pregnancy separately. Afterwards a subanalysis of women with CH and GH was carried out. Body mass index (BMI) was calculated as weight in kilograms / height in meters squared. Descriptive data are presented as a mean or median depending on the distribution pattern. Differences between the groups regarding weight and BMI were calculated by Mann–Whitney U test. We decided to use the nonparametric test because of the size of the compared groups. Spearman correlation was calculated to examine the relationship between anthropometric parameters and blood pressure in each prespecified subgroup. SPSS Statistics 23 statistical software was used for the analysis. P value of less than 0.05 was considered as statistically significant.
## Study population characteristics
The study group consisted of 61 women with HDP, 40 with GH, and 21 with CH, and 90 women in normal pregnancy. They were assessed during the third trimester. Between women in normal pregnancy and HDP there were no differences in age (30.5 ± 4.1 vs. 32 ± 4.7 years; $$p \leq 0.14$$) nor duration of pregnancy (32 ± 3.4 vs. 34 ± 3.8 weeks; $$p \leq 0.16$$). Subanalysis of the HDP group showed that women with GH had slightly higher gestational age compared with CH group at the time of assessment (Table 1). In the normal pregnancy group, 10 women ($11\%$) were overweight and 5 ($6\%$) were obese before pregnancy. In comparison $37.5\%$ ($\frac{15}{40}$) of the GH and $38.1\%$ ($\frac{8}{21}$) of the CH group were overweight before pregnancy. Obesity appeared in $20\%$ ($\frac{8}{40}$) of GH group and $4.8\%$ ($\frac{1}{21}$) of CH group. In total, prepregnancy weight and BMI were higher in the HDP group than in women with normal pregnancy (for weight 63.7 vs 72.1 kg; $p \leq 0.001$; for BMI 22.8 vs 26.2 kg/m2; $p \leq 0.001$). Interestingly, the change in weight (11.9 ± 4.6 vs. 12.2 ± 4.2 kg; $$p \leq 0.512$$) and in BMI (4.2 ± 1.8 vs. 4.4 ± 1.9 kg/m2; $$p \leq 0.352$$) during pregnancy were not different between the groups.
**Table 1**
| Unnamed: 0 | normal pregnancy | hypertensive disorders in pregnancy | p | gestational hypertension | chronic hypertension | p.1 |
| --- | --- | --- | --- | --- | --- | --- |
| prepregnancy weight [kg] | 63.7 | 72.1 | <0.001 | 74.0 | 70.0 | 0.288 |
| prepregnancy body mass index [kg/m2] | 22.8 | 26.2 | <0.001 | 26.1 | 25.8 | 0.532 |
| weight change [kg] | 11.9 | 12.2 | 0.512 | 13.0 | 10.8 | 0.163 |
| body mass index change [kg/m2] | 4.2 | 4.4 | 0.352 | 4.8 | 3.7 | 0.28 |
| age [years] | 30.5 | 32.0 | 0.14 | 31.5 | 33.0 | 0.212 |
| pregnancy duration | 32.0 | 34.0 | 0.16 | 35.0 | 33.0 | 0.04 |
| office left arm SBP [mmHg] | 115.0 | 134.0 | <0.001 | 138.0 | 129.0 | 0.018 |
| office left arm SBP [mmHg] | 70.0 | 85.0 | <0.001 | 87.5 | 80.0 | 0.034 |
| office right arm SBP [mmHg] | 117.0 | 132.0 | <0.001 | 136.0 | 128.8 | 0.015 |
| office right arm SBP [mmHg] | 70.0 | 85.5 | <0.001 | 87.0 | 78.5 | 0.008 |
| maximal SPB [mmHg] | 130.0 | 160.0 | <0.001 | 160.0 | 155.0 | 0.247 |
| maximal DBP [mmHg] | 80.0 | 100.0 | <0.001 | 99.5 | 100.0 | 0.579 |
| HR [beats/minute] | 86.0 | 90.5 | 0.3 | 88.0 | 92.5 | 0.794 |
## Blood pressure measurement results
Both office systolic and diastolic blood pressure differed significantly among all three studied groups. Maximal blood pressure values were similar between the GH and CH group. Details are provided in Table 1.
## Correlations between blood pressure and weight: normal pregnancy and hypertensive disorders in pregnancy
In the normal pregnancy group, higher GWG was associated with higher office and maximal measurements of blood pressure (Table 2, Graphs 1 and 2).
**Table 2**
| Unnamed: 0 | normal | hypertensive disorders | gestational | chronic |
| --- | --- | --- | --- | --- |
| | pregnancy | in pregnancy | hypertension | hypertension |
| | r = 0.286 | r = 0.233 | r = 0.264 | r = 0.022 |
| office left arm SBP [mmHg] and gestational weight gain | p = 0.014 | p = 0.115 | p = 0.131 | p = 0.943 |
| office left arm DBP [mmHg] and gestational weight gain | r = 0.221 | r = 0.25 | r = 0.147 | r = 0.329 |
| | p = 0.06 | p = 0.09 | p = 0.407 | p = 0.272 |
| | r = 0.479 | r = 0.379 | r = 0.338 | r = 0.408 |
| office right arm SBP [mmHg] and gestational weight gain | p < 0.001 | p = 0.008 | p = 0.05 | p = 0.147 |
| | r = 0.333 | r = 0.174 | r = 0.085 | r = 0.308 |
| office right arm DBP [mmHg] and gestational weight gain | p = 0.005 | p = 0.237 | p = 0.632 | p = 0.285 |
| maximal SPB [mmHg] and gestational weight gain | r = 0.34 | r = 0.165 | r = -0.097 | r = 0.572 |
| | p = 0.004 | p = 0.253 | p = 0.584 | p = 0.021 |
| | r = 0.37 | r = 0.063 | r = -0.252 | r = 0.739 |
| maximal DBP [mmHg] and gestational weight gain | p = 0.002 | p = 0.665 | p = 0.15 | p = 0.001 |
| | r = 0.24 | r = 0.148 | r = 0.182 | r = 0.079 |
| HR [beats/minute] and gestational weight gain | p = 0.038 | p = 0.315 | p = 0.302 | p = 0.788 |
Cumulative analysis of HDP group showed no correlation between blood pressure control parameters and GWG and BMI gain nor with prepregnancy weight and BMI values (Table 2).
**Graph 1:** *Correlation between systolic blood pressure and weight change in normal pregnancy group.* **Graph 2:** *Correlation between systolic blood pressure and body mass index change in normal pregnancy group.*
Correlations between blood pressure and weight: gestational and chronic hypertension group subanalysis *Correlation analysis* between blood pressure measurements and anthropometric parameters was calculated in both types of HDP. Interestingly, although there were no correlations present in the GH group, a correlation between GWG and both maximal systolic and maximal diastolic blood pressure values measured during pregnancy were found in the CH group. A similar correlation with BMI change was noticed (for mSBP $r = 0.561$; $$p \leq 0.029$$ and for mDBP $r = 0.681$; $$p \leq 0.005$$). Details are shown in Table 2 and Graphs 3 and 4.
**Graph 3:** *Correlation between maximal systolic blood pressure and weight change in gestational hypertension group.* **Graph 4:** *Correlation between maximal systolic blood pressure and weight change in chronic hypertension group.*
## Discussion
The present study is the first to demonstrate the different impact on blood pressure control of gestational weight gain in women with GH and CH. Additionally we observed that the higher the GWG is, the greater the blood pressure values are during normal pregnancy.
Excess GWG is a major health concern affecting almost half of pregnant women [12]. So far GWG has been described as a risk factor for GH and for superimposed preeclampsia but not as a risk factor for poorer control of blood pressure during pregnancy in patients with chronic hypertension. Most of the available data are retrospective and analyse the relationship between GWG and final diagnosis of GH, not with the values of blood pressure, as will be shown further in the discussion.
## Gestational weight gain modifies blood pressure profile in pregnancy
Presented results show that greater GWG up to the third trimester of pregnancy correlates with higher office and maximal blood pressure measurements. This finding is in line with evidence from the literature. In a study of 158 healthy pregnant women, GWG in the second and third tertiles resulted in lack of mid-trimester drop in systolic and diastolic blood pressure [13]. Macdonald-Wallis et al. describe results of 12,522 women from the Avon Longitudinal Study of Parents and Children [10]. They found that in normotensive pregnant women, with every 200g of gestational weight gain to 18 weeks of pregnancy there is about $30\%$ of increase in risk of GH and preeclampsia, irrespective of prepregnancy weight. Lei et al. analysed the relation between trajectories of diastolic blood pressure values and GWG and found that women with highly increasing weight gain trajectory were at greater risk of being in the high-J shaped trajectory for diastolic pressure values [14]. This subgroup also had the highest values of systolic blood pressure values throughout the pregnancy. It seems also that prepregnancy weight gain leads to an increased risk of HDP [15].
Despite those findings there are no clinical recommendations regarding more screening, for example with ambulatory blood pressure monitoring, in women with excessive gestational weight gain for early diagnosis of HDP.
## Gestational weight gain as a risk for GH and GH control
Excessive gestational weight gain, especially in early pregnancy, is recognised as a risk factor for GH, even independently of obesity prior to pregnancy [16]. Additionally, the greater GWG the higher this risk is across different races [17,18]. There are no data on the value of GWG and the control of GH once the diagnosis is made.
## Gestational weight gain as a risk factor for poorer control of CH during pregnancy
In our study there was a positive correlation between GWG and maximal systolic and diastolic blood pressure values during pregnancy, suggesting that it might be one of the parameters influencing blood pressure control. We did not observe correlation between GWG and measurement results obtained in the office, probably as a result of proper hypotensive treatment throughout further pregnancy.
No data on influence of GWG on blood pressure profile control in pregnant women with chronic hypertension were found. However, it is known that higher than recommended GWG increases maternal, obstetric and neonatal risks in this subgroup [19]. Excessive GWG in women without prepregnancy overweight or obesity aggravates risk of superimposed preeclampsia 3.5 times [20].
## Conclusion
In the third trimester of a normal pregnancy, a greater GWG is related to higher blood pressure values. Despite that in women with hypertensive disorders in pregnancy such dependency was not observed, a separate analysis revealed that greater weight gain in women with CH is related to higher maximal values of systolic and diastolic blood pressure. These observations underline pathophysiological differences between CH and GH. The GWG has similar influence on blood pressure in normal pregnancy and in women with CH during pregnancy, but not in women with a diagnosis of GH. This study highlights the need for further assessment of GWG and blood pressure profiles in pregnancy.
## Limitations
There are several limitations of our study. First, a small number of cases for particular subtypes of the HDP group was included. Second, we did not analyse the influence of GWG on pregnancy complications in the studied patients. Third, a 24-h ambulatory blood pressure monitoring was not performed as a part of the study protocol, which would have provided more averaged values of the measurements. And finally, the impact of treatment with antihypertensive medications was not evaluated.
## ORCID IDs of the Authors:
Tomasz Mikołaj Maciejewski 0000-0003-3761-1924 (https:// orcid.org/0000-0003-3761-1924) Ewa Szczerba 0000-0002-4921-4726 (https://orcid.org/0000-0002-4921-4726) Agnieszka Zajkowska 0000-0002-6664-9705 (https://orcid.org/0000-0002-6664-9705) Katarzyna Pankiewicz 0000-0001-7756-1963 (https://orcid.org/0000-0001-7756-1963)
Anna Bochowicz† 0000-0002-8773-0908 (https://orcid.org/0000-0002-8773-0908) Grzegorz Szewczyk 0000-0003-4143-2777 (https://orcid.org/0000-0003-4143-2777) Grzegorz Opolski 0000-0003-4744-2554 (https://orcid.org/0000-0003-4744-2554) Maciej Małecki 0000-0002-7078-4918 (https://orcid.org/0000-0002-7078-4918)
Anna Fijałkowska 0000-0002-2225-9684 (https://orcid.org/0000-0002-2225-9684)
## Contributorship
Tomasz Mikołaj Maciejewski: Conceptualisation, methodology, investigation, data curation, supervision, project administration, visualisation, writing – original draft, writing – review & editing Ewa Szczerba: Conceptualisation, methodology, investigation, resources, data curation, formal analysis, visualisation, funding acquisition, writing – original draft, writing – review & editing Agnieszka Zajkowska: Conceptualisation, investigation, resources, data curation, formal analysis, writing – review & editing Katarzyna Pankiewicz: Data curation, investigation, resources, writing – review & editing
Anna Bochowicz: Data curation, methodology, investigation, resources, supervision Grzegorz Szewczyk: Data curation, investigation, resources, supervision, writing – review & editing Grzegorz Opolski: Conceptualisation, methodology, supervision, writing – review & editing Maciej Małecki: Conceptualisation, methodology, project administration, supervision, writing – review & editing
Anna Fijałkowska: Conceptualisation, methodology, investigation, resources, data curation, formal analysis, visualisation, supervision, project administration, funding acquisition, writing – review & editing
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|
---
title: Personality disorders, depression and anxiety in mothers of children with ADHD
and anxiety disorders in Iran
authors:
- Mohsen Dadashi
- Roshanag Bateni
- Abolfazl Ghoreishi
journal: Journal of Mother and Child
year: 2022
pmcid: PMC10032326
doi: 10.34763/jmotherandchild.20222601.d-22-00016
license: CC BY 4.0
---
# Personality disorders, depression and anxiety in mothers of children with ADHD and anxiety disorders in Iran
## Abstract
### Background/aim
This study aims to assess and compare personality disorders and psychiatric disorders (depression and anxiety) in mothers of children with ADHD and anxiety disorders aged 2–16 years living in Iran.
### Material and methods
This is a descriptive cross-sectional study. Participants were 168 mothers (100 with children having ADHD and 68 with children having anxiety disorders). The Millon Clinical Multiaxial Inventory-III, the Depression Anxiety Stress Scale (DASS-21) and the Symptom Checklist-90–Revised (SCL-90-R) were used for assessing personality disorders, depression and anxiety in mothers. Collected data were analysed in SPSS software.
### Results
Of 168 mothers, only 100 completed the questionnaires completely (68 having children with ADHD and 32 with anxious children). Of 100 mothers, 61 had personality disorders, where 21 had children with anxiety disorders and 40 had children with ADHD. The most common personality disorder was depressive personality disorder ($$n = 27$$) followed by compulsive personality disorder ($$n = 15$$). No antisocial, borderline and paranoid personality disorders were observed in mothers. Based on DASS-21, 72 mothers had depression, and 84 had anxiety. Based on the SCL-90-R, 86 had depression, and 81 had anxiety. We found no statistically significant difference between the two groups of mothers in terms of personality disorders, depression and anxiety.
### Conclusion
Prevalence of depression, anxiety and personality disorders in mothers of children with anxiety disorders and ADHD in *Iran is* high, and there is no difference between them. It is recommended that psychiatric and psychological counseling be provided for these mothers.
## Introduction
Raising a child with Attention Deficit Hyperactivity Disorder (ADHD) or anxiety disorders is challenging because their symptoms are linked to dysfunctional behaviours, yielding high levels of friction for family life [1]. ADHD is one of the most common mental disorders in childhood with symptoms, including inattention (not being able to keep focus), hyperactivity (excess movement that is not fitting to the setting) and impulsivity (hasty acts that occur in the moment without thought), and it affects $8.4\%$ of children and $2.5\%$ of adults [2, 3] with an overall pooled estimate of $7.2\%$ [4]. It is more common among boys than girls [5]. It can cause academic failure, social skills problems and strained parent-child relationships [6]. It impacts not only on the child but also on parents and siblings, causing disturbances to family and marital functioning [7]. Parents and relatives of ADHD children are at high risk for ADHD, comorbid psychiatric disorders, school failure, learning disability and impairments in intellectual functioning [8, 9, 10]. A study in Iran showed that the lifetime prevalence of depression in mothers and fathers having children with ADHD is $48.1\%$ and $43\%$, respectively [11]. There are some studies focused on the characteristics of mothers of children with ADHD. For example, Pimentel et al. [ 12] and Babakhanian et al. [ 13] showed that the mothers of children with ADHD experience higher levels of parenting stress and report more behavioural problems in their children. Sfelinioti and Livaditis [14] in a review study, found that maternal depressive disorder and children’s ADHD influence each other through multiple psychosocial and biological factors. Van Batenburg-Eddes et al. [ 15] and Ayani et al. [ 16] also found a strong association between maternal anxiety and depressive symptoms with ADHD symptoms in children. Treatment of children with ADHD can have a favourable impact on their mothers’ depressive symptoms, which can consequently reduce negative parental attitudes, diminish the risk of behavioural disorders in these children, and exert a positive effect on their treatment [17].
Mothers with ADHD may also have personality characteristics that can be maladaptive. Personality characteristics have as important roles in coping, decision-making and other aspects of parenting practices. The studies on the personality of these mothers based on the NEO five dimensions have reported low levels of conscientiousness and agreeableness [1, 18], and higher levels of neuroticism [10, 19]. Positive personality traits (high levels of conscientiousness, extraversion, agreeableness and openness) can buffer the stress of rearing a child with ADHD, whereas negative personality traits (high neuroticism) can aggravate parenting stress [1]. Personality disorders of parents can predispose their children to other psychiatric disorders [20]. Identifying these disorders can help them better understand and predict future behaviours and problems in their children.
Depression and anxiety in both children and adults are frequently comorbid, suggesting that parental depression may be associated with child anxiety in addition to child depression. Evidence suggest that the association of parent and child depression may occur through child anxiety [21, 22]. In the study by Affrunti and Woodruff-Borden [23], maternal worry and depression significantly predicted lower levels of maternal-reported child anxiety and maternal anxiety predicted higher levels of maternal-reported child anxiety. Hudson and Rapee [24] found that mothers of anxious children were more negative during the interactions than mothers of non-clinical children. Identifying the status of depression and anxiety in mothers, in order to improve psychoeducational and other treatment interventions to favour family well-being and reduce parental distress, is potentially beneficial.
There is a scant research on personality and psychological status of Iranian mothers with ADHD. In a pilot study, Dadashzadeh et al. [ 25] evaluated the personality profile of parents of children with ADHD using the Millon Clinical Multiaxial Inventory-III (MCMI-III) and reported that the most common personality disorders were depressive ($25.3\%$), histrionic ($20\%$) and compulsive ($17.1\%$) disorders. They reported that personality disorders are prevalent in parents of children with ADHD in Iran, and mothers suffer from personality disorders more than fathers. Abdi and Narimani [26] compared the personality traits in mothers of children with Autism Disorder, ADHD and normal children. Using the NEO Five-Factor Inventory, their results showed that the mothers of children with ADHD had higher levels of neuroticism. We found no study in Iran on the personality profile and psychological status of mothers with ADHD in comparison with those of mothers with children suffering from anxiety disorders. In this regard, the present study aims to investigate personality disorders and psychiatric disorders (depression and anxiety) in mothers of children with ADHD and anxiety disorders in Iran and evaluate whether there are significant differences between them. We hypothesize that: (a) mothers of children with ADHD have more personality disorders than mothers of children with anxiety disorders, and (b) mothers of children with ADHD have more depression and anxiety than mothers of children with anxiety disorders.
## Participants
This is a descriptive cross-sectional study. Participants were 168 mothers (100 with children having ADHD and 68 with children having anxiety disorders) who were selected using a convenience sampling method from among those visited the psychiatric clinic of Shahid Beheshti Hospital in Zanjan, Iran, in 2018. The inclusion criteria were: *Having a* child aged 2–16 years, having a child with ADHD (diagnosed by a psychiatrist according to a Structured Clinical Interview for DSM-5 and the Intermediate Visual and Auditory Continuous Performance Test score) or anxiety disorders (diagnosed by a psychiatrist according to the DSM-5 criteria and based on the Beck Anxiety Inventory score), literacy to read and write, and willingness to participate in the study. Exclusion criteria were lack of cooperation, return of incomplete questionnaires and having any physical/mental disability. Prior to study, a written informed consent was obtained from them after explaining the study objectives and instruments to them and assuring them of the confidentiality of their information.
## Measures
Mothers were then assessed using a demographic form (surveying birth order, number, and gender of children as well as marital status, occupational status, and educational level of mothers), the Millon Clinical Multiaxial Inventory (MCMIIII), the Depression Anxiety Stress Scale (DASS-21) and the Symptom Checklist-90–Revised (SCL-90-R). The MCMI–III is a 175-item true-false self-report tool that assesses 10 clinical syndromes (Axis I) and 14 personality disorders (Axis II) based on the DSM-5. Personality disorders include schizoid, avoidant, depressive, dependent, histrionic, narcissistic, antisocial, sadistic, compulsive, negativistic, masochistic, schizotypal, borderline and paranoid. The clinical syndromes include anxiety, somatoform, bipolar, dysthymia, alcohol dependence, dysthymia, drug dependence, post-traumatic stress disorder, thought disorder, major depression and delusional disorder. In this study, we used the Persian version of this inventory to identify the personality disorders of mothers, which was validated by Sharifi et al. [ 27]. According to them, MCMI-III subscales have very good validity (0.58– 0.83) in the Iranian population. The DASS-21 is a valid and reliable self-report questionnaire that measures the emotional states of depression, anxiety and stress. It has 21 items, 7 for each three subscales. The items are rated on a 4-point Likert scale from 0 (did not apply to me at all) to 3 (applied to me very much). We used the Persian version of DASS-21 prepared by Sahebi et al. [ 28] who reported its good validity and reliability for the Iranian population. SCL-90-R includes 90 items (symptoms) assessing nine symptomatic dimensions: somatization, obsessive-compulsive disorder, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychoticism [29]. Each item is rated on a 5-point scale ranging from 0 (not at all) to 4 (extremely). We used the Persian version of this tool validated by Ardakani et al. [ 30], who reported its highly acceptable reliability for Iranian population.
## Statistical analysis
Of 168 questionnaires, only 100 returned complete. Therefore, the data of 100 mothers (68 having children with ADHD and 32 with anxious children) were included in the statistical analysis carried out in SPSS software. Data were described using descriptive statistics (frequency, percentage) and analysed using chi-square test. Kolmogorov-Smirnov test results showed that the data distribution was normal ($p \leq 0.05$), and Levene’s test showed the equality of variances ($p \leq 0.05$). The significance level was set at 0.05.
## Characteristics of participants
Most of participants were married ($97\%$ in the ADHD group; and $100\%$ in the anxiety group) and housewives ($63.23\%$ in the ADHD group and $65.62\%$ in the anxiety group) with academic degrees ($38.23\%$ in the ADHD group and $43.75\%$ in the anxiety group). Moreover, the majority of them had two children ($57.35\%$ in the ADHD group and $40.62\%$ in the anxiety group); most of these children were firstborn and male. For more information, see Table 1. Results of the chi-square test showed no significant difference between the two groups in terms of demographic factors ($p \leq 0.05$, Table 1).
**Table 1**
| Characteristics | Unnamed: 1 | Anxiety group (n = 32) % | ADHD group (n = 68) % | P value* |
| --- | --- | --- | --- | --- |
| | 1st | 19 (59.37) | 37 (54.41) | |
| Birth order of children | 2nd | 8 (25) | 27 (39.70) | 0.262 |
| | 3rd | 3 (9.37) | 3 (4.41) | |
| | 4th | 2 (6.25) | 1 (1.47) | |
| | 1 | 10 (31.25) | 17 (25) | |
| Number of children | 2 | 13 (40.62) | 39 (57.35) | 0.316 |
| | 3 | 7 (21.87) | 11 (16.17) | |
| | 4 | 2 (6.25) | 1 (1.47) | |
| Gender of children | Male | 27 (84.37) | 50 (73.53) | 0.229 |
| | Female | 5 (15.62) | 18 (26.47) | |
| Marital status of mothers | Single | 0 (0) | 2 (2.94) | 0.327 |
| | Married | 32 (100) | 66 (97.05) | |
| Occupational status of mothers | Housekeeper | 21 (65.62) | 43 (63.23) | 0.816 |
| | Employed | 11 (34.37) | 25 (33.82) | |
| | Lower than high school | 10 (31.25) | 22 (32.35) | |
| Educational level of mothers | High school diploma | 8 (25) | 20 (29.41) | 0.85 |
| | Academic | 14 (43.75) | 26 (38.23) | |
## Personality disorders in mothers
Based on the answers to the personality disorders subscale of MCMI-III, 61 mothers had personality disorders, where 21 ($65.62\%$) had children with anxiety disorders, and 40 ($58.8\%$) had children with ADHD (Table 2). The most common personality disorder among mothers in overall was depressive personality disorder ($$n = 27$$) followed by compulsive ($$n = 15$$), dependent ($$n = 12$$) and histrionic ($$n = 12$$) personality disorders. None of them had antisocial, borderline or paranoid personality disorders. Table 2 presents the frequency of personality disorders and mean scores for each group. Of 27 cases with depressive personality disorder, 10 ($31.25\%$) were the mothers of children with anxiety disorders (mean = 60.62 ± 21.57), and 17 ($25\%$) were the mothers of children with ADHD (mean = 61.07 ± 21.63). Results of the chi-square test (Table 2) showed no significant difference between the mothers in terms of personality disorders ($p \leq 0.05$).
**Table 2**
| Personality disorder | Answer | N(%) | N(%).1 | Mean ± SD | Mean ± SD.1 | Sig.* |
| --- | --- | --- | --- | --- | --- | --- |
| Personality disorder | Answer | Anxiety group (n = 32) | ADHD group (n = 68) | Anxiety group (n = 32) | ADHD group (n = 68) | Sig.* |
| Schizoid | True | 0 (0) | 1 (1.47) | 38.71 ± 20.46 | 47.07 ± 19.35 | 0.491 |
| | False | 32 (100) | 67 (98.53) | | | |
| Avoidant | True | 1 (3.2) | 3 (4.42) | 44.43 ± 21.91 | 47.02 ± 18.81 | 0.759 |
| | False | 31 (96.8) | 65 (95.58) | | | |
| Depressive | True | 10 (31.25) | 17 (25) | 60.62 ± 21.57 | 61.07 ± 21.63 | 0.511 |
| | False | 22 (68.75) | 51 (75) | | | |
| Dependent | True | 5 (15.63) | 7 (10.29) | 40.15 ± 25.43 | 34.80 ± 23.38 | 0.444 |
| | False | 27 (84.37) | 61 (89.71) | | | |
| Histrionic | True | 5 (15.63) | 7 (10.29) | 49.81 ± 23.02 | 49.19 ± 24.31 | 0.444 |
| | False | 27 (84.37) | 61 (89.71) | | | |
| | True | 0 (0) | 4 (5.88) | 41.96 ± 21.76 | 43.95 ± 21.60 | |
| Narcissistic | False | 32 (100) | 64 (94.12) | | | 0.161 |
| Sadistic | True | 1 (3.2) | 1 (1.47) | 45.59 ± 18.72 | 41.17 ± 17.22 | 0.581 |
| | False | 31 (96.8) | 67 (98.53) | | | |
| Compulsive | True | 3 (9.38) | 12 (17.65) | 32.09 ± 23.96 | 35.75 ± 27.89 | 0.280 |
| | False | 29 (90.62) | 56 (82.35) | | | |
| Negativistic | True | 2 (6.25) | 4 (5.88) | 46.59 ± 19.96 | 45.79 ± 20.37 | 0.094 |
| | False | 30 (93.75) | 64 (94.12) | | | |
| Masochistic | True | 0 (0) | 1 (1.47) | 40.40 ± 20.95 | 42.10 ± 20.54 | 0.960 |
| | False | 312 (100) | 67 (98.53) | | | |
| Schizotypal | True | 0 (0) | 1 (1.4) | 47.65 ± 14.32 | 44.19 ± 17.68 | 0.491 |
| | False | 31 (100) | 68 (98.6) | | | |
## Prevalence of depression in mothers
Based on the answers to the depression subscale of DASS-21, 72 mothers had depression (14 mild, 17 moderate, 26 severe and 15 extremely severe) overall. Table 3 presents the frequency of depression and mean scores for each group. Of 72 cases with depression, 21 ($65.62\%$) were related to the mothers of children with anxiety disorders (mean = 14.31 ± 9.79) and 51 ($75\%$) were related to the mothers of children with ADHD (mean = 17.29 ± 9.48). Results of the chi-square test (Table 3) showed no significant difference between the mothers in depression dimension of DASS-21 ($p \leq 0.05$).
**Table 3**
| Subscale | Status | N(%) | N(%).1 | Mean ± SD | Mean ± SD.1 | Sig.* |
| --- | --- | --- | --- | --- | --- | --- |
| Subscale | Status | Anxiety group (n = 32) | ADHD group (n = 68) | Anxiety group (n = 32) | ADHD group (n = 68) | Sig.* |
| Depression – DASS-21 | With | 21 (65.62) | 51 (75) | 14.31 ± 9.79 | 17.29 ± 9.48 | 0.702 |
| | Without | 11 (34.38) | 17 (25) | | | |
| Anxiety- SCL-90-R | With | 27 (84.37) | 57 (83.82) | 12.87 ± 8.01 | 15.16 ± 7.75 | 0.571 |
| | Without | 5 (15.63) | 11 (16.18) | | | |
| Depression – SCL-90-R | With | 27 (84.375) | 59 (86.75) | 2.40 ± 0.75 | 2.61 ± 0.75 | 0.507 |
| | Without | 5 (15.625) | 9 (13.25) | | | |
| | With | 24 (75) | 57 (83.82) | | | |
| Anxiety- DASS-21 | Without | 8 (25) | 11 (16.18) | 3.18 ± 1.63 | 3.70 ± 1.54 | 0.542 |
Based on the answers to the depression subscale of SCL-90-R, 86 mothers had depression; 27 ($84.37\%$) were mothers of children with anxiety disorders (mean = 2.40 ± 0.75) and 59 ($86.75\%$) were mothers of children with ADHD (mean = 2.61 ± 0.75). Results of the chi-square test (Table 3) also showed no significant difference between the mothers in depression dimension of SCL-90-R ($p \leq 0.05$).
## Prevalence of anxiety in mothers
Based on the answers to the anxiety subscale of SCL-90-R, 84 mothers had anxiety (19 mild, 60 moderate and 5 severe) overall. Table 3 presents the frequency of anxiety and mean scores for each group. Of 84 cases with anxiety, 27 ($84.37\%$) were related to the mothers of children with anxiety disorders (mean = 12.87 ± 8.01), and 57 ($83.82\%$) were related to the mothers of children with ADHD (mean = 15.16 ± 7.75). Results of the chi-square test (Table 3) showed no significant difference between the mothers in anxiety subscale of SCL-90-R ($p \leq 0.05$).
Based on the answers to the anxiety subscale of DASS-21, 81 mothers had anxiety, 24 ($75\%$) were mothers of children with anxiety disorders (mean = 3.18 ± 1.63), and 57 ($83.82\%$) were mothers of children with ADHD (mean = 3.70 ± 1.54). Results of the chi-square test (Table 3) also showed no significant difference between the mothers in anxiety subscale of DASS-21 ($p \leq 0.05$).
## Comorbidity of personality disorders and psychiatric disorders in mothers
Comorbidity of personality disorders, depression and anxiety were reported in 38 out of 100 mothers, in overall. Among mothers of children with ADHD, personality disorders (schizoid, avoidant, depressive, narcissistic, compulsive, dependent, negativistic, histrionic and sadistic) were comorbid with depression and anxiety in 25 out of 68 mothers ($36.76\%$). Among mothers of children with anxiety disorders, personality disorders (masochistic, dependent, negativistic, depressive and histrionic) were comorbid with depression and anxiety in 13 out of 32 mothers ($40.62\%$).
## Mixed personality disorders in mothers
Out of 100 mothers, 23 had mixed personality disorders, of whom 12 were mothers of children with ADHD (three with depressive-compulsive, four with depressive-dependent, two with depressive-negativistic, one with depressive-avoidant, one with avoidant-masochistic, and one with dependent-negativistic personality disorders), and 11 were mothers of children with anxiety disorders (three with histrionic-compulsive, two with depressive-dependent, and others each with depressive-avoidant, depressive-compulsive, depressive-negativistic, dependent-sadistic, depressive-dependent-histrionic and schizoid-depressive-dependent negativistic personality disorders).
## Discussion
The purpose of this study was to assess personality disorders, depression and anxiety in mothers of 100 children (68 children with ADHD and 32 children with anxiety disorders) in Iran. Using the MCMI-III tool, results showed that 61 mothers (out of 100) had personality disorders (21 with anxious children and 40 having children with ADHD), which is higher than the global rate (10–$20\%$) [31]. The most common personality disorder was depressive personality disorder followed by compulsive personality disorder. No antisocial, borderline and paranoid personality disorders were observed in mothers. Moreover, 72 mothers (out of 100) had depression based on the DASS-21 (21 with anxious children and 51 having children with ADHD), and 86 mothers had depression based on the SCL-90-R (27 with anxious children and 59 having children with ADHD). Furthermore, 84 (out of 100) had anxiety based the DASS-21 score (27 with anxious children and 57 having children with ADHD), and 81 had anxiety based on the SCL-90-R (24 with anxious children and 57 having children with ADHD). These indicate that the prevalence of depression and anxiety are high in the mothers of children with anxiety disorders and ADHD compared to its rate in mothers of healthy children [32]. Hajebi et al. [ 33] reported the 12-month prevalence of anxiety disorders in Iranian women as $19.4\%$. Gharraee et al. [ 34] reported the prevalence of major depressive disorder in Iranian women as $4.8\%$, and Pakizeh [35] reported the prevalence of personality disorders in Iranian women as $46.1\%$.
However, we found no statistically significant difference between the two groups of mothers in depression, anxiety and personality disorders, which rejects the hypotheses of this study. In the case-control study by Margary et al. in Italy [36], parents of children with ADHD reported higher levels of ADHD symptoms, depression and depressive personality disorders than parents of healthy children, where mothers displayed greater presence of depression. This is consistent with our results. Our results are also in agreement with the findings of Dadashzadeh et al. [ 25] conducted in Iran. In the study by Steinhausen et al. in Switzerland [10], parents of children with ADHD were most abnormal on all dimensions of ADHD psychopathology and the SCL-90-R.
ADHD and anxiety in children can greatly affect the health status of their mothers resulting in psychological disorders, including anxiety and depression. Mothers’ anxiety and depression disrupts mental, emotional and supportive care and support for children in the family. A study showed that children with ADHD whose mothers were depressed were less positive in their parent–child interaction [37]. Genetics also has a role in this association [38]. Research shows that parents and siblings of someone with ADHD are more likely to have ADHD themselves [39]. Genetics polymorphisms are associated with adult ADHD and personality disorder [40, 41]. Polymorphisms such as DAT1, DRD4, DRD5, 5HTT, HTR1B and SNAP25 are more common in children with ADHD [42, 43]. The GTP-binding RAS-like 2 gene (DIRAS2), which regulates neurogenesis, as well as protein phosphatase 2, regulatory subunit B, gamma (PPP2R2C) gene located in the 4P16 region, channel-interacting protein 4 (KCNIP4) and SPOCK gene, is also associated with adult ADHD and personality disorders [44, 45, 46, 47].
There were some limitations and disadvantages in this study including lack of a control group, the use of self-report tools, low sample size, not assessing fathers of children, not controlling effect of confounding factors (e.g. socioeconomic status and education) and not examining the effect size of children’s anxiety and ADHD on mothers’ personality profile and psychopathology. In this regard, further studies are recommended on fathers of children with anxiety and ADHD using controls for comparison, larger sample size from different cities, and other assessment tools. Generalisation of the results to all mothers of children with anxiety and ADHD in Iran should also be done with caution. Due to the high frequency of depression and anxiety and personality disorders in mothers of children with anxiety disorders and ADHD, it is recommended that psychiatric and psychological counseling be provided for these mothers.
## Conclusion
Prevalence of depression, anxiety and personality disorders in mothers of children with anxiety disorders and ADHD in *Iran is* high, and there is no difference between them. The occurrence of ADHD symptoms, psychopathology and personality disorders in parents of these children highlights the importance to integrate the treatment programs for children with the screening and treatment for psychopathological symptoms of the parents.
Key points
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|
---
title: Rapid and sensitive detection of superoxide dismutase in serum of the cervical
cancer by 4-aminothiophenol-functionalized bimetallic Au-Ag nanoboxs array
authors:
- Ji Xia
- Gao-Yang Chen
- You You Li
- Lu Chen
- Dan Lu
journal: Frontiers in Bioengineering and Biotechnology
year: 2023
pmcid: PMC10032346
doi: 10.3389/fbioe.2023.1111866
license: CC BY 4.0
---
# Rapid and sensitive detection of superoxide dismutase in serum of the cervical cancer by 4-aminothiophenol-functionalized bimetallic Au-Ag nanoboxs array
## Abstract
Early, efficient and sensitive detection of serum markers in cervical cancer is very important for the treatment and prognosis to cervical cancer patients. In this paper, a SERS platform based on surface enhanced Raman scattering technology was proposed to quantitatively detect superoxide dismutase in serum of cervical cancer patients. Au-Ag nanoboxs array was made by oil-water interface self-assembly method as the trapping substrate. The single-layer Au-AgNBs array was verified by SERS for possessing excellent uniformity, selectivity and reproducibility. 4-aminothiophenol (4-ATP) was used as Raman signal molecule, it will be oxidized to dithiol azobenzene under the surface catalytic reaction with the condition of PH = 9 and laser irradiation. The quantitative detection of SOD could be achieved by calculating the change of characteristic peak ratio. When the concentration was from 10 U mL−1–160 U mL−1, the concentration of SOD could be accurately and quantitatively detected in human serum. The whole test was completed within 20 min and the limit of quantitation was 10 U mL−1. In addition, serum samples from the cervical cancer, the cervical intraepithelial neoplasia and healthy people were tested by the platform and the results were consistent with those of ELISA. The platform has great potential as a tool for early clinical screening of cervical cancer in the future.
## Introduction
Among female malignancies, cervical cancer is the second leading cause of death in women, with more than 600,000 new cases each year, accounting for $5\%$ of all new cancer cases, more than $80\%$ of which occur in developing countries (Bray et al., 2018; Siegel et al., 2018; Clarke et al., 2019; Liu et al., 2019). There are even 300,000 deaths ever year and a clear trend of younger age (Li et al., 2011; Arbyn et al., 2022). Squamous cell carcinoma is the most common histological type of cervical cancer and persistent infection of HPV is the main cause of cervical squamous cell carcinoma (Li et al., 2016; Mao et al., 2018). The current diagnostic methods for cervical cancer include HPV testing, cytology testing and colposcopy, but these methods are usually invasive and have low patient acceptance. Although the popularity of vaccines and cervical cancer screening has effectively reduced the mortality rate of cervical cancer. Cervical cancer is still the malignant tumor with the highest mortality rate in women worldwide (Aggarwal, 2014). However, the early stage of cervical cancer is not easy to diagnose and existing diagnostic methods are inaccurate and expensive (Zorzi et al., 2013; Tsikouras et al., 2016). Therefore, a sensitive and non-invasive method for diagnosing cervical cancer is urgently needed. With the development of tumor ecology, biomarkers have gradually become one of the indicators for early detection and prognosis of tumors (Braham et al., 2017).
Superoxide dismutase is the most common class of antioxidant enzymes in organisms and it is also an important enzyme that regulates the metabolism of reactive oxygen species. SOD achieves cellular homeostasis by maintaining intracellular reactive oxygen species levels and redox balance, while protecting normal tissues from oxidative stress. Studies have shown that the downregulation of SOD activity is related to tumorigenesis and development. The reduction of SOD levels may lead to an increase in lipid peroxidation, resulting in rigidity and deformability of cells, which may be related to tumor migration and invasion (Khawsak et al., 2012). The SOD activity in normal human blood is about 128 U mL−1, while the SOD activity in tumor patients is significantly lower than that in normal people. For example, the SOD activity in the serum of gastric carcinoma is 27.6 ± 6.6 U mL−1 (Naidu et al., 2007), while SOD activity in the serum of patients with intestinal cancer is 79.35 ± 15.66 U mL−1 (Lee et al., 2006). Therefore, rapid and simple detection of SOD concentration is of great significance for early diagnosis of tumor. At present, there are three commonly used methods to detect SOD activity, including nitroblue tetrazole photochemical reduction method, chemiluminescence method and pyrogallol autoxidation method. Nitroblue tetrazole photochemical reduction method has strong specificity, stable determination results, good repeatability, simple instrument, but it also has complicated reagent preparation, complex operation, long determination time and expensive reagents (Manoharan et al., 2004; Qi et al., 2022). Chemiluminescence method has the advantages of high sensitivity, high accuracy, strong specificity detection. However, due to the need for special high sensitivity precision luminescence detection instruments, its clinical use is inhibited. The pyrogallol autoxidation method requires high environmental conditions and is not easy to implement. In clinical detection, turbidimetry, electron spin resonance (ESR) spectroscopy and spectrophotometry were often used for the analysis of SOD (Manju et al., 2002; Zahra et al., 2021). These methods take a long time to detect, expensive and not easy to popularize, hence, it is urgent to develop a rapid, sensitive and cheap method to detect SOD.
Surface-enhanced Raman scattering is a convenient, non-destructive and ultra-sensitive molecular fingerprinting spectroscopy method, which has been widely used in chemical, biological and food fields (Wang et al., 2018; Yang et al., 2019; Zhai et al., 2021). Theoretically, SERS is mainly based on the interaction of incident laser light with nanostructures, resulting in electromagnetic field enhancement in nanostructure gaps or junctions (hot spots). The enhancement factor depends on the size, shape, distribution and material composition of the nanostructures (Mondal and Subramaniam, 2020; Fu et al., 2021). The analyte molecules close to the hot spot region help to generate stronger Raman signals. SERS can overcome the disadvantage of low Raman spectral sensitivity and greatly expand the Raman signal. Its narrow linewidth allows the detection of multiple analytes in complex mixtures. The surface selection rule and the selectivity of resonance enhancement enable SERS to enhance only target molecules or chemical groups in extremely complex systems to obtain the spectral information of target analytes (Jiang et al., 2018; Luu et al., 2020).
Compared with common nano particles, such as gold nano particles and gold nano stars, Au-Ag nanoboxs (Au-AgNBs) with regular appearance is gradually attracting attention. Due to the inner and transmural walls of the cavity, Au-AgNBs have superior coupled electromagnetic fields between the inner and outer walls due to the coupling of the inner and outer surface fields, resulting in strong light absorption and high Raman enhancement (Xiong et al., 2005; Mahmoud et al., 2012). The pores on the surface of Au-AgNBs are expected to promote SERS activity through large electric field enhancement. In addition, due to the large surface area of Au-AgNBs, more Raman signal molecules can be accommodated on the surface to enhance the sensitivity of SERS detection (Wang et al., 2019).
High performance SERS substrate is a key problem in the application of SERS technology. Its signal strength is related to the surface morphology of the substrate adsorbed by the molecules. By assembling different kinds of nanomaterials into the substrate. The assembled substrates are divided into disordered substrates and highly ordered substrates and their repeatability is another important factor in the quantitative detection of target substances. Compared with the disordered SERS substrate, the highly ordered SERS substrate guarantees the reliability of the data due to its excellent signal uniformity and repeatability (Cheng et al., 2020; Langer et al., 2020; Yun and Koh, 2020). Au-AgNBs have the advantages of mild reaction conditions, simple steps, uniform morphology and high biocompatibility. Assembling them into orderly SERS substrates can not only increase the density of hot spots, but also enhance the SERS effect activity (Sultangaziyev and Bukasov, 2020).
In this work, a novel SERS platform for SOD detection based on Au-AgNBs array was proposed. 4-aminothiophene (4-ATP) was used as Raman reporter, which had been widely studied and used to determine SERS capability. Scheme 1 showed the schematic diagram of SERS platform preparation and SOD detection. By means of oil-water interface self-assembly, Au-AgNBs was assembled on the substrate surface of silicon wafer to form Au-AgNBs ordered nanoarray. The homogeneity, sensitivity and stability of the array were tested. Under the condition of PH = 9 and laser irradiation, with the gradual decrease of SOD concentration, 4-ATP would be oxidized into dithiol azobenzene (DMAB) under the action of surface catalytic reaction driven by plasmon, which made SERS signal appear the characteristic peak of DMAB and the SERS signal intensity changed accordingly, so as to realize the qualitative and quantitative detection of SOD. Finally, the SERS platform was used to detect SOD in clinical specimens of healthy people, patients with cervical low-grade squamous intraepithelial disease (LSIL), high-grade squamous intraepithelial disease (HSIL) and cervical cancer. The ELISA results further verified the accuracy of the method. This method had great potential in the early screening of cervical cancer.
**SCHEME 1:** *(A) Preparation of self-assembled Au-AgNBs array at oil-water interface. (B) SOD was detected by SERS platform.*
## Materials
Chloroauric acid tetrahydrate (HAuCl4-4H2O), silver nitrate (AgNO3), hydrogen peroxide, sulfuric acid and ascorbic acid (AA) were obtained from YangZhou LanTian Chemicals Co. Ltd. (China). 4-aminothiophenol (4-ATP), sodium chloride and ethanol were acquired from Jiangsu Younuo Chemicals Co. Ltd. (China). All of the materials were applied directly without further processing. Meanwhile, ELISA kits, superoxide dismutase and four necked round bottom flasks were all purchased from Sangon Biotech (Shanghai, China). Deionized water (resistivity >18.2 Ω) was used for the preparation of the specimens and throughout all the experiments. All glassware was dipped in aqua regia [HNO3/HCl = 1:3 (v/v)] for over 24 h and washed with deionized water.
## Collection, treatment, and preservation of clinical serum samples
Peripheral blood samples from clinical medical college of Yangzhou university in 50 cases of healthy subjects and 50 patients with low grade squamous intraepithelial lesion, high-grade squamous intraepithelial lesion in 50 cases of patients and 50 cases of cervical cancer patients were centrifuged at 3,000 rpm for 12 min at 4°C. Then serum samples were collected and based on its classification to store in −80°C before analysis. Consent documents were obtained from all donors. Table 1 summarizes the details of age and histopathological stage.
**TABLE 1**
| Groups | Healthy person | LSIL | HSIL | Cervical cancer |
| --- | --- | --- | --- | --- |
| Age (mean) | 29 | 38 | 42 | 48 |
| Sample | 50 | 50 | 50 | 50 |
## Synthesis of Au-AgNBs
Au-AgNBs modified by nanodots was synthesized by one-step method (Li et al., 2018). The experiment was carried in 100 mL conical tubes. First, 90 μL of $1\%$ HAuCl4 solution was added into 10 mL ultrapure water by continuously stirring for 1 min. Then, 170 μL of AgNO3 (6 mM) was dropped into the mixture and the mixture became turbid pearl white. After dropping the 125 μL of AA (0.1 M), the solution turned to distinct blueviolet color indicating the Au-AgNBs were prepared. After 10 min of continuously stirring, the Au-AgNBs were concentrated by centrifugation (4,000 rpm, 4 min). Collecting the bottom sediment, disperse the particles in 5 mL deionized water and store at 4°C.
## Manufacturing of capture substrate
The silicon wafer was divided with the size of 0.8 × 0.8 cm2, then they were placed in a beaker and used after ultrasonic cleaning with ultra-pure water and ethanol in turn. Then, the silicon wafer of appropriate size was immersed in piranha solution (hydrogen peroxide ($30\%$) was added to concentrated sulfuric acid in a ratio of 3:7 by volume) for 30 min to make the silicon wafer hydrophilic. Then ultrapure water and ethanol were used again to clean the silicon wafer for three times. Au-AgNBs prepared the array by using the method of oil-water interface self-assembly. In brief, by mixing 8 mL of the prepared Au-AgNBs solution sequentially with 4 mL of hexane in a beaker and then adding 4 mL of ethanol drop by drop, it was found that Au-AgNBs formed neat arrays at the oil-water interface. Next, the Au-AgNBs array was picked up using the prepared hydrophilic silicon wafer and placed in a ventilated place to dry. Then, the Au-AgNBs array is obtained. These prepared Au-AgNBs array were uniformly stored in a sealed glass cover at 4°C. Every time they were used, Au-AgNBs arrays made in the same batch were selected to reduce the error caused by the detection platform.
## Principle of the SERS platform
Before the test, 20 μL of 2.5 × 10−5 M 4-ATP was dropped onto the prepared Au-AgNBs array surface. Then an appropriate amount of buffer solution was added to the array surface to adjust PH = 9 and left it at room temperature for 2 min to mix evenly. Next, 20 μL of sample solution was dropped and stayed for 2 min at room temperature to make it evenly covered. The ordered array was irradiated by 785 nm laser. The laser power at the sample location was 2.3 mW. Under the continuous irradiation of the sample, the SERS spectrum of 1 s was continuously measured in 1 min steps. All SERS spectrum reported in this study were collected in a continuous mode within the range of 400–1800 cm−1. The average SERS spectra measured at 10 different points at random in one platform were used to quantify SOD in the sample solution, which ensured the authenticity and rationality of the data. The characteristic bands of 4-ATP and DMAB were listed in the table S1 (supporting information).
## Instrumentation
Uv-vis-near-infrared (UV-VIS-NIR) spectrometer (UV-3000PC, Mapuda, China) was used to detect the absorption spectrum of UV-VIS-NIR. Transmission electron microscope (TEM) images were taken with transmission electron microscope (Tecnai 12, Philips, Netherlands). Scanning electron microscopy (SEM) images were studied using an S-4800II ⅐ laser emission scanning electron microscope (Gemini SEM 300, Carl Zeiss, Germany). High-resolution TEM (HRTEM) images, selective region electron diffraction (SAED) and element mapping images were obtained using a field emission transmission electron microscope (Tecnai G2F30 S-Twin, FEI, United States). Raman spectrometer (Renishaw inVia, United Kingdom) was used to record SERS mapping with a mapping step of 1 μm and a pinhole of 25 μm. All experiments were performed at room temperature.
## Characterization of Au-AgNBs
Au-AgNBs is a new type of nanomaterial, which has the advantages of high hotspot density and good stability. TEM and SEM were used to characterize the structure and size of Au-AgNBs. The SEM image of Au-AgNBs could be seen in Figure 1A, which indicated that a large amount of Au-AgNBs could be obtained by one-step process with uniform size and good dispersion. It could be seen that in the TEM image, the hollow inner wall and outer wall of Au-AgNBs were obviously different (Figure 1B). The mean side length of Au-AgNBs was 70 nm and the thickness of the wall was 5 nm. As shown in Figure 1C, the four bright rings {111}, {200}, {220}, and {311} indicated that Au-AgNBs were polycrystalline (Gomez-Grana et al., 2013). Usually, nanocages were composed of bimetals and almost all hollow nanostructures needed to use templates to form nanocages (Zhang et al., 2010). The template was often silver nanocubes. In particular, Cl− promoted the formation of silver cube templates through the cap effect, which was consistent with the halide selective stabilization of the {100} surface of Au-AgNBs, while the silver nanocrystals were oxidized to form Au-AgNBs in the inner pore wall (Huang et al., 2009). Figure 1D indicated the plane distances between the tip crystal faces of the inner and outer walls of Au-AgNBs were 0.210 nm and 0.225 nm respectively. The basic diagram of energy dispersive X-ray energy spectrum (EDX) of Au-AgNBs could be seen from Figure 1E, it could be seen that the outer wall composition of Au-AgNBs were dominated by silver and gold. Figure 1F showed the UV-visible spectrum of Au-AgNBs with a broadband maximum of 692 nm, which indicated that a large amount of Au-AgNBs had been prepared. The physical drawing showed the picture of the Au-AgNBs solution in visible blue color. Figure 1G was the energy dispersive X-ray spectrum (EDX) of Au-AgNBs. It showed that Au-AgNBs is mainly composed of gold and silver and the peak of copper in the electron spectrum was mainly due to the use of copper mesh as the test substrate. The *Raman spectra* of 4-ATP and 4-ATP-labeled Au-AgNBs were shown in Figure 1H. 4-ATP and Au-AgNBs were connected to each other mainly through Au-S bonds (Jang and Keng, 2008). As could be seen from the figure, the *Raman spectrum* of 4-ATP showed that the SERS signal was very weak. In contrast, significantly enhanced SERS signal could be observed for 4-ATP-labeled Au-AgNBs indicating that Au-AgNBs had a strong SERS effect. The analytical enhancement factor (EF) of Au-AgNBs was calculated as EF=(ISERS/CSERS)/(IRS/CRS) (Mahmoud and El-Sayed, 2010). SERS and RS represent SERS condition and non-SERS condition, while C and I represent the concentration and intensity, respectively. When CSERS = 1 × 10−6 M, CRS = 10−1 M and the intensity measured at 1,081 cm-1, the EF calculated was 7.531×105. This effect was due to the coupling of its inner and outer surfaces, leading to strong optical absorption and high SERS enhancement. Therefore, compared with the ordinary noble metal substrate, the Au-AgNBs array had significant SERS signal enhancement ability.
**FIGURE 1:** *The representative structure of Au-AgNBs was generated in one step. (A) SEM of Au-AgNBs, (B) TEM, (C) SAED image, (D) HRTEM and EDX mapping (E) for Ag element and Au element image, (F) UV-Vis-NIR absorption spectrum of Au-AgNBs. (G) EDX spectra of Au-AgNBs. (H) SERS spectra of 4-ATP and 4-ATP-labeled Au-AgNBs.*
## Characterization of Au-AgNBs array
Excellent SERS substrate performance is the key to practical application. As shown in Figure 2A, the SEM image showed the side view of Au-AgNBs array. It could be seen that Au-AgNBs was highly uniform and orderly arranged, with an average height of about 70 nm. 40 × 40 μm2 area was randomly selected on the array marked with 4-ATP for SERS intensity mapping measurement. Each pixel in the spatial position of the mapping image represented the signal strength at 1,081 cm-1. These signals were related to the distribution of Au-AgNBs on the array surface. Although there were a few blue and yellow areas in the image, most areas show relatively stable green, indicating that the SERS substrate had a high uniformity as shown in Figure 2B. In order to more intuitively verify the uniformity of the detection substrate, 10 random points were selected on the substrate surface for SERS spectrum measurement. Figure 2C showed the SERS spectrum of the random points. It could be seen that the signal strengths of the selected points were relatively consistent. The histogram in Figure 2D intuitively showed the slight fluctuation of the spectrum and its relative standard deviation (RSD) was $7.832\%$, indicating that the Au-AgNBs array had good signal uniformity.
**FIGURE 2:** *(A) SEM of cross section and plane for monolayer Au-AgNBs array. (B) SERS mapping of Au-AgNBs array at 1,081 cm−1. (C) SERS spectra with a peak intensity of 1,081 cm−1 were obtained from 10 randomly selected points within the 40 × 40 μm2 region of the substrate of the Au-AgNBs array and (D) the histogram of spectral intensity at 1,081 cm−1.*
## Optimization of parameters
The concentration of 4-ATP and SOD had a strong influence on the detection sensitivity. By optimizing these parameters, the sensitivity of SERS detection platform could be further improved. When 4-ATP was adsorbed on the surface of Au-AgNBs, under the irradiation of laser, 4-ATP would be oxidized into DMAB. In the presence of SOD, SOD could combine with OH− in solution to remove oxide, thus inhibiting the formation of DMAB and then SERS signal showed the characteristic peak of 4-ATP. The value of I1170/I1185 was selected as the index parameter of the optimization result, the detection sensitivity was positively correlated with I1170/I1185. In the process of preparing SERS substrate, 4-ATP with different concentrations was added and then SOD solution with the same solubility of 150 U mL−1 was dripped respectively. As shown in Figure 3A, the peak intensity of I1170 gradually increased with the increase of 4-ATP concentration. It was shown in Figure 3B that when the 4-ATP concentration was 2.5 × 10−5 M, the ratio of I1170/I1185 was close to 1, and I1170/I1185 ratio remained unchanged with the increase of 4-ATP concentration. Therefore, when the SOD concentration was 150 U mL-1, the optimal 4-ATP concentration was 2.5 × 10−5 M. Similarly, by controlling the concentration of 4-ATP to 2.5 × 10−5 M and changing the concentration of SOD from 0 U mL−1–150 U mL−1, as shown in Figures 3C, D, the ratio of I1170/I1185 gradually decreased with the increase of SOD concentration. When SOD concentration was 150 U mL-1, the ratio of I1170/I1185 was close to 1 and when SOD concentration was increased again, the ratio of I1170/I1185 hardly changed, indicating that when SOD concentration was 150 U mL−1, the conversion of 4-ATP could be completely inhibited. Therefore, SOD with the concentration of 150 U mL−1 and 4-ATP with the concentration of 2.5 × 10−5 M were selected as the best concentrations.
**FIGURE 3:** *Relationship between 4-ATP concentration and SOD concentration and I1170/I1185
(A) SERS spectrum when 4-ATP concentration was changed, (B) corresponding scatter plot, (C) SERS spectrum when SOD concentration was changed, (D) corresponding scatter plot.*
## Characterization of the sensor performance
The stability of SERS substrate was evaluated. The prepared SERS substrate was stored in a sealed container at 20°C and SERS detection was taken on the SERS substrate at the 0, 5, 10, 15, 20, and 25 days respectively. As can be seen in Figure 4A, there was no significant difference in the SERS spectrum peak and spectrum shape. Figure 4B showed the corresponding scatter plot, with the peak intensity of 1,081 cm−1 as the characteristic peak and the peak intensity of the 15th day was $8.803\%$ lower than that of the 0 day. Among them, the peak intensity on the 25th day was still maintained at $80\%$ of the initial intensity compared with the peak intensity on the 0 day, indicating that the SERS array base had stable SERS enhanced effect and storage stability. As we all know, the selectivity of SERS platform was of great significance in the actual analysis of biological samples. In order to evaluate the selectivity of SERS immune substrate. The substances were selected that may exist in the detection environment as interfering substances and detected specific markers SOD and non-specific biomarkers or proteins (SCCA, CA125, IL-6, survivin, glucose) of the same concentration (100 U mL−1) in PBS buffer. I1170/I1185 was selected as the tracer of SOD. As shown in the spectrum in Figure 4C, the peak intensity of I1170 of SOD was similar to that of I1185, while the peak intensity of I1170 of other substances was obviously higher than that of I1185. The histogram of Figure 4D indicated the results more clearly. When SOD existed, the I1170/I1185 ratio was obviously lower than that of the solution without SOD. Under the above optimal conditions, the reproducibility of another important parameter of the SERS platform was studied. According to Figure 4E, the SERS spectra was studied by selecting of ten independent experiments conducted at different times. There was almost no difference between these SERS spectra. The broken line graph of the SERS spectrum was shown in Figure 4F. With 1,081 cm−1 as the characteristic peak, the peak intensity deviation was $7.625\%$. This small change showed that the SERS platform had good reproducibility. In order to study the differences between different batches of Au-AgNBs arrays, the Au-AgNBs arrays made at different batches were compared. Figure S1A showed the differences of SERS spectrum. The Au-AgNBs array marked with 4-ATP prepared at different batches were detected by SERS. As shown in Figure S1B, with 1,081 cm-1 as the reference peak, the intensity deviation of the four peaks was small ($2.407\%$), indicating that there was almost no difference between Au-AgNBs array prepared at different times.
**FIGURE 4:** *(A) SERS spectra of 4-ATP-labeled Au-AgNBs array stored for different days. (B) The scatter graph corresponding to SERS intensity at 1,081 cm−1. Specificity based on Au-AgNBs array. (C) SERS spectra of analytes (1) SOD, (2) SCCA, (3) CA125, (4) IL-6, (5) survivin, (6) glucose. (B) Histogram corresponding to I1170/I1185. Reproducibility of Au-AgNBs array. (E) SERS spectrum at 1,081 cm−1. (F) Scatter diagram of peak intensity at 1,081 cm−1.*
## Application of detection platform in serum
Under the above optimization conditions, the capability of SERS platform for rapid analysis of SOD was evaluated. In order to combine the immunosensor with practical application, it was used to detect the concentration of SOD in serum. Au-AgNBs ordered arrays with the size of 0.8 × 0.8 cm2 which adsorbed 4-ATP on their surfaces according to the detection principle were adjusted to the PH = 9. The SOD solution was diluted in the purpose-made serum (without SOD) to the concentration of 10 U mL−1, 40 U mL−1, 70 U mL−1, 100 U mL−1, 130 U mL−1 and 160 U mL−1 respectively. 20 μL of the above solutions was dropped on different SERS detection platforms, then the samples were continuously irradiated under Raman microscope. The SERS spectrum were continuously measured for 1 s in a step of 1 min until the spectral shape did not change. 10 points were selected randomly of the detection platform for measurement and the average SERS spectrum were calculated for quantitative detection of SOD in the sample solution. Figure 5A presented the SERS spectra of SOD solutions with different concentrations. It was obvious that with the increase of SOD concentration, the process of 4-ATP converting to DMAB was gradually inhibited, which was reflected in the gradual decrease of the ratio of I1170/I1185. By using the ratio of I1170/I1185 as parameter, a linear calibration chart for quantitative assessment of SOD concentration was constructed as shown in Figure 5B. When the SOD concentration was from 10 U mL−1–160 U mL−1, the ratio of I1170/I1185 was almost linearly related to the concentration of SOD. Its linear regression equation was y = −0.00332x+1.54406 and the relative coefficient (R 2) was 0.970. The limit of quantitation (LOQ) of the SERS platform for SOD was 10 U mL−1. It showed that the SERS platform had a good linear relationship at the concentration range from 10 U mL−1–160 U mL−1, which could realize SERS detection of SOD activity in serum.
**FIGURE 5:** *After applying different concentrations of SOD in serum (10 U mL−1–160 U mL−1), obtained SERS spectrum (A) and calibration curve (B).*
## Clinical serum samples analysis
SERS platform was used to analyse SOD quantitatively to prove the accuracy, reliability and clinical practicability of the analysis. Clinical serum samples of healthy people, LSIL, HSIL and the cervical cancer were studied. By calculating the ratio of I1170/I1185 in SERS spectrum, the concentration difference of SOD in serum of different populations could be calculated. The used clinical serum samples needed not to be diluted and they were directly used as sample solution. Each sample was measured three times. 10 random test points were measured on the surface of the detection platform each time. Every spectra was the average result of 30 different serum samples. These results are analyzed and the standard deviation is calculated. Figure 6A showed the mean SERS spectrum of clinical samples. It could be seen that as the disease progresses, the characteristic peak of SERS spectrum gradually changed from 4-ATP to DMAB, which was the ratio of I1170/I1185 gradually increasing. The concentration of SOD in each actual sample was determined by fitting I1170/I1185 into the linear regression equation of the calibration curve. The concentration of SOD in the serum of patients with cervical cancer was significantly lower than that of HSIL, LSIL and normal people. Figure 6B directly showed the ratio difference of I1170/I1185 in SERS images. As a detection method for biomarkers, ELISA was used to detect the actual samples and calculated the average concentration. Figure 6C allowed a more intuitive comparison of the SOD concentration in the actual samples detected by ELISA kit and SERS platform. As shown in Table 2, the average concentration of SOD in the serum of healthy people, LSIL, HSIL, and the cervical cancer detected by SERS platform were 129.1 U mL−1, 79.95 U mL−1, 57.24 U mL−1, and 28.96 U mL−1, respectively. The concentration of SOD detected by ELISA were 121.7 U mL−1, 82.19 U mL−1, 59.72 U mL−1 and 27.88 U mL−1 respectively and the relative errors of the two methods were −$3.87\%$, $4.1\%$, $2.73\%$ and −$6.08\%$ respectively. The results showed that there was no significant difference between SERS platform and ELISA detection results, which confirmed that the SERS platform could be used for early clinical screening of cervical cancer.
**FIGURE 6:** *(A) Mean SERS spectra of clinical samples. (B) I1170/I1185 histogram of clinical samples, (C) comparison histogram of SOD concentration in clinical samples detected by ELISA and SERS.* TABLE_PLACEHOLDER:TABLE 2
## Conclusion
In this work, Au-AgNBs were orderly arranged on the substrate by the method of oil-water interface self-assembly and a SERS platform capable of quantitative detection of SOD was successfully constructed by the changes of Raman signal molecular characteristic peaks. The experimental results showed that the prepared SERS platform had good performance in homogeneity, reproducibility, selectivity and stability. When the concentration was from 10 U mL−1–160 U mL−1, the platform could quantitatively detect SOD in human serum and the LOQ was 10 U mL−1. The clinical application research of its detection ability was carried out. The SERS platform was used to detect clinical serum samples of healthy people, LSIL, HSIL and cervical cancer patients. In the clinical detection of SOD, turbidimetry and electron spin resonance (ESR) spectroscopy took a long time and the cost was expensive. At the same time, they had low sensitivity and needed to buy special large instruments. On the contrary, the SERS platform had shorter detection time, the entire test could be completed in 20 min and it also had higher sensitivity, simple operation and low price. The detection results of SERS were consistent with ELISA, indicating that it has broad application prospects in early diagnosis of cervical cancer.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by Ethics Committee of Medical College of Yangzhou University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
Conception and design: JX and G-YC. Administrative support: DL. Provision of study materials or patients: YY and LC. Collection and assembly of data: JX, G-YC, YY, and LC. Data analysis and interpretation: JX, DL, and G-YC. Manuscript writing: JX and G-YC. Final approval of manuscript: All authors.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fbioe.2023.1111866/full#supplementary-material
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|
---
title: Dose-dependent effects of human umbilical cord-derived mesenchymal stem cell
treatment in hyperoxia-induced lung injury of neonatal rats
authors:
- Jing Xiong
- Qing Ai
- Lei Bao
- Yuanshan Gan
- Xiaoyu Dai
- Mei Han
- Yuan Shi
journal: Frontiers in Pediatrics
year: 2023
pmcid: PMC10032376
doi: 10.3389/fped.2023.1111829
license: CC BY 4.0
---
# Dose-dependent effects of human umbilical cord-derived mesenchymal stem cell treatment in hyperoxia-induced lung injury of neonatal rats
## Abstract
### Background
Mesenchymal stem cells (MSCs) are multipotent stromal cells that have been reported to possess great potential for the treatment of bronchopulmonary dysplasia (BPD).
### Objective
Our study aims to assess the effects of three different doses of intraperitoneal administration of human umbilical cord-derived MSCs (hUC-MSCs) on a hyperoxia-induced BPD model of newborn rat.
### Methods
Neonatal Sprague Dawley (SD) rats were reared in either hyperoxia ($75\%$ O2) or room air (RA) from postnatal days (PN) 1-14. At PN5, hUC-MSCs (1 × 106, 5× 106,or 1× 107 cells per pup) were given intraperitoneally to newborn rats exposed to $75\%$ O2 from birth; the controls received an equal volume of normal saline (NS). At PN14, the lung tissues, serum, and bronchoalveolar fluid (BALF) were collected for histologic examination, wet/dry (W/D) weight ratio analysis, engraftment, myeoloperoxidase (MPO) activity analysis, cytokine analysis, and western blot analysis of protein expression.
### Results
Compared to rat pups reared in RA, rat pups reared in hyperoxia had a significant lower survival rate ($53.3\%$) ($P \leq 0.01$). Hyperoxia-exposed rats exhibited pulmonary inflammation accompanied by alveolar-capillary leakage, neutrophile infiltration, augmented myeloperoxidase (MPO) activity, prominent alveolar simplification, and increased mean linear intercept (MLI), which was ameliorated by hUC-MSCs treatment. Increased oxidative stress and inflammatory cytokine production were also reduced. Importantly, the expression of Fas, an apoptosis-associated protein that was increasingly expressed in hyperoxia-exposed rats ($P \leq 0.05$), was downregulated after administration of hUC-MSCs ($P \leq 0.05$).
### Conclusions
Our results suggest that intraperitoneal administration of high number hUC-MSCs (1 × 107 cells) may represent an effective modality for the treatment of hyperoxia-induced BPD in neonatal rats.
## Introduction
Bronchopulmonary dysplasia (BPD) was first described by Northway in 1967 [1]. It is a chronic lung disease in preterm infants resulting from supplemental oxygen or mechanical ventilation for respiratory distress syndrome [1]. The incidence of BPD defined as the use of oxygen at 36 weeks' postmenstrual age or at discharge/transfer if before 36 weeks in neonates who survived to 36 weeks increases despite the tremendous advances in perinatal and neonatal medicine, including surfactant replacement therapy, antenatal steroids, and gentler ventilation techniques [2], and BPD remains the most common chronic disease in newborns with increased morbidity and mortality [3]. The pathogenic mechanism of BPD is multifactorial and has also evolved, from striking fibrosis and cellular proliferation to arrested lung development, including inhibition of lung alveolar and vascular development [4, 5]. Therapeutic strategies for the prevention and treatment of BPD including caffeine, vitamin A, surfactant, and steroids. In recent years, researches on mesenchymal stem cell (MSC)-based therapy have provided a novel approach for the prevention and treatment of BPD.
MSCs are multilineage cells with the ability to self-renew and differentiate into various cell types, which could be derived from bone marrow (BM), adipose, dental pulp, placenta, cord blood, and matrix [6, 7]. They can modulate the immune response, activate cell proliferation, prevent apoptosis, promote angiogenesis, and improve regenerative responses and repair to protect tissues against a variety of injuries (8–11). These properties make MSCs an innovative potential cell-based therapy for regenerative medicine, especially for pediatric diseases [12]. The most common source of MSCs in respiratory disease has been BM [13, 14]. Nowadays, more attention has been drawn to umbilical cord-derived MSCs (UC-MSCs) due to their higher proliferation rate and that they can be extracted non-invasively. Besides, UC can generate MSCs with greater immunomodulatory potential than BM-MSCs.
In BPD animal models, MSCs can be delivered intravenously, intraperitoneally, intranasally, and intratracheally. In addition to the route of administration, the number of cells is also an important preclinical question. Various preclinical studies provided proof of concept for the lung-protective effect of MSCs. However, to date, the optimal number of cells and the route of MSC administration for the prevention and treatment of BPD are unknown. Therefore, this study aims to analyze the effect of three different numbers of human UC-MSCs (hUC-MSCs) via intraperitoneal injection on BPD model of rat pups and investigate the underlying mechanism.
## Characterization and analysis of hUC-MSCs
hUC-MSCs were obtained from Chongqing Perfect Cell Biotech Co., Ltd (Chongqing, China). The cells used in this study were followed by the International Society for Cellular Therapy Guidelines. The hUC-MSCs were maintained in Mesenchymal Stem Cell Basal Medium (MSCBM, Dakewe Biotech Corp., Shenzhen, China) supplemented with EliteGro™ (Biomedical Elitecell Corp., Woodway, TX, United States), at 37°C, saturating humidity and $5\%$ CO2. They were characterized for the expression of specific cell surface markers (CD73, CD90, and CD105) through flow cytometry (Supplementary Figure S1A) and differentiation to osteogenic, adipogenic, and chondrogenic cells (Supplementary Figure S1B). The results revealed that hUC-MSCs used in this study were positive for typical MSC antigens (CD73, CD90, and CD105) but negative for hematopoietic antigens (CD34, CD45, and HLA-DR) (Supplementary Figure S1A). In addition, the hUC-MSCs showed the potential to differentiate into bone, fat, and cartilage (Supplementary Figure S1B).
## Animal model and experimental design
Sprague Dawley (SD) rats were purchased from the Experimental Animal Center of Chongqing Medical University and were raised in the Animal Laboratory Center of Pediatrics, in the Children's Hospital of Chongqing Medical University. All animal procedures and protocols were approved by the Ethics Committee of Chongqing Medical University. Time-dated pregnant SD rats were maintained in single cages at room temperature (between 20 and 24°C) with a $\frac{12}{12}$ h light-dark cycle. Rats were provided with laboratory food and water ad libitum and allowed to deliver vaginally at term. Within 24 h of birth, the litters were pooled and distributed to the newly delivered mothers randomly. Newborn rat pups were randomly divided into five experimental groups: normoxia control group (NC), hyperoxia normal saline group (HS), hyperoxia with hUC-MSCs of 1 × 106 cells group (HM1), hyperoxia with hUC-MSCs of 5 × 106 cells group (HM2), and hyperoxia with hUC-MSCs of 1 × 107 cells group (HM3). Rat pups of the NC group were maintained with a nursing mother rat in a single cage at room air (RA) throughout the experiment. Rat pups of the HS group were kept with a nursing mother in a sealed Plexiglas chamber in which the hyperoxia (oxygen concentration of $75\%$) was maintained until postnatal day (PN) 14. Humidity and environmental temperature were maintained at $50\%$ and 24°C, respectively. Nursing dams were rotated between room air and the $75\%$ hyperoxia groups every 24 h. Survival of rat pups in each group were observed daily during the experiment. All rat pups were sacrificed at PN14 under deep pentobarbital anesthesia (60 mg/kg, intraperitoneal) (Figure 1A). Lung tissues, bronchoalveolar lung fluid (BALF), and serum were collected for morphometric and biochemical analyses. Six to ten rat pups were used in each subgroup of analysis.
**Figure 1:** *Human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) restores hyperoxia-induced lung damages in bronchopulmonary dysplasia (BPD) of neonatal rats. (A) The schematic of hUC-MSCs procedure and study design; n = 14–16 per group. (B) Representative images of whole lung from all groups at postnatal day (PN) 14. (C) The histology of the lung in different groups identified using haematoxylin-eosin (HE) staining. Magnification, ×20 (scare bar = 100 µm). At PN 14, the HS group (b) demonstrated fewer and larger alveoli and heterogenous alveolar sizes as compared to the NC group (a). hUC-MSCs (1 × 106 cells, 5 × 106 cells, or 1 × 107 cells) treatment improved hyperoxia-induced damages in alveolar growth and morphological changes in HM1 (c), HM2 (d), and HM3 (e), respectively, in a dose-dependent manner. (D) Mean linear intercept (MLI) in 14-day-old rat pups. The rat pups reared in the HS group showed a remarkably higher MLI than did those of the NC group. hUC-MSCs (1 × 106 cells, 5 × 106 cells, or 1 × 107 cells) remarkably reversed the hyperoxia-induced MLI increase. **P < 0.01, ***P < 0.001, and ****P < 0.0001. Data was presented as means ± SEM, n = 6–8 per group.*
## Stem cell labeling
hUC-MSCs were transfected with lentivirus carrying GFP gene (lentiviral vector: pLVX-CMV-EGFP-PGK-PURO; virus titre: 2.6*109 TU/ml) (Sangon Biotech CO., Ltd. Shanghai, China) at different MOI [10, 25, 50, 75] in vitro according to the manufacturer's protocol. The infection efficiency of hUC-MSCs was assessed at 48 h after transfected by lentivirus via flow cytometry.
## Assessment of hUC-MSCs engraftment
A suspension of 1 × 106 cells of hUC-MSCs (the HM1 group) transfected by lentivirus carrying GFP gene in 200 µl normal saline was injected intraperitoneally per pup at PN5 after the pups had been already exposed to high oxygen. At 1, 12, 24, 48, and 72 h after injection, three pups from each timepoint were selected randomly and sacrificed after anesthesia. Left lung was fixed in $4\%$ polyformaldehyde followed by embedding for paraffin sections and DAPI immunofluorescent staining. For immunofluorescence, after deparaffination and antigen retrieval process, sections were incubated with the primary antibody overnight at 4°C according to the manufacturer's instructions. Then the slides were washed and incubated with the appropriate secondary antibody at room temperature for 50 min in dark condition. After washing, tissue sections were incubated with DAPI solution, followed by spontaneous fluorescence quenching reagent. Primary antibody used in this study was anti-GFP (Abcam ab290). Microscopy detection and images were collected by Ortho-Fluorescent Microscopy.
## hUC-MSCs treatment
For hUC-MSCs transplantation, 1 × 106 cells, 5 × 106 cells, and 1 × 107 cells in 0.2 ml normal saline were administered intraperitoneally at PN5 for HM1, HM2, and HM3 groups, respectively. For HS, an equal volume of normal saline was given intraperitoneally at PN5. After the procedure, the rat pups were allowed to recover from anesthesia and were returned to their nursing mothers. There was no mortality associated with the transplantation procedure.
## Lung histology and wet/dry (W/D) weight ratio
The whole left lung lobes of each animal were fixed in $4\%$ paraformal dehyde, serially dehydrated in increasing concentrations of ethanol, and then embedded in paraffin. Two random sections from the lungs of each animal were stained with hematoxylin and eosin. Alveolarization was quantified using the mean linear intercept (MLI) based on the previous methods [15]. Ten nonoverlaping fields of each section were acquired for morphological analysis by optical microscopy at a magnification of 20× (Nikon, Japan).
The lung W/D weight ratio was measured to assess the degree of pulmonary edema. The wet weight of the lung tissue was recorded before drying at 80°C for 48 h and reweighted until a stable dry weight was achieved. Then the lung W/D weight ratio was calculated.
## Analysis of bronchoalveolar lavage fluid (BALF)
After the animals were sacrificed at PN14, the lungs of pups were washed with phosphate-buffered saline (PBS) three times (five minutes each time) through the trachea cannula, and the washing fluid was collected and was centrifuged at 12,000 rpm for 15 min at 4°C. The supernatant was collected for determination of protein concentration using the BCA Protein Assay Kit (Beyotime, Shanghai, China). The total cell count was performed using the Countstar Automated Cell Counter (Ruiyu Biological Technology Co., LTD, Shanghai, China). Differential cell counts were made from centrifuged preparations stained with Wright-Giemsa staining, and at least 200 cells were counted in each pup.
## Analysis of myeloperoxidase (MPO) activity
Lung tissues were homogenized in normal saline with an appropriate proportion. Samples were then centrifuged at 12,000 rpm at 4°C for 20 min, and MPO activity in the supernatant was determined at 460 nm using a commercially available MPO activity colorimetric assay kit (Nanjing Jiancheng Bioengineering Institute, China).
## Analysis of MDA concentration
Lung tissues were lysed using lysis solution (MDA lysis buffer + BHT) (cat. no. ab118970; Abcam; Cambridge, UK). The cellular lysates were centrifuged at 13,000 rpm for 10 min at 4°Cand the supernatants were harvested. MDA concentration was determined using MDA kit according to the manufacturer's protocol (cat. no. ab118970; Abcam; Cambridge, UK).
## Assessment of HO-1 activity
HO-1 activity was estimated by using rat HO-1 ELISA kit (Ruixin Biological Technology Co., LTD, Quanzhou, China). It is a solid phase sandwich enzyme linked immunosorbent assay (ELISA), which uses a microtitre plate reader read at 450 nm. Activity of HO-1 was calculated from the plotted standard curve and expressed in U/L protein.
## Analysis of serum cytokine levels
A total of four serum cytokines, including (IL)-1β, IL-6, IL-10, and TNF-α, were detected simultaneously by using the MILLIPEX MAP Rat Cytokine/Chemokine Kit (EMD Millipore Corp., Billerica, MA, USA) according to manufacturer's protocol. Concisely, 200 µl assay buffer was added into each well of the plate for 10 min at room temperature with shaking and then removed. 25 µl of standard or control was added into the appropriate wells, followed by the add of assay buffer (25 µl) to the sample wells. Then the matrix solution (25 µl) was added to background, standards, and control wells with 25 µl of 1:2 diluted samples to sample wells. After mixing, 25 µl of beads were added to each well, and the plate was incubated for two hours at room temperature. After incubation, the well contents was removed, and the plate was washed 2 times. Following the addition of detection antibodies (25 µl) per well, the plate was incubated for one hour at room temperature with shaking. Then, 25 µl of Streptavidin-Phycoerythrin was added to each well, and plate was incubated for 30 min at room temperature with shaking. Well contents were gently removed and plat was washed two times. Then, 125 µl of Sheath Fluid or Drive Fluid was added into all wells, and the beads were resuspended for five minutes with shaking. Finally, the Median Fluorescent *Intensity data* were read on Luminex® and analyzed using a 5-parameter logistic or spline curve-fitting method for calculating cytokine concentrations in samples.
## Western blot analysis
Total protein of each sample was extracted from lung tissues by using radio immunoprecipitation assay (RIPA) analysis buffer and qualified by using a BCA protein assay kit (Beyotime, Shanghai, China). After denaturation, proteins (30 µg) were distinguished by $10\%$ sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to a polyvinylidene difluoride (PVDF) membrane for blotting. After incubated with blocking buffer, the membranes were incubated with appropriate primary antibodies [anti-fas, Abcam, Cambridge, UK; horseradish peroxidase (HRP)-conjugatedβ-tubulin, ABclonal, Wuhai, China] overnight at 4°C. After washing, the membranes were incubated with HRP-conjugated secondary antibodies (Zen-bioscience, Chengdu, China), and the immunoreactive bands were visualised by enhanced chemiluminescence and analyzed with ImageJ software. The relative protein expressions were calculated after normalization with β-tubulin. Data were presented as expression level relative to the control group.
## Statistical analyses
Quantitative results were expressed as means ± SEM with $$n = 6$$ to 10 rats in each group, and statistical analysis was conducted by GraphPad Prism software version 8.0 (La Jolla, CA). Groups were compared with the two-tailed unpaired t-test and one- or two-way analysis of variance (ANOVA), as appropriate. P-values of <0.05 were considered statistically significant.
## Identification of infected hUC-MSCs
At 1, 12, 24, 48, and 72 h following injection of hUC-MSCs into rats, immunofluorescence for GFP in lung tissue was conducted. The images were captured with a camera system connected to a fluorescence microscope. GFP positive cells were found in the lungs of rat pups within 1 h after hUC-MSCs injection indicating that hUC-MSCs were first home to the lung after intraperitoneal infusion. One hour after injection, the signal increased and peaked at 24 h after injection, then decreased gradually. Even at 72 h after hUC-MSCs injection, GFP positive cells were still detected in the lung sections. These data revealed that the GFP-infected hUC-MSCs were able to migrate rapidly to lung tissue in vivo and function over time (Figure 2).
**Figure 2:** *Identification of the intraperitoneal injection of human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) (the HM1 group) by fluorescence microscopy. The green fluorescent protein (GFP)-labeled cells were identified by observing the distribution of GFP-positive cells. (A) 1 h, (B) 12 h (C) 24 h, (D) 48 h, and (E) 72 h. Magnification, ×400.*
## hUC-MSCs administration increases survival rate in hyperoxia-induced BPD of neonatal rats
Exposure to hyperoxia (HS) reduced the survival rate at 14 days of age ($53.3\%$) compared to the $100\%$ survival rate of NC at the same age. Administration with hUC-MSCs (HM1, HM2, and HM3) remarkably increased the survival rate in comparison with that in the BPD group (HS), with no death in the HM1, HM2, and HM3 groups (Figure 3).
**Figure 3:** *Human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) improves survival rate of each group at postnatal day (PN) 14 showed in Kaplan-Meier survival curves. All the rat pups reared in the NC group (n = 14) and those reared in hyperoxia treated with human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) (HM1, HM2, and HM3 groups) (n = 14–16) survived. The rat pups reared in the HS group (n = 14) had a significant reduced survival rate than did those reared in the NC group. hUC-MSCs improved the hyperoxia-induced decrease of survival rate, and the differences among the groups were statistically significant. **, ##, and &&P < 0.01.*
## hUC-MSCs administration improves lung histology in hyperoxia-induced BPD of neonatal rats
Representative images of the whole lung from all groups at PN 14 are shown in Figure 1B. Representative lung sections stained with hematoxylin and eosin from newborn rats on PN14 are shown in Figure 1C. At PN14, compared to the NC group, the HS group demonstrated a histological pattern reminiscent of human BPD, characterized by severe impaired alveolar growth, as evidenced by fewer and larger alveoli and heterogenous alveolar sizes. And this was reflected in elevated MLI values compared with normoxia control rats (Figure 1D). After hUC-MSCs administration, hyperoxia-induced damage in alveolar growth and morphological changes were dramatically improved in HM1, HM2, and HM3, respectively, in a dose-dependent manner. A remarkably lower MLI was observed in the HM3 group (1 × 107 cells per pup) as compared to that of the HM1 and HM2 groups.
## hUC-MSCs administration reduces pulmonary vascular permeability in hyperoxia-induced BPD of neonatal rats
The BALF protein concentration and the lung W/D weight ratio are two commonly used indicators of pulmonary vascular permeability. A significant increase in the BALF protein concentration (Figure 4A) and the lung W/D weight ratio (Figure 4B) was observed in the hyperoxia-induced BPD rats when compared with those of the NC group, and this level was decreased by hUC-MSCs treatment in HM1, HM2, and HM3 group, in a dose-dependent manner. These results suggest that hUC-MSC treatment attenuates lung edema in hyperoxia-induced BPD rats.
**Figure 4:** *Human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) reduces lung edema and lung inflammation in hyperoxia-induced bronchopulmonary dysplasia (BPD) of neonatal rats. (A) The lung wet/day (W/D) weight ratio, (B) Total protein concentration, (C) Total cell count, (D) Neutrophil number, and (E) Macrophage number in bronchoalveolar lavage fluid (BALF) of rat pups. The rat pups reared in the HS group showed a significantly increased lung W/D weight ratio, higher BALF total protein concentration, total cell count, and increased neutrophil and macrophage accumulation than did those reared in the NC group. hUC-MSCs (1 × 106 cells, 5 × 106 cells, or 1 × 107 cells) reduced the hyperoxia-induced lung W/D weight ratio, total protein concentration, total cell count, neutrophil number, and macrophage number of BALF increases. *P < 0.05, **P < 0.01, and ****P < 0.0001. Data was presented as means ± SEM, n = 6–8 per group.*
## Impact of hUC-MSCs on differential cell counts of BALF in hyperoxia-induced BPD of neonatal rats
The total cell number in BALF was counted using the Countstar Automated Cell Counter, and differential cell counts were evaluated using centrifuged preparations stained with Wright-Giemsa staining (Figures 4C–E). The numbers of total cells, neutrophils, and macrophages in BALF were significantly increased in the HS group compared to the NC group, and those in the hUC-MSCs-treated group (HM1, HM2, and HM3) were significantly lower than those in the HS group, in a dose-dependent manner.
## hUC-MSCs administration reduces neutrophil infiltration into the lungs in hyperoxia-induced BPD of neonatal rats
To measure the degree of neutrophil infiltration in the lung, MPO activity was detected. Compared with that in the NC group, lung MPO activity in the HS group was dramatically increased (Figure 5). The increased MPO activity observed in the HS group was significantly attenuated in HM1, HM2, and HM3 groups, and this attenuation was most significant in HM3 group, next in HM2 and HM1 groups.
**Figure 5:** *Human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) reduces myeloperoxidase (MPO) activity of the lung tissue. The rat pups reared in the HS group yielded significantly higher MPO activity than did those reared in the NC group. hUC-MSCs (1 × 106 cells, 5 × 106 cells, or 1 × 107 cells) reduced the hyperoxia-induced MPO activity increase. **P < 0.01. Data was presented as means ± SEM, n = 6–8 per group.*
## hUC-MSCs administration alleviates oxidative stress in lung tissues of hyperoxia-induced BPD of neonatal rats
MDA is a marker of free radical activity. Extensive evidence has shown that oxidative stress markedly increases MDA level. Similarly, heme oxygenase (HO)-1 is an essential enzyme in heme catabolism physiologically that possesses anti-inflammatory properties and suppresses oxidative stress. In the present study, the HS group revealed a substantially higher levels of MDA concentration and HO-1 activity in lung tissues compared to the NC group. Elevated MDA level and HO-1 activity induced by hyperoxia exposure were significantly diminished upon hUC-MSCs administration in hyperoxia-exposed neonatal rats, in a dose-dependent manner (Figures 6A,B).
**Figure 6:** *Human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) reduces the level of MDA concentration (A) and HO-1 activity (B). The rat pups reared in the HS group showed significantly higher level of MDA concentration and HO-1 activity than did those reared in the NC group. hUC-MSCs (1 × 106 cells, 5 × 106 cells, or 1 × 107 cells) diminished the hyperoxia-induced MDA level and HO-1 activity increases. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. Data was presented as means ± SEM, n = 6-8 per group.*
## hUC-MSCs administration regulates cytokine levels in hyperoxia-induced BPD of neonatal rats
As depicted in Figures 7A–D, we found that the expression of the pro-inflammatory cytokines TNF-α, IL-6, and IL-1β in serum of the BPD rats were remarkably increased compared to those of the NC group. This hyperoxia-induced increases in TNFα, IL-1β, and IL-6 levels were significantly attenuated in HM1, HM2, and HM3 group in a dose-dependent manner. Conversely, we observed a decrease in anti-inflammatory cytokine IL-10 expression in rats exposed in hyperoxia, while the decreased IL-10 expression observed in the HS group was significantly augmented in HM1, HM2, and HM3 group, in a dose-dependent manner.
**Figure 7:** *Human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) reduces proinflammatory cytokine levels and increases anti-inflammatory cytokine level in rat pups. The rat pups reared in the HS group showed significantly higher levels of IL-1β, IL-6, and TNF-α and lower level of IL-10 than did those reared in the NC group. hUC-MSCs (1 × 106 cells, 5 × 106 cells, or 1 × 107 cells) reduced the hyperoxia-induced IL-1β, IL-6, and TNF-α level increases and augmented the hyperoxia-induced IL-10 level decrease. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. Data was presented as means ± SEM, n = 6–8 per group.*
## hUC-MSCs administration downregulates the apoptosis-associated protein in hyperoxia-induced BPD of neonatal rats
The expression level of apoptosis-associated protein Fas in lung tissue was determined by Western blotting (Figure 8). There was a significant increased level of Fas in the HS group compared to the NC group, and the increased expression level was downregulated by hUC-MSCs treatment in a dose-dependent manner. These results suggested that hUC-MSCs may suppress the apoptosis of hyperoxia-induced BPD in rats by modulating the expression level of Fas.
**Figure 8:** *Human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) inhibits Fas protein expression in rat pups. (A) The protein expression of Fas in the lung tissue evaluated by western blot. (B) Statistical analysis of protein expression of Fas. The rat pups of the HS group showed significantly higher protein expression of Fas than did those of the NC group. hUC-MSCs (1 × 106 cells, 5 × 106 cells, or 1 × 107 cells) downregulated the hyperoxia-induced Fas protein level increase. *P < 0.05. Data was presented as means ± SEM, n = 5 or 6 per group.*
## Discussion
BPD develops due to impaired alveolarization in preterm infants and extends into childhood with severe respiratory problems. Various animal models have been developed and continue to be refined with the aim of recapitulating the pathological pulmonary hallmarks noted in the lungs of neonates with BPD. Plenty of preclinical studies commonly use rats to make BPD models. Reasons are various, including relatively short gestation times allowing quick studies on lung development and that the term rat lungs present a similar development stage with human preterm neonates between 24 and 28 gestation weeks, which allows them a proper model for developmental lung injury [16].
MSCs are hypoimmunogenic and more tolerated by hose immune system than other types of stem cells [17], which makes them a great cell source for transplantation in the treatment of BPD. Numerous preclinical studies have shown that treatment with MSCs can alleviate neonatal lung injury in animal models mimicking BPD [18]. In particular, the discrepancy concerning the partial effect of MSCs could be explained by numerous differences, including the cell concentration at harvesting, species, hyperoxia level, and the administration route. And studies have reported that administration of MSCs via intraperitoneal injection may be as effective as via intratracheal injection or intravenous injection [12, 18]. Given that MSCs have been transplanted intrapetitoneally with a good safety record and effectiveness, we chose to administer MSCs via the intraperitoneal route in our study. Due to technical limitations, a maximum of 1 × 107 cells were transplanted. The results revealed that intraperitoneal delivery of hUC-MSCs improved the survival rate, restored the airway structure caused by exposure to hyperoxia, exhibited decreased alveolarization as evidenced by increased MLI, reduced lung edema, reduced cellular infiltration and total protein in BALF, alleviated oxidative stress, modulated levels of inflammatory cytokines, and downregulated level of apoptosis-related protein Fas in a dose-dependent manner, and that 1 × 107 donor cells seems to be optimal to maximize protective effects in the experimental model and setting.
MSCs labeled by transfection with a GFP-carrying lentivirus were administered intraperitoneally to rats at PN5 to enable visualization of localization. GFP-positive cells were seen in the lung tissue of all rats that received labeled cells one hour after transplantation, mainly in the airway, alveolar epithelial cells, and alveolar septum. Even 72 h after transplantation, GFP-positive cells were still seen in the lung tissues of all rats. This explains the potential mechanism by which treatment with hUC-MSCs intraperitoneally improves lung development through engraftment.
The oxidant/antioxidant balance is a vital component in the pathogenesis of ALI [19]. MDA is the main product of lipid peroxidation and is identified as a biological marker of oxygen stress injury [20, 21]. Substantial evidence demonstrated that heme HO-1 is an inducible enzyme with potent anti-oxidant, anti-inflammatory, and anti-apoptotic properties [22, 23]. Multiple preclinical studies have shown that HO-1 regulates the protective response in hyperoxia-induced injury (24–26). In the present study, we found that hUC-MSCs administration remarkably downregulated MDA and HO-1 expression in lung tissue, which suggests that hUC-MSCs alleviate BPD via modulating the oxidative/antioxidative balance.
A plenty of proinflammatory factors could be activated during oxidative stress, including IL-1β, IL-6, and TNF-α (27–29). Overproduction of these factors promotes chronic inflammation, which leads the development of BPD. Preclinical studies have reported that the inhibition of inflammatory factors has beneficial effects on lung injury by decreasing lung inflammation and oxidative stress in neonatal rats. Oncel et al. reported that the inhibition of TNF-α decreased the MDA levels, which helps the lung development and pulmonary vascularization [30]. Besides, studies also showed that IL-1β and IL-6 could be used as biomarkers for monitoring ALI [28, 29, 31]. As one of the most important anti-inflammatory cytokines, IL-10 is known to inhibit the synthesis of proinflammatory cytokines. Study indicated that the progression of ALI is associated with decreased expression and secretion of IL-10 [32]. In this study, we observed that hUC-MSCs significantly decreased the levels of these proinflammatory cytokines TNF-α, IL-1β, IL-6, and MPO and increased the expression of anti-inflammatory cytokine of IL-10 in the serum of BPD rats, which is consistent with the previous studies [33, 34], and probably suggests that inflammatory responses mediated by neutrophils, oxidative stress, and proinflammatory cytokines play an important role in the pathogenesis of BPD [35, 36]. Many previous studies have reported that the protective effects of MSCs transplantation against hyperoxia-induced lung injury are mainly mediated by paracrine potency rather than regenerative mechanisms [12, 37, 38]. Various paracrine mediators are known to be protective against hyperoxia-induced lung injury, including increased inflammation, oxidative stress, apoptosis, and impaired angiogenesis and alveolarization [38, 39]. In addition, a growing corpus of studies have highlighted that MSCs can modulate T-cell-mediated immunological responses [40]. In the present study, the protective effects of hUC-MSCs treatment against hyperoxia-induced lung injury were positively correlated with reduced levels of proinflammatory cytokines of TNF-α, IL-1β, and IL-6, total protein and cells accumulation including neutrophils, reduced MPO activity, MDA concentration, and HO-1 activity, in a dose-dependent manner. Our results suggest that intraperitoneal delivery of at least 1 × 106 cells of hUC-MSCs is necessary to induce paracrine effects in hyperoxia-induced lung injury of newborn rats.
The Fas-mediated cell death pathway represents typical apoptotic signaling in many cell types (41–43), and Fas signaling also was involved in hyperoxia-induced apoptosis [44]. A number of investigations demonstrated that acute lung injury increases the expression of Fas in epithelial cells (45–47). Besides, activation of the Fas signaling triggers inflammatory responses in the lung, including cytokine release from epithelial cells via activation of protein kinases [45]. Prevention of Fas expression results in the reduction of apopsis [48]. Some studies have demonstrated that hyperoxia has started apoptosis in animal models via Fas and death receptor-mediated apoptotic pathway [49, 50]. And MSCs therapy possesses protective effects on apoptosis mediated through the death receptor pathway [51]. In our study, the protein expression of Fas was markedly up-regulated in the HS group, which was remarkably down-regulated by hUC-MSCs in a dose-dependent manner. Our results suggest that hUC-MSCs transplantation saves lungs from pulmonary injury by suppressing the upregulation of hyperoxia-triggered apoptosis, along with the reduced inflammation.
## Conclusion
In conclusion, our data indicated that intraperitoneal administration of hUC-MSCs significantly ameliorated the hyperoxia-induced lung injury, including reversing decreased alveolarization and increased inflammation. The intraperitoneal delivery of 1 × 107 cells was optimal to achieve effective anti-inflammatory, anti-oxidative, and anti-apoptotic effects. However, even if this dose might be appropriate for newborn rats, it might not be suitable for humans on a kilogram basis. Further studies are needed to explore the optimal dose of hUC-MSCs for the treatment of BPD infants.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.
## Ethics statement
The animal study was reviewed and approved by The Animal Research Ethics Committee of the Children's Hospital of Chongqing Medical University, China.
## Author contributions
YS and MH designed the study. JX performed the experiment and drafted the manuscript. QA and LB analyzed the data. YG and XD revised the manuscript. YS reviewed and revised the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
Authors YG, XD, and MH were employed by company The Perfect Cell Biotechnology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fped.2023.1111829/full#supplementary-material.
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|
---
title: The impact of SGLT2 inhibitors on αKlotho in renal MDCK and HK-2 cells
authors:
- Lisa Wolf
- Michael Föller
- Martina Feger
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10032406
doi: 10.3389/fendo.2023.1069715
license: CC BY 4.0
---
# The impact of SGLT2 inhibitors on αKlotho in renal MDCK and HK-2 cells
## Abstract
αKlotho is a transmembrane protein predominantly expressed in the kidney serving as a co-receptor for phosphate homeostasis-regulating hormone FGF23 and has an extracellular domain that can be cleaved off and is a hormone. αKlotho deficiency results in accelerated aging and early onset of aging-associated diseases while its overexpression strongly expands the lifespan of mice. Moreover, αKlotho exerts health-beneficial anti-inflammatory, anti-neoplastic, anti-fibrotic, and anti-oxidant effects. Higher αKlotho levels are associated with better outcomes in renal and cardiovascular diseases. SGLT2 inhibitors are novel drugs in the treatment of diabetes by inhibiting renal glucose transport and have additional nephro- and cardioprotective effects. We explored whether SGLT2 inhibitors affect αKlotho gene expression and protein secretion. Experiments were performed in renal MDCK and HK-2 cells, and αKlotho transcripts were determined by qRT-PCR and Klotho protein by ELISA. SGLT2 inhibitors canagliflozin, sotagliflozin, and dapagliflozin enhanced whereas empagliflozin reduced αKlotho gene expression in MDCK cells. By the same token, canagliflozin, sotagliflozin, dapagliflozin, but not empagliflozin down-regulated p65 subunit of pro-inflammatory NFκB. In HK-2 cells, all SGLT2 inhibitors reduced αKlotho transcripts. Canagliflozin and sotagliflozin, however, increased Klotho protein concentration in the cell culture supernatant, an effect paralleled by up-regulation of ADAM17. Taken together, our investigations demonstrate complex effects of different SGLT2 inhibitors on αKlotho gene expression and protein secretion in renal MDCK and HK-2 cells.
## Introduction
αKlotho is a renal protein the lack of which results in a phenotype recapitulating human aging: αKlotho-deficient mice die at an age of a few weeks only whilst exhibiting aging-associated diseases affecting almost all organs including emphysema, hearing loss, infertility, or hypogonadism amongst others (1–3). The animals exhibit massive hyperphosphatemia and excess of active vitamin D, 1,25(OH)2D3, with a low-phosphate diet or low-vitamin D diet normalizing their phenotype [4, 5]. Hence, further research has demonstrated a pivotal role of αKlotho in maintaining phosphate homeostasis: αKlotho is a transmembrane protein that enhances binding affinity of fibroblast growth factor 23 (FGF23) for its renal receptor [6, 7]. FGF23 is produced in bone as a hormone and reduces production of 1,25(OH)2D3 and reabsorption of phosphate, thereby lowering serum levels of 1,25(OH)2D3 and phosphate (8–11). The joint action of FGF23 and αKlotho in the regulation of phosphate and 1,25(OH)2D3 explains why FGF23 deficiency is comparable to αKlotho deficiency in mice (12–14). In addition to being the co-receptor of FGF23, a soluble form of αKlotho exists called sKL which results from the cleavage of transmembrane αKlotho by proteases ADAM10 and ADAM17 [15, 16]. SKL has hormone-like properties and influences various cellular effects including signaling or membrane transport [17].
*In* general, the effects of αKlotho are beneficial: Also by inhibiting insulin-like-growth factor 1 (IGF-1) or Wnt signaling, αKlotho exerts anti-tumor effects [18, 19]. αKlotho has anti-inflammatory and anti-oxidant properties (20–22). Notably, αKlotho is organoprotective: It protects from stress-induced cardiac hypertrophy through suppression of transient receptor potential cation channel subfamily C member 6 (TRPC6) [23] or from ischemia/reperfusion injury [24]. Moreover, αKlotho is nephroprotective and has proven beneficial in diabetic nephropathy [25, 26]. It suppresses kidney fibrosis and delays progression of chronic kidney disease (CKD) (27–31). All these beneficial effects are likely to contribute to the $30\%$ longer lifespan in mice overexpressing αKlotho [2].
SGLT2 inhibitors such as dapagliflozin, canagliflozin, empagliflozin, or sotagliflozin were introduced as new antidiabetics in the early 2010s [32]. They efficiently lower blood glucose levels by inhibiting renal SGLT2, a Na+-dependent glucose transporter which normally accomplishes complete glucose reabsorption along with SGLT1, another isoform that is not inhibited by specific SGLT2 inhibitors (33–35). As a consequence, the patients excrete more glucose in their urine [36, 37]. Recent large clinical studies of high quality have unequivocally proven that SGLT2 inhibition has further significant and diabetes-independent cardio- and nephroprotective effects that are, in many aspects, regarded as a milestone in cardiology and nephrology (38–40). SGLT2 inhibition is the only approved drug therapy in heart failure with preserved ejection fraction (HFpEF), and in CKD, SGLT2 inhibitor therapy is the first major breakthrough since the introduction of angiotensin-converting enzyme (ACE) inhibitors in the 1990s, since the drugs convincingly delay loss of kidney function (41–43). The precise underlying mechanisms of SGLT2 inhibitor-dependent cardio- and nephroprotection have, hitherto, remained enigmatic although many beneficial effects have been characterized including the prevention of hyperfiltration in the kidney or anti-inflammatory and anti-fibrotic effects [44, 45].
Given the strong cardio- and nephroprotection provided by αKlotho, which is also, at least in part, dependent on its anti-inflammatory and anti-fibrotic effects, we hypothesized that benefits of therapy with SGLT2 inhibitors may be related to up-regulation of cellular αKlotho levels. This hypothesis has already been formulated in a recent review [46]. To verify it, we performed experiments in canine MDCK and human HK-2 kidney cell lines.
## Cell culture and treatments
Madin Darby Canine Kidney Cells (MDCK) (NBL-2) (CVCL_0422, CLS Cell Lines Service, Eppelheim, Germany) were grown in Dulbecco’s Modified Eagle Medium: Nutrient Mixture F-12 (DMEM/F-12; Gibco, Life Technologies, Darmstadt, Germany) supplemented with $5\%$ fetal bovine serum (FBS), 2 mM glutamine, 100 U/mL penicillin, and 100 μg/mL streptomycin (all from Gibco). Human kidney 2 (HK-2) cells (CRL-2190, ATCC, Manassas, VA, USA) and rat osteoblast-like UMR-106 cells (CRL-1661, ATCC) were cultured in DMEM of high glucose (Gibco) containing $10\%$ FBS, 100 U/mL penicillin, and 100 μg/mL streptomycin. Cells were incubated at 37°C and $5\%$ CO2.
For experiments, cells (150,000 MDCK or 120,000 HK-2 cells per well) were seeded into 6-well plates, grown for 24 h, and then treated for 24 h with or without canagliflozin, sotagliflozin, empagliflozin (all from Selleck Chemicals, Planegg, Germany), dapagliflozin, or phlorizin dihydrate (both from Sigma-Aldrich, Schnelldorf, Germany) at the indicated concentrations. Control cells were treated with the appropriate amount of solvent dimethyl sulfoxide. For the measurement of soluble Klotho protein in the cell culture medium, supernatants were collected after 24 h of treatment and stored at -70°C until further use.
UMR-106 cells were plated at a density of 200,000 cells per well in growth medium supplemented with 10 nM 1,25(OH)2D3 (Tocris, Bio-Techne, Wiesbaden-Nordenstadt, Germany). After 24 h, cells were treated with SGLT2 inhibitor canagliflozin, sotagliflozin, empagliflozin, dapagliflozin, or vehicle only for 24 h.
## RNA isolation and qualitative expression analysis
Total RNA from MDCK, HK-2, and UMR-106 cells was isolated using a phenol-chloroform extraction technique (RNA-Solv Reagent, Omega Bio-Tek, Norcross, GA, USA or TriFast Reagent, VWR, Bruchsal, Germany) according to the manufacturer’s protocol. For qualitative detection of SLC5A1 and SLC5A2 transcripts and quantitative gene expression analysis in MDCK cells, total RNA was extracted by means of phenol-chloroform-extraction, subjected to DNase treatment, and RNA purification (NucleoSpin RNA, Macherey-Nagel, Düren, Germany). Synthesis of complementary DNA (cDNA) was performed using 1.2 µg of total RNA, random primers, and GoScript Reverse Transcription System (Promega, Mannheim, Germany).
Polymerase chain reaction (PCR) was conducted on a Biometra TAdvanced thermocycler (Analytik Jena, Jena, Germany) using 2 µL cDNA, 0.25 µM canine forward and reverse primers or 0.5 µM human primers, respectively, 10 µL GoTaq Green Master Mix (Promega), and sterile water up to 20 µL. Conditions for PCR included 3 min at 94°C; followed by 40 cycles of 94°C for 30 s; 57°C (for human SLC5A2), 59°C (for canine SLC5A1) or 60°C (for canine SLC5A2 and human SLC5A1) for 30 s; and 72°C for 30 s.
The following primers (5’→3’ orientation) were used for qualitative and quantitative PCR analysis of SGLT$\frac{1}{2}$: SLC5A1 (canine): GTGCAGTCAGCACAAAGTGG, CGGGACACCCCAATCAGAAA;
SLC5A2 (canine): CTCTTTGCCAGCAACATCGG, CACGAACAGCGCATTCCAC; SLC5A1 (human):
GCTGCCACCATGGACAGTAG, AGATGGGGACAAACAGCCAG; SLC5A2 (human): GGAGATGAATGAGCCCCAGG,
TCATGAGCAGGGCATTGAGG.
Amplified PCR products, no reverse transcriptase controls (NRT), and no template controls (NTC) were separated (complete PCR reaction mixture of MDCK samples and 12 µL of the HK-2 samples, respectively), on a $1.5\%$ agarose gel and visualized by ethidium bromide staining.
## Quantitative real-time polymerase chain reaction
Quantitative real-time PCR (qRT-PCR) was performed on a CFX Connect Real-Time PCR System (Bio-Rad Laboratories, Feldkirchen, Germany). The qRT-PCR reaction mixture consisted of 10 µL GoTaq qPCR Master Mix (Promega), 2 µL of cDNA, specific primers and sterile water up to 20 µL. The qRT-PCR conditions were as follows: 95°C for 2 min; 40 cycles of 95°C for 10 s; 56°C for 30 s (canine KL), 57°C for 30 s (canine RELA), 58°C for 30 s (rat Fgf23 and TATA-box binding protein (Tbp)), 59°C for 30 s (human KL, ADAM10, and TBP), 60°C for 30 s (canine TBP and human ADAM17); and 72°C for 25 s.
The following primers (5’→3’ orientation) were used for qRT-PCR analysis: KL (canine): AAATGAAGCTCTGAAAGCC, AATGATAGAGGCCAAACTTC;
TBP (canine): CCTATTACCCCTGCCACACC, GCTCCCGTACACACCATCTT; RELA (canine):
AACAGCGTGGGGACTATGAC, GGGCACGGTTGTCAAAGATG; ADAM10 (human): GACCACAGACTTCTCCGGAAT,
TGAAGGTGCTCCAACCCAAG; ADAM17 (human): GGGCAGAGGGGAAGAGAGTA, TGTGGAGACTTGAGAATGCGA;
KL (human): TGGAAACCTTAAAAGCCATCAAGC, CCACGCCTGATGCTGTAACC; TBP (human):
TGCACAGGAGCCAAGAGTGAA, CACATCACAGCTCCCCACCA, Fgf23 (rat): TAGAGCCTATTCAGACACTTC,
CATCAGGGCACTGTAGATAG; Tbp (rat): ACTCCTGCCACACCAGCC, GGTCAAGTTTACAGCCAAGATTCA.
Relative mRNA transcript levels were calculated by the 2-ΔΔCt method using TBP as internal reference gene and normalized to the control group. For quantitative analysis of SGLT1 and SGLT2 expression, relative mRNA transcripts were calculated using the 2-ΔCt formula and TBP as internal reference.
## Klotho protein determination in cell culture supernatant
HK-2 supernatants were subjected to enzyme-linked immunosorbent assay (ELISA) for measurement of soluble Klotho protein (IBL, Hamburg, Germany).
## Statistics
Data are reported as means ± standard error of mean (SEM) in bar graphs with scatter plots, and n is the number of independent experiments. Data were tested for normality using Shapiro-Wilk test. Differences between control and treatment group were assessed by two-tailed paired Student’s t test, Wilcoxon matched-pairs signed rank test (for data not passing normality), one-sample t test or Wilcoxon signed rank test (for data not passing normality), as indicated in the figure legends. $P \leq 0.05$ was considered statistically significant. SPSS version 27.0 (IBM, Armonk, NY, USA) or GraphPad Prism 6.0 (GraphPad Software, San Diego, California, USA) were used for statistical analysis.
## Results
We utilized canine MDCK and human HK-2 kidney cells to study the regulation of αKlotho. At first, we investigated the expression of SGLT1 (encoded by SLC5A1) and SGLT2 (encoded by SLC5A2) in the two cell lines by PCR. According to qualitative analysis, mRNA specific for both glucose transporters was detectable in MDCK cells seemingly to a small extent (Figure 1A). For HK-2 cells, mRNA for both transporters could be verified with a markedly stronger band for SGLT2 (Figure 1B). In addition, we performed quantitative analysis and examined relative expression ($$n = 3$$ for all analyses) of SGLT1 and SGLT2 in both cell lines by qRT-PCR. As a result, cycle threshold (Ct) value for canine SLC5A1 (SGLT1) in MDCK cells was 37.46 ± 1.499, whereas a positive signal for SLC5A2 (SGLT2) could not be detected in these cells (Ct > 40 for SLC5A2). In HK-2 cells, Ct value for SLC5A1 (SGLT1) was 37.39 ± 0.661 resulting in 3.93 x 10-5 ± 2.055 x 10-5 arbitrary units of relative SLC5A1 mRNA transcription normalized to the housekeeping gene TBP. For SLC5A2 (SGLT2), Ct value was 30.35 ± 0.128 resulting in 4.04 x 10-3 ± 0.447 x 10-3 arbitrary units of relative SLC5A2 mRNA transcription normalized to TBP.
**Figure 1:** *Qualitative analysis of SGLT1 (SLC5A1) and SGLT2 (SLC5A2) mRNA abundance in MDCK and HK-2 cells. Original agarose gel photo showing SLC5A1- and SLC5A2-specific PCR products in untreated MDCK (A) and HK-2 (B) cells. base pair, bp; no template control, NTC; no reverse transcriptase, NRT.*
Next, we treated MDCK cells with increasing concentrations of the specific SGLT2 inhibitor canagliflozin, which is widely used in the treatment of patients, for 24 h and utilized qRT-PCR to assess the effect on αKlotho gene expression. As detailed in Figure 2A, canagliflozin up-regulated αKlotho gene expression in a dose-dependent manner. As a further step, we investigated whether also sotagliflozin, a dual SGLT1/SGLT2 inhibitor approved for use in patients, up-regulates αKlotho. To this end, we treated MDCK cells with different concentrations of sotagliflozin for 24 h, and measured αKlotho transcripts by qRT-PCR. Similar to canagliflozin, sotagliflozin enhanced αKlotho mRNA abundance (Figure 2B). Further experiments explored the effect of a 24 h-incubation with or without dapagliflozin (Figure 2C) or empagliflozin (Figure 2D) on αKlotho in MDCK cells. Whereas dapagliflozin (Figure 2C) up-regulated αKlotho gene expression to an extent similar to canagliflozin (Figure 2A) or sotagliflozin (Figure 2B), empagliflozin surprisingly reduced αKlotho transcripts in MDCK cells (Figure 2D).
**Figure 2:** *SGLT2 inhibition affects KL mRNA expression in MDCK cells. Arithmetic means ± SEM of relative KL mRNA abundance normalized to TBP in MDCK cells treated without (Ctr) or with canagliflozin (A; n=7), sotagliflozin (B; n=5), dapagliflozin (C; n=7), or empagliflozin (D; n=6-7) for 24 h. *p < 0.05, **p < 0.01 and ***p < 0.001 indicate significant difference from control cells. (One sample t test).*
Finally, we tested phlorizin, a non-specific glucose transporter inhibitor inhibiting both, SGLT1 and SGLT2, which is not approved for use in patients. As illustrated in Figure 3, treatment of MDCK cells with 30 µM phlorizin (24 h) resulted in a small, but statistically significant up-regulation of αKlotho transcripts whereas higher concentrations had no significant effect on αKlotho (Figure 3).
**Figure 3:** *Effect of phlorizin on KL transcript level in MDCK cells. Arithmetic means ± SEM of relative KL mRNA abundance normalized to TBP (n=7) in MDCK cells treated without (Ctr) or with phlorizin for 24 h. *p < 0.05 indicates significant difference from control-treated cells. (One sample t test).*
Owing to its anti-inflammatory properties [45], we investigated whether SGLT2 inhibitors impact on the expression of p65 subunit (encoded by RELA) of pro-inflammatory transcription factor complex NFκB. As illustrated in Figure 4, the three SGLT2 inhibitors which enhanced αKlotho down-regulated RELA expression in MDCK cells (Figures 4A–C). Empagliflozin, which did not significantly affect αKlotho, and phlorizin did not significantly modify RELA in MDCK cells (Figures 4D, E).
**Figure 4:** *Reduced expression of NFκB subunit p65 (RELA) following SGLT2 inhibitor treatment in MDCK cells. Arithmetic means ± SEM of relative RELA mRNA expression normalized to TBP in MDCK cells treated with vehicle (Ctr), canagliflozin (A; n=9), sotagliflozin (B; n=7), dapagliflozin (C; n=7), empagliflozin (D; n=9) or with phlorizin (E; n=5) for 24 h. *p < 0.05 and **p < 0.01 indicate significant difference from control-treated cells. (One sample t test).*
Our results thus far indicate that canagliflozin, sotagliflozin, and dapagliflozin, but not empagliflozin, up-regulated αKlotho in MDCK cells. We wondered whether this effect translates into enhanced Klotho protein secretion. In order to answer this question, we carried out a new series of experiments in human HK-2 cells since ELISA-based Klotho protein quantification is not feasible in canine MDCK cells. First, we examined the effect of the SGLT2 inhibitors on αKlotho transcripts in HK-2 cells. As demonstrated in Figure 5A, canagliflozin, sotagliflozin, dapagliflozin as well as empagliflozin down-regulated αKlotho gene expression in HK-2 cells in sharp contrast to MDCK cells, whereas phlorizin had no significant effect on αKlotho mRNA levels in HK-2 cells. As a second step, we employed ELISA to measure Klotho protein in the cell culture supernatant. According to Figure 5B, Klotho protein abundance was slightly but significantly higher in the supernatant of HK-2 cells treated with canagliflozin or sotagliflozin compared to control cells (Figure 5B, upper panel). Dapagliflozin or empagliflozin did, however, not significantly modify the Klotho protein amount in the cell culture supernatant (Figure 5B, lower panel).
**Figure 5:** *Impact of SGLT2 inhibition on Klotho production in HK-2 cells. (A) Arithmetic means ± SEM of relative KL mRNA expression in HK-2 cells normalized to TBP treated either with vehicle (Ctr), canagliflozin (60 µM; n=10), sotagliflozin (60 µM; n=10), dapagliflozin (100 µM; n=5), empagliflozin (100 µM; n=5), or phlorizin (100 µM; n=6) for 24 h. (B) Arithmetic means ± SEM of Klotho protein (pg) in the supernatant of HK-2 cells either treated with vehicle (Ctr), canagliflozin (n=11), sotagliflozin (n=10), dapagliflozin (n=8), or empagliflozin (n=8) for 24 h. *p < 0.05, **p < 0.01, and ***p < 0.001 indicate significant difference from control-treated cells. (A: One sample t test; B: Wilcoxon matched-pairs signed rank test (upper panel, right) or two-tailed paired Student’s t test).*
ADAM10 and ADAM17 are proteases that are involved in αKlotho shedding, a process yielding soluble Klotho in the blood or cell culture supernatant [15]. To investigate whether ADAM10 or ADAM17 regulation accounts for the different effects of gliflozins on Klotho protein in the cell culture supernatant, we tested the effect of SGLT2 inhibitors on ADAM10 and ADAM17 expression in HK-2 cells. QRT-PCR analyses revealed that none of the SGLT2 inhibitors had a significant effect on ADAM10 transcripts (Figure 6A) in HK-2 cells. However, a significant increase in ADAM17 transcript levels was observed in response to canagliflozin or sotagliflozin but not dapagliflozin or empagliflozin treatment (Figure 6B), an effect likely to contribute to the different actions of SGLT2 inhibitors on Klotho protein in the cell culture supernatant of HK-2 cells.
**Figure 6:** *Effect of SGLT2 inhibition on ADAM10 and ADAM17 mRNA expression in HK-2 cells. Arithmetic means ± SEM of relative ADAM10
(A) and ADAM17
(B) mRNA expression normalized to TBP in HK-2 cells either treated with vehicle control (Ctr), canagliflozin (60 µM; n=10), sotagliflozin (60 µM; n=10), dapagliflozin (100 µM; n=8), or empagliflozin (100 µM; n=8) for 24 h. **p < 0.01 indicates significant difference from control cells. (One sample t test or Wilcoxon signed rank test).*
Finally, we investigated whether the SGLT2 inhibitors also impact on gene expression of Fgf23, a bone-derived hormone which requires renal αKlotho as a co-receptor. To this end, UMR-106 osteoblast-like cells were treated without or with SGLT2 inhibitors for 24 h, and Fgf23 transcripts were determined by qRT-PCR. As a result, control cells exhibited Fgf23 transcripts levels relative to Tbp of 1.0 ± 0.0 arbitrary units (a.u.) ( for all: $$n = 3$$). Treatment with 100 µM dapagliflozin or 100 µM empagliflozin significantly up-regulated Fgf23 transcript levels (1.8 ± 0.1 a.u.; $p \leq 0.01$ or 2.1 ± 0.2 a.u.; $p \leq 0.05$, resp.), whereas exposure to 60 µM canagliflozin or 60 µM sotagliflozin significantly reduced *Fgf23* gene expression (0.1 ± 0.0 a.u.; $p \leq 0.01$ or 0.4 ± 0.1 a.u.; $p \leq 0.05$, resp.).
## Discussion
Our investigations revealed that common SGLT2 inhibitors used in the treatment of patients with diabetes, CKD, or heart failure modify αKlotho gene expression and Klotho protein secretion in renal MDCK and HK-2 cells.
In canine MDCK cells, canagliflozin, sotagliflozin, and dapagliflozin up-regulated αKlotho gene expression. In contrast, empagliflozin down-regulated αKlotho transcripts in MDCK cells. Importantly, empagliflozin has the highest selectivity for SGLT2 over SGLT1 among the four gliflozins tested [47]. According to our quantitative expression analysis, MDCK exhibited low SGLT1 and virtually no SGLT2 expression. Therefore, it can be assumed that exposure to empagliflozin resulted in higher remaining SGLT1 activity compared to exposure to the other SGLT2 inhibitors in MDCK cells. It is tempting to speculate that this difference in remaining SGLT1 activity may play a role in the gliflozin effect on αKlotho in MDCK cells. Certainly, αKlotho up-regulation in MDCK cells was independent of SGLT2 since this transporter was not expressed in MDCK cells. In line with this, cardioprotective effects of SGLT2 inhibitors are not necessarily related to SGLT2, since expression of SGLT2 in the heart and cardiomyocytes is controversial (48–50). Therefore, upregulation of αKlotho expression in MDCK cells by canagliflozin, sotagliflozin, and dapagliflozin is probably also an SGLT2-independent effect.
Therefore, other SGLT2-independent effects of gliflozins must account for their effect on αKlotho in MDCK cells. As a matter of fact, such effects exist and play a role in nephro- and cardioprotection by SGLT2 inhibitors: Among them are anti-inflammatory properties [51, 52]. Accordingly, we observed a small, but statistically significant down-regulation of p65 subunit (encoded by RELA) of pro-inflammatory NFκB transcription factor [53, 54] by canagliflozin, sotagliflozin, and dapagliflozin. This effect can clearly be expected to be anti-inflammatory and may be one of the SGLT2-independent effects contributing to the up-regulation of αKlotho. Interestingly, in line with its failure to significantly modify αKlotho expression, empagliflozin did not alter RELA expression, either. Therefore, suppression of inflammation may be relevant for the up-regulation of αKlotho by SGLT2 inhibitors in MDCK cells in the absence of SGLT2. Alternatively, RELA suppression may be secondary to enhancement of αKlotho in canagliflozin-, sotagliflozin-, or dapagliflozin-treated MDCK cells as αKlotho has anti-inflammatory properties [20, 22].
Given the cellular responses of HK-2 cells to SGLT2 inhibitors, the situation even becomes more complex. All SGLT2 inhibitors reduced αKlotho gene expression in HK-2 cells to a small, but statistically significant extent, an observation in contrast to MDCK cells. Our expression analysis suggested markedly higher SGLT2 than SGLT1 expression in HK-2 cells. Since the strong up-regulation of αKlotho in MDCK cells in the absence of SGLT2 expression suggests that this up-regulation is SGLT2-independent, the different expression pattern of SGLT1 and SGLT2 in MDCK cells and HK-2 cells may contribute to the contrasting effects on αKlotho transcripts in the two cell lines. Therefore, inhibition of SGLT2 in HK-2 cells may be accompanied by down-regulation of αKlotho expression.
In the kidney, highest αKlotho expression is observed in the distal tubule, but also the proximal tubule expresses it [55, 56]. Whereas MDCK cells are derived from distal tubule, HK-2 cells are of proximal tubule origin [57, 58]. The different origin of the two cell lines may also help explain the different cellular response to gliflozin treatment. Since SGLT2 is expressed in proximal tubular cells, the gliflozin effect on SGLT2 and αKlotho may be related to actions in different cell types in the kidney.
Despite the small but significant suppressive action of all four SGLT2 inhibitors on αKlotho transcripts in HK-2 cells, canagliflozin and sotagliflozin enhanced Klotho protein secretion. Hence, both SGLT2 inhibitors ultimately increased Klotho protein levels in the supernatant of HK-2 cells, an effect that can be expected to be in line with up-regulation of αKlotho gene expression by SGLT2 inhibitors in MDCK cells. In contrast, dapagliflozin and empagliflozin did not significantly affect Klotho protein levels in the cell culture supernatant. In the kidney, soluble *Klotho is* generated due to cleavage of transmembrane αKlotho by ADAM10 and ADAM17 proteinases [15]. Our expression analysis of ADAM10 and ADAM17 indeed revealed that canagliflozin and sotagliflozin, but not dapagliflozin or empagliflozin up-regulated ADAM17 expression in HK-2 cells. This effect is likely to explain why canagliflozin and sotagliflozin, but not dapagliflozin or empagliflozin increased Klotho protein concentration in the cell culture supernatant of HK-2 cells although all four gliflozin suppressed αKlotho gene expression in these cells according to qRT-PCR analysis.
Phlorizin, a dual inhibitor SGLT1/SGLT2 not in clinical use since inhibition of both Na+-dependent glucose transporters, can be expected to cause serious adverse effects, only had moderate (MDCK cells) or no significant effect (HK-2 cells) on αKlotho although its glucosuric, hence anti-diabetic effect may even be stronger than that of gliflozins. This finding corroborates the notion that the health benefits of SGLT2 inhibitors for the heart or kidneys, which may include up-regulation of αKlotho as unraveled by this study, are independent of the glucose-lowering effects of this class of drugs.
It is a major finding of this investigation that the four SGLT2 inhibitors studied yielded different results in our cell culture experiments: In MDCK cells, empagliflozin failed to up-regulate αKlotho in contrast to canagliflozin, sotagliflozin, and dapagliflozin, and in HK-2 cells, canagliflozin and sotagliflozin, but not dapagliflozin and empagliflozin increased Klotho protein in the cell culture supernatant. It is tempting to speculate whether these findings indeed translate into a different capacity of the SGLT2 inhibitors to upregulate αKlotho levels in vivo and whether this would have a clinical significance. Thus far, most clinical studies revealed similar health benefits of different gliflozins. However, it must be kept in mind that the clinical studies rarely include direct comparisons of different gliflozins. In a recent study of acute kidney injury in rats, differences between empagliflozin and sotagliflozin became apparent [59]. Whether or not the differences of the four gliflozins with regard to αKlotho matter in vivo, must be investigated in the future. A next step could be an ex vivo study addressing the effect of the four gliflozins on αKlotho in proximal tubule cells from diabetic rats to investigate whether αKlotho regulation is different in disease.
Our study brings together two novel concepts of nephro- and cardioprotection, i.e. therapy with SGLT2 inhibitors and increasing the availability of αKlotho (Figure 7). Given the well-established beneficial effects of gliflozins on the kidney and heart and promising results of Klotho delivery particularly in kidney disease in conjunction with association studies showing better outcome with higher Klotho levels in renal and cardiovascular diseases, our study sheds new light on the relationship between SGLT2 inhibitors and αKlotho levels [46].
**Figure 7:** *Proposed interdependence of SGLT2 inhibitors and αKlotho with regard to nephro- and cardioprotection.*
Our experiments with UMR-106 cells revealed that the four different SGLT2 inhibitors studied here markedly differed in their effects on FGF23. In humans, treatment with dapagliflozin or canagliflozin elevates serum FGF23 levels, effects at least in part secondary to changes in phosphate metabolism [60, 61]. Why canagliflozin and sotagliflozin directly decreased and dapagliflozin and empagliflozin directly increased *Fgf23* gene expression in cell culture experiments remains enigmatic and must be clarified in future studies.
It is a major limitation of our study that it is solely cell culture-based. Clearly, additional animal experiments and human studies are warranted to decipher the effect of SGLT2 inhibitors on αKlotho in vivo. Furthermore it would be interesting to study whether other antidiabetics also impact on αKlotho.
Taken together our study revealed distinct actions of four different SGLT2 inhibitors on αKlotho and ADAM17 gene expression and Klotho protein secretion in two different renal cell lines, distal tubular MDCK cells and proximal tubular HK-2 cells.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Author contributions
MFe and MFö designed the research. MFe, MFö, and LW interpreted data. MFe and MFö wrote the manuscript. MFe and LW performed the research and analyses. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Oxidative stress and toxicity produced by arsenic and chromium in broiler chicks
and application of vitamin E and bentonite as ameliorating agents
authors:
- Javaria Mashkoor
- Fatimah A. Al-Saeed
- Zhang Guangbin
- Abdullah F. Alsayeqh
- Shafia Tehseen Gul
- Riaz Hussain
- Latif Ahmad
- Riaz Mustafa
- Umar Farooq
- Ahrar Khan
journal: Frontiers in Veterinary Science
year: 2023
pmcid: PMC10032408
doi: 10.3389/fvets.2023.1128522
license: CC BY 4.0
---
# Oxidative stress and toxicity produced by arsenic and chromium in broiler chicks and application of vitamin E and bentonite as ameliorating agents
## Abstract
The present study investigated the adverse effects of arsenic and chromium in broilers and ascertained the role of vitamin E and bentonite in alleviating their harmful effects. For this purpose, we experimented on 180 one-day-old broiler chickens. The feed was administered to broiler chicks of groups 2, 6, 7, 8, and 9 chromium @ (270 mg.kg−1 BW). Groups 3, 6, 7, 8, and 9 were administered arsenic @ (50 mg.kg−1 BW). Groups 4, 7, and 9 received vitamin E (150 mg.kg−1 BW), and groups 5, 8, and 9 received bentonite ($5\%$), respectively. Group 1 was kept in control. All the broiler chicks treated with chromium and arsenic showed a significant ($p \leq 0.05$) decline in erythrocytic parameters on experimental days 21 and 42. Total proteins decreased significantly, while ALT, AST, urea, and creatinine increased significantly ($p \leq 0.05$). TAC and CAT decreased significantly ($p \leq 0.05$), while TOC and MDA concentrations increased significantly ($p \leq 0.05$) in chromium and arsenic-treated groups on experimental days 21 and 42. Pearson correlation analysis revealed a strong positive correlation between TAC and CAT (Pearson correlation value = 0.961; $p \leq 0.001$), similarly TOC and MDA positive correlation (Pearson correlation value = 0.920; $p \leq 0.001$). However, TAC and CAT showed a negative correlation between TOC and MDA. The intensity of gross and microscopic lesions was more in chromium (270 mg.kg−1) and arsenic (50 mg.kg−1) singly or in combination-treated groups. Thus, broiler chicks treated with chromium plus arsenic exhibited higher gross and microscopic lesion intensity than other treated groups. Fatty degeneration, severe cytoplasmic vacuolar degeneration, and expansion of sinusoidal spaces were the main lesions observed in the liver. Kidneys showed renal epithelial cells necrosis, glomerular shrinkage, and severe cytoplasmic vacuolar degeneration. Co-administration of bentonite along with chromium and arsenic resulted in partial amelioration (group 8) compared to groups 7 and 9, administered arsenic + chromium + vitamin E and arsenic + chromium + vitamin E + bentonite, respectively. It was concluded that arsenic and chromium cause damage not only to haemato-biochemical parameters but also lead to oxidation stress in broilers. Vitamin E and bentonite administration can ameliorate toxicity and oxidative stress produced by arsenic and chromium.
## Introduction
The poultry industry of Pakistan has become an integral part of livestock, contributing $61.89\%$ to agriculture and $14\%$ to GDP. Intensive poultry farming has resulted in poverty alleviation by employing 1.5 million people. With investing > Rs. 750 billion, this industry has been expanding almost $7.5\%$ per annum over the last decade, which has facilitated Pakistan to stand at 11th position among the largest poultry producer in the world and has more than enough room for additional expansion. The poultry industry contributes $38\%$ of the total meat produced in the country [1].
Heavy metals are relentless in the environment and can render bioaccumulation in the food chain. Contamination of drinking water by heavy metals can risk poultry and human health [2]. Water pollution due to heavy metals poses a potential risk to human and animal health [3]. Wastewater from industrial and domestic sources is responsible for polluting the environment and has become a significant health issue in developing countries [4]. Nowadays, heavy metal ions are among water's most toxic inorganic pollutants and have gained ecological significance [5]. Heavy metals, including arsenic and chromium, are naturally found in the earth's crust and enter the environment due to natural or anthropogenic pursuits. They have become health hazards worldwide due to their non-biodegradability and bioaccumulation. Biological activities include rock weathering and volcanic eruptions, while anthropogenic activities include mining, ores smelting, and phosphate fertilizer [6].
As a global health issue, arsenic poisoning is influencing millions worldwide because of environmental and occupational disclosure. The fundamental source of arsenic toxicity to the general population is polluted soil, water, and food products [7]. Arsenic name is derived from the Greek word “arsenikon” which means potent. Arsenic is the most common and toxic element among the most dangerous xenobiotics listed in the environment [8]. Arsenic is found in the environment, e.g., organic and inorganic; their toxicity depends on the form and oxidation state. Arsenite is more toxic than arsenate due to its high affinity to thiol protein groups, while arsenate stops phosphorylation [7].
Chromium (Cr) is a heavy metal notorious as a toxic water pollutant. It is the 21st most abundant element. It naturally exists as mineral chromite and is metallic [9]. Organic forms of chromium include chromium picolinate and chromium-enriched yeast. Among inorganic forms, metallic chromium is used to make alloys, i.e., stainless steel. Anti-corrosive property of stainless steel is due to chromium. Trivalent chromium found in water is used in the tannery and in making dyes and paints. Hexavalent chromium is the most treacherous form being utilized in chrome plating and is an important etiology of mutagenesis and cancer [10]. Anthropogenic activities, i.e., metallurgy, smelting, electroplating, tannery effluents, and agriculture, has heightened chromium level ahead of the integration ability of the environment. Hence it has become the most toxic element in terrestrial and marine environments [11, 12].
Oxidative stress is a process triggered by inequity between the production and accretion of reactive oxygen species (ROS) in cells and tissues and the capacity of a biological system to cleanse these reactive manufactured items, such as free radicals [13, 14]. Free radicals, including superoxide anion, hydroxyl radical, and nitric oxide, usually consist of partially filled orbital having unpaired electrons, which causes the reduction of a variety of organic macromolecules like lipid/protein/carbohydrate [15]. Vitamin E renders free radicals inactive due to its ability to donate hydrogen. It is lipid-soluble vitamin located in the cell membrane. It is the best antioxidant available to combat the oxidative stress induced by heavy metals [16].
Among the natural adsorbents available, bentonite is the most widely used clay to eliminate heavy metals from the body. It is used in many ways in broiler feed, such as a binding agent of bacteria/viruses and the pelleting process [17]. There are rare studies showing bentonite as an ameliorating agent in arsenic plus chromium intoxicated broiler chicks singly or amalgamation with vitamin E. Thus, this study was planned to know the arsenic plus chromium rendered oxidative stress and further amelioration with bentonite and vitamin E. This study shows how the co-administration of bentonite and vitamin E affects broiler health.
## Chemicals
Different chemicals, i.e., Potassium dichromate (K2Cr2O7) and arsenic trioxide (As2O3), were procured from Merck KGaA, Darmstadt, Germany. We procured vitamin E (α-Tocopherol acetate) from Alpharma Inc., New Jersey, USA. Bentonite was obtained from the Potohar Plateau (Punjab Province, Pakistan), where it is abundantly available.
## Experimental broiler chicks and management
The trial was conducted on 180 one-day-old broiler chicks bought from a regional hatchery and maintained these chicks in wire cages under standard housing and management conditions, i.e., humidity (60–$65\%$) and temperature (24–35°C). We fed broiler chicks a basal diet (chick starter crumbs: $21\%$ total proteins) and plenty of clean water. We administered the Newcastle disease (Nobilis® ND Lasota, Intervet SA (Pty) Ltd) vaccine to these chicks on days 2nd and 23rd. On days 8th and 21st, infectious bursal disease (IBD) [Nobilis Gumboro 228E, Intervet SA (Pty) Ltd], whereas administration of hydropericardium syndrome (BioAngara Plus, Sana Lab) vaccine was on day 19th.
## Experimental design
After 2 days of acclimatization, we randomly divided broiler chicks into nine groups having twenty each. Arsenic, chromium, vitamin E, and bentonite medications began on the 3rd day and remained until 42 days. Potassium dichromate [17], arsenic trioxide [18], and vitamin E [17] were used @ 270, 50, and 150 mg.kg−1 BW, respectively. All the treatments were administered daily in the feed. Group 1 served as control. We used 5 % bentonite in the feed [17]. Groups 2 and 3 were given chromium and arsenic, respectively. Group 4 received vitamin E while group 5 bentonite. We administered chromium plus arsenic to chicks of Group 6, whereas group 7 received chromium plus arsenic and vitamin E. Group 8 administered chromium plus arsenic and bentonite. Group 9 received chromium + arsenic + vitamin E and bentonite. We assessed each bird in each group for weight weekly, weighed feed intake in each group daily, and then calculated the cumulative average of body weight and feed intake at the end of the experiment.
## Hematobiochemical parameters
Broiler chicks ($$n = 10$$) were selected randomly from each group and killed humanely on experimental days 21 and 42 to collect blood with anticoagulant (EDTA; 1.0 mg/mL blood) for hematological studies, including total erythrocyte counts, hemoglobin concentration, hematocrit, and total leukocyte counts were determined. Briefly, total erythrocyte and leukocyte counts were figured out following the techniques already described [19] by diluting blood with Natt and Herrick solution and with the aid of a Neubauer counting chamber (Hemocytometer) were counted under a light microscope [20]. Using Drabkin's solution, hemoglobin was determined spectrophotometrically (540 nm) through the cyanmethemoglobin method.
## Biochemical studies
The collected blood without anticoagulant was centrifuged (3,000 rpm for 5 min) for serum separation and stored at −20°C. Serum biochemical parameters like total proteins (Cat # 997180), albumin (Cat # 997258), alanine aminotransferase (ALT; Cat # 30254), aspartate aminotransferase (AST; Cat # 30243), urea (Cat # 996060) and creatinine (Cat # 99108) were measured using commercial kits (M/S Canovelles; Barcelona, Spain) with the help of a chemistry analyzer. Globulin was determined by subtracting albumin from total proteins.
## Antioxidant enzymes/parameters
As mentioned under biochemical studies, serum was procured for further studies. We measured total antioxidant capacity (TAC) in the serum samples following the method already mentioned [21]. Briefly, by this method, antioxidants present in the sample lessen dark blue-green colored ABTS [2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)] radial to colorless ABTS form determined through spectrophotometer at a wavelength between 660 and 670 nm.
The earlier method figured out the catalase (CAT) levels [22]. Briefly, free radicals generated, like hydrogen peroxide, respond with molybdate founding a yellowish complex, the intensity of that is measured by spectrophotometer at a wavelength between 352 and 360 nm.
## Total oxidant status
These included total oxidant capacity (TOC) and malondialdehyde (MDA). TOS was figured out using the method already described [23]. By this method, ferrous-o-dianisidine is dissolved by oxidants present in the sample forming a ferric ion, which then oxidizes with glycerol by generating color. A spectrophotometer measures concentration at a wavelength of 560 nm.
The MDA was measured according to the earlier procedure [24]. MDA oxidizes with deoxyadenosine and deoxyguanosine in DNA, forming complex DNA-MDA and other TBARS abridge with two equivalents of thiobarbituric acid to give a fluorescent red derivative that is assayed with a spectrophotometer at a wavelength of 532 nm.
The total oxidant capacity (TOC) of broilers in various groups given chromium, arsenic, bentonite, and vitamin E in different combinations has been presented in Tables 6, 7. On experimental days 21 and 42, significantly ($p \leq 0.05$) high TAC (1.63 ± 0.05 and 1.79 ± 0.02 μMol/L, respectively) was recorded in chromium and arsenic administration broiler chicks (group 6), while the lowest ($p \leq 0.05$) values (1.15 ± 0.05 and 1.31 ± 0.01 μMol/L, respectively) was observed in control (group 1). Significantly ($p \leq 0.05$), high values of TOC were also recorded in chromium (group 2) and arsenic (group 3) treated broiler chicks compared control group. Though significantly ($p \leq 0.05$) high TOC values were observed in groups treated with bentonite and vitamin E along with arsenic and chromium (groups 7–8) but were significantly ($p \leq 0.05$) reduced TOC values than values in chromium + arsenic treated broiler chicks (group 6).
Malondialdehyde (MDA) of broilers in groups administered chromium, arsenic, bentonite, and vitamin E in different combinations have been presented in Tables 6, 7. At experimental days 21 and 42, the highest MDA (17.70 ± 0.12 and 19.92 ± 0.14 nmol/L, respectively) was recorded in administered broiler chicks (group 6), while the lowest values (9.47 ± 0.11 and 10.34 ± 0.15 nmol/L, respectively) was observed in control (group 1) with significant ($p \leq 0.05$) difference. Significantly ($p \leq 0.05$), higher values of MDA were also recorded in chromium-treated broiler chicks (group 2), followed by arsenic-treated broiler chicks (group 3) compared to the control group. However, higher MDA values were recorded in groups treated with bentonite and vitamin E plus arsenic and chromium (groups 7–9) than in the control group, but MDA values were lower than in chromium and arsenic-treated group (group 6).
## Gross and histopathology techniques
The visceral organs, including the lungs, liver, kidneys, and heart, were examined for gross lesions on each killing. Morbid tissues were preserved in $10\%$ buffered formalin and processed for histopathological studies using the routine method of dehydration and embedding in paraffin. For histopathological studies, sections 4–5 μm thick were cut and stained with hematoxylin and eosin [20]. Prepared slides were examined under a light microscope. We made the scoring of microscopic lesions based on severity (mild, moderate, and severe).
## Statistical analysis
The data thus gathered were evaluated statistically by utilizing two-factor factorials under a completely randomized design. Group means were equated by Duncan multiple range (DMR) test using Microcomputer Statistical Package [25]. Pearson correlations were calculated using Minitab Statistical Software (Minitab Release 13.1). We considered the significance level at $p \leq 0.05.$
## Feed intake, body weight, and FCR
All physical parameters like feed consumed, body weight, and FCR decreased significantly ($p \leq 0.05$) in arsenic plus chromium-administered broiler chicks (group 6) on both experimental days, i.e., 21 and 42. Nevertheless, groups 7, 8, and 9 fed vitamin E and bentonite along with arsenic plus chromium exhibited increased ($p \leq 0.05$) feed consumption and body weight. In arsenic plus chromium-fed broiler chicks (group 6), there was increased FCR compared to control broiler chicks on investigational days 21 and 42. Groups 7, 8, and 9, treated with bentonite and vitamin E along with arsenic and chromium, exhibited significantly ($p \leq 0.05$) decreased/improved FCR (Table 1).
**Table 1**
| Groups | Experimental day 21 | Experimental day 21.1 | Experimental day 21.2 | Experimental day 42 | Experimental day 42.1 | Experimental day 42.2 |
| --- | --- | --- | --- | --- | --- | --- |
| | Feed eaten (g/day) | Body weight (g) | FCR | Feed eaten (g/day) | Body weight (g) | FCR |
| G1 | 972 ± 14.2a | 745 ± 27.5a | 1.30 | 3,397 ± 26.9a | 2,074 ± 29.2a | 1.63 |
| G2 | 820 ± 9.3b | 515 ± 13.2c | 1.59 | 3,259 ± 15.2b | 1,629 ± 15.7c | 2.00 |
| G3 | 821 ± 9.7b | 520 ± 12.5c | 1.57 | 3,275 ± 19.9b | 1,675 ± 15.9c | 1.95 |
| G4 | 974 ± 13.5a | 752 ± 20.7a | 1.29 | 3,392 ± 25.7a | 2,079 ± 29.7a | 1.63 |
| G5 | 962 ± 12.9a | 741 ± 21.3a | 1.29 | 3,380 ± 23.2a | 2,069 ± 29.3a | 1.63 |
| G6 | 795 ± 19.2b | 490 ± 10.5c | 1.62 | 3,201 ± 13.7b | 1,572 ± 14.2d | 2.03 |
| G7 | 880 ± 11.3c | 627 ± 18.5b | 1.40 | 3,265 ± 21.5c | 1,859 ± 17.5b | 1.75 |
| G8 | 865 ± 11.7c | 617 ± 17.3b | 1.40 | 3,247 ± 14.2c | 1,855 ± 17.9b | 1.75 |
| G9 | 969 ± 14.3a | 747 ± 23.5a | 1.29 | 3,392 ± 25.3a | 2„067 ± 28.3a | 1.64 |
## Hematological parameters
Displayed a significant ($p \leq 0.05$) decrease in TEC, hemoglobin, hematocrit, and ESR in groups treated with chromium (group 2), arsenic (group 3), and a combination of arsenic and chromium-treated broiler chicks (group 6) compared with control broiler chicks (group 1) on experimental days 21 and 42 (Tables 2, 3). Vitamin E and bentonite treated groups, along with chromium and arsenic (groups 7, 8, and 9), showed a non-significant ($p \leq 0.05$) difference compared with control (group 1).
Leukocyte counts significantly ($p \leq 0.05$) decreased in broiler chicks treated with chromium (group 2), arsenic (group 3), chromium + arsenic (group 6), and chromium + arsenic + vitamin E (group 7) at experimental days 21 and 42 (Figure 1). In broiler chicks treated with chromium + arsenic + bentonite (group 8), TLC was higher ($p \leq 0.05$) on both experimental days than in other 2, 3, 6, and 7 groups but lower ($p \leq 0.05$) than in control, 4, 5 and 9 groups (Figure 1).
**Figure 1:** *Leukocyte counts (×103/μL) in broiler chicks on 21 and 42 experiment days administered arsenic, chromium, vitamin E, and bentonite alone or in combinations. Bars (mean ± SE) having dissimilar letters under a specific experimental day vary significantly (p < 0.05). Chromium, arsenic, and vitamin E were given @ 270, 50, and 150 mg.kg−1, respectively, while bentonite was administered @ 5%. G1 = Group 1: Control (negative); G2 = Group 2: Chromium; G3 = Group 3: Arsenic; G4 = Group 4: Vitamin E; G5 = Group 5: Bentonite; G6 = Group 6: Chromium + Arsenic; G7 = Group 7: Chromium + Arsenic + Vitamin E; G8 = Group 8: Chromium + Arsenic + Bentonite; and G9 = Group 9: Chromium + Arsenic + Vitamin E + Bentonite.*
## Biochemical parameters
Significantly ($p \leq 0.05$) decreased plasma proteins, albumin, and globulin, and urea and creatinine significantly ($p \leq 0.05$) increased in chromium (group 2), arsenic (group 3), and chromium plus arsenic (group 6) treated broiler chicks compared with control group broiler chicks (Tables 4, 5) at experimental days 21 and 42. Whereas, total proteins, albumin, and globulin showed non-significantly ($p \leq 0.05$) on experimental days 21 and 42 in broiler chicks treated with chromium and arsenic plus vitamin E or bentonite (groups 7 and 8). Interestingly, broiler chicks in chromium + arsenic + vitamin E + bentonite (group 9) showed identical results in total proteins, albumin, and globulin concentration to control broiler chicks (group 1).
ALT concentrations increased significantly ($p \leq 0.05$) in chromium (group 2), arsenic (group 3), and chromium plus arsenic (group 6) treated broiler chicks compared to control (Figure 2) on experimental days 21 and 42 were observed. Whereas, a non-significant ($p \leq 0.05$) difference was seen in the concentration of ALT in chromium + arsenic + vitamin E (group 7), chromium + arsenic + bentonite (group 8), and chromium + arsenic + vitamin E + bentonite (group 9) treated broiler chicks with the control group on 21 and 42 trail days (Figure 2).
**Figure 2:** *Alanine aminotransferase (ALT) in broiler chicks on 21 and 42 experiment days administered arsenic, chromium, vitamin E, and bentonite alone or in combinations. Bars (mean ± SE) having dissimilar letters under a specific experimental day vary significantly (p < 0.05). Chromium, arsenic, and vitamin E were given @ 270, 50, and 150 mg.kg−1, respectively, while bentonite was administered @ 5%. G1 = Group 1: Control (negative); G2 = Group 2: Chromium; G3 = Group 3: Arsenic; G4 = Group 4: Vitamin E; G5 = Group 5: Bentonite; G6 = Group 6: Chromium + Arsenic; G7 = Group 7: Chromium + Arsenic + Vitamin E; G8 = Group 8: Chromium + Arsenic + Bentonite; and G9 = Group 9: Chromium + Arsenic + Vitamin E + Bentonite.*
AST concentrations increased significantly ($p \leq 0.05$) in chromium (group 2), arsenic (group 3), chromium plus arsenic (group 6), chromium + arsenic + vitamin E (group 7), chromium + arsenic + bentonite (group 8) and chromium + arsenic + vitamin E + bentonite (group 9) treated broiler chicks compared with the control group at experimentation days 21 and 42 (Figure 3).
**Figure 3:** *Aspartate aminotransferase (AST) in broiler chicks on 21 and 42 experiment days administered arsenic, chromium, vitamin E, and bentonite alone or in combinations. Bars (mean ± SE) having dissimilar letters under a specific experimental day vary significantly (p < 0.05). Chromium, arsenic, and vitamin E were given @ 270, 50, and 150 mg.kg−1, respectively, while bentonite was administered @ 5%. G1 = Group 1: Control (negative); G2 = Group 2: Chromium; G3 = Group 3: Arsenic; G4 = Group 4: Vitamin E; G5 = Group 5: Bentonite; G6 = Group 6: Chromium + Arsenic; G7 = Group 7: Chromium + Arsenic + Vitamin E; G8 = Group 8: Chromium + Arsenic + Bentonite; and G9 = Group 9: Chromium + Arsenic + Vitamin E + Bentonite.*
## Total antioxidant capacity
The TAC of broilers in groups given arsenic, chromium, bentonite, and vitamin E in various amalgamations has been presented in Tables 6, 7. At experimental days 21 and 42, the highest TAC (1.45 ± 0.09 and 0.85 ± 0.02 nmol/L, respectively) was recorded in control broiler chicks (group 1), while significantly ($p \leq 0.05$) the lowest values (0.82 ± 0.03 and 0.30 ± 0.01 nmol/L, respectively) were observed in broiler chicks treated with chromium and arsenic (group 6). Further analysis revealed that the TAC in broiler chicks of groups 2 and 3 were also significantly ($p \leq 0.05$) low compared to the control group. Groups administered bentonite and vitamin E along with arsenic and chromium (groups 7–9) showed non-significant ($p \leq 0.05$) TAC values compared with the control group.
Values for CAT of broilers in groups fed chromium, arsenic, bentonite, and vitamin E in various permutations have been presented in Tables 6, 7. A significantly ($p \leq 0.05$) highest CAT (63.7 ± 3.07 and 73.4 ± 3.01 Kilo U/L, respectively) was recorded in control broiler chicks (group 1). In comparison, the lowest ($p \leq 0.05$) values (35.2 ± 0.19 and 47.7 ± 2.07 Kilo U/L) were observed on chromium and arsenic-administered broiler chicks (group 6) on experimental days 21 and 42, respectively. Further analysis revealed that the CAT in groups 2 and 3 broiler chicks was also significantly ($p \leq 0.05$) lower than the control group. Groups treated with bentonite and vitamin E along with arsenic and chromium (groups 7 and 9) showed non-significant ($p \leq 0.05$) CAT values compared with the control group; however, group 8 showed significantly low ($p \leq 0.05$) CAT concentration than the control group on experimental days 21 (Table 6) and 42 (Table 7).
## Pearson correlation
Pearson correlation analysis revealed a strong positive and interconnected correlation between TAC with CAT (Figure 4). We also found a strong positive and significant Pearson correlation between TOC and MDA (Pearson correlation value = 0.920; $p \leq 0.001$), like that between TAC and CAT (Pearson correlation value = 0.961; $p \leq 0.001$). However, TAC showed a negative correlation between TOC (Pearson correlation value = −0.075; $$p \leq 0.590$$) and MDA (Pearson correlation value = −0.218; $$p \leq 0.114$$). Similarly, CAT showed a negative correlation between TOC (Pearson correlation value = −0.021; $$p \leq 0.882$$) and MDA (Pearson correlation value = −0.101; $$p \leq 0.446$$). Interestingly, with the increase of antioxidant enzymes (TAC and CAT), TOC and MDA decrease and have a negative correlation (Figure 5). TAC1 to TAC6 (group 1: control, group 2: chromium; group 4: vitamin E, group 5: bentonite, and group 6: chromium + arsenic) except TAC3 (group 3: arsenic) showed weak positive Pearson correlation with total oxidant capacity and malondialdehyde (Figure 5).
**Figure 4:** *Scattered and point connected graph showing total antioxidant capacity (TAC) positive Pearson correlation with catalase in broiler chicks treated with chromium, arsenic, and their combination along and amelioration with vitamin E and bentonite clay.* **Figure 5:** *Scattered graph showing total antioxidant capacity (TAC) positive Pearson correlation (green shaded) with catalase () and negative Pearson correlation (red shaded) with oxidant enzymes, i.e., total oxidant capacity () and malondialdehyde () at experimental day 21. There were nine experimental groups. Broiler chicks were treated with chromium and arsenic, and their combination and amelioration of toxic effects with vitamin E and bentonite clay. A positive Pearson correlation has been shown in the green-shaded area from 0 to +1, whereas a negative Pearson correlation has been shown in the red-shaded area from −1 to 0. TAC also has shown a weak positive Pearson correlation with total oxidant capacity and malondialdehyde. A similar trend was observed on experimental day 42. Thus, only the results of experimental day 21 are presented here.*
## Gross and histopathology
The intensity of gross and microscopic lesions was more in arsenic and chromium singly or in combination-treated groups. It is worth mentioning that broiler chicks of group 6 were treated with chromium (270 mg.kg−1) plus arsenic (50 mg.kg−1) showed higher intensity of gross and microscopic lesions as compared with other treated groups (Table 8).
**Table 8**
| Organ | Gross/microscopic | Lesions | G2 (Cr) | G3 (As) | G6 (As + Cr) | G7 (As + Cr + Vit E) | G8 (As + Cr + BN) | G9 (As + Cr + Vit E + BN) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Lungs | Gross | Hemorrhages | ++ | ++ | ++ | + | + | + |
| | | Frothy exudate | ++ | ++ | ++ | + | + | + |
| | Microscopic | Edema | ++ | ++ | +++ | + | + | – |
| | | Congestion | ++ | ++ | +++ | + | + | – |
| | | Emphysema | ++ | ++ | +++ | + | + | – |
| Liver | Microscopic | Pyknotic nuclei | ++ | ++ | +++ | + | ++ | – |
| | | Expended sinusoidal spaces | + | ++ | +++ | + | + | – |
| | | Cytoplasmic vacuolar degeneration, | ++ | ++ | +++ | + | ++ | – |
| | | Leucocytic infiltration | ++ | ++ | +++ | + | ++ | – |
| Kidneys | Gross | Swollen | + | ++ | +++ | + | ++ | – |
| | Microscopic | Tubular necrosis | ++ | ++ | +++ | + | ++ | – |
| | | Pyknotic nuclei | ++ | ++ | +++ | + | ++ | – |
| | | Vacuolation | ++ | ++ | +++ | + | ++ | – |
| | | Congestion | ++ | ++ | +++ | + | ++ | – |
| | | Glomerular shrinkage | ++ | ++ | +++ | + | ++ | – |
Grossly, lungs were normal in size and shape in all treated and control groups except the lungs of groups 2, 3, & 6 (++), and 7, 8 & 9 (+) were hemorrhagic and frothy exudate was seen in the trachea. Microscopically, the major lesions of the lungs, like edema, congestion, and emphysema, were noted in groups 7 & 8 (+), 2 & 3 (++), and 6 (+++). However, mononuclear cell infiltration, hemorrhages, congestion, thickening of alveolar and bronchial septae, and alveolar edema along with emphysema and necrosis were also observed but with intensity. While the lungs of group 1 did not show any microscopic lesions and had well-arranged normal-sized bronchioles and alveoli.
Grossly, the liver was normal in size, shape, and consistency in all groups. Microscopically, pyknosis, condensation of nuclei, along with advanced fatty change in hepatocytes (Figure 6) and mononuclear cells infiltration and separation of cells from the basement membrane in hepatic lobule were observed in groups 7 (+), 2, 3 & 8 (++), and 6 (+++). Advanced fatty degeneration, detachment of cells from the basement membrane, and expansion of sinusoidal spaces were also observed in some treated groups like groups 6 (+++) (Table 8). Nuclear degenerative changes such as karyorrhexis, and karyolysis in broiler chicks treated with chromium + arsenic (group 6) was observed. The liver of group 1 showed no microscopic lesion, and hepatocytes had a well-preserved lobular pattern.
**Figure 6:** *Photomicrograph of the liver of broiler chicks stained with H&E (scale bar = 50): (A) control group showing no microscopic lesions, and hepatocytes have well-preserved lobular pattern, (B) group 6 (chromium + arsenic treated) showing advanced fatty change in hepatocytes (arrow heads), (C) group 2 (chromium treated) showing vacuolar degeneration (arrowheads) and congestion (arrow), and (D) group 3 (arsenic treated) showing vacuolar degeneration (arrowheads) and congestion (arrow).*
Grossly, kidneys were swollen in groups 2 & 7 (+), 3 & 8 (++), and 6 (+++). Microscopically, renal epithelial cells necrosis characterized by pyknotic nuclei, glomerular shrinkage, increased urinary spaces, and cytoplasmic vacuolar degeneration was observed in a mild form (+) in group 7, moderate (++) in groups 2, 3 & 8, and severe form (+++) in group 6 (Table 8). Degeneration, congestion, disintegration of cells from the basement membrane (Figure 7), karyorrhexis, and karyolysis were also seen in group 6 (+++). There was no microscopic lesion in group 1.
**Figure 7:** *Photomicrograph of the kidneys of broiler chicks stained with H&E (scale bar = 50): (A) control group showing normal histology, (B) group 2 (chromium treated) showing pyknosis and disintegration of cells from the basement membrane (arrows), and degeneration in the form of vacuolation (arrow heads), (C) group 3 (arsenic treated) showing congestion (arrowhead) and degenerative changes in the form of vacuolation (arrows), and (D) group 6 (chromium + arsenic treated) showing pyknosis and disintegration of cells from the basement membrane (arrows).*
Grossly, the heart was normal in size and shape in all treated groups, i.e., groups 2–9, and did not show microscopic lesions and had fairly well-arranged smooth muscles.
## Discussion
The data of the present study suggested that arsenic and chromium provoke adverse effects not only on hematobiochemical parameters but also on TAC is severely damaged, leading to oxidative stress. The current study showed a significant ($p \leq 0.05$) decrease in feed intake and body weight in broiler chicks administered with arsenic plus chromium compared with the control group. Earlier reports have also shown a significant reduction in feed intake and body weight in broiler chicks [26] and rats/mice [13] treated with chromium and arsenic, respectively. Decreased feed intake and body weight could be due to the treatment of broiler chicks with heavy metals that could have led changes in liver glycogen and triglyceride along with a disturbance in metabolic enzymes leading to weight loss [27]. Decreased feed intake and body weight could also result from metabolic deregulations due to chromium's toxic effects on the liver [11]. Another reason could be due to the inhibitory effect of chromium on the specific area of the hypothalamus in the brain regulating feed intake leading to restricted feed intake and ultimately decreased body weight [28].
Erythrocyte indices decreased significantly ($p \leq 0.05$) in the present study in broiler chicks treated with arsenic and chromium. At the same time, ESR increased significantly in arsenic plus chromium given to broiler chicks compared to the control group in the current experiment. Earlier studies reported decreased hematological parameters because of chromium and arsenic treatment in rats [29] and broilers [30], respectively. A decrease in erythrocytes numbers, hemoglobin concentration, and hematocrit levels indicates an anemic condition, which could be a decrease in the availability of iron for hemoglobin synthesis as a result of heavy metals exposure leading to the development of anemia [31]. Another possible reason could be the ability of chromium to cross the red blood cell membrane where it forms DNA protein crosslinks leading to anemia. Anemia could also occur due to the binding of chromium to the β-chain of hemoglobin; thus, hemoglobin would not be available for heme synthesis ultimately anemia develops [28]. Chromium taken up by erythrocytes undergoes reduction to the trivalent form with the help of reduced glutathione [32], chromium-hemoglobin complexes, and other intracellular proteins are sufficiently stable to retain chromium for a substantial fraction of the RBC lifetime [33]. In this mechanism, arsenic triggers eryptosis either by increasing cytosolic calcium and ceramide concentration or depleting energy [34]. The cytopathic effect of arsenic is attributed to its interference with the erythrocytes' energy production pathway leading to interference in ATP production. Arsenic also interferes with mitochondrial enzymes, so there could be no ATP production; ultimately cell lysis occurs [35].
Significantly ($p \leq 0.05$) decreased leukocyte counts were recorded in arsenic plus chromium-administered broiler chicks compared to control broiler chicks in the present experiment. Earlier studies reported decreased leukocytes following arsenic/chromium administration in rats [11, 36]. A decrease in leukocytes could be due to the inhibitory effect of heavy metals on the immune system leading to leukopenia [29]. Chromium affects the cortisol level and may be partially liable for its immunostimulatory effects [37]. Cortisol influences antibody production and the functions of lymphocytes and other leukocyte populations [38], thus leading to leukopenia. A decrease in leukocytes could result from chromium contact with biological compounds, leading to the peroxidation of these biological complexes present in the cell [11]. In effect, some negative changes, such as cell membrane impairment due to the peroxidation of unsaturated fatty acids or inhibition of both mitochondrial trans-membrane potential occur in lymphocytes [39]. Still, another possible reason could be due to the capability of chromium to enter the leukocytes and reside there till its life. In this way, it becomes lethal for the leukocytes, thus leading to leukopenia development.
In the current study, ALT and AST increased significantly ($p \leq 0.05$), while plasma proteins decreased significantly ($p \leq 0.05$) in arsenic plus chromium-administered broiler chicks compared to the control group. Earlier studies reported increased ALT and AST concentrations in rats [37] and mice [40] for chromium and arsenic, respectively. An increase in the level of ALT and AST could be due to leakage of these enzymes indicating damage to hepatocytes due to heavy metals (chromium/arsenic). Another reason for their increased levels could be a result of the biotransformation of chromium in hepatocytes rendering injury to hepatocytes [41]. Heavy metals are known to produce ROS in the body [42]. Hepatocytes develop various defensive mechanisms to block ROS consequences. Among the antioxidative enzymes, CAT concentration has been reported to be very high in liver tissue [43]. Thus, CAT provides the first line of antioxidative defense enzymatic system, leading to elevated ALT and AST. In the case of arsenic, high serum hepatic enzymes could be due to its binding to the thiol groups of enzymes and proteins of liver cells while arsenic being bio-converted from its toxic (monomethyl arsenic) to less toxic (dimethyl arsenic) metabolites [44]. This hepato-cellular membrane damage leads to elevated ALT levels and loss of functions [45].
For the decreased proteins in the present study, there is a possibility that arsenite and trivalent organic (methylated) arsenicals respond with thiols (-SH) in proteins and impede their action [46]. A decrease in plasma proteins could also be due to the impairment of podocytes (visceral epithelial cells in Bowman's capsule). The mechanism of podocyte injury could be due to the production of ROS, which is deleterious for podocyte contractile, modulating, and linkage proteins. ROS induces unrepairable damage to podocytes leading to changes in cell integrity, thus affecting the glomerular filtration rate (GFR) and leakage of proteins [47].
Urea and creatinine increased significantly ($p \leq 0.05$) in arsenic plus chromium-treated broiler chicks compared to the control group in the current study. Earlier reports showed a rise in urea and creatinine in rats [48] for chromium, ducks [49], and goats [50] for arsenic. Urea and creatinine levels could be due to ROS generation, which then causes lipid peroxidation. These lipid droplets sediment in the endothelium of glomeruli, thereby affecting GFR, ultimately damaging the membrane components and leading to necrosis. Therefore, elevated urea and creatinine occur [51]. Arsenic has a great affinity to the sulfhydryl group of glomerular filtration membrane; thus, the renal injury could be due to defective GFR [52]. After protein metabolism, ammonia is produced. The liver converts it into a less dangerous form as urea which is water soluble and only accumulates in the plasma if the renal system fails to eliminate it from the body [53].
The inequality between the production of ROS and the antioxidant defense system is oxidative stress [13, 14, 54]. ROS production is a peculiar feature of heavy metals like arsenic and chromium [46]. In this process, mitochondria are the primary organelle affected as the center of cell metabolism. Oxidative stress considerably injures proteins, lipids, and nucleic acids within the mitochondria, resulting in substantial mitochondrial changes in structures and functions [55]. Arsenic, a heavy metal, can ruin the anatomy and physiology of mitochondria and yield excess electrons that can convert oxygen (O2) into superoxide anion (O2-). Superoxide anions persuade oxidative stress and produce ROS, resulting in lipid peroxidation and MDA formation [56]. ROS also persuades DNA breakage, thus generating many molecules of 8-hydroxy-2 deoxyguanosine (8-OHdG) [57]. In the meantime, arsenic can trigger the antioxidant defense system and boost the countenance of molecules such as CAT, SOD, GST, and GPx which remove excessive free radicals and peroxides [56]. However, if the degree of oxidation exceeds the ability of these antioxidant molecules, then it will reduce the levels of CAT, SOD, GST, and GPx; this is what we have observed in our study in arsenic and chromium-treated broiler chicks.
Total antioxidant capacity is the primary measurement to evaluate the state and potential of oxidative stress. The imbalance between antioxidants and oxidants generates the condition of oxidative stress [56]. In the current study, TAC and CAT decreased significantly ($p \leq 0.05$), while TOC and MDA increased significantly ($p \leq 0.05$) in chromium and arsenic administration broiler chicks. Heavy metals like chromium and arsenic cause oxidative stress by lessening antioxidant enzymes (TAC, CAT, SOD, GPx, and glutathione reductase) and elevating lipid peroxidation in both target and non-target animals [58]. Oxidative stress mediated by ROS is a common denominator in arsenic toxicity [46].
Arsenic and chromium, as individual metals and in combination, affect animals'/birds' health more terribly. The acquaintance with these metals results in the upsurge of oxidative stress that leads to the creation of an uneven number of electrons, triggering the deterioration of proteins, RNA, and DNA and even leading to cell death [56]. However, due to the cleansing systems of bare birds/animals, exposure to different toxicants yields rapid and increased formation of ROS. ROS production triggers the lipid peroxidation process, leading to cell membrane damage and the development of TBARS [19]. As a result, the increased concentration of oxidative stress indices (TOC and MDA) in the present investigation might be connected to antioxidant enzyme diminution and misbalancing [58].
Depression in CAT concentration after feeding rats [59] chromium and goats [60] for arsenic has been reported. A decrease in CAT levels could be due to the involvement of free radicals. In this situation, SOD is slowed down due to the overproduction of free radicals leading to hyperaccumulation of superoxide because of decreased dismutation of superoxide to hydrogen peroxide; thus, decreased TAC and CAT activity is evident [13].
Levels of TOC and MDA increased significantly ($p \leq 0.05$) in chromium and arsenic-treated broiler chicks compared to the control group in the current study. Earlier studies reported increased MDA in rats [36] for chromium and cattle [61] for arsenic. MDA is a good marker of lipid peroxidation [62]. MDA is well-known toxic metabolite formed by lipid peroxidation due to oxidative stress [63]. An increase in MDA level could be due to oxygen free radicals, which further target polyunsaturated fatty acids leading to the production of lipid peroxides that then change membrane fluidity and permeability, ultimately rendering cellular damage (64–66).
Histopathological biomarkers use target organs of toxicity in heavy metal studies, mostly the liver and kidneys [67]. The liver and kidneys perform several important functions related to the metabolism and excretion of substances. Thus, lesions in such organs caused by chemical pollutants/heavy metals may negatively affect detoxification and homeostasis (68–70). The present study showed gross and microscopic lesions in chromium (270 mg.kg−1) and arsenic (50 mg.kg−1) singly or in combination-treated groups. In the liver, the main lesions were fatty degeneration, disintegration of cells from the basement membrane, expended sinusoidal spaces, and cytoplasmic vacuolar degeneration.
In contrast, renal epithelial cell necrosis, glomerular shrinkage, and cytoplasmic vacuolar degeneration were kidney lesions. In White Pekin ducks, inorganic arsenic toxicity has been reported [71]. Skin lesions due to chronic arsenic toxicity have been reported [70], as the present study was not chronic, thus, we did not observe these lesions. Chromium toxicity also produces severe lesions in the liver and kidneys [72], as observed in the present study (Table 8).
Several synthetic, as well as natural mixtures have been experienced for the amelioration of arsenic and chromium toxicity [40, 69, 73, 74]. Vitamin E, an integral part of the plasma membrane, is an effective antioxidant as it is present at the site of free-radical production; it might counteract the toxic effects of ROS [73]. The oral vitamin E and K2Cr2O7 ameliorate all these vicissitudes and ensue in normal hepatic cellular structure and contents [17, 74]. Vitamin E is the most effective fat-soluble non-enzymatic antioxidant, which safeguards the cell membrane from radical-induced peroxidation, rouses the initiation of antioxidant enzymes, and lessens the concentration of oxidative stress produced by heavy metal-induced toxicity [75, 76].
It has been explained earlier that vitamin E allows free radicals to non-concrete a hydrogen atom from the antioxidant molecule rather than from polyunsaturated fatty acids, thus breaking the chain of free radical regeneration, thus resultant antioxidant radical being a comparatively unreactive species [40]. Vitamin E, due to its hepatoprotective properties, has got wide attention, which is principally due to its capability to lessen the tempted oxidative stress in various tissues by reducing MDA levels, restoring the levels of CAT, SOD, GSH, and the recovery of impaired hepatocytes [74].
Vitamin E administration improves various hemato-biochemical and oxidative stress parameters in arsenic plus chromium-administered broiler chicks compared to the control group in the present study. Oxidative stress results from a disproportion in free radical generation and antioxidant production. A possible mechanism of vitamin E-induced restoration of antioxidant enzyme levels is breaking the chain reaction initiated by free radicals [77]. It repairs the oxidizing radicals responsible for the chain elongation by autoxidation. Its antioxidant activity could also be due to its ability to restore the cell membrane to normal by interacting with the unsaturated fatty acid chain [77]. Moreover, vitamin E is an important component of the cytoplasm and cell membrane [75]. Vitamin E is efficient only in protecting the outer cellular layer of cells from oxidation stress, although very low concentration still prevents lipids and proteins from oxidation [13, 36]. Mechanism regarding chain-breaking antioxidants states that SOD, GPx, and CAT are responsible for removing super oxides and peroxides before metal catalyzes them to generate free radicals [78]. However, some free radicals escape the protective mechanism of the enzymes, and a peroxidative chain reaction occurs. Here chain breaker antioxidants, i.e., vitamin E plays an important role by donating electrons, thus limiting this cascade of deterioration [14].
Bentonite is a clay mineral collection of fine particles with high opening volume and particular active sites [17, 79]. Bentonite is a widely used porous adsorbent aluminum phyllosilicate clay with high adsorption capability, chemical and mechanical solidity, and unique inter-lamellar structural properties [80, 81]. The metal ion belongings, preliminary concentration, adsorbent quantity, and operational circumstances (contacting time, pH, temperature, etc.) are the most important factors for the successful application of raw and altered bentonite [79]. Metals like arsenic, chromium etc., interfere with various body processes and are toxic to many organs and tissues [82], thus leading to the generation of ROS, consequently creating oxidative stress in the body [46]. Bentonite absorbs these toxic substances and acts as an ameliorating agent [80, 83]. In the present study, bentonite administration improved arsenic and chromium-treated broiler chicks compared to the control group. Partial amelioration by bentonite could be due to its ability to prevent heavy metal absorption by forming inert, stable, and insoluble complexes with toxic substances [84, 85]. Another proposed mechanism of partial amelioration of bentonite could be due to its adhesive ability and absorptive nature by which it attaches to toxic substances occurs, rendering their absorption lessened [17, 86, 87].
## Conclusion
The data suggested that arsenic and chromium aggravate adverse effects not only on hematobiochemical parameters but also lowering total antioxidants, thus enhancing oxidative stress. All the broiler chicks treated with chromium and arsenic showed a significant ($p \leq 0.05$) decline in erythrocytic parameters. Total proteins decreased significantly, while ALT, AST, urea, and creatinine increased significantly ($p \leq 0.05$). TAC and CAT decreased significantly ($p \leq 0.05$), while TOC and MDA concentrations increased significantly ($p \leq 0.05$) in chromium and arsenic-treated groups. There was a strong positive correlation between TAC and CAT (Pearson correlation value = 0.961; $p \leq 0.001$), with similar TOC and MDA positive correlation (Pearson correlation value = 0.920; $p \leq 0.001$). However, TAC and CAT showed a negative correlation between TOC and MDA. The intensity of gross and microscopic lesions was more in chromium (270 mg.kg−1) and arsenic (50 mg.kg−1) singly or in combination-treated groups. Lungs of arsenic and chromium treated broiler chicks were hemorrhagic and had frothy exudate in the trachea and microscopically, lungs edema, congestion, thickening of alveolar and bronchial septae, and necrosis were observed. Heart was also normal in size and texture in treated broiler chicks. In the liver, fatty degeneration, detachment of cells from the basement membrane, severe cytoplasmic vacuolar degeneration, and expansion of sinusoidal spaces, while in kidneys, renal epithelial cells necrosis, glomerular shrinkage, and severe cytoplasmic vacuolar degeneration were the main lesions. Co-administered bentonite along with chromium and arsenic resulted in partial amelioration compared to groups administered arsenic plus chromium plus vitamin E and arsenic plus chromium plus vitamin E plus bentonite, respectively. Vitamin E and bentonite administration can ameliorate toxicity and oxidative stress produced by arsenic and chromium.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
The animal study was reviewed and approved by Bioethics Committee, University of Agriculture, Faisalabad, Pakistan.
## Author contributions
AK tailored the research project and managed the experiment. JM executed the project with the help of STG, RH, LA, RM, and UF. JM, STG, and RH carried out laboratory work and data analysis. AK, RH, FAAS, and AFA interpreted the data. The manuscript was written by AK and checked by ZG. All authors approved the final version of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
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|
---
title: Ongoing increasing trends in central precocious puberty incidence among Korean
boys and girls from 2008 to 2020
authors:
- Sinyoung Kang
- Mi Jung Park
- Jung Min Kim
- Jin-Sung Yuk
- Shin-Hye Kim
journal: PLOS ONE
year: 2023
pmcid: PMC10032490
doi: 10.1371/journal.pone.0283510
license: CC BY 4.0
---
# Ongoing increasing trends in central precocious puberty incidence among Korean boys and girls from 2008 to 2020
## Abstract
### Background
Over the last few decades, there has been growing evidence of earlier onset and progression of puberty worldwide. This population-based longitudinal cohort study aimed to analyze the change in the annual incidence rate of central precocious puberty (CPP) among Korean children over the most recent decade, using the national registry data.
### Method
The International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) and insurance claims for gonadotropin-releasing hormone agonist (GnRHa) treatment were used to identify CPP patients who were using the Korean Health Insurance Review & Assessment Service (HIRA) database between 2008 and 2020. Patients who began GnRHa therapy before the age of 9 and 10 for girls and boys, respectively, were included in the study.
### Results
A total of 6,906 boys and 126,377 girls were diagnosed with CPP between 2008 and 2020. The annual incidence of CPP increased by 83.3 times in boys (from 1.2 to 100 per 100,000 persons) and by 15.9 times in girls (from 88.9 to 1414.7 per 100,000 persons). The age-specific annual incidence of CPP increased remarkably more in older children than in younger ones; the 2020 CPP incidence among 9-year-old boys and 8-year-old girls reached 705.2 and 7,967.3 per 100,000 persons, respectively. The annual prevalence of CPP in boys and girls increased from 2.7 to 206.5 (76.5 times) and from 141.8 to 3439.9 (24.3 times) per 100,000 persons, respectively.
### Conclusion
Based on GnRHa treatment insurance claims, our study suggests that the annual incidence of CPP has substantially increased in Korea during the past 13 years. These findings highlight the importance of meticulous judgment by doctors in determining GnRHa treatment.
## Introduction
Precocious puberty is traditionally defined as the appearance of secondary sexual characteristics before the age of 8 in girls and 9 in boys, as well as the onset of menstruation before the age of 9.5 [1]. The onset of puberty can be assessed by breast enlargement in girls and testicular enlargement in boys, as well as the activation of the hypothalamic-pituitary-gonadal (HPG) axis, detected by laboratory tests [2]. If early activation of HPG axis causes precocious puberty, the disorder is GnRH-dependent and referred to as central precocious puberty (CPP) [3].
There has been a global trend in which pubertal development has been accelerated since the mid-1900s, although the pubertal timing and tempo varied by race and ethnicity [4]. However, after the early 2000s, epidemiologic studies suggested that the pubertal timing and specific age at menarche in girls stabilized in several European nations [5–9]. In the US, despite the fact that the age of breast development among girls had decreased significantly over the past half-century, the age at menarche had decreased by only 2.5 to 4 months over a similar period, indicating that the advancement of pubertal onset in the US population is not accompanied by HPG axis activation [6]. On the other hand, a recent large population-based study indicated that the age of menarche has declined by approximately five months over the last 15 years in Korean girls, which is a much more significant change as compared to other Western countries [10]. This trend of accelerated pubertal development in adolescents was also observed in several countries, including Korea, Sweden, and Denmark, not just in girls but also in boys [11–14].
Few studies have been conducted to examine the prevalence of precocious puberty worldwide, as compared to several studies on normal pubertal timing and its secular changes among adolescents. Studies in Spain [15], Denmark [16], and Korea have reported an increase in CPP incidence. In prior research, we demonstrated that the incidence of CPP among Korean children aged less than 8 (in girls) or 9 (in boys) rose almost 15-fold between 2004 and 2010 [11]. A follow-up study from another group indicated that the increase in Korean CPP incidence was approximately 5-fold in girls and 9-fold in boys for the pediatric population aged less than 9 (in girls) or 10 years (in boys) between 2008 and 2014 [17]. Identifying the long-term trends in overall CPP cases under GnRHa treatment in the real world is essential to estimate the medical and societal burden of CPP. However, given the accelerating pubertal tempo worldwide, it is particularly crucial to evaluate CPP trends by age group in order to predict future implications on public health and to make medical decisions based on treatment targets.
In this study, we aimed to examine the most up-to-date incidences of CPP in Korea and to determine if there was a difference in CPP trends based on sex and age group.
## Study samples
The vast majority of South Korean citizens ($97\%$) are registered with a National Health Insurance Service (NHIS) and are offered insurance coverage for their medical expenses. Korean patients’ diagnoses from hospitals are recorded using the International Classification of Diseases, Tenth Revision (ICD-10) coding system for health insurance claims. The Korea Health Insurance Review and Assessment Service (HIRA) performs reviews of medical documentation for all NHIS-sponsored medical expenses to monitor the adequacy of diagnosis and treatment. Therefore, HIRA data provides a credible source of information for monitoring the incidence and prevalence of uncommon illnesses in the Korean population [18].
The HIRA allows insurance claims for GnRHa treatment only if a diagnosis of CPP is confirmed in accordance with the Korean clinical guidelines for CPP [19]: 1) an emergence of secondary sexual characteristics before age 8 in girls (Breast Tanner stage 2) and before age 9 in boys (Genitalia Tanner 2 stage), 2) advancement of bone age and acceleration of growth, and 3) a pubertal response (a peak luteinizing hormone level ≥ 5 IU/L) after a GnRH stimulation test. Of note, the HIRA sets an upper age limit at which it allows insurance claims for GnRHa therapy, which is 9 years of age in girls and 10 years in boys, to consider a lag period between the first detection of pubertal signs and diagnosis by a physician. To estimate the incidence of CPP in this study, we included CPP children (girls aged <9, boys aged <10) who registered with an ICD-10 diagnostic code for precocious puberty (E22.9 or E30.1) at the HIRA database as starting GnRHa treatment for the first time between January 1, 2008, and November 30, 2020. Due to the method used in this study to define CPP cases, it is important to note that the number of CPP cases in this study is not the direct number of cases determined from medical records, including medical histories and laboratory data, but a proxy number of CPP cases. Since the NHIS covers GnRHa treatment claims in CPP girls under the age of 12 and in CPP boys under the age of 13, we calculated prevalence by including CPP patients who had received GnRHa therapy within these age limits. Both the incidence and prevalence were determined in terms of units per 100,000 people. Throughout the study period, neither the Korean guidelines for CPP diagnosis nor the HIRA practice for GnRHa insurance coverage changed [19].
The Institutional Review Board at Inje University Sanggye Paik Hospital waived their review board’s approval requirement for this study (approval number: SGPAIK2021-07-001), because the HIRA dataset complies with South Korea’s Bioethics and Safety Act by using anonymous identifying codes to protect personal information. No informed consent was necessary.
## Incidence and prevalence calculations
The incidence rate was estimated by taking the number of population at risk in accordance with the census in each calendar year, sex, and age as denominators and the number of CPP patients who first started GnRHa treatment in each calendar year, sex, and age as numerators. The number of at-risk population was collected from the National Institute of Statistics of Korea [20]. The prevalence was also calculated using the number of populations at risk in each calendar year, sex, and age as denominators and the number of CPP patients receiving GnRHa treatment in each calendar year, sex, and age as the numerator. $95\%$ confidence intervals of incidence and prevalence estimates were generated from the asymptotic convergence of incidence estimates to the normal distribution under the assumption that the number of CPP follows the Poisson distribution. A generalized linear model (GLM) with Poisson distribution was used to evaluate the relationship of year, sex, and age with the incidence rate of CPP. All statistical data were analyzed using R 4.0.2 (The R Foundation for Statistical Computing, Vienna, Austria) and a two-tailed test. A p-value of less than 0.05 was considered statistically significant.
## The incidence of central precocious puberty
A total of 6,906 boys aged 0–9 years and 126,377 girls aged 0–8 years were diagnosed with CPP between 2008 and 2020, and the incidence of CPP was 489.3 per 100,000 girls and 22.4 per 100,000 boys (Table 1). The annual incidence of CPP among Korean children by sex and diagnostic age cutoffs is shown in Fig 1. The annual incidence of CPP among boys grew by 83.3 times (from 1.2 to 100 per 100,000 persons), while the incidence among girls increased by 15.9 times (from 88.9 to 1414.7 per 100,000 persons). When a stricter diagnostic age cutoff (under 8 and 9 years of age for girls and boys, respectively) was implemented, similar but rather gradual trends were observed, with a 49.5-fold rise in the annual incidence of CPP in boys from 0.4 to 19.8 per 100,000 and a 12-fold increase in girls from 15.4 to 187.7 per 100,000.
**Fig 1:** *The annual incidence of central precocious puberty.The error bars show the 95 percent confidence intervals for the incidence estimates.* TABLE_PLACEHOLDER:Table 1
## Age-specific incidence of central precocious puberty
Fig 2 and S1 Table show the annual incidence of CPP based on sex and age at diagnosis. The age-specific annual incidence of CPP increased significantly in all age groups, with the exception of boys aged 5 years and girls aged 0–3 years. In 2020, the incidence of CPP was greatest among 9-year-old boys and 8-year-old girls at 705.2 and 7,967.3 per 100,000 individuals, respectively (Fig 2, S1 Table). Table 2 illustrates the estimated annual change in central precocious puberty incidence rates by sex and age group. CPP incidence increased by $135.8\%$ per year in boys and $118.5\%$ per year in girls between 2008 and 2020. The age-specific annual incidence of CPP increased considerably with older age groups, with the highest rise occurring in 8-year-old boys ($138.9\%$) and in 8-year-old girls ($118.9\%$).
**Fig 2:** *The annual incidence of central precocious puberty according to sex and age at diagnosis.* TABLE_PLACEHOLDER:Table 2 Fig 3 depicts the age distribution of CPP cases according to sex and calendar year. Between 2008 and 2013, the proportion of 8-year-old CPP girls increased from $66.9\%$ to $78.4\%$; however, this proportion remained consistent after 2013. Similarly, the proportion of CPP boys aged 9 years increased from $69.7\%$ to $86.8\%$ between 2008 and 2013, but the proportion of CPP cases based on age remained stable after 2013.
**Fig 3:** *Age distribution of central precocious puberty patients according to sex and calendar year.*
## The prevalence of CPP
The annual prevalence of CPP based on sex, calendar year, and two diagnostic age limits is shown in Table 3 and Fig 4. Between 2008 and 2020, the overall prevalence of CPP was 6.8 per 100,000 boys and 250.6 per 100,000 girls. Similar to the trend in incidence, the prevalence of CPP had increased annually and reached 28.0 per 100,000 boys and 657.2 per 100,000 girls by 2020. Between 2008 and 2013, the CPP prevalence rose more sharply than between 2013 and 2020. CPP prevalence increased 31.1 times in boys over 13 years, while it grew 20.3 times in girls. It increases more among boys than girls, similar to the incidence rate (Fig 4).
**Fig 4:** *The annual prevalence of central precocious puberty according to sex, calendar year, and two diagnostic age limits.The error bars show the 95 percent confidence intervals for the prevalence estimates.* TABLE_PLACEHOLDER:Table 3
## Discussion
In this study, we found that Korean CPP incidence has accelerated in the past 13 years. The annual incidence of CPP increased by 83.3 times in boys (from 1.2 to 100 per 100,000 persons) and by 15.9 times in girls (from 88.9 to 1,144.7 per 100,000 persons), substantially more in older children than in younger children; the incidence of CPP increased most remarkably in boys and girls aged 8 years. Since CPP incidences increased across all age groups, the age distribution of CPP incidence remained constant after 2013. The prevalence of CPP followed a similar pattern to the incidence.
Precocious puberty is conventionally defined as the development of secondary sexual characteristics in girls and boys before the age of 8 and 9 years, respectively, in most countries. This criterion was established based on the old articles on the age of pubertal events from the 1940s [21]. In 1997, in response to the concern that this criterion might be outdated, large-scale national surveys on the pubertal age were conducted in the US [22]. These studies indicated that the mean age of pubertal onset in girls had been advanced as compared to prior studies (9.96 ± 1.82 years in white girls, 8.87 ± 1.93 years in African-American girls). Accordingly, the age of 7 years for white girls and 6 years for African-American girls has been proposed to define precocious puberty [23], assuming no symptoms or signs of a central nervous system disorder or other concurrent illnesses that could also contribute to sexual precocity. However, the conventional diagnostic age limit is still used in the majority of studies on precocious puberty, mainly due to the absence of nationally representative survey data on pubertal development. Furthermore, because of the delay between the initial detection of pubertal development and the clinically verified diagnosis of precocious puberty, several studies and clinicians use the age of under 9 years for girls and under 10 years for boys as a diagnostic age limit for precocious puberty [16,17,24–26].
There have been few studies on the national incidence of CPP. In a prior study, we reported a substantial increase in CPP incidence among Korean girls from 3.3 to 50.4 per 100,000 persons between 2004 and 2010 using the HIRA database [11]. A Spanish study conducted between 2000 and 2009 using clinical data from pediatric endocrinology units also showed an increasing trend of CPP among girls, but the overall incidence of Korean CPP between 2004 and 2010 was more than 20 times higher compared with Spanish CPP incidence between 1997 and 2009 (23.3 vs. 1.1 per 100,000 persons) [15]. Among Korean boys, between 2004 and 2010 the increase in CPP incidence was gradual, from 0.3 to 1.2 per 100,000 boys, and the increment was modest among girls aged < 6 years and boys aged < 7 years. In this study, we showed that the increasing secular trend in Korean CPP was accelerated over the last 13 years in both sexes and that the incidence rise was larger in boys than in girls, contrary to our earlier findings. Recently, a Danish study using national patient registry data likewise reported that the CPP incidence increased more rapidly in boys (15-fold) than in girls (6-fold) between 1998 and 2017, although the rates of CPP increase in our study were 5 to 9 times higher than those reported in Denmark in 2017 [16].
No research has previously explored the trend in CPP incidence by age in detail. In this study, we observed that CPP incidence has grown across almost all age groups. This observation and our recent report on a significant decrease in menarche age in Korean girls [10] support the notion that activation of the HPG axis is associated with earlier pubertal onset in Korean girls. The earlier onset of puberty among Korean children differs from that of US children, in whom pubertal onset was accelerated but not accompanied by HPG axis activation [24,27], the age at menarche remained constant, and the tempo of puberty was slowed [28]. We hypothesize that changes in CPP incidences over the last decade could be attributed to a general shift to the left in pubertal onset and tempo rather than an increase in pathological CPP [29], which is supported by the fact that CPP incidences have increased over the majority of age groups, with the incidence among 8-year-old girls reaching $7.96\%$ in 2020.
Another important finding in our study is that GnRH agonist treatment starting at the age of 8 accounts for 75 to $90\%$ of all cases. Despite there being no conclusive evidence that GnRHa treatment beyond the age of 7 years improves females’ adult height [30–32], GnRHa treatment has been initiated in many children ≥ 8 years old with CPP in Korea, likely reflecting severe parental concerns regarding premature menarche and children’s psychosocial health [33]. Other possible explanations might be vigorous marketing by GnRHa pharmaceutical companies, high accessibility to the healthcare system, and relatively affordable medical costs in Korea [33,34]. According to the Korean Pediatric Endocrinology society’s clinical guidelines, clinicians are recommended to regularly follow up on patients suspected to have CPP for at least 3–4 months to confirm if their symptoms and signs are rapidly progressing before they start GnRHa treatment [19]. Through this process, it is possible to prevent non-progressive CPP patients who do not need treatment from starting treatment. Additionally, it is necessary to establish the normative data of pubertal onset and tempo in Korean children, which estimated to have been accelerated in the last 20 years among the entire population, and to educate physicians and parents to improve their understanding of the normative data in Korea.
We found that the proportions of 8-year-old CPP girls and 9-year-old CPP boys increased considerably between 2009 and 2013; however, this trend halted after 2013. We previously discovered that the incidence of CPP in Korea increased significantly between 2004 and 2010, but primarily among children over the age of six [11]. We had hypothesized that a rise in parental knowledge of CPP, which led to an increase in treatment, was one of the plausible causes for this trend. If parental knowledge was the key reason for the trends in rising Korean CPP incidence in this study, the proportion of CPP children aged 8–9 years should have continuously increased, whereas CPP incidence among children aged 6 would have shown just a modest increase. In light of our new results on the age-specific incidence and proportions of CPP, parental awareness and CPP screening practices may have contributed to the early rise in CPP incidence prior to 2013, although the impact may have peaked after 2013. A trend toward earlier activation of the HPG axis in the overall Korean population may account for our recent results.
It is unclear why the onset of puberty among Korean children is accelerating. Growing research suggests that the rising prevalence of childhood obesity is a key factor in the secular trend toward earlier puberty. Several population-based longitudinal studies suggested that earlier pubertal maturation might be linked with higher adiposity in both boys and girls from many countries, including Korea [35]. The average age of menarche among Korean girls has been reduced from 13.0 years old to 12.6 years old over the last decade, and this trend was particularly pronounced in girls with overweight. Although the exact mechanisms remain to be elucidated, obesity-related hormonal changes including leptin and insulin resistance are speculated to contribute to early puberty [35]. It has been observed that obesity-related increases in circulating leptin activate hypothalamic Kiss 1 expression, which regulates GnRH pulse production and puberty onset [36]. Obesity-induced hyperinsulinemia was also shown to induce earlier pubertal onset by increasing androgen synthesis from the ovary and adrenal glands and by increasing the bioavailability of sex steroid hormones [35,37,38]. The prevalence of overweight/obesity in Korean children has rapidly increased from $15.2\%$ to $23.7\%$ between 2007 and 2017, and was higher in boys than in girls [39]. More pronounced increasing trends of overweight and obesity prevalence in boys compared with girls might partly explain why CPP incidence in boys has increased more rapidly than girls among Korean children. Although the findings are inconsistent, studies have also shown that low birth weight and preterm birth are associated with a faster pubertal onset or tempo than peers, especially when obesity is accompanied during childhood [40]. This phenomenon can be partly explained by increased insulin resistance in children born with low birth weight and preterm birth [41]. Between 1993 and 2016, the percentage of newborns born in Korea with low birth weight and preterm birth has increased approximately two to three times [42]. Prenatal and postnatal exposure to endocrine-disrupting chemicals (EDCs) is another frequently stated explanation for earlier pubertal onset. The relationship between EDC exposure and puberty has been explored for phenols and phthalates or polybrominated diphenyl ethers (PBDEs), although the findings of those studies have been inconsistent [43]. In Korea, cross-sectional studies reported significant associations between phthalate exposure and earlier menarche / central precocious puberty in girls, as well as between air pollution and earlier menarche; however, further studies are needed to confirm EDC exposure has contributed to the rapid increase in Korean CPP [44–46]. Furthermore, there has been a marked increase in psychological distress, a rise in screen time, a decrease in physical activity, and an increase in protein intake over the previous decades among Korean children [47,48], all of which may have influenced the advancement of pubertal tempo. However, since this was a registry-based study, we were unable to investigate the potential factors due to lack of access to individual medical records.
There are several limitations to this study. First, the etiology and epidemiologic risk factors could not be investigated since detailed medical information on variables associated with CPP, such as obesity status, family history of pubertal milestones, and the presence of organic diseases, was not included in the HIRA database. Second, patients who were diagnosed with CPP but did not receive GnRHa treatment may have been excluded in this study, which may have affected the accuracy of our study findings. Third, given that the precise dates of the pubertal onset in CPP cases were not recorded in the HIRA database, there is a possibility of overestimation of the CPP incidence, particularly among girls aged 8 years and boys aged 9 years. Lastly, we were not able to exclude unsustained CPP cases that began unnecessary GnRHa treatment. According to clinical guidelines, GnRHa treatment is not recommended for individuals with unsustained CPP that might not be linked with early menarche and short adult height.
In conclusion, our research revealed that the reported increase in CPP incidence in Korea that began in early 2000 has continued through 2020. This phenomenon was ubiquitous across all age groups and both sexes, and was not confined to older children or girls. This suggests that the onset of puberty among the general population in *Korea is* shifting to the left. Further research is needed to identify the precise causes of accelerated puberty onset and to establish a new pubertal timing reference in Korea.
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|
---
title: 'The social media diet: A scoping review to investigate the association between
social media, body image and eating disorders amongst young people'
authors:
- Alexandra Dane
- Komal Bhatia
journal: PLOS Global Public Health
year: 2023
pmcid: PMC10032524
doi: 10.1371/journal.pgph.0001091
license: CC BY 4.0
---
# The social media diet: A scoping review to investigate the association between social media, body image and eating disorders amongst young people
## Abstract
### Background
Eating disorders are a group of heterogenous, disabling and deadly psychiatric illnesses with a plethora of associated health consequences. Exploratory research suggests that social media usage may be triggering body image concerns and heightening eating disorder pathology amongst young people, but the topic is under-researched as a global public health issue.
### Aim
To systematically map out and critically review the existing global literature on the relationship between social media usage, body image and eating disorders in young people aged 10–24 years.
### Methods
A systematic search of MEDLINE, PyscINFO and Web of Science for research on social media use and body image concerns / disordered eating outcomes published between January 2016 and July 2021. Results on exposures (social media usage), outcomes (body image, eating disorders, disordered eating), mediators and moderators were synthesised using an integrated theoretical framework of the influence of internet use on body image concerns and eating pathology.
### Results
Evidence from 50 studies in 17 countries indicates that social media usage leads to body image concerns, eating disorders/disordered eating and poor mental health via the mediating pathways of social comparison, thin / fit ideal internalisation, and self-objectification. Specific exposures (social media trends, pro-eating disorder content, appearance focused platforms and investment in photos) and moderators (high BMI, female gender, and pre-existing body image concerns) strengthen the relationship, while other moderators (high social media literacy and body appreciation) are protective, hinting at a ‘self-perpetuating cycle of risk’.
### Conclusion
Social media usage is a plausible risk factor for the development of eating disorders. Research from Asia suggests that the association is not unique to traditionally western cultures. Based on scale of social media usage amongst young people, this issue is worthy of attention as an emerging global public health issue.
## Types and burden
Eating disorders are a group of heterogenous, disabling and deadly psychiatric illnesses that severely impair daily psychological and social functioning [1]. Characterised by disturbed body image attitudes and extreme preoccupations with weight and shape, eating disorders manifest as persistent and worrisome disordered eating behaviours [2]. International ICD-11 and DSM-5 diagnostic classification tools recognise six principal clinical eating disorders [Table 1] [3]. A supplementary Other Specified Feeding and Eating Disorder (OSFED) category captures approximately $60\%$ of cases that do not meet criteria for clinical diagnosis [4].
**Table 1**
| CLINICAL EATING DISORDERS | CLINICAL EATING DISORDERS.1 |
| --- | --- |
| Anorexia | An intense fear of weight gain and/or a disturbed body image that motivates severe dietary restriction or other weight loss behaviours |
| Bulimia | Recurrent episodes of binge eating and compensatory behaviours, e.g., purging, to prevent weight gain |
| Binge eating disorder | Recurrent episodes of compulsive overeating that leads to distress without attempts to compensate for weight gain |
| Avoidant/restrictive food intake disorder | The avoidance or restrictive intake of food in the absence of body image concerns and fear of weight gain |
| Pica | Eating non-nutritive or non-food substances for a period of one month or more |
| Rumination disorder | Involves regurgitation of food after eating in the absence of nausea, involuntary retching, or disgust |
| SUBCLINICAL OTHER SPECIFIC FEEDING AND EATING DISORDERS | SUBCLINICAL OTHER SPECIFIC FEEDING AND EATING DISORDERS |
| Orthorexia Nervosa | A pathological fixation with healthy or ‘clean’ eating, avoidance of unhealthy foods and rigid dietary and exercise practices- violations of which cause severe emotional distress |
| Atypical anorexia | Majority of symptoms of anorexia are present, but the individual is classified as being within the normal BMI range |
| Atypical bulimia | Mimics clinical bulimia but occurs less frequently and with shorter duration |
| Atypical binge eating disorder | Mimics clinical binge eating disorder but occurs less frequently and with shorter duration |
| Purging disorder | Purging or using laxatives as a mean to control weight |
| Night eating disorder | Repeatedly eating at night, either after an evening meal or waking up from sleep |
| COMMON PATHOLOGY | COMMON PATHOLOGY |
| Dieting, binging, purging, restricting, avoidance of certain food groups, compulsive or compensatory exercise behaviours and the use of laxatives or weight loss pills | Dieting, binging, purging, restricting, avoidance of certain food groups, compulsive or compensatory exercise behaviours and the use of laxatives or weight loss pills |
Eating disorders incur an estimated 6–$10\%$ increase in years lived with disability [7]. Outcomes range from cardiovascular disease, reduced bone density, to comorbid psychiatric conditions, namely depression, anxiety, obsessive compulsive disorder and specific phobias [8, 9]. Amongst young females, eating disorders are one of the leading causes of disability, often preceding amenorrhea, reduced fertility, and adverse pregnancy and neonatal outcomes [10, 11]. Anorexia has the highest mortality amongst all mental disorders: only $50\%$ of individuals fully recover [7, 8, 12].
The cost of eating disorders at a health systems level is significant, fuelled by increased hospitalisations and the significant burden placed on primary and outpatient services. At a societal level, reduced workforce participation, family members as unpaid carers and young people out of education are noteworthy outcomes of eating disorders [11].
## Epidemiology
Despite perceptions of eating disorders as a culturally bound syndrome of the West, they affect individuals worldwide [7]. Estimating global prevalence, however, is challenging. Nationally representative data are scarce, the disorder tends to be omitted from national health surveys, and multiple changes to classification have confounded existing global data [13–15]. Despite this, the most recent Global Burden of Disease study calculated that in 2019, approximately 13.9 million people suffered from Anorexia or Bulimia. A subsequent review highlighted an additional 41.9 million overlooked cases of OSFED and binge eating disorder, indicating a total global prevalence of $0.7\%$ [2]. However, since many cases never present at formal health services, actual prevalence may be much greater [16]. A review of 94 studies from Asia, Europe and North America revealed that the weighted mean of lifetime prevalence of any eating disorder was $8.4\%$ for women and $2.2\%$ for men [17].
Whilst females still represent the largest proportion of cases, the greatest increase is amongst males, athletes, those with obesity, and sexual and gender minorities [18–21]. Most eating disorders begin in adolescence but tend to persist throughout adulthood [22]. Therefore, young people constitute a subgroup of particular concern [13, 17].
## Aetiology and risk factors
The aetiology of eating disorders is complex; no single risk factor accounts for their manifestation [23]. Rather, prevalence is hypothesised to be the result of numerous biological, psychological, psychosocial, and behavioural factors [Table 2].
**Table 2**
| BIOLOGICAL FACTORS |
| --- |
| • Genetic predisposition• Gender: female-male ratio 10:1 for restrictive type eating disorders and 2:1 for bulimic spectrum eating disorders• Obsessive-compulsive or autistic spectrum traits• Susceptibility to appetite dysregulation• Metabolic vulnerability and high BMI• Environmental influences in the perinatal period• Early puberty |
| PSYCHOSOCIAL FACTORS |
| • Parental eating problems or eating disorder in first-degree biological relatives• Peer stress (e.g. bullying, weight teasing)• Trauma• Culture• Internalisation of the thin / fit ideal• Media• Middle-to-high socioeconomic status• Acculturation (adoption of western beauty ideal) |
| PSYCHOLOGICAL FACTORS |
| • Personality traits (rigidity, attention to detail, perfectionism, neuroticism)• Negative emotionality• Increased sensitivity to social ranking and threat• Body image concerns: dissatisfaction or disturbance• Low self-esteem• Appearance schemas |
| BEHAVIOURAL FACTORS |
| • Extreme weight control behaviours e.g., compulsive exercise, dieting, use of laxatives and purging• Overconcern with weight and shape• Social isolation• Body avoidance or checking |
Body image, a multidimensional psychological construct encompassing how we think, feel and act towards our bodies—has been recognised as the most salient and consistent predictor of eating disorder symptomatology [9, 25, 26]. Although grounded in physical appearance, body image is rarely synonymous with it—individuals often view themselves through a lens of dysmorphia, seeing fatness, ugliness, or an endless list of flaws. The need to ‘fix’ what is ‘faulty’ is thought to precede compensatory disordered eating and appearance altering behaviours [27]. Owing to pubertal weight gain, wavering self-esteem, and a strong desire to fit in, body image concerns often begin in adolescence [28].
## The rise of social media
With increasing eating disorder prevalence, attention has turned to the growth of social media. In 2020, social media reached $49\%$ of the global population [29]. Platforms including Facebook (FB), YouTube (YT), Snapchat (SC), Instagram (IG), WeChat and TikTok have created a new online world for today’s youth. Recent reports reveal that $91\%$ of UK and US adolescents use social media, with over $50\%$ checking these at least once per hour [30]. Users can choose who to follow or message, what content to engage with or upload, what to highlight or conceal. Using filters and editing tools, individuals can alter their identities and dictate how they and their lives are perceived by others [20].
What is posted and well-received is not coincidental–it is dynamic, shaped by broader social and cultural ideals related to beauty [31]. Online, young people are exposed to the ever-changing societal ideals of the ‘desired body’, with perfection as the often-unattainable end goal [9].
## Rationale for review
Body image dissatisfaction and eating disorder pathology amongst young people is rising. According to a recent UK Government report, $95\%$ of under 18’s report that they would change their appearance, and body image was one of the top three anxieties amongst Australian youths [32, 33]. An estimated $13\%$ of young people experience an eating disorder by the age of 20, and 15–$47\%$ endorse disordered eating cognitions and behaviours [23]. Exploratory evidence indicates that social media usage may be partly to blame [34, 35].
Research has highlighted factors such as the ease of accessing harmful eating disorder-promoting content, the pervasiveness of personalised ‘for you page’ algorithms and the explosion of weight loss trends that inspire extreme fitness or thinness [20, 36, 37].
In parallel, recent publications have drawn attention to rising concerns of modern social media platforms and public health, and the need to understand app engagement amongst younger demographics [38].
Despite this, the association between social media, body image and eating disorders remains relatively unexplored. However, with 41.9 million neglected cases of eating disorders in 2019, combined with unprecedented social media exposure amongst young people, this issue warrants further review from a global health perspective. Is social media a plausible risk factor for the development of body image concerns and recent rise in eating disorders? If so, is it a global or western phenomenon?
## Research aim and objectives
Our review aims to systematically map out and critically review the existing global literature on the relationship between social media, body image and eating disorders amongst young people. We provide a glossary of key terms to aid global health audiences unfamiliar with terminology related to social media, body image and eating disorders [Table 3].
**Table 3**
| Term | Description |
| --- | --- |
| Appearance comparison tendencies | The degree to which an individual tends to compare themselves to others |
| Body image | Thoughts, feelings, and perceptions related to one’s body, perceived attractiveness, and self-worth |
| Bonespiration | A social media trend that idealises a very thin body through photos of people with protruding bones |
| Disordered eating | Abnormal food or eating behaviours relevant to eating disorders, such as extreme dieting, eliminating certain food groups, laxative use, binging or purging |
| Eating disorder | A group of psychiatric illnesses characterised by disturbed relationships with food, body image and exercise |
| Eating disorder pathology | Used interchangeably with disordered eating |
| Ecological Momentary Assessment | A type of study aiming to research people’s thoughts and behaviours in real time by repeatedly sampling them in their natural environment |
| Facebook | A social networking app where users can create a profile, add other users as ‘friends’, send messages, comment or like photos, post status updates, share videos and receive notifications when other users update their profiles |
| Filter | A feature that allows you to apply pre-set edits to enhance or change a photo or video |
| Fitspiration | A social media trend aiming to inspire and motivate a healthy and fit lifestyle |
| Generation Z | Individuals born between 1997–2012 |
| Hashtag | User-generated labelling of content which aims to categorise content thematically e.g., #food, #health #fitness. Users can search hashtags to see all content related to that topic |
| Appearance focused social media | Social media platforms that use images or videos as the main mode of communication, e.g., Snapchat, Instagram, TikTok or YouTube |
| Instagram | An interactive social networking app that allows users to share videos and photos to their profiles, ‘follow’ and interact with other users (peers, brands, celebrities, influencers) and apply filters |
| Instagram captions | Text that appears underneath an Instagram photo or video to add context |
| Mediator | Any mechanism that may underlie the relationship between exposure and outcome |
| Moderator | Any variable that may strengthen or weaken the relationship between exposure and outcome |
| Pre-existing body image | Feelings related to one’s body independent of any manipulation or association with other variables |
| Self-objectification | Occurs when an individual internalises a third-person perspective of themselves as an object to be evaluated and judged based on their appearance |
| Self-schema | The extent to which an individual cares about their appearance and how this influences their behaviour |
| Selfie | A photo taken of the self (usually with a smartphone) that can be shared on social media |
| Snapchat | An instant messaging app where conversations consist of pictures, videos and text which disappear after a set amount of time |
| Social media | User-generated platforms allowing for content creation and sharing, exchange of information, advertising and marketing, social networking, and formation of virtual communities |
| Social media influencer | Content creators with large followings and established online credibility. They may be paid by brands to advertise products or may share content aiming to influence or inspire other users (e.g. fitness, recipes, fashion, or skincare related) |
| Social networking sites | Used interchangeably with social media |
| Sociotrophy | A personality trait characterised by a need to please others and low autonomy / lack of independence |
| Thin / fit ideal internalisation | The degree to which an individual adopts and endorses the societal ideal of looking thin or fit |
| Thinspiration | A social media trend aiming to motivate individuals to reach an extremely low bodyweight |
| TikTok | A video sharing social networking app that allows users to create and share videos with background sounds/music |
| WeChat | A Chinese mobile social media app which allows for the instant exchange of messages, voice notes, images, videos, user location sharing and the creation of a profile |
| YouTube | An online video sharing platform where users can create, watch or subscribe to content and other ‘YouTubers’ |
In this review, we assess whether social media use could be a plausible and significant risk factor for the development of subclinical and clinical eating disorders on a global scale. We identify populations of young people affected, primary outcomes and any moderating or risk enhancing factors. We also explore pathways that may mediate the relationship between body image concerns and eating disorders/disordered eating behaviours within distinct social media platforms. Finally, we highlight gaps in the literature and recommend areas of focus for future research and for global health.
## Methodology
We used Arksey and O’Malley’s framework and the updated PRISMA checklist for scoping reviews [39, 40] to guide our approach.
## Search strategy and study selection
We searched MEDLINE, PyscINFO and Web of Science databases in May 2021 and updated our search on 20th July 2021. We identified appropriate search terms through preliminary reading and listing relevant Medical Subject Headings. Keywords were related to four principal concepts: social media, body image, eating disorders and young people [Table 4]. We entered keywords manually and used the “*” symbol to capture all potential word-endings. A full search strategy for PsycINFO is in S1 Fig.
**Table 4**
| CONCEPT | SEARCH TERMS |
| --- | --- |
| Social Media | “social media” or “social networking site*” or Instagram or TikTok or Snapchat or Facebook or Twitter or Pinterest or YouTube |
| Body Image | “body image” or “body perception” or “body surveillance” or “body shame” or “body comparison” or “body checking” or “body image avoidance” or “body image disturbance” or “body dissatisfaction” or “body negativity” or “negative affect” or “body positivity” or “body acceptance” or “body appreciation” or “positive affect” or “body satisfaction” |
| Eating disorders | “eating disorder*” or “disordered eating” or “anorexia” or “bulimia” or “binge eating disorder*” or OSFED or “orthorexia” or “body dysmorphia” or “atypical anorexia” or “compulsive exercise” or “extreme dieting” or “clean eating” or binging or fasting or overeating or undereating or purging or fitspiration or thinspiration or “thin ideal” or “pro-eating disorder” or pro-anorexia or pro-bulimia |
| Young people | “youth*” or “adolescent*” or “young adult*” or “teen*” or “young people” or “young person” |
We exported search results from each database to the reference manager Zotero. First, we removed duplicates. Next, we screened titles and abstracts and eliminated irrelevant papers. Subsequently, we assessed full-text articles against predetermined eligibility criteria, and recorded reasons for exclusion [Table 5]. We identified additional studies through hand searching reference lists. Both authors independently screened titles and abstracts and reviewed full-text articles for inclusion. One reviewer carried out data extraction from all included studies, and the other reviewer independently verified that all entered information was correct. Any discrepancies were resolved by enlisting the help of additional reviewers who were not part of the study but had the relevant background in global health and nutrition.
**Table 5**
| INCLUSION | EXCLUSION |
| --- | --- |
| All studies | All studies |
| • English language• Peer reviewed papers• Published between January 2016 and July 2021• All study types, including previous systematic reviews and meta-analyses• All social media platforms (singular, multiple, or general)• All geographical locations• Any study setting: community based, clinical, online• Participants aged 10–24 or with a combined mean age of ≤ 24• All subgroups of young people (gender, sexuality, ethnicity, eating disorder status)• Exploration of social media use and body image / disordered eating outcomes, and relevant mediating or moderating factors | • Theses, non-academic grey literature, books, and book chapters• Studies with no full text available• Focus on mass media or general internet use rather than social media• Focus on social media related interventions for eating disorders |
| Additional criteria for quantitative studies | Additional criteria for quantitative studies |
| • Appropriate measure of social media use and valid tools to confirm body image or disordered eating outcomes | |
| Additional criteria for qualitative studies | Additional criteria for qualitative studies |
| • Exploration of themes related to social media use and body image/disordered eating | |
To allow for sufficient depth of analysis and documentation of individual differences, we included studies involving young people (defined by the WHO as individuals aged 10–24 years), irrespective of gender, sexuality, ethnicity, or existing eating disorder status. If papers did not indicate the age range of participants, we included them if the reported mean age was ≤ 24. Our review was not restricted by geographic location or country income grouping, as a deliberate measure to develop a global understanding of social media use and body image or eating disorder, without making assumptions about the existence or nature of the phenomenon in countries categorised as low- or middle-income. We included studies on any social media platform (singular, multiple, or general), but not those focusing on mass media and / or internet use. We excluded studies exploring social media interventions and body image / disordered eating outcomes because they were beyond the scope of our review. Papers published between January 2016 and July 2021 were eligible.
## Data charting process
We used a data extraction table to synthesise relevant information from each study, starting with study type, country, and World Bank Income classification [41]. We also recorded number of participants, gender (percentage female), age range (mean and standard deviation), sexual orientation and ethnicity. After testing the extraction framework on a small number of included articles, BMI and eating disorder prevalence were added as additional participant characteristics.
We extracted information on study objectives, social media platform(s), underlying theoretical framework (stated or indicated) and definitions of exposure (social media usage), outcome (body image or eating disorder / disordered eating), mediator or moderator variables.
## Critical appraisal
Formal critical appraisal is not a requirement of scoping reviews [42]. However, we aimed to analyse the relationship between social media, body image and eating disorder pathology and the plausibility of social media as a risk factor for clinical / subclinical eating disorders to guide future global health research and policy, and thus felt that a rigorous understanding of the quality of evidence was necessary.
We used three validated critical appraisal tools to account for study design, including: (i) the Joanna Briggs Institute (JBI) checklist for analytical cross-sectional studies for cross-sectional, ecological momentary assessment, mixed methods, and longitudinal observational studies; (ii) an adapted version of the JBI tool for quasi-experimental studies for experimental and mixed methods experimental studies; and (iii) the Critical Appraisal Skills Checklist (CASP) for qualitative studies.
We gave studies one point for every checklist item fully met, and half a point when the item was partially met, and then calculated the proportion of checklist items met by each study. We categorised study quality as High (≥ $75\%$ checklist covered), Moderate ($50\%$-$74\%$ checklist covered), and Low (<$50\%$ checklist covered).
## Theoretical framework
Our review and appraisal of the evidence was informed by Rodgers’ 2016 integrated theoretical framework [43] on the influence of internet use on body image concerns and disordered eating pathology. Interactions with others as well as individual online behaviours are important pathways linking internet use to body image and eating pathology, and a hypothesised feedback loop between the two reinforces the addictive nature of social media and sustained motivations for use despite potentially adverse outcomes. Whilst previous frameworks have focused on singular theoretical perspectives that explain this relationship, Rodgers incorporates five theories to provide mechanistic insight, including (i) sociocultural theory, (ii) self-objectification and feminist theory, (iii) impression management involving self-discrepancy and true-self theories, (iv) social identity theory, and (v) gratification theory.
We used Rogers’ framework in our scoping review in two ways. First, we assessed whether each included article addressed any of the framework’s specific pathways and noted any underlying theoretical assumptions that were outside of Rogers’ model but could potentially extend it. Second, we evaluated included studies to understand how well the current scope of literature matches Rogers’ proposed view, and how we could extend and update the integrated model in light of emerging evidence on social media use, body image and disordered eating published since 2016.
## Ethics
The review was based on previously published studies and therefore no ethical approval or participant consent was required.
## Results
This section details the findings of our review, beginning with an overview of search results and study characteristics. We then summarise the main exposures, outcomes, mediators and moderators identified in the literature. We present our synthesis of the literature in a framework describing a self-perpetuating cycle of risk.
## Study selection
We identified 273 articles from database searches, and after de-duplication, screening abstracts, reading full-texts, and hand-searching reference lists, we included 50 studies (45 quantitative and 5 qualitative) in our review (Fig 1) (See S1 Table for individual summaries of the 50 studies).
**Fig 1:** *PRISMA flow diagram.*
## Study design
We reviewed 45 quantitative (30 cross-sectional, 6 experimental, 5 mixed methods, 2 ecological momentary assessment, and 2 longitudinal observational studies) and 5 qualitative studies. Qualitative studies collected data through focus group discussions ($$n = 4$$) and interviews ($$n = 1$$).
As discussed, the scope of evidence in this review is limited by the cross-sectional design adopted by most studies, the homogeneity of included participants, and limited geographic scope. Moving forward, researchers should consider conducting longitudinal studies with representative samples and cross-cultural comparisons covering all regions. This would provide greater clarity regarding the true directionality of the relationship, and grant insight into the impact of social media on young people across the life course. Likewise, ongoing qualitative studies with young people would aid understanding and provide rich data on a topic that is unique to this age group.
While our review describes certain risk factors, mediators and moderators in the extended framework, the nature of their role was not conclusive (including gender and personality traits). Future research would benefit from focusing on these to see whether they are as meaningful as current evidence suggests. The increase in eating disorder incidence among males and gender differences described by a small number of studies suggest that this particular moderator requires further investigation [111].
Next, previous research has highlighted that certain subcategories, including those with high BMI, athletes, and young people who identify as lesbian, gay, bisexual and transgender (LGBT) are more susceptible to body image dissatisfaction and eating disorders [112]. Study populations in this review tended to be White school or university students and did not reflect the intersectionality nor diversity of national populations. Thus, it would be beneficial to assess the relative impact of social media on eating disorders across these high-risk groups.
Finally, prior evidence suggests that media penetration and adoption of ‘western ideals’ increases the risk of eating disorder pathology [113]. Despite global scope, this review did not retrieve any studies from low-income countries. Therefore, this assumption requires further testing in these contexts. Future research should explore the relationship in low income, non-western cultures, which may be important as social media use rises globally.
## Study location
Ninety percent of studies ($$n = 45$$) were conducted in high-income countries. The largest number of studies were conducted in Australia ($$n = 12$$) and the United States ($$n = 11$$). Canada, Italy, Singapore, and United Kingdom were included in three studies each, Spain and Ireland in two each, and Belgium, France, Germany and Sweden in one each. Studies conducted in Sri Lanka ($$n = 1$$), China ($$n = 2$$), Malaysia ($$n = 1$$) and Thailand ($$n = 1$$) comprised the evidence base on upper-middle-income and lower middle-income countries, and none included populations in low-income countries.
## Participants
$48\%$ of studies ($$n = 24$$) included only female participants, $2\%$ of studies ($$n = 1$$) included male participants, and $50\%$ ($$n = 25$$) included both genders. One study reported transgender participants, although this subcategory constituted only $9.3\%$ of the study’s total sample [48].
Where ethnicity was reported, >$75\%$ of studies ($$n = 18$$) reported majority White participants, $17.4\%$ ($$n = 4$$) reported majority Chinese participants, and <$5\%$ reported majority Hispanic participants.
Only 17 studies described participants’ BMI. Most participants ($82\%$) were of a healthy BMI (18.5 to 25), with smaller proportions who met criteria for overweight ($12\%$) and underweight ($6\%$) BMI categories. Four studies ($8\%$) identified participants with an existing eating disorder.
Most studies recruited participants from university ($$n = 20$$) and secondary schools ($$n = 19$$), with fewer studies using online ($$n = 6$$), community ($$n = 4$$) and clinical ($$n = 1$$) settings to recruit young people.
## Underlying theoretical frameworks
Most studies borrowed ideas from multiple theories. $78\%$ of papers referred to Sociocultural Theory, with an emphasis on social comparison. $26\%$ referred to Gratification’s Theory, $24\%$ reported Self-objectification and Feminist Theory, $24\%$ highlighted Impression Management Theory, and $12\%$ of studies mentioned Social Identity Theory. We identified seven theories outside of Rodgers’ framework [S2 Table].
## Social media platforms assessed
Twelve studies assessed general social media usage, 18 indicated multiple platforms, and nine focused on appearance orientated sites only (Facebook, Instagram, Snapchat, YouTube). Where specified, most studies investigated Instagram ($$n = 15$$), followed by Facebook ($$n = 5$$) and WeChat ($$n = 1$$).
## Motivation for social media usage
Across studies, reasons for social media usage included: identity management, fitting in with friends, posting content for peer feedback, and seeking out weight loss, fitness, or pro-eating disorder material.
## Quality appraisal
Many quantitative studies ($$n = 28$$) were of moderate quality. Generally, unfulfilled criteria included failure to address confounding variables, and lack of control group in experimental conditions. Most qualitative studies ($$n = 4$$) comprised high-quality evidence (>$75\%$ checklist met), although they did not always consider ethical issues or the positionality of the researcher [S3–S5 Tables].
## Relationships between social media, body image and eating disorders
The overarching relationship between social media use, body image and eating disorders operates through a range of mechanisms. Typically, social media usage led to body image concerns, eating disorder or disordered eating outcomes, and poor mental health via the mediating pathways of social comparison, thin / fit ideal internalisation, and self-objectification.
## Specific social media usage
Approximately $58\%$ of studies ($$n = 29$$) investigated specific types of social media exposure, including time, frequency, use of appearance-focused platforms, and investment in appearance related activity [Table A in S1 Data].
## Time
Seven studies investigated the relationship between time spent on social media and body image or eating disorder-related outcomes. Time was significantly associated with these variables in two studies, although both papers failed to acknowledge other social media activities and possible mediators [49, 50]. Three cross-sectional studies discovered that time spent on social media was associated with body image dissatisfaction via the mediating pathways of social comparison and thin ideal internalisation [51–53], indicating that the relationship between exposure and outcome is more nuanced than the mere number of hours spent online.
## Frequency
High frequency of social media usage and body image dissatisfaction was supported by two studies [54, 55].
## Appearance focused social media platforms
Three cross sectional studies indicated that appearance focused platforms, namely Instagram and Snapchat, are significantly associated with body image concerns, eating disorder pathology, anxiety and depressive symptoms [51, 56, 57].
## Investment in appearance related activities
17 studies identified that investment in appearance related activities (‘selfie’ avoidance, manipulation and posting edited photos, and significantly investing in ‘likes’ and ‘comments’) may be noteworthy exposures. These activities were consistently associated with body image dissatisfaction and risk of eating disorder pathology across a range of cross-sectional, experimental, and qualitative study designs ($$n = 14$$).
There were anomalies to this trend ($$n = 3$$). First, two studies found that posting ‘selfies’ on Instagram led to higher body esteem, rather than body image dissatisfaction [58, 59]. However, included participants may have had significantly higher pre-existing body esteem, rather than this emerging as a consequence of posting.
## Social media trends
Two prominent hashtags featured in the literature, drawing on the idea of social media as a source of inspiration or aspiration towards greater fitness (#Fitspiration) and thinness (#Thinspiration) ([Table B in S1 Data]).
#Fitspiration. Eight studies investigated the impact of the fitspiration trend on body image dissatisfaction and eating disorder pathology with mixed results: $50\%$ supported the relationship, $25\%$ partly supported it, and $25\%$ refuted it. Three moderate- to high- quality experimental studies demonstrated that exposure to fitspiration imagery relative to control images resulted in body image dissatisfaction and negative mood for participants, pointing towards a causal relationship [60–62]. Qualitative insight highlighted that for some, fitspiration inspired healthy eating and exercise. Others felt extreme pressure to ‘eat clean’ or exercise to excess, with subsequent bingeing and disordered eating outcomes [63, 64]. A mixed methods study of fitspiration followers on Instagram found that $17.7\%$ were at risk of developing an eating disorder, $17.4\%$ demonstrated high levels of psychological distress, and $10.3\%$ displayed addictive levels of physical exercise [65].
#Thinspiration and pro-eating disorder content. Three studies explored the relationship of the #thinspiration trend with body image and eating disorders. A mixed methods study concluded that the hashtag glorified “emaciated people” and “bone-thin girls”, promoting starvation as a lifestyle choice instead of a symptom of mental illness. Posts provided individuals with tips on how to lose weight and hide an eating disorder [48]. A cross-sectional study found that $96\%$ of included participants followed the thin-ideal on social media, of whom $86\%$ met the criteria for a clinical/subclinical eating disorder, and $71\%$ and $65\%$ reported symptoms of depression and anxiety, respectively [66]. Whilst these statistics are alarming, the study relied on self-reported symptoms and used novel eating disorder diagnostic tools that had not been extensively validated.
## Eating disorder pathology
20 studies explored eating pathology as an outcome of social media usage [Table C in S1 Data].
## Clinical/subclinical eating disorders
Five cross-sectional studies yielded statistically significant associations between social media usage and various clinical eating disorders. These ranged from night eating syndrome [67], to binge eating disorder [68] and bulimia nervosa [69].
One cross-sectional and one qualitative study indicated orthorexia nervosa symptomatology amongst participants, ranging from obsessions with ‘clean eating’ to avoidance of ‘demonised’ foods and compulsive exercise behaviours [64, 70]. A study of 713 participants confirmed orthorexia nervosa prevalence of $49\%$ [70], far greater than the estimated <$1\%$ in the general UK population. However, participants were recruited from ‘fitness’ Instagram pages, and thus unlikely to be representative of all social media users or the general population.
## Disordered eating behaviours
More commonly, 11 studies found statistically significant associations between social media usage and disordered eating behaviours, including bingeing, purging, use of laxatives and extreme dieting. One cross-sectional study found that $51.7\%$ of adolescent girls and $45\%$ of boys engaged in meal skipping and excessive exercise [57]. Although the sample size was large ($$n = 993$$), behaviours were self-reported and study quality was low.
## Eating disorder maintenance or recovery
Two studies explored the effect of social media on eating disorder maintenance or recovery. A mixed-methods study found that only $3\%$ of 499 participants with clinical/subclinical eating disorders used social media to aid recovery or as a form of treatment. The remaining $97\%$ indicated that it hampered recovery, one stating that “when I get really hungry, I go into these sites to get motivation to not eat for a bit longer” [48].
## Body image concerns
33 studies demonstrated significant associations between social media usage and body image dissatisfaction, including body shame, low self-esteem and body related anxiety [Table D in S1 Data]. Of these, five hypothesised that body image dissatisfaction preceded subsequent eating disorder pathology [50, 71].
## Mental health
Although not the primary focus of the research, nine studies revealed significant associations between social media usage, body image concerns or disordered eating pathology, and poor mental health [Table E in S1 Data]. Outcomes included low mood ($$n = 4$$), anxiety and depressive symptoms ($$n = 5$$).
## Mediators
We identified three key mediators [Table F in S1 Data].
## Thin / fit ideal internalisation
12 studies investigated thin / fit internalisation as a mediator between social media usage and body image or disordered eating outcomes. Eleven ($92\%$) indicated that it is a plausible mediator, across cross sectional ($$n = 7$$), experimental ($$n = 1$$) and qualitative ($$n = 3$$) study designs. In a qualitative study, female participants ($$n = 27$$) in focus group discussions reported feeling pressure to adhere to an ever-changing ideal [72]. In qualitative interviews, a sample of Swedish adolescents reported feelings of alienation following failure to adhere to the ‘toned but not too muscular’ ideal, whilst focus groups with Irish adolescents revealed participants’ feelings of self-blame and disgust [64, 73].
## Appearance comparisons
21 studies explored the mediator of appearance comparisons on social media, with 19 ($90\%$) reporting a significant relationship. Comparisons tended to be ‘upward’ and yielded feelings of inadequacy and self-loathing [53, 63, 68]. In contrast, an observational longitudinal study revealed that comparisons on Facebook did not predict body image dissatisfaction six months later [74]. However, Facebook use was marked as ‘outdated’ amongst younger participants (mean age in this study was 14.7 years) which could account for the non-significant finding.
## Self-objectification
Six studies recognised self-objectification as a significant mediator. Generally, participants reported self-criticism, picking out flaws in photos and purposively posting photos accentuating certain body parts [48, 51, 73].
## Moderators
The relationship between social media and body image / disordered eating was inflected by several moderating factors, broadly categorised as biological [Table G in S1 Data], cognitive [Table H in S1 Data] and socio-environmental [Table I in S1 Data] characteristics.
## Gender
18 studies investigated gender as a moderator, 14 of which found significant differences between males and females. Generally, girls invested heavily in photos of themselves, endorsed the thin / fit ideal, made more comparisons with others, and engaged in higher levels of disordered eating pathology, specifically dieting and emotional eating [50, 57, 75]. In contrast, boys endorsed a more muscular ideal, with goals of functionality and fitness, rather than weight loss [76, 77].
Qualitative focus groups implied that social media and body image is perceived as a gendered subject, with boys feeling reluctant to admit adverse effects due to stigma or fear of emasculation [63]. As most studies implemented self-report tools, and over half comprised mixed gender participants, this may have distorted findings. Remaining studies ($$n = 4$$) found no difference between males and females. Discrepancy may be due to variance in the assessment tools utilised for body image or disordered eating outcomes and differential sex ratios and mean age of participants.
## BMI
Five studies investigated BMI as a moderator. Three indicated high BMI as strengthening the relationship between social media, body image dissatisfaction and eating disorder pathology. The two anomalies to this trend both included participants with abnormally low average BMIs [71, 78]. All studies relied upon self-reported BMI, thus increasing risk of measurement error and bias.
## Pre-existing body image concerns
Four studies indicated that inherent body image concerns (shame and low self-esteem) predicted certain social media behaviours and heightened susceptibility of eating disorder pathology. In contrast, body appreciation appeared to buffer against this effect [71], however, self-compassion was not a moderator [79].
## Risk of eating disorder
Five studies highlighted that high-risk individuals with elevated eating disorder scores or a pre-existing eating disorder are more inclined to seek out damaging content on social media (such as thinspiration or weight loss). They may be more susceptible to mediating factors (internalisation and comparison) and are thus at heightened risk of further clinical / subclinical eating disorders [78, 80, 81].
## Social media literacy
Three studies indicated low social media literacy amongst participants, including difficulty switching off from damaging posts and an inability to recognise edited versus unedited posts [63, 73, 82]. In contrast, focus groups with adolescent girls in the US showed that having studied social media in school, girls were critical of its artificiality, reported that they could appreciate others’ beauty without jealousy, were self-accepting and did not feel the need to seek compliments online. Some engaged in comparisons, although this did not appear to lead to body image dissatisfaction and eating disorder pathology [83].
## A self-perpetuating cycle of risk?
Our synthesis indicates that certain social media exposures and individual risk factors can strengthen this relationship, whilst numerous moderators may weaken, or even disrupt it. This led us to extend Rodgers’ framework [43] in light of new research, referred to here as a ‘self-perpetuating cycle of risk’ [Fig 2].
**Fig 2:** *A self-perpetuating cycle of risk to show the relationship between social media usage, body image and eating disorder pathology.*
Our findings indicate that specific features of social media usage (appearance focused platforms, investment in photos, and engagement with fitspiration and thinspiration trends) lead to body image concerns, disordered eating pathology and mental health outcomes. This relationship is shaped by the mediating pathways of thin / fit ideal internalisation, appearance comparisons, and self-objectification, which have been supported by additional meta-analyses [3, 84, 85].
However, due to the cross-sectional nature of most studies, it is impossible to identify the direction of causality: for example, do body image dissatisfaction and disordered eating occur because of social media usage, or do these pre-exist, encourage engagement in certain online activities, and result in unfavourable clinically significant outcomes?
Our revised framework recognises both possibilities. It is plausible that specific individual risk factors (particularly high BMI, poor body image and existing eating disorders) combined with differential motivations for social media use (identity formation, gratifications from peer feedback) encourage certain behaviours when users engage with social media (photo manipulation, searching for thinspiration and fitspiration content), strengthen the effects of mediators, and increase risk of poor body image, disordered eating, and mental health outcomes. This shifts away from the fatalistic notion that social media causes poor body image and eating disorders in all users. Instead, it suggests that certain individuals are simply more vulnerable to its deleterious effect.
This relationship is not linear. Results highlight that social media is highly addictive, and individuals use it despite negative outcomes [59]. In fact, to ‘fix’ their poor body image, users may be even more inclined to do so (e.g., manipulate photos to obtain more likes)—indicated by a hypothesised feedback loop. It is this which may trigger the self-perpetuating cycle of risk. However, this cycle can be broken. Several moderators, or buffers, that have the potential to disrupt it. Many studies showed that whilst individuals still internalised the ideal or compared themselves to others, high social media literacy and body appreciation prevented this from resulting in body image dissatisfaction, disordered pathology and poor mental health [71, 83].
## Discussion
Our scoping review of 50 studies conducted in 17 countries found that social media usage is a plausible risk factor for the development of clinical / subclinical eating disorders across a range of country income groupings. We extended current thinking to describe how social media provides a platform of perfectionism, often embeds unhealthy ideals of disordered eating and fitness and can hamper recovery from eating disorders. We also identified mediators, moderators and important risk factors that shape this relationship and offer opportunities to intervene.
Taken as a whole, the literature underlines a complex, yet meaningful relationship between social media usage, body image concerns, and disordered eating pathology. While our review points to potentially large scale implications among the approximately 3.9 billion social media users worldwide, it is important to note that not every user has poor body image or an eating disorder [29]. This begs the question, what makes certain individuals more susceptible?
## Significance for global health
Disturbingly high prevalence of body image dissatisfaction, disordered eating pathology and comorbid mental health outcomes were reported amongst young social media users in this review [57, 66, 70]. Given the sheer scale of social media reach (approximately $60\%$ of the world’s young people), a large proportion of young people could be exposed to the self-perpetuating cycle of risk [29]. Significantly, findings were reflected amongst Asian samples, mirroring concerns regarding the explosion of eating disorders in Asia [86, 87] which has a population of 4.6 billion. Social media use as a risk factor for eating disorder pathology clearly warrants attention outside of high-income western countries [17].
Currently, up to $80\%$ of eating disorder sufferers remain out of formal healthcare systems, with many presenting at late stages [66]. Denial, stigma, and fears that one’s disorder isn’t serious enough already hampers treatment seeking [14]. Today, young people are often immersed in a digital world where desires to change one’s body, excessive exercise and preoccupation with food appears normal [88]. How can young people identify that they have a problem, when their behaviours seem to be nothing out of the ordinary?
Eating disorders, clinical or subclinical, are serious psychiatric disorders with a range of comorbid health outcomes [28, 89]. Unfortunately, they are often misunderstood, omitted from nationally representative health surveys, and viewed as less important than other mental health disorders [12]. Reports of 41.9 million neglected eating disorder cases in 2019, combined with a surge in cases recorded by health systems and charities alike calls for a serious reconceptualisation of the disorder [36, 90].
This issue also sits within the wider arena of adolescent mental health. The WHO Global Strategy for Women’s, Children and Adolescent Health coupled with The Mental Health Action Plan 2013–2020 demonstrate that investment in young people and their mental health yields invaluable gains for society [91, 92]. Although eating disorders were recognised by The National Institute of Mental Health as a priority area for adolescents in 2007 with prevalence equalling that of bipolar and substance use disorders, they remain absent from these seminal reports and constitute a fraction of global mental health research [12, 93, 94].
Since 2007, the rise of social media has brought new challenges. Despite its dominance in the lives of Generation Z, we are only just beginning to learn about its impact [95, 96]. Of significance, The UK Royal Society for Public Health (RSPH) published a damning report on social media and mental health, whilst social media, body image and eating disorders have been recognised as emerging policy areas for the UK, Australian and US government alike [97–100]. In 2020, The UK launched their Online Harms White Paper to promote a regulatory framework of online safety and stimulate innovative social media intervention to protect young users. Eating disorder risk, however, remains absent from many of these youth centred goals in the UK [101].
Despite this rising concern, regulation of social media remains weak, with significant gaps between ‘safety policies’ and the real-life experiences of users [80]. Age, anonymity, and the pervasiveness of algorithms play a key role in this. Although required that users must be at least 13 years of age, most popular social media platforms have no robust means of age authentication. Recent figures reveal that up to $42\%$ of children under the minimum age have a social media profile [102]. Users of all ages can join under any email address and disguise their identity through use of aliases. Once online, access to content is generally unrestricted, whilst algorithms suggest personalised content based on prior user engagement. In April 2021 Instagram sparked headlines after ‘appetite suppressants’, ‘fasting’ and ‘weight loss’ was recommended to certain users based on their previous searches [103]. Although rectified, content that glorifies eating disorders remains highly accessible, with little promise of intervention. More critically, internal research by Facebook conducted in 2019 leaked to the media in September 2021 showed not only that $40\%$ of Instagram users who reported that they felt unattractive said that dissatisfaction began while using the platform [104], but also suggests that Facebook knew about the app’s potential to harm teenage girls’ mental health [105].
Further to this, evidence suggests that social media literacy amongst the young is low, and clinical recognition of eating disorder symptomatology and online risk factors poor [106]. In parallel, social media has been described as more addictive than alcohol and cigarettes, body image dissatisfaction and eating disorder pathology are on the rise, and the mental health of today’s youth is declining [98]. In light of COVID-19 lockdowns and further shifts towards an online world, this issue demands greater global attention [55].
## Recommendations and future research
As the topic is in its infancy, our recommendations for intervention and future research are generally exploratory.
## Community level
Primarily, opportunity lies in raising awareness of social media and its possible connection to body image dissatisfaction and eating disorder symptomatology. In the UK, only $23\%$ of young people learn about body image at school, although $78\%$ believe that this would be useful [32]. Through investments in social media literacy [83], young people could learn to appreciate body diversity, navigate social media through a critical lens, and challenge the artificiality of the societal ideal of beauty. Open discussions between students, teachers, and parents could reduce the stigma associated with eating disorders and help young people to identify body image concerns and eating problems before they manifest as serious disorders. Preliminary research investigating social media literacy has shown promising outcomes [107].
## Societal and health systems level
At a societal level, recognition of the issue within government and health systems is paramount. To optimise positive social media use, communication between these players and social media companies should be enabled. From this, policies to enhance age verification, minimise access to pro-eating disorder content and increase the health and safety of users could be developed [106]. Recently, the UK Government Equalities Office approached social media influencers and advertising companies to devise strategies to enhance body diversity online [97]. Results are yet to materialise, although there is evidence of an emerging online ‘body positivity’ movement [95, 108].
Within a clinical setting, appreciating eating disorders as serious mental health problems, screening patients presenting relevant symptomatology regardless of body size, and integrating social media as an additional factor within treatment plans could be advantageous [109]. At the global level, we recommend building health system capacity in low and middle-income countries in preparation for the potential future burden of eating disorders as a progressive strategy. However, based on funding, budget cuts, and the priority that other health issues currently take, the likelihood of this being implemented is unclear [110].
## Limitations of this review
First, due the cross-sectional nature of most included studies and generally ‘moderate’ quality, causation between social media usage and outcomes cannot be presumed. Likewise, with variance in tools used to assess mediators and outcomes, most of which were self-reported, measurement error and bias are possible.
Secondly, despite the reviews’ inclusive geographic scope, studies were concentrated within middle-high income countries in Europe, Asia, and Australasia. Although few cross-cultural differences emerged, scarcity of evidence from low-income countries means that generalisability is indicative rather than conclusive. Likewise, included samples were relatively homogenous. Most participants were of White ethnicity, average BMI, and female. Paucity of diverse evidence limits meaningful insight into any subcategories of particular risk.
Next, although the social media platforms covered in the literature were extensive, the digital landscape evolves rapidly. For example, TikTok (the most downloaded mobile application in 2020) is the focus of significant media concerns regarding body image yet is only mentioned by one study [55]. It may be that findings still fail to capture the most recent trends.
## Conclusion
In the 21st century, social media use amongst a developmentally susceptible age category is unprecedented and largely unregulated. ‘ Likes’ and comments can validate identity, the societal ideal of beauty appears ubiquitous, and most people (albeit enhanced and filtered) appear to be perfect. In pursuit of acceptance, popularity and validation, the common option is to follow suit–to manage one’s own online identity to meet the ideal marked by others, to manipulate and scrutinise ‘selfies’, and once posted, angst over the numbers of likes or comments received. However, despite one’s best efforts, this online change is rarely good enough. Through the lens of social media, someone else can always look better, skinnier, or prettier [73]. Likewise, pro-eating disorder content is rife, and the ‘healthy’ #fitspiration trend may be fuelling new waves of disturbed eating and exercise pathology. The outcome is a population of young people at risk of corroded body image, gaping discrepancies between their actual and ‘polished’ online selves, and an increased likelihood of engaging in compensatory disordered eating behaviours, as our review has shown.
In parallel, cases of eating disorders are escalating, with prevalence far exceeding what was previously thought. Although it is not possible to isolate one single cause, the plausible link between social media, body image dissatisfaction and eating disorders is alarming. Based on the scale of social media usage, this could impact the wellbeing of a significant proportion of the world’s young- particularly those who are already vulnerable. Where the body of evidence is so recent, and social media ever evolving, the ramifications of this are not yet fully clear.
However, with significant strides being made in the realm of global adolescent mental health, intervention is clearly possible. Through recognition, funding, research, and prioritisation, there is hope that this issue will receive more attention, and concern will translate into tangible action. Our goal should be to have a generation of young people who are body positive, who use social media in a progressive way, who eat food because it is a basic human need, and who do not measure self-worth by the circumference of their thighs. We must also aim for widespread education and early identification of at-risk individuals, so that eating disorder symptomatology can be challenged long before it presents at formal healthcare settings.
The burden of body image dissatisfaction and eating disorders as a global health issue has been ignored for too long. The rise of social media has reinforced the need to turn attention to the ‘Cinderella disease’ of mental health, which this scoping review has demonstrated as worthy of a place on the global public health agenda.
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|
---
title: O-GlcNAc glycosylation orchestrates fate decision and niche function of bone
marrow stromal progenitors
authors:
- Zengdi Zhang
- Zan Huang
- Mohamed Awad
- Mohammed Elsalanty
- James Cray
- Lauren E Ball
- Jason C Maynard
- Alma L Burlingame
- Hu Zeng
- Kim C Mansky
- Hai-Bin Ruan
journal: eLife
year: 2023
pmcid: PMC10032655
doi: 10.7554/eLife.85464
license: CC BY 4.0
---
# O-GlcNAc glycosylation orchestrates fate decision and niche function of bone marrow stromal progenitors
## Abstract
In mammals, interactions between the bone marrow (BM) stroma and hematopoietic progenitors contribute to bone-BM homeostasis. Perinatal bone growth and ossification provide a microenvironment for the transition to definitive hematopoiesis; however, mechanisms and interactions orchestrating the development of skeletal and hematopoietic systems remain largely unknown. Here, we establish intracellular O-linked β-N-acetylglucosamine (O-GlcNAc) modification as a posttranslational switch that dictates the differentiation fate and niche function of early BM stromal cells (BMSCs). By modifying and activating RUNX2, O-GlcNAcylation promotes osteogenic differentiation of BMSCs and stromal IL-7 expression to support lymphopoiesis. In contrast, C/EBPβ-dependent marrow adipogenesis and expression of myelopoietic stem cell factor (SCF) is inhibited by O-GlcNAcylation. Ablating O-GlcNAc transferase (OGT) in BMSCs leads to impaired bone formation, increased marrow adiposity, as well as defective B-cell lymphopoiesis and myeloid overproduction in mice. Thus, the balance of osteogenic and adipogenic differentiation of BMSCs is determined by reciprocal O-GlcNAc regulation of transcription factors, which simultaneously shapes the hematopoietic niche.
## Introduction
Mammalian bones support body structure, protect vital organs, and allow body movement. In addition, they provide an environment for hematopoiesis in the bone marrow (BM). Most bones in mammals are formed through endochondral ossification, which is initiated by mesenchymal condensation, followed by the differentiation of chondrocytes and perichondrial progenitors (Kobayashi and Kronenberg, 2021). Perichondrial progenitors expressing Osterix (encoded by the *Sp7* gene) co-migrate with blood vessels into the primary ossification center, giving rise to osteoblasts and transient stromal cells in the nascent BM cavity (Chen et al., 2014; Liu et al., 2013; Maes et al., 2010; Mizoguchi et al., 2014). At the perinatal stage, Osterix+ progenitors contribute to osteo-lineages and long-lived BM stromal cells (BMSCs) that exhibit trilineage differentiation potential into osteocytes, chondrocytes, and adipocytes.
The decision of BMSC fate is controlled by a transcriptional network of pro-osteogenic and anti-adipogenic transcription factors that pre-establishes osteogenic enhancers in BMSCs for rapid bone formation (Rauch et al., 2019). RUNX family transcription factor 2 (RUNX2), by regulating osteogenic genes including Sp7, determines the osteoblast lineage from the multipotent BMSCs. Mice with Runx2 mutations completely lack skeletal ossification and die of respiratory failure (Komori et al., 1997). Runx2-haploinsufficient mice show specific skeletal abnormalities characteristic of human cleidocranial dysplasia (CCD), including persistent fontanels, delayed closure of cranial sutures, rudimentary clavicles, and dental abnormalities (Otto et al., 1997; Takarada et al., 2016). On the other hand, adipogenesis is driven by downregulation of pro-osteogenic factors, remodeling of the chromatin, and activation of adipogenic transcription factors, such as C/EBPs and PPARγ (Aaron et al., 2021; Rauch et al., 2019). BM adiposity is associated with bone loss in osteoporosis caused by aging, menopause, and anorexia nervosa (Bethel et al., 2013; Fazeli et al., 2013; Liu et al., 2015; Scheller et al., 2016). However, it is incompletely understood how these distinct types of transcription factors act cooperatively to determine lineage differentiation during neonatal skeletal development.
BMSCs and their lineage-differentiated progeny (e.g. osteoblasts and adipocytes) provide a niche microenvironment for hematopoiesis (Bianco and Robey, 2015; Calvi and Link, 2015; Morrison and Scadden, 2014; Wei and Frenette, 2018). Recent studies using single-cell technologies and lineage tracing experiments have started to unveil the complexity and heterogeneity of niche cell types, niche factors, and their actions. For example, BMSC-derived stem cell factor (SCF, encoded by the *Kitl* gene) and CXC chemokine ligand 12 (CXCL12) are required for the maintenance and differentiation of hematopoietic stem/progenitor cells (HSPCs) (Asada et al., 2017; Ding et al., 2012). A prominent subpopulation of perivascular BMSCs express adipocyte markers (Dolgalev and Tikhonova, 2021; Zhong et al., 2020; Zhou et al., 2017), and support steady-state and metabolic-stressed myelopoiesis by secreting SCF (Zhang et al., 2019). Meanwhile, osteolineage cells are crucial for lymphopoiesis (Wei and Frenette, 2018). Depleting Osterix+ cells halts B cell maturation and causes immune failure (Yu et al., 2016). IL-7, the most crucial factor for lymphoid progenitors, is expressed by a subset of BMSCs (Fistonich et al., 2018). While it is well accepted that myeloid and lymphoid progenitors may reside in distinct BM niches, it is unclear how BMSC heterogeneity is established during early development and whether cytokine expression is coordinated and controlled by the fate-defining transcriptional network in BMSCs.
Post-translational modifications (PTMs), including phosphorylation, acetylation, and ubiquitination, allow the precise regulation of stability, localization, and activity of BM transcriptional factors, such as RUNX2 (Chen et al., 2021; Kim et al., 2020), C/EBPs (Wang et al., 2022), and PPARγ (Brunmeir and Xu, 2018). It remains poorly defined how these modifications are coordinated in a spatio-temporal manner to calibrate skeletal development. Thousands of intracellular proteins are dynamically modified by a single O-linked N-Acetylglucosamine (O-GlcNAc) moiety at serine or threonine residues, termed O-GlcNAcylation (Hart et al., 2007; Ruan et al., 2013b; Yang and Qian, 2017). O-GlcNAc transferase (OGT), using UDP-GlcNAc derived from the hexosamine biosynthetic pathway as the substrate, controls diverse biological processes such as gene transcription, protein stability, and cell signaling (Hanover et al., 2012; Ruan et al., 2014; Ruan et al., 2012; Ruan et al., 2013a). In cell culture, O-GlcNAcylation promotes osteogenesis (Kim et al., 2007; Nagel and Ball, 2014) and suppresses adipogenesis (Ji et al., 2012). However, the physiological relevance of O-GlcNAcylation in skeletal development and remodeling has not been established. Here, we studied OGT in balancing osteogenic versus adipogenic programs and in controlling niche function of BMSC in mice. The multifaceted role of protein O-GlcNAcylation is achieved through reciprocal regulation of pro-osteogenic, pro-lymphopoietic RUNX2 and pro-adipogenic, pro-myelopoietic C/EBPβ.
## Loss of OGT in perinatal BMSCs leads to bone loss
To determine the in vivo role of protein O-GlcNAcylation in bone development, we deleted the X Chromosome-located *Ogt* gene using the Sp7GFP:Cre mice (Figure 1A). Floxed Ogtfl/fl were bred with Sp7GFP:Cre to generate Ogt conditional knockout (cKO) mice. Compared to Sp7GFP:Cre littermate controls, newborn Ogt cKO mice showed no obvious change in long bone formation (Figure 1B) but had a profound defect in the mineralization of flat bones of the calvaria (Figure 1C), suggesting impaired intramembranous ossification during the prenatal stage.
**Figure 1.:** *Impaired osteogenesis in Ogt cKO mice.(A) Mating strategy to generate Ogt cKO mice. Note that the Ogt gene is located on Chr. X, thus males are hemizygous Ogtfl/Y. (B, C) Whole mount Alizarin red and Alcian blue staining of newborn mice. (D, E) Long bone length (D) and gross morphology of 4–6 weeks old mice. (F, G) Goldner’s trichrome (F) and Safranin O (G) staining of femurs from 4-week-old mice. (H–L) Micro-CT of 6-week-old mice (H, n=3–4). Bone volume (BV, I), BV/tissue volume ratio (BV/TV, J), trabecular thickness (Tb.Th, K), and trabecular number (Tb.N, L) were calculated. Data are presented as mean ± SEM. *, p<0.05 by unpaired student’s t-test.*
At 4–6 weeks of age, Ogt cKO mice were modestly shorter than the controls (Figure 1D and E). Histological analyses showed decreased bone volume and osteoblast number (Figure 1F) and shortened growth plate (Figure 1G) in Ogt cKO mice. Micro-CT scanning further showed that Ogt cKO mice had reduced trabecular bone volume, bone volume to tissue volume ratio, trabecular thickness, and trabecular numbers in the distal femur (Figure 1H–L). Ogt cKO mice represent typical bone and dental defects (Figure 1—figure supplement 1) as observed in Runx2-haploinsufficient mice (Otto et al., 1997; Takarada et al., 2016), suggesting that O-GlcNAcylation might control RUNX2 function. Moist food was provided to these animals after weaning to prevent malnutrition.
## RUNX2 O-GlcNAcylation promotes osteogenesis
To investigate how OGT controls osteogenic differentiation of BMSCs, we first isolated primary BMSCs from control and Ogt cKO mice and induced them into osteoblast cells. Alkaline phosphatase staining revealed a reduction in mineralization of Ogt cKO BMSCs (Figure 2A). Similarly, treating mesenchymal C3H10T$\frac{1}{2}$ cells with an OGT inhibitor, OSMI-1, reduced mineralization (Figure 2B) and ablated calcium deposition (Figure 2C) after osteogenic differentiation. Parathyroid hormone (PTH) is a bone anabolic agent that requires RUNX2-dependent signaling (Krishnan et al., 2003). We found that PTH treatment of C3H10T$\frac{1}{2}$ cells increased global protein O-GlcNAcylation (Figure 2D). The ability of PTH to activate osteogenesis is completely abolished when OGT was inhibited by OSMI-1 (Figure 2E). Pharmacological activation of O-GlcNAcylation enhances RUNX2 activity and promotes osteogenic differentiation (Kim et al., 2007; Nagel and Ball, 2014). We mutated three known O-GlcNAc sites on RUNX2, Ser 32 and Ser 33 in the N-terminal transactivation domain and Ser 371 in the proline/serine/threonine-rich domain (Figure 2F), to alanine (3A), and found that mutant RUNX2 possessed less O-GlcNAcylation (Figure 2G). O-GlcNAcase (OGA) inhibition by Thiamet-G (TMG) increased O-GlcNAcylation of wildtype (WT) RUNX2, but to a much less extent in the 3A mutant (Figure 2G). OGT inhibition by OSMI-1 or O-GlcNAc mutation both impaired the transcriptional activity of RUNX2 on a luciferase reporter (Figure 2H). OSMI-1 could still suppress of luciferase activity of RUNX2-3A (Figure 2H), suggesting additional, unidentified O-GlcNAc sites (Figure 2G), which requires future investigation. Nevertheless, when overexpressed in C3H10T$\frac{1}{2}$ cells, RUNX2-3A substantially lost the ability to induce osteogenic differentiation (Figure 2I) or RUNX2-target gene expression (Figure 2J). These data demonstrate that O-GlcNAcylation is essential for RUNX2 activity and osteogenesis.
**Figure 2.:** *RUNX2 O-GlcNAcylation is required for osteogenesis.(A) Alkaline phosphatase (ALP) staining of control and Ogt cKO BMSCs differentiated to the osteogenic lineage. (B, C) Primary BMSCs, in the presence or absence of the OGT inhibitor OSMI-1, were induced for osteogenesis and stained for ALP (B) and Alizarin Red S (C). (D) Primary BMSCs were treated with PTH for the indicated time and subjected to Western blotting of total protein O-GlcNAcylation. (E) BMSCs were treated with PTH alone or together with OSMI-1, osteogenic differentiated, and stained for ALP (n=3). (G) Flag-tagged wildtype (WT) and O-GlcNAc mutant (3A) RUNX2 plasmids were overexpressed in HEK293 cells, and their O-GlcNAcylation was determined by Flag immunoprecipitation followed with O-GlcNAc western blot. (H) 6xOSE-luciferase activity in COS-7 cells transfected with WT or 3A-mutant RUNX2, in the presence or absence of the OGT inhibitor, OSMI-1. (I, J) C3H10T1/2 cells with lentiviral overexpression of RUNX2 were osteogenically differentiated and stained with ALP or Alizarin Red S (I). Expression of Bglap and Rankl was determined by RT-qPCR (J). Data are presented as mean ± SEM. *, p<0.05; **, p<0.01; and ***, p<0.001 by two-way ANOVA (H) or one-way ANOVA (J). Representative images from at least three biological replicates were shown in A, B, C, E, and I.
Figure 2—source data 1.Raw uncropped images for panel D.
Figure 2—source data 2.Raw uncropped images for panel G.*
In adult mice, Sp7 expression is restricted to osteoblast precursors. We treated Ogt cKO mice from pregnancy with doxycycline (Dox) and withdrew Dox at 10 weeks of age to induce Cre expression and OGT depletion only during adulthood (Figure 3A). Micro-CT showed that Ogt cKO mice had reduced bone volume, trabecular thickness, and bone mineral density (Figure 3B–E). Together, these results support the functional indispensability of OGT in the committed osteolineage for adult trabecular bone remodeling.
**Figure 3.:** *Adult-onset deletion of OGT impairs trabecular bone formation.(A) Dox treatment timeline in Ogt cKO to achieve osteoblast-specific deletion of OGT. (B–D) Micro-CT (B) showing reduced bone volume/tissue volume (C), trabecular thickness (D), and trabecular bone mineral density (E). Data are presented as mean ± SEM.*, p<0.05 by unpaired student’s t-test.*
## C/EBPβ O-GlcNAcylation inhibits the adipogenic specification of BMSCs
The osteogenic and adipogenic differentiation of BMSCs is generally considered mutually exclusive (Ambrosi et al., 2017). Concomitant with bone loss, we observed a massive accumulation of adipocytes in the bone marrow of Ogt cKO mice, shown by hematoxylin & eosin staining (Figure 4A) and immune-staining of the lipid droplet protein – perilipin (Figure 4B). Pdgfrα and Vcam1 (encoding CD106) have been recently identified as surface markers of adipogenic lineage cells in the BM that also express the Lepr and *Adipoq* genes (Figure 4—figure supplement 1; Baryawno et al., 2019; Zhong et al., 2020). Flow cytometric analysis of BMSCs revealed that Ogt cKO mice possessed more PDGFRα+VCAM1+ adipogenic progenitors than littermate controls (Figure 4C). To directly test if OGT deficiency biases BMSC differentiation toward the adipogenic lineage, we first induced the adipogenic differentiation of primary BMSCs and found increased lipid deposition in Ogt cKO mice (Figure 4D). Even under an osteogenic induction condition, adipo-lineage markers such as Adipoq and Vcam1 were significantly upregulated by OGT deficiency (Figure 4E and F). Furthermore, treating C3H10T$\frac{1}{2}$ mesenchymal cells with an OGA inhibitor TMG to increase protein O-GlcNAcylation, was able to substantially reduce perilipin protein expression (Figure 4G) and Pparg and *Adipoq* gene expression (Figure 4H, I). These data indicate that OGT inhibits the adipogenic program of BMSCs.
**Figure 4.:** *O-GlcNAcylation inhibits BM adipogenesis.(A, B) H&E (A) and Perilipin immunofluorescent staining (B) on femur sections from 4-week-old mice. (C) Flow cytometric quantification of PDGFRa+VCAM1+ preadipocytes frequencies within the live BM cells (n=3). (D) Adipogenic differentiation of primary BMSCs from control and Ogt cKO mice. Lipid was stained with Oil Red O and quantified to the right (n=4). (E, F) Primary BMSCs were osteogenic differentiated for 0 or 15 days. Expression of Adipoq (E) and Vcam1 (F) genes was determined by RT-qPCR (n=3). (G–I) C3H10T1/2 cells, treated with or without TMG, were adipogenic differentiated. Western blotting for perilipin and O-GlcNAc of differentiated cells (G) and RT-qPCR for adipogenic marker Pparg (H) and Adipoq (I) expression. (J–M) Adipogenic differentiation of C3H10T1/2 cells infected with lentiviral C/EBPβ. Oil Red O was stained (J) and quantified (K). Pparg (L) and Adipoq (M) gene expression was determined by RT-qPCR. Data are presented as mean ± SEM.*, p<0.05; **, p<0.01; and ***, p<0.001 by unpaired student’s t-test (C, D, K), one-way ANOVA (L, M), and two-way ANOVA (E, F, H, I).
Figure 4—source data 1.Mass spectrometry search results of all protein modifications (Table S1) and PPARγ2 O-GlcNAc sites (Table S2).
Figure 4—source data 2.Raw uncropped images for panel G.*
We went on to determine the O-GlcNAc targets of OGT in suppressing adipogenesis. As an osteogenic regulator, RUNX2 also reciprocally suppresses the adipogenic program (Ahrends et al., 2014). However, such suppression was not dependent on O-GlcNAcylation, because O-GlcNAc-deficient RUNX2 displayed similar efficiency as the wildtype protein to reduce lipid deposition and perilipin expression in differentiated C3H10T$\frac{1}{2}$ cells (Figure 4—figure supplement 2). It is possible that O-GlcNAc on RUNX2 selectively facilitates the recruitment of transcriptional co-activators for osteogenesis but does not suppress the chromatin remodeling needed for the activation of adipogenic transcriptional factors.
PPARγ1 is O-GlcNAcylated at T54 in the A/B activation domain (Ji et al., 2012), corresponding to T84 in PPARγ2, the major isoform in adipocytes (Figure 4—figure supplement 3A). Mutating T84 in PPARγ2 did not ablate the ability of the OGA inhibitor TMG to suppress adipogenesis in C3H10T$\frac{1}{2}$ cells (data not shown), suggesting the existence of other unidentified O-GlcNAc sites on PPARγ2 or other target proteins than PPARγ2. Through mass spectrometry, we were able to map four additional O-GlcNAc sites on PPARγ2 (Figure 4—figure supplement 3A and Figure 4—source data 1). Intriguingly, mutating these four sites or together with T84 to alanine, render PPARγ2 incompetent to induce transcription and adipogenesis (Figure 4—figure supplement 3B, C). It suggests that PPARγ2 O-GlcNAcylation is essential for adipocyte maturation, but likely does not mediate the anti-adipogenic effect of OGT in perinatal BMSCs.
We then looked to C/EBPβ, an early transcription factor that specifies the adipogenic fate of BMSCs (Cao et al., 1991; Darlington et al., 1998). It has been reported that OGT modifies C/EBPβ to inhibit its transcriptional activity (Li et al., 2009; Qian et al., 2018). As expected, ablating O-GlcNAcylation of C/EBPβ (2A mutation) promotes adipogenic differentiation of C3H10T$\frac{1}{2}$ cells (Figure 4J and K). Taken together, we concluded that, by O-GlcNAcylating and reciprocally regulating RUNX2 and C/EBPβ, OGT is required for the proper allocation of skeletal progenitors into osteogenic versus adipogenic lineages during development.
## OGT deficiency disrupts the BM niche
Skeletal development is concomitant with the establishment of definitive hematopoiesis in the BM. To test if OGT deficiency affects the niche function of Sp7+ cells for B-cell lymphopoiesis, we performed flow cytometry analyses of bone marrow of 4-week-old mice (Figure 5A, Figure 5—figure supplement 1 and Figure 5—source data 1; Hardy et al., 1991). No changes in the percentage of lineage-Sca-1+Kit+ (LSK) progenitor cells, common lymphoid progenitors (CLPs), Fraction A that contains pre-pro-B cells were observed between control and Ogt cKO mice (Figure 5B–D). While frequencies of Fraction B and C pro-B, pre-B, and immature B in Ogt cKO mice were drastically reduced (Figure 5E–I), demonstrating a developmental blockage from pre-pro-B to pro-B cells. In the peripheral blood, there was specific loss of CD19+B220+ B cells but not CD4+ or CD8+ T cells (Figure 5J–L). B-cell dysfunction observed here was similar to the phenotype in mice when all Sp7+ cells were depleted (Yu et al., 2016) or IL-7 was deleted in BMSCs (Cordeiro Gomes et al., 2016), indicating that O-GlcNAcylation is essential for the Sp7+ lineage cells to establish a niche environment for B-cell lymphopoiesis.
**Figure 5.:** *Impaired B lymphopoiesis and myeloid skewing in Ogt cKO mice.(A) Schematic view of B cell development in the BM and blockade by stromal OGT deficiency (red X). (B–C) Flow cytometric quantification of LSK (B) and CLP (C) among live BM cells (n=4–6). (D–I) Flow cytometric quantification of fraction A (D), fraction B (E), fraction C (F), fraction C’ (G), fraction D (H), and immature B (I) frequencies among live BM lymphocytes (n=6–7). (J–L) Flow cytometric quantification of B220+ B cell (J), CD4+ T cell (K), and CD8+ T cell (L) percentages in the blood (n=3–4). (M, N) CMP/CLP ratio (M) and GMP/EMP ratio (N) in the BM (n=6–7). (O, P) Complete blood counting showing numbers of RBC (O) and neutrophil (P) (n=7–9). Data are presented as mean ± SEM. *, p<0.05; **, p<0.01; and ***, p<0.001 by unpaired student’s t-test.
Figure 5—source data 1.Antibodies used for flow cytometry.*
BM adiposity is associated with myeloid overproduction in conditions including aging, irradiation (Ho et al., 2019), osteopenia (Kajkenova et al., 1997), and obesity (Singer et al., 2014), indicating the supportive function of marrow adipocytes on demand-adapted myelopoiesis. Consistently with the increased BM adiposity found in Ogt cKO mice, we also observed biased HSPC differentiation toward the myeloid lineage, as shown by increased ratio of common myeloid progenitor (CMP) to common lymphoid progenitors (CLPs) and ratio of granulocyte-monocyte progenitors (GMP) to megakaryocyte-erythrocyte progenitors (MEP) in the BM (Figure 5M and N). As a result, increased numbers of red blood cells and neutrophils were observed in the blood of Ogt cKO mice (Figure 5O and P). Together, these results demonstrate that OGT deficiency in neonatal BMSCs establishes a BM environment that promotes myelopoiesis and simultaneously impairs B cell development.
## Transcriptional regulation of niche cytokines by RUNX2 and C/EBPβ O-GlcNAcylation
BMSC-derived SCF (encoded by the *Kitl* gene) and IL-7 are required for the myeloid differentiation and B-cell development, respectively (Asada et al., 2017; Cordeiro Gomes et al., 2016; Ding et al., 2012). We sought to test if their expression is controlled by the same transcriptional network determining BMSC fate. Adipogenic differentiation of mesenchymal C3H10T$\frac{1}{2}$ cells concomitantly increased Kitl while decreased *Il7* gene expression (Figure 6A and B). Simultaneous treatment with the OGT inhibitor OSMI-1 dampened Il7 expression before differentiation but enhanced Kitl expression in differentiated adipocytes (Figure 6A and B). On the other hand, osteogenic differentiation suppressed Kitl transcription, which could be further inhibited by TMG that elevated global O-GlcNAcylation (Figure 6C). While Il7 mRNA levels were not evidently affected by osteogenic differentiation, TMG stimulated its expression (Figure 6D). O-GlcNAcylation inhibits the adipogenesis specified by C/EBPβ but supports osteogenesis determined by RUNX2. In concert, C/EBPβ overexpression in C3H10T$\frac{1}{2}$ cells activated Kitl transcription and suppressed Il7 expression, which was further exacerbated by O-GlcNAc-deficient C/EBPβ (Figure 6E and F). However, RUNX2 overexpression decreased Kitl mRNA levels (Figure 6G). When compared to the wildtype, O-GlcNAc-defective RUNX2 was impaired in inducing Il7 expression (Figure 6H). Collectively, these results reveal that protein O-GlcNAcylation, by acting on BMSC lineage transcriptional factors, establishes a pro-lymphopoietic niche during neonatal bone development and at the same time prevents the myeloid-skewing, adipogenic BM environment.
**Figure 6.:** *O-GlcNAc regulation of niche cytokine expression.(A, B) C3H10T1/2 cells were treated with vehicle or OGT inhibitor OSMI and differentiated for adipocytes (n=4). Kitl (A) and Il7 (B) gene expression was determined by RT-qPCR. (C, D) C3H10T1/2 cells were treated with vehicle or OGA inhibitor TMG and induced for osteogenic differentiation (n=6). Kitl (C) and Il7 (D) gene expression was determined by RT-qPCR. (E–H) C3H10T1/2 cells were infected with lentiviruses expressing WT and O-GlcNAc-deficient C/EBPβ (E, F) or RUNX2 (G, H). Expression Kitl (E, G) and Il7 (F, H) was measured by RT-qPCR (n=6). (I) Proposed action of protein O-GlcNAcylation in regulating the BMSC niche function. Data are presented as mean ± SEM. *, p<0.05; **, p<0.01; ***, p<0.001 by two-way ANOVA (A–D) and one-way ANOVA (E–H).
Figure 6—source data 1.Sequences of oligos used for RT-qPCR.*
## Discussion
Post-translational modification networks exist in the bone-BM organ to regulate its development and remodeling. Given that definitive hematopoiesis is matured in perinatal BM, it is tempting to hypothesize that the regulatory mechanisms guiding the development of bone also establish the BM niche for hematopoiesis. However, experimental evidence has been largely lacking so far. In the present study, we examined the vital role of the under-studied protein O-GlcNAcylation in determining the osteogenic versus adipogenic fate specification of BMSCs and in balancing the pro-lymphopoietic and pro-myelopoietic niche function of BMSCs. We showed that, by modifying and reciprocally regulating RUNX2 and C/EBPβ, O-GlcNAc orchestrates the early development of skeletal and hematopoietic systems (Figure 7).
**Figure 7.:** *Working model of O-GlcNAc signaling in bone-BM development.*
Multiple temporally and spatially distinct types of progenitors contribute to bone development and maintenance. In the early embryo, Sp7+ progenitors give rise to fetal bone tissues and transient stromal cells that disappear in early postnatal life (Mizoguchi et al., 2014). Perinatally, Sp7+ progenitors contribute to osteolineage cells and long-lived perivascular BMSCs that can be labeled by leptin receptor (Lepr) and adiponectin (Adipoq) (Zhong et al., 2020; Zhou et al., 2017). Recent evidence suggests that a significant portion of adult BMSCs and osteoblasts originate from collagen II (Col2)- and aggrecan (Acan)-expressing chondrocytes (Ono et al., 2014). Due to the fact that Sp7GFP:Cre targets osteoblasts, BMSCs, and a subset of chondrocytes (Chen et al., 2014; Liu et al., 2013), the current study could not delineate the exact developmental stages and the primary cellular compartments where OGT instructs bone development. Nonetheless, our ex vivo experiments and adult-onset targeting of OGT in Sp7+ osteoblasts, together with prior published in vivo and in vitro evidence (Andrés-Bergós et al., 2012; Nagel and Ball, 2014; Nagel et al., 2013), certainly reveal the indispensability of protein O-GlcNAcylation for chondro-osteogenic differentiation. While this study primarily focused early life bone development, it is warranted to further investigate the role of OGT in the transition to appositional remodeling during adulthood (Shu et al., 2021) and in osteoporosis pathogenesis during aging. Moreover, bone-forming skeletal stem cells (SSCs) are identified in other anatomical regions of long bones, such as growth plate, periosteum, and endosteum (Ambrosi et al., 2019). It remains undetermined whether O-GlcNAcylation is abundant in and controls the development and function of these SSC populations.
O-GlcNAcylation is required for PPARγ to drive adipogenesis, but why did not OGT-deficient BMSCs arrest their differentiation after being committed to the adipogenic lineage. One speculation is that PPARγ O-GlcNAcylation is extremely low in homeostatic conditions, which might help explain the rare appearance of adipocytes in young BM. If so, the loss of PPARγ O-GlcNAcylation in Ogt cKO mice would not block adipogenesis. Second, the differentiation of marrow adipose tissue (MAT) is distinct from peripheral white adipose tissue (WAT). For instance, adipogenic BMSCs in adult mice already express large amount of Adipoq, which is only present in mature WAT adipocytes. Certain forms of genetic lipodystrophy (e.g. mutations in CAV1 and PTRF) selectively lose peripheral WAT but preserve MAT (Scheller et al., 2015). These findings suggest that MAT might be less dependent on PPARγ or able to adopt alternative differentiation when PPARγ is absent or inhibited. In fact, a recent publication reported a secondary adipogenic pathway in lipodystrophic 'fat-free' mice (Zhang et al., 2021). Lastly, our preliminary examination of old Ogt cKO mice revealed the resolution of BM adiposity, indicating that PPARγ and its O-GlcNAc modification become essential for the adipogenic differentiation of adult BMSCs.
Protein O-GlcNAcylation senses glucose availability (Hardivillé and Hart, 2014; Ruan et al., 2012), hormonal cues (Ruan et al., 2014; Ruan et al., 2017; Whelan et al., 2008), cellular stress (Martinez et al., 2017; Ruan et al., 2017), and immune signals (Chang et al., 2020; Liu et al., 2019; Zhao et al., 2020; Zhao et al., 2022) to maintain cellular and tissue homeostasis. Osteogenic differentiation of mesenchymal cells induces global O-GlcNAc levels (Kim et al., 2007; Nagel and Ball, 2014); however, the upstream mechanistic regulators of osteoblastic O-GlcNAcylation remain enigmatic. High glucose has been shown to promote O-GlcNAcylation and osteogenic differentiation of cartilage endplate stem cells (Sun et al., 2019). BMSCs preferentially use glycolysis for bioenergetics to support their self-renewal and multipotency (Ito and Suda, 2014; van Gastel and Carmeliet, 2021). Active aerobic glycolysis also fuels the high anabolic demand during bone formation. It would be important in the future to determine whether flux of the hexosamine biosynthetic pathway, a branch of glycolysis (Ruan et al., 2013b), increases to provide more UDP-GlcNAc for O-GlcNAc modification. We also showed here that PTH treatment increased protein O-GlcNAcylation. Signaling through the PTH receptor activates the cAMP-protein kinase A (PKA)-CREB pathway and the accumulation of inositol trisphosphate (IP3) and diacylglycerol (DAG), which further increase intracellular Ca2+ and PKC, respectively (Datta and Abou-Samra, 2009). Future experiments are required to determine if OGT enzymatic activity can be regulated by these signaling nodes, for example Ca2+/calmodulin-dependent protein kinase II (CaMKII) (Ruan et al., 2017). Sex differences in skeletal development, maintenance, and aging have been well appreciated. Whether BMSC O-GlcNAc signaling is differentially regulated between male and female animals, particularly during puberty and aging, is an important question that remains unaddressed. Since only male mice were investigated in the current study, it is unclear if the reciprocal regulation of RUNX2 and PPARγ by O-GlcNAcylation in determining the bone-fat balance is equally vital in females.
The BM microenvironment, composed of BMSCs, osteoblasts, adipocytes, sympathetic nerves, and vascular endothelial cells, has been highlighted as an important extrinsic factor for the maintenance and differentiation of distinct hematopoietic lineage progenitors (Bianco and Robey, 2015; Calvi and Link, 2015; Morrison and Scadden, 2014; Wei and Frenette, 2018). While the concomitant development, remodeling, and aging of the skeletal and hematopoietic systems have been observed in various pathophysiological conditions, mechanisms underlying the coordinated regulation of the two systems are less understood. Our current study has provided the first evidence that RUNX2, permitted by O-GlcNAcylation, not only is indispensable for the osteoblast development, but also establishes the endosteal niche for B lymphocytes by driving IL-7 expression (Figure 7). When OGT is deficient, the perivascular BMSCs are prone to adipogenic differentiation, which also activates C/EBPβ-dependent SCF expression and myelopoiesis. During aging, the parallel dysfunction of the skeletal and hematopoietic systems leads to osteoporosis, marrow fat accumulation, and myeloid hematopoietic skewing (Geiger et al., 2013). Whether BMSC aging is associated with O-GlcNAc decline and whether the balance between RUNX and C/EBPβ leads to bone-fat imbalances and niche dysfunction require future investigations.
## Methods
**Key resources table**
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
| --- | --- | --- | --- | --- |
| Genetic reagent(Mus musculus) | Sp7GFP:Cre (B6.Cg-Tg(Sp7-tTA,tetO-EGFP/cre)1Amc/J) | Jackson Laboratory | RRID:IMSR_JAX:006361 | |
| Genetic reagent(Mus musculus) | Ogtfl/fl (B6.129-Ogttm1Gwh/J) | Jackson Laboratory | RRID:IMSR_JAX:004860 | |
| Cell line (Mus musculus) | Primary BMSC | This paper | | From long bones of mouse |
| Cell line (Mus musculus) | C3H10T1/2 | ATCC | CCL-226 | Verified by ATCC and tested negative for mycoplasma |
| Cell line (Homo sapiens) | HEK293FT | Invitrogen | R70007 | Verified by Invitrogen and tested negative for mycoplasma |
| Cell line (Cercopithecus aethiops) | COS7 | ATCC | CRL-1651 | Verified by ATCC and tested negative for mycoplasma |
| Transfected construct (Mus musculus) | 6xOSE2-luc | Phimphilai et al., 2006 | | |
| Transfected construct (Renilla reniformis) | pGL4-hRluc | Promega | #E688A | |
| Transfected construct (Mus musculus) | PPREx3-TK-luc | Addgene | #1015 | |
| Antibody | Anti-Perilipin(Rabbit monoclonal) | Cell Signaling Technology | 9349T | IF(1:200) |
| Antibody | Anti-B220 (Rat monoclonal) | Life Technologies | 67-0452-82 | FC(1:200) |
| Antibody | Anti-CD43(Rat monoclonal) | BD Biosciences | 553271 | FC(1:200) |
| Antibody | Anti-CD24 (Rat monoclonal) | Biolegend | 101822 | FC(1:1000) |
| Antibody | Anti-Ly-51 (Rat monoclonal) | Biolegend | 108305 | FC(1:200) |
| Antibody | Anti-CD127 (Rat monoclonal) | Tonbo Biosciences | 20–1271 U100 | FC(1:200) |
| Antibody | Anti-CD25 (Rat monoclonal) | Life Technologies | 63-0251-82 | FC(1:200) |
| Antibody | Anti-CD19(Rat monoclonal) | Biolegend | 115545 | FC(1:200) |
| Antibody | Biotin-conjugated lineage antibodies(Rat monoclonal) | Biolegend | 133307 | FC(1:200) |
| Antibody | Anti-CD4(Rat monoclonal) | Biolegend | 100403 | FC(1:200) |
| Antibody | Anti-CD5(Rat monoclonal) | Biolegend | 100603 | FC(1:200) |
| Antibody | Anti-CD8(Rat monoclonal) | Biolegend | 100703 | FC(1:200) |
| Antibody | Anti-CD127-APC(Rat monoclonal) | eBioscience | 17-1271-82 | FC(1:100) |
| Antibody | Anti-c-Kit-APC-eFluor780(Rat monoclonal) | eBioscience | 47-1171-82 | FC(1:400) |
| Antibody | Anti-Sca-1-Super Bright 436(Rat monoclonal) | eBioscience | 62-5981-82 | FC(1:100) |
| Antibody | Anti-CD34-PE(Rat monoclonal) | Biolegend | 152204 | FC(1:100) |
| Antibody | Anti-FcγR-PerCP-eFluor710(Rat monoclonal) | eBioscience | 46-0161-80 | FC(1:400) |
| Antibody | Anti-CD150-BV605(Rat monoclonal) | Biolegend | 115927 | FC(1:100) |
| Antibody | Anti-CD48-BUV395(Rat monoclonal) | BD Biosciences | 740236 | FC(1:100) |
| Antibody | Anti-CD45-BUV395(Rat monoclonal) | BD Biosciences | 564279 | FC(1:400) |
| Antibody | Anti-Ter119-BV421(Rat monoclonal) | Biolegend | 116234 | FC(1:400) |
| Antibody | Anti-CD31-BV421(Rat monoclonal) | Biolegend | 102424 | FC(1:400) |
| Antibody | Anti-PDGFRa-Super Bright 600(Rat monoclonal) | eBioscience | 63-1401-82 | FC(1:100) |
| Antibody | Anti-VCAM1-PE(Rat monoclonal) | Biolegend | 105713 | FC(1:100) |
| Recombinant DNA reagent | RUNX2-WT | This paper | pLV-EF1a-RUNX2-WT-IRES-Hygro | See Methods; available upon request |
| Recombinant DNA reagent | RUNX2-3A/ RUNX2-3SA | This paper | pLV-EF1a-RUNX2-3Mut-IRES-Hygro | See Methods; available upon request |
| Recombinant DNA reagent | PPARλ2-WT | This paper | pLVX- PPARλ2-WT-Puro | See Methods; available upon request |
| Recombinant DNA reagent | PPARλ2-T84A | This paper | pLVX- PPARλ2-T84A-Puro | See Methods; available upon request |
| Recombinant DNA reagent | PPARλ2-4A | This paper | pLVX- PPARλ2-4A-Puro | See Methods; available upon request |
| Recombinant DNA reagent | PPARλ2-5A | This paper | pLVX- PPARλ2-5A-Puro | See Methods; available upon request |
| Recombinant DNA reagent | C/EBPβ-WT | This paper | pCDH-CMV-Cebpb-WT-P2a-Puro | See Methods; available upon request |
| Recombinant DNA reagent | C/EBPβ–2A | This paper | pCDH-CMV-Cebpb-2MUT-P2a-Puro | See Methods; available upon request |
| Commercial assay or kit | Q5 Site-Directed Mutagenesis Kit | NEB | #E0554 | |
| Commercial assay or kit | Dual-Luciferase Assay System | Promega | E1910 | |
| Commercial assay or kit | Transporter 5 Transfection Reagent | Polysciences | 26008–1 A | |
| Chemical compound, drug | Parathyroid hormone (PTH) | Genscript | RP01001 | |
| Chemical compound, drug | OSMI-1 | Sigma | SML1621-5MG | |
| Chemical compound, drug | Thiamet-G (TMG) | Biosynth | MD08856 | |
| Chemical compound, drug | Doxycycline food | Bio-Serv | S3888 | |
| Chemical compound, drug | IBMX | CAYMAN | 13347 | |
| Chemical compound, drug | Dexamethasone | Sigma | D4902 | |
| Chemical compound, drug | Insulin | Sigma | 91077 C | |
| Chemical compound, drug | Rosiglitazone | Sigma | R2408-10MG | |
| Chemical compound, drug | Ascorbic acid | Sigma | A4403-100MG | |
| Chemical compound, drug | β-Glycerophosphate | Santa cruz | sc-220452 | |
| Software, algorithm | FlowJ | BD Life Sciences | V10 | |
## Animals
All animal experiments were approved by the institutional animal care and use committee of the University of Minnesota (protocol # 2112–39682 A). All the mice were group-housed in light/dark cycle- (6am-8pm light), temperature- (21.5 ± 1.5 °C), and humidity-controlled (30–$70\%$) room, and had free access to water and regular chow (Teklad #2018) unless otherwise indicated. Moist food was provided to constitutive Sp7GFP:Cre animals to circumvent tooth defects and prevent malnutrition. All mice were maintained on a C57BL6 background. Due to the X-chromosome localization of the *Ogt* gene, only male mice were used in the study if not specified in the text or figures. To suppress Cre activity, designated breeders were fed a diet containing 200 mg/kg doxycycline (Bio-serv, S3888).
## BMSC isolation, culture, and differentiation
BMSC were isolated from the long bones as described previously (Zhu et al., 2010). The fragments of long bones were digested with collagenase II for 30 min. The released cells were discarded, and the digested bone fragments were cultivated in the BMSCs growth medium (alpha-MEM supplemented with $10\%$ FBS). Once confluent, cells were switched to either adipogenic differentiation medium (alpha-MEM supplemented with $20\%$ FBS, 500 µM IBMX, 1 µM Dexamethasone, 10 µg/ml Insulin and 1 µM Rosiglitazone) for the first 2 days. The medium was then changed to adipocyte differentiation base medium (α-MEM supplemented with $20\%$ FBS, 10 µg/ml Insulin and 1 µM Rosiglitazone) for the next 4 days followed by oil red O staining. For osteogenic differentiation, cells were induced with osteoblast differentiation medium (α-MEM supplemented with $10\%$ FBS, 0.3 mM ascorbic acid, 10 mM β-glycerophosphate, 0.1 µM Dexamethasone) for 14 days followed by ALP staining or for 28 days followed by Alizarin red staining.
## Cell culture, plasmids, and lentiviruses
HEK 293, COS7, and C3H10T$\frac{1}{2}$ (ATCC, CCL-226) cells were cultured with DMEM plus $10\%$ of FBS. The mouse RUNX2-Myc/DDK plasmid was purchased from OriGene (MR227321), then subcloned into pLV-EF1a-IRES-Hygro (Addgene #85134). Mouse PPARγ2 with a N-terminal MYC tag was subcloned into pLVX-Dsred-puro plasmid. C/EBPβ plasmids were kindly provided by Dr. Xiaoyong Yang at Yale University and then subcloned into pCDH-CMV-P2a-Puro. O-GlcNAc sites were mutated into alanine with Q5 Site-Directed Mutagenesis Kit (NEB#E0554). Lentivirus was packed as previously described (Huang et al., 2022). Briefly, 293 FT cells were transfected with over-expression plasmids pSPAX2, and pMD2.G. Media with lentivirus were filtered and added into C3H10T$\frac{1}{2}$ cells. Seventy-two hr after infection, cells were then selected with drugs according to the resistance genes they possessed.
## Luciferase assay
For Runx2 luciferase assay, empty or RUNX2 vectors were transfected into COS7 cells with Lipofectamine, together with 6xOSE2-luc (Phimphilai et al., 2006) and pGL4-hRluc vectors in which either firefly or Renilla luciferase genes were expressed under the control of the RUNX2-specific or the constitutive SV40 promoter, respectively. After 6 hr, cells were washed three times and with the addition of 50 µM OSMI-1. Cells were incubated for an additional 48 hr in growth medium containing $5\%$ serum. Luminescent signals were generated using the Dual-Luciferase Assay System (Promega). Relative light units (RLU) for the 6xOSE2 reporter were normalized against pGL4-hRLuc values as an internal control for transfection efficiency. For PPARγ2 luciferase assays, C3H10T$\frac{1}{2}$ cells were transfected with Transporter 5 Transfection Reagent (Polysciences) following manufacture’s protocol. PPARγ2 transcriptional activity was determined using the PPREx3-TK-luc reporter (Addgene, #1015).
## Histology
Bone tissues were fixed in formalin solution at 4 °C for 24 hr. Tissue embedding, sectioning, and hematoxylin and eosin staining were performed at the Comparative Pathology Shared Resource of the University of Minnesota. For immunostaining, the tissues were embedded in OCT then cut into 7 µm slides. After three times of PBS wash, the slides were incubated with blocking buffer ($3\%$ BSA in PBS) for 1 hr, then immersed with anti-Perilipin (Cell Signaling Technology, #9349) antibody overnight at 4 °C. For immunofluorescence, PBS-washed slides were incubated with a fluorescent secondary antibody at room temperature for 1 hr, and then mounted with VECTASHIELD Antifade Mounting Medium with DAPI after three times of PBS wash. A Nikon system was used for imaging. Goldner’s trichrome and Safranin O staining were performed at Servicebio, China.
## micro-CT
The samples were scanned with an in vitro micro-CT device (Skyscan 1272, Bruker micro-CT) with scanning parameters of: Source Voltage = 60 kV, Source Current = 166 µA, exposure 897ms/frame, average of 3 frames per projection, Rotation Step (deg)=0.200 and 0.25 mm Aluminum filter. The specimens were scanned at high resolution (2016×1344 pixels) with an Isotropic voxel size of 7.1 μm. Reconstructions for X-ray projections and re-alignment were performed using the Skyscan software (NRecon and DataViewer) (v. 1.7.3.1, Brüker micro-CT, Kontich, Belgium). Ring artefact and beam hardening corrections were applied in reconstruction. Datasets were loaded into SkyScan CT-Analyzer software for measurement of BMD. Calibration was performed with 0.25- and 0.75 mg/mL hydroxyapatite mice phantoms provided by SkyScan. For cancellous and cortical bone analysis, the scanning regions were confined to the distal metaphysis, 100 slices starting at 0.5 mm proximally from the proximal tip of the primary spongiosa for the cancellous portion and 100 slices starting at 4.5 mm proximally from the center of intercondylar fossa for the cortical portion.
## O-GlcNAc mass spectrometry
Myc-tagged PPARγ2 was co-transfected with OGT into 15 cm-dishes of 293T cells and purified by immunoprecipitation with anti-c-Myc agarose beads (Pierce), followed by PAGE gel electrophoresis. The corresponding PPARγ2 band was cut for in gel Trypsin (Promega) digestion. Tryptic peptides were analyzed by on-line LC-MS/MS using an Orbitrap Fusion Lumos (Thermo) coupled with a NanoAcquity UPLC system (Waters) as we previously reported (Liu et al., 2019; Zhao et al., 2022). Peaklists were generated using PAVA (UCSF) and searched using Protein Prospector 5.23.0 against the SwissProt database and a randomized concatenated database with the addition of the recombinant PPARγ2 sequence. HexNAcylated peptides were manually verified.
## Real-time RT-PCR
RNA was isolated with Trizol and reverse transcribed into cDNA with the iScript cDNA Synthesis Kit. Real-time RT-PCR was performed using iTaq Universal SYBR Green Supermix and gene-specific primers (Figure 6—source data 1) on a Bio-Rad C1000 Thermal Cycler.
## Flow cytometry
For PDGFRa+VCAM1+preadipocytes, BM cells were stained with anti-CD45, anti-Ter119, anti-CD31, anti-PDGFRa, and anti-VCAM1. We gated the PDGFRa+VCAM1+ cells after excluding the CD45+Ter119+CD31+ cells. For B-cell lymphopoiesis, BM cells were stained in PBS containing $1\%$ (w/v) bovine serum albumin on ice for 30 min, with anti-B220 (Life Technologies, 67-0452-82), anti-CD43 (BD Biosciences, 553271), anti-CD24 (Biolegend, 101822), anti-Ly-51 (Biolegend, 108305), anti-CD127 (Tonbo Biosciences, 20–1271 U100), anti-CD25 (Life Technologies, 63-0251-82) and anti-CD19 (Biolegend, 115545). For hematopoietic stem and progenitor cells, BM cells were stained with a cocktail of biotin-conjugated lineage antibodies CD3e, B220, Ter119, Mac-1 and Gr-1 (Biolegend, 133307), CD4 (Biolegend, 100403), CD5 (Biolegend, 100603), CD8 (Biolegend, 100703), followed by Streptavidin-AF488 (Biolegend, 405235). Cells were then stained with CD127-APC (eBioscience, 17-1271-82), c-Kit-APC-eFluor780 (eBioscience, 47-1171-82), Sca-1-Super Bright 436 (eBioscience, 62-5981-82), CD34-PE (Biolegend, 152204) and FcγR-PerCP-eFluor710 (eBioscience, 46-0161-80), CD150-BV605 (Biolegend, 115927), and CD48-BUV395 (BDBioscience, 740236). Fixable Viability Dye was used to exclude dead cells as instructed by the manufacturer. A complete list of used antibodies was shown in Figure 5—source data 1. Flow cytometry was performed on an LSR Fortessa H0081 or X20 and analyzed with FlowJo.
## Quantification and statistical analysis
Results are shown as mean ± SEM. N values (biological replicates) and statistical analysis methods are described in figure legends. The statistical comparisons were carried out using two-tailed unpaired Student’s t-test and one-way or two-way ANOVA with indicated post hoc tests with Prism 9 (Graphpad). Differences were considered significant when $p \leq 0.05$; *, $p \leq 0.05$; **, $p \leq 0.01$; ***, $p \leq 0.001.$
## Funding Information
This paper was supported by the following grants:
## Data availability
All data generated or analyzed during this study are included in the manuscript and supporting file. Supplementary tables have been provided for Mass spectrometry, primer sequences, and antibody list.
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---
title: Enteroendocrine cell lineages that differentially control feeding and gut motility
authors:
- Marito Hayashi
- Judith A Kaye
- Ella R Douglas
- Narendra R Joshi
- Fiona M Gribble
- Frank Reimann
- Stephen D Liberles
journal: eLife
year: 2023
pmcid: PMC10032656
doi: 10.7554/eLife.78512
license: CC BY 4.0
---
# Enteroendocrine cell lineages that differentially control feeding and gut motility
## Abstract
Enteroendocrine cells are specialized sensory cells of the gut-brain axis that are sparsely distributed along the intestinal epithelium. The functions of enteroendocrine cells have classically been inferred by the gut hormones they release. However, individual enteroendocrine cells typically produce multiple, sometimes apparently opposing, gut hormones in combination, and some gut hormones are also produced elsewhere in the body. Here, we developed approaches involving intersectional genetics to enable selective access to enteroendocrine cells in vivo in mice. We targeted FlpO expression to the endogenous Villin1 locus (in Vil1-p2a-FlpO knock-in mice) to restrict reporter expression to intestinal epithelium. Combined use of Cre and Flp alleles effectively targeted major transcriptome-defined enteroendocrine cell lineages that produce serotonin, glucagon-like peptide 1, cholecystokinin, somatostatin, or glucose-dependent insulinotropic polypeptide. Chemogenetic activation of different enteroendocrine cell types variably impacted feeding behavior and gut motility. Defining the physiological roles of different enteroendocrine cell types provides an essential framework for understanding sensory biology of the intestine.
## Introduction
The gut-brain axis plays a critical role in animal physiology and behavior. Sensory pathways from the gut relay information about ingested nutrients, meal-induced tissue distension, osmolarity changes in the intestinal lumen, and cellular damage from toxins (Bai et al., 2019; Brookes et al., 2013; Prescott and Liberles, 2022; Richards et al., 2021; Williams et al., 2016). Responding neural circuits evoke sensations like satiety and nausea, coordinate digestion across organs, shift systemic metabolism and energy utilization, and provide positive and negative reinforcement signals that guide future consumption of safe, energy-rich foods (Andermann and Lowell, 2017; Sternson and Eiselt, 2017; Zimmerman and Knight, 2020). Moreover, manipulations of the gut-brain axis have been harnessed clinically through gut hormone receptor agonism or bariatric surgery to provide powerful therapeutic approaches for obesity and diabetes intervention (Richards et al., 2021; Seeley et al., 2015).
Enteroendocrine cells are first-order chemosensory cells of the gut-brain axis and are sparsely distributed along the gastrointestinal tract (Gribble and Reimann, 2019). Like taste cells, enteroendocrine cells are epithelial cells with neuron-like features, as they are electrically excitable, release vesicles upon elevation of intracellular calcium, and form synaptic connections with second-order neurons through specialized extrusions called neuropods (Bohórquez et al., 2015; Reimann et al., 2012). Single-cell RNA sequencing approaches revealed a diversity of enteroendocrine cell types that produce different gut hormones (Beumer et al., 2018; Gehart et al., 2019; Haber et al., 2017). Superimposing cell birthdate on the enteroendocrine cell atlas through an elegant genetically encoded fluorescent clock revealed five major enteroendocrine cell lineages defined by expression of either glucose-dependent insulinotropic polypeptide (GIP), ghrelin, serotonin (called enterochromaffin cells), somatostatin, or a combination of glucagon-like peptide 1 (GLP1), cholecystokinin (CCK), and/or neurotensin (Gehart et al., 2019).
Enteroendocrine cell-derived gut hormones evoke a variety of physiological effects (Drucker, 2016). GLP1 and CCK are satiety hormones released following nutrient intake, ghrelin is an appetite-promoting hormone whose release is suppressed by nutrients, and serotonin can be released by non-nutritive signals like irritants, force, and catecholamines. Sugar-induced release of GIP and GLP1 causes the incretin effect which rapidly promotes insulin release and lowers blood glucose (Holst et al., 2009). CCK, serotonin, and other gut hormones additionally regulate a variety of digestive functions, including gut motility, gastric emptying, gastric acidification, absorption, gallbladder contraction, and exocrine pancreas secretion.
The functions of individual enteroendocrine cell types could in some cases be inferred by summing the actions of their expressed hormones. For example, chemogenetic activation of enteroendocrine cells in the distal colon which express insulin-like peptide 5 triggers a multipronged physiological response that includes appetite suppression through a peptide YY (PYY) receptor, improved glucose tolerance through GLP1, and defecation indirectly through the serotonin receptor HTR3A (Lewis et al., 2020). However, a challenge in generalizing this approach is that some enteroendocrine cells release hormones with apparently opposing functions (Gehart et al., 2019; Haber et al., 2017), and moreover, many gut hormones are also produced by other cell types in the body (Lee and Soltesz, 2011; Okaty et al., 2019). To overcome these challenges, we developed approaches involving intersectional genetics to obtain highly selective access to major transcriptome-defined enteroendocrine cell lineages. Chemogenetic activation of each of these enteroendocrine cell types produced variable effects on gut physiology and behavior. Obtaining a holistic model for enteroendocrine cell function provides a critical framework for understanding the neuronal and cellular logic underlying gut-brain communication.
## Selective access to enteroendocrine cells in vivo through intersectional genetics
We first sought to identify genetic tools that broadly and selectively mark enteroendocrine cells. Transcription factors such as Atoh1, Neurogenin3, and NeuroD1 are expressed in enteroendocrine cell progenitors and/or precursors and act in early stages of enteroendocrine cell development (Li et al., 2011). We obtained Atoh1-Cre (both knock-in and transgenic lines), Neurog3-Cre, and Neurod1-Cre mice and crossed them to mice containing a Cre-dependent tdTomato reporter (Rosa26CAG-lsl-tdTomato herein defined as lsl-tdTomato). Neurog3-Cre and Neurod1-Cre lines labeled a sparse population of intestinal epithelial cells characteristic of enteroendocrine cells, although the Neurog3-Cre line additionally labeled other cells in intestinal crypts and in occasional mice produced broad labeling of intestinal epithelium; neither Atoh1-Cre line tested displayed selective labeling of enteroendocrine cells (Figure 1—figure supplement 1A; Schonhoff et al., 2004). Two-color analysis of tdTomato and gut hormone expression verified tdTomato localization in enteroendocrine cells of Neurod1-Cre; lsl-tdTomato mice, consistent with prior findings (Figure 1—figure supplement 1B; Li et al., 2012). Single-cell RNA sequencing of tdTomato-positive cells obtained from these mice (see below) also verified selective enteroendocrine cell labeling.
Neurod1-Cre mice provide broad, indelible, and selective marking of enteroendocrine cells within the intestine, but NeuroD1 is also expressed in a variety of other tissues, including the brain, retina, pancreas, peripheral neurons, and enteric neurons (Figure 1B and C, Figure 1—figure supplement 1D and E; Cho and Tsai, 2004; Li et al., 2011). Knockout of NeuroD1 is lethal, causing severe deficits in neuron birth and survival, as well as in the development of pancreatic islets and enteroendocrine cells (Gao et al., 2009; Naya et al., 1997). We employed an intersectional genetic strategy of combining Cre and Flp recombinases to limit effector gene expression to enteroendocrine cells. Villin1 (Vil1) is expressed with high selectivity in the lower gastrointestinal tract (el Marjou et al., 2004; Maunoury et al., 1992), so we generated a knock-in mouse allele (Vil1-p2a-FlpO) that drives FlpO recombinase expression from the endogenous Vil1 locus. Vil1-p2a-FlpO mice displayed expression of a Flp-dependent Gfp allele in epithelial cells throughout the entire length of the intestine with striking specificity (Figure 1A, Figure 1—figure supplement 1C). Reporter expression was not observed in most other tissues examined, including most brain regions, spinal cord, peripheral ganglia, and enteric neurons; rare GFP-expressing cells were noted in taste papillae, epiglottis, pancreas, liver, and thalamus (Figure 1C, Figure 1—figure supplement 1C and D; Höfer and Drenckhahn, 1999; Madison et al., 2002; Rutlin et al., 2020). Combining Neurod1-Cre and Vil1-p2a-FlpO alleles (Neurod1INTER) yielded highly selective expression of an intersectional reporter gene encoding tdTomato (Rosa26CAG-lsl-fsf-tdTomato herein defined as inter-tdTomato) in enteroendocrine cells, with only occasional cells observed in pancreas, and no detectable expression in other cell types labeled by either allele alone (Figure 1C, Figure 1—figure supplement 1D and E).
**Figure 1.:** *Establishing intersectional tools for genetic access to enteroendocrine cells in vivo.(A) Bright-field microscopy and native GFP fluorescence microscopy of intestinal tissue from Vil1-p2a-FlpO; fsf-Gfp mice (left) and fsf-Gfp mice (right). Scale bars: 5 mm. (B) Cartoon depicting intersectional genetic strategy to access enteroendocrine cells. (C) Native reporter fluorescence in cryosections (20 μm, except 50 μm for cortex and dorsal root ganglion) or wholemounts (tongue) of fixed tissues indicated from Neurod1-Cre; lsl-tdTomato mice (left), Vil1-p2a-FlpO; fsf-Gfp mice (middle), and Neurod1INTER; inter-tdTomato mice (right). Scale bars: 100 μm for all except 500 μm for tongue. Intestine sections from duodenum (middle) or jejunum (left, right). See Figure 1—figure supplement 1.*
## Charting enteroendocrine cell diversity and gene expression
*Our* general goal was to use intersectional genetics to access subtypes of enteroendocrine cells that express different gut hormones. We first used single-cell RNA sequencing approaches to measure the extent of enteroendocrine cell diversity, compare findings with existing enteroendocrine cell atlases, and establish a foundation for genetic experiments. Enteroendocrine cells represent <$1\%$ of gut epithelial cells, so we used genetic markers for enrichment. NeuroD1 is expressed early in the enteroendocrine cell lineage, and we observed by two-color expression analysis that Neurod1-Cre mice target at least several enteroendocrine cell types (Figure 1—figure supplement 1B). Since prior enteroendocrine cell atlases were derived from cells expressing an earlier developmental marker, Neurog3 (Gehart et al., 2019), we sought to compare the repertoire of enteroendocrine cells captured by Neurod1-Cre and Neurog3-Cre mice.
tdTomato-positive cells were separately obtained from the intestines (duodenum to ileum) of Neurod1-Cre; lsl-tdTomato mice and Neurog3-Cre; lsl-tdTomato mice by fluorescence-activated cell sorting (Figure 2—figure supplement 1A). Using the 10X Genomics platform, mRNA was captured from individual cells, and barcoded single-cell cDNA was generated. Single-cell cDNA was then sequenced and unsupervised clustering analysis was performed using the Seurat pipeline (Hafemeister and Satija, 2019; Stuart et al., 2019). Transcriptome data was obtained for 5,856 tdTomato-positive cells from Neurog3-Cre; lsl-tdTomato mice and 1841 tdTomato-positive cells from Neurod1-Cre; lsl-tdTomato mice. Twenty-five percent of Neurog3-lineage cells ($\frac{1454}{5856}$) and $87\%$ of NeuroD1-lineage cells ($\frac{1595}{1841}$) expressed classical markers for enteroendocrine cells (Figure 2—figure supplement 1B and C). Moreover, the full diversity of known enteroendocrine cell types was similarly captured by both Cre lines, with Neurog3-Cre mice additionally labeling many other cells, including paneth cells, goblet cells, enterocytes, and progenitors (Figure 2—figure supplement 1C). These findings are consistent with NeuroD1 acting later than Neurogenin3 in the enteroendocrine cell lineage, but prior to cell fate decisions leading to enteroendocrine cell specialization (Jenny et al., 2002).
Since Neurog3-Cre and Neurod1-Cre mice similarly labeled all known enteroendocrine cell lineages, transcriptome data was computationally integrated for analysis of enteroendocrine cell subtypes. Selective clustering analysis of 3049 enteroendocrine cells from both mouse lines revealed 10 distinct cell clusters, with one cluster representing putative progenitors (Figure 2A, Figure 2—source data 1). Cell clusters were compared with previously described enteroendocrine cell types based on expression of signature genes encoding hormones and transcriptional regulators (Figure 2A–C; Gehart et al., 2019). We observed three classes of enterochromaffin cells that similarly express serotonin biosynthesis enzymes (Tph1) and associated transcription factors (Lmx1a), but differentially produce Tac1, Cartpt, Pyy, Ucn3, and Gad2 (Figure 2B). Six other cell types preferentially express either Gip (K cells), Cck (I cells), Gcg (GLP1 precursor, L cells), Nts (N cells), Sst (D cells), and Ghrl (X cells), with L, I, and N cells thought to be derived from a common cell lineage (Beumer et al., 2020; Gehart et al., 2019). Strong segregation was observed for some signature genes, such as Tph1 in enterochromaffin cells and Sst in D cells. In other cases, signature hormone genes like Cck and Ghrl were enriched in particular cell clusters but expression was not absolutely restricted and also observed at lower levels in other cell clusters (Figure 2B). We note that glutamate transporters were not readily detected in our transcriptomic data (Figure 2B, Figure 2—figure supplement 2). Thus, each enteroendocrine cell subtype expresses a hormone repertoire with distinct patterns of enrichment but also sometimes partial overlap.
**Figure 2.:** *An enteroendocrine cell atlas reveals differential hormone and receptor expression.(A) A uniform manifold approximation and projection (UMAP) plot of enteroendocrine cell transcriptomic data reveals 10 cell clusters. (B) Violin plots showing expression of genes encoding key transcriptional regulators, hormones, other secreted molecules, and receptors across enteroendocrine cell subtypes. Gene loci used for genetic targeting are highlighted with dashed boxes. (C) Normalized expression of enriched signature genes (see Figure 2—source data 1 for a gene list) in single enteroendocrine cells. The dendrogram (top) depicts the relatedness (quantified by position along the Y-axis) between cell clusters based on gene expression. (D) For each enteroendocrine cell type, examples of gene loci used for genetic targeting (top, also highlighted in B), expressed cell surface receptor genes (middle) and expressed hormone and neurotransmitter-related genes (bottom). Genes were selected among the top 30 differentially expressed genes. See Figure 2—figure supplement 1.
Figure 2—source data 1.Signature genes with differential expression across enteroendocrine cell types.*
Enteroendocrine cells also express various cell surface receptors to detect nutrients, toxins, and other stimuli. For example, enteroendocrine cells detect sugars through the sodium-glucose cotransporter SGLT1 (encoded by the gene Slc5a1), with sodium co-transport thought to lead directly to cell depolarization (Gorboulev et al., 2012; Reimann et al., 2008). This mechanism is distinct from sugar detection by taste cells or pancreatic beta cells. Gustatory sensations of sweet (and savory/umami) involve taste cell-mediated detection of sugars (and amino acids) through heterodimeric G protein-coupled receptors termed T1Rs (Yarmolinsky et al., 2009), while pancreatic beta cells respond to sugar through increased metabolic flux, ATP-gated potassium channel closure, and depolarization. Expression of Slc5a1 was observed in multiple enteroendocrine cell subtypes, and highest in K, L, D, and N cells, while abundant expression of T1Rs was not detected in any enteroendocrine cell type (Figure 2B). These findings are consistent with the ability of taste blind mice lacking T1Rs to develop a preference for sugar-rich foods through SGLT1-mediated post-ingestive signals of the gut-brain axis (Sclafani et al., 2016; Tan et al., 2020). In addition, free fatty acid receptor genes Ffar1 and Ffar4 were broadly expressed in several enteroendocrine cell lineages, but largely excluded from enterochromaffin cells (Figure 2B). Orthogonally, the toxin receptor gene Trpa1 was enriched in enterochromaffin cells (Bellono et al., 2017), but not abundantly expressed in other enteroendocrine cells (Figure 2B and D). Enterochromaffin cells also reportedly sense force through the mechanosensory ion channel PIEZO2 (Alcaino et al., 2018); Piezo2 transcript was not readily detected in our transcriptomic data, but is enriched in enteroendocrine cells from colon that we did not analyze (Billing et al., 2019; Treichel et al., 2022; Figure 2B). Thus, enteroendocrine cells often express multiple cell surface receptors, suggesting polymodal response properties, and some receptors are expressed by multiple enteroendocrine cell types.
## Genetic access to subtypes of enteroendocrine cells
Next, we obtained genetic tools for selective access to each major enteroendocrine cell lineage. We chose several combinations of Cre and FlpO lines to achieve intersectional genetic access to different enteroendocrine cells based on the cell atlas. [ 1] Vil1-Cre; Pet1-FlpE (Pet1INTER) mice broadly target enterochromaffin cells, while [2] Tac1-ires2-Cre; Vil1-p2a-FlpO (Tac1INTER) and [3] Npy1r-Cre; Vil1-p2a-FlpO (Npy1rINTER) mice target different enterochromaffin cell subtypes. [ 4] Vil1-Cre; Sst-ires-FlpO, [5] Gip-Cre; Vil1-p2a-FlpO, [6] Cck-ires-Cre; Vil1-p2a-FlpO, and [7] Gcg-Cre; Vil1-p2a-FlpO mice respectively target D, K, I, and L cells (Figure 2D), and are herein referred to as SstINTER, GipINTER, CckINTER, and GcgINTER mice.
Mice of each intersectional allele combination were crossed to inter-tdTomato mice, and reporter expression was analyzed across tissues, including in the brain, tongue, airways, pancreas, stomach, and intestine (duodenum to rectum) (Figure 3—figure supplements 1 and 2). Each of these seven intersectional combinations produced sparse labeling of intestinal epithelial cells, as expected for labeling of enteroendocrine cell subtypes (Figure 3—figure supplement 1). Striking selectivity for enteroendocrine cells was observed across analyzed tissues for intersectional combinations targeting D, K, L, and I cells; sparse labeling was rarely observed in gastric endocrine cells and pancreatic islets, and absent from all other tissues examined. For example, Cck-ires-Cre alone (without intersectional genetics) drove reporter (lsl-tdTomato) expression in many tissues, including the brain, spinal cord, and muscle, and within the intestine, in enteroendocrine cells as well as enteric neurons, extrinsic neurons, and cells in the lamina propria; however, in CckINTER; inter-tdTomato mice, expression was not observed in the brain, spinal cord, or muscle, and within the intestine, was highly restricted to a subset of enteroendocrine cells, and not observed in other intestinal cell types (Figure 3—figure supplement 1). Similarly restrictive reporter expression was observed in SstINTER; inter-tdTomato, GipINTER; inter-tdTomato, and GcgINTER; inter-tdTomato mice. We did note that Tac1INTER and Npy1rINTER alleles more broadly labeled rectal epithelium, and Npy1rINTER additionally labeled taste cells as well as rare cells in the airways and epiglottis (Figure 3—figure supplements 2 and 3B). We also note that other genetic tools were inefficient at targeting enteroendocrine cells, including Nts-ires-Cre and Mc4r-t2a-Cre mice (Figure 3—figure supplement 3A).
Hormone expression can be dynamic in individual enteroendocrine cells, and Cre/Flp lines provide an indelible marker for transiently expressed genes (Beumer et al., 2018; Gehart et al., 2019). Thus, Cre/Flp lines enable in vivo lineage tracing to measure enteroendocrine cell dynamics. We used two-color expression analysis to investigate the repertoires of enteroendocrine cells captured by different intersectional lines. Two-color analysis involved visualization of native reporter fluorescence and immunohistochemistry for GLP1, CCK, SST, and/or serotonin in the duodenum, jejunum, ileum, colon, and rectum (Figure 3—figure supplement 4, Figure 3—figure supplement 4—source data 1). SstINTER mice showed enriched targeting of somatostatin cells throughout the intestine (SstINTER cells in duodenum, jejunum, ileum, colon, and rectum: 98.9, 66.4, 66.0, 82.4, and $66.0\%$ express somatostatin, 0.3, 0.0, 0.0, 0.8, and $0.0\%$ express serotonin, 0, 0, 0, 0, and $0\%$ express CCK, and 0, 0, 0.9, 2.3, $0\%$ express GLP1). The PetINTER driver also captured cells with other hormones, suggesting that some enteroendocrine cells can either transiently or durably express markers of multiple lineages or can switch identity from enterochromaffin cells to other enteroendocrine cell types (PetINTER cells in duodenum, jejunum, ileum, colon, and rectum: 6.3, 2.0, 4.6, 0.9, and $0\%$ express somatostatin, 86.7, 47.9, 43.7, 52.5, and $49.4\%$ express serotonin, 8.2, 6.5, 3.7, 0.3, and $0.6\%$ express CCK, and 4.6, 17.2, 45.7, 18.5, and $25.7\%$ express GLP1). Tac1-ires2-Cre and Npy1r-Cre both labeled subsets of serotonin cells ($100\%$ of labeled cells produce serotonin in each line), with Tac1-ires2-Cre labeling a higher percentage of serotonin cells in duodenum ($78.4\%$) than Npy1r-Cre ($5.0\%$) (Figure 3—figure supplement 3A). Both GcgINTER and CckINTER mice labeled the majority of GLP1 and CCK cells; these cell types are within the same developmental lineage, and CCK and proglucagon are frequently coexpressed in the same EE cells (Habib et al., 2012). GcgINTER mice did not effectively label either somatostatin or serotonin cells (GcgINTER labeled in duodenum, jejunum, ileum, colon, and rectum 75.5, 67.6, 89.4, 95.9, and $99.0\%$ of GLP1 cells, 34.8, 50.0, 0, 0, and $0\%$ of CCK cells, 0.0, 0.0, 20.8, 34.3, and $22.2\%$ of somatostatin cells, and 1.5, 0.2, 1.8, 0, and $0.8\%$ of serotonin cells). CckINTER mice were less selective (CckINTER labeled in duodenum, jejunum, ileum, colon, and rectum 68.6, 54.0, 60.7, 71.3, and $23.8\%$ of GLP1 cells, 90.8, 87.9, 96.2, 54.2, and $26.8\%$ of CCK cells, 24.2, 15.1,28.3, 38.1, and $22.1\%$ of somatostatin cells, and 11.6, 20.6, 15.9, 0.9, and $0.5\%$ of serotonin cells), and a substantial fraction (at least $13.7\%$ in duodenum) targeted other enteroendocrine cells that do not express these four hormones (Figure 3—figure supplement 4B and C). It is possible that the Cck-ires-Cre allele simply displays inefficient targeting efficiency and/or that it drives reporter expression at early developmental time points with subsequent switching or refinement of cell identity. Together, these experiments measure the extent of selectivity achievable with each genetic tool, with some intersectional combinations providing highly selective genetic access to classes of enteroendocrine cells in vivo.
Next, we assessed the spatial distribution of each enteroendocrine cell lineage along the proximal-distal axis in the duodenum, jejunum, ileum, colon, and rectum by quantifying the number of reporter-positive cells (Figure 3). PetINTER and SstINTER cells were most enriched in the duodenum and colon (Figure 3). SstINTER cells were the sparsest of enteroendocrine cell types, consistent with observations from scRNA-seq data (Figures 2A and 3). GipINTER cells and GcgINTER cells displayed strikingly distinct spatial patterns. GipINTER cells were enriched proximally, with almost no tdTomato+ cells observed in distal intestine. In contrast, GcgINTER cells were present along the entire proximal-distal axis and were enriched in colon and rectum. Thus, various enteroendocrine cell subtypes display distinct spatial distributions along the gastrointestinal tract.
**Figure 3.:** *Differential targeting of enteroendocrine cell types using intersectional genetic tools.(Left) UMAP plots based on single-cell transcriptome data showing expression of indicated genes across the enteroendocrine cell atlas. (Middle) Number of cells expressing inter-tdTomato reporter in five 20 μm sections from intestinal regions of mice indicated, dots: individual animals, n: 2–4 mice, mean ± sem. (Right) Representative images of native tdTomato fluorescence in intestinal tissue from mouse lines indicated. Scale bars: 100 μm. See Figure 3—figure supplements 1–4.*
## Physiological responses to enteroendocrine cell activation
Direct study of enteroendocrine cell function has been challenging due to a lack of specific genetic tools. Hints come from Neurogenin3 point mutations in human infants or intestine-targeted Neurog3 knockout, which cause loss of enteroendocrine cells, severe malabsorptive diarrhea, and increased mortality (Mellitzer et al., 2010; Wang et al., 2006). We sought to develop cell type-specific genetic tools for enteroendocrine cell manipulation, reasoning that they might provide a specific approach to define the repertoire of evoked physiological and behavioral responses.
We first developed chemogenetic approaches for acute stimulation of all enteroendocrine cell types in freely behaving mice. Chemogenetic strategies involved designer G protein-coupled receptors (so-called DREADDs) that respond to the synthetic ligand clozapine-N-oxide (CNO) (Roth, 2016). Neurod1INTER mice were crossed to contain an intersectional reporter allele (Rosa26CAG-fsf-eGFP-FLEX-hM3Dq-mCherry herein defined as inter-hM3Dq-mCherry) that enables expression of a Gαq-coupled DREADD (hM3Dq) only in cells expressing both Cre and Flp recombinase (Sciolino et al., 2016). Since this approach yielded rare reporter expression in pancreatic islets, we used an additional control mouse line, Ptf1a-Cre; Vil1-p2a-FlpO (Ptf1aINTER), which targets sparse Vil1-expressing pancreatic cells but not intestinal cells (Figure 4—figure supplement 1; Kawaguchi et al., 2002).
First, we examined the effect of global enteroendocrine cell activation on gut motility as assessed by movement of charcoal dye following oral gavage. Neurod1INTER; inter-hM3Dq-mCherry mice, Ptf1aINTER; inter-hM3Dq-mCherry mice, and control Cre-negative Vil1-p2a-FlpO; inter-hM3Dq-mCherry littermates were injected intraperitoneally (IP) with CNO (fed ad libitum, daytime). After 15 min, charcoal dye was administered, and after an additional 20 min, the gastrointestinal tract was harvested. Charcoal transit distance was calculated by genotype-blinded measurement of the charcoal dye leading edge. In control animals lacking DREADD expression, the leading edge of charcoal dye traversed part of the intestine (littermate controls lacking Neurod1-Cre: 22.6 ± 1.2 cm; littermate controls lacking Ptf1a-Cre: 22.8 ± 2.0 cm) (Figure 4, Figure 4—source data 1). Chemogenetic activation of all enteroendocrine cells in Neurod1INTER; inter-hM3Dq-mCherry mice accelerated gut transit, with the charcoal leading edge traversing 30.8 ± 1.5 cm of the intestine. When DREADD signaling was instead activated in all epithelial cells using Vil1-Cre; lsl-hM3Dq mice, gavaged dye failed to enter the intestine at all (Figure 4—figure supplement 1A). CNO-accelerated gut transit was not observed Ptf1aINTER; inter-hM3Dq-mCherry mice (22.6 ± 2.6 cm) containing DREADD expression only in pancreatic cells (Figure 4, Figure 4—figure supplement 1B and C). Based on these observations, the observed effects in Neurod1INTER; inter-hM3Dq-mCherry mice are due to enteroendocrine cells rather than pancreatic cells, and the net effect of activating all enteroendocrine cells is to promote gut transit.
**Figure 4.:** *Enteroendocrine cell types that accelerate or slow gut transit.Mice of genotypes indicated were injected with CNO (IP, 3 mg/kg) and gavaged orally with charcoal dye. Intestinal tissue was harvested, and the distance between the pyloric sphincter and the charcoal dye leading edge was measured. Representative images (left) and quantification (right) of gut transit. Scale bars: 1 cm, circles: individual mice, n: 5–14 mice, mean ± sem, *p<0.05, **p<0.01 by a Mann–Whitney test with Holm–Šídák correction. See Figure 4—figure supplement 1.
Figure 4—source data 1.Quantification of gut transit.*
Next, we examined the effects of activating different enteroendocrine cell subtypes on gut motility. We additionally generated [1] Pet1INTER; inter-hM3Dq-mCherry; [2] Tac1INTER; inter-hM3Dq-mCherry; [3] Npy1rINTER; inter-hM3Dq-mCherry; [4] SstINTER; inter-hM3Dq-mCherry; [5] GipINTER; inter-hM3Dq-mCherry; [6] CckINTER; inter-hM3Dq-mCherry; and [7] GcgINTER; inter-hM3Dq-mCherry mice, with Cre-negative FlpO-positive inter-hM3Dq-mCherry littermates serving as controls (Figure 4). As above, CNO was injected (IP) into ad libitum-fed animals followed by oral charcoal gavage. Pet1INTER cells promoted gut transit (Pet1INTER: 29.8 ± 1.6 cm, Cre-negative littermates: 22.1 ± 1.5 cm), while SstINTER and GipINTER cells had no significant effect (SstINTER: 24.5 ± 2.0 cm, Cre-negative littermates: 18.8 ± 1.6 cm; GipINTER: 23.2 ± 1.0 cm, Cre-negative littermates: 22.0 ± 1.8 cm). Interestingly, single-cell transcriptome data revealed multiple subtypes of enterochromaffin cells, and we observed accelerated gut transit upon chemogenetic activation of Tac1INTER cells (Tac1INTER: 36.2 ± 1.4 cm, Cre-negative littermates: 21.0 ± 1.1 cm) but not Npy1rINTER cells (Npy1rINTER: 21.8 ± 2.0 cm, Cre-negative littermates: 24.3 ± 2.1 cm). These findings raise the possibility that each enterochromaffin cell subtype may privately communicate with different downstream extrinsic and/or enteric neurons to control gut physiology. In contrast, CckINTER and GcgINTER cells slowed gut motility (CckINTER: 7.1 ± 0.3 cm, Cre-negative littermates: 21.5 ± 2.8 cm; GcgINTER: 7.9 ± 0.7 cm, Cre-negative littermates: 22.4 ± 1.9 cm). Ingested food slows gut motility to promote nutrient absorption, while ingested toxins may accelerate gut motility to purge luminal contents (Nozawa et al., 2009; Van Citters and Lin, 2006). Consistent with these findings, CCK and GLP1 are released by nutrients while serotonin signaling is required for certain toxin responses (Drucker, 2016; Gribble and Reimann, 2019). Simultaneous activation of both pathways, as done in Neurod1INTER; inter-hM3Dq-mCherry mice, masks the slowing of gut transit by CckINTER and GcgINTER cells. These findings suggest a hierarchy where neural circuits that mediate toxin responses may achieve priority over those that mediate nutrient responses, at least under conditions of equal and maximal activation. Altogether, we characterize enteroendocrine cell subtypes that have different and sometimes opposing effects on digestive system physiology.
## Enteroendocrine cells that regulate feeding behavior
Next, we examined the effect of global enteroendocrine cell activation on feeding behavior. Fasted mice expressing DREADDs in all enteroendocrine cells (Neurod1INTER; inter-hM3Dq-mCherry) or in sparse pancreatic cells (Ptf1aINTER; inter-hM3Dq-mCherry), and their control littermates lacking Cre recombinase, were injected (IP) with CNO and given access to food for 2 hr at dark onset (Figure 5A). Animals lacking DREADD expression, or with sparse DREADD expression only in pancreas, ate robustly (~1 g of food over a 2 hr period). In contrast, CNO-induced activation of enteroendocrine cells caused a $26\%$ reduction in food intake (Figure 5B, Figure 5—source data 1).
**Figure 5.:** *Enteroendocrine cell types that reduce feeding.(A) Timeline for behavioral assay. (B) Mice of genotypes indicated were fasted overnight, injected with CNO (IP, 3 mg/kg), and total food intake was measured during 2 hr ad libitum food access, circles: individual mice, n: 8–13 mice, mean ± sem, *p<0.05 by a Mann–Whitney test with Holm–Šídák correction. See Figure 5—figure supplement 1.
Figure 5—source data 1.Quantification of feeding behavior.*
To interrogate the roles of different enteroendocrine cell subtypes in feeding regulation, similar experiments were then performed in [1] Pet1INTER; inter-hM3Dq-mCherry; [2] Tac1INTER; inter-hM3Dq-mCherry; [3] Npy1rINTER; inter-hM3Dq-mCherry; [4] SstINTER; inter-hM3Dq-mCherry; [5] GipINTER; inter-hM3Dq-mCherry; [6] CckINTER; inter-hM3Dq-mCherry; and [7] GcgINTER; inter-hM3Dq-mCherry mice, with Cre-negative littermates again serving as controls. Chemogenetic activation of enterochromaffin cells reduced feeding behavior (Figure 5B, $52.1\%$ reduction). Similar results were seen upon chemogenetic activation of Tac1 and Npy1r cells (Figure 5—figure supplement 1A, Tac1-ires2-Cre: $48.5\%$ reduction, Npy1r-Cre: $79.6\%$ reduction), but we note that these intersectional allele combinations also drove expression in taste cells and rectal epithelium, cell types that could also potentially drive changes in feeding behavior. In contrast, activation of SstINTER and GipINTER cells did not change feeding behavior (Figure 5B). Activating GcgINTER cells also reduced feeding (compared to Cre-negative littermates, GcgINTER: $52.4\%$ reduction), but surprisingly, activating CckINTER cells lowered feeding only in fed but not fasted mice (Figure 5—figure supplement 1B, Figure 5—figure supplement 1—source data 1). This observation is likely due to Cck-ires-Cre and Gcg-Cre alleles targeting at least partially distinct populations of enteroendocrine cells. Chemogenetic activation of GcgINTER (single CNO injection) caused a durable reduction of feeding for several hours, with total food intake normalizing by 11 hr, and also evoked a decrease in water intake and the respiratory exchange ratio, but not locomotion (Figure 5—figure supplement 1C). For comparison, activating somatostatin cells reduced the respiratory exchange ratio but did not change feeding, water intake, or locomotion. Altogether, we find that some but not all enteroendocrine cells can regulate food intake, and can do so with varying efficacy.
## Conclusion
Here we developed a toolkit involving intersectional genetics for systematic access to each major enteroendocrine cell lineage (Figure 6A). We then used chemogenetic approaches to delineate major response pathways of the gut-brain axis (Figure 6B). Serotonin-producing enterochromaffin cells express the irritant receptor TRPA1 (Bellono et al., 2017) and chemogenetic activation blocks feeding behavior and promotes gut transit, presumably for toxin clearance. Furthermore, different enterochromaffin cell subtypes can have different effects on gut motility, suggesting at least partially nonoverlapping communication pathways with downstream neurons. These findings are consistent with a role for enterochromaffin cells in toxin-induced illness responses, and interestingly, pharmacological blockade of the serotonin receptor HTR3A is a clinical mainstay for nausea treatment (Freeman et al., 1992). Other enteroendocrine cell types, including those that produce CCK, GIP, GLP1, neurotensin, and somatostatin, express nutrient receptors yet elicit different physiological and behavioral responses. For example, GLP1 cells slow gut motility, presumably to promote nutrient absorption and decrease feeding behavior (Gribble and Reimann, 2019). Additional studies are needed to define gut-brain pathways that mediate nutrient reward, and why receptors for specific nutrients are expressed across a dispersed ensemble of enteroendocrine cells. Together, these experiments provide a highly selective method for accessing enteroendocrine cells in vivo and a direct measure of their various roles in behavior and digestive physiology.
**Figure 6.:** *Differential regulation of physiology and behavior by enteroendocrine cell subtypes.(A) A dendrogram depicting cell types targeted by different genetic tools. (B) Summary of feeding and gut transit data obtained for genetic tools that target different enteroendocrine cell types, *only observed in fed state.*
## Materials and methods
**Key resources table**
| Reagent type (species) or resource | Designation | Source or reference | Additional information |
| --- | --- | --- | --- |
| Strain, strain background (Mus musculus) | Atoh1-Cre knock-in | Yang et al., 2010 | |
| Strain, strain background (M. musculus) | Pet1-FlpE | Jensen et al., 2008 | |
| Strain, strain background (M. musculus) | Ptf1a-Cre | Kawaguchi et al., 2002 | |
| Strain, strain background (M. musculus) | Gip-Cre | Svendsen et al., 2016 | |
| Strain, strain background (M. musculus) | Atoh1-Cre transgenic | Jax 011104 | |
| Strain, strain background (M. musculus) | Neurog3-Cre | Jax 006333 | |
| Strain, strain background (M. musculus) | Neurod1-Cre | Jax 028364 | |
| Strain, strain background (M. musculus) | Sst-ires-Cre | Jax 013044 | |
| Strain, strain background (M. musculus) | Sst-ires-FlpO | Jax 028579 | |
| Strain, strain background (M. musculus) | Vil1-Cre | Jax 021504 | |
| Strain, strain background (M. musculus) | Gcg-Cre | Jax 030542 | |
| Strain, strain background (M. musculus) | Cck-ires-Cre | Jax 012706 | |
| Strain, strain background (M. musculus) | Nts-ires-Cre | Jax 017525 | |
| Strain, strain background (M. musculus) | Mc4r-t2a-Cre | Jax 030759 | |
| Strain, strain background (M. musculus) | Npy1r-Cre | Jax 030544 | |
| Strain, strain background (M. musculus) | Tac1-ires2-Cre | Jax 021877 | |
| Strain, strain background (M. musculus) | Rosa26CAG-lsl-tdTomato, Ai14 (lsl-tdTomato) | Jax 007914 | |
| Strain, strain background (M. musculus) | Rosa26CAG-lsl-fsf-tdTomato, Ai65 (inter-tdTomato) | Jax 021875 | |
| Strain, strain background (M. musculus) | Rosa26CAG-fsf-eGFP-FLEX-hM3Dq-mCherry, (inter-hM3Dq-mCherry) | Jax 026943 | |
| Strain, strain background (M. musculus) | lsl-hM3Dq | Jax 026220 | |
| Strain, strain background (M. musculus) | C57BL/6 | Jax 000664 | |
| Strain, strain background (M. musculus) | Vil1-p2a-FlpO | This paper | |
| Commercial assay or kit | Chromium single-cell 3’ reagent kit v3 | 10X Genomics | |
| Peptide, recombinant protein | TrypLE express | Thermo Fisher 12604013 | |
| Other | FBS | VWR10802-772 | 5%See ‘Single-cell RNA sequencing’ |
| Peptide, recombinant protein | DNase | Worthington Biochemical LK003172 | 100 U/ml |
| Other | TO-PRO-3 | Thermo Fisher T3605 | 1:10,000See ‘Single-cell RNA sequencing’ |
| Other | Calcein Violet | Thermo Fisher 65-0854-39 | 1:10,000See ‘Single-cell RNA sequencing’ |
| Other | Normal donkey serum | Jackson Immuno 017-000-121 | 5%See ‘Tissue histology’ section |
| Other | Bovine serum albumin | Jackson Immuno 001-000-161 | 1%See ‘Tissue histology’ |
| Other | DAPI Fluoromount-G | Southern Biotech 0100-20 | See ‘Tissue histology’ |
| Antibody | Anti-CCK (rabbit polyclonal) | Abcam ab27441 | 1:1000 |
| Antibody | Anti-CRE (rabbit polyclonal) | Cell Signaling 15036 | 1:500 |
| Antibody | Anti-GLP1 (rabbit polyclonal) | Novus 2622B MAB10473 | 1:2000 |
| Antibody | Anti-NTS (rabbit polyclonal) | Immunostar 20072 | 1:2000 |
| Antibody | Anti-SST (rabbit polyclonal) | Novus 906552 MAB2358 | 1:1000 |
| Antibody | Anti-5HT (goat polyclonal) | Abcam ab66047 | 1:2000 |
| Antibody | Donkey anti-rabbit Alexa488 | Jackson Immuno 711-545-152 | 1:500 |
| Antibody | Donkey anti-rabbit AlexaCy3 | Jackson Immuno 711-165-152 | 1:500 |
| Antibody | Donkey anti-rabbit AlexaCy5 | Jackson Immuno 711-175-152 | 1:500 |
| Antibody | Donkey anti-rabbit Alexa680 | Thermo Fisher A32802 | 1:500 |
| Antibody | Donkey anti-goat Alexa488 | Jackson Immuno 705-545-147 | 1:500 |
| Chemical compound, drug | Clozapine N-oxide dihydrochloride | Fisher ScientificTocris 6329/10 | 3 mg/kg |
## Mice
All animal husbandry and procedures were performed in compliance with institutional animal care and use committee guidelines. All animal husbandry and procedures followed the ethical guidelines outlined in the NIH Guide for the Care and Use of Laboratory Animals (https://grants.nih.gov/grants/olaw/guide-for-the-care-and-use-of-laboratory-animals.pdf), and all protocols were approved by the institutional animal care and use committee (IACUC) at Harvard Medical School (protocol #04424). Atoh1-Cre knock-in (Yang et al., 2010), Pet1-FlpE (Jensen et al., 2008), Ptf1a-Cre (Kawaguchi et al., 2002), and Gip-Cre (Svendsen et al., 2016) mice were described before; Atoh1-Cre transgenic [011104], Neurog3-Cre [006333], Neurod1-Cre [028364], Sst-ires-Cre [013044], Sst-ires-FlpO [028579], Vil1-Cre [021504], Gcg-Cre [030542], Cck-ires-Cre [012706], Nts-ires-Cre [017525], Mc4r-t2a-Cre [030759], Npy1r-Cre [030544], Tac1-ires2-Cre [021877], lsl-tdTomato (Ai14, Rosa26CAG-lsl-tdTomato, 007914), inter-tdTomato (Ai65, Rosa26CAG-lsl-fsf-tdTomato, 021875), inter-hM3Dq-mCherry (Rosa26CAG-fsf-eGFP-FLEX-hM3Dq-mCherry, 026943), lsl-hM3Dq [026220], and C57BL/6 [000664] mice were purchased (Jackson Laboratory). Both male and female mice between 8 and 24 weeks old were used for all studies, and no differences based on sex were observed. All mice were maintained in the C57BL/6 genetic background. Mouse breeding involved paternal Cre alleles, paternal Flp alleles, and/or maternal effector genes. Vil1-Cre produced occasional germline recombination of loxP sites that resulted in ectopic inter-hM3Dq-mCherry gene expression; mice with such ectopic expression were excluded based on genotyping of reporter allele DNA extracted from ear tissue with primer 1 (stop cassette forward): atgtctggatctgacatggtaa; primer 2 (hM3Dq cassette reverse): tctggagaggagaaattgcca; primer 3 (GFP cassette reverse): ttgaagtcgatgcccttcag; intact allele: ~490 bp, recombined allele: ~290 bp. Vil1-p2a-FlpO mice were generated by CRISPR-guided approaches at Boston Children’s Hospital Mouse Gene Manipulation Core. Cas9 protein, CRISPR sgRNAs (targeting the stop codon of Vil1 locus), and an ssDNA (containing a p2a-FlpO cassette with 150 bp homology arms) were injected into the pronucleus of C57BL/6 embryos. Founder mice were screened by allele specific PCR analysis with primers flanking the 5′ junction (primer 1: aacagaagttccttaaacaagcca; primer 2: aacaggaactggtacagggtcttg; ~930 bp), FlpO internally (primer 1: acaagggcaacagccaca; primer 2: tcagatccgcctgttgatgt; ~830 bp), and the 3′ junction (primer 1: accccctggtgtacctgga; primer 2: tagccctcccttttgagtgtga; ~840 bp), followed by Sanger sequencing to validate the allele. Selected Vil1-p2a-FlpO founder mice were viable, fertile, and back crossed to C57BL/6 mice for at least three generations.
## Single-cell RNA sequencing
Enteroendocrine cells were acutely harvested using a protocol modified from previous publications (Haber et al., 2017; Sato et al., 2009). Intestinal tissue was obtained from Neurog3-Cre; lsl-tdTomato mice (one adult male), or Neurod1-Cre; lsl-tdTomato (three adult females), cut longitudinally, washed (cold phosphate-buffered saline [PBS]), cut into small ~5 mm pieces, and incubated (gentle agitation, 20 min, 4°C) in EDTA solution (20 mM EDTA-PBS, Ca/Mg-free) in LoBind Protein tubes (Eppendorf 0030122216). The specimen was shaken, the tissue allowed to settle, and the supernatants collected. The residual tissue was again incubated similarly with EDTA solution, and supernatants were combined, and centrifuged (300 × g, 5 min, 4°C) Pellets were washed (2×, PBS [Ca/Mg-free] supplemented with $5\%$ fetal bovine serum [FBS], 4°C) and incubated (37°C, 2 min) in protease solution (TrypLE express, Thermo Fisher 12604013) supplemented with DNase (100 U/ml, Worthington Biochemical LK003172). The suspension containing dissociated cells was centrifuged (300 × g, 5 min), washed (2×, PBS [Ca/Mg-free] containing $5\%$ FBS, 4°C) The resulting pellet was resuspended in FACS buffer ($5\%$ FBS in DMEM/F12, HEPES, no phenol red) containing DNase (100 U/ml), TO-PRO-3 (Thermo Fisher T3605, 1:10,000) to label dead cells, and Calcein Violet (Thermo Fisher 65-0854-39, 1:10,000) to label living cells. Cells were filtered (1 × 70 um, 1 × 40 um) and tdTomato+, Calcein Violet+, TO-PRO-3- cells were collected by fluorescence activated cell sorting using a FACS Aria (BD Biosciences). Collected cells were then loaded into the 10X Genomics Chromium Controller, and cDNA prepared and amplified according to manufacturer’s protocol (10X Genomics, Chromium single-cell 3′ reagent kit v3, 12 cycles per amplification step). The resulting cDNA was sequenced on a NextSeq 500 at the Harvard Medical School Biopolymers Facility. Sequence reads were aligned to the mm39 mouse transcriptome reference, and feature barcode matrices were generated using 10X Genomics CellRanger. Unique transcript (UMI) count matrices were analyzed in R v4.1.1 using Seurat v4.0.5 (Beutler et al., 2017; Satija et al., 2015). The cell barcodes were filtered, removing cells with a high number of UMIs (>125,000) or high percentage of mitochondrial genes (>$25\%$). The filtered UMI count matrix was transformed using SCTransform (Hafemeister and Satija, 2019). Transformed matrices from Neurog3 and Neurod1 samples were integrated (nFeature = 3000), and integrated matrices used for cluster identification and UMAP projections. Additional clusters of low-quality cells (defined by low-average UMI counts and low-average feature counts across the cluster) were removed. To examine the diversity among enteroendocrine cells, cell barcodes belonging to enteroendocrine cells from Neurog3 and Neurod1 samples were identified and reanalyzed separately. Matrices of enteroendocrine cells from Neurog3 and Neurod1 samples were transformed and integrated (nFeature = 3000). *Differential* gene expression (Wilcoxon ranked-sum test) was conducted on UMI counts matrices that were log normalized and scaled. Seurat’s BuildClusterTree function was used to spatially arrange clusters based on relative similarity in gene expression. Two serotonergic clusters were merged post hoc (to become cluster EC_3) due to the absence of any single signature gene that effectively distinguished them. Gene expression data in all UMAP plots is shown as a natural log of normalized UMI counts. Further details and full parameters of analysis will be provided on GitHub upon publication: https://github.com/jakaye/EEC_scRNA, copy archived at (Hayashi, 2023).
## Tissue histology
For histology, mice were perfused intracardially with PBS and then fixative ($4\%$ paraformaldehyde/PBS). Intestinal regions and other organs were dissected (duodenum: first 2 cm after the pyloric sphincter, jejunum: middle 2 cm, ileum: last 2 cm before the cecum, colon: first 2 cm after the cecum, and rectum: last 2 cm accessible via the pelvic cavity) and postfixed (1–2 hr, 4°C). Samples were then incubated in $30\%$ sucrose/PBS (overnight, 4°C), embedded in Tissue-Tek OCT, frozen, cryosectioned, and placed on glass slides. Slides were incubated with primary antibodies at dilutions indicated below (overnight, 4°C, PBS supplemented with $0.05\%$ Tween20, $0.1\%$ TritonX, and either $5\%$ normal donkey serum or $1\%$ BSA) and then with fluorophore-conjugated secondary antibodies (1:500, 2 hr, RT). Sections were mounted (DAPI Fluoromount-G, Southern Biotech 0100-20), coverslipped, and imaged using a Nikon A1R confocal microscope, an Olympus FV1000 confocal microscope, or a Zeiss Axiozoom V16 fluorescent stereoscope. Microscope images are presented as z-projections. Quantification of tdTomato expression and antibody staining was performed manually using a Nikon Ti2 inverted microscope. Antibodies were rabbit anti-CCK (Abcam ab27441, 1:1000), rabbit anti-CRE (Cell Signaling 15036, 1:500), rabbit anti-GLP1 (Novus 2622B MAB10473, 1:2000), rabbit anti-NTS (Immunostar 20072, 1:2000), rabbit anti-SST (Novus 906552 MAB2358, 1:1000), goat anti-5HT (Abcam ab66047, 1:2000), donkey anti-rabbit Alexa488, Cy3, Cy5, Alexa680 (Jackson Immuno Research, Thermo Fisher, 1:500), donkey anti-goat Alexa488 (Jackson Immuno Research, 1:500).
## Gut transit measurements
DREADD-expressing and control animals (ad libitum fed) were injected with CNO (3 mg/kg, IP). After 15 min, charcoal dye (200 μl, $10\%$ activated charcoal, $10\%$ gum Arabic in water), or for Figure 4—figure supplement 1, carmen red dye (200 μl, $6\%$ carmen red, $0.5\%$ methyl cellulose in water), was gavaged orally, and 20 min later, mice were euthanized and the gastrointestinal tract was harvested. The distance between the pyloric sphincter and the charcoal dye leading edge was measured by an observer blind to animal genotype. All animals were naive to CNO exposure, except for some Gip-Cre mice due to limited availability of mice.
## Feeding measurements
Experimental mice were individually housed for 3 days and habituated to feeding from a ceramic bowl. Animals were either fed ad libitum or fasted for the last 20–22 hr in a new clean cage with some bedding material from the previous cage. CNO was injected (3 mg/kg, IP), and food pellets presented 15 min later at the onset of darkness. Food intake was measured over the course of 2 hr by weighing the amount of residual food, with genotypes revealed post hoc to achieve a genotype-blinded analysis. Studies involved fasted mice that were naive to prior CNO exposure or fed mice that were either naive to CNO or acclimated for at least a week after prior CNO exposure.
## Body composition and indirect calorimetry
Body composition (lean mass and fat mass) was first analyzed for each experimental group with a 3-in-1 Echo MRI Composition Analyzer (Echo Medical Systems, Houston, TX), and no significant differences were observed. Animals were then placed in a Sable Systems Promethion indirect calorimeter maintained at 23°C ± 0.2°C. Mice were singly housed in metabolic cages with corn cob bedding and ad libitum access to Labdiet 5008 chow ($\frac{56.8}{16.5}$/26.6 carbohydrate/fat/protein). After 18 hr, all mice were injected with PBS (IP) for acclimatation to handling and mild injection stress. The following day, mice were injected with CNO (3 mg/kg, IP) approximately 30 min before dark onset. Animals were then analyzed for food and water consumption, body weight, distance traveled, and respiratory exchange ratio. Statistical analysis was performed with CalR (Mina et al., 2018).
## Statistical analysis
Graphs represent data as mean ± sem, as indicated in figure legends. All data points were derived from different mice except some mice in Figure 4 (Gip-Cre; Vil1-p2a-FlpO; inter-hM3Dq-mCherry mice) were previously used in feeding assays and some mice in Figure 5—figure supplement 1 (Ptf1a-Cre; Vil1-p2a-FlpO; inter-hM3Dq-mCherry: $\frac{21}{21}$ mice, Cck-ires-Cre; Vil1-p2a-FlpO; inter-hM3Dq-mCherry: $\frac{7}{19}$ mice, Gip-Cre; Vil1-p2a-FlpO; inter-hM3Dq-mCherry: $\frac{10}{10}$ mice, and Vil1-Cre; Sst-ires-FlpO; inter-hM3Dq-mCherry: $\frac{9}{16}$ mice) were previously used in prior feeding assays for Figure 5. When mice were reused, they were acclimated for at least a week after prior CNO exposure.
Sample sizes (from left to right): Figure 3 (Pet1: 4, 4, 3, 3, 3; Sst: 4, 4, 3, 2, 4; Gip: 4, 4, 4, 2, 4; Cck: 4, 4, 3, 4, 4; Gcg: 3, 3, 3, 2, 3), Figure 3—figure supplement 4A (serotonin antibody: 17, 17, 16, 10, 13; Sst antibody: 18, 18, 12, 10, 13; Cck antibody: 18, 18, 12, 13, 13; GLP1 antibody: 18, 18, 13, 10, 13), B (serotonin antibody: 3, 3, 3, 3, 3; Sst antibody: 4, 4, 3, 3, 3; Cck antibody: 3, 3, 3, 3, 3; GLP1 antibody: 3, 3, 3, 3, 3), C (serotonin antibody: 4, 4, 4, 2, 4; Sst antibody: 4, 4, 4, 2, 4; Cck antibody: 4, 4, 4, 2, 4; GLP1 antibody: 4, 4, 4, 2, 4), D (serotonin antibody: 4, 4, 4, 2, 4; Sst antibody: 4, 4, 4, 2, 4; Cck antibody: 4, 4, 4, 2, 4; GLP1 antibody: 4, 4, 4, 2, 4), E (serotonin antibody: 3, 3, 2, 3, 3; Sst antibody: 3, 3, 2, 3, 3; Cck antibody: 4, 4, 2, 3, 3; GLP1 antibody: 4, 4, 2, 3, 3), F (serotonin antibody: 3, 3, 2, 3, 3; Sst antibody: 3, 3, 2, 3, 3; Cck antibody: 3, 3, 2, 3, 3; GLP1 antibody: 3, 3, 2, 3, 3), Figure 4 [13, 14, 8, 8, 8, 10, 6, 7, 8, 9, 12, 12, 5, 5, 5, 5, 8, 8], Figure 4—figure supplement 1A [4, 5], Figure 5 [10, 10, 10, 11, 9, 10, 8, 8, 13, 12, 9, 9, 9, 9], Figure 5—figure supplement 1A [4, 7, 6, 6,], B [9, 12, 9, 8, 4, 6, 9, 10], Figure 5—figure supplement 1C [7, 9, 3, 5].
Statistical significance was measured using a Mann–Whitney test with Holm–Šídák correction on Prism 9 (GraphPad) for Figure 4, Figure 5, and Figure 5—figure supplement 1A and B, a Mann–Whitney test on Prism 9 (GraphPad) for Figure 4—figure supplement 1A, and ANCOVA and ANOVA on CalR for Figure 5—figure supplement 1C (Mina et al., 2018).
## Source data
The source data Excel file contains raw numerical data used for all bar graphs and statistical analyses. Single-cell transcriptome data is available with a GEO GSE accession number GSE224223.
## Materials availability statement
Vil1-p2a-FlpO mice will be deposited in Jackson Laboratory and made generally available upon reasonable request.
## Declaration of interest
SDL and FMG are consultants for Kallyope, Inc.
## Funding Information
This paper was supported by the following grants:
## Data availability
The source data excel file contains raw numerical data used for all bar graphs and statistical analyses. Single-cell transcriptome data are available at NCBI Gene Expression Omnibus with accession GSE224223.
The following dataset was generated: HayashiM KayeJA DouglasER JoshiNR GribbleF ReimannF LiberlesSD 2023Enteroendocrine cell lineages that differentially control feeding and gut motilityNCBI Gene Expression OmnibusGSE224223
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|
---
title: Evolutionary origins and interactomes of human, young microproteins and small
peptides translated from short open reading frames
authors:
- Clara-L. Sandmann
- Jana F. Schulz
- Jorge Ruiz-Orera
- Marieluise Kirchner
- Matthias Ziehm
- Eleonora Adami
- Maike Marczenke
- Annabel Christ
- Nina Liebe
- Johannes Greiner
- Aaron Schoenenberger
- Michael B. Muecke
- Ning Liang
- Robert L. Moritz
- Zhi Sun
- Eric W. Deutsch
- Michael Gotthardt
- Jonathan M. Mudge
- John R. Prensner
- Thomas E. Willnow
- Philipp Mertins
- Sebastiaan van Heesch
- Norbert Hubner
journal: Molecular Cell
year: 2023
pmcid: PMC10032668
doi: 10.1016/j.molcel.2023.01.023
license: CC BY 4.0
---
# Evolutionary origins and interactomes of human, young microproteins and small peptides translated from short open reading frames
## Summary
All species continuously evolve short open reading frames (sORFs) that can be templated for protein synthesis and may provide raw materials for evolutionary adaptation. We analyzed the evolutionary origins of 7,264 recently cataloged human sORFs and found that most were evolutionarily young and had emerged de novo. We additionally identified 221 previously missed sORFs potentially translated into peptides of up to 15 amino acids—all of which are smaller than the smallest human microprotein annotated to date. To investigate the bioactivity of sORF-encoded small peptides and young microproteins, we subjected 266 candidates to a mass-spectrometry-based interactome screen with motif resolution. Based on these interactomes and additional cellular assays, we can associate several candidates with mRNA splicing, translational regulation, and endocytosis. Our work provides insights into the evolutionary origins and interaction potential of young and small proteins, thereby helping to elucidate this underexplored territory of the human proteome.
## Graphical abstract
## Highlights
•Most sORF-encoded human microproteins emerged in primates and often evolved de novo•We identify new human sORF-encoded peptides that are smaller than 16 amino acids•We detect interacting proteins for 266 sORF-encoded proteins in MS-based screens•This study builds a resource to investigate small and evolutionarily young proteins
## Abstract
Sandmann et al. explore the evolution and interactomes of the smallest and evolutionarily youngest members of the human proteome. They identify previously unknown human short ORF-encoded microproteins and show that conserved and young microproteins that emerged de novo in primates can engage with vital biological processes.
## Introduction
Ribosome profiling (Ribo-seq)1 has revealed the translation of thousands of short open reading frames (sORFs) in human cell lines and tissues,2 which can result in the production of short proteins denoted as microproteins, micropeptides, or short ORF-encoded polypeptides (SEPs).3 Many human microproteins have been selected for functional characterization based on high inter-species conservation2,4 and a minimum amino acid (aa) size. However, evolutionarily young microproteins can have biological roles and have for instance been implicated in cell survival,5,6,7 human brain development,8 and cancer.9,10,11 Similarly, a lower size cutoff for protein investigations seems unjustified: peptides as small as 11 aa can control morphogenetic development in insects12,13,14 and muscle metabolism in humans,15 respectively, and short bioactive peptides cleaved from bigger precursor proteins can function as peptide hormones.16 These examples suggest that many more human sORFs encoding young microproteins, as well as very short peptides, may have yet unknown biological roles.
Here, we investigated these two underappreciated elements of the human proteome. To this end, we first analyzed the conservation and evolutionary mechanisms of origin of 7,264 human-translated Ribo-seq ORFs included in a recently published GENCODE reference catalog.2 We found that almost $90\%$ were evolutionarily young, of which 4,101 emerged de novo from ancestral non-coding regions.
To gain first insights into the potential bioactivity of these young microproteins, we selected 45 microproteins of recent evolutionary origin from the set of 7,264 sORFs and performed a high-throughput protein interaction screen on peptide matrix (PRISMA).17,18,19,20 *For this* approach, proteins are divided into short peptides that are synthesized onto cellulose membranes and incubated with a protein lysate. The protein interactome of each peptide is then analyzed by mass spectrometry (MS), making PRISMA well suited to detect interactions mediated by short linear motifs (SLiMs) within disordered protein regions,17,18,19,20 which are common in microproteins.21 Since the GENCODE Ribo-seq ORF catalog2 introduced a lower length cutoff of 15 aa, we next analyzed previously published Ribo-seq datasets of human tissues22,23 and performed (targeted) MS searches to identify and validate translated sORFs potentially encoding peptides below this cutoff (denoted sORFs3–15 aa and peptides3–15 aa, respectively). We identified 221 translated sORFs3–15 aa, including 38 with endogenous peptide-level evidence, and showed that many of these are also translated in rodents and conserved across mammals. We designed an additional PRISMA screen that incorporated each of these 221 peptides3–15 aa.
With our two PRISMA screens, we identified a number of recently evolved microproteins and very small peptides that interacted with proteins involved in cellular processes including splicing, translational regulation, and endocytosis. In line with these interactome results, we performed independent cellular assays that indicated that several candidates can modulate translation and endocytosis, respectively.
We here present evolutionary and interactome analyses of two underexplored parts of the human proteome. Our study can serve as a resource and blueprint to investigate the cellular roles of young and small human proteins that are being detected at a rapid rate but have been difficult to characterize.
## Most human sORFs are young and have emerged de novo
Recently, a community-driven effort supported by major gene and protein annotation projects (GENCODE-Ensembl, UniProt, HGNC, PeptideAtlas, and HUPO) produced a reference catalog of human Ribo-seq ORFs.2 This catalog comprises 7,264 human sORFs longer than 15 aa (average length 43 aa) found in presumed long non-coding RNAs (lncRNAs), untranslated regions (UTRs), and alternative mRNA coding frames (Figures 1A and 1B). We assessed the amino acid conservation of the putative microproteins encoded by these sORFs and compared our results with [1] a negative control set in form of length-matched sORFs sampled from UTRs and [2] a positive control set composed of 527 annotated proteins encoded by known short protein-coding sequences (sCDSs; length <100 aa, average length 76 aa) (STAR Methods).Figure 1Most human sORFs are young and have emerged de novo(A) Phylogenetic tree of the mammalian taxa comprising 120 mammalian species used for sORF genomic alignments ($$n = 7$$,264). sORFs were classified into lncRNA-ORFs (lncORFs), upstream ORFs (uORFs), upstream overlapping ORFs (uoORFs), internal ORFs (intORFs), and downstream ORFs (dORFs). For comparison, we included 527 sCDS. The heatmap displays the pairwise aa identity (%) of all sORFs and sCDSs (columns) across the 120 species’ genomes (rows).(B) Numbers of evaluated sORFs and sCDS separated by ORF biotype.(C) Conservation scores (CSs) calculated across non-primate mammalian species. Dotted lines represent the CS cutoff of 8 (STAR Methods). sORFs and sCDS with (red) or without (light blue) significant protein sequence conservation are displayed below.(D) Dot plots displaying the average and $95\%$ confidence interval of sORF, sCDS, and untranslated ORF truncation introduced by the most upstream stop codon in the aligned counterpart regions of the sequences. sORFs are divided by biotype and conservation of aa sequences. Internal sORFs (intORFs) were not considered due to additional constraints acting to preserve the frame of the sequence.(E) Top: total numbers of conserved (CS ≥ 8) and young sORFs (CS < 8). Bottom: schematic of the classification of young sORFs ($$n = 6$$,506) based on conservation of ORF structures. We defined three levels of conservation: humans, old world monkeys, and primatomorpha.(F) Numbers of evolutionarily young sORFs per level of conservation of ORF structures.(G) Violin plots with the numbers of human23 (left) and macaque23 (right) brain Ribo-seq reads mapped to human brain translated ORFs ($$n = 830$$), by absence (light blue) or presence (dark blue) of conservation in macaque. Statistical differences were assessed by Wilcoxon signed-rank test. Horizontal bars represent the median values. ns, not significant.(H) Percentages of sORFs translated in the human brain with aligned counterpart regions translated in macaque. sORFs are divided by biotype and by the presence (dark blue) or absence (rlight blue) of conservation in macaques.(I) Schematic of modes of sORF evolution and numbers of young sORFs per category.
We found that most of the putative 7,264 sORF-encoded microproteins ($$n = 6$$,506; $89.6\%$) lack significant protein homology across non-primate mammals and could therefore be classified as evolutionarily young (Figures 1A–1D and S1A–S1C; Table S1; STAR Methods). The remaining $10.4\%$ were conserved across non-primate mammals—a significantly higher fraction than observed in the negative control set ($1.0\%$) (Figure S1D). This indicated that the putative microproteins were more conserved than expected by chance. However, the fraction of conserved microproteins encoded by sORFs ($10.4\%$) was still lower than what we observed for annotated sCDS ($71.4\%$ conserved) (Figures 1C and S1D; Table S1).
Since primatomorpha species (primates and colugos) are closely related, counterpart sequences of human sORFs are highly similar across their genomes, showing an average aa identity of $90.62\%$ (Figure 1A). Hence, sORF-encoded microprotein homologs in primatomorpha cannot be solely identified based on sequence similarity since unconstrained regions have not diverged enough. Therefore, we additionally evaluated the conservation of ORF structures across primatomorpha for all 6,506 young sORFs by reconstructing their ancestral sequences and inspecting the positional conservation of the start codon and the presence of an intact ORF (i.e., an ORF that is not truncated by premature stop codons) (Figures 1E, S1A, and S1E–S1J; STAR Methods). Within the subset of young sORFs, we found 4,914 ($75.5\%$) with conserved structures across primatomorpha (Figures 1E and 1F). There were 1,370 ORF structures that emerged during old-world monkey evolution (after the human-macaque split), and 222 ORFs were human specific. The conservation of untranslated and translated ORF structures showed a similar phylogenetic distribution across primatomorpha lineages (Figures 1D and S1D). However, we observed a significantly higher proportion of conserved ATG initiation codons in young sORFs compared with untranslated control sequences ($63.30\%$ ± $16.14\%$ versus $35.83\%$ ± $12.41\%$, Wilcoxon signed rank test, p value = 1.45 × 10−21, Figure S1F). These results suggest that there is significant selection pressure acting to preserve the initiation codons of young sORFs but not their frame structures.
We next compared published Ribo-seq data of human, macaque, and mouse brain samples and asked whether the absence of conserved ORF structures in non-human species led to a decrease in the levels of translation of the aligned counterpart sequences. Indeed, in comparison with regions with conserved ORF structures, we found that regions with non-conserved ORF structures exhibited lower levels of expression and ribosome occupancy, lower periodicity bias in ribosome footprints, and fewer actively translated ORFs (Figures 1G, 1H, S1G, and S1H).
When we traced the genomic changes (i.e., DNA mutations) that led to the formation of the ORFs throughout primate evolution, we found that most young sORFs ($63.0\%$) emerged de novo from ancestral non-CDSs of which 162 evolved in the human lineage (Figure 1I; STAR Methods). Far less common was emergence through CDS duplication or fission of older protein-coding regions ($3.3\%$ and $0.5\%$, respectively) or through inserted endogenous retrovirus (ERV) elements and Alu repeats ($1.3\%$). The mode of evolution could not be determined for the remainder of the investigated young sORFs ($31.9\%$) (Figure 1I).
## Interactome profiling of microproteins translated from young sORFs with PRISMA
Demonstrating that proteins engage in specific interactions with other proteins is a well-established step in gathering evidence toward putative protein functionality.24 We applied PRISMA17,18,19,20 to investigate the interactomes of young microproteins and the sequence motifs through which they mediate protein-protein interactions (PPIs) (Figure 2A; STAR Methods). We selected 45 recently evolved microproteins with a median length of 53 aa based on their reproducible translation across human tissues and cell lines,2,25,26,27 and for having protein-level evidence2,5,6,22,28,29 from in vitro translation assays ($\frac{21}{45}$), ectopic expression in cultured cells ($\frac{39}{45}$), and endogenous detection by (targeted) MS-based proteomics as reported by previously published studies2,22,29 ($\frac{30}{45}$) (Figure 2B top panel; Table S2; Data S1; STAR Methods). Nineteen out of 45 young microproteins were translated from presumed lncRNAs that had been associated with human diseases and six affected transcriptional profiles after microprotein overexpression or knockout based on previous publications5,6 (Table S2; STAR Methods). We included 15 conserved microproteins with a median length of 65 aa for comparison, some with known interactomes (e.g., MRPL33,30 MIEF1-MP [MP, microprotein]31), as well as the well-characterized wild-type (WT) and mutated peptides of SOS1 and GLUT118,32 as positive controls (STAR Methods).Figure 2Interactome profiling of microproteins translated from young sORFs with PRISMA(A) Schematic of PRISMA including 60 microproteins and four assay controls.(B) Top: protein evidence per microprotein (Table S2). Bottom: conserved (red) and young (blue) microproteins were sorted based on the highest interaction score (product of fold change and p value).(C–H) Volcano plots with interactomes of the (C) SOS1 wild-type (WT) control peptide, (D) GLUT1 mutant control peptide, and (E) annotated mitochondrial microprotein MRPL33 (interactors from all tiles are summarized). Additional examples of conserved microproteins are shown in Figure S2M. Volcano plots of summarized interactome results of the three young microproteins (F) RP11-644F5.11-MP, (G) RP11-464C19.3-MP, and (H) SNHG8-MP, the latter being enriched for essential proteins (52 out of 106 interactors; padj = 0.00013; Fisher’s exact test). Additional examples of young microproteins are shown in Figure S2N.(I) Percentage of essential proteins detected in the interactomes of conserved and young microproteins. No statistical differences were found among both groups (assessed by two-tailed Student’s t test). The horizontal lines indicate $25\%$, $50\%$, and $75\%$ quartiles, respectively.(J) Interaction scores for eleven young microproteins whose top interactor is an essential protein. Asterisks mark microprotein interactomes significantly enriched for essential proteins (assessed by Fisher’s exact test, FDR < 0.05) (Table S3).
For PRISMA, each of the selected microproteins was divided into 15 aa long, overlapping peptides (tiles) with an offset of eight aa. This way, each aa per microprotein was represented in two subsequent tiles except for the first and last eight aa of each microprotein. Following this approach and including the control peptides, we analyzed 490 peptides in total (Figures 2A, S2A, and S2B; Table S3; STAR Methods). In order to determine specific versus non-specific interactions, we compared protein identifications in each peptide spot against all other peptide spots. The ratio of each log2-fold change to its standard error was computed for each detected protein, and the resulting p values were adjusted using the Benjamini and Hochberg method to control the false discovery rate (FDR). As performed previously,18,32 we determined the significance cutoff based on known interactors of the SOS1 and GLUT1 control peptides (STAR Methods), retaining as many known interactors and as few non-reported interactors as possible. After applying this significance filter, we retained roughly $1.4\%$ of all identified proteins as significant binders and detected up to $89\%$ and $80\%$ of known SOS1 and GLUT1 interactors (Figures 2C and 2D; Table S3; STAR Methods). Additionally, we identified the expected sequence-specific interactome changes provoked by a single amino acid substitution between their WT and mutant versions (Figure S2C) and recapitulated previously reported, endogenous interactors for conserved microproteins (including MRPL3330 and MIEF1-MP31; Figures 2E and S2M). These results demonstrate that PRISMA can identify sequence-specific and biologically relevant protein interactions.
Young microproteins formed specific interactions with different classes of proteins (Figures 2B, bottom panel, 2F–2H, and S2F; Table S3). For example, the mitochondrial proteins GOT2 and SLC25A33 stood out as the most significantly enriched interactors for the microproteins RP11-644F5.11-MP and RP11-464C19.3-MP (Figures 2F and 2G), respectively, in line with their previously observed mitochondrial localization.22 Between young and conserved microproteins, there were no apparent differences in the specificity, number, strength, or phylogenetic age of interactors (Figures S2G–S2L). Notably, young and conserved microproteins both displayed an equal capacity to interact with proteins required for cell survival33 (Figures 2H–2J; Table S3), and the interactomes of 11 young microproteins were enriched for essential proteins (Table S3). These results suggest that young proteins have the potential to engage in vital cellular processes.
## SLiMs may drive microprotein-protein interactions
SLiMs17,18,19—three to ten aa-long stretches within intrinsically disordered regions of proteins34—might be important contributors to the interaction potential of microproteins since microproteins are less structured and enriched for such motifs.21,35 We identified putative disordered regions within the 60 investigated microproteins using the disorder prediction tool IUPred36 and interrogated the eukaryotic linear motif (ELM) resource for functional sites in proteins37 to annotate potential SLiMs (STAR Methods). Our predictions revealed 1,428 SLiMs in total, of which 429 are located within putative disordered regions of the microprotein candidates. Out of these 429, our PRISMA design covered 412 complete motifs. It should be noted that these included 87 proteolytic cleavage sites and 71 SLiMs whose binding capacity depends on additional requirements, e.g., post-translational modifications (PTMs) or free C-terminal regions (Table S3) and are thus not likely to be validated by our PRISMA screen.
Based on known protein-SLiM interactions annotated in the ELM resource,37 we searched for matches within interactomes of each microprotein tile, i.e., if a microprotein tile carrying a SLiM bound to an interactor that is known to bind this particular motif. In total, we detected 47 matching protein-SLiM interactions (Table S3; STAR Methods). Those included proline-rich sequences within the young microproteins PALLD-uORF-MP and RAB12-uoORF-MP that interacted with proteins containing Src homology 3 (SH3) domains involved in actin cytoskeleton organization and endocytosis (Figures 3A and 3B), in a manner similar to the proline-rich SLiM of the SOS1 control peptide (Figures 2C and S2C). Furthermore, six young and three conserved microproteins interacted with kinases and harbored kinase phosphorylation and docking motifs (Figures 3A and 3C). A general enrichment of kinases in interactomes of microprotein tiles carrying such motifs could not be observed (p value = 0.079, Fisher’s exact test; STAR Methods). However, we detected phosphorylated tryptic peptides after MP overexpression for two of the nine microproteins (THAP7-uORF-MP and JHDM1D-AS1-MP) (Table S3; STAR Methods), indicating that the lack of PRISMA-wide significance does not necessarily preclude the potential relevance of our observed kinase interactions. Figure 3SLiMs may drive microprotein-protein interactions(A) Heatmap with fold changes of kinases and SH3-domain-containing proteins that interact with microproteins carrying a phosphorylation/kinase-docking motif or a proline-rich motif.(B) Peptide sequence and volcano plot with PRISMA results of a RAB12-uoORF-MP-derived peptide carrying a proline-rich motif (underlined). SH3-domain-containing proteins are highlighted in red.(C) Peptide sequence and volcano plot with PRISMA results of the GAS5-MP-derived peptide carrying a phosphorylation motif (underlined). Kinases are highlighted in red.(D) Heatmap with fold changes of interactors detected in two overlapping peptides within one microprotein. Only microprotein tiles that share at least three interactors are plotted (Table S3).(E) Peptide sequences and volcano plots with PRISMA results of tile 2 and tile 3 of PVT1-MP. Splicing factors are highlighted in red.(F) Immunofluorescence stainings of FLAG-tagged PVT1-MP after overexpression in HeLa cells. Cell nuclei were stained with DAPI, mitochondria with anti-ATPIF1 antibody, and PVT1-MP-3xFLAG with anti-FLAG antibody. Scale bar represents 20 μm.(G) PLA in HeLa cells transfected with V5-tagged PVT1-MP and FLAG-tagged SRSF2. Red spots indicate PVT1-MP-V5 and SRSF2-FLAG interactions (additional images in Figure S3C). Cell nuclei were stained with DAPI. Controls: anti-FLAG single primary antibody only; anti-V5 single primary antibody only; both primary antibodies were omitted. As an additional control, the PLA was performed in untransfected HeLa cells (Figure S3C). Scale bar represents 20 μm.(H) Peptide sequences and volcano plots with PRISMA results of tile 9 and tile 10 of LINC01128-MP. Tile 10 lacks the first amino acid of the clathrin box motif. Clathrins are highlighted in red.(I) Immunofluorescence stainings of FLAG-tagged LINC01128-MP after overexpression in HeLa cells. Cell nuclei were stained with DAPI, mitochondria with anti-ATPIF1 antibody, CLTC with anti-CLTC antibody, and LINC01128-MP-3xFLAG with anti-FLAG antibody. Scale bar represents 20 μm.(J) Representative images of fluorescently labeled transferrin (green) and EEA1 (red) detection in HeLa WT and LINC01128-MP knockout (KO) cells. Cell nuclei were stained with DAPI (gray) and EEA1 with anti-EEA1 antibody. Scale bar represents 10 μm. Images with lower magnification are shown in Figure S3H.(K) Beeswarm plot for quantification of transferrin and EEA1 co-localization in HeLa WT and LINC01128-MP KO cells using Manders’ coefficient tM1. Each dot represents one analyzed cell. Per experiment, an average of 30 cells were quantified ($$n = 3$$). Statistical significance was determined using Student’s t test.(L) Volcano plot depicting significantly differentially expressed genes (in blue, −0.26 ≤ log2(FC) ≥ 0.26, padj = 0.05) in RNA-seq data of wild-type versus LINC01128-MP KO cells. LINC01128 is highlighted in red and its transcript levels are not differentially expressed between wild-type and KO cells (padj = 0.15); also see Figure S3H.
We next focused on 190 interactors that were detected in pairs of neighboring, partly overlapping tiles within 39 microproteins (Figure 3D; Table S3). Their repeated identification strengthened these interactions and the confidence that they were mediated by the overlapping sequence. For instance, two overlapping tiles of the young and nuclearly localized microprotein PVT1-MP each bound five serine/arginine-rich splicing factors (SRSF1, SRSF2, SRSF5, SRSF6, and SRSF7; enriched GO term: regulation of mRNA splicing, padj = 0.011; CORUM: spliceosome, padj = 0.009), likely provoked by a stretch of three consecutive arginines within the sequence shared by both tiles (EGRRRAAS, Figures 3D–3F and S3A). This arginine stretch, which is located in a region of low complexity close to an RGG motif38 (Table S3), resembles basic patches found in some human but mostly viral RNA-binding proteins.38 As cell lysates were treated with benzonase before the PRISMA binding assay, these PPIs should be direct and not mediated by RNA. To support the PRISMA results, we tested SRSF2 and SRSF6 in overexpression studies and observed a partial co-localization with PVT1-MP with both splicing factors in HeLa cells (Figure S3B). Moreover, we selected SRSF2 to perform an in situ proximity ligation assay (PLA). PLA is better suited to corroborate protein interactions than co-localization experiments as the PLA signal, provoked by hybridization of antibody-conjugated PLA probes, only occurs when the target proteins are in close proximity (<40 nm) to each other.39 We observed a PLA signal in HeLa cells overexpressing V5-tagged PVT1-MP and FLAG-tagged SRSF2 but not in any of the negative controls (Figures 3G and S3C). These results indicate that the interaction between PVT1-MP and SRSF2 detected with PRISMA can indeed occur in living cells.
We also found two overlapping tiles within the young LINC01128-MP to interact with multiple clathrin heavy-chain (CLTC and CLTCL1) and light-chain proteins (CLTA and CLTB) involved in vesicular trafficking and endocytosis. The binding to CLTC and CLTCL1 may be driven by a partially shared clathrin box motif (LLLLD, Figures 3D and 3H), which is usually present in endocytic cargo adaptor proteins (APs).40,41 Within LINC01128-MP, the motif resides in a C-terminal extension that evolved in the human lineage through the loss of a stop codon (Figure S3D). In line with the observed binding profile, LINC01128-MP localized to clathrin-rich foci (Figures 3I and S3E), and the knockdown of the presumed LINC01128 lncRNA decreased endocytosis of epidermal growth factor (EGF) and transferrin in a previous genome-wide small interfering RNA (siRNA) screen performed by Collinet et al.42 The microprotein had remained undetected at that time, and it was not investigated whether the effect could be attributed to the lncRNA molecule or the potential microprotein. To further investigate the role of LINC01128-MP in endocytosis and trafficking, we employed CRISPR-Cas9 to create a pool of cells carrying short insertions and deletions (indels) that lead to premature stop codons truncating the endogenous sORF encoding for LINC01128-MP (Figure S3F; STAR Methods). When assessing transferrin trafficking in WT cells and the pool of modified cells (with $93\%$ carrying premature STOP codons, Figure S3G), we observed a similar effect as in the study by Collinet and colleagues,42 specifically a reduction of transferrin accumulation in early endosomes by approximately $30\%$ (Figures 3J, 3K, and S3H; STAR Methods). RNA sequencing followed by differential expression analysis revealed that the sORF-truncating mutations did not significantly alter LINC01128 transcript levels (Figures 3L and S3I; STAR Methods) suggesting that the loss of the microprotein contributed to reduced transferrin accumulation. Although our approach reduced changes to the LINC001128 lncRNA to a minimum, we cannot completely exclude the impact of the short indels on potential RNA-mediated roles. Notably, genes differentially expressed upon microprotein truncation are enriched for extracellular matrix proteins and components of the plasma membrane. These are involved in signaling receptor binding and growth factor activity, processes associated with endocytosis and vesicular trafficking43,44 (Figure S3J). Combined, these observations suggest a role for LINC01128-MP in the control of intracellular trafficking and endocytosis.
## sORFs smaller than 16 aa (sORFs3–15 aa) are highly translated in multiple tissues and often conserved across mammals
Next, we investigated the smallest potential peptides translated from independent ORFs in the human genome. For this purpose, we expanded the lower size cutoff of the currently cataloged Ribo-seq ORFs2 to include ORFs below 16 aa in length (denoted sORFs3–15 aa). We reanalyzed Ribo-seq data obtained from 96 human samples of five different tissues (brain, testis, liver, kidney, and heart)22,23 using RiboTaper45 and ORFquant46-methods that leverage the 3-nt periodicity in ribosome footprints to newly identify translated ORFs. This resulted in the detection of 221 translated sORFs3–15 aa (Figures 4A and 4B; Table S4) of which $69\%$ were independently identified in at least two out of five analyzed tissues (Figure 4C). We validated the translation of $\frac{182}{221}$ ($82\%$) sORFs3–15 aa using a probabilistic algorithm for ORF detection (PRICE47). We further retrieved a set of predicted translation initiation sites (TISs) from a deep learning transformer model trained on the sequence context of canonical TISs48 and observed that TISs from sORFs3–15 aa were significantly more likely to initiate translation than untranslated TISs from the same transcripts (p values ranging from 0.04 to 5.17 × 10−82; Figure S4A; STAR Methods). The translation of the majority of these newly identified sORFs3–15 aa was thus substantiated by independently developed frameworks that utilize ribosome footprint periodicity, probabilistic inference, and machine learning. Figure 4sORFs smaller than 16 aa (sORFs3–15 aa) are highly translated in multiple tissues and often conserved across mammals(A) Detection of 221 candidate sORFs3–15 aa using ribosome profiling in five human tissues.(B) Distribution of sORF3–15 aa length separated by sORF biotype and source (gray: GENCODE catalog).(C) Numbers of sORFs called in each human tissue.(D) Genomic view of three loci with uORFs3–15 aa and the respective mainORFs. *The* gene orientation of SNRPN was reversed for clarity.(E) Ratio of P-sites per aa of the uORFs3–15 aa versus their respective mainORFs.(F) Normalized P-sites for all candidate sORFs3–15 aa whose structures are mapped and conserved in mouse ($$n = 166$$) and rat ($$n = 150$$). Gray bars represent sORFs3–15 aa without conserved structures or with a length of less than $70\%$ of the human ORF. Heatmaps are individually sorted by mean P-sites of the respective tissues.(G) Schematic of the PRM-MS assay.(H) Peptide sequence and chromatograms of fragment ions from synthetic and endogenous signature peptides of the SVIL-AS1-peptide3–15 aa in K562 cells and the human heart. The star represents the oxidation of methionine. The dot product (dp) indicates the similarity to the matching spectrum of the synthetic peptide and ranges from 0 to 1 with higher scores indicating better similarities. We note that the detected peptide also matches an alternative microprotein isoform of SVIL-AS1 of 81 aa (Table S4).
The vast majority of sORFs3–15 aa ($92\%$) were translated from 5′ UTRs of protein-coding genes (uORFs) and detected at much higher levels than the downstream primary CDS (average fold change of 3.06 ± 1.09 per tissue (Figures 4D and 4E). As the conservation of smaller sORFs is challenging to determine with approaches based on sequence homology,49 we calculated the conservation of the sORF structures across 27 primates, 92 mammals, and three non-mammalian vertebrate species. We found that 181 out of 221 sORF3–15 aa structures (∼$82\%$) were conserved across mammals, and for 36 cases, the conservation was extended to vertebrates (Figures S4B–S4D; STAR Methods). In support of this, most were found to be translated in at least two out of four mouse tissues ($61\%$; heart, brain, liver, and testis) and two rat tissues ($76\%$; heart and liver)22,23,50 (Figure 4F; STAR Methods). We further validated the translation of a highly conserved peptide encoded by USP10-uORF in bird tissues (Figure S4C). Noticeably, sORF3–15 aa structures displayed higher levels of species conservation compared with longer sORFs. This is possibly explained by their localization to 5′ UTRs, as these regions contain conserved sequences that can impose additional evolutionary constraints on sORFs3–15 aa located there, as well as the reduced likelihood of disrupting substitutions truncating very short sequences (Figure S4G; STAR Methods).
To collect proteomic evidence for the peptides translated from the identified sORFs3–15 aa, we searched the Human Proteome Map51 resource as well as global proteome52,53 and immunopeptidomic6,54,55 datasets. For the peptide identifications of both shotgun and immunopeptidome data, we applied an FDR filter of <0.01 on the peptide level using the reverse-sequence-based target decoy approach implemented in MaxQuant.56 The protein FDR filter was disabled, as had been performed previously for identifications of small proteins.45,57 Moreover, the peptides3–15 aa were included in the search database of the recent human HLA 2022-09 PeptideAtlas build29,58 (STAR Methods), and analyzed with the PeptideAtlas build pipeline29,58 and the Trans-Proteomic Pipeline58 using a non-specific (no protease) search strategy. In total, this led to the endogenous identification of 27 peptides from public datasets, including 16 from the HLA PeptideAtlas build. Five peptides3–15 aa were identified in multiple datasets with up to four different, unique peptides. Fourteen of the peptides3–15 aa detected in immunopeptidomics datasets were supported by at least one MS peptide that was in silico predicted to be a high-affinity binder of the major histocompatibility complex I (MHC I) (Figure S4F; Table S4).
To enhance the endogenous detection of these very small peptides, we additionally set up a targeted MS assay (parallel reaction monitoring [PRM]) in five human hearts and three cell lines (HEK239T, K562, and HeLa) (Figures 4G and 4H; Table S5; STAR Methods). Each identification was manually confirmed based on the analytical runs of synthetic peptide mixtures as well as the internal library-based fragment ranking, and only peptides detected in at least two biological replicates with a dot product of ≥0.7 were considered. This yielded evidence for 18 peptides3–15 aa, increasing the peptide-level evidence to 38 out of 221 candidates (Tables S4 and S5).
## Peptides encoded by sORFs3–15 aa have distinct interaction profiles
To interrogate their ability to interact with other proteins, we synthesized all 221 peptides3–15 aa in their entirety and investigated the interactomes via PRISMA (Figures 5A and S5A–S5G; Table S4). Hierarchical clustering of the enrichment values of all proteins identified in each peptide’s pull down revealed several peptide features (length, hydrophobicity, and isoelectric point) that contributed to specific binding profiles (Figure 5B).Figure 5Peptides encoded by sORFs3–15 aa have distinct interaction profiles(A) Schematic of the PRISMA approach with all 221 sORF-encoded peptides3–15 aa.(B) Hierarchical clustering of the enrichment values of all interacting proteins per peptide3–15 aa (STAR Methods). Factors potentially influencing the clustering (length, number of pull-downs per peptide3–15 aa [logarithmic scale], in-frame P-sites per aa [logarithmic scale], hydrophobicity, and isoelectric point) are depicted below the heatmap.(C) Volcano plot of the peptide encoded by MTMR3-uORF. Proteins assigned to the GO term clathrin-dependent endocytosis (GO:0072583) as well as the clathrin-binding protein CLINT1 are highlighted in red.(D) Left: string network of all significantly bound proteins of the MTMR3-uORF-peptide. Lines indicate confidence based on experiments, databases, and co-occurrence; high confidence (0.7). Right: MTMR3-uORF-peptide sequence (di-leucine motif highlighted) and GO enrichment analysis of its interactors.(E) Genomic view and sequence alignment of the highly conserved MTMR3-uORF locus in four human (left) and four mouse (right) tissues.22,23(F) Volcano plots summarizing the PRISMA results of the peptides3–15 aa translated from GATA4-uORF, VPS8-uORF, AC093642.6-lncORF, and STAT1-uORF.
After applying stringent filtering (STAR Methods), we detected on average 16 significant interaction partners for 166 out of 221 peptides3–15 aa (Figure S5H), several of which stood out for their particular interaction profiles (Figures 5C–5F and S5I). For instance, a peptide of only 5 aa translated from a highly conserved uORF within the MTMR3 gene (peptide sequence MLLWL) bound four clathrins (CTLA, CLTB, CLTC, and CLTCL1) as well as the clathrin assembly protein PICALM and the clathrin interactor CLINT1 (Figures 5C–5E). Interestingly, the interactome of this peptide resembled that of the GLUT1 mutant peptide carrying a di-leucine motif (Figures 2D, S5F, and S5G), which is known to recruit clathrins via adapter proteins.18 Furthermore, the interactomes of several peptides were enriched for proteins from distinct subcellular compartments, i.e., the nucleus (Figure 5F, panels 1–3), suggesting an organelle-restricted function. A seven aa long peptide encoded by the uORF of STAT1 interacted specifically with four proteins regulating mitotic spindle assembly and cytokinesis (Figure 5F, panel 4). These four proteins are not known to interact with each other, which excludes a secondary binder effect and supports direct interaction of the uORF-peptide with these proteins.
## Peptide interactomes can predict modulators of cellular function
Out of the 221 small peptide interactomes, we identified 16 hydrophilic uORF-derived peptides3–15 aa rich in arginine residues that predominantly interacted with components of the translational machinery (Figures 6A–6C and S6A–S6F). In order to assess whether the presence of the translated uORF impacted downstream translation, we set up reporter assays for five of these candidates. To retain the natural sequence context of the uORFs, we inserted the candidates embedded within their endogenous 5′ UTRs in front of the luciferase reporter. As controls for each candidate, we included ATG mutants (interrupted sORF translation) as well as arginine-to-alanine mutants (translated sORF with an altered sequence) (Figure 6D, upper panel; STAR Methods). In four cases, the presence of the intact uORF significantly reduced reporter translation rates when compared against the respective uORF with mutated ATG (Figure 6D). For two candidates (ASB15-uORF and PECAM1-uORF), the arginine-to-alanine mutants reversed the observed effect, i.e., downstream translation was not impacted (Figure 6D). This indicates that the arginines are important for the repression of downstream translation of the main ORF. However, we cannot discern if the amino acid or the encoding nucleotides are responsible for the effect. Figure 6Peptide interactomes can predict modulators of cellular functions(A) GO enrichment analysis of all interacting proteins of 16 ribosome-binding peptides3–15 aa compared with all other peptides.(B) Violin plot with hydrophobicity values of the 16 ribosome-binding peptides3–15 aa compared with all other peptides3–15 aa. Horizontal lines indicate the mean ± standard deviation.(C) Number of arginines of the 16 ribosome-binding peptides3–15 aa compared with all other peptides3–15 aa, normalized to the total number of amino acids.(D) Schematic and results of the luciferase reporter assay performed with five randomly selected ribosome-binding peptides3–15 aa. The significance was calculated using ANOVA and Tukey post hoc test.(E) Volcano plots of four AP-binding peptides3–15 aa. Proteins assigned to the GO term vesicle-related transport (GO:0016192) are highlighted in red.(F) Circos plot of all peptides3–15 aa that interact with endocytic proteins.(G) Peptide sequences of the four AP-binding peptides3–15 aa (aromatic aa highlighted in red, di-hydrophobic motifs underlined) and GO enrichment analysis of their interactomes.(H) Representative immunofluorescence images of fluorescently labeled RAP internalized by BN16 cells treated with DMSO, dynasore, PPARD- and ARMC1-uORF-peptide, respectively. Scale bar represents 200 μm.(I) Results of the RAP endocytosis assay (five replicates per condition). Values were normalized to total protein content, and samples without RAP treatment were subtracted and then normalized to the treatment with RAP only (=$100\%$). The PPARD-uORF-peptide, which did not bind APs, was included as a control (Figure S6J). The statistical significance was calculated using ANOVA and Tukey post hoc test.
PRISMA also revealed four peptides3–15 aa enriched for aromatic amino acids that bound APs involved in clathrin-mediated endocytosis (Figures 6E–6G, S6G, and S6H). Two of these peptides3–15 aa contain a tandem di-hydrophobic motif that was previously proposed as a non-classical AP-binding motif.59 When assessing the impact of the AP-binding peptides3–15 aa on endocytosis in vitro (Figure S6I), we found that two out of three reduced the cellular uptake of a ligand by the endocytic receptor low-density lipoprotein receptor-related protein 2 (LRP2) in BN16 cells by $50\%$ (Figures 6H and 6I). This effect was akin to the reduction observed upon treatment with dynasore (Figures 6H and 6I), a pharmacological inhibitor of clathrin-mediated endocytosis, but not upon treatment with a control peptide3–15 aa that did not interact with APs in PRISMA (Figure S6J). Based on these results, we hypothesized that the binding between these uORF-peptides and APs may inhibit clathrin recruitment during the formation of new vesicles and thereby reduce endocytosis (Figure S6K).
In summary, our findings illustrate how PPIs detected by PRISMA can hint toward putative biological roles of previously unknown peptides, stimulating future efforts into the mechanistic roles of both evolutionarily young and very small peptides translated from short ORFs in the human genome.
## Discussion
Microproteins have gained increasing attention in recent years, but the biological significance of human evolutionarily young and very small microproteins has remained less well studied.2,5,7,22,60,61,62,63 We aimed to address this knowledge gap by investigating the evolutionary origins and interactomes of the putative microproteins and peptides encoded by young and very small ORFs.
To define the fraction of young human microproteins, we evaluated the conservation of the amino acid sequences of over 7,000 recently cataloged human sORFs across more than 90 mammalian species and found that most were not conserved to non-primate mammals. We further present a detailed resource describing the evolutionary age and mechanisms of origin of these sORFs across primatomorpha, which included the genomes of 27 different primate and colugo species. Since the genomes of these species are highly similar, standard sequence similarity search methods were not sufficient to reliably infer homology for constrained proteins and estimate their evolutionary ages.64 We circumvented these limitations by assessing the conservation of translated sORF structures, i.e., we evaluated the conservation of the start codon and the presence of an intact ORF in the ancestor sequences of human young sORFs. We further estimated that 4,101 sORFs emerged de novo during primatomorpha evolution, including 162 human-specific ones. When translated into stable microproteins, this would increase the number of human-specific de novo microproteins reported previously7 by an order of magnitude. This substantiates observations that sporadic protein evolution “from scratch” may occur at a higher rate than previously thought.65,66,67,68 Of note, our evolutionary analyses are conservative because the presence of a homologous ORF sequence in other primate species does not indicate its expression per se. In primates, the extent of transcription and translation of young ORFs with conserved structures will require future studies that generate new pan-transcriptomes, -translatomes, and -proteomes from non-human primates.
In order to further investigate these novel microproteins, we employed PRISMA, which is highly suitable for the analysis of microprotein interactomes due to several technical advantages: [1] its high scalability allows the inclusion of hundreds of peptides which increases the statistical power to define significant interactions, [2] it does not rely on the ectopic expression of CDS vectors, [3] it does not require the addition of a tag (e.g., HA and FLAG), which is particularly problematic for small proteins, and [4] it does not depend on antibody-based affinity pull-downs that introduce antibody-specific background binding.
We show that young microproteins can bind proteins that are involved in diverse cellular processes, including proteins essential for cell survival. Moreover, our results illustrate how short sequence features such as SLiMs, which are prevalent within intrinsically disordered regions of microproteins,21 may contribute to the ability of recently evolved microproteins to engage with the more conserved human proteome. This capacity to interact may be present as early as at, or shortly after their evolutionary origin, without the need to evolve larger and more complex three-dimensional structures. For example, and although we could not completely rule out potential RNA-mediated effects, LINC01128-MP KO experiments suggested that this microprotein plays a role in transferrin accumulation in early endosomes in vitro, potentially enabled through interactions of the microprotein’s human-specific C terminus with endocytic proteins. This supports the idea that many young proteins can quickly become functional after they emerge de novo.69,70 Further investigations are, however, necessary to delineate how LINC01128 may affect endocytosis.
Furthermore, we identified 221 novel small translated sORFs3–15 aa by Ribo-seq in five human tissues. We demonstrated the translation of most of them by independently developed methodologies that exploit ribosome footprint periodicity (RiboTaper,45 ORFquant,46 and PRICE47), machine learning-based inference of TISs transformer,48 and evolutionary sequence alignments demonstrating conservation across mammals. The fact that the sORFs3–15 aa were translated at high levels and the majority of their structures were conserved to rodents, as supported by Ribo-seq, might indicate biological relevance. Of note, we obtained putative peptide-level evidence for 38 out of 221 predicted very small peptides3–15 aa with proteomics technologies. Each of these 38 peptides3–15 aa was smaller than the smallest human peptide translated from an individual sORF annotated as protein-coding to date (MOTS-c15; 16 aa). We would like to point out that MS identification of such small peptides is technologically challenging4 and the possibility of false-positive identifications cannot be completely excluded.
Following the PRISMA results obtained for newly detected peptides3–15 aa, we highlight a group of hydrophilic, arginine-rich uORF-encoded peptides3–15 aa that bound components of the translational machinery. We observed in a luciferase reporter assay that four translated uORFs3–15 aa reduced downstream translation, potentially through cis-mediated effects such as reinitiation inhibition71 or ribosome stalling.72 For two candidates, the translational inhibition of the downstream reporter seemed to depend on the peptide sequence and charge (i.e., presence of arginine residues). However, we cannot exclude that the effect stems from the underlying nucleotide change. We continued to show that members of a separate class of novel peptides3–15 aa could bind APs of the endocytic machinery and were capable of reducing endocytosis levels by $50\%$ in vitro. As a means to control for a possible influence that the transactivating transcriptional activator (TAT) sequence attached to our candidate peptides might have on endocytosis, we included a control peptide that did not bind any endocytosis-related proteins in the PRISMA screen. This peptide did not influence endocytosis levels, indicating that the TAT-peptide per se does likely not (solely) impact the observed effect. We hypothesize that the candidate peptides3–15 aa that reduce endocytosis might hinder clathrin recruitment and vesicle formation by competitive binding to APs. Interestingly, the protein encoded by the main CDS of one of these uORF-peptides—INSIG2—is known to mediate the feedback control of cholesterol synthesis.73 This hints that the INSIG2-uORF could contribute to the impact of INSIG2 on circulating cholesterol levels by regulating endocytic uptake of cholesterol-rich LDL particles, possibly indicating a co-evolution of the uORF and the main protein. In light of the great potential that similarly sized synthetic peptides have shown as pharmacological compounds,74 these peptides3–15 aa may also be exploited therapeutically as modulators of endocytosis, or new inhibitors of translation, a mechanism commonly used in antibiotics.75 In summary, our study provides new insights into the evolutionary origins and potential roles of evolutionarily young microproteins and very small peptides in humans. We describe motif-resolution interactomes for hundreds of human microproteins and peptides, which may serve as a basis for candidate-focused, independent validation experiments. We anticipate that these insights will set the stage for future investigations of this underexplored part of the human proteome, which will be crucial for our understanding of protein evolution, adaptation, and human-specific biology.
## Limitations of the study
Although the interactions detected by PRISMA suggest that small peptides and young microproteins can take part in fundamental cellular processes, we recognize certain limitations of the assay. Longer microproteins need to be tiled into smaller segments, which leads to the loss of the natural protein context, and the impact of globular protein folds and domains on microprotein interactomes will be missed. Moreover, PRISMA employs synthetic peptides and does not recapitulate a peptide’s stability, expression level, and concentration within cells. As a possible instability of the microproteins and peptides3–15 aa would preclude them from building stable interactions in vivo, PRISMA can only yield information on possible interactions that can be used to derive functional hypotheses but need further validation. Furthermore, the use of a cell protein lysate may yield interactions that would otherwise not occur due to cellular compartmentalization, it is not possible to discriminate between direct and indirect interactions, and we lose the fraction of cell-type-specific interactions that may occur in cell types other than the cell lysate used in this study. We highlight that PRISMA works with any cell or tissue lysate that is not suitable for conventional interactome approaches, such as hard-to-transfect cells (e.g., cardiomyocytes) or (human) disease-specific clinical samples. Lastly, we did not evaluate the impact of PTMs on microprotein interactomes, which will be of interest in future studies, particularly in regard to SLiMs that depend on PTMs.41
## Key resources table
REAGENT or RESOURCESOURCEIDENTIFIERAntibodiesMouse monoclonal anti-FLAG (M2), 1:500Sigma-AldrichCat#F1804; RRID: AB_262044Rabbit monoclonal anti-ATPIF1 (D6P1Q), 1:1000Cell Signaling TechnologyCat#13268; RRID: AB_10949890Rabbit polyclonal anti-Clathrin Heavy Chain (P1663), 1:100Cell Signaling TechnologyCat#2410; RRID: AB_2083156Alexa Fluor 488 anti-rabbit, 1:500InvitrogenCat#A11070; RRID: AB_142134Alexa Fluor 594 anti-mouse, 1:500InvitrogenCat#A11005; RRID: AB_141372Rabbit monoclonal anti-V5-Tag (D3H8Q), 1:500Cell Signaling TechnologyCat#13202; RRID: AB_2687461Chicken polyclonal anti-BirA, 1:500BioFront TechnologiesCat#BID-CP-100, RRID: not availableAlexa Fluor 488 anti-mouse, 1:500InvitrogenCat#A11001, RRID: AB_2534069Alexa Fluor 488 anti-chicken, 1:500InvitrogenCat#A11039, RRID: AB_142924Alexa Fluor 594 anti-rabbit, 1:500InvitrogenCat#A-11037, RRID: AB_2534095Mouse monoclonal anti-EEA1, 1:100BD Transduction LaboratoriesCat#610457, RRID: AB_397830Alexa Fluor 555 anti-mouse, 1:500InvitrogenCat#A31570, RRID: AB_2536180Biological sampleshuman heart tissue of five adult cardiomyopathy patients:Sample 1: female, DCM, age range 55-60Sample 2: male, DCM, age range 30-35Sample 3: male, HCM, age range 40-45Sample 4: male, HCM, age range 55-60Sample 5: female, HCM, age range 50-55Harvard Medical School, Boston USA, previously used in van Heesch et al.22N/AChemicals, peptides, and recombinant proteinsAlexa 647-conjugated transferrinInvitrogenCat#T23366Lysyl EndopeptidaseWakoCat#125-05061Trypsin GoldPromegaCat#V5280PRISMA synthetic peptides on a cellulose membraneJPTPRISMA - peptidesCrude synthetic peptides for PRM assayJPTSpikeTides/Maxi SpikeTidesProteinase KSigma-AldrichCat#3115879001Poly-D-LysineSigma-AldrichCat#P0899-50MGcOmplete, EDTA-free Protease Inhibitor CocktailRocheCat#11873580001synthetic peptides with TAT-sequencePepscancustomDAPIThermo FisherCat#R37606Critical commercial assaysDuolink® In Situ Proximity Ligation Assay Starter Kit, Red, Mouse/RabbitSigma-AldrichCat#DUO92101-1KTAlexa Fluor 594 Protein Labeling KitThermo FisherCat#A10239Dual Glo® Luciferase Assay SystemPromegaCat#E2920Deposited dataCataloged set of 7,264 Ribo-seq ORFsMudge et al.2https://www.gencodegenes.org/pages/riboseq_orfs/MS data: PRISMA (microproteins >15aa)This paperProteomeXchange (via PRIDE76): PXD033629, PXD033630MS data: microprotein pull-down with phosphosite identificationThis paperProteomeXchange (via PRIDE76): PXD033631MS data: PRISMA (peptides3-15aa)This paperProteomeXchange (via PRIDE76): PXD033651MS data: PRMThis paperProteomeXchange (via PRIDE76): PXD036997RNA-seq data for LINC01128 knockout and wild type cell linesThis paperEuropean Nucleotide Archive (ENA): PRJEB57619Public human ribosome profiling data used for detection of sORFs3-15aavan Heesch et al.22 and Wang et al.23left ventricular heart tissue: EGA accession code EGAS0000100326322; kidney: EGA accession code EGAS0000100326322; liver: EGA accession code EGAS0000100326322 and ArrayExpress accession code E-MTAB-724723; brain: ArrayExpress accession code E-MTAB-724723; testis: ArrayExpress accession code E-MTAB-724723Ribosome profiling data of mouse and rat tissuesvan Heesch et al. ,22 Wang et al. ,23 and Witte et al.50mouse heart: ENA accession code PRJEB2920822; mouse liver: ArrayExpress accession code E-MTAB-724723; mouse brain: ArrayExpress accession code E-MTAB-724723; mouse testis: ArrayExpress accession code E-MTAB-724723; rat heart: ENA accession code PRJEB3809650 and rat liver: ENA accession code PRJEB3809650RNA sequencing datasets of HEK293T cellsSchueler et al.77NCBI Sequence Read Archive (SRA) SRR1107836 and SRR1107837Microscopy data: original images from immunofluorescence stainingsThis paperMendeley Data https://doi.org/10.17632/ckgdgty885.178Supplemental excel tables Tables S1, S2, S3, S4, and S5This paperMendeley Data https://doi.org/10.17632/ckgdgty885.178Experimental models: Cell linesBrown Norway rat yolk sac carcinoma (BN16) cellsATCCATCC® CRL-2180HeLa cellskindly provided by A. Woehler, MDCN/AHeLa LINC01128-MP KO cells and wild type cellsSynthego Inc.N/AHEK293T/17 cellsATCCATCC® CRL-11268K562 cellskindly provided by T. Blankenstein, MDCN/ASoftware and algorithmsBLASTp (v.2.7.1)Altschul79https://blast.ncbi.nlm.nih.gov/Blast.cgiPhyloCSFLin et al.80https://github.com/mlin/PhyloCSF/wikiPRANK (v.170427)Löytynoja81http://wasabiapp.org/software/prank/STAR (v.2.5.2b)Dobin et al.82https://github.com/alexdobin/STARBEDTools (v.2.27.1)Quinlan83https://bedtools.readthedocs.io/en/latest/UCSC LiftoverLee et al.84https://genome.ucsc.edu/goldenPath/help/hgTracksHelp.html#LiftoverStringtie (v.1.2.1)Pertea et al.85https://ccb.jhu.edu/software/stringtie/RepeatMasker (v.4.1.0)Smit and Hubley86https://www.repeatmasker.org/RepeatMasker/MaxQuant (v.1.5.2.8. and v.1.6.0.1)Cox and Mann56https://www.maxquant.org/R (v.3.6.1)R Core Team87https://www.r-project.org/IUPred (v.1.0)Dosztányi et al.36https://iupred2a.elte.hu/‘elm_classes.tsv’ file (version 1.4; 15 January 2018)Gouw et al.37http://elm.eu.org/gProfiler2 (v0.2.0)Reimand et al.88https://cran.r-project.org/web/packages/gprofiler2/index.htmlDESeq2 (v1.26.0)Love et al.89https://bioconductor.org/packages/release/bioc/html/DESeq2.htmlRiboseQCCalviello et al.90https://github.com/ohlerlab/RiboseQCPRICE (v1.0.3b)Erhard et al.47https://github.com/erhard-lab/priceTIS TransformerClauwaert et al.48https://github.com/jdcla/TIS_transformerMSFraggerKong et al.91https://msfragger.nesvilab.org/Trans-Proteomic PipelineDeutsch et al.58http://www.tppms.org/Proteomapper (v.1.5)Mendoza et al.92http://www.tppms.org/tools/pm/Skyline (v21.02)MacLean et al.93https://skyline.ms/project/home/software/Skyline/begin.viewFijiSchindelin et al.94https://imagej.net/software/fiji/NetMHCpan-4.1Reynisson et al.95https://services.healthtech.dtu.dk/service.php?NetMHCpan-4.1ORFquant (v1.00)Calviello et al.46https://github.com/lcalviell/ORFquantOtherPython, R and Bash scripts used for the analysisThis paperZenodo https://doi.org/10.5281/zenodo.755381796Github: https://github.com/jorruior/riboseq_orfs_analyses
## Lead contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Norbert Hubner ([email protected]).
## Materials availability
This study did not generate new unique reagents.
## Cell culture
Human female HeLa cells (kindly provided by A. Woehler, MDC), HeLa LINC01128-MP KO cells (Synthego Inc., Redwood City, CA) and human female HEK293T/17 cells (CRL-11268, ATCC) were cultured in a humidified incubator at 37°C with $5\%$ CO2 using Dulbecco’s modified eagle medium (DMEM) with high glucose (4.5 g/l), $10\%$ fetal bovine serum (FBS), 2 mM L-glutamine and 1 mM sodium pyruvate. Brown Norway rat yolk sac carcinoma (BN16) cells (CRL-2180, ATCC, sex unknown) were cultivated in DMEM supplemented with $10\%$ FBS and $1\%$ Penicillin/Streptomycin. Human female K562 cells (kindly provided by T. Blankenstein, MDC) were cultured in RPMI medium supplemented with $10\%$ FBS, $1\%$ Penicillin/Streptomycin, 1 mM sodium pyruvate, 1 mM non-essential amino acids and 0.05 mM beta-mercaptoethanol. The medium was renewed every two to three days and cells were passaged at 80-$90\%$ confluency using standard trypsinization methods. Since K562 cells grow in suspension they were passaged without trypsin treatment.
## Human primary material
Targeted proteomics (PRM) experiments for in vivo detection of microproteins and peptides3-15 aa were performed on human heart tissue of adult cardiomyopathy patients with HCM ($$n = 3$$; mutations in MYH7 (2x) and MYBPC3) and DCM ($$n = 2$$; mutations in LMNA (2x)) obtained from Harvard Medical School, Boston USA, approved by the Mass General Brigham Human Research Protection Committee (Protocol 1999P010895); Harvard Longwood Campus Institutional Review Board (Protocol M11135). Samples were previously used in a study by van Heesch et al.22
## Retrieval of sORF and control sequences
We retrieved a set of 7,264 Ribo-seq ORFs longer than 15 aa (denoted sORFs) annotated as part of the Phase I GENCODE ORF annotation project (Table S1).2 For our analyses, we combined sORFs annotated as downstream overlapping ORFs (doORFs) and downstream ORFs (dORFs), since doORFs are a rare category that represents only $0.8\%$ of all sORFs. This resulted in five considered sORF biotypes: lncRNA ORFs (lncORFs, encoded by presumed long non-coding RNAs and processed transcripts), upstream ORFs (uORFs, encoded by 5′ UTR sequences), upstream overlapping ORFs (uoORFs, encoded by 5′ UTR sequences and partially overlapping an annotated CDS in an alternative frame), internal ORFs (intORFs, fully overlapping an annotated CDS in an alternative frame), and downstream ORFs (dORFs, encoded by 3′ UTR sequences).
In order to determine the significance of our findings in subsequent evolutionary analyses, we defined two additional control sets. Firstly, we selected a set of 527 annotated CDS sequences (Ensembl v.10197) from genes in which all annotated protein isoforms were shorter than 100 amino acids (aa), selecting the longest CDS per gene and discarding incomplete CDSs without annotated start and/or stop codons (sCDS, Table S1). Secondly, we generated negative controls of untranslated ORF sequences. To this end we sampled length-matched sequences starting with ATG codons from non-coding regions of genes hosting uORFs and lncORFs and we translated them in silico. These untranslated regions did not overlap any annotated CDS or translated sORF sequence included in this study. We excluded genes hosting ORFs overlapping with conserved CDS sequences (uoORF, intORF, dORF), since the overlapping coding sequences can impose additional constraints on these ORFs. *We* generated a set of 2,068 and 2,914 length-matched untranslated regions in lncRNAs and 5′ UTRs, respectively. This set covers $93.54\%$ of the genes containing uORFs and lncORFs included in the analysis. For the rest of the cases, we could not extract any compatible sequence from the corresponding untranslated regions.
## Whole-genome alignments across mammalian species
We downloaded a comparative genomics resource that comprises pre-built whole-genome nucleotide alignments across 120 mammalian species98 to calculate the extent of conservation of all human ORFs across the mammalian lineage. Additionally, we downloaded Cons30 multiple alignments from UCSC84 comprising 27 different primate species (non-primate species were removed from the alignment). For a set of ten species with high-quality genomes (rhesus macaque, mouse, cow, dog, horse, elephant, opossum, chicken, western clawed frog, zebrafish) we also retrieved whole-genome Liftover chains from UCSC.84 We included chicken, western clawed frog, and zebrafish as evolutionary outgroups to find potential ORFs with vertebrate conservation that extended beyond the mammalian clade. Finally, for every ORF we designed a custom script to define local multiple alignments including the species where the region could be fully aligned, discarding partial or ambiguous alignments.
## Protein sequence conservation of sORFs and sCDS
Standard homology-detection approaches are not adequate for discovering sORF homologues in full transcriptomes or genomes due to the extense search space and the short length of the query sequences.49,99 Hence, we instead estimated the levels of protein similarity of sORF- and sCDS-encoded microproteins (>15 aa), as well as untranslated ORF controls, to evaluate the significance of the similarity across a reduced set of previously aligned counterpart regions in mammals extracted from whole-genome alignments. When at least one genome region was aligned to an ORF, we ran BLASTp (v.2.7.1)79 against species-specific databases containing all aligned regions and extracted the E-value of the alignment of the ORF against each specific counterpart region. Next, we calculated a conservation score (CS) for each encoded microprotein, defined as the negative logarithmic value (-log10) of the median E-values across all aligned species. Since each species-specific database contained variable numbers of counterpart regions due to differences in genome quality and divergence, we decided to use E-values instead of bit-scores as they are adjusted by the size of the sequence databases. However, pairwise alignment bit-scores computed by BLAST can also clearly distinguish between young and conserved sequences (Figure S1C). Because of the high genome similarity across primate species, we limited this score to non-primate mammalian species, where unconstrained genome sequences show higher divergence. We therefore excluded all mammalian species that were classified as primates or colugos (primatomorpha). Phylogenetic reconstructions have shown that the genomes of colugo and primate species are quite related, hence colugos can be phylogenetically classified as a sister taxon to primates.100 Unaligned species were not considered, so counterpart regions which evolved across different branches of mammalian evolution were evaluated for conservation.
Finally, we selected a CS significance cutoff ≥ 8 for consistent amino acid sequence similarity conservation across non-primate mammals. We estimated a FDR < 0.01 by randomly extracting 10,000 size-matched sequences from untranslated regions of the same genes that host sORFs and calculating the distribution of CS scores for these sequences. Hence, amino acid sequences below the CS threshold are defined as ‘conserved proteins’ through mammalian evolution, as opposed to the rest of sequences that encode ‘evolutionarily young’ proteins without conserved protein homologues in non-primate mammals. Of the 7,264 cataloged sORFs and 527 sCDS, 758 sORF-encoded microproteins ($10.43\%$) and 379 sCDS-encoded microproteins ($71.37\%$) were conserved across mammals, including at least 375 sORF-encoded microproteins and 257 sCDS-encoded microproteins with significant conservation in some vertebrate species (translated orthologous regions of chicken, western clawed frog, and/or zebrafish with pairwise E-value < 10-4). Conserved proteins were aligned to an average of 71 non-primate mammalian species, with $95\%$ of ORF sequences aligned to 5 or more species. $69.3\%$ and $59.7\%$ of the non-conserved sORFs could be aligned to at least one and more than five non-primate mammalian species, respectively.
Of note, the fraction of sORF-encoded microproteins with detectable homologues in non-primate mammals was higher for ORFs overlapping protein-coding sequences (∼30-$35\%$ of uoORFs and intORFs) compared to other ORF biotypes (∼4-$5\%$ of lncORFs, uORFs and dORFs) (Figure S1D). Conserved sORF-encoded microproteins were longer than young ones (median length, 64 versus 37 amino acids, Figure S1A) and exhibited higher levels of protein similarity than both non-conserved sORF-encoded microproteins as well as untranslated ORF sequences from the same transcripts (Figure S1B).
Furthermore, we searched for signatures of evolutionary protein-coding potential in the set of young sORFs, sCDS, and untranslated controls by running PhyloCSF80 with default parameters. PhyloCSF scores were calculated across the retrieved multiple alignments for primates and for mammals. Young sORFs displayed similar PhyloCSF80 scores to untranslated control sequences across primates and mammals, indicating that the codon sequences of young sORFs were not constrained at the protein-coding level (Figure S1E).
## Conservation of sORF structures
We firstly adapted a previously published method101 and used PRANK v.17042781 to reconstruct ancestral sequences based on the built Cons120 and Cons30 whole-genome multiple alignments. Next, we evaluated the conservation of young sORF, sCDS and untranslated ORF structures (structural conservation) across ancestral sequences in the primatomorpha lineage. An ORF structure was considered as conserved in an ancestral region if the ATG translation initiation site (TIS) was present in the same position or within an in-frame window 6 nt downstream of the human ATG position, and if ≥ $70\%$ of the sequence did not contain stop codons truncating the ORF. For the cases with different alignments in Cons120 and Cons30, we selected the one with the highest level of conservation. The considered lineages were humans (<6-8 Million years ago (Mya), age 0), old world monkeys (∼35 Mya, age 1) and primatomorpha (including primates and the only two living colugo species; ∼88 Mya, age 2). Hence, these categories define the most distant ancestral primatomorpha sequence predicted to contain the full ORF structure. These ORFs could be fixed across all species from the lineage, they could be segregating and only be present in a subset of species from the lineage, or they might have evolved convergently in independent primate and non-primate lineages (although with highly divergent protein sequences).
The numbers of species assigned to each lineage and the genome releases used to map sequencing datasets are described in Table S1.
## Expression and translation of sORFs in mammals
To determine if the absence of a conserved ORF structure in a specific species was supported by the absence of ribosome occupancy and/or translation in the same region, we downloaded human, macaque and mouse Ribo-seq data from brain tissue, three replicates each (ArrayExpress accession number E-MTAB-724723). Ribo-seq reads were trimmed for adapters, filtered to remove common rRNA, tRNA and mitochondrial RNA contaminants, and mapped to the human, macaque, or mouse genome using STAR v2.5.2b82 with maximum of 2 mismatches. Next, we ran ORFquant 1.0046 to call translated ORFs in the human brain and subsetted 830 translated sequences that were also found in the set of 7,264 cataloged sORFs. Subsequently, we extracted local sORF coordinates from non-human species genomes using LiftOver chain alignments and ran BEDTools v2.27.183 to quantify the number of reads overlapping these sequences. In both macaque and mouse species, the absence of conservation of sORF structures led to a strong significant drop in the number of mapped Ribo-seq reads (macaque: median of 79 reads vs 14 reads for conserved and non-conserved ORF structures, Wilcoxon signed rank test p-value = 9.11 x 10-33; mouse: median of 97 reads vs 8 reads for conserved and non-conserved ORF structures; Wilcoxon signed rank test, p-value = 2.58 x 10-61; Figures 1F; Figure S1G), while the levels of ribosome occupancy remained constant in human. We additionally applied a simple binomial probability test to determine how many of the macaque and mouse brain counterpart regions had significant Ribo-seq periodicity biases (p-value < 0.01), as previously done by Patraquim and colleagues.102 In support of our method, counterpart regions of human brain ORFs without conserved structures displayed lower periodicity biases in macaque and mouse (Figure S1H). However, we still found a group of ORFs without conserved structures but with significant periodicity in macaque ($27\%$) and mouse ($18\%$). Thus, a small proportion of the non-conserved ORFs are still translated into shorter ORFs (<$70\%$ of human length sequence) or use alternative translation initiation or splice sites.
Moreover, we ran ORFquant to call translated ORFs in the macaque brain and found that counterpart regions of human ORFs without conserved structures were depleted from actively translated ORFs in macaque (Test of equal proportions, p-value < 0.05; Figure 1G). In addition, we retrieved a unified set of translated ORFs from several mouse tissues103 and determined how many of these ORFs overlapped regions aligned to human sORFs with different levels of conservation (Figure S1I). As expected, mouse regions that could be aligned to evolutionarily young ORFs contained a very low proportion of translated ORFs ($5.30\%$) compared to conserved sORFs present in mammals ($39.66\%$). This indicated that, for human brain translated ORFs, the corresponding truncated ORF sequences were not actively translated in other non-human species.
We next estimated how many counterpart regions potentially containing cataloged sORFs were supported by RNA expression evidence in other species. Chimpanzee, macaque, mouse, cow, dog, horse, elephant and opossum transcriptomes and genomes were retrieved from Ensembl v.98. Chimpanzee and macaque RNA-seq data from brain, heart, liver and testis were downloaded from Gene Expression Omnibus with accession code GSE69241.104 RNA-seq reads were trimmed for adapters and mapped to each corresponding genome using STAR v2.5.2b82 with a maximum of 4 mismatches. Chimpanzee and rhesus macaque RNA-seq data from the four tissues were combined to assemble a species-specific reference-guided transcriptome with Stringtie v1.2.185 (parameters -M 0.5 -j 3 -p 4 -f 0.2). For each species, we then searched for annotated and/or assembled transcripts overlapping the previously generated LiftOver regions. We found that 90-$93\%$ and 74-$92\%$ of the counterpart regions of conserved sORFs overlapped transcripts expressed in primate and non-primate mammalian lineages, respectively. In contrast, evolutionarily young sORFs were less commonly expressed in rodents ($60\%$), especially in more distant mammalian lineages, such as ferae ($32\%$). This indicated that, in mammals, the presence of conservation is often linked to the presence of expression (Figure S1J). A similar trend could be observed for the expression of primate-specific versus mammalian- and vertebrate-specific sORF3-15 aa (Figure S4B).
## Mode of evolution of sORFs
To identify the mode of evolution of evolutionarily young sORFs, we ran BLASTp to inspect whether any of the ORF sequences displayed significant homology to any other annotated protein in the human, macaque, mouse, cow, dog, horse, elephant and opossum proteomes (Ensembl v.98). For sORFs with significant matches (E-value < 10-4), we further assessed if the candidate orthologous protein was translated in the same aligned genomic region or a different one by analyzing the previously generated LiftOver coordinates for these species. Orthologs found in a different region were defined as ‘CDS duplications’, while the ones found in the same aligned genomic region were defined as ‘CDS fissions’, provided that both the sORF and the rest of the human protein exhibited homology to different parts of the same protein in the other species.
For the remainder of the sequences, we re-analyzed the generated multiple and LiftOver alignments to trace the sORFs back to their evolutionary origins. We classified an sORF as ‘de novo’ if the age of the aligned region predated the age of the ORF, hence being able to spot the mutations responsible for the birth of the ORF sequence. For the cases where the region could not be aligned beyond the lineages where the ORF structure was conserved, we downloaded transposable element annotations from RepeatMasker v.4.1.086 and inspected whether both the ORF and the region emerged as a result of the insertion of an endogenous retrovirus (EVR) or alu element (category ‘EVR/Alu derived’). The mode of evolution of the remaining sORF sequences could not be correctly assessed with the available data, since the ancestral region evolved at the same time as the ‘orphan’ ORF sequence, and we classified these cases as ‘not known’. A comparison of the main findings of our approach and the one recently developed by Vakirlis and colleagues7 is available in the following section.
## Comparison of the modes of evolution with a previous resource
We compared our resource on modes of evolution with a recent study from Vakirlis et al. that evaluated the modes of evolution of 715 ORFs structrures.7 Vakirlis et al. used aligned sequences from the UCSC 100-way phylogenetic tree to reconstruct the ancestral sequences and evaluate frame conservation –independent of the presence or absence of an initiating ATG codon- and the expression of the loci in other species. This set of 715 ORFs was extracted from a published ORF dataset,6 which was one of the seven datasets included in the GENCODE sORF resource that we included in our study. Of these 715 ORFs evaluated by Vakirlis et al., 452 overlapped our sORF list. The reasons why 263 ORFs were excluded from our dataset were diverse: some overlapped in-frame annotated coding sequences and pseudogenes, had a length under the selected cutoff (< 16 aa), or could not be fully mapped to annotated GENCODE transcripts. Vakirlis et al. found 155 out of 715 ORFs to have evolved de novo, of which 94 overlapped with our ORF set. We similarly classified 60 out of these 94 ($63.82\%$) ORFs as de novo evolved, while for 31 cases we could not assign any mode of evolution (‘not known’). Only for 3 out of 94 ($3.19\%$) cases we found a duplication event that would disqualify the ORF as having emerged de novo as per our analysis. These are: [1] c10riboseqorf103 (RPARP-AS1_104210065_114aa), which is encoded by an antisense lncRNA and the ORF contains a partial duplicated region which overlaps with the 3′ UTR of the sense gene c10orf95, [2] c19riboseqorf102 (ZNF585A_37701369_35aa), which is a uORF harboring a partially duplicated region of the local neighbor gene AC012309.1, and [3] cXnorep31 (ZNF81_47696378_89aa), which contains an exon derived from an AluSz element that is also integrated in the coding sequences of several primate genes. Moreover, Vakirlis et al. reported 7 de novo ORFs as being human specific. Three out of these 7 overlapped the ORF list evaluated in our study and we also classified them as emerged de novo, although we defined an older primatomorpha origin for one of the ORFs (MALAT1_65266767_39aa / c1riboseqorf84) and a human origin for two of them (PTPRF_43996733_27aa / c1riboseqorf84 and TTC9C_62496064_18aa / c11riboseqorf70). Our study additionally identified 160 human-specific de novo ORFs, including 147 cases that were not evaluated previously by Vakirlis et al. and likely therefore not yet reported as de novo human-specific ORFs. We classified an additional 13 ORFs as being human specific that Vakirlis et al. reported as being conserved in non-primate mammals. Although our results generally overlap very well, the observed discrepancies in the mode of evolution for some of the cases might be due to differences in the parameters for the computational methods (i.e., we require conservation of the initial ATG codon) and the selection of distinct genome-wide alignments with different numbers of primate, mammalian and vertebrate species.
## Candidate selection for PRISMA of microproteins translated from recently evolved sORFs
We selected 45 evolutionarily young microproteins (CS < 8, longer than 15 aa) to investigate their interactomes with PRISMA. This included 41 microproteins putatively encoded by cataloged Ribo-seq ORFs (32 lncORFs, 5 uORFs, 4 uoORFs) as well as 4 microproteins putatively encoded by novel sORFs outside annotated gene regions discovered by us in a recent study (3 lncORFs and 1 uORF).22 We additionally included 15 microproteins translated from conserved mammalian sORFs to compare the interactomes of young and conserved proteins of similar protein sizes. Four out of the 15 selected conserved microproteins were translated from lncORFs, three from uORFs and eight were sCDS (annotated small proteins). Four of the conserved sCDS (MIEF1-MP, MTLN, NDUFB3 and MRPL33) evolved during vertebrate evolution and had been included in interactomics studies before.30,31,105,106 The properties, conservation and mode of evolution of each candidate were calculated and reported in Table S2.
In addition to the levels of conservation, we based the selection of the PRISMA candidates on the likelihood of these sORFs to be translated into stable microproteins. Therefore, we collected evidence for sORF translation in multiple datasets, as well as the exogenous and endogenous detectability of their translation products (microproteins). Moreover, we considered the potential relevance of the sORF or the host gene in disease. Details on how we collected all this information are described below.
## Ribo-seq datasets
We counted the number of Ribo-seq studies that supported the translation of candidate sORFs. To this end, we retrieved evidence from the study of Mudge and colleagues that collected data from seven Ribo-seq studies to create a first consolidated sORF catalog.2
## ORF databases
We investigated how many of the candidate sORFs were reported in three public ORF annotation databases: SmProt,25 MetamORF26 and sORF.org.27 Nine were detected in one database, 12 in two and 16 in all three databases.
## In vitro translation evidence
We searched for candidate sORFs that were able to produce detectable microproteins in coupled in vitro transcription:translation assays, as described and reported in our previous study,22 and found 23 out of 24 candidates that were tested.
## Antibody-based microprotein detection after overexpression
We surveyed our previous study22 as well as three others5,6,28 for sORFs that produced detectable microproteins after ectopic expression in cultured human cells. In all four studies, epitope-tagged microproteins were overexpressed in human cell lines and detected either via immunoblotting or immunofluorescence. Additionally, in the current study we overexpressed 31 microproteins with a C-terminal 3xFLAG-tag and detected protein expression of 26 candidates by immunofluorescence (details in STAR Methods section detection of overexpressed microproteins by immunofluorescence and Data S1).
## Endogenous mass spectrometric evidence
We collected protein expression evidence of 15 candidate sORF-encoded microproteins from our previous study, based on targeted mass spectrometry (selected reaction monitoring, SRM) data,22 and acquired evidence for four more candidates (PVT1-MP, MRPL33, NDUFB3 and MIEF1-MP) in a targeted mass spectrometry assay (parallel reaction monitoring, PRM) performed in this study (details in STAR Methods section parallel reaction monitoring (PRM) proteomics). We additionally searched for identified peptide spectrum matches (PSMs) that uniquely mapped to sORF-encoded microproteins in a previous meta-analysis of 16 published MS searches, including both shotgun MS and HLA peptidomics studies.2 For 32 sORFs, we found at least one PSM mapping to their encoded amino acid sequences. We also retrieved evidence of sORFs protein expression from PeptideAtlas [2022-01].29 For seven sORFs, we found evidence of PSMs uniquely mapping to their respective translated microproteins in HLA peptidomics datasets. However, for five out of seven cases this evidence was limited to single peptide identifications. While these peptides were not assigned to any other translated sequences in the human proteome, further manual curation will be required to validate the collected MS evidence, especially when only supported by a unique PSM.
## Candidate disease relevance and prior indications of potential microprotein function
We collected indications for disease relevance to prioritize candidates. To this end, we took into account whether the microprotein host gene was a presumed lncRNA that had been implicated in disease, anticipating that the encoded, but previously missed microprotein might have contributed to the observed phenotypic effect. We interrogated the manually curated database EVLncRNAs 2.0,107 which contains lncRNAs whose function and disease association was validated by low-throughput and targeted experiments. Similarly, we searched for sORFs experimentally interrogated in different functional assays to find candidates with reported biologically active roles. First, we used the output data from the functional assay on 553 ORFs from Prensner and colleagues5 and found 11 ORFs whose microproteins were included in our PRISMA design. We extracted two functional scores based on CRISPR phenotype and transcriptional activity score (transcriptional perturbation after overexpression). An ORF scored positive if the CRISPR phenotype was significant in the original study (CRISPR phenotype = 1) and/or the transcriptional activity score was higher than or equal to 0.2. None of the 11 microproteins had an effect on cell growth, but five young (RP11-140K17.3-MP, DANCR-MP, PRR34-MP, SNGH8-MP, and SNHG6-MP) and three conserved microproteins (MTLN, MKKS-uORF-MP, and MIEF1-MP) altered cellular transcription profiles after overexpression. Second, we selected 771 sORFs translated in the dataset published by Chen and colleagues,6 one of the studies selected for the GENCODE Ribo-seq ORF catalogue, and retrieved CRISPR and Perturb-seq scores from the same study for 10 ORFs encoding microproteins included in our PRISMA design. An ORF was positive if the CRISPR score was significant in the original study (p-value ≤ 0.5) and/or the Perturb-seq assay found pathways affected significantly after knocking-out the ORF. Four conserved microproteins (RNF10-uORF-MP, TUG1-MP, MIEF1-MP and IFRD1-uORF-MP) influenced cell viability and one young microprotein (PPP1R15A-uORF-MP) and three conserved microproteins (MIEF1-MP, LINC00881-MP and IFRD1-uORF-MP) scored in the Perturb-seq assay. Combined, the merger of both studies yielded 13 ORFs whose microproteins were included in our PRISMA design that either affected cellular growth or transcription profiles in at least one of the two investigated studies, of which seven would fall into the ‘young’ category, and six would be considered conserved. Additionally, of the 221 peptides3-15 aa, 23 had been previously interrogated by Chen and colleagues, while Prensner and colleagues only included candidates >15 aa. Of those peptides3-15 aa included by Chen and colleagues, six scored positive, i.e., impacted cell viability.
## Summary of selection criteria
The selection criteria for each candidate are summarized in Table S2. Briefly, 30 out of 45 young and 11 out of 15 conserved sORFs were found to be translated in at least two independent sORF studies. Exogenous protein-level support for encoded microproteins was obtained for 44 sORFs from either in vitro translation assays (nyoung = $\frac{21}{45}$; nconserved = $\frac{3}{15}$) or antibody-based detection after overexpression (nyoung = $\frac{39}{45}$; nconserved = $\frac{13}{15}$). Mass spectrometric detection supported endogenous production of 29 young and 12 conserved microproteins in human cell lines and tissues. Seven young and six conserved microproteins were translated from ORFs with hits in CRISPR and overexpression screens, and 22 were translated from lncRNAs implicated in disease, including cardiovascular disease [10] and cancer [17].
## Detection of overexpressed microproteins by immunofluorescence
*Synthetic* gene fragments containing the codon-optimized coding sequence of candidate microproteins with a C-terminal 3xFLAG tag were synthesized and cloned into a customized plasmid for mammalian expression by Genewiz Europe (Leipzig, Germany; constructs available upon request). The 3xFLAG-tagged human microproteins were overexpressed in HeLa cells and visualized using immunofluorescence as described by us previously.22 Human HeLa cells were grown on glass slides in 12-well plates for 24 h and transfected with plasmids encoding c-terminally 3xFLAG-tagged microproteins using Lipofectamine 2000 according to manufacturer’s instructions. The plasmids used are available upon request. 24 h post transfection cells were fixed with $4\%$ paraformaldehyde (PFA) for 10 min at room temperature (RT) and washed three times with ice cold phosphate-buffered saline (PBS). Cells were permeabilized and blocked for 1 h with $2.5\%$ bovine albumin serum (BSA), $10\%$ anti-goat serum (NGS) and $0.1\%$ Triton X in PBS. After washing the cells, overexpressed microproteins were stained with anti-FLAG mouse monoclonal antibody (1:500 in PBS with $5\%$ BSA, F1804, Sigma Aldrich) for 1 h at RT. Mitochondria (1:1000 rabbit anti-ATPIF1 in PBS with $5\%$ BSA, #13268, Cell Signaling Technology, Danvers, MA, USA) and in the case of LINC01128-MP overexpression clathrin-coated vesicles (1:100 rabbit anti-Clathrin Heavy Chain (P1663) in PBS with $5\%$ BSA, #2410, Cell Signaling) were co-stained in this step. Afterwards cells were washed and incubated with fluorochrome-labeled secondary antibodies (1:500 in PBS with $5\%$ BSA, Alexa Fluor 488 anti-rabbit and Alexa Fluor 594 anti-mouse (Invitrogen, Carlsbad, CA, USA) for 30 minutes at RT. Cells were washed again, stained with 4-6-diamidino-2-phenylindole (NucBlue Fixed Cell ReadyProbes Reagent, R37606, Thermo Fisher) for 5 minutes at RT and mounted onto glass slides using ProLongTM Gold antifade reagent (Molecular Probes; InvitrogenTM). Images were taken with a LEICA SP8 confocal microscope using a 63x objective and analyzed using ImageJ (v1.53c).108
## Experimental setup
We adapted the PRISMA assay from the PRISMA assay introduced previously by others and ourselves.17,18,19,20 Each of the 60 putative microproteins was divided into 15 aa long, overlapping peptides (tiles) with an offset of eight aa. This resulted in 478 tiles (minimum of two, maximum of 21, average of eight tiles per microprotein). To evaluate if arbitrary peptides derived from untranslated RNA sequences would serve as a suitable control for our screen, we generated a set of arbitrary peptides which we translated in silico from non-coding regions (5′ UTRs or lncRNA exons without translation in our Ribo-seq data). These sequences start with ATG codons and are located in the genes hosting the 271 microproteins and peptides included in the PRISMA analysis, with length-matched distributions. We only considered regions that were not covered by annotated coding sequences nor by sequences from the set of 7,264 sORFs>15 aa and 221 sORFs3-15 aa analyzed in our manuscript. We found that the 45 young microproteins included in the PRISMA analysis present similar amino acid compositions compared to untranslated sequences, as observed in the PCA (Figure S2D). This is in line with our observation that most of the young microproteins recently emerged de novo from non-coding RNA sequences. Moreover, we generated 10,000 sets of shuffled sequences for each of the 45 young microproteins included in the PRISMA screen and compared the numbers of SLiMs predicted in these sequences. The numbers of SLiMs in young microproteins and shuffled sequences are not statistically different (978 SLiMs in young microproteins and an average of 948 SLiMs in shuffled sequences, p-value = 0.16, Figure S2E). Because of this, we expect the extent and specificity of the interactomes of untranslated sequences, shuffled sequences and young microproteins to be rather similar, indicating that arbitrary or random peptides would not serve as a helpful control. Instead, we included four characterized control peptides derived from the proteins SOS1 and GLUT1 that had been investigated in previous proteomic interaction screens.18,32 In total, 490 peptides were spot-synthesized (SPOT synthesis technology) on three cellulose membranes (JPT Inc., Berlin, Germany), with the GLUT1 and SOS1 peptide controls present on each membrane. Each spot carries approximately five nmol of peptide covalently bound to the cellulose-ß-alanine-membrane. Peptides were acetylated at their free N-termini to enhance stability and to better recapitulate the uncharged nature of a protein backbone. All three biologically distinct membranes were ordered and processed in triplicates. Sequences of spotted tiles can be found in Table S3. The spotted peptides have been referred to as “baits”, the proteins that are bound by these peptides as “prey”.
The impact of peptides on receptor-mediated endocytosis was tested in an endocytosis assay in cultured BN16 cells expressing the endocytic receptor low-density lipoprotein receptor-related protein 2 (LRP2/megalin).119 To do so, we ordered synthetic peptides including three candidate peptides and one control peptide from Pepscan (Lelystad, Netherlands) with a TAT-sequence at the N-terminus (GRKKRRQRRRPQ) to facilitate the entry into the cytoplasm of the cells, a linker sequence (Ahx), and a fluorescent 5[6]-FAM to be able to visualize the internalized peptides (FAM-{Ahx}-TAT-peptide). We decided to use a TAT-peptide for shuttling the peptides into the cells, as we believed that it would not influence the results of the assay. The mode of cell entry of TAT is still not completely clear and likely depends on the type of cargo and target cell.120,121 Endocytosis, macropinocytosis, but also direct membrane translocation or inverted micelle formation are mechanisms proposed for TAT delivery into cells.121 To additionally assure that the TAT has no impact on the assay results, we added a control peptide into the assay that did not bind any endocytosis-related proteins in the PRISMA screen (PPARD-uORF-peptide). If the TAT itself would affect endocytosis levels more than the delivered peptide, we assume that all tested TAT-peptides would produce similar results.
## Protein lysate preparation
HEK293T/17 cells were grown in 14 cm dishes (Sarstedt) as described above. All following steps were performed on ice and only ice-cold buffers were used. Cells were washed with PBS, scraped, transferred into falcon tubes and centrifuged for 5 min at 1000 g. After an additional wash with PBS cell pellets were resuspended in lysis buffer (50 mM HEPES pH 7.6 at 4°C, 150 mM NaCl, 1 mM EGTA, 1 mM MgCl2, $10\%$ Glycerol, $0.5\%$ Nonidet P-40, $0.05\%$ SDS, $0.25\%$ sodium deoxycholate and cOmplete™ EDTA-free protease inhibitor (Roche) (0.7 mL per 14 cm dish) and incubated for 30 min on ice. Five μL (1250 U) of Benzonase (Merck) were added, followed by another 15 min incubation and 15 min centrifugation step at 20,000 g. The supernatant was transferred into a fresh tube and the protein concentration was determined with the Pierce™ BCA Protein Assay Kit (Thermofisher Scientific) following manufacturer's instructions. The protein concentration was adjusted to 5 mg/mL with lysis buffer. The protein extract was directly used for the PRISMA assay.
## Sample preparation for mass spectrometric analysis
Membranes with spot-synthesized peptides were equilibrated at RT for 15 min in wash buffer (50 mM HEPES pH 7.6 at 4°C, 150 mM NaCl, 1 mM EGTA, 1 mM MgCl2, $10\%$ Glycerol), blocked with 1mg/mL tRNA (Invitrogen; diluted in wash buffer) for 10 min and washed again twice with wash buffer for 5 min. Afterwards, membranes were incubated with HEK239T protein lysate (5 mg/mL) for 2 h at 4 °C while shaking, followed by 3 washing steps with a wash buffer for 5 min at 4 °C, and were dried for 15 min at RT.
Peptide spots were punched out using a 2 mm mouse ear puncher and transferred directly into 20 μL of urea sample buffer (6M Urea, 2M Thiourea, 10mM HEPES). Samples were reduced in 12 mM dithiothreitol (DTT) solution for 30 min at RT and alkylated in 40 mM chloroacetamide for 45 min at RT in the dark. To digest proteins bound to the spotted peptides, samples were diluted with 100 μL of 50mM ammonium bicarbonate (pH 8.5) buffer containing trypsin (Promega; 5 μg/mL) and LysC (Wako; 5 mAU/mL), and incubated overnight at RT. The proteolytic digestion was stopped by adding 4 μL $25\%$ trifluoroacetic acid. Peptides were extracted and desalted using StageTip protocol.109
## LC-MS/MS
Peptides were eluted using Buffer B ($80\%$ acetonitrile and $0.1\%$ formic acid), organic solvent was evaporated using a speedvac (Eppendorf) and samples were diluted in Buffer A ($3\%$ acetonitrile and $0.1\%$ formic acid). Peptides were separated on a 20 cm reversed-phase column (inner diameter 75 μm, packed with ReproSil-Pur C18-AQ 3 μm resin (Dr. Maisch GmbH)) using a 45 min gradient with 250 nl/min flow rate of increasing Buffer B concentration on a High Performance Liquid Chromatography (HPLC) system (ThermoScientific). Peptides were ionized using an electrospray ionization (ESI) source (ThermoScientific) and analyzed on an Orbitrap Fusion instrument (ThermoScientific). Precursor survey scans were performed at 120K resolution with a 2 × 105 ion count target. Dynamic exclusion for selected precursor ions was 30 s. MS/MS was performed with a 1.6 m/z isolation window, HCD fragmentation with normalized collision energy of 32, ion count target of 1x104 and maximum injection time of 300 ms. The instrument was operated in top speed mode with 3 s cycles. Replicates were measured in batches with a different run order for each batch. A blank run was placed after each analytical run.
## Data analysis
The resulting raw files were analyzed using the MaxQuant software package 1.6.0.1.56 The internal Andromeda search engine was used to search MS2 spectra against a decoy human UniProt database (Human.2019-07) and an in-house database containing PRISMA peptide and microprotein sequences. The search included variable modifications of methionine oxidation, N-terminal acetylation, deamidation (N and Q) and carbamidomethyl cysteine as fixed modification. The FDR was set to $1\%$ for peptide and protein identifications. Unique and razor peptides were considered for quantification. Retention times were recalibrated based on the built-in nonlinear time-rescaling algorithm. MS2 identifications were transferred between runs with the “match between runs” function. The integrated LFQ quantitation algorithm was applied.
Following analyses were done using R v.3.6.187 and adapted from Meyer and colleagues18 with slight modifications. The resulting text files were filtered to exclude reverse database hits, potential contaminants, and proteins only identified by site. Missing LFQ-values were imputed with random noise simulating the detection limit of the mass spectrometer. Imputed values were taken from a log normal distribution with 0.3x the standard deviation of the measured, logarithmized values, down-shifted by 1.8 standard deviations. By doing this, we obtained a distribution of quantitative values for each protein across samples. We excluded replicates with sample identifications of over two standard deviations away from its other replicates, as well as samples whose correlation value was over two standard deviations away from the other correlations between replicates. This led to the exclusion of two peptide spots from interactome analyses (Table S3). For determination of specific interactions, i.e., to separate specific binders from background, we compared protein identifications in each peptide spot against all other peptide spots excluding spots of the same microproteins using moderated t-tests (limma v3.40.6110). Only proteins with at least two valid values for the peptide spot were considered. The resulting p-values were adjusted using Benjamini-Hochberg correction. Adjusted p-values and fold-changes (log2 space) were plotted as volcano plots. To determine significance cutoffs, we used a graphical formula combining a fold-change and p-value cutoff18,111: −log10(p)≥c|x|−x0 with x: enrichment factor of a protein, p: p-value of adjusted moderated t-test, x0: fixed minimum enrichment, c: curvature parameter. The curvature parameter c determines the maximum acceptable p-value for a given enrichment x. The parameters c and x0 can be optimized based on prior knowledge of known true and false positives.32,111 Here, cutoffs were chosen according to known interaction partners of the SOS1 and GLUT1 control peptide.111 This resulted in a cutoff of x0 = 3, $c = 4$ that was applied to all other peptide spots. Ultimately, the PRISMA approach enabled us to define significant interactors for synthesized peptides individually. The total interactome of one microprotein was defined as the summary of interactors detected in all synthesized peptides that were derived from that microprotein.
## Quality control by replicate measurement assessment
Principal component analysis showed that no batch bias was identified between the replicate membranes (data not shown). Further, for all three membranes triplicate measurements of each spotted peptide correlated well with a median Pearson's R of 0.73, 0.72, and 0.87, respectively, and were significantly higher than the median of correlations of random triplets (Figure S2A). This was similar to what was observed in a previous peptide array screen.18 The number of identified proteins per peptide spot ranged from 159 to 2,380 and was comparable across membranes (median of 973, 1,012 and 1,011 IDs for membrane 1, 2 and 3, respectively) (Figure S2B).
No batch bias was identified between the replicate membranes (Figures S5A and S5B) and the triplicates of the same peptide correlated with a mean correlation coefficient (Pearson’s R) of 0.82, indicating good data reproducibility (Figure S5C). Six samples were excluded at this step because their correlation or number of interactors deviated more than two standard deviations from the other two samples. Taken together, a total of 3,784 unique binding proteins (including 64 bait peptides) were identified, with a range of 191 to 2,711 proteins binding to the individual peptides (mean identifications per peptide: 1,376 proteins, Figure S5D). The wide range of binding proteins could be expected due to the exploratory nature of the screen: some peptides might not be biologically relevant and will therefore not display any binding capacity, which in turn adds greater weight to the ones that do bind other proteins specifically. In order to distinguish between specific (transient) interactions and unspecific background binding, each triplicate was analyzed with regard to all other peptide pull-downs (used as background) via label-free quantification.
## Quality control by evaluation of assay control peptides
The spotted SOS1 peptide is known to interact with SH3-domain containing proteins via its proline-rich motif, while motif disruption in the mutant peptide leads to the loss of this interaction capability (loss-of-interaction mutant).18,32 In the case of GLUT1, a proline (P) to leucine (L) mutation creates a dileucine motif in the mutant peptide which mediates the binding to adaptor and clathrin proteins.18 Interactions with clathrins do not occur in the wild type peptide and lead to aberrant endocytosis of GLUT1 in GLUT1 deficiencies (gain-of-interaction mutant).18 Reassuringly, we only found clathrins to be significantly enriched in pulldowns of the GLUT1 mutant peptide but not of the wild type peptide (Figures 2D and S2C) and detected up to eight of the nine known SOS1 binding partners (Figures 2C and S2C). As expected, mutation of the SOS1 proline-rich motif led to the loss of most SH3 domain proteins except for CD2AP, GRB2 and BIN1 (Figure S2C).
Overall, the assay controls demonstrated the ability of our peptide array approach to detect biologically relevant PPIs as well as its specificity to the spotted peptide sequence. For 481 out of 488 analyzed peptides, we identified between 1 and 94 significant protein interactors (with a median of 14), resulting in 13 to 333 interactors per microprotein (with a median of 107 interactors) (Figure 2B; Table S3). Interactors that were detected in the interactomes of multiple microproteins tended to have lower interaction scores (product of fold change and p-value) than interactors that were found in fewer or only one microprotein interactome. However, this is likely due to the comparison approach against all other peptides, which will penalize proteins that are detected in many peptide pulldown (Figures 2B and S2F).
We used the known interaction partners of GLUT1 and SOS1 to determine the appropriate significance cutoff,18,32 resulting in a cutoff of $c = 6$ and x0=2.8, equal to two standard deviations (Figures S5F and S5G). The expected binding behavior of GLUT1 and SOS1 is described above (PRISMA “Quality control by evaluation of assay control peptides”). For GLUT1, reassuringly, we find all known binding partners enriched in the mutant but not in the wild type peptide. For SOS1, we find all previously reported binding partners in the wild type, and only $\frac{3}{9}$ in the mutant. Additionally, we find four previously unreported proteins with SH3 domains in the wild type but not in the mutant. Our very specific assay controls demonstrate the ability to detect previously reported, biologically relevant proteins, as well as its specificity to the spotted peptide sequence. In total, we identify between zero to 212 proteins per peptide (mean identification of 11 proteins per peptide, Figure S5H). We found that the shortest peptides (below 6 aa) have slightly lower numbers of interaction partners overall.
## Quality control based on bait identification for PRISMA of young microproteins
As part of the PRISMA quality control we assessed if the spotted peptides (baits) were identified and enriched in the expected samples, i.e., in the samples that contained the part of the membrane the bait peptide was synthesized on. We reasoned that the high amount of synthesized peptides should lead to their identification by peptide-spectrum matches in case a suitable tryptic peptide is produced upon enzymatic digestion. Thus, we only considered bait identifications that were detected “by MS/MS” in at least two replicates, while hits derived “by matching” were excluded. Because of the short length of the baits (15 aa), the possibilities to produce tryptic peptides suitable for MS is rather limited and indeed we do not detect all, but 193 out of 480 unique baits. 108 of those ($56\%$) were exclusively identified in the expected samples (data not shown). 79 baits were enriched in the expected sample, but also in other samples. However, for 71 of those the median LFQ intensity across all three replicates was higher in the correct samples compared to LFQ intensities in unexpected samples. In seven cases the bait was detected with a higher intensity in an unexpected sample than in the correct sample, and seven baits were only enriched in unexpected samples (data not shown). In total four samples were completely excluded from the analysis and 13 were flagged and only used to determine the interactome of the entire microprotein but not to investigate motif-driven interactions (Table S3).
## Expression of microproteins and peptides3-15aa in HEK293 cells
Since we used a protein lysate from HEK293T cells for the PRISMA analysis, we assessed the expression level of the microprotein and peptide-encoding genes in this cell line. Therefore, we downloaded a public RNA sequencing dataset of HEK293T cells (NCBI Sequence Read Archive (SRA) accession codes SRR1107836 and SRR110783777) and calculated the counts per million (CPM) from the mapped and quantified raw reads. A gene was determined as expressed if the mean of the two runs per gene was above or equal to 1. We detected the genes of $\frac{39}{60}$ microproteins ($65\%$) (Table S3) and $\frac{194}{221}$ peptides3-15 aa ($88\%$) (Table S4) as expressed in HEK293 cells.
## Annotation and enrichment of essential proteins detected in microprotein interactomes
We downloaded a list of proteins that were shown to be essential for survival of human cells in a study by Blomen and colleagues.33 We only included genes that affected cell viability in both tested cell lines (KMB7 and HAP1). This resulted in a list of 1,734 proteins based on which we annotated microprotein interactors as essential proteins. We calculated if specific microprotein interactomes were enriched for essential proteins using Fisher’s exact test (Table S3).
## Phylogenetic origin of microprotein interaction partners
We extracted the phylogenetic origins of microprotein interactors from a public resource published by Zhang and colleagues (http://gentree.ioz.ac.cn/download.php; “Ensembl Ver95 (hg38)”),112 who assigned annotated protein-coding genes to 14 different evolutionary branches. We used this resource to annotate the evolutionary origin of 2,357 out of 2,423 microprotein interactors identified in PRISMA (Table S3). We collapsed the 14 branches into four evolutionary groups: Vertebrates (Branch 0 - 2: Euteleostomi, Tetrapoda and Amniota), Mammals (Branch 3 - 7: Mammalia, Theria, Eutheria, Boreoeutheria, Euarchontoglires), Primates (Branch 8 - 12: Simiiformes, Catarrhini, Hominoidea, Hominidae, Homininae) and Humans (Branch 13).
## Computational prediction of disordered regions and short linear motifs within microproteins and peptides
Intrinsically unstructured (disordered) regions in peptide sequences were predicted using IUPred v.1.0 in the “short” disorder mode and disorder values were averaged over the sequence.36 For the detection of short linear motifs (SLiMs), also called eukaryotic linear motifs (ELMs), the ‘elm_classes.tsv’ file (version 1.4; 15 January 2018) was downloaded from the ELM resource for functional sites in proteins.37 We then filtered the peptide sequences for matches to any of the motifs falling in regions with an average disorder value ≥ 0.5. Five of the 60 microproteins subjected to PRISMA did not harbor any predicted SLiMs, the remaining 55 microproteins contained 429 SLiMs within disordered regions. Of those, 412 were captured within 159 of the tiled peptides spotted for the PRISMA screen (Table S3). Moreover, 174 of 221 peptides3-15 aa contained 514 SLiMs within disordered regions all captured in the PRISMA approach (Table S4).
## Detection of protein domain-SLiM matches in PRISMA interactomes
We first annotated known protein domains for each microprotein and peptide interactor using the R packages “ensembldb” and “EnsDb. Hsapiens.v86.”97 Next we extracted SLiMs that were known to bind the respective protein domains from a public resource published by Kumar and colleagues (http://elm.eu.org/; “elm_interactiondomains.tsv”).41 We reported a domain-SLiM match when a SLiM was present in a microprotein tile or peptide3-15 aa that bound an interactor carrying the SLiM-binding protein domain. In total we detected 47 protein domain-SLiM matches within disordered regions of 34 microprotein tiles (Table S3) and 30 protein domain-SLiM matches within 18 peptides3-15 aa (Table S4).
## Kinase-enrichment by microproteins with kinase-related SLiMs
In total, 117 out of 481 microprotein tiles were predicted to carry one of 35 different kinase phosphorylation and docking motifs within putative disordered microprotein regions extracted from the ELM resource for functional sites in proteins.37 We detected 158 microprotein-kinase interactions in the entire interactome screen, 17 of which were detected in the interactomes of fifteen tiles (derived from six young and three conserved microproteins) of the 117 tiles harboring a kinase phosphorylation or docking motif. Fisher’s exact test revealed that kinases were not enriched in interactomes of microprotein tiles that carry a kinase phosphorylation or docking motif within disordered regions (p-value = 0.079, Fisher’s exact test).
## Phosphorylation of kinase-binding microproteins with domain-SLiM matches
We investigated the phosphorylation of the nine microproteins from the SLiM-domain match analysis that interacted with kinases and carried phosphorylation or kinase docking motifs (RP11-12K22.1-MP, JHDM1D-AS1-MP, RP11-620J15.3-MP, SLCO5A1-uORF-MP, GAS5-MP, ABR-uORF-MP, MKKS-uORF-MO, RP11-140K17.3_2-MP and THAP7-uORF-MP, Table S3). Therefore, 3xFLAG-tagged microproteins were overexpressed, immunoprecipitated and analyzed by MS as performed previously22 with slight modifications. HEK293T/17 were seeded in triplicates on poly-D-Lysine (Sigma, Germany) coated 10 cm dishes and transfected with 28 μg plasmid-DNA of FLAG-tagged microproteins using TransFectin (BioRad, California) following manufacturer’s instructions. Two days post transfection cells were washed twice with ice-cold phosphate-buffered saline (PBS), scraped in 1.5 mL ice-cold PBS and transferred into Eppendorf tubes. After centrifugation at 950 g for five min at 4 °C, cell pellets were lysed in 200 μL lysis buffer (150 mM NaCl, 50 mM Tris pH 7.5, $1\%$ IGPAL-CA-630, 2x Complete protease inhibitor without EDTA) for 30 min on ice. Lysates were centrifuged at 20,800 g for 15 min at 4 °C and supernatants were added to 30 μL $50\%$ antibody-coupled magnetic bead solution (M2-magnetic beads, Sigma, Germany) and 300 μL wash buffer 1 (150 mM NaCl, 50 mM Tris pH 7.5). Beads were washed 3x in 150 μL wash buffer 1 before usage. After incubating the samples for 2 h at 4 °C in an overhead shaker, samples were washed once with 1 mL wash buffer 2 (150 mM NaCl, 50 mM Tris pH 7.5, $0.05\%$ IGPAL-CA-630) and three times with wash buffer 1. Supernatants were removed and magnetic beads were frozen at 80 °C until analyzed by mass spectrometry. Beads were resuspended in 20 μL urea buffer (6 Murea, 2 Mthiourea, 10 mM HEPES, pH 8.0), reduced for 30 min at 25C in 12 mM DTT solution, followed by alkylation in 40 mM chloroacetamide for 20 min in the dark at 25 °C. Samples were first digested with 0.5 μg endopeptidase LysC (Wako, Osaka, Japan) for 4 h. After adding 80 μL 50 mM ammonium bicarbonate (pH 8.5) samples were digested with 1 μg sequence grade trypsin (Promega) overnight at 25 °C. The peptide-containing supernatant was removed and collected into a fresh tube. Beads were washed twice with 50 μL 50mMammonium bicarbonate (pH 8.5) and the supernatants were pooled. Samples were acidified by adding 1 μL formic acid to stop the digestion.
Peptides were extracted, desalted and diluted as described in the previous section for PRISMA. Peptides were separated on a reversed-phase column (20 cm fritless silica microcolumns with an inner diameter of 75 μm, packed with ReproSil-Pur C18-AQ 1.9 μm resin (Dr. Maisch GmbH)) using a 90 min gradient with a 250 nL/min flow rate of increasing Buffer B concentration (from $2\%$ to $60\%$) on a High-Performance Liquid Chromatography (HPLC) system (Thermo Fisher Scientific) and ionized using an electrospray ionization (ESI) source (Thermo Fisher Scientific) and analyzed on an Thermo Q Exactive Plus instrument, which was run in data dependent mode selecting the top 10 most intense ions in the MS full scans, selecting ions from 350 to 2000 m/z, using 70 K resolution with a 3 × 106 ion count target and 50 ms injection time. Tandem MS was performed at a resolution of 17.5 K. The MS2 ion count target was set to 5 × 104 with a maximum injection time of 250 ms. Only precursors with charge state 2–6 were sampled for MS2. The dynamic exclusion duration was set to 30 s with a 10-ppm tolerance around the selected precursor and its isotopes. Data were analyzed using MaxQuant v1.5.2.8. The internal Andromeda search engine was used to search MS2 spectra against a human UniProt database (HUMAN.2017-01) and an in-house bait protein sequence database containing forward and reverse sequences. The search included variable modifications of methionine oxidation, N-terminal acetylation and serine, threonine and tyrosine phosphorylation, and fixed modification of carbamidomethyl cysteine. Minimal peptide length was set to seven amino acids and a maximum of 3 missed cleavages was allowed. The FDR was set to $1\%$ for peptide and protein identifications. Unique and razor peptides were considered for quantification. Retention times were recalibrated based on the built-in nonlinear time-rescaling algorithm. MS2 identifications were transferred between runs with the “Match between runs” option for biological replicates, in which the maximal retention time window was set to 0.7 min. We detected four phosphorylated tryptic peptides from three of the overexpressed microproteins (RP11-12K22.1-MP, JHDM1D-AS1-MP and THAP7-uORF- MP). Two of these peptides contained phosphorylation motifs (Table S3).”
## Gene ontology analysis
Gene ontology (GO)113 enrichment on small peptide and microprotein interactomes identified with PRISMA was performed with gProfiler2 v0.2.0,88 with default parameters. As a custom background, we used all identified proteins in the respective PRISMA screen.
## Co-localization analysis of PVT1-MP with SRFS2 and SRSF6
*Synthetic* gene fragments containing the codon-optimized coding sequence of PVT1-MP with a C-terminal V5-tag was synthesized and cloned into a customized plasmid for mammalian expression by Genewiz Europe (Leipzig, Germany; construct available upon request). Overexpression plasmids for SRSF2-FLAG and SRSF6-BirA-Myc-His were kindly provided by M. Gotthardt, MDC (constructs available upon request). PVT1-MP-V5 was co-overexpressed in HeLa cells with SRSF2-FLAG and SRSF6-BirA-Myc-His in equimolar ratios, respectively. Briefly, human HeLa cells [35 000] were grown on 8-well chamber slides for 24 h and transfected with the respective plasmids using Lipofectamine 3000 according to manufacturer’s instructions. 24 h post transfection cells were fixed with $4\%$ paraformaldehyde (PFA) for 10 min at room temperature (RT) and washed three times with ice cold phosphate-buffered saline (PBS). Cells were permeabilized and blocked for 1 h with $2.5\%$ bovine albumin serum (BSA), $10\%$ anti-goat serum (NGS) and $0.1\%$ Triton X in PBS. After washing the cells, overexpressed PVT1-MP-V5 was stained with anti-V5 rabbit monoclonal antibody (1:500 in PBS with $5\%$ BSA, #13202, Cell Signaling Technology), SRSF2-FLAG with anti-FLAG mouse monoclonal antibody (1:500 in PBS with $5\%$ BSA, F1804, Sigma Aldrich), and SRSF6-BirA-Myc-His with anti-BirA chicken polyclonal antibody (1:500 in PBS with $5\%$ BSA, BID-CP-100, BioFront Technologies) for 2 h at 4 °C. Afterwards cells were washed and incubated with fluorochrome-labeled secondary antibodies (1:500 in PBS with $5\%$ BSA, Alexa Fluor 488 anti-mouse, Alexa Fluor 488 anti-chicken and Alexa Fluor 594 anti-rabbit (Invitrogen, Carlsbad, CA, USA) for 1 h at RT. Cells were washed again and stained with 4-6-diamidino-2-phenylindole (NucBlue Fixed Cell ReadyProbes Reagent, R37606, Thermo Fisher) for 5 minutes at RT. Images were taken with a LEICA SP8 confocal microscope using a 63x objective and analyzed using ImageJ (v1.53c).108
## Proximity ligation assay
In situ proximity ligation assay (PLA) was performed to corroborate the interaction between PVT1-MP and SRSF2 using the Duolink® In Situ Proximity Ligation Assay Starter Kit (Red, Mouse/Rabbit, DUO92101-1KT, Sigma-Aldrich) according to manufacturer’s instructions. V5-tagged PVT1-MP and FLAG-tagged SRSF2 were co-overexpressed in HeLa cells as described above for co-localization experiments. Following fixation with $4\%$ paraformaldehyde (PFA) for 10 min at RT, cells were permeabilized in $0.1\%$ Triton X in PBS for 1 h at RT, and blocked in Duolink® blocking solution for 1 h at 37 °C. PVT1-MP-V5 and SRSF2-FLAG were stained overnight at 4 °C with anti-V5 rabbit monoclonal antibody (1:500 in Duolink® antibody diluent, #13202, Cell Signaling Technology) and with anti-FLAG mouse monoclonal antibody (1:500 in Duolink® antibody diluent, F1804, Sigma Aldrich), respectively. Cells were washed twice for 5 minutes in 1x Duolink® Wash Buffer A at RT and incubated with PLUS and MINUS PLA probes (1:5 in the Duolink® Antibody Diluent) for 1 h at 37 °C. After washing cells 3x for 5 min in 1x Duolink® Wash Buffer A at RT, cells were incubated for 30 min at 37 °C with Duolink® ligase in 1x ligation buffer and washed again in 1x Duolink® Wash Buffer A at RT. For amplification, cells were incubated with Duolink® polymerase (1:80) in 1x amplification buffer for 100 min at 37 °C. Cells were washed 2x 10 min in 1x Duolink® Wash Buffer B, 1x 1 min in 0.1x Duolink® Wash Buffer B, and stained with 4-6-diamidino-2-phenylindole (NucBlue Fixed Cell ReadyProbes Reagent, R37606, Thermo Fisher) for 5 minutes at RT. As negative controls, transfected cells were stained with only anti-V5 rabbit monoclonal antibody, only anti-FLAG mouse monoclonal antibody, or no primary antibodies and PLA probes. Additionally, untransfected cells were stained with both primary antibodies and PLA probes. Images were taken with a LEICA SP8 confocal microscope using a 63x objective and analyzed using ImageJ (v1.53c).108
## Generation of LINC01128-MP knock-out cells and RNA-seq analysis
A HeLa LINC01128-MP knock-out (KO) cell pool was generated by Synthego Inc. (Redwood City, CA) using CRISPR/Cas9 with GAUCCAAGGCAGGCACUCAA as guide RNA targeting the N-terminal region of the LINC01128-MP encoding sORF. Non-homologous end joining (NHEJ) following the Cas9-mediated double-strand break led to insertions and deletions (indels) that cause premature STOP codons and ultimately a disruption of the microprotein-encoding sORF (Figure S3F).
At passage four, RNA-sequencing was performed for triplicates of wild type (WT) cells and KO cell pools to assess i) the proportion of indels within the targeted cell pool and ii) the potential impact the introduced indels have on the expression of LINC01128 transcripts. RNA-seq data was analyzed by trimming adapters and mapping each sample to the human genome (hg38, Ensembl v.98) using STAR v2.5.2b82 with a maximum of 4 mismatches.
Our analyses revealed that $93.7\%$ of all reads mapped to sequences carrying different indels that all lead to a frameshift resulting in premature STOP codons and ultimately disruption of the sORF encoding LINC01128-MP, while only $6.3\%$ of reads mapped to the wild type sequence. A single adenine insertion was most common within the KO cell pools ($69.4\%$), while a single guanine deletion, a 14 bp deletion and a 13 bp deletion contributed with $9\%$, $9.5\%$ and $5.7\%$, respectively (Figure S3G). From these data, we can estimate that at least $87.4\%$ of cells carry a mutation on both alleles.
Differential RNA-seq expression analysis was performed using DESeq2 v1.26.0.89 Differentially expressed genes were selected based on adjusted p-values ($p \leq 0.05$) and log fold changes (logFC < -0.18 or logFC > 0.18) and showed that LINC01128 transcript expression was not significantly altered through the introduced indels in comparison to wild type cells (Figures 3L and S3I).
## The role of LINC01128-MP in transferrin endocytosis
For studying the uptake of fluorescently labeled transferrin-Alexa 647 in HeLa WT and HeLa LINCO1128-MP KO cells, cells from the same batch of cells that had been analyzed by RNA-seq (see above) were washed in PBS and incubated in DMEM without FBS for 30 min at 37°C. Subsequently, 5 μg/mL transferrin-Alexa 647 in DMEM were added to the cells for another 10 min at 37°C. Cells were washed several times in PBS and fixed in $4\%$ PFA for 8 min on ice. Standard immunocytochemical analysis was carried out by incubation of cells with primary mouse anti-EEA1 antibody (1:100; BD Transduction Laboratories). Bound primary antibody was visualized using secondary antiserum conjugated with Alexa Fluor 555 (1:500; Invitrogen). Alexa 647-conjugated transferrin was purchased from Invitrogen (T23366). Nuclei were counterstained with DAPI (1:8000; Roche). Image acquisitions were carried out with a Leica TCS SP8 confocal microscope using a 63x PL APO CS2 oil immersion objective (NA 1.4). Manders’ coefficient tM1 was determined with the Coloc 2 plugin from ImageJ/Fiji. Student’s t-test was applied using Graph Pad Prism 7. Three independent experiments were performed. Per experiment and cell line, an average of 30 cells were analyzed for quantification.
## Detection of sORFs3-15aa in human tissues
The first GENCODE annotation set of Ribo-seq ORFs only included sequences with a minimum length of 16 amino acids.2 To identify very small novel ORFs translated in human tissues, we applied our previously published approach to detect actively translated ORFs in human left ventricular heart tissue (European Genome-Phenome Archive (EGA) accession code EGAS00001003263).22 We decided to use this dataset as discovery tissue due to the high sample size ($$n = 80$$) and the high average codon periodicity (average of $85.4\%$ in-frame reads for read lengths of 29 base pairs). We modified our original method for ORF detection22 and removed the minimum length cutoff for ORF assignment, leading to the identification of 287 new translated ORFs starting with ATG and with a size of 15 amino acids or shorter (denoted sORFs3-15 aa). 221 of these ORFs met our translation rate cutoff, which required high levels of translation (minimum number of 290 raw P-sites and a minimum of $70\%$ in-frame P-sites), resulting in their selection for further analysis (Table S4). We next inspected the translation of these 221 sORFs3-15 aa in other human tissues by analyzing four publicly available ribosome profiling datasets corresponding to kidney, ($$n = 6$$, EGA accession code EGAS0000100326322), liver ($$n = 7$$, EGA accession code EGAS0000100326322 and ArrayExpress accession code E-MTAB-724723), brain ($$n = 3$$, ArrayExpress accession code E-MTAB-724723) and testis ($$n = 3$$, ArrayExpress accession code E-MTAB-724723).
## Conservation of sORFs3-15aa in mammalian tissues
We defined the levels of conservation of sORF3-15 aa by extending our analysis of conservation of sORF structures (see STAR Methods section ‘conservation of sORF structures’) to include non-primate mammals and three vertebrates, including four additional branches: euarchontoglires (∼95 Mya), boreoeutheria (∼102 Mya), placentalia (∼107 Mya), mammalia (∼225 Mya) and vertebrata (>300 Mya).
For comparison, we inspected the translation of 183 sORFs3-15 aa with conserved structures in rodents. To this end, we retrieved datasets corresponding to mouse heart ($$n = 6$$, European Nucleotide Archive (ENA) accession code PRJEB2920822), mouse liver ($$n = 3$$, ArrayExpress accession code E-MTAB-724723), mouse brain ($$n = 3$$, ArrayExpress accession code E-MTAB-724723), mouse testis ($$n = 3$$, ArrayExpress accession code E-MTAB-724723), rat heart ($$n = 30$$, ENA accession code PRJEB3809650) and rat liver ($$n = 30$$, ENA accession code PRJEB3809650). We additionally retrieved chicken brain Ribo-seq samples ($$n = 3$$, ArrayExpress accession code E-MTAB-724723) to inspect the translation of USP10-uORF, an sORF3-15 aa with conserved structure and length in mammals and birds. Ribo-seq datasets were filtered and mapped following a similar approach as for the mammalian Ribo-seq datasets (details in STAR Methods section ‘expression and translation of sORFs in mammals’). Using the mapped data from the described tissues, we called translated ORFs per sample running ORFquant 1.0046. Next, we pooled the datasets by tissue and we extracted P-site counts with RiboseQC.90 In-frame P-site counts were quantified for each annotated CDS and sORF3-15 aa. Finally, raw P-site counts were subjected to a normalization procedure (estimateSizeFactorsForMatrix; DESeq2 v1.26.089) and divided by the total number of codons in each sequence. Additionally, we calculated the PhyloCSF80 scores with default parameters of all 221 sORFs3-15 aa (Figure S4E). PhyloCSF scores were calculated across the retrieved multiple alignments for primates and for mammals. We found that a total of 30 ($13.57\%$) and 44 sORFs3-15 aa ($19.91\%$) have positive PhyloCSF scores in primates and mammals. It is important to note that PhyloCSF performs better for sequences >30 nucleotides and can fail to determine the constraints of very short sequences,80,114 which is why the determined PhyloCSF results can not be $100\%$ reliable.
## Effect of genomic location and length in the conservation of sORF structures
Compared to sORFs3-15 aa, cataloged sORFs (Ribo-seq ORFs, longer than 15 aa) contained a higher proportion of lncORFs ($30\%$ vs $8\%$), and a lower proportion of uORFs ($42\%$ vs $92\%$). Therefore, we inspected whether this difference in the biotype proportions had an effect on the observed patterns of conservation between cataloged sORFs and novel sORFs3-15 aa. We considered two main sORF biotypes: upstream ORFs (uORF, which contains 3,083 cataloged sORFs and 203 sORFs3-15 aa) and lncRNA ORFs (lncORF, which contains 2,208 cataloged sORFs and 17 sORFs3-15 aa). Next, we checked how many of the sORFs had conserved structures in five different species: mouse, rat, cow, horse and cat. An sORF structure was considered as conserved in the counterpart region of the compared species if the ATG translation initiation site (TIS) was present in the same position or within a window 6 nt down-stream of the human ATG position, and if ≥ $70\%$ of the sequence did not contain stop codons truncating the ORF. For comparison, we randomly sampled non-translated ORF sequences from the same regions harboring translated sORFs (5′ UTRs for uORFs, lncRNA exons for lncORFs). We observed that both translated and non-translated lncRNA sequences were less highly conserved than 5′ UTR sequences (Figure S4G). UTR regions are enriched in promoters, secondary structures and binding motifs that are evolutionary constrained. Hence, we concluded that sORFs3-15 aa are more likely to overlap these elements and be maintained over longer evolutionary times.
Secondly, we evaluated whether the intrinsic length differences between cataloged sORFs and sORFs3-15 aa affected the different observed patterns of conservation between both groups. We consistently observed a continuum, in which the proportion of sORFs with conserved structures decreased with ORF length in the five analyzed species (Figure S4G). For the set of untranslated random sequences extracted from 5′ UTRs and lncRNAs, we also observed a similar negative trend between ORF length and conservation of ORF structures. This indicates that, even if not translated, disabling substitutions in very short ORFs (< 15 aa) are less likely to occur across evolutionary time.
## Additional evidences of sORF3-15aa translation
We alternatively validated the translation of the set of 221 sORFs3-15 aa in the same discovery set of 80 human left ventricular tissue Ribo-seq samples by running PRICE v1.0.3b.47 This method uses an expectation–maximization algorithm to compute probabilistic inferences of codon activities. sORFs3-15 aa with a p-value < 0.05 were defined as translated. Moreover, we retrieved a public resource containing the relative probabilities of each human transcript position to contain a translation initiation site (TIS). These probabilities were calculated using TIS Transformer,48 a deep learning model based on information embedded in processed transcript sequences. Per each transcript containing a sORF3-15 aa, we extracted and ranked the probabilities of all possible ATG triplets in the transcript sequence.
## Detection of sORF-translated peptides3-15aa in public mass spectrometry data
We collected protein expression evidence for translated sORFs3-15 aa from previously published datasets.6,51,52,54,55 Therefore, we re-analyzed the raw spectra using MaxQuant v1.6.1056 with settings indicated in the respective dataset, added a false discovery rate (FDR) filter of <0.01 using the reverse-sequence based target decoy approach implemented in MaxQuant,56 and disabled the protein FDR filter, as it was previously done for the identification of small proteins.45,57 We added the peptide sequences together with the human UniProt database (HUMAN.2019-07) into the search space. We also searched the analyzed results of a published dataset of translated sORFs for our candidate peptides.53 Moreover, we included the peptides3-15 aa in the search database of the recent Human HLA 2022-09 PeptideAtlas build, in which the 51 million MS/MS spectra from 49 immunopeptide datasets deposited to ProteomeXchange115 were processed with the PeptideAtlas build pipeline.29,58 The reference database used was the comprehensive THISP level 3 database116 plus the candidate peptide sequences. Processing was performed with MSFragger91 and the Trans-Proteomic Pipeline58 using a non-specific (no protease) search strategy and other parameters as appropriate for each of the 49 datasets. The Human HLA 2022-09 PeptideAtlas build contains 15 million peptide-spectrum matches (PSMs), among which 794 PSMs mapped to 16 of the short peptide sequences described herein. This PeptideAtlas build also included two of the individually analyzed studies.6,51,52,54,55 All peptides detected in the respective datasets can be found in Table S4. Of note, only $\frac{116}{221}$ peptides3-15 aa are theoretically detectable with standard shotgun MS, because they produce tryptic peptides above six aa after enzymatic digest. To ensure a proper identification, peptides additionally require to be unique within the digested proteome, which further reduces the number of peptides able to be detected. $\frac{193}{221}$ peptides are theoretically detectable in immunopeptidomics, since no digestion is required. To ensure the validity of peptide identifications, each identification was required to be unique within the proteome. This was assessed with the tool Proteomapper v.1.592 (http://www.peptideatlas.org/map/), which maps input sequences to the proteome, in order to exclude that the identified peptides stem from fragments of longer proteins. Our analysis showed that all identified peptides are either unique within the proteome or, if present within other proteins, cannot be cleaved into the identified peptides because of lacking tryptic restriction sites. However, four of the identified peptides could theoretically stem from semi-tryptic digestion of canonical proteins and four might be derived from longer alternative sORF isoforms encoded by the same gene that had been detected in previous ribosome profiling studies2 (Table S4).
## PRM proteomics
The parallel reaction monitoring (PRM) analysis was performed similarly to the targeted mass spectrometry approach carried out by us previously,22 except for some modifications. To select appropriate tryptic and LysC-proteolytic signature peptides, we first digested candidate microproteins and peptides3-15 aa in silico with i) trypsin and ii) LysC using the online tool MS-Digest (http://prospector.ucsf.edu). We selected tryptic or LysC-proteolytic peptides that were unique across the human proteome digested with the respective enzyme, had a minimum length of six aa and fell into a mass to charge (m/z) range of 10 - 1850 m/z. Selected peptides were purchased as synthetic peptides of crude quality (JPT Inc., Berlin, Germany), which were resuspended in $20\%$ acetonitrile (100 mM ammonium bicarbonate) and measured (1 pmol per peptide) on a Q-Exactive HF-X mass spectrometer (Thermo Fisher Scientific) using data dependent acquisition mode (DDA) with a mass resolution of 60,000 for the MS scans and 15,000 for the MS/MS scans, considering precursor ions with charge state 1 to 6. Precursor fragmentation efficiency was evaluated using normalized collision energies (NCE) ranging from 25 to 35. The recorded spectra were analyzed using MaxQuant v1.6.3.456 applying a custom-made database containing the predicted sequences, with carbamidomethylation of cysteines as fixed and oxidation of methionines as variable modification. Based on the observed precursor signal, retention time, charge state and MS/MS fragment information, a library based PRM method was developed using the Skyline software package v3.693 with the following settings: precursor charge state 1 to 4; ion charge state 1 and 2; ion types y, p, b, a, z; auto-selection of matching transitions enabled; ion match tolerance = 0.05 m/z; method match tolerance = 0.055 m/z. For each candidate the most abundant variant together with the corresponding fragment ions (five or more) were selected. In total, we included 42 tryptic and 16 LysC precursor targets for 24 microproteins, and 77 tryptic and 193 LysC precursor targets for 149 peptides3-15 aa (Table S5). A subset of precursor targets did not generate fragmentation patterns suitable for PRM analysis and were therefore excluded. The final set consisted of 39 tryptic and 9 LysC precursor targets for 24 microproteins, and 73 tryptic and 155 LysC precursor targets for 129 peptides3-15 aa. As positive controls, proteotypic peptides from high abundant housekeeping genes (tryptic peptides: GAPDH, ACTA, and HIST1H2; LysC peptides: GAPDH and H2BC1) and sCDS were included (tryptic peptides: PLN and MIEF1-MP; lysC peptides: NDUFB3, MTLN, MPRL33, and MIEF1-MP). Based on their retention time profile, targets were split into two (tryptic peptides) and three (LysC peptides) PRM inclusion lists. Parameters for the positive controls were added to each list. Analytical PRM measurements were performed on a High Performance Liquid Chromatography (HPLC) system (ThermoScientific) using a 98 min gradient of increasing Buffer B concentration (from $2\%$ to $60\%$, 250 nl/min flow rate) coupled to an Q-Exactive HF-X mass spectrometer (Thermo Fisher Scientific). Instrument parameters were set to 30,000 resolution with 2e5 AGC target value, 100 ms maximum ion injection time, 30 min retention time window and adjusted normalized collision energy (NCE) values for each target.
Analytical PRM measurements were performed on human tissue and cell line samples. Therefore, pulverized human heart tissue of five patients and three biological replicates of PBS-washed cell pellets of HEK239T, HeLa and K562 cells were resuspended in lysis buffer ($1\%$ weight per volume (w/v) sodium deoxycholate (SDC), 10 mM DTT, 40 mM chloroacetamide (CAA), 1 mM ethylenediaminetetraacetic acid (EDTA), 100 mM Tris, pH 8.5) and boiled for 10 min at 95°C. After cooldown, samples were incubated with Benzonase (Merck Germany) to digest nucleic acids. For tryptic peptide samples, 10 μg of protein extract was digested with 0.2 μg sequence grade endopeptidase LysC (Wako, Osaka, Japan) and 0.2 μg sequence grade trypsin (Promega). LysC peptide samples were obtained by digesting 10 μg of protein extract with 0.2 μg sequence grade endopeptidase LysC only (Wako, Osaka, Japan). All digests were performed at 37°C for 16 hours. The digest was stopped by acidifying each sample to pH < 2.5 by adding $10\%$ trifluoroacetic acid solution. After centrifugation to pellet insoluble material (14,000 rpm, 10 min), the peptides were extracted and desalted using the StageTip protocol.109 Peptide samples were eluted from StageTips ($80\%$ acetonitrile, $0.1\%$ formic acid), dried down, resolved in sample buffer ($3\%$ acetonitrile and $0.1\%$ formic acid) and analyzed on the mass spectrometer as described above. For heart tissue samples, two technical replicates for each biological replicate were performed. Cell line samples were analyzed by one analytical run per replicate.
PRM data analysis was carried out using the Skyline software package v21.02.93 Analytical runs of synthetic peptide mixtures as well as the internal library-based fragment ranking were used to manually confirm the peak assignment. Peptides detected in at least two biological replicates with a dot product of ≥ 0.7 were considered robust identifications. For these peptides, also peaks with a dot product ≥ 0.6 were reported in the remaining samples. For peaks passing the quality filter, the total peak area, retention time and dot product values were exported and are available for all technical and biological replicates (Table S5). We were able to detect four out of the six sCDS. The control proteins GAPDH, ACTA, HIST1H2, and H2BC1 were robustly identified in all replicates of the five heart samples and the three cell lines. We detected signature peptides for two microproteins and for 18 peptides3-15 aa. Of the latter 18, two peptides could also stem from semi-tryptic digestion of canonical proteins and four might be also derived from longer alternative sORF isoforms encoded by the same gene (Table S4).
## Setup, Preparation, LC-MS/MS & Data analysis
The PRISMA approach described above was used for the peptide dataset of 221 candidate peptides with minor adaptations. We decided to use the same PRISMA controls as for the microproteins after analysis of amino acid frequencies (Figure S2C): four peptides derived from SOS1 and GLUT1 (SOS1wt, SOS1mt, GLUT1wt, GLUT1mt) that had been previously investigated.18,32 All 221 candidate peptides as well as the four PRISMA controls were spot-synthesized onto a cellulose membrane (JPT Peptide Technologies Inc., Berlin, Germany). We were able to synthesize each peptide in full length due to their short size. The assay was performed in triplicates, i.e., three membranes with identical peptides. The sequences of all spotted peptides were identical to all identified peptides and are listed in Table S4 together with the sequences of the four control peptides. Preparation for MS (i.e., incubation of the membrane with HEK239T protein lysate, punching out the peptide spots, digesting the peptide spots with trypsin and LysC, preparation of the StageTips), LC-MS/MS (i.e., elution of the peptides, separation of the peptides on a HPLC system, ionization and analysis of the peptides on an Orbitrap Fusion instrument with above settings), as well as raw data analysis and filtering steps for identification of PPIs were performed as described in the above PRISMA screen for microproteins.
## Quality control based on bait identification for PRISMA of peptides3-15aa
Similar to what was described for PRISMA, we assessed if the spotted peptides (baits) were identified and enriched in the expected samples (Figure S5E). We detected 64 baits in total. It was to be expected that not all bait peptides could be detected, as the short length of the peptides reduces the possibility to produce tryptic peptides suitable for MS. $\frac{29}{64}$ baits were found exclusively in expected samples. 28 were found in multiple samples but with higher mean LFQ intensity across the three triplicates in the expected sample compared to the unexpected sample. One bait was found exclusively in an unexpected sample, but it was only identified “by matching” and not “by MS/MS” and also had a relatively low LFQ intensity. As explained above, the high amount of peptide synthesized on the membrane should lead to a robust identification by peptide-spectrum matches and hits identified only “by matching” are more likely false positives. Six baits were not significantly enriched in any sample.
## Peptide properties
Peptide hydrophobicity values were calculated using the Krokhin model.117 For prediction of the peptides’ MHC presentation, we used the tool NetMHCpan-4.195 which predicts binding of peptides to MHC molecules of known sequence using a neural network. We selected the 12 indicated MHC supertype representatives for binding analysis. In total, 105 peptides were reported to be presented on one or more MHC supertype representatives. The results, indicating for how many MHC supertype representatives a peptide is predicted to be presented, are listed in Table S4.
## Analysis of CORUM complexes in interactomes of peptides3-15aa
CORUM complexes118 were downloaded from http://mips.helmholtz-muenchen.de/corum/ (Human_CORUM_coreComplexes_2018-09-03_symbol.gmt). We annotated a complex when at least two proteins of a CORUM complex were present in the interactome of a specific peptide.
## Luciferase assay for ribosome-binding peptides
We identified 16 peptides that clustered together in the global hierarchical clustering, indicating that their interactomes were very similar. For the reporter assay, we selected five out of 16 peptides encoded by sORFs3-15 aa within 5′ UTRs. We created two mutants for each of the five candidates: one with a mutated start codon (ATG > ACG) that impedes translation of the uORF, and one where we mutated charged arginines to non-charged, inert alanines. We created this second control (i.e., the charge mutant) in order to assess the impact of the peptide’s sequence and/or charge on its effect on downstream (luciferase) translation. The respective 5′ UTRs were synthetized and cloned into the backbone in front of the Renilla luciferase using the NheI restriction site (psiCHECK-2, constructs available upon request). We then confirmed the plasmid sequences using Sanger Sequencing. For the reporter assay (Dual Glo Luciferase Assay System, Promega, E2920), human HeLa cells were grown in a white 96-well plate with a transparent bottom. All plasmids including internal controls were transfected using LipoJet Reagent. The assay was performed 48 hours after transfection, according to the manufacturer’s manual. 50 μL of Dual Glo Luciferase Agent was added to the cells, which were then incubated for 30-40 min at RT. The cells were checked for complete lysis under a microscope to ensure proper read out of the luminescence signal. The level of luminescence was measured using the fluorescent plate reader Safire2 (Tecan) and the program Magellan with a luminescence integration time of 100 ms. Immediately after the measurement, 50 μL of the Stop & Glo Reagent was added. The mixture was incubated for exactly the same time span as after the addition of the first reagent. After incubation, the luminescence levels were measured again using the same plate reader, program and settings. We performed four biological replicates with three to four technical replicates each. The resulting luminescence levels were analyzed in Excel and plotted in R. First, the background of untransfected cells was subtracted from the values of transfected samples. Then, the ratio of Renilla luciferase to Firefly luciferase was calculated, followed by the calculation of the fold changes in respect to the ATG mutant for each biological replicate individually. An ANOVA was performed for statistical analysis.
## Endocytosis assay and analysis
For studying the uptake of fluorescently labeled receptor-associated protein (RAP), the protein was recombinantly expressed and purified to homogeneity from E. coli and labeled with Alexa Fluor 594 (Alexa Fluor 594 Protein Labeling Kit, A10239, Thermo Fisher). BN16 cells were seeded at a density of 50k cells/well (24-well-plate) in DMEM supplemented with FBS as described above. The next day, cells were incubated in DMEM without FBS for 30 min at 37°C, followed by pre-treatment with 50μM peptides, DMSO or dynasore in DMSO for 60 min at 37°C. Then, 10 μg/ml RAP was added to the cell supernatant for another 60 min at 37°C. At this point, images of the cells were acquired with a fluorescence microscope directly using the cell plates. For the fluorescence quantification, cells were washed several times in PBS and lysed in RIPA fluorescence lysis buffer (50 mM Tris pH 7.4, 150 mM NaCl, $1\%$ NP40, protease inhibitor (cOmplete, Merck)) for one hour on ice. Lysates were cleared by centrifugation at 12,000 rpm for 10 min at 4°C. Uptake of RAP by LRP2 was then assessed by measuring the fluorescence of RAP in cell lysates, which was measured in triplicates in black 96-well-plates (655079, Greiner Bio-One) using a fluorescence reader (Tecan). The results were normalized to total protein content. Values of blank samples (without RAP) were subtracted from all measurements. Values of the various experimental conditions are displayed as normalized to the condition with RAP only (set to $100\%$).
## Immunofluorescence of peptides3-15aa
Human HeLa cells were grown in ibidi chambers for 24 h. Cells were starved (i.e., kept in DMEM medium without FCS) for 30 minutes and then treated with 50 μM of respective peptide for 1 h (sequences see “Endocytosis assay” above). Cells were washed 3x with PBS for 5 minutes each and subsequently fixed with 200 μL of ice cold $4\%$ PFA per ibidi well for 10 minutes at RT. Afterwards, cells were washed on a shaker twice with ice cold PBS for 5 min each. Cells were stored in PBS at 4°C in the dark until imaging. For staining the mitochondria, cells were blocked for 1 h at RT in the dark using $3\%$ BSA, $10\%$ NGS, and $0.1\%$ Triton X, and washed again three times for 5 minutes each. Cells were stained with an antibody against mitochondria (ATPIF1, 1:1000, ab110277, abcam) for 1h at RT or overnight. Afterwards, cells were washed three times in ice-cold PBS for 5 minutes each and incubated with the secondary antibody against mouse (1:500, Alexa 594 anti-mouse, Invitrogen) for 30 minutes at RT. Cells were washed again and stained with 4′,6-Diamidin-2-phenylindol (DAPI, NucBlue Fixed Cell ReadyProbes Reagent, R37606, Thermo Fisher) for 5 minutes at RT and stored at 4°C in the dark until visualization. Images were visualized with a Leica SP8 confocal microscope using a 63x objective and the corresponding software. Negative controls with only secondary antibody were used in order to avoid false positive results (autofluorescence). Images were analyzed using Fiji.94
## Quantification and statistical analysis
*The* generation of figures and statistical analyses were performed using custom scripts and R v.3.6.1.87 A detailed list of software used for data processing, quantification and analysis is stated in the respective STAR Methods sections and the key resources table. Statistical parameters such as the value of n, mean/median, standard deviation (SD) and significance level (including the type of statistical test) are reported in the STAR Methods, figures and/or in the figure legends. The values of “n” represent sample numbers of human or animal tissue (STAR Methods sections “experimental model and subject details” and “detection of sORFs3-15aa in mammalian tissues”), the number of sORFs (results text, Figures 1A, 1E, 1G, and S1), number of microproteins (Figure S2K), number of experiments (Figure 3K) and the number of sORFs3-15 aa (Figures 4F and S4B), uORFs and lncORFs (Figure S4G). Statistical parameters used to indicate differential expression were derived from DESeq2 (STAR Methods: section “generation of LINC01128-MP knock-out cells and RNA-seq analysis”), or otherwise the type of statistical test (e.g., Mann-Whitney U test or t test) is annotated in the figure legend and indicated in the STAR Methods segment specific to each analysis. Unless stated otherwise, statistical analyses are two-sided tests performed using R. In the PRISMA analysis, samples were excluded that deviated more than two standard deviations away from its other replicates regarding number of sample identifications as well as samples whose correlation value was over two standard deviations away from the other correlations between replicates. False discovery rate (FDR) was estimated using the Benjamini & Hochberg method.
## Supplemental information
Document S1. Figures S1–S6, Data S1, and supplemental references Table S1. Sequence conservation and mode of evolution of 7,264 translated human sORFs and annotated sCDS, related to Figure 1Table with information on sequence conservation and mode of evolution of the 7,264 analyzed translated human sORFs and 527 annotated sCDS. Table S2. Metadata on microproteins selected for PRISMA, related to Figure 2Table with information on the 60 human microproteins selected for PRISMA, including Ribo-seq-evidence, MS evidence (including PRM), sequence conservation, and mode of evolution. Table S3. PRISMA results for 60 microproteins, related to Figures 2 and 3Table with information on microprotein PRISMA setup and results. Table S4. Identification of peptides3-15aa and PRISMA results, related to Figures 4, 5, and 6Table with information on all identified peptides3-15aa, including Ribo-seq evidence, MS evidence, level of conservation, and information on PRISMA setup and results. Table S5. PRM results for peptides3-15aa, related to Figure 4Table with information on setup and results of PRM experiments performed for peptides3-15aa. Document S2. Article plus supplemental information
## Data and code availability
•All MS and RNA-seq data created in this study have been deposited online and are publicly available as of the date of publication. Microscopy data reported in this paper have been deposited to Mendeley Data and are publicly available as of the date of publication. The DOI is listed in the key resources table. This paper also analyzes existing, publicly available data. These accession numbers for the datasets are listed in the key resources table.•All original code has been deposited at GitHub as well as Zenodo and is publicly available as of the date of publication. The DOI is listed in the key resources table.•Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
## Author contributions
Conceptualization, N.H., J.R.-O., C.-L.S., J.F.S., and S.v. H.; methodology, N.H., M.G., P.M., J.R.-O., C.-L.S., J.F.S., S.v. H., and T.E.W.; validation, A.C., E.W.D., M.K., M.M., R.L.M., C.-L.S., J.F.S., Z.S., and M.Z.; formal analysis, E.W.D., M.K., R.L.M., J.R.-O., C.-L.S., J.F.S., Z.S., and M.Z.; investigation, A.C., J.G., M.K., M.M., N. Liang, N. Liebe, C.-L.S., A.S., J.F.S., and M.Z.; resources, M.G., J.M.M., and J.R.P.; data curation, J.R.-O., C.-L.S., and J.F.S.; writing – original draft, J.R.-O., C.-L.S., and J.F.S.; writing – review & editing, E.A., N.H., J.R.-O., C.-L.S., J.F.S., and S.v. H. with input from all authors; visualization, J.R.-O., C.-L.S., and J.F.S.; supervision, N.H., M.G., P.M., S.v. H., and T.E.W.; project administration: N.H. and S.v. H.; funding acquisition, N.H. and S.v. H.
## Declaration of interests
The authors declare no competing interests.
## Inclusion and diversity
We support inclusive, diverse, and equitable conduct of research.
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|
---
title: Prevalence and predictors of outcomes among ESRD patients with COVID-19
authors:
- Claire S. Baptiste
- Esther Adegbulugbe
- Divya Shankaranarayanan
- Zahra Izzi
- Samir Patel
- Rasha Nakity
- Richard L. Amdur
- Dominic Raj
journal: BMC Nephrology
year: 2023
pmcid: PMC10033174
doi: 10.1186/s12882-023-03121-5
license: CC BY 4.0
---
# Prevalence and predictors of outcomes among ESRD patients with COVID-19
## Abstract
### Background
End-stage renal disease patients on hemodialysis (ESRD) patients are at high risk for contracting COVID-19. In this propensity matched cohort study, we examined the prevalence of COVID-19 in emergency room (ER) patients and examined whether clinical outcomes varied by ESRD status.
### Methods
Patients who visited George Washington University Hospital ER from April 2020 to April 2021 were reviewed for COVID-19 and ESRD status. Among COVID-positive ER patients, the propensity for ESRD was calculated using a logistic regression model to create a propensity-matched sample of ESRD vs non-ESRD COVID-19 patients. A multivariable model examined whether ESRD was an independent predictor of death and other outcomes in COVID-19 patients.
### Results
Among the 27,106 ER patients, 2689 of whom were COVID-positive ($9.9\%$). The odds of testing positive for COVID-19 were 0.97 ([$95\%$ CI: 0.78–1.20], $$p \leq 0.76$$) in ESRD vs non-ESRD patients after adjusting for age, sex, and race. There were 2414 COVID-positive individuals with non-missing data, of which 98 were ESRD patients. In this COVID-positive sample, ESRD patients experienced a higher incidence of stroke, sepsis, and pneumonia than non-ESRD individuals. Significant independent predictors of death included age, race, sex, insurance status, and diabetes mellitus. Those with no insurance had odds of death that was $212\%$ higher than those with private insurance (3.124 [1.695–5.759], $p \leq 0.001$). ESRD status was not an independent predictor of death (1.215 [0.623–2.370], $$p \leq 0.57$$). After propensity-matching in the COVID-positive patients, there were 95 ESRD patients matched with 283 non-ESRD individuals. In this sample, insurance status continued to be an independent predictor of mortality, while ESRD status was not. ESRD patients were more likely to have lactic acidosis ($36\%$ vs $15\%$) and length of hospital stay ≥ 7 days ($48\%$ vs $31\%$), but no increase in odds for any studied adverse outcomes.
### Conclusions
In ER patients, ESRD status was not associated with higher odds for testing positive for COVID-19. Among ER patients who were COVID positive, ESRD was not associated with mortality. However, insurance status had a strong and independent association with death among ER patients with COVID-19.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12882-023-03121-5.
## Introduction
COVID-19 is a world-wide public health emergency [1, 2]. The risk for developing severe symptoms and death from COVID-19 is higher in patients who are socioeconomically disadvantaged, and in those with large burden of comorbidities [2, 3]. End-stage renal disease patients on maintenance hemodialysis (ESRD patients) are highly susceptible to contracting COVID-19 because many receive in-center hemodialysis at least three times per week, limiting their ability to isolate and socially distance themselves [4, 5]. In order to better understand the prevalence and clinical outcome among ESRD patients with COVID-19 infection, we conducted a propensity matched retrospective analysis of all the patients admitted to the George Washington University Hospital (GWUH) with a diagnosis of COVID-19 from April 2020 to April 2021.
## Cohort selection
We queried the GWUH EHR (Cerner EHR platform) for patients who visited GWUH Emergency Department April from 2020 to April 2021. This time period begins roughly from the onset of the pandemic until peak vaccination distribution for COVID-19. We extracted information including demographics, diagnosis codes, clinical notes, procedures, imaging results, laboratory values, medication lists, visit summaries, and ancillary results, among other clinical subject areas. The data were integrated with billing and administrative data from a variety of other sources. As Fig. 1 illustrates, among the 27,106 individuals who visited the GWUH emergency room during the study period, we identified adults positive for COVID-19 using ICD-10 code U07.1 and further screened for patients with ESRD using the ICD-10 code N18.6. Patients with any missing data point were excluded from this study ($$n = 275$$).Fig. 1Patient Flowchart. The patient flowchart depicts the screening process of patients that visited the emergency department between April 1, 2020 and April 1, 2021. Abbreviations: ER, emergency room, ESRD, end stage renal disease
## Statistical analysis
Starting with the full ER sample of patients, we examined whether demographics (age, sex, and race) and ESRD status differed between those with and without COVID positivity using chi-square and multivariable logistic regression. Among COVID-positive ER patients, we examined differences in demographics and outcomes between patients with vs without ESRD in univariable analysis using chi-square or Fisher’s Exact test, 2-tailed between-groups t-tests, or Kruskal–Wallis tests. Counts and frequencies were reported for most variables; for length of stay (LOS) and ventilation duration, the mean and range were reported. Among all COVID-positive ER patients, a multivariable logistic regression was then used to determine whether ESRD was an independent predictor of death. Other variates in the model included age, sex, race, insurance, diabetes, hypertension, heart failure, coronary artery disease (CAD), and obesity.
In COVID-positive patients, the propensity for ESRD was calculated using a logistic regression model predicting ESRD using pre-hospitalization variables, including age, sex, insurance, heart failure, coronary artery disease, and obesity. Greedy Matching was then used to match ESRD to non-ESRD patients 1-to-3. The probability of ESRD was derived from the propensity model. Matching was done on this probability (requiring ± $2\%$ for a match) as well as race (requiring an exact match). In the matched sample, Fisher exact test or Kruskal–Wallis test was used to compare pre-hospitalization variables and outcomes. In order to account for non-independent outcomes among matched groups of patients, general estimating equations (GEE) were used to nest cases within matched groups.
SAS (version 9.4, Cary, NC) was used for data analysis with $p \leq 0.05$ considered statistically significant.
## Results
Among the 27,106 individuals who visited the GWUH emergency room during the study period, 2689 ($9.9\%$) tested positive for COVID-19, and 115 of those were ESRD patients. After excluding individuals with missing information, we analyzed data on 2414 individuals, of which 98 were ESRD patients. ESRD patients were more likely to be older, Black individuals, have public insurance, heart failure, and coronary artery disease.
## Analysis of data from all ER patients
The odds for testing positive for COVID-19 was higher among males (1.20 [1.10–1,31], $p \leq 0.001$), Blacks (3.04 [2.64–3.51], $p \leq 0.001$), and age ≥ 50 years (1.13 [1.04–1.24], $$p \leq 0.005$$). After adjusting for demographics, ESRD status was not associated with COVID-19 positivity (0.97 [0.78–1.20], $$p \leq 0.76$$).
## Results in COVID-19 positive patients
During hospitalization, ESRD patients had higher incidence of stroke, sepsis, and pneumonia than non-ESRD individuals (Table S4). Not surprisingly, more patients with ESRD received continuous renal replacement therapy (CRRT) and had longer hospital stays. In a multivariable logistic regression model predicting mortality, the receiver operator curve (ROC) area under the curve (AUC) was 0.83, indicating good discrimination (Table 1). Significant predictors of death included age, race, sex, insurance status, and diabetes mellitus. Each year of age raised the odds for death by $6\%$, after accounting for all other covariates (1.06 [1.05–1.08], $p \leq 0.001$). The adjusted odds of death were reduced in White vs. Black patients by $63\%$ (0.37 [0.17–0.78], $$p \leq 0.009$$). Those with public insurance had a $70\%$ increase in odds of death vs those with private insurance (1.70 [1.05 -2.76], $$p \leq 0.03$$), and those with no insurance had odds of death that was $212\%$ higher than those with private insurance (3.12 [1.70 -5.76], $p \leq 0.001$). Having diabetes mellitus raised the odds of death by $62\%$ (1.62 [1.12 -2.35], $$p \leq 0.01$$). However, ESRD status did not have a significant association with death (1.22 [0.62–2.37], $$p \leq 0.57$$).Table 1Multivariable logistic regression model predicting mortality using the full sample Adjusted OR $95\%$ Wald Confidence Limits P -Value Age1.0641.0511.077 < 0.001Race Asian vs Blacks0.6450.1233.3960.61 Multiracial vs Blacks1.5220.3935.8920.54 Unknown vs Blacks0.7710.4901.2130.26 Whites vs Blacks0.3670.1740.7760.009Sex (Female vs Male)0.4520.3160.648 < 0.001Insurance (None vs Private)3.1241.6955.759 < 0.001Insurance (Public vs Private)1.6981.0462.7550.03Heart Failure1.4130.7902.5270.24CAD1.4570.9432.2510.09Obesity1.7220.9053.2770.10Diabetes1.6171.1152.3460.01Hypertension0.8950.6101.3130.57ESRD status1.2150.6232.3700.57 CAD Coronary artery disease
## Analysis of propensity matched samples
After matching by propensity score and race, there were 378 individuals, including 95 patients with ESRD who matched with 283 individuals without ESRD. There were no significant differences in demographics or comorbidities among the matched samples. Among the outcomes examined in the propensity matched samples, ESRD patients were more likely to have lactic acidosis ($36\%$ vs $15\%$) and length of hospital stay ≥ 7 days ($48\%$ vs $31\%$) (Table 2). The median length of stay was 6.5 [2.8 – 13.3] days in ESRD patients and 3.0 [0.6 – 9.4] days in non-ESRD patients ($p \leq 0.001$). There was no significant increase in odds for death in ESRD patients (0.93 [0.46–1.88], $$p \leq 0.84$$).Table 2Outcomes by ESRD status in propensity-matched sample Outcome ESRD $$n = 95$$ Matched Controls $$n = 283$$ Univariable p -value Adjusted OR for ESRD vs Control Adjusted p -value Stroke6 ($6.3\%$)11 ($3.9\%$)0.391.67 (0.58–4.77)0.34Shock3 ($3.2\%$)7 ($2.5\%$)0.721.29 (0.37–4.48)0.69Lactic acidosis34 ($35.8\%$)42 ($14.8\%$) < 0.00013.19 (1.92–5.30) < 0.001Intubation2 ($2.1\%$)16 ($5.7\%$)0.260.36 (0.09–1.48)0.16Sepsis16 ($16.8\%$)30 ($10.6\%$)0.151.71 (0.88–3.32)0.12MI01 ($0.4\%$)0.99NaNaPneumonia15 ($15.8\%$)27 ($9.5\%$)0.131.77 (0.90–3.49)0.10ICU admission2 ($2.1\%$)9 ($3.2\%$)0.740.65 (0.14–3.14)0.60Mortality12 ($12.6\%$)38 ($13.4\%$)0.990.93 (0.46–1.88)0.84LOS ≥ 7 days46 ($48.4\%$)87 ($30.7\%$)0.0032.12 (1.34–3.35)0.001On Ventilator2 ($2.1\%$)10 ($3.5\%$)0.740.59 (0.14–2.43)0.46Univariable p is for Fisher Exact test. Adjusted p is from the GEE model accounting for correlated outcomes in matched groups Na Too few events to calculate, MI Myocardial infarction, ICU Intensive care unit, LOS Length of Stay
## Discussion
In this single center retrospective study involving 27,106 individuals, we found 2414 individuals who tested positive for COVID-19. Within that sub-sample, there were 98 patients who had ESRD. Those ESRD patients were not at increased risk for testing positive for COVID-19. Insurance status was an independent predictor of mortality among patients testing positive for COVID-19. While ESRD patients were predominantly using public insurance, ESRD status was not independently associated with increased odds for death. ESRD patients were more likely to have longer length of hospital stay than non-ESRD patients.
A year after data collection, there have been about 80 million confirmed COVID-19 cases in the U.S. and about 983,000 deaths [6]. In Washington D.C., the most recent corresponding numbers are 134,623 and 1,319, respectively [7]. The risk for developing severe symptoms and death from COVID-19 is higher in patients who are socioeconomically disadvantaged, and in those with large burden of comorbidities, which is consistent with the recent literature [8, 9]. A recent meta-analysis of 34 studies reported high COVID-19 prevalence and case fatality rates among ESRD patients [10].
Despite having many risk factors for poor outcomes, we did not find increased odds for death in ESRD patients, both in the analysis of the entire cohort and in the propensity matched sample. Published findings from two other retrospective studies report opposite findings, stating that ESRD status is an important risk factor for mortality in COVID-19 patients [11, 12]. Nonetheless, these studies only analyzed one to two months of data and retrieved data from a single site in different places, which were New York or the Alborz province in Iran [11, 12].
Furthermore, hyperlactatemia has traditionally been a marker of poor prognosis in critically ill patients [13]. In our study, ESRD patients had 3.19 higher odds for having elevated lactate levels and 2.12 higher odds for ≥ 7 days of hospital stay. A recent systematic literature review, comprising of 19 studies, found that substantially elevated lactate values were neither consistently present in all COVID-19 patients with poor outcomes, supporting our results [14].
The study has limitations associated with the retrospective study design and relatively smaller number of ESRD patients from a single center. These findings need to be confirmed in a larger multicenter cohort study.
To conclude, we found that insurance status has a strong independent association with death among individuals with COVID-19. ESRD status was not associated with higher odds for testing positive for COVID-19. Among individuals with COVID-19 positive test result, ESRD patients did not have a higher odd for adverse outcomes compared to matched individuals without ESRD. This may be due to increased patient awareness and proactive strategies implemented by the hospital and dialysis providers during the pandemic.
## Supplementary Information
Additional file 1: Supplementary Table S1. Characteristics of patients with and without COVID-19 diagnosis in the entire cohort. Supplementary Table S2. Patient Characteristics in the entire cohort. Supplementary Table S3. Association between Clinical Characteristics and COVID-19 diagnosis using a generalized estimating equations for the multivariable model in the entire cohort. Supplementary Table S4. Inpatient Outcomes by ESRD Status. Supplementary Table S5. Patient characteristics by ESRD/dialysis status in the propensity matched sample.
## References
1. 1.COVID-19 Public Health Emergency of International Concern (PHEIC) Global research and innovation forum. Who.int. Available from: https://www.who.int/publications/m/item/covid-19-public-health-emergency-of-international-concern-(pheic)-global-research-and-innovation-forum. [cited 2022 May 6].
2. 2.CDC. COVID-19 overview and infection prevention and control priorities in non-U.S. healthcare settings. Centers for Disease Control and Prevention; 2022. Available from: https://www.cdc.gov/coronavirus/2019-ncov/hcp/non-us-settings/overview/index.html. [cited 2022 May 6].
3. 3.CDC. Health equity considerations and racial and ethnic minority groups. Centers for Disease Control and Prevention; 2022 Available from: https://www.cdc.gov/coronavirus/2019-ncov/community/health-equity/race-ethnicity.html. [cited 2022 May 6].
4. Corbett RW, Blakey S, Nitsch D, Loucaidou M, McLean A, Duncan N. **Epidemiology of COVID-19 in an urban dialysis center**. *J Am Soc Nephrol* (2020.0) **31** 1815-1823. DOI: 10.1681/ASN.2020040534
5. Hsu CM, Weiner DE, Aweh G, Miskulin DC, Manley HJ, Stewart C. **COVID-19 among US dialysis patients: risk factors and outcomes from a national dialysis provider**. *Am J Kidney Dis* (2021.0) **77** 748-756.e1. DOI: 10.1053/j.ajkd.2021.01.003
6. 6.CDC. COVID data tracker. Centers for Disease Control and Prevention; 2020. Available from: https://covid.cdc.gov/covid-data-tracker/. [cited 2022 May 6].
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8. 8.Foster HME, Ho FK, Mair FS, Jani BD, Sattar N, Katikireddi SV, et al. The association between a lifestyle score, socioeconomic status, and COVID-19 outcomes within the UK biobank cohort. BMC Infect Dis. 2022; 22(273). 10.1186/s12879-022-07132-9.
9. Mena GE, Martinez PP, Mahmud AS, Marquet PA, Buckee CO, Santillana M. **Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile**. *Science* (2021.0) **372** eabg5298. DOI: 10.1126/science.abg5298
10. Nopsopon T, Kittrakulrat J, Takkavatakarn K, Eiamsitrakoon T, Kanjanabuch T, Pongpirul K. **Covid-19 in end-stage renal disease patients with renal replacement therapies: a systematic review and meta-analysis**. *PLoS Negl Trop Dis* (2021.0) **15** e0009156. DOI: 10.1371/journal.pntd.0009156
11. Rastad H, Ejtahed H, Shafiee G, Safari A, Shahrestanaki E, Khodaparast Z, Hassani NS, Rezaei M, Nazari M, Zakani A, Niksima MM, Azimzadeh M, Karimi F, Tajbakhsh R, Qorbani M. **The risk factors associated with COVID-19-related death among patients with end-stage renal disease**. *BMC Nephrol* (2021.0) **22** 33-33. DOI: 10.1186/s12882-020-02221-w
12. Ng JH, Hirsch JS, Wanchoo R, Sachdeva M, Sakhiya V, Hong S, Jhaveri KD, Fishbane S. **Outcomes of patients with end-stage kidney disease hospitalized with COVID-19. Kidney international**. *Clin Chem Lab Med* (2020.0) **98** 1530-1539. DOI: 10.1016/j.kint.2020.07.030
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|
---
title: Neurological symptoms and comorbidity profile of hospitalized patients with
COVID-19
authors:
- Renata Carvalho Cremaschi
- Carla Alessandra Scorza Bahi
- Angelo Amato Vincenzo de Paola
- Jaquelina Sonoe Ota Arakaki
- Paulo Roberto Abrão Ferreira
- Nancy Cristina Junqueira Bellei
- Vanderci Borges
- Fernando Morgadinho Santos Coelho
journal: Arquivos de Neuro-Psiquiatria
year: 2023
pmcid: PMC10033191
doi: 10.1055/s-0043-1761433
license: CC BY 4.0
---
# Neurological symptoms and comorbidity profile of hospitalized patients with COVID-19
## Abstract
Background The neurological manifestations in COVID-19 adversely impact acute illness and post-disease quality of life. Limited data exist regarding the association of neurological symptoms and comorbid individuals.
Objective To assess neurological symptoms in hospitalized patients with acute COVID-19 and multicomorbidities.
Methods Between June 2020 and July 2020, inpatients aged 18 or older, with laboratory-confirmed COVID-19, admitted to the Hospital São Paulo (Federal University of São Paulo), a tertiary referral center for high complexity cases, were questioned about neurological symptoms. The Composite Autonomic Symptom Score 31 (COMPASS-31) questionnaire was used. The data were analyzed as a whole and whether subjective olfactory dysfunction was present or not.
Results The mean age of the sample was 55 ± 15.12 years, and 58 patients were male. The neurological symptoms were mostly xerostomia ($71\%$), ageusia/hypogeusia ($50\%$), orthostatic intolerance ($49\%$), anosmia/hyposmia ($44\%$), myalgia ($31\%$), dizziness ($24\%$), xerophthalmia ($20\%$), impaired consciousness ($18\%$), and headache ($16\%$). Furthermore, $91\%$ of the patients had a premorbidity. The 44 patients with subjective olfactory dysfunction were more likely to have hypertension, diabetes, weakness, shortness of breath, ageusia/hypogeusia, dizziness, orthostatic intolerance, and xerophthalmia. The COMPASS-31 score was higher than that of previously published controls (14.85 ± 12.06 vs. 8.9 ± 8.7). The frequency of orthostatic intolerance was $49\%$ in sample and $63.6\%$ in those with subjective olfactory dysfunction (2.9-fold higher risk compared to those without).
Conclusion A total of $80\%$ of inpatients with multimorbidity and acute COVID-19 had neurological symptoms. Chemical sense and autonomic symptoms stood out. Orthostatic intolerance occurred in around two-thirds of the patients with anosmia/hyposmia. Hypertension and diabetes were common, mainly in those with anosmia/hyposmia.
## INTRODUCTION
The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds mainly to the angiotensin-converting enzyme type 2 (ACE2) receptor in the olfactory and respiratory epithelium. The virus's interaction can cause anosmia/hyposmia. Because ACE2-expressing cells are widely distributed, the SARS-CoV-2 reaches extrapulmonary sites as well. 1 The neurological manifestations were early linked to the coronavirus disease 2019 (COVID-19). 2 The pathophysiology is related to immune activation, neuroinflammation, and damage to brains blood vessels. 3 Anosmia was associated with low in-hospital mortality. 4 5 Although chemosensory symptoms are primary presentations of neurological features in COVID-19, a broad spectrum of other neurological symptoms accompany them. A sizeable proportion of the patients with anosmia have headache, and others develop mild-to-severe complications, including life-threatening cerebrovascular events. 6 7 A few symptoms of the illness overlap with the symptoms of autonomic dysfunction. Particularly in the early stages of COVID-19, syncope and silent hypoxemia are the clinical presentations of autonomic impairment, which could be associated with the intrinsic mechanism of the viral infection or other non-COVID causes. 8 Some patients in recovery have experienced long-term autonomic symptoms, which are reported in long-COVID. 9 10 Cardiovascular autonomic dysfunction was confirmed. 11 Dysautonomia was overlooked in critically ill COVID-19 patients. 12 The studies evaluating COVID-related neurological manifestations are heterogeneous, because they include symptoms and complications. The association between high-risk preexisting comorbidities for COVID-19 hospitalization and related deaths has been established in the literature. 13 About $43.9\%$ of the patients with COVID-19 and neurological symptoms have comorbid conditions. 14 The impact of morbidities in the neurological symptoms of COVID-19, especially those with a strong ACE2 receptor expression that enhances the viral entry into the host cells, has not been well emphasized.
Even though anosmia/hyposmia is one of the most frequent neurological symptoms observed, not much is known regarding the differences of the other neurological symptoms and the preexisting diseases in patients, regardless of whether subjective olfactory impairment is present or not. This study investigates inpatients with multiple comorbidities from a public reference hospital of Sao Paulo, in the course of the initial outbreak of the pandemic. The objectives were to assess the frequency of neurological symptoms in patients hospitalized for the treatment of COVID-19. We also evaluated the association between subjective anosmia/hyposmia and other neurological symptoms or comorbidities.
## Standard protocol approval and patient consent
The University's Institutional Ethics Committee (Comitê de Ética em *Pesquisa da* Universidade Federal de São Paulo) approved the study, with the ethics board approval number $\frac{0547}{2020.}$ All participants in the study provided written informed consent for the research.
## Study design and sample
This cross-sectional study describes neurological symptoms in patients admitted to a public tertiary referral center for high complexity cases, which turned into a COVID-19 care center. The study was conducted at the São Paulo Hospital, from the Federal University of Sao Paulo (Brazil).
The inclusion criteria were symptomatic patients aged 18 or older, with diagnostic confirmation of SARS-CoV-2 infection by nasopharyngeal swab reverse-transcriptase polymerase chain reaction (RT-PCR). The exclusion criteria were pregnancy or puerperium, dementia as a premorbidity, assessment during mechanical ventilation, or hemodynamic instability. The sampling method was noncasual and convenient, intended to represent a sample of real-life inpatients.
## Data acquisition
The hospital capacity was organized to treat COVID-19 patients separately. The occupancy rates were high, but an exact overall value was inaccurate. All patients sited at the infectious diseases and pneumology units (48 beds available) were invited to participate, between June 2020 and July 2020. Data collection was performed once a week, during five consecutive weeks. Before recruitment, the assistant physician confirmed the stable clinical condition of each patient, enabling them to join in the research. Two expert neurologists (FMSC, RMCC) interviewed the patients to fill out electronic forms with answers on the experienced symptoms. The completion of all items was checked before sending them for data processing. Clinical data were retrieved manually from electronic medical charts. The reported medications comprised those in use before admission.
According to the manufacturer's protocols, all quantitative RT-PCR analyses were made in a single laboratory located at the hospital. Additionally, RNA was isolated from the subject's nasopharyngeal swabs using the Quick-RNA Viral Kit (Zymo Research, Irvine, CA, USA). After extraction, the samples were tested for SARS-CoV-2 using a multiplex RT-PCR commercial kit (Gene Finder, South Korea). Early in the first wave of COVID-19, the identification of the variants was not yet implemented. Different commercially available kits were used in the samples of subjects who had been initially diagnosed at other hospitals. At admission, the degree of radiological involvement was confirmed using chest computed tomography to identify the typical ground-glass opacities extension.
## Neurological symptoms
As there was no standardized measure of neurological symptoms in COVID-19, a survey was created, based on those of the initial report in China. 2 The Composite Autonomic Symptom Score 31 (COMPASS-31) was added to investigate symptoms of orthostatic intolerance and secretomotor function, which are frequently underrecognized. Later, in the context of long-COVID, this questionary was suggested as a screening tool to track symptoms changes of autonomic dysfunction. 15 Olfactory dysfunction (OD), partial hyposmia or total anosmia, is the reduced ability to smell during sniffing (orthonasal olfaction) or eating and drinking (retronasal olfaction). The retronasal olfaction contributes to the taste, whose dysfunction can be partial hypogeusia or total ageusia. The interviewers asked whether smell and taste were reduced or absent during sniffing, chewing, drinking, or digesting food. Orthostatic intolerance, xerophthalmia, and xerostomia were identified according to questions number one, nine, and ten of COMPASS-31. Questions number two and three characterized the frequency and intensity of orthostatic intolerance.
The COMPASS-31 is a 31-item survey, organized in 6-scale domains, which assesses the presence, severity, distribution, frequency, and progression of autonomic symptoms. Regarding the clinical relevance of the domain, the scores provide the total weighted score, which ranges from 0 to 100. The higher scores indicate a more significant symptom load. 16 As some COVID-19 symptoms are similar to autonomic symptoms, a score with gastrointestinal domain scored as zero was named COMPASS-31(-GI). The Mayo Clinic Autonomic Laboratory validated the Brazilian Portuguese language version of COMPASS-31. The overall score of patients with COVID-19 was compared to those of healthy controls in previously published literature (mean 8.9 ± 8.7). 17 Table 2 shows that the mean number of days from the onset of illness to the neurological symptoms was 1.48 ± 3.41. The neurological symptoms occurred in $80\%$ of the sample. The patients in the s̅OD group reported $64.3\%$ of other neurological symptoms rather than anosmia/hyposmia. The most frequent symptoms were xerostomia ($71\%$), ageusia/hypogeusia ($50\%$), orthostatic intolerance ($49\%$), anosmia/hyposmia ($44\%$), myalgia ($31\%$), dizziness ($24\%$), xerophthalmia ($20\%$), impaired consciousness due to syncope, seizures, daytime sleepiness or insomnia ($18\%$), and headache ($16\%$). The OD group had a higher percentage of ageusia/hypogeusia (97.7 vs. $12.5\%$, OR = 301, $95\%$ confidence interval [CI] 35.59 to 2,545.42), dizziness (34.1 vs. $16.1\%$, OR = 2.70, $95\%$ CI 1.04 to 6.96) orthostatic intolerance (63.6 vs. $37.5\%$, OR = 2.91, $95\%$ CI 1.28 to 6.61), and xerophthalmia (29.5 vs. $12.5\%$, OR = 2.93, $95\%$ CI 1.05 to 8.16) than s̅OD. Orthostatic intolerance occurred more frequently and with moderate and severe intensity in OD. Other neurological symptoms did not differ. The sample had a higher COMPASS-31 mean score than that of healthy controls previously published (14.85 ± 12.06 vs. 8.9, $t = 4.939$, $95\%$ CI 3.56 to 8.35, $p \leq 0.001$). The COMPASS-31 and COMPASS-31(-GI) mean scores for the OD group were higher than for the s̅OD one.
**Table 2**
| Unnamed: 0 | Unnamed: 1 | OD | OD.1 | Unnamed: 4 |
| --- | --- | --- | --- | --- |
| | Overall | without | with | p - value |
| | (N = 100) | (N = 56) | (N = 44) | |
| Delay, days 1 | 1.48 ± 3.41 | 1.39 ± 3.48 | 1.55 ± 3.39 | 0.84 |
| Neurological symptom, % | 80 | 64.3 | 100 | <0.001 |
| Ageusia/hypogeusia 2 | 50 | 12.5 | 97.7 | <0.001 |
| Vision impairment | 11 | 8.9 | 13.6 | 0.45 |
| Dizziness 3 | 24 | 16.1 | 34.1 | 0.03 |
| Irritability | 12 | 7.1 | 18.2 | 0.09 |
| Impaired consciousness 4 | 18 | 23.2 | 11.4 | 0.12 |
| Confusion | 9 | 8.9 | 9.1 | 0.97 |
| Muscle weakness 5 | 2 | 1.8 | 2.3 | 1 |
| Myalgia | 31 | 23.2 | 40.9 | 0.05 |
| Headache | 16 | 12.5 | 20.5 | 0.28 |
| Orthostatic intolerance 6 | 49 | 37.5 | 63.6 | 0.01 |
| Frequency | | | | |
| Never | 51 | 62.5 | 36.4* | 0.02 |
| Rarely | 14 | 12.5 | 15.9 | |
| Occasionally | 19 | 17.9 | 20.5 | |
| Frequently | 15 | 7.1 | 25* | |
| Almost always | 1 | 0 | 2.3 | |
| Intensity | | | | 0.01 |
| | 51 | 62.5* | 36.4* | |
| Mild | 23 | 23.2 | 22.7 | |
| Moderate | 20 | 12.5 | 29.5* | |
| Severe | 6 | 1.8* | 11.4* | 0.03 |
| Xerophthalmia 7 | 20 | 12.5 | 29.5 | 0.03 |
| Xerostomia | 71 | 64.3 | 79.5 | 0.09 |
| COMPASS-31 TWS (0–100) | 16.69 ± 11.52 | 12.92 ± 13.72 | 20.90 ± 23.47 | 0.002 |
| TWS minus GI (0–75) | 13.82 ± 9.71 | 10.58 ± 12.16 | 17.21 ± 20.26 | 0.003 |
## Comorbidity profile
As a complete database was available, the co-existing conditions were obtained manually in medical records. The Charlson Comorbidity Index was calculated by the investigators. The method uses 17 comorbidities associated with mortality to classify prognostic comorbidity. 18 The severity of comorbid diseases is mild (scores 1–2), moderate (scores 3–4), and severe (scores ≥ 5).
The associated diseases are presented in Table 3. The majority of the patients ($91\%$) had a comorbid condition. The most frequent premorbidities were hypertension ($60\%$), diabetes ($40\%$), obesity ($33\%$), smoking history ($25\%$), and cardiac ($23\%$) or end-stage renal ($31\%$) diseases. The OD group had a higher percentage of hypertension (72.7 vs. $50\%$) and diabetes (59.1 vs. $25\%$), compared to s̅OD. There were no significant differences between these groups concerning other coexisting disorders or the Charlson Comorbidity Index, according to which $65\%$ of the patients had a moderate to severe scores.
**Table 3**
| Unnamed: 0 | Unnamed: 1 | OD | OD.1 | Unnamed: 4 |
| --- | --- | --- | --- | --- |
| | Overall | without | with | p- value |
| | (N = 100) | (N = 56) | (N = 44) | |
| Associated medical disease, % | Associated medical disease, % | | | |
| Hypertension | 60 | 50 | 72.7 | 0.02 |
| Diabetes mellitus | 40 | 25 | 59.1 | 0.001 |
| Obesity (BMI > 30 kg/m 2 ) | 33 | 32.1 | 34.1 | 0.83 |
| Smoking history 1 | 25 | 26.8 | 22.7 | 0.64 |
| Cardiac diseases 2 | 23 | 25 | 20.5 | 0.59 |
| Chronic pulmonary disease 3 | 10 | 12.5 | 6.8 | 0.34 |
| End-stage renal disease | 31 | 30.4 | 31.8 | 0.87 |
| Kidney transplantation | 24 | 23.2 | 25 | 0.83 |
| Hemodialysis | 7 | 7.1 | 6.8 | 1 |
| Malignancy | 9 | 8.9 | 9.1 | 0.97 |
| Asthma | 7 | 3.6 | 11.4 | 0.23 |
| Vasculiti 4 | 7 | 8.9 | 4.5 | 0.46 |
| Cerebrovascular | 7 | 3.6 | 11.4 | 0.23 |
| Peripheral artery and aneurysm | 2 | 0 | 4.5 | 0.37 |
| Falciform anemia | 2 | 1.8 | 2.3 | 1 |
| Pulmonary tuberculosis | 1 | 1.8 | 0 | 1 |
| Other 5 | 24 | 30.4 | 15.9 | 0.09 |
| Charlson Comorbidity Index grade, % | Charlson Comorbidity Index grade, % | | | 0.64 |
| None (CCI = 0) | 9 | 10.7 | 6.8 | 0.49 |
| Mild (CCI = 1 – 2) | 26 | 26.8 | 25 | 0.84 |
| Moderate (CCI = 3 – 4) | 39 | 41.1 | 36.4 | 0.63 |
| Severe (CCI ≥ 5) | 26 | 21.4 | 31.8 | 0.24 |
## Statistical analysis
Data were examined using the Statistical Package Social Sciences (SPSS, IBM Corp. Armonk, NY, USA) software for windows 8, version 21.0. Data were analyzed as a whole, and whether the patient has subjective OD or not (s̅OD). Normality and homogeneity tests and parametric analyses were used in the descriptive statistics. The Student T-test was used to compare COMPASS-31 overall total weighted score (TWS) and controls. The Pearson chi-squared and Fisher exact tests were used to analyze categorical variables. Generalized Linear Models, according to the distribution of the dependent variable (gamma and Poisson log-linear), were used to obtain COMPASS-31 scores, with OD as the independent variable. Logistic binary regression, with OD as the predictor, was used to compare the magnitude of the risk factors (odds ratio, OR) in a few neurological symptoms. The results are presented as means ± standard deviation and percentages (%). The significant level considered was $p \leq 0.05.$
## Demographics
A total of 124 surveys were obtained among the 118 patients enrolled. After reviewing the medical records, our study consisted of 100 patients. The clinical, laboratory, and imaging features of the patients are summarized in Table 1. Among the sample, 44 patients had OD. The mean age of the sample was 55.02 ± 15.12 years, with 39 patients being over 60 years of age. There were 58 male patients. The sample's mean body mass index (BMI) was above normal limits. Furthermore, OD patients had a higher percentage of weakness, shortness of breath, and constipation, compared to s̅OD. The mean lymphocyte count was also higher for OD patients than for s̅OD. There was a higher percentage of thrombocytopenia in s̅OD, compared with OD. Regarding chest imaging, $60.7\%$ of the patients in the s̅OD group had < $25\%$ disease severity, and $43.2\%$ of those in the OD group had 25 to $49\%$ extension.
**Table 1**
| Unnamed: 0 | Unnamed: 1 | Overall | OD | OD.1 | p- value |
| --- | --- | --- | --- | --- | --- |
| | | Overall | without | with | p- value |
| | | (N = 100) | (N = 56) | (N = 44) | |
| Age, years | Age, years | 55.02 ± 15.12 | 55.16 ± 15.26 | 54.84 ± 15.11 | 0.91 |
| Age category, % | 18–60 years | 61 | 57.1 | 65.9 | 0.37 |
| Age category, % | >60 years | 39 | 42.9 | 34.1 | 0.37 |
| Male gender, % | Male gender, % | 58 | 66.1 | 47.7 | 0.06 |
| BMI, kg/m 2 | BMI, kg/m 2 | 27.66 ± 5.96 | 27.49 ± 5.38 | 27.79 ± 6.42 | 0.80 |
| Regular medications use | Antihypertensives 1 | 66 | 67.9 | 63.6 | 0.65 |
| Regular medications use | Antidepressants and neuroleptics | 6 | 3.6 | 9.1 | 0.40 |
| Regular medications use | Immunosuppressants | 31 | 30.4 | 31.8 | 0.87 |
| Regular medications use | Oral corticosteroids | 27 | 26.8 | 27.3 | 0.95 |
| Regular medications use | Oral anticoagulants | 4 | 3.6 | 4.5 | 1 |
| General symptoms, % | Anorexia | 50 | 42.9 | 59.1 | 0.10 |
| General symptoms, % | Fever/chill | 66 | 66.1 | 65.9 | 0.98 |
| General symptoms, % | Weakness | 60 | 50.0 | 72.7 | 0.02 |
| General symptoms, % | Cough | 68 | 62.5 | 75.0 | 0.18 |
| General symptoms, % | Sore throat | 25 | 19.6 | 31.8 | 0.16 |
| General symptoms, % | Rhinorrhea | 21 | 23.2 | 18.2 | 0.54 |
| General symptoms, % | Shortness of breath | 66 | 57.1 | 77.3 | 0.03 |
| General symptoms, % | Diarrhea | 50 | 50 | 50 | 1 |
| General symptoms, % | Nausea/vomiting | 35 | 35.7 | 34.1 | 0.86 |
| General symptoms, % | Constipation | 15 | 7.1 | 25 | 0.01 |
| Viral load quantitation, % | High | 44 | 40.0 | 59.5 | 0.07 |
| Viral load quantitation, % | Low | 45 | 60.0 | 40.5 | 0.07 |
| Viral load quantitation, % | Missing 2 | 13 | 6 | 7 | 0.07 |
| Delay to confirmation, days 3 | Delay to confirmation, days 3 | 6.08 ± 4.24 | 6.25 ± 4.49 | 5.86 ± 3.93 | 0.65 |
| White blood cells 4 | Leucocyte count | 9,164 ± 6,581 | 8,636 ± 6,831 | 9,835 ± 6,262 | 0.36 |
| White blood cells 4 | Neutrophils count | 6,859 ± 4,815 | 6,638 ± 5,286 | 7,140 ± 4,182 | 0.60 |
| White blood cells 4 | Lymphocytes count | 1,202 ± 876 | 1,011 ± 724 | 1,445 ± 995 | 0.01 |
| Platelet | Count 4 *1000 | 214.85 ± 95.71 | 198.84 ± 92.86 | 235.23 ± 96.45 | 0.05 |
| Platelet | Thrombocytopenia, % (<100,000 4 ) | 27 | 35.7 | 15.9 | 0.02 |
| Serum creatinine 5 | Serum creatinine 5 | 2.82 ± 7.07 | 2.28 ± 3.04 | 3.51 ± 10.12 | 0.39 |
| Blood urea nitrogen 5 | Blood urea nitrogen 5 | 63.47 ± 53.59 | 66.29 ± 61.09 | 59.95 ± 42.85 | 0.56 |
| C-reactive protein 6 | C-reactive protein 6 | 104.23 ± 79.82 | 104.96 ± 78.76 | 103.3 ± 82.07 | 0.91 |
| Lactose dehydrogenase 7 | Lactose dehydrogenase 7 | 384.12 ± 164.71 | 351.3 ± 140.63 | 420.23 ± 182.7 | 0.05 |
| Chest CT category, % 8 | 0 (none) | 7 | 5.4 | 9.1 | 0.001 |
| Chest CT category, % 8 | 1 (<25%) | 43 | 60.7 | 20.5* | 0.001 |
| Chest CT category, % 8 | 2 (25–49%) | 31 | 21.4 | 43.2* | 0.001 |
| Chest CT category, % 8 | 3 (50–75%) | 18 | 12.5 | 25.7 | 0.001 |
| Chest CT category, % 8 | 4 (>75%) | 1 | 0.0 | 2.3 | 0.001 |
Both groups had a high percentage of patients on antihypertensive agents, and a low percentage on antidepressants and neuroleptics. Almost all the patients were using ACE inhibitors. The viral load quantitation was missing for 13 patients who had the samples collected at a different hospital. There were no significant differences between the groups in the use of medications, other general symptoms, delay to confirm SARS-Cov2 infection, and other laboratory testing on admission. The Supplementary Material Table S1 shows in-hospital standard of care of the patients. Finally, the OD group received more antiviral treatment than s̅OD (11.4 vs. $1.8\%$), but other differences were not seen, including clinical outcomes and mortality.
## DISCUSSION
Among hospitalized multimorbidity patients with laboratory-confirmed COVID-19, the neurological symptoms were very frequent, developing shortly after the onset of typical flu symptoms. Our strong results are the presence of chemical sense impairment and underestimated autonomic symptoms. Most of the patients without anosmia/hyposmia also reported another neurological symptom.
The neurological features in COVID-19 vary according to the sample and data collection. Our frequency of olfactory and gustatory dysfunction is comparable to the 46.8 and $52.3\%$ already described. Headache and dizziness were reported in $7.5\%$ and $6.1\%$ of patients, respectively. Diabetes ($31.1\%$) and hypertension ($13.5\%$) were the most common associated comorbidities. 19 The pooled prevalence of central nervous system or mental associated disorders in COVID-19 is around $50.68\%$. The most frequent symptom was OD ($36.20\%$ in 10 studies of the meta-analysis). 20 The main neurological symptoms detected among COVID-19 patients were fatigue ($42.9\%$), gustatory ($35.4\%$) and olfactory ($25.3\%$) dysfunctions, $10\%$ headache ($10\%$), and dizziness ($6.7\%$) in another meta-analysis. 14 An European multinational study with 6,537 SARS-infected patients reported the frequency of headache ($18.5\%$), impaired sense of smell ($9.0\%$) and taste ($12.8\%$), and delirium ($6.7\%$). The patients were mostly hospitalized in complicated/critical ($53\%$) and uncomplicated phases. 21 As expected, the three most frequent diseases found in our patients are risk factors for COVID-19, which seem to negatively affect the clinical course and morbimortality outcomes, but this is not definitely clear. 22 Our groups without and with OD were demographically similar, but the one with OD constituted a majority of those with hypertension, diabetes, and a higher extension on chest imaging.
The SARs-CoV-2 upregulates ACE2 expression in patients with hypertension, which can increase blood pressure and determine pneumonia. As diabetes has impaired T-cell function and increased interleukin-6, pneumonia-like symptoms can exist in these patients with COVID-19. 23 The association between COVID-19 and hypertension has generated considerable discussion. Hypertension is normally accompanied by many comorbidities that are major factors for disease severity, and there is little evidence of ACE inhibitors influencing tissue expression or activity of ACE2 in humans. 24 The increased risk of hyposmia after recovery of patients with type 2 diabetes and mild pneumonia associated with COVID-19, as well as animal experiments, indicate that diabetes could dampen the first-line defense of nasal immunity. There is an impaired nasal-associated lymphoid tissue immunity in diabetes type 2. 25 The extended autonomic system includes the neuroendocrine and neuroimmune systems. 18 The link between diabetes and OD is controversial. Adults with diabetes on more aggressive treatments showed a trend toward severe hyposmia/anosmia in the pocket smell test, without an association between disease duration and self-reported symptoms. 26 There were no differences in an objective odor test between patients with and without chronic complications of diabetes. 27 The slightly increased autonomic load in our sample might be explained, at least partially, by diabetes. The COMPASS-31 scores in all the groups of this study were lower than those of diabetics with cardiovascular autonomic neuropathy and polyneuropathy. 28 A cut-off of 28.67 indicates autonomic dysfunction in diabetes. 29 Even though the symptoms were interrogated during COVID-19, and despite the confounders, such as medication side effects and dehydration, diabetes was a possible factor for orthostatic intolerance in our study.
The elevated frequency of ageusia/hypogeusia in those with OD in our study was likely due to gustatory function being mediated through the sense of smell, pain perception, and somatosensory pathway. Elevated olfactory and gustatory dysfunction occur in mild-to-moderate COVID-19 through sense questionnaires. Of the $18.2\%$ of patients without nasal obstruction or rhinorrhea, $79.7\%$ had OD. 7 Chemosensory loss of smell and taste occurred in outpatients with influenza-like symptoms and COVID-19. 30 Our frequency of headache was a quarter of the one detected in adult symptomatic patients with laboratory-confirmed COVID-19 from the northeast of Brazil, whose characteristics were usually bilateral and severe, but rarely continuous. We found a comparable frequency of anosmia/hyposmia and ageusia/hypogeusia, but not the elevated risk of headache in those with impairment of smell and taste. 6 The autonomic symptoms can arise as para- and postinfectious manifestations, especially in viral infections such as herpes simplex and mononucleosis. A case-control study from Colombia showed that in *Zika virus* outbreaks, the COMPASS-31 was elevated, mainly in the orthostatic, secretomotor, and bladder domains, about 63 weeks after disease onset. 31 Two-thirds of the patients with anosmia/hyposmia had orthostatic intolerance answering to: você se sentiu tonto, desorientado, aéreo ou teve dificuldade de pensar quando levantou após ter ficado sentado ou deitado? ( did you feel dizzy, disoriented, lightheaded or had difficulty thinking when you got up from sitting or lying down?). They had a 2.9-fold higher risk, compared to those without anosmia/hyposmia. Orthostatic intolerance was more frequent and more intense in those with anosmia/hyposmia. This multifactorial symptom is associated with hyperadrenergic state and hypovolemia, among other factors that may have been present in COVID-19. Similarly, the elevated frequency of xerostomia could be associated with different causes.
Our COMPASS-31 score is almost 2-fold higher in healthy individuals than noted in previous studies, and another analysis reduced the overstatement of gastrointestinal symptoms as a bias. As the patients were asked about the presence and progression of such symptoms during COVID-19, our findings confirm these symptoms in the acute disease, especially in patients with subjective OD. Our scores are comparable to those of outpatients with post-COVID, using the same instrument. Orthostatic hypotension and hyposmia/hypogeusia occurred in 13.8 and $37.1\%$ of them; 9 autonomic symptoms may start within the first weeks of illness, or after hospital discharge. 10 32 33 The most frequent autonomic symptoms in patients referred to autonomic testing after COVID-19 were orthostatic such as lightheadedness ($93\%$) and headache ($22\%$). Orthostatic intolerance without tachycardia or hypotension was more frequent than postural orthostatic tachycardia syndrome. 34 Cardiovascular reflex alterations in early COVID-19 without associated diseases were remarkable in those with mild disease or with confirmed interstitial pneumonia. Orthostatic hypotension frequency was $33\%$. 11 Impaired consciousness occurred in $18\%$ of our sample. Syncope and presyncope occurred around $4.2\%$ in COVID-19, mainly due to unexplained causes. 35 *As a* deficient compensatory heart rate increase was observed in a few patients with syncope during the acute hypocapnic hypoxemia, researchers hypothesize that SARS-CoV-2 could have caused ACE2 internalization in midbrain nuclei changing the baroreflex and chemoreceptor responses. 36 *The neuropathogenesis* in COVID-19 includes a direct attack on the nervous system, and indirect effects of systemic factors, with postinfectious immune-mediated complications. 37 The neurotropism of SARS-CoV-2 may partially contribute to respiratory failure. 38 Older adults and those with multiple diseases have reduced homeostatic capabilities. The happy or silent hypoxemia, a term applied to the deficient blood oxygenation without dyspnea, is a blood-gas disorder that may occur due to an autonomic impairment. The allostasis, coordinated alterations to maintain homeostasis, is present in COVID-19 stress response. 39
In conclusion, this study has limitations. Data collection was during the increasing rate of cases. To reduce the risk of in-hospital contamination, owing to the access of units with and without COVID-19 cases, it was not possible to recruit controls. We could not use objective methods to measure sense functions. We were unable to collect the duration of flow oxygen therapy, which excites nerve endings (common chemical sense) to cause burning sensations. They can confound taste symptoms and xerostomia. Given the small number of patients without diabetes, but with anosmia, a subcategory analysis was not possible.
The COVID-19 neurological complications are associated with a poor in-hospital outcome and a high risk of mortality. 40 However, the growing evidence of OD as a prognostic marker of better in-hospital outcome for patients with COVID-19 was not observed in this study, probably due to the sample size (Supplementary Material). 4 The causes underlying the neurological symptoms of inpatients with COVID-19, especially those with associated diseases, can be multiple and unclear. Our results reinforce that they can be related to the COVID-19 itself, comorbidities, pharmacotherapies, and other factors. Given that the symptoms may persist, their recognition is critical to recovery, mainly those not frequently explored. The management of orthostatic symptoms could reduce the severity of chronic illness in long-COVID.
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|
---
title: 'A Potential Protective Effect of Alcohol Consumption in Male Genital Lichen
Sclerosus: A Case-Control Study'
authors:
- Joey El Khoury
- Jessica Andraos
- Anthony Kanbar
- Rami Halabi
- Serge Assaf
- Anthony Mina
- Sabine El Breidi
- Charbel Dabal
- Charbel El Hachem
- Rodrigue Saad
- Antoine Kassis
- Maher Abdessater
- Raghid El Khoury
journal: Advances in Urology
year: 2023
pmcid: PMC10033206
doi: 10.1155/2023/7208312
license: CC BY 4.0
---
# A Potential Protective Effect of Alcohol Consumption in Male Genital Lichen Sclerosus: A Case-Control Study
## Abstract
### Materials and Methods
A nested case-control study design was chosen. Subjects enrolled were adult male patients who had a circumcision between January 2010 and December 2020 at our university hospital, with a confirmed LSc diagnosis on pathology. Cases were matched with controls by age with a ratio of 1: 1, all of whom were circumcised and had a negative pathology report. Data collection consisted of sociodemographic, behavioral, and past medical and familial history characteristics.
### Results
A total of 94 patients were enrolled. The mean age was 49.81 (±22.92) in the group of men with LSc. No significant differences in sociodemographic characteristics (age and BMI) were found between the two compared groups. Smoking cannot predict LSc as opposed to alcohol consumption, which we found to be a protective factor against the appearance of LSc ($$p \leq 0.027$$). Men with LSc had significantly higher rates of diabetes ($$p \leq 0.021$$) and hypertension ($$p \leq 0.004$$). No associations were found between LSc and the presenting chief complaints, family history of LSc, and past penile trauma.
### Conclusion
In this study, we were able to compare multiple variables between 47 circumcised patients diagnosed with LSc and a control group. We found that LSc patients showed higher rates of diabetes and hypertension. A potential protective effect of alcohol consumption is to be explored in future projects with bigger sample sizes and higher statistical powers.
## 1. Introduction
Lichen sclerosus (LSc) is an acquired mucocutaneous disorder of unclear etiology with a predilection to the anogenital area. Even though it remains uncommon, extragenital manifestations exist and mainly concern the oral mucosa and the skin. In males, LSc primarily occurs in the genital area; anal involvements and extragenital manifestations are rarely found in this population [1]. Multiple nomenclatures have denoted LSc throughout the years, depending on emerging histological findings, clinical manifestations, or even lesion sites. Male genital LSc has been known as “balanitis xerotica obliterans” (BXO). This term is currently used for very advanced cases of LSc [2]. LSc affects both genders, with a female-to-male ratio varying from 10: 1 to 5: 1. The exact prevalence is unknown. The incidence ranges between $\frac{1}{300}$ ($0.33\%$) and $\frac{1}{1000}$ ($0.1\%$) in both genders [2].
The main clinical manifestation of LSc, dyspareunia, is the result of preputial and urethral dysfunctions. Although LSc can be silent and asymptomatic, it presents a wide array of sexual and urinary symptoms. The lesions can cause pruritis, tears, and sometimes bleeding. Phimosis, paraphimosis, and urethral strictures are some of the more common clinical signs. Subsequently, this dermatosis is considered to have a high burden on the patient's urinary and sexual well-being [3].
The diagnosis of LSc is mainly clinical, especially when the lesion is typical. A biopsy is seldom performed to confirm the diagnosis but is reserved only for suspicious cases of neoplasia, hyperpigmentation, or even for cases of clinical uncertainty [4]. Most often, these preputial lesions are described as atrophic plaques of ivory color or leukoderma, with sometimes the presence of hypertrophic scaly patches with telangiectasia and sparse purpura. In some cases, the glans can be eroded by the aforementioned lesions, especially in its premeatal area [5].
Topical corticosteroids have become the first-line therapy for LSc, having proven their efficacy. Symptoms are mostly relieved within the first few days of therapy. Other known alternatives and complementary therapies are moisturizers and immunosuppressors, such as topical calcineurin inhibitors, for patients who did not respond to the ultrapotent corticosteroid therapy or in whom the mainstay therapy is contraindicated [6]. In other cases, whenever medical therapy proves to be ineffective, surgical intervention is prompted. Circumcision is the main surgery performed, alongside other additional procedures when needed, such as a meatotomy or urethroplasty. Although surgery is considered highly curative, recurrences may rarely occur [7]. In addition, circumcision has been proven to play an essential role in improving LSc patients' quality of sexual life [8].
The etiologies and exact pathophysiology of LSc are still unknown, but what is known is that the connection between genital LSc and squamous cell carcinoma (SCC) is well established, and the prevalence has been estimated to be between $2\%$ and $8\%$ [9]. Throughout the literature, many assumptions were discussed but the subject is still debatable. Edmonds et al. and his associates published multiple studies about LSc, and suggested that genetic and environmental factors, inflammation, autoimmunity, and urine exposure play important roles in the pathogenesis of the condition; a genetically defined autoimmune pathway has been the most popular pathogenesis explored in the literature [3].
We aim, through this case-control study, to potentially associate genital LSc with risk factors and certain patient characteristics in Lebanese adult males.
## 2.1. Ethical Considerations
We started by obtaining the Notre Dame des Secours—University Hospital (NDS-UH) in Byblos, Lebanon's Institutional Review Board approval prior to data collection.
## 2.2. Study Design
We conducted a retrospective nested case-control study on patients who underwent circumcision in that establishment, between January 2010 and December 2020. All included cases were consenting male adults (>18 years of age) with a proven diagnosis of LSc on pathology.
## 2.3. Sample Selection and Matching
All cases of circumcision were screened for possible eligibility; 48 of those with a positive LSc diagnosis on pathology were selected. Only one case was excluded since the patient was deceased.
All cases are male; therefore, the matching process was based only on age, with up to a five-year margin. Besides age, no other factors were matched, and controls were selected randomly from patients who underwent circumcision with normal foreskin found on pathology during the same period (matching ratio 1: 1).
## 2.4. Data Collection
Medical records for all patients were pulled, followed by data extraction of variables of interest, to minimize any recall bias. An excel sheet was filled with sociodemographic characteristics, past medical history (diabetes, hypertension, and others), habits, and allergies. We followed the data extraction process with a subsequent phone call to each subject to verify the accuracy and fill in any missing data. All 94 patients were compliant and consented to take part in this study. Alcohol consumption behavior was assessed using the “Alcohol Use Disorder Addiction Test” (AUDIT) which is a 10-item questionnaire with scores ranging from 0 for participants with low risk of addiction to 20+ for likely addicted patients [10].
## 2.5. Statistical and Data Analyses
SPSS v.28 (IBM SPSS Inc., Chicago, IL, USA) was used for the statistical analysis, with a $95\%$ confidence interval (CI) and subsequently a $5\%$ margin of error. We expressed quantitative values as averages and standard deviations, whereas qualitative values as frequencies and percentages. LSc associations with categorical variables were evaluated using the Pearson chi-square or Fisher's tests, depending on conditions. Other associations with quantitative variables were assessed using the independent samples t-test. Cramer's V was used to investigate the size of the effect of statistically significant associations. The level of statistical significance was set at $p \leq 0.05.$
## 3. Results
Control and LSc groups were compared searching for statistically significant differences, pertaining to sociodemographic, behavioral, and medical history variables, alongside the chief symptom on presentation (Table 1).
As planned, no significant age difference between both groups was noted ($$p \leq 0.360$$). Furthermore, BMI, smoking status, the chief presenting symptom, the presence or absence of allergies, past penile trauma, or a family history of LSc were all found to yield no statistical difference.
Table 2 displays statistical associations between study participants and alcohol use, diabetes, and hypertension.
A χ2 test of independence was performed to examine the relationship between multiple sociodemographic and behavioral factors, the chief presenting symptom, and past medical history on one side, against having LSc. We have found that having diabetes is statistically associated with developing LSc (χ2 = 5.343, df = 1, and $$p \leq 0.021$$) with an OR = 3.564.
Hypertension can also be added to the list of associated diseases in our sample. ( χ2 = 8.428, df = 1, and $$p \leq 0.004$$) with an OR = 3.938.
As for the magnitude of these associations, using Cramer's V as an effect size measurement [11], we found the previously mentioned statistical relations to be of medium (Cramer's $V = 0.238$) and strong (Cramer's V approximately 0.3) magnitudes, respectively.
Regarding alcohol use, we have found that low-to-moderate alcohol use was inversely associated with LSc (χ2 = 4.896, df = 1, and $$p \leq 0.027$$). Having an OR of 0.365, alcohol use could potentially be a protective factor against LSc. That statistical relation is of medium strength (Cramer's $V = 0.228$) [11].
## 4. Discussion
Our research efforts focused on the clinical associations of LSc with medical and behavioral conditions. We have found higher rates of diabetes mellitus in men suffering from LSc, with an OR of 3.564 (1.165–10.903). This finding is well supported by the literature. Hofer et al. have found that diabetic patients were twice as likely to contract LSc in their sample of 485 men. Other associations found in that sample were a higher BMI and the presence of coronary artery disease in patients with LSc. This led Hofer and his team to support the hypothesis that lifestyle and metabolic variables may facilitate the development of LSc [12]. In another study conducted by Bromage et al., almost one third of diabetic subjects had a preputial phimosis [13], which was also the chief complaint of 23 patients in our sample inflicted with LSc ($48.9\%$).
In 2015, another team of researchers was able to argue that hypertensive patients were twice as likely to develop LSc. In our sample, this odds ratio is increased to approximately four times. This study specifically links LSc to multiple metabolic syndrome components, including hypertension, an argument that does align with our findings [14]. Both of the aforementioned studies also highlighted that a higher than average BMI and smoking status were linked with LSc, contrary to our work where no significant associations were found [12, 14].
On the other hand, alcohol consumption was revealed to have a protective effect against LSc with an OR of 0.365 (0.147–0.904) in our sample. To date, and after an extensive search for published papers on that subject, no similar results were found. To explain this phenomenon, we are reminded that LSc is of unclear etiology and is rather multifactorial. Multiple papers have already postulated that the etiology of LSc is probably the irritable effects of urine [15, 16]. In our case, we would like to emphasize two suspected hypotheses: autoimmunity and vascular compromise of smaller terminal vessels [17].
The first hypothesis implies the presence of an autoimmune mechanism for the appearance of LSc lesions in the male genitalia. This pathway is poorly understood, especially in men, since female genital LSc is more cumbersome and thus more prone to better scientific research. The autoimmunity hypothesis arises from papers associating patients inflicted with LSc with autoimmune pathologies, highlighting a role for both cellular and humoral-mediated pathways. One of the earliest works on the matter was published in 1988, studying autoimmune disorders in 350 women with confirmed LSc on biopsy. They found that $42\%$ of their sample had autoantibody levels at more than 1: 20 and that $21.5\%$ were diagnosed with at least one autoimmune-related disease [18]. Comparatively, a more recent paper examining vulvar LSc puts this prevalence at $28\%$ and demonstrates the presence of elevated circulating autoantibody levels in their sample [19]. Contrarily, in an observational and descriptive case series of 329 patients with male genital LSc, only $7\%$ of participants had autoimmune disorders, whilst less than $1\%$ had a family history of LSc [3]. Other papers were able to associate LSc with localized scleroderma and morphea [20, 21] and autoimmune thyroid disease [22], concomitant with the presence of the respective positive serologic autoantibody titers. Furthermore, an in-depth look into the presumed immunoregulatory factors of LSc reveals a primordial role for CD4+, CD8+, and FOXP3+T-regulatory cells, as evidenced by stained immunohistology data from two different data sets [23, 24]. Taking into consideration all the aforementioned arguments, a postulated role for alcohol consumption becomes apparent. An interference of alcohol with LSc immunoregulation is of interest: impairment of CD4+ cell activation and proliferation by S-adenosylmethionine (SAMe) after catalysis by methionine adenosyltransferase II (MAT II). Ethanol, most commonly known as alcohol, reduces MAT II's enzymatic activity by reducing the transcription of MAT2A. In consequence, diminished intracellular SAMe levels lead to activation-induced caspase-3-dependent cell death (AICD) in T helper CD4+ lymphocytes [25]. Furthermore, other animal studies have found that alcohol abuse leads to decreased splenic cellularity and weight, and by consequence reduced numbers of CD8+ T cells. In alcohol-fed mice, CD8+ cell proliferation was proven to be reduced [26, 27]. Another theory was built around a similar rationale, discussing a potential beneficial role of alcohol consumption in oral lichen planus [28].
Second, metabolic syndrome may be incriminated in LSc, as supported by our data. Moreover, Hofer et al. were able to associate their sample of men with LSc with a higher BMI than their control group and increased rates of diabetes, a finding in common between both our investigations, in addition to increased rates of coronary artery disease and smoking [12]. Another published paper went to the extent of suggesting oral glucose tolerance tests and tight medical glycemic control as a way to improve cutaneous lesions in patients with diabetes mellitus [29]. Subsequently, a presumed compromise of genital microvessels is included in the multifactorial spectrum hypotheses of male genital LSc.
A protective effect of alcohol consumption on cardiovascular disease has been studied and proven on multiple occasions in academic papers. Moderate alcohol consumption is said to reduce the risk of diabetes mellitus type 2 by around $30\%$ and lower the risk of mortality in diabetic patients [30]. In addition, moderate drinking was also linked to lower rates of morbidity and mortality in cardiovascular diseases, such as hypertension, peripheral artery disease, and strokes [31]. Another mechanism of action of alcohol on cardiovascular protection is via modulating inflammation. Alcohol reduces interleukin and C-reactive protein levels, resulting in a reduction of oxidative stress levels [32]. Vascular wall oxidative stress, more commonly known as the increase of free radicals fabrication at the expense of the physiological ability to counterbalance this phenomenon by the use of antioxidants, is essential in hypertension and vascular wall disease [33]. In a similar fashion, researchers have found that moderate alcohol consumption reduces all risks of microvascular complications in a cohort of diabetic patients [34].
Our study was limited by the sample size, which reflected the nature of the disease in question. LSc is a rare disease, thus the sample of only 47 affected patients was collected from our center. This is a thought-provoking paper, introducing new perspectives in understanding LSc on a global scale. A larger sample could achieve considerable conclusions regarding the role of alcohol consumption in LSc.
## 5. Conclusion
Our study aimed to compare multiple determinants between 47 circumcised patients diagnosed with LSc and a respective age-matched control group. We have found that in our sample, LSc was associated with increased rates of diabetes and hypertension. A potential protective effect of alcohol consumption was also found. Further research ought to be conducted on this subject to confirm this established connection between alcohol and LSc protections.
## Data Availability
The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.
## Ethical Approval
The study was reviewed and approved by the Ethical Review Board of Notre Dame des Secours—University Hospital—Byblos, Lebanon.
## Conflicts of Interest
The authors declare that they have no conflicts of interest.
## Authors' Contributions
R.K. conceived and planned the research project. J.K. and J.A. were involved in sampling, data collection, and writing the manuscript with the support and oversight of R.K. and all the remaining authors. The remaining authors also contributed to the literature review, interpretation, and analysis of data, provided critical feedback, and helped in shaping the final manuscript. All authors read and approved the final manuscript.
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|
---
title: Construction of a Prognostic Model for Predicting Colorectal Cancer Prognosis
and Response to Immunotherapy Based on Cuproptosis-Associated lncRNAs
authors:
- Yi Yang
- Xiaoli Wang
- Jin Lu
- Zhiyong Dong
- Ruixiang Hu
- Wenhui Chen
- Songhao Hu
- Guanhua Lu
- Biao Huang
- Shiliang Dong
- Lu Wang
- Cunchuan Wang
journal: Journal of Oncology
year: 2023
pmcid: PMC10033210
doi: 10.1155/2023/2733232
license: CC BY 4.0
---
# Construction of a Prognostic Model for Predicting Colorectal Cancer Prognosis and Response to Immunotherapy Based on Cuproptosis-Associated lncRNAs
## Abstract
Colorectal cancer (CRC) is a common and highly lethal gastrointestinal malignancy. Immunotherapy has shown positive efficacy in the treatment of CRC; however, only a minority of patients benefit from immunotherapy. The aim of this study is to construct a cuproptosis-related lncRNA (CRLs) risk score model to predict the prognosis and immune infiltration of CRC patients. Firstly, we synthetically analyzed 19 cuproptosis-related genes (CRGs) from CRC samples derived from the TCGA and obtained 33 CRLs that were significantly associated with prognosis. Next, we defined three cuproptosis modification patterns via consensus clustering analysis (C1, C2, and C3). Further analysis showed that there were significant differences in the abundance of B cells, NK cells, fibroblasts, monocytes, CD8+ cells, bone marrow dendritic cells, and cytotoxic lymphocytes in different clusters. In addition, the LASSO regression screened out 6 individual CRLs (AC009315.1, PLS3-AS1, ZEB1-AS1, AC007608.3, AC010789.2, and AC010207.1) closely related to the prognosis of CRC. We found that the low-risk group had better survival prognoses in patients. Furthermore, the high-risk group had lower immune scores and exhibited lower CD8+ T cell infiltration. Moreover, the low-risk group had lower immune exclusion, immune dysfunction and TIDE scores than the high-risk group. Interestingly, the lncRNAs in our risk model were positively associated with most immune checkpoints. CD274 (PD-L1), CTLA4, and HAVCR2 (TIM3) were positively correlated with risk scores. Moreover, MSI-H patients had lower risk scores than MSI-L patients, and IPS scores were significantly higher in the low CRLs score group. In conclusion, we constructed a novel risk score model with6 lncRNAs related to cuproptosis, which may be a potential biomarker for evaluating the prognosis and immune treatment for CRC.
## 1. Introduction
In the past, surgery and chemotherapy were the main treatment methods for colorectal cancer (CRC) [1]. In recent years, with the advent of immunotherapy, targeted therapy and other treatment strategies, the prognosis of colorectal cancer patients has been significantly improved [2]. However, the prognosis of patients with advanced CRC remains poor, largely due to the lack of highly specific prognostic biomarkers [3]. So far, the TNM staging system is the most commonly used prognostic indicator in clinical practice, but its overall specificity is insufficient [4]. Therefore, it is crucial to explore more sensitive and specific markers for the prognosis of CRC.
Cuproptosis is a newly discovered form of programmed cell death, which is different from known programmed cell death such as apoptosis, ferroptosis, pyroptosis, and necroptosis; relies on intracellular overload of copper ions to cause cellular death. Excessive respiration produces cytotoxicity and eventually induces cell death [5]. Recent studies have shown that cuproptosis regulation is involved in the development and response to therapy of multiple tumor types [6–8]. Numerous proteins, such as CDKN2A, FDX1, DLD, DLAT, LIAS, GLS, LIPT1, MTF1, PDHA1, and PDHB, have been identified that affect tumor cell proliferation and migration and are associated with cuproptosis [9]. Therefore, revealing the occurrence and development mechanism of cuproptosis may provide positive help for the treatment of CRC.
In recent years, immunotherapy has emerged as a promising alternative therapy for CRC patients. However, due to tumor heterogeneity, only a minority of patients benefit from immunotherapy [10]. Given the evidence suggests that intratumoral infiltrating leukocytes are closely associated with the efficiency of immune responses, including in CRC [11]. Therefore, the discovery and identification of novel immune-related gene targets are crucial to accurately predict the immune response of CRC.
LncRNAs are RNAs containing more than 200 nucleotides that cannot be translated into proteins [12]. LncRNAs play an important role in the occurrence and progression of various solid cancers, including CRC [13–15]. More interestingly, a series of prognostic models constructed based on public databases and by analyzing the expression of lncRNAs showed excellent predictive ability [16, 17]. A prognostic model based on a collection of various regulatory functions in tumors may be a positive direction for the exploration of prognostic markers in the future. However, there has been no report on the construction of a prognostic model based on cuproptosis-related lncRNAs (CRLs).
In this study, we obtained RNA-sequencing (RNA-seq) data from the TCGA database and identified 6CRLs significantly associated with prognostic and then developed a prognostic model. In addition, we verified the CRLs model with training and validation cohort and explored its underlying mechanisms through enrichment analysis. Finally, we assessed the relationship between risk scores and immune cell infiltration, drug sensitivity, and immunotherapy efficacy. Our findings will help predict the prognosis of colorectal cancer patients and provide references for clinical immunotherapy.
## 2.1. Data Collection and Correlation Analysis
The Cancer Genome Atlas (TCGA) database was used to retrieve the RNA transcriptome dataset and the associated CRC clinical data. Genes were divided into protein-coding genes and lncRNA genes based on information from the annotated human genome. Additionally, the levels of 19 cuproptosis-related genes (CRGs) expression were evaluated. To evaluate the relationship between lncRNAs and CRGs, we employed Pearson correlation coefficients. CRLs were those with an absolute correlation coefficient of >0.4 and a p value less than 0.001. After that, patients were split into the training group and the validation group. The data that were retrieved were then used for bioinformatics analysis.
## 2.2. Construction of Risk Model
To find lncRNA predictive characteristics connected to cuproptosis in the training data set, univariate Cox regression analysis and minimal absolute shrinkage and selection operator (lasso) penalized Cox regression analysis were utilized. Each CRC patient's risk score was determined using the following formula: Risk score is equal to Expi ∗ i, where Expi and bi are the expression and coefficient of each lncRNA, respectively.
## 2.3. Estimation of Tumor-Microenvironment Cell Infiltration
In this study, we applied the method of Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) to quantify 22 types of immune cells in the tumor and normal tissue [18]. We also applied the microenvironmental cell population counter (MCPcecther) method using the R package “MCPcether” to quantify the absolute abundance of eight immune cell populations and two stromal cell populations in tumor tissues from RNA-seq data.
## 2.4. Prediction of Small Molecule Drugs
The “limma” R package was used to find differentially expressed genes (DEGs) between high- and low-risk groups. Then, in order to identify which potential target chemicals would be helpful, we submitted the first 1000 DEGs to the CMAP database [19].
## 2.5. Statistical Analysis
R software (version 4.1.3, available at https://www.r-project.org) was used for computational and statistical analyses. Their response to immunotherapy was compared using the Wilcoxon rank sum test. The distinctions between the high- and low-risk categories were ascertained using Kaplan-Meier curves and log-rank testing. p values under 0.05 were regarded as statistically significant for all analyses.
## 3.1. Identification of Cuproptosis-Related lncRNAs in CRC Patients
We first analyzed the CRC mRNA dataset from the TCGA database and obtained the expression profiles of 19 CRGs and 16,876 lncRNAs. Next, we screened out 2450 CRLs by Pearson correlation analysis (|R| > 0.4, $p \leq 0.001$) (Figure 1(a)). We further performed coexpression and univariate Cox regression analysis and obtained 33 CRLs that were significantly associated with prognosis (Figure 1(b)). In addition, we compared the expression of the obtained 33 CRLS in tumor tissue and normal tissue, and the results showed that there were significant differences in their expression levels (Figures 1(c) and 1(d)).
## 3.2. Cuproptosis-Related Genotyping and GSVA Analysis
Cuproptosis is closely associated with prognosis in solid malignancies [5, 20, 21]. Based on the above hypothesis, we stratified samples with qualitatively different CRC based on the expression of 19CRGs via consensus clustering analysis. The results showed that we identified three different clusters of modified patterns, including 100 cases in cluster 1 (with high CRGs and namely C1), 197 cases in cluster 2 (with medium CRGs and namely C2), and 246 cases in cluster 3 (with low CRGs and namely C3) (Figure 2(a) and Supplementary Figure 1). The further survival analysis showed that C1 had a worse survival advantage than C2 and C3 (Figure 2(b)). In addition, MCP counter algorithm was used to calculate the infiltration of 9 immune cells in the three molecular subtypes of CRC, and the differences were analyzed [22]. The results showed that the abundance of B cells, NK cells, and fibroblasts in C2 was higher than that in C3, the abundance of monocytes and CD8+T cells in C3 was lower than that in C1 and C2, the abundance of bone marrow dendritic cells and cytotoxic lymphocytes in C2 was higher than that in C1 and C3, and there was no difference in neutrophils and endothelial cells in C1, C2, and C3 group (Figures 2(c)–2(k)). Next, we compared the enrichment differences of KEGG and HALLMARK signaling pathways in C1 and C3 groups by GSVA analysis, and the heat map showed that all pathways with statistical significance were enriched in the C1 group (Figures 3(a) and 3(b)). These results strongly suggest that CRGs may participate in the immune function of CCR via multiple signaling pathways.
## 3.3. Constructing and Evaluating a Risk Score Model Based on CRLs in CRC
A variety of studies showed that prognostic models based on lncRNAs have guiding significance for patient prognosis [23]. Therefore, the purpose of this study is to establish a CRLs-based model to facilitate the prognostic prediction of CRC. We previously obtained 33 CRLs with prognostic values, which were further screened by LASSO regression. A total of 6 lncRNAs were obtained, and their risk coefficients were calculated. Specifically, the risk score model for predicting CRC prognosis based on 6 CRLs is shown as follows: risk score = expression value of AC009315.1 ∗ 0.15835365544805 + expression value of PLS3-AS1 ∗ 0.100587632623172 + expression value of ZEB1-AS1 ∗ 0.0274302502732273 + expression value of AC007608.3 ∗ 0.0549982300165668 + expression value of AC010789.2 ∗ 0.166645095217608 + expression value of AC010207.1 ∗ 0.403357464363707. Next, we randomly divided the CRC cohort patients into two cohorts, a training cohort ($$n = 382$$) and a validation cohort ($$n = 161$$), in a 7: 3 ratio. In the training and validation cohort, the patients were divided into low-risk and high-risk groups based on the median risk score. Surprisingly, we found that the low-risk group had a higher survival rate than the high-risk group, both in the training and validation cohort (Figures 4(a) and 4(b)). The AUC values of the 1-year, 3-year and 5-year ROC curves of the training cohort were 0.756, 0.737, and 0.649, respectively, while the AUC values of the 1-year, 3-year and 5-year ROC curves of the validation cohort were 0.688, 0.652, and 0.728, respectively, (Figures 4(c) and 4(d)). In addition, in the training and validation cohort, the signature divided the integrated cohort into low-risk and high-risk groups based on the median risk score (Figures 4(e) and 4(f)). The above results indicated that the risk score model based on CRLs has a good predictive efficiency for the prognosis of CRC patients.
## 3.4. The Correlation Analysis between the Clinical Features and CRLs Risk Score Model for CRC Patients
We previously established a risk score model based on CRLs and found that it could accurately predict the survival prognosis of CRC patients. To further explore the value of this model, we analyzed the correlation of this model with the clinical features (age, gender and stage) of CRC patients (Figure 5(a)). The results showed that the low-risk group had better survival prognosis in patients aged >65 years, male, stage I-II, stage III-IV, T3-4, N1-2, and M0. There was no difference in survival prognosis between high and low-risk groups in patients aged ≤65 years, female, T1-2, N0, and M1 (Figures 5(b)–5(d)).
## 3.5. Enrichment and Drug Sensitivity Analysis of CRLs Risk Score Model
In order to clarify the specific mechanism of the CRLs risk score in CCR, we further analyzed the potential functional pathway of the high-risk and low-risk groups. The results showed that the differentially expressed genes in the high-risk group and the low-risk group were mainly enriched in multiple signaling pathways, such as DNA packaging, chromatin assembly and neutrophil extracellular trap formation (Figures 6(a) and 6(b)). In addition, we further analyzed the association between the CRLs risk score and the efficacy of chemotherapy in the treatment of CRC. It showed that the high-risk group was associated with lower half inhibitory centration (IC50) of chemotherapeutic drugs, such as AZ8055, Paclitaxel, and AKT inhibitor VII, while the IC50 of Cisplatin, 5-Fu, and Trametinib was higher (Figures 6(c)–6(h)). The results showed that the CRLs risk score model could be used as a predictor of chemical sensitivity in the future.
## 3.6. The Relationship between TME and CRLs Risk Score in CRC
The immune microenvironment of tumors is closely related to tumor progression. Tumor cells interact with immune cells, thereby inhibiting the function of immune cells and finally leading to tumor immune escape [24, 25]. Therefore, we continued to investigate whether the CCR immune microenvironment was associated with CRLs risk scores. We assessed the immune microenvironment of CRC by the ESTIMATE algorithm and observed the differences in the stromal score and immune score between the high-risk group and the low-risk group. As shown in Figure 7(a), lower immune scores were exhibited in the high-risk group. The distribution of 22 immune cells in CRC patients is shown in Figure 7(b). Next, we further calculated the infiltration abundance of immune cells by the CIBORESORT algorithm. The results showed that the infiltrating abundance of CD8+ T cells in the low-risk group was higher than that in the high-risk group (Figure 7(c)). Moreover, the boxplot of immune function analysis showed that the scores of chemokine receptors, HLA and MHC in the high-risk group were significantly lower than those in the low-risk group (Figure 7(d)). Immune checkpoints are important predictors for assessing immunotherapy response [26]. Therefore, we evaluated the association of 12 immune checkpoints with CRLs. As shown in Figure 7(e), all lncRNAs in the CRLs risk model were positively associated with most immune checkpoints. Finally, we analyzed the relationship of four common immune checkpoints with risk scores, and the results showed that CD274 (PD-L1), CTLA4, and HAVCR2 (TIM3) were positively associated with risk scores (Figure 7(f)). The above data strongly suggested that CRGs play an important role in the regulation of the CCR immune microenvironment.
## 3.7. Correlataion between Immunotherapy Responsiveness and CRLs Risk Score
MSI is an important indicator for evaluating the efficacy of immunotherapy in CRC [27]. Therefore, we explored the association of MSS, MSI-L and MSI-H with CRLS scores. The result showed that MSI-H patients had lower risk scores than MSI-L patients (Figure 8(a)). In recent years, IPS and TIDE have been widely used to evaluate the efficacy of immunotherapy [28, 29]. Our analysis revealed that IPS scores were significantly higher in the low CRLs score group (Figures 8(b) and 8(c)). Consistently, the low-risk group had a lower immune exclusion, immune dysfunction and TIDE scores than the high-risk group (Figures 8(d)–8(f)). These findings indirectly suggest that CRLs may play a key role in mediating immune responses in CRC.
## 4. Discussion
In recent years, the gradual increase in the incidence of CRC has attracted many researchers to lucubrate its occurrence, development and treatment. The resistance of tumors to antitumor therapy has made people gradually realize the importance of programmed cell death, such as autophagy, pyroptosis and ferroptosis [30–32]. Cuproptosis is a newly discovered type of cell death that can be induced by a variety of drugs [33]. Therefore, a full understanding of the specific mechanisms of cuproptosis is critical to guide the treatment of CCR.
In this study, firstly, we found that CRGs were closely associated with CCR immune cell infiltration. Next, we identified 6 CRLs significantly associated with prognostic and then developed a prognostic model. In addition, we validate the accuracy of the CRLs model and initially explore its underlying mechanisms. Finally, we evaluated the relationship between risk scores and immune cell infiltration, drug sensitivity, and immunotherapy efficacy.
In the past decade, more and more studies attempted to establish lncRNA-based prognostic models in order to provide guidance for the prognosis of various malignant tumors. Tang et al. analyzed the expression of ferroptosis-related lncRNAs in head and neck squamous cell carcinoma in a public database, constructed a prognostic model, and further confirmed that it has a good predictive effect. The AUC area for 1 year, 3 years, and 5 years is 0.78, 0.83, and 0.71, respectively [34]. Song et al. analyzed the expression of pyroptosis-related lncRNAs in lung cancer tissues and constructed a prognostic model with good predictive ability. The AUC area for 1 year, 3 years and 5 years is 0.757, 0.728, and 0.685, respectively [35]. In this study, the areas under the AUC curve of our prognostic model at 1 year, 3 years, and 5 years were 0.756, 0.737, and 0.649, respectively. Compared with previous studies, this model shows no weak predictive ability and has good clinical application value.
The immune microenvironment of tumors is regulated by a variety of cells, including tumor cells themselves, immune cells, and fibroblasts [36]. Among them, immune cells play a major role in regulating the tumor immune microenvironment [37]. In recent years, efforts have been made to explore new approaches to treat CCR. The advent of immunotherapy has brought new hope to this idea. A variety of evidence indicates that infiltrating lymphocytes play an important role in the prognosis of various solid tumors and have potential predictive value [38]. In this study, we found that different groups of CRGs have differences in the distribution of immune cells, which indirectly suggests the existence of a relationship between CRGs and the immune microenvironment. More interestingly, we constructed a prognostic model based on CRLs and also showed an association between the risk score and the proportion of immune cells in the tumor microenvironment. This result further supported the relationship between cuproptosis and the immune microenvironment of CCR. However, its specific mechanism needs to be further studied in the future.
Cuproptosis is a novel mode of cell death for which research is currently rather limited. In this study, we found that there were significant differences in the infiltration of various immune cells under different patterns of CRLs. More interestingly, the risk scores of the prognostic models constructed based on CRLs were also significantly different from the immune microenvironment of CRC and its multiple immune checkpoints. These data strongly suggested that there is a strong interrelationship prior to cuproptosis and immunity. Given the existence of an extremely complex network of molecular interactions within cells. In addition, there are some unsatisfactory aspects of this study. Firstly, all data in this study were obtained from public databases, lacking further support from clinical data. Secondly, the mechanism by which the CRLs model regulates the immune microenvironment has not been thoroughly investigated. These issues deserve further research in the future.
In conclusion, in this study, we revealed multiple roles of CRGs and CRLs in CCR. Firstly, CRGs were closely related to CCR immune cell infiltration. Secondly, the risk scoring model based on CRLs has a good predictive ability for the overall survival of CCR. In addition, the risk score of CRLs might have potential guiding value for the application of various antitumor drugs. Moreover, the risk score of CRLs was closely related to the immune cell infiltration of CCR. Finally, the CRLs risk model might have potential instructive value for immunotherapy.
## Data Availability
The data and result in this study are available from the corresponding author for reasonable request.
## Ethical Approval
The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of The First Affiliated Hospital of Jinan University.
## Conflicts of Interest
The authors declare that they have no conflicts of interest.
## Authors' Contributions
Yi Yang, Xiaoli Wang, and Jin Lu contributed equally to this work. All authors read and approved the final manuscript.
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|
---
title: Extracellular Vesicles Secreted by TGF-β1-Treated Mesenchymal Stem Cells Promote
Fracture Healing by SCD1-Regulated Transference of LRP5
authors:
- Zihui Zhou
- Chenyang Guo
- Xulong Sun
- Zhengwei Ren
- Jie Tao
journal: Stem Cells International
year: 2023
pmcid: PMC10033213
doi: 10.1155/2023/4980871
license: CC BY 4.0
---
# Extracellular Vesicles Secreted by TGF-β1-Treated Mesenchymal Stem Cells Promote Fracture Healing by SCD1-Regulated Transference of LRP5
## Abstract
Bone fracture repair is a multiphased regenerative process requiring paracrine intervention throughout the healing process. Mesenchymal stem cells (MSCs) play a crucial role in cell-to-cell communication and the regeneration of tissue, but their transplantation is difficult to regulate. The paracrine processes that occur in MSC-derived extracellular vesicles (MSC-EVs) have been exploited for this study. The primary goal was to determine whether EVs secreted by TGF-β1-stimulated MSCs (MSCTGF-β1-EVs) exhibit greater effects on bone fracture healing than EVs secreted by PBS-treated MSCs (MSCPBS-EVs). Our research was conducted using an in vivo bone fracture model and in vitro experiments, which included assays to measure cell proliferation, migration, and angiogenesis, as well as in vivo and in vitro gain/loss of function studies. In this study, we were able to confirm that SCD1 expression and MSC-EVs can be induced by TGF-β1. After MSCTGF-β1-EVs are transplanted in mice, bone fracture repair is accelerated. MSCTGF-β1-EV administration stimulates human umbilical vein endothelial cell (HUVEC) angiogenesis, proliferation, and migration in vitro. Furthermore, we were able to demonstrate that SCD1 plays a functional role in the process of MSCTGF-β1-EV-mediated bone fracture healing and HUVEC angiogenesis, proliferation, and migration. Additionally, using a luciferase reporter assay and chromatin immunoprecipitation studies, we discovered that SREBP-1 targets the promoter of the SCD1 gene specifically. We also discovered that the EV-SCD1 protein could stimulate proliferation, angiogenesis, and migration in HUVECs through interactions with LRP5. Our findings provide evidence of a mechanism whereby MSCTGF-β1-EVs enhance bone fracture repair by regulating the expression of SCD1. The use of TGF-β1 preconditioning has the potential to maximize the therapeutic effects of MSC-EVs in the treatment of bone fractures.
## 1. Introduction
The repair of bone fractures includes bone remodeling, angiogenesis, and formation of cartilage callus [1, 2]. Both the regeneration of bone and the paracrine communication that take place during this process rely heavily on angiogenesis [3, 4]. It facilitates the process for immune cells and bone precursor cells to be transported to the damaged area, and it boosts the delivery of oxygen and nutrients to the callus that is healing [5, 6].
Mesenchymal stem cells (MSCs) are multipotent stromal cells that have the potential to differentiate into a wide range of mesenchymal tissues, such as bone and cartilage, and several other tissues [7, 8]. It is speculated that MSCs work in tandem with innate and adaptive immune systems, osteoblasts, and osteoclasts to repair bone injury and restore bone function following a bone fracture [9, 10]. At the site of injury, MSCs produce bioactive compounds and vesicles containing proteins, nucleic acids, and lipids [11, 12].
Cell-to-cell communication at the site of injury is thought to be mediated by extracellular vesicles (EVs) that migrate through cells [13, 14]. MSC-derived EVs (MSC-EVs) carry a cargo of RNA and proteins that stimulate multiple physiological processes, including angiogenesis and extracellular matrix remodeling, to boost the process of tissue repair [15]. Human MSC-EVs are thought to participate in tissue repair through the activation of the transforming growth factor- (TGF-) β pathway [16, 17]. In addition, TGF-β1 is recognized to have a crucial role in the process of angiogenesis in humans. Furthermore, defects in associated pathways can lead to vascular diseases such as hereditary hemorrhagic telangiectasia [18]. However, it is unclear whether TGF-β1-stimulated MSCs secreting EVs (MSCTGF-β1-EVs) can accelerate bone fracture healing as well as whether such an improvement is dependent by EV-mediated signaling.
There are indications that the sterol-responsive element-binding protein- (SREBP-) 1 transcription factor is involved in the regulation of the TGF-β1 signaling pathway [19]. Recent evidence suggests that SREBP-1 influences TGF-β1 signaling through the exosome regulation of TGF-β1 receptor-1 [20]. SREBP-1 is upregulated with the suppression of stearoyl-coenzyme A desaturase-1 (SCD1) expression [21], a protein associated with osteogenesis and fracture risk in menopausal women with diabetes [22, 23].
In a series of in vitro and in vivo gain and loss of function experiments, we confirmed that MSCTGF-β1-EV-derived SCD1 has a functional role in the bone fracture healing process. MSCTGF-β1-EVs promoted angiogenesis, proliferation, and migration, whereas the knockdown of SREBP-1 resulted in a significant decrease of SCD1 in MSCs and EVs, abolishing the effects of TGF-β1. In the treatment of bone fractures, the use of MSCTGF-β1-EVs may prove to be an effective and promising technique.
## 2.1. Cell Culture and TGF-β1 Treatment
According to the findings of a previous investigation [24], MSCs were isolated from the umbilical cord tissue of human donors ($$n = 3$$, ages 23–29 years) who were in good condition. Briefly, umbilical cords that were collected from healthy neonatal deliveries were washed and then cut into pieces that were 10 mm3 in volume after the cord vessels were removed. The pieces were cultured in Dulbecco's modified Eagle's medium (DMEM) containing $10\%$ fetal bovine serum (FBS, Gibco) and $1\%$ penicillin-streptomycin (Invitrogen, Carlsbad, CA, USA) antibiotics at 37°C with $5\%$ CO2 until they reached 70–$80\%$ confluency. Flow cytometry (FACSCalibur, BD Biosciences, San Jose, California, USA) was utilized to validate the expression of MSC surface markers. The markers CD73, CD73, CD90, CD14, CD34, and CD45 were utilized for this validation (BD Biosciences, San Jose, CA, USA). In this study, MSCs were primed with 10 ng/mL TGF-β1 or treated with PBS as vehicle control for 24 h. Human umbilical vein endothelial cells (HUVECs) were obtained from the American Type Culture Collection (CRL-1730, Rockville, MD, USA) and cultured in DMEM with $10\%$ FBS at 37°C with $5\%$ CO2 and deprived of serum for 24 h before treatment with TGF-β1 (10 ng/mL). This study was approved by the Ethics Committee of Shanghai General Hospital and conducted in accordance with the Declaration of Helsinki.
## 2.2. EV Isolation and Identification
MSCs were allowed to reach $80\%$ confluency and then transferred to EV-depleted FBS for 48 h. The media was collected by centrifugation at 2,000 × g for 10 min at 4°C, and then, cellular debris was removed by filtration using a 0.22 μm sterile filter (Millipore, Burlington, MA, USA). The filtered supernatant was concentrated to 200 μL in an Amicon Ultra-15 Centrifugal Filter Unit (Millipore, Burlington, MA, USA) at 4,000 × g. EVs were recovered by ultracentrifugation at 100,000 × g for 60 min at 4°C in an Optima L-100 XP Ultracentrifuge (Beckman Coulter, Indianapolis, IN, USA). The fraction containing the EVs was verified with the EV markers TSG101, CD63, CD81, and HSP70 and by using transmission electron microscopy (TEM; Tecnai 12, Philips, Best, The Netherlands).
## 2.3. Femoral Fracture Model and X-Ray Imaging
The Animal Research Committee at Shanghai Jiao Tong University Affiliated Sixth People's Hospital reviewed and gave its approval to all of the experiments that involved animals. The murine femoral fracture was carried out as described previously [25]. Briefly, Kirschner's wire (K-wire; 1.0 mm) was inserted into the femoral marrow cavity of anesthetized mice (C57BL/6 background, 10–12 weeks, $$n = 36$$) and bone forceps were used to create a mid-diaphyseal fracture. The mice were divided into three groups ($$n = 12$$ in each group): PBS, MSCPBS-EV, and MSCTGF-β1-EV groups. Next, MSCPBS-EVs or MSCTGF-β1-EVs (a total of 200 μg of EV protein was precipitated in a volume of 200 μL of PBS) or an equal volume of PBS was injected immediately near the fracture on days 3, 5, and 7 after surgery. The wounds were closed with sutures, and the mice received daily buprenorphine to control pain postsurgery. The progress of the fractures was monitored in the Faxitron MX-20 X-ray system (Faxitron, Tucson, AZ, USA) 21 d after surgery. The K-wire was removed, and femurs were collected from euthanized mice, fixed in $4\%$ paraformaldehyde for 24 h, decalcified in $10\%$ ethylenediaminetetraacetic acid (EDTA), and embedded in paraffin for further analysis.
## 2.4. Micro-Computed Tomography (CT) Imaging
Femurs fixed in $4\%$ paraformaldehyde were scanned by micro-CT at a resolution of 18 μm using a SkyScan 1172 (Bruker, Billerica, MA, USA) at 50 kV and 200 μA. Three-dimensional images were constructed and bone morphometric parameters were obtained using a CT analyzer (Bruker, Billerica, MA, USA). Assessment of micro-CT scans of the samples from the PBS, MSCPBS-EV, and MSCTGF-β1-EV groups, sacrificed at 21 d postoperatively, was used to quantify the between-group differences in new bone formation at the osteotomy site. The following new bone structural parameters were calculated and statistically analyzed from the region of interest at the osteotomy site: bone volume density (BV/TV, %), trabecular number (Tb. N, mm−1), trabecular thickness (Tb. Th, mm), trabecular spacing (Tb. Sp, mm), and bone mineral density (BMD) [26].
## 2.5. Micro-CT Analysis of Angiogenesis at the Fracture Sites
Micro-CT examination using a contrast agent was utilized to investigate the vascular networks in the area of the fractures. In a nutshell, a radiopaque silicone rubber compound was perfused into the heart prior to the removal of the fractured femurs. After that, the micro-CT system (SkyScan 1172, Bruker) was used to scan the femurs after they had been removed. After being submerged in $10\%$ EDTA solution for 10 d, they were examined once more to reveal the callus's vascular structure. The use of a CT analyzer (Bruker) allowed for the generation of three-dimensional reconstructions.
## 2.6. Three-Point Bending Mechanical Test
A mechanical test was performed within 24 h of the sacrifice at room temperature. To determine the biomechanical properties of the femur samples, they were subjected to three-point bending using a three-point bending device (H25KS, Hounsfield Test Equipment, Surrey, UK). The femur samples were loaded in the anterior-posterior direction at a rate of 5 mm per minute until failure was achieved. The Vernier graphical analysis software was used to examine the ultimate load, stiffness, and energy to failure of the material.
## 2.7. Histochemical Analysis
Hematoxylin and eosin (H&E), toluidine blue (TB), and safranin O-fast green staining were performed on sections of femur tissue deparaffinized with xylene. To stain with H&E, sections were stained in hematoxylin for 5 min and eosin for 2 min and then rinsed briefly in water. For TB staining, sections were incubated in TB for 2–3 min and then rinsed with xylene. For safranin O-fast green staining, sections were stained with $0.02\%$ fast green for 1 min, $1.0\%$ acetic acid for 30 s, and $1.0\%$ safranin O for 10 min and rinsed in xylene.
## 2.8. Immunofluorescence Staining
Following the deparaffinization of the sections in xylene, they were placed at room temperature for an overnight incubation with primary antibodies against Ki-67 and CD31 (1: 500, Abcam, Cambridge, UK). Goat secondary antibodies conjugated with Alexa Fluor 488 and Alexa Flour 594 (Jackson ImmunoResearch, West Grove, PA, USA) were added, and the samples were incubated for 1 h at room temperature. Nuclei were stained with DAPI, and fluorescent images were obtained using a fluorescence microscope. All experiments were performed in triplicate.
## 2.9. Real-Time PCR
Total RNA was extracted from cells and EVs using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer's instructions. A Reverse Transcription Kit (Toyobo, Osaka, Japan) was used to synthesize cDNA. Real-time PCR was conducted on an ABI 7900 Real-Time PCR system (Applied Biosystems, Foster City, CA, USA) using SYBR Green PCR master mix from Applied Biosystems (Foster City, CA, USA). Levels of expression were normalized to GAPDH and evaluated using the 2−ΔΔCT method. The primer sequences are listed as follows: SCD1-F: GAGGCACCTACATTGGATGCT, SCD1-R: CGTAGACATAGGACCGCTCA; SREBP-1-F: ACCATCGGCACCCGCTGCTTTAAAGAT, SREBP-1-R: TGAATGGTGGCTGCTGAGTGTTTCCTG; and GAPDH-F: CTCACCGGATGCACCAATGTT, GAPDH-R: CGCGTTGCTCACAATGTTCAT.
## 2.10. Cell Counting Kit-8 (CCK-8) Assay
HUVEC proliferation was assessed using CCK-8 (Sigma-Aldrich, St. Louis, MO, USA). The cells (2.0 × 103 cells/100 μL medium) were cocultured with either PBS or 100 μg/mL of MSCPBS-EVs or MSCTGF-β1-EVs. After 24, 48, or 72 h, the cells in each well were incubated for 2 h at 37°C with CCK-8 (10 μL/well) solution, and the optical density was read on a microplate reader at 450 nm. All experiments were performed in triplicate.
## 2.11. 5-Ethynyl-2-Deoxyuridine (EdU) Assay
Following the instructions from the manufacturer, an EdU assay kit (Thermo Fisher Scientific, Waltham, MA, USA) was used to measure proliferation of cells. The cells (2.0 × 104) were either visualized with fluorescence microscopy (Carl Zeiss Microscopy GmbH, Jena, Germany) or counted by flow cytometry (FACSCalibur) with an iClick EdU Andy Fluor 647 Flow Cytometry Assay Kit (Genecopoeia, Germantown, MD, USA). All experiments were performed in triplicate.
## 2.12. Tube Formation Assay
The angiogenic properties of EV-treated HUVECs were determined by tube formation using Matrigel (BD Biosciences, San Jose, CA). EV-treated HUVECs (2.0 × 104) were seeded onto Matrigel-coated 96-well plates. Tube formation was observed under an optical microscope 6 h after plating, and the lengths of tubes were measured randomly in five separate fields using ImageJ software (National Institutes of Health, Bethesda, MD, USA). All experiments were performed in triplicate.
## 2.13. Migration Assays
For the purpose of determining the migration of HUVECs, a Transwell assay was utilized. The cells (2.0 × 105) were seeded into the upper chamber of a 24-well Transwell plate (Corning, NY, USA). The cells that migrated to the lower chamber were stained with crystal violet. A wound assay was also performed to determine the level of migration in EV-treated cells. A confluent layer of cells (2.0 × 105 cells/well) was scratched with a pipette tip, and the level of migration was observed 12 h later. All experiments were performed in triplicate.
## 2.14. Luciferase Reporter Assay
To determine the possible interaction between SREBP-1 and SCD1 promoter, we used a luciferase reporter assay. The putative SREBP-1 binding site was mutated in the promoter of SCD1, and the mutated sequence and wild-type sequence were inserted separately into pmir-GLO-promoter vectors (Promega, Madison, WI, USA). SREBP-1 was inserted into a reporter vector, and the constructs were transfected into cells. A Dual-Luciferase Reporter Kit (Promega, Madison, WI, USA) was used to measure the luciferase activity in transfected cells and normalized to Renilla. All experiments were performed in triplicate.
## 2.15. Chromatin Immunoprecipitation (ChIP) Assay
To confirm the luciferase reporter assay results, we conducted a ChIP assay. The cells were fixed in $1\%$ formaldehyde and then centrifuged at 800 × g for 5 min at 4°C. The cells were lysed, and chromatin was cut up into small pieces using an Ultrasonic Cell Disruptor (Covaris, Waltham, MA, USA). SREBP-1 binding to the SCD1 promoter was confirmed by adding anti-SREBP-1 antibodies (Abcam, Cambridge, MA, USA), and the target sequence in the immunoprecipitated fragments was detected by PCR.
## 2.16. Western Blot Analysis
Proteins were extracted from cells using RIPA buffer (89900, Thermo Fisher Scientific, Grand Island, NY, USA), and protein concentration was measured by using a BCA assay (23225, Thermo Fisher Scientific), and equal quantities of protein were separated by SDS-PAGE. Proteins were transferred to polyvinylidene fluoride (PVDF) membranes (Millipore) and blocked with $5\%$ bovine serum albumin. Blocked PVD membranes were incubated overnight at 4°C with primary antibodies at dilutions recommended by the manufacturers. The following primary antibodies were used for western blot analysis: anti-SCD1 (Abcam, ab236868, 1: 1,000), anti-GAPDH (Abcam, ab8245, 1: 1,000), anti-TSG101 (Abcam, ab83, 1: 1,000), anti-CD81 (Abcam, ab286173, 1: 500), anti-CD63 (Abcam, ab1318, 1: 1,000), anti-HSP70 (Abcam, ab2787, 1: 1,000), anti-β-actin (Abcam, ab7817, 1: 2,000), anti-SREBP-1 (Abcam, ab28481, 1: 1,000), anti-LRP5 (Abcam, ab223203, 1: 1,000), and anti-LRP6 (Abcam, ab231779, 1: 1,000). Membranes were then incubated with secondary antibodies for 1 h at room temperature and immunoreactive bands were detected by enhanced chemiluminescence (Thermo Fisher Scientific). The density of the bands was analyzed by ImageJ.
## 2.17. Statistical Analysis
Data are presented as means ± SD from three independent experiments for the in vitro study and in the in vivo study ($$n = 12$$). Statistical significance was calculated by Student's t test when comparing two sets of data. One-way ANOVA followed by the Bonferroni multiple comparison test was used for comparing more than two sets of data. $P \leq 0.05$ was considered statistically significant. Data were analyzed with GraphPad software 8.0 (GraphPad Software, CA, USA) and SPSS 19.0 (IBM, NY, USA).
## 3.1. Identification of MSCs
The cultivated normal MSCs had a morphology that was very similar to that of fibroblasts when viewed using an inverted light microscope (Figure 1(a)). These cells were all flexible and adherent, and they were long and polygonal. Furthermore, we established that MSCs are capable of differentiating into osteogenic, chondrogenic, and adipogenic lineages (Figure 1(a)). MSCs exhibited a characteristic MSC immunophenotype by expressing positive levels of CD73, CD90, and CD105 but not CD14, CD34, or CD45 (Figure 1(d)). This is known as a positive immunophenotype. After treating the MSCs with PBS and TGF-β1, we checked if the shape and characteristics of the cells had changed. These cells displayed a pattern characteristic of MSCs, as shown in Figures 1(b) and 1(c). They were highly positive for the markers CD73, D90, and CD105, whereas CD14, CD34, and CD45 were negative (Figures 1(e) and 1(f)). According to these data, treatment with PBS or TGF-β1 did not result in any changes to the typical morphology or immunotype of MSCs. In addition, it was found that MSCs treated with either PBS or TGF-β1 were capable of differentiating into osteogenic, chondrogenic, and adipogenic cell lines (Figures 1(b) and 1(c)). In conclusion, normal MSCs, MSCs treated with PBS, or MSCs treated with TGF-β1 were all plastic-adherent cells that were either long or polygonal in shape. This established a strong basis for the studies that we carried out later.
## 3.2. TGF-β1 Promotes SCD1 Expression and EV Release from MSCs
To confirm that TGF-β1 could control the expression of SCD1 in MSCs, TGF-β1 (10 ng/mL) was used to activate MSCs. PBS-stimulated MSC-EVs (MSCPBS-EVs) and TGF-β1-stimulated MSC-EVs (MSCTGF-β1-EVs) were recovered from cell supernatants 24 h later. TEM was used to determine the morphology of the EVs, and the nanoparticle tracking analysis (NTA) method was used to quantify the EVs (Figure 2(a)). The number of MSCTGF-β1-EVs was substantially higher than MSCPBS-EVs (Figure 2(b)). The expression of particular EV markers such as TSG101, CD63, CD81, and HSP70 [27] in the medium was evaluated by western blot analysis and found to be higher in cells treated with TGF-β1 (Figure 2(c)). Compared to the MSCPBS-EV group, the MSCTGF-β1-EV group had higher levels of SCD1 mRNA and protein expression (Figures 2(d) and 2(e)). In conclusion, we successfully obtained MSCTGF-β1-EVs and confirmed the overexpression of SCD1 in MSCTGF-β1-EVs.
## 3.3. MSCTGF-β1-EV Transplantation Promotes Bone Fracture Repair in Mice
To determine whether MSCTGF-β1-EVs could influence the bone healing process in vivo, we created a femoral fracture model in mice and compared callus formation after injecting the mice with MSCTGF-β1-EVs, MSCPBS-EVs, or a PBS control. Figures 3(a) and 3(b) depict radiograph and 3D micro-CT scanned images of the femurs taken 21 d postfracture. Significant increases in the ultimate load, stiffness, and energy to failure were seen in the MSCTGF-β1-EV group compared to both the MSCPBS-EVs and PBS groups, indicating a considerable improvement in mechanical attributes (Figure 3(c)). Callus development and bone bridging development were evaluated using H&E, TB, and safranin O-fast green staining across the three treatment groups (Figures 3(d)–3(f)). There was a similar degree of bone repair in the PBS and MSCPBS-EV-treated femurs, but the femurs treated with MSCTGF-β1-EVs had more cartilaginous and osseous callus formation with bone bridging at a more advanced stage than in the control mice. Moreover, 3D micro-CT scanned images revealed that vascularization was far more advanced in the femurs treated with MSCTGF-β1-EVs than in the MSCPBS-EV-treated mice or the PBS control group. Bone deposition and bridging occurred more rapidly in the femurs of mice treated with MSCTGF-β1-EVs compared to mice treated with MSCPBS-EVs and the PBS control group. We also assessed levels of CD31, α-SMA, SCD1, and low-density lipoprotein receptor-related proteins (LRP) 5 at the site of injury (Figure 3(g)). The CD31 marker on endothelial cells is a reliable indicator of recent angiogenesis [28], α-SMA is a marker of osteoprogenitors in the periosteum during fracture healing [29], and LRP5 is used to indicate the level of osteogenesis and bone density [30]. All the markers were significantly upregulated in response to MSCTGF-β1-EV treatment at the site of the fracture. Figure 3(h) further demonstrates that compared to the MSCPBS-EV group and the PBS group, the BV/TV of callus in the MSCTGF-β1-EV group was considerably greater. The trabecular thickness (Tb. Th) and trabecular number (Tb. N, mm−1) of the fracture in the MSCTGF-β1-EV group were also significantly higher when compared to the MSCPBS-EV group and the PBS group. Tb. Sp in the MSCTGF-β1-EV group was lower than the MSCPBS-EV group and the PBS group. BMD at the fracture healing areas in the MSCTGF-β1-EV group was significantly higher than in the MSCPBS-EV group and the PBS group. Overall, these results indicate that transplantation with MSCTGF-β1-EVs promotes the healing of bone fractures in vivo more effectively than MSCPBS-EVs.
## 3.4. MSCTGF-β1-EVs Induce HUVEC Proliferation, Migration, and Tube Formation In Vitro
To better understand the role of MSCTGF-β1-EVs in fracture healing and angiogenesis, we measured proliferation, migration, and tube formation in HUVECs treated with 100 μg/mL of MSCTGF-β1-EVs, MSCPBS-EVs, and PBS. The reaction to MSCTGF-β1-EVs was greater than the response to MSCPBS-EVs in terms of cell proliferation, migration, and wound closure in HUVECs compared to the PBS-treated group. After treatment with MSCPBS-EVs, HUVECs showed significant improvements in cell proliferation, migration, and wound closure when compared to the PBS-treated group; the response to MSCTGF-β1-EVs was larger than the response to MSCPBS-EVs (Figures 4(a)–4(d)). In comparison to the PBS control group, increased levels of tube formation were seen in HUVECs that had been treated with MSCTGF-β1-EVs, MSCPBS-EVs, or both. The highest levels of tube formation were seen in HUVECs that had been treated with MSCTGF-β1-EVs (Figure 4(e)). According to these findings, the contribution of MSCTGF-β1-EVs to fracture repair and angiogenesis is greater than that of MSCPBS-EVs.
## 3.5. SCD1 Is Transferred to HUVECs by EVs
We next evaluated SCD1 for its ability to be transmitted to HUVECs via EVs. RT-PCR and western blotting confirmed that SCD1 was expressed in MSCsTGF-β1, MSCTGF-β1-EVs, and targeted HUVECs and that silencing SCD1 could lower the levels of its protein (Figure 5(a)). This demonstrates that si-NC- and si-SCD1-transfected MSCTGF-β1-EVs were delivered to the target HUVECs efficiently. Although the size of EVs produced by si-SCD1-transfected cells differed to those from si-NC-transfected cells, NTA analysis of MSCTGF-β1-EVs revealed a similar amount of EVs in both types of cells (Figure 5(b)). The expression of the EV markers TSG101, CD81, CD63, and HSP70 was similar in MSCTGF-β1-EV-si-NC and MSCTGF-β1-EV-si-SCD1 (Figure 5(b)). Cy3-labeling confirmed that the levels of EV-SCD1 in the cytoplasm of HUVECs were lower after the administration of si-SCD1 from MSCTGF-β1-EVs (Figure 5(d)). These results confirm the transfer of EV-SCD1 into HUVECs.
## 3.6. SCD1 Knockdown Reduces MSCTGF-β1-EV-Stimulated Proliferation, Angiogenesis, and Migration In Vivo and In Vitro
Whether the differential expression of SCD1 could influence the characteristics of MSCTGF-β1-EVs was then determined both in vivo and in vitro. Callus tissues from mice administered with MSCTGF-β1-EVs, MSCTGF-β1-EV-si-NC, or MSCTGF-β1-EV-si-SCD1 were probed with Ki-67 (red immunofluorescence) and CD31 (green immunofluorescence) 21 d postfracture (Figure 6(a)). The level of cell proliferation was lower in tissue treated with MSCTGF-β1-EV-si-SCD1 than in tissue treated with MSCTGF-β1-EV-si-NC. Similarly, the functional effects of silencing SCD1 in vivo were observed in vitro in HUVECs treated with either MSCTGF-β1-EV-si-NC or MSCTGF-β1-EV-si-SCD1. HUVECs treated with MSCTGF-β1-EV-si-NC showed a higher amount of cell proliferation, as evaluated by CCK-8 and EdU staining (Figures 6(b) and 6(c)). Wound healing experiments and tube formation both showed similar effects on migration (Figure 6(d)) and angiogenesis (Figure 6(e)), respectively. In conclusion, SCD1 downregulation by RNA interference reduces MSCTGF-β1-EV-mediated cell proliferation, migration, and angiogenesis in vivo and in vitro.
## 3.7. SREBP-1 Is Required for the SCD1 Transcriptional Response
We used RNA interference to silence SREBP-1 in MSCTGF-β1-EVs to determine whether SREBP-1 was involved in the effects that SCD1 had on cell proliferation and angiogenesis (Figure 7(a)). Stimulating MSCs with TGF-β1 causes an increase in nuclear translocation of SREBP-1 (Figure 7(b)). However, SREBP-1 silencing results in reduced expression of SCD1 in MSCsTGF-β1 and MSCTGF-β1-EVs (Figure 7(c)). The proposed SREBP-1 binding site was mutated in the promoter of SCD1, and the relative luciferase activity confirmed an interaction between SREBP-1 and SCD1 (Figures 7(d) and 7(e)). Moreover, a ChIP assay indicated that the binding of SREBP-1 to the SCD1 promoter is enhanced by TGF-β1 stimulation (Figure 7(f)). Overall, these results indicate that SREBP-1 regulates SCD1 expression and that this regulation is enhanced by TGF-β1 stimulation.
## 3.8. EV-SCD1 Promotes HUVEC Proliferation, Angiogenesis, and Migration in Association with LRP5
During fracture healing, the activation of the Hippo signaling system [31] and the HIF-1/VEGF38 pathway [32] has been shown to increase angiogenesis and vascular remodeling. We first examined changes in key proteins in angiogenesis-related pathways after MSCTGF-β1-EVs, MSCTGF-β1-EV-si-NC, and MSCTGF-β1-EV-si-SCD1 were administered to HUVECs. The levels of HIF-1α and VEGF in HUVECs in the MSCTGF-β1-EV group were significantly increased. In contrast, the expression of SCD1 was suppressed, and the protein expression of HIF-1α/VEGF was lowered. However, the phosphorylation and protein levels of YAP and TAZ did not change significantly (Figure 8(a)). This suggests that the HIF-1α/VEGF pathway is necessary for EV-SCD1-induced HUVEC angiogenesis. Next, the expression levels of specific proteins involved in fracture healing were measured after MSCTGF-β1-EVs, MSCTGF-β1-EV-si-NC, or MSCTGF-β1-EV-si-SCD1were administered to HUVECs. The protein expression level of LRP5 in HUVECs was significantly increased by the addition of MSCTGF-β1-EVs and MSCTGF-β1-EV-si-NC whereas MSCTGF-β1-EV-si-SCD1 had little impact (Figure 8(b)), and there was no major change in the protein expression of LRP6. To further investigate the association between SCD1 and LRP5 expressions, the effect of overexpressing LRP5 on the proliferation (Figures 8(c) and 8(d)), migration (Figure 8(e)), and angiogenesis (Figure 8(f)) of HUVECs was measured by CCK-8, EdU, wound healing assays, and tube formation. The overexpression of LRP5 resulted in higher levels of cell proliferation, migration, and angiogenesis in HUVECs (Figures 8(c)–8(f)). Based on these findings, it appears that LRP5 is involved in the process by which MSCTGF-β1-EV-SCD1 promotes proliferation, angiogenesis, and migration in HUVECs.
## 4. Discussion
Regenerative medicine involving the use of MSCs offers great potential in optimizing the healing of bone fractures and bone abnormalities that are difficult to cure [33, 34]. However, there are restrictions on the use of MSCs in clinical applications because they are difficult to transplant, have a short life span, and have the potential to induce cancer [35]. MSC-EVs provide a useful alternative that resolves the pluripotent issues associated with stem cells [36]. Because of their ability to impact various biological processes, including both angiogenesis and osteogenesis, MSCs have been selected to release EVs to aid the repair process during bone healing [14, 37, 38]. This paracrine effect has been exploited in several studies to deliver specific repair factors to the site of bone injury [39, 40].
In this study, we examined the role of MSCTGF-β1-EVs in angiogenesis and fracture repair. TGF-β1 is a pleiotropic cytokine that is known to modulate MSCs by regulating differentiation and homeostasis and has been used successfully to stimulate the repair of fractures [41, 42]. In agreement with the published studies, we found that MSCTGF-β1-EVs enhanced callus development in an in vivo bone fracture model. When injected locally at the site of injury, MSCTGF-β1-EVs were able to promote bone healing, which manifested as increased bone volume density, trabecular number, trabecular thickness, and BMD, while reducing trabecular spacing in a mouse fracture model. In addition, the results of the immunohistochemical study showed an increased expression of CD31, α-SMA, SCD1, and LRP5 at the site of injury, which indicated that levels of angiogenesis and osteogenesis were increased in response to the paracrine effect of MSCTGF-β1-EVs.
LRP5 and LRP6 form a complex with Wnt and Frizzled to control the activation of β-catenin phosphorylation [43]. The Wnt/β-catenin pathway is a conserved cascade of signaling pathways involved in the proliferation, differentiation, and regulation of stem cells [44]. A recent study has found that SCD1 provides a feedback loop to control the level of Wnt/β-catenin signaling by modulating LRP5 and LRP6 expressions [45]. In this study, we found that LRP5 and SCD1 protein expressions were significantly upregulated after MSCTGF-β1-EV treatment at the site of injury. The addition of MSCTGF-β1-EVs or MSCTGF-β1-EV-si-NC greatly raised the expression level of LRP5 in HUVECs, whereas the addition of MSCTGF-β1-EV-si-SCD1 had minimal impact on the level of expression. The protein expression of LRP6 in HUVECs was relatively unaffected, indicating that the expression of SCD1 upregulates LRP5. LRP5 seemed to have a greater impact on cell proliferation, migration, and angiogenesis. Several studies have also observed differences in the signaling activity of LRP5 and LRP6 [46, 47]. Wnt3a is known to regulate bone metabolism in conjunction with LRP5 and LRP6 [45]. LRP6 is believed to have a more active role in the Wnt3a-mediated differentiation of osteoblasts. However, dramatic losses in bone density are observed when LRP5 is mutated [48], which signifies that LRP5 and LRP6 play different roles in osteogenesis [49].
In our study, we found that SREBP-1 interacts with SCD1 to stimulate cell proliferation and angiogenesis. Furthermore, we discovered that knocking down SREBP-1 significantly reduced SCD1 expression in MSCsTGF-β1 and MSCTGF-β1-EVs, thereby abolishing the effects of TGF-β1-EVs. This observation supports the results found in previous studies where SREBP-1 interacts with SCD1 to control Wnt signaling and LRP5 and LRP6 expressions [45]. SREBP-1 is thought to be activated by TGF-β1 [19]; therefore, our results indicate that MSCTGF-β1-EVs promote bone healing through the activation of SREBP-1-mediated SCD1 transcription and the subsequent upregulation of LRP5.
There are some limitations to our research, such as the absence of validated clinical data. In addition, the results of our studies have indicated that suppressing SCD1 leads to a decrease in the amount of proliferation, angiogenesis, and migration of HUVECs that is mediated by MSCTGF-β1-EVs in both in vivo and in vitro conditions; however, we have not investigated the effects of SCD1 overexpression. We also did not analyze the regulatory effects of SREBP-1 on LRP5. Furthermore, there is no comprehensive investigation of the particular mechanisms performed by SREBP-1 and LRP5 in animal models, which may be the primary focus of research in the near future. Studies in a follow-up phase could potentially include investigations into connected pathways.
## 5. Conclusions
In summary, the paracrine processes of MSCTGF-β1-EVs in a mouse model of bone fracture and HUVECs were examined in our study. The primary objective of this study was to compare the effects of MSCTGF-β1-EVs and MSCPBS-EVs on the healing of bone fractures. We found that MSCTGF-β1-EVs were more effective at promoting angiogenesis, proliferation, and migration than MSCPBS-EVs. In addition, the findings of this study reveal a strategy through which MSCTGF-β1-EVs enhance bone fracture repair via SCD1 in a chain reaction of contacts including SREBP-1 and LRP5. Our findings lead us to the conclusion that preconditioning with TGF-β1 is an efficient strategy for the therapeutic optimization of MSC-EVs in bone fracture healing (Figure 9).
## Ethical Approval
This study was approved by the Ethics Committee of Shanghai General Hospital and conducted in accordance with the Declaration of Helsinki.
## Conflicts of Interest
The authors have no conflict of interest to declare.
## Authors' Contributions
Zihui Zhou performed the research, analyzed the data, and wrote the first version of the paper. Chenyang Guo, Xulong Sun, and Zhengwei Ren analyzed the data. Jie Tao designed and edited the manuscript.
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|
---
title: 'Distal Radius Extra-Articular Fractures: The Impact of Anatomical Alignment
on Patient’s Perceived Outcome (A Single Center Experience)'
journal: Cureus
year: 2023
pmcid: PMC10033218
doi: 10.7759/cureus.36541
license: CC BY 3.0
---
# Distal Radius Extra-Articular Fractures: The Impact of Anatomical Alignment on Patient’s Perceived Outcome (A Single Center Experience)
## Abstract
Purpose: The effects of the anatomical alignment of distal radial extra-articular fractures on the patient's perceived outcome have been drawing much attention recently, and much controversy exists in the literature. The primary purpose of this study was to explore the relationship between the radiological parameters of reduction (radial inclination, radial length, and radial tilt) and the patient's perceived functional outcome, which was quantified using the DASH questionnaire.
Methods: The study included one hundred twenty-four patients with distal radial extra-articular fractures managed by closed reduction and casting. Their radiological (anatomical) outcome was determined by measuring the radial inclination, tilt, and length. Subjective functional outcome was quantified using the DASH score, calculated from the Arabic-translated DASH questionnaire at three months and six months after cast removal.
Results: the mean DASH score was 31.56 SD± 9.1 at three months and 29 SD± 3.89 at six months, and the acceptable radiological results for radial tilt, radial inclination, and radial length (according to McDermid's criteria for acceptable reduction) were $77.4\%$, $88.7\%$ and $74.4\%$, respectively. There was a significant linear correlation between two radiological parameters (radial tilt and radial length) and the DASH score at three-month follow-up, which was more profound among patients under 70 years old and with diabetes mellitus. At the six-month follow-up, there was no significant relationship between the radiological parameters and the DASH score.
Conclusion: This study confirmed that radiological outcome affects the early patient-perceived outcome, with a more significant effect among patients under 70 and diabetics. Nonetheless, over time, there will be no significant relationship between the quality of reduction and patients' perceived outcomes. And this phenomenon requires further investigation.
## Introduction
Distal radius fractures (DRFs) are prevalent in menopausal women, and their incidence rises exponentially after menopause until age 65 compared to males. [ 1]. In every five fractures, four are female patients. Osteoporosis is the main risk factor for DRFs among the elderly; therefore, these fractures are also known as fragility fractures [2]. DRFs commonly occur after simple falls, and their presence increases the risk of other fragility fractures like hip fractures [3]. Most DRFs among elderlies are extra-articular, unlike younger individuals, who experience a higher rate of intra-articular fractures [4]. Extra-articular DRFs are often managed by closed reduction and six weeks of immobilization in a cast when acceptable reduction criteria have been met [5]. Radial length (>5mm), radial angulation or tilt (< 15° dorsal or 20° volar), and radial inclination (>15°) are among the radiological markers for acceptable reduction [6-11].
Traditionally, grip strength and wrist joint range of motion are used as metrics to measure the functional progress that has been made after DRF treatment. However, these objective measures do not reflect the patient’s perceived outcome [12,13]. Therefore, patient-reported outcome (PRO) tools were developed to measure and quantify patients perceived outcomes following treatment, such as patient-reported wrist evaluation (PRWE) and the disability of arm, hand, and shoulder (DASH) questionnaires [12-14].
According to numerous studies, good radiographic (anatomical) reduction parameters positively affect the patient's perceived outcome [15]. Therefore, these findings favor surgical management for distal radial extra-articular fractures to achieve more anatomical reduction. Other studies, however, reported no discernible difference between the results of operative versus conservative treatment of DRFs in the elderly [16]. Moreover, surgical treatment for DRFs has its own financial and medical burdens, especially in under-resourced countries.
Published literature is scarce regarding the management of DRFs among Sudanese patients. This study sought to evaluate the impact of radiological parameters of acceptable reduction on the patient-perceived outcomes among Sudanese following conservative treatment of extraarticular DRFs.
## Materials and methods
Study design This prospective observational Hospital-based study included a cohort of patients with DRFs. It was conducted from September 1, 2013 to September 1, 2016 in the orthopedic department at EL-Mack Nimr University Hospital in Shendai city, Sudan. The hospital had 200 beds and was the only hospital that provided orthopedic services in the city.
Study population and sample size All patients aged 50 years and above with distal radius extra-articular fractures managed with closed reduction and cast application were included during the study period. All patients were treated by closed reduction and cast immobilization. Post-reduction x-ray was done immediately to check reduction quality, and any patient who had loss reduction or required re-manipulation during the six-week follow-up was removed from the study. Patients with distal radial fractures with articular extension, open fractures, bilateral fractures, and patients managed operatively were excluded. Initially, 143 patients who met the inclusion criteria were invited to the study. Their contact information was registered at the time of cast removal (an average of six weeks from the trauma after clinical and radiological evidence of union). However, only 131 patients returned to the referred clinic after three months for follow-up, and of those, only 124 patients agreed to be enrolled in the study. Of the 124, only 73 returned for follow-up at six months (Figure 1).
**Figure 1:** *Study flow chart.*
Data collection Patients’ Demographics Patients' age, gender, mode of trauma, affected limb, the timing of the initial management, fracture classification (according to AO, “Arbeitsgemeinschaft für Osteosynthesefragen” classification), and co-morbidities were all recorded in the datasheet.
Radiological Parameters Standard anteroposterior (AP) and lateral wrist x-ray views were used to determine the radiological parameters of reduction in order to assess the anatomical outcome; all x-rays were performed by three senior radiology technicians with experience ranging from five to 10 years. Two orthopedic surgeons with a minimum experience of five years and one senior orthopedic trainee measured the radial length, radial inclination, and radial tilt using a standard ruler and a goniometer (Figures 2A, 2B). All raters independently measured the same x-ray films for every patient included in the study. Interclass correlation coefficients were used to assess interrater reliability. Moreover, the radiological (anatomical) outcome was classified according to McDermid’s criteria for acceptable reduction into acceptable and unacceptable for each parameter [11].
**Figure 2:** *Radiographic measurements of the distal radius. (A) Lateral view of the wrist joint and the yellow lines for the measurements of Radial tilt. (B) Anteroposterior view of the wrist joint.Blue lines are for measurement of the radial length, red lines for the measurement of radial inclination.*
Patients Perceived Outcomes Patients reported functional outcomes were assessed using the Arabic-translated version of the DASH questionnaire, which has 30 items and a 5-point Likert scale for each item. The DASH score must also be calculated using a minimum of 27 responses; higher values denote more disability [17].
Ethical considerations Informed consent was obtained from all individual participants included in the study. The research ethics committee of Shendi University provided its institutional permission for the research protocol. The procedures used in this study adhere to the tenets of the Declaration of Helsinki.
Statistical analysis *The data* were analyzed using IBM SPSS version 25, JASP version 0.16, and Microsoft Excel 2019 edition. For categorical variables, frequency and percentages tables were used. The Kolmogorov-Simirove test of normality revealed that all variables were normally distributed; therefore, means and standard deviations (SD) were presented. Inferential statistics were done using the following tests: for the relationships between the continuous variables, Person’s correlation was used, independent samples t-test and two-way ANOVA were used for the relationship between continuous and categorical variables, and mixed ANOVA for repeated measurements was used to assess the change in DASH score between follow-ups. A p-value of less than 0.05 and a confidence Interval of $95\%$ were considered significant.
## Results
Demographics The mean age was 68.73 SD ±9 years, and $58.1\%$ were below 70 years old. Most patients, $79\%$, were females, $62.9\%$ of fractures involved the dominant hand, and most injuries were reported as secondary to a simple fall $93.5\%$. Of most patients, $72.6\%$ received management less than 24 hours from the trauma, and according to the AO classification, the most frequently encountered type of fracture, $82.3\%$, was A2. The most frequent comorbidities were diabetes mellitus $35.5\%$ and hypertension $22\%$, respectively (Table 1).
**Table 1**
| Variable | Frequency | Frequency.1 | Percent |
| --- | --- | --- | --- |
| Gender | | | |
| Male | 26 | 26.0 | 20.968 |
| Female | 98 | 98.0 | 79.032 |
| Mode of Trauma | Mode of Trauma | | |
| Other | 8 | 8.0 | 6.452 |
| Simple fall | 116 | 116.0 | 93.548 |
| limb | | | |
| Dominant | 78 | 78.0 | 62.903 |
| Non-Dominant | 46 | 46.0 | 37.097 |
| AO classification | | | |
| A2 | 102 | 102.0 | 82.258 |
| A3 | 22 | 22.0 | 17.742 |
| Time of mang. | | | |
| < 24 hours | 90 | 90.0 | 72.581 |
| >24 hours | 34 | 34.0 | 27.419 |
| Age70 | | | |
| <70 | 72 | 72.0 | 58.065 |
| >70 | 52 | 52.0 | 41.935 |
| DM | | | |
| Yes | 44 | 44.0 | 35.484 |
| No | 80 | 80.0 | 64.516 |
| Hypertension | | | |
| Yes | 28 | 28.0 | 22.58 |
| No | 96 | 96.0 | 77.419 |
Using t-test for independent samples, patients who were less than 70 years old showed significantly higher DASH mean scores (Mean = 33.81, SD = 10.71) compared to patients who were more than 70 years old (Mean= 28.46, SD = 4.78); t[104] = 3.74, $$p \leq 0.01$$ with moderate effect size (Cohens $d = 0.67$) according to Cohen's convention for effect size [18]. Furthermore, diabetic patients reported significantly high DASH mean scores (Mean = 36.6, SD = 11.1) compared to non-diabetics (Mean = 28.5, SD = 6.6); t [60] = 4.42 with large effect size ($d = 0.87$). Other demographic variables did not differentiate in DASH scores among different groups.
Radiological parameters and DASH score at three months ($$n = 124$$) The intraclass correlation coefficients (ICC) were used to assess inter-observer reliability utilizing a two-way mixed model and mean ratings (ICC 3,1) for absolute agreement. The ICC estimates and $95\%$ confidence intervals (CI) of the radiological parameters were as follows: For radial length, the ICC = 0.96 with $95\%$ CI (0.71-0.98); p-value (<0.001); for Radial inclination, the ICC = 0.92 with $95\%$ CI (0.50-0.97); p value < 0.001, and for radial tilt the ICC = 0.82 with $95\%$ CI (0.81-0.92), p-value < (0.001). Therefore, based on these results, the inter-rater reliability is considered excellent according to Cohen's criteria for classifying ICC values [19].
The mean DASH score was (31.56 SD± 9.1) at three months ($$n = 124$$), the mean radial tilt was 0.84°±SD16.53° (volar angulation was given a positive sign while a negative sign was given to dorsal angulation), the mean radial length was (6.76 ±SD 3.36 mm), and the mean radial inclination was (19.90°±SD4.24°). According to McDermid's criteria for acceptable reduction, the radiological outcome was acceptable in ($88.7\%$) for radial inclination, ($74.2\%$) for radial length, and ($77.4\%$) for radial tilt.
The correlation between the radiological measures and the DASH score was examined across all patients using Person’s correlation. Radial tilt and DASH score had a significant moderately negative (inverse)correlation (r = -0.596, $p \leq 0.001.$ Additionally, there was a significant negative, weak correlation (r = -0.448, $p \leq 0.001$) between radial length and DASH score. Moreover, there was a weak but significant positive(direct) correlation ($r = 0.256$, $p \leq 0.001$) between radial inclination and DASH score. However, subgroup correlation analysis revealed a strong correlation between radial tilt and radial length with the DASH mean scores among patients younger than 70 years old. Moreover, to compare correlation coefficients between age groups, Fisher Z transformation of correlation coefficients (r) was utilized to calculate the Z statistic, which was 2.5 ($$p \leq 0.006$$) for the radial length and 3.3 ($$p \leq 0.001$$) for the radial tilt (Table 2).
**Table 2**
| Unnamed: 0 | Unnamed: 1 | Unnamed: 2 | Radial length | Radial inclination | Radial inclination.1 | Radial tilt | Unnamed: 7 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| DASH All patients | Pearson Correlation | -.448** | -.448** | .256** | -.595** | -.595** | -.595** |
| DASH All patients | Sig. (2-tailed) | .000 | .000 | .004 | .000 | .000 | .000 |
| DASH All patients | N | 124 | 124 | 124 | 124 | 124 | 124 |
| DASH >70 years | Pearson Correlation | -.437** | -.437** | .462** | -.576** | -.576** | -.576** |
| DASH >70 years | Sig. (2-tailed) | .000 | .000 | .000 | .000 | .000 | .000 |
| DASH >70 years | N | 72 | 72 | 72 | 72 | 72 | 72 |
| DASH <70 years | Pearson Correlation | -.764** | -.764** | .149 | -.853** | -.853** | -.853** |
| DASH <70 years | Sig. (2-tailed) | .000 | .000 | .292 | .000 | .000 | .000 |
| DASH <70 years | N | 52 | 52 | 52 | 52 | 52 | 52 |
| Group wise Correlation Analysis | Z observed score | 2.5 P = (0.006) | 2.5 P = (0.006) | -- | 3.3 P = (0.001) | 3.3 P = (0.001) | 3.3 P = (0.001) |
Using the independent samples t-test, patients with unacceptable radial length and radial tilt (according to McDermid's criteria) demonstrated higher mean DASH scores, which were statistically significant (Table 3), with larger effect size (Cohen’s d) among individuals under 70 years of age (Table 4). There was no statistically significant difference in the DASH mean scores regarding radial inclination.
A two-way ANOVA was conducted to examine the effect of interactions between patients' age groups, DM, and radiological parameters (according to McDermid's criteria) on the DASH score. There was a statistically significant interaction between the effects of patients' age and radial tilt (F [1, 120] = 19.217, $p \leq .001.$) and radial length (F [1, 120] = 7.923, $$p \leq 0.006.$$) on DASH score. Furthermore, there was a statistically significant interaction between the effects of DM and radial tilt (F [1, 120] = 6.315, $$p \leq 0.014.$$) and radial length (F [1, 120] = 4.936, $$p \leq 0.023$$) on DASH score. Moreover, Simple main effects analysis showed that patients < 70 and diabetic patients with unacceptable radial tilt and radial length had higher mean DASH scores compared to their counterparts. Interaction of radial inclination with patient age groups and DM did not show a significant difference in DASH mean scores.
Radiological parameters and DASH score at six months ($$n = 73$$) The mean DASH score for patients who returned after six months was (29 SD± 3.89). Using mixed ANOVA for repeated measurements with Greenhouse-Geisser correction, the DASH mean scores were significantly lower at six months compared to three months (F [1,72] = 69.34, p.001), with a Bonferroni adjustment for confidence interval, the mean difference was 7.12 ($95\%$ CI. 5.42 - 8.83). Additionally, patients over 70 years old and people with diabetes exhibited substantially less improvement in their DASH mean scores compared to other groups when age groups and DM diagnosis were added as fixed effects to the model (Figures 3, 4).
**Figure 3:** *Descriptive plot showing the variation of DASH scores along time (three months, six months) among different age groups.N=73* **Figure 4:** *Descriptive plot showing the variation of DASH scores along time (three months, six months) among diabetics and non-diabetics.N=73*
There was a noticeably smaller mean difference in mean DASH scores between the acceptable and unacceptable groups for the three radiological parameters at six months compared to the mean difference at three months, which was statistically significant for radial length and radial tilt (Table 5).
**Table 5**
| Cases | Mean difference b/w unacceptable groups at 3-month and 6-month | df | F | p |
| --- | --- | --- | --- | --- |
| time ✻ Radial inclination | -1.717 | 1 | 0.652 | 0.422 |
| time ✻ Radial tilt | 13.846 | 1 | 64.13 | < .001 |
| time ✻ Radial length | 12.001 | 1 | 32.641 | < .001 |
## Discussion
Many experts have asserted that a positive outcome is more likely when the anatomy has been correctly rebuilt after a DRF [20,21]. There is disagreement about the radiological measure that most accurately predicts the outcome. It is frequently believed that radial shortening is the most significant radiological parameter [20-22]. Nevertheless, the significance of restoring a normal palmar tilt for wrist function and carpal alignment has also been given a high value [21,23]. Since Mason's study, the radial inclination has not received much attention [24].
Although several authors have reported results supporting a correlation between radiological and patient-perceived results, others describe a lack of correlation. Our investigation demonstrated that dorsal angulation and radial shortening had a negative impact on early patient-perceived outcome (DASH), with the effect being most obvious in patients under the age of 70, and this could be explained by the higher functional demands in the younger population. And these results were in agreement with Kumar et al. [ 25], who also reported that patients younger than 60 had worse PRO compared to older patients. However, they did not report any changes in PRO during follow-ups.
Our study also noted that all patients showed an improvement in PRO at six months, with younger patients showing the greatest improvement. Additionally, radiological characteristics had no discernible effect on the PRO, and these findings were consistent with those of Chang et al. [ 26], Anzurat et al. [ 27], and Gutiérrez et al. [ 15].
Patients with diabetes mellitus had less favorable PRO than other patients at the three-month follow-up, and diabetic patients who had unsatisfactory radial tilt and radial length reported substantially worse PRO than other patients. Although their PRO scores improved over the six months, their pace of progress was slower than that of non-diabetic patients. And these outcomes were consistent with research by Alsubheen et al. [ 28].
To the best of our knowledge, the impact of the three radiological parameters of reduction on the patient-perceived outcome, measured by a DASH score following extra-articular DRFs, has not been demonstrated before in Sudanese people.
One of the significant limitations of our investigation was that we lost nearly $50\%$ of our sample by the six-month follow-up attributable to no-shows or refusals to continue the study. The follow-up period in our study was supposed to last one year. Although the DASH questionnaire was in the Arabic language, some patients found it difficult to respond properly, and we made an effort to reduce information bias by asking our junior collages to assist in filling out the questionnaire, which added to the workload, especially in busy clinic settings, this was another study limitation.
## Conclusions
In conclusion, our study confirmed that radiological outcome impacts the early patient-perceived outcome, with a more significant effect among patients under 70 and diabetics. Nonetheless, over time, there will be no significant relationship between the quality of reduction and patients' perceived outcomes. At three months, younger patients had higher DASH scores than patients over 70, which might be explained by their higher functional demands. The DASH scores of younger patients were lower after six months of follow-up, which might be attributed to a variety of factors, including the temporal effect of improved wrist mobility and physiotherapy. And in fact, further research into this phenomenon is required. Therefore, we draw the conclusion that conservative therapy is still a decent choice, especially in countries with limited resources; yet the treating doctors in the emergency department should make all efforts to achieve the highest possible acceptable reduction for fractures.
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|
---
title: Group 3 innate lymphoid cells secret neutrophil chemoattractants and are insensitive
to glucocorticoid via aberrant GR phosphorylation
authors:
- Li Xiu He
- Ling Yang
- Ting Liu
- Yi Na Li
- Ting Xuan Huang
- Lan Lan Zhang
- Jian Luo
- Chun Tao Liu
journal: Respiratory Research
year: 2023
pmcid: PMC10033286
doi: 10.1186/s12931-023-02395-5
license: CC BY 4.0
---
# Group 3 innate lymphoid cells secret neutrophil chemoattractants and are insensitive to glucocorticoid via aberrant GR phosphorylation
## Abstract
### Background
Patients with neutrophil-mediated asthma have poor response to glucocorticoids. The roles and mechanisms of group 3 innate lymphoid cells (ILC3s) in inducing neutrophilic airway inflammation and glucocorticoid resistance in asthma have not been fully clarified.
### Methods
ILC3s in peripheral blood were measured by flow cytometry in patients with eosinophilic asthma (EA) and non-eosinophilic asthma (NEA). ILC3s were sorted and cultured in vitro for RNA sequencing. Cytokines production and signaling pathways in ILC3s after IL-1β stimulation and dexamethasone treatment were determined by real-time PCR, flow cytometry, ELISA and western blot.
### Results
The percentage and numbers of ILC3s in peripheral blood was higher in patients with NEA compared with EA, and negatively correlated with blood eosinophils. IL-1β stimulation significantly enhanced CXCL8 and CXCL1 production in ILC3s via activation of p65 NF-κB and p38/JNK MAPK signaling pathways. The expression of neutrophil chemoattractants from ILC3s was insensitive to dexamethasone treatment. Dexamethasone significantly increased phosphorylation of glucocorticoid receptor (GR) at Ser226 but only with a weak induction at Ser211 residues in ILC3s. Compared to human bronchial epithelial cell line (16HBE cells), the ratio of p-GR S226 to p-GR S211 (p-GR S226/S211) was significantly higher in ILC3s at baseline and after dexamethasone treatment. In addition, IL-1β could induce Ser226 phosphorylation and had a crosstalk effect to dexamethasone via NF-κB pathway.
### Conclusions
ILC3s were elevated in patients with NEA, and associated with neutrophil inflammation by release of neutrophil chemoattractants and were glucocorticoid (GC) resistant. This paper provides a novel cellular and molecular mechanisms of neutrophil inflammation and GC-resistance in asthma.
Trial registration The study has been prospectively registered in the World Health Organization International Clinical Trials Registry Platform (ChiCTR1900027125)
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12931-023-02395-5.
## Background
Group 3 innate lymphoid cells (ILC3s) have crucial roles in immunity and tissue homeostasis [1, 2], which are widely distributed throughout the body and are constitutively present at mucosal barrier sites such as the lung, liver, gut, spleen, skin and secondary lymphoid tissues [3]. ILC3s lack the diversified antigen receptors expressed on T cells and B cells and are defined by expression of the Retinoid-related orphan receptorγt (RORγt) and the secretion of IL-17, IL-22 in response to IL-23 and IL1-β [4, 5]. A recent study found ILC3 (IL-17+RORγt+) cells were responsible for the development of airway hyperreactivity (AHR) induced by IL-1β in obese mice asthma model, and blockade of IL-1β abolished the AHR and reduced the number of ILC3 cells [6]. Meanwhile, these ILC3s were found to be increased in non-allergic neutrophilic asthma mice [7] and a similar IL-17+ ILCs were also found in bronchoalveolar lavage fluid from human patients with severe asthma[6]. However, the roles of ILC3 in asthmatic patients with airway neutrophilic inflammation are still not clear.
Asthma is a heterogeneous disease with two broad inflammatory phenotypes: eosinophilic asthma (EA) and non-eosinophilic asthma (mainly mediated by neutrophil) (NEA) [8, 9]. Neutrophil mediated asthma occurs in about $50\%$ of corticosteroid resistant/insensitive asthma cases [10]. Evidence suggests IL-17 produced by Th17 cells appears to drive neutrophil-predominant steroid-resistant asthma [11, 12] and increases the production and release of chemokine IL-8 via airway epithelial cells, further propagating the chemotactic neutrophil response [13]. Since ILC3s can mimic the function of T helper type 17 (Th17) cells [1], they may also contribute to corticosteroid resistance in asthma, which is supported by a recent study that found the proportions of lung ILC3s in a murine neutrophilic asthma model was not altered by fluticasone propionate treatment [7]. However, the mechanisms behind the ILC3 associated steroid resistance are unknown. Therefore, in this study we investigated the relationship between ILC3s and neutrophilic inflammation in asthma patients and the mechanisms of corticosteroid resistance, which will provide a better understanding of the innate immunity and treatment target for NEA.
## Human subjects
Adult patients with asthma were consecutively recruited from Respiratory Outpatient Department of West China Hospital, Sichuan University from June 2019 to December 2020, according to diagnosis of Global Initiative for Asthma (GINA) recommendations [14]. Patients were stratified by baseline blood eosinophil counts 300/μL or greater and less than 300 cells/μL as eosinophilic asthma (EA) and non-eosinophilic asthma (NEA), respectively [15, 16]. Detailed inclusion and exclusion criteria for subjects were described as previously reported [17]. The healthy control population enrolled healthy volunteers from West China Hospital. The study was approved by the Institutional Review Board (IRB) at West China Hospital, Sichuan University (Chengdu, China) (No. 2019–856). All participants provided written informed consent.
## Flow cytometry analysis of peripheral blood
Fresh blood was collected in EDTA-treated tubes and peripheral blood mononuclear cells (PBMCs) were isolated as previously reported [18, 19] and stained with Zombie Aqua (Live/Dead) together with antibody panel 1(Additional file 4: Table S1). Lineage (Lin) markers included CD19/CD56/CD14/CD11b/CD11c/CD123/FcεRI. Group 2 innate lymphoid cell (ILC2) was defined as Lin−CD45+CD3−CD4−CD8−CD127+CRTH2+, Group 1 innate lymphoid cell (ILC1) was defined as Lin−CD45+CD3−CD4−CD8−CD127+CRTH2−CD117− and ILC3 was defined as Lin-CD45+CD3−CD4−CD8−CD127+CRTH2−CD117+ [20–22] (Additional file 1: Fig. S1).
## Cell lines and reagents
Human bronchial epithelial cell line (16HBE cells) were received as a gift from Tianfu Life Science Center of Sichuan University West China Hospital. Human IL-2, recombinant human IL-1β and IL-23 were purchased from Peprotech. NF-κB inhibitor (TPCA-1), p38 inhibitor (SB203580) and JNK inhibitor (SP600125) were purchased from Selleck. Dexamethasone was purchased from Sigma.
## ILC3s sorting and culture
PBMCs from healthy donors were labeled with the antibody panel 2 (Additional file 4: Table S1) after CD3 depletion by using CD3 Microbeads (Miltenyi Biotech). ILC3 cells were sorted with the FACSAria III (BD, Franklin Lakes, NJ).
The cells were cultured in RPMI with $10\%$ human serum, 1× l-glutamine, 1× penicillin/streptomycin, 1× sodium pyruvate, 1× nonessential amino acids, and 250 U/mL IL-2. Details were described in our previously published study [23].
## Intracellular staining
For RORγt staining, cultured ILC3s were incubated with antibody for RORγt after surface staining with Zombie Aqua (Live/Dead) together with antibody panel 3 (Additional file 4: Table S1) and fixation/permeabilization with FOXP3/Transcription Factor Staining Buffer set (eBioscience) [24]. ILC3 was defined as CD3-RORγt+. Cells with purity ≥ $90\%$ were used for subsequent experiments.
For staining of IL-22, IL-17A and CXCL8, cultured ILC3s were stimulated with Phorbol 12-myristate 13-acetate (PMA) cocktail in the presence of Brefeldin A (Cell Activation Cocktail with Brefeldin, Biolegend) for 6 h at 37 ℃. After staining with Zombie Aqua (Live/Dead) and fixation with IC Fixation Buffer (eBioscience), cells were permeabilized with a permeabilization buffer (Invitrogen) and labeled with corresponding antibodies (Additional file 4: Table S1). The above samples were acquired on a BD LSR II flow cytometer.
## RNA extraction and quantitative real-time polymerase chain reaction (PCR)
Total RNA was extracted from ILC3 cells using RNeasy Mini Kit (QIAGEN) following the manufacturer’s instructions and reverse-transcribed to cDNA using HiScript III RT SuperMix for qPCR (+gDNA wiper) Kit (Vazyme, China). Primers sequences were presented in Additional file 4: Table S2. Data correction was performed using Bio-Rad CFX Maestro and analyzed by 2−ΔΔCt method.
## RNA sequencing (RNA-seq)
ILC3s were stimulated with or without IL-1β + IL-23 for 48 h before sending to Beijing Novogene Bioinformation Technology Co., Ltd. (Beijing, China) in TRIzol Reagent for RNA-Seq. Standard methods were used to extract RNA, and RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Bioanalyzer. 1 μg RNA was used for cDNA library preparation after purification using Poly-T oligo-attached magnetic beads. Sequencing was performed on the Illumina Novaseq 6000 (Novogene Bioinformatics Technology Co., Ltd., Tianjin, China) platform [25].
## Enzyme-linked immunosorbent assay (ELISA)
Cell supernatants were analyzed by Human CXCL8 Uncoated ELISA kit (Thermo Fisher Scientific) and human CXCL1 ELISA kit (Elabscience, China) following manufacturer’s recommendations.
## Western blot
Proteins of cells were extracted with radioimmunoprecipitation (RIPA) lysis buffer (Beyotime, China) with phenylmethanesulfonyl fluoride (PMSF) and protease inhibitor cocktail and quantified by BCA Protein Assay Kit (Beyotime, China). The process was performed as previously reported [26]. Antibody and antigen complexes were detected using ECL chemiluminescent kit (Beyotime, China) by ChemiDoc™ MP Imaging System (Bio-Rad, USA). Antibodies used in western blot were presented in Additional file 4: Table S3.
## Statistical analysis
Continuous variables are expressed as mean ± standard deviation (SD) or median (interquartile range, IQR). Categorical variables are summarized as frequencies and proportions. Differences between groups were analyzed with Student’s t test or Mann–Whitney test. And for three or more groups, Analysis of Variance or the Kruskal–Wallis test were performed, Tukey HSD (Tukey Honest Significant Differences) were used for multiple comparisons as post hoc test. Categorical data were compared using Chi-square test or Fisher exact test. Test of Spearman was performed for evaluating correlations. Differences were considered statistically significant at *$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$ and ****$P \leq 0.0001.$ Data was analyzed by Prism 6.0 (GraphPad) and flow cytometry data was analyzed by FlowJo 9.7.6 software (Tree Star).
For sequencing data, differential gene expression analysis was performed using the DESeq2 R package (1.20.0). The P-value was adjusted using the Benjamini & Hochberg method for controlling the false discovery rate. Genes with adjusted P-value < 0.05 and absolute value of log2(FoldChange) ≥ 1 were assigned as differentially expressed genes. Gene Ontology (GO) enrichment analysis and KEGG pathways analysis of differentially expressed genes were conducted by the clusterProfiler R package, and terms with adjusted P-value < 0.05 were considered significantly enriched.
## ILC3 counts are higher in patients with NEA and they show a negative correlation with blood eosinophils
Twenty patients with NEA and eleven patients with EA were enrolled in this study, respectively. And eleven healthy donors were included as healthy control (HC). In the NEA group, blood eosinophils count (170.00 ± 87.12 × 106/L vs 570.00 ± 201.15 × 106/L, $P \leq 0.001$) and percentage (2.95 [1.30, 4.23]% vs 9.70 [5.70, 13.50]%, $P \leq 0.001$), fractional exhaled nitric oxide (FeNO) (54.00 [27.25, 68.25] ppb vs 87.00 [60.00, 153.0]ppb, $$P \leq 0.032$$) were lower compared to the EA group (Table 1, Fig. 1A), and the percentage of blood neutrophils was higher (Additional file 2: Fig. S2A). To determine the difference of ILC subsets between EA and NEA, the proportions of ILC subsets in peripheral blood were measured (Additional file 1: Fig. S1). Due to the imbalance of age between HC and patients with asthma, we firstly checked the relationship between age and ILC subsets and found a negative correlation with ILC3 (r = − 0.29, $$P \leq 0.067$$) and positive correlation with ILC2 ($r = 0.35$, $$P \leq 0.023$$) but not ILC1(Fig. 1B). After adjusting for age, the percentage (18.33 ± $5.64\%$ vs 9.30 ± $8.16\%$, $$P \leq 0.0212$$) and numbers (139.00 [89.50, 224.50] vs 45.00 [2.00, 135.00], $$P \leq 0.021$$) of ILC3s in NEA group was higher than EA group (Fig. 1C). The percentage of ILC3s was negatively correlated with blood eosinophils counts (r = − 0.57, $P \leq 0.001$) and percentage (r = − 0.55, $$P \leq 0.0013$$) (Fig. 1D), but was not correlated with blood neutrophils counts and percentage (Additional file 2: Fig. S2B).Table 1Demographic and clinical characteristics of study subjectsVariableNEAEAHCP-valueN201111Male/Female$\frac{9}{115}$/$\frac{66}{50.866}$Age, years48.20 ± 11.6443.18 ± 8.8427.00 ± 1.67*,#< 0.001BMI, kg/m223.13 ± 2.4222.63 ± 1.6221.85 ± 2.710.141Asthma duration, years4.00 (1.00, 10.00)10.00 (1.50, 16.00)–0.640Spirometry (Prebronchodilator) FEV1,L1.92 (1.48, 2.85)1.40 (0.94, 2.33)–0.157 FEV1, % predicted79.440 ± 22.1059.04 ± 29.21–0.036 FEV1/FVC66.98 ± 12.0258.69 ± 15.73–0.125ACT score15.40 ± 3.8414.82 ± 2.89–0.665ACQ score2.05 ± 1.022.34 ± 0.50–0.392Allergy, n (%)7(35.0)7(63.6)–0.132FeNO, ppb54.00 (27.25, 68.25)87.00 (60.00, 153.0)–0.032Blood Leukocytes, × 106/L6667.78 ± 2080.046202.22 ± 1190.515524.00 ± 1676.330.451 Eosinophils, × 106/L170.00 ± 87.12###570.00 ± 201.15***100.00 ± 62.45###< 0.001 Eosinophils, %2.95 (1.30, 4.23)###9.70 (5.70, 13.50)***1.00 (0.80, 0.10)###< 0.001 Neutrophils, × 106/L3895.00 (3182.50, 4612.50)3020.00 (2525.00, 3925.00)3520.00 (2105.00, 4710.00)0.254 Neutrophils,%61.90 (57.83, 65.13)52.7 (51.05, 58.30)62.7 (44.90, 70.60)0.115Data are represented as mean ± SD, median (interquartile range, IQR) or frequency (%)*$P \leq 0.05$, **$P \leq 0.005$, ***$P \leq 0.001$ vs. NEA, with Tukey HSD test#$P \leq 0.05$, ##$P \leq 0.005$, ###$P \leq 0.001$ vs. EA, with Tukey HSD testACT: Asthma Control Test; BMI: Body mass index; EA: Eosinophilic asthma; FEV1: Forced expiratory volume in one second; FeNO: Fractional exhaled nitric oxide; FVC: Forced vital capacity; NEA: Non-eosinophilic asthmaFig. 1ILC3s elevated in patients with NEA compared to EA. A Blood eosinophils count and percentage among healthy control (HC) and patients with NEA and EA. B Correlation between subsets of ILCs and age. C Percentage and numbers (in every 50 × 105 recorded lymphocyte) of ILC1, ILC2 and ILC3 in HC, NEA and EA patients. D Correlation between ILC subsets and blood eosinophils count and percentage. **** $p \leq 0.0001$, ***$p \leq 0.001$, **$p \leq 0.01$, *$p \leq 0.05$, ns $p \leq 0.05$
## IL-1β induces neutrophil chemoattractants expression in ILC3s
In order to explore relationship between ILC3s and neutrophilic inflammation, ILC3s were sorted and cultured in vitro. Because of plasticity in vitro [27, 28], ILC3s were first identified by flow cytometry based on the transcription factor RORγt [4], and only cells with RORγt+ILC3 purity ≥ $90\%$ were used for further experiments. Meanwhile, the cytokines IL-17A and IL-22 secreted by in vitro cultured cells were explored for further confirmation [5]. So, intracellular staining of transcription factor (RORγt) and cytokines (IL-17A and IL-22) by flow cytometry was performed, and results showed the expression of RORγt and production of IL-17A and IL-22 upon PMA stimulation in the in vitro cultured cells (Fig. 2A), confirming these cells were ILC3s. Fig. 2Flow cytometry gating strategy for ILC3 identification and cytokines production profiles of ILC3s. A ILC3s were identified by transcription factor RORγt and the secretion of IL-17, IL-22 and CXCL8. Cytokines-producing cells of ILC3s were detected by Flow cytometry. Blue, ILC3s without stimulation. Red, ILC3s stimulated with PMA + ionomycin. Numbers represent percentages of cells producing IL-17A or IL-22 or CXCL8 in ILC3s. B Time course of CXCL8, CXCL1, TNF-α and GM-CSF mRNA expression in ILC3s after IL-1β (50 ng/mL) stimulation. C Protein level of CXCL8 and CXCL1 in culture supernatant of ILC3s with or without IL-1β (50 ng/mL) stimulation after 24 h. D, E Effect of IL-1β and IL-23 on CXCL8 expression in ILC3s Next, we measured the capability of release of neutrophil chemoattractants on ILC3s. CXCL8, one of the most potent chemokines for neutrophil migration [29, 30], was significantly enhanced after PMA or IL-1β stimulation in ILC3s at both transcriptome and protein level, peaked at 30 min and 4 h, respectively (Fig. 2A–C). In addition, IL-1β also induced production of other neutrophil chemoattractants in ILC3s including CXCL1, *Tumor necrosis* factor (TNF-α) and Granulocyte–macrophage colony-stimulating factor (GM-CSF) (Fig. 2B, C). Previous study reported the stimulation of ILC3s by IL-1β plus IL-23 [31–33], therefore, we sought to determine whether there is any additive or synergistic effect of IL-1β and IL-23. We found that IL-1β + IL-23 significantly increased the production of CXCL8 in ILC3s in a similar extent as IL-1β alone, and IL-23 alone did not induce CXCL8 production, indicating no additive or synergistic stimulation effect of IL-1β and IL-23 in ILC3s (Fig. 2D, E).
## ILC3s are involved in inflammation response via NF-κB and MAPK pathways
To investigate the pathways that regulate the production of neutrophil chemoattractants in ILC3s, RNA sequencing was performed on in vitro cultured ILC3s with or without IL-1β + IL-23 stimulation. IL-1β + IL-23 stimulation was associated with 760 up-regulated and 450 down-regulated genes, and the top five overexpressed genes were IL-22, CXCL8, CCR7, IGFBP4, and CD22 (Fig. 3A). IL-1β + IL-23 induced expression of cytokines and chemokines such as CSF2, TNFSF4, IL-22, CXCL8 and IL-17(Fig. 3B), which have been previous identified to be effector cytokines of ILC3s [5]. The GO gene set enrichment analyses showed response to inflammation was an important biological process of ILC3s (Fig. 3C, left panel), and CXCL8 was involved in “inflammatory response” during cell activation (Fig. 3D).Fig. 3RNA sequencing of in vitro cultured ILC3s with or without IL-1β + IL-23 stimulation. A Volcano plots of differential expression genes (DEGs) in ILC3s stimulated by IL-1β + IL-23. The top five differential expressed genes were IL-22, CCR7, IGFBP4, CD22 and CXCL8. B *Heatmap analysis* of effector cytokines in ILC3s. In the heatmap, the redder the color is, the higher the expression is, and the greener the expression is, the lower the expression is. C Function terms of DEGs by Gene Ontology (GO) enrichment analysis and KEGG pathway analysis of DEGs in ILC3s upon IL-1β + IL-23(50 ng/mL) stimulation. D *Heatmap analysis* of DEGs in inflammation response. E The clustering heat map of DEGs involved in NF-κB signal pathway. F, G Western blot analysis by antiphospho-p65 Ab, antiphospho-p38 Ab, antiphospho-JNK Ab and antiphospho-Erk Ab. Cell extracts were prepared after stimulation with IL-1β (50 ng/mL) for indicated times. H Phosphorylation and inhibition of p65, p38 and JNK by specific inhibitors in ILC3s. Western blot analysis of p-p65, p-p38 and p-JNK were performed with cell extracts from ILC3s, preincubated for 1 h with p65 NF-κB inhibitor TPCA-1, p38-MAPK inhibitor SB203580 or JNK-MAPK inhibitor SP600125 and then stimulated with IL-1β (50 ng/mL) for 10 min. I Expression of CXCL8 protein in culture supernatant of ILC3 cells after IL-1β stimulation with or without corresponding inhibitors. ILC3s were preincubated for 1 h with indicated inhibitors and then stimulated with IL-1β (50 ng/mL) for 24 h The KEGG pathway analysis showed the NF-κB ($$P \leq 0.006$$) (Fig. 3C, right panel) and MAPK signaling pathway ($$P \leq 0.071$$) (data not shown) were participated in ILC3s upon IL-1β + IL-23 stimulation. Further enrichment analysis also showed CXCL8 was involved in the NF-κB signaling pathway (Fig. 3E). To verify these signaling pathways, we detected the activation of key proteins in NF-κB and MAPK pathways in ILC3s by western blotting, and found IL-1β stimulation induced phosphorylation of p65, p38 and JNK, but had no effect on the activation of Erk (Fig. 3F, G). These increased phosphorylation were significantly inverted by the corresponding inhibitors (Fig. 3H). The enhancement of CXCL8 level by IL-1β was completely inhibited by p65 NF-κB inhibitor (TPCA-1) and partially inhibited by p38-MAPK inhibitor (SB203580) and JNK-MAPK inhibitor (SP600125) (Fig. 3I).
## IL-1β-induced neutrophil chemoattractants expression in ILC3s is insensitive to dexamethasone due to aberrant GR phosphorylation
Patients with neutrophilic asthma usually have poor response to corticosteroids [34–36], and our previous study has found that the numbers of ILC3s in asthma patients were not reduced after treatment with prednisolone [23]. Therefore, we speculated that ILC3s might contribute to the steroid resistance. To test this hypothesis, in vitro cultured ILC3s were treated with dexamethasone (Dex) and the expression levels of CXCL8 and CXCL1 were detected. 16HBEs were selected as reference cells, which were sensitive to dexamethasone treatment, as Dex significantly reduced CXCL8 production induced by IL-1β (Fig. 4A). We found that the CXCL8 and CXCL1 production of ILC3s in response to IL-1β was not inhibited by Dex at protein level, but partially reduced by Dex at transcriptome level (Fig. 4B, C). These findings suggest the steroid resistance of ILC3s in terms of neutrophil chemoattractants expression. Fig. 4Effect of dexamethasone (Dex) on IL-1β-induced CXCL8 and CXCL1 production and phosphorylation of glucocorticoids receptor (GR) at S226 and S211 in ILC3s. A Expression of CXCL8 in 16HBEs with and without Dex treatment. B, C, Expression of CXCL8 and CXCL1 in ILC3s with Dex treatment by indicated concentrations. Cells were pre-treated with Dex (10 to 1000 ng/mL) for 1 h before 24 h stimulation with IL-1β (50 ng/mL), supernatants were collected and assayed for CXCL8 and CXCL1 release by ELISA, and ILC3 cells were extracted for qPCR analysis. D Western blotting analysis of GR in 16HBEs and ILC3s after stimulation with Dex (1000 ng/mL) for 15 min. E, F Western blotting analysis of phosphorylation of GR S226 and S211 in 16HBEs (E) and ILC3s (F) after stimulation with Dex (1000 ng/mL) for 15 min. G Ratio of p-GR S226/S211 in 16HBEs and ILC3s at baseline and after dexamethasone treatment. H Western blotting analysis of phosphorylation of MAPK in ILC3s with Dex stimulation (1000 ng/mL) for 15 min. I Effects of Dex and corresponding inhibitors on phosphorylated GR at S226 and S211 in ILC3s. J GR phosphorylation at S226 and S211 in ILC3s with indicated stimulation. Cells were stimulated with IL-1β (50 ng/mL) or dexamethasone (1000 ng/mL) alone or combination for 15 min. ILC3s were preincubated for 1 h with corresponding inhibitors then stimulated with dexamethasone (1000 ng/mL) for 15 min, then lysed and assessed for phosphorylation of GR S226 or GR S211 by Western blotting Next, we investigated the mechanisms that mediate steroids resistance in ILC3s. The effects of glucocorticoids (GCs) are mediated by their binding and activation of GC receptor (GR) [37], and phosphorylation status and sites of GR can dictate cells respond to GCs [38]. Therefore, we firstly compared the levels of GR between ILC3s and 16HBEs, and no difference was found between ILC3s and 16HBEs at baseline or after treatment of dexamethasone (Fig. 4D). Then, we measured the phosphorylation levels of two sites of GR, i.e. p-GR S226 and p-GR S211, and the results showed that the level of p-GR S226 was significantly up-regulated by Dex in both 16HBEs and ILC3s. However, there was a significant increase in the phosphorylation of GR S211 in 16HBEs and only a weak increase in ILC3s in response to Dex treatment (Fig. 4E, F and Additional file 3: Fig. S3). In addition, we also found that the ratio of p-GR S226 to p-GR S211 (p-GR S226/S211) was significantly higher in ILC3s than 16HBEs at baseline and after dexamethasone treatment (Fig. 4G). Taken together, these data indicate the steroid resistance of ILC3s is mediated by the enhanced phosphorylation at Ser226 and weak phosphorylation of GR at Ser211, with a higher ratio of p-GR S226/S211 than that of 16HBEs.
Finally, we explored the potential signaling pathways that regulate phosphorylation of GR in ILC3s. MAPK signaling pathway has been found to directly phosphorylate GR [38], and in ILC3s we found Dex treatment increased JNK MAPK phosphorylation, but not phosphorylation of p38 MAPK or Erk MAPK (Fig. 4H), which was further confirmed by the inhibitory experiment showing that the GR phosphorylation at S226 in response to dexamethasone was significantly inhibited by JNK-MAPK inhibitor SP600125 (Fig. 4I). Interestingly, we also found p65 NF-κB inhibitor (TPCA-1) also abolished the effect of Dex on phosphorylation of S226 (Fig. 4I), indicating a role of NF-κB signaling pathway in GR activation. Indeed, evidence has shown GR and NF-κB interacted with each other, dexamethasone treatment could reduce the binding of NF-kB chromatin and high levels of NF-κB attenuated GR function [39]. Since IL-1β significantly increased activity of NF-κB and JNK MAPK (Fig. 3F, G), we then sought to explore the crosstalk effects of dexamethasone and IL-1β in ILC3s. As expected, the level of GR phosphorylation at S226 was also significantly elevated by IL-1β treatment alone or combination with dexamethasone for 15 min (Fig. 4J), suggesting the crosstalk effect between dexamethasone and IL-1β in corticosteroids-insensitivity in ILC3s.
## Discussion
The pro-inflammatory roles of ILC3 in asthma AHR and inflammation have been known for several years [6], but the underlying mechanisms have not been fully illustrated. Previous studies have reported the production of IL-17 by ILC3 might be a key contributor to the airway neutrophilic inflammation through neutrophil recruitment in patients with asthma [40–42]. Here, we illustrated that percentage of ILC3s in peripheral blood of NEA patients was increased compared with EA patients and were negatively correlated with blood eosinophils. However, a phase II study with brodalumab (a human anti-IL-17 receptor monoclonal antibody) failed to show the treatment effect in subjects with moderate to severe asthma [43], which suggests other mechanisms might mediate neutrophilic inflammation in asthma. In our study, we found ILC3s produced neutrophil chemoattractants including CXCL8, CXCL1, TNF-α and GM-CSF after IL-1β stimulation, suggesting ILC3s may mediate airway neutrophilia in asthma by release of neutrophil chemoattractants. Meanwhile, we noticed a relatively high level of CXCL8 and CXCL1 released by ILC3s without stimulation, this might suggest the contribution of ILC3s in the recruitment of neutrophils in physiological condition but we could not exclude the possibility that they were due to the effect of rIL-2 in the culture medium [44, 45].
One of the clinical characteristics of patients with NEA is the poor response to corticosteroids, leading to a higher severity of disease and difficult-to-control asthma [46]. In this study, the expression of CXCL8 and CXCL1 in ILC3s upon IL-1β stimulation was insensitive to dexamethasone, suggesting that neutrophilic inflammation mediated by IL-1β-ILC3-CXCL8/CXCL1 axis may be involved in the development of steroid-resistant of asthma. To find out the mechanisms that mediate this insensitivity, we focused on the GR, which is the key receptor responsible for the physiological and pharmacological effects of glucocorticoids. GR can be phosphorylated at over 20 sites, while Ser211 and Ser226 are the two well-characterized sites. Phosphorylation at Ser211 promotes nuclear translocation and enhances transcriptional activity, while phosphorylation at Ser226 inhibits transcriptional activity and promotes nuclear export and has an inhibitory effect on GR function [38, 47]. The lack of Ser211 phosphorylation has been suggested to be a possible mechanism for GC resistance of human lymphoid cells [48]. Evidences have also shown the expression of GR Ser226 in PBMCs from severe asthma patients was significantly higher than that from non-severe asthma patients after dexamethasone treatment [49, 50]. In our study, we found the enhanced Ser226 phosphorylation while weak Ser211 phosphorylation upon dexamethasone treatment in ILC3s, and compared to GC-sensitive 16HBEs, the ratio of p-GR S226 to p-GR S211 (p-GR S226/S211) was significantly higher in ILC3s at baseline and after dexamethasone treatment, which well explained the insensitivity of ILC3s to dexamethasone treatment.
The phosphorylation of GR has been reported to be controlled by the p38 MAPK and Erk/JNK MAPK signaling pathways, of which p38 MAPK promotes the Ser211 phosphorylation while Erk/JNK MAPK down-regulates Ser211 phosphorylation and increases Ser226 phosphorylation [48, 51, 52]. In our study, we confirmed the regulatory effect of JNK MAPK on Ser226 phosphorylation by using its inhibitor SP600125, and the JNK MAPK activity was enhanced by dexamethasone treatment but not p38 MAPK activity, which reveals the intrinsic regulatory mechanisms associated with insensitivity of ILC3 to corticosteroids. Furthermore, phosphorylation of GR usually happens after binding to its ligand (GC), but a ligand-independent fashion as part of the crosstalk with other signaling pathways has also been reported [38]. A study of ligand-independent phosphorylation of GR showed TNF-α induced phosphorylation of the unliganded human GR at Ser-226 not Ser-211 [53]. In our study, we found IL-1β could induce Ser226 phosphorylation and the NF-κB inhibitor TPCA-1 demolished this effect in ILC3s. Meanwhile, IL-1β and dexamethasone could activate the same signaling pathway of JNK MAPK in ILC3s. This evidence implies that elevated IL-1β in neutrophil dominant asthma may have a crosstalk effect on dexamethasone [54], which aggravates corticosteroids insensitivity in neutrophilic asthma [55]. However, we could not exclude the possibility that the IL-1β induced phosphorylation at Ser226 might be through TNF-α, which has been shown to impair glucocorticoid receptor phosphorylation at Ser211 [56, 57] and directly phosphorylate GR at Ser226 by activating JNK MAPK [58], because in our study, TNF-α was enhanced by IL-1β in ILC3s.
However, there are several limitations that need to be addressed in this study. Firstly, due to the COVID-19 pandemic, we were unable to obtain induced sputum samples from asthma patients, thus unable to detect ILC3 in airways and distinguish neutrophilic asthma from NEA; therefore, EA and NEA were classified according to blood eosinophil counts 300/μL. Secondly, different subgroups of ILC3 have not been investigated, which could provide more information of ILC3 heterogeneity. Third, we did not directly compare the stimulation responses of ILC3 from healthy control and different asthma phenotypes/severities, which might imply the functional differences of ILC3 in the context of disease and asthma phenotypes. Fourth, we did not investigate other potential mechanisms that could be involved in the regulation of glucocorticosteroids such as GR isoforms (GRβ) and the lack of GRE in CXCL8 gene. Finally, we focused on patients with asthma, however, it would be interesting to investigate the roles of ILC3 in a broader spectrum of diseases such as patients with chronic obstructive pulmonary disease (COPD) as they are generally less sensitive to inhaled steroids and age dependent, and evidence showed ILC3s were notably increased in lung tissue from patients with COPD [59].
In conclusion, ILC3s are elevated in patients with NEA compared to EA and are crucial contributors to the neutrophilic airway inflammation via production of neutrophil chemoattractants through p65 NF-κB and p38/JNK MAPK signaling pathways. Specifically, the expression of neutrophil chemoattractants induced by IL-1β is insensitive to dexamethasone, which is due to aberrant GR phosphorylation with increased phosphorylation at Ser226 and weak phosphorylation at Ser211. Our findings provide evidence of the roles of innate immunity in neutrophilic inflammation and steroids insensitivity, which help to discover novel treatment targets for future drug development and better management of neutrophilic phenotype of asthma.
## Supplementary Information
Additional file 1: Figure S1. Flow cytometric gating strategy for ILCs in PBMCs. ILC2 populations were gated as Lin−CD45+CD3−CD4−CD8− CD127+CRTH2+, ILC1 populations were gated as Lin−CD45+CD3−CD4−CD8− CD127+CRTH2−CD117−, and ILC3 populations were gated as Lin−CD45+CD3−CD4−CD8−CRTH2−CD127+CD117+ Lineage markers contained CD11b, CD11c, CD14, CD19, CD56, CD123, and FcεRI. ILCs, innate lymphoid cells; ILC1, group 1 innate lymphoid cell; ILC2, group 2 innate lymphoid cell; ILC3, group 3 innate lymphoid cell. Additional file 2: Figure S2. A, Blood neutrophils count and percentage among healthy control (HC) and patients with NEA and EA. B, Correlation between ILC subsets and blood neutrophils count and percentage. **** $p \leq 0.0001$, *** $p \leq 0.001$, ** $p \leq 0.01$, * $p \leq 0.05$, ns $p \leq 0.05.$Additional file 3: Figure S3. A, Level of p-GR S226 and p-GR S211 in HBEs with or without dexamethasone treatment. B, Level of p-GR S226 and p-GR S211 in ILC3s with or without dexamethasone treatment. Additional file 4: Table S1. Antibodies used in flow cytometry and cell sorting. Table S2. Primer sequences used in RT-PCR. Table S3. Antibodies used for western blotting.
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|
---
title: Selective PPARγ modulator diosmin improves insulin sensitivity and promotes
browning of white fat
authors:
- Jian Yu
- Yepeng Hu
- Maozheng Sheng
- Mingyuan Gao
- Wenxiu Guo
- Zhe Zhang
- Dongmei Wang
- Xia Wu
- Jin Li
- Yantao Chen
- Wenjun Zhao
- Caizhi Liu
- Xiangdi Cui
- Xin Chen
- Cheng Zhao
- Huang Chen
- Junjie Xiao
- Shijie Chen
- Cheng Luo
- Lingyan Xu
- Xuejiang Gu
- Xinran Ma
journal: The Journal of Biological Chemistry
year: 2023
pmcid: PMC10033317
doi: 10.1016/j.jbc.2023.103059
license: CC BY 4.0
---
# Selective PPARγ modulator diosmin improves insulin sensitivity and promotes browning of white fat
## Body
The prevalence of type 2 diabetes has increased considerably worldwide in recent decades, accompanied with increased incidences of associated comorbidities and growing trends of morbidity and mortality, posting increasing economic burden and social disadvantages [1, 2]. Although the frontline diabetic drugs including GLP-1 analog, SLGT2 inhibitor, and metformin were widely used, natural compounds and their derivations that have potential use as therapeutics or daily supplements with glycemic control effects are still in high demands.
Adipose tissues play critical roles in glucose and lipid homeostasis [3]. Adipose tissues have been divided into three types: brown, beige, and white adipose tissues, according to their location, morphology, and function [4, 5]. Excess accumulation of white adi2pose tissue can induce detrimental metabolic effects, whereas beige and brown adipose tissues have the potential to improve metabolism [6]. The beige (also known as “brown-like”) adipose tissue mainly exists in the subcutaneous fat pads with inducible thermogenic capacity in response to stimulations like cold or β3 agonists [7]. In addition to sympathetic nervous systems and β-adrenergic signaling, beige adipocytes could also be activated in a cell-autonomous manner [8]. Beige adipocyte activation increases energy expenditure and thermogenesis, as well as functions as a metabolic sink to consume excess lipid and glucose, thus reduces obesity and hyperlipidemia and improves insulin sensitivity [9], emphasizing beige fat as an attractive target for obesity and diabetes treatments.
Peroxisome proliferator–activated receptor γ (PPARγ) is a member of the nuclear receptor family of transcription factors, which governs adipocyte differentiation and thermogenesis, insulin sensitivity, and inflammation inhibition [10, 11, 12]. Classic PPARγ-full agonist thiazolidinediones (TZDs) could activate PPARγ and increase insulin sensitivity of peripheral tissues (fat tissue, skeletal muscle, liver, etc.) to achieve a hypoglycemic effect [13]. However, their usage has been limited in clinical treatment because of unwanted side effects, such as weight gain, cardiovascular risk, edema, and osteoporosis [14]. Thus, selective PPARγ modulators acting through alternative mechanisms to modulate PPARγ activity without activating the full transcriptional program were developed [15]. Particularly, it has been found that high-fat feeding activates the protein kinase cyclin-dependent kinase 5 (Cdk5) resulting in phosphorylation of PPARγ at Ser273. Compounds blocking PPARγ phosphorylation at Ser273 would reverse a specific set of diabetic gene programs and exhibit antidiabetic effects [16]. Recently, studies demonstrated that dephosphorylation of PPARγ at Ser273 with PPARγ partial agonist or nonagonist PPARγ ligands including SR1664, MRL24, GQ-16, and CDK5 inhibitor roscovitine conveyed insulin-sensitizing and/or antiobesity effects, whereas at the same time avoided undesirable adverse effects of TZDs [17, 18, 19]. These observations indicate that blocking phosphorylation of PPARγ Ser273 represents a novel avenue of drug development for diabetes and obesity treatments.
Diosmin is a flavone glycoside, a natural compound extracted from dehydrated pericarps of different citrus fruits. Diosmin is a safe and nontoxic clinical medication regularly used for treating venous disease, that is, chronic venous insufficiency and acute or chronic hemorrhoidal disease [20, 21]. Mechanistically, diosmin exerted its protective role on venous system by prolonging the vasoconstriction effects of β-adrenergic signaling and decreasing capillary permeability [22]. Diosmin also exhibited anti-inflammatory, antioxidation activities, and anticancer through its regulation on NF-κB signaling pathway and phosphatidylinositol 3-kinase/Akt pathway [23, 24, 25]. Recently, in addition to its function in vascular protection and inflammation, metabolic implications of diosmin were documented as it shows liver protective function by improving bile duct ligation–induced liver defects and promote glucose homeostasis in models of type I diabetes [26, 27].
In the present study, computer virtual screening was performed in a natural small-molecule compound library to screen for compounds with potent binding to PPARγ. We identified diosmin that characterized binding affinity with PPARγ and blocked its S273 site. Our results demonstrated that diosmin treatment induced antidiabetic effects in beige fat and adipocytes without affecting adipogenesis. Moreover, local administration of compounds in adipose tissues has attracted great attentions for enhanced treatment efficiency and minimized side effects. For example, it has been shown that local administration of nanoparticle-formulated rosiglitazone in subcutaneous fat (inguinal white adipose tissue [iWAT]) induces adipose tissue browning and prevents obesity [28]. Besides, we have developed a highly efficient fluoropolypeptide for the delivery of siRNA-based therapeutics directly into iWAT to combat obesity and metabolic diseases [29]. In addition, we and others have recently shown that local hyperthermia therapy on beige fat with photothermal nanoparticles induces thermogenesis to combat obesity without obvious systemic side effects [30, 31]. Considering these previous studies, in the present study, we selected local injection of diosmin into iWAT and found that diosmin reduced obesity and hepatic steatosis under high-fat diet (HFD) via browning of white fat and enhanced energy expenditure without obvious side effects. These results indicated that diosmin is a potential agent for the treatment of obesity and diabetes.
## Abstract
Peroxisome proliferator–activated receptor γ (PPARγ) is a master regulator of adipocyte differentiation, glucolipid metabolism, and inflammation. Thiazolidinediones are PPARγ full agonists with potent insulin-sensitizing effects, whereas their oral usage is restricted because of unwanted side effects, including obesity and cardiovascular risks. Here, via virtual screening, microscale thermophoresis analysis, and molecular confirmation, we demonstrate that diosmin, a natural compound of wide and long-term clinical use, is a selective PPARγ modulator that binds to PPARγ and blocks PPARγ phosphorylation with weak transcriptional activity. Local diosmin administration in subcutaneous fat (inguinal white adipose tissue [iWAT]) improved insulin sensitivity and attenuated obesity via enhancing browning of white fat and energy expenditure. Besides, diosmin ameliorated inflammation in WAT and liver and reduced hepatic steatosis. Of note, we determined that iWAT local administration of diosmin did not exhibit obvious side effects. Taken together, the present study demonstrated that iWAT local delivery of diosmin protected mice from diet-induced insulin resistance, obesity, and fatty liver by blocking PPARγ phosphorylation, without apparent side effects, making it a potential therapeutic agent for the treatment of metabolic diseases.
## Diosmin is a selective PPARγ modulator and enhances glucose uptake
To identify novel PPARγ ligand that blocks PPARγ pS273, computer virtual screening was performed using molecular docking strategy with 3000 natural small-molecule compounds. Among the positive candidates, four compounds including diosmin, hesperidin, polydatin, and amygdalin featured highest absolute binding free energy. Among them, diosmin piqued our interest as it features strongest binding affinity, whereas molecular docking studies predicted its binding pocket nearing PPARγ S273 (Fig. 1, A–C). Indeed, compared with other three compounds, diosmin increased a specific set of diabetic genes dysregulated in the scenario of CDK5-induced PPARγ S273 phosphorylation in a similar pattern as the PPARγ full agonist rosiglitazone did [16] (Fig. 1D).Figure 1Diosmin (Dios) blocks PPARγ phosphorylation in beige adipocytes. A, binding free energy, (B) chemical structure, and (C) molecular docking model of Dios, hesperidin, polydatin, and amygdalin. D, expression of gene sets regulated by PPARγ S273 phosphorylation in beige adipocytes treated with control, rosiglitazone (Rosi), Dios, hesperidin, polydatin, and amygdalin ($$n = 3$$). E, microscale thermophoresis assay to assess the binding of PPARγ and Dios. F, transcriptional activity of a PPARγ-responsive element (PPRE) after treatment with Rosi or Dios ($$n = 3$$). G, representative images and quantity of Oil Red staining and (H) expression of adipogenic marker genes of beige adipocytes treated with control (Con), Rosi, or Dios ($$n = 3$$). The scale bar represents 50 μm. I, glucose uptake in beige adipocytes treated with Con, Rosi, or Dios ($$n = 3$$). Data are presented as mean ± SEM and ∗$p \leq 0.05$, ∗∗$p \leq 0.01$ compared with control group. PPARγ, peroxisome proliferator–activated receptor γ.
We further conducted microscale thermophoresis (MST) experiment, which confirmed interactions between PPARγ and diosmin, indicated its potential usage for diabetic control as a lead compound (Fig. 1E). Of note, compared with rosiglitazone, diosmin showed weak transcriptional activity in a PPARγ-responsive element–driven luciferase assay to the similar extent of the reported PPARγ nonagonist SR1664 and partial agonist MRL24 [16, 17] (Fig. 1F). Besides, in contrast to rosiglitazone, we found that diosmin has minor effects on adipocyte differentiation as shown by Oil Red staining and classic adipogenic marker gene expression (Fig. 1, G and H). Via 2-deoxyglucose (2-DG) uptake assay, we showed that diosmin significantly increased 2-DG uptake upon insulin treatment in beige adipocytes, suggesting its binding to PPARγ for enhanced glucose utilization (Fig. 1I). Overall, these data suggest that diosmin functions as a PPARγ selective modulator to enhance glucose uptake in beige adipocytes.
## Diosmin blocks PPARγ phosphorylation and improves diabetic gene programs both in vitro and in vivo
Since diosmin has been predicted to bind near the PPARγ S273 site and that we have shown diosmin activates the diabetic gene programs regulated by PPARγ pS273 as potently as rosiglitazone (Fig. 1D), we then examined whether diosmin exerts its modulator functions by blocking PPARγ S273 phosphorylation. Tumor necrosis factor alpha (TNFα) has been reported to induce PPARγ S273 phosphorylation [16]. Of note, similar to rosiglitazone, diosmin inhibited TNFα-induced PPARγ pS273 in a dose-dependent manner in beige adipocytes (Fig. 2A). Of note, both rosiglitazone and diosmin treatment showed no obvious effect on CDK5 activity, with CDK5 inhibitor roscovitine as a positive control (Fig. 2B). Besides, we found that diosmin addition had no effect on the phosphorylation of Rb protein, another well-characterized CDK5 substrate (Fig. 2C), indicating diosmin may not impact PPARγ phosphorylation through its regulation on CDK5 activity. Moreover, consistent with previous results [32], palmitic acid induced PPARγ S273 hyperphosphorylation and worsened previously reported PPARγ pS273-regulated diabetic gene programs in beige adipocytes [16, 17, 33, 34], which were significantly reversed by diosmin treatment (Fig. 2, D and E). Furthermore, diosmin administration decreased PPARγ S273 phosphorylation and improved diabetic gene programs in beige adipocytes, whereas these effects were lost in adipocytes treated with PPARγ antagonist GW9662, possibly because of the confirmational change of PPARγ upon GW9662 treatment (Fig. 2, F and G). Roscovitine is a specific inhibitor of CDK5, the kinase responsible for PPARγ S273 phosphorylation [19]. Interestingly, compared with diosmin administration alone, combined treatment of diosmin and roscovitine failed to further reduce PPARγ S273 phosphorylation and modulate diabetic gene programs (Fig. 2, H and I), indicating that PPARγ pS273 is indispensable for diosmin functionality. Overall, these data showed that diosmin modulates diabetic gene programs via inhibition of PPARγ S273 phosphorylation in vitro. Figure 2Diosmin is a selective PPARγ modulator. A, TNF-α induced phosphorylation of PPARγ S273 in beige adipocytes treated with rosiglitazone or diosmin at indicated doses. B, CDK5 activity assay in beige adipocytes treated with control, diosmin, rosiglitazone, or roscovitine ($$n = 3$$). C, protein levels of p-Rb and Rb in beige adipocytes treated with control, diosmin, or roscovitine ($$n = 3$$). D, protein levels of p-PPARγ (S273) and PPARγ and (E) expression of gene sets regulated by PPARγ S273 phosphorylation in beige adipocytes treated with control, PA, or diosmin + PA ($$n = 3$$). F, protein levels of p-PPARγ (S273) and PPARγ and (G) expression of gene sets regulated by PPARγ S273 phosphorylation in beige adipocytes treated with control, diosmin, or diosmin + GW9662 ($$n = 3$$). H, protein levels of p-PPARγ (S273) and PPARγ and (I) expression of gene sets regulated by PPARγ S273 phosphorylation in beige adipocytes treated with control, diosmin, or diosmin + roscovitine ($$n = 3$$). Data are presented as mean ± SEM and ∗$p \leq 0.05$, ∗∗$p \leq 0.01$ compared with control group. PPARγ, peroxisome proliferator–activated receptor γ.
We then set out to unravel the effects of diosmin administration in vivo. For acute gene expression analysis, control, diosmin, rosiglitazone, hesperidin, polydatin, and amygdalin were injected unilaterally into inguinal fat pads of wildtype C57BL/6 mice (Fig. 3A). After 3 days, we found that diosmin decreased PPARγ S273 phosphorylation levels, elevated insulin signaling pathway including p-IRβ, p-AKT, and p-GSK3β, as well as improved PPARγ pS273-related diabetic genes in iWAT, similar to rosiglitazone (Fig. 3, B–D), suggesting that diosmin modulated PPARγ phosphorylation, insulin signaling, and diabetic gene programs in vivo. Interestingly, we found that hesperidin, polydatin, and amygdalin exhibited significantly weaker transcriptional activity compared with the full agonist rosiglitazone (Fig. S1), wheres they showed minimal effects on PPARγ phosphorylation and insulin sensitivity improvement in vivo (Fig. S2).Figure 3Acute diosmin (Dios) administration improves diabetic gene programs in iWAT of mice. A, experimental model of acute control (Con) or Dios administration in mice with iWAT unilateral injection ($$n = 4$$). B, protein levels of S273 p-PPARγ, (C) p-IRβ, p-AKT, and p-GSK3β, (D) expression of gene set regulated by PPARγ S273 phosphorylation in iWAT of mice after acute Dios administration. Data are presented as mean ± SEM and ∗$p \leq 0.05$, ∗∗$p \leq 0.01$ compared with control group. iWAT, inguinal white adipose tissue; PPARγ, peroxisome proliferator–activated receptor γ.
## Diosmin induced brown gene programs both in vitro and in vivo
In addition to diabetic gene programs, we found that diosmin also increased brown gene programs in beige adipocytes in a similar pattern as rosiglitazone did, which was also recapitulated in iWAT of mice after diosmin administration acutely, in addition to increased protein levels of uncoupling protein 1 (UCP1) (Fig. 4, A–C). Moreover, increased protein levels of UCP1 and mRNA levels of brown gene programs after diosmin treatment were blunted in adipocytes treated with PPARγ antagonist GW9662 (Fig. 4, D and E). Meanwhile, sole diosmin treatment promoted UCP1 protein level and the expression of brown gene programs, whereas combined usage of diosmin and CDK5 inhibitor roscovitine showed no further effects (Fig. 4, F and G).Figure 4Diosmin administration induces browning gene programs both in vitro and in vivo. A, heatmap of expression levels of brown gene programs in beige adipocytes treated with control, rosiglitazone, diosmin, hesperidin, polydatin, or amygdalin ($$n = 3$$). B, uncoupling protein 1 (UCP1) protein levels and (C) expression levels of brown gene programs in inguinal white adipose tissue (iWAT) of mice after acute diosmin administration ($$n = 4$$). D, UCP1 protein levels and (E) expression levels of brown gene programs in beige adipocytes treated with control, diosmin, or diosmin + GW9662 ($$n = 3$$). F, UCP1 protein levels and (G) expression levels of brown gene programs in beige adipocytes treated with control, diosmin, or diosmin + roscovitine ($$n = 3$$). Data are presented as mean ± SEM and ∗$p \leq 0.05$, ∗∗$p \leq 0.01$ compared with control group.
Moreover, we also examined the effects of diosmin on insulin sensitivity and thermogenic gene programs in mice via acute oral delivery, using rosiglitazone as a positive control. Indeed, rosiglitazone and diosmin both significantly promoted insulin sensitivity in mice iWAT, including decreased PPARγ S273 phosphorylation, improved diabetic genes dysregulated by PPARγ pS273, and elevated pIRβ, pAKT, and pGSK3β levels (Fig. S3, A–D), as well as increased brown gene programs and UCP1 protein levels (Fig. S3, E and F) in iWAT. Notably, oral administration of rosiglitazone significantly increased adipogenic gene expression in iWAT, which was absent upon diosmin administration (Fig. S3E). These data suggest that diosmin may exhibit a potential insulin-sensitizing and white fat browning effects in mice.
## Diosmin ameliorated insulin resistance and obesity in mice under HFD
We thus examined whether diosmin administration chronically could ameliorate insulin resistance and obesity in mice under HFD. Indeed, similar to rosiglitazone, 12-week diosmin intervention improved insulin sensitivity in mice as shown by lower fasting and random glucose levels, better performances in glucose and insulin tolerance tests, as well as decreased body weights and fat mass compared with control group (Figs. 5, A–E and S4). These phenotypes were accompanied with enhanced thermogenic capacity and energy expenditure as demonstrated by increased core temperature during cold exposure (Fig. 5F) as well as enhanced oxygen consumption and carbon dioxide production in diosmin-treated group compared with control groups, without obvious changes in locomotor activity and food intake (Fig. 5, G–I). Furthermore, serum analysis showed that diosmin decreased serum total cholesterol, total triglyceride, and low-density lipoprotein–cholesterol levels (Fig. 5J). These data suggested that diosmin treatment improved insulin sensitivity, alleviated obesity, and hyperlipidemia in mice under HFD.Figure 5Diosmin (Dios) improves insulin sensitivity and promotes browning of white fat in mice under high-fat diet (HFD).A–J, metabolic performances of HFD mice treated with control (Con), rosiglitazone (Rosi), or Dios ($$n = 5$$). A, experimental model of chronic control, Rosi, or Dios administration in mice with bilateral inguinal white adipose tissue (iWAT) injection. B, glucose tolerance test (GTT) and area under the curve (AUC). C, insulin tolerance test (ITT) and AUC. D, body weight and mice appearance; The scale bar represents 2 cm. E, fat mass. F, rectal temperature changes of mice during 5 h cold exposure. G, energy expenditure as shown by oxygen consumption and carbon dioxide production. H, total locomotor activity. I, food intake. J, analysis of serum parameters. Data are presented as mean ± SEM and ∗$p \leq 0.05$, ∗∗$p \leq 0.01$ compared with control group.
## Diosmin ameliorated adiposity and hepatic steatosis with reduced inflammation in HFD mice
Next, we investigated the intrinsic alternation of metabolic tissues in diosmin-treated mice under HFD. Detailed analysis revealed that diosmin administration reduced adipose tissue weights with smaller adipocyte sizes in iWAT and epididymal WAT as shown by H&E staining and cross-sectional area quantification, as well as cross-sectional area frequency distribution (Fig. 6, A–D). Besides, inflammatory gene expressions were inhibited in iWAT of diosmin-treated mice under HFD, which was consistent with the role of PPAR in anti-inflammation (Fig. 6E).Figure 6Diosmin (Dios) ameliorates adipose tissue lipid accumulation and reduces hepatic steatosis of mice under high-fat diet (HFD).A–I, analysis of adipose tissues and liver in HFD-fed mice treated with control (Con), rosiglitazone (Rosi), or Dios ($$n = 5$$). A, tissue weights of brown adipose tissue (BAT), inguinal white adipose tissue (iWAT), and epididymal whote adipose tissue (eWAT) fat pads. B, representative images of H&E staining of fat tissues; The scale bar represents 100 μm. C, quantitative analysis of adipocyte sizes. D, cross-sectional area (CSA) frequency distribution of iWAT and eWAT. E, expression levels of inflammatory genes in iWAT. F, liver weights. G, representative H&E staining of liver. The scale bar represents 100 μm. H, liver triglyceride levels. I, expression levels of inflammatory genes in liver. Data are presented as mean ± SEM and ∗$p \leq 0.05$, ∗∗$p \leq 0.01$ compared with control group.
Meanwhile, diosmin-treated mice showed decreased extent of liver steatosis as shown by reduced lipid infiltration and intrahepatic triglyceride levels, as well as suppressed inflammatory gene expressions compared with controls (Fig. 6, F–I). These data suggest that diosmin reduced adiposity and hepatic steatosis as well as ameliorating tissue inflammation in HFD.
Of note, although similarly to rosiglitazone treatment, local delivery of diosmin exhibited relative stronger effects in reducing adipose tissue weights and adipocyte sizes in iWAT and epididymal WAT (Fig. 6, A–D).
## Local diosmin administration in iWAT shows no obvious side effects in water retention and cardiovascular systems
Oral PPARγ full-agonist treatment such as rosiglitazone causes an array of side effects, including abdominal fat accumulation, fluid retention, and increased risk of cardiac dysfunction [35]. We found that local injection of rosiglitazone also caused increased fluid retention in mice as assessed by MRI, whereas in contrast, diosmin treatment showed no side effects on fluid retention (Fig. 7A). Moreover, serum parameter analysis showed no apparent toxicity effects upon diosmin treatment, whereas rosiglitazone treatment caused trends of increased serum urea nitrogen and creatinine levels that are indicative of increased kidney stresses (Table S1). Interestingly, local administration of diosmin and rosiglitazone did not lead to heart weight increase, heart muscle hypertrophy, or enlargement of cardiac cavity (Fig. 7, B and C). Consistently, M-mode echocardiography showed no obvious abnormalities in cardiac perimeters, that is, ejection fraction, fractional shortening (FS), wall thickness, left ventricular end systolic/diastolic diameter, as well as expressions of myocardial hypertrophy and fibrotic genes compared with control mice (Fig. 7, D–I), suggesting iWAT local delivery of diosmin or rosiglitazone did not cause cardiac overload. Figure 7Local diosmin (Dios) administration in inguinal white adipose tissue (iWAT) shows no obvious side effects in water retention and cardiovascular systems. A–I, analysis of fluid retention and cardiovascular functions in high-fat diet (HFD)-fed mice treated with control (Con), rosiglitazone (Rosi), or Dios ($$n = 5$$). A, fluid retention; (B) heart weights; (C) H&E staining of heart. The scale bar represents 10 μm. D, M-mode echocardiography. E and F, ejection fraction and fractional shortening. G and H, wall thickness (left ventricular anterior systolic/diastolic wall thickness (LVAW(s); LVAW(d)), left ventricular posterior systolic/diastolic wall thickness (LVPW(s); LVPW(d)), and left ventricular end systolic/diastolic diameter (LVIDS/LVIDD)). I, expression levels of hypertrophy and fibrotic genes in heart. Data are presented as mean ± SEM and ∗$p \leq 0.05$, ∗∗$p \leq 0.01$ compared with control group.
Taken together, these data suggest that local administration of diosmin in iWAT significantly ameliorated insulin resistance, obesity, hyperlipidemia, and hepatic steatosis, without causing the classical adverse side effects of TZDs in vivo.
## Discussion
In the present study, we performed computer virtual screening from thousands of natural small molecules and their derivatives to look for potential PPARγ selective modulator that blocks phosphorylation of PPARγ at Ser273 site previously shown to exhibit metabolic benefits without apparent adverse effects. Molecular docking, chemical analysis, and molecular analysis revealed diosmin as a potential lead compound, which binds to PPARγ and blocks PPARγ Ser273 phosphorylation in beige adipocytes. Diosmin improves glucose uptake and diabetic gene sets dysregulated upon PPARγ S273 phosphorylation without altering adipogenic ability in beige adipocytes. In vivo, we found that diosmin improved insulin sensitivity as well as reduced body weight and enhanced energy expenditure in mice fed with HFD. Furthermore, diosmin promoted browning of iWAT and ameliorated hepatic steatosis with attenuated proinflammatory responses in iWAT and liver, with no apparent adverse effects such as edema or cardiovascular disease risk previously reported in PPARγ full agonist usage.
Diosmin is a natural compound obtained from hesperidin, a substance commonly found in citrus fruit and Rutaceae family, through semisynthesis [36, 37]. In this study, we identified diosmin as a selective PPARγ modulator. PPARγ antagonist GW9662 or CDK5 inhibitor abolished the effects of diosmin, overall suggesting diosmin may function via PPARγ and its S273 phosphorylation. PPARγ has been shown to play an important role in vascular protection, whereas its activation suppresses the oxidative stress and apoptosis of endothelial cells, protects against vascular thrombosis, and alleviates atherosclerosis [38, 39, 40]. Interestingly, diosmin also has a long history of clinical implication and has been widely used in the treatment of chronic venous disorders, including lymphatic drainage, microcirculation, venous tone, and capillary hyperpermeability clinically for its effectiveness and safeness [20, 41]. Moreover, diosmin also has anti-inflammatory and antioxidant properties [42, 43]. In clinical practices, flavonoid fraction containing $90\%$ diosmin treatment scavenged oxygen free radicals in human neutrophils as well as decreased HbA1c level in type 1 diabetic patients [44]. Besides, dietary supplement flebotrofine, containing diosmin improved the antioxidant and antithrombotic profile in both type 1 and type 2 diabetes [45]. It is possible that these functions of diosmin and diosmin-containing substances may be exerted through its modulation on PPARγ. Future modifications based on diosmin as the lead compound may provide more attractive candidates against diabetes via blocking PPARγ S273 phosphorylation.
Beige fat has been considered as an attractive targeting tissue against metabolic diseases, considering its inducible activation in promoting energy metabolism and improving serum parameters as metabolic sink [46, 47]. PRDM16 and PGC-1α form complex with PPARγ to regulate energy homeostasis. Of note, fat-specific PGC-1α deficiency develops insulin resistance and hyperlipidemia, and the effects were majorly caused by beige fat as shown by decreased expression of thermogenic and mitochondrial genes, whereas gene expression patterns in brown fat were not altered [48]. In addition, adipocyte-specific deletion of PRDM16 inhibited beige adipocyte function in subcutaneous fat and promoted obesity, as well as aggravated insulin resistance and hepatic steatosis in mice under HFD, while caused minimal effects on brown fat [49]. These findings indicate that beige fat plays an important role in metabolism. Considering the importance of PPARγ in beige fat functionality in concert with PGC-1α and PRDM16, as well as the unique role of beige fat in metabolism, we administrated diosmin locally in beige fat, instead of intraperitoneally or intravenously, to pursue a direct action on beige fat and metabolic improvement, while at the same time avoid any potential adverse effects via systemic administration. Further attempts of different delivery routes could be tested.
Notably, local injection of rosiglitazone caused increased fluid retention and serum kidney stress markers in mice, which side effect was absent in diosmin group, suggesting the existence of systemic exposure in mice injected with compound injection in iWAT. Interestingly, iWAT local delivery of both diosmin and rosiglitazone showed no overt cardiovascular defect including parameters in heart weights, ejection fraction, FS, wall thickness, left ventricular end systolic/diastolic diameter, as well as expressions of myocardial hypertrophy and fibrotic genes compared with control group. Overall, these data suggested that kidney and heart have different susceptibility toward compound exposure, and that different compounds have different impact on kidney. Systematic analysis of the pharmacokinetics in iWAT local compound delivery would be informative. In addition, acute oral delivery of rosiglitazone and diosmin both significantly promoted insulin sensitivity and brown gene programs in mice iWAT, whereas diosmin administration did not induce adipogenic genes as rosiglitazone did. Further analysis of the effects of long-term oral delivery of diosmin is warranted to systemically examine chronic metabolic changes. Overall, we have shown that diosmin has its unique features in treating obesity and metabolic derangement without overt adverse effect compared with rosiglitazone, thus may serve as a desirable alternative therapy for treating metabolic diseases.
PPARγ Ser273 phosphorylation has been shown to play a critical role in insulin resistance. Based on this feature, a series of compounds were developed to block PPARγ Ser273 phosphorylation and exerted hypoglycemic function without the usual adverse side effects of classical TZDs [35]. Among these compounds, SR1664 and MRL24 showed no effects on reducing obesity, whereas we and others showed that in addition to improved glycemic control, diosmin, GQ-16, and roscovitine have additional effects on fat metabolism, including reducing body weight gain and hepatic steatosis of mice under HFD, possibly through enhanced formation of beige adipocytes in white adipose tissues and elevated energy expenditure [18, 19]. It has been reported that PPARγ deacetylation (K268 and K293) via SIRT1 upon long-term TZD treatment protected against diet-induced obesity by enhancing browning of white adipose tissue and increasing energy expenditure, thus uncouples the adverse effects of TZD in weight gain from insulin sensitization [50, 51]. Considering K268 and K293 locates closely to Ser273, those compounds, including diosmin, with antiobesity properties, may also block PPARγ acetylation and recruit the BAT program coactivator such as PRDM16, to selective induce BAT genes and repress visceral WAT genes associated with insulin resistance, which need further investigation.
Of note, the PPARγ partial agonists or nonagonist PPARγ ligands mentioned previously including SR1664, MRL24, GQ-16, and CDK5 inhibitor roscovitine have not been approved for clinical use. Meanwhile, a recent study showed that Gleevec, a well-known anticancer drug that exhibits dramatic effectiveness for the treatment of chronic myelocytic leukemia and gastrointestinal stromal tumors [52], also blocks the CDK5-mediated PPARγ S273 phosphorylation and improves insulin sensitivity without classical PPARγ agonism and related side effects. However, *Gleevec is* a very expensive anticancer drug, which limited its wide use for other milder but persisting diseases. Comparing with Gleevec, diosmin only costs $\frac{1}{1000}$ of Gleevec in price and exhibited excellent antidiabetic effect, thus has great potential for the usage in patients with chronic metabolic diseases.
In conclusion, the present study reveals a previously unrecognized role of diosmin, a conventional drug features long-time clinical use and safeness, in improving insulin sensitivity by blocking PPARγ phosphorylation and reducing obesity without the side effects of classic PPARγ full agonists.
## Virtual screening and molecular docking study
The crystal structure of human 7-bound PPARγ ligand-binding domain (Protein Data Bank code: 5GTO) was retrieved from the RCSB Protein Data Bank and imported into SYBYL software (Tripos) for the subsequent structural analysis and docking simulation. The ligand database consists of 3000 compounds from the Targetmol Bioactive Natural Compound Library (https://www.tsbiochem.com). After the protein and the ligand candidate preparation, molecular docking was carried out using the Surflex-Dock module of SYBYL with default settings, and “binding energy” was used as the indicator to choose the ligand with the lowest score, as well as target compounds were selected for further biological evaluation.
## Cell culture
Immortal beige preadipocytes were generously provided by Professor Qiurong Ding (Chinese Academy of Sciences) and cultured in Dulbecco’s modified Eagle's medium supplemented with $20\%$ fetal bovine serum and $1\%$ penicillin/streptomycin at 37 °C with $5\%$ CO2. Beige adipocytes were differentiated as the following protocol. When preadipocytes reached confluency (day 0), cells were induced with 1 μg/ml insulin (catalog no.: HI0240; Eli Lilly), 0.5 mmol/l 3-isobutyl-1-methylxanthine (catalog no.: I7018, Sigma–Aldrich), 1 nmol/l T3 (catalog no.: T2877; Sigma–Aldrich), 1 μmol/l dexamethasone (catalog no.: D4902; Sigma–Aldrich) in the presence or the absence of 1 μmol/l rosiglitazone (catalog no.: R2408; Sigma–Aldrich). After 2 day differentiation, cells were switched to the maintenance medium containing 5 μg/ml insulin with or without 1 μmol/l rosiglitazone every 2 days. Differentiated beige adipocytes were treated with 0.3 mM palmitate (catalog no.: P0500; Sigma–Aldrich) for 48 h to mimic insulin resistance in vitro. For drug treatment, cells were treated with diosmin (catalog no.: D111390; Aladdin) instead of rosiglitazone for the whole differentiation process. GW9662 and roscovitine were purchased from MCE (catalog nos.: HY-30237 and HY-16578). Lipid accumulation in beige adipocytes treated with rosiglitazone or diosmin was detected by Oil Red O staining and measured at 520 nm by SpectraMax 190 microplate reader (Molecular Devices).
## Gene expression analyses
Total RNA was isolated from cultured cells or tissues by RNAiso Plus (catalog no.: 9109; TaKaRa), and 1 μg of total RNA was reversed transcribed into complementary DNA using the PrimeScript RT reagent Kit (catalog no.: PR047Q; TaKaRa). Quantitative real-time PCR was performed with the SYBR green fluorescent dye mix (catalog no.: 11143ES50; Yeasen) on the PCR system (LightCycle 480; Roche). Relative mRNA levels were calculated using the 2−ΔΔCt method, and sequences of primers used for real-time PCR were listed in Table S2.
## MST
The purified recombinant His-tagged PPAR protein was first labeled with the RED-tris-NTA second-generation dye. Diosmin and rosiglitazone were diluted from 2.5 mM and 10 M, respectively, and incubated with 200 nM labeled PPARγ at room temperature for 20 min in MST buffer (25 mM Tris–HCl, pH 8.0, 75 mM NaCl, $0.05\%$ [v/v] Triton X-100). Then the samples were loaded into Monolith NT. Automated Premium Capillary Chip and analyzed via Monolith NT. Automated instrument. The data analysis was performed using MO Affinity Analysis (version 2.3).
## Cdk5 activity assay
Differentiated beige adipocytes were treated with rosiglitazone, diosmin, or roscovitine, and the cells were harvested and subjected to CDK5 activity assay using a CDK5/P25 kinase assay kit (catalog no.: HL50150; CG biotec). Total protein was measured using Enhanced BCA Protein Assay Kit (catalog no.: P0010S; Beyotime) as per the manufacturer's protocol.
## Immunoprecipitation and protein analyses
Differentiated beige adipocytes treated with rosiglitazone or diosmin were preincubated with TNF-α (50 ng/ml). Protein from cultured cells or tissues was extracted by the radioimmunoprecipitation assay buffer, and equal amounts of total protein were loaded into $10\%$ acrylamide gels for general analysis and transferred to NC membranes (catalog no.: 66485; PALL). Membranes were incubated in $5\%$ bovine serum albumin for 2 h and with primary antibodies overnight at 4 °C, including anti-p-PPARγ (1:2000 dilution) (catalog no.: bs-4888R; Bioss biotech), anti-PPARγ (1:1000 dilution) (catalog no.: sc-7273; Santa Cruz), anti-p-IRβ (1:2000 dilution) (catalog no.: 3025; Cell Signaling Technology), anti-p-AKT (1:2000 dilution) (catalog no.: 13038; Cell Signaling Technology), anti-p-GSK3β (1:2000 dilution) (catalog no.: 9322; Cell Signaling Technology) (1:2000 dilution), anti-UCP1 (catalog no.: Ab10983; Abcam), or β-actin (1:2000 dilution) (catalog no.: sc-47778; Santa Biotechnology). After washed three times with PBS with Tween-20, the corresponding horseradish peroxidase–conjugated secondary antibodies were then incubated for 2 h at room temperature. Detection was performed using Odyssey CLx Imaging System (LI-COR).
## Reporter gene assay
Human embryonic kidney 293 cells were transfected with PPRE luciferase reporter plasmid, PPARγ, RXRα, and Renilla using Lipofectamine 2000 (catalog no.: 11668019; Invitrogen). Then the cells were treated with rosiglitazone or diosmin for 24 h after an overnight transfection. The cells were harvested and performed with the reporter gene assay using a Double-luciferase reporter assay kit (catalog no.: FR201; Transgen). Luciferase activity was normalized to renillia activity.
## Glucose uptake assay
The beige adipocytes treated with or without diosmin were washed twice by PBS and maintained in fetal bovine serum–free medium for 2 h. After washing twice by PBS, cells were incubated with insulin for 0.5 h at 37 °C followed by the stock solution of radioactive 2-DG, which was added into each well for 0.5 h at 37 °C. The uptake was terminated by washing the cells with precooling PBS, and 300 μl of 0.05 M NaOH was added into each well for lysing cells. The entire cell lysate was transferred to a scintillation vial containing 3 ml liquid scintillation cocktail. Quantify the radioactivity associated with the cells using a liquid scintillation counter.
## Animal studies
Eight-week-old male C57BL/6J mice at around 22 to 25 g were purchased from Shanghai Research Center for Model Organisms and fed with a 12:12-h light/dark cycle with free access to food and water. All efforts were made to minimize animal suffering. For acute iWAT local drug treatment, mice were injected with 10 mg/kg diosmin, hesperidin, polydatin, amygdalin, or rosiglitazone in one side of the inguinal fat pads and injected with solvent control in the other side of inguinal fat pads in C57BL/6J mice and were sacrificed after 3 days. For acute oral delivery, 10 mg/kg rosiglitazone, diosmin, or solvent control was gavaged in C57BL/6J mice and sacrificed after 3 days. For chronic drug treatment, mice under HFD (catalog no.: D12492; Research Diet) were bilaterally injected in inguinal fat pads with 10 mg/kg diosmin, rosiglitazone, or solvent control in mice on Monday and Thursday of each week for 12 weeks. The body weight and food intake were monitored weekly. Body fat content was determined every 2 weeks by AccuFat-1050 MRI system (Meg-Med). After 12-week treatment, metabolic parameters were analyzed. For acute cold exposure, mice were individually caged without bedding and exposed to 4 °C for 6 h. Rectal temperature was measured each hour (Braintree). For determination of whole-body energy expenditure and basal metabolic rate of mice, oxygen consumption (VO2), carbon dioxide production (VCO2), and physical activity were measured using Comprehensive Lab Animal Monitoring System (Columbus Instruments) for 72 h. For the glucose tolerance test, mice were fasted for 16 h and injected with d-glucose in saline solution (1.5 g/kg) intraperitoneally, and plasma glucose levels were measured at 0, 15, 30, 60, 90, and 120 min (AlphaTrak Blood Glucose Monitor System; Abbott). For the insulin tolerance test, mice were injected with insulin in saline solution (1.25 U/kg body weight) intraperitoneally, and plasma glucose levels were measured at 0, 15, 30, 60, 90, and 120 min. Fluid retention was determined by AccuFat-1050 MRI system (Meg-Med). All animal studies were carried out following the guidelines approved by the Ethics Committee of Animal Experiments of East China Normal University (m20200604).
## Histological and immunohistochemistry analyses
Adipose and liver tissues were dissected and fixed in $10\%$ neutral formalin, whereas heart tissues were fixed in $4\%$ paraformaldehyde. All these samples were dehydrated and embedded in molten paraffin wax, and then paraffin blocks were cut into 5 mm sections and were subjected to hematoxylin and eosin staining. Sections were examined by light microscopy (Abaton Scan 300/Color scanner).
## Serum parameter and liver triglyceride level determination
Serum parameters including total cholesterol, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol levels were measured with the commercial kits (A111-2, A112-2, and A113-2; Jiancheng). Serum chemistry tests including alanine aminotransferase, aspartate aminotransferase, creatine kinase, lactate dehydrogenase, urea nitrogen, creatinine, uric acid, albumin, and total protein were performed with the commercial kits (MAK052, MAK055, MAK116, MAK066, MAK006, MAK080, MAK077; 09753 and 71285-M; Sigma–Aldrich). Triglyceride was extracted using $5\%$ NP-40 solution in liver and heated at 90 °C for twice. Then it was cooled down to room temperature and centrifuged at 12,000 rpm for 2 min to collect transparent supernatant. The serum and hepatic levels of triglyceride were measured using TG kit (K952; BioVision).
## Echocardiography
Mice were anesthetized by 1.5 to $2\%$ isoflurane and reduced to 0.5 to $1\%$ once the mouse was asleep. Oxygen gas was flowing at 2 ml/min. The chest skin of the mouse was shaved using a hair remover, and the heart function was evaluated with a 30 MHz high-frequency ultrasound transducer (Visualsonics; VEVO 2100). 2D image and M-mode echocardiographic images were studied in the parasternal short-axis view at the level of the papillary muscles. Left ventricular end-diastolic diameter and left ventricular end-systolic dimension (LVESD) were measured, and FS was calculated as follows: FS% = ([LVEDD − LVESD]/LVEDD) × $100\%$.
## Statistical analyses
Two-tailed unpaired Student’s t test and two-way ANOVA were performed to evaluate statistical significance using GraphPad Prism software (GraphPad Software, Inc). The p values were designed as follows: ∗$p \leq 0.05$ and ∗∗$p \leq 0.01.$ All values specified in the figures were represented as mean ± SEM.
## Data availability
All data are contained within the article and supporting information.
## Supporting information
This article contains supporting information.
Supplemental Tables S1 and S2 Supplemental Figure S1Transcriptional activity of PPARγ under Hesperidin, Polydatin or Amygdalin treatment. A, transcriptional activity of a PPARγ-responsive element (PPRE) after treatment with Hesperidin, Polydatin and Amygdalin ($$n = 3$$). Data are presented as mean ± SEM and ∗$p \leq 0.05$, ∗∗$p \leq 0.01$ compared to control group. Supplemental Figure S2Acute Hesperidin, Polydatin and Amygdalin iWAT local administration did not show effects on diabetic gene programs. Mice were injected with 10 mg/kg Rosiglitazone (Rosi), Hesperidin (Hesp), Polydatin (Poly) or Amygdalin (Amyg) into one side of inguinal fat pads and injected with solvent control (Con) in the other side of inguinal fat pads and sacrificed after 3 days and analyze for (A) Expression of gene sets regulated by PPARγ S273 phosphorylation in iWAT local treated with control, Rosiglitazone, Hesperidin, Polydatin and Amygdalin ($$n = 4$$). B, protein levels of S273 p-PPARγ, (C) p-IRβ, p-AKT and p-GSK3β in iWAT of mice. Data are presented as mean ± SEM and ∗$p \leq 0.05$, ∗∗$p \leq 0.01$ compared to control group. Supplemental Figure S3Acute Diosmin oral administration improves diabetic gene programs in iWAT of mice. A, experimental model of acute oral gavage of control (Con), Rosiglitazone (Rosi) or Diosmin (Dios) in mice at 10 mg/kg and sacrificed after 3 days. ( $$n = 5$$). B, protein levels of S273 p-PPARγ, (C) p-IRβ, p-AKT and p-GSK3β, (D) expression of gene set regulated by PPARγ S273 phosphorylation in iWAT of mice after acute Diosmin or Rosiglitazone administration. E, expression levels of brown gene programs, adipogenic marker genes, (F) UCP1 protein levels in iWAT of mice after acute Diosmin or Rosiglitazone administration. Data are presented as mean ± SEM and ∗$p \leq 0.05$, ∗∗$p \leq 0.01$ compared to control group. Supplemental Figure S4Diosmin improves glucose metabolism in mice under HFD.A and B, the fasting and random glucose levels of HFD fed mice treated with control (Con), Rosiglitazone (Rosi) or Diosmin (Dios) ($$n = 5$$). Data are presented as mean ± SEM and ∗$p \leq 0.05$, ∗∗$p \leq 0.01$ compared to control group.
## Conflict of interest
The authors declare that they have no conflicts of interest with the contents of this article.
## Author contributions
L. X., X. G., and X. M. conceptualization; J. Y., Y. H., M. S., M. G., W. G., Z. Z., D. W., X. W., Xiangdi Cui, Xin Chen, and C. Z. methodology; J. L., W. Z., C. L., and J. X. software; M. G., W. G., Z. Z., D. W., X. W., Xin Chen, C. Z., and H. C. validation; J. Y., Y. H., M. S., Y. C., and S. C. formal analysis; J. Y., Y. H., M. S., M. G., W. G., Z. Z., D. W., X. W., J. L., Y. C., W. Z., C. L., Xin Chen, C. Z., H. C., J. X., S. C., and C. L. investigation; J. L., Y. C., W. Z., C. L., H. C., J. X., S. C., and C. L. resources; X. G. and X. M. writing–original draft; C. L. and L. X. writing–review & editing; L. X., X. G., and X. M. supervision; L. X., X. G., and X. M. project administration.
## Funding and additional information
This project is supported by funds from $\frac{10.13039}{501100012166}$National Key Research and Development Program of China (grant no.: 2019YFA09004500), $\frac{10.13039}{501100001809}$National Natural Science Foundation of China (grant nos.: 32022034, 32222024, 32271224, and 32071148), $\frac{10.13039}{501100003399}$Science and Technology Commission of Shanghai Municipality (grant nos.: 22ZR1421200 and 21140904300), $\frac{10.13039}{501100005230}$Natural Science Foundation of Chongqing, China (grant no.: CSTB2022NSCQ-JQX0033), Key Research and Development Project of Zhejiang Province (grant no.: 2021C03069), $\frac{10.13039}{501100004731}$Natural Science Foundation of Zhejiang Province (grant no.: LY20H070003), $\frac{10.13039}{501100004106}$ECNU public platform for Innovation [011], and the instruments sharing platform of School of Life Sciences.
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|
---
title: Molecular dynamics simulations reveal the importance of amyloid-beta oligomer
β-sheet edge conformations in membrane permeabilization
authors:
- Dirk Matthes
- Bert L. de Groot
journal: The Journal of Biological Chemistry
year: 2023
pmcid: PMC10033322
doi: 10.1016/j.jbc.2023.103034
license: CC BY 4.0
---
# Molecular dynamics simulations reveal the importance of amyloid-beta oligomer β-sheet edge conformations in membrane permeabilization
## Body
The progression of Alzheimer’s disease (AD) is marked by synaptic dysfunction, inflammatory processes, and neuronal loss that eventually result in irreversible neurodegeneration and dementia [1, 2]. The abnormal accumulation of amyloid-beta peptide[1-42] (Aβ42) is linked via several proposed pathways to the observed proteostatic stress and dyshomeostasis in the brain of AD patients [3, 4, 5, 6]. Aβ42 self-assembly into soluble neurotoxic oligomeric aggregates, Aβ42 interactions with binding receptors, or downstream accumulation of reactive oxygen species represent some of the potential targets in a diverse set of therapeutic intervention strategies for AD [7, 8]. Soluble Aβ oligomers and insoluble fibrillar amyloid plaques do not show the same correlation with cognitive impairment and build-up in AD brain [8, 9], as small Aβ assemblies are suggested to elicit potent neurotoxic activity in the early stages of AD [9]. Yet to date, a full understanding of the complex molecular processes has not been achieved. The causal involvement of Aβ42 in the human neuropathology via a linear cascade of events therefore remains disputed despite decades of intense research [10]. Along these lines, analysis of human cerebrospinal fluid showed that oligomeric Aβ aggregate sizes and structures are heterogeneous and their mechanisms of toxicity vary during the disease progression [11]. The direct interaction with cellular membranes and detrimental effects on cell viability caused by oligomeric Aβ aggregates as small as dimers [12, 13] are, however, well established in vitro [2, 14, 15, 16, 17, 18]. In fact, smaller aggregates were identified as the most potent agents at disrupting membrane integrity [19, 20, 21], suggesting that membrane-associated Aβ oligomers are an important piece to the puzzle. The ability of low molecular-weight Aβ42 oligomers to permeabilize membranes, including a variety of N-terminally truncated Aβ42 variants [22, 23], has sparked the quest to explore the putative presence of ion channel-like [24, 25, 26] and amyloid pore structures [23, 27, 28, 29, 30]. Several studies have demonstrated that Aβ42 induces permeation of common physiological ions across planar lipid bilayers and plasma membranes [24, 29, 31, 32, 33], with a preferential flow of calcium and potassium over sodium ions [24].
Still, the inherent heterogeneity and transient nature of the conformational states populated during aggregate formation has thwarted the elucidation of high-resolution structures in general [34] and of pore-forming oligomers in particular [35]. Recent advances in functional and structural characterization of small Aβ42 oligomers in membrane mimics therefore critically contribute to the understanding of the molecular basis of pore formation in a near physiological environment [20, 29, 30, 33]. Ciudad et. al [33] studied preparations of β-sheet pore-forming oligomers (βPFOs) with solid-state NMR spectroscopy and proposed a membrane-inserted aggregate structure composed of four Aβ42 peptide chains (PDB ID: 6RHY). The structural model exhibits a six-stranded, antiparallel β-sheet with two distinct subunits. Two antiparallel β-strands from the Aβ42 C-terminus (β3, residues G29-I41) make up the aggregate core, with two flanking β-hairpins, each formed by residues G9-A21 (β1) and G29-V40 (β2). Additionally, high-order Aβ42 oligomers, in particular octameric structures, are observed. In both tetramer and octamer structures, exposed β-strands constitute the edge of the Aβ42 βPFOs transmembrane domain (TMD) [33]. The N-terminal edge β-strand harbors residues 11 to 16 that have previously been implicated in the formation of stable intramembrane Aβ oligomers [14, 18]. Most of the previous Aβ42 βPFO structure models were predominantly characterized by β-barrel or cylindrin-like conformations that avoid exposed edge strands by continuous intermolecular hydrogen bonding [29, 30, 36, 37] as an important difference to βPFOs with the 6RHY fold. Covalently stabilized oligomer conformations from Aβ fragments form nonfibrillar β-sandwich structures, however, exclusively with β-turn-β folds [34, 38]. According to Ciudad et al., membrane-inserted Aβ42 oligomers with the 6RHY fold expose hydrophilic edge strands that are able to facilitate water permeation and lipid head group perturbation by a mechanism termed edge conductivity [33]. Due to their small size, the novel βPFOs with the 6RHY fold are suitable model systems to study the oligomer-induced disruption of a model membrane by means of all-atom molecular dynamics (MD) simulations. Here, we address the question if pore formation and ion permeation are common characteristics of many low molecular-weight Aβ oligomer structures or only associated with certain conformational states. We probe the principal relationship between the three-dimensional Aβ42 oligomer structure and membrane permeabilization for more than a millisecond in total simulation time. We monitor the stability of the aggregates and reveal the molecular determinants that render Aβ42 βPFOs with the 6RHY fold capable of ion permeation.
## Abstract
Oligomeric aggregates of the amyloid-beta peptide[1-42] (Aβ42) are regarded as a primary cause of cytotoxicity related to membrane damage in Alzheimer’s disease. However, a dynamical and structural characterization of pore-forming Aβ42 oligomers at atomic detail has not been feasible. Here, we used Aβ42 oligomer structures previously determined in a membrane-mimicking environment as putative model systems to study the pore formation process in phospholipid bilayers with all-atom molecular dynamics simulations. Multiple Aβ42 oligomer sizes, conformations, and N-terminally truncated isoforms were investigated on the multi-μs time scale. We found that pore formation and ion permeation occur via edge conductivity and exclusively for β-sandwich structures that feature exposed side-by-side β-strand pairs formed by residues 9 to 21 of Aβ42. The extent of pore formation and ion permeation depends on the insertion depth of hydrophilic residues 13 to 16 (HHQK domain) and thus on subtle differences in the overall stability, orientation, and conformation of the aggregates in the membrane. Additionally, we determined that backbone carbonyl and polar side-chain atoms from the edge strands directly contribute to the coordination sphere of the permeating ions. Furthermore, point mutations that alter the number of favorable side-chain contacts correlate with the ability of the Aβ42 oligomer models to facilitate ion permeation in the bilayer center. Our findings suggest that membrane-inserted, layered β-sheet edges are a key structural motif in pore-forming Aβ42 oligomers independent of their size and play a pivotal role in aggregate-induced membrane permeabilization.
## Extent of membrane permeabilization induced by Aβ42 pore-forming oligomers examined on the μs time scale
Simulation models of pore-forming β-sheet oligomers with the 6RHY fold are based on the Aβ42 βPFO structure reported by Ciudad et al. [ 33] (Fig. 1A). The disordered, solvent-exposed N-terminal regions could not be assigned or did not show lipid interactions experimentally [33]. We therefore built a β-sheet model (T) confining the Aβ[1-42] molecules to two subunits of the transmembrane motif with residues 6 to 42 and 22 to 42, respectively (Fig. 1, A and B, see Experimental procedures). In addition, a tetramer with full-length Aβ[1-42] was set up (Fig. 1B). Two octameric β-sandwich models were obtained by packing the hydrophobic core of two N-terminally truncated transmembrane β-sheet tetramers side-by-side. We considered only two sterically favorable packing modes of the tetrameric β-sheet subunits: up-down and face-to-face (O-AP), as well as face-to-back (O-P, Fig. 1C).Figure 1Simulation model of Aβ42 pore-forming oligomers with the 6RHY fold. A, amino acid sequence of full-length Aβ42 is shown together with the transmembrane domain of Aβ42 pore-forming oligomer models with the 6RHY fold highlighted by boxes with broken lines. B, renderings of corresponding full-length Aβ42 tetramer β-sheet and its N-terminally truncated transmembrane domain model (T). Structures are shown in cartoon representation, side-chain atoms of residues 13 to 16 (HHQK) and 35 (M) are indicated as sticks. C, initial coordinates for two octameric β-sandwich oligomer models: O-AP and O-P. D, snapshot of membrane-embedded Aβ42 O-AP model. E, close up view after 10 nanoseconds of MD simulation shows the formation of polar defects along either edge of the Aβ42 oligomer model. Phospholipid atoms from the head group region are shown as spheres and highlighted by color (red - oxygen, orange - phosphorus). Water molecules inside the lipid bilayer are shown as sticks (blue). White bars on the right indicate 28 slices along the membrane normal used to monitor the presence of water and polar lipid atoms across the membrane. F, time traces show spontaneous formation of a continuous and stable polar defect expressed as reaction coordinate ξ. Arrows indicate corresponding ξ values for polar defects along the two β1-strand pair edges shown in (E). MD, molecular dynamics.
The full-length and N-terminally truncated (T) tetramer β-sheet as well as the octamer Aβ42 β-sandwich oligomer (O-AP and O-P) models were embedded in a lipid bilayer consisting of zwitterionic 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC, Figs. 1D and S1).
For each of the N-terminally truncated transmebrane domain structure models, 10 independent 2.5 μs long MD simulations were carried out with two different MD force fields (AMBER99SB∗/Slipids referred to as AMBER and CHARMM36m/CHARMM36 lipid force field referred to as CHARMM, see Experimental procedures). A detailed summary of all simulated systems and performed MD simulations is provided in Tables S1–S3.
The tetrameric and octameric Aβ42 βPFO models show a different spontaneous permeabilization behavior consistently in each of the individual simulation runs and among the employed force fields (Figs. 1, E and F, and 2, A and B). An increased water permeability of the transmembrane region along either β-sheet edge was revealed by simulations of T compared to a flat POPC bilayer without embedded Aβ42 βPFO (Fig. 2, A–C and Movie S1). However, no substantial perturbation of the lipid head group region is observed (Fig. 2C). The presence of water and lipid oxygen atoms in the hydrophobic center of the membrane was characterized by the polar transmembrane defect in terms of a reaction coordinate analysis [39] (see Experimental procedures, Fig. 2B). Accordingly, no polar defects that span the entire lipid bilayer width are seen based on the reaction coordinate ξ (Fig. 2B). Simulations of the full-length Aβ42 β-sheet tetramer and the truncated T βPFO model show no significant differences in terms of structural stability or water permeability of the bilayer. A slightly higher lipid head group perturbation for the full-length system is observed due to the sporadic and transient formation of partially open pores (Fig. S2, B and C). Neither the full-length nor the N-terminally truncated Aβ42 pore-forming tetramers induce ion permeation across the POPC bilayer (Fig. S2A). To ensure that the observed trends are robust, the T βPFO simulations with the AMBER force field were extended from 2.5 μs up to 5 μs. Indeed, no eventual pore-opening events occur even on longer time scales (Fig. S3).Figure 2Extent of Aβ42 oligomer–induced membrane permeabilization. A, representative snapshots for three Aβ42 oligomer models (T, O-AP, O-P) taken at $t = 2.5$ μs of simulation time with the AMBER force field. Lipid phosphorus (orange) and lipid oxygen (red) atoms with direct contact to β1-strands of the aggregates are shown as colored spheres. The remaining protein and lipid atoms as well as water molecules are not shown for clarity. B, distributions of the polar transmembrane defect reported (ξ) for AMBER (black) and CHARMM (burgundy) simulations. Shading indicates the standard error. As reference, the distribution of the continuous polar defect reaction coordinate for an unperturbed POPC bilayer is reported (broken lines). C, partial density profiles for water (blue), polar lipid groups (red), lipid phosphates (orange), potassium (purple), and chloride (light-green) ions across the POPC bilayer shown for AMBER and CHARMM simulations. Data are averaged over multiple independent trajectories, shading indicates the standard error. The partial density profiles for an unperturbed lipid bilayer are reported as reference (broken lines). POPC, 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine.
In contrast, simulations of both O-AP and O-P structures show a higher density of water and polar lipid groups in the hydrophobic membrane interior, indicating formation of a hydrophilic pore. The time traces shown in Figure 1E illustrate the penetration of water and polar lipid groups in the hydrophobic membrane center, i.e. the formation of continuous and stable polar defects within 200 ns for a single representative simulation (see also Movie S2). The O-AP β-sandwich model most favorably induces fully formed and water filled pores (ξ = 1) along its β-strand edges (Fig. 2, A and B) in both tested force fields. The spontaneously formed pores stay open and result in stable transmembrane defects on a multi-μs time scale (Fig. S3). As evident from the partial density profiles in Figure 2C, chloride anions and potassium cations also enter into and out of the bilayer center. The position probability distributions show that cations permeate the bilayer more readily during the simulations (Fig. 2C). The O-P β-sandwich model shows a higher permeation barrier for potassium ions (AMBER: by 2.5 kJ/mol, CHARMM: by 7.5 kJ/mol) and an increased fraction of only partially open pores compared to the O-AP model (Fig. 2, B and C).
## The HHQK domain shows specific interactions with lipid head groups
Residue-based contact mapping shown in Figure 3, A and B reveals direct interactions between polar lipid head groups and water molecules with the hydrophilic residues 13 to 16 (HHQK domain) of the β1-strand, regardless of the initial oligomer model. The simulated T, O-AP, and O-P models slightly tilt during the simulations and show a high average contact frequency of H14 and K16 side-chains to the lipid phosphate groups, whereas the H13 and Q15 side-chains predominantly interact with the positively charged choline group of the lipids. Furthermore, lipid carbonyls show increased contact frequency to the side-chain atoms of Q15 and K16, as well as to the backbone atoms of H14 and K16 due to hydrogen bonding (Figs. S4 and S5). It is of note that the extent of pore formation and pattern of lipid aggregate interactions agree almost quantitatively between both force fields (Figs. 3B, S4 and S5). The direct lipid contacts between the HHQK domains on either β-sheet edge of the T βPFO are established on a sub-μs time scale (see Movie S1). The highest density of lipid-contacting HHQK residues is observed approx. 1 nm away from the bilayer center and towards the lipid head group region from opposite membrane leaflets (Fig. 3, C and D). The layered β-sheets in the O-AP and O-P models feature two HHQK domains on each side of the exposed β-strand edges (Fig. 3C). The direct interactions of lipid head groups and water with this wider patch of hydrophilic residues facilitate and stabilize the opening of fully hydrated pores (Fig. 3, C and D).Figure 3The N-terminal HHQK domain of Aβ42βPFOs with the 6RHY fold shows specific interactions with lipid head groups. A, averaged contact frequencies of polar lipid groups and water molecules to protein residues for three Aβ42 oligomer models (T, O-AP, O-P) mapped onto the initial simulation structure shown in cartoon representation (side and front view). Contact frequencies for each oligomer model are averaged over AMBER and CHARMM simulations, respectively. B, detailed breakdown of the average contact frequencies for residues from the transmembrane β-strands (β$\frac{1}{2}$/3) to polar lipid groups (red) and water (blue) are compared for the two tested force fields. The standard error is indicated by error bars. HHQK domain residues are encircled. C, renderings of initial simulation coordinates for T, O-AP, and O-P models indicate the position of the HHQK domain (front view). Side-chain atoms for residues 13 to 16 and 35 are shown in stick representation. D, corresponding density profiles show the location of residues 13 to 16 (HHQK domain) inside the POPC layer. Shading indicates the standard error. βPFO, β-sheet pore-forming oligomer; POPC, 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine.
Continuous polar transmembrane defects are most abundantly induced by the O-AP model due to the antiparallel β1-strand alignment that results in the HHQK domains spanning the whole center of the bilayer on either edge (Fig. 3, C and D). The preferential location of the HHQK domains relative to the bilayer center thus rationalizes the varying extent of stable pore formation induced by the tested oligomer models.
Similar to the T model, the hydrophobic β2- and β3-strands of the β-sandwich octamer models show no substantial interactions with polar lipid atoms, water molecules, or ions (Figs. S4 and S5). Consequently, the lipid bilayer remains unperturbed along the hydrophobic faces of the β-sheet core. The resulting pore shape can be thus best described as a nonconcentric toroid that is restricted to both β-sandwich edges (Fig. S6, A and B). As water or ions are only in contact with the outermost, exposed β-sheet edges in the membrane center, no permeation pathway in the aggregate interior is observed in the simulations (Fig. S6C).
## Aβ42 βPFO stability and pore formation along layered β-sheet edges depends on β1-strand insertion
The ability of Aβ42 octamers with layered β-sheet edges to induce pore formation is associated with characteristic lipid contacts via residues 13 to 16 (HHQK) from the N-terminal β1-strand. This led us to further probe the stability of Aβ42 βPFO models and their capacity for pore formation with a particular focus on the role of the peptide’s N-terminus. To do so, aggregate isoforms were derived by selectively removing the HHQK domain bearing β1-strands from the TMD of the octameric β-sandwich model structure to mimic a 6RHY type aggregate without membrane-inserted N-terminus (Figs. 4 and 5A). We obtained an octameric β-sandwich model with layered β-sheet edges formed only by the hydrophobic β2/β3 strands from the C-terminus of Aβ42 (Figs. 4 and 5A). Based on the previous analysis, only the O-AP model was considered a good template for the smaller Aβ42 βPFOs with the 6RHY fold (Fig. 4). We also tested tetrameric and hexameric β-sandwich models with and without N-terminal truncation (Fig. 4A). The latter, ’Janus-faced’ β-sandwich aggregates, exhibit by design a hydrophilic side-by-side β1-strand pair edge on one side (identical to the octamer) and a hydrophobic side-by-side β$\frac{2}{3}$-strand pair edge on the other side of its TMD structure (Figs. 4 and 5A). As expected from the previous findings, also the smaller, ’Janus-faced’ β-sandwich conformations form continuous polar transmembrane defects along the side-by-side β1-strand pair edge with a high propensity. Yet, independent of tested oligomer size and conformation, no pore formation occurs along side-by-side pair edges formed by β2/β3-strands (Fig. 4A).Figure 4Summary of Aβ42βPFO models with the 6RHY fold. Overview over all Aβ oligomers obtained from initial coordinates of the O-AP model. Individual aggregate structures are distinguished by unique identifiers and grouped according to their β-sheet edge conformation: (A) Aβ42 models with at least one side-by-side β1-strand pair edge and (B) Aβ42 models without side-by-side β1-strand pair edge. For each system, the oligomer size (number of Aβ42 molecules) and the number of β-strands is plotted. Circle colors were chosen to match the composition of the outermost β-strands, i.e. the β-sheet edges of the 6RHY oligomer models: all β1 (hydrophilic) - yellow, all β2/β3 (hydrophobic) - cyan, β1 and β2/β3 (’Janus-faced’) - green. βPFO, β-sheet pore-forming oligomer. Figure 5Aβ42βPFO stability and pore formation along layered β-sheet edges depends on N-terminal insertion. A, starting structures of up-down, face-to-face (left) octameric and hexameric β-sandwich oligomer models (center) with and (right) without β1-strand edges. Accompanying panels report the occurrence of polar transmembrane defects (ξ) averaged over multiple independent simulations. Distributions are reported in total (black line) and separately along each individual β-strand edge (colored lines). B, the average polar transmembrane defect is shown for a total of seven β-sandwich oligomer models with layered β-sheet edges, with and without side-by-side β1-strand pair edges (left panel). Symbols indicate averages over all independent simulations per oligomer model, error bars denote the standard error. The symbol outline colors indicate the force field used (black - AMBER; burgundy - CHARMM). Additional symbol annotations are the same as in Figure 4. The average rmsd of the transmembrane β-strands with respect to the simulation starting structure are shown as function of the average polar transmembrane defect (right panels). The symbols report individual averages for each independent simulation per oligomer model. C, representative snapshots of selected simulations illustrate the extent of the polar defect and conformational deviation for each oligomer model shown in (B). βPFO, β-sheet pore-forming oligomer.
The stability of the TMD β-strands in terms of rmsd and capacity to form continuous polar membrane defects of the tested aggregate model structures is summarized in Figure 5B. Aβ42 β-sandwich oligomers without any intra-membrane, N-terminal β1-strands do not form pores and cause only small, if any detectable perturbations of the lipid bilayer in the simulations (Fig. 5B). The β-sandwich octamer with β1-strands is the most stable among the tested oligomer models, maintaining the high initial β-sheet secondary structure content throughout the simulated μs time scale. All smaller oligomers show sizable deviations from the initial structural models (Fig. 5C). The ’Janus-faced’ tetrameric and hexameric β-sandwich conformations are less stable than the octamer model in AMBER and CHARMM simulations, due to frequent separations of the mated β-sheets at the β2/β3-strand edge. The βPFO derivatives with layered β-sheets composed only of hydrophobic β2/β3-strands do not appear to be stable either (Fig. 5, B and C). Instead, even the larger βPFO models with six or eight TMD β-strands show twisted and sheared β-sheet layers. We also observed the conversion to single β-sheet and β-barrel–like states, in each case resulting in an increased amount of interchain β-sheet contacts (Fig. 5C).
## Aβ42 oligomers without layered β-sheet edges are not able to form stable pores
To examine whether side-by-side β1-strand pair edges are necessary to induce spontaneous pore formation, we studied additional Aβ42 βPFO models without layered β-sheet edges, varying in number and composition of their TMD β-strands (Fig. 4B). The respective β-sheet aggregates were obtained by selectively removing β1/β2-strands from the T and O-AP 6RHY structure models (Fig. 4B). Deleting a single β1-strand on either edge of the O-AP is sufficient to significantly diminish the otherwise high membrane permeability induced by this oligomer (Fig. 6A). The extent of transmembrane polar defects decrease linearly upon removal of further β-strands from octameric to trimeric structures (Fig. 6D). In fact, none of the tested oligomeric states without layered β-sheet edges are able to form continuous pores or large polar defects in the membrane. Figure 6Aβ42 oligomers without layered β-sheet edges do not form stable pores. A, starting structure of up-down, face-to-face octameric β-sandwich oligomer model without layered β-sheet edges. Accompanying panels report the occurrence of polar transmembrane defects (ξ) averaged over multiple independent simulations. Distributions are reported in total (black line) and separately along each individual β-strand edge (colored lines). Models of a (B) hexameric and (C) tetrameric Aβ42 oligomers derived from β-barrel structure templates without any β-sheet edges. D, the average polar transmembrane defect is shown for a total of 8 Aβ42 oligomer models without layered β-sheet edges or without any β-sheet edges (left panel). Additional symbol annotations are the same as in Figure 4. E, the average rmsd of the transmembrane β-strands with respect to the simulation starting structure is shown as a function of the average polar transmembrane defect (right panels). F, representative snapshots of selected simulations illustrate the extent of the polar defect and conformational deviation for each oligomer model.
Independent of aggregate size, the majority of structure models is stable on the multi-μs simulation time scale as judged by the small rmsd of the TMD β-strands (Fig. 6E). The T model, which is also the building block of the larger tested asymmetric β-sandwich oligomers, is the most stable. The hexameric, asymmetric β-sandwich oligomer without membrane-inserted β1-strands (Hexa-0pe-0.2.4) is the least stable aggregate model and equilibrates towards single β-sheet and β-barrel-like states, similar to what is observed for the symmetric β-sandwich structures entirely composed of the hydrophobic, C-terminal TMD β-strands.
Our results indicate that (β-sandwich) aggregate conformations are unstable in the absence of polar lipid contacts to the entire length of the intramembrane β-sheet edges. Instead, closed arrangements are favored, where the edges of both β-sheet layers establish hydrogen bonds with one another. To further test this premise, we sought to examine preformed β-barrel oligomers that are not based on the 6HRY structure model. We selected a tetrameric β-barrel (Tetra-0pe-2OTK derived from PDB ID: 2OTK/5W4J) and hexameric cylindrin (Hexa-0pe-3SGO derived from PDB ID: 3SGO) with different folds as additional structure models for Aβ42 βPFOs without any β-sheet edges (see Experimental procedures, Figs. 6, B and C and S7). The tetrameric β-barrel based on 2OTK Aβ hairpin conformations as principal structural motif shows a high degree of stability during the simulations. The cylindrin-like hexamer structure formed by the C-terminal part of the Aβ-peptide [37] is also very stable within POPC bilayers. However, both models show no pore formation or capacity to permeate ions across the lipid bilayer. The β-barrel structures are devoid of water contacts in the center of the membrane, indicating that also no aqueous interior pores are present (Figs. 6C and S8 and S9).
## Phospholipid interactions stabilize Aβ42 oligomer models with the 6RHY fold
A comparison of tetrameric oligomer conformations simulated in aqueous solvent and POPC lipid bilayers indicates that lipid–protein interactions have a significant stabilizing effect on β-sheet and β-sandwich tetramers with the 6RHY fold (Fig. 7A). In particular, the T model, while stably immersed in a phospholipid bilayer, converts to more compact and curved aggregates in the absence of lipids and shows a partial loss of extended β-structure content (Fig. 7, B and C). The Tetra-0pe-2.2.2 and Tetra-2pe-2.2.2 models form incomplete β-barrel topologies with increased hydrogen bonding at the β-sheet edges (Fig. 7, B and C). Interestingly, the tetrameric and hexameric Aβ42 β-barrel models (Fig. S7) without free edge β-strands display a stability in water comparable to the membrane-embedded structures (Fig. 7, B and D). Both AMBER and CHARMM force fields show the same trends in terms of solvent media (lipid bilayer or water)–dependent aggregate stability for the tested structure models (β-sheet, β-sandwich, or β-barrel) with evidence of a higher overall stability in the AMBER simulations (Fig. 7B).Figure 7The phospholipid environment stabilizes Aβ42 oligomer models with the 6RHY fold. A, the panel reports the average rmsd of the transmembrane β-strands with respect to the simulation starting structure for tetrameric β-sheet and β-sandwich models, as well as hexameric and tetrameric Aβ42 β-barrel models simulated with and without a phospholipid environment. Additional symbol annotations are the same as in Figure 4. B, the average rmsd of the transmembrane β-strands are shown as a function of the average β-structure content for all independent trajectories. Simulation data of Aβ42 oligomer models performed in water are highlighted by shaded background. Snapshots of selected simulations with (C) 6RHY fold and (D) β-barrel structures without any β-sheet edges illustrate the effect of the simulation environment (immersed in POPC bilayer vs water only) on the structural stability. POPC, 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine.
## Pore formation and ion permeation occurs only along stable β-sheet pair edges but independent of oligomer size
To corroborate the emerging trends, we conducted a systematic investigation of smaller βPFOs (dimers to hexamers) with at least one side-by-side β1-strand pair edge (Fig. 4A). As done before, subsets of the TMD β-strands from the O-AP template were selected to arrive at the respective 6RHY βPFO model structures. Together with the three oligomer models presented in Figure 5A, a total of nine Aβ42 βPFO models with at least one side-by-side β1-strand pair edge were compiled (Fig. 4A). The stability of the βPFO aggregate models, specifically of the side-by-side β1-strand pair edge, shows a striking correlation with the ability to form stable, aqueous pores (Fig. 8A). Aggregates larger than the trimer form stable hydrated edge pores during the majority of all individual multi-μs long simulations. However, also smaller dimeric and trimeric βPFO aggregates retain stable side-by-side β1-strand pair edges in multiple simulation runs, resulting in pore formation (Fig. 8, A). Interestingly, the lateral addition of a β1/β2-strand hairpin to the tetrameric β-sheet model (T) is already sufficient to create a stable, pore-forming side-by-side β1-strand pair edge motif (Figs. 4A and 8A, Penta-1pe-3.3.2).Figure 8Formation of ion-conducting pores depends on side-by-side β1-strand pair edge stability. A, the average polar transmembrane defect is shown for a total of 9 Aβ42 oligomer models with at least one side-by-side β1-strand pair edge (left panel). Additional symbol annotations are the same as in Figure 4. The average rmsd of the side-by-side β1-strand pair edge with respect to the simulation starting structure (depicted in B) are shown as function of the average polar transmembrane defect (right panels). B, front and side views of selected simulation snapshots illustrate the deviation from the initial side-by-side β1-strand pair edge conformation taken from individual Aβ42 βPFO models (rest of aggregate not shown). C, average partial density profiles for polar lipid groups and potassium ions are shown as a function of side-by-side β1-strand pair edge stability. Conformations from AMBER (left) and CHARMM (right) simulations are grouped based on rmsd in three pools (I, II, III). Shading indicates the standard error. Relative conformation populations within the three pools are provided. D, representative O-AP simulation snapshot shows multiple potassium ions (purple spheres) spontaneously entering the lipid bilayer center. Lipid oxygen atoms are shown as red spheres. βPFO, β-sheet pore-forming oligomer.
The simulations reported in Figure 8A were carried out with charged lysine side-chains at position 16 compatible with pH 7.4 (K16/LysH+). We note that the O-AP, Hexa-1pe-3.3.3, and Tetra-2pe-2.2.2 models with two neutral K16/LysH0 (pH 9.0) also show membrane perturbing characteristics. However, the extent of pore formation and HHQK domain interactions with the lipid head group region is decreased compared to simulations with ionized K16 (Fig. S10). The O-AP and Hexa-1pe-3.3.3 aggregate models with neutral K16 side-chains retain conformations that are similar to the ones with ionized K16 or stay even closer to the initial structure model in both force fields (Fig. S11).
Figure 8C shows the correlation between the rmsd of the side-by-side β1-strand pair edge and extent of edge conductivity considering K16/LysH+ and K16/LysH0 simulations. The most stable layered β-sheet edges show the lowest energetic barrier for potassium ions entering the hydrophobic bilayer center (Fig. 8, C and D). In contrast, no ion permeation is registered when the side-by-side β1-strand pair edge conformations exhibit a rmsd higher than 0.6 nm from the initial structure (Fig. 8, B and D). In the simulations, such high rmsd values are caused, e.g., by twisting and shearing of the initially parallel β-sheet layers that separate both HHQK domains. Once the side-by-side β1-strand structure deforms further or closes due to interstrand hydrogen bond formation, the edge conductivity is lost and not compatible with ion permeation anymore (Fig. 8, B and C). The pore-forming characteristics of well aligned, layered β-sheet edges with persistent close intersheet contacts between the two HHQK domains (low rmsd) are consistent in AMBER and CHARMM simulations. The underlying distributions of βPFO model conformations are, however, skewed differently (Fig. 8C).
For smaller βPFO models, the difference in the stability of the side-by-side β1-strand pair edge is more pronounced in the CHARMM force field and sheds light on the force field dependence of the simulation outcome (Figs. 8A and S12). In particular, the tetrameric β-sandwich conformations (Tetra-2pe-2.2.2) are less stable in CHARMM simulations than the AMBER simulations (Fig. S12, A–C). Indeed, pore-forming structures sampled during AMBER simulations and spawned in the CHARMM force field reequilibrate within 2 μs towards conformations that do not perturb the bilayer significantly (Fig. S12, D and E).
In conclusion, our simulations indicate that aggregate stability is likely governed by nuanced lipid–protein and protein–protein interactions and may be force field dependent for smaller, metastable β-sandwich oligomer models.
## Ion conducting properties of lipid-stabilized pores is defined by specific interactions with side-by-side β1-strand pair edge
To disentangle individual contributions to cation permeation in the membrane center, we monitored direct protein contacts to potassium ions crossing the bilayer in tetrameric, hexameric, and octameric βPFO models, all of whom feature at least one antiparallel side-by-side β1-strand pair edge structure. Figure 9A shows the side-by-side β1-strand pair edge up close and highlights the carbonyl oxygens, along with the histidine (H13, H14), glutamine (Q15), and phenylalanine (F19, F20) side-chains. Figure 9Ion permeability is facilitated by direct and specific interactions with side-by-side β1-strand pair edge. A, the side-by-side β1-strand pair edge structure is shown with all backbone atoms, additionally side-chain atoms of residues 12 to 20 are depicted by sticks. Carbonyl oxygen atoms from the backbone and from polar side-chains are highlighted by transparent spheres. Lipid phosphorus atoms are shown as orange spheres. B, simulation snapshot of the side-by-side β1-strand pair edge structure in contact with a potassium ion entering the hydrophobic region around the membrane center. A close up view of the ion’s direct atomic contacts to protein, lipid, and water molecules are shown. C, the average composition of the first hydration shell of potassium ions spontaneously permeating across the POPC bilayer is shown as a function of the K16 protonation state in two force fields (left, AMBER and right, CHARMM). The average coordination number of the cation is plotted as a function of the ion position along the bilayer axis. Shading indicates the standard error. The sum of all direct contacts with protein atoms (yellow) are shown together with individual coordination numbers: backbone carbonyls of residues 9 to 21 (gray), as well as H13 (cyan), H14 (dark-blue), and Q15 (magenta) side-chains. POPC, 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine.
Backbone and side-chain atoms from residues 9 to 21 comprising the β1-strand exhibit transient but direct contacts to potassium ions as found from a contact analysis (Fig. S5). Residues H13 and F20, located at opposite ends of the β1-strand and close to the lipid-water interface, have the highest average contact frequency (Fig. S5). Residues H14, Q15, and F19 show direct cation contacts, as well. An example of a potassium permeation event in the bilayer center with direct backbone and side-chain atom interactions is depicted in Figure 9B and Movie S3, showing that side-chain and backbone carbonyls can also coordinate the monovalent cation in concert. We calculated the average composition of the first hydration shell of potassium ions with a distance cutoff of 0.35 nm. A hydration profile of potassium ions crossing the bilayer along the side-by-side β1-strand pair edges for AMBER and CHARMM simulations is shown in Figure 9C. Hydrated potassium ions (bulk) maintain a high number of surrounding waters upon entering the transmembrane region due to the polar nature of the formed pore. The ions experience a loss of one to two coordinating waters in the membrane center on average. The lost water coordination is compensated by contacts with polar lipid and protein atoms. Notably, in the center most part of the bilayer, exposed carbonyl oxygens from the protein backbone (β1-strand) and Q15 side-chain atoms contribute directly to the coordination sphere of permeating potassium ions (Fig. 9C). Similarly, close contacts between the potassium ions and the imidazole nitrogen from the H13 and H14 side-chain were observed.
The K16/LysH0 simulations show that the K16 protonation state does influence the ion coordination patterns, showing increased contacts to the β1-strand for permeating ions (Fig. 9C). Concordantly, fewer water and lipid oxygens coordinate the potassium ions moving across the bilayer center than K16/LysH+, in particular for CHARMM simulations.
## Mutations in the β1-strand emphasize the specificity of β-sheet edges in Aβ42 βPFOs
In subsequent computer experiments, we aimed to explore the specificity of the layered β-sheet edges formed by Aβ′s N-terminus. Specifically, we tested how single residues impact the pore forming and ion conduction ability of these βPFO models. Experimental data suggest that certain mutations in the β1-strand likely affect the cytotoxicity compared to the WT Aβ peptide. The F19G mutation, for instance, was shown to abolish toxicity [40, 41]. An attenuation of toxicity was also observed by altering amino acids 13 through 17 (GGQGL) [42] or through methylation of nitrogen atoms from the imidazole rings of H13 and H14 [43]. In contrast, a 1:1 mixture of K16N and WT Aβ produces highly toxic oligomers [44].
We simulated these three sets of mutations for the hexameric β-sandwich model (Hexa-2pe-2.2.4): The F19 side-chain [40, 41] is located on opposite ends of the side-by-side β1-strand pair edge (Fig. 10A), whereas the H13A; H14A; Q15A (Fig. 10B) and K16N [44] (Fig. 10C) mutations directly affect the HHQK domains and either delete or introduce a side-chain carbonyl group. All probed variants show a minor impact on the extent of pore formation and stability of the 6RHY fold but change the ability to permeate ions across membrane compared to the WT Aβ42 βPFO model. The F19G and alanine mutations caused a significant reduction in ion permeation, increasing the energetic barrier by more than 4 kJ/mol, respectively (Fig. 10, A and B). The K16N mutant increase the capacity of the hydrophilic transmembrane β-sheet edge to bind ions (Fig. 10C), thus lowering the energetic barrier in the bilayer center. This observation agrees with the previously discussed direct residue-ion contacts, in particular with the cation coordination of the side-chain carbonyls (Fig. 9C). The impact of deleting the inward facing aromatic residue phenylalanine (F19) and both histidines (H13/H14) in the side-by-side β1-strand pair edge furthermore points to a relevance of cation-pi interactions for the entry of ions in the transmembrane region. Figure 10Point mutants emphasize the specificity of the side-by-sideβ1-strand pair edge structure and HHQK domain. Front view of side-by-side β1-strand pair edge structure for (A) F19G, (B) H13A; H14A; Q15A, and (C) K16N/WT mutants of the hexameric β-sandwich oligomer (Hexa-2pe-2.2.4). Locations of mutated residues are indicated by dashed circles and shown in close-up (center panels). Side-chain atoms of the WT oligomer model are shown as gray sticks in the background for comparison. Extent of transmembrane defects and partial density profiles of water (blue), all polar lipid groups (red), lipid phosphates (orange), and potassium ions (purple) across the POPC bilayer are shown for each mutant, respectively. As reference, the partial density profiles and polar defect reaction coordinate for an unperturbed POPC bilayer (broken lines) and the WT hexameric β-sandwich oligomer model (dotted lines) are also reported. Shading indicates the standard error. POPC, 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine.
## Discussion
Small, oligomeric, and membrane-bound structures may represent the most neurotoxic aggregates during Aβ misfolding and self-assembly [2, 6, 8, 9, 11, 13, 16, 19, 20, 21, 26, 28]. Determining the mechanistic underpinnings of pathological membrane permeabilization from a structure-activity standpoint, however, remains a considerable challenge due to the scarcity of experimentally determined atomic structures. MD simulations allow to investigate isolated oligomeric states and gauge their conformational stability and dynamical interactions based on 3D structure models. Several theoretical models of Aβ oligomers have been presented in the past: de novo assembled from Aβ monomers [45, 46, 47, 48], by threading the Aβ sequence on β-barrel and cylindrin-like scaffolds [37, 49], or by modeling of Aβ conformations based on fibrillar cross-β structure motifs [50, 51]. An in-depth review of computer simulations on Aβ oligomer models in solution or bound to model membranes is given elsewhere [48, 52].
Here, we probed the principal relationship between membrane permeabilization and 3D structures of low molecular-weight Aβ42 oligomers, ranging from dimer to octamers in size, using all-atom MD simulations. Only membrane-embedded Aβ42 oligomers with the 6RHY fold and exposed edges of mated β-strands from the Aβ N-terminus spontaneously form and maintain continuous, lipid-stabilized pores. By comparing the behavior of Aβ42 truncation variants and isoforms, we demonstrate that β-sandwich models with the 6RHY fold lacking a membrane-inserted N-terminus do not form pores and show a significantly reduced stability. We suggest a specific mechanism of ion permeation across edge-conductivity pores involving direct protein-ion contacts with side-by-side pair of β1-strands, as well as an interdependence between the overall aggregate stability and the anchoring of the HHQK domain in the membrane.
Among the tested oligomer 6RHY models, the tetrameric β-sheet oligomer and octameric β-sandwich with identical subunits are the most stable, in line with the experimentally observed homogeneous Aβ42 oligomer size populations [29, 33]. Based on our simulation results, the tetramer β-sheet model (T) with the 6RHY fold could play a crucial role in the formation of larger Aβ42 βPFOs. The tetramer β-sheet was stable and invariably anchored in the two-dimensional matrix of phospholipid bilayers. This could effectively reduce the combinations of available aggregate packings in the nucleation stage and thus provide the necessary templates to facilitate the octameric β-sandwich structure formation. We additionally found that the tetrameric β-sheet could serve as stable building block in pentameric and hexameric oligomers that form by lateral addition of individual Aβ molecules. The simulations furthermore support the notion of at least metastable subpopulations of dimers, trimers, and tetramers with a side-by-side pair edge motif of β1-strands. It is known that phospholipids are crucial for the formation and stability of Aβ42 oligomers [17, 29, 30, 33]. The presence of lipid membranes and their composition modulates Aβ aggregate structures and toxicity [19, 29, 30, 33, 53]. Our study provides additional evidence that tetrameric β-sheet and β-sandwich conformations with the 6RHY fold are only stable as lipid–protein complexes and significantly less stable in aqueous solution. Our findings therefore suggest that edge pore formation may entail favorable lipid–protein interactions, critical in stabilizing the distinct features of Aβ42 oligomers with the 6RHY fold. Moreover, tight and specific lipid–protein interactions as found here would also be consistent with detergent-like effects of Aβ oligomers [17, 21]. The hydrophobic C-terminal part of Aβ is known to be important for the intramembrane self-assembly pathway [18] and is found as the key constituent of several Aβ42 oligomer structures with intermolecular antiparallel β-sheets [29, 36, 54]. All tested Aβ42 aggregate models without the membrane-interacting β1-strand exhibit a significant amount of β-structure, however, are restricted to conformational states with compact β-barrel–like and twisted β-sheets. The structural conversion of truncated 6RHY oligomers without β1-strands highlights interstrand hydrogen bond formation as a dominant driving force for transmembrane β-strand conformations in the absence of additional lipid–protein interactions [55]. Osterlund et al. reported that incubation of Aβ monomers in a micellar environment results in Aβ42 oligomer sizes up to hexamers, whose collisional cross sections determined by native mass spectrometry data are compatible with isotropic or β-barrel structures [30]. The underlying building block chosen for the putative structural model was a 2OTK β-turn-β fold [56] that does not involve the peptide’s N-terminal part in the β-sheet core [30]. From additional simulations of a tetrameric β-barrel based on 2OTK Aβ hairpin conformations and preformed cylindrin-like hexamers [37], we conclude that closed, antiparallel β-sheet arrangements of membrane-inserted Aβ molecules represent a conformational state with high relative stability, both in lipid bilayers and aqueous solvent. Despite the comparable stability of these oligomer states, a considerable degree of variability, e.g. in the number of β-strands and β-sheet topology may explain the structural polymorphism observed in low molecular-weight Aβ oligomers [3, 11, 29, 30, 33, 34, 36, 37, 54].
The current study provides evidence that small Aβ oligomers show a common membrane permeabilization mechanism by formation of edge conductivity pores involving exposed β-strand layers from the N-terminus. Stable edge pore formation was observed for a number of oligomer sizes, ranging from tetramers to octamers, however, only if stable, exposed β-sheet edge double layers are present. The continuous lining of hydrophilic residues 13 to 16 (HHQK domain) found in the antiparallel pairings of the β1-strand edge provides an intuitive explanation for the observed difference in pore formation ability for tetramer β-sheet and octamer β-sandwich models. Previous studies have implicated the HHQK domain in the formation of stable intramembrane Aβ oligomers [18]. Mutations in this region of the Aβ42 sequence that disrupt protein–lipid interactions were shown to abolish pore formation [42, 57].
We have investigated the permeation mechanism of monovalent potassium ions for edge pore-forming Aβ42 oligomers with the 6RHY fold and found direct protein–ion interactions via carbonyl groups from the exposed protein backbone and side-chain atoms from the HHQK domain. The octameric β-sandwich models with two double layer β-sheet edges represent the most conductive states in terms of ion permeation. Our findings indicate that the ability to induce stable, toroidal shaped transmembrane defects is a necessary prerequisite for ion permeation. Membrane permeabilization of ionic species, however, is sensitive to distortions of layered β-sheet edge conformation, as well as to mutations in the N-terminal β1-strand. We simulated several mutations that are known to either impair (F19G and H13A, H14A, Q15A) or enhance (K16N) Aβ toxicity [41, 42, 44]. Interestingly, these mutations decrease or increase the number of favorable side-chain ion contacts in the membrane-inserted N-terminal β1-strand as observed in the simulations and affect the extent of ion permeation into the bilayer center accordingly. Previous in vitro studies demonstrated the inhibition of Aβ42 oligomer-induced ion conductance and toxicity by binding of small peptide fragments to the HHQK domain [14] and treatment with histidine-associating compounds [58]. We conclude that direct and specific protein interactions exist along the ion-conducting pathway. The permeation process is therefore governed by Aβ42 oligomer edge interactions beyond the attraction of hydrophilic lipid head groups into the membrane center that stabilize the pore. It is of further note that the N-terminal part of Aβ is also well known to bind divalent metal ions, such as Zn2+ and Cu2+ [59, 60], which are found to enrich in Aβ plaques.
Specifically, H13 and H14 from two adjacent Aβ monomers were shown to coordinate with Zn2+ ions [59], rapidly inducing toxic, off-pathway Aβ42 oligomers [61]. The side-by-side pair edge of β1-strands in both tested packing modes (parallel or antiparallel β1-strand alignment) results in two HHQK domains in close proximity, compatible with such a scenario. Therefore, it would be interesting to investigate the conformation-specific interactions of divalent metal ions to 6RHY type oligomers in greater detail.
We would like to stress that none of the tested low molecular-weight Aβ42 oligomer models, varying in size and β-sheet topology, exhibited a membrane permeabilization mechanism with an interior pathway for either water or ions. Aβ42 βPFO models may differ in structure and permeation mechanism depending on the respective oligomer size distribution. Previously suggested pore models [17, 36, 50, 62, 63] do not feature exposed transmembrane β-sheet edges and often lack the N-terminal domain altogether [30, 36, 50]. Ion channel–like Aβ42 structures with inner pores that are sufficiently stable and wide to conduct solvated ions would likely require folding into highly regular and long transmembrane β-barrels [62, 63] or formation of much larger, high molecular-weight assemblies with annular or concentric pores [50, 63]. Atomistic MD simulations of a number of putative membrane-embedded Aβ structures (hepta-, octa-, and 16-mer β-barrels) furthermore show that oligomers formed by the C-terminal part of Aβ and with mainly hydrophobic inner surface area fail to support stable aqueous pore formation in lipid membranes [48]. The present simulations corroborate and refine on Aβ42 oligomer–phospholipid interactions and induced water permeation across the transmembrane region findings from MD simulations using CHARMM36 force field parameters only with order-of-magnitude longer simulations on the μs time scale [33]. Compared to the previous simulations of 6RHY oligomer models [33], we did not apply a transmembrane voltage in the current study to circumvent pore initiation or pore expansion by electroporation effects.
Our results on pore formation and ion permeation (e.g. partial densities, lipid-protein contacts, ion coordination profiles across the lipid bilayer) from simulations of T and O-AP models on the μs time scale are robust regarding the two tested popular force fields (AMBER99SB∗/Slipids and CHARMM36m/CHARMM36 lipid force field). Moreover, the observed trends of aggregate stability and extent of polar defects for membrane-embedded T (with and without β1-strand), O-P, and O-AP, as well as both β-barrel structure models are independent of the force field used. Our simulations predict that the protonation state of lysine side-chains at position 16 from the β1-strand influences the aggregate conformations, consistent with experimental evidence of more stable oligomeric preparations at pH 9.0 over pH 7.4 [29, 33]. In simulations of tetrameric and hexameric β-sandwich oligomers (Tetra-2pe-2.2.2, Hexa-2pe-2.2.4), the CHARMM force field reproducibly favors aggregate reorganization towards structures that do not form pores in contrast to AMBER simulations. Here, the extent of pore formation and insertion depth of the HHQK domain is sensitive to the employed combinations of protein, lipid, and water force field in simulations. Theoretical work on plain lipid bilayers has highlighted barrier height differences for the formation of aqueous pores in MD simulations with respect to the applied force field, i.e. for moving a hydrophilic phospholipid headgroup to the hydrophobic bilayer center [39, 64]. A potential overstabilization of the bilayer state [64], as well as shortcomings of additive MD force fields to describe ion-induced membrane defects as a function of bilayer thickness [65], therefore may explain the observed differences for the smaller, presumably metastable, oligomers models. The observed slow conformational changes furthermore underscore the need for MD simulations beyond the hundreds of nanosecond time scale to properly asses the kinetic stability of membrane-embedded β-sheet oligomers [48].
Based on our investigation of multiple low molecular-weight Aβ oligomer models, we surmise that hydrophilic edge pore formation induced by 6RHY type structures is compatible with a wide range of experimental observations. It has recently been suggested that toxic Aβ oligomers can be defined by structural constraints, however, are not necessarily of the same size [30, 33, 66]. It is therefore intriguing to speculate that toxic βPFOs are not necessarily of one particular oligomer size but rather tied to the presence of layered β-sheet edges formed by Aβ′s N-terminal β-strands, stably inserted in the membrane. We have shown that this structural motif plays a key role in both, the Aβ lipid membrane interactions and characteristics of ion permeation.
Finally, the exploration of structural and functional features of membrane-inserted Aβ42 oligomers as outlined above provides an intriguing working model to further the development of therapeutic intervention strategies. Unraveling and understanding the molecular basis of aggregate-induced membrane permeabilization and cytotoxicity may assist future structure-based inhibitor design. Along these lines, the layered β-sheet edge conformations of Aβ42 oligomers bearing the N-terminal HHQK domain provide an intriguing target. Monoclonal antibodies [22, 23, 67] that recognize the side-by-side pair edge motif of β1-strands or specific regions in the N-terminus of Aβ42 could have the potential to neutralize oligomer-induced pore formation and ion permeation. A number of small molecules with specific binding properties to either exposed backbone epitopes [68, 69], histidine [58], or lysine [70] side-chains have been shown to effectively modulate Aβ42 oligomerization and to interfere with Aβ42 pore formation. Capping the formation of membrane-embedded Aβ oligomers with layered β-sheet conformations could be explored as another strategy to block pore-forming Aβ42 oligomers [71]. The present results offer the opportunity to revisit and confer the mode of action for these and other known Aβ oligomer inhibitors [7, 67, 72, 73, 74] at the molecular level. In order to achieve enhanced efficacy, the ability to bind near or in the cellular membrane is expected to be an important property of drugs targeting Aβ42 βPFOs.
## Simulation details
The GROMACS 2018 and 2020 simulation software package [75, 76] was used to set up, carry out, and analyze the MD simulations. Settings for production runs were chosen as follows: the long range electrostatic interactions were treated using the Particle Mesh Ewald method [77, 78]. Bonds in protein and lipid molecules were constrained using the P-LINCS [79] algorithm. Water molecules were constrained using SETTLE [80] algorithm. Neighbor lists were updated with the Verlet list scheme [76, 81]. For production runs, the simulated systems were kept at a temperature of 300 K by applying the velocity-rescaling [82] algorithm. Initial velocities for the production runs were taken according to the Maxwell-Boltzmann distribution at 300 K. The pressure was held constant by using the Parrinello-Rahman barostat [83] with a semi-isotropic coupling in the xy-plane.
## Simulation protocol: AMBER99SB∗/Slipids force field
For all simulations with the AMBER99SB∗-ILDN [84, 85, 86] force field, we employed the TIP3P water model [87] together with ion parameters by Dang et al. [ 88, 89]. For the description of the lipids, a modified version [90] of the all-atom Slipids force field [91, 92, 93] was used. Bonds between all atoms in protein and lipid molecules were constrained. The conversion of aliphatic hydrogen atoms to virtual sites [94] in all protein and lipid molecules allowed to set the integration time step to 4 fs to speed up the simulations. A real space cut-off for the electrostatic interactions was set at 1.0 nm. The van-der-Waals interactions were cut off at 1.0 nm. A dispersion correction for energy and pressure was applied.
## Simulation protocol: CHARMM36m/CHARMM36 lipid force field
All simulations with the CHARMM36m [95, 96] protein force field utilized the CHARMM36 lipid parameters [97] together with the CHARMM-modified [98] TIP3P water model. The integration time step was set to 2 fs. The neighbor lists for nonbonded interactions were updated every 20 steps. Real-space electrostatic interactions were truncated at 1.2 nm. The van der Waals interactions were switched off between 1.0 to 1.2 nm and short-range electrostatic interactions were cut-off at 1.2 nm.
## Aβ(6–42) β-sheet and β-sandwich models (6RHY fold)
If not stated otherwise, all simulations of oligomeric amyloid-β peptide aggregate structures were initiated from or generated based on atomic coordinates from previously published solid-state NMR spectroscopy and CCS data [33]. Octameric β-sandwich structures were derived by simple symmetry operations (translation and/or rotations) of the tetrameric β-sheet conformation (PDB ID: 6RHY) using the PyMOL visualization software (99, http://www.pymol.org/pymol). In the majority of the simulations, the disordered, solvent-exposed N-terminal regions that could not be assigned or did not show lipid interactions experimentally [33] were truncated. And the N-termini were capped with acetyl groups. Smaller oligomer models and aggregate isoforms were obtained by selectively removing individual Aβ molecules from the common structure templates (tetrameric β-sheet, octameric β-sandwich). F19G; H13A, H14A, Q15A, and K16N mutants were generated by substituting individual residue side-chains in the WT 6RHY model conformation using the ’Mutagenesis’ functionality PyMOL.
In addition, two distinct tetrameric and hexameric Aβ β-barrel model structures were simulated. Aβ(6–42) β-barrel model (2OTK fold): This structure model was generated analogous to the protocol described by Nowick et al. [ 100] using atomic coordinates of X-ray crystallographic structures formed by macrocyclic mimics of the Aβ(16–36) β-hairpin (PDB ID: 5W4J) [38]. After deletion of the delta-linked ornithine residues from each macrocycle using PyMOL, an oligomer structure with resolved Aβ(16–22 and 30–36) β-strands in each monomer were obtained. The previously reported monomeric β-hairpin structure of Aβ(16–40) (PDB ID: 2OTK) [56] with the same intramolecular hydrogen bond register was fitted on the structural template (5W4J), resulting in a combined model of a high-resolution Aβ(16–40) tetramer. Missing N- and C-terminal residues were modeled in random coil conformation with PyMOL (Fig. S7). Aβ[21-42] β-barrel model: This structure model was generated analogous to the protocol described by Do et al. [ 37]. Threading of the Aβ (28–38) sequence onto the backbone structure of the hexameric αB-crystallin cylindrin revealed favorable side-chain packing among several tested C-terminal Aβ fragments [37]. Thus, the side-chains in the X-ray crystal structures of the αB-crystallin cylindrin hexamer (PDB ID: 3SGO) were replaced to match the Aβ(28–38) amino acid stretch using PyMOL.
Missing N- and C-terminal residues were modeled in random coil conformation with PyMOL (Fig. S7). After the modeling stage, both structures were equilibrated in a water box to allow structure relaxation before arriving at the final models.
In all simulation systems (see Table S2), the titratable amino acids were protonated according to their standard protonation states at pH 7, while also taking into account the solvent exposure and electrostatic interactions with neighboring polar groups. Thus, aspartic and glutamic side-chains were simulated with negative charge and all histidine side-chains were set to neutral [33, 101]. All lysine side-chains were simulated as positively charged (LysH+). In three additional sets of simulations of β-sandwich models with the 6RHY fold, the lysine residues at position 16 were also simulated in neutral state (K16/LysH0, see Table S2).
All production runs were preceded by a multistep equilibration of the system. First, the protein part was separately energy minimized in water. Second, the aggregates were immersed in a phospholipid bilayer. Depending on the simulated oligomer model, the following two sizes of POPC membrane patches were used: a lipid bilayer composed of [1] 250 (used for O-AP and O-P β-sandwich models and the full-length Aβ tetramer) or [2] 170 (used for all other oligomer models) POPC lipid molecules spanning the xy-plane of the periodic simulation box (see Fig. S1). Both membrane patches were prepared with a water slab of 2.5 nm thickness on top and bottom of the bilayer using the CHARMM-GUI membrane builder webserver [102]. The simulations with full-length Aβ molecules were set up with a 6.0 nm thick water slab on top and bottom of the bilayer. Lipid-solvent contacts were equilibrated for 1 ns at 300 K. Next, the β-sheet structure was embedded into the solvated lipid bilayer. Subsequently, K+ and Cl− ions (ionic strength: 600 mM) were added in the aqueous phase. The whole system was simulated for an additional 1 ns. Position restraints with a force constant of 1000 kJ mol−1 nm−2 were applied to the heavy atoms of the protein backbone to allow relaxation of protein-solvent and protein-lipid contacts. Reference simulations without inserted β-sheet aggregates were carried out with a pure POPC membrane patch composed of 170 lipid molecules. Depending on the two types of initial bilayer sizes, the entire solvated systems consisted of roughly 42,000 to 67,000 atoms (Table S1).
## Analysis protocol
From the individual simulation trajectories, samples were collected every 100 ps and used throughout subsequent analyses. The first 500 ns of each trajectory were not considered for analyses to ensure that the results are not biased by the initial equilibration of the simulation system. The extent of the pore formation process was quantified by partial density profiles across the lipid bilayer and the occurence of continuous polar transmembrane defects.
## Partial density profiles
The partial densities of water molecules, polar lipid groups and ions across the simulation box and along the membrane normal direction were computed using the gmx density tool. Histogram binning was done relative to the center of all lipid atoms. Partial density profiles were averaged over all independent trajectory replicates per simulation system.
## Reaction coordinate for polar transmembrane defects
To monitor and quantify polar transmembrane defects in the model membrane, i.e. the presence of water molecules and the polar lipid head groups in the hydrophobic center of the phospholipid bilayer, a reaction coordinate analysis was carried out based on the work by Awasthi et al. [ 39]. The reaction coordinate was defined as the parameter ξ and indicates the fraction of slices along the 2.8 nm thick transmembrane region that are occupied by at least one lipid or water oxygen atom in each frame. In total, 28 equally distributed slices along the membrane normal with 0.1 nm increments were used. A value of 0 for ξ thus means that no slice in the hydrophobic transmembrane slab is occupied by polar atoms. A value of 1 for ξ means that all slices are occupied by polar atoms, therefore indicating a continuous polar defect in the membrane. Reaction coordinate values for each simulation frame from all independent trajectory replicates per simulation system were combined, sorted into 1-d bins and normalized. The probability pi = ξi,j/ξtotal was calculated, where ξi,j is the number of frames j in the bin i and ξtotal is the total number of frames. The logarithm of p(ξi) multiplied by a constant factor (-RT, with $T = 300$ K) results in a free energy value in units of kJ/mol.
## Contact analysis and mapping
The frequency of short-range interatomic contacts between the peptide oligomer structures and water, polar lipid groups, as well as ions was quantified for every frame using the contact search algorithm provided by the g contacts program [103]. Pairwise residue-based contacts with a cutoff distance of 0.5 nm between heavy atoms of the analyzed groups were averaged over all independent trajectory replicates per simulation system.
## RMSD of atomic positions
Comparison of oligomer model conformations obtained by the MD simulations with respect to the starting structure by calculating rmsd of mainchain and Cβ atoms from the (transmembrane) β-strands.
## Visualization
Renderings of atomic coordinates were carried out with the molecular visualization software PyMOL [99].
## Data availability
MD data generated during this study and source data for this paper are deposited in the open research data repository Edmond at https://doi.org/$\frac{10.17617}{3.}$UR3ECA. Additional raw MD data generated in this study are available from the corresponding author upon reasonable request.
## Supporting information
This article contains supporting information.
Movie-4A Movie-8B movie-8BPERM Supplementary Tables and Figures
## Conflict of interests
The authors declare that they have no conflicts of interest with the contents of this article.
## Author contributions
D. M. and B. L. d. G. conceptualization; D. M. formal analysis; D. M. investigation; D. M. writing–original draft; D. M. visualization; B. L. d. G. writing–review and editing; B. L. d. G. project administration.
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|
---
title: 'Antibiotic exposures and the development of pediatric autoimmune diseases:
a register-based case–control study'
authors:
- Laura K. Räisänen
- Sohvi E. Kääriäinen
- Reijo Sund
- Elina Engberg
- Heli T. Viljakainen
- Kaija-Leena Kolho
journal: Pediatric Research
year: 2022
pmcid: PMC10033398
doi: 10.1038/s41390-022-02188-4
license: CC BY 4.0
---
# Antibiotic exposures and the development of pediatric autoimmune diseases: a register-based case–control study
## Abstract
### Background
Antibiotics have been associated with several individual autoimmune diseases (ADs). This study aims to discover whether pre-diagnostic antibiotics are associated with the onset of ADs in general.
### Methods
From a cohort of 11,407 children, 242 developed ADs (type 1 diabetes, autoimmune thyroiditis, juvenile idiopathic arthritis (JIA), or inflammatory bowel diseases) by a median age of 16 years. Antibiotic purchases from birth until the date of diagnosis (or respective date in the matched controls $$n = 708$$) were traced from national registers.
### Results
Total number of antibiotic purchases was not related to the onset of ADs when studied as a group. Of specific diagnoses, JIA was associated with the total number of antibiotics throughout the childhood and with broad-spectrum antibiotics before the age of 3 years. Intriguingly, recent and frequent antibiotic use (within 2 years before diagnosis and ≥3 purchases) was associated with the onset of ADs (OR 1.72, $95\%$ CI 1.08–2.74). Regardless of frequent use in childhood ($40\%$ of all antibiotics), penicillin group antibiotics were not related to any ADs.
### Conclusions
Use of antibiotics was relatively safe regarding the overall development of ADs. However, broad-spectrum antibiotics should be used considerately as they may associate with an increased likelihood of JIA.
### Impact
Increasing numbers of antibiotic purchases before the age of 3 years or throughout childhood were not associated with the development of pediatric autoimmune diseases. Broad-spectrum antibiotics were related to the development of autoimmune diseases, especially juvenile idiopathic arthritis in children, while penicillin group antibiotics were not. The use of broad-spectrum antibiotics in children should be cautious as they may carry along a risk for autoimmune disease development.
## Introduction
Autoimmune diseases (ADs) are disorders in which the immune system attacks healthy tissues. Some ADs such as type 1 diabetes (DM), autoimmune thyroiditis (AIT), juvenile idiopathic arthritis (JIA), and inflammatory bowel diseases (IBD) may have overlapping genetic pathways1,2 and similarities in their pathogenesis involving T-cell organ inflitrations.3–7 The gut is the largest organ harboring T cells, and therefore is the place where most antigen-immune system contact occurs.8 To interact with the immune system, antigens have to penetrate the gut mucosal barrier and be introduced by the antigen-presenting cells to the T cells.9 Gut mucosal barrier may be breached due to disrupted gut microbiota homeostasis—for instance after antibiotic exposures, leading to influx of antigens and excess stimulation of the immune system.10 Sequentially, this may contribute to the onset of ADs.11–14 For unknown reasons, the incidences of pediatric-onset DM, AIT, JIA, and IBD in Finland are particularly high, presuming a presence of mutual environmental risk factors in addition to potential genetic predisposition.15–19 In the eastern neighboring countries of Finland (Estonia and Russian Karelia), the incidence of ADs such as DM is lower than in Finland. This finding was suggested to derive from different exposures to environmental pathogens and microbes.20 In line with this, Finnish children used more antibiotics during the first and second years of life compared with Estonian children, and this is reflected in the composition of the gut microbiota and immune stimulation.21 Previous studies have connected antibiotic exposures (especially in early childhood) with the onset of JIA and Crohn’s disease, while findings regarding DM were controversial.22–27 AIT has been related to tetracycline use in adolescence, but the mechanism has remained unspecified.28 Most of the previous studies on exposures to antibiotics and the development of ADs addressed each disease individually in different settings, making it challenging to estimate whether antibiotic exposures could be associated with the development of pediatric autoimmunity in general, yet manifesting as different diseases. This register-based case–control study focuses on the potential relationship between antibiotics and the onset of ADs in general.22–25 Our aim was to investigate whether the development of ADs (represented by DM, AIT, JIA, and IBD) is associated with [1] number of antibiotic exposures during different periods in childhood and [2] exposures to particular types of antibiotics.
## Data sources for the study population
The study population was derived from Finnish Health in Teens (Fin-HIT) cohort—a nationwide prospective school-based cohort to address health behaviors of Finnish children and adolescents, comprising 11,407 children (born 2000–2005) without specific exclusion criteria. More details on recruitment and characteristics of the cohort have been described elsewhere.29 The cohort represents children from densely populated areas across Finland, with relatively high maternal socioeconomic status.15 *Using a* unique personal identity code of every Finnish resident, children in the Fin-HIT cohort were linked to three well-established national registers: [1] the Special Reimbursement Register (SRR)—containing records on patients with chronic diseases requiring medication (including entry dates and physician verified diagnosis), who are entitled to drug refunds regardless of their socioeconomic status; [2] the Drug Purchase Register (DPR)—containing data on all purchased drugs by prescriptions in Finland (including dispensation dates and pharmaceutical information); and [3] the Medical Birth Register (MBR)—containing information on gestational age, delivery modes, and postnatal antibiotic treatment before discharge. These registers are maintained by the Finnish Social Insurance Institution (SRR and DPR) and Finnish Institute for Health and Welfare (MBR).
## Identifying children for the matched case–control study
The outcome of this study was the diagnosis of at least one AD by the end of the follow-up on December 31, 2018—when the median age of the participants was 16 years. DM, JIA, and IBD (including Crohn’s disease, ulcerative colitis (UC), and IBD unclassified (IBDU)) diagnoses were obtained from the SRR using International Classification of Diseases, 10th revision (ICD-10) codes: E10 for DM; M08 for JIA; K50 (Crohn’s disease) and K51 (UC/IBDU) for IBD. AIT diagnoses were obtained from the DPR using ATC (Anatomical Therapeutic Chemical) code H03AA01 for thyroxin—the prescription-only drug used for AIT. The excellent coverage of these registers has been described previously.30,31 DPR was chosen for identifying AIT because thyroxin is inexpensive and therefore, not everyone using this medication is applying special reimbursement and registered in the SRR.
Of the 11,407 children in the Fin-HIT cohort, 242 developed a primary AD after the first year of life and generated the case group. Depending on the availability of potential controls, each child in the case group was matched with one to four children from the same cohort of similar age (0–4 days of differences in age to ensure an equal length of the observation period for potential antibiotic exposures), sex, and residential area (to decrease the impact of other environmental factors). Furthermore, preterm birth has been identified as a potential risk factor for ADs.15 Therefore, gestational age (preterm/term), and delivery mode (cesarean section/vaginal delivery) were also considered in the matching. Due to the limited number of potential controls, most children born preterm and/or with cesarean section had only one matching control.
## Number and types of antibiotic purchases
Data on perinatal antibiotic treatment during pregnancy and in the birth hospital were obtained from the DPR and MBR, respectively. Outpatient antibiotic purchases were collected from the DPR using ATC codes starting with J01. The data were collected from birth until two months prior to the index date (date of diagnosis for children with ADs/compatible date for their matched controls). The two-month period was chosen to reduce the possibility of including antibiotic purchases during the symptomatic phase of ADs.
Antibiotic exposures were analyzed in several observation periods based on the age distribution of antibiotic purchases (Fig. 1): [1] throughout childhood—from birth to the index date; [2] during the first year of life (infancy); [3] in the toddler phase—from the age 1 year up to third birthday/index date; [4] during preschool to adolescence—from the age of 3 years to the index date; and [5] within 2 years before the index date. The association between pre-diagnostic antibiotic purchases in each observation period and the development ADs as a group or individually as diagnoses of DM, AIT, JIA, or IBD were analyzed. Due to observed nonlinear associations between antibiotic exposures and the onset of ADs (Fig. 2), the antibiotic purchases were categorized into different groups. When concerning the total number of antibiotic exposures until the index date we used three groups: <4 courses; 4–8 courses; and >8 courses. Regarding shorter observation periods antibiotic purchases were categorized into three groups as follows: no purchases; 1–2 courses (occasional); and ≥3 courses (frequent).Fig. 1Purchased antibiotic courses before index datea in the matched case–controlb study. The numbers of tetracycline, clindamycin, fluoroquinolone, and other antibiotics courses were relatively low. aIndex date = date of diagnosis for children who developed autoimmune diseases (type 1 diabetes, autoimmune thyroiditis, juvenile idiopathic arthritis, and inflammatory bowel diseases) and respective date for their matching controls. bCases = 242 children who developed autoimmune diseases by the end of follow-up (December 31, 2018) at a median age of 16 years. Each child in the case group was matched with one to four children of similar age, sex, residential area, gestational age (preterm/term), and delivery method (cesarean section/vaginal), comprising control group of 708 children. Fig. 2Nonlinear relationship between the number of antibiotic purchases in different periods of childhood and onset of autoimmune diseasesa.aAutoimmune diseases in this study were type 1 diabetes, autoimmune thyroiditis, juvenile idiopathic arthritis, and inflammatory bowel diseases. Index date = date of diagnosis for children who developed autoimmune diseases and compatible date for their age, sex, residential areas, gestational age (preterm/term), and delivery method (Cesarean section/vaginal delivery) matched controls.
Different types of antibiotics were categorized based on ATC codes (Supplementary Table 2) into five groups: (A) penicillins; (B) macrolides; (C) cephalosporins; (D) amoxicillin-clavulanic acid; and (E) sulfonamides and trimethoprim. Clindamycin, tetracyclines, fluoroquinolones, and other antibiotics such as nitrofurantoin and metronidazole were considered in the analysis regarding the total number of antibiotic purchases but dismissed from the subgroup analysis due to low frequency of usage.
## Statistical analysis
The background data of cases and controls are presented as mean and standard deviation (SD), median (interquartile range, IQR), or number/proportion (%). A matched case–control study design with an equal length of the observation period in which these exposures may occur in cases and in their controls was used. The pre-diagnostic antibiotic exposures of each case were compared with the antibiotic exposures of his/her matched control(s) until the index date, and the association between antibiotic exposures and the development of ADs was estimated using conditional logistic regression with strata analysis.32 Results were presented with odds ratio (OR) and $95\%$ confidence interval (CI). The software used was IBM SPSS Statistics 26.0 and a $5\%$ statistical significance level was adopted.
## Results
The background characteristics of the 242 children who developed ADs (cases) and their 708 matched controls who did not develop ADs are presented in Table 1. The ADs were diagnosed at a median age of 11 (IQR 6–13.8) years. The distribution of antibiotic purchases by age for cases with AD and their controls are shown in Fig. 1. A similar age-related pattern in purchases was noted when cases and controls were compared. Of all purchased antibiotics, $44.3\%$ ($$n = 3825$$) were bought before the age of 3 years (Fig. 1). Throughout childhood, only 14 ($5.8\%$) children in the case group and 34 ($4.8\%$) in the matched control group had no antibiotic purchases ($$p \leq 0.596$$). Age at and the type of the first antibiotic purchase did not differ between cases and matched controls (Supplementary Table 3). Also, perinatal antibiotic exposures were similar between cases and their controls (Table 1).Table 1Background characteristics of children in the study population. DM ($$n = 102$$)AIT ($$n = 68$$)JIA ($$n = 54$$)IBD ($$n = 27$$)Casesa ($$n = 242$$)Matched controlsb ($$n = 708$$)Age at the end of follow-up (years), mean ± SD16.5 ± 1.617.1 ± 1.116.6 ± 1.316.8 ± 1.216.7 ± 1.416.8 ± 1.4Sex, N (%) Girl41 (40.2)47 (69.1)43 (79.6)13 (48.1)140 (57.9)407 (57.5) Boy61 (59.8)21 (30.9)11 (20.4)14 (51.9)102 (42.1)301 (42.5)Residential area, N (%) Capital (south)34 (33.3)20 (29.4)10 (18.5)11 (40.7)72 (29.8)248 (35.0) Inner south6 (5.9)7 (10.3)13 (24.1)4 (14.8)30 (12.4)93 (13.1) West9 (8.8)18 (26.5)10 (18.5)2 (7.4)36 (14.9)92 (13.0) East33 (32.4)18 (26.5)9 (16.7)5 (18.5)63 (26.0)161 (22.7) North20 (19.6)5 (7.4)12 (22.2)5 (18.5)41 (16.9)114 (16.1)Age of diagnosis/age at the index date, N (%) <6 years36 (35.3)3 (4.4)19 (35.2)2 (7.4)59 (24.4)154 (21.8) 6–12 years42 (41.2)21 (30.9)18 (33.3)11 (40.7)88 (36.4)273 (38.6) >12 years24 (23.5)44 (64.7)17 (31.5)14 (51.9)95 (39.3)281 (39.7)Gestational age, N (%) Term (≥37 weeks)90 (88.2)63 (92.6)49 (90.7)24 (88.9)217 (89.7)682 (96.3) Preterm (<37 weeks)9 (8.9)4 (5.9)4 (7.4)3 (11.1)20 (8.3)26 (3.7) Missing3 (2.9)1 (1.5)1 (1.9)05 (2.0)0Delivery mode, N (%) Vaginal80 (78.4)58 (85.3)47 (87.0)21 (77.8)198 (81.8)644 (91.0) Cesarean section19 (18.6)9 (13.2)7 (13.0)6 (22.2)40 (16.5)64 (9.0) Missing3 (2.9)1 (1.5)004 (1.7)0Maternal antibiotic purchase during pregnancy, N (%) None79 (77.5)58 (85.3)45 (83.3)23 (85.2)198 (81.8)776 (80.9) Up to 60 days before delivery5 (4.9)1 (1.5)006 (2.5)36 (3.8) >60 days before delivery18 (17.6)9 (13.2)9 (16.7)4 (14.8)38 (15.7)147 (15.3)Postnatal antibiotic exposure at the birth hospital before discharge None96 (94.1)64 (94.1)49 (90.7)24 (88.9)224 (92.6)679 (95.9) Received antibiotics5 (4.9)3 (4.4)5 (9.3)3 (11.1)16 (6.6)29 (4.1) Missing1 (1.0)1 (1.5)002 (0.8)0aCases = children with autoimmune diseases (represented with DM (type 1 diabetes mellitus), AIT (autoimmune thyroiditis), JIA (juvenile idiopathic arthritis), and IBD (inflammatory bowel diseases)). Nine children have two diagnoses.bEach child in the case group was matched with one to four children of similar age, sex, residential area, gestational age (preterm/term), and delivery mode (cesarean section/vaginal). Due to limited potential controls, most children born preterm/with cesarean section have only one control instead of four.
## Number of antibiotic purchases
The total number of antibiotic purchases from birth to the index date was not associated with the development of the studied ADs as one group (Table 2). However, we found a nonlinear and timing-dependent relationship between the number of antibiotic exposures and the onset of ADs (Fig. 2). The highest odds for developing ADs were observed in children receiving ≥3 courses of antibiotics within 2 years before the index date when compared with those without antibiotic purchases (OR 1.72, $95\%$ CI 1.08–2.74) (Table 2). The median age at this stage was 9 (IQR 4–12) years. Table 2Association between numbers of antibiotic purchases in different periods and the development of an autoimmune disease (DM, AIT, JIA, or IBD)a. Antibiotic purchases at different ages, N (%)Casesa ($$n = 242$$)Matched controlsb ($$n = 708$$)Odds ratio ($95\%$ CI)cThroughout childhood (from birth to the index date)dAD <4 courses53 (21.9)174 (24.6)1.00 (Ref) 4–8 courses82 (33.9)223 (31.5)1.32 (0.87–2.01) >8 courses107 (44.2)311 (43.9)1.22 (0.79–1.88)DM <4 courses28 (27.5)81 (28.9)1.00 (Ref) 4–8 courses36 (35.3)101 (36.1)1.07 (0.60–1.93) <8 courses38 (37.3)98 (35.0)1.27 (0.69–2.36)AIT <4 courses11 (16.2)25 (13.2)1.00 (Ref) 4–8 courses24 (35.3)52 (27.4)1.08 (0.45–2.58) >8 courses33 (48.5)113 (59.5)0.68 (0.29–1.60)JIA <4 courses10 (18.5)56 (35.9)1.00 (Ref) 4–8 courses15 (27.8)45 (28.8)2.91 (1.05–8.05) >8 courses29 (53.7)55 (35.3)6.60 (2.12–20.5)IBD <4 courses5 (18.5)11 (15.1)1.00 (Ref) 4–8 courses9 (33.3)21 (28.8)0.90 (0.21–3.86) >8 courses13 (48.1)41 (56.2)0.69 (0.17–2.79)Infancy (<age of 1 year)AD None128 (52.9)356 (50.3)1.00 (Ref) 1–2 courses85 (35.1)262 (37.0)0.98 (0.71–1.36) ≥3 courses29 (12.0)90 (12.7)0.86 (0.53–1.38)DM None59 (57.8)147 (52.5)1.00 (Ref) 1–2 courses34 (33.3)102 (36.4)0.92 (0.55–1.53) ≥3 courses9 (8.8)31 (11.1)0.58 (0.24–1.39)AIT None37 (54.4)89 (46.8)1.00 (Ref) 1–2 courses22 (32.4)73 (38.4)0.73 (0.38–1.41) ≥3 courses9 (13.2)28 (14.7)0.73 (0.31–1.74)JIA None24 (44.4)82 (52.6)1.00 (Ref) 1–2 courses19 (35.2)56 (35.9)1.32 (0.65–2.68) ≥3 courses11 (20.4)18 (11.5)2.26 (0.93–5.54)IBD None14 (51.9)31 (42.5)1.00 (Ref) 1–2 courses12 (44.4)29 (39.7)0.99 (0.41–2.40) ≥3 courses1 (3.7)13 (17.8)0.21 (0.03–1.75)Toddler phase (from age of 1 up to third birthday/the index date)dAD None45 (23.3)146 (20.6)1.00 (Ref) 1–2 courses73 (30.2)216 (30.5)1.12 (0.71–1.76) ≥3 courses124 (51.2)346 (48.9)1.16 (0.76–1.78)DM None21 (20.6)56 (20.0)1.00 (Ref) 1–2 courses27 (26.5)96 (34.3)0.71 (0.36–1.43) ≥3 courses54 (52.9)128 (45.7)1.17 (0.62–2.21)AIT None11 (16.2)40 (21.1)1.00 (Ref) 1–2 courses25 (36.8)44 (23.2)2.48 (1.00–6.17) ≥3 courses32 (26.4)106 (55.8)1.18 (0.50–2.81)JIA None13 (24.1)35 (22.4)1.00 (Ref) 1–2 courses12 (22.2)57 (36.5)0.51 (0.18–1.46) ≥3 courses29 (53.7)64 (41.0)1.44 (0.60–3.45)IBD None2 (7.4)10 (13.7)1.00 (Ref) 1–2 courses9 (33.3)17 (23.3)6.85 (0.74–63.1) ≥3 courses16 (59.3)46 (63.0)4.20 (0.49–35.8)Preschool to adolescence (from age of 3 years to index dateAD None33 (13.6)96 (13.6)1.00 (Ref) 1–2 courses38 (15.7)147 (20.8)0.82 (0.45–1.48) ≥3 courses151 (62.3)40 (57.3)1.28 (0.75–2.18)DM None20 (19.6)54 (19.3)1.00 (Ref) 1–2 courses13 (12.7)71 (25.4)0.59 (0.24–1.49) ≥3 courses57 (55.9)126 (45.0)1.60 (0.73–3.53)AIT None7 (10.3)13 (6.8)1.00 (Ref) 1–2 courses10 (14.7))29 (15.3)0.55 (0.17–1.85) ≥3 courses51 (75.0)148 (77.9)0.64 (0.23–1.81)JIA None6 (11.1)27 (17.3)1.00 (Ref) 1–2 courses9 (16.7)35 (22.4)1.89 (0.52–6.78) ≥3 courses33 (61.1)72 (46.2)3.94 (1.16–13.4)IBD None1 (3.7)2 (2.7)1.00 (Ref) 1–2 courses6 (16.2)12 (16.4)1.16 (0.75–17.8) ≥3 courses18 (66.7)52 (71.2)0.80 (0.06–10.3)Purchases within 2 years before the index dateAutoimmune diseases None120 (49.6)371 (52.4)1.00 (Ref) 1–2 courses70 (28.9)235 (33.2)0.98 (0.69–1.40) ≥3 courses52 (21.5)102 (14.4)1.72 (1.08–2.74)DM None54 (52.9)153 (54.6)1.00 (Ref) 1–2 courses27 (26.5)83 (29.6)0.93 (0.53–1.63) ≥3 courses21 (20.6)44 (15.7)1.34 (0.62–2.87)AIT None39 (57.4)101 (53.2)1.00 (Ref) 1–2 courses21 (30.9)64 (33.7)1.11 (0.56–2.18) ≥3 courses8 (11.8)25 (13.2)0.92 (0.33–2.55)JIA None24 (44.4)76 (48.7)1.00 (Ref) 1–2 courses14 (25.9)56 (35.9)0.91 (0.39–2.10) ≥3 courses16 (29.6)24 (15.4)2.39 (0.95–6.00)IBD None10 (37.0)37 (50.7)1.00 (Ref) 1–2 courses9 (33.3)27 (37.0)1.25 (0.44–3.56) ≥3 courses8 (29.6)9 (12.3)3.85 (0.93–15.9)aAD = all autoimmune diseases together. Cases = children with ADs, $$n = 242$$ (represented with DM (type 1 diabetes mellitus), $$n = 102$$; AIT (autoimmune thyroiditis), $$n = 68$$; JIA (juvenile idiopathic arthritis), $$n = 54$$; and IBD (inflammatory bowel diseases), $$n = 27$$). Nine children had two diagnoses. Throughout childhood, only 48 children ($5.8\%$) had no record of antibiotic purchases.bEach child in the case group was matched with one to four children of similar age, sex, residential area, gestational age (preterm/term), and delivery mode (cesarean section/vaginal).cOdds ratio and CI ($95\%$ confidence interval) were obtained using conditional logistic regression.dAntibiotic purchases throughout childhood including postnatal antibiotics. Only 14 children in the case group and 34 in the control group had no antibiotic purchases. Index date = date of diagnosis for children who developed autoimmune diseases and compatible date for their matching controls. Italic values indicate statistical significance $p \leq 0.05.$
Regarding individual diagnoses, onset of JIA was more common in children receiving more than 4 courses of antibiotics (4–8 courses OR 2.91, $95\%$ CI 1.05–8.05 and >8 courses OR 6.60, $95\%$ CI 2.12–20.5) than in those receiving <4 antibiotic courses through the entire study period from birth to the index date (Table 2). Also, the development of JIA was associated with ≥3 antibiotic courses during preschool to adolescence when compared to the respective group with no antibiotic purchases (OR 3.94, $95\%$ CI 1.16–13.4). No such associations were observed regarding DM, AIT, and IBD (Table 2).
## Types of antibiotic purchases
Penicillins were the most commonly purchased antibiotics ($40\%$ of all antibiotics, of which over $80\%$ were amoxicillin), followed by macrolides ($20\%$ of all antibiotics, of which over $80\%$ were azithromycin) (Supplementary Table 2). When purchases of penicillins, macrolides, cephalosporins, amoxicillin-clavulanic acid, sulfonamides, and trimethoprim were analyzed separately, none of them was associated with the onset of ADs in general (Fig. 3 and Supplementary Table 4). However, during the toddler phase purchases of amoxicillin-clavulanic acid (OR 1.18 $95\%$ 1.01–1.37); and within 2 years before the index date purchases of macrolides were associated with the onset of ADs in general (OR 1.24, $95\%$ CI 1.01–1.51).Fig. 3Association between types of antibiotic purchases in different exposure periods and the development of an autoimmune disease (AD), represented by type 1 diabetes (DM), autoimmune thyroiditis (AIT), juvenile idiopathic arthritis (JIA), or inflammatory bowel diseases (IBD)a.aCases = children with ADs (DM, AIT, JIA, or IBD). Nine children have two diagnoses. OR odds ratio, CI confidence interval. Analyses were performed using conditional logistic regression. Index date = age of diagnosis for children who developed autoimmune diseases and compatible date for their matching controls.
For individual diagnoses, the development of JIA was associated with purchases of broad-spectrum antibiotics (cephalosporins, macrolides, and amoxicillin-clavulanic acid), (Fig. 3 and Supplementary Table 4) in three different time periods: throughout childhood, infancy, and toddler phase. The highest ORs for these antibiotics were seen in infancy (cephalosporins OR 2.54, $95\%$ CI 1.01–6.38; macrolides OR 1.80, $95\%$ CI 1.08–3.01; and amoxicillin-clavulanic acid OR 1.93, $95\%$ CI 1.12–3.32, respectively). On the other hand, the development of DM was associated with purchases of sulfonamides and trimethoprim during preschool to adolescence (OR 1.35, $95\%$ CI 1.03–1.77) (Fig. 3 and Supplementary Table 4). These findings did not apply to any other individual diagnoses. Finally, purchases of penicillin were not associated with any types of ADs in this study.
## Discussion
Our study is the first to investigate the association of the number and types of antibiotic exposures in different stages of childhood with the onset of four common pediatric ADs (DM, AIT, JIA, or IBD) in a mutual setting. The total number of antibiotic purchases from birth to the index date was not associated with the development of the studied ADs as one group. However, we found a nonlinear and timing-dependent relationship between the number of antibiotic exposures and the onset of ADs. Furthermore, although the total exposure to antibiotics throughout childhood was not related to the development of these ADs, the more recent and frequent exposures within 2 years prior to the diagnosis were associated. This finding was further supported by purchases of macrolides 2 years prior diagnosis, which increased the risk of ADs. Regarding specific diagnosis of AD, the number of antibiotic exposures throughout childhood was associated with the onset of JIA. Intriguingly, early exposures to broad-spectrum antibiotics were associated with JIA as well. Despite being the most common antibiotic used in childhood, penicillins (predominantly amoxicillin) were safe to use at any age in relation to the development of ADs.
Antibiotic purchases during the first year of life were not associated with the development of any ADs. On contrary, higher exposures to antibiotics at later stages, i.e., close to the age of diagnosis (the median age of 9 years) were associated with the onset of ADs. How do these findings align with previous studies? A Swedish register-based study presented a connection between prescribed antibiotics during infancy and the onset of DM,26 while studies from other countries assessing parental reports or prescription records have not reported a significant association between early childhood antibiotic exposures and DM.27,33,34 Studies from the United Kingdom, Finland, and Sweden have associated early life antibiotic exposures with the onset of JIA.22,23,35 As for IBD, early antibiotic exposures have been related to Crohn’s disease, but this association was not apparent regarding UC.25,36,37 However, a recent meta-analysis did not confirm the relationship between antibiotics and IBD.38 Most studies have assumed a linear association between antibiotic exposure and the onset of a particular AD, i.e. the risk increases with increasing antibiotic exposure. Our study challenges this presumption, as we did not detect linear associations between antibiotic exposures and onset of ADs. In fact, penicillins (as the most common antibiotic type to treat pediatric infections) were not related to the development of any ADs at any age. Since infancy is the most susceptible period for common infectious diseases and consequently the period of most frequent antibiotic use,39 it is reassuring that antibiotics used in early childhood hardly increased the risk for developing pediatric ADs.
We reported that recent purchases of macrolides (within the two years before a diagnosis) were related to obtaining an AD in general, while purchases of sulfonamides and trimethoprim during preschool and adolescence were particularly associated with DM. Exposures to amoxicillin-clavulanic acid during the toddler phase were related to the development of an AD as well, but this finding was most likely driven by the association between this antibiotic and JIA. Intriguingly, in addition to amoxicillin-clavulanic acid, the use of other broad broad-spectrum antibiotics such as macrolides and cephalosporins before the age of three years was also associated with JIA but not with DM, AIT, or IBD. The reason for this finding can only be speculated. JIA is a group of complex, multifactorial, and heterogenous diseases.40 *The pathogenesis* of JIA involves several types of immunological cells, with interacting mechanisms that are not entirely known. For instance, JIA has been treated with non-steroid anti-inflammatory drugs, which have not been used in other ADs—suggesting a broader spectrum of inflammatory responses in its disease mechanism. Therefore, we suggest that early childhood infections, antibiotics, or both of them together, might influence a disease mechanism of JIA that is rather different than those of other ADs. What this disease mechanism might be is still beyond our understanding and warrants further studies.
Antibiotic exposures could be interpreted as exposures to infections, which might act as triggers for ADs.41 In our study, penicillins were the most common antibiotics used, yet having no prominent association with ADs. Furthermore, a recent cohort study from Sweden showed that while early antibiotic use was associated with JIA, the infections causing the antibiotic exposures were not.35 Finally, both antibiotic use (especially among children under the age of 5 years) and ADs are more common in industrialized countries than in developing countries, while infections are generally more common in developing countries.42–46 Therefore, infections may not be the most plausible explanation in relating antibiotics and ADs.
Since antibiotics have been shown to have an influence on gut microbial homeostasis,47 antibiotic exposures could be related to ADs through altering gut microbiota composition, often seen in different autoimmune diagnosis.12,48–53 Magnitude and type of gut microbiota modification varies according to given antibiotics, hence recovery time after different types of antibiotic exposures may vary as well.54–56 For example, macrolides targeting and inhibiting intracellular ribosomal protein synthesis have both a broad spectrum and a long-term influence on gut microbiota that may persist even for several years.57–59 In addition, macrolides have immunomodulatory properties.60 These characteristics may yield a summative response in the immune system. In our study, azithromycin was the most often used antibiotic among macrolides. Azithromycin has a broad bacteriostatic spectrum, a marked tissue penetration, a high stability, and a low clearance rate due to its long half-life, which enable it to reach a higher cellular concentration compared to penicillin.57 These characteristics may explain the long-term influences of azithromycin on gut microbiota compared with penicillin. Furthermore, a previous Fin-HIT study showed that azithromycin presented the strongest inverse association with salivary microbiota diversity.61 *Since dysbiosis* of gut and salivary microbiota have also been associated with ADs,62 we suggest that macrolides might catalyze long-term dysbiosis, explaining their association with ADs. Further studies to examine the potential link between the use of broad-spectrum antibiotics, the duration of their influence on gut microbiota, gut dysbiosis, and the onset of ADs are warranted.
The strength of our study lies in the comprehensive and excellent coverage of longitudinal data from national registers, which has been shown before.63 For example, we were able to trace purchased antibiotics as outpatients rather than just prescribed. In addition, we studied several ADs in a mutual setting, using a comprehensive Fin-HIT cohort with small variations in socioeconomic status as the source of the study population.15 The controls were matched for age (with a maximum difference of four days), sex, residential area, gestational age, and delivery method to limit the number of potential confounding factors. This matching design provided an additional benefit by indirectly limiting the role of the season as a confounder—since season-related factors, such as infections and daylight exposures, would similarly influence both cases and controls. In addition, our study setting made it possible to examine the association between childhood antibiotic exposures at different stages of childhood and onsets of the four pediatric ADs together, and to reliably compare one disease to another.
As for limitations, we lack information on the children’s genetic susceptibility to infections or to ADs. We also did not know why the antibiotics were purchased—for treating infections (and if so, for what kind of infection) or for prophylactic purposes—and on whether secondary antibiotic courses for the same infection were needed. In addition, we had no access to the antibiotics given during inpatient care. Yet, antibiotic treatments during hospitalization are often continued orally after discharge, and our data cover these post-discharge antibiotic purchases. Finally, we have no guarantee on the consumption of the purchased antibiotics. However, since monitoring antibiotic consumption of over 11,000 children for over a decade is technically not possible, a study design based on antibiotic purchases is the second-best option, which we used in this study.
## Conclusion
Use of antibiotics throughout childhood can be considered relatively safe in relation to the development of pediatric ADs. Antibiotics in the penicillin group are unlikely to be associated with the development of any ADs. In contrast, broad-spectrum antibiotics should be used considerately as they may associate with the development of ADs, especially JIA. In conclusion, the development of an AD is a multifactorial process in which antibiotics have a role to play, but the importance of that role still needs to be determined.
## Supplementary information
Supplementary Information The online version contains supplementary material available at 10.1038/s41390-022-02188-4.
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|
---
title: Fetal endothelial colony-forming cell impairment after maternal kidney transplantation
authors:
- Nadia Meyer
- Thu Huong Vu
- Lars Brodowski
- Bianca Schröder-Heurich
- Constantin von Kaisenberg
- Frauke von Versen-Höynck
journal: Pediatric Research
year: 2022
pmcid: PMC10033415
doi: 10.1038/s41390-022-02165-x
license: CC BY 4.0
---
# Fetal endothelial colony-forming cell impairment after maternal kidney transplantation
## Abstract
### Background
Successful pregnancies are nowadays possible after kidney transplantation but are associated with a higher incidence of maternal and fetal complications. Immunosuppressive therapy causes cardiovascular side effects but must be maintained during pregnancy. Little is known about the consequences of maternal kidney transplantation on offspring’s endothelial health. Endothelial colony forming cells (ECFCs) represent a highly proliferative subtype of endothelial progenitor cells and are crucial for vascular homeostasis, repair and neovascularization. Therefore, we investigated whether maternal kidney transplantation affects fetal ECFCs’ characteristics.
### Methods
ECFCs were isolated from umbilical cord blood of uncomplicated and post-kidney-transplant pregnancies and analyzed for their functional abilities with proliferation, cell migration, centrosome orientation and angiogenesis assays. Further, ECFCs from uncomplicated pregnancies were exposed to either umbilical cord serum from uncomplicated or post-kidney-transplant pregnancies.
### Results
Post-kidney-transplant ECFCs showed significantly less proliferation, less migration and less angiogenesis compared to control ECFCs. The presence of post-kidney-transplant umbilical cord serum led to similar functional aberrations of ECFCs from uncomplicated pregnancies.
### Conclusions
These pilot data demonstrate differences in ECFCs’ biological characteristics in offspring of women after kidney transplantation. Further studies are needed to monitor offspring’s long-term cardiovascular development and to assess possible causal relationships with immunosuppressants, uremia and maternal cardiovascular alterations.
### Impact
Pregnancy after kidney transplantation has become more common in the past years but is associated with higher complications for mother and offspring. Little is known of the impact of maternal kidney transplantation and the mandatory immunosuppressive therapy on offspring vascular development. In this study we are the first to address and detect an impairment of endothelial progenitor cell function in offspring of kidney-transplanted mothers. Serum from post-transplant pregnancies also causes negative effects on ECFCs’ function. Clinical studies should focus on long-term monitoring of offspring’s cardiovascular health.
## Introduction
Since the first pregnancy resulted in a live birth following a kidney transplantation with consecutive immunosuppression in 1967, the number of female transplant recipients of child-bearing age has steadily increased and the issue of post-transplantation pregnancies has become considerably more important1. Due to modern immunosuppressive drugs, the risk of rejection has decreased and the fertility of women often restores after kidney transplantation2. Nevertheless, the management of these pregnancies is challenging and associated with higher rates of maternal and perinatal complications3.
Life-long immunosuppression is necessary to avoid graft rejection but might present a potential hazard for the offspring during pregnancy and afterwards. Most immunosuppressive drugs reach the fetal circulation by crossing the placental barrier4.
It is well-known that the intrauterine milieu and complications during pregnancy co-determine offspring’s health and cardiovascular risk. While exposure to adverse conditions, e.g. preeclampsia and diabetes, is associated with cardiovascular impairment in children in later life, data on offspring exposed to immunosuppressants in utero are limited and the impact of maternal transplantation and immunosuppressive therapy during pregnancy on offspring’s cardiovascular health has not been studied yet5–14.
Endothelial progenitor cells (EPCs) are impaired in several cardiovascular diseases15–17 and are considered as one of the strongest biomarkers to evaluate endothelial dysfunction and cardiovascular risk18–21. Endothelial colony-forming cells (ECFCs), a highly proliferative subgroup of EPCs, play an important role in angiogenesis and vascular repair and contribute to endothelial integrity22,23.
At the time of birth, cord blood-derived ECFCs are easily accessible and functional impairment has been reported in pregnancy complications which are associated with long-term cardiovascular impairment of the offspring24–26. In this study, we therefore tested whether fetal ECFCs are affected in pregnancies following maternal kidney transplantation.
## Patients
The study was approved by the Institutional Review Board of Hannover Medical School (approval no. 1443–2012 and 504–2009). Written informed consent was obtained from each participant.
Umbilical cord blood was collected from 12 uncomplicated and 6 post-kidney-transplant pregnancies for ECFC isolation, serum extraction or both. Uncomplicated pregnancies were defined as normotensive and without proteinuria, preexisting diabetes, hypertensive, vascular or renal disease, smoking or the use of illicit drugs ending with the delivery of a healthy baby. For the experiments, patients were matched by maternal age, BMI and gestational age at delivery.
## ECFC isolation, culture and characterization
Umbilical cord blood was collected into sterile EDTA-tubes immediately after delivery. Serum was extracted from separate serum tubes and stored at −80 °C until use for further experiments. ECFCs were isolated as previously described25,27,28 and cultured in endothelial growth medium 2 (EGM-2) consisting of endothelial basal medium (EBM-2; Lonza, Basel, Switzerland) supplemented with supplier provided supplements, $10\%$ fetal bovine serum (FBS; Harvard Bioscience, Holliston, MA) and $1\%$ penicillin/streptomycin (P/S; Bio&Sell, Feucht, Nürnberg, Germany) at 37 °C, $5\%$ CO2. Day of appearance of ECFC colonies and total colony number were evaluated. ECFC colonies were noted as circumscribed cell monolayers with cobblestone-like morphology. Well-defined colonies were expanded and characterized by flow cytometry with typical phenotype markers (CD31 +, CD45-, and CD133-) by using appropriate antibodies (CD31, 130-117-390, BD Biosciences, San Jose, CA; CD45, 555483, BD Biosciences; and CD133, 130-090-826, Miltenyi Biotec, Bergisch Gladbach, Germany) and corresponding isotype controls (BD Biosciences, Miltenyi Biotec). ECFCs were used for experiments in cell culture passages 4–6.
## Cell impedance assay
For continuous monitoring of live cell proliferation, morphology and viability we used the xCelligence system (Roche, Basel, Switzerland), an impedance-based real-time analysis. The change in impedance is measured via the cell index, a dimensionless parameter reflecting cell adhesion, migration and proliferation and was calculated with the xCelligence Real-Time Cell Analyzer. The electrical impedance caused by adherent cells is converted into cell indices by the xCelligence software (v.1.2.1)28.
For ECFC comparison, 0.25 × 104 cells from 4 control and 4 post-transplant ECFC lines were seeded in quadruplicates in EGM-2 with $10\%$ FBS and $1\%$ P/S onto a gold-coated E-Plate View 96-well plate (Roche) and then placed into the Real-Time Cell Analyzer SP station, positioned in a 37 °C incubator with $5\%$ CO2 supply. Following adherence, the cell indices were aligned for all lines and then continuously monitored for 72 h.
To analyze serum effects, 0.25 × 104 cells from 5 control ECFC lines were seeded in quadruplicates in EGM-2 with $2.5\%$ FBS and $1\%$ P/S and analyzed as described above. When a stable cell index was reached, $2.5\%$ pooled control or pooled transplant serum were added to the cells. Cell impedance was recorded for 72 h.
## In vitro angiogenesis assay
To compare the capacity to form capillary tubule-like networks, 1.4 × 104 cells/well from 4 control and 4 post-transplant ECFC lines were incubated in triplicates in 96-well plates pre-coated with 30 µL growth factor reduced Matrigel (BD Biosciences) for 14 h in EBM-2 with $5\%$ FBS and $1\%$ P/S24.
In separate experiments, 5 control ECFC lines were treated with either $5\%$ control or $5\%$ transplant serum in EBM-2 with $1\%$ P/S. Phase contrast microscopic images were taken with a Leica DMI 6000 B microscope (Leica, Wetzlar, Germany). Total tube length and number of branch points in each visual field were calculated with ImageJ 1.50b (National Institutes of Health)26. Branch points were defined as nodes with connections to at least 3 tubes.
## Migration assay
To assess migration ability 5 × 104 cells from 4 control and 4 post-transplant ECFC lines were seeded on gelatin-coated (Sigma-Aldrich, St. Louis, Missouri) wells of 6-well culture plates with EGM-2 containing $10\%$ FBS and $1\%$ P/S and grown to confluence. The cell monolayers were scratched with a sterile pipette tip to create a wound and washed with PBS. Afterwards, cells were cultured in fresh EBM-2 with $2.5\%$ FBS and $1\%$ P/S.
To analyze serum effects on ECFCs’ migration ability, 5 control ECFC lines were seeded, grown and scratched as described above. Then $5\%$ control or transplant serum was added to the medium. Phase contrast microscopic images were immediately taken after scratching and then again after 18 h with a Leica DMI 6000 B microscope. Non-populated scratch areas were quantified by ImageJ 1.50b and subtracted to obtain the remigrated area.
## Centrosome orientation assay
In several cell types, there is a correlation between the position of the centrosome and the direction of cell movement: the centrosome is located behind the leading edge, suggesting that it serves as a control device for directional cell movement29. A change in the direction of cell movement precedes a re-orientation of the centrosome in the intended direction and is a sign of polarity of migrating cells30. During migration, the centrosome is positioned between the nucleus and the leading part, indicating the migrational status and direction31. To study differences in centrosome orientation, ECFCs were grown to confluence on coverslip glasses in 6-well culture plates and scratches were performed. After 1 h, cells were washed with PBS, fixed with $3\%$ formaldehyde and $2\%$ sucrose and permeabilized with $0.2\%$ Triton X-100 (Sigma-Aldrich). Cells were incubated with an antibody against pericentrin (ab28144; Abcam, Cambridge, UK) in $2\%$ normal goat serum (Thermo Fisher Scientific, Waltham, Massachusetts) and PBS for 2 h, washed 3x with PBS and incubated with Alexa Fluor anti-mouse IgG 546 (Thermo Fisher Scientific) for 2 h. Nuclear DNA was stained with 4′,6-diamidino-2-phenylindole (DAPI; Thermo Fisher Scientific), and coverslip glasses were mounted in mounting medium (ProLongGold; Thermo Fisher Scientific). Images were acquired randomly along the scratch with a Leica DMI 6000 B microscope (Leica).
## Statistical analysis
Normality distribution was tested by Shapiro-Wilk or D’Agostino normality test. Students paired t test was applied for serum associated data, students unpaired t test was used to analyze cell related data. Welch’s correction was administered in case of unequal variances. Not normally distributed data were assessed by Wilcoxon matched-pairs signed rank test or Mann Whitney test, respectively. Experimental data of biological replicates are presented as mean and standard error in the text. The obtained individual measured values (n) from each experiment were analyzed with Prism 9 (GraphPad Software, La Jolla, CA). P-values at < 0.05 were considered statistically significant and indicated in the figures as follows: * $p \leq 0.05$, ** $p \leq 0.01.$
## Patient characteristics
The kidney transplantation related clinical data for the transplanted women recruited for the study are given in Supplemental Table 1. The average time that has elapsed since transplantation was 5.5 years ± 1.5 years. All transplanted women received immunosuppression with tacrolimus. The mean tacrolimus plasma concentration was 5.4 µg/l ± 0.4 µg/l. All concentrations were in line with the average concentrations reported by Hebert et al.32 The comparison of pregnancy associated clinical and demographic data for women who provided umbilical cord blood are given in Table 1. Maternal age, gravidity, parity, maternal pre-pregnancy BMI, birth weight, birth weight percentile, delivery mode and sex of the baby were not statistically different between the control and the transplant group. The transplanted women had higher blood pressures at delivery (systolic: 144.7 ± 9.6 mmHg; diastolic: 87.3 ± 5.1 mmHg) compared to the control group (systolic: 112.9 ± 2.4 mmHg, $p \leq 0.001$; diastolic: 68.0 ± 1.6 mmHg, $$p \leq 0.004$$), although none had developed preeclampsia. The mean maternal serum creatinine concentration of the transplant group (Tx, 124 ± 11 µmol/l) was significantly higher than the control group (Con, 53 ± 3 µmol/l, $$p \leq 0.001$$) whereas the mean GFR was significantly lower (Tx 55 ± 9 ml/min/1,73 m2 vs. Con 123 ± 3 ml/min/1,73 m2, $p \leq 0.001$), respectively. Table 1Demographic and clinical characteristics of the study population who provided cord blood. Control group ($$n = 12$$)Transplant group ($$n = 6$$)p valueMaternal characteristicsMaternal age (years)29.50 ± 1.2829.00 ± 1.980.83Ethnicity0.34 European9 [75]3 [50] Asian3 [25]3 [50]Primigravida4 [30]4 [67]0.32Primipara8 [67]6 [100]0.25Maternal pre-pregnancy BMI (kg/m2)24.12 ± 1.2527.22 ± 1.650.16Gestational weight gain15.36 ± 2.1014.57 ± 4.310.85Gestational SBP, pre-delivery (mmHg)112.9 ± 2.4144.7 ± 9.6<0.001Gestational DBP, pre-delivery (mmHg)68.0 ± 1.687.3 ± 5.10.004Gestational age at delivery (weeks)37.49 ± 0.8934.74 ± 1.790.18Caesarean delivery9 [75]5 [83]>0.99Conception mode spontaneous12 [100]5 [83]0.33Gestational diabetes mellitus00NAPIH or preeclampsia00NATocolysis2 [17]1 [17]>0.99ASA prophylaxis0 [0]3 [50]0.03Creatinine (µmol/l)53 ± 3124 ± 110.001GFR (ml/min/1,73 m2)123 ± 355 ± 9<0.001Neonatal characteristicsBirth weight (g)3055 ± 2282534 ± 4180.25Birth weight percentile44.75 ± 7.3452.00 ± 9.060.56Sex of the baby male8 [67]4 [67]>0.99Preterm birth < 37 weeks3 [25]3 [50]0.34RDS prophylaxis3 [25]2 [33]>0.99Data are presented as mean ± standard error or number (% of total); ASA acetylsalicylic acid, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, GFR glomerular filtration rate, NA not applicable, PIH pregnancy induced hypertension, RDS respiratory distress syndrome.
## Lower cell index of fetal ECFCs under transplant conditions
The time to the appearance of the first ECFC colony (Tx 8.5 ± 0.9 days vs. Con 9.2 ± 2.4 days, $$n = 4$$–5, $$p \leq 0.81$$) and the total number of colonies formed (Tx 9.0 ± 3.2 colonies vs. Con 13.4 ± 5.4 colonies, $$n = 4$$–5, $$p \leq 0.53$$) did not differ between the control and the transplant group.
The cell index of offspring from women with a kidney transplant, determined by real-time cell analysis, kept up with ECFCs derived from control umbilical cord blood in the first 24 h (Tx 22,149 ± 2,596 vs. Con 23,196 ± 1216, $$n = 4$$, $$p \leq 0.73$$), slowed down after 48 h (Tx 33,610 ± 8,250 vs. Con 46,468 ± 2,118, $$n = 4$$, $$p \leq 0.18$$) and showed a significantly lower increase after 72 h (Tx 35,240 ± 10,006 vs. 61,979 ± 4,164, $$n = 4$$, $$p \leq 0.049$$), Fig. 1a. Fig. 1Lower cell index of fetal ECFCs under transplant conditions.a Overlay of growth curves of ECFCs from healthy or post-transplant pregnancies. The cell index of transplant ECFCs is significantly lower after 72 h. b Overlay of growth curves of ECFCs treated with control or transplant serum. The presence of transplant serum leads to a significantly lower cell index after 24, 48 and 72 h. $$n = 4$$–5; Con control, Tx transplant, CS control serum, TS transplant serum. * $p \leq 0.05$, ** $p \leq 0.01.$
In the presence of transplant serum (TS) the cell index was markedly lower when compared to the incubation with serum from healthy controls (CS) (TS 16,209 ± 1,592 vs. CS 24,681 ± 1,871 after 24 h, $$p \leq 0.005$$; TS 27,470 ± 4,239 vs. CS 43,005 ± 5,986 after 48 h, $$p \leq 0.008$$; TS 38,962 ± 7,623 vs. CS 55,689 ± 10,260 after 72 h, $$p \leq 0.04$$, $$n = 5$$), Fig. 1b.
## Reduced tube formation ability of fetal ECFCs after maternal transplantation and after incubation with transplant cord serum
An in vitro angiogenesis assay was performed to reflect ECFCs’ ability to form de novo vessels in vivo. ECFCs derived from umbilical cord blood from transplant patients showed significantly lower tube lengths and lower number of branch points than ECFCs derived from umbilical cord blood from control patients (tube lengths: Tx 3.02 × 107 µm ± 0.70 × 107 µm vs. Con 4.93 × 107 µm ± 0.34 × 107 µm, $$n = 4$$, $$p \leq 0.048$$; branch points: Tx 41 ± 13 vs. Con 106 ± 17, $$n = 4$$, $$p \leq 0.02$$), Fig. 2a, b.Fig. 2Reduced tube formation ability of fetal ECFCs after maternal transplantation and after incubation with transplant cord serum.a ECFCs of post-transplant pregnancies show less tube formation ability. b Representative images of control (a) and transplant (b) ECFCs. c Incubation with transplant serum impairs ECFCs’ angiogenesis. d Representative images of ECFCs treated with control (c) or transplant serum (d). Images were obtained after 14 h. Scale bar 500 µm. Box plots represent median, 25th and 75th percentile, whiskers the minimum and the maximum. $$n = 4$$–5. Con control, Tx transplant, CS control serum, TS transplant serum. * $p \leq 0.05.$
ECFCs’ ability to form capillary-like structures in Matrigel was significantly impaired in presence of umbilical cord serum from transplant patients in comparison to umbilical cord serum from healthy controls (TS 3.22 × 107 µm ± 0.42 × 107 µm vs. CS 3.62 × 107 µm ± 0.44 × 107 µm, $$n = 5$$, $$p \leq 0.047$$). The difference in the number of branch points did not reach significance (Tx: 52 ± 10 vs. Con 62 ± 11, $$n = 5$$, $$p \leq 0.30$$), Fig. 2c, d.
## Impaired migration of fetal ECFCs after maternal transplantation and after incubation with transplant cord serum
We addressed the migration capacity of transplant patients’ offspring’s ECFCs in a scratch wound healing assay. Transplant ECFCs showed significantly less wound closure after 18 h than control ECFCs (relative remigrated area: Tx 0.72 ± 0.08 vs. Con 1.00 ± 0.12, $$n = 4$$, $$p \leq 0.045$$), Fig. 3a, b.Fig. 3Impaired migration of fetal ECFCs after maternal transplantation and after incubation with transplant cord serum.a ECFCs from post-transplant pregnancies are less capable to migrate. b Representative images of control (a) and transplant (b) ECFCs after 18 h of migration. c Incubation with transplant serum impairs ECFC migration. d Representative images of ECFCs treated with control (c) or transplant serum (d). Scale bar 1000 µm. Box plots represent median, 25th and 75th percentile, whiskers the minimum and the maximum. Cell-free area after 18 h was subtracted from cell-free area at start to calculate remigrated area. Mean of control group was set to 1. $$n = 4$$–5. Con control, Tx transplant, CS control serum, TS transplant serum. * $p \leq 0.05$, ** $p \leq 0.01.$
In presence of transplant serum, ECFCs remigrated about half as much of the area as when incubated with control serum (relative remigrated area: TS 0.46 ± 0.07 vs. CS 1.00 ± 0.07, $$n = 5$$, $$p \leq 0.001$$), Fig. 3c, d.
## Delayed pro-migratory positioning of fetal ECFCs from kidney transplanted women
Pericentrin staining was applied to gain insight in centrosome orientation. In accordance to the findings in the scratch wound healing assay, the proportion of ECFCs in direction towards the wound as well as the ratio of forwards and backwards directed cells were significantly lower in the transplant group compared to the control group (forwards: Tx 0.48 ± 0.02, $$n = 4$$ vs. Con 0.68 ± 0.04, $$n = 7$$, $$p \leq 0.006$$; backwards: Tx 0.52 ± 0.02, $$n = 4$$, vs. Con 0.32 ± 0.04, $$n = 7$$, $$p \leq 0.006$$; ratio: Tx 0.94 ± 0.06, $$n = 4$$, vs. Con 2.38 ± 0.34, $$n = 7$$, $$p \leq 0.01$$), Fig. 4.Fig. 4Delayed pro-migratory positioning of fetal ECFCs from kidney transplanted women. Centrosome localization is indicated by pericentrin staining (red) in immunofluorescence 1 h after the scratch performance. Nuclei were counterstained with DAPI (blue). Transplant ECFCs show less forwards (a) and more backwards (b) orientation than control ECFCs. Ratio of forwards and backwards orientation is calculated in c. Representative images of control and transplant ECFCs are shown in d and e. White lines indicate scratch borders. White arrows show the migration direction of the cells: forwards (towards the scratch) and backwards (away from the scratch). At least 37 cells were counted per line. Scale bar 50 µm. Box plots represent median, 25th and 75th percentile, whiskers the minimum and the maximum. $$n = 4$$–7, Con control, Tx transplant. * $p \leq 0.05$, ** $p \leq 0.01.$
## Discussion
In this study, we report a significant impairment of main biological characteristics of fetal ECFCs from mothers with a kidney transplant. A similar effect was observed when ECFCs derived from healthy pregnancies were exposed to umbilical cord serum from pregnancies after maternal kidney transplantation. To our knowledge, we are the first to address endothelial progenitor cells in transplant patients’ offspring as surrogate marker for vascular health.
The foundation for cardiovascular diseases in later life can already be laid during pregnancy7,9. Therefore, it is pivotal to identify risk factors for cardiovascular health as early as possible, when classical risk factors are not yet visible. This might pave the way for early intervention and primary prevention. Pregnancies after kidney transplantation carry a higher risk for the development of pregnancy complications, e.g. pregnancy-induced hypertension, preeclampsia, IUGR, and preterm birth33—well-known risk factors for future cardiovascular impairment of mothers and offspring12,34–36.
Fetal EPCs, which are considered to be involved in vascular homeostasis and repair37, appear to be an adequate model in this context to study vascular health. Our findings are in line with previous studies which describe an impairment of offspring ECFC number and function in gestational diseases or in the newborn period which are associated with later cardiovascular impairment of the progeny. In infants with bronchopulmonary dysplasia, a lung disease associated with prematurity, decreased numbers of ECFCs were reported38. In preeclampsia, a hypertensive disorder of pregnancy, the number of ECFC colonies was lower compared to controls39 and cells showed reduced proliferation, migrated less40 and formed fewer tubules24 which corresponds to our findings in transplant ECFCs. Recently, another study drew the link between ECFCs, neonatal complications and future cardiovascular diseases. It was demonstrated that in former preterms elevated systolic blood pressure significantly correlated with alterations in ECFC proliferation and tube formation41. These findings support our assumption that ECFCs are a suitable marker for vascular impairment.
We additionally demonstrated a negative effect on ECFCs derived from healthy pregnancies when incubated with cord serum of transplant pregnancies compared to cord serum of healthy controls. It seems possible that this observation may be the consequence of a substance circulating in the materno-placental-fetal system. The underlying disease that led to the transplantation is often associated with numerous cardiovascular risk factors and end organ damage, which only partially regress after the transplantation. The post-kidney-transplant cohort in our study still showed significantly higher concentrations of creatinine as an example for circulating urinary substances. In this context, EPCs have shown to be reduced in numbers, function and differentiation in chronic kidney disease patients as well as in uremia42,43. Another potential cause for our observations is the mandatory use of immunosuppressive agents, e.g. tacrolimus. Adverse effects include hypertension, hypercholesterinemia and hyperglycemia with the corresponding effects on the vascular system, leading to endothelial dysfunction44. Rabbits exposed to calcineurin inhibitors in utero were reported to be asymptomatic at birth, but presented hypertension, proteinuria and chronic kidney disease in adulthood implying possible long-term effects of intrauterine exposure to calcineurin inhibitors14,45. Regarding pregnancies in humans, it has been reported that, while trying to maintain target whole blood concentrations, dosage titration leads to a considerable increase of unbound tacrolimus concentrations, which is suggested to have important clinical implications46. Tacrolimus can accumulate in placenta as well as in ex vivo perfused placental tissue what could be associated with cytotoxic effects on placental level47. Unfortunately, the amount of serum obtained from umbilical cord was insufficient to record the concentration of tacrolimus or other metabolites in the newborns included in our study. However, we recently demonstrated calcineurin inhibitor induced functional impairment of ECFCs already in therapeutic concentrations28. These findings support our hypothesis of a possible contribution of immunosuppressive medication to reduced ECFC function in offspring of transplant patients. Apart from circulating substances themselves, there might also be a role for exosomes derived from endothelial or circulating cells. It has been shown that conditioned media or exosomes derived from ECFCs of patients with a known cardiovascular disease led to dysregulation of migration and impairment of tube formation of healthy ECFCs. It was stated that this effect is possibly mediated via the introduction of RNAs including miRNAs48.
*In* general, the pathogenesis of fetal complications is difficult to assess as there are many interacting factors such as the intrauterine exposure to immunosuppressive agents, higher incidence of preterm birth as well as the concomitant maternal pathologies like high blood pressure that can influence the fetal outcome14,24,49. Although not significant, mean gestational age was shorter in the transplant recipients in our study. This could have influenced the study results, but the literature is not clear on this. Baker et al. reported that preterm cord blood grew more ECFC colonies due to a higher proliferative capacity than term blood did and ECFCs had a similar angiogenic capacity50. This is in line with higher numbers of ECFC colonies in the study by Munoz-Hernandes et al.39 but in contrast to the findings by Ligi et al. who describe similar ECFC colony numbers but impaired function of preterm ECFCs51. It is worth noting that three out of six women in the transplant group have taken low-dose acetylsalicylic acid (ASA), which in pregnancy is used in the prevention of preeclampsia. In this context Hu et al. found a favorably impact on EPC migratory function and on the prevention of senescence using low-dose ASA52. At high-dose ASA they and Chen et al. observed an impairment of EPC function52,53. Considering the positive effect of low-dose ASA in clinical but also in in vitro studies on vascular and endothelial health one would expect this to be reflected in ECFC characteristics of the transplant cohort. However, the effects of the aberrant milieu in transplant recipients seem to mask the favorable impact of ASA in our study. Altogether, the history of kidney transplantation and the complications mentioned cannot be discussed independently of one another and should be understood as invitation for interdisciplinary thinking.
Although the number of pregnancies after kidney transplantation has increased, the single-center experience still remains quite low, leading to a small sample size in our pilot study. As in the latter we already detected considerable effects, we wish to share our results to encourage other researchers to contribute to the further elucidation of underlying mechanisms. Confirmatory studies including more participants are needed to corroborate our results and to better reflect the kidney transplant patients’ variety. Also, higher sample sizes would allow to adjust the results for the impact of potential confounders, e.g. gestational age at delivery. The transplant population of our study displayed heterogenous characteristics. For the six women included there were five different causes that led to the former kidney failure. Further, the time between transplantation and pregnancy differed from 2 to 10 years. The validity of our results is correspondingly limited. Another limitation of our study is reflected in the short assay time. Our in vitro analyses covered a period from 14 to 72 h, so no conclusions can be drawn about long-term effects.
So far, there are very few long-term follow-up studies targeting children of transplanted mothers. Most of the available information is limited essentially to classical parameters such as height, weight and head circumference monitoring which fortunately were unremarkable in the majority of children14,54,55. Nevertheless, as systemic alterations after intrauterine calcineurin inhibitor exposure were detected only in adult rodents45, Boulay et al. consequently concluded that the lack of symptoms in children might not be predictive of the absence of long-term effects14. Therefore, and considering the results of our study, further efforts are clearly needed to get a broader picture of possible consequences of maternal transplantation on their offspring’s cardiovascular health. Continuous multidisciplinary long-term follow-up studies should be implemented to open up the possibility for early interventions when facing cardiovascular risk.
## Supplementary information
Supplemental Table S1 The online version contains supplementary material available at 10.1038/s41390-022-02165-x.
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|
---
title: Spatial mapping of mitochondrial networks and bioenergetics in lung cancer
authors:
- Mingqi Han
- Eric A. Bushong
- Mayuko Segawa
- Alexandre Tiard
- Alex Wong
- Morgan R. Brady
- Milica Momcilovic
- Dane M. Wolf
- Ralph Zhang
- Anton Petcherski
- Matthew Madany
- Shili Xu
- Jason T. Lee
- Masha V. Poyurovsky
- Kellen Olszewski
- Travis Holloway
- Adrian Gomez
- Maie St. John
- Steven M. Dubinett
- Carla M. Koehler
- Orian S. Shirihai
- Linsey Stiles
- Aaron Lisberg
- Stefano Soatto
- Saman Sadeghi
- Mark H. Ellisman
- David B. Shackelford
journal: Nature
year: 2023
pmcid: PMC10033418
doi: 10.1038/s41586-023-05793-3
license: CC BY 4.0
---
# Spatial mapping of mitochondrial networks and bioenergetics in lung cancer
## Abstract
Mitochondria are critical to the governance of metabolism and bioenergetics in cancer cells1. The mitochondria form highly organized networks, in which their outer and inner membrane structures define their bioenergetic capacity2,3. However, in vivo studies delineating the relationship between the structural organization of mitochondrial networks and their bioenergetic activity have been limited. Here we present an in vivo structural and functional analysis of mitochondrial networks and bioenergetic phenotypes in non-small cell lung cancer (NSCLC) using an integrated platform consisting of positron emission tomography imaging, respirometry and three-dimensional scanning block-face electron microscopy. The diverse bioenergetic phenotypes and metabolic dependencies we identified in NSCLC tumours align with distinct structural organization of mitochondrial networks present. Further, we discovered that mitochondrial networks are organized into distinct compartments within tumour cells. In tumours with high rates of oxidative phosphorylation (OXPHOSHI) and fatty acid oxidation, we identified peri-droplet mitochondrial networks wherein mitochondria contact and surround lipid droplets. By contrast, we discovered that in tumours with low rates of OXPHOS (OXPHOSLO), high glucose flux regulated perinuclear localization of mitochondria, structural remodelling of cristae and mitochondrial respiratory capacity. Our findings suggest that in NSCLC, mitochondrial networks are compartmentalized into distinct subpopulations that govern the bioenergetic capacity of tumours.
A study describing an approach that combines imaging and profiling techniques to structurally and functionally analyse lung cancer in vivo, revealing heterogeneous mitochondrial networks and an association between bioenergetic phenotypes and mitochondrial organization and function.
## Main
NSCLC is a heterogeneous disease at a histological, genetic and metabolic level4. Mitochondria are essential regulators of cellular energy and metabolism, playing a critical role in sustaining growth and survival of tumour cells5. The mitochondria organize into dynamic networks such that the structural architecture of their outer and inner membrane dictates cellular electron transport chain (ETC) activity and respiratory capacity2,6. However, our understanding of how mitochondrial networks are structurally and functionally regulated in NSCLC at an in vivo level is limited.
To better understand mitochondrial bioenergetics in NSCLC, we recently developed and validated a voltage-sensitive, positron emission tomography (PET) tracer known as [18F]4-fluorobenzyl triphenylphosphonium ([18F]FBnTP)7,8. This tracer allowed us to measure relative changes in the mitochondrial membrane potential (ΔΨ) in autochthonous KRAS-driven mouse models of NSCLC9. PET imaging of NSCLC tumours in KRAS-driven genetically engineered mouse models (GEMMs) identified that lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) had distinctly different uptake values for the radiotracers [18F]FBnTP and 18F[FDG], suggestive of functionally distinct metabolic and bioenergetic phenotypes9. Therefore, we sought to determine whether [18F]FBnTP uptake in NSCLC tumours directly correlated with OXPHOS activity in vivo.
## In vivo profiling of OXPHOS in NSCLC
To evaluate the OXPHOS signatures in NSCLC, we coupled PET imaging of lung tumours using the radiotracers [18F]FBnTP and [18F]FDG followed by ex vivo respirometry analysis of mitochondrial complex I and II activity (Fig. 1a). [ 18F]FBnTP was used to compare the ratio of mitochondrial ΔΨ in the tumour to that of the heart, whereas [18F]FDG was used to measure glucose flux in tumours. Representative [18F]FBnTP and [18F]FDG PET–CT images of KrasG12D;p53−/−;Lkb1−/− (KPL) and KrasG12D;Lkb1−/− (KL) mice identified synchronous lung tumours with differential uptake of the radiotracers (Fig. 1b and Extended Data Fig. 1a), as previously described9. Lung tumours with high [18F]FBnTP and low [18F]FDG uptake were denoted as [18F]FBnTPHI;[18F]FDGLO and glycolytic tumours with the opposite phenotype were termed [18F]FBnTPLO;[18F]FDGHI. In GEMMs, inactivation of Lkb1 drives the development of both LUAD and LUSC subtypes10. Therefore, we confirmed tumour subtypes by staining for surfactant protein C (SP-C) and TTF1 to verify LUAD histology and for glucose transporter 1 (GLUT1) and cytokeratin 5 (CK5) to verify LUSC histology, as previously described11 (Extended Data Fig. 1b–d).Fig. 1In vivo characterization of mitochondrial bioenergetics and respiration capacity among NSCLC subtypes.a, Schematic depicting experimental approach of [18F]FBnTP and [18F]FDG PET imaging followed by respirometry on frozen tumour samples measuring mitochondrial complex I and complex II MRC. T1, tumour 1; T2, tumour 2. b, Representative [18F]FBnTP (left) and [18F]FDG (right) transverse PET–CT images of KPL mice. Uptake of PET probe was measured as the maximum percentage of injected dose (PID) per gram of tissue. Numbers in brackets after T1 and T2 indicate ratio of uptake tumour to heart. H, heart. c, Correlation between the tumour/heart ratio of [18F]FBnTP uptake and complex I MRC of tumours from KPL, KL, Kras, KP and LPP mice ($$n = 30$$ tumours, $$n = 18$$ LUAD tumours and $$n = 12$$ LUSC tumours). The MRC values are normalized to mitochondrial content quantified by MitoTracker Deep Red (MTDR). The grey shading represents s.e.m. One-tailed F-statistics. d, MRC of complex I in frozen xenografts from human cells (H1975, A549, A549 Rho, RH2 and Tu686); data are mean ± s.e.m. ( $$n = 3$$ biological replicates per cell line). One-way analysis of variance (ANOVA), Dunnett test. e, [18F]FBnTP ($$n = 25$$ LUAD tumours, $$n = 22$$ LUSC tumours) and [18F]FDG ($$n = 22$$ LUAD tumours, $$n = 22$$ LUSC tumours) uptake of xenografts from human NSCLC cell lines (H1975 and RH2). Unpaired two-tailed t-test, lines indicate mean value. Source data The ETC generates a proton gradient to maintain ΔΨ and drive OXPHOS. Therefore, we examined whether uptake of [18F]FBnTP correlated with the OXPHOS activity in lung tumours. Following PET imaging, ex vivo respirometry was carried out on snap-frozen lung tumours that allowed for the direct measurement of complex I and II maximal respiratory capacity (MRC)12. Here OXPHOS signatures were defined by the combination of [18F]FBnTP tracer uptake and complex I and II MRC in tumours. We identified a threefold upregulation in complex I and II MRC in [18F]FBnTPHI tumours versus [18F]FBnTPLO tumours (Extended Data Fig. 1e,f). Analysis of mitochondrial respiration in LUAD and LUSC tumours from a larger cohort of KPL and KL mice identified a significant increase in complex I and II MRC in LUAD cells versus LUSC cells (Extended Data Fig. 1f,g). Our analysis of mitochondrial activity in KPL and KL GEMMs showed that LUSC cells had significantly lower OXPHOS signatures compared with those of LUAD cells.
Next, we broadened our correlative examination of complex I and II MRC and [18F]FBnTP uptake in GEMMs across five different genetic backgrounds: KPL and KL, as well as KrasG12D;p53−/− (KP), KrasG12D (Kras) and Lkb1−/−;p53−/−;Pten−/− (LPP) mice (Extended Data Fig. 2a,b). We identified a direct and significant correlation between [18F]FBnTP uptake and complex I and II MRC (Fig. 1c and Extended Data Fig. 2d,f). Clear histologic stratification was observed, as LUAD cells had higher [18F]FBnTP uptake and complex I and II MRC compared with those of LUSC cells (Fig. 1c and Extended Data Fig. 2d,f,h). Conversely, [18F]FDG uptake was inversely correlated with complex I and II MRC (Extended Data Fig. 2c,e,g).
We then evaluated [18F]FBnTP uptake and complex I and II MRC in human LUAD and squamous cell carcinoma (SCC) cell lines to determine whether OXPHOS signatures were conserved across species. The human LUAD cell lines (H1975 and A549), LUSC cell line (RH2), and head and neck squamous cell carcinoma (HNSCC) cell line (Tu686) were implanted into mouse tumour xenografts. H1975 and A549 tumours had significantly higher complex I and II MRC than tumours from RH2, Tu686 or A549 Rho cells (Fig. 1d and Extended Data Fig. 2i). We also confirmed that A549 Rho cells lacked expression of ETC proteins and uptake of [18F]FBnTP (Extended Data Fig. 2j–l). PET imaging of xenografts showed a significantly higher [18F]FBnTP uptake in H1975 tumours versus RH2 tumours (Fig. 1e, left). Conversely, RH2 tumours had significantly higher [18F]FDG uptake than H1975 tumours (Fig. 1e, right). Collectively, our results demonstrated that [18F]FBnTP uptake directly correlated with complex I and II activity in both human and mouse NSCLC. Although LUAD cells tended to have higher OXPHOS signatures than LUSC, given the heightened metabolic heterogeneity that is common to human NSCLC4, we anticipate that OXPHOS signatures may vary between NSCLC tumour subtypes.
## PET-guided 3D SBEM imaging of NSCLC
The fact that we observed functionally distinct OXPHOS signatures in NSCLC tumour subtypes suggests that these tumours may have equally distinct structural organization of their mitochondrial networks. To investigate this, we developed a workflow that paired functional PET imaging with micro-computed tomography (microCT) and ultra-resolution three-dimensional serial block-face electron microscopy13 (3D SBEM; Fig. 2a). The incorporation of microCT imaging allowed us to bridge gaps in resolution scales between whole-tumour imaging with PET and ultrastructure imaging with SBEM. We first carried out [18F]FBnTP and [18F]FDG PET–CT imaging on KRAS(G12D)-driven GEMMs to distinguish mitochondrial activity and glucose flux in tumours (Fig. 2b). We identified regions of high versus low radiotracer uptake within each tumour that were used to guide SBEM analysis (Fig. 2b and Extended Data Fig. 3a–c). We then carried out microCT and histological analysis to: provide an overview of the tumour tissue density from the periphery to core regions; spatially orientate tumours in an x–y–z plane; and differentiate viable versus necrotic tissue (Fig. 2c, Extended Data Fig. 3d,e,h and Supplementary Video 1).Fig. 2PET-guided multi-modality imaging to characterize spatial architecture of mitochondrial networks in NSCLC.a, Schematic of multi-modality imaging technique and analysis approach. b, [18F]FDG transverse (left) and 3D-rendered (right) PET–CT images. [ 18F]FDGHI tumour (KL) was identified with heterogeneous regions of high and low [18F]FDG uptake. Scale bar, 5 mm. c, 3D-rendered microCT image of isolated lung lobe with tumour in b. Dashed line represents the orientation of sectioning plane on the tumour. Scale bar, 5 mm. d, Left: high-resolution microCT images on heavy-metal-stained tumour block. Selected region for SBEM imaging is indicated by white arrowhead. Scale bars, 500 μm (top) and 1 mm (bottom). Right: representative subvolume of SBEM images. Scale bar, 10 μm. e, The landscape of SBEM-imaged OXPHOSLO LUSC tumour volume after individual cell segmentation and cell-type classification. LUSC, blue; neutrophil (NTPH), yellow; red blood cell (RBC), purple; LUAD, red. f, Quantification of different cell types in e.*Source data* *With this* workflow in place, we next carried out high-resolution microCT scans on tumours to verify that the tumour sections had proper depth of penetration of heavy metal staining and contained viable, tumour-dense tissue (Fig. 2d, Extended Data Fig. 3f,g,i and Supplementary Video 2). Last, sequential SBEM images were acquired and compiled to generate 3D tumour volumes (Fig. 2d and Supplementary Videos 3–5). SBEM imaging readily facilitates quantitative analysis on large-volume content-rich datasets based on the following three features: tomography (serial sectioning) and 3D rendering of tissue; a large field of view with content-rich images; and nanometre resolution.
The reduced [18F]FBnTP uptake in LUSC raised the question as to whether these tumours had reduced vasculature as compared to LUAD. We quantified tumour volume and vasculature using microCT analysis and immunohistochemical staining for the endothelial marker CD34. The data showed no notable differences in vasculature densities (Extended Data Fig. 4a,b), or CD34 staining (Extended Data Fig. 4c,f), between OXPHOSHI LUAD and OXPHOSLO LUSC. By contrast, CD34 staining was significantly increased in normal tissue versus tumour (Extended Data Fig. 4d,e).
*Having* generated a 3D tumour volume, we next built a topographical map of the tumour’s cellular landscape to identify specific cell types within the tumour. We carried out cell segmentation and labelling of each cell type (tumour versus immune versus endothelial) based on cellular morphological features (Fig. 2e, Extended Data Fig. 5a and Supplementary Video 6). In LUSC, the main immune cell types identified were neutrophils, whereas macrophages were most predominant in LUAD (Fig. 2e,f and Extended Data Fig. 5b–e). These results are consistent with those of previous work in mouse models and human NSCLC14,15, and demonstrate the development of an in vivo imaging workflow that enabled us to bridge non-invasive PET imaging of whole tumours with high-resolution microCT and ultra-resolution SBEM.
## Spatial and structural mapping of mitochondria
Having obtained SBEM datasets from OXPHOSHI LUAD and OXPHOSLO LUSC tumours, we carried out a quantitative structural and spatial analysis of mitochondrial networks within tumour cells. Mitochondria in 2D SBEM images appeared phenotypically similar in both LUAD and LUSC (Fig. 3a). These images accurately represented cross-sectional views of the mitochondria; however, they did not characterize the higher-order organization of mitochondrial networks. By contrast, 3D renderings of mitochondrial networks (shown in red) identified evident phenotypic differences in which LUAD cells had predominantly fused, elongated mitochondria whereas LUSC cells had predominantly fragmented mitochondria (Fig. 3b). These results demonstrated that qualitatively, 3D rendering of tumours identified differences in mitochondrial structure and spatial distribution between LUAD and LUSC cells. Fig. 3Structural and spatial analysis of mitochondrial networks in SBEM-imaged NSCLC tumour volumes.a, Representative 2D SBEM images of an OXPHOSHI LUAD cell and an OXPHOSLO LUSC cell. b, 3D reconstruction of nucleus (blue) and mitochondrial (red) networks segmented from NSCLC cells in a. Yellow boxes show elongated mitochondria (i) and fragmented mitochondria (ii,iii). Scale bars, 3 μm. c–f, Density plots measuring mitochondrial sphericity (c), volume (d), length (e) and spatial distribution relative to nucleus surface (f) for OXPHOSHI LUAD cells (n > 50,000 mitochondria) and OXPHOSLO LUSC cells (n > 22,000 mitochondria). g,h, SBEM images and 3D reconstruction of representative type I (i), II (ii) and III (iii) crista structures identified in OXPHOSHI LUAD cells and OXPHOSLO LUSC cells. Scale bars, 500 nm (h). i, Illustration of type I, II and III crista structures. Outer mitochondrial membrane (OMM), matrix and inner mitochondrial membrane (cristae) are indicated. j, Percentage of mitochondrial type I, II and III crista distribution in OXPHOSHI LUAD cells ($$n = 3$$ biological replicates, n > 1,200 mitochondria) and OXPHOSLO LUSC cells ($$n = 3$$ biological replicates, n > 750 mitochondria). Data are mean ± s.e.m. Unpaired two-tailed t-test. k, Percentage of type I and III crista distribution in human LUAD (H1975, A549) and SCC (RH2, Tu686) cells. Data are mean ± s.e.m. ( $$n = 3$$ biological replicates, n > 2,000 mitochondria). Unpaired two-tailed t-test. Source data Next we developed quantitative methods to analyse mitochondrial structure across our content-rich SBEM datasets. To achieve a comprehensive analysis of the large mitochondrial content within the SBEM volumes, we developed a deep learning convolutional neural network (CNN). The CNN achieved robust and accurate trinary segmentation that included partitioning of the image space of mitochondria, nucleus and background classes. We segmented the mitochondria and nuclei in about 200 LUAD and about 150 LUSC cells from each tumour volume and manually annotated nuclei and mitochondria in 13 2D SBEM images—10 for training and 3 for validation. Our network outputs class logits at each pixel, with the class yielding the highest logits denoting its label. The resulting segmentation yielded an average precision of 0.916 and a recall of 0.927 versus manual registration (Extended Data Fig. 6a,b). We used the CNN to infer the segmentation of 200–500 serial 2D SBEM images to generate 3D renderings of mitochondria and nuclei in lung tumours.
Mitochondria are dynamic organelles that continually remodel their networks resulting in pools that vary in both size and shape. Utilizing our deep learning CNN, we then analysed and quantified 20,000–50,000 mitochondria per tumour section with morphological measurements taken for length, total volume, sphericity (roundness) and spatial distribution in our SBEM tumour volumes. Mitochondria in OXPHOSLO LUSC were predominantly fragmented with increased sphericity versus OXPHOSHI LUAD cells (Fig. 3b,c). Density plots measuring total mitochondrial length and volume showed a narrow distribution in LUSC versus a broad Gaussian distribution of peaks in LUAD cells (Fig. 3d,f and Extended Data Fig. 7a–c). These results showed that mitochondria within OXPHOSLO LUSC cells were smaller and more fragmented than OXPHOSHI LUAD cells.
We followed these results with an analysis of mitochondrial dynamics in human tumour cell lines and found that the OXPHOSLO RH2 and Tu686 SCC lines had significantly higher mitochondrial fragmentation (as measured by mitochondrial circularity, aspect ratio and size) than OXPHOSHI H1975 and A549 LUAD cell lines (Extended Data Fig. 7d–g). Our analysis of human and mouse tumour cells identified a more uniform organization of mitochondrial dynamics in OXPHOSLO SCC cells than in OXPHOSHI LUAD cells.
Moreover, we observed that mouse LUAD and LUSC had different spatial distribution of their mitochondria throughout the cell (Fig. 3b). To further evaluate, we developed another algorithm to measure the spatial distance between individual mitochondria and their corresponding nucleus in both SBEM and fluorescent images (Extended Data Fig. 8a–d). In SBEM images from OXPHOSHI LUAD cells, we discovered a broad spatial distribution of mitochondrial networks across the cytoplasm—spanning from the nucleus to the plasma membrane. By contrast, the mitochondria in OXPHOSLO LUSC were enriched at perinuclear regions of the cell (Fig. 3f, Extended Data Fig. 8e and Supplementary Videos 7 and 8). Next we analysed the spatial distribution of mitochondria relative to the nucleus in human tumour cell lines and found that mitochondrial networks in OXPHOSHI LUAD A549 and H1975 cells were localized predominantly throughout the cytoplasm, whereas mitochondrial localization was perinuclear in OXPHOSLO squamous RH2 and Tu686 cells (Extended Data Fig. 8f). These results indicate that OXPHOSLO SCC cells maintained a more spatially restricted and structurally homogeneous pool of fragmented perinuclear mitochondria (PNM) than OXPHOSHI LUAD cells.
The SBEM imaging at a resolution of 5–6 nm enabled us to investigate not only mitochondrial dynamics but also the organization of the ultrastructure of the cristae, which constitute the mitochondrial inner membrane. It is well established that the structural organization of the cristae directly impacts the bioenergetic capacity of the mitochondria2,3,6. The differences in OXPHOS signatures between NSCLC tumour subtypes suggested that OXPHOSHI LUAD cells may have distinctly different crista organization from OXPHOSLO LUSC cells. Among the OXPHOSHI LUAD and OXPHOSLO LUSC tumour volumes analysed, we identified three main crista structures that have been previously described in mammalian cells. These structures include: highly organized orthodox or lamellar cristae that we classified as type I (ref. 16); sparse and disorganized cristae that we classified as type II (ref. 17); and condensed cristae that we classified at type III (ref. 18). Representative images of type I–III cristae in mouse and human NSCLC cells are shown in Fig. 3g–i and Extended Data Fig. 9a–f.
Orthodox, lamellar cristae support robust OXPHOS activity in cells, whereas disorganized and condensed cristae are associated with defects in cellular OXPHOS activity16,18–20. We therefore analysed and compared crista density in mouse and human OXPHOSHI LUAD versus OXPHOSLO LUSC cells. The crista density was quantified using a Weka segmentation (ImageJ) program21 (Extended Data Fig. 9g). Morphological analysis identified that LUAD cells had higher crista density and basal oxygen consumption rate (OCR) than LUSC cells (Extended Data Fig. 9h,i). Next we carried out an analysis of crista architecture in human and mouse OXPHOSHI LUAD and OXPHOSLO SCC cells. Quantification of crista types showed that OXPHOSHI LUAD cells had a mixed population of type I, II and III cristae. By contrast, OXPHOSLO SCC cells were enriched for condensed type III cristae with a significant reduction in type I cristae (Fig. 3j,k and Extended Data Fig. 9j). This corresponded to significantly lower basal OCR in squamous versus LUAD cells (Extended Data Fig. 9k). In sum, our structural analysis of mitochondria showed that human and mouse OXPHOSLO tumour cells consistently lacked organized type I cristae compared to OXPHOSHI tumour cells.
## Peri-droplet mitochondria enriched in LUAD
SBEM imaging of content-rich tumour volumes allowed for 3D mapping of not only mitochondrial networks, but also other cellular organelles. We discovered that OXPHOSHI LUAD cells contained a vast number of lipid droplets (LDs) that were nearly absent in OXPHOSLO LUSC cells (Fig. 4a,b). Integrated throughout these LDs was a subpopulation of mitochondria in OXPHOSHI LUAD cells that contacted single or clustered LDs to form peri-droplet mitochondria (PDM; Fig. 3a bottom left panel). PDM have been identified in brown adipose tissue22, heart23 and skeletal muscle24 but have not been described in lung cancer. In brown adipose tissue, PDM were characterized by elevated rates of mitochondrial respiration (that is, OXPHOS) versus cytoplasmic mitochondria (CM)25.Fig. 4Enrichment of LDs and PDM in OXPHOSHI LUAD cells. Data are mean ± s.e.m. ( $$n = 3$$ biological replicates), unpaired two-tailed t-test unless specified otherwise. a, 3D reconstruction of LDs (green), mitochondria (red) and nucleus (blue) in an OXPHOSHI LUAD cell and an OXPHOSLO LUSC cell. Zoomed-in images (lower panels) are of the regions outlined in white from the 3D reconstructed cells (upper panels). The lower left panel is a side view of the interaction between mitochondria and LDs. The lower right panel comprises a front and back view of LDs in close proximity to but not contacting mitochondria. Scale bars, 3 μm. b, Quantification of the total volume and number of LDs in 3D-rendered LUAD and LUSC cells imaged by SBEM (n > 150 LDs). c, Percentage of spatially compartmentalized mitochondria in OXPHOSHI LUAD cells ($$n = 3$$ biological replicates, n > 1,200 mitochondria) and OXPHOSLO LUSC cells ($$n = 3$$ biological replicates, n > 750 mitochondria). d, Co-staining of oil red O and haematoxylin in OXPHOSHI LUAD (H1975) and OXPHOSLO LUSC (RH2) human xenografts. Scale bars, 40 μm. e,f, Ratio of area between oil red O and haematoxylin staining for OXPHOSHI LUAD and OXPHOSLO LUSC xenografts (e, $$n = 5$$ LUAD tumours, $$n = 5$$ LUSC tumours) and GEMMs (f, $$n = 6$$ LUAD tumours, $$n = 7$$ LUSC tumours). g, Co-staining of MTDR (purple), BODIPY (green) and Hoechst (blue) in H1975 and RH2 cells. Scale bars, 3 μm. h,i, Average number of LDs and PDM in human LUAD and SCC cells (n > 300 cells per cell line). j, 2D SBEM image (left) and 3D reconstruction (right) of PDM and associated crista structure of an OXPHOSHI LUAD cell. Scale bar, 500 nm. k, Percentage of type I, II and III cristae in PDM population (n > 400 mitochondria). One-way ANOVA, Dunnett test. l, Percentage of change in basal OCR of human LUAD and SCC cells in response to UK5099, etomoxir and BPTES. m, Cell count of H1975 and RH2 cells proliferating under the conditions of normal medium (25 mM glucose), and medium with no free fatty acids (FFAs), low glucose (12 mM) or no glutamine. Source data On the basis of the presence of PDM in OXPHOSHI LUAD cells, we asked whether mitochondria may be organized into discrete subpopulations within these tumours. Using SBEM images from mouse LUAD and LUSC tumours, we grouped mitochondria into subpopulations based on their interaction with cellular organelles. These subpopulations include: PNM, localized to the nucleus; PDM that contacted LDs; and CM that were scattered throughout the cytoplasm and lacked both nuclear and LD contacts. OXPHOSHI LUAD cells had a similar distribution of PNM, PDM and CM populations compared to OXPHOSLO LUSC cells, which were nearly absent of LDs (Fig. 4a,c). To confirm our observations in the SBEM images, we measured LDs in vivo in both NSCLC xenografts and GEMMs by oil red O staining of tumours to label neutral lipids (triglycerides and diacylglycerols). We found a significant enrichment in lipids in OXPHOSHI LUAD cells that was absent in OXPHOSLO SCC cells (Fig. 4d–f and Extended Data Fig. 10a,b).
We next investigated whether PDM were present in human NSCLC tumour subtypes. Analysis of The Cancer Genome Atlases for human LUAD and LUSC identified that gene expression of DGAT1, a regulator of LD biogenesis, and that of PLIN5, which regulates LD formation26,27, were significantly upregulated in LUAD versus LUSC (Extended Data Fig. 10c,d). We then stained the OXPHOSHI LUAD cell lines H1975, H1651 and A549 and the OXPHOSLO SCC cell lines RH2 and Tu686 with BODIPY to identify LDs. Our results showed that the OXPHOSHI LUAD cell lines had a significant increase in LDs and PDM formation compared with OXPHOSLO squamous cell lines (Fig. 4g–l and Extended Data Fig. 10e,f). These results demonstrate that human and mouse OXPHOSHI LUAD cells had a significantly higher number of LDs and PDM compared with OXPHOSLO squamous tumours.
Given the nanometre resolution of our SBEM images, we next examined the crista architecture in PDM from OXPHOSHI LUAD cells. Representative 2D and 3D images from tumour volumes showed elongated mitochondria that were interlaced throughout LD clusters and contacted multiple LDs (Fig. 4j). Notably, PDM exhibited densely packed orthodox type I cristae that aligned vertically at the mitochondria–lipid contact sites (Fig. 4k and Extended Data Fig. 10g). The enrichment of PDM for more OXPHOS-proficient type I cristae agrees with the data in Fig. 3, as well as previous studies in brown adipose tissue25.
We next examined both OXPHOS activity and nutrient preferences for glucose, glutamine or fatty acids in PDM-rich versus PDM-deficient human OXPHOSHI LUAD and OXPHOSLO SCC cell lines. We measured the percentage of change in basal and maximal OCR following treatment with inhibitors of pyruvate metabolism (UK5099), glutamine metabolism (BPTES) or fatty acid oxidation (etomoxir) in the H1975 and A549 versus RH2 and Tu686 cell lines. Our results showed that PDM-enriched LUAD cell lines utilize pyruvate, glutamine and fatty acid oxidation to support respiration, whereas PDM-deficient squamous cell lines were reliant on pyruvate and glutamine inhibition, but not fatty acid oxidation (Fig. 4l and Extended Data Fig. 10h).
Subsequently, we sought to understand whether PDM-rich NSCLC cells relied on different nutrient sources compared to PDM-deficient ones. We examined whether restriction of nutrients (glucose, glutamine or free fatty acids would limit growth of H1975 or RH2 cells. Our results showed that restricting free fatty acids significantly inhibited growth of H1975 cells but not RH2 cells (Fig. 4m). Both cell types were highly dependent on glutamine, as previously described in lung tumour cells28. H1975 cells grew well in low-glucose conditions, whereas RH2 cells did not grow, agreeing with previous studies that identified LUSC are reliant on both glucose and glutamine for cell survival11,29. In sum, we discovered PDM as a mitochondrial subpopulation that were enriched in OXPHOSHI LUAD cells. Our data suggest that PDM-rich LUAD cells relied on a broad source of nutrients to support OXPHOS and growth whereas nutrient dependency in PDM-deficient squamous tumour cells was restricted to glucose and glutamine metabolism.
## Glucose regulation of mitochondrial motility
Mitochondria move along the cytoskeleton (microtubules, actin and intermediate filaments) aided by motor, adaptor and transmembrane proteins30,31. Notably, motor adaptors sense glucose flux through O-GlcNAcylation by O-GlcNAc transferase (OGT). Here glucose is shunted into the nutrient-sensing hexosamine biosynthetic pathway, which activates OGT resulting in arrest of mitochondrial motility32. High glucose flux is a hallmark of hypermetabolic squamous tumours of the lung, head and neck as evidenced by elevated [18F]FDG uptake11,29 (Fig. 1e). We asked whether the perinuclear localization of mitochondria identified in hypermetabolic SCC cells was due to suppression of mitochondrial trafficking along the cytoskeleton as a result of high glucose flux (Fig. 3b and Extended Data Fig. 11a,b). We thus proposed a model in hypermetabolic SCC cells in which high glucose flux serves to confine mitochondria to the nucleus by repression of mitochondrial motility through activation of the hexosamine and OGT pathways (Fig. 5a).Fig. 5Glucose flux regulates mitochondrial motility and remodels crista structure through hexosamine pathway in OXPHOSLO LUSC.Data are mean ± s.e.m. ( $$n = 3$$ biological replicates), unpaired two-tailed t-test unless specified otherwise. a, Diagram of proposed model that glucose flux regulates the remodelling of mitochondrial cristae and reduction of OXPHOS function through hexosamine pathway. b–e, Basal mitochondrial displacement in human LUAD (H1975 and A549) and SCC (RH2 and Tu686) cells (b) and vehicle (Veh)- or treatment-driven mitochondrial displacement in RH2 cells (c–e). RH2 cells were treated with KL-11743 at indicated concentrations for 72 h (c), low-glucose (5.5 mM) and galactose medium for 24 h (d), or the hexosamine pathway inhibitors azaserine (0.5 μM) and OSM1 (25 μM) for 72 h (e). $$n = 150$$ per cell line or per treatment condition. One-way ANOVA, Dunnett test (c). f, Western blots of RH2 cells treated with indicated concentrations of KL-11743 for 72 h probed with indicated antibodies. g, Mitochondrial displacement in RH2 and H1975 cells treated with Ctrl siRNA (siCtrl) and OGT siRNA (siOGT) for 72 h. $$n = 150$$ per treatment condition. h, Percentage of type I, II and III cristae in RH2 cells treated with indicated concentrations of KL-11743. n > 1,500 mitochondria. One-way ANOVA, Dunnett test. i, Mitochondrial maximal OCR in RH2 cells treated with indicated concentrations of KL-11743 for 72 h. One-way ANOVA, Dunnett test. j,k, [18F]FBnTP uptake (j) and complex I and II MRC (k) of subcutaneous xenografts of human LUSC (RH2) cells treated with vehicle or KL-11743 (100 mg kg−1, 10 days). ( j) $$n = 22$$ tumours for Veh; $$n = 29$$ tumours for KL-11743.*Source data* We measured mitochondrial motility at basal levels or following inhibition of glucose flux in hypermetabolic, glycolytic RH2 and Tu686 versus less glycolytic H1975 and A549 cell lines11 (Fig. 1e). Mitochondrial motility was quantified by measuring the displacement of individual mitochondria over time, as previously described33. Basal glucose uptake and extracellular acidification rate was notably increased, whereas mitochondrial motility was significantly decreased in RH2 and Tu686 versus H1975 and A549 cell lines (Fig. 5b, Extended Data Fig. 11c–e and Supplementary Videos 9 and 10). We next measured mitochondrial motility following inhibition of glucose flux or the hexosamine pathway. Glucose uptake was restricted by modification of cell culture medium (low glucose (12 mM) or galactose), targeted inhibition of glucose transport through the pan GLUT1 and GLUT3 inhibitor KL-11743 (ref. 34) or RNA-mediated interference knockdown of the GLUT1 transporter that all led to a marked increase in mitochondrial motility (Extended Data Fig. 11f–h). The hexosamine pathway was inhibited using azaserine, which inhibits glutamine-fructose-6-phosphate transaminase [isomerizing] 2 (GFPT2), and the OGT inhibitor OSMI-1, as previously shown35. Inhibition of glucose uptake and the hexosamine pathway induced a significant increase in mitochondrial motility in RH2 and to a lesser extent H1975 cells (Fig. 5c–e and Extended Data Fig. 11i,j).
Next we investigated the role of OGT in the regulation of mitochondrial motility in NSCLC cells. In RH2 cells, glucose restriction, via treatment with KL-11743, azaserine, OSMI-1 or RNAi-mediated OGT knockdown, led to a decrease in total cellular O-GlcNAcylation (Fig. 5f and Extended Data Fig. 11k–m). Additionally, OGT knockdown significantly increased mitochondrial motility in RH2 and H1975 cells (Fig. 5g). In sum, these results demonstrate that glucose flux regulated mitochondrial motility in NSCLC tumour cells through the hexosamine pathway and OGT.
We then explored whether the increased mitochondrial motility as a result of glucose flux inhibition induced remodelling of mitochondrial networks. In RH2 cells, KL-11743 treatment induced a redistribution of mitochondria from nuclear to cytoplasmic localization (Extended Data Fig. 11b,n,o). We discovered that inhibition of glucose flux induced remodelling of crista architecture marked by a significant increase in type I cristae and a concomitant decrease in type III cristae in RH2 cells (Fig. 5h and Extended Data Fig. 11p,q). Glucose restriction, inhibition of the hexosamine pathway and OGT knockdown in RH2 and/or H1975 cells yielded similar results to KL-11743 treatment (Extended Data Fig. 11r–u).
The enrichment of well-formed orthodox type I cristae in NSCLC cells following crista remodelling suggested that OXPHOS activity may be upregulated following inhibition of glucose flux and/or the hexosamine–OGT pathway. We measured the maximal OCR in tumour cells following inhibition of glucose flux, the hexosamine pathway and OGT and these all induced a significant increase in basal and maximum OCR, as well as the complex I and II MRC in RH2, and to a lesser extent in H1975 cells (Fig. 5i and Extended Data Fig. 11v–z). Last, we examined OXPHOS signatures in vivo by carrying out [18F]FBnTP PET imaging on RH2 tumour xenografts treated with KL-11743 or vehicle. KL-11743 induced a significant increase in [18F]FBnTP uptake in RH2 tumours versus vehicle (Fig. 5j). Ex vivo respirometry carried out on tumours showed that KL-11743 treatment induced a significant increase in complex I and II MRC versus vehicle (Fig. 5k). These results demonstrate that inhibition of glucose flux and the hexosamine–OGT pathway led to significantly increased mitochondrial motility, enrichment of type I cristae and the upregulation of OXPHOS activity in glycolytic squamous tumours and to a lesser extend in LUAD cells.
Last, we investigated the role of microtubules, actin and vimentin in the regulation of mitochondrial motility in H1975 and RH2 NSCLC cell lines. Disruption of both microtubule and actin networks by treatment with latrunculin A or nocodazole, respectively, led to a notable decrease in mitochondrial motility in both cell lines (Extended Data Fig. 12a,b). By contrast, siRNA-mediated knockdown of vimentin led to a significant increase in mitochondrial motility (Extended Data Fig. 12c,d). We next confirmed that both latrunculin A and nocodazole treatment induced a significant decrease in the distance of mitochondria from the nucleus resulting in an enrichment of PNM, whereas knockdown of vimentin led to a significant increase (Extended Data Fig. 12e–h). Last, we measured basal OCR after disruption of the components of the cytoskeleton and identified that latrunculin A and nocodazole treatment induced a notable decrease in basal OCR whereas vimentin knockdown induced a significant increase (Extended Data Fig. 12i–l). These results indicate that both microtubules and active networks support mitochondrial motility, cytoplasmic distribution of mitochondria and respiratory capacity whereas vimentin functions in an antagonistic and suppressive manner.
## Discussion
In summary, we present an in vivo structural and functional analysis of mitochondrial networks and bioenergetic phenotypes across mouse and human NSCLC tumours. The ex vivo respirometry we carried out on NSCLC tumours identified significant increases in complex I and complex II respiratory activity in [18F]FBnTP-positive LUAD cells, whereas glycolytic LUSC cells with low [18F]FBnTP uptake had persistently low complex I activity. Supporting this, it was recently shown that ubiquinol oxidization is required for lung tumour growth, emphasizing a critical role of complex III and the ETC in lung tumorigenesis36. These results underscore the diversity of bioenergetic activity among NSCLC tumour subtypes. The functional diversity we discovered between LUAD cells and LUSC cells accurately predicted distinct structural mitochondrial phenotypes among lung tumour subtypes. PET-guided 3D SBEM, combined with CNN machine learning analysis, facilitated the large-scale mapping of the structural and spatial distribution of mitochondrial networks within [18F]FBnTP-positive LUAD cells and [18F]FBnTP-negative LUSC cells. The broad diversity of mitochondrial structures identified in LUAD and LUSC tumours correlated with equally diverse bioenergetic profiles and metabolic dependencies in these histological subtypes. In addition, we identified a previously unrecognized compartmentalization of mitochondrial subpopulation in LUAD cells in which PDM populations were enriched in these tumours.
The closely regulated and uniform organization of mitochondrial networks we profiled in LUSC cells corresponded with reduced metabolic flexibility compared with LUAD cells. LUSC were more reliant on glucose and glutamine metabolism to support OXPHOS and less so on fatty acid oxidation, whereas LUAD cells utilized glucose, glutamine and fatty acids to support cellular respiration. These results agree with previous studies that demonstrated LUSC cells rely on glucose and glutamine to support tumour metabolism and are selectively sensitive to inhibition of these pathways11,28. Structure–function studies defining the relationship between mitochondrial architecture and metabolic dependencies may hold promise as an emerging diagnostic and therapeutic strategy that can be leveraged to exploit bioenergetic and metabolic liabilities unique to lung cancer subtypes. We anticipate that coupling PET imaging with 3D SBEM will have dynamic applications beyond that of lung cancer and enrich our understanding of how mitochondrial bioenergetics impact human disease.
## Cell culture
Cells were cultured in Dulbecco’s modified Eagle’s medium (Thermo Fisher Scientific) supplemented with $10\%$ fetal bovine serum (Hyclone) and $1\%$ penicillin/streptomycin (Gibco). A549 cells and H1975 cells were obtained from ATCC. The lung squamous cell line (human) RH2 were established in the laboratory of S.M.D. (UCLA). The head and neck squamous cell line (human) Tu686 was a gift from the laboratory of M.S.J. (UCLA). The mouse lung squamous cell line derived from mouse 5 (LPP) and mouse LUAD cell line derived from mouse 4 (LPP) were established in our laboratory. After resecting the tumour tissues, they were minced with razors and digested with collagenase/dispase (Sigma). Lysate was filtered through a 70-μm cell strainer. Dissociated single cells were centrifuged and resuspended in Dulbecco’s modified Eagle’s medium ($10\%$ fetal bovine serum). The cells were plated in tissue culture dishes and the medium was changed until there were enough colonies to expand the culture. All cells were grown at 37 °C in $5\%$ CO2 in a humidified incubator, and a test for mycoplasma was carried out using the LookOut Mycoplasma PCR Detection Kit (Sigma). Cell identities were confirmed by Laragen Inc. The endoribonuclease-prepared siRNA used in this study was: GLUT1 (SLC2A1) (EHU028011, Sigma); OGT (EHU082301, Sigma).
## GEMMs of lung tumour
We used five GEMMs in this study: [1] Kras-Lox-Stop-Lox-G12D; Rosa26-Lox-Stop-Lox-Luc (Kras); [2] Kras-Lox-Stop-Lox-G12D; LKB1 Lox/Lox; Rosa26-Lox-Stop-Lox-Luc mice (KL); [3] Kras-Lox-Stop-Lox-G12D; P53 Lox/Lox; Rosa26-Lox-Stop-Lox-Luc mice (KP); [4] Kras-Lox-Stop-Lox-G12D; LKB1 Lox/Lox; P53 Lox/Lox; Rosa26-Lox-Stop-Lox-Luc (KPL); [5] LKB1 Lox/Lox; P53 Lox/Lox; PTEN Lox/Lox; Rosa26-Lox-Stop-Lox-Luc (LPP). Lung tumours were induced by Ad5-CMV-Cre (VVC-U of Iowa-1174) or LentiCre (Kerafast) delivered intranasally as described previously37. Tumour growth was routinely monitored by bioluminescence imaging using an IVIS imager (PerkinElmer). All animal experiments were approved by UCLA’s Animal Research Committee (ARC) and carried out following ARC protocols and requirements. The tumour burden endpoints (morbidity; weight loss no greater than $20\%$; laboured breathing; impingement of animal’s ability to ambulate, eat and drink) allowed by our Institutional Animal Care and Use Committee were not exceeded. Lung tumours from different GEMM mice were collected and snap frozen in liquid nitrogen. Snap-frozen samples were stored at −80 °C until respirometry assay and western blotting analysis.
## Subcutaneous implantation in NSG mice
A549, H1975, A549 Rho, RH2 and Tu686 cells were cultured in vitro under the conditions described above. Cells were collected and suspended in PBS, then mixed with Matrigel Membrane Matrix (Corning) and implanted subcutaneously on the flanks of NSG mice (2–4 × 106 cells per flank). All animal experiments were approved by UCLA’s ARC and carried out following ARC protocols and requirements. The tumour burden endpoints (tumour volume no greater than 2,000 mm3; morbidity; weight loss no greater than $20\%$; laboured breathing; impingement of animal’s ability to ambulate, eat and drink) allowed by our Institutional Animal Care and Use Committee were not exceeded. For the treatment study of the GLUT1 and GLUT3 inhibitor KL-11743 (C6), mice were treated with C6 (100 mg kg−1) delivered by oral gavage for 8 days. Subcutaneous tumours were either snap frozen in liquid nitrogen or fixed in $10\%$ formalin overnight. Snap-frozen samples were stored at −80 °C until respirometry assay. Formalin-fixed samples were sent to the Translational Pathology Core Laboratory at UCLA for embedding and sectioning.
## [18F]FBnTP synthesis
Synthesis of the radioactive [18F]FBnTP probe was carried out as previously described9,38.
## PET–CT imaging
PET–CT imaging and analysis were carried out on GNEX as previously described37,39. PET signals were measured as percentage of injected dose per gram after 1 h uptake and normalized to heart signal. Tumours with a tumour-to-heart [18F]FBnTP uptake ratio of ≥0.5 are defined as [18F]FBnTPHI; those with a ratio of <0.5 are defined as [18F]FBnTPLO. Tumours with a tumour-to-heart [18F]FDG uptake ratio of ≥0.2 are defined as [18F]FDGHI; those with a ratio of <0.2 are defined as [18F]FDGLO (ref. 26).
## Ex vivo respirometry analysis on frozen tissues
Tumour tissues were isolated from GEMMs of lung cancer and snap frozen using liquid nitrogen. Frozen samples were stored in −80 °C until use in the Seahorse experiments. Frozen tissues were thawed on ice and homogenized in MAS buffer (70 mM sucrose, 220 mM mannitol, 5 mM KH2PO4, 5 mM MgCl2, 1 mM EGTA, 2 mM HEPES pH 7.4) with protease inhibitor cocktail (Roche). Homogenates were centrifuged at 1,000g for 10 min at 4 °C and supernatant was collected. Protein concentrations were determined by BCA assay kit (Thermo Fisher). Homogenates (12 µg per well for human samples and 6 µg per well for mouse samples) were loaded into a Seahorse XF96 microplate in MAS buffer (20 µl each well) and centrifuged at 2,000g for 5 min at 4 °C. After centrifugation, the volume was increased to 150 µl by adding 130 µl MAS containing cytochrome c (10 µg ml−1). At port A, substrates of NADH (1 mM) were injected to determine the respiratory capacity of mitochondrial complex I; succinate (5 mM) + rotenone (2 µM) were injected to determine the respiratory capacity of mitochondrial complex II. The following compounds were injected so that final concentrations were as follows—port B: rotenone (2 µM) + antimycin (4 µM); port C: TMPD (0.5 mM) + ascorbic acid (1 mM); port D: azide (50 mM). OCR rates were measured using a Seahorse XF96 Extracellular Flux Analyzer (Agilent Technologies) and normalized to mitochondrial content quantified by MTDR. Homogenates were stained with 500 nM MTDR for 10 min followed by two wash steps to remove the dye (Thermo Fisher). MTDR fluorescence was read on a Tecan Spark plate reader (Ex: 633 nm; Em: 678 nm).
## In vitro respirometry analysis on cultured cells
Cells were seeded into a Seahorse XF96 microplate before the assay and maintained in a tissue culture incubator (37 °C in $5\%$ CO2) to reach 90–$100\%$ density before running the Seahorse assay. To measure complex I and complex II respiratory capacity, cells were permeabilized with 4 nM XF plasma membrane permeabilizer (Agilent). Permeabilized cells were started in state 3 with substrates of pyruvate (5 mM) + malate (0.5 mM) and 4 mM ADP for respiration-driven through complex I; succinate (5 mM) + rotenone (2 µM) and 4 mM ADP for complex II. Following state 3 measurements, injections included the following—port A: oligomycin (2 µM); ports B and C: FCCP (B: 0.75 µM and C: 1.35 µM); port D: rotenone (1 µM) + antimycin (2 µM). To measure OCR and extracellular acidification rate in intact cells, cells were washed twice and incubated with freshly prepared assay medium (Seahorse XF Base Medium + 2 mM l-glutamine + 1 mM pyruvate + 10 mM glucose) for 30 min. The following compounds were injected in the order of oligomycin (2 µM), FCCP ($\frac{0.75}{1.35}$ µM), rotenone (1 µM) + antimycin A (2 µM). Cell count (per well) was determined by the number of nuclei stained with Hoechst (10 µg ml−1) and quantified by an Operetta High-Content Imaging System (PerkinElmer). OCR and extracellular acidification rate were normalized to cell count per well. For nutrient-dependent respirometry analysis, conditions of UK5099 (5 µM), BPTES (3 µM) and etomoxir (3 µM) were applied.
## Whole-animal perfusion and tissue fixation
Fixative solution ($2\%$ paraformaldehyde, $2.5\%$ glutaradehyde, 0.15 M cacodylate and 2 mM Ca2+) was freshly prepared every time. Animals were anaesthetized by ketamine (200 mg kg−1) and xylazine (10 mg kg−1) delivered by intraperitoneal injection. Anaesthesia took effect after several minutes. The depth of anaesthesia was tested using tail pinch and paw prick, and it was ensured that the breathing did not stop. The body cavity was cut with scissors, and cut up the midline to the sternum. The heart was exposed and a needle was placed into the left ventricle and then the right atrium was snipped with iridectomy scissors. The animal was perfused for 30 s at the flow speed of 8 ml min−1 with Ringer’s solution supplemented with $2\%$ xylocaine and 1,000 U herapin. Then the valve on the pump was switched on to fixative solution and perfusion was carried out for 5–8 min. After continuous perfusion, mouse lung tumour was collected, placed in ice-cold fixative solution and fixed overnight in the fridge. Fixed tissues were washed three times with 0.15 M cacodylate buffer (2 mM Ca2+) and stored in the same buffer at 4 °C until further processing.
## Sample preparation for SBEM imaging
Post-fixed tissues of mouse lung tumour were washed in 0.15 M cacodylate buffer (2 mM Ca2+). Tissues were stained for 1 h with $2\%$ osmium and $1.5\%$ potassium ferrocyanide in 0.15 M cacodylate buffer (2 mM Ca2+). Tissues were washed five times (5 min each time) with double-distilled (dd)H2O, and then placed in filtered TCH buffer (0.05 g thiocarbohydrazide in 10 ml ddH2O) for 20 min at room temperature. Tissues were washed five times (5 min each time) with ddH2O and then stained with $2\%$ osmium in ddH2O for 30 min at room temperature. Next, tissues were stained with $2\%$ uranyl acetate overnight at room temperature and lead aspartate solution ($0.66\%$ (w/v) lead in 0.03 M aspartic acid) for 30 min in a 60 °C oven. Tissues were dehydrated in serial ice-cold ethanol ($70\%$, $90\%$ and $100\%$) and ice-cold acetone after washing in ddH2O. Tissues were then embedded in serial Durcupan resin ($50\%$, $75\%$, $100\%$) and solidified in a 60 °C oven for 2 days.
## SBEM
SBEM volumes were collected on a Zeiss Gemini 300 microscope equipped with a Gatan 3View 2XP microtome system. The volumes were collected at 2.5 kV using a 30-μm aperture, the gun in analytic mode, and the beam in high-current mode. Focal charge compensation with nitrogen gas was used to mitigate charging artefacts. The dwell time was 1 μs. The pixel size was either 5 or 6 nm, and the Z step size was always 50 nm. Following data collection, the images were converted to.mrc format and cross-correlation was used for rigid image alignment of the slices using the IMOD image processing package40.
## MicroCT
MicroCT imaging was carried out on a Zeiss Versa 510 microscope. The wet lung specimens were imaged with no contrast staining in buffer at 40 kV, using 360° of specimen rotation and 801 projection images were collected. The pixel size was 11.5 μm. Specimens stained for SBEM and embedded in epoxy were imaged at 80 kV, using 360° of specimen rotation and 1,601 projection images were collected. The pixel size was between 5 and 8 μm.
## 3D visualization and analysis of SBEM images
Individual cells were segmented using Amira software (Thermo Scientific). Sequential SBEM images were processed with median filtering and contrast adjustment to enhance the appearance of intracellular space. Interactive thresholding was applied to distinguish cellular and extracellular space and generate binary images. Binary images were processed with the separate object algorithm to fragment cells without clear extracellular boundaries. The connected components algorithm was applied to create individual labels for each identified cell. Segmented individual cells in each stack were visualized with a voxelization rendering.
## Machine-learning-based segmentation of nucleus and mitochondria in SBEM images
Our method segments nuclei (class 1) and mitochondria (class 2) from the background (class 0) in a 3D SBEM volume. The method takes individual slices from the volume (2D images) as input and predicts the segmentation for the slices independently. To carry out the segmentation, a deep CNN was used, which is a function that takes an image as input and outputs logits (or softmax confidence) corresponding to each class. The class with the highest response is chosen for each pixel to yield the segmentation map for each slice. Specifically, an encoder–decoder architecture based on U-Net41, with three modifications was chosen. First, ResNet42 blocks (with 32, 64, 128, 256 and 256 filters) was used instead of standard convolutional blocks. Second, as mitochondria are small objects, we chose to limit spatial downsampling to one-eighth of the original image. To do so, the max-pooling operation was removed after the third layer. Third, an atrous spatial pyramid pooling43 layer was used with strides 6, 12, 18 and 24 as the last layer of our encoder to increase the field of view of our filters. All weights use random uniform initialization, and we used leaky-ReLU activations. Our model is trained using the standard cross-entropy loss and is optimized using Adam44 with β1 = 0.9 and β2 = 0.999. We used an initial learning rate of 1 × 10−4 and decreased it to 1 × 10−5 after 150 epochs for a total of 200 epochs. We use a batch size of 8 and resize each image to 768 × 768. We carry out random horizontal and vertical flips, rotations between [−20°, 20°], zero-mean additive Gaussian noise with standard deviation of 0.08, and crop sizes up to $90\%$ of the image height and width as data augmentation. Each augmentation was applied with a $50\%$ probability. Our model was trained on ten densely labelled images, and was validated on three additional labeled images that are not included in the training set. Training takes about 8 h on an Nvidia GTX 1080 GPU, and inference takes about 11 ms per 2D image. Our segmentation model was evaluated using the Sorensen–Dice index (DICE), the Jaccard index (also referred to intersection-over-union, IoU), precision and recall. DICE and IoU are metrics to gauge the similarity of two sets, commonly used to evaluate segmentation results. Our segmentation model achieves an average of 0.92 DICE score, 0.86 IoU, 0.916 precision and 0.927 recall, as compared to that of manual registration.
## Mitochondrial motility and mitochondrial crista analysis
A total of 2 × 105 cells were plated per well into CELLview 4-compartment glass-bottom tissue culture dishes (Greiner Bio-One) 48 h before the imaging section, and maintained in a tissue culture incubator (37 °C in $5\%$ CO2). Cells were stained with TMRE (15 nM, Thermo Fisher) for 1 h to analyse mitochondrial motility, and with 10-N-nonyl acridine orange (100 nM, Thermo Fisher) for 1–3 h to analyse mitochondrial cristae. Live-cell imaging was carried out on the Zeiss LSM 880 with Airyscan using the alpha Plan-Apochromat 100×/1.46 Oil DIC M27 objectives. Images were deconvolved by Airyscan Processing in ZEN software. Image analysis was carried out using ImageJ (Fiji) in the order of: background subtraction, crop region of interest, adjust thresholds and measure parameters. Mitochondrial motility was analysed using a program developed in the laboratory of O.S.S. To measure mitochondrial motility, each field of view was imaged every 20 s, 10 times (yielding a total of 10 frames). Mitochondrial placement in frame 1 of each time series is identified as the reference point. The following 9 frames ($$n = 2$$, 3, 4…10) in the image set were then overlaid on the reference image, and the overlapping area between frame n and frame 1 was calculated and denoted as overlap_area(n). The non-overlapping mitochondria area between frame (n) and the reference image was denoted as travel_area(n) and calculated with the formula: travel_area(n) = mitochondria_area(n) − overlap_area(n). Mitochondrial motility was then calculated as the displacement ratio for each frame, ratio(n) = travel_area(n)/overlap_area(n). We then fitted a linear regression to the ratio for each image frame (n > 1) with the time intervals (20 s, 40 s…180 s), and the regression line is set in the form of ratio = a (slope) × time intervals + b (intercept). The value of “a (slope)” for each time series of image sets is our readout of mitochondrial motility. Macros were designed for automated segmentation and quantification of mitochondrial crista structure and density according to previously described methods19,21. The original images used for classification of mitochondrial cristae were 2D images, and the consensus for classification was obtained from two.
## Quantification of distance between mitochondria and nucleus in fluorescent images
Given the centre of a mitochondria, we want to find its minimum distance to the surface of a nucleus, which is segmented as an ellipse. The result can be obtained by solving a constrained optimization problem using Lagrange multipliers. The original ellipse of the nucleus is tilted by a degree and is centred at an arbitrary coordinate. To ease the computation, we translate and rotate the original ellipse to a standard ellipse that is centred at [0,0] with the major axis aligned with the x axis and the minor axis aligned with the y axis. The centre of the mitochondria is mapped to the new coordinate system accordingly. Through substitution, the gradient of the objective function that involves only y is a fourth-degree polynomial, which has at most four possible roots that satisfy the criteria. We then choose the coordinate (x,y) on the ellipse that has the minimal distance to the target nucleus.
## Immunohistochemistry
Immunohistochemistry staining and analysis were carried out as previously described11. Tissue slides were probed with the following antibodies: CD34 (1:800, ab8158 Abcam); TTF1 (1:1,000, M3575 Dako); CK5 (1:1,000, ab52635 Abcam).
## Oil red O staining
The oil red O staining was carried out following the protocols of a commercial kit (StatLab, KTOROPT). Frozen tissues were fixed by O.C.T compound (Fisher HealthCare) and sectioned at the Translational Pathology Core Laboratory (UCLA). The frozen tissue sections were fixed in $10\%$ formalin for 5 min, and then rinsed in distilled water. Slides were immersed in propylene glycol for 2 min, followed by immersion in preheated oil red O solution at 60 °C for 6 min. Slides were then washed in $85\%$ propylene glycol for 1 min and running distilled water for 1 min. The nuclei were stained with modified Mayer’s haematoxylin for 1 min and rinsed in distilled water for 1 min. The slides were mounted with aqueous medium and covered with a coverslip.
## Western blotting
Whole-cell lysates of lung tumours isolated from GEMMs were generated by homogenizing snap-frozen tumour tissues in lysis buffer (20 mM Tris pH 7.5, 150 mM NaCl, $1\%$ (v/v) Triton X-100, 50 mM sodium fluoride, 1 mM EDTA, 1 mM EGTA, 2.5 mM pyrophosphate, 1 mM sodium orthovanadate, complete protease inhibitor cocktail). Whole-cell lysates were centrifuged at 2,000g for 5 min and supernatants were transferred to empty tubes. Supernatants were stored at −80 °C until use. Whole-cell lysates of in vitro-cultured cells were generated by homogenizing the cells SDS lysis buffer (100 mM Tris pH 7.5, 100 mM NaCl, $1\%$ SDS, protease inhibitor cocktail) followed by heat inactivation at 90 °C for 10 min. Protein concentration was determined by BCA assay (Thermo Fisher). Lysates were run on 4–$12\%$ Bis-Tris gels (Thermo Fisher) to separate the proteins, and then transferred to PVDF membrane. Membranes were stained with Poceau S to confirm transfer efficiency. Membranes were then probed with the following antibodies: SP-C (1:5,000, AB3786 Millipore); GLUT1 (1:2,000, GT11-A, Alpha Diagnostic); NDUFS1 (1:1,000, ab169540, Abcam); O-linked N-acetylglucosamine (1:1,000, ab2739, Abcam); SDHA (1:1,000, 5839, Cell Signaling Technology); SDHC (1:1,000, ab155999, Abcam); actin (1:5,000, A3853, Sigma); tubulin (1:2,500, T9026, Sigma).
## Statistics and reproducibility
Statistical analyses were carried out on GraphPad Prism 9 or R studio. Differences between groups were determined using unpaired two-tailed t-test or one-way ANOVA if more than two groups were compared. For treatment studies, Dunnett’s test was used to compare every mean to a control mean. For non-treatment studies, Tukey’s test was used to compare every mean to every other mean. Data are presented as mean ± s.e.m. unless specified otherwise. Numbers of biological replicates are indicated in the figure legends. All experiments were repeated in at least duplicate. No statistical methods were used to predetermine sample size.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Online content
Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-05793-3.
## Supplementary information
Supplementary Fig. 1Source data for western blots. Reporting Summary Supplementary Video 13D-rendered microCT volume of lung lobe with tumour. Supplementary Video 2Tumour region selected for SBEM tumour volumes. Supplementary Video 33D-rendered SBEM tumour volume. Supplementary Video 43D-rendered partial SBEM tumour volume. Supplemental Video 5Slice-by-slice view of an SBEM tumour volume. Supplementary Video 6Colour-coded cellular segmentation in a 3D-rendered SBEM tumour volume. Supplementary Video 7Representative 3D images of PNM networks in LUSC cells. Supplementary Video 8Representative 3D images of mitochondrial networks in LUAD cells. Supplementary Video 9Mitochondrial motility within A549 cells. Supplementary Video 10Mitochondrial motility within RH2 cells.
The online version contains supplementary material available at 10.1038/s41586-023-05793-3.
## Extended data figures and tables
Extended Data Fig. 1PET/CT imaging, histology and respirometry analysis on KrasG12D/+ and Lkb1−/− driven tumors.a, Representative transverse images of PET/CT probed with 18F-BnTP (top) and 18F-FDG (bottom) in KrasG12D/+; Lkb1−/− (KL) mice. Uptake of PET probe was measured by the maximum percentage of inject dose per gram (%ID/g). Ratio of %ID/g by tumor to %ID/g by heart is labeled as %ID/g (tumor/heart). H-heart, T-tumor. b, Whole cell lysates of lung tumors T1 and T2 isolated from KrasG12D/+; Lkb1−/−; p53−/− (KPL) mouse were immunoblotted with the antibodies of GLUT1 and SP-C. c,d, Immunohistochemical staining of TTF-1 and CK-5 in sections from 18F-BnTPHI tumor (mouse 2, left panel) and 18F-BnTPLO tumor (T2 in mouse 3, right panel). Scale bar = 500 μm. e,f, Maximal respiration capacity (MRC) of mitochondrial Complex I (CI) and Complex II (CII) isolated from frozen tissues of T1 and T2 in mouse 1 (KPL) (e) and from T in mouse 2 (KL) and T2 in mouse 3 (KL) (f). Data are $$n = 3$$ technical replicates, box was interleaved low-high, line at mean. g,h, MRC of Complex I and Complex II in LUAD and LUSC from KPL mice ($$n = 8$$ tumors, 3 LUAD tumors and 5 LUSC tumors) (g) and from KL mice ($$n = 13$$ tumors, 7 LUAD tumors and 6 LUSC tumors) (h). Data are mean ± s.e.m., unpaired two-tailed t-test. Source data Extended Data Fig. 2PET/CT imaging, histological markers and respirometry analysis on 5 different genetically modified mouse models (GEMMs) and xenografts of human cells.a, Whole cell lysates of lung tumors isolated from KPL, KL, KrasG12D/+ (Kras), KrasG12D/+; p53−/−(KP) and Lkb1−/−; p53−/−; Pten−/−(LPP) mice immunoblotted with the antibodies of GLUT1 and SP-C. b, Sample ID, genotype and histology of each tumor are listed. c, Correlation between %ID/g (tumor/heart) of 18F-FDG uptake and Complex I MRC of tumors from KPL, KL, Kras, KP and LPP mice ($$n = 30$$ tumors, $$n = 18$$ LUAD tumors and $$n = 12$$ LUSC tumors). One-tailed F-statistics. d–g, Correlation between %ID/g (tumor/heart) of 18F-BnTP (d,f) and 18F-FDG (e,g) uptake and MRC of Complex II (d,e) and Complex I+II (f,g) in tumors from KPL, KL, Kras, KP and LPP mice ($$n = 30$$ tumors, $$n = 18$$ LUAD tumors and $$n = 12$$ LUSC tumors). One-tailed F-statistics. h, MRC of Complex I and Complex II in frozen LUAD cells and LUSC cells from Kras, KL, KP, KPL and LPP mice ($$n = 30$$ tumors, 18 LUAD tumors and 12 LUSC tumors. Data are mean ± s.e.m., unpaired two-tailed t-test. i, MRC of Complex II in frozen xenografts from human cells (H1975, A549, A549 Rho, RH2 and Tu686); Data are mean ± s.e.m. ( $$n = 3$$ biological replicates per cell line). One-way ANOVA, Dunnett test. j, Immunoblots of Complex I subunit (NDUFS1) and Complex II subunits (SDHA and SDHC) in whole cell lysates from LUAD (H1975, A549, A549 Rho), LUSC (RH2) and HNSCC (Tu686) cell lines. k, Transverse images of PET/CT probed with 18F-BnTP (right) and 18F-FDG (left) in subcutaneous xenografts implanted with A549 cells. Uptake of PET probe was measured by the maximum percentage of inject dose per gram (%ID/g). l, Coronal view of PET/CT overlayed images probed with 18F-BnTP (right) and 18F-FDG (left) in subcutaneous xenografts implanted with A549 Rho cells. Uptake of PET probe was measured by the maximum percentage of inject dose per gram (%ID/g). Source data Extended Data Fig. 3PET guided microCT analysis to select regions for SBEM imaging.a,b,c, Transverse view and 3D reconstruction of PET/CT overlayed images probed with 18F-BnTP (a,c) and 18F-FDG (b) of 18F-FDGHI LUSC tumor in Fig. 2b (a) and 18F-BnTPHI LUAD tumor (b,c). d, MicroCT image showed the position of tumor (white outlined) in the lung lobe (orange outlined). Dense tumor region is distinguished from tissue sparse necrotic area (brown outlined) by tissue density. e,h, Hematoxylin and eosin (H&E) staining of sections from OXPHOSLO LUSC tumor (e) and OXPHOSHI LUAD tumor (h). Scale bar = 500 μm. ( e) Arrows indicated necrotic area and selected region for SBEM imaging in OXPHOSLO LUSC tumor. ( h) Arrow indicated the tumor landmark, box indicated selected region for SBEM imaging in OXPHOSHI LUAD tumor. f, High-resolution microCT image of selected SBEM region with high 18F-FDG signal and dense tumor tissue indicated in (e). Scale bar = 200 μm. g,i, Cross-sections of XY, YZ and XZ planes in 3D rendered microCT images on heavy-metal stained OXPHOSLO LUSC tumor (g) and OXPHOSHI LUAD tumor (i). Selected regions for SBEM imaging were indicated in the red boxes.
Extended Data Fig. 4Vascular density in lung normal tissues and tumor tissues measured by microCT and endothelial marker.a,b, Reconstruction of vascular structure (a) segmented from microCT images (b) by gaussian and binary morphological filters in OXPHOSLO LUSC (left panel) and OXPHOSHI LUAD (right panel) tumors. The density of vasculature was indicated. c,d, Representative of immunohistochemical staining of CD 34 on the section of a OXPHOSLO LUSC tumor (c, left panel). The threshold of positive CD 34 staining was identified using QuPath and indicated by red labeling (c, right panel). Scale bar (c) = 500 μm. Selected normal tissue region and tumor region in black boxes were zoomed-in (d). Scale bar (d) = 50 μm. e,f, Density of positive CD 34 staining in normal tissues ($$n = 10$$) and tumor tissues ($$n = 10$$) (e), and in LUAD ($$n = 6$$) and LUSC ($$n = 6$$) (f). Data are mean ± s.e.m., unpaired two-tailed t-test. Source data Extended Data Fig. 5Individual cell segmentation and cell type identification in LUSC and LUAD SBEM volumes.a, SBEM volume (75 μm*75 μm*12 μm) of OXPHOSLO LUSC tumor (left panel). Inter-cellular space between LUSC cells and neutrophils is colored as yellow and images were processed in Amira (central panel) with steps of binary smoothing, adaptive thresholding, Gaussian filter and variance to achieve individual cell segmentation. Individual cells were segmented from serial 2D SBEM images were reconstructed in 3D volume (right panel). Scale bar = 10 μm. b, Morphological features and special organelle structures were used to distinguish the cell types of LUSC, neutrophil (NTPH), LUAD and red blood cell (RBC) in OXPHOSLO LUSC SBEM volume. Scale bar = 3 μm. c, Representative 2D SBEM image of OXPHOSHI LUAD tumor. Scale bar = 15 μm. d, Morphological features and special organelle structures identified in the cell types of LUAD and macrophage from OXPHOSHI LUAD SBEM images. Scale bar = 3 μm. e, The landscape of SBEM imaged OXPHOSHI LUAD tumor volume after individual cell segmentation and cell-type classification (left panel). Quantification of different cell types (right panel). LUAD-red, macrophage-yellow, red blood cell (RBC)-purple. Source data Extended Data Fig. 6Machine learning based trinary segmentation of nucleus, mitochondria and background in SBEM images.a,b, manual labeling of nucleus (green) and mitochondria (red) was used as ground truth (left panel). U-Net encoder decoder architecture of convolution neural network (CNN) was trained for the trinary segmentation of class 1 (nucleus), class 2 (mitochondria) and class 3 (background) in OXPHOSHI LUAD and OXPHOSLO LUSC SBEM volumes (right panel). Scale bar = 6 μm.
Extended Data Fig. 7Morphological analysis of mitochondrial networks in mouse and human NSCLC cells cultured in vitro.a, Representative confocal Airyscan images stained with Mitotracker deep red (MTDR, red) and Hoechst (blue) of mouse NSCLC cells derived from OXPHOSHI LUAD (mouse 4, LPP) and OXPHOSLO LUSC (mouse 5, LPP). Scale bar = 5 μm. b,c, Violin plots showing mitochondrial morphological descriptors (circularity and aspect ratio) in mouse LUAD and LUSC cells, n > 150 cells per cell line, 3 biological replicates; unpaired two-tailed t-test. d, Representative confocal Airyscan images stained with MTDR (red) and Hoechst (blue) of human OXPHOSHI LUAD (H1975, A549) and OXPHOSLO SCC (RH2, Tu686) cell lines. Scale bar = 4 μm. e–g, Violin plots of mitochondrial morphological descriptors (circularity and aspect ratio) and mitochondrial size (area) in human LUAD cell lines (H1975, A549) and SCC cell lines (RH2, Tu686). Data are from $$n = 3$$ biological replicates, n > 240 cells per cell line, unpaired two-tailed t-test. Source data Extended Data Fig. 8Spatial analysis of mitochondrial networks in mouse and human NSCLC cells cultured in vitro.a, Illustration of the distance between nucleus and mitochondrial meshed surface using mtk program in Imod. Scale bar = 5 μm. b, Mitochondrial spatial distribution was relative to nucleus and measured by the distance between individual mitochondria to the surface of corresponding nucleus in OXPHOSHI LUAD (left panel) and OXPHOSLO LUSC (right panel) cells imaged by SBEM. Scale bar = 3 μm. c,d, Schematic of the method developed for measuring the distance between nucleus and mitochondria in 2D confocal Airyscan images. Nucleus and mitochondria are segmented in ImageJ and reconstructed in ellipse shape with the parameters of centroid coordinates, major and minor axes, and angle. The shortest distance between mitochondrial centroid and nucleus ellipse equation is estimated by solving the Lagrangian function. e,f, Violin plots of the spatial distribution of mitochondria network in mouse (e) and human (f) OXPHOSHI LUAD and OXPHOSLO LUSC/SCC cells. Data are from $$n = 3$$ biological replicates, n > 150 cells per cell line (e), n > 240 cells per cell line (f), unpaired two-tailed t-test. Source data Extended Data Fig. 9Mitochondrial cristae types in mouse and human NSCLC cells imaged by SBEM and confocal Airyscan.a, Representative of mitochondrial cristae structure in SBEM images of OXPHOSHI LUAD (left panel) and OXPHOSLO LUSC (right panel). The classification of type I, II and III cristae was indicated in the representative images. b–e, Representative of mitochondrial cristae structure of OXPHOSHI LUAD (b,c) and OXPHOSLO LUSC (d,e) cells imaged by confocal Airyscan and processed by WEKA segmentation (ImageJ). The classification of type I, II and III cristae was indicated in the representative images. Scale bar (b) = 3 μm. Scale bar (d) = 2 μm. f, Transmitted electron microscopy (TEM) image of OXPHOSLO LUSC (RH2) cell. Zoomed-in images from red boxes illustrated type II cristae structure. Scale bar = 3 μm. g, Workflow of mitochondrial cristae segmentation and quantification in SBEM images by trainable WEKA segmentation (ImageJ). h, *Mitochondrial cristae* density (cristae number/mitochondrial area) in OXPHOSHI LUAD and OXPHOSLO LUSC cells imaged by SBEM ($$n = 20$$ mitochondria per tumor type). Data are mean ± s.e.m., unpaired two-tailed t-test. i, *Mitochondrial cristae* density (cristae number/mitochondrial area) in human OXPHOSHI LUAD (H1975, A549) and OXPHOSLO SCC (RH2, Tu686) cells. Data are mean ± s.e.m. ( $$n = 2$$ biological replicates, >100 cells per cell line). j, Percentage of type II cristae distribution in human LUAD (H1975, A549) and SCC (RH2, Tu686) cells. Data are mean ± s.e.m. ( $$n = 3$$ biological replicates, n > 2,000 mitochondria). Unpaired two-tailed t-test. k, Basal OCR of in human OXPHOSHI LUAD (H1975, A549) and OXPHOSLO SCC (RH2, Tu686) cells. Data are mean ± s.e.m. ( $$n = 3$$ biological replicates). Unpaired two-tailed t-test. Source data Extended Data Fig. 10Differential accumulation of lipid droplets (LDs) between OXPHOSHI LUAD and OXPHOSLO SCC.a,b, Co-staining of oil red o and hematoxylin in OXPHOSHI LUAD and OXPHOSLO LUSC tumors from xenografts of human cells (a: H1975, RH2) and GEMMs (b: KL). Scale bar = 400 μm. c,d, The expression levels of Plin5 and DGAT1 in LUAD and LUSC tumors from The Cancer Genome Atlas (TCGA) analysis ($$n = 364$$ LUAD cells, $$n = 527$$ LUSC cells). Unpaired two-tailed t-test. e, Representative confocal Airyscan images stained with MitoTracker DeepRed (MTDR, purple), bodipy (green) and Hoechst (blue) in cultured human LUAD (H1651) and HNSCC (Tu686) cells. Scale bar = 5 μm. f, Quantification of total area of LDs per cell in human LUAD and SCC cells. Data are mean ± s.e.m. ( $$n = 3$$ biological replicates, n > 300 cells per cell line). Unpaired two-tailed t-test. g, Heat map showing the percentage of mitochondrial population classified by both spatial distribution and cristae types in OXPHOSHI LUAD cells. Data are mean value from $$n = 3$$ biological replicates (n > 1,200 mitochondria). h, Percentage of change in maximal OCR of human LUAD and SCC cells in response to UK-5099, ETO and BPTES. Data are mean ± s.e.m. ( $$n = 3$$ biological replicates), unpaired two-tailed t-test. Source data
Extended Data Fig. 11Glycolytic LUSC cells rescue OXPHOS activity by glucose restriction and inhibition of hexosamine pathway. Data are mean ± s.e.m. ( $$n = 3$$ biological replicates), unpaired two-tailed t-test unless specified. a, Representative 2D SBEM image showing PNM and associated type III cristae in OXPHOSLO and glycolytic LUSC. b, Heat map showing the percentage of mitochondrial population classified by both spatial distribution and cristae types in OXPHOSLO LUSC cells. Data are mean value from $$n = 3$$ biological replicates (n > 750 mitochondria). c,d, Colorimetric assay measuring glucose uptake (c) and ECAR rate (d) in human LUAD (H1975, A549) and SCC (RH2, Tu686) cells. ( c) $$n = 2$$ biological replicates. e, Representative of mitochondrial displacement by overlaying images from different time points in OXPHOSHI LUAD (A549) and OXPHOSLO LUSC (RH2) cells. Scale bar = 3 μm. f,g, Colorimetric assay measuring glucose uptake (f) and ECAR rate (g) in RH2 cells treated with vehicle (Veh) or KL-11743 with indicated concentrations for 72 h. (f) $$n = 2$$ biological replicates. ( g) One-way ANOVA, Dunnett test. h, Mitochondrial displacement in RH2 cells treated with si-ctrl and si-GLUT1 for 72 h ($$n = 2$$ biological replicates, n > 60 per treating condition). i,j, Mitochondrial displacement in H1975 cells treated with low glucose (5.5 mM) and galactose medium for 24 h (i), hexosamine pathway inhibitors azaserine (0.5 μM) and OSM1 (25 μM) for 72 h (j). n > 100 per treating condition. k–m, Western blots were probed with indicated antibodies on lysates of RH2 cells treated with low glucose (5.5 mM) and galactose medium for 24 h (k), hexosamine pathway inhibitors azaserine (0.5 μM) and OSM1 (25 μM) for 72 h (l), and on lysates of RH2 and H1975 cells treated with si-ctrl and si-OGT for 72 h (m). n, Representative of confocal Airyscan imaged RH2 cells stained with MTDR (red) and Hoechst (blue) of after treatment of vehicle (Veh) or KL-11743 (200 nM) for 72 h, scale bar = 4 μm. o, Quantification of the spatial distribution of mitochondrial network in RH2 cells treated with Veh or KL-11743 with indicated concentrations. $$n = 300$$ cells per treating condition. One-way ANOVA, Dunnett test. p, Representative of confocal Airyscan imaged cristae structure (type I, II and III) in RH2 cells treated with Veh or KL-11743 (200 nM, 72 h) and stained with 10-N-nonyl acridine orange (NAO) and followed by Weka segmentation (ImageJ). q–u, Percentage of type I, II, III cristae in RH2 and H1975 cells treated with low glucose (5.5 mM) and galactose medium for 24 h (q), hexosamine pathway inhibitors azaserine (0.5 μM) and OSM1 (25 μM) for 72 h (r,s) and si-ctrl and si-OGT for 72 h (t,u). n > 600 mitochondria per treating condition. v,w, Mitochondrial basal OCR in RH2 and H1975 cells treated with hexosamine pathway inhibitors azaserine (0.5 μM) and OSM1 (25 μM) for 72 h (v) and si-ctrl and si-OGT for 72 h (w). x–z, Mitochondrial basal OCR (x) and Complex I (y) and Complex II (z) MRC in RH2 cells treated with indicated concentrations of KL-11743 for 72 h. One-way ANOVA, Dunnett test. Source data Extended Data Fig. 12Mitochondrial motility, spatial distribution and respiration in OXPHOSHI LUAD and OXPHOSLO LUSC cells treated with the cytoskeleton disruptors. Data are mean ± s.e.m. ( $$n = 3$$ biological replicates), unpaired two-tailed t-test unless specified. a-d, Mitochondrial displacement in H1975 (a,c) and RH2 (b,d) cells treated with latrunculin A (1 μM) and nocodazole (6.7 μM) for 12 h (a,b), and treated with si-ctrl and si-Vimentin for 72 h (c,d). n > 90 cells per treating condition. e-h, Quantification of the spatial distribution of mitochondrial network in H1975 (e,g) and RH2 (f,h) cells treated with latrunculin A (1 μM) and nocodazole (6.7 μM) for 12 h (e,f), and treated with si-ctrl and si-Vimentin for 72 h (g,h). n > 150 cells per treating condition. i–l, Mitochondrial basal OCR in H1975 (i,k) and RH2 (j,l) cells treated with latrunculin A (1 μM) and nocodazole (6.7 μM) for 12 h (i,j), and treated with si-ctrl and si-Vimentin for 72 h (k,l). Source data
## Source data
Source Data Fig. 1 Source Data Fig. 2 Source Data Fig. 3 Source Data Fig. 4 Source Data Fig. 5 Source Data Extended Data Fig. 1 Source Data Extended Data Fig. 2 Source Data Extended Data Fig. 4 Source Data Extended Data Fig. 5 Source Data Extended Data Fig. 7 Source Data Extended Data Fig. 8 Source Data Extended Data Fig. 9 Source Data Extended Data Fig. 10 Source Data Extended Data Fig. 11 Source Data Extended Data Fig. 12
## Extended data
is available for this paper at 10.1038/s41586-023-05793-3.
## Peer review information
Nature thanks Luca Scorrano, Hiroki Ueda and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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|
---
title: The dietary sweetener sucralose is a negative modulator of T cell-mediated
responses
authors:
- Fabio Zani
- Julianna Blagih
- Tim Gruber
- Michael D. Buck
- Nicholas Jones
- Marc Hennequart
- Clare L. Newell
- Steven E. Pilley
- Pablo Soro-Barrio
- Gavin Kelly
- Nathalie M. Legrave
- Eric C. Cheung
- Ian S. Gilmore
- Alex P. Gould
- Cristina Garcia-Caceres
- Karen H. Vousden
journal: Nature
year: 2023
pmcid: PMC10033444
doi: 10.1038/s41586-023-05801-6
license: CC BY 4.0
---
# The dietary sweetener sucralose is a negative modulator of T cell-mediated responses
## Abstract
Artificial sweeteners are used as calorie-free sugar substitutes in many food products and their consumption has increased substantially over the past years1. *Although* generally regarded as safe, some concerns have been raised about the long-term safety of the consumption of certain sweeteners2–5. In this study, we show that the intake of high doses of sucralose in mice results in immunomodulatory effects by limiting T cell proliferation and T cell differentiation. Mechanistically, sucralose affects the membrane order of T cells, accompanied by a reduced efficiency of T cell receptor signalling and intracellular calcium mobilization. Mice given sucralose show decreased CD8+ T cell antigen-specific responses in subcutaneous cancer models and bacterial infection models, and reduced T cell function in models of T cell-mediated autoimmunity. Overall, these findings suggest that a high intake of sucralose can dampen T cell-mediated responses, an effect that could be used in therapy to mitigate T cell-dependent autoimmune disorders.
Consumption of high doses of the sweetener sucralose has immunomodulatory effects in mice, as a result of reduced T cell proliferation and differentiation.
## Main
Sucralose is a commonly used, calorie-free sweetener that is about 600 times sweeter than sucrose6. Despite its limited absorption7, circulating sucralose can be detected in humans following consumption of sucralose-containing food or drinks8, with consumption of 250 mg sucralose resulting in plasma sucralose levels of around 1 μM within 90–120 min (ref. 8). The maximum acceptable daily intake (ADI) of sucralose for humans has been established as 15 mg per kg (body weight) by the European Food Safety Authority (EFSA) or 5 mg per kg (body weight) by the US Food and Drug Administration (FDA). Allometric scaling on the basis of body surface area (BSA) equivalents can be used to convert human doses of drugs to mouse doses by adjusting for the increased metabolic rate in mice9. By allowing mice ad libitum access to water containing 0.72 mg ml−1 or 0.17 mg ml−1 of sucralose, we calculated—using BSA equivalents—that the consumption of sucralose over 10 weeks was near the equivalent of the ADI recommended by either EFSA (at the 0.72 mg ml−1 dose) or FDA (at the 0.17 mg ml−1 dose) (Fig. 1a). As expected, we were able to detect increasing amounts of circulating sucralose corresponding with increased consumption in mice (Fig. 1b and Extended Data Fig. 1a), reaching a plasma concentration of around 1 µM at the highest dose of sucralose, consistent with the levels that can be achieved in humans8.Fig. 1Sucralose impairs T cell proliferation and differentiation.a, Sucralose (Scrl) intake in mice given 0.72 mg ml−1 (blue; $$n = 6$$) or 0.17 mg ml−1 (aquamarine; $$n = 6$$) Scrl. In box plots, whiskers show the minimum and maximum values, box margins represent the first and third quartile and the central line is the median value. Dashed lines indicate the BSA-adjusted EFSA (black) and FDA (purple) maximum ADI. Scrl concentrations are indicated in mg ml−1 throughout. b, Circulating Scrl levels in mice given water containing different Scrl concentrations for 2 weeks. $$n = 4$$ individual mice per condition. c, Schematic of the experimental design. CFSE, carboxyfluorescein succinimidyl ester. d, Homeostatic proliferation of CD8+ and CD4+ donor T cells in individual Rag2−/− recipient mice given plain water ($$n = 6$$) or Scrl ($$n = 5$$). e, Histograms of CD8+ T cell proliferation in the presence of Scrl, AceK, NaS or control medium (Ctrl). f, Human CD8+ T cell proliferation in the presence of Scrl, AceK, NaS or control medium. g, Paired comparison of the percentage of proliferated CD8+ T cells in f. $$n = 3$$ independent donors. h, Representative flow cytometry plot of in vitro polarized CD4+ TH1 cells expressing IFNγ and TBET (also known as TBX21). i, The percentage of TH1 cells in h. $$n = 3$$ technical replicates per condition. j, Representative flow cytometry plot of CD8+ T cells expressing CD8 and IFNγ. k, Quantification of CD8+IFNγ+ cells in j. $$n = 3$$ (Ctrl) or $$n = 4$$ (Scrl, AceK and NaS) technical replicates per condition. l,m, Mice were given plain water ($$n = 9$$) or 0.72 mg ml−1 of either Scrl ($$n = 12$$) or NaS ($$n = 11$$). l, Body composition (lean versus fat mass). m, Average energy expenditure measured continuously during night (grey area) and day (white area). n, Multidimensional scaling of the faecal gut microbiome from mice given water ($$n = 5$$), 0.72 mg ml−1 Scrl ($$n = 5$$), 0.17 mg ml−1 Scrl ($$n = 5$$) or $10\%$ (w/v) glucose ($$n = 5$$) for 2 (left) or 12 (right) weeks. Data are mean ± s.d. ( b,i,k) or mean ± s.e.m. ( d,l,m). Significance was tested using unpaired (d) or paired (g) two-tailed Student’s t-test; one-way ANOVA with Tukey’s (i,k) or Dunnet’s multiple comparison test for lean and fat mass independently (l) or two-way ANOVA (m). Data are representative of two (d) or three (e,h–k) independent experiments. Source data
## Effect on T cell proliferation and differentiation
Previous reports using different models have suggested that high doses of sucralose can have either pro-inflammatory or anti-inflammatory activities2–4. To test a possible effect of sucralose on the immune system, we profiled various immune compartments in mice given 0.17 or 0.72 mg ml−1 sucralose or the chemically unrelated sweetener sodium saccharin (NaS). In these studies, neither dose of sucralose or NaS had any detectable effect on the homeostatic levels of CD11b+ myeloid cells (including monocytes and neutrophils), B220+ B cells, CD8+ T cells and CD4+ T cells (including T regulatory (Treg) cells), natural killer cells and dendritic cells (Extended Data Fig. 1b). To assess the effect of sucralose on immune responses, we challenged mice given 0.72 mg ml−1 sucralose or water with sheep red blood cells (sRBCs) to activate a germinal centre B cell response or with lipopolysaccharide (LPS) or interleukin-4 (IL-4) complex to activate a myeloid response. Sucralose did not change the number of splenocytes or B220+ B cells or affect germinal centre B cell formation (Extended Data Fig. 1c–f). Similarly, bone marrow-derived macrophages cultured in sucralose containing media did not display altered IL-1β, IL-6 or IL-12p70 production upon LPS stimulation, nor were there differences in LPS-induced IL-1β production in plasma of mice given sucralose compared with controls (Extended Data Fig. 1g,h). Finally, we did not observe any effect on the alternative activation or expansion of macrophages elicited by IL-4 complex in mice exposed to sucralose (Extended Data Fig. 1i,j). To examine the effect of sucralose on T cell proliferation, we measured the homeostatic expansion of donor T cells in sucralose-treated Rag2−/− recipient mice (Fig. 1c). Both CD8+ and CD4+ T cells showed reduced homeostatic proliferation in sucralose-treated mice at both doses (Fig. 1d and Extended Data Fig. 1k). To evaluate whether sucralose has a direct effect on T cells, we performed in vitro T cell proliferation assays in the presence of increasing doses of sucralose or two chemically unrelated sweeteners: acesulfame potassium (AceK) and NaS. Sucralose alone showed a dose-dependent ability to inhibit the proliferation of CD8+ and CD4+ T cells (Fig. 1e and Extended Data Fig. 2a). This negative effect on T cell proliferation was observed following both high- and low-dose activation with anti-CD3 antibody, even in the presence of co-stimulation (combined anti-CD3 and anti-CD28) (anti-CD3/CD28) (Extended Data Fig. 2b). Similarly, sucralose, but not NaS or AceK, inhibited the proliferation of human CD8+ T cells and the human T cell leukaemia cell line, Jurkat (Fig. 1f,g and Extended Data Fig. 2c). However, none of the sweeteners affected CD8+ or CD4+ T cell viability (Extended Data Fig. 2d). T cell differentiation and effector function also have a critical role in determining T cell responses. We found that the polarization of CD4+ and CD8+ T cells towards interferon-γ (IFNγ)-producing lineages—CD4+ T helper 1 (TH1) cells and CD8+ effector T (Teff) cells, respectively—was significantly decreased in the presence of sucralose but not with the other sweeteners (Fig. 1h–k). Together, these results indicate that high sucralose exposure decreases T cell proliferation and differentiation.
## Systemic effects of sucralose
Dietary intake of sucralose has the potential to affect food intake and metabolic parameters in mice. However, we found that up to 12 weeks of exposure to either dose of sucralose or NaS did not affect food intake or body weight in mice (Extended Data Fig. 3a,b). As expected, the average intake of liquid was higher in mice given water containing sweetener (Extended Data Fig. 3c), and the consumption remained consistent over the 12-week period (Extended Data Fig. 3d). Sucralose did not alter the lean or fat mass of the mice (Fig. 1l) and did not significantly affect fasting insulin levels or glucose tolerance (Extended Data Fig. 3e–g). Finally, we detected no major sucralose-related effect on energy expenditure (Fig. 1m), respiratory exchange ratio or locomotor activity (Extended Data Fig. 3h,i). Sucralose has previously been shown to affect the gut microbiota in some10,11 but not all12 studies. We found no consistent shift in the bacterial species detected in the stool of sucralose-treated mice (Fig. 1n). Furthermore, the weight and length of the caecum did not change, and we did not detect signs of diarrhoea (such as pale watery stool) in sucralose-treated mice (Extended Data Fig. 4a,b). Closer analysis of the bacterial composition revealed larger changes in glucose-treated mice, but only minor differences in sucralose-treated mice, compared with mice given water (Extended Data Fig. 4c–g).
## No clear role for the sweet taste receptor
Our in vitro studies suggested that sucralose has a direct effect on T cells, modulating their proliferation and differentiation. The established function of sucralose is to activate the canonical sweet taste receptor (STR), a G-protein-coupled receptor that is responsible for the perception of sweet taste. The STR is a member of the type 1 taste receptors (T1Rs) and is a heterodimer comprising the T1R2 and T1R3 subunits13. Other members of this family include the umami receptor14 (a T1R1–T1R3 heterodimer) and possibly T1R3 homodimers15. In addition to the taste buds, the subunits of these receptors are expressed in several different cell types, including cells in the gastrointestinal tract16, pancreas17, brain18 and adipose tissue19, although analysis of published RNA expression data suggests that their expression in T cells is low20. There is also evidence that the STR can have a role in regulating innate immunity21. Sucralose, AceK and NaS have been reported to bind to different regions of T1R2 and T1R3 (ref. 22) and activate downstream signalling from the canonical STR (T1R2–T1R3) and possibly the T1R3 homodimer22,23. This binding results in increased intracellular calcium levels24,25, which we did not detect when we treated Jurkat T cells with sucralose in the absence of T cell receptor (TCR) stimulation (Extended Data Fig. 4h). Furthermore, even at high concentrations (2 mM), NaS and AceK did not evoke the same effect as sucralose on primary T cell responses (Fig. 1e and Extended Data Fig. 2a). Together, our data suggest that activation of the STR is unlikely to mediate the sucralose phenotype in T cells, leading us to explore alternative mechanisms to explain this effect.
## Sucralose impairs TCR-dependent proliferation
Sucralose could potentially affect T cell metabolism, but we found no difference in glucose uptake between sucralose and control treated cells (Extended Data Fig. 5a) and no change in glucose metabolism, as measured by the conversion of labelled [13C6]glucose into pyruvate, lactate or malate in either CD4+ or CD8+ T cells (Extended Data Fig. 5b,c). Taking an unbiased approach, we carried out RNA-sequencing analysis (RNA-seq) on activated T cells exposed to sucralose, NaS or control medium for 24 and 48 h. Principal component analysis (PCA) showed that T cells activated in the presence of sucralose displayed a unique expression profile compared with control cells or cells treated with NaS (Extended Data Fig. 5d). Enrichment analysis identified several pathways affected by sucralose, including those associated with proliferation, as expected (Extended Data Fig. 5e). As these T cells were activated through their TCR with anti-CD3, we tested whether the effect of sucralose was specific to TCR-dependent proliferation using a high concentration (100 ng ml−1) of IL-2 to induce TCR-independent proliferation26. CD8+ T cell expansion induced by this approach was not reduced by sucralose (Extended Data Fig. 5f), suggesting that sucralose specifically impedes TCR-dependent proliferation. Upon activation, T cells upregulate migratory receptors, cytokine receptors and costimulatory and inhibitory molecules27. Sucralose had no significant effect on the expression of CD44, CD69 or PD1 activation markers (Extended Data Fig. 5g,h), and despite lower CD25 expression, its downstream targets—STAT5 phosphorylation and IL-2 levels—were not modified by sucralose (Extended Data Fig. 5i,j). Furthermore, IL-2 supplementation did not rescue TCR-mediated proliferation in the presence of sucralose (Extended Data Fig. 5k). These results indicate that sucralose does not affect all aspects of T cell activation.
## Effect on cell membranes and PLCγ1 activation
To understand at which point sucralose affects TCR signalling, we activated downstream pathways of the TCR with phorbol 12-myristate 13-acetate (PMA) (to induce PKC-driven RAS activation) and ionomycin (to increase intracellular calcium). We did not detect any effect of sucralose in response to stimulation with PMA or ionomycin (Extended Data Fig. 6a); we therefore focused on earlier TCR-induced signalling events (Fig. 2a). A key response downstream of TCR stimulation is the phosphorylation and activation of PLCγ1, which cleaves phosphatidylinositol-4,5-bisphosphate (PtdInsP2) into inositol-1,4,5-trisphosphate (InsP3) and diacylglycerol28,29 (DAG) (Fig. 2a). Sucralose-exposed T cells showed a clear delay in PLCγ1 phosphorylation at early timepoints following TCR activation (Fig. 2b). Similarly, Jurkat T cells cultured in sucralose showed diminished PLCγ1 phosphorylation (Extended Data Fig. 6b). In primary mouse T cells, we detected slightly delayed ERK phosphorylation (Extended Data Fig. 6c), which recovered within 5 min. We did not observe differences in early events of TCR signalling, including ZAP70 and LAT activation, and in immunoprecipitation experiments, we did not observe major defects in the association of ZAP70 with CD3ζ in Jurkat cells following stimulation (Extended Data Fig. 6d,e). The principal effect of sucralose is therefore to limit PLCγ1 activation without substantially affecting other early events downstream of the TCR. We next considered mechanisms through which sucralose could impede TCR signalling to PLCγ1. We analysed whole-cell, cytosolic and membrane fractions of Jurkat T cells that had been exposed to sucralose (Extended Data Fig. 6f), finding that sucralose was associated predominantly with cell membranes (Fig. 2c). Using the mass spectrometry imaging platform cryo-OrbiSIMS, we examined the spatial distribution of sucralose in activated T cells. In line with previous work30, depth-profiling data indicated that sucralose did not accumulate inside the cells (Fig. 2d). Furthermore, spectroscopic data indicated that sucralose was efficiently washed off the cell surface of Jurkat T cells, suggesting that it does not interact stably with the cell membrane (Extended Data Fig. 6g). However, previous studies indicated that sucralose affects lipid packing and membrane fluidity in lipid membranes31. We found that T cell membranes from sucralose-treated cells were consistently shifted to a lower order that is associated with reduced responses32 (Fig. 2e and Extended Data Fig. 6h,i). These changes in membrane order correlated with a reduction in PLCγ1 clustering and colocalization with TCRβ on the cell surface in response to TCR stimulation (Fig 2f,g and Extended Data Fig. 6j). PLCγ1 clustering has been shown to be required for signal transduction33, and this defect in sucralose-treated cells could explain their incomplete activation of PLCγ1 (Fig 2b).Fig. 2Sucralose decreases intracellular calcium flux downstream of the TCR.a, Schematic of the TCR signalling cascade. ER, endoplasmic reticulum. b, Western blot of phosphorylated and total PLCγ1 in anti-CD3-stimulated T cell lysates. c, Liquid chromatography–mass spectrometry (LC–MS) quantification of Scrl in whole-cell lysate, cytosolic fraction and membrane fraction of Jurkat T cells exposed to 0.5 mM Scrl. Whole-cell lysate of Jurkat T cells grown in T cell medium with (WC Scrl) or without (WC TCM) 0.5 mM Scrl are shown as controls. $$n = 4$$ independent preparations. d, Cryogenic OrbiSIMS analysis of Scrl-treated mouse T cells shows the intensity–depth profile above background (grey shaded area) for Scrl [CClNa2O]+ and lipid cell marker [C16H1105]+ fragments. Inset, ion intensity map for the [C2HO]+ cell marker (m/$z = 40.99$), illustrating the 8 cells quantified (circled). TIC, total ion count. e, The percentage of CD4+ T cells with intermediate (left) and low (right) membrane order activated in the presence or absence of Scrl. $$n = 17$$ biological replicates. f, Representative 3D reconstruction (z-stacks) from naive T cells cultured with or without Scrl and activated with anti-CD3. Scale bars, 2 µm. g, Average volume of PLCγ1 clusters. $$n = 3$$ average volumes of at least 3 cells per image in separate fields. h, Representative flow cytometry plot for calcium flux using INDO1 in T cells activated with anti-CD3 and streptavidin. i, The percentage of T cells undergoing calcium flux. j, Representative flow cytometry plot for intracellular calcium flux with INDO1 in the presence of 1 mM EDTA. k, The percentage of T cells undergoing intracellular calcium flux. $$n = 3$$ technical replicates per condition. l,m, T cells were activated with anti-CD3/CD28 in the presence of DMSO or ionomycin (Iono) (125 ng ml−1) with or without 0.5 mM Scrl. l, The percentage of proliferating T cells. $$n = 3$$ technical replicates/condition. m, Intracellular cytokine staining for IFNγ and TBET. $$n = 5$$ technical replicates per condition. Data are mean ± s.e.m. ( d) or mean ± s.d. ( c,g,i,k,l,m). Significance was tested using unpaired (g,i,k,l) or paired (e) two-tailed Student’s t-test; one-way ANOVA with Tukey’s multiple comparison test (m). Data are representative of two (f,g) or three (b,h–m) independent experiments. Source data
## Reduced intracellular TCR calcium release
As calcium release from intracellular stores is downstream of PLCγ1 activation34, we next examined whether sucralose affected calcium flux upon TCR engagement. Using flow cytometry, we found that sucralose reduced TCR-dependent calcium flux in T cells (Fig. 2h,i). Calcium is first released from internal stores into the cytosol, which is followed by uptake of extracellular calcium35,36. To determine which calcium source is affected by sucralose, we treated the cells with EDTA to inhibit entry of extracellular calcium (Extended Data Fig. 7a). Sucralose-treated naive T cells retained reduced TCR-dependent calcium flux compared with control cells under these conditions (Fig. 2j, k). These results therefore point to a defect in the release of intracellular calcium stores downstream of the TCR.
To test whether sucralose affected the ability of cells to store calcium, we used thapsigargin to block calcium entry into the endoplasmic reticulum. T cells treated with thapsigargin exhibited similar cytosolic calcium accumulation in the presence or absence of sucralose and, following the addition of extracellular calcium, elicited similar calcium mobilization under both conditions (Extended Data Fig. 7b,c). These results suggested that intracellular calcium stores were unaffected by sucralose; to further verify this observation, we used ionomycin in the absence of exogenous calcium to induce the release of calcium from intracellular stores. Again, we found that under these conditions, sucralose did not affect intracellular calcium release (Extended Data Fig. 7d,e). Our data indicate that sucralose affects TCR- and PLCγ1-dependent intracellular calcium release without changing overall intracellular calcium storage. In line with these observations, we were able to partially rescue proliferation and cytokine production with ionomycin (Fig. 2l,m and Extended Data Fig. 7f,g).
Calcium is an important second messenger in other immune cell types, such as dendritic cells and B cells, so we assessed whether sucralose influenced these populations. Sucralose did not impair calcium flux in in vitro-generated conventional type 1 or type 2 dendritic cells (cDC1s, cDC2s) and plasmacytoid dendritic cells (pDCs) in response to ATP37 (Extended Data Fig. 7h–j). Similarly, we did not detect sucralose-dependent changes in calcium responses downstream of B cell receptor engagement (Extended Data Fig. 7k). Together, these data are consistent with an ability of sucralose to selectively impair TCR-mediated intracellular calcium release and proliferation.
## In vivo tumour-specific T cell responses
To expand our in vitro observations, we examined the effects of sucralose on tumour-specific T cell responses in vivo. Using the model antigen ovalbumin (OVA) to induce a major histocompatibility complex type I (MHCI)-restricted CD8+ T cell response38, we measured tumour-specific responses against subcutaneous EL4 cancer cells expressing OVA (EL4-OVA cells). There were variable degrees of OVA-specific T cell infiltration, but this was consistently lower in the tumours derived from mice given 0.72 mg ml−1 sucralose (Fig. 3a and Extended Data Fig. 8a). Re-stimulation of the tumour infiltrates with an OVA peptide (SIINFEKL) showed dampened IFNγ production in CD8+ Teff cells from sucralose-exposed mice (Fig. 3b,c). Although mice treated with 0.17 mg ml−1 sucralose displayed no change in antigen-specific T cells, we observed a significant, but less pronounced, reduction in the function of these cells (Extended Data Fig. 8b,c). To further test antigen-specific CD8+ T cell responses, we adoptively transferred CD8+ OT-I donor T cells that recognize OVA into recipient mice given 0.72 mg ml−1 sucralose or water followed by EL4-OVA challenge (Fig. 3d). Consistent with weakened T cell function, we observed increased tumour growth and reduced rejection in mice treated with sucralose (Fig. 3e). Furthermore, OT-I cells activated in vitro in the presence of sucralose displayed decreased cytotoxic activity against EL4-OVA cells (Extended Data Fig. 8d). In a third model, we extended previous work showing that the rejection of infrared fluorescent protein (iRFP)-expressing Pdx1-KrasG12D pancreatic tumour cells injected into MHC-mismatched recipients is dependent on a T cell response39 (Fig. 3f). We observed a significant delay in tumour rejection in mice treated with 0.72 mg ml−1 sucralose compared with control mice (Fig. 3g). This effect of sucralose was T cell-specific, as pancreatic tumour cells grew at equivalent rates in T cell-deficient mice given water or sucralose (Fig. 3h).Fig. 3Sucralose treatment limits T cell-specific responses in vivo.a–c, CD8 antigen-specific responses to subcutaneous EL4-OVA tumour growth in mice given water ($$n = 8$$) or 0.72 mg ml−1 Scrl ($$n = 7$$). a, Quantification of intratumoral CD8–MHC tetramer (Kb)–OVA-specific T cells. b, Representative flow cytometry plot of intratumoral cells re-stimulated with OVA peptide and analysed for IFNγ and CD44. c, Percentage OVA-specific T cells expressing IFNγ. d,e, The OT-I tumour-rejection model. Mice given water or 0.72 mg ml−1 Scrl ($$n = 10$$ per condition). d, Schematic overview of the model. e, Volumes of EL4-OVA tumours. f,g,h, MHC-mismatched tumour model using KrasG12D pancreatic ductal adenocarcinoma (PDAC) cells in recipient mice given water ($$n = 9$$) or 0.72 mg ml−1 Scrl ($$n = 8$$). f, Schematic overview of the model. g, Tumour growth in FVB recipient mice. h, Tumour growth in Rag2−/− recipient mice. i–k, C57BL/6J mice given water ($$n = 7$$) or 0.72 mg ml−1 Scrl ($$n = 7$$) challenged with LmOVA. i, The percentage of splenic OVA-specific CD8+ T cells. j, Representative flow cytometry plot of splenocytes re-stimulated with OVA peptide and analysed for expression of CD44 and IFNγ. k, The percentage of splenic CD8+ T cells expressing IFNγ. l, Representative proliferation of Jurkat T cells in T cell media (Ctrl) or exposed to acute (Scrl), chronic (Scrl on/on) or transient (Scrl on/off) 0.5 mM Scrl. $$n = 3$$ per condition. representative of 3 independent experiments. m–p, T cell responses at day 7 after LmOVA infection. Mice given water ($$n = 7$$), 0.72 mg ml−1 Scrl ($$n = 6$$) or 0.72 mg ml−1 Scrl for 2 weeks followed by water for one week ($$n = 7$$, Scrl off). m, Schematic experimental overview. n, Percentage of splenic Kb–OVA-specific CD8+ T cells. o, The frequency of splenic Ki67+CD8+ T cells. p, The frequency of total IFNγ and granzyme B (GZMB) expression in splenic CD8+ T cells after re-stimulation with OVA peptide. Data are mean ± s.e.m. ( a,c,e,g–i,k,l,n–p). Each dot represents a biological (a,c,e,g–i,k,n–p) or technical (l) replicate; data are representative of two (e,g,i,k) or three (l) independent experiments. Significance was tested using unpaired two-tailed Student’s t-test (a,c,i,k); Brown–Forsythe and Welch ANOVA test with Dunnett’s T3 comparison (n–p); two-way ANOVA (e,g,h). NS, not significant. Source data
## Effect of sucralose on response to infection
We also assessed the effect of sucralose on CD8+ Teff responses in an infection model by challenging wild-type mice with Gram-positive *Listeria monocytogenes* expressing OVA (LmOVA). Treatment with 0.72 mg ml−1 sucralose did not affect splenocyte numbers at day 7 after infection (Extended Data Fig. 8e), but caused a reduction in the frequency of splenic OVA-specific CD8+ T cells (Fig. 3i and Extended Data Fig. 8f). Furthermore, SIINFEKL re-stimulation of infected splenocytes from day 7 revealed a significant decrease in the frequency and number of CD8+ T cells producing IFNγ (Fig. 3j,k and Extended Data Fig. 8g,h) from sucralose-treated mice, suggesting impaired function. In line with reduced cytokine production, we observed increased bacterial load in the liver at day 3 after infection, although this was not evident in the spleen (Extended Data Fig. 8i). To investigate the permanence of the sucralose effect, we measured the proliferation of Jurkat T cells pre-cultured in sucralose for 2 weeks. In this model, removal of sucralose resulted in the recovery of the normal proliferation rate, suggesting that the response to sucralose is reversable (Fig. 3l and Extended Data Fig. 8j). To confirm this in vivo, we used the LmOVA model to show that removal of sucralose one week before the challenge (Fig. 3m) partially rescued the development of antigen-specific T cells, their proliferation and cytotoxic function (Fig. 3n–p and Extended Data Fig. 8k).
## Sucralose mitigates autoimmune T cell responses
Our observations that sucralose can dampen T cell responses in vitro and in vivo prompted us to determine whether sucralose could also have therapeutic value by limiting T cell-mediated autoimmunity. Female NOD/ShiLtJ mice provide a spontaneous model of type 1 diabetes characterized by hyperglycaemia and insulitis between 12 and 30 weeks of age, which is caused by T cell-mediated destruction of the pancreatic islets40,41. Mice given either sucralose dose showed lower frequencies of hyperglycaemia and delayed development of type 1 diabetes, an effect that was independent of weight gain (Fig. 4a,b and Extended Data Fig. 9a,b). As a second model of T cell-mediated autoimmunity, we measured T cell-induced colitis by adoptively transferring immunodeficient CD45.2 Tcra−/− mice with congenic CD45.1 naive CD4+ T cells42 (Fig. 4c). In mice treated with 0.72 mg ml−1 sucralose, we observed reduced frequencies and numbers of donor CD45.1+CD4+ T cells, with no effect on total mesenteric lymph node (mLN) leukocytes (Fig. 4d and Extended Data Fig. 9c–e). Re-stimulation of the mLN at day 21 showed lower frequencies and reduced total numbers of pro-inflammatory IFNγ-producing CD4+ T cells in sucralose-treated mice (Fig. 4e,f and Extended Data Fig. 9f,g). Lowering the dose of sucralose led to a reduction in proliferating IFNγ-producing CD4+ T cells, without affecting the frequencies of donor CD4+ T cells and colon length at day 21 (Fig.4g,h and Extended Data Fig. 9h,i). These data suggest that supplementation with sucralose mitigates T cell-mediated autoimmune responses. Fig. 4Sucralose treatment dampens T cell-mediated inflammation in models of autoimmunity.a,b, NOD/ShiLtJ type 1 diabetes model in mice given water ($$n = 8$$), 0.72 mg ml−1 Scrl ($$n = 9$$) or 0.17 mg ml−1 Scrl ($$n = 9$$). a, Schematic of the model. b, Disease-free survival. c, Schematic of the T cell-induced colitis model. d–f, CD45.2 Tcra−/− recipient mice treated with ($$n = 6$$) or without ($$n = 5$$) 0.72 mg ml−1 Scrl. d, The percentage of congenic CD45.1+CD4+ donor T cells in the mLN 3 weeks after transplantation. e, Representative flow cytometry plot of lymphocytes from the mLN that were re-stimulated and analysed for the expression of IFNγ and CD4. f, The percentage of CD4+CD45.1+ donor cells in the mLN that express IFNγ. g,h, Analysis of mLNs from recipient CD45.2 Tcra−/− mice given water ($$n = 8$$) or 0.17 mg ml−1 Scrl ($$n = 8$$). g, The total frequency of CD45.1+CD4+ donor T cells. h, The frequency of CD4+ donor T cells expressing Ki67 and IFNγ. Data are mean ± s.e.m. ( d,f–h). Each dot (d,f–h) represents a biological replicate; data are representative of two (d–f) or three (g,h) independent experiments. Significance was tested using unpaired two-tailed Student’s t-test (d,f–h) and log rank Mantel–Cox test (b) for either dose of *Scrl versus* water. Source data In sum, this work has revealed an unexpected role for high doses of sucralose in modulating immunity by affecting T cell proliferation and effector function. Notably, although the doses of sucralose used in this study are clearly higher than those resulting from normal human dietary consumption of sucralose-sweetened drinks and foods, they are relevant to the ADI recommendation when BSA-adjusted for mice. Our findings do not, therefore, provide evidence that normal sucralose intake is immunosuppressive, but they do demonstrate that at high (but achievable) doses, sucralose has an unexpected effect on T cell responses and functions in autoimmune, infection and tumour models. Our observation that sucralose lowers membrane order and reduces calcium flux is consistent with previous studies43, suggesting that the sucralose effect on the plasma membrane could drive the defect in PLCγ1 activation and calcium release. However, the precise mechanistic details of how sucralose affects TCR signalling remain to be determined. Further experiments using primary T cells and super high-resolution microscopy are necessary to better evaluate the nanocluster formation upon TCR engagement, assess the recruitment of LAT in the presence of sucralose and establish a causal link between these observations. Although our results support a direct effect of sucralose on TCR signalling, we cannot exclude the possibility that sucralose may also affect T cells through additional mechanisms, such as epigenetic changes in response to long-term sucralose exposure or an ability to modulate taste receptors that are not shared with other sweeteners. Furthermore, although we did not observe major changes in the microbiome, such alterations have been noted previously11 and are likely to contribute to the overall response to sucralose intake. Surprisingly, our data suggest that sucralose does not impede calcium signalling in other immune cell types such as B cells or dendritic cells. It is possible that the membrane composition of T cells makes them particularly sensitive to sucralose and it remains to be determined whether sucralose affects other cell types, including other immune cells, in conditions not tested in this study. In conclusion, our study adds to the evidence that sucralose is not an inert molecule and may affect human health. If translatable, our work suggests that treatment with doses of sucralose similar to those used in this study may be beneficial for various conditions arising from unrestrained T cell activity.
## Methods
Antibodies used in this study are listed in the Supplementary Information.
## Mice and in vivo models
For all mouse experiments, at least four mice per group were used. For more complex models, we used more animals to compensate for the increased expected variability. Mice were randomly assigned to a treatment group. For metabolic phenotyping mice were divided in groups after measuring starting body weight to have weight-matched cohorts.
C57BL/6J, FVB/NJ, Tcra−/−, and Rag2−/−, Rag2−/− OT-I and B6.SJL-PtprcaPepcb/BoyCrl mice were bred and housed at the Francis Crick Institute animal facility. NOD/ShiLtJ mice were purchased from Charles River Laboratory. Animal experiments were subject to ethical review by the Francis Crick Animal Welfare and Ethical Review Body and regulation by the UK Home Office project licence P319AE968. All mice were housed under conditions in line with the UK Home Office guidelines. Mice were kept in a 12-h day:night cycle starting at 07:00 until 19:00. Food and water were available ad libitum and rooms were kept at 21 °C at $55\%$ humidity. For sucralose treatment, sucralose (Merck) was dissolved in drinking water at the final concentration of 0.72 or 0.17 mg ml−1 as indicated. The sucralose solution was filtered through a 0.2 μM filter before being added to drinking bottles. Sucralose solutions were replaced once or twice a week and sucralose consumption was measured by the change in weight of the drinking bottles. A similar procedure was followed to prepare any solution that was given to mice. All procedures were performed following the Animals (scientific procedures) Act 1986 and the EU Directive 2010.
## Physiological measures
Food intake, solution intake and body weight.
Individually caged male C57BL/6J 7–8-week-old littermates were used for measurements of body weight, food intake and solution intake. Body weight and food intake were measured weekly. Solution intake was measured twice weekly by measuring the weight of the solutions. Fresh solutions were provided after every measurement every 3–4 days.
## Metabolic phenotyping
Male mice were individually housed in metabolic cages of a combined indirect calorimetry system (TSE PhenoMaster, TSE systems), and after a 48 h acclimatization period, we continually measured O2 consumption, CO2 production, respiratory exchange ratio (RER), energy expenditure, and locomotor activity (that is, horizontal and vertical beam breaks) of individual mice in 15-min intervals for a total of 108 h. Body composition was measured using Echo-MRI device (Echo-MRI). This animal study was approved by the Animal Ethics Committee of the government of Upper Bavaria (Germany).
## Glucose tolerance test
Individually housed male C57BL/6J mice aged 7–8 weeks were given either water or the different sweeteners as indicated for more than 12 weeks. Mice were food deprived for 6 h before receiving an oral gavage of 2 mg kg−1 of glucose (Merck, 158968). Blood glucose was measured using a glucometer (Accu-CHEK) before the oral gavage and every 30 min up to 120 min post gavage.
## Gut microbiome analysis
Faecal samples were freshly collected from individually caged littermates C57BL/6J male mice aged 8 weeks exposed to the different drinking solutions as indicated. Samples were collected after 2 and 12 weeks of treatment. At least two faecal pellets were collected for each mouse, snap frozen in liquid nitrogen and stored at −80 °C until extraction. Faecal DNA was isolated using QIAamp PowerFecal DNA KIT (Qiagen, 12830-50). The 16S amplicons were prepared for sequencing by indexing PCR by a reduced-volume reaction based on the Illumina 16S Metagenomics Sequencing Protocol (15044223 Rev. B). In brief, 2 μl of each sample was combined with 3.25 μl H2O, 6.25 μl 2× NEB Q5 High-Fidelity DNA Polymerase Master Mix (M0492L) and 1 μl of a unique 10 µM UDI primer from the set Nextera IDT-8nt (384 Indexes). Samples were incubated at 95 °C for 3 min, followed by 10 cycles of 95 °C for 30 s, 55 °C for 30 s, 72 °C for 30 s, followed by a final extension at 72 °C for 5 min.
A bead-based clean-up was carried out with Beckman Coulter SPRIselect (B23319) in a 0.8× ratio. The quality of the purified libraries was assessed using a D1000 ScreenTape on an Agilent 24200 TapeStation. Libraries were sequenced on the Illumina MiSeq platform. 2× 300 paired-end reads were produced using the 600 cycle MiSeq Reagent kit v3. Libraries were loaded at 8 pM concentration with $20\%$ PhiX.
The fastq files were processed using DADA2 (v1.18)44, truncating the forward (respectively reverse) reads to 280 and 210 bases and trimming them by 17 and 21 bases, respectively, with a maximum of two expected errors. Taxa and species assignment was carried out using v132 of the SILVA database. The processed data were then analysed in R v4.0.3 (ref. 45) with the phyloseq package46 aggregating the count data to the genus level. DESeq2 (v1.30 (ref. 47)) was used to estimate the log fold changes and P values between experimental groups whilst accounting for an observed batch effect that crossed the experimental groups.
## Homeostatic proliferation
C57BL/6J Rag2−/− male and female mice aged 8–10 weeks were provided either water or sucralose (0.72 mg ml−1 or 0.17 mg ml−1) ad libitum for 2 weeks. CFSE-stained lymphocytes from C57BL/6J donors (stained according to manufacturer’s instructions, BioLegend) were then injected intravenously at 1 × 106 cells per mouse in PBS. At day 3 post injection spleens were assessed for CFSE dilution of donor T cells by flow cytometry.
## Tumour challenge and rejection models
Mice were exposed to either water or sucralose (0.72 mg ml−1) in the drinking water ad libitum 2 weeks before tumour challenge and exposed to solutions until the end of the experiment.
Pdx1-cre; KrasG12D PDAC cells expressing iRFP39 were subcutaneously injected into the left flank of FVB/NJ or C57BL/6J Rag2−/− mice at 1 × 106 cells per mouse in PBS. Growth was monitored by in vivo imaging and mice were taken at a humane endpoint as dictated by the UK Home Office and the animal license. Humane endpoints were maximum tumour size of 1.2 cm, tumour ulceration or $10\%$ weight loss, as authorized in the UK Home Office project licence P319AE968. None of these limits were exceeded.
For in vivo imaging, iRFP fluorescence was measured (excitation: 685 nm and emission: 730 nm) using the LiCOR Odyssey Pearl Imager and analysed with Image Studio v5 (LiCOR).
EL4-OVA cells (1 × 106 cells per mouse) resuspended in PBS were subcutaneously injected into the left flank of C57BL/6J recipient mice either exposed to water or sucralose (0.72 mg ml−1 or 0.17 mg ml−1) 2 weeks before challenge and until the end of the experiment, 10 days post injection. Tumours were digested and analysed for OVA-specific CD8+ T cells and IFNγ production in response to SIINFEKL peptide stimulation, followed by intracellular cytokine staining. Samples were acquired on the BD LSR Fortessa and on the BD FACSymphony. Flow cytometry data were analysed using FlowJo v10 (TreeStar).
The OT-I tumour-rejection assay was performed by exposing C57BL/6J recipients to either water or sucralose (0.72 mg ml−1) for 2 weeks, followed by intravenous injection of 0.3 × 106 TCR-transgenic OT-I T cells from Rag2−/− OT-I donor mice. The next day mice were injected subcutaneously in the left flank with EL4-OVA cancers cells (1 × 106 cells per mouse). Growth was monitored by calliper measurements and mice were taken at a humane endpoint as dictated by the UK Home Office and the animal license. Tumour volume was calculated as volume = (length × width2)/2, with the length as the longest diameter and width measured as the perpendicular tumour diameter. Humane endpoints were maximum tumour size of 1.2 cm, tumours ulceration or $10\%$ weight loss, as authorized in the UK Home Office project licence P319AE968. None of these limits were exceeded.
## L. monocytogenes infection
Male C57BL/6J mice (aged 8–10 weeks) were given either water or sucralose (0.72 mg ml−1) for 2 weeks before infection and remained on the solutions throughout the experiment. The sucralose washout experiment involved mice exposed to water for 3 weeks, mice exposed to 0.72 mg ml−1 of sucralose 2 weeks before injection and maintained on sucralose until the end of the experiment, and the washout group, which was provided sucralose for 2 weeks followed by water for 1 week before LmOVA challenge (Fig. 3m). In brief, mice were injected intravenously with a sublethal dose of LmOVA (1 × 105 colony-forming units per mouse) and were euthanized 7 days post infection. Splenocytes were analysed for the presence of OVA-specific CD8+ T cells (using fluorochrome conjugated MHC tetramer complex, Baylor College of Medicine, USA) and cytokine production by CD8+ T cells in response SIINFEKL peptide re-stimulation, followed by intracellular cytokine staining. Samples were acquired on the BD LSR Fortessa and on the BD FACSymphony. Flow cytometry data were analysed using FlowJo v10 (TreeStar).
Bacterial load was measured at day 3 post infection. Spleen and liver were isolated and weighed, followed by tissue disruption. Spleen and liver homogenates were resuspended in PBS and $\frac{1}{10}$ serial dilutions were performed in PBS. Fifty microlitres of each serial dilution was plated on brain heart infusion plates and placed into a bacterial incubator at 37 °C overnight. The number of colony-forming units were counted the following day and normalized to tissue weight.
## T cell-induced colitis model
Six- to eight-week-old male C57BL/6J Tcra−/− mice were given either water or sucralose (0.72 mg ml−1 or 0.17 mg ml−1) ad libitum for 2 weeks before T cell transfer and until the end of the experiment. 0.5 × 106 cells congenic CD45.1+CD4+CD45RB+ T cells were injected per mouse. Inflammation at day 21 was assessed by intracellular cytokine staining followed by flow cytometric analysis. Humane endpoints were maximum weight loss $15\%$ and chronic diarrhoea as authorized in the UK Home Office project licence P319AE968. None of these limits were exceeded.
## Type 1 diabetes model
Female NOD/ShiLtJ were purchased from Charles River laboratory. Starting from 8 weeks of age, age-matched and weight-matched mice were given either water or sucralose (either 0.72 mg ml−1 or 0.17 mg ml−1). To monitor the development of type 1 diabetes, blood glucose was monitored once a week using a glucometer (Accu-CHEK). Mice with a non-fasting glucose level exceeding 13.9 mmol l−1 were measured a second time the following day and mice with consecutive blood glucose exceeding 13.9 mmol l−1 were considered diabetic. Humane endpoints were defined as consecutive non-fasting blood glucose measurements exceeding 13.9 mmol l−1 as authorized in the UK Home Office project licence P319AE968. None of these limits were exceeded.
## LPS-induced systemic inflammation model
Male C57BL/6J mice aged 8–10 weeks were randomly assigned either to water or sucralose (0.72 mg ml−1) for 2 weeks before LPS challenge and until the end of the experiment. Mice were injected intraperitoneally with LPS from *Escherichia coli* (0111:B4 Sigma L4391) at a dose of 0.1 mg kg−1. Mice were euthanized 3 h after challenge and blood was collected by cardiac puncture in EDTA-coated tubes.
## In vivo proliferation of peritoneal macrophages
C57BL/6J mice (one cohort of male mice aged 8–10 weeks and one cohort of female mice aged 8–10 weeks) were given sucralose (0.72 mg ml−1) or water ad libitum for 2 weeks and until the end of the experiment. Mice were then injected intraperitoneally with long-lasting IL-4 complex (5 µg IL-4 (Peprotech): 25 µg ml−1 anti-IL-4 monoclonal antibody, clone 11B11; BioXcell) or PBS (control) every second day. Mice were sacrificed after the second injection. Peritoneal macrophages were collected by injecting 5 ml of PBS into the peritoneal cavity. The exudate was collected and stained for flow cytometry. Samples were acquired on the BD LSR Fortessa. Flow cytometry data were analysed using FlowJo v10 (TreeStar).
## sRBC cell immunization
Female C57BL/6J mice (aged 8–10 weeks) were randomly assigned to 2 groups: water treatment or sucralose treatment (0.72 mg ml−1). Mice were exposed to sucralose for 2 weeks before immunization and until the end of the experiment. sRBCs (Antibodies Online, cat. ABIN770402) were prepared in HBSS and mice were intraperitoneally immunized with 2 × 109 sRBC in PBS. Germinal centre B cells were analysed in the spleen at day 7 post immunization by cell surface staining of B220 (clone RA3-6B2), GL-7 (clone GL7), and CD95 (clone SA367H8). Samples were acquired on the BD LSR Fortessa and on the BD FACSymphony. Flow cytometry data were analysed using FlowJo v10 (TreeStar).
## Human CD8+ T cell isolation and culture
Human peripheral blood was collected from healthy, non-fasted individuals into heparinized Vacuettes (Greiner Bio-One). Mononuclear cells were isolated by layering whole blood (1:1) onto Lymphoprep (StemCell Technologies) and centrifuged at 805g for 20 min at room temperature. Human CD8+ T cells were isolated downstream using magnetic microbeads (Miltenyi Biotec; cat. 130-096-495). Isolated CD8+ T cells (1.0 × 106 ml−1) were stained with CFSE (BioLegend) and activated with plate-bound anti-CD3 (2 μg ml−1; HIT3a, BioLegend) and free anti-CD28 (20 μg ml−1; CD28.2, BioLegend) in the presence or absence of NaS, AceK and sucralose (0.5 mM) in IMDM (Gibco) at 37 °C in $5\%$ CO2-in-air for 3 days. After 3 h the media was supplemented with $10\%$ hyclone fetal calf serum. After 72 h, cells were collected and stained with viability dye DRAQ7 (BioStatus). Cells were acquired (Novocyte, Agilent) and analysis performed using FlowJo version 10 (TreeStar).
Participants were recruited from the staff and student populations at Swansea University, Wales UK. Potential participants responded to ethics committee approved advertising by contacting the local clinical research facility. The clinical research facility oversaw recruitment through informed written consent in response to an ethically approved participant information sheet that explained the study. Participant recruitment was conducted by the Joint Clinical Research Facility at Swansea University with no selection bias. Informed written consent and ethical approval was obtained from Wales Research Ethics Committee 6 (13/WA/0190).
## Murine T cell isolation and cell culture
CD8+ T cells and CD4+ T cells were isolated from spleens and peripheral lymph nodes, prepared into single-cell suspensions, and lysed for red blood cells (10× RBC lysis buffer, Biolegend). T cells were isolated by negative isolation kits (StemCell Technologies) and following manufacturer’s protocol. T cells were then activated and cultured as previously described48 using plate-bound anti-CD3 and anti-CD28 antibodies in T cell medium (TCM) (IMDM, $10\%$ fetal bovine serum (FBS), $1\%$ penicillin-streptomycin and 50 μM β-mercaptoethanol).
Mouse Pdx1-KrasG12D pancreatic cancer cell line was derived from the primary pancreatic tumours from the Pdx1-cre pancreatic cancer model. Cells were maintained in culture in DMEM, $10\%$ FBS, and $1\%$ penicillin-streptomycin. The EL4-OVA thymoma cell line was maintained in TCM with 0.4 mg ml−1 of G418 (Roche Diagnostic GmbH). All cells were incubated at 37 °C and $5\%$ CO2 humidified incubators.
## T cell proliferation assay
Isolated naive T cells were stained with CFSE from BioLegend or with the VPD450 dye (BD Horizon) following the manufacturer’s protocol. T cells were then activated with anti-CD3 clone 145-2c11 (2 μg ml−1) and anti-CD28 clone 37.51 (1 μg ml−1), unless otherwise specified, with or without the indicated sweeteners for 3 days in a 37 °C, $5\%$ CO2 humidified incubator followed by flow cytometry analysis. For IL-2 (Peprotech) supplementation, IL-2 at 20 ng ml−1 was added to selected wells in the presence of anti-CD3/CD28. Ionomycin-rescue experiments were conducted by adding an additional 125 μg ml−1 of ionomycin (Sigma) in the presence of anti-CD3/CD28. All functional grade antibodies were purchased from eBioscience, Thermo Fisher Scientific.
TCR-independent proliferation was achieved either by supplementing the media with 100 ng ml−1 of IL-2 or with a high dose combination of PMA (10 ng ml−1) and ionomycin (500 ng ml−1) or low dose of PMA (1 ng ml−1) and ionomycin (50 ng ml−1). T cells were allowed to proliferate for 3–4 days.
## T cell differentiation assays
TH1 cells. Naive CD4+ T cells were isolated using the manufacturer’s instructions and activated by seeding 2 × 106 cells per well in a 24-well tissue culture plate coated with anti-CD3 (5 μg ml−1) and anti-CD28 (2 μg ml−1). TCM was supplemented with IL-2 (20 ng ml−1, Peptrotech), IL-12 (40 ng ml−1, Peptrotech), and anti-IL-4 (BioLegend, clone 11B11, 504102). CD4+ T cells were re-stimulated with PMA–ionomycin–GolgiStop cocktail followed by surface and intracellular cytokine staining for IFNγ (clone XMG1.2) and TBET (clone 4B10) 3 days post differentiation.
CD8+ Teff cells. Naive CD8+ T cells were isolated using the manufacturer’s protocol and activated using 250,000 cells per well of a 96-well plate coated with anti-CD3 (5 μg ml−1) and anti-CD28 (2 μg ml−1). TCM was supplemented with IL-2 (20 ng ml−1). CD8+ T cells were re-stimulated, followed by surface staining and intracellular cytokine staining for IFNγ expression.
## Cytotoxic T cell assay
OVA-specific CD8+ Teff cells from Rag2−/− TCR-transgenic OT-I mice were expanded in vitro with the SIINFEKL peptide (10 μg ml−1) in TCM with or without 0.5 mM sucralose for 3 days. Live lymphocytes were selected using the Lympholyte M cell separation density gradient centrifugation method (CEDARLANE LABS) and then cocultured with EL4-OVA cells stained with VPD450 (BD Biosciences) starting at a 1:1 ratio and serially diluted to 32:1 (EL4-OVA:OT-I). Percentage of dead EL4-OVA cells was assessed by annexin V and propidium iodide (Invitrogen, eBioscience) staining as per the manufacturer’s instructions. Samples were acquired on the BD LSR Fortessa. Flow cytometry data was analysed using FlowJo v10 (TreeStar).
## Immunofluorescence and 3D reconstruction
13 mm borosilicate glass coverslips (thickness 1.5 mm, VWR) were placed in a 24-well plate and coated with 50 mg ml−1 of poly-d-lysine (Sigma) for 30 min at room temperature, followed by one water wash, and allowed to air dry. Two million naive T cells were gently placed over the coated slides and allowed to attach in a humidified 37 °C incubator for 30 min. T cells were activated with anti-CD3–biotin (5 mg ml−1) and streptavidin (20 mg ml−1) for 10 min at 37 °C. Unattached cells were aspirated and $4\%$ PFA was added, and cells fixed for 10 min at room temperature, followed by one PBS wash and aspiration. Samples were blocked with $0.4\%$ Triton X-100/PBS/$10\%$ bovine serum albumin solution. Primary antibody for PLCγ1 (Santa Cruz, clone E-2, sc7290) was diluted at 1:300 in the staining buffer ($0.4\%$ Triton X-100/PBS/$2\%$ bovine serum albumin) and incubated in the dark at room temperature for 1 h. After a PBS wash, fluorescent secondary antibody (Alexa488 Goat primary antibody to mouse IgG, Abcam 150113) was diluted at 1:500 in staining buffer and incubated in the dark at room temperature for a further 1 h. After a PBS wash, samples were incubated with DAPI solution (1:10,000 dilution in PBS; BD Pharmingen) for 5 min at room temperature. Slides were washed once with PBS, inverted and placed on antifade mounting media (VECTASHIELD) on superfrost microscope slides (Thermo Scientific, 12372098). Slides were sealed with CoverGrip coverslip sealant. Images were taken on a Zeiss Upright 710 using the ZEN (v2.3) program with a 63× oil objective and 1.4 NA. The 488 and 405 nm lasers were used for excitation and z-stacks were collected as 16 bits per pixel (average size of 240 × 240 pixels). Data were analysed using Imaris v9.5.1 software using the volume application.
## Bone marrow-derived macrophages and LPS stimulation in vitro
Bone marrow-derived macrophages were generated from femurs flushed with PBS. Red blood cells were lysed using 10× RBC lysis buffer (BioLegend), and remaining cells were plated on petri dishes at 5 × 106 cells per 10 cm dish in IMDM supplemented with $10\%$ FBS, $1\%$ pen/strep, 50 μM β-mercaptoethanol and 25 ng ml−1 of M-CSF (Peprotech). Five days after differentiation, sweeteners were added for an additional 2 days at final concentration of 0.5 mM. LPS (Sigma E. coli O111:B4 LPS25) was added at day 7 post differentiation at a final concentration of 1 ng ml−1 and BD GolgiStop (1:1340 dilution) for 4 h followed by surface and intracellular cytokine staining for tumour necrosis factor (TNF) (clone MP6-XT22) and IL-1β-pro (clone NJTEN3). Samples were acquired on the BD LSR Fortessa and on the BD FACSymphony. Flow cytometry data was analysed using FlowJo v10 (TreeStar).
## Flow cytometry
Single-cell suspensions were stained for surface markers in PBS for 20 min at 4oC. Intracellular proteins (cytokines) were assessed using the FOXP3/Transcription staining buffer set (Invitrogen, eBioscience) following the manufacturer’s instructions. Cells were permeabilized for 30 min and stained for intracellular proteins for a minimum of 1 h at 4 °C. Fluorochromes were purchased from BioLegend, eBioscience (ThermoFischer Scientific), BD Pharmigen, or TONBO Scientific. All surface flurorescent antibodies were used at a dilution of 1:300. Re-stimulation was performed using PMA (Sigma-Aldrich), ionomycin (Sigma-Aldrich) and GolgiStop (BD Biosciences) for 4 h as previously described49. For antigen-specific response using ovalbumin, SIINFEKL (OVA257–264 produced by the Peptide Chemistry Facility, Francis Crick Institute) peptide was used for re-stimulation of single-cell suspensions (10 µg ml−1 with GolgiStop (BD Biosciences)) for 6 h followed by surface staining and intracellular cytokine staining. All intracellular antibodies were used at a dilution of 1:250. OVA-specific CD8+ T cells were distinguished using the Kb–OVA-PE staining (Baylor College of Medicine, USA) and surface markers for 30 min, followed by immediate acquisition. Dead cells were distinguished using the fixable viability dye efluor780 from Invitrogen, eBioscience. Single-cell suspensions were fixed and permeabilized using the FOXP3 Transcription staining buffer set (Invitrogen, eBioscience). Samples were acquired on the BD LSR Fortessa and on the BD FACSymphony. Flow cytometry data were analysed using FlowJo (TreeStar).
## Calcium flux assays
Calcium flux assay were performed using either INDO-1AM or Fluo-3am as probe, as indicated.
INDO-1AM. Lymph nodes were made into single-cell suspensions and FLT3L-generated dendritic cells were incubated in 2 μM final concentration of INDO-1AM (BD Biosciences) in RPMI supplemented with 50 μM β-mercaptoethanol, and $1\%$ FBS for 30 min in a 37 °C, $5\%$ CO2 humidified incubator. Lymphocytes were washed and spun at 1,300 rpm for 5 min followed by cell surface staining for CD4+ (clone GK1.5), CD8+ (clone 2.43), B220 (clone RA3-6B2; Invitrogen eBioscience) and viable cells were distinguished using the Fixable Viability Dye 780 (Invitrogen, eBioscience) for 15 min. Cell suspensions were spun and resuspend in the aforementioned media at 3 × 106 cells per ml and kept on ice. cDC cultures were stained for CD11c (clone N418), B220 (clone RA3-6B2), MHCII (clone AF700), SIRPΑ1 (clone p84) and XCR1 (clone ZET). Cells were heated to 37 °C for 5 min before acquisition on the LSR Fortessa (BD Biosciences). Anti-CD3–biotin (5 μg ml−1) (clone 145-2C11; Invitrogen eBioscience) was added during baseline reading for 1–2 min, followed by 20 μg ml−1 of streptavidin (Invitrogen). For B cells, 20 μg ml−1 anti-IgM (Jackson ImmunoResearch, 115-006-020) was injected into the tube after baseline reading. Dendritic cell samples were injected with 1 mM ATP after baseline reading. Calcium flux was determined by the ratio between 400 nm (bound) to 500 nm (free) readings. Samples were acquired on the BD LSR Fortessa. Flow cytometry data was analysed using FlowJo v10 (TreeStar). To quantify the percentage of responders, cells were gated after the addition of streptavidin for 250 s.
FLUO-3AM. Single-cell suspensions were obtained from lymph nodes. Cells were loaded with the calcium indicator Fluo-3AM (Abcam) as follows: cells resuspended in TCM in presence or absence of sucralose were incubated for 30 min with 5 μM Fluo-3 AM diluted 1:1 (v/v) in $20\%$ (w/v) Pluorinic F-127 acid (Merck, P2443) at 37 °C followed by a further incubation of 15 min at room temperature. Excess Fluo-3AM was removed by two consecutive washes with cold calcium-free PBS. Cells were finally resuspended in calcium-free PBS supplemented with 10 mM glucose, 2 mM glutamine and 50 μM β-mercaptoethanol and kept on ice. Cells were warmed to 37 °C for 5 min before acquisition using the LSR Fortessa. Baseline reading were recorded for 1–2 min followed by stimulation of intracellular calcium release with either 1 μM thapsigargin (Merck) or 100 ng ml−1 ionomycin (Merck). For quantification, Fluo3 intensity was plotted against time using FlowJo (TreeStar) kinetic option with the mean value and Gaussian smoothing. A range of the same length of time was then selected for both basal and treatment (thapsigargin or ionomycin) condition and the area under the curve was obtained.
## Jurkat T cell proliferation and sucralose wash-off in vitro experiment
Jurkat T Cells were cultured in RPMI supplemented with $10\%$ FBS, 50 μM β-mercaptoethanol and $1\%$ penicillin-streptomycin. Proliferation was measured by counting cells with a CASY counter in presence of the different sweeteners as indicated.
Sucralose wash-off in vitro experiment. For acute sucralose exposure, Jurkat T cells were cultured in presence or absence of 0.5 mM sucralose for 5 days. For chronic exposure: sucralose on/on and sucralose on/off Jurkat T cells were pre-exposed to 0.5 mM sucralose for 2 weeks before sucralose removal (sucralose on/off) or continuous sucralose exposure (sucralose on/on).
## Membrane order measurement
T cells isolated from C57BL/6J male mice aged between 6 and 10 weeks—were activated with anti-CD3 (2 μg ml−1) and anti-CD28 (1 μg ml−1) for 3 days. Cells were then stained for protein surface markers and viability dye before being loaded with the phase sensitive membrane probe 2 μM Di-4-ANEPPDHQ (ANEq, Thermo Fisher, D36802) in RPMI in the presence of $0.02\%$ Pluronic F-127 (Merck, P2443). The loading was performed for 30 min at 37 °C followed by 15 min at room temperature. Cells were kept at 37 °C and protected from light before acquisition. ANEq fluorescence emission was measured at 570 nm (high order) and 610 nm (low order) using a BD LSR Fortessa. Flow cytometry data was analysed using FlowJo v10 (TreeStar).
## Immunoblotting
Cells were lysed using RIPA buffer (Millipore) supplemented with $1\%$ SDS and phosphatase and protease inhibitor cocktail (La Roche), denatured at 95 °C, and resolved on NuPAGE polyacrylamide pre-cast gels (Invitrogen, Thermo Fisher Scientific). Gels were transferred onto nitrocellulose membranes (GE Healthcare). T cell or Jurkat T cell lysates were probed as indicated in the manuscript. Working dilution of primary and secondary antibodies are listed in the antibodies section. All uncropped and unprocessed scans and images are in the Source Data files.
## Immunoprecipitation
Jurkat T cells were cultured in RPMI supplemented with $10\%$ FBS, 50 μM β-mercaptoethanol and $1\%$ penicillin-streptomycin in the presence or absence of 0.5 mM sucralose for 48 h. Cells were washed once with PBS and resuspended at the concentration of 30 × 106 cells ml–1 in DPBS containing $1\%$ FBS, 5 mM glucose, 2 mM glutamine, 50 μM β-mercaptoethanol and $1\%$ penicillin-streptomycin in the presence or absence of 0.5 mM sucralose.
Cells were kept on ice and activated with 5 μg ml−1 anti-CD3 (OKT3) at 37 °C as indicated. The reaction was stopped by adding ice-cold PBS. Proteins were extracted using immunoprecipitation lysis buffer containing $0.2\%$ Triton X-100, 50 mM Tris-HCl pH 7.5, 150 mM NaCl, protease inhibitor cocktail (Thermo Scientific) and phosphatase inhibitor cocktail (Cell Signaling) at 4 °C for 30 min. Total protein content was quantified using a BCA assay. CD3ζ was immunoprecipitated from 1.5 mg total protein using an anti-CD3ζ antibody (Santa Cruz), using 4 μg antibody per 0.5 mg total protein and Protein A/G plus Agarose (Thermo Fisher) for 2 h at 4 °C. Immunoprecipitated proteins were washed three times with immunoprecipitation lysis buffer and western blots were performed as indicated.
## Tumours
Excized tumours were kept in ice-cold medium until processing. Tumours were chopped into 1-mm pieces and digested in digestion buffer containing $0.012\%$ collagenase, $0.012\%$ dispase, 0.1 mg ml−1 DNase I, $1\%$ FBS in Krebs Ringer bicarbonate buffer (KRB) for 45–60 min at 37 °C with gentle oscillation. Cold DMEM supplemented with $10\%$ FBS (10 ml) was used to neutralize the digestion. Single cells were filtered through 100-μm cell strainer and collected by centrifugation at 300g for 5 min.
## Plasma
Blood was collected by cardiac puncture into EDTA-coated tubes. Blood was spun in 1.5 ml Eppendorf tubes at 2,000g for 15 min at 4 °C. The supernatant (plasma) was collected and stored at −80 °C.
## Enzyme linked immunosorbent assay
ELISAs were performed on T cell supernatants for IL-2 (Invitrogen, eBioscience) and serum from LPS-challenged mice was assessed for circulating IL-1β levels (Sigma). ELISAs were performed following the manufacturer’s procedures.
## Insulin
Blood was collected by cardiac puncture in EDTA-coated tubes. Samples were then centrifuged at 2,000g for 15 min to remove red blood cells and plasma insulin was measured using Insulin ELISA KIT (Alpco, 80-INSMS-E01) per the manufacturer’s instructions.
## RNA isolation and RNA-seq
CD4+ T cells were isolated from lymph nodes and spleen from male C57BL/6J mice using CD4+ T cell negative isolation kits (StemCell Technologies) according to the manufacturer’s protocol. 2 × 106 CD4+ T cells per well were plated in 24-well plates precoated overnight with 5 μg ml−1 anti-CD3 and 2 μg ml−1 anti-CD28 treated with various sweeteners (0.5 mM of sweetener). T cells were collected 24 h and 48 h after plating and total RNA was isolated using TriPure Isolation Reagent (Merck) according to manufacturer’s instructions. RNA was quantified using a nanodrop (deNovix).
mRNA capture and library preparation were performed by the Advanced Sequencing Facility at the Francis Crick Institute using the KAPA mRNA HyperPrep Kit (Roche). Technical triplicate libraries were sequenced on an Illumina HiSeq 4000 platform to generate on average 50 million 101-bp single-end reads per sample.
Raw reads were quality and adapter trimmed using cutadapt (version 2.10) before alignment50. Reads were mapped and subsequent gene-level counted using RSEM 1.3.1 (ref. 51) and STAR 2.7.6 (ref. 52) against the mouse genome GRCm38 using annotation release 95, both from Ensembl. Normalization of raw count data and differential expression analysis was performed with the DESeq2 package (version 1.30.1)47 within the R programming environment (v4.0.3)45. The following pairwise comparisons were performed: sucralose 24 h and TCM 24 h; sucralose 48 h and TCM 48 h; saccharin 24 h and TCM 24 h; saccharin 48 h and TCM 48 h; sucralose 24 h and saccharin 24 h; sucralose 48 h and saccharin 48 h; sucralose 48 h and sucralose 24 h; saccharin 48 h and saccharin 24 h; TCM 48 h and TCM 24 h, with the contrast function, from which genes differentially expressed (adjusted P value being less than 0.01) between different conditions were determined. Gene lists were used to look for pathways and molecular functions with over-representation analysis using DAVID53,54.
## Sucralose quantification and stable isotope tracing by LC–MS
Stable isotope tracing of U-[13C]glucose (Cambridge Isotopes) was performed in glucose-free IMDM (The Francis Crick Media Services) supplemented with $10\%$ dFBS. CD4+ and CD8+ T cells were activated with anti-CD3 (5 mg ml−1) and anti-CD28 (2 mg ml−1) for 48 h in the presence and absence of 0.5 mM sucralose. T cells were counted, washed, and replaced with the 10 mM U-[13C]glucose solution at a concentration of 2 × 106 cells ml−1 followed by a 4 h pulse. Cells were washed twice with ice cold PBS and metabolites were extracted using extraction buffer containing $50\%$ methanol, $30\%$ acetonitrile and $20\%$ water for 10 min at 4 °C. For sucralose detection in Jurkat T cells, cells were exposed to 0.5 mM sucralose for 48 h unless otherwise indicated, and washed 2 times with ice cold PBS. Metabolites were extracted as described above using 1 ml of extraction buffer for 3.5 × 106 cells. For sucralose detection in plasma, the extraction procedure was adapted from ref. 55. In brief, metabolites were extracted as follows: plasma samples were allowed to thaw on ice for 30–60 min. Ice-cold methanol was subsequently added to 50 µl of plasma in a ratio 3:1 (v/v). After a short vortex step, the mixture was incubated on ice for 5 min. Centrifugation (13,000 rpm, 10 min, 4 °C) was used to pellet the protein. The supernatant was transferred to an Eppendorf tube and dried in a rotary vacuum concentrator. Polar metabolites were phase-partitioned from apolar metabolites by addition of 350 μl chloroform:methanol:water (1:3:3 v/v/v, containing 0.375 mol of [13C]valine as an internal standard). The phases were separated by centrifugation (13,000 rpm, 10 min, 4 °C). The polar phase was transferred into a LC–MS vial equipped with an insert and dried in a rotary vacuum concentrator. Finally, 75 µl of a mixture of H2O:methanol (1:1) were added to the dried extract. The samples were subsequently analysed by LC–MS. For the preparation of the sucralose calibration curve, 50 µl of plasma was spiked with various concentrations of the standard. The extraction and sample preparation were then performed as described above.
Metabolites and sucralose were analysed by LC–MS using a Q-EXACTIVE Plus (Orbitrap) mass spectrometer from Thermo Fisher Scientific coupled with a Vanquish UHPLC system from Thermo Fisher Scientific. The chromatographic separation was performed on a SeQuant ZicpHILIC (Merck Millipore) column (5 μm particle size, polymeric, 150 × 4.6 mm). The injection volume was 5 μl, the oven temperature was maintained at 25 °C, and the autosampler tray temperature was maintained at 4 °C. Chromatographic separation was achieved using a gradient program at a constant flow rate of 300 μl min−1 over a total run time of 25 min. The elution gradient was programmed as decreasing percentage of B from $80\%$ to $5\%$ for 17 min, holding at $5\%$ of B for 3 min and finally re-equilibrating the column at $80\%$ of B for 4 min. Solvent A was 20 mM ammonium carbonate solution in water supplemented by 1.4 ml l−1 of a solution of ammonium hydroxide at $35\%$ in water and solvent B was acetonitrile. Mass spectrometry was performed with positive/negative polarity switching using a Q-EXACTIVE Plus Orbitrap (Thermo Scientific) with a HESI II probe. Mass spectrometry parameters were as follows: spray voltage 3.5 and 3.2 kV for positive and negative modes, respectively; probe temperature 320 °C; sheath and auxiliary gases were 30 and 5 arbitrary units, respectively; and full scan range: 70–1,050 m/z with settings of AGC target and resolution as balanced and high (3 × 106 and 70,000), respectively. Data were recorded using Xcalibur 4.2.47 software (Thermo Scientific). Mass calibration was performed for both ESI polarities before analysis using the standard Thermo Scientific Calmix solution. To enhance calibration stability, lock-mass correction was also applied to each analytical run using ubiquitous low-mass contaminants. Parallel reaction monitoring (PRM) acquisition parameters were the following: resolution 17,500; collision energies were set individually in HCD (high-energy collisional dissociation) mode. Metabolites were identified and quantified by accurate mass and retention time and by comparison to the retention times, mass spectra, and responses of known amounts of authentic standards using TraceFinder 4.1 EFS software (Thermo Fisher Scientific). Absolute quantification of sucralose was achieved with the use of a calibration curve built with the responses of known amounts of standard spiked in sucralose-free plasma.
## Subcellular fractionation
Cell fractions were obtained as described previously56 with minor modifications as reported below.
## Metabolomics
Jurkat T cells were cultured with or without 0.5 mM sucralose for 48 h. In total, 3.5 × 106 cells were washed twice with ice-cold PBS then digested with 1 ml PBS containing 0.5 mg ml−1 digitonin (BioVision) to release cytoplasmic content, followed by centrifugation for 10 s at 13,500g. Two-hundred microlitres of the supernatant, containing the cytoplasmic fraction, was collected in a new, pre-chilled tube containing 800 µl of ice-cold extraction buffer. The pellet was resuspended in 100 μl of ice-cold extraction buffer. The metabolites were extracted and processed as described above. For whole cells controls, cells (3.5 × 106) cultured in presence or absence of 0.5 mM sucralose were washed twice with ice-cold PBS and extracted in 1 ml ice-cold extraction buffer.
## Western blot
Jurkat T cells were cultured with or without 0.5 mM sucralose for 48 h. Cells (3.5 × 106) were washed once with ice-cold PBS then digested with 1 ml PBS containing 0.5 mg ml−1 digitonin to release cytoplasmic content followed by centrifugation for 10 s at 13,500g. The supernatant containing the cytoplasmic fraction was collected in a new, pre-chilled tube. The pellet was resuspended in 1 ml ice-cold PBS. Cells (3.5 × 106) untreated with digitonin were used as whole-cell controls. All the samples were stored at −80 °C for at least 12 h and sonicated 3 times for 10 s on ice. The purity of the fraction was tested using specific antibodies as indicated.
## Sucralose localization by cryo-OrbiSIMS
Primary or Jurkat T cells were treated for 48 h with 0.5 mM deuterium-labelled sucralose (SCBT). Sucralose localization was performed by cryo-OrbiSIMS analysis using a Hybrid-SIMS instrument (IONTOF GmbH, Thermo Fisher Scientific) at the National Physical Laboratory incorporating an Orbitrap Q-Exactive HF analyser and a time of flight (ToF) analyser57, 58. T cells were deposited onto silicon wafers coated with 50 nm gold, excess media removed, and frozen by plunging into liquid nitrogen. The samples were transported under liquid nitrogen and mounted on a custom Leica sample holder under liquid nitrogen in a Leica VCM and transferred into the OrbiSIMS onto a sample stage pre-cooled to −160 °C. For charge compensation, a 21 eV electron flood gun was used with a current of −21 μA, and argon gas flooding in the analysis chamber with a pressure of 9.7 × 10−7 mbar. All acquisitions were performed in positive polarity with an extraction voltage of 2 kV and a sample potential of ~−80 V. 3D analysis was performed using the ToF analyser with a cycle time of 200 μs. A 30 keV Bi+ liquid metal ion gun (LMIG) with a spot size of ~500 nm was used as the analysis beam with a current of 1.74 pA, and a 10 keV Ar2081+ gas cluster ion beam (GCIB) was used as the sputter beam with a current of 0.618 nA. The 3D image was acquired using 10 shots per pixel per frame and 3 frames per scan with a field of view of 200 μm × 200 μm in sawtooth raster mode, with non-interlaced sputtering of 440 s between frames with a crater size of 500 μm × 500 μm. Analysis was performed at a temperature of ~−160 °C throughout. The instrument liquid nitrogen dewars were loosely covered with polyethylene bags to prevent excessive ice formation. The m/z scale of the mass spectrum was calibrated using the following peaks: CH+, CH2+, CH3+ and Na+. Sucralose peaks were identified compared with analysis of a pure standard and confirmed by isotope cluster distribution. Spectral analysis was performed using the Orbitrap analyser with a cycle time of 200 μs. A 20 keV Ar3500+ GCIB was used as the analysis beam with a current of 172 pA, a duty cycle of $30\%$ and a spot size of ~20 μm. The analysis was performed using a 40 μm × 40 μm field of view and a 20 μm pixel size with random raster mode. The collisional cooling He pressure was 3.9 × 10−2 mbar. The mass range was 150–600 m/z and the injection time was 2,000 ms at a mass resolution of 240,000 Δm/m with a transient time of 512 ms. Mass calibration was performed using silver clusters generated from a silver sample. Data were acquired and analysed using SurfaceLab 7.3 (IONTOF GmbH). For the depth profile shown in Fig. 2d, relative increasing depth is indicated by consecutive data points. After each data point, the GCIB sputter removes material. The grey region in Fig. 2d indicates the region below which the ion signal is considered to be noise, this value is calculated from an untreated control sample. The data represents the mean and s.e.m. of eight separate cells located within the same field of view normalized to total ion count. The cell regions of interest were selected using the ion [C2HO]+ as a cell-specific marker.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Online content
Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-05801-6.
## Supplementary information
Supplementary InformationThis file contains Supplementary Figs. 1–4 and a list of reagents. Reporting Summary Supplementary Fig. 1Uncropped western blots The online version contains supplementary material available at 10.1038/s41586-023-05801-6.
## Extended data figures and tables
Extended Data Fig. 1Sucralose detection in mice and its effect on macrophage, B cell and T cell responses.a). Sucralose peak detection by LC-MS in the plasma of mice fed with water or 0.72 mg ml−1 of sucralose for 2 weeks. b) Box plots of frequencies of B220+ B cells, CD8+ T cells, CD4+ T cells, CD4+FOXP3+ T cells (Treg), CD11b+ pan myeloid population, CD11b+NK1.1+ cells (NK cells), CD11c+ dendritic cells (DCs), monocytes (CD11b+Ly6C+) and neutrophils (CD11b+Ly6G+) within mesenteric lymph nodes, peripheral lymph nodes, and the spleen of mice fed with water), 0.17 mgml−1 Scrl, 0.72 mg ml−1 Scrl, and 0.72 mg ml−1 sodium saccharin (NaS) ($$n = 6$$ per treatment) for over 12 weeks. As assessed by flow cytometry. Min-max box plot: centre lines show median, box limits are 1st and 3rd quartiles, with whiskers indicating the min and max value. ( c-f) C57BL/6J mice fed with water ($$n = 10$$) or 0.72 mg ml−1 Scrl ($$n = 8$$) and immunized with sheep red blood cells (sRBC). Each dot represents a biological replicate. c) Total splenocyte numbers at day 7 post sRBC immunization. d) Percentage of splenic B220+ B cells 7 days post SRBC immunization. e) Representative density plot of gated B220+ B cells and the frequency of germinal centre (GC) B cells (GL7+CD95+) at day 7 post immunization. f) Quantification of the percentage of GC B cells as depicted in Extended Data Fig. 1e, of the splenic B220+ B cell population (left) and as a percentage of total splenocytes (right). g) Pairwise comparison of pro-IL1β+TNF+ ($$n = 7$$/condition), TNF+IL6+ ($$n = 4$$/condition) and TNF+IL12p70+ ($$n = 3$$/condition) production assessed by flow cytometry in bone marrow derived macrophages (BMDMs) stimulated with lipopolysaccharide (LPS) either in control media or in presence of 0.5 mM of Scrl. Paired dots indicate biologically independent samples. h) IL1β plasma concentration from water ($$n = 7$$) or 0.72 mg ml−1 sucralose ($$n = 7$$) fed C57BL/6J mice for 2 weeks prior to LPS challenge (0.1 mg kg−1). ( i–j) Generation of alternatively activated macrophages (CD11b+F$\frac{4}{80}$+CD301+CD206+) in C57BL/6J mice intraperitoneally injected with IL4 complex (IL4c) or PBS fed water (or 0.72 mg ml−1 Scrl. Each dot represents a single mouse. $$n = 5$$ biological replicates per group. i) Frequencies. j) Absolute numbers. k) Homeostatic proliferation at day 3 of naïve CFSE-loaded CD8+ and CD4+ T cells injected to Rag2−/− recipients fed water ($$n = 6$$) or 0.17 mg ml−1 Scrl ($$n = 5$$) for two weeks and until the end of the experiment. Means represent ± s.e.m for biological replicates (c, d, f, g, h, i, j, k). Statistical significance was determined using 2-tailed unpaired (c, d, f, h, k) or paired (g) Student’s t test; one-way ANOVA with Tukey’s multiple comparison test (i, j), or ordinary one-way ANOVA for identical immune populations/condition (b). Data are representative of 2 (h–i) or 3 (c–f, k) independent experiments. Source data Extended Data Fig. 2Sucralose limits T cell proliferation without affecting viability.a) CD4+ T cell proliferation assay performed with αCD3 (5 μg ml−1) and αCD28 (1 μg ml−1) for 3 days in the presence of serial dilutions of the indicated sweeteners. b) Proliferation of CFSE stained CD8+ T cells exposed to 0.5 mM of the indicated sweeteners and activated with a high dose of αCD3 (5 μg ml−1) or low dose of αCD3 (1 μg ml−1) in the presence or absence of αCD28 (1 μg ml−1). c) Jurkat T cell proliferation in control media or media supplemented with the indicated sweeteners. $$n = 3$.$ d) Percentage of viable CD8+ (left) and CD4+ (right) T cells 24 h post activation with αCD3 (2 μg ml−1) and αCD28 (1 μg ml−1). Cell viability was determined by Fixable Viability Dye eFluor780® exclusion. Means represent ± s.e.m. for biological (d) or technical (c) replicates. Statistical significance was determined using one-way ANOVA with Tukey’s (c) or Dunnet’s (d) multiple comparison test. Data are representative of 3 independent experiments (c). Source data Extended Data Fig. 3Sucralose does not affect whole body mouse metabolism.(a–g) Individually caged C57BL/6J mice fed water, 0.17 mg ml−1 Scrl, 0.72 mg ml−1 Scrl) or 0.72 mg ml−1 sodium saccharin (NaS) for 12 weeks. $$n = 6$$ per treatment. a) Average cumulative food intake. b) Average body weight. c) Average solution intake. d) Weekly solution intake. e) *Fasting plasma* insulin levels. ( f–g) Glucose tolerance test (GTT) of mice receiving an oral bolus of 2 mg kg−1 glucose. f) Blood glucose. g) Area under the curve calculated using the trapezoid method for the GTT. h–i) C57BL/6J mice fed for over 8 weeks with water ($$n = 8$$ for RER and 9 for locomotor activity), 0.72 mg ml−1 Scrl ($$n = 12$$) or 0.72 mg ml−1 NaS ($$n = 11$$). h) Respiratory Exchange Ratio (RER). i) Locomotor activity. Data are displayed as mean ± s.d. ( d) ± s.e.m. ( a, b, c, e, f, g, h, i). Each dot represents a single mouse. Statistical significance was determined using one-way Anova with Tukey’s (a, b, c) or Dunnet’s (e, g) multiple comparison test. Source data Extended Data Fig. 4Microbiome analysis of mice treated with sweeteners and sucralose effect on calcium flux.(a–b) C57BL/6J mice fed water, 0.17 mg ml−1 Scrl, 0.72 mg ml−1 Scrl or 0.72 mg ml−1 NaS for 12 weeks. Each dot represents an individual mouse. $$n = 6$$ per treatment. a) Caecum weight. b) Caecum length. ( c–g) Gut microbiome analysis of mice fed for 2 and 12 weeks with water), 0.17 mg ml−1 Scrl, 0.72 mg ml−1 Scrl or $10\%$ w/v glucose. $$n = 5$$ per treatment. c) Heatmaps showing alteration in abundance data in mice exposed to different drinking solutions as indicated compared to time-matched samples obtained from water-fed mice. Top graphs: samples collected after 12 weeks exposure; lower graphs samples collected after 2 weeks exposure. The variation of abundance is shown at Phylum, Class, Order, Family and Genus level. ( d–f) Volcano plots of regulated genera in samples collected after 12 weeks of treatment. Water-treated animals were used as control. The fold changes were estimated using a negative binomial model from the DESeq2 package in R using its default settings for accounting for different library sizes between samples. A generalized linear model accounted for batch along with the interaction of treatment and time, to provide estimates of time effects within treatment (and vice versa). P-values were calculated using a Wald test and then adjusted using the Benjamini-Hochberg method to control for false discovery rate. Comparisons: d) $10\%$ glucose vs water, e) 0.17 mg ml−1 Scrl vs water and f) 0.72 mg ml−1 Scrl vs water. Genera with a logarithmic fold change > 0.6 and an adjusted P-value < 0.05 are considered upregulated; Genera with a logarithmic fold change < 0.6 and an adjusted P-value < 0.05 are considered downregulated. g) List of upregulated (red) or downregulated (blue) genera in the different comparison as indicated in (d, e, f). h) Representative kinetics diagram (left) of FLUO-3AM calcium flux in response to 0.5 mM Scrl and corresponding quantification (right) of FLUO-3AM intensity in Jurkat T cells. $$n = 4$$ technical replicates. Data are representative of 3 independent experiments. The means represents ± s.e.m. for biological replicates (a, b), one-way ANOVA with Dunnet’s multiple comparison test (a, b), and 2-tailed paired Student’s t test (h) were used for statistical analysis. Source data
Extended Data Fig. 5Sucralose does not impede glucose metabolism, TCR-independent proliferation, IL2-STAT5 signalling, and activation markers, but reduces membrane order.a) Flow cytometry analysis of 2NBDG-glucose uptake of T cells activated for 24 h with αCD3 and αCD28 in T cell media (TCM) with (Scrl) or without (Ctrl) 0.5mM sucralose. Graph shows the increase in mean fluorescence intensity (MFI) over time. $$n = 3$$ technical replicates except for sucralose at 5 min $$n = 2$.$ ( b–c) CD4+ (b) and CD8+ (c) T cells activated for 48 h in control media or in presence of 0.5 mM Scrl followed by stable isotope tracing analysis of U-[13C]-glucose. Mass isotopomer distribution (MID) of 13C6-glucose-derived carbon into pyruvate, lactate, and malate as indicated. $$n = 4$$ (Ctrl), $$n = 3$$ (Scrl) for CD4+ T cells, and $$n = 5$$ (Ctrl) and $$n = 4$$ (Scrl) for CD8+ T cells. Dots represent technical replicates. Values below 104 were considered zero. d) PCA from RNAseq of CD4+ T cells activated with αCD3 and αCD28 for 24 h and 48 h in control medium or medium supplemented with 0.5 mM of either NaS or Scrl. Dots represent technical replicates. e) Top 20 enrichment pathways identified using DAVID. Pathways are ordered by p-values from most significant (top, dark red) to less significant (bottom, grey). f) IL2 (100 ng/ml)-induced proliferation of VPD450-stained CD8+ T cells in the presence or absence of 0.5 mM sucralose for 4 days. ( g–h) Expression of the activation markers CD44, CD69, PD1 and CD25 in CD8+ T cells (g) and CD4+ T cells (h) activated for 24 h with αCD3 (2 μg ml−1) and αCD28 (1 μg ml−1) in the presence or absence of 0.5 mM Scrl. $$n = 3$$ technical replicates. i) Western blot analysis probing for phospho-STAT5(Tyr694) and STAT5 as a loading control from protein lysates of 24 h-activated T cells in the presence of control media or 0.5 mM Scrl. Each lane is a pool of T cells collected from a single well. j) Concentration of IL2 detected by enzyme linked immunosorbent assay (ELISA) from supernatant of T cells activated for 24 h with αCD3/CD28. Each dot represents a single well of technical replicates ($$n = 4$$/condition). k) Cell proliferation histogram overlay of VPD450-loaded T cells activated with αCD3/CD28, with or without 20 μg -ml−1 of IL-2, and either in control medium (grey) or in presence of 0.5 mM Scrl (blue). Data presented with mean value ± s.e.m. ( a, b, c, g, h) or ± s.d. ( j) Statistical significance was tested using, mixed-effects model (REML) with Sidak’s multiple comparison test (a); 2-way ANOVA (b, c), unpaired (g–j) 2-tailed student’s t-test. Data are representative of 3 independent experiments (a, f–k). Source data Extended Data Fig. 6Sucralose reduces membrane order.a) Flow cytometry histogram overlay of T cells loaded with VDP450 and activated with either high concentration of PMA (10 μg ml−1) and ionomycin (500 ng ml−1) (left) or low concentration (1 ng ml−1 PMA and 50 ng ml−1 ionomycin) (right), respectively. b) Western blot analysis of Jurkat T cells cultured with or without 0.5 mM Scrl and probed for p-PLCγ1 and PLCγ1 total. ( c–d) Western blot analysis of T cells pre-treated for 2 h with or without 0.5 mM Scrl and activated with αCD3 (5 μg ml−1) for 1, 2, and 5 min. Membranes were probed for (c) p-ERK$\frac{1}{2}$ and total ERK$\frac{1}{2}$ expression, using β-Actin as loading control. All proteins detected on the same membrane. ( d) Identical protein lysates were probed for p-ZAP70Tyr319, ZAP70, and β-Actin as loading control on one membrane and p-LCK, LCK and vinculin as loading control were probed on a second membrane. Second set of protein lysates were probed for p-LAT, LAT and β-Actin as loading control on the same membrane. e) Immunoprecipitation (IP) of CD3ζ chain from Jurkat T cells cultured media in presence or absence of 0.5 mM Scrl for 2 days. ( Left) Cells were stimulated with 5 µg ml−1 αCD3 for 0’, 2’, 5’ min as indicated. ( Right) Western blot analysis of protein lysates used for IP showing ZAP70, LCK and CD3ζ expression with β-Actin as loading control. All proteins detected on the same membrane. f) Western blot analysis of protein lysates from whole cell fractions, cytoplasmic fractions and membrane fractions were probed for Na-K ATPase (membrane marker), GAPDH (cytoplasmic marker) and TOM20 (mitochondrial marker). All proteins were detected on the same membrane. g) Cryogenic Argon GCIB Orbitrap mass spectra acquired through the entire depth of cells until substrate signal was detected, showing the presence of fragments of deuterium labelled sucralose (d-Scrl) in a sample containing Jurkat T cells treated with 0.5 mM d-Scrl for 48 h, and subsequent absence of any sucralose-related ions in a sample containing d-Scrl treated cells which were subsequently washed with PBS to remove any traces of d-Scrl in the media. Masses annotated with chemical formulas derive from d-Scrl. Identifications were performed in comparison with a standard of pure d-Scrl. h) Representative flow cytometry plot of membrane orders as detected with the Di-4-ANEPPDHQ dye. Low, intermediate, and high membrane order populations are denoted. i) *Paired analysis* of Intermediate and Low membrane order of CD8+ T cells activated with αCD3/CD28 for 3 days with or without 0.5 mM Scrl. $$n = 20$$ biologically replicates. j) Representative 3D reconstruction (Z-stacks) from naïve T cells pre-treated with or without 0.5 mM Scrl, followed by TCR-crosslinking. Images show colocalization of TCRβ (red), PLCγ1 (green), and nuclei are stained with DAPI. Bar = 3 μm. Statistical significance was tested using, paired (i) 2-tailed student’s t-test. Data represent one of 3 independent experiments (a–f, h–j). Source data Extended Data Fig. 7Sucralose selectively reduces TCR-induced intracellular calcium flux.a) Representative intracellular calcium flux in naïve CD4+ T cells activated with αCD3-biotin crosslinked by streptavidin with or without 1 mM EDTA. Intracellular calcium flux is plotted as INDO1 ratio over time. b) Representative kinetic plot measuring calcium flux with FLUO-3AM using thapsigargin 1 μM (Tg) (first arrow) followed by 2 mM calcium chloride (CaCl2) (second arrow) in the presence or absence of 0.5 mM Scrl. The line represents mean values with gaussian smoothing. c) FLUO-3AM peak intensities upon Tg and CaCl2 treatments in CD4+ T cells in media lacking calcium salt. $$n = 3$$ biological replicates per condition. The statistical significance of Tg and CaCl2 vs Basal was tested using a paired 2-tailed Student’s t test with p values as reported in the figure. 2-way anova with Sidak’s multiple comparison test was used to test the effect of sucralose on the 3 conditions (Basal, Tg, and CaCl2). No significant effect was discovered. ( d–e) Pairwise comparison of changes in FLUO-3AM peak intensity with ionomycin treatment in CD4+ (d) and CD8+ (e). $$n = 3$$ biological replicates per condition. T cells were tested in calcium-free media with or without 0.5 mM Scrl (blue). Each dot represents a biological replicate. f) Representative histogram plot of VPD450 dilution of total T cells activated with αCD3 (2 μg ml−1) and αCD28 (1 μg ml−1) in the presence of DMSO or 125 ng ml−1 of ionomycin with or without 0.5 mM Scrl. g) Representative flow cytometry plot of T cells activated with αCD3 (2 μg ml−1) and αCD28 (1 μg ml−1) in the presence of DMSO or 125 ng ml−1 of ionomycin with or without 0.5 mM Scrl and restimulated for ICS. Cytokine production is identified as Tbet+IFNγ+ populations. ( h–j) FLT3-ligand generated conventional DC1 ($$n = 3$$/condition, technical replicates) (h), cDC2 ($$n = 3$$/condition, technical replicates) (i) and plasmacytoid DCs ($$n = 3$$/condition, technical replicates) (j) differentiated in the presence or absence of 0.5 mM Scrl. Graphs on the left show the percentage of cells undergoing calcium flux in response to 1mM ATP. The representative flow cytometry plots (right) show the INDO1 ratio over time in response to ATP. k) Isolated naïve B cells stained with INDO1 and activated with αIgM (20 μg ml−1) in presence or absence of 0.5 mM Scrl. Graph (left) represents the percentage of calcium responding cells downstream of αIgM stimulation and on the right is a flow cytometry representative plot. $$n = 5$$ technical replicates per condition. Data present as mean value ±s.d. ( c) or ±s.e.m. ( h–k). Data representative of 3 independent experiments (h–k). Statistical significance was tested using paired (c–e) or unpaired 2-tailed Student’s t test (h–k). Source data Extended Data Fig. 8Sucralose treatment in mice reduces antigen-specifc CD8+ T cell responses.a) Representative density plot of intratumoral CD8+ T cells recognizing the MHC-peptide tetramer complex (Kb-OVA) from EL4OVA tumours from mice fed water or 0.72 μg ml−1 Scrl 10 days post challenge. ( b, c) Mice fed water or 0.17 μg ml−1 Scrl. $$n = 8$$ biological replicates per group and each dot represents an individual mouse b) Intratumoral frequency of CD8+ T cells recognizing the MHC-peptide tetramer complex (Kb-OVA) from EL4OVA tumours. c) Intratumoral frequency of CD8+IFNγ+ T cells from tumour bearing mice post re-stimulation with the OVA peptide. d) Cytotoxic T cell assay of OT-I T cells activated with OVA-peptide either in TCM with (Scrl) or without (Ctrl) 0.5 mM Scrl followed by co-culture with EL4OVA cancer cells. $$n = 5$$ for technical replicates. ( e–h) Splenocytes from C57BL/6J mice fed water or 0.72 mg ml−1 Scrl followed by bacterial challenge with LmOVA (105 CFU permouse). $$n = 7$$ biological replicates per group. e) Total splenocyte numbers 7 days post infection. f) Representative density plot of OVA-specific CD8+ T cells identified by Kb-OVA and CD8+ surface staining. g) Frequency of CD8+ T cells re-stimulated with OVA-peptide and analyzed for IFNγ expression. h) Absolute numbers of splenic CD8+ T cells in Extended Data Fig. 8g. i) The bacterial load (colony forming units per mg of tissue) of the liver (left) and spleen (right) at day 3 post LmOVA (105 CFU per mouse) infection of mice fed water or 0.72 mg ml−1 Scrl; $$n = 6$$ (spleen) or 7 (liver) of biological replicates per group. j) Absolute cell number of Jurkat T cells grown in media with or without Scrl as indicated at day 5. $$n = 3$$ technical replicates. k) Absolute splenic numbers of CD8+ T cells producing IFNγ and Granzyme B (GZMB) in response to OVA-peptide re-stimulation of mice infected with LmOVA (105 CFU per mouse) 7 days post infection. Mice were fed water ($$n = 7$$), 0.72 mg ml−1 Scrl for 2 weeks ($$n = 6$$) or 0.72 mg ml−1 Scrl treatment for 2 weeks followed by one-week water washout ($$n = 7$$, Scrl off). Data presented as mean value ± s.e.m. ( b-e, g-k). Each dot represents a sample derived from an individual mouse (b, c, e, g, h, i, k). Statistical significance was tested using single or multiple unpaired 2-tailed Student’s t test (b-e, g-i) or a One-way ANOVA with a Tukey’s Multiple Comparison (j, k). Data representative of 2 (d, e, i, k) or 3 (j) independent experiments. Source data
Extended Data Fig. 9Sucralose delays the onset of T1D and reduces inflammatory T cells in a T-cell induced colitis model in mice.a-b). NOD/ShiLtJ mice fed with either water ($$n = 8$$), 0.17 or 0.72 mg ml−1 Scrl ($$n = 9$$ for each dose). a) Weekly blood glucose measurements. b) Weekly weight measurements. ( c–g) T cell-induced colitis model of CD4+CD45RB+CD45.1+ congenic T cells transferred into CD45.2 TCRα−/− recipients that were fed either water ($$n = 5$$) or 0.72 mg ml−1 Scrl ($$n = 5$$). Inflammation was assessed at 21 days post T cell transfer. c) Total numbers of mesenteric lymph node (mLN) leukocytes. d) Representative density plot of the percentage of CD4+ CD45.1+ donor cells in the mLN of TCRα−/− recipients. e) Total numbers of donor congenic donor T cells in the mLN. f) Percentage of congenic CD4+ T cells producing IFNγ+ in the mLN. g) The absolute number of congenic CD4+IFNγ+ T cells within the mLN. ( h–i) T-cell induced colitis model of CD45.1 congenic naive T cells adoptively transferred into CD45.2 TCRα−/− recipients that were fed either water or 0.17 mg ml−1 sucralose. h) Colon length at 21 days post transfer ($$n = 8$$/group). i) Frequency of CD45.1 CD4+ donor T cells producing IFNγ in the colonic lamina propria at 21 days post transfer. Water ($$n = 7$$) versus 0.17 mg ml−1 sucralose ($$n = 6$$). Data presented as mean value ±s.e.m. ( a–c, e–i). Each dot represents an individual mouse (a, c, e–i). Statistical significance was tested using unpaired 2-tailed Student’s t test (c, e–i) and mixed-effects model (REML) (b). Data are representative of 2 (c, e–g) or 3 (i, h) individual experiments. Source data
## Source data
Source Data Fig. 1 Source Data Fig. 2 Source Data Fig. 3 Source Data Fig. 4 Source Data Extended Data Fig. 1 Source Data Extended Data Fig. 2 Source Data Extended Data Fig. 3 Source Data Extended Data Fig. 4 Source Data Extended Data Fig. 5 Source Data Extended Data Fig. 6 Source Data Extended Data Fig. 7 Source Data Extended Data Fig. 8 Source Data Extended Data Fig. 9
## Extended data
is available for this paper at 10.1038/s41586-023-05801-6.
## Peer review information
Nature thanks Ping-Chih Ho and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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---
title: Low doses to the heart in daily practice for treating left-sided breast cancer
using accelerated partial-breast irradiation by multicatheter brachytherapy and
deep-inspiration breath-hold using a SIB
authors:
- Stefan Knippen
- Sven Schönherr
- Michael Schwedas
- Tobias Teichmann
- Simon Howitz
- Matthias Mäurer
- Andrea Wittig-Sauerwein
- Marciana-Nona Duma
journal: Strahlentherapie Und Onkologie
year: 2023
pmcid: PMC10033472
doi: 10.1007/s00066-023-02047-z
license: CC BY 4.0
---
# Low doses to the heart in daily practice for treating left-sided breast cancer using accelerated partial-breast irradiation by multicatheter brachytherapy and deep-inspiration breath-hold using a SIB
## Abstract
### Purpose
The aim of this study was to analyze the heart dose for left-sided breast cancer that can be achieved during daily practice in patients treated with multicatheter brachytherapy (MCBT) accelerated partial-breast irradiation (APBI) and deep-inspiration breath-hold (DIBH) whole-breast irradiation (WBI) using a simultaneous integrated tumor bed boost (SIB)—two different concepts which nonetheless share some patient overlap.
### Materials and methods
We analyzed the nominal average dose (Dmean) to the heart as well as the biologically effective dose (BED) and the equivalent dose in 2‑Gy fractions (EQD2) for an α/β of 3 in 30 MCBT-APBI patients and 22 patients treated with DIBH plus SIB. For further dosimetric comparison, we contoured the breast planning target volume (PTV) in each of the brachytherapy planning CTs according to the ESTRO guidelines and computed tangential field plans. Mean dose (Dmean), EQD2 Dmean, and BED Dmean for three dosing schemes were calculated: 50 Gy/25 fractions and two hypofractionated regimens, i.e., 40.05 Gy/15 fractions and 26 Gy/5 fractions. Furthermore, we calculated tangential field plans without a boost for the 22 cases treated with SIB with the standard dosing scheme of 40.05 Gy/15 fractions.
### Results
MCBT and DIBH radiation therapy both show low-dose exposure of the heart. As expected, hypofractionation leads to sparing of the heart dose. Although MCBT plans were not optimized regarding dose to the heart, Dmean differed significantly between MCBT and DIBH (1.28 Gy vs. 1.91 Gy, $p \leq 0.001$) in favor of MCBT, even if the Dmean in each group was very low. In MCBT radiation, the PTV–heart distance is significantly associated with the dose to the heart ($p \leq 0.001$), but it is not in DIBH radiotherapy using SIB.
### Conclusion
In daily practice, both DIBH radiation therapy as well as MCBT show a very low heart exposure and may thus reduce long term cardiac morbidity as compared to currently available long-term clinical data of patients treated with conventional tangential field plans in free breathing. Our analysis confirms particularly good cardiac sparing with MCBT-APBI, so that this technique should be offered to patients with left-sided breast cancer if the tumor-associated eligibility criteria are fulfilled.
## Introduction
Breast cancer is the most frequent cancer in women. Due to significant improvements in treatment, long-term survivorship is frequent [1]. Nonetheless, treatment could come with a cost: both, systemic therapies and radiotherapy have an impact on cardiac morbidity [1, 2].
As an example, drug-related cardiac morbidity can be caused by trastuzumab, which is associated with the occurrence of left-ventricular dysfunction and chronic heart failure (CHF) [3, 4]. Well known are also the effects of anthracyclines, causing myocardial damage which can progress to symptomatic CHF. This cardiotoxicity is dose related, progressive, and irreversible [5].
Postoperative radiotherapy for left-sided breast cancer has also an influence on cardiac morbidity. Radiation-related cardiac toxicity is also irreversible and dose dependent. Darby et al. calculated a linear increase in severe cardiac events of $7.4\%$ per gray mean cardiac dose [6]. Radiation-related cardiac toxicity is a late-occurring event, manifesting clinically 10 or more years after breast cancer treatment [7] so that late sequelae observed clinically today reflect radiation techniques used in clinical practice 10 years ago. Moreover, doses to cardiac substructures like the LAD are predictive for defined cardiac events after radiation therapy, as shown for esophageal cancer [8]. With modern treatment techniques, the rate of serious sequelae may be lower than previously thought [9] and several modern radiotherapy techniques significantly improved the therapeutic ratio [10]. This makes it even more important to evaluate the contribution of current radiation techniques to cardiac morbidity in clinical practice. Numerous strategies are available to lower the cardiac dose, such as radiobiologically optimized dosing and fractionation regimes, reduction of target volume, or technical means to distance the heart from the target volume. All appropriate means for heart sparing have to be evaluated against the oncologic risk constellation but also in terms of other patient-related factors like age, individual anatomy, concurrent disease, and locally available techniques to guide individually optimized treatment decisions.
In detail, the following techniques can be considered: deep-inspiration breath-hold (DIBH) is the best-studied technique for heart sparing. Treating exclusively the tumor bed—and thus a smaller volume (as in accelerated partial-breast irradiation, APBI [11])—could also result in better heart sparing. Patients for APBI have to be carefully selected to achieve an equivalent outcome compared to whole-breast radiation therapy [11, 12]. There is some overlap of patients who, on the one hand, can be treated by APBI, and who on the other hand, if treated by WBI, would get a tumor bed boost (e.g. pT2 tumor ≤ 3 cm or patients aged between 45 and 50 years) [12, 13]. The data on APBI for the topic of dose exposure to the heart are, however, not conclusive. A study of Alonso et al. [ 14] compared heart doses of patients treated with single-catheter intraoperative radiation therapy (IORT) to whole-breast irradiation (WBI) with deep-inspiratory breath-hold. They found that the mean heart dose (Dmean) was significantly lower with DIBH-WBI compared to IORT. Similarly, a study of Dutta et al. [ 15] found the mean heart dose to be higher in IORT than in DIBH-WBI or DIBH-external beam APBI.
On the other hand, a study on APBI with multicatheter brachytherapy (APBI-MCBT) found a significantly lower dose in the APBI-MCBT group as compared to WBI [16]. Compared to CyberKnife® (CK) radiation, MCBT-APBI performed better in terms of protection of the skin and ribs, whereas CK treatment did show some lower values for non-target breast. The dose parameters for the heart did not differ significantly between the two techniques [17]. Major et al. provide a comprehensive literature review of dosimetric studies between brachytherapy and external-beam radiation therapy including single-fraction boost with BT and VMAT, APBI with MCBT, IMRT, and CyberKnife® (Accuray, CA, USA) [18]. All of the summarized studies show excellent target coverage and sparing of OARs for breast BT. A plan analysis comparing IMRT-APBI and MCBT-APBI showed that the mean dose to the heart was lower with IMRT ($2\%$ vs. $4.5\%$), but as a consequence of IMRT planning, the dose to the lung became larger. Regarded as an absolute value, the MCBT mean heart dose was only 1.3 Gy [19].
We consistently perform both techniques (DIBH and MCBT) for breast cancer patients in our department. The technique is chosen with regard to the ESTRO and ASTRO APBI recommendations [20, 21], respecting the patients’ preferences. The aim of this dosimetric plan analysis is to assess what degree of heart sparing can be achieved in daily routine practice by multicatheter brachytherapy and DIBH.
## Materials and methods
A total of 30 patients treated with multicatheter brachytherapy for left-sided breast cancer were chosen from our database. Twenty-nine of these patients received MCBT as accelerated partial-breast irradiation, one patient (aged 37 years old) received MCBT as boost following external-beam radiation therapy (EBRT). A retrospective EBRT planning approach was done for these patients as described below. These treatment plans were compared to the treatment plans of 22 consecutive left-sided breast cancer patients treated with DIBH radiation therapy. As one could expect, none of the MCBT patients received neoadjuvant chemotherapy, and all fulfilled the treatment criteria for APBI [21].
For the MCBT patients, contouring was performed as described in the paper of Strnad et al. [ 20]. Briefly, in a pre-interventional CT, entry and exit points of the guide needle were localized using in-room lasers. Then, the relevant areas of the surgical scar were implanted with single leader catheters. The PTV safety margin was calculated by considering the size of free resection margins (total size of safety margin was always set to 20 mm), which was the sum of the surgical and added safety margins. The PTV was limited to chest wall/pectoral muscles. The evaluated MCBT dose prescription was 32 Gy in eight fractions administered twice daily for all cases, according to the GEC-ESTRO recommendations [22]. For the dosimetric analysis of this study, the mammary gland CTV (according to the ESTRO contouring guideline [23, 24]) and the heart (according to the Feng et al. atlas [25]) were defined retrospectively on the brachytherapy planning CT (see Fig. 1). Planning target volumes (PTV) were generated by adding a safety margin of 1 cm to the CTV (adapted to natural borders) and were used for external-beam radiotherapy treatment planning in free breathing. The tangential field treatment plans created for this study in free breathing (TF-FB) had a PTV dose prescription of 50 Gy in 2‑Gy single doses (normofractionated whole-breast irradiation, nWBI); 40.05 Gy in 2.67-Gy fractions (moderately hypofractionated WBI, mhWBI), which is seen as today’s standard of care when given without a SIB [26]; and 26 Gy in 5 fractions for the ultrahypofractionated FAST-Forward regimen (uhWBI) [27].Fig. 1Tangential field treatment plan in a brachytherapy planning CT: 1 demarks the brachytherapy PTV (red); 2 the mammary gland; 3 the EBRT PTV, the red arrow shows the air within a brachytherapy single leader catheter; 4 the measure of the PTV–heart distance (white arrows) for WB All DIBH patients were treated with a simultaneous integrated boost (SIB) to the primary tumor bed. Dose prescription for the DIBH-WBI PTV was 50.4 Gy in 1.8-Gy and 63 Gy in 2.25-Gy fractions for the SIB [28]. For further comparisons, we calculated tangential field plans (TF-DIBH) without a boost for the treated 22 cases with the standard dosing scheme of 40.05 Gy/15 fractions [13].
Herein, we report the nominal average dose (Dmean) to the heart as well as the mean biologically effective dose (BED) and the mean equivalent dose in 2‑Gy fractions (EQD2) for an α/β of 3 [29, 30].
We additionally analyzed the distance between the outer PTV and the heart contour. For this purpose, the contoured PTV was evaluated slice by slice in the planning CT with a measurement tool, and the minimum measured heart–PTV distance was noted by two independent physicians (see Fig. 1). In case of differences, the respective CT was reviewed simultaneously by both, and differences were clarified. The statistical analyses were performed with IBM SPSS Statistics v. 26 (IMB Corp., Armonk, NY, USA). Correlations between the heart Dmean values were analyzed by t-test, correlations between the heart Dmean and the PTV–heart distance by Spearman’s correlation. A value of $p \leq 0.05$ was considered to be statistically significant.
## Results
Median age of patients treated with MCBT was 63 years ($95\%$ CI 57.89–63.78, range 37–76), median age of the patients treated with DIBH was 57 years ($95\%$ CI 53.18–60.35, range 41–73). Dmean data are summarized in Table 1 and discussed below. Table 1Results summaryMCBTTF-FB nWBITF-FB mhWBITF-FB uhWBIDIBH-SIBTF-DIBHDmean (Gy)1.284.173.332.121.911.42Dmean EQD2 (Gy)0.812.652.161.471.180.84Dmean BED (Gy)1.354.423.62.451.971.47MCBT Multicatheter brachytherapy, TF-FB tangential field radiation therapy in free breathing, nWBI normofractionated whole breast radiation therapy, mhWBI moderate hypofractionated whole breast radiation therapy, uhWBI ultra hypofractionated whole breast radiation therapy, DIBH deep inspiration breath-hold, SIB simultaneous integrated boost
## MCBT-treated patients
The heart Dmean for MCBT (for a summed dose of 32 Gy) was 1.28 Gy ($95\%$ CI 1.11–1.44, range 0.32–2.0), for TF-FB nWBI was 4.17 Gy ($95\%$ CI 3.70–4.63, range 2.16–7.2), for TF-FB mhWBI 3.33 Gy ($95\%$ CI 2.96–3.71, range 1.73–5.76), and for TF-FB uhWBI 2.12 Gy ($95\%$ CI 1.86–2.38, range 0.92–3.74).
The EQD2 heart Dmean for MCBT was 0.81 Gy ($95\%$ CI 0.7–0.92, range 0.19–1.3), for TF-FB nWBI was 2.65 Gy ($95\%$ CI 2.34–2.96, range 1.33–4.73) for TF-FB mhWBI 2.16 Gy ($95\%$ CI 1.9–2.42, range 1.08–3.9), and for TF-FB uhWBI was 1.47 Gy ($95\%$ CI 1.27–1.67, range 0.59–2.8).
The BED heart Dmean for MCBT was 1.35 Gy ($95\%$ CI 1.17–1.53, range 0.32–2.17) for TF-FB nWBI was 4.42 Gy ($95\%$ CI 3.9–4.94, range 2.22–7.89) for TF-FB mhWBI 3.6 Gy ($95\%$ CI 3.17–4.03, range 1.8–6.5) and for TF-FB uhWBI was 2.45 Gy ($95\%$ CI 2.11–2.79, range 0.98–4.67).
## DIBH-treated patients
The heart Dmean for DIBH treatment was 1.91 Gy ($95\%$ CI 1.67–2.16, range 0.94–2.98), EQD2 Dmean was 1.18 Gy ($95\%$ CI 1.02–1.33, range 0.57–1.86), and BED Dmean was 1.97 Gy ($95\%$ CI 1.71–2.22, range 0.95–3.10).
Tangential field plans of the DIBH cases (TF-DIBH) with one of today’s most common fractionation schemes of 40.05 Gy in 15 fractions yielded a heart Dmean of 1.42 Gy ($95\%$ CI 1.18–1.66, range 0.67–2.46), an EQD2 Dmean of 0.84 Gy ($95\%$ CI 0.67–1.02, range 0.09–1.56) and a BED Dmean of 1.47 Gy ($95\%$ CI 1.21–1.73, range 0.68–2.59).
Analyzing heart Dmean (numerical, EQD2, and BED) for MCBT and DIBH-SIB patients by t-test showed that although the absolute Dmean in each group was very low, they differed significantly. Numerical Dmean was 1.28 Gy vs. 1.91 Gy ($p \leq 0.001$), EQD2 was 0.81 Gy vs. 1.18 Gy ($p \leq 0.001$), and BED was 1.35 Gy vs. 1.97 Gy ($p \leq 0.001$). The boxplot for EQD2 *Dmean is* shown in Fig. 2. As one could expect, none of the TF-FB plans were able to perform better regarding the heart dose than the DIBH plans, even if using the FAST-Forward regimen: EQD2 Dmean TF-FB uhWBI 1.47 Gy vs. 1.18 Gy EQD2 DIBH ($p \leq 0.001$).Fig. 2Heart EQD2 Dmean in MCBT and DIBH SIB *Statistical analysis* by t-test of the heart Dmean (numerical, EQD2, and BED) for MCBT and TF-DIBH did not show any significant differences. The calculated values were as follows for MCBT vs. TF-DIBH: Dmean 1.28 vs. 1.42 Gy ($$p \leq 0.31$$), Dmean EQD2 0.81 vs. 0.84 Gy ($$p \leq 0.7$$), and Dmean BED 1.35 vs. 1.47 Gy ($$p \leq 0.4$$). Thus, the most often used hypofractionation scheme achieves doses to the heart as low as those achieved by MCBT. Part of the results of the statistical analysis is shown in Table 2.Table 2Comparison of heart Dmean values and corresponding p-valuesTreatmentDIBH-SIBp-valueTF-DIBHp-valueMCBTDmean EQD2 (Gy)0.81 vs. 1.18< 0.0010.81 vs. 0.84NSDmean BED (Gy)1.35 vs. 1.97< 0.0011.35 vs. 1.47NSMCBT Multicatheter brachytherapy, DIBH deep inspiration breath-hold, SIB simultaneous integrated boost, TF tangential field radiation therapy The mean heart–PTV distance for MCBT patients was 33.1 mm ($95\%$ CI 27.2–39 mm, range 8–78 mm) and for DIBH patients 7.8 mm ($95\%$ CI 6.4–9.2 mm, range 3–16 mm), and means differed significantly between these two groups ($p \leq 0.001$). The heart Dmean for MCBT was significantly associated with the heart-PTV distance ($p \leq 0.001$), as shown by Spearman’s correlation. The distance–dose distribution for MCBT is shown as a scatterplot in Fig. 3. There was no significant association between the heart-PTV distance with regard to the heart Dmean for DIBH plans ($$p \leq 0.398$$).Fig. 3Scatterplot heart distance in mm/heart, Dmean MCBT
## Discussion
As we hypothesized, mean heart doses in MCBT radiation therapy were lower than in free-breathing nWBI, mhWBI, and uhWBI, as well as in SIB-DIBH treatments. Hypofractionation translates into a lower dose to the heart, which is a logical consequence of lower nominal total prescription dose. Nonetheless, the ultrahypofractionated regimen of the FAST-Forward trial has not become a standard yet. In 2020, the trial’s 5‑year follow-up data were published. Although with the potential to become a new standard, to date, 15–16 fractions remain the most frequently used hypofractionated regimen. In our retrospective dosimetric analysis, the heart Dmean for the mhWBI treatment plans was 3.33 Gy, which was more than 2.5-fold the dose of the MCBT plans. There is also literature supporting the use of DIBH instead of brachytherapy. The studies of Alsono et al. and Dutta et al. [ 14, 15] both found better heart sparing by DIBH compared to balloon brachytherapy. The Alonso et al. [ 14] study comprised 34 patients: 17 patients with left-sided breast cancer treated with a multicatheter balloon in a phase I clinical trial and 17 patients with left-sided tumors who had undergone lumpectomy and adjuvant WBI-DIBH. The mean heart BED was lower with WBI-DIBH as compared to balloon brachytherapy (0.62 vs. 1.3 Gy, $$p \leq 0.0001$$). Dutta et al. [ 15] analyzed 52 consecutive patients with left-sided breast cancer treated with either balloon brachytherapy ($$n = 17$$; $76\%$ outer breast, Contura Hologic® five-channel balloon), adjuvant external-beam APBI-DIBH ($$n = 18$$; $56\%$ outer breast, $6\%$ cavity boost), or WBI-DIBH without SIB ($$n = 17$$, $76\%$ outer breast, $53\%$ with lumpectomy cavity boost). Mean heart BED was higher with balloon brachytherapy, at 1.26 Gy compared to 0.48 Gy and 0.24 Gy for WBI-DIBH and APBI-DIBH, respectively ($p \leq 0.001$). The results themselves are intriguing, especially as over $75\%$ of patients in the brachytherapy group had a tumor in the outer breast and, thus, it is expected, due to the heart–PTV distance, that the Dmean of the heart would be lower in the APBI-brachytherapy group. Similarly, Holliday et al. found a higher BED to cardiac structures with APBI using single-entry catheter APBI ($$n = 5$$), Contura® balloon ($$n = 11$$), and the SAVI® system ($$n = 39$$) than using DIBH-techniques [31].
However, all these studies used single-entry devices, with some of them capable of modifying the radiation dose distribution (more than one lumen).
Multicatheter brachytherapy—which can better modulate the dose to the PTV and thus significantly increase dose conformity—should be expected to perform better with regard to heart sparing.
Lettmaier et al. [ 16] found a significantly lower radiation exposure to all organs at risk using MCBT-APBI. They created two physical treatment plans for each of 16 patients with left-sided breast cancer, one for sole external-beam radiotherapy and one for partial-breast brachytherapy using MCBT. The exposed dose to a prespecified volume (D0.5cc, D1cc, up to D50cc) of the heart was significantly lower using MCBT than WBI, with D0.5cc being 11.82 Gy vs. 44.06 y, D1cc being 10.72 Gy vs. 41.91 Gy, and D50cc being 5.6 Gy vs. 18.17 Gy.
The smaller, nontangential PTV in PBI often results in a longer distance to the heart compared to WBI. In our cohort, the PTV to heart distance in DIBH patients was 7.8 mm, which is a result of the target definition process, because the adjacent thoracic wall is an integral part of the PTV. During beam-on time in DIBH radiation therapy, the PTV to heart distance should be reproducible. MCBT planning CTs were done in free breathing. It is possible that the PTV to heart distance that was measured shrinks during the patient’s exhale phase, and that the real heart dose is somewhat higher. It should be pointed out the dose to the LAD could differ, because the heart *Dmean is* not a perfect surrogate parameter for it, but this is beyond the scope of this article [32]. In principle, it is feasible to apply APBI in DIBH. MCBT-APBI is not suitable for every breast cancer patient [21]; nevertheless, there is some overlap of patients that can be treated by sole ABPI, or, if treated by WBI, would get a tumor bed boost [12, 13]. The reason for the discrepancy regarding results using APBI-brachytherapy in the published literature is the outcome of the different techniques that are used for APBI. Single-entry devices have no or few possibilities for 3D-optimized dose distribution, whereas MCBT offers the complete armamentarium of modern radiation planning and dose optimization. Patients treated with neoadjuvant chemotherapy do not qualify for APBI, so for these patients, DIBH offers the possibility of whole-breast radiation with low doses to the heart, also using an SIB, when indicated. Only some specialized centers are equipped and experienced enough to offer MCBT, but these should offer MCBT-APBI to suitable patients as an alternative to WBI.
There are of course several limitations to our study. The TF-FB planning was performed retrospectively on the MCBT CT datasets. But even if the mammary gland is slightly compressed by the brachytherapy catheters, an anatomic shift of the bony thorax is unlikely, and we thus considered the treatment plans similar to daily FB routine treatments. We further chose this approach (retrospectively planning WBI on MCBT CTs) in order to have a fair comparison to the MCBT dose that is delivered in FB. In most departments, DIBH is standard for left-sided WBI, and our retrospective plan analysis showed that this technique can achieve comparably low doses to the heart, despite the SIB. It is important to note that in our analysis, the heart was retrospectively contoured in the MCBT plans and that no dose optimization to the cardiac structures was performed during MCBT treatment planning. Optimizing for specific heart constraints would probably result in even lower doses than those presented herein for the MCBT treatment plans. On the other hand, normofractionated DIBH plans were optimized to the heart structures and, although using an SIB concept, reached low doses of 1.91 Gy heart Dmean. Further, as shown by the calculation of hypofractionation TF-DIBH plans, DIBH will result in very low doses. A hypofractionted regimen is now carried out as a daily routine. It should be noted that the combination of hypofractionation and a boost given as SIB, which could also provide some advantage for sparing radiation dose to the heart, but is currently seen as an experimental therapy per German S3 guideline [13].
## Conclusion
Both MCBT-APBI and DIBH using an SIB can lead to low doses to the heart and, thus, may have an impact on cardiac morbidity. This may be even more relevant as the subgroup suited for MCBT-APBI in general shows good prognostic characteristics. On the basis of an informed-consent decision process, MCBT-APBI carried out at experienced centers should be offered to left-sided breast cancer patients who fulfill the eligibility criteria as one possible treatment modality. Without optimizing the dose to the heart during the planning process, results as low as with DIBH radiation therapy can be achieved.
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|
---
title: Overexpression of CHAF1A is associated with poor prognosis, tumor immunosuppressive
microenvironment and treatment resistance
authors:
- Xia Sun
- Qiushuang Ma
- Yahong Cheng
- Huangwei Huang
- Jing Qin
- Mengchen Zhang
- Sifeng Qu
journal: Frontiers in Genetics
year: 2023
pmcid: PMC10033519
doi: 10.3389/fgene.2023.1108004
license: CC BY 4.0
---
# Overexpression of CHAF1A is associated with poor prognosis, tumor immunosuppressive microenvironment and treatment resistance
## Abstract
Background: As distinct marker of proliferating cells, chromatin assembly factor-1 (CAF-1) was critical in DNA replication. However, there is paucity information about the clinical significance, functions and co-expressed gene network of CHAF1A, the major subunit in CAF-1, in cancer.
Methods: *Bioinformatic analysis* of CHAF1A and its co-expression gene network were performed using various public databases. Functional validation of CHAF1A was applied in breast cancer.
Results: Overexpression of CHAF1A was found in 20 types of cancer tissues. Elevated expression of CHAF1A was positively correlated with breast cancer progression and poor patients’ outcome. The analysis of co-expression gene network demonstrated CHAF1A was associated with not only cell proliferation, DNA repair, apoptosis, but cancer metabolism, immune system, and drug resistance. More importantly, higher expression of CHAF1A was positively correlated with immunosuppressive microenvironment and resistance to endocrine therapy and chemotherapy. Elevated expression of CHAF1A was confirmed in breast cancer tissues. Silencing of CHAF1A can significantly inhibit cell proliferation in MDA-MB-231 cells.
Conclusion: The current work suggested that overexpression of CHAF1A can be used as diagnostic and poor prognostic biomarker of breast cancer. Higher expression of CHAF1A induced fast resistance to endocrine therapy and chemotherapy, it may be a promising therapeutic target and a biomarker to predict the sensitivity of immunotherapy in breast cancer.
## Introduction
Cancer is the leading cause of death which is a major threaten to public health worldwide (Sung et al., 2021). Cancer cell proliferation is the fundamental precondition for disease development and progression. Among the proteins involved in the DNA assembly into chromatin, the chromatin assembly factor-1 (CAF-1) plays important role to promote assembly of chromatin and histone proteins deposition on to the DNA (Shen et al., 2020). CAF-1, an outstanding marker of proliferating cell (Polo et al., 2004), has been found to be the key regulator in DNA replication and chromatin restoration (Zheng et al., 2018). The expression of CAF-1 is positively correlated with the expression of Ki-67 in cancers (Sykaras et al., 2021).
CAF-1 is a nuclear complex containing three subunits in human cells, CHAF1A (p150), CHAF1B (p60), and RBBP4 (p48) (Sykaras et al., 2021). Among the three subunits, CHAF1A is the major one and plays essential role in CAF-1 complex (Liu et al., 2017; Tao et al., 2021). It is shown that cells were not in S phase when Chaf1a failed to bind to mouse heterochromatin-binding protein-1 (Hp1) during mitosis (Murzina et al., 1999). However, homozygous deletion of Chaf1a in mice was fatal to mice embryos. And absence of Chaf1a in these embryos led to changes of the nuclear organization in constitutive heterochromatin (Houlard et al., 2006). In recent years, studies have suggested that the elevated expression of CHAF1A is closely correlated with the development of some types of cancer, such as neuroblastoma, lung cancer, ovarian cancer, and gastric cancer (Barbieri et al., 2014; Liu et al., 2017; Xia et al., 2017; Zheng et al., 2018). Therefore, CHAF1A is considered to be one of the important oncogenic factors. However, CHAF1A has rarely been reported in breast cancer. More importantly, the precise mechanism of breast cancer development and progression remained unclear. Therefore, it was crucial to find a novel and reliable biomarker for diagnosis, prognosis and prediction of treatment response in breast cancer.
In our study, we took a comprehensive approach to investigate the genomic alterations of CHAF1A and demonstrate CHAF1A expression profiles in various cancer types. Our study not only confirmed CHAF1A abnormally high expression in cancers especially in breast cancer, but also demonstrated a strong correlation between CHAF1A overexpression, breast cancer molecular subtype, prognosis and treatment response. Co-expression network analysis was conducted for further investigation of the underlying roles of CHAF1A. Tumor immunosuppressive microenvironment was also explored to find out the association between CHAF1A and immune cells. Here, we provide evidence to demonstrate that CHAF1A could be served as a promising biomarker for breast cancer diagnosis, prognosis, sensitivity of immunotherapy and target of therapeutics.
## COSMIC (catalogue of somatic mutations in cancer) database analysis
The COSMIC database (Tate et al., 2019) is a comprehensive platform to explore somatic mutations in human cancers. The latest version was released on 28 May 2021 (v94), which included gene mutations, copy number variations, genomic rearrangements and gene fusions across 1,491,089 cancer samples. As such, genomic alterations of CHAF1A were summarized using COSMIC database.
## Assessment of CHAF1A expression from Oncomine database
Oncomine (Rhodes et al., 2007) is a cancer database with genome-wide expression analyses of 715 datasets and a total of 12764 normal and 86733 tumor samples. Through “differential analysis” module, the expression of a single gene could be analyzed across various cancer types compared with corresponding normal samples.
## TIMER 2.0
TIMER 2.0 (Li et al., 2016; Li et al., 2017; Li et al., 2020) is a database for comprehensive investigation of immune cells infiltrated in cancer tissues in a large variety of malignant diseases. There are three major modules for analysis of cancer exploration, immune association and immune estimation, including gene expression, gene correlation, immune infiltration in this study.
## UALCAN
UALCAN (Chandrashekar et al., 2017; Chen et al., 2019; Chen et al., 2022) is an on-lined data-mining resource to analyze gene expression and protein expression profile across various cancer types based on publicly available cancer OMICS data, including TCGA, CBTTC and CPTAC. It provides patient survival information for lincRNA-coding, miRNA-coding and protein-coding genes at the same time, which could discover candidate proteins that may be used as tumor biomarkers.
## Kaplan-Meier Plotter analysis
The Kaplan-Meier plotter (Győrffy, 2021; Lánczky and Győrffy, 2021) is an on-line platform which contains the expression of 30,000 genes and the survival data over 25,000 patients from 21 cancer types. The correlation of CHAF1A expression (Jetset Best Probe: 214426_x_at) and patients’ survival were investigated by applying a log-rank test.
## cBioPortal (cBio cancer genomics portal) analysis
The cBioPortal (Cerami et al., 2012; Gao et al., 2013) is an open source which provides a comprehensive platform for exploration, interactive visualization and analysis of large-scale cancer genomic datasets for scientific research. The co-expression data of CHAF1A were downloaded in cBioportal database, which was used for further investigation.
## OmicShare online platform
OmicShare (Su et al., 2019; Wang et al., 2020) is a platform for comprehensive data analysis. It contains multiple modules for different uses, such as heatmap, Gene Ontology (GO) enrichment, senior bubble plot, pathway enrichment and so on.
## GSEA (gene set enrichment analysis)
GSEA (Mootha et al., 2003; Subramanian et al., 2005) is a bioinformatic software that analyze and determine the statistically significant and concordant differences between two datasets based on a priori defined set of genes. The enrichment of CHAF1A co-expression genes were used to investigate potential functions and mechanisms of CHAF1A. Normalized enrichment score (NES) was calculated, and nominal p-value <0.05 was considered as statistically significant enrichment terms.
## GEPIA (gene expression profiling interactive analysis)
GEPIA (Tang et al., 2017) is a platform containing 9,736 tumors samples and 8,587 normal samples based on TCGA and Genotype-Tissue Expression (GTEx) projects. It is applied for the pan-cancer analysis of the RNA-sequencing expression data, including gene expression, gene correlation, survival rate and so on.
## Immunohistochemistry (IHC) and western blot
Breast cancer tissues and adjacent benign tissues were obtained in Qilu Hospital of Shandong University. The study was approved by the Ethics Committee of School of Basic Medical Sciences of Shandong University. Preparation of paraffin-embedded tissue sections, immunohistochemical and western blot analyses were performed as previously reported (Qu et al., 2018). The anti-human CHAF1A antibody (ab126625, Abcam, Cambridge, UK) was used to detect the expression of CHAF1A.
## Cell culture and shRNA transfection
MDA-MB-231 cells were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). Cells were cultured with Dulbecco’s Modified Eagle’s Medium (DMEM, CM15019, Macgene, China) with $10\%$ FBS (S711-001S, Lonsera, Uruguay). Cells were maintained as monolayer cultures at 37°C in a humidified incubator with no CO2 atmosphere. shRNA of CHAF1A was obtained from GenePharma (Shanghai, China). Cells were transfected with shRNA using polybrene (GenePharma, Shanghai, China) according to the manufacturer’s guidelines.
## CCK8 analysis
The CCK8 Cell Counting Kit (Vazyme, Biotech Co., Ltd) assay was performed using the protocol reported (Qin et al., 2021). MDA-MB-231 cells with stable silencing of CHAF1A using shRNA were seeded in 96-well plates. After replaced with fresh culture medium, 10 μL CCK-8 solution was added to each well and incubated at 37 °C for 3 h. The absorbance was determined at 450 nm on microplate absorbance reader (Bio-rad, United States) at 0h, 48h, and 96 h, respectively.
## Statistical analysis
All of these analyses were taken with $p \leq 0.05$ as the significance threshold, unless specificlly mentioned. ANOVA was applied to study the expression of CHAF1A in GEPIA database. Log-rank p-value was used for Kaplan-Meier Plotter analysis, and nominal p-value was used for GSEA analysis. Students’ t-test was used to analyze the data in Oncomine, UALCAN and CCK8. Spearman’s Correlation was used for selection of co-expression genes in cBioportal database and TIMER database. A statistic significant correlation between candidate gene(s) and immune cell(s) was considered if |Rho| > 0.1.
## Genomic alterations of CHAF1A
In order to identify the contribution of CHAF1A gene in human cancers, COSMIC (v94 GRCh38) (Tate et al., 2019) was applied for genomic alteration assessment. In the latest released version of COSMIC, CHAF1A was tested in 39,615 cancer samples across 40 different types of cancer. The total mutation frequency of CHAF1A was $1.105\%$, while missense mutation counted for $0.792\%$ and the rest were nonsense and synonymous mutations (Table 1). Compared with mutation, Insertion and Deletion were rarely found in cancers. Only one Frameshift Insertion ($0.003\%$), three Inframe Deletion ($0.008\%$) and five Frameshift Deletion ($0.012\%$) were found in cancers (Table 1). The total frequency of copy number alterations was $0.089\%$ in cancers, including $0.008\%$ for copy number gain while $0.081\%$ for copy number loss (Table 1). As shown in Supplementary Table S1, one copy number gain was found in malignancies of central nervous system, haematopoietic and lymphoid, and lung, respectively. Copy number loss was found in 12 malignancies, with the highest rate of $1.154\%$ in upper aerodigestive tract. Taken together, the data indicated that no major alterations in either sequence or copy number of the CHAF1A gene were responsible for cancer development.
**TABLE 1**
| Genetic alteration | Number | Percentage |
| --- | --- | --- |
| Substitution Nonsense | 14 | 0.035% |
| Substitution Missense | 314 | 0.792% |
| Substitution Synonymous | 110 | 0.277% |
| Inframe Insertion | 0 | 0 |
| Frameshift Insertion | 1 | 0.003% |
| Inframe Deletion | 3 | 0.008% |
| Frameshift Deletion | 5 | 0.012% |
| Complex Mutation | 0 | 0 |
| Others | 14 | 0.035% |
| Copy Number Gain | 3 | 0.008% |
| Copy Number Loss | 32 | 0.081% |
## Overexpression of CHAF1A in human cancers
Using TIMER 2.0 database (Li et al., 2017; Li et al., 2020), the gene expression of CHAF1A was investigated in various cancer types. Among the 22 paired tumor and corresponding normal samples, the expression of CHAF1A was significantly elevated in 20 types of malignancies compared with normal tissues (Figure 1). In consistent with the result from TIMER 2.0 database, it was also found abnormally high expression of CHAF1A in 36 study cohorts covering 17 types of cancers in Oncomine database (Rhodes et al., 2007) (Supplementary Table S2). In both two databases, breast cancer cohort contains the greatest amount of patient samples. The patient number included in breast cancer cohorts is more than other types of cancer, and with statistical significance as shown in Figure 1 and Supplementary Table S2. In addition, breast cancer is a leading cause of death in female. Thereby, we focused on breast cancer in the further investigation.
**FIGURE 1:** *The expression of CHAF1A is significantly higher in malignant tissues compared with normal tissues in various cancer types. In TIMER 2.0 database, CHAF1A is significantly highly expressed in 20 types of malignancies compared with normal tissues. The statistical significance was annotated by stars (*: p < 0.05; **: p < 0.01; ***: p < 0.001).*
## Correlation of CHAF1A expression with breast cancer phenotype and prognosis
To further investigate the clinical significance and application of CHAF1A in breast cancer, the correlation between the expression of CHAF1A and breast cancer phenotype in UALCAN database was analyzed (Chandrashekar et al., 2017; Chen et al., 2019; Chen et al., 2022). Both mRNA expression and protein expression of CHAF1A were significantly elevated in breast cancer samples compared with normal samples (Figures 2A, B). Furthermore, among the subtypes of breast cancer, the expression of CHAF1A was significantly higher in triple negative breast cancer (TNBC) compared to either luminal or HER2 positive breast cancers in both mRNA level and protein level (Figures 2C, D). In addition, the expression of CHAF1A was higher in TP53 mutant tissues compared to TP53 wild type tissues in breast cancer (Figure 2E).
**FIGURE 2:** *Elevated expression of CHAF1A in breast cancer tissues was significantly correlated with breast cancer patients’ poor prognosis. By applying UALCAN database (A,B) Both mRNA expression and protein expression of CHAF1A were dramatically increased in breast cancer tissues. (C,D) CHAF1A was overexpressed in all subtypes of breast cancer samples compared with normal ones. And compared with either Luminal or HER2 positive breast cancers, CHAF1A showed the highest expression in TNBC. (E) The expression of CHAF1A was higher in TP53 mutant breast cancer tissues compared to either TP53 wild type or normal tissues (***: p < 0.001). In Kaplan-Meier Plotter survival analysis. (F) The breast cancer patients with higher expression of CHAF1A showed poor overall survival (OS) compared to patients with lower CHAF1A expression. (G) The breast cancer patients with higher expression of CHAF1A took less time to develop disease recurrence. (H) The breast cancer patients with higher CHAF1A expression took shorter time to develop cancer distant metastasis.*
Then, we determined the potential effect of elevated CHAF1A expression on patients’ outcome. Elevated expression of CHAF1A was significantly correlated with shorter overall survival (OS) (Figure 2F), shorter recurrence free survival (RFS) (Figure 2G), and shorter distant metastasis free survival (DMFS) (Figure 2H) of breast cancer patients obtained from Kaplan-Meier plotter (Győrffy, 2021) survival analysis. The HR was 1.33, 1.25, and 1.48, respectively. As such, the data suggest that elevated expression of CHAF1A is a prognostic biomarker of poor patients’ outcome in breast cancer. The mechanisms underlying these events need to be further investigated.
## Co-expression gene network of CHAF1A and its potential mechanisms
Co-expression gene networks currently have been frequently used for exploration for the functional roles of the target genes (Villa-Vialaneix et al., 2013). In cBioPortal database (Cerami et al., 2012; Gao et al., 2013), the list of co-expression genes of CHAF1A in breast invasive carcinoma were downloaded, including three cohorts of TCGA data: Nature 2021, Cell 2015, and Firehose Legacy (Network, 2012; Ciriello et al., 2015). In order to get more precision co-expression networks, the genes shown consistent positive correlation score and consistent negative correlation score in all three cohorts were selected for further investigation.
The KEGG pathway annotation and enrichment of co-expression genes were generated with OmicShare online platform (Su et al., 2019; Wang et al., 2020). Figure 3 displayed the comprehensive functions and mechanisms of CHAF1A with statistical significance. The major functional roles of CHAF1A and its co-expressed genes focused on metabolism, genetic information processing, environmental information processing, cellular processes, organismal systems and human diseases (Figure 3A). Especially, the effect on metabolism (amino acid metabolism and energy metabolism), immune system, and drug resistance are more closely connected with potential response to cancer treatment. Among the top 20 KEGG pathway enrichment (Figure 3B), the co-expression network was covered cancer metabolism, stemness, microRNA, cell cycle, RNA splicing, TGF-β pathway, apoptosis and DNA repair.
**FIGURE 3:** *Analysis of CHAF1A’s functional roles and potential mechanisms. (A) The KEGG annotation of CHAF1A co-expression gene network was indicated by OmicShare. The length of the bar stands for the number of genes enriched in each function. (B) The top 20 pathways of CHAF1A co-expression gene network were generated by using OmicShare. The size of node stands for the exact number of genes that enriched in pathway, and the colour stands for p-value. (C) The enrichment of cancer hallmarks based on the CHAF1A co-expression gene network using GSEA.*
In addition, the pathways involved in cancer hallmarks were investigated using GSEA (Mootha et al., 2003; Subramanian et al., 2005). As shown in Figure 3C, the co-expression network in TCGA breast cancer cohorts was significantly positively enriched in E2F targets pathway, G2M checkpoint pathway, mitotic spindle pathway, DNA repair pathway, MYC target pathway, MTORC1 signaling pathway, and glycolysis pathway. The results from GSEA analysis were specific to signaling pathways, while still consistent with that from OmicShare KEGG pathway annotation and enrichment. Interestingly, cancer metabolism was of the most outstanding generated by various analyses, which suggested that CHAF1A might be critical to breast cancer development and progression, thus might provide novel therapeutic target for the treatment of breast cancer.
## Association of elevated CHAF1A expression with immunosuppressive tumor microenvironment
Since the co-expression network analysis indicated that immune system might be involved (Figure 3A), we looked deep into the tumor-infiltrating immune cells of breast cancer samples to investigate the correlation between CHAF1A and immune response in TIMER 2.0 database (Li et al., 2016; Li et al., 2017; Li et al., 2020). In breast cancer, it was found that the expression of CHAF1A was negatively correlated with CD8+ T cell, but positively correlated with regulatory T cell (Treg) and MDSC (Myeloid-derived suppressor cell) (Figure 4A).
**FIGURE 4:** *The correlation of CHAF1A with immune infiltrating cells and with immune checkpoint markers in breast cancer tissues. (A) Higher expression of CHAF1A was negatively associated with cancer infiltrating CD8+ T cells, but positively associated with infiltrating regulatory T cells and MDSC cells in breast cancer as indicated in TIMER 2.0 database. (B) The expression of CHAF1A was positively correlated with the expression of CD274 and CTLA4 in breast cancer in TIMER 2.0 database. (C) The expression of CHAF1A was positively correlated with the expression of CD274 and CTLA4 in breast cancer in GEPIA database.*
Furthermore, we checked the correlation between CHAF1A and immune checkpoints in TIMER 2.0 database. The expression of CHAF1A was positively correlated with that of both CD274 and CTLA4 (Figure 4B). This correlation between CHAF1A and either CD274 or CTLA4 was also confirmed by GEPIA database (Figure 4C). Thereby, the elevated expression of CHAF1A might be related with suppressive tumor microenvironment resulting in poor survival outcome. However, these patients might response to immunotherapy.
## Elevated CHAF1A expression used as a promising predictive biomarker to therapeutic treatment
In order to explore whether elevated CHAF1A expression detected in breast cancer patients’ samples could be used to predict the sensitivity of the patients to therapeutic treatment, the correlations between the oncolytic response of patients and elevated CHAF1A expression were determined in Kaplan-Meier plotter (Győrffy, 2021).
It was shown that the breast cancer patients with elevated expression of CHAF1A showed significantly shorter recurrence free survival whether receiving endocrine therapy or chemotherapy (Figure 5A). Since the mechanisms of endocrine therapy and chemotherapy are different, the breast cancer patients were divided into two different groups, one group received endocrine therapy and the other one received chemotherapy. Patients with elevated expression of CHAF1A showed significantly shorter recurrence free survival in each group (Figures 5B,C). As such, breast cancer patients with higher expression of CHAF1A might progress into treatment resistance to endocrine therapy and chemotherapy in shorter time.
**FIGURE 5:** *Resistance to endocrine therapy and chemotherapy treatment of breast cancer patients correlated with elevated CHAF1A expression as found by Kaplan-Meier Plotter. Breast cancer patients with elevated expression of CHAF1A showed shorter time to develop disease recurrence when they received (A) chemotherapy and endocrine therapy, (B) chemotherapy treatment, and (C) endocrine therapy treatment as found by Kaplan-Meier Plotter analysis.*
## CHAF1A highly expressed in human breast cancer tissues and its potential function
Clinical tissues were used to validate the expression of CHAF1A in patients. The expression of CHAF1A is much higher in breast cancer tissues compared with benign ones as shown by the IHC images (Figure 6A). To further investigate the function of CHAF1A in breast cancer, shRNA targeting CHAF1A was used to treat MDA-MB-231 cells. The expression of CHAF1A was significantly downregulated as shown in Figure 6B. Moreover, cell proliferation was inhibited when CHAF1A was silencing in MDA-MB-231 cells (Figure 6C), indicating that CHAF1A played important role in breast cancer cell growth. As such, CHAF1A may be a potential therapeutic target of breast cancer.
**FIGURE 6:** *CHAF1A highly expressed in breast cancer tissues and inhibition of cell proliferation by silencing of CHAF1A. (A) Higher expression of CHAF1A was confirmed in clinical breast cancer tissues compared with normal tissues by IHC. (B) The expression of CHAF1A was dramatically downregulated using shRNA of CHAF1A in MDA-MB-231 cells. (C) Cell proliferation of MDA-MB-231 cells was significantly inhibited after CHAF1A silencing.*
## Discussion
During the past decades, therapeutic options in cancers have been fast developed. The current treatment paradigm is now focused on mechanism-based therapeutics with selectivity, such as targeted therapy and immunotherapy (Vanneman and Dranoff, 2012). However, neither targeted therapy or immunotherapy can reach effective and durable results. Increasing evidences indicate that some targeted therapy can promote the anti-cancer immune response. As such, the combined use of both targeted therapy and immunotherapy may generate synergistic anti-cancer efficacy for patients (Bergholz et al., 2020). Thereby, the screening of immune-related therapeutic target is of great interests in cancer.
In this study, we have reported CHAF1A was overexpressed in 20 types of cancers. To rule out the potential effect of genomic alterations of CHAF1A in cancers, mutation, insertion, deletion and copy number alterations were investigated in COSMIC and Oncomine. The frequency of genetic alterations was too low to lead cancers development and progression. Elevated expression of CHAF1A has been reported to be associated with several solid cancers (Xu et al., 2016; Liu et al., 2017; Xia et al., 2017; Zheng et al., 2018; Tao et al., 2021), it was found that this phenomenon was commonly shared in more cancer types, indicating that abnormal higher expression of CHAF1A could be a potential biomarker for cancer diagnosis. Moreover, there was no study about the functional roles and mechanisms of CHAF1A in breast cancer. Thereby, we have shown the comprehensive profile of CHAF1A in breast cancer.
As a hormone related cancer type, the subtype of breast cancer classification is based on the expression of three major factors: estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2). TNBC is typically characterized by lack of expression of ER, PR and HER2 (Garrido-Castro et al., 2019). More importantly, TNBC is the most aggressive subtype of breast cancer (Ensenyat-Mendez et al., 2021). Overexpression of CHAF1A was found in breast cancer tissues compared to benign tissues. This was confirmed in IHC analysis by using clinical breast cancer tissues compared with normal ones. Furthermore, the expression of CHAF1A was significantly higher in TNBC than either luminal or HER2 positive breast cancer. In addition, it was shown that the expression of CHAF1A was much higher in TP53 mutant breast cancer patients. Since it was reported that the frequency of TP53 mutation was high in TNBC (Network, 2012), it was consistent with our finding that the expression of CHAF1A was not only higher in TP53 mutant breast cancer, but also higher in TNBC. In addition, we demonstrated statistically significant correlation between elevated CHAF1A expression and poor breast cancer patients’ outcome. The results suggest that the elevated expression of CHAF1A may serve as a poor prognostic biomarker in breast cancer. Furthermore, silencing of CHAF1A can significantly inhibit MDA-MB-231 cell proliferation, which suggests that CHAF1A can serve as potential therapeutic target of breast cancer.
To identify the mechanisms of overexpression of CHAF1A in breast cancer, the bioinformatics analysis was applied using CHAF1A co-expression network. The major biological effects and signaling pathways demonstrated that CHAF1A played important roles in breast cancer progression. Some of these functions and mechanisms of CHAF1A were reported in other cancer types, however, most of these were shown in the first time. In ovarian cancer, it was found that CHAF1A was involved in DNA repair, apoptosis, and cell cycle (Xia et al., 2017), which were confirmed in our study by pathway enrichment as shown in KEGG pathways (such as cell cycle pathway, p53 signaling pathway, DNA replication pathway and mismatch repair pathway) and GSEA enrichment (such as E2F targets pathway, G2M checkpoint pathway, mitotic spindle pathway and DNA repair pathway). It was reported that CHAF1A could bind to the DNA promoter region of c-Myc to enhance the transcriptional expression of c-Myc in gastric cancer (Zheng et al., 2018), which was consistent with the current finding that CHAF1A was positively correlated with Myc targets. These findings indicated that CHAF1A played essential role to promote breast cancer growth. As such, CHAF1A could be potential therapeutic target in breast cancer.
More importantly, it was found that CHAF1A could affect cancer metabolism and immune system. In recent years, it has been demonstrated that cancer metabolism was important since it can affect immunotherapy and chemotherapy in cancer treatment (Zaal and Berkers, 2018; Bader et al., 2020; Desbats et al., 2020; DePeaux and Delgoffe, 2021). It was interesting to find that the elevated expression of CHAF1A was negatively correlated with CD8+ cell, but positively correlated with Treg and MDSC in breast cancer. Furthermore, we found that elevated expression of CHAF1A is positively associated with glycolysis in breast cancer. Since it is widely acknowledged that glycolysis is important factor to induce immunosuppressive microenvironment in cancer (Reinfeld et al., 2022). Thereby, it indicated that elevated CHAF1A might contribute to activation of glycolysis, which induced the immunosuppressive microenvironment. As well, the expression of CHAF1A was positively correlated with immune checkpoints, i.e., CD274 and CTLA4. As such, breast cancer patients with higher expression of CHAF1A might benefit from immune checkpoint inhibitors. And CHAF1A targeting therapy combined with immune checkpoint inhibitors might achieve synergistic effect for breast cancer patients with increased expression of CHAF1A.
Cancer metabolism reprogramming has been found to mediate drug resistance in patients (Chen et al., 2020). To further analyze the correlation between CHAF1A and the response to treatment, breast cancer patients received either endocrine therapy or chemotherapy were enrolled in Kaplan-Meier plotter. Since we found that CHAF1A could induce metabolic reprogramming in breast cancer, it was not surprising that patients with increased CHAF1A expression developed drug resistance in a relatively short period of time. This was consistent with the research finding that elevated expression of CHAF1A could promote thymidylate synthetase activity, leading to 5-FU resistance in gastric cancer (Wang et al., 2019). In addition, it was reported that abnormally elevated expression of CHAF1A could regulate the metabolic pathways of some amino acids, such as methionine, eventually inducing 5′-methylthioadenosine (MTA) accumulation in neuroblastoma (Tao et al., 2021). Homozygous deletion of the methylthioadenosine phosphorylase (MTAP) is frequently found in some types of cancer, and the application of purine analogue has been shown to be effective therapeutic option in MTAP deletion cancer patients (Tang et al., 2018). Since elevated expression of CHAF1A may cause accumulation of MTA via regulating amino acid metabolism, it is possible that purine analogue might be potential treatment option in MTAP deletion breast cancer patient with higher expression of CHAF1A.
The current work mainly investigated the bioinformatic analysis of CHAF1A. Thereby, further functional study of CHAF1A would better validate its potential role as biomarker and target in breast cancer. That is also the limitation of this study.
*In* general, the current study demonstrated that elevated expression of CHAF1A can be used as diagnostic biomarker in various types of human cancers. Moreover, elevated expression of CHAF1A is a promising prognostic predictor and potential biomarker of drug resistance in breast cancer. In addition, it may serve as a promising therapeutic target and biomarker to predict the sensitivity of immunotherapy in breast cancer patients.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
## Ethics statement
The study was approved by the Ethics Committee of School of Basic Medical Sciences of Shandong University.
## Author contributions
Concept and design: SQ. Data acquisition: XS, QM, YC, HH, and JQ, Analysis and interpretation of data: XS, SQ, QM, YC, HH, JQ, and MZ. Draft of the manuscript: XS, SQ. Obtained funding: XS and SQ. All of the authors listed read and approved the final manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1108004/full#supplementary-material
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|
---
title: Porcine anti-human lymphocyte immunoglobulin depletes the lymphocyte population
to promote successful kidney transplantation
authors:
- Limin Zhang
- Haoyong Zou
- Xia Lu
- Huibo Shi
- Tao Xu
- Shiqi Gu
- Qinyu Yu
- Wenqu Yin
- Shi Chen
- Zhi Zhang
- Nianqiao Gong
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10033525
doi: 10.3389/fimmu.2023.1124790
license: CC BY 4.0
---
# Porcine anti-human lymphocyte immunoglobulin depletes the lymphocyte population to promote successful kidney transplantation
## Abstract
### Introduction
Porcine anti-human lymphocyte immunoglobulin (pALG) has been used in kidney transplantation, but its impacts on the lymphocyte cell pool remain unclear.
### Methods
We retrospectively analyzed 12 kidney transplant recipients receiving pALG, and additional recipients receiving rabbit anti-human thymocyte immunoglobulin (rATG), basiliximab, or no induction therapy as a comparison group.
### Results
pALG showed high binding affinity to peripheral blood mononuclear cells (PBMCs) after administration, immediately depleting blood lymphocytes; an effect that was weaker than rATG but stronger than basiliximab. Single-cell sequencing analysis showed that pALG mainly influenced T cells and innate immune cells (mononuclear phagocytes and neutrophils). By analyzing immune cell subsets, we found that pALG moderately depleted CD4+T cells, CD8+T cells, regulatory T cells, and NKT cells and mildly inhibited dendritic cells. Serum inflammatory cytokines (IL-2, IL-6) were only moderately increased compared with rATG, which might be beneficial in terms of reducing the risk of untoward immune activation. During 3 months of follow-up, we found that all recipients and transplanted kidneys survived and showed good organ function recovery; there were no cases of rejection and a low rate of complications.
### Discussion
In conclusion, pALG acts mainly by moderately depleting T cells and is thus a good candidate for induction therapy for kidney transplant recipients. The immunological features of pALG should be exploited for the development of individually-optimized induction therapies based on the needs of the transplant and the immune status of the patient, which is appropriate for non-high-risk recipients.
## Introduction
Kidney transplantation (KTx) is the most effective treatment for end-stage renal disease [1, 2]. The development of immunosuppressant therapies has greatly improved organ and recipient survival (3–5). However, acute rejection (AR) and delayed graft function (DGF) remain treatment obstacles that impair KTx outcomes, especially in the current era of donation after circulatory death (DCD) [6, 7]. Induction therapy, based on the conventional triple maintenance immunosuppressive therapy, can reduce the incidence of postoperative AR and DGF, allow for reduced immunosuppressant doses, and prolong transplanted kidney and recipient survival (8–10). To date, induction therapy has been used in the majority of recipients as a part of immunosuppressive protocol.
Induction therapy can be subdivided into two strategies involving either the use of non-lymphocyte-depleting monoclonal antibodies or lymphocyte-depleting polyclonal antibodies [11]. The non-lymphocyte-depleting strategy is represented by the interleukin-2 (IL-2) receptor antagonist basiliximab, which inhibits lymphocyte proliferation [12, 13]. The lymphocyte-depleting strategy is represented by the rabbit anti-human thymocyte immunoglobulin (rATG). In the current era of DCD, given the weaker immunosuppressive potency of non-depleting treatment compared to lymphocyte-depleting drugs, basiliximab use is limited to recipients at high risk of AR or DGF [14]. In addition, while the depleting rATG effectively inhibits the incidence of AR, it also increases the risks of corresponding complications such as lymphopenia and infection [15, 16]; however, little is known about its effects on the lymphocyte populations [17, 18].
In addition to rATG, the porcine anti-human lymphocyte immunoglobulin (pALG) is also a lymphocyte-depleting agent. pALG is produced by immunizing pigs with human-derived thymocytes. Unlike rATG which is made in rabbits, pALG origins are from a genetically closer species to humans and therefore improved antigenic similarity, which may result in weaker cytolytic capacity on human lymphocytes than rATG [19]. pALG has been successfully used as first-line treatment in acquired severe aplastic anemia (20–22). It has also been employed for other hematological indications such as graft versus host disease prophylaxis [23]. Meanwhile, pALG has been used in transplantation including hematopoietic stem cell transplantation [19, 24, 25] and kidney transplantation [26]. Pharmacokinetic studies show that, in humans, pALG reaches peak concentration immediately after administration and then markedly decreases over 2-3 months to undetectable levels [27]. However, its immediate effects on the lymphocyte population remain unknown; such effects can directly influence the clinical course by affecting the propensity for AR, kidney function, inducing a cytokine storm, or inducing immunocompromise and facilitating the development of secondary infections during its period of physiological activity.
In this study, we retrospectively assessed a cohort of kidney recipients with organs from DCD donors who received induction therapy using pALG. The binding capability and the modulatory impact of pALG on peripheral blood mononuclear cells (PBMCs) were analyzed, and the outcomes were tracked. Our work provides important data to optimize immunosuppressive protocols by considering individual patient variables in treatment planning.
## Study cohort
This retrospective study was designed to elucidate the impact of pALG on lymphocyte populations in kidney transplant recipients. The inclusion criteria were as follows: over 18 years of age; first-time KTx; negative PRA before transplantation; using a triple immunosuppressive regime as tacrolimus (Tac)+ mycophenolic acid (MPA)+prednisone. We excluded individuals with multiple organ transplantation; previous organ transplantation; or dual kidney transplantation. From March 2022 to September 2022, 12 KTx recipients with DCD kidneys were administered pALG as the induction therapy and enrolled in this study. The demographic characteristics of these recipients are shown in Table 1. We also recruited six additional recipients with DCD kidneys who received rATG ($$n = 3$$) or basiliximab ($$n = 3$$), and three additional recipients with live donor kidneys who did not receive any induction therapy (untreated). These nine additional recipients had the same demographic characteristics as the pALG group. This study adheres to the Declaration of Helsinki and the Declaration of Istanbul, and was been approved by the Medical Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (No. TJ-IRB20221216).
**Table 1**
| Characteristic | pALGn=12 | rATGn=3 | Basiliximabn=3 | Untreatedn=3 | P value |
| --- | --- | --- | --- | --- | --- |
| Characteristic | pALGn=12 | rATGn=3 | Basiliximabn=3 | Untreatedn=3 | P value |
| Donors | Donors | Donors | Donors | Donors | Donors |
| Male gender, n (%) | 10 (83.3) | 1 (33.3) | 3 (100.0) | 2 (66.7) | 0.265 |
| Mean age, yrs (SD) | 53.3 (10.7) | 55.7 (14.8) | 52.3 (8.1) | 50.0 (16.7) | 0.947 |
| Mean BMI, kg/m2 (SD) | 24.2 (4.1) | 20.9 (1.9) | 24.6 (6.2) | 22.7 (3.0) | 0.613 |
| Mean HLA mismatch, n (SD) | 4.7 (0.7) | 3.7 (1.2) | 4.0 (1.0) | 3.3 (0.6) | 0.053 |
| Donation classification, n (%) | Donation classification, n (%) | Donation classification, n (%) | Donation classification, n (%) | Donation classification, n (%) | Donation classification, n (%) |
| Deceased donor | 12 (100.0) | 3 (100.0) | 3 (100.0) | 0 (0.0) | |
| Living donor | 0 (0.0) | 0 (0.0) | 0 (0.0) | 3 (100.0) | |
| Mean creatinine before donation, μmol/L (SD) | 87.1 (48.5) | 27.0 (7.0) | 73.3 (19.8) | 75.7 (8.0) | 0.181 |
| Mean eGFR before donation, mL/min/1.73m2 (SD) | 95.7 (27.5) | 123.2 (3.4) | 103.1 (18.4) | 95.5 (12.6) | 0.352 |
| Mean warm ischemia time, min (SD) | 20.6 (2.3) | 17.5 (2.1) | 19.0 (2.8) | 1.7 (0.3) | |
| Mean cold ischemia time, h (SD) | 9.0 (3.3) | 8.5 (0.5) | 10.7 (2.7) | 1.6 (0.2) | |
| Recipients | Recipients | Recipients | Recipients | Recipients | Recipients |
| Male gender, n (%) | 9 (75.0) | 1 (33.3) | 2 (66.7) | 2 (66.7) | 0.701 |
| Mean age, yrs (SD) | 37.2 (7.1) | 37.7 (5.1) | 37.9 (7.0) | 34.3 (9.5) | 0.310 |
| Mean BMI, kg/m2 (SD) | 20.1 (2.6) | 21.7 (2.9) | 21.3 (2.7) | 23.0 (1.9) | 0.112 |
| Dialysis type, n (%) | | | | | 0.686 |
| HD | 11 (91.7) | 3 (100.0) | 2 (66.7) | 3 (100.0) | |
| PD | 1 (8.3) | 0 (0.0) | 1 (33.3) | 0 (0.0) | |
| Mean duration of dialysis, months (SD) | 44.5 (30.2) | 25.0 (15.4) | 28.7 (5.7) | 8.7 (5.8) | 0.167 |
## Immunosuppressive therapy
The agents used for induction therapy included pALG, rATG or basiliximab. The initiation and maintenance immunosuppressive regimes were administered as Tac+ MPA+ prednisone. The detailed dosages and drug regimens are shown in Figure 1.
**Figure 1:** *Induction therapy and immunosuppressive protocol for kidney transplantation. Induction therapy was provided using pALG, rATG, basiliximab or untreated. Methylprednisolone was administrated at a daily dose of 500mg to all recipients from POD0 to POD2. Initial and maintenance immunosuppression therapy was tacrolimus + mycophenolic acid+ prednisone. The dosage and usage were shown. D, day. W, week. M, month.*
## Recipient management
Postoperatively, recipients were monitored daily for the first week and every 2 days for the second week. All recipients were followed up for 3 months. Preoperative data on postoperative day (POD) 3, 5, 7, 14, 30, 60 and 90 were documented, including red blood cell (RBC), white blood cell (WBC), neutrophil, lymphocyte, serum creatinine (sCr), and the estimated glomerular filtration rate (eGFR). Meanwhile, within one week of transplantation, peripheral blood mononuclear cells (PBMCs) were isolated for further analysis. Clinical sequela including DGF and AR was recorded. DGF was defined as the requirement of hemodialysis in the first week after transplantation [28]. AR was diagnosed based on the Banff 2019 criteria [29].
## pALG quantification and binding affinity
In the recipients who received pALG, we tested the serum concentration of pALG and the pALG binding affinity to PBMCs based on fluorescence signal intensity preoperatively and on POD 3, 5, and 7. To determine the serum pALG concentrations, ELISA plates were coated with rabbit anti-swine IgG (Fab’2) (5 μg/mL, Sigma, Billerica MA, USA) overnight at 4°C. The plates were blocked with $10\%$ fetal calf serum (Gibco, Thornton, Australia)/phosphate buffered solution (PBS) (HyClone, Utah, USA) for 2-3 hours at room temperature. 50 μL of plasma samples were diluted with $1\%$ fetal calf serum/PBS and added to each well and incubated at room temperature for 1 hour. Series diluted pALG were used as standards. Plates were washed 5 times with PBS containing $0.05\%$ Tween-20 (Sigma, Louis, MO, USA). 100 μL of diluted goat anti-porcine IgG-HRP antibody (Southern Biotech, Birmingham, AL, USA) was added and incubated for 1 hour. Plates were washed 7 times followed by the addition of 100 μL Tetramethylbenzidine Substrate Solution (Sigma, Louis, MO, USA) and incubated until color development was complete. 100 μL 2N H2SO4 stopping solution was then added. The optical density was measured at 450 nm with a microtiter plate reader.
PBMCs were isolated from 5ml fresh anticoagulant whole blood by density gradient centrifugation using Ficoll-Paque *Plus medium* (GE Healthcare, Uppsala, Sweden) and washed with Ca/Mg-free phosphate buffered solution (PBS) (HyClone, Utah, USA). To remove the red blood cells, 2 mL GEXSCOPE® red blood cell lysis buffer (Singleron, Nanjing, China) was added at 25°C for 10 minutes. The solution was then centrifuged at 500g for 5 min and suspended in PBS. The blood samples were centrifuged at 400g for 5 min at 4°C, and the supernatant was discarded. After removing red blood cells, PBMCs were isolated by centrifugation at 400g for 10 min at 4°C. The supernatant was discarded and the PBMCs were resuspended by PBS to obtain a single-cell suspension. After that, PBMCs were incubated with fluorescent-conjugated goat anti-porcine IgG (Southern Biotech, Birmingham, AL, USA), and then detected by flow cytometry. To determine the pALG binding affinity in serum, PBMCs were obtained from normal human peripheral blood by the method described above. The isolated PBMCs were diluted to 2×106 cells/mL. A pALG gradient was diluted over at least 7 concentrations to generate a set of standards. 100 μL PBMC suspension was placed in a centrifuge tube and 10 μL pALG solutions of different concentrations or 10 μL recipient serum were added. After incubation at 4°C for 1h, the PBMC suspension was centrifuged and the supernatant was removed. Then 100 μL of diluted fluorescent-conjugated goat anti-porcine antibody (Southern Biotech, Birmingham, AL, USA) was added to each tube. After incubation in dark at 4°C for 30 min, the binding affinity was detected by flow cytometry.
## Single-cell sequencing analysis
PBMCs from the pALG group were isolated by the previously described methods [30]. Single-cell suspensions (2×105 cells/mL) with PBS (HyClone, Utah, USA) were loaded onto microwell chip using the Singleron Matrix® Single Cell Processing System. Barcoding Beads were then collected from the microwell chip, followed by reverse transcription of the mRNA captured by the Barcoding Beads to obtain cDNA, and conduct PCR amplification. The amplified cDNA was then fragmented and ligated with sequencing adapters. The scRNA-seq libraries were constructed according to the protocol of the GEXSCOPE® Single Cell RNA Library Kits (Singleron, Hangzhou, China) [31]. Individual libraries were diluted to 4 nM, pooled, and sequenced on Illumina novaseq 6000 with 150 bp paired end reads. Scanpy v1.8.2 was used for quality control, dimensionality reduction and clustering under Python 3.7 [32]. For each sample dataset, after filtering expression matrix, 15,243 cells were retained for the downstream analyses. The raw count matrix was normalized by total counts per cell and logarithmically transformed into normalized data matrix. Principle component analysis was performed on the scaled variable gene matrix, and top 20 principle components were used for clustering and dimensional reduction. Cell clusters were visualized by using Uniform Manifold Approximation and Projection (UMAP). To identify differentially expressed genes (DEGs), we used the Seurat FindMarkers function based on Wilcox likelihood-ratio test with default parameters, and selected the genes expressed in more than $10\%$ of the cells in a cluster and with an average log (Fold Change) value greater than 0.25 as DEGs. The cell type identity of each cluster was determined with the expression of canonical markers found in the DEGs using the SynEcoSys database. The cluster identification criteria involved two steps. The first was classification and the second was annotation of cell type. First, unsupervised clustering was carried out. According to the similarity of the transcriptome, corresponding expression patterns were gathered together for clustering. Then the cells were annotated according to the up-regulated marker genes in each cell group. The total number of cells and the number of subjects were shown in Supplementary Table 1. Heatmaps were generated by Seurat v3.1.2 DoHeatmap. *The* genes in the Heatmap were all DEGs, and the top 3 genes (ranked by AVG_logFC value) were then selected and shown in the plot. Mean expression in groups of heatmaps was the average expression of the gene in the subpopulation.
## Immune cell subset profiling and cytokine quantification
Recipient whole blood was collected preoperatively and on POD 3, 5, and 7. Erythrocytes were lysed. The resulting cell suspension was incubated with Pacific Blue-conjugated anti-CD3, PE-Cy5-conjugated anti-CD4, FITC-conjugated anti-CD8, PE-conjugated anti-CD16, APC-conjugated anti-CD19, PE-conjugated anti-CD25, APC-Cy7-conjugated anti-CD127, and PE-conjugated anti-CD56 antibodies (4A Biotech, Beijing, China) in dark at 4°C for 20 minutes. We gated mononuclear cells with PE-Cy7-conjugated anti-CD45 antibodies after excluding cell debris or non-single cells on the basis of FSC/SSC plots. In parallel, PBMCs were thawed and washed in PBS once and incubated with PE-conjugated anti-CD11c, FITC-conjugated anti-CD68, and FITC-conjugated anti-HLA-DR antibodies (4A Biotech, Beijing, China) in dark at 4°C for 20 minutes. The catalog numbers of the antibodies were shown in Supplementary Table 2. Human Fc receptor blocking solution (4A Biotech, Beijing, China) was used as an Fc block. The proportions of DC (CD11C+, HLA-DR+), CD4+T cell (CD3+, CD4+), CD8+T cell (CD3+, CD8+), Treg (CD3+, CD4+, CD25+, CD127-), B cell (CD3-, CD19+), macrophage (Mϕ, CD68+), NK cell (CD3-, CD16+, CD56+), NKT cell (CD3+, CD16+, CD56+) were detected by flow cytometry.
Serum cytokine proteins were detected by using the Bioagent LEGENDplex TM test kit (Biolegend, San Diego, CA, USA), with IL-2 and IL-6 as targets.
## Statistics
Count data are summarized as proportions (%). Measurement data are summarized as mean ± standard deviation or mean. Fisher’s exact test was used to test differences between categorical variables. *Univariate* general linear models or Kruskal-Wallis H tests were used to compare differences according to the results of normal distribution and homogeneity of variance. All statistical analyses were performed by using IBM SPSS Statistics 24.0 (IBM, Armonk, NY, USA). $P \leq 0.05$ indicates a statistically significant difference.
## Demographics
The demographic characteristics of all recipients and their corresponding donors are shown in Table 1. The donor gender, age, BMI, human leukocyte antigen mismatch, sCr, and eGFR before the donation were similar between groups. The recipient gender, age, BMI, type of dialysis and duration of dialysis were also comparable.
## pALG has a high binding affinity to PBMCs
After drug administration, we tested the serum concentration of pALG. A cumulative effect was seen during the 5-dose injection cycle with the concentration of up to 277 μg/mL at day 5 (Figure 2A). Thereafter, the serum level decreased gradually. As a lymphocyte-depleting antibody, the affinity of the bound pALG on PBMCs was examined by its flow cytometry MFI values within one week after drug administration. On day 5, the MFI values peaked at 191,457 (Figure 2B), suggesting that pALG has a high affinity to its target cells. After incubating PBMCs derived from healthy volunteers with serum, the MFI value showed a peak of only 29,193 at day 3 and then decreased (Figure 2C). These findings indicate that most of the administered pALG quickly binds to its target PBMCs to exert its effects.
**Figure 2:** *Serum pALG concentration and its binding affinity to PBMCs within 7 days after kidney transplantation. (A) The serum concentration of pALG. (B) The affinity of pALG binding to recipient PBMCs. (C) Serum pALG binding affinity to PBMCs from healthy volunteer. Data are expressed as mean (solid line) and SD (shaded area). PBMC, peripheral blood mononuclear cell.*
## pALG depletes blood lymphocytes
After binding to its target cells, pALG directly depletes them which should be reflected by changes in blood cell counts. Routine blood tests showed that the number and proportion of lymphocytes decreased immediately and markedly after pALG administration; this effect was maximal on POD3 at 0.19◊109/L and $2.6\%$, respectively. Afterward, the number and proportion of lymphocytes gradually increased reaching a clinically normal range within 3 months (Figure 3A). These trends indicate that pALG effectively and temporarily depletes lymphocytes. Interestingly, the decrease and recovery of lymphocytes were faster in recipients who received pALG compared to those who received rATG, but slower than those receiving basiliximab.
**Figure 3:** *Changes in routine blood tests during the 3 months of follow-up. (A) The number and proportion of lymphocytes. (B) The number and proportion of neutrophils. (C) The number of WBC. (D) The number of RBC. Shaded area represents the normal range. WBC, white blood cell. RBC, red blood cell. POD, postoperative day.*
We also evaluated neutrophils and whole WBCs. The number and proportion of neutrophils increased significantly from POD0 to POD3 and then decrease gradually (Figure 3B). These changes likely stem from the high-dose methylprednisolone and stress responses to the transplant. WBCs showed similar trends as neutrophils (Figure 3C). We also tested RBCs but found no relationship with pALG administration (Figure 3D).
Therefore, pALG treatment resulted in robust but moderate lymphocyte depletion, with relatively fast recovery to normal levels after the cessation of treatment.
## pALG effects on immune cell composition
To more precisely determine the effects of pALG on the immune cell pool, we performed single-cell sequencing on PBMCs obtained from one recipient preoperatively and on POD7. In the cell cluster analysis, we identified 5 cell types (Figure 4A, left) including B cells, erythrocytes, mononuclear phagocytes (MPs), neutrophils and T cells. The bar diagram shows the percentage of each cluster (Figure 4A, middle. Neutrophils, T cells and MPs were the main cell populations. Compared to the preoperative state, at POD7 neutrophils were more abundant while T cells and MPs were well less abundant. We show the highly expressed gene markers of each cell cluster in Figure 4A, right. After that, we analyzed the compositions of T cell subsets (Figure 4B), MPs (Figure 4C), neutrophil subsets (Figure 4D) and B cells (Figure 4E); this included cell clustering analysis (left), percentage of different cell clustering (middle) and the marker genes expressed in different cell subsets (right). We identified pALG-associated trends among the different cell types. At POD7, pALG treatment increased naïve T cells and reduced CD8 effector T cells (CD8 Teff), reduced non-classical monocytes and cDCs but increased classical monocytes, and increased neutrophils 1, 2, and 4 but reduced neutrophils 3. The neutrophils identified in this study should be the low-density neutrophils which could be co-segregated with PBMCs as previous study reported [33]. There were few B cells and we found little difference after pALG. These findings show that pALG most affects the T cell and innate immune cell (MPs and neutrophils) populations.
**Figure 4:** *Single-cell sequencing analysis. The analysis of PBMCs’ composition in pALG group on POD0 and POD7. [(A–E), left] UMAP cluster cell analysis of PBMCs, T cells, MPs, neutrophils and B cells. Each dot corresponds to a single cell. [(A–E), middle] Percentage of different cell types. [(A–E), right] Heatmaps shows the 3 genes with the highest expression levels in each subcluster, with columns representing selected marker genes. UMAP, uniform manifold approximation and projection. MPs, mononuclear phagocytes. CD8 Teff, CD8 effector T cells. Mono, monocytes. cDCs, conventional DCs.*
## pALG modulates the component of lymphocyte repertoire
The change in immune cell subpopulations after transplantation reflects the effect of pALG on the immune system, as shown in Figure 5A. As antigen-presenting cells, DCs showed a mild decrease in abundance within 5 days and recovery at POD7. CD4+T cells, CD8+T cells, Tregs, and NKT cells all decreased significantly to their lowest level on POD3 - 5 and then gradually recovered, indicating that pALG robustly depletes T cells. On the contrary, we observed a small increase in B cells. Mϕ and NK cells increased on POD3 and then began to decrease; this may reflect acute immune responses to surgery. The potency of pALG on immune cell depletion was between that of rATG and basiliximab/untreated control. Furthermore, the proportion of CD4+T cells and Mϕ on POD5 among different groups had statistically significant differences. These findings suggest that pALG is a mild depletor of antigen-presenting cells, moderate depletor of T lymphocytes, and does not deplete B cells.
**Figure 5:** *Recipients’ immune cell populations after kidney transplantation. (A) Proportions of DC, CD4+T cell, CD8+T cell, Treg, B cell, Mϕ, NK cell and NKT cell in PBMCs or CD45+T cells at 0 day, 3 days, 5 days and 7 days. Mϕ, macrophage. (B) Serum levels of IL-2 and IL-6. (*p<0.05, **p<0.01).*
After administration, lymphocyte-depleting antibody immediately depletes lymphocyte, thereby liberating intracellular cytokines, thus inducing a cytokine storm. Compared with rATG administration, pALG administration resulted in lower IL-2 levels. pALG administration increased IL-6 to higher levels than seen after rATG administration, but given the baseline differences the fold increase after pALG administration was much lower than after rATG administration, and IL-6 decreased quickly thereafter (Figure 5B). Although we compared the trend of IL-2 and IL-6 levels between pALG and rATG group, there were no statistical differences. These findings indicate that pALG induces moderate lymphocytolysis compared to rATG, and therefore may reduce the risk of a cytokine storm.
## pALG results in a low rejection risk
We observed no AR after pALG administration, other induction therapies, or in the no therapy controls during the 3 months of follow-up.
## Renal function and graft and recipient survival
Renal function was evaluated by sCr levels and eGFR. In recipients who received pALG, sCr gradually decreased to approximately 200 μmol/L post-transplantation, and was maintained around 200 μmol/L at 3 months after KTx (Figure 6A); eGFR increased gradually and was maintained at around 50 mL/min/1.73m2 during the follow-up (Figure 6B). Recovery of renal function in the recipients receiving pALG therapy was similar to those receiving rATG or basiliximab, and no statistical differences were observed. At 3 months post-transplantation, eGFR in the rATG group showed a small advantage over the other groups. All the kidney grafts and recipients survived during the observation period.
**Figure 6:** *Kidney function during the 3 months after kidney transplantation. (A) Serum Cr level. (B) eGFR. Cr, creatinine. eGFR, estimated glomerular filtration rate.*
## Complications
All recorded complications are shown in Table 2. One recipient receiving pALG underwent DGF and recovered at POD10. There were three cases of infection; one pALG case developed a mild pulmonary infection with no specific identified pathogen and was cured after 5 days of treatment with cefoperazone sodium and sulbactam sodium, one pALG case was infected with JC viruria and virus cleared 1 month after the Tac was decreased, and one basiliximab case developed Pneumocystis jiroveci pneumonia which was cured by using caspofungin and a reduced Tac dose. Two donor-derived infections (DDI) were identified; *Candida parapsilosis* in one pALG case and *Candida glabrata* in one rATG case, both of which were cured by appropriate antibiotics following sensitivity testing.
**Table 2**
| Complication | pALG n=12 | rATGn=3 | Basiliximab n=3 | Untreated n=3 |
| --- | --- | --- | --- | --- |
| Complication | pALG n=12 | rATGn=3 | Basiliximab n=3 | Untreated n=3 |
| DGF, n | 1 | 0 | 0 | 0 |
| Infections, n | 0 | 0 | 0 | 0 |
| Pneumonia | 1 | 0 | 1 | 0 |
| JC viruria | 1 | 0 | 0 | 0 |
| DDI, n | 1 | 1 | 0 | 0 |
## Discussion
Induction therapy is integrated into post-transplant immunosuppression protocols to modulate immunological reactivity and to protect the donor organ [16]. In recipients who are at low risk for rejection, non-lymphocyte-depleting antibodies are used most often, while high-risk recipients and those with low-quality donor organs usually receive lymphocyte-depleting antibodies. Precisely targeted treatments are critical for successful transplantation [8, 11]. In the current DCD era, donor organs carry greater immunogenicity potential and are of lower quality; as such, lymphocyte-depleting antibody treatment has been extended to low-risk DCD recipients at relatively lower doses (34–36). The optimal balance between treatment benefits (immunological and kidney protective) and risks (infections) of the typical lymphocyte-depleting antibody rATG still remains to be properly elucidated. pALG has been already used in the hematology and transplantation fields [20, 23], and offers an appropriate option to build an individualized induction therapy based on relevant immunological characteristics.
The first step toward elucidating the immunological characteristics of pALG is to determine its binding affinity to the target cells. In this study, we isolated PBMCs from 12 recipients who received pALG and used flow cytometry to test the binding affinity within one week of administration. The mean MFI values reached 191,457 at POD5. We also incubated serum pALG with PBMCs derived from healthy volunteers to examine its affinity potency, finding only a low MFI of 29,193 on POD3. We conclude that pALG has a high binding affinity to PBMCs and most pALG binds to its target PBMCs immediately after administration. This finding also indicates that continuous monitoring of pALG after the therapy is ceased imparts no clinical value.
The bound pALG should deplete the target cells, which can be measured directly by counting peripheral blood cell populations. To elucidate the lymphocyte depletion effects of pALG, matched recipients who received rATG, basiliximab, or no induction were also tested. We found that lymphocytes were markedly decreased immediately after pALG administration, while basiliximab exhibited only mild effects on lymphocyte numbers. Moreover, pALG showed a similar ability to deplete lymphocytes as rATG, but allowed a faster recovery after treatment cessation. Neutrophils, WBCs, and RBCs were also changed after transplantation, but this was most likely due to methylprednisolone and/or surgery-related factors rather than the induction therapy.
PBMCs and lymphocytes are complex cells with myriad subtypes. To further characterize the effect of pALG impact on immune cells, we conducted longitudinal single-cell sequencing of PBMCs that were derived from one pALG recipient. We found that T cells and innate immune cells (MPs and neutrophils) were most affected by pALG administration. PBMCs were then analyzed by flow cytometry. DCs showed a small decrease within 5 days and recovered by POD7, indicating that pALG may inhibit the initiation of immune responses by suppressing DC antigen presentation. T cells, including CD4+T cells, CD8+T cells, Tregs, and even NKT cells all decreased significantly to their lowest level 3-5 days after pALG administration and then recovered gradually. We also found a modest increase in B cells, which indicates that pALG does not deplete this population. Mϕ and NK cells were increased on POD3 and then began to decline. Because pALG significantly depleted lymphocytes, the absolute number and proportion of neutrophils were increased within three days of surgery, which was followed by a slow decline after the cessation of pALG. Furthermore, Hassani M et al. found that low-density neutrophils were co-segregated with PBMCs from whole blood by Ficoll separation [33]. Therefore, the neutrophils identified by single-cell sequencing analysis should be the low-density neutrophils. And the proportion of neutrophils in this study was consistent with previous study that the proportion of neutrophils in PBMCs of patients after kidney transplantation could reach $70\%$ or even higher through the single-cell sequencing analysis [37]. These differences in innate immune cells may reflect the complex post-operative immune changes that occur in response to the surgery, inflammation, and pALG administration. Taken together, pALG modulates the lymphocyte repertoire mainly by depleting T cells, which gradually recovers after drug cessation.
The use of lymphocyte-depleting antibodies can trigger hyperactive immune responses, which are sometimes termed “the cytokine storm”, due to strong lymphocytolysis. In this work, we used IL-2 and IL-6 as a proxy measure of lymphocytolysis and the development of a cytokine storm (38–40). pALG administration led to lower IL-2 (absolute) and IL-6 (relative) compared with rATG administration. However, because our groups showed markedly different baseline values of these cytokines, larger studies are needed to verify this potential safety advantage of pALG over rATG.
Knowing that pALG drops to undetectable levels within 2-3 months [27], we conducted our observation over 3 months. Within the first week of administration, we tested the concentration kinetics, binding affinity, and lymphocyte populations. We also closely followed the clinical recovery of the cohort. Over the three months, we did not identify any cases of organ rejection and found a low rate of complications. sCr levels were found to gradually decrease and were maintained at < 200 μmol/L, and eGFR increased and maintained at approximately 50 mL/min/1.73m2. Based on previous experience with pALG, we administered it in five daily doses of 500mg each, from POD 0 to POD 4. Our immunological analyses and clinical outcome measures demonstrate that this protocol successfully balances treatment efficacy and patient safety. Our data suggest that our induction therapy strategy was appropriate. Although we aimed to elucidate the acute effects of pALG administration on immune cells, longer-term investigations are necessary and ongoing. In addition, due to the small sample size, there were no statistical differences among those groups for most of the parameters. Therefore, we will further expand the sample size in future studies to compare the effects between pALG and rATG or basiliximab.
In conclusion, pALG modulates lymphocytes mainly by a moderate T cell depletion effect. pALG can be used as induction therapy for kidney transplant recipients with high efficacy and safety. These immunological features of pALG treatment should inform the development of optimal and individualized induction therapies for patients according to their transplant and immune status needs, especially for non-high-risk recipients. The current cohort study should be followed-up by large sample, multi-center prospective studies.
## Data availability statement
The data of single-cell sequencing presented in the study are deposited in the Gene Expression Omnibus repository, accession number GSE226328.
## Ethics statement
The studies involving human participants were reviewed and approved by Medical Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
NG and ZZ: study design and manuscript reviewing. LZ, HZ, HS, TX, SG and SC: literature research and manuscript drafting. LZ, HZ, XL, HS, SG, SC, ZZ and NG: clinical studies conduct and data collection. LZ, HZ, XL, TX, QY and WY: data collection, interpretation and statistical analysis. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1124790/full#supplementary-material
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|
---
title: 'The causality between intestinal flora and allergic diseases: Insights from
a bi-directional two-sample Mendelian randomization analysis'
authors:
- Qiubai Jin
- Feihong Ren
- Dan Dai
- Nan Sun
- Yiyun Qian
- Ping Song
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10033526
doi: 10.3389/fimmu.2023.1121273
license: CC BY 4.0
---
# The causality between intestinal flora and allergic diseases: Insights from a bi-directional two-sample Mendelian randomization analysis
## Abstract
### Background
Growing evidence shows a significant association between intestinal flora and allergic diseases, specifically atopic dermatitis (AD), allergic rhinitis (AR), and allergic asthma (AA). However, the causality has not yet been clarified.
### Objective
We conducted a bidirectional two-sample Mendelian randomization (TSMR) analysis to study the causal relationships between intestinal flora classification and AD, AR, or AA.
### Materials and methods
We obtained summary data of intestinal flora, AD, AR, and AA from a genome-wide association research. The inverse-variance weighted method is the primary method for analyzing causality in the TSMR analysis. Several sensitivity analyses were conducted to examine the stability of TSMR results. Reverse TSMR analysis was also performed to assess whether there was a reverse causality.
### Results
A total of 7 bacterial taxa associated with AD, AR, and AA were identified by the current TSMR analysis. Specifically, the genus Dialister($$P \leq 0.034$$)and genus Prevotella($$P \leq 0.047$$)were associated with a higher risk of AD, whereas class Coriobacteriia ($$P \leq 0.034$$) and its child taxon, order Coriobacteriales ($$P \leq 0.034$$) and family Coriobacteriaceae ($$P \leq 0.034$$), all had a protective effect on AR. In addition, the family Victivallaceae ($$P \leq 0.019$$) was identified as a risk factor for AR. We also noticed a positive association between the genus Holdemanella ($$P \leq 0.046$$) and AA. The reverse TSMR analysis didn’t suggest any evidence of reverse causality from allergic diseases to the intestinal flora.
### Conclusion
We confirmed the causal relationship between intestinal flora and allergic diseases and provided an innovative perspective for research on allergic diseases: targeted regulation of dysregulation of specific bacterial taxa to prevent and treat AD, AR, and AA.
## Introduction
The incidence of atopic dermatitis (AD), allergic rhinitis (AR), and allergic asthma (AA) have dramatically increased over the past three decades, resulting in a considerable burden to society [1]. AD is a common inflammatory skin disease characterized by recurrent eczematous lesions. AR is a non-infectious inflammatory disease of the nasal mucosa, with typical symptoms of paroxysmal sneezing, watery nasal discharge, nasal itching and congestion. AA is an airway inflammatory disease that results in recurring wheezing, chest tightness, shortness of breath, and mucus production. Currently, there is no effective radical treatment for AD, AR, or AA. Glucocorticoids and antihistamines are commonly used for treating these three allergic diseases; but symptoms tend to rebound after drug withdrawal [2]. AD, AR and AA are associated with genetic, dietary, and environmental factors (such as air pollution and exposure to allergens); however, the underlying causes remain unclear [3]. Therefore, there is an urgent need to identify potential causal risk factors for AD, AR and AA.
The intestinal flora is a dynamic ecosystem known as the ‘second genome’ [4]. Intestinal dysbiosis can cause metabolic disorders of intestinal microorganisms and further lead to immune dysfunction [5]. With an in-depth study of the gut-skin and gut-lung axes, more attention has been paid to the effect of intestinal flora on the skin and respiratory tract (6–8). Several observational studies have reported that the abundance of certain intestinal flora changes significantly in patients with AD, AR, and AA compared to healthy individuals, indicating a potential correlation between intestinal flora and these three allergic diseases (9–11). Intestinal flora can regulate adaptive immunity by maintaining the balance between effector T cells (Th1, Th2, and Th17) and regulatory T cells, which may explain the effect of intestinal flora on AD, AR and AA [12, 13].
However, owing to the lack of evidence from randomized controlled trials, it is unclear whether there is a definite causality between intestinal flora and these three allergic diseases. As the gold standard for causal inference in epidemiological studies, randomized controlled trials are sometimes difficult to conduct because of ethical limitations and high costs. Two sample Mendelian randomization (TSMR) is an effective alternative [14]. Genome-wide association studies (GWAS) have made great contributions to the identification of genetic variants related to diseases, mainly single nucleotide polymorphism (SNP), which can increase our understanding of the genetic basis of many complex traits in human diseases [15]. TSMR uses genetic variation related to exposure as an alternative indicator of exposure to study the causality between exposure and outcome [16]. The selected SNPs are also called instrumental variances (IVs). TSMR simulates randomization based on the random distribution of genetic variants during gametogenesis, conceptually similar to randomized controlled trials [17]. Since these genetic variants precede diseases progression and are independent of lifestyle and environmental factors after birth, TSMR can minimize the influence of confounding factors and reverse causality [16].
In this study, we used the latest available GWAS database published in 2021 [18] for TSMR analysis to investigate the possible causality between intestinal flora and AD, AR, and AA, to provide an innovative perspective for the research of allergic diseases: targeted regulation of specific bacterial taxa to prevent and treat AD, AR, and AA.
## Study design
TSMR was used to analyze the causal relationship between intestinal bacterial taxa and allergic diseases (AD, AR, and AA). The overall design of this study is shown in Figure 1. To obtain reliable results, three hypotheses need to be satisfied when performing TSMR analysis [1]: there is a strong correlation between genetic variants and exposure factors [2]; there is no correlation between genetic variants and confounders; and [3] genetic variants can only affect the outcome through exposure factors, but not through other methods, that is, horizontal pleiotropy is not allowed (Figure 1). Genetic variants that satisfy these three hypotheses can be included in TSMR analysis as instrumental variables [16].
**Figure 1:** *Overview of present MR analyses and assumptions. AD, atopic dermatitis; AR, allergic rhinitis; AA, allergic asthma; SNP, single nucleotide polymorphism.*
## Data sources and selection of instrumental variables
Summary statistics of the intestinal flora were obtained from a large-scale GWAS study by the MiBioGen consortium (https://mibiogen.gcc.rug.nl), involving 18340 European ethnic participants from 11 countries with 122,110 loci of variation [18]. We screened the IVs of intestinal bacterial taxa at five levels (phylum, class, order, family, and genus) from this GWAS. Fifteen bacterial taxa without specific species names were eliminated. The GWAS statistics for AD, AR, and AA were obtained from the data released by FinnGen Research (https://r7.finngen.fi/) in July 2022. The diagnostic criteria of AD were based on ICD-8, ICD-9 and ICD-10 standards and the GWAS statistics contain 16,383,295 loci of variations from 10,277 cases and 278,795 controls. The diagnostic criteria of AR were based on ICD-9, and ICD-10 standards, and the GWAS statistics of AR contain 16383313 loci of variation from 8430 cases and 298829 controls. The diagnostic criteria of AA were based on ICD-10 standards and the GWAS statistics of AR contain 16383313 loci of variation from 8430 cases and 298829 controls.
To obtain more complete results, we used a genome-wide significance threshold (5 × 10-8) and a locus-wide significance threshold (1 × 10-5), respectively to screen SNPs related to exposure [19, 20]. Linkage disequilibrium analysis was performed to satisfy TSMR’s hypothesis 1. The linkage disequilibrium correlation coefficient was set to r2<0.001 and clumping window >10000kb to ensure no linkage disequilibrium among the included IVs. To avoid horizontal pleiotropy, IVs associated with risk factors for allergic diseases were excluded using PhenoScanner V2 [21]. Palindromic and incompatible SNPs were excluded when harmonizing the statistics of exposure and outcome, and SNPs related to exposure that could not be matched in the GWAS outcome statistics were excluded. To avoid the influence of weak instrument bias on causal inference, we used the formula F=β2 exposure/SE2 exposure to calculate the strength of the IVs [16, 20, 22] and eliminate IVs with F < 10 [23].
## Statistical analysis
TSMR was conducted to analyse the causality between bacterial taxa and AD, AR, and AA. In the absence of horizontal pleiotropy, the inverse-variance weighted (IVW) method can be the primary method for analyzing causality in TSMR analysis [24]. Before that, we implemented the Cochrane’s Q test to evaluate the heterogeneity between the IVs. If heterogeneity was detected($P \leq 0.05$), the random-effects IVW model could provide a more conservative estimate; otherwise, the fixed-effect IVW model would be used [25]. Other methods of TSMR analysis, including the weighted median estimator (WM) and MR-Egger regression [26], can supplement the IVW method and provide wider confidence intervals [27]. These three TSMR methods for causal inference have their model assumptions. The IVW method is suitable for situations where horizontal pleiotropy does not exist [24]; the WM method assumes that less than $50\%$ of IVs have horizontal pleiotropy [28]. The MR-Egger regression assumes that more than $50\%$ of IVs are affected by horizontal pleiotropy [26].
If the result of the TSMR analysis was nominally significant ($P \leq 0.05$), we considered that there might be a causal relationship between the flora and outcome [29]. If the causality between bacterial taxa and outcome is identified as significant by two or more TSMR methods, the result is considered robust [5].
The existence of horizontal pleiotropy may challenge the second TSMR hypothesis; therefore, we adopted various methods to monitor possible horizontal pleiotropy. Specifically, the p-value of the MR-Egger intercept test and MR pleiotropy residual sum and outlier (MR-PRESSO) global test can be used to assess the existence of horizontal pleiotropy, and $P \leq 0.05$ was considered statistically significant [5, 30]. The MR-PRESSO outlier test can adjust horizontal pleiotropy by detecting and removing outliers [31], and the number of distributions in the MR-PRESSO analysis was set to 1000 [17].
Additionally, we conducted a leave-one-out sensitivity analysis of the identified significant results to determine whether the causal relationship of the TSMR analysis was caused by a single SNP [32]. Finally, a reverse TSMR analysis was performed between allergic diseases (AD, AR and AA) and the identified significant bacterial taxa using positive TSMR analysis to examine whether a reverse causal association existed. The reverse TSMR procedure was the same as that for the above TSMR analysis. TSMR analyses were performed using the ‘TwoSampleMR’ (version 0.5.6) in R software (version 4.2.1).
## Ethical approval
The GWAS data used in this study were public de-identified data. The ethics committee approved these data; therefore, there was no need for additional ethical approval.
## Results
IVs were screened according to the conditions described above. The details of the SNPs, that were eventually included in the TSMR analysis of intestinal flora and allergic diseases, are presented in Supplementary Table 1. After harmonization, the number of SNP involved in each pair of bacterial taxa and allergic diseases was more than three. The F-statistics of all SNPs were greater than ten, indicating that there are no weak IVs. Moreover, it should be noted that there is an inclusive relationship between intestinal flora classifications. Thus, the SNPs included in the class and their relevant order may overlap heavily. For example, SNPs of the order Coriobacteriales, class Coriobacteriia, and family Coriobacteriaceae.
## Results of the TSMR analysis (locus-wide significance, P<1×10-5)
The causal relationship between each pair of bacterial taxa and allergic disease was analyzed using the three TSMR methods (Supplementary Table 2). Twenty-five potential causal associations between bacterial traits and allergic diseases were identified using one or more TSMR methods (Figure 2). Among them, two bacterial taxa related to AD, four bacterial taxa associated with AR and one bacterial taxon related to AA were cross-validated using the IVW and WM methods (Table 1 and Figure 3). We mainly focused on these seven relatively stable causal relationships.
**Figure 2:** *Causal analysis of intestinal flora and allergic diseases (locus-wide significance, P<1×10-5). MR-PRESSO, Mendelian Randomization Pleiotropy Residual Sum and Outlier; IVW, inverse-variance weighted method; WM, weighted median estimator; AD, atopic dermatitis; AR, allergic rhinitis; AA, allergic asthma.* TABLE_PLACEHOLDER:Table 1 **Figure 3:** *Forest plot of the causality between cross-validated 7 bacterial taxa with the risks of AD, AR, or AA. IVW, inverse-variance weighted method; WM, weighted median estimator; AD, atopic dermatitis; AR, allergic rhinitis; AA, allergic asthma.*
We also performed the leave-one-out sensitivity analysis for the identified significant bacterial taxa, and the results further validated the robustness of our results (Supplement Figure 1). In the absence of heterogeneity, horizontal pleiotropy, and outliers, the results of TSMR analysis are credible.
## AD
Seven causal associations from bacterial taxa to AD were identified by the IVW method. Considering the cross-validation, the results of the two bacterial taxa remained stable. In specific, our TSMR analysis found that genus Dialister (OR: 0.839, $95\%$ confidence interval (CI): 0.714-0.987, $$P \leq 0.034$$) and genus Prevotella (OR: 0.924, $95\%$ CI: 0.854-0.999, $$P \leq 0.047$$) were associated with a higher risk of AD. In the sensitivity analysis, Cochrane’s Q test did not suggest evidence of heterogeneity in the genus Dialister ($$P \leq 0.214$$) and genus Prevotella ($$P \leq 0.672$$) (Supplementary Table 3). The MR-Egger intercept test observed no horizontal pleiotropy in the genus Dialister ($$P \leq 0.188$$) and genus Prevotella ($$P \leq 0.914$$) (Supplementary Table 4). Similarly, MR-PRESSO global test didn’t detect any horizontal pleiotropy in the genus Dialister ($$P \leq 0.231$$) and genus Prevotella ($$P \leq 0.667$$) (Supplementary Table 5). For the MR-PRESSO outlier test, no outlier was found in the genus Dialister and genus Prevotella (Supplementary Table 5).
## AR
Eleven causal relationships from bacterial taxa to AR were identified by the IVW method. Considering the cross-validation, the results of the four bacterial taxa remained stable. Specifically, class Coriobacteriia (OR: 0.789, $95\%$ CI: 0.634-0.982, $$P \leq 0.034$$) and its child taxon, order Coriobacteriales and family Coriobacteriaceae, all had a protective effect on AR. On the contrary, the family Victivallaceae (OR: 1.107, $95\%$ CI: 1.017-1.205, $$P \leq 0.019$$) was associated with a higher risk of AR. In the sensitivity analysis, Cochrane’s Q test did not suggest evidence of heterogeneity in class Coriobacteriia ($$P \leq 0.071$$), family Coriobacteriaceae ($$P \leq 0.071$$), family Victivallaceae($$P \leq 0.938$$) and order Coriobacteriales($$P \leq 0.071$$) (Supplementary Table 3). No horizontal pleiotropy was observed by MR-Egger intercept test in class Coriobacteriia ($$P \leq 0.609$$), family Coriobacteriaceae ($$P \leq 0.609$$), family Victivallaceae($$P \leq 0.507$$) and order Coriobacteriales($$P \leq 0.609$$) (Supplementary Table 4). Similarly, MR-PRESSO global test didn’t detect any horizontal pleiotropy in class Coriobacteriia ($$P \leq 0.081$$), family Coriobacteriaceae ($$P \leq 0.081$$), family Victivallaceae($$P \leq 0.938$$) and order Coriobacteriales($$P \leq 0.949$$) (Supplementary Table 5). For the MR-PRESSO outlier test, no outlier was found in class Coriobacteriia, family Coriobacteriaceae, family Victivallaceae and order Coriobacteriales(Supplementary Table 5).
## AA
Three causal associations from bacterial taxa to AA were identified by the IVW method. Considering the cross-validation, only one bacterial taxon remained stable. Specifically, our TSMR analysis found that genus Holdemanella (OR: 1.124, $95\%$ CI: 1.002-1.261, $$P \leq 0.046$$). In the sensitivity analysis, Cochrane’s Q test did not suggest evidence of heterogeneity in the genus Holdemanella ($$P \leq 0.198$$) (Supplementary Table 3). No horizontal pleiotropy was observed by the MR-Egger intercept test in the genus Holdemanella ($$P \leq 0.886$$) (Supplementary Table 4). Similarly, MR-PRESSO global test didn’t detect any horizontal pleiotropy in the genus Holdemanella ($$P \leq 0.227$$) (Supplementary Table 5). For the MR-PRESSO outlier test, no outlier was found in the genus Holdemanella(Supplementary Table 5).
## Results of the TSMR analysis (genome-wide significance threshold, P<5 × 10-8)
In the TSMR analysis of intestinal flora as a whole and allergic diseases, the IVW, WM, and MR-Egger regression methods did not find any significant causal associations. In the sensitivity analysis, Cochrane’s Q test did not suggest evidence of heterogeneity, MR-Egger intercept test and MR-PRESSO global test didn’t detect any horizontal pleiotropy, and the MR-PRESSO outlier test didn’t find any outliers. All the results are shown in Supplementary Table 6.
## Reverse TSMR analysis
The results of reverse TSMR analysis are presented in Supplementary Table 7. Considering cross-validation, we did not find any reverse causal relationships between the intestinal flora classification shown in Table 1 and allergic diseases.
## Discussion
Using large-scale GWAS statistics, seven bacterial traits associated with AD, AR, and AA were identified by the current TSMR analysis (Figure 4). In accordance with some prospective observational studies [10, 33] and animal experiments [34, 35], our TSMR study also revealed that the genus Dialister and genus Prevotella may be protective factors for AD. Th2-skewed and Th17-skewed immune dysregulation are some of AD’s most significant pathogenesis mechanisms [36], whereas Treg cells can inhibit Th2 and Th17 allergic inflammation and restore immune tolerance [37]. Furthermore, the genus *Dialister is* a propionate producer in the intestinal tract [38], and genus Prevotella can break down fibers and produce propionate and butyrate [39]. Several studies have discovered that the content of short-chain fatty acids (SCFAs) such as propionate and butyrate in patients with AD is significantly lower than that in healthy individuals [40, 41]. As important SCFAs, propionate and butyrate can inhibit the Th2-skewed and Th17-skewed inflammation in AD. Specifically, both can inhibit histone deacetylase [42] and induce the differentiation of peripheral CD4+T cells to Treg cells, thus producing anti-inflammatory cytokine IL-10 and inhibiting the function of Th2 and Th17 cells [43]. In addition, some studies have shown that butyrate can inhibit the release of histamine and other inflammatory mediators from mast cells by inhibiting the interaction between Immunoglobulin E and mast cells [44]. Therefore, we speculate that the protective effect of these two bacterial taxa on AD might be related to SCFAs, especially propionate and butyrate.
**Figure 4:** *Bacterial taxa associated with AD, AR or AA identified by the current MR analysis. The blue arrow indicates that the bacterial taxa is the protective factor of the outcome and the red arrow indicates that the bacterial taxa is the risk factor of the outcome. MR, Mendelian randomization; IVW, inverse-variance weighted method; WM, weighted median estimator.*
The Genus Holdemanella was considered as risk factor for AA based on the TSMR analysis. Reportedly, the abundance of the genus *Holdemanella is* negatively correlated with the propionate content in patients with diabetes and cognitive impairment [45]. Thus, we speculate that the genus Holdemanella may promote Th2 inflammation in AA by affecting SCFAs such as propionate [46]. However, the exact mechanism is still unclear and it is necessary to further study the possible role of the genus Holdemanella.
Class Coriobacteriia and its child taxa, order Coriobacteriales, and family Coriobacteriaceae, all have negative effects on AR, whereas, family *Victivallaceae is* the risk factor for AR. The relationship between these intestinal florae and allergic diseases was cross-validated using two IVW and WM methods. However, the function of this bacteria is poorly understood. Currently, there are no studies on the relationship between these bacterial taxa and allergic diseases, and the specific flora of AR and AA were reported for the first time in this study. Therefore, our study may provide a new perspective for mechanistic research on AR and AA.
In addition to the seven stable causal associations above, the IVW method yielded several interesting results, which are supported by previous studies. We also discovered that the family Bacteroidaceae and its child taxon genus Bacteroides were both risk factors for AD. Studies have reported that the abundance of the genus Bacteroides in patients with AD is significantly higher than that in healthy people [47], and its proportion is positively correlated with the severity of AD symptoms [33]. Lipopolysaccharide, the metabolite of the family Bacteroidaceae and genus Bacteroides, can promote Th2 inflammation in AD [48]. Moreover, the genus Bacteroides and genus Prevotella share a common ancestor but have inhibitory effects on each other [49]. Genus *Bacteroides is* dominant in people who consume protein and animal fat in their main diet. In contrast, genus *Prevotella is* predominant in people who take fruits and vegetables as their main diet [50]. The research of Nosrati et al. showed that vegetable intake could improve AD symptoms [51]. This suggests that the relationship between intestinal flora and AD can be studied from the perspective of diet in the future. After all, it is much easier to adjust eating habits than to change genetic or environmental factors.
Butyrate, a metabolite of gut microbiota, may be an important connector between gut microbiota and allergic diseases [52]. Genus Subdoligranulum [53] and genus Collinsella [54], both butyrate producers, were also determined to be protective factors of AR and AA by the IVW method. However, there are some differences in the role of butyrate in patients with AR and AA. Specifically, Th2-skewed and Th17-skewed immune dysregulation are important pathogenic mechanisms of AR [55]. Similarly, the genus Subdoligranulum may also inhibit Th2 and Th17-mediated inflammation in AR by producing butyrate. In addition, IL-4, an important cytokine in Th2 inflammation, can impair the airway epithelial barrier function in AR patients. In this case, butyrate can improve the function of the airway epithelial barrier [56], which may explain the protective effect of genus Subdoligranulum on AR. Moreover, the inflow of eosinophils into the lung parenchyma is a hallmark of AA [46]. Butyrate flows into eosinophils through monocarboxylate transporters and promotes apoptosis [57]. In addition, type 2 innate lymphoid cells (ILC2) can promote T2 immunity in AA [46]. Butyrate can inhibit the release of type 2 immune factors such as IL-5 and IL-13 by ILC2 cells, which can reduce the inflammatory response of AA [58].
This study has several advantages. Firstly, this is the first bi-directional TSMR study to reveal the causal relationship between intestinal flora and allergic diseases (AD, AR, and AA), which is not disturbed by confounding factors or reverse causality. Second, we set strict conditions for the screening of instrumental variables, and only when more than two TSMR methods identify the causal relationship can it be considered conceivable. Thirdly, we provided evidence for research on the intestinal-skin and intestinal-lung axes from a genetic perspective. Seven bacterial taxa associated with AD, AR, and AA were identified using TSMR analysis. These identified significant bacterial taxa could serve as candidate microbiome interventions in future clinical trials of allergic diseases. Meanwhile, our findings may provide an innovative perspective for research on allergic diseases: targeted regulation of specific bacterial taxa such as supplementing beneficial bacteria and inhibiting the growth of harmful bacteria to prevent and treat AD, AR and AA.
There are also some limitations of this study. Firstly, the number of instrumental variables involved in GWAS statistics of intestinal flora is limited, and there are no data available at the species level. Secondly, we could not determine whether there were overlapping participants in the GWAS data of the exposures and outcomes involved in this study. Thirdly, demographic data were lacking in the original research; therefore, we could not perform subgroup analysis on factors such as gender. Our results need to be verified by further clinical and basic research. In future study, we will increase the sample size and more accurately explore the relationship between intestinal flora and allergic diseases at the species level.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author.
## Author contributions
PS and QJ designed the study. NS and YQ collected the data. QJ, FR, DD, and NS performed the computations and manuscript writing. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1121273/full#supplementary-material
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|
---
title: Long-term effects of repeated multitarget high-definition transcranial direct
current stimulation combined with cognitive training on response inhibition gains
authors:
- Zhihua Guo
- Rui Qiu
- Huake Qiu
- Hongliang Lu
- Xia Zhu
journal: Frontiers in Neuroscience
year: 2023
pmcid: PMC10033537
doi: 10.3389/fnins.2023.1107116
license: CC BY 4.0
---
# Long-term effects of repeated multitarget high-definition transcranial direct current stimulation combined with cognitive training on response inhibition gains
## Abstract
### Background
Few studies have investigated the effects of repeated sessions of transcranial direct current stimulation (tDCS) combined with concurrent cognitive training on improving response inhibition, and the findings have been heterogeneous in the limited research. This study investigated the long-lasting and transfer effects of 10 consecutive sessions of multitarget anodal HD-tDCS combined with concurrent cognitive training on improving response inhibition compared with multitarget stimulation or training alone.
### Methods
Ninety-four healthy university students aged 18–25 were randomly assigned to undergo different interventions, including real stimulation combined with stop-signal task (SST) training, real stimulation, sham stimulation combined with SST training, and sham stimulation. Each intervention lasted 20 min daily for 10 consecutive days, and the stimulation protocol targeted right inferior frontal gyrus (rIFG) and pre-supplementary motor area (pre-SMA) simultaneously with a total current intensity of 2.5 mA. Performance on SST and possible transfer effects to Stroop task, attention network test, and N-back task were measured before and 1 day and 1 month after completing the intervention course.
### Results
The main findings showed that the combined protocol and the stimulation alone significantly reduced stop-signal reaction time (SSRT) in the post-intervention and follow-up tests compared to the pre-intervention test. However, training alone only decreased SSRT in the post-test. The sham control exhibited no changes. Subgroup analysis revealed that the combined protocol and the stimulation alone induced a decrease in the SSRT of the low-performance subgroup at the post-test and follow-up test compared with the pre-test. However, only the combined protocol, but not the stimulation alone, improved the SSRT of the high-performance subgroup. The transfer effects were absent.
### Conclusion
This study provides supportive evidence for the synergistic effect of the combined protocol, indicating its superiority over the single intervention method. In addition, the long-term after-effects can persist for up to at least 1 month. Our findings also provide insights into the clinical application and strategy for treating response inhibition deficits.
## 1. Introduction
Response inhibition comprises the ability to withhold irrelevant or context-inappropriate responses following changes in the environment so that one can make flexible and goal-directed behavioral responses, which is one of the core components of executive function (Verbruggen and Logan, 2009; Diamond, 2013). It is an essential factor for self-adaptation and self-regulation of the dynamics of actions (Aron, 2007; Sandrini et al., 2020). Response inhibition is closely associated with many other cognitive abilities, such as impulse control, working memory (WM), and cognitive inhibition (Dalley and Robbins, 2017; Zhao et al., 2018; Xu et al., 2020; Weidler et al., 2022). It is commonly impaired in many psychiatric disorders, such as substance use disorder, psychopathy, attention deficit hyperactivity disorder (ADHD), and schizophrenia (Hughes et al., 2012; van Rooij et al., 2015; Kohl et al., 2019; Gillespie et al., 2022).
Due to its great importance, the neural substrates and the approach to enhancing response inhibition have recently received increasing attention. Accumulating evidence has identified a frontal-basal ganglia network engaged in response inhibition, including the right inferior frontal gyrus (rIFG), the pre-supplementary motor area (pre-SMA), and the basal ganglia (Aron and Poldrack, 2006; Duann et al., 2009; Aron et al., 2014; Hannah and Aron, 2021). Transcranial direct current stimulation (tDCS) is a promising and widely used neuromodulatory technique for regulating cortical activity and neuroplasticity and enhancing cognitive function (Nitsche and Paulus, 2001; Pisoni et al., 2018). It is a suitable tool to infer the causality for the links between brain function and corresponding behavioral changes (Filmer et al., 2014; Gbadeyan et al., 2016; Yavari et al., 2018). tDCS is safe, non-invasive, tolerable, and easy-to-operate (Bikson et al., 2016) and has been found to effectively enhance response inhibition via anodal stimulation targeting rIFG or pre-SMA (Hsu et al., 2011; Jacobson et al., 2011; Ditye et al., 2012; Kwon and Kwon, 2013b,a; Stramaccia et al., 2015; Sandrini et al., 2020; Fujiyama et al., 2021).
New forms of tDCS emerge as research into the effect of tDCS on enhancing response inhibition progresses. High-definition tDCS (HD-tDCS) is an optimized form of conventional pad-tDCS with high spatial precision and produces more prominent behavioral and neurophysiological effects (Kuo et al., 2013; Sehatpour et al., 2021). Multitarget stimulation refers to simultaneous stimulation with the same polarity on multiple functionally related brain cortices, which can modulate the cortical activity more efficiently and enhance tDCS effects more prominently than conventional single-target stimulation (Hill et al., 2018; Gregoret et al., 2021; Guo et al., 2022a). Given behavioral and neuroimaging evidence, a previous study has shown that multitarget high-definition stimulation of rIFG and pre-SMA is more effective in improving response inhibition compared with the commonly used single-target stimulation on rIFG or pre-SMA alone (Guo et al., 2022a).
Importantly, repeated sessions of tDCS can increase efficacy through cumulative effects, yield long-lasting after-effects and stable changes in brain function, and are tolerated and safe (Nitsche et al., 2008; Cohen Kadosh et al., 2010; Paneri et al., 2016; Turski et al., 2017; Di Rosa et al., 2019; Song et al., 2019). Since cognitive training and tDCS both modulate neuroplasticity, combining tDCS and related cognitive training that involves the same or similar neural network may generate a synergistic and additional effect (Elmasry et al., 2015; Val-Laillet et al., 2015; Allenby et al., 2018; Berryhill and Martin, 2018; Wilkinson et al., 2019; Schneider et al., 2021). This combined approach can affect the trained tasks and be generalized to other untrained cognitive functions (transfer effect), including near and far transfer effects (Filmer et al., 2017a; Berryhill and Martin, 2018; Brem et al., 2018; Forcano et al., 2018; Smits et al., 2021).
However, limited studies focused on whether repeated tDCS combined with concurrent behavioral task training further extends response inhibition performance relative to a single intervention method, and the findings are heterogeneous among these few studies. Some studies have shown that this combination can induce greater response inhibition enhancement or better clinical outcomes (improved abstinence rate of alcohol), with the effects lasting 1 or 2 weeks (Dousset et al., 2021; Dubuson et al., 2021). However, according to some findings, this combination cannot produce additional benefits for response inhibition performance at post-intervention or follow-up sessions (Smits et al., 2021; Westwood et al., 2021; Zhou and Xuan, 2022). Additionally, the near and far transfer effects generated by this combined approach have scarcely been explored and warrant further studies. For instance, a previous study using tDCS together with stop-signal task (SST) training found that non-trained task (implicit association task) showed no evidence of intervention effects (Smits et al., 2021). To date, no researchers have investigated the effect of repeated daily multitarget tDCS (a new stimulation montage) combined with concomitant cognitive training on extending performance improvements of response inhibition. In addition, its long-term after-effects and transfer effects should be examined.
To fill the research gap, we designed this study to investigate the effects of 10 consecutive sessions of multitarget anodal HD-tDCS targeting rIFG and pre-SMA combined with concurrent cognitive training on improving response inhibition compared with 10 repeated sessions of multitarget stimulation or training alone, including long-lasting effects and transfer effects. Based on available research, we hypothesized that [1] the combined approach would extend and enhance performance improvements of response inhibition compared to multitarget stimulation or cognitive training alone, and the improvement effects would persist to follow-up session (i.e., long-term after-effect), [2] multitarget stimulation or cognitive training alone would induce response inhibition improvements compared to sham tDCS, and [3] the transfer effects would be absent. To the best of our knowledge, this study is the first to examine the effects of repeated daily multitarget anodal HD-tDCS combined with concurrent cognitive training on response inhibition, providing a preliminary insight into strategies to enhance response inhibition ability for both psychiatric and non-psychiatric populations.
## 2.1. Participants
Ninety-four healthy university students were included in this study. Prior to inclusion, the participants were screened to ensure they were ≥18 years of age and unfamiliar with tDCS-related research. They reported no neuropsychiatric disorders or use of psychotropic medication. All the participants ($$n = 94$$, mean age = 20.88 ± 1.77 years, range = 18–25 years, 41 males) had a normal or corrected-to-normal vision, no contraindications to tDCS (e.g., metal implants in the head, open wounds in the scalp, a family or personal history of epilepsy), and were right-handed as assessed with the Edinburgh Handedness Inventory (Oldfield, 1971). The participants were also evaluated in hyperactivity/impulsivity and inattention using the Adult ADHD Self-report Scale (ASRS), and only those with scores of <17 in both subscales were included because individuals with a score of ≥17 on either subscale were likely to have ADHD (Kessler et al., 2005; Yeh et al., 2008). The participants were randomly assigned to four groups: [1] real stimulation combined with SST training, $$n = 24$$ (stimulation + training group); [2] real stimulation, $$n = 21$$ (stimulation group); [3] sham stimulation combined with SST training, $$n = 24$$ (sham + training group); and [4] sham stimulation, $$n = 25$$ (sham control). Each group underwent intervention separately, without knowing each other. We used G*power 3.1.9.6 to compute a prior sample size with a medium effect size of 0.25, two-tailed α of 0.05, and power (1-β) of 0.80, and a sample of 52 participants was planned (13 per group) (Cohen, 1992; Faul et al., 2007). Written informed consent was obtained from all the participants after the experimental procedure was explained to them. They were free to withdraw from the study at any stage. All the experimental protocols were reviewed and approved by the Tangdu Hospital Ethics Committee, Air Force Medical University, and were performed under the Declaration of Helsinki. After finishing the experiment, the participants received monetary compensation for their time.
## 2.2. Design and procedure
The current study had a single-blind, randomized, parallel-group, and sham-controlled design. The participants were blind to the intervention conditions and study hypotheses. Before undertaking the experiment, the participants were asked to complete a brief questionnaire to collect their demographic information, the ASRS scores, and assess their eligibility for tDCS. There were 13 sessions in this study: pre-intervention test, 10 intervention sessions, post-intervention test, and a follow-up test after a month. After the pre-test, the participants were randomly assigned to four intervention conditions. Each participant received 10 sessions of corresponding intervention for 20 min per day on 10 consecutive days. The training did not start until a stable holding current was obtained to avoid the confounding effect of current fluctuations (Zhou and Xuan, 2022). Side effects and blinding efficacy were evaluated via interviews with the participants after finishing the intervention sessions. All the participants completed the measurements before the intervention (pre-intervention test), the day after the end of the intervention (post-intervention test), and 1 month after intervention (follow-up test). The test contents were identical every time (Figure 1), including the Barratt Impulsiveness Scale-Version 11 (BIS-11), SST, color-word Stroop task, N-back task, and attention network test (ANT). The BIS-11 lasted for about 5 min; the test SST, Stroop task, and N-back task each lasted for about 10 min; the ANT lasted for about 16 min. In addition to SST, which assessed response inhibition, other tasks examined the potential transfer effects (near transfer: Stroop task; far transfer: N-back task and ANT). Before each measurement, BIS-11 was used to assess changes in self-reported impulsivity. The tasks were computerized and run on E-prime 3.0 software (Psychology Software Tools, Inc., Sharpsburg, PA, USA). The behavioral tasks were administered in a randomized order (Martin et al., 2013; Dubuson et al., 2021). Before beginning each task, the participants were instructed on how to perform the task; then, a standardized written instruction appeared on the screen.
**FIGURE 1:** *Experimental procedure. The study followed a single-blind, randomized, parallel-group, and sham-controlled design. The order of SST, Stroop task, ANT, and N-back task were randomized.*
## 2.3. High-definition transcranial direct current stimulation
Multitarget HD-tDCS was delivered using an M × N-9 HD-tES Stimulator (Soterix Medical, Inc., New York, NY, USA), following the procedures for HD-tDCS usage specified in a previous study protocol (Villamar et al., 2013). The stimulation procedure in this study used multitarget HD-tDCS on rIFG and pre-SMA from our previous study (Guo et al., 2022a). The electrodes were localized according to the international 10-10 EEG system (Jurcak et al., 2007). Anodes were placed at C2 (1.48 mA) and FT8 (1.02 mA) (a total current intensity 2.5 mA), with return cathodes at Fz (−0.51 mA), C4 (−0.52 mA), P4 (−0.36 mA), FT10 (−0.53 mA), TP8 (−0.17 mA), and FC4 (−0.41 mA) (Figure 2A). The electric field and current flow were simulated (Figures 2B, C and Supplementary Figures 1–5) using HD-explore and HD-Targets software (Soterix Medical, Inc., New York, NY, USA). This simulation method has been widely used in prior studies and proved effective (Shen et al., 2016; Stephens and Berryhill, 2016; Reinhart and Nguyen, 2019). Participants in the sham stimulation condition underwent the same procedure as the real stimulation condition. The panel of the instrument was not visible to the participants. The current intensity of each electrode was smaller than 1.5 mA, which has been shown to be safe and reliable enough to improve cognitive performance (Villamar et al., 2013; Bikson et al., 2016; Hogeveen et al., 2016; Abellaneda-Perez et al., 2021; Zhou et al., 2021). Real stimulation was applied for 20 min with a ramp-up of 30 s at the beginning and a ramp-down of 30 s at the end. Sham stimulation consisted of a 30 s ramp-up and a 30 s ramp-down at the beginning and end, respectively, with no current during the intervening time, facilitating blinding by mimicking the sensations of real tDCS without actual neurophysiological changes (Di Rosa et al., 2019; Sharma et al., 2021). After stimulation sessions, the participants guessed which kind of stimulation they received (real or sham) and rated the confidence level based on a numeric analog scale ranging from 0 = absolute guess to 10 = absolutely sure. Additionally, participants completed a side-effect survey to report their dominant sensations (e.g., itching, tingling, burning, metallic taste, no special sensation) during the stimulation, and an 11-point scale was used to evaluate the intensity of sensations they felt, ranging from 0 = no sensation to 10 = strongest sensation imaginable (Hill et al., 2017).
**FIGURE 2:** *Electrode configuration and computational neurostimulation modeling of multitarget HD-tDCS. (A) Electrodes configuration. (B) A 3D view of the simulated electric field. (C) The section view of simulated electric field and current direction. The color bar represents the field intensity. The arrow points in the direction of the current flow, and the length indicates the current flow intensity. L, left; R, right; F, front; B, back.*
## 2.4.1. Barratt impulsiveness scale-version 11
Barratt impulsiveness scale-version 11 was employed to evaluate the impulsivity of the participants. It comprises 30 items and can be divided into three dimensions: attentional impulsivity, motor impulsivity, and non-planning impulsivity, with 10 items in each dimension (Patton et al., 1995; Bari and Robbins, 2013). In the current study, we used the revised Chinese version of BIS-11 (Li et al., 2011). It is reliable and has been widely used in previous studies (Ran et al., 2021; Guo et al., 2022b). Each item can be rated from 1 to 5 based on a five-point Likert scale. The dimensional score and total score range from 0 to 100 after being converted, with higher scores indicating higher levels of impulsivity (Li et al., 2011; Ran et al., 2021). The internal consistency of the BIS scale and its three subscales were good in our sample, with the Cronbach’s α ranging from 0.70 to 0.91 at an arbitrary test time point.
## 2.4.2. Stop-signal task
We used SST to evaluate the response inhibition performance (Logan et al., 1984; Verbruggen and Logan, 2008; Verbruggen et al., 2019). The task settings were identical to our previous study (Guo et al., 2022a). In the pre-potent go trials ($75\%$ of total trials), the participants were instructed to discriminate the direction of the right arrow or left arrow go signal on the screen by pressing the corresponding key (F for the left arrow and J for the right arrow) on a standard keyboard as quickly and accurately as possible. However, in the stop trials ($25\%$ of total trials), a small red square (stop signal) was presented above the arrow after an interval (stop signal delay, SSD), indicating the need to withhold their initiated response. The SSD was dynamically adjusted stepwise (initial SSD = 250 ms, 50-ms step, range = 0–1250 ms) to ensure that each participant had an approximately $50\%$ successful inhibition rate. Figure 3A presents the details of the task parameters. We estimated the primary outcome measure using the stop-signal reaction time (SSRT) determined by the integration method (Verbruggen et al., 2019), with shorter SSRT indicating superior response inhibition. SSRT was determined as follows: [1] calculating p(response| stop-signal), which means the probability of response to a stop signal; [2] ranking all RT of go trials from the minimum to the maximum with go omissions assigning the maximum RT (RT distribution); [3] calculating nth RT which corresponds to the p(response| stop-signal)-percentile of the RT distribution; and [4] using nth RT minus mean SSD to calculate SSRT. In addition to SSRT, other SST performance metrics, such as stop accuracy (the probability of inhibiting responses on stop stimulus) and goRT (mean RT on correct go trials), were also assessed.
**FIGURE 3:** *Detailed information about procedures of behavioral tasks. (A) SST. (B) Color-word Stroop task. (C) ANT. (D) 2-back task. (E) 3-back task.*
The SST was not only the test task for all groups but also the training task for the two groups using SST training. The test SST included a practice block of 48 trials and a formal test block of 200 trials ($25\%$ stop-signal trials), while the training SST consisted of 48 practice trials and 400 formal trials (30-s rest when finishing 200 trials). The training SST finished within the stimulation duration to guarantee the identical training amount. All the trials were presented at random.
## 2.4.3. Color-word Stroop task
The participants performed a classical color-word Stroop task at the pre-test, post-test, and follow-up test, which is a measure of cognitive inhibition (Lu et al., 2020a; Parris et al., 2021; Wu et al., 2021b; Zhou and Xuan, 2022). The Stroop task was used to explore the near-transfer effect of various interventions on cognitive inhibition. The task included a practice block of 15 trials and two test blocks of 45 trials each, with a 30-s rest between formal experimental blocks. The stimulus was chosen randomly from one of three Chinese characters (“红” for red, “绿” for green, and “黄 for yellow) printed in different colors of ink, either red, green, or yellow (Lu et al., 2020a). The practice block was presented with feedback, and the participants did not proceed to the formal test block until $80\%$ accuracy was achieved. The formal test block had no feedback. Each trial began with a fixation cross (+) at the center of the screen for 300 ms, which was replaced by a Stroop stimulus. The participants were instructed to press “D” for red, “F” for yellow, and “J” for green on the keyboard, according to the color rather than the meaning of the Chinese character, as quickly and accurately as possible. The stimulus interface lasted up to 1500 ms or was terminated with a blank screen (800–1000 ms) immediately after a key-press response (Figure 3B). During the congruent trial, the word matched the color (e.g., “红” in red), while in the incongruent trial, the word conflicted with the ink color (e.g., “红” in yellow). In our task, $40\%$ of trials were incongruent, and all the trials were presented randomly (Fu et al., 2019). We adopted the Stroop effect as the primary outcome. It was characterized by a longer reaction time in incongruent conditions compared with color-word congruent conditions and measured by the mean correct RT in incongruent trials, subtracting the mean correct RT in congruent trials. A lower Stroop effect indicated a higher inhibitory performance (Stroop, 1935; Fu et al., 2019; de Boer et al., 2021).
## 2.4.4. Attention network test
Attentional network test (ANT) is a classic task to study attention ability, which simultaneously measures the efficiency of individual alerting, orienting, and executive control networks involved in attention (Fan et al., 2002; Goldin et al., 2014; Lu et al., 2020b). The ANT was used to measure the transfer effect on attentional function. In our study, the ANT featured identical visual and timing parameters to those previously described (Fan et al., 2002). The target was preceded with one of the four cues, namely no cue, center cue, double cue, and spatial cue, and was flanked on either side by two arrows pointing in the same direction (congruent condition), opposite direction (incongruent condition), or no direction (neutral condition). The participants were asked to identify the direction (left/right) of the targeted arrow in the upper or lower visual hemifield by pressing a corresponding key (“F” for the left arrow, “J” for the right arrow) as quickly and accurately as possible. A session included a 24-trial practice block and two test blocks of 96 trials each (Rinne et al., 2013). The participants did not enter the test block until $60\%$ accuracy of the practice block was achieved. The trials were presented in a random order. There was a 30-s rest between two experimental blocks to avoid mental fatigue in the participants. Figure 3C presents more details. Outcome measures included the following: [1] conflict effect = RT (incongruent)–RT (congruent); [2] orienting effect = RT (central cue)–RT (spatial cue); and [3] alerting effect = RT (no cue)–RT (double cue) (Fan et al., 2002). The higher the orienting and alerting effects, the better the attentional processing; the lower the conflict effect, the better the ability to deal with interference.
## 2.4.5. N-back task
To probe the far transfer effect on the WM, we used an N-back task that is widely used to measure WM performance (Owen et al., 2005; Alizadehgoradel et al., 2020; Kaminski et al., 2020). We used a 2-back combined with a 3-back task with two blocks of each kind of task, and the 2-back task was conducted before the 3-back task. A cue appeared before each task block to alert the participants whether the next block was a 2-back or 3-back block. A number stimulus ranging from 1 to 9 appeared on the screen every time, and the participants were instructed to press the “J” key when the targets were identical to the ones presented two numbers before in a 2-back task block or three numbers before in a 3-back task block; otherwise, they pressed “F” in the keyboard. There were 62 trials in a 2-back task block and 63 trials in a 3-back task block, and the participants could have a 30-s rest between blocks. The participants had to finish the practice block before the test block started. Figures 3D, E present the details of the time sequence of the trials. The mean RT of correct responses and response accuracy were assessed as a result, and shorter RT and higher accuracy rates indicated better WM performance (Alizadehgoradel et al., 2020; Nejati et al., 2020).
## 2.5. Data pre-processing
Concerning SST, five participants were excluded from further analyses because they showed [1] stop accuracy <0.25 or >0.75 or [2] SSRT <50 ms (Congdon et al., 2012). After exclusion, the sample for SST analysis consisted of 89 subjects ($$n = 23$$, 21, 22, 23 for groups 1 to 4, respectively). Five participants were excluded from the Stroop effect analysis due to RT exceeding ± 3 SD of the mean (Fu et al., 2019). After exclusion, the Stroop task analysis was based on $$n = 23$$, 21, 22, 23 for groups 1 to 4, respectively. As for the N-back task, four participants with accuracy or RT exceeding ± 3 SD of the mean were excluded, leaving 90 participants for further analyses ($$n = 23$$, 20, 23, 24 for groups 1 to 4, respectively). Concerning ANT, five participants were excluded due to RT deviating >3 SDs of the mean. The final sample for ANT analysis comprised 89 participants ($$n = 22$$, 21, 21, 25 for groups 1 to 4, respectively). Notably, the number of participants varied by measure because of data filtering of corresponding behavioral measures, which was common practice in previous studies (Biggs et al., 2015; Dagan et al., 2018).
## 2.6. Statistical analysis
We used the IBM SPSS statistical package version 26 to conduct data analyses. The normality in the distribution of data was evaluated using the Shapiro-Wilk test, and the homogeneity of variances was confirmed using Levene’s test. When necessary, the sphericity assumption was verified by Mauchly’s sphericity test, and Greenhouse-Geisser was applied when the sphericity assumption was not met. Categorical variables such as gender and blinding were represented as count or proportion and examined by the chi-squared test. Continuous variables such as accuracy and RT were presented as mean ± standard deviation (SD). One-way analysis of variance (ANOVA) was used to test baseline performance and continuous data that measured once such as demographic variables. If the outcome measures differed at baseline (i.e., pre-test), they were analyzed by creating contrasts (δ values) between the post-test or follow-up test and pre-test to eliminate the interference of baseline, thereby ensuring that any performance changes would be attributable to the intervention. In addition, one-way ANOVA with Bonferroni’s-corrected statistical threshold was used to test group differences of δpost–pre or δfollow–up–pre.
Each behavioral task and its outcome measures and BIS-11 scores were tested using a series of 4 × 3 repeated-measures ANOVA (RM-ANOVA) with group (stimulation + training/combined condition, stimulation, sham + training, sham control) as between-subject factor and time (pre-test, post-test, follow-up test) as within-subject factor. Post-hoc tests were performed using Bonferroni’s-corrected pairwise comparisons. To further detect the effects of different intervention conditions on improving response inhibition, we conducted a subgroup analysis of SSRT. The participants in each group were separated into high-performance (HP) and low-performance (LP) subgroups based on baseline SSRT via a median-split method (Whelan et al., 2012; Schmicker et al., 2021). Subgroup analysis for each condition was performed using a 2 (subgroup: HP and LP) × 3 (time: pre-test, post-test, and follow-up test) RM-ANOVA. To explore possible relationships between SST and other behavioral tasks, we computed correlations of baseline outcome measures (excluding participants according to data filtering criteria of both tasks) using bivariate Pearson’s correlation analysis (two-tailed test). For exploring purposes, the statistical threshold of correlation analysis was not corrected. Concerning RM-ANOVAs, the significant interaction term was the focus of this study. The statistical significance level was set at α = 0.05. For ANOVAs, partial eta-squared (ηp2) was calculated as measure of effect sizes.
## 3.1. Demographics and baseline performance
As shown in Table 1, the four groups were matched. There were no significant differences in demographic and basic characteristics between the groups (ps > 0.05), including gender distribution, age, years in education, scores of hyperactivity/impulsivity and inattention subscales of ASRS, and sleep duration per night. In addition, one-way ANOVA for scores of BIS-11 and outcome measures of SST, Stroop task, N-back task, and ANT revealed no significant differences in the variables at baseline between the groups (ps > 0.05), except for 2-back accuracy, 3-back accuracy, and orienting effect (Table 1).
**TABLE 1**
| Variable | Stimulation + training | Stimulation | Sham + training | Sham control | F/χ2 | p |
| --- | --- | --- | --- | --- | --- | --- |
| n | 24 | 21 | 24 | 25 | | |
| Gender (male/female) | 10/14 | 10/11 | 10/14 | 11/14 | 0.213 | 0.976 |
| Age (years) | 20.83 (1.74) | 20.71 (1.95) | 20.88 (1.75) | 21.08 (1.73) | 0.170 | 0.917 |
| Education (years) | 15.42 (1.77) | 15.24 (1.79) | 15.46 (1.93) | 15.64 (1.73) | 0.192 | 0.902 |
| ASRS-inattention | 12.00 (2.83) | 12.57 (2.01) | 13.17 (2.53) | 12.24 (2.51) | 0.983 | 0.404 |
| ASRS-hyperactivity/impulsivity | 9.33 (2.88) | 9.00 (2.92) | 9.46 (2.86) | 9.64 (3.16) | 0.187 | 0.905 |
| Sleep duration per night (hours) | 7.00 (0.83) | 6.81 (0.87) | 6.75 (0.74) | 6.64 (0.57) | 0.968 | 0.412 |
| BIS-11 | BIS-11 | BIS-11 | BIS-11 | BIS-11 | BIS-11 | BIS-11 |
| Non-planning impulsivity | 28.65 (14.52) | 30.83 (13.45) | 31.88 (13.48) | 28.20 (11.78) | 0.416 | 0.742 |
| Motor impulsivity | 29.27 (9.22) | 32.62 (8.27) | 32.50 (8.20) | 32.60 (10.29) | 0.788 | 0.504 |
| Attentional impulsivity | 31.88 (9.00) | 34.76 (9.74) | 33.02 (6.84) | 29.40 (8.14) | 1.644 | 0.185 |
| SST | SST | SST | SST | SST | SST | SST |
| SSRT (ms) | 274.73 (28.47) | 272.06 (32.82) | 277.56 (34.75) | 274.03 (36.57) | 0.101 | 0.959 |
| Stop accuracy | 0.51 (0.07) | 0.51 (0.04) | 0.53 (0.06) | 0.50 (0.06) | 1.337 | 0.268 |
| GoRT (ms) | 565.20 (202.08) | 506.71 (152.52) | 569.66 (216.79) | 497.66 (221.20) | 0.796 | 0.499 |
| Stroop task | Stroop task | Stroop task | Stroop task | Stroop task | Stroop task | Stroop task |
| Stroop effect (ms) | 114.58 (47.34) | 121.60 (67.99) | 131.96 (72.02) | 100.98 (57.96) | 1.005 | 0.395 |
| ANT | ANT | ANT | ANT | ANT | ANT | ANT |
| Orienting effect (ms) | 122.74 (27.01) | 132.02 (24.90) | 107.10 (30.62) | 127.16 (32.99) | 2.899 | 0.040 |
| Conflict effect (ms) | 51.34 (32.41) | 56.25 (20.94) | 39.24 (26.98) | 50.42 (28.06) | 1.438 | 0.237 |
| Alerting effect (ms) | 52.42 (32.14) | 54.35 (25.96) | 45.88 (25.19) | 50.95 (27.54) | 0.356 | 0.785 |
| N-back task | N-back task | N-back task | N-back task | N-back task | N-back task | N-back task |
| 2-back accuracy | 0.82 (0.07) | 0.68 (0.22) | 0.71 (0.12) | 0.70 (0.18) | 3.207 | 0.027 |
| 2-back RT (ms) | 652.56 (72.61) | 657.90 (96.93) | 685.37 (65.40) | 657.05 (81.27) | 0.815 | 0.489 |
| 3-back accuracy | 0.73 (0.11) | 0.69 (0.12) | 0.62 (0.10) | 0.71 (0.15) | 3.652 | 0.016 |
| 3-back RT (ms) | 640.07 (74.14) | 660.87 (57.69) | 651.52 (97.77) | 611.24 (111.20) | 1.335 | 0.268 |
## 3.2. HD-tDCS safety, blinding efficacy, and electric field simulation
All the participants tolerated the stimulation well, and only mild side effects (i.e., tingling, burning, itching) were reported. Most of the participants reported tingling sensation, with 19 ($79.2\%$), 15 ($71.4\%$), 21 ($87.5\%$), and 21 ($84.0\%$) subjects in groups 1 to 4, respectively. Moreover, there was no significant difference in the ratings of the intensity of tingling sensations between the four intervention conditions [F[3,72] = 1.704, $$p \leq 0.174$$, η2p = 0.066]. There were 24 ($100\%$), 19 ($90.5\%$), 23 ($95.8\%$), and 24 ($96\%$) participants in groups 1 to 4, respectively, who believed that they underwent real stimulation. No significant differences were found between the groups in the number of participants reporting real or sham stimulation (χ2 = 2.385, $$p \leq 0.45$$). The confidence level scores were also non-significant when they were compared between the stimulation + training (8.21 ± 1.29), stimulation (7.57 ± 2.40), sham + training (7.38 ± 2.16), and sham control (8.24 ± 1.76) conditions [F[3,90] = 1.245, $$p \leq 0.298$$, η2p = 0.04]. The electric field modeling showed that the electric field distribution generated by multitarget HD-tDCS was focused around the anodes and the electric field and current flow produced was largely restricted within the ring of return electrodes (Figures 2B, C).
## 3.3. Stop-signal task
A significant group × time interaction effect on SSRT was observed [F[6,170] = 2.161, $$p \leq 0.049$$, η2p = 0.071] (Figure 4A). The main effects of time and group were also significant (ps < 0.05). Post hoc analysis with a Bonferroni’s-correction showed a significant decrease in SSRT both in the stimulation + training and stimulation alone groups form pre-intervention to post-intervention ($$p \leq 0.005$$ and $p \leq 0.001$, respectively) and from pre-intervention to follow-up test ($$p \leq 0.008$$ and $$p \leq 0.003$$, respectively). It also revealed a significant decrease in SSRT between pre-intervention and post-intervention in the sham + training group ($$p \leq 0.037$$) but not between pre-intervention vs. 1-month follow-up ($$p \leq 0.737$$). Post hoc analysis showed no significant changes in SSRT in the sham control group (ps > 0.999). There were no significant group × time interaction effects for the stop accuracy [F(5.36,152.95) = 0.387, $$p \leq 0.869$$, η2p = 0.013] and goRT [F(5.42,153.69) = 0.776, $$p \leq 0.578$$, η2p = 0.027], and the main effects were all non-significant (ps > 0.05).
**FIGURE 4:** *The effects of different intervention conditions in relation to the stop-signal task. (A) Significant interaction between group and time. (B) Subgroup analysis in the stimulation + training group. (C) Subgroup analysis in the stimulation group. HP, high performance; LP, low performance. All error bars represent standard deviation.*
Subgroup analysis showed a significant subgroup × time interaction for SSRT in both stimulation + training [F[2,42] = 3.538, $$p \leq 0.038$$, η2p = 0.144] and stimulation conditions [F[2,38] = 5.105, $$p \leq 0.011$$, η2p = 0.212]. The main effects of time and subgroup reached significance in the stimulation + training group (ps < 0.05), and the time main effect was significant in the stimulation group [F[2,38] = 13.182, $p \leq 0.001$, η2p = 0.41]. In the combined intervention (stimulation + training) condition, the Bonferroni’s-corrected post hoc analysis showed significantly decreased SSRT between pre-intervention and follow-up in the HP subgroup ($$p \leq 0.002$$), and between pre-test and post-test ($p \leq 0.001$) and between pre-test and follow-up test in the LP subgroup ($p \leq 0.001$) (Figure 4B). In the stimulation-alone condition, the SSRT significantly decreased in the post-test ($p \leq 0.001$) and follow-up test ($p \leq 0.001$) compared to the pre-test in the LP subgroup but not in the HP subgroup (Figure 4C). For the sham + training and sham control conditions, the interactions of subgroup × time were not significant ($$p \leq 0.214$$ and 0.098, respectively). The main effect of the subgroup was significant in the sham + training group [F[1,20] = 4.568, $$p \leq 0.045$$, η2p = 0.186]. There were no significant main effects in the sham control group (ps > 0.05).
## 3.4. Transfer tasks
In the Stroop task, the main effect of time was significant [F[2,170] = 24.085, $p \leq 0.001$, η2p = 0.221] due to decreased Stroop effect at the post-test (95.73 ± 5.44 ms) and follow-up test (73.36 ± 4.79 ms) compared to the pre-test (117.28 ± 6.53 ms). The interaction effect of group × time and the main effect of the group were not significant (ps > 0.05). In the ANT, one-way ANOVA showed that both the orienting effect δ values were not significant (ps > 0.05). The time effects for conflict [F(1.82,155.04) = 11.705, $p \leq 0.001$, η2p = 0.121] and alerting [F[2,170] = 4.057, $$p \leq 0.019$$, η2p = 0.046] effects were significant but not interaction terms or group effects (ps > 0.05). Concerning the N-back task, the baseline 2-back accuracy significantly differed between the combined intervention and stimulation conditions (Table 1), with the former exhibiting significantly higher accuracy than the latter ($$p \leq 0.047$$). One-way ANOVA showed that the δpost–pre and δfollow–up–pre for 2-back accuracy reached significance ($$p \leq 0.024$$ and 0.017, respectively, with corrected α = 0.025), but not 3-back accuracy (ps > 0.05). Post hoc analysis revealed that the combined intervention condition exhibited a smaller 2-back accuracy for δpost–pre ($$p \leq 0.022$$) and δfollow–up–pre ($$p \leq 0.013$$) compared to the stimulation condition. The main effects of time for 2-back RT and 3-back RT were significant (ps < 0.001) due to the reduction of RT at the post-test and follow-up test compared to the pre-test, but the interaction terms and the group effects were not significant (ps > 0.05).
## 3.5. Barratt impulsiveness scale-version 11
None of the group × time interactions and main effects of time and group for non-planning impulsivity, motor impulsivity, and attentional impulsivity reached significance (ps > 0.05).
## 3.6. Correlation analysis
Pearson’s correlation analysis showed that SSRT was significantly and negatively associated with 2-back (r = −0.259, $$p \leq 0.015$$) and 3-back (r = −0.239, $$p \leq 0.024$$) accuracy but was not correlated with the Stroop effect in the Stroop task or orienting, conflict, alerting effects in ANT (ps > 0.05).
## 4. Discussion
To the best of our knowledge, this randomized, parallel, and sham-controlled study is the first to examine whether repeated daily multitarget HD-tDCS applied to rIFG and pre-SMA, combined with concurrent SST response inhibition training, enhanced the response inhibition improvements. Consistent with the study hypotheses, our main findings showed that the combined protocol could generate a synergistic effect, compared to the single intervention condition, which also improved the response inhibition compared to the sham control. The decreased SSRT suggests improved response inhibition (Verbruggen and Logan, 2008; Verbruggen et al., 2019). According to the current results of SSRT, the combined protocol and the stimulation alone significantly improved response inhibition after the intervention, and the improvement persisted for up to at least 1 month. Given that the training alone only produced post-intervention effects, this condition was inferior to the combined condition and the stimulation alone in the long-term effects. However, the combined condition not only enhanced the LP subgroup performance but also improved the HP subgroup performance at the follow-up session compared to the stimulation-alone condition, which only enhanced the response inhibition of the LP subgroup. According to the compensation hypothesis (Shaw and Hosseini, 2021; Teixeira-Santos et al., 2022), the effects of cognitive enhancement techniques, such as tDCS and cognitive training, depend on baseline performance, and individuals with high baseline performance are difficult to be enhanced because they may already be near the peak level of cognitive ability. Therefore, there is less room for improvement. Conversely, individuals with low baseline performance have more room for improvement and are predisposed to enhancement. Many studies favor the compensation hypothesis (Krebs et al., 2021; Wu et al., 2021a,b; Assecondi et al., 2022). Despite the high baseline performance of the HP subgroup in this study, the combined protocol produced an improved effect at the follow-up session. Overall, the repeated daily HD-tDCS combined with SST training yielded the most significant effects and extended the improvement effects of stimulation or training alone.
The main finding is consistent with numerous previous studies that repeated tDCS accompanied by cognitive training could induce a synergistic effect after the intervention (Filmer et al., 2017b; Dousset et al., 2021; Dubuson et al., 2021; Schneider et al., 2021; Corrêa et al., 2022; Han et al., 2022; Lo et al., 2022; Szymkowicz et al., 2022). Importantly, the response inhibition improvement in this study persisted for up to 1 month following the intervention, consistent with previous studies in which repeated sessions of tDCS combined with concurrent cognitive training could produce after-effects that persisted from 1 week to 1 month (Dousset et al., 2021; Dubuson et al., 2021; Lee and Kim, 2021; Pisano et al., 2022). However, in the two studies involving response inhibition (Dousset et al., 2021; Dubuson et al., 2021), the after-effects lasted for 1 or 2 weeks, which differs from the 1-month after-effects in our study. This inconsistency may be attributed to the duration of intervention in previous studies that used four or five daily sessions of 20 min compared with 10 daily sessions of 20 min in this study. Most previous studies did not focus on the effects of combined condition on response inhibition, and among the few relevant studies, some findings rule out the synergistic effect of tDCS combined with response inhibition training (Smits et al., 2021; Westwood et al., 2021; Zhou and Xuan, 2022). However, our study provides evidence to support the higher efficacy of the combined protocol than commonly used single training or tDCS, providing further support for the limited literature on the efficacy of combined protocol in further improving response inhibition.
Previous studies have proposed that the best effects of tDCS are achieved when the stimulated neural network is already activated or pre-activated (e.g., via a behavioral task that involves the same brain region). Simultaneous activation of shared neural networks by both applied tDCS and performing relevant tasks can produce a synergistic effect. In addition, repeated tDCS and cognitive training may interactively facilitate the beneficial effect which occurs through specific neuroplastic changes such as the N-methyl-D-aspartate (NMDA)-dependent mechanism (Gilmore et al., 2018; Wilkinson et al., 2019; Breitling et al., 2020; Schneider et al., 2021; Westwood et al., 2021). The SST was widely used to study response inhibition and has been shown to engage the rIFG and pre-SMA (Aron and Poldrack, 2006; Duann et al., 2009; Watanabe et al., 2015; Hannah and Aron, 2021). Based on previous studies, we speculate that the neural mechanisms underlying the synergistic effect in our study may lie in the neural plasticity changes of the shared response inhibition cortices, including rIFG and pre-SMA, which were activated and shaped by the SST training and multitarget HD-tDCS. However, future studies are warranted, including the use of neuroimaging tools such as tDCS-compatible fMRI or magnetic resonance spectroscopy (MRS) to record simultaneous brain activity during the tDCS combined with SST training.
Additionally, we found that the repeated sessions of multitarget stimulation or SST training alone could improve response inhibition compared with the sham control condition, consistent with our hypothesis. The favorable effect of multitarget stimulation over sham control on response inhibition is in line with our previous study (Guo et al., 2022a). It is also similar to published studies indicating that multitarget stimulation exerted more significant effects on motor function than sham control (Dagan et al., 2018). Furthermore, this study explored the long-term effects of the multisession multitarget stimulation and found the improvement persisted for 1 month after intervention, similar to a previous study in which 10 repeated sessions of tDCS over dorsolateral prefrontal cortex (DLPFC) could improve task performance for 1 month after the intervention (Alizadehgoradel et al., 2020). This finding also showed that SST training alone improved response inhibition ability after the intervention. Not surprisingly, training is one of the crucial cognitive enhancers, and several studies have confirmed that SST training plays an important role in facilitating response inhibition (Berkman et al., 2014; Zhou and Xuan, 2022).
We found the good performance of SST was associated with high N-back accuracy at baseline, suggesting a correlation between response inhibition and WM in the mechanism. This is consistent with previous studies that at a behavioral level, response inhibition and WM are correlated (Alderson et al., 2010, 2017; Raiker et al., 2012), and at a functional level, response inhibition and WM both activate the rIFG (McNab et al., 2008). The scores of BIS-11 subscales measuring trait impulsivity showed no changes in this study, which is consistent with a previous study that revealed no variations of BIS-11 under the influence of time and intervention (training combined with either real or sham stimulation) (Gilmore et al., 2018). According to previous studies, personality traits increase in stability during puberty and remain relatively stable after that (Hayes et al., 2017). Therefore, the absence of an intervention effect is probably because the trait impulsivity assessed via BIS-11 remained relatively stable in our sample that comprised adults aged 18 years and older.
Although the 2-back accuracy δ values of the stimulation condition were higher than those of the combined protocol, this was attributed to the baseline difference between the two conditions. Since the 2-back accuracy of the combined protocol was significantly higher than the stimulation condition, it had less room for improvement (Shaw and Hosseini, 2021; Teixeira-Santos et al., 2022). Therefore, the difference was unrelated to the interventions. Overall, the transfer effects on the Stroop task, ANT, and N-back task, which measure cognitive inhibition, attentional function, and WM, respectively, showed no group differences attributable to the intervention. A previous study showed that seven daily sessions of SST training positively impacted the Stroop task performance, while the anodal stimulation on pre-SMA combined with SST training did not (Zhou and Xuan, 2022). This is partly consistent with our findings, but some discrepancy exists in that the SST training had no transfer effects in our study. This discrepancy might have arisen from the variations in the number of formal SST training trials; the SST training comprised 400 trials per session in our study, whereas the SST training consisted of 720 trials per session in the previous study. Furthermore, the total number of trials was less in our study (4000 vs. 5,040 trials). Concerning the transfer effect on attention and WM, previous studies have revealed that 10 online (i.e., tDCS concurrent with the task) sessions of tDCS + dual N-back training could produce a transfer effect to an untrained test of attention and WM at follow-up (Martin et al., 2013), or five sessions of multiple-task cognitive training with tDCS could lead to a near-transfer effect of attention gains (Boroda et al., 2020). However, no studies on online tDCS combined with response inhibition training have explored transfer effects on ANT or N-back. Therefore, they cannot be directly compared with our study. The transfer effect should be further considered and investigated.
In this study, to stimulate pre-SMA, we placed central anode at C2. A circuit was formed between the anode and cathodes, which led to current density and electric field existing between the electrodes—between the anode at C2 and the cathodes at Fz and FC4. The detailed simulation (Supplementary Figures 1–5) showed that the electric field extended through the anterior portion of Area 6 (Area 6a and 6ma) to the transition of Area 6 and Area 8 (Area i6-8 and s6-8). It cannot be excluded that parts of the motor area were stimulated as well, but fortunately this brain cortex has not been shown to be involved in the response inhibition process, which did not impact the interpretation of the findings in this study. Furthermore, there may be some confusions arising from the anode placement of pre-SMA because some previous studies placed the central anode at Fz to stimulate pre-SMA (Berglund-Barraza et al., 2020; Chiang et al., 2021). This is because there may be some ambiguity in what people are calling “pre-SMA.” We see that some places call Area 8 pre-SMA and others call the anterior Area 6 pre-SMA. Here we adopted the latter definition.
The current study has important theoretical and clinical implications. Regarding the theoretical implications, our findings support the synergistic effect of combining tDCS and concurrent cognitive training, indicating better improvement effects than the single intervention method. Moreover, we provided evidence that the combined protocol can be effectively applied in the field of response inhibition enhancement, with the long-lasting after-effects persisting for at least 1 month. Regarding the clinical implication, this study may provide insights into the treatment strategy for the clinical populations with inhibition-deficit-related mental diseases, who need to enhance response inhibition.
Despite these important implications, this study has some limitations. First, this study did not use neuroimaging method; therefore, we cannot infer the neural plasticity changes caused by the intervention. In the future, we plan to study brain functional and structural changes induced by this combined protocol. Second, the long-term after-effects were not investigated thoroughly. We only conducted a 1-month follow-up test, and further long-term effects were unknown, which should be dealt with in future studies. Third, this study focused on only young, healthy adults; therefore, it is not known how generalizable our findings are to other populations, such as the clinical sample, and the applicability of our results to other populations requires replication in other samples. Fourth, the study used a single-blinded design due to experimental constraints, possibly weakening the power of this study. Future studies should use more rigorous experimental designs to minimize potential bias, such as the Rosenthal effect. Fifth, the focality of multitarget anodal HD-tDCS in this study has to be improved. The electric field simulation result showed that the maximal electric field strength achieved underneath the anodes C2 and FT8, which we intended to stimulate pre-SMA and rIFG. However, the anode at C2 may also stimulated right motor cortex. Hence, in this study, the electric field produced by the stimulation protocol covered pre-SMA but the precision and focality were not enough, indicating the multitarget stimulation protocol needs to be improved to increase the focality of stimulation. Finally, due to the inter-individual variations of the cortical anatomy and reactivity to stimulation, the individual MRI data should be collected to improve the spatial localization accuracy and the individualized multitarget stimulation protocol for optimal effectiveness is highlighted, and this personalized application might be developed in the future.
## 5. Conclusion
The present study is the first to use multitarget stimulation combined with concurrent SST training to explore the enhanced improvement effect of response inhibition of this protocol compared to stimulation or training alone. We found that 10 daily sessions of combined interventions and the stimulation alone improved response inhibition, and the effects persisted for 1 month. The training alone only caused improved performance after the intervention. Furthermore, the combined protocol could modulate the performance of the individuals with high baseline response inhibition, which was not seen in the stimulation-alone condition. Notwithstanding the absence of transfer effects, it is too early to conclude that there is no transfer effect, and further studies are warranted. Thus, this study provides supportive evidence for the synergistic effect of the combined protocol. In addition, the long-term after-effect can persist for at least 1 month. Our findings also provide insights into the clinical application and strategy for treating response inhibition deficits.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by the Tangdu Hospital Ethics Committee, Air Force Medical University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
ZG and XZ: concept and design. ZG, RQ, HQ, and HL: acquisition, analysis, and interpretation of data. ZG: drafting of the manuscript. XZ: obtained funding. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2023.1107116/full#supplementary-material
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|
---
title: Cannabidiol treatment improves metabolic profile and decreases hypothalamic
inflammation caused by maternal obesity
authors:
- Fernanda da Silva Rodrigues
- Jeferson Jantsch
- Gabriel de Farias Fraga
- Victor Silva Dias
- Sarah Eller
- Tiago Franco De Oliveira
- Márcia Giovenardi
- Renata Padilha Guedes
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10033544
doi: 10.3389/fnut.2023.1150189
license: CC BY 4.0
---
# Cannabidiol treatment improves metabolic profile and decreases hypothalamic inflammation caused by maternal obesity
## Abstract
### Introduction
The implications of maternal overnutrition on offspring metabolic and neuroimmune development are well-known. Increasing evidence now suggests that maternal obesity and poor dietary habits during pregnancy and lactation can increase the risk of central and peripheral metabolic dysregulation in the offspring, but the mechanisms are not sufficiently established. Furthermore, despite many studies addressing preventive measures targeted at the mother, very few propose practical approaches to treat the damages when they are already installed.
### Methods
Here we investigated the potential of cannabidiol (CBD) treatment to attenuate the effects of maternal obesity induced by a cafeteria diet on hypothalamic inflammation and the peripheral metabolic profile of the offspring in Wistar rats.
### Results
We have observed that maternal obesity induced a range of metabolic imbalances in the offspring in a sex-dependant manner, with higher deposition of visceral white adipose tissue, increased plasma fasting glucose and lipopolysaccharides (LPS) levels in both sexes, but the increase in serum cholesterol and triglycerides only occurred in females, while the increase in plasma insulin and the homeostatic model assessment index (HOMA-IR) was only observed in male offspring. We also found an overexpression of the pro-inflammatory cytokines tumor necrosis factor-alpha (TNFα), interleukin (IL) 6, and interleukin (IL) 1β in the hypothalamus, a trademark of neuroinflammation. Interestingly, the expression of GFAP, a marker for astrogliosis, was reduced in the offspring of obese mothers, indicating an adaptive mechanism to in utero neuroinflammation. Treatment with 50 mg/kg CBD oil by oral gavage was able to reduce white adipose tissue and revert insulin resistance in males, reduce plasma triglycerides in females, and attenuate plasma LPS levels and overexpression of TNFα and IL6 in the hypothalamus of both sexes.
### Discussion
Together, these results indicate an intricate interplay between peripheral and central counterparts in both the pathogenicity of maternal obesity and the therapeutic effects of CBD. In this context, the impairment of internal hypothalamic circuitry caused by neuroinflammation runs in tandem with the disruptions of important metabolic processes, which can be attenuated by CBD treatment in both ends.
## 1. Introduction
Maternal malnourishment before and during pregnancy is a growing worldwide concern known to bear several implications for fetal development that lead to long-term consequences on offspring health and well-being [1, 2]. Interestingly, since the pioneering studies on the effects of the perinatal environment, such as the Developmental Origin of Health and Disease (DOHaD) theory, the context transitioned from the lack of nutrients due to hunger and starvation to excess due to the global obesity pandemic [3]. In fact, globalization and urbanization have gradually led to an increase in the consumption of “junk food” (i.e., ultra-processed, rich in fats, and sugar) associated with the reduction of physical activity, a phenomenon called “nutritional transition” [4, 5]. In this context, it is important to highlight that obesity and the consumption of mentioned “junk food” most often co-occur, making it difficult to discriminate the effects of obesity and its metabolic profile per se from those related to nutritional aspects of the foods consumed [6].
A broad range of studies has demonstrated that an abnormal inflammatory milieu during in utero development triggers the so-called “early-life programming” of the offspring metabolism [3, 7]. A number of potential pathways underlying the effects of maternal obesity include increased sustained inflammation, changes in lipid transport and storage, dysregulation of glucose metabolism, and modifications to the microbiome, which triggers increased translocation of lipopolysaccharides (LPS) to the bloodstream and circulating levels of pro-inflammatory cytokines (8–10). These inflammatory mediators are able to cross the placental barrier and create a harmful environment for developing fetal tissues [11, 12]. Other than that, the increased insulin resistance, glucose levels, and lipids, with a potentially elevated supply of nutrients to the developing fetus, contribute to setting persistent changes in the offspring’s energy balance, appetite regulation, lipid and glucose homeostasis, and gut dysbiosis. Overall, as a result, maternal obesity substantially raises the risk of offspring obesity, insulin resistance, type 2 diabetes, high blood pressure, and adverse lipid profile [7, 10, 13].
The hypothalamus is the predominant brain area that controls energy balance by integrating information from the body and initiating appropriate behavioral, humoral, and neural outputs. Current evidence indicates hypothalamic inflammation as a likely mechanism for the dysregulation of the homeostatic control of energy balance, which might lead to an increased susceptibility to metabolic alterations and obesity in the offspring [14, 15]. Abnormal insulin signaling during neurodevelopment leads to malformation of neural projections that affect hypothalamic function and plasticity, resulting in altered energy homeostasis in the offspring [16, 17]. These effects can also be attributed to enhanced activation of resident immune cells, such as astrocytes and microglia, with the consequent secretion of inflammatory cytokines, such as tumor necrosis factor-alpha (TNFα), interleukin (IL)-6, and IL-1β [18].
The extent to which changes in the offspring’s habits and resolutive approaches throughout life can modify the effects of perinatal maternal obesity is yet not known, but a few anti-inflammatory approaches seem to exert positive effects. The endocannabinoid system has been extensively studied in the context of obesity and inflammation, showing a close relationship with energy metabolism and the feeding circuitry [19]. Cannabidiol (CBD) is a non-psychotropic terpenophenol isolated from *Cannabis sativa* with anti-inflammatory and antioxidant effects discussed to be beneficial for diverse immunological states [20]. It has been suggested that the hydroxyl groups of the phenol ring in CBD structure interfere with free radical chain reactions, which confers CBD its antioxidant activity [20, 21]. Furthermore, the modulation of endocannabinoid signaling via downregulation of CB1 receptor activity and upregulation of CB2 receptor activity results in reduced reactive oxygen species (ROS) production and reduced pro-inflammatory signaling [22, 23].
Besides the endocannabinoid receptors CB1 and CB2, CBD is also known to interact with other systems and receptors relevant to metabolic homeostasis, such as the G protein-coupled receptor 55 (GPR55), Transient Receptor Potential Vanilloid (TRVP) channel and nuclear peroxisome proliferator-activated receptors (PPARs) (24–27). This broad spectrum of communication among systems translates into modulatory roles in diverse metabolic aspects throughout the entire body, from lipid metabolism and storage in the liver to mitochondrial activity and energy expenditure in the adipose tissue [28, 29]. Furthermore, CBD was also shown to interact with glucose metabolism by improving glucose tolerance [30, 31], the brain-gut axis by mitigating microbiome dysbiosis [30, 32, 33], and hypothalamic anorexigenic neuromodulators [34]. In this sense, beyond directly improving a range of physiological aspects related to obesity, modulation of the endocannabinoid system seems to also be effective on tempering eating behaviors, such as high-fat and high-sucrose food intake [35], hyperphagia [36], sucrose self-administration [37] and binge eating [38], which sets the stage for it as a potential intervention on maternal obesity-related metabolic dysfunctions.
Here, we investigated the effects of CBD treatment on maternal obesity-induced hypothalamic inflammation and metabolic outcomes on the early-adulthood of the offspring.
## 2.1. Animals
Eighteen female Wistar rats (3-weeks-old) were obtained from the animal facility of the Federal University of Health Sciences of Porto Alegre (UFCSPA). The animals were group-housed (3 animals per cage) under standard laboratory conditions at a controlled temperature (23 ± 1°C) and 12-h light:dark cycle. This study was approved by UFCSPA Institutional Animal Care and Use Committee under protocol N° $\frac{751}{21.}$ All experiments were designed and performed to minimize the number and suffering of subjects, following the international laws that regulate the care of laboratory animals.
## 2.2. Experimental groups and diet
Three-week-old female breeders ($$n = 9$$) were placed on either control diet (CT) composed by standard chow (Nuvilab® CR-1 Nuvital®, Curitiba, PR, Brazil) (3.4 kcal/g, $63\%$ carbohydrates, $26\%$ protein, and $11\%$ fat) or a Cafeteria Diet (CAF) composed by standard chow plus bacon mortadella (Perdigão®), strawberry wafers (Isabela®), chocolate cookies (Isabela®), pizza-flavored crackers (Parati®), white chocolate (Harald®), sausage (Alibem®), and orange-flavored soda (Sukita®) (4.3 kcal/g, $43\%$ carbohydrates, $14\%$ protein, and $43\%$ fat) with water ad libitum. CAF group was fed with three menus with different combinations among the foods mentioned interchanged every 2 days, to maintain novelty and stimulate consumption. CAF was chosen as an obesogenic diet since it mimics the Western dietary habits in a more translational manner than regular high-fat and/or high-sugar diets, once CAF provides the variety of textures, options and palatability that contribute to hedonic eating and are not present on manufactured chows [39, 40]. The diets were maintained for 12 weeks prior to and during mating with a 3-months-old male, throughout gestation, lactation, and until weaning. Dam weight and the weight of consumed diets were recorded weekly. Day of parturition was considered postnatal day zero (PND0).
To reduce the impact of litter effects, litters were adjusted to seven to nine pups per dam with an equal proportion of males to females when possible. All offspring were weaned at PND21, placed on standard chow and weighed weekly. Litters were divided equally among treatment groups and by sex, which created four groups per sex: CT mother + Vehicle (CT-Veh), CT mother + Cannabidiol (CT-CBD), CAF mother + Vehicle (CAF-Veh), and CAF mother + Cannabidiol (CAF-CBD).
Treatment started at the same day of weaning (PND21) and consisted of CBD oil diluted in corn oil for a dose of 50 mg/kg (Prati-Donaduzzi®, Toledo, PR, Brazil) or corn oil (vehicle), both in a volume of 1 mL/kg by oral gavage. The treatment was administered 7 days a week for 3 weeks (Figure 1). Treatment dose and duration were chosen based on previous studies on different cognitive-assessment models with oral administration of CBD (41–45). Furthermore, we have performed a pilot study assessing doses of 2,5 mg/kg, 10 mg/kg, and 50 mg/kg, to which 50 mg/kg showed most significant positive results (data not shown).
**FIGURE 1:** *Experimental design. CT, control chow-fed dam; CAF, cafeteria diet-fed dam; Veh, offspring treated with vehicle (corn oil); CBD, offspring treated with cannabidiol (50 mg/kg); PND, post-natal day.*
## 2.3. Tissue processing
At the end of the 3 weeks of treatment, on PND42, animals were euthanized by decapitation. The gonadal visceral adipose tissue was weighed, truncal blood was centrifuged, and plasma was separated. The brain was dissected immediately and all tissues were snap-frozen in liquid nitrogen and stored in −80°C for further processing and analysis.
## 2.4. Biochemical analysis
Fasting plasma levels of glucose, total cholesterol and triglycerides were quantified using enzymatic colorimetric kits (Labtest, Lagoa Santa, Brazil). Insulin levels in the plasma were determined by enzyme-linked immunosorbent assay (ELISA) (Insulin ELISA kit, Cat# RAB0904; Sigma-Aldrich, St. Louis, MO, USA). Subsequently, the homeostatic model assessment (HOMA-IR) index was calculated to determine insulin resistance through the following formula: glucose (mg/dL) × insulin (uU/mL)/22.5.
## 2.5. LPS quantification
Plasma (150 μL) was hydrolyzed with 75 μL of NaCl 150 mM and 300 μL of HCl 8M and then incubated for 4 h at 90°C. Afterward, 3 mL of hexane were added, and samples were centrifuged at 3,500 rpm for 10 min. The upper organic phase was withdrawn, and the residue was reconstituted in 50 μL of methanol, transferred to a vial, and an aliquot of 3 μL was injected into the analytical system. A Nexera-i LC-2040C Plus system coupled to a LCMS-8045 triple quadrupole mass spectrometer (Shimadzu, Kyoto, Japan) was used for the analysis.
## 2.6. RT-qPCR
Total RNA was isolated from the hypothalamus using TRIzol® (Invitrogen, Brazil) according to the manufacturer’s instructions. The quantification of total RNA was done by spectrometry BioSpec-nano® (Shimazu, Kioto, Japan) at 260 and 280 nm. For cDNA synthesis, 1,000 ng of RNA were reverse transcribed according to the manufacturer’s instructions (GoScript Reverse Transcription Kit, Promega, Brazil). To conduct real time quantitative polymerase chain reaction (RT-qPCR), cDNA was added to a reaction mix (10 μL final volume) containing 100 nM gene-specific primers and universal SYBR green supermix (Applied Biosystems, Thermo Fisher Scientific CA, USA). All samples were run in duplicate and were analyzed on an QuantStudio Real-Time PCR instrument (Applied Biosystems, Thermo Fisher Scientific CA, USA) for quantitative monitoring of PCR product formation. *Relative* gene expression was normalized to β-Actin controls and assessed using the 2-ΔΔCT method. Primer sequences are as follows: β-Actin: F: TATGCCAACACAGTGCTGTCTGG; β-Actin: R: TACTCCTGCTTGCTGATCCACAT; Iba1: F: GCAAG GATTTGCAGGGAGGA; Iba1: R: CGTCTTGAAGGCCTCCAG TT; GFAP: F: CGAAGAAAACCGCATCACCA; GFAP: R: CC GCATCTCCACCGTCTTTA; TNFα: F: TGGCGTGTTCATCCG TTCTCTACC; TNFα: R: CCCGCAATCCAGGCCACTACTT; IL6: F: GACCAAGACCATCCAACTCATC; IL6: R: GCTTAG GCATAGCACACTAGG; IL1β: F: TGAGGCTGACAGACCCCAA AAGAT; IL1β: R: GCTCCACGGGCAAGACATAGGTAG.
## 2.7. Data analysis and statistics
Data were analyzed using Graphpad Prism 9 statistical software (GraphPad Software, San Diego, CA, USA). Two-way ANOVA with a Bonferroni post hoc analysis was performed within sexes. The main effects were: maternal diet and CBD treatment. The interaction between these two factors was also analyzed. The results were expressed as the mean ± standard error of the mean (SEM). Outliers were removed using the ROUT test, and statistically significant differences were considered at $p \leq 0.05.$
## 3.1. Cafeteria diet induces obesity in female Wistar rats after 9 weeks of diet
The dams from both CT and CAF groups were weighed every week throughout the experiment to assess the impact of the diets on weight gain. Repeated measures two-way ANOVA has shown a significant diet effect (F1,16 = 8.705; $$p \leq 0.0094$$). From the 9th week of diet, CAF-fed female Wistar rats presented significantly higher body weight than the CT group ($$p \leq 0.0349$$), which persisted until mating in the 12th week ($$p \leq 0.0062$$). Despite no differences in body weight being shown during most of gestational and lactational time, except for the 15th week ($$p \leq 0.0298$$), the difference became significant again right after weaning of the offspring on the 19th week ($$p \leq 0.0012$$) (Figure 2).
**FIGURE 2:** *Dams’ body weight throughout the experiment. Cafeteria diet-fed (CAF) dams presented significantly higher body weight when compared to control diet (CT) from the 9th week of diet, which was consistent until mating (12th week of diet) and after weaning of the offspring (19th week of diet). Data are presented as mean ± SEM. n = 9/group. *p < 0.05 **p < 0.01.*
## 3.2. Cafeteria-induced maternal obesity increases visceral white adipose tissue deposits even though it does not affect offspring total body weight
The offspring was weighed weekly from weaning (PND21) to euthanasia (PND42) to determine weight gain in early-life and throughout treatment and visceral white adipose tissue (WAT) was weighed at euthanasia. No groups showed any differences in weight gain related to neither maternal diet nor CBD treatment (Figure 3A), however, both male and female offspring of CAF-fed dams, presented an increase in WAT (maternal diet effect: F1,32 = 17.02; $$p \leq 0.0002$$ and F1,31 = 24.92; $p \leq 0.0001$ respectively). Indeed, untreated male offspring of obese dams (CAF-Veh) showed heavier visceral WAT when compared to the offspring of control dams (CT-Veh) ($$p \leq 0.0005$$). Also, both female CAF-Veh and CAF-CBD had more visceral fat than CT ones ($$p \leq 0.0008$$ and $$p \leq 0.0084$$). However, CBD treatment was able to reduce the deposition of visceral fat on male CAF-CBD when compared to CAF-Veh ($$p \leq 0.0256$$) (Figure 3B).
**FIGURE 3:** *Offspring’s body weight and visceral white adipose tissue (WAT). Neither male nor female offspring showed influences of maternal diet or cannabidiol (CBD) treatment in weight at weaning (PND21) and the following 3 weeks of treatment (A). Maternal diet increased visceral fat deposit in both male and female cafeteria diet (CAF)-Veh offspring with CBD effect only in males (B). Data are presented as mean ± SEM. n = 8–10/group. *p < 0.05 **p < 0.01 ***p < 0.001.*
These data suggest a complex energy-balance disruption on the offspring of obese mothers, with an increase in the accumulation of visceral fat while maintaining total body weight. Also, CBD treatment seems to exert a positive effect in a sex-dependent manner.
## 3.3. Female offspring lipid profile is more affected by CAF-induced maternal obesity with partial effects of cannabidiol treatment
Plasma cholesterol and triglyceride levels were assessed in order to evaluate the biochemical profile of the offspring. On female offspring, there was a maternal diet effect (F1,31 = 11.29; $$p \leq 0.0023$$) and an interaction between diet and CBD treatment (F1,31 = 6.027; $$p \leq 0.0208$$) on total cholesterol levels. CAF-CBD had higher levels of plasma cholesterol than CT-CBD ($$P \leq 0.0008$$) (Figure 4A). Regarding triglycerides, there was a maternal diet effect (F1,32 = 4.997; $$p \leq 0.0325$$). CAF-Veh presented higher levels of triglycerides when compared to CT-Veh ($$P \leq 0.0166$$), while CAF-CBD showed lower levels when compared to CAF-Veh ($$P \leq 0.0395$$) (Figure 4B). There were no significant differences among male groups.
**FIGURE 4:** *Offspring’s plasma levels of total cholesterol and triglycerides. Female cafeteria diet (CAF)-cannabidiol (CBD) presented higher levels of plasma cholesterol than control diet (CT)-CBD (A). Maternal diet increased triglyceride levels in females, but CBD treatment was able to revert this effect (B). No differences were seen in males. Data are presented as mean ± SEM. n = 8–10/group. *p < 0.05 ***p < 0.001.*
Together, these data suggest that CAF-induced maternal obesity affects lipid metabolism in the offspring in a sex dependent manner, with apparently more severe effects in females. However, even though CBD treatment did not exert any effects on total cholesterol, it was able to revert the increased triglyceride levels in females.
## 3.4. Cannabidiol treatment reverts insulin resistance caused by maternal obesity in male offspring
Plasma levels of fasting glucose and insulin were evaluated, and the HOMA-IR was determined in order to assess glucose metabolism and insulin resistance in the offspring. In male offspring we found a maternal diet effect on glucose levels (F1,33 = 10.60; $$p \leq 0.0026$$). CAF-Veh had higher fasting glucose than CT-Veh ($$p \leq 0.0072$$), with no effect of CBD (Figure 5A). On insulin, there was an interaction between maternal diet and CBD treatment (F1,34 = 4.916; $$p \leq 0.0334$$). CAF-Veh showed higher insulin levels than CT-Veh ($$p \leq 0.0458$$), while CAF-CBD had lower insulin levels than CAF-Veh ($$p \leq 0.0354$$) (Figure 5B). Consequently, on the HOMA-IR there was an interaction between maternal diet and CBD treatment (F1,32 = 7.674; $$p \leq 0.0093$$). CAF-Veh showed an increased HOMA-IR when compared to CT-Veh ($$p \leq 0.0068$$), while CAF-CBD had a lower index than CAF-Veh ($$p \leq 0.0029$$) (Figure 5C).
**FIGURE 5:** *Plasma levels of glucose and insulin and calculated homeostatic model assessment (HOMA-IR) index of the offspring. Maternal obesity increased fasting glucose levels in both male and female offspring with no cannabidiol (CBD) effect (A). Maternal obesity increased levels of plasma insulin in males, but CBD treatment was able to revert this damage (B). Male cafeteria (CAF)-vehicle (Veh) presented an elevated HOMA-IR index, which was alleviated by CBD treatment (C). Data are presented as mean ± SEM. n = 8–10/group. *p < 0.05 ***p < 0.01.*
In female offspring, fasting glucose levels showed a maternal diet effect (F1,30 = 15.30; $$p \leq 0.0005$$). Both female CAF-Veh and CAF-CBD showed higher levels of plasma glucose when compared to their CT ($$p \leq 0.0377$$ and $$p \leq 0.0099$$) (Figure 5A). However, we did not find differences regarding insulin levels (Figure 5B) and HOMA-IR (Figure 5C) in female offspring.
These findings suggest that CAF-induced maternal obesity affects glucose metabolism and promotes insulin resistance in the offspring in a sex-dependent manner. Opposed to what was observed in lipid metabolism, glucose disturbances appear to be more severe in males. On the other hand, CBD treatment was able to reduce plasma insulin in male offspring of obese dams to control levels, which led to an improved HOMA-IR in this group as well.
## 3.5. Cannabidiol treatment reverts LPS-induced endotoxemia caused by maternal obesity
Plasma levels of LPS were measured to evaluate metabolic endotoxemia. In male offspring, we found a maternal diet effect (F1,28 = 7.215; $$p \leq 0.0120$$). Male CAF-Veh showed a higher concentration of LPS than CT-Veh ($$p \leq 0.0148$$), while CAF-CBD had lower levels when compared to CAF-Veh ($$p \leq 0.0470$$) (Figure 6). In female offspring, there were maternal diet (F1,28 = 32.46; $p \leq 0.0001$) and treatment (F1,28 = 15.81; $$p \leq 0.0004$$) effects and an interaction between both (F1,28 = 14.88; $$p \leq 0.0006$$). Female CAF-Veh presented higher levels of plasma LPS than CT-Veh ($P \leq 0.0001$), while CAF-CBD had lower levels than CAF-Veh ($p \leq 0.0001$) (Figure 6). Thus, CBD treatment seems to be effective to reduce plasma levels of LPS in the offspring of obese dams.
**FIGURE 6:** *Plasma levels of lipopolysaccharides (LPS) of the offspring. Maternal obesity increased circulating LPS in both males and females, but cannabidiol (CBD) treatment was able to decrease its levels. Data are presented as mean ± SEM. n = 8–10/group. *p < 0.05 ****p < 0.0001.*
## 3.6. Cannabidiol treatment rescues hypothalamic neuroinflammation resulted from maternal obesity
Real time quantitative polymerase chain reaction (RT-qPCR) was conducted to evaluate gene expression of TNFα, IL6, IL1β, GFAP, and IBA-1 in the hypothalamus.
In male offspring, the gene expression of TNFα showed a maternal diet (F1,27 = 4.939; $$p \leq 0.0348$$) and CBD treatment (F1,27 = 6.035; $$p \leq 0.0207$$) effects, and also an interaction between both factors (F1,27 = 16.34; $$p \leq 0.0004$$). Male CAF-Veh showed higher levels of TNFα mRNA than CT-Veh ($$p \leq 0.0002$$), while CAF-CBD had lower levels than CAF-Veh ($$p \leq 0.0001$$) (Figure 7A). Regarding IL6 gene expression, there was an interaction between maternal diet and CBD treatment (F1,26 = 20.20; $$p \leq 0.0001$$). Male CAF-Veh showed higher levels of IL6 mRNA than CT-Veh ($$p \leq 0.0054$$), while CAF-CBD had lower levels than CAF-Veh ($$p \leq 0.0003$$) (Figure 7B). Regarding IL1β expression, there was a maternal diet effect (F1,26 = 4.919; $$p \leq 0.0355$$), with no differences among groups on the post-hoc test (Figure 7C). Regarding GFAP expression, there were maternal diet (F1,27 = 6.816; $$p \leq 0.0146$$) and CBD treatment (F1,27 = 7.402; $$p \leq 0.0113$$) effects. CAF-Veh presented much lower levels of GFAP mRNA than CT-Veh ($$p \leq 0.0055$$), while CT-CBD had lower levels than CT-Veh as well ($$p \leq 0.0045$$) (Figure 7D). There were no differences in IBA-1 expression (Figure 7E).
**FIGURE 7:** *Relative gene expression of tumor necrosis factor-alpha (TNFα), interleukin 6 (IL6), interleukin 1β (IL1β), glial fibrillary acidic protein (GFAP), and ionized calcium-binding adapter molecule 1 (IBA1) in the hypothalamus of the offspring. Maternal obesity increased the expression of TNFα (A) and IL6 (B) in both males and females, with a positive effect of cannabidiol (CBD) treatment. Maternal obesity increased the expression of IL1β only in females, with no effect of CBD (C). Both maternal obesity and CBD treatment decreased GFAP expression in males and females when compared to control diet (CT) (D). No difference was found in IBA1 expression (E). Data are presented as mean ± SEM. n = 6–8/group. *p < 0.05 **p < 0.01 ***p < 0.001.*
In female offspring, there was an interaction between maternal diet and CBD treatment (F1,28 = 4.250; $$p \leq 0.0486$$) regarding the gene expression of TNFα. Female CAF-Veh presented higher levels of TNFα mRNA than CT-Veh ($$p \leq 0.0156$$), while CAF-CBD had lower levels than CAF-Veh ($$p \leq 0.0351$$) (Figure 7A). Regarding IL6 expression, there was a maternal diet effect (F1,28 = 5.637; $$p \leq 0.0247$$), a treatment effect (F1,28 = 6.014; $$p \leq 0.0207$$) and an interaction (F1,28 = 6.057; $$p \leq 0.0203$$). CAF-Veh presented higher levels of IL6 mRNA than CT-Veh ($$p \leq 0.0039$$), while CAF-CBD had lower levels than CAF-Veh ($$p \leq 0.0034$$) (Figure 7B). There was a maternal diet effect (F1,25 = 15.62; $$p \leq 0.0006$$) on IL1β gene expression. Both CAF-Veh and CAF-CBD showed higher levels of IL1β mRNA than their CT ($$p \leq 0.0150$$ and $$p \leq 0.0255$$), with no effect of CBD treatment (Figure 7C). Regarding GFAP expression, there was an interaction between maternal diet and CBD treatment (F1,27 = 8.541; $$p \leq 0.0069$$). CAF-Veh showed lower levels of GFAP mRNA than CT-Veh ($$p \leq 0.0123$$), while CT-CBD had lower levels than CT-Veh as well ($$p \leq 0.0068$$) (Figure 7D). No differences were found in IBA-1 expression among groups (Figure 7E).
These findings indicate that maternal obesity leads to hypothalamic inflammation in the offspring. Nonetheless, treatment with CBD reduced the gene expression of the proinflammatory cytokines.
## 4. Discussion
Genetic and epidemiological studies provide evidence supporting the contribution of a transgenerational background of parental obesity to the development of obesity itself and further metabolic risks in the offspring (13, 46–48). Other than understanding the underlying mechanisms through which parental obesity takes its toll on the offspring’s health, an increasing body of research has been raising resolutive approaches. However, most of them rely on preventive measures targeted at the pre-conception and/or gestational period [49]. Here, to address the problem once the damage is already set, we investigated the effects of CBD treatment on the offspring as a way to attenuate the negative outcomes of maternal obesity.
An increasing number of studies have addressed the pleiotropic role of the endocannabinoid system on metabolic regulation at the central and peripheral levels. Endocannabinoid regulation of metabolism is extremely relevant to the central nervous system (CNS), especially in the hypothalamus where it plays a pivotal role on energy balance and feeding behaviors, contributing not only for the pathogenicity of obesity but also the development of eating disorders (34, 36–38, 50, 51). Nonetheless its receptors are also expressed in peripheral organs such as the adipose tissue, liver, skeletal muscle, pancreas, kidney, and gastrointestinal tract [52], hence its particularly promising modulation in the context of obesity and metabolic disorders [53, 54]. Here, we show that CBD treatment is able to revert a number of metabolic dysfunctions and neuroinflammation arising from maternal consumption of CAF during pregnancy and lactation, including higher visceral adiposity, insulin resistance, endotoxemia, and overexpression of inflammatory markers in the hypothalamus of the offspring.
Besides obesity itself, the consumption of a diet rich in saturated fats and carbohydrates is also associated with the development of metainflammation, a chronic and self-sustained state of low-grade inflammation [55]. In this study, we demonstrated that both male and female offspring of obese mothers had higher visceral WAT deposits and fasting glucose levels, followed by elevated plasma cholesterol and triglycerides levels in females and insulin in males. Previous studies with different models of maternal obesity have established that both the gestation and the lactation-suckling periods are critical for WAT development, impacting epigenetic regulation of key genes for energy metabolism–such as dopamine and opioid genes related to food behavior [56] and hypothalamic nutrient sensors [46]–and altering long-term adiposity set points [57, 58]. In a model of maternal high-fat diet (HFD), the offspring of obese mothers showed an increased expression and activity of stearoyl-CoA desaturase-1 (SCD1), a key enzyme of fatty acid (FA) metabolism. SCD1 converts saturated FAs, such as palmitate and stearate, to monounsaturated FAs, the predominant substrates for triglyceride synthesis [59]. It is important to highlight that CAF, which closely mimics the Western urban eating patterns, is not only high in sugar but also saturated fats, with palmitate being the most predominant FA, hence the prominent impact of this dietary pattern on lipid profile [60, 61].
Interestingly, no effect of maternal obesity was found on total body weight, even though WAT deposition was altered. This finding could be due to a diminished muscle mass that may have compensated for the heavier adiposity. In previous studies, both 3- and 12-weeks old pups from CAF-fed mothers showed equal or lower body weight and lean mass but greater fat accumulation than controls, which has been described as the thin-outside-fat-inside phenotype [62, 63]. Also, since in our study the offspring were fed a normal diet after weaning, we were able to show that the metabolic impairments observed were independent of the offspring’s own diet. However, in previous studies, when offspring of CAF-fed dams were given CAF after weaning, there was no increase in body weight at puberty (4 weeks of life), but animals had higher weight at adulthood (16 weeks of life), and no difference in visceral adiposity was reported in male offspring. Thus, differences in body composition seem to be dependent on the post-weaning diet as well as the sex of the offspring [64, 65].
Cannabidiol (CBD) treatment was able to mitigate most of the metabolic dysfunctions caused by maternal obesity, reducing visceral fat content and IR in males, plasma triglyceride levels in females, and plasma LPS in both sexes. CB1 receptor activation is generally considered a powerful orexigenic signal; thus, the endocannabinoid system’s inhibition is beneficial for treating obesity and related metabolic diseases. Since CBD is an allosteric modulator of CB1 receptors, inhibiting its activation by endogenous ligands or exogenous agonists might trace the pathway through which CBD attenuates peripheral disturbances arising from maternal obesity [66]. CB1-KO mice maintained on a normocaloric, standard diet have been shown to have a decreased body weight gain over time, which was associated with increased energy expenditure and elevated β[3]-adrenergic receptor and uncoupling protein-1 (UCP1) mRNA levels in the brown adipose tissue, suggestive of enhanced peripheral sympathetic activation and thermogenesis [66].
Diets high in saturated FAs, such as the Western diet, increase the uptake and storage of sphingolipids and their essential fractions, such as ceramide, sphingosine, sphinganine, and sphingomyelin. Interestingly, previous data suggest that phytocannabinoids and other agonists of CB1 or CB2 receptors can modulate sphingolipid concentrations in specific organs under the increased availability of FAs in the diet. CBD significantly lowered the concentration of sphingolipids in the adipose tissue [67], the skeletal muscle [31], and the brain by increasing catabolism, inhibiting salvage and/or de novo synthesis, which restores the tissue’s insulin sensitivity and, therefore, attenuates IR [68]. Other than that, other mechanisms are proposed for the beneficial effects of CBD on metabolic disorders in peripheral organs, such as the protective effect of CBD on adipose-derived stem cells against endoplasmic reticulum stress and its complications related to IR and diabetes [69] and attenuation of oxidative stress and inflammatory response, associated with an improved n-6/n-3 polyunsaturated fatty acids (PUFAs) ratio in the white and red skeletal muscle [70], indicating a narrow relationship between the endocannabinoid system and hormonal and energetic balance.
It is worth noting that the sexual dimorphisms observed in this study regarding glucose metabolism and IR corroborate with what has been demonstrated in different models of obesity and maternal obesity [71]. Female sex hormones play a fundamental role in dimorphic insulin signaling since estrogens increase insulin sensitivity in metabolic tissues and upregulate insulin transcription in pancreatic beta cells as well as GLUT4 synthesis in adipose tissue and muscle [72]. Female mice fed an HFD showed reduced susceptibility to developing obesity-induced IR and WAT inflammation when compared to HFD-fed males. Meanwhile, HFD-fed males treated with estradiol presented the same protective effect as females, indicating that the dimorphic effects of obesity on IR may be due to estrogen-mediated reductions in WAT inflammation [73]. Furthermore, a recent study has shown that an androgen-driven gut microbiome may also be responsible for the increased susceptibility to IR in males since gut microbiome depletion abolishes sex-biased glucose metabolism in HFD-fed mice [74].
A growing body of evidence suggests that males are more sensitive to intrauterine hyperglycemia as well; hence both animal and human studies show the same pattern of higher risk for obesity and IR in male offspring of obese/diabetic mothers. In a model of maternal high-sucrose diet (HSD), female HSD offspring were shown to be more glucose intolerant, while male counterparts were more insulin resistant [75]. Furthermore, human cohort and prospective studies have shown a strong correlation between offspring metabolic impairments and maternal diabetes for males but not for females (76–78). This relationship may be explained by the fact that, during preimplantation, the male embryo is believed to have a greater ability to adapt to the adverse environment and, as a result, has a higher sensitivity to programming influences [79].
In addition, our results showed that CBD effectively reverted the increase in plasma LPS levels in both male and female CAF offspring. Obesogenic diets change the gut microbiota composition by altering the Firmicutes: Bacteroidetes ratio, the two most detected bacterial phyla in rodents as well as humans. In normal-weight animals this relation is characterized by a high ratio of Bacteroidetes to Firmicutes, while the opposite is found in obese counterparts [80, 81]. It has been proposed that *Firmicutes bacteria* are more effective in extracting energy from food than Bacteroidetes, thus promoting a more efficient absorption of calories and boosting weight gain [82, 83]. This imbalance resulting from obesogenic diets can be traced back to the overabundance of refined sugars and fats as well as the low intake of vegetables, fruits and dietary fibers (84–87). The simultaneous collapse of the gut barrier with increased permeability allows high levels of LPS, Gram negative bacteria’s most potent immunogenic component, to reach the bloodstream and initiate a diffuse inflammatory process named endotoxemia [88]. The structural components of LPS are recognized by B cells via cluster of differentiation 14 (CD14) and toll-like receptor 4 (TLR4), thus leading to nuclear factor kappa-B (NFkB) activation and the release of pro-inflammatory cytokines, such as TNFα and IL1β [89]. During pregnancy, these pro-inflammatory mediators, together with LPS itself, can interact with the placenta and cause a range of disturbances, including immature blood vessels, hypoxia, increased inflammation, autophagy, and altered stress markers [90, 91]. In that sense, several models of maternal immune activation rely on prenatal exposure to LPS, resulting in a myriad of altered physiological and neurological outcomes in the offspring [92, 93]. The data seen here indicates that the endotoxemia caused by obesogenic diets affects not only the pregnant mother but can be seen in the offspring later on, independently of the offspring’s own diet. These findings corroborate with previous studies demonstrating that maternal obesity during gestation and/or lactation negatively impacts the offspring’s gut microbiota [94]. On the other hand, CBD has rescued this damage on the offspring, lowering plasma LPS to control levels. Even though we have not performed gut-specific analysis, previous studies in different pre-clinical and clinical models lead us to infer that the effects observed may be due to the influence of CBD on gut microbiota composition (95–97) and/or a protective effect on maintaining gut barrier integrity (98–102).
Regarding the alterations provoked by maternal overnutrition on CNS neuroinflammation, here we show that the treatment with CBD was able to rescue hypothalamic inflammation by reducing gene expression of TNFα and IL6 in the offspring of obese dams. The hypothalamus is one of the main homeostatic centers of the CNS and, therefore, needs to be effectively responsive to fluctuations in peripheral systems. However, due to its naturally increased permeability in order to better receive and respond to signals coming from metabolic organs, the hypothalamus is also one of the first brain regions to suffer with systemic disruptions, resulting in neuroinflammation [103, 104]. In the present study, we have demonstrated molecular alterations that are trademarks of neuroinflammation in the hypothalamus of the offspring born from CAF-fed dams. In both sexes, the expressions of TNFα and IL6 were increased in CAF-Veh animals, while the expression of IL1β was increased only in females. These findings corroborate with previous data from a different model of maternal obesity that demonstrates that mice born from mothers fed a HFD diet have increased expression of these inflammatory markers in the hypothalamus compared to the offspring of lean parents [105].
Tumor necrosis factor-alpha (TNFα), IL6, and IL1β are well-known pro-inflammatory cytokines involved in microglial and astrocytic activation in the entire nervous tissue. However, especially in the hypothalamus, they have a remarkable role in the modulation of hypothalamic feeding circuits. It has been previously demonstrated that HFD and high-carbohydrate diets stimulate orexigenic neuropeptide Y/agouti-related peptide (NPY/AgRP) neurons to produce advanced glycation end products, which activate TNFα, enhancing microglia reactivity. This scenario results in the dysfunction of anorexigenic neurons, altering the appetite-regulatory circuits [106]. In addition, Proopiomelanocortin (POMC) neurons, which present anorexigenic activities, also suffer a significant impact from maternal obesity [107]. The melanocortin system plays an important role on the regulation of appetite, energy expenditure, and metabolism, therefore, impairments in the POMC and melanocortin 4 receptor (MC4R) pre- and post-translational processing are forerunners for the development of obesity [108, 109]. Decreased activity in POMC cells has been shown to be associated with increased food intake and obesity [107] and has been demonstrated in the offspring of obese mothers (110–113). When observing the precise localization of NPY and POMC in the hypothalamus of the offspring of obese mothers, Ornellas and collaborators found that NPY nerve fibers from the ARC to the periventricular nucleus and around the third ventricle were increased, while POMC were diminished in the same areas [105]. Variations in the reactivity and/or distribution of hypothalamic astrocytes also seem to affect synaptic organization and POMC responsiveness to glucose, which is associated with energy and metabolic imbalances [114, 115].
Although endocannabinoid signaling has been implicated in the modulation of both food intake and energy expenditure, a complete understanding of its role in the hypothalamus is still lacking. A recent study demonstrated that a HFD diet in CB1 receptor-deficient mice contributes to the offspring’s nutritional programming, resulting in increased susceptibility to metabolic challenges both perinatally and during adulthood [116]. Additionally, maternal HFD has been shown to upregulate CB1 hypothalamic expression in the offspring, which was associated with leptin pathway impairment and increased susceptibility to obesity (117–119). Other than that, few studies have evaluated cannabinoid modulation in the context of parental obesity, however, the findings shown here are still in line with different models that show the anti-inflammatory effects of CBD on other neuroinflammatory conditions (95, 120–122). Elevated hypothalamic endocannabinoid content has been associated with higher orexigenic signaling of ghrelin (123–125) and defective leptin signaling, observed in genetic models of obesity such as obese Zucker rats and db/db and ob/ob mice [126, 127]. These findings suggest that endocannabinoid mediators contribute to hyperphagia and obesity, which also supports the restorative effects of CBD treatment, once it reduces endocannabinoid signaling, especially through CB1 receptors [128]. When it comes to inflammation, effects of CBD via CB2 receptor are more distinguished, since this receptor is more predominantly expressed on immune cells, including glial cells. CB2 expression is upregulated in microglia stimulated with pro-inflammatory cytokines, indicating a significant role of CB2 in the regulation of neuroinflammatory states [129]. In line with this, CBD has been shown to exert a CB2-dependant anti-inflammatory effect on microglial inflammation [23, 130].
Astrogliosis is a very well-established marker for obesity-related neuroinflammation [16, 131]. Variations in the reactivity and/or distribution of hypothalamic astrocytes seem to affect synaptic organization and responsiveness to peripheral fluctuations, which is associated with energy and metabolic imbalances [114, 115]. In animal models of obesity, gene and protein expression of the glial fibrillary acidic protein (GFAP), an astrocyte marker, are commonly higher in obese groups when compared to control animals (131–134). Interestingly, we have found that GFAP gene expression was reduced in the hypothalamus of the offspring of obese mothers. This result may have been induced by an adaptative reprogramming mechanism in response to the exposure to a harmful intrauterine environment during neurodevelopment, indicating that molecular mechanisms that rule maternal obesity-induced neuroinflammation may differ from the ones associated with obesity in the individual itself (135–137). Reduction in astrocyte expression can be deleterious during neurodevelopment since these cells play a pivotal role in synapse maturation, and their reduced expression is related to a range of neurological disorders (138–140). We have observed the same reduction of GFAP expression in CBD-treated CT offspring, however, we cannot affirm that the same detrimental effect applies. The reduction in GFAP expression of CAF offspring is a response to a prenatal immune challenge, while the reduction seen in CT-CBD is more likely to be the result of the anti-inflammatory activity of CBD [141, 142].
Regarding microglial activation, unlike previous studies [143, 144], we have not found any differences in the gene expression of ionized calcium-binding adapter molecule 1 (IBA1) in the hypothalamus of the offspring of obese mothers. However, the expression of IBA1 is related to the proliferation and distribution of microglial cells and not the polarization toward a pro-inflammatory state [145]. Furthermore, it has recently been described that prenatal immune stress blunts microglia reactivity throughout life [146], which means that the expression levels of microglial cells may remain at control levels, but their innate reactivity to immune stressors can be defective.
*These* gene expression patterns are consistent with impaired energy and metabolic regulation in the hypothalamus, which might have originated the peripheral deficits observed in the offspring of obese mothers. Together, these results indicate an intricate interplay between peripheral and central counterparts in both the pathogenicity of maternal obesity and the modulation of the endocannabinoid system by CBD. In this context, the impairment of internal hypothalamic circuitry caused by neuroinflammation runs in tandem with the disruptions of important metabolic processes, which can be attenuated by CBD treatment in both ends.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The animal study was reviewed and approved by Comissão de Ética no Uso de Animais–Universidade Federal de Ciências da Saúde de Porto Alegre.
## Author contributions
FR, MG, and RG: conceptualization. FR, JJ, GF, VD, SE, and TD: in vivo experimental procedures and ex vivo tissue and sample analysis. FR, JJ, and RG: statistical analysis. MG and RG: critical revision of the manuscript. All authors have writing, read, edited, and approved the final version of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
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|
---
title: Association between preeclampsia in daughters and risk of cardiovascular disease
in parents
authors:
- Frederikke Lihme
- Saima Basit
- Lucca Katrine Sciera
- Anne-Marie Nyboe Andersen
- Henning Bundgaard
- Jan Wohlfahrt
- Heather A. Boyd
journal: European Journal of Epidemiology
year: 2023
pmcid: PMC10033554
doi: 10.1007/s10654-023-00972-y
license: CC BY 4.0
---
# Association between preeclampsia in daughters and risk of cardiovascular disease in parents
## Body
Women with a history of preeclampsia have greatly increased risks of cardiovascular disease (CVD) compared with women with no such history [1–7]. The links between preeclampsia and CVD are incompletely understood; the increased CVD risk observed post-preeclampsia could be due to behavioral risk factors or heritable predispositions common to both diseases, sequelae directly attributable to preeclampsia, or exacerbation of an existing predisposition to CVD by the additional cardiovascular system stress of preeclampsia [6].
Consistent with the suggestion that preeclampsia and CVD have overlapping features and vulnerabilities [6], we hypothesized that preeclampsia and CVD share common heritable mechanisms. Genetic predisposition to CVD before pregnancy might increase the risk of endothelial dysfunction, poor placentation, and organ dysfunction during pregnancy, contributing to preeclampsia risk [3, 8, 9]. Pregnancy could therefore represent a unique opportunity to identify women susceptible to vascular dysfunction and assess CVD risk in both the woman herself and her family. Conversely, CVD in parents might help to inform preeclampsia risk assessment in daughters.
We conducted a register-based cohort study to investigate whether preeclampsia and CVD co-aggregate in families. By examining the association between preeclampsia in daughters and CVD in parents, and considering the impact of multiple affected daughters, timing of preeclampsia onset, and timing of CVD in parents, we explored whether the diseases might share underlying risk factors and if so, whether common determinants of disease are most likely to be heritable, behavioral or both.
## Abstract
Preeclampsia and cardiovascular disease (CVD) might share heritable underlying mechanisms. We investigated whether preeclampsia in daughters is associated with CVD in parents. In a register-based cohort study, we used Cox regression to compare rates of CVD (ischemic heart disease, ischemic stroke, myocardial infarction) in parents with ≥ 1 daughters who had preeclampsia and parents whose daughters did not have preeclampsia in Denmark, 1978–2018. Our cohort included 1,299,310 parents, of whom 87,251 had ≥ 1 daughters with preeclampsia and 272,936 developed CVD during 20,252,351 years of follow-up (incidence rate $\frac{135}{10}$,000 person-years). Parents with one daughter who had preeclampsia were 1.19 times as likely as parents of daughters without preeclampsia to develop CVD at age < 55 years (hazard ratio [HR] 1.19, $95\%$ confidence interval [CI] 1.13–1.25). Having ≥ 2 daughters who had preeclampsia yielded an HR of 1.88 ($95\%$ CI 1.39–2.53). The corresponding HRs for CVD at ≥ 55 years of age were 1.13 ($95\%$ CI 1.12–1.15) and 1.27 ($95\%$ CI 1.16–1.38). Patterns of association were similar for all CVD subtypes. Effect magnitudes did not differ for mothers and fathers ($$p \leq 0.52$$). Analyses by timing of preeclampsia onset in daughters suggested a tendency toward stronger associations with earlier preeclampsia onset, particularly in parents < 55 years. Preeclampsia in daughters was associated with increased risks of CVD in parents. Increasing strength of association with increasing number of affected daughters, equally strong associations for mothers and fathers, and stronger associations for CVD occurring before age 55 years suggest that preeclampsia and CVD share common heritable mechanisms.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s10654-023-00972-y.
## Study cohort
Using the Danish Civil Registration System, the National Patient Register and the Medical Birth Register (see eMethods, Data Sources, in the Supplement) [10–12], we identified all women in Denmark ≥ 15 years of age with one or more pregnancy of > 20 weeks’ duration between 1978 and 2017. We then identified each woman’s parents using the Danish Family Relations Database (see eMethods, Data Sources, in the Supplement); the parents constituted the study cohort. Parents who experienced one of the study outcomes before the first eligible pregnancy in a daughter in the study period were excluded (Fig. 1). The study was approved by Statens Serum Institut’s Compliance Department and registered with the Danish Data Protection Agency; under Danish law, neither informed consent nor ethics committee approval is required for strictly register-based studies.
Fig. 1Study cohort assembly and exclusions
## Number of daughters with preeclampsia (exposure)
The exposure of interest was the number of daughters with preeclampsia complicating one or more pregnancies. A daughter was considered to have preeclampsia in a given pregnancy if she was registered in the National Patient Register with preeclampsia, eclampsia, or the hemolysis, elevated liver enzymes and low platelets (HELLP) syndrome (see eMethods, Definitions, in the Supplement). We further classified preeclampsia according to gestational age at delivery (a proxy for timing of onset) into early preterm (delivery < 34 completed weeks’ gestation), late preterm (delivery between 34 and 36 completed weeks’ gestation) and term (delivery ≥ 37 completed weeks’ gestation) preeclampsia; hereafter, when we refer to timing of preeclampsia onset, we refer to this proxy classification. When we examined the importance of timing of preeclampsia onset in daughters, we used a variable combining the number of daughters with preeclampsia and the timing of preeclampsia onset in each daughter (see eMethods, Definitions, in the Supplement).
We considered number of daughters with preeclampsia as a time-dependent variable. A parent could contribute person-time to several exposure groups, changing exposure status from unexposed to exposed the first time a daughter had preeclampsia and changing exposure category with each additional daughter who experienced a pregnancy with preeclampsia. Once exposed, a parent could not become unexposed again.
## Cardiovascular disease in parents (outcome)
A parent was considered to have had CVD on the date of his/her first registration in the National Patient Register or Causes of Death Register with myocardial infarction, cerebrovascular infarction (ischemic stroke) or ischemic heart disease (not including myocardial infarction) (see eMethods, Definitions, in the Supplement). Parents contributed to the analyses with their earliest registered CVD, if any. If at first registration of CVD more than one type of event was registered and one of the events was a myocardial infarction, the parent was classified as having had a myocardial infarction. If no myocardial infarction was registered at first registration of CVD but both an ischemic stroke and ischemic heart disease were registered, one of the two events was selected at random. We further classified CVD as early-onset (< 55 years of age) and later-onset (≥ 55 years of age), because our evaluation of the proportional hazards assumption suggested that it might not be met for parental age. Although the American College of Cardiology defines premature heart disease as heart disease in women < 65 years of age and men < 55 years of age [13], we chose to use the 55-year cutoff for both sexes because residual plots (see Statistical analysis) suggested that 55 years would be a good cutoff for both groups and to simplify the comparison of estimates for fathers and mothers.
## Covariates
We considered parental birth year and sex (i.e., whether the parent was a mother or a father), number of daughters contributing pregnancies, parental diabetes (see eMethods, Definitions, in the Supplement) and total number of children to be potential confounders; parental sex and number of daughters contributing pregnancies were also evaluated as potential effect modifiers. We assumed that total number of children was a time-independent variable (i.e., that parents had had all their children by the start of follow-up) but treated number of daughters contributing with pregnancies as a time-dependent variable, because this number could increase during the follow-up period. We included parental birth year to help account for possible time trends in preeclampsia and CVD diagnoses. We did not adjust for hypertension in parents or daughters, as doing so would block the very link between preeclampsia in daughters and CVD in parents that we aimed to study, namely any association attributable to a heritable mechanism common to preeclampsia and CVD. ( See the simplified acyclic directed graph and associated explanation in the eMethods in the Supplement for further clarification.)
## Statistical analysis
Parents were followed from the date of delivery in the first eligible pregnancy in a daughter to the first of the following events (competing risks analysis [14], see eMethods in the Supplement): [1] myocardial infarction; [2] ischemic stroke; [3] ischemic heart disease; [4] death due to non-cardiovascular causes; [5] emigration; [6] registration as “missing” in the Civil Registration System; or [7] 31 August 2018 (end of follow-up). Follow-up ended when a parent experienced one of the above events, regardless of whether they experienced an event of a different type later on. We used a cause-specific hazards model (Cox regression) with parental age as the underlying time scale to estimate hazard ratios comparing rates of CVD in parents with daughters with a history of preeclampsia and parents whose daughters had been pregnant but had no history of preeclampsia. The baseline hazards were stratified by parental birth year (five-year intervals), parental sex, number of daughters with pregnancies in the study period, and total number of children. We used Wald chi-squared tests across all exposure levels to determine whether hazard ratio magnitudes for the individual CVD types differed from one another. In a sensitivity analysis, we further adjusted the estimates for parental diabetes as a time-dependent variable.
Potential violations of the proportional hazards assumption were checked by plotting cumulative Martingale residuals [15]. We found that the assumption might not be met for parental age and that age 55 years was a good cutoff to use for stratification. Therefore, with the exception of results stratified by timing of preeclampsia onset (where power was an issue), we present results stratified by parental age (< 55 years and ≥ 55 years).
All analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC).
## Results
The cohort consisted of 1,299,310 persons (690,194 mothers and 609,116 fathers) who had one or more daughters with eligible pregnancies between 1978 and 2017 and who had not been registered with CVD before the start of follow-up (Fig. 1). Table 1 presents basic characteristics of the cohort at the start of follow-up. More mothers than fathers were < 50 years of age at the start of follow-up, whereas more fathers were ≥ 60 years of age. Almost half of all parents ($46\%$) had two children; $13\%$ had only one child, while $14\%$ had four or more. The majority of parents ($56\%$) had one daughter, $34\%$ had two daughters, and only $10\%$ had three or more daughters. ( However, not all daughters became pregnant during the study period and therefore not all contributed to the analysis). During follow-up, 87,251 parents had one or more daughters with preeclampsia.
Table 1Characteristics of parents of daughters with one or more pregnancies of at least 20 weeks’ duration in Denmark between 1978 and 2017CharacteristicNumber (%)Mothers ($$n = 690$$,194)Fathers ($$n = 609$$,116)Total ($$n = 1$$,299,310)Age at the start of follow-up (years)< 4019,000 (2.8)5,423 (0.9)24,423 (1.9)40–4479,965 (11.6)39,013 (6.4)118,978 (9.2)45–49152,927 (22.2)103,545 (17.0)25,647 (19.7)50–54183,061 (26.5)156,568 (25.7)339,629 (26.1)55–59140,808 (20.4)148,454 (24.7)289,262 (22.3)60–6475,103 (10.9)92,549 (15.2)167,652 (12.9)65–6929,016 (4.2)42,585 (7.0)71,601 (5.5)≥ 7010,314 (1.5)20,979 (3.4)31,293 (2.4)Number of daughters at the start of follow-up†1391,928 (56.8)336,766 (55.3)728,694 (56.1)2230,923 (33.5)206,351 (33.9)437,274 (33.7)355,245 (8.0)53,200 (8.7)108,445 (8.4)≥ 412,098 (1.8)12,799 (2.1)24,897 (1.9)Total number of children at the start of follow-up192,279 (13.8)79,183 (13.0)171,462 (13.2)2321,041 (46.5)273,233 (44.9)594,274 (45.7)3187,670 (27.2)167,445 (27.5)355,115 (27.3)≥ 489,204 (12.9)89,255 (14.7)178,459 (13.7)Diabetes at the start of follow-upYes9,877 (1.4)12,920 [2,1]22,797 (1.8)No680,317 (98.6)596,196 (97.9)1,276,513 (98.2)† Including daughters not contributing with pregnancies We followed the cohort for 20,252,351 person-years, during which 4,823 parents were lost to follow-up (128 were lost to the Civil Registration System and 4,695 emigrated). Median follow-up time was 16.0 years (interquartile range: 8.5–24.3 years) for mothers and 13.1 years (interquartile range: 6.7–20.7 years) for fathers. Overall, 272,936 parents were registered with CVD during follow-up (incidence rate $\frac{135}{10}$,000 person-years). Among mothers with CVD ($$n = 113$$,645), 28,079 ($24\%$) had a myocardial infarction as their first event (incidence rate $\frac{24.3}{10}$,000 person-years), 28,499 ($25\%$) had an ischemic stroke (incidence rate $\frac{24.6}{10}$,000 person-years), and 57,067 ($50\%$) had ischemic heart disease (incidence rate $\frac{49.3}{10}$,000 person-years). The corresponding numbers for fathers ($$n = 159$$,291) were 54,879 ($34\%$), 33,986 ($21\%$), and 70,426 ($44\%$) (incidence rates 63.3, 39.2 and 81.2 per 10,000 person-years), respectively.
Having one daughter with a history of preeclampsia was associated with a $19\%$ increase in the rate of any parental CVD at < 55 years of age, compared with having one daughter whose pregnancy history did not include preeclampsia (hazard ratio [HR] 1.19, $95\%$ confidence interval [CI] 1.13–1.25) (Fig. 2, eTable 1). Having two or more daughters with a history of preeclampsia was more strongly associated with parental CVD in this age group (HR 1.88, $95\%$ CI 1.39–2.53) (Fig. 2, eTable 1). The corresponding hazard ratios for any CVD in parents ≥ 55 years of age were 1.13 ($95\%$ CI 1.12–1.15) and 1.27 ($95\%$ CI 1.16–1.38), respectively. The number of daughters contributing to the analyses (i.e., the number of daughters a parent had who were pregnant during the study period) did not modify the strength of the associations between number of daughters with a history of preeclampsia and parental CVD (eTable 2, P for interaction = 0.12).
Fig. 2Hazard ratios for cardiovascular disease in parents by parental age, number of daughters with preeclampsia and type of cardiovascular disease. Hazard ratios for cardiovascular disease in parents at < 55 years of age with 1 affected daughter (red) and ≥2 affected daughters (green) and at ≥55 years of age with 1 affected daughter (blue) and ≥2 affected daughters (black), compared with parents with daughters with no history of preeclampsia (reference group). Hazard ratios were estimated in a cohort of parents with daughters having ≥1 pregnancies of at least 20 weeks’ duration in Denmark in the period of 1978–2017. All hazard ratios were estimated with the baseline hazards stratified by parental birth year (five-year intervals), parental sex, number of daughters with pregnancies in the study period, and total number of children. Age was the underlying time scale in the Cox model.
When we examined CVD types separately, the pattern of increasing strength of association with increasing number of daughters with preeclampsia appeared most pronounced for events occurring at < 55 years of age (Fig. 2, eTable 1). Hazard ratios for myocardial infarction at < 55 years increased from 1.24 ($95\%$ CI 1.14–1.35) for parents with one affected daughter to 1.73 ($95\%$ CI 1.00-2.99) for parents with two or more affected daughters, compared with parents whose daughters had no history of preeclampsia. The corresponding estimates for myocardial infarctions at ≥ 55 years of age were 1.17 ($95\%$ CI 1.13–1.20) and 1.45 ($95\%$ CI 1.24–1.70), respectively. A similar pattern was observed for ischemic stroke and ischemic heart disease (Fig. 2, eTable 1), although hazard ratios for ischemic stroke in parents > 55 years of age did not differ by number of affected daughters. Testing for differences in pattern across the three CVD types showed no differences ($$P \leq 0.80$$) by event type; however, estimates for the associations with preeclampsia in two or more daughters were based on relatively small numbers of parental events (Fig. 2, eTable 1).
Separate analyses by parental sex showed that associations for mothers and fathers did not differ from one another for any outcome (Fig. 3, eTables 3 A and B, all p-values ≥0.05). However, in mothers, the HRs for any CVD at < 55 years and at ≥ 55 years differed from one another ($$P \leq 0.02$$), whereas in fathers the estimates for the two age groups were similar ($$P \leq 0.44$$).
Fig. 3Hazard ratios for any cardiovascular disease in parents by parental sex, age and number of daughters. Hazard ratios for cardiovascular disease in parents at < 55 years of age with 1 affected daughter (red) and ≥2 affected daughters (green) and at ≥55 years of age with 1 affected daughter (blue) and ≥2 affected daughters (black), compared with parents with daughters with no history of preeclampsia (reference group). Hazard ratios were estimated in a cohort of parents with daughters having ≥1 pregnancies of at least 20 weeks’ duration in Denmark in the period of 1978–2017. All hazard ratios were estimated with the baseline hazards stratified by parental birth year (five-year intervals), number of daughters with pregnancies in the study period, and total number of children. Age was the underlying time scale in the Cox model.
When we examined gestational age at preeclampsia onset in daughters, the results suggested that preterm preeclampsia, particularly in multiple daughters, might be most strongly associated with CVD in parents (Table 2). However, we often lacked power to detect differences across exposure groups and strata; in particular, there were few families with two or more daughters with early-onset preeclampsia and CVD among parents. Consequently, confidence intervals around most hazard ratios were very wide.
Table 2Hazard ratios for combined cardiovascular disease in parents of daughters with one or more pregnancies of at least 20 weeks’ duration in Denmark between 1978 and 2017, by parental age, number of daughters with a history of preeclampsia and timing of preeclampsia onset in these daughtersTiming of preeclampsia onset†Number of daughters with preeclampsiaCombined cardiovascular disease‡Parental age < 55 yearsParental age ≥ 55 yearsNo. of eventsNo. of person-years (x103)HR§$95\%$ CINo. of eventsNo. of person-years (x103)HR§$95\%$ CINo preeclampsia021,2163,741.01Ref214,52414,123.01RefEarly preterm (< 34 weeks)113114.51.461.23–1.741,16668.61.161.10–1.232< 50.31.800.67–4.81522.91.220.93–1.61Late preterm (34–36 weeks)117523.01.281.10–1.481,8121071.161.10–1.2121,0000.62.731.47–5.07844.11.361.10–1.69Term11,1741751.151.09–1.2212,8617481.131.11–1.152262.31.641.12–2.4228815.51.221.09–1.37Any preeclampsia≥ 3< 50.052.700.38–19.3170.71.400.87–2.26CI, confidence interval. HR, hazard ratio† The exposure variable is a single categorical variable combining the timing of preeclampsia and the number of affected daughters. Because the exposure variable was time-dependent, parents could contribute person-time to more than one exposure category. Because we did not have enough power to consider every possible combination separately, we combined some categories. Two daughters with preeclampsia: If at least one daughter had early preterm preeclampsia, the parent was classified as exposed to early preterm preeclampsia. If no daughter had early preterm preeclampsia and at least one daughter had late preterm preeclampsia, the parent was classified as exposed to late preterm preeclampsia. Three or more affected daughters: all preeclampsia combined, regardless of timing of onset‡ Myocardial infarction, ICD-8 code 410 or ICD-10 codes I21-I23; ischemic stroke, ICD-8 codes 433.09, 433.99, 436.01, 436.09, 436.90 or 436.99 or ICD-10 code I63; ischemic heart disease, ICD-8 codes 411–414 or ICD-10 codes I20, I24 or I25§ All hazard ratios were estimated with the baseline hazards stratified by parental birth year (five-year intervals), parental sex, number of daughters with pregnancies in the study period, and total number of children Additional adjustment for parental diabetes affected hazard ratio magnitudes very little (eTable 4). The strength of the observed associations did not depend on daughters’ parity (P for interaction = 0.59, eTable 5). In a sensitivity analysis restricted to the first daughter contributing a pregnancy in the study period, adjustment for the daughter’s age at the start of follow-up changed the results very little (eTable 6). Finally, when we repeated the analyses using generalized estimating equations to account for potential correlation of CVD outcomes within families (to avoid the possibility that the observed associations were driven by the co-aggregation of preeclampsia and CVD within a few large families), HRs and CIs were identical (out to the fourth decimal place) to those obtained in our main analyses (data not shown).
## Discussion
In a cohort of almost 1.3 million parents, preeclampsia in daughters was associated with an increased parental risk of CVD, especially for CVD occurring before age 55 years. The associations were equally strong for mothers and fathers. The strength of the associations increased markedly with an increasing number of affected daughters for all events except ischemic stroke at ≥ 55 years of age, suggesting a possible dose-response relationship. However, there were too few ischemic strokes at ≥ 55 years of age among persons whose daughters had had preeclampsia to allow us to conclude that the pattern of association differed for different CVD types. The association with preeclampsia in two or more daughters appeared to be particularly strong for parental CVD occurring before 55 years of age. Our results also suggested that preeclampsia in daughters with onset at earlier gestational ages might be most strongly associated with parental CVD.
Many studies have linked preeclampsia with increased risk of later CVD [2, 4, 5, 16, 17]. Less is known about potential familial predisposition to both conditions [18]. Of previous studies investigating family history of CVD as a risk factor for preeclampsia, two found no association [19, 20], whereas five reported odds ratios between 1.58 and 3.65 [21–25]. The studies ascertained family history of CVD (often in any relative and at any age) via interview either during pregnancy or retrospectively, once the outcome of pregnancy was known, which might have introduced significant recall bias. No study has previously investigated offspring preeclampsia as a risk factor for CVD in parents.
Previous studies were not designed to determine whether preeclampsia and CVD are linked by shared predispositions or preeclampsia causes de novo cardiovascular damage that results in CVD [2, 9, 18, 26, 27]. Our study results favor the former, as preeclampsia in a daughter cannot cause CVD in a parent. The finding of equal strength of association for mothers and fathers supports the hypothesis that preeclampsia is linked with CVD through common heritable factors. Stronger associations for mothers than for fathers could have been explained by the known, strong familial aggregation of preeclampsia in the female line (the paternal contribution is decidedly smaller [28]) coupled with mothers’ increased risk of CVD after their own preeclampsia. Our findings that parental risk of CVD increased with the number of daughters with preeclampsia, associations with multiple affected daughters were stronger for early CVD in parents than for later events, and associations might be stronger for early-onset preeclampsia in daughters, all further support a role for common heritable factors in explaining the associations. CVD later in life and late-onset preeclampsia share common risk factors with a behavioral component, including overweight, the metabolic syndrome, and diabetes, which might suggest that such factors could drive the observed associations if the conditions also clustered strongly in the same families. However, early CVD and preeclampsia with onset < 37 weeks are both much less strongly associated with the above-mentioned conditions and other behavioral risk factors.
While our findings do not exclude contributions from shared behavioral factors, they indicate a role for heritable genetic mechanisms common to preeclampsia and CVD. Genome-wide association studies of early-onset preeclampsia have identified genetic variants shared by the two conditions [8], suggesting that joint susceptibility to preeclampsia and CVD might be mediated by inherited predisposition to hypertension, inflammation, endothelial dysfunction or other abnormalities resulting in vascular damage, rather than predisposition to more behaviorally contingent factors such as overweight and diabetes [29–31].
The inclusion of the entire Danish population in the cohort minimized the risk of selection bias, and we addressed previous studies’ potential problems with recall bias by using prospectively collected data on both preeclampsia and CVD and restricting to outcomes in parents. The large study population and long follow-up period provided us with excellent power to examine outcomes temporally far removed from the exposure, examine associations with individual ischemic outcomes, and categorize exposure and outcome by timing of onset, which has not previously been done.
Registration of myocardial infarctions and ischemic strokes in the National Patient *Register is* virtually complete and validation of registered diagnoses against medical records found positive predictive values exceeding $92\%$ and $97\%$, respectively [11, 32]. Registration of ischemic heart disease, which can initially be diagnosed by general practitioners (who do not report to the National Patient Register), is probably less complete or at least delayed, but registered diagnoses have not been validated. Registration of preeclampsia is incomplete, with a sensitivity of $69.3\%$ overall [33]. However, the specificity exceeds $99\%$ for all preeclampsia subtypes, indicating that registered diagnoses overwhelmingly reflect true instances of preeclampsia. Due to the excellent specificity of registered preeclampsia and CVD diagnoses, the effect of any bias due to misclassification on our results was likely negligible.
We could not adjust for certain comorbidities that can aggregate in families and are associated with both preeclampsia and CVD, including the metabolic syndrome, overweight, and maternal preeclampsia. Registration of metabolic syndrome diagnoses only began recently; BMI is only registered in connection with pregnancy and then only since 2003. Registration of pregnancy complications in the National Patient Register began in 1978; consequently, only a minority of mothers in the study cohort had their own pregnancy experience registered and a sensitivity analysis restricted to this small group of mothers would not have had meaningful statistical power. The finding of a similar pattern of association in fathers, however, indicates that clustering of preeclampsia in the maternal line cannot explain the familial co-aggregation of preeclampsia and CVD.
We also lacked the information to allow us to adjust for potential confounding by shared familial socioeconomic and lifestyle factors. How and to what extent such factors might have been shared trans-generationally in our study population is difficult to assess, making it challenging to determine how any residual confounding by these factors might have affected the observed results. Confounding by shared socioeconomic and lifestyle factors would be expected to be independent of parental age, whereas we observed stronger associations in parents at younger ages (< 55 years) than in older parents. This suggests that the associations in younger parents are more likely to be chiefly related to genetic factors than to be the product of residual confounding by socioeconomic and lifestyle factors. However, the associations observed in older parents may well be partially explained by such factors. While the results of this study have no immediate implications for clinical practice, they suggest potential modifications to risk assessment practices for both preeclampsia and CVD. Future research should determine whether knowledge of preeclampsia history in daughters could help identify persons at increased risk of CVD, potentially by testing the effect of adding this information to existing algorithms [34, 35] for assessing CVD risk. Conversely, new research could also investigate whether knowledge of early-onset CVD in a woman’s parents might improve prediction of preeclampsia risk, which is especially difficult in nulliparous women.
Having one or more daughters with a history of preeclampsia was associated with increases in the risk of CVD in parents. Associations were particularly pronounced for parental CVD occurring before 55 years of age. Our findings suggest that preeclampsia and CVD share common heritable mechanisms.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1
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|
---
title: Improvement in binge eating and alexithymia predicts weight loss at 9-month
follow-up of the lifestyle modification program
authors:
- Chiara Conti
- Maria Di Nardo
- Roberta Lanzara
- Maria Teresa Guagnano
- Valentina Cardi
- Piero Porcelli
journal: Eating and Weight Disorders
year: 2023
pmcid: PMC10033561
doi: 10.1007/s40519-023-01560-5
license: CC BY 4.0
---
# Improvement in binge eating and alexithymia predicts weight loss at 9-month follow-up of the lifestyle modification program
## Abstract
### Purpose
The aim of this longitudinal study was to examine the extent to which improved levels of binge eating (BE) behaviors, alexithymia, self-esteem, and psychological distress would predict a reduction in body mass at 9-month follow-up, following a lifestyle modification program for weight loss in obese or overweight patients.
### Methods
A convenience sample of 120 obese or overweight patients were recruited. Body mass index (BMI), binge eating (BES), levels of alexithymia (TAS-20), perceived stress (PSS), depressive symptoms (SDS), and self-esteem (RSE) were assessed during their first medical examination (T1), and after a weight-loss treatment period of 9 months (T2).
### Results
Compared with unimproved patients, improved patients reported a significant decrease in binge eating ($$p \leq 0.04$$) and perceived stress symptoms ($$p \leq 0.03$$), and a significant improvement in self-esteem ($$p \leq 0.02$$) over time. After controlling for gender, self-esteem, depressive symptoms, and perceived stress, baseline BMI (OR = 1.11, $95\%$ CI [1.04,1.19]), ΔBES (OR = 0.99, $95\%$ CI [0.98,0.99]), and ΔTAS-20 (OR = 1.03, $95\%$ CI [1.01,1.05]) significantly and independently predicted a ≥ $5\%$ reduction in body mass from baseline.
### Conclusions
Our finding supports the suggestion to consider psychological outcomes such as emotional aspects and dysfunctional eating behaviors when planning a weight loss programs to prevent a negative outcome.
Level of evidence: Level III, case–control analytic study.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s40519-023-01560-5.
## Introduction
Obesity is a public health disease with widespread medical, psychological, and social implications [1]. Given the obesity epidemic worldwide, significant interventions have been developed for its management. Lifestyle modification, including dieting and exercise, represents one of the core recommended interventions to achieve weight loss and target morbid obesity, overweight and related comorbidities [2]. However, up to $86\%$ of the patients undergoing lifestyle modification do not achieve clinically significant weight loss [3] that is, as indicated by the National Heart, Lung, and Blood Institute [4], losses of 5–$10\%$ of initial weight. This can result in weight-regain, relapse from weight loss and its associated comorbidities, and a decrease in quality of life [5]. Thus, identifying predictors of long-term successful weight control is especially important to guide treatment development and identify patients who need increased support or an alternative approach for weight loss [6].
Clinical and sub-clinical eating behaviors, such as binge eating (BE), are usual in subjects seeking weight loss interventions and have been proposed as important mechanisms involved in weight-related changes [7]. Binging behavior, defined by the incorporation of large amounts of food that is accompanied by a sense of loss of control and overeating [8], has been successfully defined in scientific literature. Specifically, BE rates in overweight and obese individuals are at least twice as high as in normal-weight subjects [9], and BE occurs in about $30\%$ of overweight or obese subjects seeking weight loss treatment [10]. In addition, more than $65\%$ of subjects with Binge Eating Disorder (BED) are obese [11], and more than $25\%$ of individuals seeking treatment for obesity fulfill the criteria for BED consistent with DSM-5 [12]. The high prevalence of BE in overweight and obese subjects is alarming, and it might clarify some of the reduced advantages from weight loss treatment, including greater attrition, more subjective barriers, less weight loss, and greater weight regain [12]. Furthermore, weight-related difficulties are associated with psychological problems including psychological distress, depression, and poor self-esteem [13]. Self-esteem and psychological distress typically are included in studies examining the outcomes of weight-related difficulties interventions as it is unclear how they are differentially associated with the outcomes of these interventions [14]. On the other hand, when weight loss programs work, then they might have a positive impact on psychological functioning. For example, in a meta-analysis of psychological outcomes of weight loss interventions [15], authors noted a consistent and significant decrease in depressive symptoms and an improvement in self-esteem following the intervention. However, there is insufficient evidence to assess the impact of weight loss programs on BE at intervention-end [14]. This highlights the importance of assessing multiple psychological processes when evaluating treatment outcomes, both in terms of treatment mediators and in terms of concurrent primary outcomes [16].
Alexithymia is a multifaceted personality dimension widely observed in patients with obesity, especially those with comorbid BE [17, 18]. It is composed of two higher‐order factors: a deficit of affect awareness (as difficulty identifying feelings [DIF] and difficulty describing feelings [DDF]) and operatory thinking (externally‐oriented thinking [EOT] and poor imaginal processes) [19]. These features are assumed to reflect the cognitive deficits in processing and regulation of emotions related to anxiety and depression, and to influence health-related behaviors and symptom onset [20]. High alexithymia has been identified among the risk factors for the onset of several psychological and physical health issues (for a review, see [19]), including reduced weight management, sedentary lifestyle [21], and disordered eating behavior [22]. It has been hypothesized that the alexithymic difficulty in the cognitive processing of emotions induces an amplification of somatic sensations associated with emotional arousal [23]. This could explain the tendency to regulate tension through uncontrolled behaviors, such as BE [24]. Furthermore, individuals with obesity who show higher alexithymic difficulties in identifying and describing their feeling states, are less likely to complete the treatment program and obtain benefits from it. Alexithymia significantly predicts attrition, and unsuccessful weight-loss in obese outpatients [25] and predicts poor treatment outcomes following group cognitive behavioral therapy for overweight and obese patients [26].
This longitudinal study examined the extent to which BE, alexithymia, self-esteem, and psychological distress predicted reduction of body mass (≥ $5\%$ reduction in body mass from baseline) at 9-month follow-up, following a lifestyle modification program for weight loss in obese or overweight patients. The aim of the present study was twofold: (a) to investigate whether BE, alexithymia, distress, and self-esteem would differ between patients not reaching the weight loss threshold (unimproved group) and patients reaching a reduction of ≥ $5\%$ in body mass compared to baseline (improved group); and (b) to explore whether and to what extent changes over time in BE and alexithymia would be associated with treatment outcome (weight loss). Based on previous findings, it was hypothesized that: (a) unimproved patients would exhibit higher levels of BE, alexithymia, and psychological distress, and lower self-esteem than improved patients both at baseline and follow-up; and (b) changes in BE and alexithymia levels over the course of treatment would predict treatment outcome (weight loss) at 9-month follow-up.
## Participants and procedure
A sample of 120 treatment-seeking obese and overweight outpatients were enrolled at the Clinical Nutrition Centre of the University Clinical Hospital of Chieti (Italy), consecutively selected from referrals to a dietary control program for any medical reason. Data were collected from April 2017 to December 2018. Patients were evaluated during their first medical examination (T1), and after a weight-loss treatment period of 9 months (T2). All the participants were involved in a non-surgical weight loss program, which was aimed at dietary change, weight control, balanced daily food intake, paced eating, and healthy lifestyle (see Weight loss program section).
To maximize ecological validity, patients aged 18 to 65 and with body mass index (BMI) ≥ 25 were included. Participants were selected for inclusion only if they were enrolled in a weight loss program which did not include the use of drugs. Documented current or past diagnosis of schizophrenia or other psychotic disorders, cognitive impairment, pregnancy, severe medical comorbidity (e.g., thyroid dysfunction, diabetes, chronic liver disease, and any other physical diseases which could interfere with eating behavior), or inability to perform/understand the self‐rating scales were considered exclusion criteria. Patients were evaluated for medical history and past or current psychopathology by a team of expert physicians and psychologists.
## Weight loss program
Standard lifestyle recommendations were provided in written format during weekly 20–30-min individual sessions. Each session was led by a physician and a nutritionist and focused on a discussion around the implementation of a healthy lifestyle and included weight and metabolic monitoring. In addition, patients met a nutritionist once every 3 months until the weight loss goal was achieved. Patients were encouraged to reduce their weight (healthy low-calorie, low-fat diet) and increase their physical activity (moderate-intensity activity, such as walking for at least 150 min per week) following the Food Guide Pyramid [27] and National Cholesterol Education Program guidelines [28].
## Sociodemographic and clinical characteristics
An ad hoc semi-structured questionnaire was used to collect information on sociodemographic characteristics, such as age, gender, marital status, and socioeconomic status (SES). The relationship between educational attainment and job position was used to determine SES [29]. Patient medical records were used to calculated BMI and collect information on the weight history (i.e., years from the first weight-loss treatment). The cutoff of BMI ≥ 25 was used to determine overweight, analysing the ratio of weight in kilograms to the square of height in meters (kg/m2).
## Binge-eating behavior
The severity of BE behavior was measured using the 16‐item Binge Eating Scale (BES) [30]. The BES was originally developed to assess affective, cognitive, and behavioral aspects of BE symptoms in patients with obesity. Scores range from 0 to 46, with a score of ≥ 27 have conventionally served as threshold for severe BE, ≥ 18 for moderate BE, and ≤ 17 for minimal or no BE. Within this sample, Cronbach’s α was 0.86 at T1 and 0.89 at T2.
## Alexithymia
Levels of alexithymia were measured using the 20-item Toronto Alexithymia Scale (TAS-20) [31]. Each item is scored on a 5-point Likert scale ranging from 1 (= strongly disagree) to 5 (= strongly agree). Scores range from 20 to 100 with a score of ≥ 61 used as the threshold for high alexithymic traits. In addition to the total score, the TAS-20 yields scores for three-factor scales: [1] DIF, a measure of the difficulty to discriminate between feelings and bodily sensations of emotional arousal; [2] DDF, a measure of the difficulty to describe feelings to other people; and 3) EOT, a measure of the tendency to focus on concrete and factual details of external reality and to avoid emotional nuances of emotional life. Within this sample, Cronbach’s α was 0.74 at T1 and0.73 at T2 for the total scale.
## Self-esteem
Self-esteem was measured using the 10-item Rosenberg Self-Esteem Scale (RSE) [32]. Each item is scored on a 4-point Likert scale ranging from 0 (= strongly disagree) to 3 (= strongly agree). Scores range from 0 to 30 with higher scores indicating a stronger sense of self-esteem. Within this sample, Cronbach’s α was 0.81 at T1 and 0.83 at T2.
## Depressive symptoms
The Zung Self-Rating Depression Scale (SDS) [33] is a 20-item self-report scale that is used widely to evaluate the severity of psychological and somatic depressive symptoms. The scale was developed based on factor-analytic studies of major depressive disorder as defined in the DSM series and includes all the current DSM-5-TR [8] criteria. Each item is scored on a 5-point Likert scale ranging from 1 (= none or a little of the time) to 4 (= all or almost all the time). Scores range from 20 to 80 with higher scores indicating more severe depressive symptoms. SDS scores are classified as normal (< 50), mild depression (50–59), moderate-to-marked major depression (60–69), and severe-to-extreme major depression (> 70). Within this sample, Cronbach’s α was 0.81 at T1 and 0.80 at T2.
## Perceived stress
The Perceived Stress Scale (PSS) [34] is a 14-item self-report scale that measures stress from a psychological perspective. Each item is scored on a 5-point Likert scale ranging from 0 (= never) to 4 (= very often). Scores range from 0 to 56 with higher scores indicating a higher level of perceived stress. Within this sample, Cronbach’s α was 0.78 at T1 and 0.84 at T2.
## Weight outcome measure
Although treatment goals for obesity are numerous, weight loss is the most important. The National Heart, Lung, and Blood Institute [4] panel of experts has indicated the goal of weight loss therapy is to reduce body mass by 5–$10\%$ of baseline. While this weight may still be in the overweight or obese range, this modest weight loss can decrease the risk factors for chronic diseases related to obesity [35]. In the present study, the cut-point of $5\%$ of weight loss was used as a cut-point to categorize patients into improved and unimproved outcome groups. Weight data were obtained during the first medical examination (baseline, T1) and after a weight-loss treatment period of 9 months (follow-up, T2).
Non-surgical weight loss is generally reported as a percentage of the initial weight, with a metric called percentage total weight loss (%TWL), expressed as the proportion of change from pre- (T1) to post-treatment (T2) and calculated as follows:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\% {\text{TWL}} = \left[{\left({{\text{initial weight}} - {\text{current weight}}} \right)/\left({\text{initial weight}} \right)} \right] \times {1}00.$$\end{document}%TWL=initial weight-current weight/initial weight×100.
## Statistical analyses
Data analysis was performed using SPSS 26.0 for Windows. Descriptive statistics were reported in terms of mean and standard deviation [Mean (SD)] or absolute frequencies. The level of significance was set at $95\%$. Alpha for all tests was set at 0.05, with all p values being adjusted for multiple comparisons with the false discovery rate method, using the Benjamini–Hochberg procedure [36].
A three-step strategy was used for data analysis.
First, independent and paired-sample Student’s t tests or chi-square tests (χ2) were used to compare between- and within-group differences in socio-demographic and clinical variables over time for improved and unimproved patients. Cohen’s d and Cramer’s φ were used as measures of effect size.
Second, repeated-measures analysis of covariance (ANCOVA) was used to compare between-group differences in psychological variables that are based on repeated observations while controlling for a confounding variable. The repeated-measures ANCOVA included BE, levels of alexithymia, self-esteem, depressive symptoms, and perceived stress as dependent variables, the timepoints T1 and T2 as a within-subject factor, BMI at baseline as a covariate, and TWL%-related groups as the between-subject factor. The partial eta-squared (η2) was used as a measure of effect size.
Third, binary logistic regression analysis was used to investigate how change in psychological variables (i.e., BE, levels of alexithymia, self-esteem, depressive symptoms, and perceived stress) predicted the weight outcome. The measurement of change over time (Δ) in psychological variables, expressed as the proportion of change from T1 to T2, was calculated as follows: [(T2–T1)/(T1)] × 100. The weight outcome was considered as a dependent variable (dummy coded: 0 = unimprovement; 1 = improvement). The independent variables were gender, baseline BMI, changes in BE, levels of alexithymia, self-esteem, depressive symptoms, and perceived stress. Six regression steps were processed and regression coefficients, confidence intervals (CI), odds ratio (OR), and p values were estimated. In the first step, gender and baseline BMI were entered as control variables. In the next steps, we added the variables that we were interested in. Specifically, to evaluate the contribution of BE and alexithymia to the outcome before adjusting for other clinical variables, ΔBES and ΔTAS-20 were included in the second and third steps. In the following steps, we added other key variables (ΔRSE in the fourth step, ΔSDS in the fifth step, and ΔPSS in the sixth step). In particular, we aimed to investigate the extent to which each factor would significantly distinguish between the two outcome groups.
## Characteristics of the sample
Figure 1 describes the flow of participation in the study. One hundred and ninety-eight participants were screened for eligibility. One hundred and fifty ($75.6\%$) were eligible and participated in the study. Of the 150 participants assessed at T1, 30 ($20\%$) were lost at follow-up and did not complete the measures at T2, and 120 ($80\%$) were included in the present study. No baseline differences were found between patients who completed and those who did not completed the follow-up (Table S1).Fig. 1Consort diagram describing the flow of participation in the study (created using MS Office) Table 1 reports the socio‐demographic and clinical characteristics of the sample. Table 1Socio‐demographic and clinical characteristics of the study sample ($$n = 120$$)VariableTotal sampleN = 120Improved groupN = 63 ($52.5\%$)Unimproved groupN = 57 ($47.5\%$)t/χ2pd/φAge, mean (SD)48.92 (14.20)48.03 (13.87)50.11 (14.64)0.790.4200.15Gender Men41 ($34.2\%$)27 ($65.9\%$)14 ($34.1\%$)4.450.0310.19 Women79 ($65.8\%$)36 ($45.6\%$)43 ($54.4\%$)SES Middle-low65 ($52.4\%$)35 ($53.9\%$)30 ($46.1\%$)0.680.4120.08 Middle-high55 ($47.6\%$)28 ($50.9\%$)27 ($49.1\%$)*Marital status* Unmarried60 ($50\%$)20 ($48.4\%$)21 ($51.6\%$)0.040.8300.02 Married60 ($50\%$)43 ($50.7\%$)42 ($49.3\%$)History of overweight (years), mean (SD)7.61 (9.23)6.62 (8.91)9.03 (9.68)1.060.2950.26BMI (T1), mean (SD)37.58 (8.02)39.69 (7.89)35.39 (7.72)2.990.0030.56BMI (T2), mean (SD)35.22 (7.58)35.56 (7.51)34.72 (7.61)0.590.5500.12SES socioeconomic status Included patients were mostly females ($$n = 79$$, $65.8\%$), with a mean age of 48.92 years (SD = 14.20 years) and had been overweight for 7.61 years (SD = 9.23 years). Most of the participants were from a middle-low socioeconomic status ($$n = 65$$, $52.4\%$) and $50\%$ were married ($$n = 60$$). According to the NHLBI criteria (see Methods section), $52.5\%$ ($$n = 63$$) of the sample achieved the $5\%$ weight loss criterion at T2. Compared with unimproved, improved patients were predominantly men ($$n = 27$$, $65.9\%$; φ = 0.19) and had a higher baseline BMI ($d = 0.56$). No differences were found on age, SES, marital status, and history of overweight between the two groups.
## Within‑group comparisons over time
Table 2 reports the differences in clinical variables between baseline (T1) and follow-up (T2) in the total sample. Table 2Comparisons of BMI, binge eating (BES), alexithymia (TAS-20), self-esteem (RES), depressive symptoms (SDS), and psychological distress (PSS) before (T1) and after (T2) treatment in total sampleVariableTotal sample($$n = 120$$)tp’dT1Mean (SD)T2Mean (SD)BMI37.58 (8.02)35.22 (7.58)6.440.0020.30BES10.34 (7.63)7.91 (8.89)3.560.0020.29TAS-2047.63 (12.77)45.41 (12.59)2.640.0030.18TAS-DIF15.63 (6.72)14.38 (6.39)2.910.0090.19TAS-DDF13.04 (5.25)12.27 [5]1.970.0640.15TAS-EOT18.96 (4.54)18.76 (5.21)0.480.6300.04RSE19.11 (4.41)20.08 (4.66)2.610.0180.21SDS46.51 (10.33)45.91 (11.21)0.720.5280.04PSS23.05 (7.26)21.62 (8.92)2.140.0450.18p’ = Benjamini–Hochberg adjusted p valueBES Binge Eating Scale, TAS-20 Toronto Alexithymia Scale–20, TAS-DIF Toronto Alexithymia Scale–Difficulty Identifying Feelings, TAS-DDF Toronto Alexithymia Scale–Difficulty Describing Feelings, TAS-EOT Toronto Alexithymia Scale–Externally Oriented Thinking, RSE Rosenberg Self-Esteem Scale, SDS Zung Self-Rating Depression Scale, PSS Perceived Stress Scale All patients reported overall improvement as compared to before treatment. Statistically significant T1 vs. T2 differences were found for BMI ($d = 0.30$), BES ($d = 0.29$), TAS-20 ($d = 0.18$) and its affective factor DIF ($d = 0.19$), RSE ($d = 0.21$), and PSS ($d = 0.18$) (all small effect sizes).
## Between‑group comparisons over time
Table S2 (online appendix) describes the differences in clinical and psychological variables at baseline (T1) and follow-up (T2) between improved and unimproved groups. When the two groups were compared at baseline, unimproved patients had significantly higher levels of alexithymia ($d = 0.45$) and depressive symptoms ($d = 0.47$) compared to improved patients (all moderate effect sizes). When the two groups were compared at follow-up, unimproved patients had higher BES ($d = 0.44$), PSS ($d = 0.58$), SDS ($d = 0.65$), and lower RSE ($d = 0.60$) (all moderate effect sizes).
Table 3 reports the results of repeated measure ANCOVA. The baseline level of BMI was used as a covariate to investigate its influence on patients’ weight loss at follow-up. Table 3Comparisons of psychological variables before (T1) and after (T2) treatment between improved and unimproved groupsVariable mean (SD)TWL%-related groupsTimeTime*BMI(T1)Time*GroupImprovedN = 60UnimprovedN = 56Fp’η2Fp’η2Fp’η2BES T19.88 (6.95)10.82 (8.34)2.500.1900.020.300.6010.004.120.0500.03 T26.25 (6.42)9.18 (7.97)TAS-20 T145.38 (12.34)50.75 (12.78)0.150.6900.000.920.4250.001.640.2450.01 T244 (12.19)47.52 (12.92)RSE T119.55 (4.08)18.39 (4.73)4.310.1400.042.350.3500.025.450.0500.04 T221.35 (4.21)18.70 (4.84)SDS T144.43 (9.74)48.70 (10.73)0.720.4900.0010.4250.011.150.2760.01 T242.60 (10.04)49.41 (11.54)PSS T122.20 (6.76)24.18 (7.611)3.640.1400.032.170.3500.024.910.0500.04 T219.42 (7.56)23.91 (9.38)p’ = Benjamini–Hochberg adjusted p valueBES Binge Eating Scale, TAS-20 Toronto Alexithymia Scale–20, RSE Rosenberg Self-Esteem Scale, SDS Zung Self-Rating Depression Scale, PSS Perceived Stress Scale Both time and baseline BMI covariate did not show a significant effect on any variables. Compared with unimproved patients, improved patients reported a significant decrease in BES ($F = 4.12$, p’ = 0.050) and PSS ($F = 4.91$, p’ = 0.050), and a significant improvement in RSE ($F = 5.45$, p’ = 0.050) over time with effect sizes in the small range.
## Predicting weight outcome
Table 4 shows binary logistic regression model with the %TWL score as a binary outcome criterion (improvement/unimprovement). Gender, baseline BMI, and changes (Δ) in psychological features served as independent variables. Table 4Logistic regression model examining changes in psychological variables from baseline to end of treatment as predictors of treatment outcomes at 9-month follow-upVariablesStep 1Step 2Step 3Step 4Step 5Step 6βOR [$95\%$ CI]βOR [$95\%$ CI]βOR [$95\%$ CI]βOR [$95\%$ CI]βOR [$95\%$ CI]βOR [$95\%$ CI]Gender1.921.80 [0.78, 4.16]2.191.96 [0.80, 4.76]2.692.14 [0.86, 5.29]2.740.97 [0.85–1.12]3.212.37 [0.92, 6.07]3.332.41 [0.94, 6.22]BMI (T1)4.89*1.06 [1, 1.13]7.67**1.03 [0.80, 4.76]7.85**1.09 [1.03, 1.17]8.50**1.06 [0.93–1.22]8.13**1.11 [1.03, 1.17]8.54**1.11 [1.04, 1.19]ΔBES9.46**0.99 [0.98, 0.99]10.91**0.99 [0.98, 0.99]9.91**0.93 [0.82–1.06]10.07**0.99 [0.98, 0.99]9.46**0.99 [0.98, 0.99]ΔTAS-203.58*1.02 [1, 1.04]4.53*0.95 [0.85–1.05]5.83**1.03 [1.01, 1.05]6.17**1.03 [1.01, 1.05]ΔRSE2.611.10 [0.86–1.42]1.881.01 [0.99, 1.03]1.981.01 [0.99, 1.03]ΔSDS2.430.98 [0.95, 1]1.570.98 [0.96, 1.01]ΔPSS0.800.99 [0.98, 1.01]R20.110.230.270.290.320.33ΔR20.110.120.040.020.030.01χ29.39**11.31**3.88*2.812.610.80BES Binge Eating Scale, TAS-20 Toronto Alexithymia Scale–20, RSE Rosenberg Self-Esteem Scale, SDS Zung Self-Rating Depression Scale, PSS Perceived Stress Scale*$p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001$ In the first model, gender and baseline BMI explained $11\%$ of %TWL, with only baseline BMI showing the greatest OR of 1.06 ($95\%$CI [1, 1.13]). Adding ΔBES (OR = 0.99, $95\%$CI [0.98, 0.99]) produced an added prediction of $12\%$ (Model 2). When ΔTAS-20 was added in Model 3, it significantly predicted an added $4\%$ of the variance with a significant OR of 1.02 ($95\%$CI [1, 1.04]). Adding ΔRSE, ΔSDS, and ΔPSS (Models 4, 5, and 6) did not contribute to explain a significant added variance. The final model (predictive accuracy = $72.7\%$) showed that baseline BMI (OR = 1.11, $95\%$CI [1.04, 1.19]), ΔBES (OR = 0.99, $95\%$CI [0.98, 0.99]), and ΔTAS-20 (OR = 1.03, $95\%$ CI [1.01, 1.05]) significantly and independently predicted %TWL.
## Discussion
The aim of this longitudinal study was to examine the extent to which BE, alexithymia, self-esteem, and psychological distress would predict a reduction of body mass at 9-month follow-up, following a lifestyle modification program for weight loss in obese or overweight patients. The main result suggests that improvement in BE and alexithymia may play a clinically significant role in predicting a decrease in body mass at 9-month follow-up. In particular, after controlling for gender, self-esteem, depressive symptoms, and perceived stress, overweight and obese patients who did not benefit from weight loss control program were more alexithymic and had more BE behaviors. Our finding is consistent with a growing number of studies that have established that an improvement in psychological outcomes is associated with a reduction of body mass after a lifestyle modification program for weight loss [15, 37]. In addition, this result is in line with literature that has revealed a mechanism based on emotional dysregulation and uncontrolled eating underlying problems related to body mass [38]. This supports the recommendation to include psychological outcomes such as emotional aspects and dysfunctional eating behaviors when designing a weight loss program to prevent a negative outcome.
In our first hypothesis, we expected that unimproved patients would report higher levels of BE, alexithymia, and psychological distress, and lower self-esteem than improved patients both at baseline and follow-up. This hypothesis was partially confirmed. At baseline, patients who did not improve had significantly higher levels of alexithymia and depressive symptoms compared to improved patients. This result is in line with evidence suggesting depressive symptoms and difficulties in identifying and describing feelings as risk factors for negative weight loss program outcomes. The finding that depressive symptoms are associated with negative weight loss program outcomes is well-established [37, 39]. Depressive symptoms and emotional regulation strategies may predict negative weight loss program outcomes and, in turn, negative treatment outcomes might predict the onset and the maintenance of psychological distress [17, 37, 39]. For example, functional emotion regulation strategies, like reappraisal, predicted weight loss in a sample of adolescents undergoing inpatient obesity treatment [40]. Alexithymic personality traits are frequently observed in people with obesity and eating disorders [17, 18] and individuals with obesity or overweight who show higher difficulties in identifying and describing their feeling states, are less likely to complete the treatment program and obtain benefits from it [25, 26]. This is in line with evidence suggesting that individuals with high levels of alexithymia may be less likely to obtain benefits from treatment due to difficulties in building collaborative therapeutic relationships [40], limited adaptive coping in stressful situations [19], associations with unhealthy behaviors [41]. Furthermore, it is possible that difficulties to discriminate affective states from bodily feelings, and hunger from satiety could have a negative impact on adherence to dietetic recommendations [12]. Indeed, neuroimaging evidence show that alexithymia is associated with reduced neural responses to emotional stimuli from the external environment, and with enhanced neural activity in somatosensory and sensorimotor regions [42]. Deficits in the cognitive processing and regulation of emotions are associated with depression and may reinforce each other. This may further contribute to affect health-related behaviors, symptom formation, and numerous mental and physical health issues (for a review, see [19]).
In this study, $52.5\%$ of the patients achieved the $5\%$ weight loss criterion at a 9-month follow-up. This result is in line with the literature showing a percentage of successful participants ranging from $14.7\%$ to $67\%$ in short-to-medium-term lifestyle interventions [3]. When the two groups of improved and unimproved patients were compared at follow-up, those who had benefited more reported a significant decrease in BE and perceived stress and depressive symptoms, and a significant improvement in self-esteem following treatment. In fact, when weight loss programs work, then the weight loss might have a positive impact on psychological functioning [15]. For example, recent studies indicate that weight loss due to caloric restriction or gastric bypass surgery improves depressive symptoms among obese patients with depression [43, 44].
In our second hypothesis, we expected that changes in BE and alexithymia over the course of treatment would predict treatment outcomes. This hypothesis was confirmed. Baseline BMI and improvement in BE and alexithymia predicted successful outcome, over and above changes in self-esteem and psychological distress. Instead, in our sample no significant changes in depressive symptoms were reported at follow-up. Indeed, change in depressive symptoms did not predict treatment outcome. The result that BE symptoms and alexithymia levels predicted successful outcome is coherent with evidence that psychological treatments targeting emotions and problematic eating behaviors, often result in a significant increase in the efficacy of therapeutic weight loss interventions [38]. Indeed, overweight and obese patients with comorbid BE, tend to have lower levels of emotional awareness and difficulty in using emotion regulation strategies (e.g., [45]) and less favorable prognosis compared to those without BE (e.g., [46]). Some hypotheses to explain how alexithymic difficulties in affective awareness are associated with the key mechanisms in the onset and maintenance of BE and to explain how these mechanisms contribute to the health and weight issues in obese and overweight patients have been established. For example, it seems that individuals with higher alexithymic characteristics abuse external regulators, such as food for regulating emotional arousal [47], being unable to cognitively process their emotions adaptively [48]. Our results are in line with studies that have shown a mechanism based on emotional dysregulation and bingeing behaviors underlying issues related to body mass and suggest that clinicians certainly need to identify and monitor alexithymia and BE in their patients, and include them among their therapeutic interventions strategies for reducing alexithymia and BE and for mitigating their effects on the health and weight issues.
## Strength and limits
The longitudinal study design, the high response rate, and the use of well-validated questionnaires are some of the major strengths of this study. However, there are also some limitations to acknowledge. First, the naturalistic design and the lack of a control group limit the extent to which causal conclusions can be drawn about the relationship between BE, alexithymia, self-esteem, psychological distress, and body mass reduction in patients who engage in a lifestyle modification program. Second, a consecutive non‐probabilistic sample was used in this study, which may have hindered our findings’ validity due to the risk of selection bias. For example, participants were selected from a specific clinical site and results might not generalize to patients attending different interventions. In addition, this study is based on a substantial percentage of individuals who volunteered for weight loss treatments, thus limiting the generalizability to patients who do not seek treatment. Third, psychological variables were assessed only with self-report. Self-reported measures involve a significant amount of personal insight and can be influenced by subjective biases and social desirability effects. Finally, several potential predictors were not controlled for, so $27\%$ of the variance in weight loss was explained by other factors. There were several unmeasured variables (e.g., smoking habit, lack of exercise, alcohol abuse, and personality disorders) that could help us further understand the relationship between psychological factors and weight loss achievement. Therefore, future research will benefit from exploring other factors as potential influences on lifestyle modification programs.
To conclude, with these limitations in mind, results from the current study indicate that overweight and obese patients who present higher BE, and, mainly, alexithymic difficulties in the cognitive processing of emotions, are less likely to gain advantages from the treatment program. This shows the significance of evaluating BE and alexithymic deficits when assessing treatment outcomes, both in terms of treatment mediators and in terms of concomitant primary outcomes. Therefore, the results of the current study emphasize the importance of recognizing individuals with BE and alexithymic deficits before the start of treatment and during weight loss intervention to address psychological treatments on these outcomes. Tailored treatments on outcomes such as the decrease of BE and difficulties in the cognitive processing of emotions should be prudently assessed by clinicians to improve the therapeutic effectiveness of weight control program.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 20 KB)
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|
---
title: 'Predictive value of ferroptosis-related biomarkers for diabetic kidney disease:
a prospective observational study'
authors:
- You Wu
- Yunwei Sun
- Yiwei Wu
- Kecheng Zhang
- Yan Chen
journal: Acta Diabetologica
year: 2023
pmcid: PMC10033569
doi: 10.1007/s00592-022-02028-1
license: CC BY 4.0
---
# Predictive value of ferroptosis-related biomarkers for diabetic kidney disease: a prospective observational study
## Abstract
### Aims
To explore the predictive value of ferroptosis-related (FR) biomarkers for diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM).
### Methods
This prospective observational study enrolled patients with T2DM at the Second Hospital of Jilin University between December 2021 and March 2022. DKD was measured by the urinary albumin-to-creatinine ratio. Receiver operating characteristic curve (ROC) analysis was performed to assess the predictive value of ferroptosis-related biomarkers for DKD.The risk factors for massive proteinuria were performed by multivariable logistic regression analysis.
### Results
Finally, 118 patients (53.0 ± 12.2 years, 76 males) were enrolled, 52 of them without DKD (had normal proteinuria), while 66 with DKD. ( Forty-one had microproteinuria, and 25 had massive proteinuria.) FR biomarkers, including acyl-CoA synthase long chain family member 4 (ACSL4), malondialdehyde (MDA), and reactive oxygen species (ROS), were significantly higher in the massive proteinuria group than in the other groups, while glutathione peroxidase 4 (GPX4) was significantly lower (all $P \leq 0.05$). The area under the ROC of the combination of GPX4, ACSL4, MDA, and ROS for predicting DKD was 0.804 ($P \leq 0.001$). Additionally, multivariate logistic regression analysis showed that the course of disease and ferritin levels were independent risk factors for massive proteinuria, while high serum iron, transferrin, and GPX4 levels were independent protective factors for massive proteinuria in patients with T2DM (all $P \leq 0.05$).
### Conclusions
The GPX4, ACSL4, MDA, and ROS combination might have a good predictive value for DKD. Additionally, the course of disease, ferritin levels, serum iron, transferrin, and GPX4 were independently associated with massive proteinuria.
## Introduction
Type 2 diabetes mellitus (T2DM) is a common endocrine disorder characterized by variable degrees of insulin resistance and deficiency, resulting in hyperglycemia [1]. T2DM is one of the most common chronic diseases and can lead to life-threatening complications and reduce life expectancy [2, 3]. The 10th edition of the Diabetes Map predicts that 783 million people worldwide will have T2DM in 2045, and China will reach 174 million patients [4]. Potential complications of T2DM include cardiovascular disease, neuropathy, nephropathy, retinopathy, and increased mortality [1, 5].
Kidney disease (KD) is one of the microvascular complications of diabetes, in which the diabetic kidney gradually enters the stage of chronic kidney disease (CKD) [6, 7]. The timely diagnosis and treatment of KD in diabetes (DKD) can delay its progression and the occurrence of complications, but a study in 2021 showed that about half of the patients with T2DM are already in CKD stage 3 when diagnosed [8]. Therefore, specific biomarkers for DKD are being sought, but the search has remained unsuccessful. In addition, there are differences between animal models used in preclinical studies of DKD and clinical studies regarding age, renal function at onset, and combination medication. These differences lead to the poor predictive value of animal experiments for the results of clinical trials. DKD is difficult to treat, and controlling DKD progression is not ideal [9]. Various phenotypes of DKD increase the difficulties for early diagnosis and intervention [10], and new biomarkers are necessary.
Ferroptosis was initially found to be involved in the development of ischemia–reperfusion injury, stroke, and cancer [11, 12], but there are relatively few studies on the role of ferroptosis in T2DM and its complications. Like other regulatory cell death pathways, ferroptosis is controlled by a complex regulatory network [11, 12]. Intervention on any pathway’s members can have a therapeutic effect on diseases. Ferroptosis inhibitors were first observed to improve β-cell damage in pancreatic islets in vitro [13], and the role of ferroptosis was subsequently found in diabetic complications, such as diabetic cardiomyopathy and diabetic retinopathy [14, 15]. For DKD, the iron levels in the kidneys of diabetic rats and the urine of diabetic patients are elevated, and a low-iron diet or iron chelators can delay the development of DKD in diabetic patients [16], which is indirect evidence of the involvement of ferroptosis in the pathogenesis of DKD. In the process of lipid peroxidation, the increased acyl-CoA synthase long chain family member 4 (ACSL4) activity increases the sensitivity of the cell membrane to ferroptosis [17]. The iron in the unstable iron pool simultaneously generates a large amount of reactive oxygen species (ROS) that participate in peroxidation and ferroptosis [18, 19]. On the other hand, glutathione peroxidase 4 (GPX4) prevents ferroptosis [20].
Therefore, this study aimed to explore the predictive value of ferroptosis-related (FR) biomarkers for diabetes kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM).
## Study design and participants
This prospective observational study enrolled patients with T2DM at the Second Hospital of Jilin University between December 2021 and March 2022. This study was approved by the Ethics Committee of the Second Hospital of Jilin University. All participants signed the informed consent form.
The inclusion criteria were [1] 18–75 years of age, [2] the diagnosis of T2DM conformed to the “Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes” (2020 edition) [21]. The exclusion criteria were [1] type 1 diabetes mellitus, monogenic diabetes syndrome, or other special types of diabetes, [2] acute complications of diabetes or other serious complications, [3] combined with severe liver, kidney, or heart disease, [4] anemia or used iron treatment in the past 3 months, or [5] tumor, inflammation, infection, or any disease that could affect serum iron metabolism-related biomarkers.
## Data collection and definitions
The demographic clinical data of the patients, such as age, sex, course of disease, height, and weight, were collected. Body mass index (BMI) was calculated as BMI = weight (kg)/m2. Venous blood was collected after 8 h of fasting to measure the general biochemical biomarkers. Glycated hemoglobin (HbA1C) was determined by high-performance liquid chromatography (HLC-723G8). An automatic biochemical analyzer (UniCel DxC 800 Synchron) was used to determine fasting plasma glucose (FPG), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), serum creatinine (Scr), cystatin C (Cys-C), and serum iron metabolism related biomarkers (serum iron, ferritin, and transferrin). The estimated glomerular filtration rate (eGFR) was calculated: female eGFR = 130 × (Scr/62)a × (Cys-C/0.80)b × (0.995)age [age ≤ 62 years, a = − 0.248; age > 62, a = − 0.601; Cys-C ≤ 0.08 mg/L, b = − 0.375; Cys-C > 0.08 mg/L, b = − 0.711]; male eGFR = 135 × (Scr/80)a × (Cys-C/0.80)b × (0.995)age [age ≤ 80, a = − 0.207; age > 62, a = − 0.601; Cys-C ≤ 0.08 mg/L, b = − 0.375, Cys-C > 0.08 mg/L, b = − 0.711].
The UACR was measured using an IMMAGE 800 analyzer. According to the diagnosis of DKD conformed to the “Chinese Guidelines for the Prevention and Treatment of Diabetic Kidney Diseases” (2021 edition) [22], patients were divided into non-DKD and DKD group. Patients in non-DKD group had normal proteinuria (the urinary albumin to creatinine ratio [UACR] < 30 mg/g), while in DKD group were divided into microproteinuria subgroup (UACR 30–300 mg/g) and massive proteinuria subgroup (UACR > 300 mg/g).
## Methods for the detection of ferroptosis-related biomarkers
Cubital venous blood (2 mL) was collected from the patients after fasting for 8–12 h in the morning. After standing at room temperature for 30 min, the samples were centrifuged at 3500 rpm for 10–15 min. The serum samples were collected and stored at − 80 °C. The levels of the ferroptosis-related biomarkers GPX4, ACSL4, ROS, and MDA were detected by enzyme-linked immunosorbent assay kits (OUSAID, Hunan, China); the lowest detection concentrations were 3.75 ng/mL, 33.75 pg/mL, 15 IU/mL, and 0.15 nmol/mL, respectively. Antibody specific for Human ferroptosis-related biomarkers (GPX4, ACSL4, MDA, and ROS) has been pre-coated onto a microplate. Standard, samples and HRP-linked detect antibody specific for ferroptosis-related biomarkers are pipetted into the wells and ferroptosis-related biomarkers present is bound by the immobilized antibody and detect antibody following incubation. After washing away any unbound substances, streptavidin-HRP is added. After washing, substrate solution is added to the wells and color develops in proportion to the amounts of ferroptosis-related biomarkers bound in the initial step. The color development is stopped and the intensity of the color is measured.
## Statistical analysis
SPSS 26.0 statistical software (IBM Corp., Armonk, NY, USA) was used for data analysis. The continuous data that conformed to the normal distribution (according to the Kolmogorov–Smirnov test) were described as means ± standard deviation (SD) and analyzed using ANOVA. Continuous data with a skewed distribution were described as medians (upper and lower quartiles) and analyzed using the Kruskal–Wallis H-test. Categorical data were expressed as n (%) and analyzed using the chi-square test. The Pearson correlation analysis was used to analyze the correlations of continuous data that conformed to the normal distribution, while the Spearman correlation analysis was used to analyze the correlations between categorical data and continuous data with a skewed distribution. Receiver operating characteristics (ROC) curves were drawn using GraphPad Prism 9 (GraphPad Software Inc., San Diego, CA, USA), and the area under the curve (AUC) was calculated to evaluate the predictive value of ferroptosis-related biomarkers for DKD. Sensitivity, specificity, cutoff value, positive predictive value (PPV) and negative predictive value (NPV) were calculated by using MedCalc statistical software (MedCalc Software Ltd, Ostend, Belgium). The risk factors for massive proteinuria were performed by multivariable logistic regression analysis. Two-sided P values < 0.05 were considered statistically significant.
## Results
The participant flowchart is depicted in Fig. 1. Finally, 118 participants were enrolled, with an average age of 53.0 ± 12.2 years and including 76 males and 42 females. Among them, the course of the disease, HbA1C, and ALT in the normal proteinuria group were lower than in the massive proteinuria group (all $P \leq 0.05$). eGFR and serum iron in the massive proteinuria group were lower than in the other two groups (all $P \leq 0.05$). The levels of ACSL4, MDA, and ROS in the massive proteinuria group were higher than in the other groups (all $P \leq 0.05$). UACR, transferrin, and GPX4 levels gradually decreased with the aggravation of proteinuria, while ferritin levels gradually increased (all $P \leq 0.05$) (Table 1).Fig. 1Study flowchartTable 1Characteristics of the patientsVariablesNon-DKD ($$n = 52$$)DKD ($$n = 66$$)PNormal proteinuria ($$n = 52$$)Microproteinuria ($$n = 41$$)*Massive proteinuria* ($$n = 25$$)Age51.8 ± 10.3651.5 ± 12.856.2 ± 14.40.320Sex (%)Male37 ($71.2\%$)23 ($56.1\%$)16 ($64.0\%$)0.314Female15 ($28.8\%$)18 ($43.9\%$)9 ($36.0\%$)Height (cm)168.6 ± 7.9168.0 ± 9.8163.2 ± 21.30.190Weight (kg)75.9 ± 13.877.8 ± 15.573.5 ± 11.50.467BMI (kg/m2)26.6 ± 3.427.6 ± 4.226.2 ± 2.90.247Course of disease (years)5.0 (1.0, 10.0)7.0 (2.0, 11.0)10.0 (5.5, 16.5)0.021cFPG (mmol/L)9.34 ± 3.079.90 ± 3.5410.24 ± 3.520.501HbA1C (%)7.88 ± 1.228.52 ± 1.789.16 ± 1.16 < 0.001cALT (U/L)29.00 (21.00, 45.00)24.00 (17.00, 48.00)19.00 (15.00, 33.50)0.033cAST (U/L)22.00 (16.00, 25.75)19.00 (17.00.27.50)18.00 (14.50, 25.50)0.183TG (mmol/L)2.19 (1.44, 3.10)2.53 (1.83, 3.64)2.23 (1.16, 3.12)0.239TC (mmol/L)5.68 ± 1.015.83 ± 1.635.54 ± 1.570.773HDL-C (mmol/L)1.06 (0.93, 1.28)1.14 (0.95, 1.31)1.04 (0.86, 1.21)0.314LDL-C (mmol/L)3.11 ± 0.872.87 ± 1.132.97 ± 1.370.540BUN (mmol/L)5.06 (3.93, 6.04)5.30 (4.32, 6.04)5.52 (4, 82, 7.93)0.079Scr (μmol/L)71.00 (62.25, 78.75)75.00 (63.00, 87.00)81.00 (62.50, 90.00)0.057UACR (mg/g)9.86 (5.44, 16.00)87.69 (45.66, 121.51)676.26 (479.26, 1241.63) < 0.001deGFR (ml/min)98.35 ± 12.7594.34 ± 23.4879.06 ± 32.010.018bcIron (µmol/L)21.44 ± 5.5519.03 ± 6.1514.42 ± 3.21 < 0.001bcFerritin (µg/L)117.00 (85.75, 175.75)203.00 (138.00, 237.50)378.00 (319.00, 439.50) < 0.001dTransferrin (g/L)2.44 ± 0.352.04 ± 0.351.46 ± 0.69 < 0.001dGPX4 (ng/mL)164.09 ± 29.80145.55 ± 25.38119.70 ± 23.73 < 0.001dACSL4 (pg/mL)1249.99 ± 343.901368.30 ± 347.541602.55 ± 268.45 < 0.001bcMDA (nmol/mL)7.43 ± 1.297.80 ± 1.969.48 ± 1.35 < 0.001bcROS (IU/mL)416.82 ± 117.90440.22 ± 136.23498.81 ± 108.690.026bcaThere was a statistical difference between the normal proteinuria group and the microproteinuria group ($P \leq 0.05$)bThere was a statistical difference between the microproteinuria group and the massive proteinuria group ($P \leq 0.05$)cThere was a statistical difference between the normal proteinuria group and the massive proteinuria group ($P \leq 0.05$)dThe data in the three groups were all statistically different ($P \leq 0.05$)BMI body mass index, FPG fasting plasma glucose, HbA1C glycated hemoglobin, ALT alanine aminotransferase, AST aspartate aminotransferase, TG triglycerides, TC total cholesterol, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, BUN blood urea nitrogen, *Scr serum* creatinine, UACR urine albumin-creatinine ratio, eGFR estimated glomerular filtration rate, MDA malondialdehyde, ROS reactive oxygen species GPX4 was negatively correlated with FPG, HbA1C, UACR, and ferritin and positively correlated with transferrin (r = − 0.205, r = − 0.290, rs = − 0.463, rs = − 0.348, and $r = 0.366$, respectively; all $P \leq 0.05$) (Fig. 2A, B, Table 2). ACSL4 was positively correlated with HbA1C, UACR, and ferritin and negatively correlated with serum iron and transferrin ($r = 0.207$, rs = 0.359, rs = 0.269, r = − 0.262, and r = − 0.184, respectively; all $P \leq 0.05$) (Fig. 2C, D, Table 2). MDA was positively correlated with UACR and negatively correlated with transferrin (rs = 0.364 and r = − 0.417, respectively; both $P \leq 0.05$) (Fig. 2E, Table 2). ROS was positively correlated with HbA1C and BUN and negatively correlated with transferrin ($r = 0.206$, rs = 0.196, and r = − 0.235, respectively; all $P \leq 0.05$) (Fig. 2F, Table 2).Fig. 2A *Correlation analysis* of GPX4 with fasting plasma glucose (FPG), glycated hemoglobin (HbA1C), and urine albumin to creatinine ratio (UACR). B *Correlation analysis* of GPX4 with ferritin and transferrin. C *Correlation analysis* of ACSL4 with HbA1C, urine UACR, and ferritin. D *Correlation analysis* of ACSL4 with serum iron and transferrin. E *Correlation analysis* of malondialdehyde (MDA) with UACR and transferrin. F *Correlation analysis* of reactive oxygen species (ROS) with HbA1C, blood urea nitrogen (BUN), and transferrinTable 2Correlation analysis of ferroptosis-related biomarkers and clinical biomarkersVariablesGPX4ACSL4MDAROSr/rsPr/rsPr/rsPr/rsPAge− 0.0370.692− 0.0760.412− 0.0310.7420.1250.176Sex0.0700.4500.0500.595− 0.0170.852− 0.0170.858Height (cm)− 0.0200.827− 0.0660.475− 0.0620.5020.0080.932Weight (kg)− 0.1250.1780.0190.8390.0990.2860.0050.956BMI (kg/m2)− 0.0680.4630.0540.5650.1030.2670.0220.810Course of disease (years)− 0.0840.3640.1320.1530.1660.0720.0510.582FPG (mmol/L)− 0.2050.0260.0930.318− 0.0050.9560.1030.266HbA1C (%)− 0.2900.0010.2070.0240.1110.2310.2060.025ALT (U/L)0.0040.968− 0.1630.079− 0.0860.357− 0.0350.709AST (U/L)0.0240.795− 0.1300.160− 0.0580.535− 0.0270.770TG (mmol/L)− 0.0930.319− 0.0160.862− 0.0620.503− 0.1140.220TC (mmol/L)0.0570.541− 0.0040.965− 0.0960.303− 0.0490.599HDL-C (mmol/L)0.0980.293− 0.0220.814− 0.0560.546− 0.0550.557LDL-C (mmol/L)0.0270.774− 0.0380.685− 0.0170.858− 0.0060.946BUN (mmol/L)− 0.1310.1580.0700.451− 0.0400.6640.1960.033Scr (μmol/L)− 0.0370.6920.1000.2800.0060.945− 0.0550.552UACR (mg/g)− 0.463 < 0.0010.359 < 0.0010.364 < 0.0010.1270.172eGFR (ml/min)0.0740.425− 0.1560.092− 0.0470.617− 0.0850.359Iron (µmol/L)0.1590.085− 0.2620.004− 0.1280.169− 0.0390.679Ferritin (µg/L)− 0.348 < 0.0010.2690.0030.1430.1220.0560.549Transferrin (g/L)0.366 < 0.001− 0.1840.046− 0.417 < 0.001− 0.2350.010The bold means that the P value is less than 0.05BMI body mass index, FPG fasting plasma glucose, HbA1C glycated hemoglobin, ALT alanine aminotransferase, AST aspartate aminotransferase, TG triglycerides, TC total cholesterol, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, BUN blood urea nitrogen, *Scr serum* creatinine, UACR urine albumin/creatinine ratio, eGFR estimated glomerular filtration rate, MDA malondialdehyde, ROS reactive oxygen species *The microproteinuria* and massive proteinuria groups were combined, and the participants were divided into the non-DKD ($$n = 52$$) and DKD ($$n = 66$$) groups. The ROC curves were drawn to explore the clinical predictive value of ferroptosis-related biomarkers on diabetic kidney disease (Fig. 3A). GPX4, ACSL4, MDA, and ROS all had a certain predictive value for the occurrence of DKD in patients with T2DM. GPX4 had the best predictive value. Using GPX4 167.5 as the best threshold displayed $88\%$ sensitivity and $58\%$ specificity (Table 3). The AUC of the combination of GPX4, ACSL4, MDA, and ROS for predicting DKD was 0.804 ($P \leq 0.001$) (Fig. 3B).Fig. 3A Receiver operating characteristics (ROC) curve of GPX4, ACSL4, malondialdehyde (MDA), and reactive oxygen species (ROS) for predicting diabetic kidney disease. B ROC curve of the combination of GPX4, ACSL4, MDA, and ROS for predicting diabetic kidney diseaseTable 3Predictive value of ferroptosis-related biomarkers for diabetic kidney diseaseVariablesAUCCut-offSensitivity (%)Specificity (%)PPVNPVGPX4 (ng/mL)0.761 < 167.5087.8857.6918.897.7ACSL4 (pg/mL)0.674 > 1511.3951.5276.9219.993.5MDA (nmol/mL)0.660 > 8.7643.9488.4629.793.4ROS (IU/mL)0.607 > 532.0042.4282.6921.492.8HbA1C glycated hemoglobin, ALT alanine aminotransferase, MDA malondialdehyde, ROS reactive oxygen species Additionally, univariate logistic regression analysis showed that the course of the disease, HbA1C, serum iron, ferritin, transferrin, GPX4, ACSL4, MDA, and ROS were potential risk factors for massive proteinuria (all $P \leq 0.05$) (Table 4). Furthermore, multivariate logistic regression analysis suggested that the course of disease (odds ratio [OR] = 1.345, $95\%$ CI: 1.036–1.746, $$P \leq 0.026$$) and ferritin levels (OR = 1.030, $95\%$ CI 1.006–1.055, $$P \leq 0.014$$) were independent risk factors for massive proteinuria, while high serum iron (OR = 0.418, $95\%$ CI 0.205–0.853, $$P \leq 0.017$$), transferrin (OR = 0.053, $95\%$ CI 0.008–0.363, $$P \leq 0.003$$), and GPX4 (OR = 0.935, $95\%$ CI 0.879–0.994, $$P \leq 0.031$$) levels were inversely independent protective factors for massive proteinuria in patients with T2DM (Table 4). Table 4Univariate and multivariate logistic analysis of the influence of massive proteinuria in T2DMVariablesUnivariate logistic regressionMultivariate logistic regressionOR, $95\%$ CIPOR, $95\%$CIPCourse of disease1.111, (1.034, 1.193)0.0041.345, (1.036,1.746)0.026HbA1C (%)1.857, (1.295, 2.665)0.00110.130, (0.990,103.651)0.051ALT (U/L)0.977, (0.950, 1.004)0.094–Iron (µmol/L)0.734, (0.638, 0.844) < 0.0010.418, (0.205,0.853)0.017Ferritin (µg/L)1.008, (1.004, 1.012) < 0.0011.030, (1.006,1.055)0.014Transferrin (g/L)0.003, (0.000, 0.024) < 0.0010.053, (0.008,0.363)0.003GPX4 (ng/mL)0.941, (0.919, 0.964) < 0.0010.935, (0.879,0.994)0.031ACSL4 (pg/mL)1.004, (1.002, 1.005) < 0.0011.004, (0.998,1.010)0.194MDA (nmol/mL)2.222, (1.547, 3.192) < 0.0017.942, (0.524,120.475)0.135ROS (IU/mL)1.006, (1.001, 1.010)0.0091.026, (1.992,1.061)0.136The bold means that the P value is less than 0.05AUC area under the curve, Cut-off cut off value, PPV positive predictive value, NPV negative predictive value
## Discussion
The results suggest that the combination of GPX4, ACSL4, MDA, and ROS might have a good predictive value for DKD. Additionally, the course of disease, ferritin levels, serum iron, transferrin, and GPX4 were independently associated with massive proteinuria. These findings might provide a prediction model consisted of ferroptosis-related biomarkers for DKD in patients with T2DM.
Ferroptosis is regulated by multiple factors. ACSL4 activity increases the sensitivity of cell membrane to ferroptosis [17]. ROS is both a consequence and an actor of ferroptosis, and MDA is an oxidation product [18, 19]. On the other hand, GPX4 prevents ferroptosis [20]. The present study showed that the levels of ACSL4, MDA, and ROS in the massive proteinuria group were higher than in the other two groups, and the expression of GPX4 was lower than that of the other two groups, as supported by the theoretical concepts of these four molecules in ferroptosis. In addition, GPX4 was negatively correlated with UACR, while ACSL4 and MDA were positively correlated with UACR. The univariable logistic regression analyses showed that the four biomarkers were associated with massive proteinuria, but only GPX4 was an independent factor in the multivariable regression analysis. It is supported by Wang et al. [ 23], who also observed a significant increase in ACSL4 level, a significant decrease in GPX4 level, and an increase in the content of lipid peroxidation products in the DKD mouse model. In addition, the ACSL4 inhibitor rosiglitazone reduced the content of lipid peroxidation products [23]. In addition to animal models, decreased GPX4 expression was also found in kidney biopsies from patients with DKD [24]. Feng et al. [ 25] reported that ferroptosis could aggravate the renal injury and tubular fibrosis in DKD mice through the hypoxia-inducible factor-1α pathway. The glutathione system is the main pathway limiting ferroptosis [26]. The inhibition of GPX4 increases ferroptosis [27]. The conditional depletion of GPX4 in mice increases ferroptosis and leads to apoptosis, necroptosis, and pyroptosis [28–30]. These results highlight the central role of GPX4 in regulating ferroptosis, and ferroptosis is involved in the pathogenesis of DKD [31].
At present, the gold standard for the diagnosis of DKD is still renal biopsy, but because of its invasiveness, some patients can refuse or be ineligible because of comorbidities. In addition, it requires equipment, supplies, and skilled operators. In this context, screening for DKD using a simple blood draw is attractive. According to the ROC analysis, the AUC of GPX4 alone was 0.761, while adding ACSL4, MDA, and ROS increased the AUC to 0.804. Therefore, combining the above four biomarkers had a certain predictive power on DKD.
Iron homeostasis in the body is strictly regulated; when iron is in excess, highly toxic hydroxyl radicals are generated through the Fenton reaction, which induces oxidative stress [10, 17]. It has been reported that iron restriction can prevent the progression of DKD, and in DKD rats, treatment with the iron chelator deferiprone was found to reduce renal inflammatory infiltration, fibrosis, and oxidative stress and to have a protective effect on the kidneys [32]. In this study, the levels of serum iron and transferrin in the massive proteinuria group were higher than in the normal proteinuria group, while ferritin levels were higher than in the other two groups. Serum iron, transferrin, and ferritin were independently associated with massive proteinuria in DKD. In a retrospective study in China, the differences in serum iron, transferrin, and ferritin at baseline were compared between T2DM and DKD patients, and it was found that lower serum transferrin was a predictor of DKD progression to ESRD, supporting the present study [33]. Ferritin is the main form of iron storage, reflecting the body’s iron overload to a certain extent. When ferritin is saturated, excess iron enters the kidney in the form of non-transferrin-bound iron and catalyzes the production of ROS, resulting in cell damage, accompanied by increased serum ROS and MDA levels and inflammation (as shown by interleukin-6 and tumor necrosis factor-α in the kidneys), while the antioxidants such as glutathione and superoxide dismutase in the kidneys are decreased [34]. The univariable regression analysis in this study also found that HbA1C is a risk factor for massive proteinuria in DKD patients. Therefore, it is necessary to strengthen early glycemic control to reduce the decline of eGFR. However, the current HbA1C control goals are not unified. The KDIGO guidelines recommend a target range of $6.5\%$ to $8.0\%$ for HbA1C based on each patient’s degree of hypoglycemia risk [35]. In the natural history of DKD, hyperglycemia is the main factor driving its progression [36]. Hyperglycemia can lead to increased advanced glycation products, the activation of the polyol pathway and endothelial cell damage, vascular proliferation, and podocyte and renal tubular epithelium damage by activating downstream cytokines [36]. In addition to glycemia, the course of the disease was also independently associated with massive proteinuria in patients, supported by Wei et al. [ 37].
This study had limitations. The patients were from a single hospital, resulting in a small sample size. Renal biopsies could not be performed for ethical reasons, but future studies should enroll patients who undergo renal biopsy for medical reasons and examine the associations among histological changes and ferroptosis-related biomarkers. Various immune and inflammatory markers associated with the pathogenesis of DKD were not measured either. Information on whether patients had fatty liver was not collected.
In conclusion, the combination of GPX4, ACSL4, MDA, and ROS might have a good predictive value for DKD. Additionally, the course of disease, ferritin levels, serum iron, transferrin, GPX4 were independently associated with massive proteinuria. Nevertheless, the prediction model consisted of ferroptosis-related biomarkers for DKD in patients with T2DM needs validation by multicenter with large sample size.
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|
---
title: 'Physical activity and risk of chronic kidney disease: systematic review and
meta-analysis of 12 cohort studies involving 1,281,727 participants'
authors:
- Samuel Seidu
- Mohammad Abdool
- Abdullah Almaqhawi
- Thomas J. Wilkinson
- Setor K. Kunutsor
- Kamlesh Khunti
- Tom Yates
journal: European Journal of Epidemiology
year: 2023
pmcid: PMC10033580
doi: 10.1007/s10654-022-00961-7
license: CC BY 4.0
---
# Physical activity and risk of chronic kidney disease: systematic review and meta-analysis of 12 cohort studies involving 1,281,727 participants
## Abstract
The role of regular physical activity in preventing vascular and non-vascular disease is well established. Chronic kidney disease (CKD) is a major cause of global morbidity and mortality and largely preventable, but it is uncertain if regular physical activity can reduce the risk of CKD. Using a systematic review and meta-analysis of published observational cohort studies in the general population, we sought to assess the association between physical activity and CKD risk. Relevant studies with at least one-year of follow-up were sought from inception until 02 May 2022 in MEDLINE, Embase, Web of Science, and manual search of relevant articles. Relative risks (RRs) with $95\%$ confidence intervals (CIs) for the maximum versus the minimal amount of physical activity groups were pooled using random effects meta-analysis. The quality of the evidence was evaluated using the GRADE tool. A total of 12 observational cohort studies comprising 1,281,727 participants and 66,217 CKD events were eligible for the analysis. The pooled multivariable-adjusted RR ($95\%$ CI) of CKD comparing the most versus the least physically active groups was 0.91 (0.85–0.97). The association was consistent across several study level subgroups. Exclusion of any single study at a time from the meta-analysis did not change the direction or significance of the association. There was no evidence of small study effects among contributing studies. The GRADE quality of the evidence was low. In the general population, individuals who are most physically active have a lowered risk of CKD compared to those who are not or least physically active. CRD42022327640.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s10654-022-00961-7.
## Introduction
Chronic kidney disease (CKD), conventionally characterized by the presence of kidney damage and reduced function, is a direct and major cause of global morbidity and mortality [1]. Chronic kidney disease is a major contributor to poor health outcomes of noncommunicable diseases (NCDs); it is associated with an 8–10 fold increased risk of cardiovascular disease (CVD) mortality and multiplies risk in diabetes and hypertension [2, 3]. A major societal effect of CKD is the immense healthcare costs associated with its potential outcome—end-stage renal disease (ESRD)—and the loss of productivity associated with this [2, 4]. Though risk factors for CKD vary by setting, major risk factors include diabetes, hypertension and metabolic syndrome [5, 6]. In developing countries, HIV and exposure to heavy metals and toxins play an additional role [7, 8]. Chronic kidney disease has a substantial effect on global public health and is largely preventable and treatable. Globally, in 2017, 1.2 million people died from CKD [9]. The prevalence and incidence of CKD continues to rise because of an ageing population and an increasing burden due to its major risk factors [2]. There is therefore an urgent need to identify modifiable risk factors that can reduce the risk of CKD or slow its progression.
The health benefits of physical activity are well documented and include reduction in the risk of several vascular and non-vascular diseases [10–13]. Physical activity also reduces the risk, duration or severity of infectious diseases [14, 15] and has mental health benefits [16]. Several cross-sectional studies have reported associations between physical activity and risk of CKD with inconsistent results [17–19]. Zhu and colleagues in a recent meta-analysis of 8 cross-sectional studies showed little evidence of an association between the highest vs. lowest level of physical activity and risk of CKD [20]. However, cross-sectional study designs do not address the issue of temporality. The evidence on the prospective association between physical activity and CKD is also controversial. Whereas, some studies have reported evidence of associations between physical activity and risk of CKD [21–23], others have reported no evidence of an association [24, 25]. In their recent meta-analysis, Zhu and colleagues also pooled the results of 6 observational cohort studies [20]. However, a number of relevant observational cohort studies were not included in the meta-analysis and others have since been published since this last review [20]. Furthermore, the results of these additional studies have been inconsistent. Hence, there is a need to re-evaluate the relationship in more detail. In this context, our aim was to evaluate the association between physical activity and future risk of CKD in general population settings using a systematic review and meta-analysis of all published observational cohort studies to date.
## Data sources and searches
We registered the protocol for this systematic review and meta-analysis in the PROSPERO prospective register of systematic reviews (CRD42022327640). The conduct and reporting of this review adhered to PRISMA and MOOSE guidelines [26, 27] (Appendices 1–2). MEDLINE and Embase were searched from inception to 02 May 2022 with no language restrictions. The search strategy used a combination of MESH words or terms relating to the exposure (“physical activity”, “exercise”, “aerobic training”) and outcome (“chronic kidney disease”, “kidney failure”, “renal disease”). Details of the search strategy are presented in Appendix 3. One author (SKK) initially screened the titles and abstracts of the retrieved citations to assess their potential for inclusion. This was conducted using Rayyan (http://rayyan.qcri.org), an online bibliographic tool that helps to expedite the screening process using a process of semi-automation [28]. Full texts of the selected titles and abstracts were retrieved and detailed evaluation was done, which was independently conducted by three authors (SKK, MA and SS). To identify potential articles missed by the search of databases, manual scanning of reference lists of relevant studies and review articles was performed, and Web of Science was used to do a cited reference search.
## Study selection
We included all population-based observational cohort (retrospective or prospective designs) studies that had evaluated the relationship between physical activity and risk of incident CKD in adult general populations and reported at least one year follow-up duration for the ascertainment of outcomes. For all CKD outcomes, we accepted the range of definitions as reported by the included studies. The following studies were excluded: (i) case–control and cross-sectional studies because of their lack of temporality; (ii) those involving elite athletes and/or evaluated competitive or endurance sports; and (iii) those evaluating the associations between measures of fitness (eg, cardiorespiratory fitness, physical fitness, exercise capacity) and risk of CKD; and (iv) those conducted in people with pre-existing diseases.
## Data extraction and risk of bias assessment
Using a standardized data collection form which has been used for previous reviews of a similar nature [12, 13, 15], one author (SKK) extracted relevant data from the eligible studies and two other authors (MA and SS) independently checked the data using the original articles. We extracted data on the following study characteristics: first author surname and year of publication, geographical location, year of recruitment/baseline data collection, specific study design, demographic characteristics (age and percentage of males), sample size, duration of follow-up, physical activity type and assessment method, definition of CKD, number of CKD events, risk comparisons, the most fully-adjusted risk ratios (odds ratios (ORs), relative risks (RRs), and hazard ratios) for CKD (and corresponding $95\%$ confidence interval [CIs]), list of covariates adjusted for, and level of adjustment (‘+’ defined as minimally adjusted analysis, i.e. age and/or sex; ‘++’ as adjustment for conventional risk factors for CKD excluding inflammation, i.e. age and/or sex plus body mass index, socioeconomic status, alcohol consumption, smoking, and comorbidities; and ‘+++” as adjustment for conventional risk factors plus inflammation). When there were multiple publications of the same cohort, we extracted data from the most comprehensive study to avoid double counting the same cohort in the pooled analysis. The criterion for selection was the one with the most extended follow-up or analysis covering the largest number of participants and events. The risk of bias within individual observational studies was assessed using the Cochrane Risk of Bias in Non-randomised Studies—of Interventions (ROBINS-I) tool [29]. The risk of bias is assessed for the following domains: confounding, participant selection, classification of interventions, deviations from intended interventions, missing data, outcome measurements, and selective reporting. For each domain, the risk is quantified as low risk, moderate risk, serious risk, or critical risk and then an overall judgement of the risk of bias is provided for each study. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) tool was also used to assess the quality of the body of evidence, based on the following criteria: study limitations, inconsistency of effect, imprecision, indirectness and publication bias [30].
## Data analysis
Relative risks with $95\%$ CIs were used as the summary measures of association. Hazard ratios and ORs were assumed to approximate the same measure of RR based on the assumptions of rare outcomes [31], consistent with previous studies [12, 13]. All studies categorised physical activity exposure (e.g., leisure-time physical activity, total or any physical activity) into user-defined categories or quantiles. Due to the varied reporting of the RR comparisons across studies, they could not be transformed to consistent comparisons (e.g., top versus bottom quantiles of the distribution of physical activity) using standard statistical methods [32–34]. However, to provide some consistency and enhance comparison and interpretation of the findings, the extreme groups (i.e., the top versus bottom or maximum versus the minimal amount of physical activity) reported for each study were used for the analyses. Several previous meta-analyses have utilised this approach [12, 13] and it is considered reliable as there is documented data that pooled estimates from transformed and untransformed data are qualitatively similar [33]. When a study reported specific types of physical activity in addition to any or total physical activity, we only used risk estimates for any or total physical activity in the pooled analysis as done for previous similar reviews [12, 13]. Relative risks were pooled using a random effects model to account for the effect of heterogeneity [35]. The extent of statistical heterogeneity across studies was quantified by standard chi-square tests and the I2 statistic [36, 37]. To determine the degree of heterogeneity, we also estimated $95\%$ prediction intervals, which provide a region in which about $95\%$ of the true effects of a new study are expected to be found [38, 39]. We explored for evidence of effect modification on the association (sources of heterogeneity) using pre-specified study-level characteristics such as geographical location, observational cohort design (prospective vs retrospective), the average age at baseline, the average duration of follow-up and number of CKD events, which was conducted using stratified analysis and random effects meta-regression [40]. To test the robustness of the observed association, we conducted a sensitivity analysis by investigating the influence of omitting each study in turn on the overall result (stata module –metaninf-). To explore for small study effects, we visually inspected constructed Begg’s funnel plots [41] and performed Egger’s regression symmetry test [42]. We employed Stata version MP 17 (Stata Corp, College Station, Texas) for all statistical analyses.
## Study identification and selection
We identified 864 potentially relevant citations following the search of databases, manual screening of relevant articles and Web of Science citation search. After the initial screening of titles and abstracts, 33 articles were selected for full-text evaluation. After detailed evaluation of the full-texts, 21 articles were excluded because of the following reasons: (i) exposure was not relevant ($$n = 7$$); (ii) outcome not relevant ($$n = 7$$); (iii) duplicate of a cohort already included in the review ($$n = 3$$); (iv) population not relevant ($$n = 2$$); and (v) study design not relevant ($$n = 2$$). Overall, 12 articles based on 12 unique studies comprising 1,281,727 participants and 66,217 CKD events were eligible for the review [21–25, 43–49] (Fig 1). All 12 articles were identified from the search of databases. Fig. 1PRISMA flow diagram
## Study characteristics and risk of bias
The study design and population characteristics of the eligible observational cohort studies evaluating the associations between physical activity and the risk of CKD are summarized in Table 1. The studies were published between 2003 and 2022. Ten studies were based on prospective cohort designs, with two being retrospective cohort designs [47, 48], and they were all based in general population participants. The average age of participants at baseline ranged from approximately 39 to 74 years, with a weighted mean of 47 years. All studies recruited men and women, except for one which was based on only men [47]. Six studies were based in Asia (Japan, Singapore, and Taiwan), 5 in North America (USA), and 1 in Australasia (Australia). All 12 studies assessed total physical activity exposure through self-reported or interview-administered questionnaires; the categorisation of physical activity varied across studies. The average duration of follow-up ranged from 1.0 to 24.0 years, with a weighted mean of 4.8 years. Chronic kidney disease was mostly defined as an estimated glomerular filtration rate (GFR) of <60 mL/min/1.73 m2 and/or proteinuria. The degree of confounder adjustment varied across studies, but all studies adjusted for established risk factors; only one study adjusted for inflammation as measured by C-reactive protein. All 12 studies were at serious risk of bias (i.e., were judged to be at serious risk of bias in at least one domain, but not at critical risk of bias in any domain) (Appendix 4).Table 1Baseline characteristics of observational studies included in review (2003–2022)Author, year of publicationName of studyCountryBaseline yearMales, %Mean/median age, yearsFollow-up duration, yearsPhysical activity comparisonsNo. of CKD casesNo. of participantsDefinition of CKDCovariates adjusted forStengel 2003 [43]NHANES IIUSA1976–198047.049.313.2High vs low PA1899082Incident CKD was defined as either treatment of end-stage kidney disease due to any cause or death related to CKDSmoking, alcohol, BMI, age, gender, race, DM, CVD, hypertension, SBP, total cholesterol, and estimated GFRWhite 2011 [44]AusDiabAustralia1999–200045.551.65.0≥150 min/week vs 0 min/week5496318Final estimated GFR of <60 mL/min/1.73 m2Age, sex and kidney function at baselineHawkins 2015 [24]Health ABCUSA1997–199848.273.510.0Top vs bottom third3382435Incident CKD was defined as a follow-up eGFR less than 60 ml/min/1.73m2 17 in individuals with baseline GFR >60 ml/min/1.73m2Age, baseline GFR, sex, race, smoking status, study site, hypertension medication use, heart failure, diabetes status, pulse pressure, BMI, HDL, triglycerides, TC, CRP, and television watchingJafar 2015 [22]Singapore Chinese Health StudySingapore1993–199844.156.115.3Any vs never64259,552NRAge, sex, interview year, BMI, dialect, education level, self-reported history of physician diagnosed hypertension, DM, heart disease or stroke, alcohol use, smoking, intake of ginseng, and protein intakeFoster 2015 [46]Framingham OffspringUSA1998–200145.259.06.6Highest vs lowest PA1711802Incident CKD was defined as an estimated GFR of <60 mL/min/1.73 m2Lifestyle factors, age, sex, baseline estimated GFR, BMI, hypertension, DM, and dipstick proteinuriaOgunmoroti [45] 2016MESAUSA2000–200247.262.010.2Ideal vs poor4546506Age, sex, race/ethnicity, education, and incomeMichishita 2016 [47]Fukuoka UniversityJapan2008100.051.65.0Habitual moderate exercise vs no23252Estimated GFR <60 ml/min/1.73 m2 and/or proteinuriaAge, BMI, smoking habit, drinking habit, estimated GFR, HbA1c levels, and systolic and diastolic blood pressure at baselineWakasugi 2017 [49]SHCJapan2008–200936.963.71.0Regular vs no regular exercise294899,404Proteinuria corresponding to ≥30 mg/dl of albumin-to-creatinine ratioAge, hypertension, diabetes, hypercholesterolemia, smoking status, BMI, alcohol intake, regular exercise, and healthy eating habitsGuo 2020 [21]MJ CohortTaiwan1996–201451.639.14.2High vs low PA10,596199,421The incident CKD was identified by medical assessment. Defined as an eGFR of less than 60 mL/min/1.73 m2 or reported a physician diagnosis of CKDAge, sex, education and baseline estimated GFR, physical labour at work, smoking status, alcohol consumption, vegetable and fruit intake, calendar season and calendar year, BMI, hypertension, DM, dyslipidaemia, self-reportof a physician diagnosis of CVD and self-report of a physician diagnosis of cancer, and urinary protein levelParvathaneni 2021 [23]ARICUSA1987–198944.954.024.0Highly active vs inactive482014,537Incident CKD defined as estimated GFR <60 mL/min/1.73 m2 at follow up and ≥$25\%$ decline in estimated GFR relative to baselineAge, sex, race-center, education, smoking status, DASH diet score, diabetes, CHD, hypertension, antihypertensive medication, BMI, and baseline estimated GFRYamamoto 2021 [25]J-ECOHJapan2006–200790.042.810.6High vs inactive401317,331Incident CKD was defined as an estimated GFR of <60 mL/min/1.73 m2 and/or proteinuria determined using the dipstick testBaseline estimated GFR, age, sex, smoking status, alcohol consumption, occupation, job position, overtime work, shift work, commuting mode, sleep duration, other types of physical activity, hypertension, DM, history of CVD, dyslipidemia, hyperuricemia, and BMISuzuki 2022 [48]JMDC Claims DatabaseJapan2005–201660.746.04.0Ideal vs non-ideal41,474865,087Proteinuria corresponding to ≥30 mg/g of albumin-to-creatinine ratioAge, sex, smoking status, BMI, dietary habits, blood pressure, fasting glucose, and TCBMI body mass index, CHD coronary heart diseae, CKD chronic kidney disease, CRP C-reactive protein, CVD cardiovascular disease, DM diabetes mellitus, GFR glomerular filtration rate, HbA1c glycated haemoglobin, HDL high density lipoprotein, NR not reported, PA physical activity, SBP systolic blood pressure TC total cholesterol. Study Abbreviations: ARIC Atherosclerosis Risk in Communities, AusDiab Australian Diabetes, Obesity and Lifestyle Study, Health ABC Health, Aging and Body Composition, J-ECOH Japan Epidemiology Collaboration on Occupational Health, MESA Multi-Ethnic Study of Atherosclerosis, NHANES National Health and Nutrition Examination Survey, SHC Specific Health Checkups and Guidance System in Japan
## Physical activity and risk of CKD
In pooled analysis of 12 studies, the multivariable-adjusted RR ($95\%$ CI) of CKD comparing the most physically active versus the least physically active groups was 0.91 (0.85–0.97) (Fig. 2). The $95\%$ prediction interval for the pooled RR was 0.75 to $1.09\%$, which is the range within which the true RR for any new single study will usually fall. There was substantial heterogeneity between the contributing studies (I2 = $71\%$, 48 to $84\%$; $p \leq 0.001$). Exclusion of any single study at a time from the meta-analysis did not change the direction or significance of the association (Appendix 5).Fig. 2Observational cohort studies of physical activity and risk of chronic kidney disease included in meta-analysis. The summary estimate presented was calculated using random effects models and was based on fully adjusted estimates; sizes of data markers are proportional to the inverse of the variance of the relative ratio; CI, confidence interval (bars); PA, physical activity; RR, relative risk; ++, adjustment for conventional risk factors excluding inflammation, i.e. age and/or sex plus body mass, socioeconomic status, alcohol consumption, smoking, and comorbidities
## Subgroup analysis and assessment of small study effects
The association between physical activity and CKD risk was consistent across several subgroups, with no significant evidence of effect modification by any of the study level characteristics (Fig. 3). A funnel plot of the 12 studies reporting on the associations between physical activity and risk of CKD showed no evidence of asymmetry (Appendix 6), which was consistent with Egger’s regression symmetry test ($$p \leq 0.11$$). Furthermore, there was no evidence of such selective reporting when studies were grouped by size in meta-regression analysis (Fig. 3).Fig. 3Relative risks for chronic kidney disease comparing maximal versus minimal amount of physical activity, grouped according to several study-level characteristics. CI, confidence interval (bars); PA, physical activity; RR, relative risk; *, p-value for meta-regression
## GRADE summary of findings
GRADE ratings for the overall incidence of CKD are reported in Appendix 7. GRADE quality of the evidence was very low.
## Key findings
Given the uncertainty regarding the prospective relationship between physical activity and CKD risk, we re-evaluated the association by conducting a meta-analysis of all published population-based observational cohort studies limited to general populations. In a pooled analysis of 12 observational cohort studies comprising over 1.2 million participants, comparing the most versus the least physically active groups was associated with a $9\%$ lowered risk of CKD. The association was consistent across several relevant subgroups and in sensitivity analysis that involved recalculating the pooled estimate on exclusion of a single study at a time. The quality of the evidence was very low.
## Comparison with previous studies
The only relevant review on the topic is that by Zhu and colleagues which explored the relationship between physical activity and CKD risk using a systematic review and dose–response meta-analysis of both observational cross-sectional and cohort studies [20]. Their pooled analysis of 8 cross-sectional studies showed weak evidence of an association between physical activity and risk of CKD. Comparing the highest versus lowest level of physical activity, they observed a $16\%$ reduced risk of CKD in pooled analysis of 6 observational cohort studies. Despite the comprehensive nature of the previous review [20], there were some limitations which included pooling estimates across cross-sectional and cohort studies in their dose–response analysis and the limited number of observational cohort studies identified despite a search end date of March 2020. The current study represents the most contemporary evidence on the relationship between physical activity and CKD risk in general population participants. Our review involved about five-fold more participants than the previous meta-analysis [20], providing more power to investigate the magnitude of the association. We showed a $9\%$ risk reduction in CKD and our assessment of publication bias showed no significant evidence of small study bias, which was contrary to that reported by Zhu et al. [ 20].
## Explanations for findings
Exercise training and physical activity types such as aerobic and resistance training have the ability to positively modulate dysglycaemia, high blood pressure, obesity, dyslipidemia, and inflammation [50, 51], which are all major risk factors for CKD. Habitual physical activity may also protect against CKD via improved cardiovascular and renal endothelial dysfunction, improved insulin sensitivity, alleviation of sympathetic overactivity, slowing down the atherosclerotic process, and reduction in adipocytokines, which can damage the kidney endothelium [52–55].
## Implications of findings
The current findings on the potential for high levels of physical activity to reduce the risk of CKD add to the accumulating evidence base on the health benefits of physical activity, especially in reducing the incidence of NCDs. Current physical activity guidelines recommend a minimum of 150 min/week of moderate-intensity or 75 min/week of vigorous-intensity aerobic PA/ exercise for adults, given that these levels are associated with substantial benefits in the majority of people [56–58]. However, it is documented that many individuals do not even meet these minimum levels [59, 60]. Data on worldwide trends in insufficient physical activity from 2001 to 2016 showed that the global age-standardized prevalence of insufficient physical activity was $27.5\%$ [61]. Given the strong link between physical activity and major NCDs, it was agreed by the World Health Organization member states that one of the ways to improve the prevention and treatment of NCDs, was to achieve a $10\%$ relative reduction in the prevalence of insufficient physical activity by 2025 [62]. Chronic kidney disease even in its early stages is associated with extremely high morbidity and mortality, enormous economic burden and loss of productivity [2]; hence, it is a disease that warrants urgent attention. Physical activity in any form has health benefits and there is a need to promote physical activity urgently via clinical practice and population wide approaches. It has been suggested that implementing the following policies might increase population levels of physical activity in order to reduce physical inactivity by $10\%$ by 2025: improving provision of infrastructure for non-motorised modes of transportation such as walking and cycling and encouraging their use; promoting participation in active leisure time activities; creating more opportunities for physical activity in public open spaces and parks; addressing cultural barriers that might lead to reduced participation in physical activity; and providing opportunities for safe and accessible leisure-time activities to women, who have been documented to have lower levels of physical activity [61]. Finally, though physical activity is an important strategy for the primary prevention of CKD, it may only be one piece of the puzzle. In addition to engaging in habitual physical activity, other strategies include adoption of healthy lifestyles such as consuming a healthy diet, achieving healthy weights, and avoiding tobacco use as well as beneficial modulation of modifiable risk factors such as obesity, smoking, hypertension, hyperlipidaemia and diabetes.
## Strengths and limitations
The strengths of the current evaluation include (i) the use of only observational cohort studies with at least one year follow-up, hence ensuring temporality in the association; (ii) ability to explore if the association is modified by clinically relevant study level characteristics; (iii) evidence of no significant small study effects (publication bias); (iv) assessment of the risk of bias for each individual study and the certainty of the evidence using well-established tools; and (v) sensitivity analysis to test the robustness of the association. There were several limitations, but these were mostly inherent to the studies and not the methodological approach. First, there was variation in the assessment and categorisation of physical activity exposures across studies, which could have introduced biases in our pooled results. For example, whereas some studies reported risk comparisons as high vs low, others reported it as any vs never. This did not enable transformation into consistent comparisons such as quantiles; hence comparisons could only be made between the most and least physically active. This approach is however, consistent with previous studies [12, 13, 63, 64]; it is unlikely this approach will impact the findings as there is evidence showing that pooled results from untransformed data of extreme categories are not very different from results based on transformed data [33]. Furthermore, because most studies did not quantify a unit of measurement for physical activity, a dose–response relationship could not be assessed. Second, the definition of CKD varied across studies. For instance, some studies used the estimated GFR for defining CKD, whereas others used proteinuria or the albumin-to-creatinine ratio. However, the majority of studies used estimated GFR of <60 mL/min/1.73 m2 and/or proteinuria. Furthermore, our leave-one-out sensitivity analysis showed our results were robust. Third, there was a potential for misclassification bias given that physical activity was self-reported. Fourth, given the varying degree of adjustment across studies, we could not evaluate the impact of a uniform approach to statistical adjustment. However, all studies adjusted for several established risk factors for CKD. Fifth, given that diabetes and CVD may exist in the causal pathway between physical activity and CKD and could be mediators, it could be argued that the pooled estimate is over-adjusted as the majority of studies adjusted for these potential mediators. However, this is unlikely given that these comorbidities are well established risk factors and potential confounders. Sixth, there was potential for small study effects [65] which is known to threaten the validity of the results in a meta-analysis [66], given that some of the smaller studies such as Stengel et al. [ 43] and Michishita et al. [ 47] reported larger effect estimates than even the larger studies. However, our assessment of publication bias (the most well-known reason for small study effects) using a variety of methods showed no evidence of small study effects. Seventh, all studies were judged to be at serious risk of bias in at least one domain of the Cochrane risk of bias tool. Finally, given the use of observational study designs with physical activity exposures assessed at baseline, there was potential for biases such as residual confounding, reverse causation, and regression dilution. None of the studies accounted for lag-time bias to minimise reverse causation. Additionally, the findings cannot be attributed to cause and effect. A meta-analysis of individual participant data with objective measures of physical activity and their repeat measures may better quantify the association between physical activity and CKD risk and ascertain if there is a dose–response relationship.
## Conclusion
In the general population, individuals who are most physically active have a lowered risk of CKD compared to those who are not or least physically active.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 207 KB)
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|
---
title: Identification of immune-related genes in diagnosing atherosclerosis with rheumatoid
arthritis through bioinformatics analysis and machine learning
authors:
- Fuze Liu
- Yue Huang
- Fuhui Liu
- Hai Wang
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10033585
doi: 10.3389/fimmu.2023.1126647
license: CC BY 4.0
---
# Identification of immune-related genes in diagnosing atherosclerosis with rheumatoid arthritis through bioinformatics analysis and machine learning
## Abstract
### Background
Increasing evidence has proven that rheumatoid arthritis (RA) can aggravate atherosclerosis (AS), and we aimed to explore potential diagnostic genes for patients with AS and RA.
### Methods
We obtained the data from public databases, including Gene Expression Omnibus (GEO) and STRING, and obtained the differentially expressed genes (DEGs) and module genes with Limma and weighted gene co-expression network analysis (WGCNA). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis, the protein–protein interaction (PPI) network, and machine learning algorithms [least absolute shrinkage and selection operator (LASSO) regression and random forest] were performed to explore the immune-related hub genes. We used a nomogram and receiver operating characteristic (ROC) curve to assess the diagnostic efficacy, which has been validated with GSE55235 and GSE57691. Finally, immune infiltration was developed in AS.
### Results
The AS dataset included 5,322 DEGs, while there were 1,439 DEGs and 206 module genes in RA. The intersection of DEGs for AS and crucial genes for RA was 53, which were involved in immunity. After the PPI network and machine learning construction, six hub genes were used for the construction of a nomogram and for diagnostic efficacy assessment, which showed great diagnostic value (area under the curve from 0.723 to 1). Immune infiltration also revealed the disorder of immunocytes.
### Conclusion
Six immune-related hub genes (NFIL3, EED, GRK2, MAP3K11, RMI1, and TPST1) were recognized, and the nomogram was developed for AS with RA diagnosis.
## Introduction
Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by chronic inflammation that commonly affects individuals aged 50–60 years [1]. Patients with RA experience symmetrical joint pain and swelling, which may lead to joint deformity and progressive joint damage [2].
RA patients also have an increased risk of cardiovascular morbidity and mortality [3]. Atherosclerosis (AS), the accumulation of a fibrofatty lesion in the artery wall with the infiltration of immunocytes such as macrophages, T cells, and mast cells, is a potential reason for coronary and carotid artery disease [4, 5]. Recent evidence suggests that there are similar pathological processes and risk factors in both RA and AS, with chronic inflammation and immune dysfunction being the most significant (5–9).
While the underlying mechanism linking RA and AS is still unknown, it is clear that both conditions involve chronic inflammation and immune infiltration. For example, AS is an inflammatory process that can lead to plaque rupture, thrombosis, and vessel occlusion [10, 11]. In patients with RA, immunological processes can occur many years before diagnosis, during the pre-RA phase [12]. Furthermore, many pathological processes of the artery wall in AS are reflected in RA synovial inflammation, including the infiltration of macrophages and type 1 T helper cells, which have secondary effects on the artery via mediators produced in the synovium [7]. Therefore, identifying immune infiltration and associated inflammatory molecules may have early diagnostic efficacy for RA patients with AS, which is significant in avoiding severe cardiovascular consequences.
In this study, we downloaded RA and AS datasets from the Gene Expression Omnibus (GEO) database and screened for differentially expressed genes (DEGs) using Limma. We identified significant module genes via weighted co-expression network analysis (WGCNA) and performed functional enrichment analysis. We constructed a protein–protein interaction (PPI) network for the intersection genes and identified candidate genes using machine learning algorithms, including the least absolute shrinkage and selection operator (LASSO) and random forest (RF), and immune cell infiltration analysis. We evaluated the key immune-associated diagnostic genes for AS with RA using nomogram and receiver operating characteristic (ROC) curve assessments. This study is useful in screening immune-related diagnostic biomarkers for AS in RA patients.
## Data collection and data processing
We retrieved four gene expression datasets from the GEO database (https://www.ncbi.nlm.nih.gov/geo/), namely, GSE55457, GSE55235, GSE100927, and GSE57691 [13]. The GSE55457 dataset included 11 control samples and 12 RA samples, while GSE55235 included 10 control samples and 10 RA samples. The GSE100927 dataset contained 35 control samples and 69 AS samples, and GSE57691 contained 10 control samples and 9 AS samples. We normalized the gene expression data using the R package “optparse.” The study procedures are summarized in Figure 1.
**Figure 1:** *Workflow of the analysis.*
## Differentially expressed gene screening
We obtained DEGs between RA and the control group with p adj < 0.05 and |log2Fold change (FC)| > 1.2 in GSE55457, and between AS and the control group with p adj < 0.05 and |log2FC| > 1.2 in GSE100927. The R software package Limma was used in this analysis. The DEGs were visualized via the Sangerbox platform (http://vip.sangerbox.com/).
## Weighted gene correlation network analysis
In this study, we utilized the “WGCNA” package in R software to investigate the association between genes and phenotypes by constructing a gene co-expression network [14]. Firstly, we removed $50\%$ of genes with the smallest median absolute deviation (MAD). Secondly, we calculated Pearson’s correlation matrices for all pairwise genes and constructed a weighted adjacency matrix using the average linkage method and a weighted correlation coefficient. The “soft” thresholding power (β) was then used to calculate the adjacency, which was converted into a topological overlap matrix (TOM). To group genes with similar expression profiles into modules, we performed average linkage hierarchical clustering based on the TOM-based dissimilarity measure with a minimum gene group size of 50. Finally, we calculated the dissimilarity of module eigengenes, selected a cut line for the module dendrogram, and merged several modules. WGCNA was employed to identify significant modules in AS, and a visualized eigengene network was created.
## Function enrichment analysis
To explore the biological functions of genes, we utilized the “clusterProfile” package in R software [15]. First, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, using a p-value < 0.05 [16, 17]. The results were visualized using the Sangerbox platform (http://vip.sangerbox.com/). We then identified the intersection of DEGs in both AS and the critical module genes of RA, as well as the intersection of DEGs in RA and the critical module genes of RA. We performed GO and KEGG analyses based on these intersections.
## Protein–protein intersection network construction
To investigate the interaction among proteins, pathways, and co-expression, we utilized the STRING database (https://cn.string-db.org/) to construct the protein–protein intersection (PPI) network of the DEGs for AS and the critical module genes [18]. Cytoscape software was used to identify the significant interacted genes [19]. Only the genes that interacted with each other were chosen for further analysis.
## Machine learning
To further investigate the potential candidate genes for the diagnosis of AS with RA, we performed LASSO and RF analyses. LASSO, a machine learning technique that combines variable selection and regularization, can enhance predictive accuracy [20]. On the other hand, RF is a predictive algorithm that does not impose restrictions on variable conditions, making it capable of providing predictions without apparent variations [21]. We employed the R software’s “glmnet” and “randomforest” packages to conduct LASSO and RF analyses, respectively. The intersection of the two results can serve as the candidate hub genes for diagnosis [22, 23].
## Nomogram construction and receiver operating characteristic evaluation
In order to determine the importance of the candidate genes for the diagnosis of AS with RA, we constructed a nomogram using the “rms” R package. The nomogram consisted of “Points,” which indicated the score of the candidate genes, and “Total Points,” which showed the total sum of all gene scores. The nomogram was an important tool for predicting the diagnosis of AS with RA. We further evaluated the prognostic value of the candidate genes and the nomogram by performing ROC analysis. The ROC analysis generated the area under the curve (AUC) and $95\%$ confidence interval (CI), and an AUC value > 0.7 was considered to have great diagnostic efficacy.
## Immune infiltration analysis
To estimate the infiltration of immune cells based on gene expression profiles, we utilized CIBERSORT, an analytical tool. We evaluated the proportion of immune cells in AS and control groups using this platform [24]. The bar plot was used to visualize the proportion of various immune cells, while the vioplot was used to compare the proportions of these cells between the AS and control groups. The heatmap with Sangerbox platform was used to depict the association of immunocytes [25].
Because the key diagnosis genes that were correlated with RA can regulate the pathogenesis of AS and be mainly enriched in immunity, the immune infiltration analysis can better explore the effect of immunity in AS. For AS and the control groups, the proportion of 22 kinds of immunocytes are shown in Figure 8A. The box plot presented that compared with the control group, naïve B cells, plasma cells, CD4+ naïve T cells, CD4+ memory-activated T cells, follicular helper T cells, activated NK cells, monocytes, M0 macrophage, M1 macrophage, M2 macrophage, resting mast cells, and activated dendritic cells had a lower level in the AS group, while memory B cells, regulatory T cells, gamma delta T cells, and activated mast cells had a high level, as shown in Figure 8B. The correlation of 22 types of immunocytes demonstrates that CD4+ memory resting T cells were positively related to monocytes ($r = 0.55$), monocytes were negatively related to M0 macrophage (r = −.64), CD4+ memory resting T cells were negatively related to M0 macrophage, (r = −0.72), and all the associations are shown in Figure 8C. In summary, the different level of infiltration of immunocytes in RA patients may serve as a potential treatment target.
**Figure 8:** *Immune infiltration analysis between AS and control. (A) The proportion of 22 immunocytes in all samples visualized from the bar plot. (B) Comparison of the proportion of 22 kinds of immunocytes between AS and control groups shown in the vioplot. (C) Association of 22 immunocyte-type compositions. *p < 0.05; **p < 0.01; ***p < 0.001.*
## Statistical analysis
Statistical analysis was conducted to analyze the data obtained in this study. The ROC curve and AUC were constructed using SPSS Version 26.0 (IBM Corporation, Armonk, NY, USA), and the $95\%$ CI was calculated. The proportion of various immunocytes between the RA and control groups was compared using the Mann–Whitney U-test via GraphPad Prism Version 8.3.0 (GraphPad Software, San Diego, CA, USA). A p-value<;0.05 was considered statistically significant.
## Identification of differentially expressed genes
A total of 2,705 DEGs were identified from the RA combined dataset with a p-value < 0.05 and |log2FC| > 1.2. The volcano plot and heatmap presented in Figures 2A, B, respectively, illustrate the differential expression pattern of these DEGs. Similarly, for AS, a total of 5,322 DEGs were identified using the same cutoff criteria of p-value < 0.05 and |log2FC| > 1.2. Figures 3A, B depict the differential expression pattern of these DEGs for AS.
**Figure 2:** *Different expression genes between AS and control groups. (A) Red and green represent DEGs with significantly higher and lower expression level in AS groups, respectively. (B) The heatmap showed the top 20 genes that were significantly expressed in the RA and control groups.* **Figure 3:** *Identification of DEGs via Limma and WGCNA module genes in RA. (A) The volcano plot represents DEGs, of which the red and green triangles refer to significant genes. (B) The heatmap shows the top 20 upregulated and downregulated DEGs from the RA dataset, which are shown in red and blue colors. (C, D)
β = 7 is chosen as the soft threshold based on the scale independence and average connectivity. (E) Clustering dendrogram of the RA and control samples. (F) Gene co-expression modules with different colors under the gene tree. (G) Heatmap of eigengene adjacency. (H) Heatmap of correlation between module genes and RA shows that the pink module has the highest association with RA. For each pair, the top left triangle is colored to represent the correlation coefficient; the bottom right one is colored to indicate the p-value. (I) Correlation plot between module membership and gene significance of magenta module genes.*
## Weighted gene co-expression network analysis and critical module identification
We constructed a scale-free co-expression network using the weighted gene co-expression network analysis (WGCNA) to identify the most associated module in RA. A “soft” threshold β of 7 was chosen based on the scale independence and average connectivity (Figures 3C, D). The clustering dendrogram of RA and control was generated, and 26 gene co-expression modules in different colors were obtained with a module merge threshold of 0.25 and a minimum size of 50, as shown in Figures 3E–G. Clinical correlation analysis results showed that the pink module had the highest association ($r = 0.73$, p-value < 0.001) with RA Figure 3H. Thus, we selected the pink module, which consisted of 206 genes, for further analysis. We conducted a correlation analysis between module membership and gene significance, and found a significant positive correlation between them (correlation coefficient = 0.64, p-value < 0.001) Figure 3I. These results indicated that the genes in the pink module were most closely related to RA.
## Functional enrichment analysis of RA
To validate the reliable extent of GSE55457, we implemented enrichment analysis for the intersection of genes from Limma and module genes. A total of 164 common genes were obtained, as shown in Figure 4A.
**Figure 4:** *Function enrichment analysis of the intersection of genes for RA. (A) The intersection of DEGs via Limma and WGCNA module genes includes 164 genes, which were shown in the Venn diagram. (B) KEGG analysis of the intersection of genes. Various significant pathways and associated genes are represented with different colors. (C–E) The GO analysis includes biological process, cellular component, and molecular function. The y-axis represents GO terms, and the x-axis represents gene ratio involved in corresponding GO terms. The size of circles represents gene numbers, and their color refers to p-value.*
KEGG analysis elucidated that common genes were involved in “p53 signaling pathway” and “Apoptosis”, as shown in Figure 4B. The results of GO analysis revealed that common genes were enriched in biological process (BP) terms, including “immune system process”, “immune response”, and “regulation of immune response”, as shown in Figure 4C. For cellular component (CC) ontology, the common genes are involved in “cytosol”, “nuclear part”, and “nuclear lumen”, as shown in Figure 4D. For molecular function (MF), the results showed that “drug binding” was the most significant term in common genes, as shown in Figure 4E.
The results showed that the common genes for RA were associated with immune response, which were highly related to the pathogenesis of RA.
## Enrichment analysis of AS with RA and screening node genes via the protein–protein interaction network
The intersection of the DEGs for AS and the module genes for RA included 53 genes, as seen in Figure 5A. To explore the relationship between RA-related genes with the pathogenesis of AS, enrichment analysis was performed based on these genes. The KEGG analysis showed that 53 genes mainly enriched in “NF-kappaB signaling pathway” and “Neurotrophin signaling pathway”, which were all closely associated with the immune system, as shown in Figure 5B. GO analysis revealed that genes were involved in “NF-kappaB signaling pathway”, “I-kappaB phosphorylation” (BP), “cytosol”, “cytoskeleton” (CC), and “transferase activity” (MF), as shown in Figures 5C–E.
**Figure 5:** *Functional enrichment analysis of common genes from RA with AS and the recognition of node genes with the PPI network. (A) Venn diagram shows 53 genes are recognized from the intersection of genes in RA with Limma and SLE with WGCNA. (B) KEGG analysis of 53 common genes. (C–E) GO analysis (biological process, cellular component, and molecular function) of 53 common genes. (F) The PPI network demonstrates that 23 genes interact with each other. (G) The column shows the gene nodes of 23 genes in the PPI network.*
A PPI network was constructed, in which 22 genes can interact with each other, as shown in Figure 5F. The node genes were ranked by node numbers in Figure 5G.
## Identification of candidate hub genes via machine learning
LASSO regression and RF machine learning algorithms were utilized to identify potential candidate genes associated with the diagnosis of AS with RA. LASSO regression analysis identified 22 genes that were closely associated with the disease (Figures 6A, B). In the RF algorithm, we evaluated the importance of genes based on indicators such as mean decrease accuracy (MDA) and mean decrease gini (MDG) (Figures 6C, D). The AUC and $95\%$ CI of these genes in LASSO regression and the intersection of MDA and MDG in RF machine learning algorithms were calculated, and the ROC curves were plotted. The results showed high accuracy for the LASSO regression (AUC 0.999, CI 0.971–1) and RF machine learning algorithms (AUC 0.995, CI 0.971–0.986) (Figures 6E, F). The intersection of the top 15 most important genes from RF and 22 genes from LASSO were visualized in Figure 6E, which identified six genes (NFIL3, EED, GRK2, MAP3K11, RMI1, and TPST1) as key diagnosis genes for the final validation.
**Figure 6:** *Machine learning in identifying key diagnosis genes for RA with AS. (A, B) Key genes identified in the LASSO model. Twenty-two genes are the most suitable for diagnosis. (C) The random forest algorithm ranks the top 15 most important genes based on MDA and MDP. (D) The intersection of genes of the above two algorithms is shown in the Venn diagram. (E) The ROC curve of the LASSO model. (F) The ROC curve of random forest algorithm.*
## Diagnosis value evaluation
We constructed the nomogram with six key diagnosis genes, as shown in Figure 7B. The AUC and $95\%$ CI of these genes were calculated with the construction of ROC curves to evaluate the diagnostic efficacy as shown in Figure 7A. The results were as follows: NFIL3 (AUC 0.907, CI 0.8515–0.9622), EED (AUC 0.915, CI 0.8582–0.9712), GRK2 (AUC 0.986, CI 0.9669–1), MAP3K11 (AUC 0.954, CI 0.9089–0.9984), RMI1 (AUC 0.953, CI 0.9157–0.9903), TPST1 (AUC 0.815, CI 0.723–0.9076), and nomogram (AUC 0.996, CI 0.9839–1). We validated the model with GSE55235 and GSE57691, as shown in Figure 7A. *All* genes and nomogram showed a high value of diagnosis for AS with RA.
**Figure 7:** *Construction of the nomogram and the diagnosis value assessment. (A) The ROC curve of each candidate gene (NFIL3, EED, GRK2, MAP3K11, RMI1, and TPST1), nomogram, and the validation in GSE55235 and GSE57691. (B) Nomogram for diagnosis RA with AS.*
## Discussion
Accumulation of plaque in the artery wall, known as, is a primary cause of cardiovascular diseases and is closely associated with complications of the heart, brain, and kidney (26–28). Due to the difficulty in diagnosing and treating AS, finding an appropriate diagnostic biomarker is crucial to improve the prognosis [29]. AS and RA share similar pathological processes, and the mortality rate of AS in RA patients is significantly increasing [30]. Therefore, we performed bioinformatics analysis and machine learning methods to construct a nomogram to evaluate the diagnostic efficacy of AS in RA patients. We identified six key immune-related candidate genes (NFIL3, EED, GRK2, MAP3K11, RMI1, and TPST1) and constructed a nomogram.
Nuclear-factor interleukin 3 (NFIL3), also known as E4BP4, is a new biomarker for diagnosing AS in RA patients. NFIL3 exerts a transcriptional repressing function by binding to an activation transcription factor (ATF) DNA consensus sequence site [31]. As a crucial transcription factor in the immune system, the expression level of NFIL3 is regulated by cytokines and mainly found in natural killer cells, B lymphocytes, T lymphocytes, and other immune cells (31–33). As a crucial transcription factor in the immune system, the expression level of NFIL3 is regulated by cytokines and mainly found in natural killer cells, B lymphocytes, T lymphocytes, and other immune cells (34–36). Inhibition of NFIL3 expression in CD4+ T cells decreases the level of IL10, worsening autoimmune encephalomyelitis [37]. NFIL3 promotes the Th2 lineage while inhibiting the Th17 lineage and suppresses the production of IL-12 p40 in macrophages, which is associated with the progression of colitis [37, 38]. Additionally, the anti-inflammatory effect of NFIL3 in immunity plays a crucial role in autoimmune diseases. NFIL3 has a high expression level in CD4+ T cells of patients with systemic lupus erythematosus (SLE) and suppresses the activation and self-reactivity of T cells and subsequent autoimmune response by downregulating CD40L [39]. T follicular helper cells in patients with SLE also show a high level of NFIL3 but a low level of phosphorylation [40]. Furthermore, the deficiency of NFIL3 is associated with juvenile idiopathic arthritis and induces more severe arthritis [41]. The significant increase in NFIL3 in patients with RA may be associated with the production of multiple pro-inflammatory cytokines and RA progression [42]. However, the association of NFIL3 with AS is still unclear. Due to the pro-inflammatory effect of NFIL3 in patients with RA, and the inflammation being a crucial factor in plaque rupture and stability, we suggest that NFIL3 could be a candidate diagnostic gene for AS in RA patients.
Embryonic ectoderm development (EED) is a nuclear factor and a transcriptional repressor. It is a member of the polycomb repressive complex and is involved in the proliferation and differentiation of lymphocytes as well as embryonic development (43–45). WAIT-1, a protein cloned from EED, interacts with integrins at the plasma membrane and plays a crucial role in immunity [46, 47]. The activation of the integrin receptor can recruit EED to the plasma membrane, where it participates in the antigen receptor transduction in T cells [44, 48]. EED also interacts with the neutral sphingomyelinase 2, which is involved in inflammation, heart failure, AS, and other biological processes [45, 49, 50]. The production of ceramide via sphingomyelin hydrolysis is involved in the formation of atherogenic plaques, making the sphingomyelinase an important target in the treatment of AS [50]. The production of ceramide via sphingomyelin hydrolysis is involved in the formation of atherogenic plaques, making the sphingomyelinase an important target in the treatment of AS.
G protein-coupled receptor (GPCR) kinase 2 (GRK2) is a key node in multiple signaling networks and interacts with various cellular proteins associated with signal transduction. This interaction further promotes signal propagation after GPCR activation [51]. The signal transduction involves various cells’ activation, including endothelial cells. Excessive angiogenesis is an important factor in the development of inflammatory diseases, such as RA [52, 53]. A high expression level of GRK2 has been detected in the synovial tissues of RA patients [51]. It has been proven that GRK2 participates in the progression of AS. The mouse with a GRK2 deficiency demonstrates defective angiogenesis and increasing chemokine and adhesion molecules as AS progresses [54]. Moreover, GRK2 is a potential upstream kinase for vinculin via mediating phosphorylation of vinculin, which further induces the disruption of the VE-cadherin/catenin complex, promoting the generation of atherogenesis [55]. In this study, GRK2 is identified as one of the candidate diagnosis biomarkers for AS with RA.
Mitogen-activated protein kinase 11 (MAP3K11) is a potential target for immune treatment due to its expression in T cells and its regulatory role in T-cell activation and cytotoxicity [56]. In addition, MAP3K11 is upregulated by mechanical stress and is associated with the differentiation of bone marrow stromal cells [57, 58]. MAP3K11 has also been identified as a target for AS, as its inhibition can reduce the expression of key genes in coronary artery disease and the migration of vascular smooth muscle cells (59–61). It can also be used as a diagnosis marker.
RecQ-Medoayed Genome Instability 1 (RMI1) is crucial for maintaining genomic stability and regulates adipocyte hyperplasia to maintain energy stability [62]. RMI1 is upregulated by obesity and high-glucose conditions and plays a role in maintaining genome integrity during replicative stress [63, 64].
Protein-tyrosine sulfotransferase 1 (TPST1) catalyzes the sulfuration of tyrosine residues within the acidic motif of polypeptides [65]. It has been proven that TPST1 can regulate immune and inflammatory response through catalyzing sulfation [66] involved in regulating immune and inflammatory responses through tyrosine sulfation [67]. Additionally, tyrosine sulfation contributes to monocyte recruitment, a major factor in AS development, making drugs inhibiting TPST1 favorable in AS treatment [68, 69]. In this study, TPST1 is selected as a candidate diagnosis biomarker.
It has been identified that immune cells and inflammation play a crucial role in the pathogenesis of AS [70]. The interactions between immunocytes and the production of pro-inflammatory and anti-inflammatory chemokines have an important influence in the plaque rupture [5, 71, 72]. In AS patients and the animal models of AS, it has been observed that circulating monocytes are associated with the size and stage of plaque [73, 74]. Monocytes can further differentiate into macrophages, the key component of plaque, and become foam cells after the accumulation [70]. Dendritic cells also participate in the adaptive immune response to AS-associated antigens and the formation of foam cells, further promoting the development of AS [75, 76]. Furthermore, Th1 cells are the main type of CD4+ T cells in AS, which produce a large number of pro-inflammatory cytokines, while Th2 cells can produce IL-13 and IL-5 to antagonize atherosclerosis (77–79). The expression level of Tregs has decreased with the progression of AS [80, 81]. Moreover, B2 cells, which participate in antibody production, dependent on T cells, promote the progression of AS. In our study, naïve B cells, plasma cells, CD4+ naïve T cells, CD4+ memory-activated T cells, follicular helper T cells, activated NK cells, monocytes, M0 macrophage, M1 macrophage, M2 macrophage, resting mast cells, and activated dendritic cells had a lower level in AS patients, while memory B cells, regulatory T cells, gamma delta T cells, and activated mast cells had a high level in AS patients, consistent with previous studies. In summary, the study on immune manifestation and inflammatory cytokines can favor the diagnosis and treatment for AS.
## Conclusion
In this study, we have successfully identified six immune-related hub genes (NFIL3, EED, GRK2, MAP3K11, RMI1, and TPST1) using bioinformatics analysis and machine learning algorithms. *These* genes have shown a potential to serve as diagnostic candidate genes for AS in RA patients. Furthermore, our study has also highlighted the immune dysfunction in AS with RA. We have also constructed a nomogram for diagnosing AS with RA, which can aid in clinical decision-making. Overall, our findings may provide new insights into the pathogenesis and diagnosis of AS with RA. Further validation studies are warranted to confirm the clinical relevance of these genes in AS with RA.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material.
## Author contributions
YH, FHL and HW participated in reviewing the articles. FZL wrote the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1126647/full#supplementary-material
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---
title: Characterization of the salivary microbiome before and after antibiotic therapy
via separation technique
authors:
- Katarzyna Pauter-Iwicka
- Viorica Railean
- Michał Złoch
- Paweł Pomastowski
- Małgorzata Szultka-Młyńska
- Dominika Błońska
- Wojciech Kupczyk
- Bogusław Buszewski
journal: Applied Microbiology and Biotechnology
year: 2023
pmcid: PMC10033590
doi: 10.1007/s00253-023-12371-0
license: CC BY 4.0
---
# Characterization of the salivary microbiome before and after antibiotic therapy via separation technique
## Abstract
### Abstract
In the present research, the MALDI-TOF MS technique was applied as a tool to rapidly identify the salivary microbiome. In this fact, it has been monitored the changes occurred in molecular profiles under different antibiotic therapy. Significant changes in the composition of the salivary microbiota were noticed not only in relation to the non antibiotic (non-AT) and antibiotic treatment (AT) groups, but also to the used media, the antibiotic therapy and co-existed microbiota. Each antibiotic generates specific changes in molecular profiles. The highest number of bacterial species was isolated in the universal culture medium ($72\%$) followed by the selective medium ($48\%$ and $38\%$). In the case of non-AT patients, the prevalence of *Streptococcus salivarius* ($25\%$), *Streptococcus vestibularis* ($19\%$), *Streptococcus oralis* ($13\%$), and *Staphylococcus aureus* ($6\%$) was identified while in the case of AT, *Streptococcus salivarius* ($11\%$), *Streptococcus parasanguinis* ($11\%$), *Staphylococcus epidermidis* ($12\%$), *Enterococcus faecalis* ($9\%$), *Staphylococcus hominis* ($8\%$), and Candida albicans ($6\%$) were identified. Notable to specified that the Candida albicans was noticed only in AT samples, indicating a negative impact on the antibiotic therapy.
The accuracy of the MALDI-TOF MS technique was performed by the 16S rRNA gene sequencing analysis—as a reference method. Conclusively, such an approach highlighted in the present study can help in developing the methods enabling a faster diagnosis of disease changes at the cellular level before clinical changes occur. Once the MALDI tool allows for the distinguishing of the microbiota of non-AT and AT, it may enable to monitor the diseases treatment and develop a treatment regimen for individual patients in relation to each antibiotic.
### Key points
The salivary microbiota of antibiotic-treated patients was more bacteria variety MALDI-TOF MS is a promising tool for recording of reproducible molecular profiles *Our data* can allow to monitor the treatment of bacterial diseases for patients
## Introduction
Since the time when the first antibiotics were introduced to treat bacterial infections, drug resistance to pathogens has become a serious health problem. The reasons include various factors such as an irresponsible dosage of antibiotics, naturally occurring mutations, and the transmission of drug-resistant strains. Microorganisms can have both positive and negative impacts. Many of them can make food go bad and cause serious diseases. For this reason, it is extremely important to search for quick and reliable methods to identify the basic infectious agents such as bacteria, which is particularly important in the medical diagnostics (Jackowski et al. 2008; Pauter et al. 2020).
Personalized treatments are one of the most important achievements of modern medicine (Garzón et al. 2020). For this field to develop it is necessary for specialists in the field of biology, genetics, biotechnology, bioinformatics, and pharmacology to cooperate with the medical community. This leads to an innovative approach in the diagnostics and, in consequence, in the medical treatment by improving or adapting the pharmacological therapy to the individual needs of patients, the so-called targeted pharmacological therapy, “tailor-made therapy” or personalized medicine (Borg-Bartolo et al. 2020).
On the other hand, the diversity and composition of saliva microbiota seem highly important for the human health and disease. Hence, in the recent years, saliva has attracted widespread interest as a means of simple and rapid testing because the composition of it might reflect the health status. The quick identification of the pathogen causing the infection will enable the implementation of an appropriate therapy (Jackowski et al. 2008; Pauter et al. 2020). Currently, the MALDI-TOF MS is used with great success (Złoch et al. 2020b). The worthwhile point is that this technique is often chosen in the identification of microorganisms for routine clinical testing (Hou et al. 2019; Duncan and DeMarco 2019; Van Belkum et al. 2017).
The human oral microbiome is one of the most active environments for many species of bacteria, where they undergo an extensive interspecies competition to form a multispecies biofilm structure. These bacteria are also present in saliva; they constitute many hundreds and thousands of species, some of which are unique to this specific habitat (Gao et al. 2018). Streptococcus salivarius is considered to be the first human oral colonizer at birth and can therefore play a role in setting up immune homeostasis and controlling the inflammatory reactions of the host. Streptococcus mitis, Streptococcus oralis, and *Streptococcus anginosus* prefer to colonize on oral soft tissues and saliva, while *Streptococcus sanguinis* tends to colonize on teeth (Abranches et al. 2018). There are also opportunistic species among *Streptococcus bacteria* like Streptococcus mutants. Its contribution to caries development is well established (El-sherbiny 2014;Koo and Bowen 2014). Moreover, various Lactobacillus species, especially L. fermentum, L. rhamnosus, L. salivarius, L. casei, L. acidophilus, and L. plantarum are frequent mouth inhabitants and studies show that they antagonize harmful microorganisms and reinforce the dental health (Koll-Klais et al. 2005; Badet and Thebaud 2008; Wasfi et al. 2018).
The other group of microbes in the oral cavity includes Candida species, especially during a long-term antibiotic therapy (Muzyka and Glick 1995). In many individuals, C. albicans is a minor component of their oral flora, which does not generate any clinical symptoms (Cannon and Chaffin 1999). In contrast, when the balance of the microbiota in the oral cavity is disturbed, candida seeks to colonize the oral tissue by creating a biofilm with Streptococcus, which plays a pathogenic role (Tsui et al. 2016; Koo et al. 2018).
According to the recent medical reports and current scientific knowledge, a change in the balance of the oral bacterial composition has the potential to signal pathological conditions. This includes diseases such as halitosis (Haraszthy et al. 2007), caries (Guo and Shi 2013), and periodontosis (Ko et al. 2020), but also systemic diseases including breathing diseases (Gomes-Filho et al. 2010), diabetics (Sabharwal et al. 2019) along with cardiovascular diseases (Fernandes et al. 2014), and cancer (Mager et al. 2005). The characteristics of the salivary microbiome in obese subjects also received attention (Al-Rawi and Al-Marzooq 2017). Nevertheless, healthy salivary bacterium should be identified primarily to describe the changes caused by the disease, which may eventually lead to the development of diagnostic tools to improve the treatment or prevent the disease (Espuela-Ortiz et al. 2019). Additionally, these several studies indicate that salivary bacteria biomarkers in the oral cavity constitute a recognized diagnostic and prognostic tool for a variety of diseases. Hence, many activities were undertaken using hyphenated methods based on the bacterial ribosomal proteins determination (MALDI-TOF MS) (Stîngu et al. 2008; Sun et al. 2016) along with volatile organic compounds (VOCs) detection (gas chromatography-mass spectrometry, GC-MS) (Milanowski et al. 2019). Nevertheless, most of the research on oral microbes utilize the 16SrRNA-based technique (Hrynkiewicz et al. 2008).
The literature often focuses on pathogenic microorganisms and the assessment of their significance in the etiology and course of infectious diseases along with the spread of drug resistance to commonly used antibacterial drugs. However, what is interesting is the fact whether and what differences in the prevalence of the bacterial strain colonization occur in people with bacterial infections undergoing the antibiotic therapy compared to non-antibiotic therapy. Hence, in this study, the salivary microbiota after antibiotic treatment was described. The MALDI-TOF MS technique as a tool to provide a rapid diagnosis and identification of microbiota was used; different media were investigated in order to achieve a complimentary microbiota identification. At the same time, we utilized the 16S rRNA gene sequencing to determine the selected salivary bacteria in order to obtain information on the effectiveness and accuracy of the investigated spectrometric method. Additionally, the present research focused on checking the ability of the MALDI technique for the investigation of fast monitoring of the patients under antibiotic therapy. The samples from 14 patients not treated with antibiotics were intended to determine possible changes in the local population related to both local epidemiological factors and hospitalization factors. This study was performed to determine the possible impact of the hospital environment on changes in the patient’s microbiome after 10 days of hospitalization with or without antibiotic treatment. Therefore, in the population of patients subjected to antibiotic therapy, the focus was on changing the above-mentioned profile, which allowed to separate the changes resulting from the hospitalization itself from those caused by antibiotic therapy. As we have presented in the profile of patients undergoing antibiotic therapy, we have noticed an increase in the diversity of strains; the emergence of bacteria typically associated with the surgical ward. The interesting point also was to compare if the MALDI technique would differentiate each administrated antibiotic in the relevant time period. Moreover, the optimal conditions of the growth medium for the identification of microorganism by using the MALDI-TOF MS were examined. A correlation between protein profile changes of the non-AT and AT microbiota was performed and studied in details. Additionally, the impact of antibiotic and pathogen’s presence on the patients’ therapy was described.
## Saliva samples preparation protocol
In this study, 38 samples were investigated; the saliva samples were provided from fourteen non-AT and twenty-four AT patients, who were hospitalized in very serious condition. The present research involved demographic data from 38 consecutive patients admitted to the Department of General, Gastroenterological and Oncological Surgery of the Nicolaus Copernicus University in Toruń. Patient data is strictly identified and marked with both name and surname, 11-digit ID number, description of the medical history number, date of sampling and type of antibiotic, its dose, and disease being the reason for its recommendation. Patients with diseases in the oral cavity were not eligible for the study. In some of the qualified patients, there were no indications for antibiotic therapy. This group was used to determine the local status of the salivary microbiome dependent on both population and hospitalization effects as mentioned. Initially, 42 patients were qualified for the study in the proportion of 14 non-antibiotic vs 28 antibiotic/2: 1 ratio; however, due to difficulties in obtaining the appropriate sample volume, 2 patients treated with an antibiotic were disqualified from the experiment—which was also a limitation of the study. Among the patients receiving antibiotic therapy, the participants undergoing the diabetic foot, surgical wounds, sinusitis, and phlegmon have been immediately subjected to the antibiotic therapy: azithromycin, amoxicillin, ciprofloxacin, clindamycin, cefotaxime and levofloxacin, metronidazole, and piperacillin. Non-antibiotic therapy patients were selected as a control. The average age of the subjects was 58.9 years old. Of these participants, $68\%$ were men and the rest $32\%$ were women. The present research did not classified the patients to the specific patient data (age, sex) once we were restricted in the sample number collection. Generally, all the patients were instructed to avoid eating, drinking, and brushing their teeth for 2 h before the saliva collection.
For the sample cultivation, an universal growth medium—Brain Heart Infusion (BHI) (Sigma-Aldrich, Germany) and two selective mediums such as the Vancomycine Resistant Enterococci Agar Base (VRE) (Sigma-Aldrich, Germany), and the Azide Blood Agar BASE (AZB) (Sigma-Aldrich, Germany) were chosen. The saliva was diluted with sterile peptone water (Sigma-Aldrich, Germany) in a 1:9 (v/v) ratio. The cultivation was performed by the serial dilution method based on the procedure of Abouassi and co-workers (Abouassi et al. 2014) with a slight modification. The peptone water was used instead of $0.9\%$ NaCl to support the growth of the fastidious microorganisms. Subsequently, 100 μL of each suspension was streaked on the Petri dishes containing the chosen culture medium. Thereafter, they were incubated for 24 h (BHI, AZB) or 48 h (VRE) at a constant temperature of 37 °C in aerobic conditions. Moreover, the number of colony-forming units (CFU/mL) was determined by the colony counter (IUL S.A., Barcelona, Spain) and compared in non-AT and AT patients.
## MALDI-TOF MS measurements
All fresh colonies isolated in the different medium (as described in “Saliva samples preparation protocol” section) were then used for the identification. Owing to problems with the identification by the MALDI-TOF MS on AZB (considered as a selective medium), the respective medium was changed to the Tryptic Soy Agar (TSA, Sigma-Aldrich, Germany). TSA is considered a universal medium and applied as a routine diagnostic medium. The colonies isolated on the AZB medium were transferred to the TSA medium, incubated for 24 h at 37 °C in aerobic conditions, then identified using the MALDI tool.
The standard extraction protocol was adopted from our previous study, Pauter et al. with some changes (Pauter et al. 2022). The modification included suspending the pellet in 150 μl of distilled water and adding 450 μl of ethanol. Afterwards, the pellet was centrifuged for 5 min at 20 °C, 14, 400 rpm, then the supernatant was removed. Subsequently, the vacuum concentration was used to dry the pellet (8–10 min). The $70\%$ formic acid (Merck, 98–$100\%$, Germany), acetonitrile (Fluka Analytical Sigma Aldrich, Germany), was added into the dried pellet [1,1], and then centrifuged (2 min, 20 °C, 13, 000 rpm). Next, 1μl of the material was dropped into the MALDI target, left to dry and covered with 1 μl matrix alpha-cyano-4-hydroxycinnamic acid (HCCA) (Sigma-Aldrich, Switzerland). The external calibration of the instrument was performed using the Bacterial Test Standard, (BTS, Bruker, Bremen, Germany). Each spot was analyzed in duplicate in order to minimize the effect of changes in the sample preparation. The MALDI-TOF mass spectra measurements were carried out by an Ultraflextreme instrument (Bruker Daltonik, Bremen, Germany) operated in the positive ion mode using the BrukerBiotyper 1.1 software (Bruker Daltonik GmbH). The Flex Analysis 2.4 software (Bruker Daltonik GmbH, Bremen, Germany) was used to visualize the MS spectra. Moreover, the data were analyzed automatically by the MBT Compass software (Bruker Daltonik GmbH, Bremen, Germany) and the mass spectra were compared with the spectra of known microbial isolates from commercial libraries provided by Bruker Daltonik. Based on this data, the phyloproteomic tree (dendrogram) was prepared. The spectra match was evaluated by a proprietary algorithm and generated a logarithmic value (score) ranging from 0.0 to 3.0.
## Statistical analysis
The heat maps, the hierarchical clustering analysis, and radar chat were generated using the STATISTICAL Release version 7.0 software and Microsoft Excel 2010. All raw data were taken into consideration and correlated on the basis of the used medium and antibiotic, while the identified microbiota was performed.
## 16S rRNA gene sequencing
To correlate the data obtained from the identification by the MALDI-TOF MS, the 16 rRNA sequencing method was performed. There was an attempt to select one species (Neisseria perflava, Enterococcus faecalis, Staphylococcus aureus, Streptococcus epidermidis, Streptococcus salivarius, Streptococcus pneumoniae, Staphylococcus cohnii, and Lactobacillus plantarum) each of the identified microbial genus. Moreover, *Bacillus subtilis* was also chosen, based on the unreliable low score (1.6) generated by the MALDI-TOF MS.
The procedure of the DNA isolation was carried out according to the protocol supplied in the DNeasyUltraClean Microbial Kit (QIAGEN, Wrocław, Poland). The polymerase chain reaction amplification was performed using universal primers as forward: 27F(5-AGAGTTTGATCMTGGCTCAG-3) and reverse primers: 1492R(5-GGTTACCTTGTTACGACTT-3). After that, the PCR amplification products were purified and the sequencing of the amplified fragments was performed by using the Sanger dideoxy method by Genomed (Warsaw, Poland). Then, from the received sequences, the contigs were submitted using the BioEdit Sequence Alignment Editor ver. 7.2.5 software (12.11.2013) (Hall 1999). Finally, the Basic Local Alignment Search Tool (BLAST) database, available in the National Center for Biotechnology Information (NCBI) was used to compare the consensus sequences with the known 16S rRNA gene sequences deposited in the GenBank. The accession numbers of the studied DNA sequences were determined.
## Ethical considerations
This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Bioethical Commission of Collegium Medicum in Bydgoszcz of Nicolaus Copernicus University in Torun, Poland, according to the agreement number $\frac{477}{2021}$ – 14.09.2021. A written informed consent was obtained from all the participants.
## Results
In the present research, the emphasis is put on the microbiota differences in patients with non- and under-antibiotic therapy. Moreover, to date, no work has been focused on the investigation of protein profile changes of isolate species identified in the patient’s microbiota not undergoing and undergoing the antibiotic therapy.
The colony-forming unit results indicate that in the saliva samples collected from the AT group bacteria cells were found between 105 and 107 CFU/mL. However, the number of CFU in the non-AT patients was noticed more than 107 CFU/mL ($64\%$ of patients). Only in the case of non-AT2, non-AT6, non-AT7, non-AT8, and non-AT10, ($36\%$) the bacterial count was around 106 CFU/mL. The slight differences in the abundance can be associated with various lifestyles and distinct genotypes of the hosts (Ling et al. 2013). Figure 1A presents, the heat map combined with the dendrogram representing the total of isolated bacteria (%) in both group of patients (AT and non-AT) and differences between medium to isolate possible microbiota. The heat map was created to show the complimentary of the culture medium applied. The most percentage of isolated bacteria were observed in the universal growing media (BHI), which reported $72\%$, followed by the VRE medium ($48\%$), and AZB medium ($38\%$). Figure 1A shows that Streptococcus salivarius, Streptococcus parasanguinis, *Streptococcus oralis* and Streptococcus vestibularis, Enterococcus faecalis, *Staphylococcus hominis* were identified in all the cultivation medium. Fig. 1The abundance of isolated microorganisms in correlation with the used growth media (A) and the comparison between both non-AT as well as AT of the S. salivarius protein profiles in dependence of used culture media (B) and comparison between non-AT and AT patients on S. vestibularis, example – Z1–Z6 representing common and characteristic MS signals of each (C) Information regarding isolates identified in all the samples by the MALDI-TOF MS was presented in details (name of strain, score, and antibiotic used) in Table 1 (non-AT) and Table 2 (AT). Additionally, the used culture medium was also included. According to Tables 1 and 2, it can be observed that the log (score) value (level of identification) was in most cases above 2.0.Table 1 Identification results of all isolates identified in non-AT patients using MALDI-TOF MSPatient nameAntiboticBest much in MALDI Biotyper databaseScore valueNon-antibiotic treatment (non AT) Non-AT1-*Streptococcus salivarius* 0807M25049501 IBS (BHI)2.06Streptococcus pneumoniae DSM 11868 DSM (BHI)*2.11Streptococcus parasanguinis DSM 6778T DSM_2 (VRE)1.85 Non-AT2-*Streptococcus vestibularis* DSM 5636T DSM (BHI/VRE/TSA)2.11Streptococcus sanguinis CCUG 29269 CCUG_corr (BHI)2.35 Non-AT3-*Streptococcus salivarius* 0807M25049501 IBS (BHI)2.34Streptococcus mitis V17_201158 MUZ (BHI)2.05Streptococcus parasanguinis 14137939_2 MVD (TSA)1.91 Non-AT4-*Streptococcus vestibularis* DSM 5636T DSM (BHI)1.93Streptococcus salivarius 0807M25049501 IBS (BHI/VRE)2.07Streptococcus salivarius DSM 20560T DSM (TSA)2.07 Non-AT5-*Rothia mucilaginosa* DSM 20446 DSM (BHI)1.80Streptococcus vestibularis DSM 5636T DSM (TSA)2.05 Non-AT6-*Streptococcus vestibularis* DSM 5636T DSM (BHI)1.94Streptococcus parasanguinis CCUG 55521 CCUG (BHI)2.31Streptococcus oralis DSM 20379 DSM (VRE/TSA)2.24 Non-AT7-*Streptococcus salivarius* DSM 20560T DSM (BHI)2.30Neisseria perflava DSM 18009T DSM (BHI)2.45Streptococcus anginosus DSM 20563T DSM (VRE)2.20Escherichia coli ATCC 25922 CHB (TSA) *2.29 Non-AT8-*Streptococcus salivarius* 0807M25049501 IBS (BHI/VRE/TSA)2.17 Non-AT9-*Streptococcus oralis* DSM 20395 DSM (BHI/VRE)2.20Streptococcus oralis DSM 20627T DSM (TSA)2.26 Non-AT10-*Streptococcus salivarius* 0807M25049501 IBS (BHI)2.11Staphylococcus aureus ssp aureus DSM 4910 DSM (TSA) *2.51 Non-AT11-*Streptococcus salivarius* DSM 20560T DSM (BHI)2.27Neisseria perflava 1621 PGM (BHI)2.35Staphylococcus aureus ssp aureus DSM 4910 DSM (VRE) *2.28Lactobacillus plantarum DSM 1055 DSM (VRE/TSA)2.25 Non-AT12-Neisseria flavescens C1 2 PGM (BHI)2.18Neisseria perflava DSM 18009T DSM (BHI)2.49Streptococcus oralis DSM 20379 DSM (TSA)2.48 Non-AT13-*Streptococcus vestibularis* DSM 5636T DSM (BHI)2.13Streptococcus gordonii DSM 6777T DSM (BHI)2.01Staphylococcus aureus ATCC 33591 THL (VRE/TSA)2.12 Non-AT14-*Streptococcus vestibularis* DSM 5636T DSM (BHI/TSA)2.08Rothia dentocariosa DSM 43762T DSM (BHI)2.04Streptococcus salivarius DSM 20560T DSM (VRE)1.80Table 2Identification results of all isolates identified in AT patients using MALDI-TOF MSPatient nameAntibioticBest much in MALDI Biotyper databaseScoreAntibotic treatment (AT) AT1AzithromycinStreptococcus oralis DSM 20395 DSM (BHI)2.26Streptococcus salivarius 0807M25049501 IBS (VRE/TSA)2.10 AT2AmoxicillinStreptococcus parasanguinis CS 50_4 BRB (BHI)2.19 AT3PiperacillinStaphylococcus haemolyticus Mb18803_2 (VRE)2.15Enterococcus faecium DSM 13589 DSM (VRE) *2.29 AT4Ciprofloxacin + metronidazoleStreptococcus salivarius 0807M25049501 IBS (BHI)2.24Staphylococcus hominis ssp novobiosepticus DSM 15614T DSM (AZB)2.42 AT5ClindamycinEscherichia coli DH5alpha BRL (BHI) *2.26Streptococcus oralis DSM 20627T DSM (BHI)2.08Enterococcus faecalis 20247_4 CHB (VRE/TSA) *2.46 AT6ClindamycinCandida albicans ATCC 10231 THL (BHI) *2.16Streptococcus sanguinis DSM 20567T DSM (BHI)2.13Staphylococcus epidermidis 10547 CHB (VRE) *2.30Lactobacillus plantarum DSM 1055 DSM (TSA)2.30 AT7CiprofloxacinStreptococcus salivarius DSM 20560T DSM (BHI)2.36Streptococcus parasanguinis 14137939_2 (BHI)2.18Enterococcus faecium DSM 17050 DSM (VRE) *2.40Streptococcus vestibularis DSM 5636T DSM (TSA)2.19 AT8ClindamycinCandida glabrata DSM 11950 DSM (BHI) *2.29Rothia dentocariosa B16575_bh8 IBS (BHI)2.16Staphylococcus epidermidis DSM 1798 (VRE)*2.22Lactobacillus plantarum DSM 1055 DSM (VRE)2.33Streptococcus salivarius 0807M25049501 (TSA)2.06 AT9CiprofloxacinStreptococcus oralis DSM 20379 DSM (BHI/TSA)2.14Lactobacillus paracasei ssp paracasei DSM 2649 (VRE)2.11 AT10PiperacillinStreptococcus parasanguinis CS 50_4 BRB (BHI)2.05Candida albicans DSM 6569 DSM (VRE) *2.15Staphylococcus epidermidis 10547 CHB (TSA)2.08 AT11MetronidazoleRothia mucilaginosa DSM 20445 DSM (BHI)2.23 AT12PiperacillinStreptococcus parasanguinis CS 50_4 BRB (BHI)2.00Bacillus subtilis ssp subtilis DSM 10T DSM (BHI) *1.60Streptococcus salivarius 0807M25049501 IBS (VRE)2.21Staphylococcus epidermidis 10547 CHB (TSA)2.23 AT13Clindamycin + levofloxacinStreptococcus salivarius 0807M25049501 IBS (BHI)2.20Streptococcus parasanguinis 14137939_2 MVD (TSA)2.29 AT14ClindamycinEnterococcus faecalis DSM 20409 DSM (BHI) *2.54 AT15ClindamycinCandida albicans ATCC 10231 THL (BHI) *2.04Serratia marcescens DSM 12481 DSM (BHI)2.38Enterococcus faecalis DSM 20409 DSM (VRE) *2.35Enterococcus faecalis DSM 2570 DSM (TSA)2.34 AT16ClindamycinCandida dubliniensis 99 PSB (BHI) *2.01Enterococcus faecium DSM 13589 DSM (VRE) *2.29Staphylococcus epidermidis DSM 1798 DSM (TSA)2.06 AT17ClindamycinNeisseria flavescens C1 2 PGM (BHI)2.27Micrococcus luteus IMET 11249 HKJ (TSA)2.10 AT18ClindamycinNeisseria perflava DSM 18009T DSM (BHI)2.18 AT19Cefotaxime- AT20CefotaximeRothia mucilaginosa BK2995_09 ERL (BHI)2.37Streptococcus parasanguinis CS 50_4 BRB (BHI)2.44Streptococcus parasanguinis 14137939_2 MVD (VRE)2.26Candida albicans DSM 6569 DSM (VRE) *1.95Staphylococcus epidermidis DSM 1798 DSM (TSA)2.13 AT21AmoxicillinStaphylococcus cohnii ssp urealyticus DSM 6718T(BHI) *2.13 AT22ClindamycinNeisseria mucosa 1591 PGM (BHI)2.12Staphylococcus hominis ssp novobiosepticus DSM 15614T DSM (BHI/TSA)2.43Staphylococcus epidermidis ATCC 14990T THL (VRE)2.16 AT23ClindamycinRothia dentocariosa RV_BA1_032010_D LBK (BHI)2.35Staphylococcus hominis 18 ESL (VRE)2.17Staphylococcus hominis ssp novobiosepticus DSM 15614T DSM (TSA)2.15 AT24ClindamycinNeisseria flavescens C1 2 PGM (BHI)2.24Staphylococcus epidermidis ATCC 12228 THL (VRE)1.83Enterococcus faecalis 20247_4 CHB (TSA) *1.80 The only one - B. subtilis (AT12) was found to be below 1.7 (log (score) = 1.6). Moreover, it is necessary to underline that S. aureus and S. pneumoniae were noticed only in non-AT group, whereas E. faecalis, E. faecium, S. epidermidis, B. subtilis, S.cohnii and yeasts C. albicans, C. glabrata, and C. dubliniensis were found only in AT group. Based on the shown date, it can be noticed that the identification for S. salivarius cultivated on various medium was similar (score value for non-AT4 was 2.07), (Table 1). However, the protein profile of the identified bacteria differed (Fig. 1B). The comparison of the protein profile of *Streptococcus salivarius* based on the culture medium was presented in Fig. 1B. In the case of the non-AT patients, the mass spectra show that the signals at m/$z = 4451$ and m/$z = 5968$ are similar in BHI, VRE, and TSA. Moreover, it can be observed that the differences in intensities of generated signals depend on the culture medium used. However, the signal 2762 m/z was recorded only in the universal media (BHI, TSA). In contrast, this signal disappeared in the case of the VRE medium, and the new signal was registered at 2984 m/z. Moreover, the signal m/$z = 7888$ was observed in the protein profile of S. salivarius identified on the BHI and VRE growing media in the non- and treated group. It is notable that some signals are present only in one mass spectra of non-AT4 in the case of each medium: 9030 and 10268 m/z in BHI; 9443 m/z in VRE and 10840 m/z in TSA. Based on the mass spectra of S. salivarius in the patient group treated with antibiotics (AT13 and AT1), the common signals (m/$z = 2984$; m/$z = 4451$; m/$z = 5968$) can be noticed. Furthermore, the m/$z = 10068$ was noticed in the universal media (BHI and TSA). However, the differences in the protein profile of the studied bacteria strain were also recorded. The signals at 9090 m/z were observed in BHI and at 9444 m/z in the VRE growing medium.
It is notable that the relative intensities and the noise level of the signals of the registered protein profiles depended on the growing employed media. Hence, the use of the selective culture media that contains various components, including antibiotics, salts, and pH indicators can be the only limitation associated with the use of the mass spectrometric techniques. Some components, such as salts, are well-known inhibitors for the mass spectrometry, and various media can induce changes in the bacterial protein expression (Metwally et al. 2015). Therefore, it is supposed that the disappearance of some signals in the mass spectra recorded after the use of the VRE medium of S. salivarius can be correlated with the salt mixture present in this selective culture medium. Karamonová and co-workers (Karamonová et al. 2013) established the optimal cultivation media for the identification of *Cronobacter sakazakii* bio groups using the MALDI-TOF MS. They studied the universal growth media, such as TSA, BHA, Blood Agar Base (Blood Agar Base with sheep blood ($5\%$), BA), and selective cultivation medium *Enterobacter sakazakii* Isolation Agar (ESIA). It was observed that the intensity of the recorded mass spectra was lower in the case of the ESIA medium than in the universal media (TSA, BHA, BA). It was suggested that the unsatisfied intensity of the protein profile of *Cronobacter sakazakii* CB03 can be caused by the deficient composition of the selective medium rather than the universal media and the presence of specific (selective) substances (sodium desoxycholate, sodium chloride, and crystal violet) as inhibitors of growing microbial competitors. Finally, the TSA medium was chosen to further analysis by the MALDI-TOF MS (Karamonová et al. 2013).
Additionally, the mass spectra of *Streptococcus vestibularis* between the non-antibiotic treatment (non-AT5) and the antibiotic treatment (AT7) patients were also compared (Fig. 1C). The common signals at 3944, 4452, and 5968; 6755, 7888 m/z (Fig. 1C) and at 2984 m/z (Fig. 1C-Z2), 5135; 5188 m/z (Fig. 1C-Z4), and 6311m/z (Fig. 1C-Z6) were found in both studied groups. Moreover, the signals at 2731, 2743, 2762, 2778, 2800, 2816 m/z (Fig. 1C-Z1), at 2907 m/z (Fig. 1C-Z2), at 4723; 4788 m/z (Fig. 1C-Z3), and at 6139 m/z (Fig. 1C-Z6) were characteristics only for the non-AT patients. However, in the protein profile of S. vestibularis (AT7), the non-common signals m/z 4923 (Fig. 1C-Z3), m/z 5363, 5391, and 5557 (Fig. 1C-Z5) were also observed. According to the UNIPROT database, the common signals registered at 4452 m/z indicated the 50S ribosomal protein L36 (structural constituent of ribosome) (Maeder and Draper 2005) while those registered at 5968 m/z were found to be responsible for the defense response to bacterium (Bacteriocin-type signal sequence) (Wescombe et al. 2009). Remarkably, the disappearance of the signal at m/z 4723 in the AT group, responsible for the DNA binding and transpose activity can be associated with the mechanism of antibiotics, in this case, ciprofloxacin (it inhibits the DNA replication) (LeBel 1988).
In the next step, the recorded mass spectra for all the isolates were matched to the reference spectra (MSPs), and the phyloproteomic tree was created. Figure 2 represents the hierarchical clustering of the identified isolates and correlation to the reference species (from the MALDI database). Based on the MSP dendrogram (Fig. 2), 11 main clusters indicating genus (Micrococcus, Enterococcus, Serratia, Escherichia, Rothia, Candida, Staphylococcus, Bacillus, Streptococcus, Lactobacillus, and Neisseria) were noticed. The relationship between the identified microorganisms and the reference strains was found. The showing longest distance level correlation between the bacteria strain was observed in the cluster belonging to the Staphylococcus genus. Moreover, the cluster describing the *Enterococcus genus* indicates the shortest distance among the identified species. According to the dendrogram, a close relationship between the Serratia and *Escherichia genus* can be noticed. Moreover, the *Bacillus genus* was also included in the phyloproteomic tree (Fig. 2), and (marked by #), despite the low identification level using the MALDI-TOF MS technique (1.7 >). Furthermore, a close relationship between *Streptococcus salivarius* and *Streptococcus vestibularis* was noticed. According to the research previously published by our group (Złoch et al. 2020b), the problem of distinguishing those species could be overcome by using the MALDI-TOF MS to create protein and lipid profiles. Fig. 2The phyloproteomic tree of all identified isolates based on the obtained MSP identification via MALDI Biotyper platform Considering the manufacturer’s guidelines, the low score value of 1.6 (Table 2), only for *Bacillus subtilis* was obtained while the standard method for the bacteria identification (16S rRNA gene sequencing) was performed. Furthermore, to confirm the accuracy of the MALDI results for all the identified 11 genus of bacteria, the one species from each cluster was selected (Neisseria perflava, Lactobacillus plantarum, Streptococcus salivarius, Staphylococcus aureus, Staphylococcus epidermidis, and Enterococcus faecalis). In addition, the *Streptococcus pneumoniae* as a serious pathogen and *Staphylococcus cohnii* showing the high level of the antibiotic resistance were also chosen. However, the genus with the low abundance of percentage in the identification including Candida, Rothia, Escherichia, Serratia, and Micrococcus was considered in the PCR analysis.
Then, the results received by the MALDI-TOF MS (score > 2.00) were correlated with the 16S rRNA gene sequencing method (excluding the Bacillus subtilis). However, the low identification level was verified in the PCR analysis. On the basis of the data from the PCR assay, the value of identification was over $99.5\%$ for all the studied bacteria species. Moreover, the following accession numbers were given to the bacteria: B. subtilis (MZ336018); N. perflava (MZ191898); *Lactiplantibacillus plantarum* (the previous form Lactobacillus plantarum) (A taxonomic note on the genus Lactobacillus) (MZ411566); S. salivarius (MZ191906); S. aureus (MZ191908); S. epidermidis (MZ411533); E. faecalis (MZ191905); S. pseudopneumoniae (MZ191882), and S. cohnii (MZ191897).
Based on the PCR method, in one case, only the identification compared to the MALDI- TOF MS was slightly different. The protein profile of S. pneumoniae was identified as S. pseudopneumoniae using the 16S rRNA gene sequencing method. Lucia Gonzales–Siles et al. studied the genomic markers for the differentiation and identification of both Streptococcus species. The presence of these unique markers was confirmed by the PCR with reference strains and clinical isolates (Gonzales-Siles et al. 2020).
Summarily, 29 already identified species were represented as predominant species in both non-AT and AT salivary sample groups (Fig. 3). The diversity in the salivary bacteria in the AT group vs the non-AT was observed. Figure 3 (up) illustrates the heat map representing the abundance of all identified species of microorganisms (%).Fig. 3The heat map (up) representing the abundance of identified isolates and radar chat (down) showing the % distribution predominance of all identified microorganism species in non-AT and AT group Moreover, based on the created heat map, the % distribution predominance of all identified microorganism species was performed and shown as a form of the radar chart (Fig. 3 down).
Regarding the investigated radar chat, the differences between the salivary microbiota of non-AT and AT patients were found. It can be observed that the *Streptococcus salivarius* ($25\%$) and *Streptococcus vestibularis* ($19\%$) dominated in the non-antibiotic treatment patients. Another predominant bacteria species in patients with the normal salivary microbiome were *Streptococcus oralis* and Staphylococcus aureus.
The blue area of the web chart (Fig. 3 down) shows that in the non-AT group, the 16 species of bacteria (from S. salivarius to R. mucilaginosa) were identified. Compositionally, the most abundant microorganism present in the AT patients were Streptococcus salivarius($11\%$) *Streptococcus parasanguinis* ($11\%$), *Staphylococcus epidermidis* ($12\%$), *Enterococcus faecalis* ($9\%$), *Staphylococcus hominis* ($8\%$), and Candida albicans ($6\%$).
The orange color indicates that the saliva of patients under the antibiotic therapy was more bacterially rich than the non-AT group. It was also observed that pathogenic microorganisms dominated in the group. It can be assumed that the type of the antibiotic treatment influenced the salivary bacteria composition in the AT patients. In comparison with patients with normal (physiological) salivary microbiota, more diversity of microorganism and more abundance of pathogenic bacteria can be noticed in the AT group, which can be associated with stress conditions under the antibiotic treatment.
The correlation between the identified pathogen and the antibiotic was shown in the cluster analysis (Figs. 4 and 5). For further discussion, the most predominant species identified in both non-AT and AT samples were taken into consideration. Fig. 4Heat map (A) and formed clusters (vertical left) illustrating the differences between registered common and characteristic signals in both groups; hierarchical clustering distinguishing non-AT and AT for S. vestibularis (A, up), for S. salivaris, S. parasanguinis, and S. oralis (B)Fig. 5Heat map (A) and formed clusters (vertical left) illustrating the differences between registered common and characteristic signals only in AT; hierarchical clustering distinguishing different AT for C. albicans (A, up), for S. epidermidis, S. hominis, and E. faecalis (B) *The analysis* of the intraspecific proteomic variation within distinct microbial species derived from the saliva of patients treated and untreated with antibiotics revealed the impact of the treatment undertaken, associated microbiota, and applied culture conditions on the generated protein profiles (Figs. 4 and 5). Regarding species that occurred in both patients group, obtained results indicated the influence of the antibiotic used and the type of the culture medium on the variation in the proteomic composition of the bacteria (Fig. 4). As the example of the species S. vestibularis is shown (Fig. 4A), such differences cover a wide range of m/z with various frequencies. In that particular case, the most different protein pattern was noted for the strains isolated from the patient non-AT6 and AT7 and can be attributed to the synergic effect of the culture medium composition (TSA vs. BHI) and antibiotic treatment (ciprofloxacin). A similar effect was observed for S. oralis (Fig. 4B), where isolates collected from the AT patients and cultured on the BHI comprised a distinct group. Moreover, S. oralis isolated from the non-AT also revealed a clear grouping according to the culture medium—VRE vs. TSA. Although all S.oralis from the AT patients were isolated using BHI, their proteome differentiation was much higher than that of isolates detected among the non-AT, which can be associated with a different antibiotic treatment—ciprofloxacin (AT9), azithromycin (AT1), and clindamycin (AT5). Regarding the other two strains found in both patients group—S. salivarius and S. parasanguinis—the comparative analysis revealed the highest proteomic variations; however, grouping according to drug-treated and untreated patients was not as evident as in the former cases. Nonetheless, S. parasanguinis strains isolated from the AT patients were placed into different clusters according to the type of antibiotic used, where strains from piperacillin-treated patients (AT10 and 12) were much more similar to each other than those from ciprofloxacin, amoxicillin, and clindamycin with levofloxacin (Fig. 4B). Additionally, great diversity noted for S. parasanguinis can be related to various microbiota found in the saliva samples. Indeed, strains isolated from samples accompanied by the presence of such microorganisms as Candida albicans (AT10 and 20) or *Bacillus subtilis* (AT12) demonstrated a more unique proteins pattern than those comprised of streptococci only (AT2 or non-AT6). A similar phenomenon was noted for S. salivarius, where the strains showing extremely different protein profiles came from samples significantly different in the species composition—non-AT7 (additionally N. perflava, S. anginosus, E. coli) vs. non-AT8 (S. salivarius only)—or the antibiotic used—AT13 (clindamycin with levofloxacin) vs AT4 (clindamycin with metronidazole). All in all, comparing species derived from the saliva of treated and untreated patients, the impact on the proteomic diversification increases as follows: the type of the antibiotic used>coexistent microbiota>culture medium type.
Similarly, a comparison of the proteomic diversity among microbial species that occurred only in the AT patients was performed (Fig. 5). The effect of the culture medium used was noted for C. albicans (Fig. 5A) along with E. faecalis strains (Fig. 5B). Interestingly, in the case of E. faecalis, the strain isolated as a single microorganism from patient AT14 demonstrated a far more different proteins pattern compared to the rest of the strains which were isolated from samples occupied by pathogenic microorganisms like C. albicans, S. marcescens, N. flavescens or E. coli. In turn, the proteomic intraspecific diversification of the staphylococci (S. hominis and S. epidermidis) most likely resulted from the type of antibiotic treatment undertaken—ciprofloxacin with levofloxacin vs. clindamycin in the case of former and piperacillin vs. clindamycin or cefotaxime in the latter one. Moreover, among S. epidermidis strains the most distinct proteins profile was demonstrated by the isolate AT12—the only one that derived from the sample in which the presence of Candida spp. was not observed.
## Discussion
Intraspecific differences in microbial protein profiles depending on culture media compositions were recognized as significant since $50\%$ of the peaks of all bacteria are non-ribosomal proteins, which are more or less metabolic status dependent (Mellmann et al. 2008). It was proved in the work of Złoch et al. ( Złoch et al. 2020a) where changing the culture conditions significantly influenced the differentiation of S. aureus strains based on their protein patterns. As the authors pointed out, it may result from the induction of some new metabolic pathways in bacteria leading to the appearance of more discriminant signals.
According to previous reports (Monedeiro et al. 2021; Szultka-Młyńska et al. 2021) the culture media could be useful for the separation of each bacterial strains from the sample obtained from the hospital. In this context, the present research have also used different media to select and identify the individual strain. Moreover, it has been investigated how the composition of the used media can influence the isolation and identification of each bacteria in order to establish an optimal media in this way regarding the biomedical approach. *In* general, the identification of microorganisms by MALDI is considered culture independent, since most of the proteins present in bacterial cells are ribosomal proteins (about $50\%$ or more), so that reliable classification of bacteria for most genera and species is certain regardless of the culture media used. Nevertheless, in addition to ribosomal proteins, the bacterial extracts studied also contain other non-ribosomal proteins that are more or less metabolism dependent. Such proteins can affect the identification result primarily in the case of bacteria belonging to groups of closely related species (e.g., *Bacillus subtilis* or cereus group, S. salivarius group or S. mitis/oralis) leading to misidentification. Knowing that in the case of closely related species, the genomic and proteomic differences are very small, even slight variation in the culturing conditions may matter.
Nevertheless, the results of our studies showed that the impact of the culture medium type on the intra-specific variation of the proteomes was lower than the effect of the antibiotic treatment and the presence of the co-exist microbiota. It was shown that the interaction that occurred between microbes can alter the expression of their membrane proteins. Such a phenomenon was noted in the work of Kumar and Ting, where amounts of seven classes of proteins on the S. aureus surface were elevated upon coculturing with P. aeruginosa (Kumar and Ting 2015). Found proteomic changes included proteins related to host-microbe interactions such as virulence, adhesion, and resistance, which explains the increased fatality of infections with the simultaneous presence of *Staphylococcus aureus* and *Pseudomonas aeruginosa* compared to the colonization of the individual bacterial species (Fazli et al. 2009). A similar phenomenon was observed in our studies where for instance E. faecalis and S. salivarius that co-existed with other microbial species demonstrated distinct protein patterns from the ones isolated as a monoculture. Since most of the accompanying species represented pathogenic microorganisms, the detected intra-specific proteome variation of the mentioned bacterial species may be partially explained by the horizontal transfer of the genes related to virulence factors. Bacterial genomes frequently contain a significant amount of foreign DNA, which is DNA that originated from another organism and was inserted into the genome of a bacterium (Ochman et al. 2000). The DNA mobilized into the host bacterium is referred to as mobile genetic elements (MGEs), which have a huge impact on the shape of the bacterial genomes and promote intra-specific variability (Heuer and Smalla 2007). Enterococcus faecalis harbors a pathogenicity island containing several virulence factors and is known for its fast adaptation to the clinical environment by the acquisition of antibiotic resistance and pathogenicity traits generating it the third leading cause of hospital-associated infections (Laverde Gomez et al. 2011). Although the horizontal gene transfers occur more frequently between closely related bacteria, they also occur among distantly related species. Nevertheless, horizontally acquired (or lost) genes can also contribute to ecological adaptation, and they are likely to be major drivers of niche differentiation among bacteria (Smillie et al. 2011). Moreover, different habitats can be expected to support different levels of intra-species diversity and to be subject to distinct selection pressures (Ellegaard and Engel 2016). Considering clinical specimens, antibiotics demonstrate highly selective pressure on the bacteria populations. Besides, causing the shifts in the species composition, it was proved that antibiotics (at certain concentrations which depend on their class) are also responsible for the high phenotypic variation even at a single bacterial population level (Lee et al. 2018). Indeed, the presence of the antibiotic demonstrated the highest impact on the proteomic intra-specific diversity of the investigated salivary microorganisms, including Candida spp. Moreover, such an impact depends on the type of the antibiotic used and tested microorganisms.
Our results indicated significant differences in the saliva microbiota between non-antibiotic treatment and antibiotic treatment patients. We noticed the dominated and characteristic microorganisms in the non-AT (*Streptococcus salivarius* ($25\%$) and *Streptococcus vestibularis* ($19\%$), *Streptococcus oralis* ($13\%$) and *Streptococcus parasanguinis* ($6\%$)) and in the AT (*Streptococcus salivarius* ($11\%$), *Streptococcus parasanguinis* ($11\%$), *Staphylococcus epidermidis* ($12\%$), *Enterococcus faecalis* ($9\%$), *Staphylococcus hominis* ($8\%$), and Candida albicans ($6\%$)) groups. The salivary microbiota of antibiotic-treated patients was characterized by a more bacteria variety; the appearance of the Candida albicans species was noticed only in the AT patient indicating a negative impact of the antibiotic administration on the patient microbiota. Moreover, the proteomic analysis showed the influence of the antibiotic therapy, composition, and abundance of saliva microbiota and used growth medium on the recorded protein profiles. Remarkably, the MALDI-TOF MS analysis represents a promising method for a large-scale, less labor intensive, rapid, and cost-effective record of reproducible molecular profiles of microorganisms, particularly the salivary bacteria. It is notable that the proposed approach enables the early administration of the specie-specific antimicrobial therapy in the patients. Therefore, our data can allow a medical diagnosis to be confirmed and they may also enable us to monitor the treatment of diseases and develop drugs for individual patients. Moreover, the presented study can be the base to develop methods enabling a faster diagnosis and the detection of disease changes at the cellular level before clinical changes occur. To summarize, the obtained results suggest that tracking proteomic intra-specific variation within microorganisms may be a promising tool for the future use to examine the effectiveness of the applied antibiotic curation and to check whether one deals with mono- or polymicrobial cultures including the presence of the other pathogenic species.
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|
---
title: Therapeutic effects of CD133 + Exosomes on liver function after stroke in type
2 diabetic mice
authors:
- Poornima Venkat
- Huanjia Gao
- Elizabeth L. Findeis
- Zhili Chen
- Alex Zacharek
- Julie Landschoot-Ward
- Brianna Powell
- Mei Lu
- Zhongwu Liu
- Zhenggang Zhang
- Michael Chopp
journal: Frontiers in Neuroscience
year: 2023
pmcid: PMC10033607
doi: 10.3389/fnins.2023.1061485
license: CC BY 4.0
---
# Therapeutic effects of CD133 + Exosomes on liver function after stroke in type 2 diabetic mice
## Abstract
### Background and purpose
Non-alcoholic fatty liver disease (NAFLD) is known to adversely affect stroke recovery. However, few studies investigate how stroke elicits liver dysfunction, particularly, how stroke in type 2 diabetes mellitus (T2DM) exacerbates progression of NAFLD. In this study, we test whether exosomes harvested from human umbilical cord blood (HUCBC) derived CD133 + cells (CD133 + Exo) improves neuro-cognitive outcome as well as reduces liver dysfunction in T2DM female mice.
### Methods
Female, adult non-DM and T2DM mice subjected to stroke presence or absence were considered. T2DM-stroke mice were randomly assigned to receive PBS or Exosome treatment group. CD133 + Exo (20 μg/200 μl PBS, i.v.) was administered once at 3 days after stroke. Evaluation of neurological (mNSS, adhesive removal test) and cognitive function [novel object recognition (NOR) test, odor test] was performed. Mice were sacrificed at 28 days after stroke and brain, liver, and serum were harvested.
### Results
Stroke induces severe and significant short-term and long-term neurological and cognitive deficits which were worse in T2DM mice compared to non-DM mice. CD133 + Exo treatment of T2DM-stroke mice significantly improved neurological function and cognitive outcome indicated by improved discrimination index in the NOR and odor tests compared to control T2DM-stroke mice. CD133 + Exo treatment of T2DM stroke significantly increased vascular and white matter/axon remodeling in the ischemic brain compared to T2DM-stroke mice. However, there were no differences in the lesion volume between non-DM stroke, T2DM-stroke and CD133 + Exo treated T2DM-stroke mice. In T2DM mice, stroke induced earlier and higher TLR4, NLRP3, and cytokine expression (SAA, IL1β, IL6, TNFα) in the liver compared to heart and kidney, as measured by Western blot. T2DM-stroke mice exhibited worse NAFLD progression with increased liver steatosis, hepatocellular ballooning, fibrosis, serum ALT activity, and higher NAFLD Activity Score compared to T2DM mice and non-DM-stroke mice, while CD133 + Exo treatment significantly attenuated the progression of NAFLD in T2DM stroke mice.
### Conclusion
Treatment of female T2DM-stroke mice with CD133 + Exo significantly reduces the progression of NAFLD/NASH and improves neurological and cognitive function compared to control T2DM-stroke mice.
## Introduction
Diabetes mellitus (DM) leads to a 3–4 fold higher risk of ischemic stroke (Mast et al., 1995; Goldstein et al., 2006; Adams et al., 2007). Ischemic stroke patients with DM exhibit a distinct risk-factor and etiologic profile and a worse outcome than non-DM patients (Scott et al., 1999; Capes et al., 2001; Putaala et al., 2011). Type 2 DM (T2DM) constitutes ∼$90\%$ of diabetic patients and is associated with non-alcoholic fatty liver disease (NAFLD) (Patel et al., 2011). Non-alcoholic steatohepatitis (NASH) is a form of NAFLD in which steatosis in the liver is accompanied by inflammation and cell damage. NAFLD is a chronic liver disease that is present in 50–$60\%$ of patients with T2DM, and the two conditions often act synergistically to aggravate the severity of outcomes from ischemic stroke (Megherbi et al., 2003; Putaala et al., 2011; Seo et al., 2016; Li et al., 2018; Weinstein A. et al., 2018; Chatzopoulos et al., 2021). Given the global prevalence of NAFLD, and the increased post-stroke morbidity and mortality in the expanding diabetic population, there is a compelling need to elucidate the brain-liver interaction after stroke in the setting of T2DM and to develop therapeutic approaches specifically designed, not only to reduce stroke induced direct neurological and cognitive deficits, but also to reduce liver damage driven by the interaction of brain and liver.
Exosome based therapeutics is emerging for neurological diseases, cancers, and autoimmune diseases (Peterson et al., 2007; Cheng et al., 2011; Ding et al., 2011; Zhu et al., 2011; Minnerup et al., 2012; Corral-Fernández et al., 2013; Wei et al., 2013). Exosomes are extracellular vesicles naturally released by all cells and they transport nucleic acids, proteins, lipids, and metabolites thereby facilitating intercellular communication (El-Andaloussi et al., 2012; Yáñez-Mó et al., 2015; Kalluri and LeBleu, 2020). Systemic delivery of exosomes that are derived from mesenchymal stromal cells or endothelial cells, primarily accumulate in liver and spleen, and can pass the blood brain barrier without any apparent adverse effects (Xin et al., 2013; Lai et al., 2014; Yang et al., 2015; Venkat et al., 2019; Wang et al., 2020; Choi et al., 2021). CD133 is a marker for hematopoietic stem and progenitor cells, and CD133 + /KDR + identifies endothelial progenitor cells (EPC) (Friedrich et al., 2006; Thum et al., 2007). We previously reported that treatment of T2DM stroke in male mice with exosomes harvested from human umbilical cord blood (HUCBC) derived CD133 + cells (early progenitor endothelial cells in cord blood, here referred to as CD133 + Exo), significantly improves cardiac function via reducing inflammation, oxidative stress, fibrosis, and increasing microRNA 126 expression at 28 days post-stroke compared to control T2DM-stroke mice (Venkat et al., 2021). Treatment of stroke in diabetic mice with miR-126 enriched EPC-exosomes administered intravenously at 2 h after stroke significantly reduces infarct size, improves neurological function, as well as increases cerebral blood flow and angiogenesis in the peri-infarct region compared to control T2DM-stroke mice (Wang et al., 2020). While the therapeutic effects of CD133 + Exo, EC-Exo, and EPC-Exo on stroke outcome and cardiac function have been previously reported, the effect of CD133 + Exo on the liver function in diabetic stroke mice has not been studied. In this study, we report for the first time that among the major peripheral organs, i.e., heart, kidney and liver, stroke elicits earlier, and stronger acute phase response in the liver of diabetic mice. We also present a novel therapeutic approach using CD133 + Exo not only to improve neurological and cognitive outcome post-stroke in T2DM mice but also to reduce the progression of NAFLD/NASH after stroke.
## Materials and methods
All procedures were carried out in accordance with the National Institutes of Health (NIH) Guide for the Care and Use of Laboratory Animals and with the approval of Institutional Animal Care and Use Committee (IACUC) of the Henry Ford Health System. This manuscript is prepared following ARRIVE guidelines (Kilkenny et al., 2010).
## Photothrombotic ischemic stroke model
The photothrombotic stroke model is a minimally invasive small vessel occlusion model which induces reproducible and well-defined infarcts in the frontal and parietal cortex and prolonged sensorimotor impairment in mice (Labat-gest and Tomasi, 2013; Chen et al., 2017; Uzdensky, 2018). Briefly, mice were anesthetized in a chamber with $3.5\%$ isoflurane and maintained with $1.5\%$ isoflurane in a mixture of $70\%$ N2O and $30\%$ O2 using a facemask. The head was fixed on the stereotaxic instrument and animals placed on a water circulating heating pad set to 37°C during the surgical procedure. A skin incision was made, and a black roundabout rubber sheet was placed on the skull surface such that the sensorimotor area (1.5–3 mm lateral; 0.5–1 mm anterior of bregma) was exposed by the inner circle aperture. A photosensitive dye (Rose Bengal, 40 mg/kg, i.p.) mixed with saline (4 ml/kg) was injected and 5 min later, a cold light illuminator was fixed close to the skull surface and turned on for 20 min. Activation of the dye induces endothelial damage with platelet activation and thrombosis, resulting in local blood flow interruption and infarction (Labat-gest and Tomasi, 2013; Uzdensky, 2018). The rubber was removed, incision was sutured, and routine post-surgical monitoring, analgesics (Buprenorphine SR, 1 mg/Kg, subcutaneous), and supportive care was provided.
## Experimental groups, randomization, and blinding
Diabetic women have a higher risk of stroke than men (Peters et al., 2014). The incidence of NAFLD is higher in males than in females as evidenced by longitudinal studies (Kojima et al., 2003; Ballestri et al., 2017). However, with advancing age (≥50 years), women have a higher risk of NASH and advanced NAFLD fibrosis than men (Summart et al., 2017; Balakrishnan, 2021). Thus, we employ female T2DM and control mice in this study. Female, adult, 3–4 m old, T2DM (BKS.Cg-m +/+ Leprdb/J, Jackson Laboratory, Bar Harbor, ME, USA) and control non-DM (db +, Jackson Laboratory, Bar Harbor, ME, USA) mice were used and following experimental groups employed: [1] Non-DM (WT, $$n = 6$$), [2] Non-DM-stroke (WT-Stroke, $$n = 15$$), [3] T2DM ($$n = 8$$), [4] T2DM-stroke ($$n = 12$$), and [5] T2DM-stroke + Exo ($$n = 13$$). CD133 + Exo (20 μg/200 μl PBS) was administered via a tail vein once at 3 days after stroke. The treatment regimen was selected based on our prior study in which we demonstrated that that treatment of T2DM stroke with CD133 + Exo administered once intravenously at 3 days after stroke at a dose of 20 μg/200 μl PBS significantly improves cardiac function at 28 days post-stroke compared to control T2DM-stroke mice (Venkat et al., 2021). At day 28 after stroke, fasting blood glucose was tested using a glucose analyzer (AgaMatrix Advanced blood glucose monitoring system) and blood lipids measured using CardioChek Plus analyzer. End-point measurements such as neurological and cognitive testing, histochemical staining, imaging, and quantification analysis were performed by investigators who were blinded to the experimental groups. The investigator who performed behavioral testing was not involved in performing stroke surgery or treatment administration.
## Neurological and cognitive function assessment
To assess neurological function post-stroke, the modified neurological severity score (mNSS) and adhesive removal tests were performed on days 1, 7, 14, 21, and 28 after stroke. The mNSS test is a composite of motor, sensory, balance, and reflex tests in which the absence of a tested reflex or abnormal response is scored as one point. Thus, on a scale of 0–18, 0 indicates normal neurological function, i.e., no deficits and a score of 18 indicates severe neurological deficits (Chen et al., 2001). The adhesive removal test evaluates somatosensory dysfunction (Bouet et al., 2009). Two small pieces of adhesive paper tapes were applied on both the forepaws with equal pressure and the time taken to sense the stimulus and to remove the tapes was recorded. The maximum trial time was 120 s. Three individual trials that were separated by at least 5 min were carried out for each animal on each testing day.
To evaluate cognitive impairment, novel object recognition (NOR) test and odor test were performed at 25–28 days after stroke using ANY-Maze video tracking and analysis software (Stoelting Co., Wood Dale, IL, USA). All animals were habituated to the testing environment 1 day prior to testing. To test short-term memory, a NOR test with a retention delay of 4 h was employed following previously described methods (Culmone et al., 2022). Briefly, in the 5 min trial phase, mice freely explored 2 identical plastic objects that were placed equidistant from the walls of a testing chamber. During the 5 min test phase, one object from the trial phase was replaced with a novel object and the time spent exploring each object was recorded. If the total exploration time was less than 10 s, animals were excluded from analysis. Sniffing, pawing, or probing with whiskers within 1 cm of an object was considered as exploration. The discrimination index was calculated as the ratio of time spent exploring the novel object to the total exploration time. To test long-term memory, odor test was employed following previously described methods (Spinetta et al., 2008; Culmone et al., 2022). Briefly, two sets of novel odors (N1 and N2) were obtained by placing beads in the home cage of donor mice (same sex, different strains) for 1 week. All experimental animals were separated to single housing and four wooden beads1 were placed in each cage to habituate mice to presence of beads and to collect familiar odor beads (F). On the 2 days of testing, animals were familiarized with novel odor N1 during three 1 min trials. On the 3 days of testing, mice were subjected to a 1 min test phase during which two familiar odor beads (F), one N1 odor bead and one N2 odor bead were placed in the center of the cage. The time spent exploring each odor bead (F, N1, N2) was recorded and discrimination index was calculated as the ratio of time spent exploring the novel odor N2 to the total time spent exploring all beads. Mice that were inactive and failed to explore any of the beads were excluded from the analysis.
## CD133 + Exo isolation
CD133 + cells (SER-CD34-F, ZenBio, Durham, NC, USA) were cultured using custom media (ZenBio, Durham, NC, USA) as per manufacturer instructions. Briefly, Stem Cell Expansion Supplement (100×) was thawed at room temperature and added to the Custom Stem Cell Expansion Medium at 1:100 ratio. CD133 + cells were cultured for 1 week and media was collected and filtered with 0.22 μM syringe filter to remove any particulate matter. Exosomes were harvested from collected media using ExoQuick (System Biosciences, Palo Alto, CA, USA). 2 ml ExoQuick was added for every 10 ml media and stored overnight at 4°C. Media was centrifuged at 1,500 g for 30 min, supernatant removed, and the pellet resuspended in PBS. Protein concentration was determined using BCA Protein Assay Kit (Pierce) (Thermo Fisher Scientific, Waltham, MA, USA) following standard protocol. CD133 + Exo were verified by transmission electron microscopy (TEM), western blot detection of common exosome marker proteins [CD9 (1:1000 Abcam, Waltham, MA, USA, Cat#223052), CD81 (1:1000 Abcam, Waltham, MA, USA, Cat#109201), CD63 (1:1000, Abcam, Waltham, MA, USA, cat# ab134045), ALIX (1:1000 Cell Signaling, Danvers, MA, USA, Cat#2171), and Calnexin (1:1000 Biolegend, San Diego, CA, USA, Cat#699401)] and exosome size and quantification were performed using NanoSight NS300 (Malvern Panalytical, Malvern, UK). Ultrastructure and nanosize analysis demonstrated that CD133 + Exo had intact spherical/donut-shaped morphology with a mean diameter of 144.3 ± 4.6 nm (Figure 1G).
**FIGURE 1:** *CD133 + Exo treatment significantly improves neuro-cognitive outcome in T2DM stroke mice. (A,B) Stroke in T2DM mice induced severe neurological deficits indicated by higher mNSS scores and longer adhesive removal times compared to T2DM sham and non-DM stroke (WT-stroke) mice. Intravenous administration of CD133 + Exo at 3 days after stroke reduced neurological impairment indicated by significantly lower mNSS scores on days 21 and 28 after stroke as well as significantly shorter adhesive removal time on days 7, 14, and 28 after stroke when compared to T2DM-stroke mice. (C,D) Stroke in T2DM mice induced significant cognitive impairment indicated by lower discrimination index in NOR test and odor test at 28 days after stroke compared to T2DM sham and WT-stroke mice. Treatment of stroke with CD133 + Exo significantly improved both short-term and long-term memory evaluated by NOR and Odor test compared to T2DM stroke mice. (E) There were no significant differences in the lesion volume among non-DM stroke, T2DM-stroke and CD133 + Exo treated T2DM-stroke mice. (F) There were no significant differences (p > 0.05) in blood glucose, total cholesterol or triglyceride levels at 28 days after stroke among T2DM, T2DM-stroke and CD133 + Exo treated T2DM-stroke mice. (G) Characterization of CD133 + Exo by NTA, TEM and Western blots. Non-DM (WT, n = 6), non-DM-stroke (WT-Stroke, n = 15), T2DM (n = 8), T2DM-stroke (n = 12), and T2DM-stroke + Exo (n = 13).*
## Immunohistochemistry and quantification analysis
Animals were anesthetized with ketamine (80 mg/kg, i.p) and (xylazine 13 mg/kg, i.p), transcardially perfused with $0.9\%$ saline and decapitated. The brain was excised, immersion fixed in $4\%$ paraformaldehyde overnight, cut into seven equally spaced (1 mm each) coronal blocks using a mouse brain matrix and embedded in paraffin. A series of adjacent 6 μm thick sections was cut from each block and stained with hematoxylin and eosin (H&E) for ischemic lesion volume measurement. Lesion volume was measured using ImageJ (NIH) and is presented as a volume percentage of the lesion area compared with the contralateral hemisphere. Brain coronal tissue sections (6 μm) were prepared and antibody against VWF (1:400; Dako) (Agilent Technologies, Santa Clara, CA, USA) and αSMA (α-smooth muscle actin; smooth muscle cell marker, mouse monoclonal IgG 1:800, Dako) were used (Agilent Technologies, Santa Clara, CA, USA). Bielschowsky silver and Luxol fast blue staining was used to stain axons and myelin, respectively. Control experiments consisted of similar procedures without addition of primary antibody. For each brain section, 6–8 fields of view of the ischemic border zone (IBZ) or ipsilateral striatum were digitized under a 20 × objective (Olympus BX40) using a 3-CCD color video camera (Sony DXC-970MD) interfaced with an MCID image analysis system (Imaging Research, St. Catharines, ON, Canada). Data were averaged to obtain a single value for each animal and presented as percentage of positive area or number of positive cells/mm2.
The left lateral lobe of the liver was excised and cut into three sections. One section was snap frozen in liquid nitrogen, one section was immersion fixed in $4\%$ paraformaldehyde overnight and embedded in paraffin, and the third section was embedded in cryoprotective optimal cutting temperature compound (OCT) solution and flash frozen in 2-methyl butane on dry ice and then stored at −80°C. Oil red O staining of 15 μm thick frozen liver sections was used to evaluate neutral triglycerides and lipid content. H&E staining of 6 μm thick paraffin embedded sections was used to evaluate steatohepatitis and NAFLD Activity Score (NAS) which is the unweighted sum of scored for steatosis (0–3), lobular inflammation (0–3), and hepatocellular ballooning (0–2) (Kleiner et al., 2005; Trovato et al., 2014). Picrosirius red staining of 6 μm thick paraffin embedded sections was used to evaluate liver fibrosis.
## Alanine aminotransferase activity
During euthanasia, blood was collected under deep anesthesia via retro-orbital bleeding. Blood was allowed to clot at room temperature for 30 min and centrifuged at 1,500 g for 10 min to collect serum. Serum ALT activity was measured using an ALT Activity Assay kit (MAK052, MilliporeSigma, St. Louis, MO, USA) following manufactures instructions.
## Western blot
To test the acute phase response of peripheral organs after stroke, additional sets of T2DM-sham and T2DM-stroke mice were sacrificed at 1 and 3 days after stroke ($$n = 3$$/group). Animals were anesthetized with ketamine (80 mg/kg, i.p) and (xylazine 13 mg/kg, i.p), transcardially perfused with $0.9\%$ saline and decapitated. The heart, liver and kidney were excised and snap frozen in liquid nitrogen. Protein was isolated from heart, liver and kidney tissue using Trizol (Thermo Fisher Scientific, Waltham, MA, USA) following standard protocol. Protein concentration was measured using the BCA kit (Thermo Fisher Scientific, Waltham, MA, USA) and 40 μg of protein/lane was loaded in a $10\%$ SDS PAGE precast gel (Thermo Fisher Scientific, Waltham, MA, USA). The gel was placed in a tank and run for approximately 90 min at 120 V. The gel was subsequently transferred to a nitrocellulose membrane using the iBlot transfer system (Thermo Fisher Scientific, Waltham, MA, USA). The membrane was blocked in $2\%$ I-Block (Thermo Fisher Scientific, Waltham, MA, USA) in 1 × TBS-T for 1 h, and then primary antibodies against either β-actin (1:10,000, Abcam, Waltham, MA, USA, cat# ab6276), serum amyloid A (SAA, 1:1000, Abcam, Waltham, MA, USA, cat # ab199030), NOD-like receptor protein 3 (NLRP3, 1:1000, Cell Signaling, Danvers, MA, USA, cat# 15101), Interleukin-1β (IL-1β, 1:1000, Abcam, Waltham, MA, USA, cat# ab2105), IL-6 (1:1000, Fisher Scientific, Hampton, NH, USA, cat# 700480), *Tumor necrosis* factor α (TNFα, 1:1000, Abbiotec, Escondido, CA, USA, cat# 250844), Toll like receptor 4 (TLR4, 1:500, Santa Cruz, Dallas, TX, USA, cat# sc-10741) were used. Secondary antibodies (anti-mouse, Jackson ImmunoResearch, West Grove, PA, USA) were added at 1:3,000 dilution in $2\%$ I-Block in 1 × TBS-T at room temperature for 1 h. The membranes were washed with 1 × TBS-T, and then Luminol Reagent (Santa Cruz, Dallas, TX, USA) was added. The membranes were then developed using a FluorChem E Imager system (ProteinSimple, San Jose, CA, USA). Grayscale images were analyzed using ImageJ and normalized to β-actin.
## Statistical analysis
Data are presented as mean ± SEM. Data were evaluated for normality; ranked data were used for analysis when data were not normally distributed. To study the combination of T2DM and stroke effects, we used two factorial design with factors T2DM and stroke and Analysis of variance (ANOVA) was used for single measurements collected at day 28 (NOR test, odor test, and immunostaining measurements) and analysis of variance and covariance (ANCOVA) was used for repeated measurements (mNSS and adhesive removal test). The analysis would first test two-factor interaction, followed by assessment of additive effect (no interaction), supper-additive or sub-additive interaction effects. Repeated analysis of variance (ANCOVA) was used to test CD133 + Exo effect on function recovery for mNSS and adhesive removal test in mice with both T2DM and stroke. Statistical significance was detected at $p \leq 0.05.$ *Data analysis* was performed by a Biostatistician.
## CD133 + Exo treatment significantly improves neuro-cognitive outcome in T2DM stroke mice
Stroke in T2DM mice induced severe neurological deficits (Figures 1A, B) indicated by higher mNSS scores and longer adhesive removal times as well as significant cognitive impairment (Figures 1C, D) indicated by lower discrimination index in NOR test and odor test compared to T2DM sham and non-DM stroke mice. Super-additive effect was observed on mNSS scores at day 28 and sub-additive effects were observed on the adhesive removal test on days 1, 7, 14, and 21 after stroke. Treatment of stroke with CD133 + Exo significantly reduced neurological impairment and improved both short-term and long-term memory compared to T2DM stroke mice (Figures 1A-D). However, there were no significant differences ($p \leq 0.05$) in the lesion volume among non-DM stroke, T2DM-stroke and CD133 + Exo treated T2DM-stroke mice (Figure 1E). There were no significant differences ($p \leq 0.05$) in blood glucose, total cholesterol or triglyceride levels at 28 days after stroke among T2DM, T2DM-stroke and CD133 + Exo treated T2DM-stroke mice (Figure 1F).
## CD133 + Exo treatment of T2DM stroke significantly increases vascular and white matter/axon remodeling
To test the effects of CD133 + Exo treatment on vascular and white matter/axonal remodeling in the ischemic brain, we measured arterial blood vessel and vascular density in the IBZ based on αSMA and vWF immunostaining, respectively, and myelin and axon density in the ipsilateral white matter striatal bundles using Luxol fast blue and Bielschowsky silver staining, respectively. T2DM-stroke significantly alters vascular density and reduces axon and myelin density in the IBZ compared to T2DM Sham. CD133 + Exo treatment of T2DM-stroke significantly increased arterial blood vessel density, vessel density, myelin density and axon density in the ischemic brain compared to T2DM-stroke mice at 28 days after stroke (Figures 2A-D).
**FIGURE 2:** *CD133 + Exo treatment of T2DM stroke significantly increases vascular and white matter/axon remodeling. (A,B) CD133 + Exo treatment of T2DM-stroke significantly increases arterial blood vessel density (αSMA) and vessel density (vWF) in the ischemic brain compared to T2DM-stroke mice. (C,D) CD133 + Exo treatment of T2DM-stroke significantly increases axon density (Bielschowsky silver, BS) and myelin density (Luxol fast blue, LFB) in the ischemic brain compared to T2DM-stroke mice at 28 days after stroke. T2DM (n = 8), T2DM-stroke (n = 12), and T2DM-stroke + Exo (n = 13).*
## T2DM-stroke induces liver immune-response earlier and stronger than in other peripheral organs
The acute phase response is typically initiated in response to most inflammatory triggers to coordinate the innate immune response (McColl et al., 2007). Triggers for the acute phase response include elevations of cytokines IL-1β, IL-6, and TNFα, which can bind directly to hepatocytes and induce the synthesis and subsequent release of acute phase response proteins (McColl et al., 2007). Therefore, we examined the expression of inflammatory factors, cytokines and acute phase response related proteins at 1 and 3 days after stroke in T2DM mice. Figure 3 data show that stroke in T2DM mice induced earlier and higher inflammatory factor, cytokine, and acute phase response protein expression such as SAA, NLRP3, IL1β, IL6, TNFα and TLR4 in the liver compared to heart and kidney, as measured by Western blot. Combination of T2DM and stroke had a super-additive effect on the increased expression of SAA and IL6.
**FIGURE 3:** *T2DM-stroke induces liver immune-response earlier and stronger than in other peripheral organs. (A,B) Western blot was used to measure the expression of inflammatory factors, cytokines, and acute phase response related proteins at 1 and 3 days after stroke in T2DM mice. Stroke in T2DM mice induces earlier and higher cytokine and acute phase response protein expression such as SAA, NLRP3, IL1β, IL6, TNFα and TLR4 in the liver compared to heart and kidney. The bands correspond to the precursor form for IL1β and TNFα. n = 3/group.*
## Stroke in T2DM-mice exacerbates NAFLD progression while CD133 + Exo treatment significantly reduces steatosis, fibrosis, NAS, and ALT activity
Since NAS is a composite of steatosis, lobular inflammation, and hepatocellular ballooning (Kleiner et al., 2005; Trovato et al., 2014), higher NAS scores indicate more severe NAFLD. Figure 4 shows that T2DM-stroke mice exhibited significantly increased liver steatosis measured by Oil red O staining, increased ballooning degeneration of hepatocytes, increased fibrosis measured by PicroSirius Red, increased serum ALT activity, and higher NAS compared to T2DM-Sham mice, with super-additive effect observed on hepatocyte ballooning, fibrosis, NAS and ALT. CD133 + Exo treatment of T2DM-stroke mice significantly decreased liver steatosis, hepatocellular ballooning, fibrosis, serum ALT activity, and NAS compared to T2DM-stroke mice at 28 days after stroke (Figures 4A-H). Thus, CD133 + Exo treatment significantly attenuates the progression of NAFLD/NASH in T2DM stroke mice.
**FIGURE 4:** *Stroke in T2DM-mice exacerbates NAFLD progression while CD133 + Exo treatment significantly reduces steatosis, fibrosis, NAS, and ALT activity. (A–C) Representative images of Oil red O (ORO), hematoxylin and eosin (H&E) staining and PicroSirius Red (PSR). T2DM-stroke mice exhibit significantly increased liver steatosis (D), increased ballooning degeneration of hepatocytes (E), increased fibrosis (F), increased serum ALT activity (G), and higher NAS (H) compared to T2DM-Sham mice. CD133 + Exo treatment of T2DM-stroke mice significantly decreased liver steatosis, hepatocellular ballooning, fibrosis, serum ALT activity, and NAS compared to T2DM-stroke mice at 28 days after stroke. NAS is the unweighted sum of scores for steatosis (0–3), lobular inflammation (0–3) and hepatocellular ballooning (0–2). Non-DM (WT, n = 6), non-DM-stroke (WT-Stroke, n = 15), T2DM (n = 8), T2DM-stroke (n = 12), and T2DM-stroke + Exo (n = 13).*
## Discussion
In this study, we report for the first time that stroke in T2DM mice induces acute phase immune response earlier and stronger in the liver compared to heart and kidney and worsens NAFLD compared to T2DM sham or stroke in non-DM mice. We also report that treatment of T2DM stroke with CD133 + Exo not only improves neuro-cognitive outcome but also attenuates the progression of NAFLD/NASH, thereby holding promise as a novel therapeutic agent for the treatment of T2DM stroke, and potentially, directly for NAFLD/NASH.
There is increasing evidence of the clinical importance of dysfunctional brain–peripheral organ interactions (Anthony et al., 2012; Ren et al., 2018; Zhao et al., 2019; Li et al., 2020; Yan et al., 2020). Our prior work, as well as others have demonstrated that stroke in non-DM and T2DM mice induces significant and progressive cardiac dysfunction compared to corresponding control mice (Yan et al., 2020; Chen et al., 2021). T2DM aggravates such cerebral-cardiac syndrome (Lin et al., 2021; Venkat et al., 2021). Acute ischemic stroke patients who developed acute kidney injury had higher risk of mortality at 3 months after stroke (Qureshi et al., 2020; Zhao et al., 2020). Thus, we tested the expression of acute phase proteins and inflammatory factors and cytokines in kidney, heart and liver tissue at 1 and 3 days after stroke. Acute phase proteins are a class of proteins whose concentration dramatically increases, or increases in the circulation in response to stimulation by pro-inflammatory cytokines (Güleç et al., 2022). IL-6 is the major inducer for hepatic secretion of acute phase proteins while IL-1β, TNFα and interferon gamma (IFN-γ) are other cytokines that mediate acute phase response (Güleç et al., 2022). In this study, we report for the first time that stroke in T2DM mice induces earlier and higher inflammatory factor, cytokine, and acute phase response protein expression such as SAA, NLRP3, IL-1β, IL-6, TNFα, and TLR4 in the liver compared to heart and kidney. This novel finding improves our understanding of the brain-peripheral organ interaction sequelae and may impact the treatment of diabetic stroke. The liver is the principal organ contributor to circulating level of chemokines and acute phase protein, SAA after focal brain injury (Wicker et al., 2019). After acute brain injury, chemokine expression by the liver results in neutrophil recruitment and hepatic damage (Campbell et al., 2005, 2008; Nizamutdinov et al., 2017), contributing to multi-organ dysfunction (Villapol, 2016), and amplification of the local injury responses (Campbell et al., 2008; Villapol, 2016). SAA is synthesized in the liver and is induced in response to pro-inflammatory stimuli such as IL-1β, IL-6, and TNFα. Serum SAA increases more than 1,000 fold during severe acute-phase inflammation, and SAA is modestly elevated in chronic inflammation (Shridas et al., 2018). SAA has pro-inflammatory activities and participates in systemic modulation of innate and adaptive immune responses, lipid metabolism/transport, metabolic endotoxemia, obesity and insulin resistance, and in the induction of extracellular-matrix-degrading enzymes (Li et al., 2016; Griffiths et al., 2017). Thus, SAA has the potential to serve as a novel, early plasma biomarker for T2DM and NAFLD (Li et al., 2016; Griffiths et al., 2017). SAA has strong chemotactic activity for neutrophils and macrophages, induces the expression of a variety of inflammatory cytokines, and stimulates NLRP3 inflammasome activity, mediated in part by TLR4 (Chami et al., 2019; Yu et al., 2019). Therefore, early inflammation in the liver is likely a key contributor to post-stroke NAFLD progression in T2DM mice.
Stroke in diabetic patients elicits a strong bilateral brain-liver interaction, with significant associations between changes in biomarkers of liver dysfunction, ischemic lesion volume and systemic inflammation (Muscari et al., 2014; Cassidy et al., 2015; Abdeldyem et al., 2017; Li et al., 2017, 2018; Weinstein G. et al., 2018; Weinstein et al., 2019). Immune communication between the brain and peripheral organs contributes to and amplifies the pathophysiological sequelae of stroke and neural injury (Maier et al., 2006; Lu et al., 2009; Denes et al., 2010; Anthony et al., 2012; Tobin et al., 2014; Wendeln et al., 2018). The liver is a key organ mediating the inflammatory interaction between brain and peripheral organs (Decker et al., 1985; Freitas-Lopes et al., 2017). Preclinical and clinical data indicate that brain injury induces liver damage and hepatic inflammation (Villapol, 2016; Zhang et al., 2018). Acute stroke increases liver damage identified by increased serum ALT at 3 h after middle cerebral artery occlusion stroke in rats (Yang et al., 2003), and hyperlipidemia exacerbates liver damage by promoting oxidative stress, inflammation and hepatocyte apoptosis (Gong et al., 2012). Focal brain injury elicits a rapid hepatic response and the subsequent production of chemokines by the liver acts as an amplifier of the focal cerebral injury (Anthony et al., 2012; Villapol, 2016; Zhang et al., 2018). Post-stroke inflammatory response in the brain changes over time, and the elevation of proinflammatory factors and cytokines in the CNS can precede or follow elevated levels in the blood (Maier et al., 2006; Lu et al., 2009; Anthony et al., 2012; Tobin et al., 2014; Shi et al., 2021). NAFLD when accompanied by fibrosis is strongly associated with systemic inflammation and elevation of hepatic as well as plasma IL-6 expression (Wieckowska et al., 2008). Liver fibrosis is associated with a higher risk of ischemic stroke particularly in women (Bateman et al., 2016). Our data indicate that female T2DM mice subjected to stroke exhibit significantly increased serum ALT activity and increased hepatic fibrosis, steatosis and NAFLD/NASH progression compared to T2DM control and non-DM stroke mice. In addition, our data show that treatment of T2DM stroke with CD133 + Exo significantly improves neurological function, cognitive function, white matter and vascular remodeling in the ischemic brain as well as reduces hepatic injury and progression of NAFLD/NASH compared to T2DM stroke mice.
## Limitations and future studies
The role of the gut microbiota in mediating stroke pathogenesis and outcome is emerging. However, in this study, we only tested acute phase response in the heart, kidney and liver and found that among these major organs, stroke elicits stronger and earlier acute phase response in the liver. Given the increase in post-stroke morbidity, mortality, and neurological and cognitive deficits in the diabetic population and the global prevalence of NAFLD, it is important to investigate how cerebral ischemic stroke exacerbates NAFLD in T2DM. In a retrospective, hospital-based observational study of ischemic stroke patients, $64.2\%$ ($\frac{206}{321}$) patients presented with NAFLD (Baik et al., 2019). These stroke patients with NAFLD had less severe strokes with lower NIHSS scores and favorable functional outcome at 90 days after stroke (Baik et al., 2019). However, as the study authors point out, the proportion of patients with comorbidities such as diabetes, atrial fibrillation, or peripheral arterial occlusive disease, was similar or even lower in the group of NAFLD-stroke patients compared to stroke patients without NAFLD (Baik et al., 2019). Therefore, further studies are needed to verify the effect of NAFLD on stroke outcomes in diabetic population as well as evaluate whether NAFLD is aggravated post-stroke and its effect on stroke outcome. The mechanisms by which CD133 + Exo treatment improves neurocognitive recovery as well as reduces NAFLD/NASH progression, and how liver dysfunction induced by T2DM stroke possibly feeds-back to amplify neurological dysfunction have not been investigated in the current study. NAFLD is characterized by triglyceride accumulation in the cytoplasm of hepatocytes and measurement of hepatic triglycerides and free fatty acids as well as evaluation of the therapeutic effects of CD133 + Exo in a T2DM only model and NASH only model is warranted. In addition, studies investigating sex and age dependence of T2DM stroke on liver dysfunction and therapeutic response to CD133 + Exo treatment warrant investigation.
## Conclusion
Stroke in T2DM mice induces acute phase immune response earlier and more intensely in the liver compared to heart and kidney. Stroke in T2DM mice induces significant NAFLD indicated by increased steatosis, hepatocellular ballooning, inflammation, and fibrosis in the liver. CD133 + Exo treatment significantly improves neurocognitive function, vascular and white matter remodeling in the ischemic brain as well as attenuates the progression of NAFLD/NASH in the liver of T2DM stroke mice. Thus, CD133 + *Exo is* a novel therapeutic agent that can improve both brain and liver function after stroke in T2DM mice.
## Disclosures
Intellectual Property relating to the topic of this manuscript is subject to patent application ($\frac{62}{586}$,102) fully owned by Henry Ford Health System.
## Data availability statement
The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
This animal study was reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) of Henry Ford Health.
## Author contributions
PV: writing—original draft preparation, visualization, and formal analysis. HG, EF, AZ, and JL-W: investigation. ZC: investigation and formal analysis. BP: behavioral testing. ZZ: writing—review and editing and supervision. ZL: investigation and writing—review and editing. ML: formal analysis (statistical). MC: conceptualization, writing—review and editing, and supervision. All authors read and agreed to the published version of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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---
title: 'U.S. health professionals’ perspectives on orthorexia nervosa: clinical utility,
measurement and diagnosis, and perceived influence of sociocultural factors'
authors:
- Christina M. Sanzari
- Julia M. Hormes
journal: Eating and Weight Disorders
year: 2023
pmcid: PMC10033613
doi: 10.1007/s40519-023-01551-6
license: CC BY 4.0
---
# U.S. health professionals’ perspectives on orthorexia nervosa: clinical utility, measurement and diagnosis, and perceived influence of sociocultural factors
## Abstract
### Purpose
This study examined U.S. health professionals’ perspectives on the clinical utility, measurement, and etiology of orthorexia nervosa (ON).
### Methods
Participants ($$n = 100$$) were U.S. health professionals with experience working clinically with eating disorders, including trainees, Ph.D. psychologists, social workers/mental health counselors, and medical health professionals. After reviewing the proposed ON criteria, participants responded to questions regarding the clinical utility, diagnosis, and measurement of ON, and sociocultural influence on the emergence of ON. Views of ON as a useful diagnostic category were examined as a function of participants’ current involvement in clinical versus research activities.
### Results
Participants mostly ($71.9\%$) agreed that ON should be a distinct clinical diagnosis. Participants who endorsed ON as a valid diagnosis spent more time on clinical work and less time engaged in research compared to participants who disagreed (both ps < 0.05). Approximately $27\%$ of participants believed additional components should be added to the proposed ON diagnostic criteria. Participants indicated that sociocultural factors have considerable influence on the development of ON, namely the diet and weight loss industry, and the perceptions that biological/organic/vegan and low fat/low carb/gluten free food are the healthiest.
### Conclusion
Professionals who spent more time working clinically with eating disorders were more likely to endorse ON as a unique disorder, and professionals who spent more time on research were more likely to disagree. To the extent that professionals who spend more time on research may shape the narrative around ON more visibly, this study underscores the importance of listening to practitioners' experiences in applied settings.
Level of evidence: Level V: Opinions of authorities, based on descriptive studies, narrative reviews, clinical experience, or reports of expert committees.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s40519-023-01551-6.
## Introduction
Orthorexia nervosa (ON), a condition that is characterized by a pathological obsession with eating healthy food [1], has been widely described in the scientific literature and popular press, but has not been formally recognized as a disorder in either the Diagnostic and Statistical Manual of Mental Disorders (DSM) [2] or the International Classification of Diseases (ICD) [3]. An ongoing debate as to whether ON is a unique eating disorder (ED) inhibits rigorous research on the condition. An important contribution to this debate is the opinions of practicing health professionals with experience working clinically with ED patients. While recent studies have examined professionals’ opinions of ON as a meaningful diagnosis outside of the U.S. [4–6], knowledge of U.S. health professionals’ opinions on this matter is lacking. Given that the DSM and ICD are the standard classifications of mental disorders used by mental health professionals and insurance companies in the U.S., specifically surveying U.S. health professionals is a critical extension of prior work. The lack of consensus on the clinical utility, diagnostic criteria and measurement, and sociocultural influence of this disordered eating pattern cause problems for the interpretation of much of the ON literature.
## Clinical utility
Clinical utility refers to a diagnostic concept whose defining features provide useful information such as prognosis, likely treatment outcomes, and/or testable propositions about biological correlates [7]. Prior surveys of clinicians working with ED patients in Belgium, the Netherlands, Australia, and New Zealand found that a majority reported they had observed ON in their own practice and thought it should be a distinct, clinically recognized disorder [4–6].
A key consideration regarding the clinical utility of ON is whether it constitutes a meaningful presentation that is distinct from other EDs. Generally, the main arguments against ON involve consideration of the disordered eating pattern as being better accounted for by existing diagnoses. Similar to other EDs, perfectionism, obsessive–compulsive traits, general psychopathology, dieting, poor body image, and drive for thinness have been shown to be positively associated with greater ON symptoms [8]. In contrast, although weight and shape concerns are considered to be integral to the psychopathology of AN and BN [9], their relevance is less clear for ON. In the original criteria proposed by Dunn and Bratman [10], dietary restriction to promote optimum health may lead to weight loss for individuals with ON, but the desire to lose weight is absent, hidden or subordinated to ideation about healthy eating. Extant research supports this proposed criterion, finding a strong preoccupation with healthy eating to be negatively correlated with overweight preoccupation and appearance evaluation in a sample of university students [11]. However, a recent study compared ON symptoms to ED and symptoms and found that not only are ON symptoms related to body weight and shape concerns, but also ON symptoms correlated with prioritizing weight above health with respect to food selection [12]. ON has also been proposed to fall under the Obsessive Compulsive and Related Disorders category of the DSM-5, mainly due to symptom overlap with obsessive–compulsive disorder [9, 13]. Indeed, in a sample of U.S. college students, ON was significantly associated with obsessive–compulsive behaviors [14].
## Diagnosis and measurement
Currently, four sets of diagnostic criteria for ON have been proposed [10, 15–17] and four ON assessments have typically been used in past research [1, 16, 18, 19]. The lack of consensus for defining and measuring the proposed construct results in significant issues with research on ON. Further, the assessments of ON that are currently available and used in research have been criticized for low internal consistency, questionable reliability, and incomplete assessment of proposed criteria [20]. Yet, one of these measures, the ORTO-15, continues to be used in research, resulting in unreliable and problematic findings. Prevalence ratings for ON as measured by the ORTO-15 have been found to be as high as $81.9\%$ [21], evidently over-pathologizing what are most likely normative food choice behaviors. Furthermore, the ORTO-15 produces a wide range of prevalence rates. For example, using the ORTO-15 criteria, one study found a prevalence rate of ON of $71\%$ among U.S. college students; however, when they considered whether participants’ diets had led to impairment in everyday living or medical problems, they reported ON rates as less than $1\%$ [22].
Current diagnostic criteria and assessment tools for ON have been criticized for not comprehensively measuring what some believe to be distinct components of the proposed disordered eating pattern. For example, compulsive or excessive exercise has been linked to ON symptomatology in previous studies [23–25], suggesting that the addition of this behavior to the diagnostic criteria or assessment for ON may capture the phenomenon more comprehensively. However, it is important to note that excessive exercise is not considered a diagnostic criterion in anorexia nervosa (AN) despite significant co-occurrence [2, 26]. In addition, a clearer criterion regarding the role of weight or shape concerns in ON would be helpful, given the inconsistency of existing literature on the topic [11, 12]. Other additional criteria specifically intended to differentiate ON from other related disorders would be beneficial for the measurement of ON.
## Sociocultural factors contributing to the emergence of ON
In addition to considerations regarding clinical utility and measurement and diagnostic issues, there is debate about etiologic trajectories that may potentially be unique or specifically relevant to our understanding of ON. Particular attention in the literature has been directed toward the influence of sociocultural factors on the emergence of ON [27, 29]. The rising popularity of health and fitness and the ubiquity of social media are two areas of focus within the sociocultural lens. The modern fixation on “healthy eating” can be traced back to the late-nineteenth early-twentieth century popularization of different diet and weight loss practices, spearheaded by companies such as Weight Watchers and Atkins that are still recognizable names today. Obesity became a salient concern in the mid-twentieth century, and it brought a transformation in the rationale for weight loss from individual goals to societal responsibility to combat rising rates [30]. This societal pressure to be thin resulted in the conceptualization of “healthism”, or the idea that the responsibility to prevent disease relies on the individual and their choice to change their own circumstances [31]. “ Healthism” made a person’s weight synonymous with their health and well-being, and solidified the moralization of food choice or process by which preferences are converted into a marker of values and perceived individual morality, that perpetuates modern ideas today [31, 32].
The growing popularity of social media platforms has also been hypothesized as contributing to the emergence of ON [33]. In a content analysis of the social media platform Instagram posts designed to promote fitness, results suggest that the majority of pictures portrayed thin and toned females, often with objectifying elements [34]. Although ostensibly promoting health and well-being, the characteristics associated with these posts, such as the thin body ideal, over-emphasis on appearance, and objectification have been linked to poorer body image and disordered eating. Indeed, one study found that higher Instagram use was associated with higher scores on the ORTO-15, indicating a greater tendency towards ON-related symptoms [28].
Results from prior studies outside of the U.S. suggest that many health professionals attribute sociocultural factors to the emergence of ON [29], although less is known about how health professionals in the U.S. conceptualize this disordered eating condition. Understanding how sociocultural factors may influence the development of ON, and which factors (e.g., social media) are most impactful, can inform prevention and treatment for ON that may differ slightly from the conceptualization of sociocultural factors related to risk in other EDs.
As was done in the original studies, participants were asked to what extent they believed certain worldviews and values, markets and industries, mass media and beauty ideals influenced the emergence of ON, which each potential factor being rated on a scale from 1 (no influence at all) to 5 (great influence). For the purposes of the current study, we changed the language of each question from “to what extend do you consider each of the following parts of the modern Western culture to influence the emergence of Orthorexia” to “to what extent do you consider each of the following sociocultural factors2 to influence the emergence of Orthorexia.” We also expanded the original survey’s questions about the influence of digital media by asking about social media separately from other digital media.
The total mean score percentage on the sociocultural influence ratings in this sample was $78.8\%$ (SD = 0.10, range: 40–$96\%$). Participants rated the diet and weight loss industry, and the perceptions that biological/organic/vegan and low fat/low carb/gluten free food are the healthiest as the most influential sociocultural factors in the emergence of ON (see Table 2 for descriptives).Table 2Mean scores and SD for each item in the sociocultural factors questionnaireSociocultural factorMeanSDIndividualism3.101.24Materialism3.101.30Capitalism3.521.34Food industry4.230.92Diet and weight loss industry4.700.63Fitness industry4.640.64Fashion industry3.761.16Cosmetic surgery industry3.511.22Broadcast media4.010.86Social media4.630.60Other digital media3.940.87Printed media3.451.00Outdoor advertisements3.020.92Thin body ideal4.250.93Muscular body ideal4.160.95Fast food is unhealthy4.510.81Biological/organic/vegan food is the healthiest4.660.64Low fat/low cab/gluten free food is the healthiest4.660.57Regular exercise is best for the body3.761.08Eating fast food2.991.25Trends of having healthy diets4.470.85 Perceived sociocultural influence on ON was significantly and positively correlated with time allocated to clinical work ($r = 0.25$ $p \leq 0.05$), but unrelated to time allocated to research (r = − 0.20, $$p \leq 0.06$$). No significant differences in perceived sociocultural influence on ON emerged for the different professions. In response to the question, “do you think there are any types of digital media in particular that influence the emergence of Orthorexia?” Instagram was specifically mentioned by 64 participants, whereas 26 participants identified Tik Tok, 12 mentioned Facebook and seven mentioned Twitter as particularly influential on the emergence of ON. For example, one participant responded:“*Social media* in general (Facebook, TikTok, Instagram, Twitter) have all been labeled by patients I have treated in the past year as sources of inspiration and guidance on eating in orthorexic patterns.”
## Current study
We aimed to replicate and expand on prior studies [4, 29] by assessing U.S. health professionals’ perspectives on: [1] ON as a distinct, clinically recognized disorder, [2] best practices in measurement and diagnosis of ON, and [3] supports for the idea that various sociocultural factors may be linked to the emergence of ON. Findings will represent an important contribution to the ongoing debate about possible inclusion of the proposed ON diagnosis in the next iteration of the DSM, and for our understanding of this condition.
## Methods
All study procedures were approved by the appropriate Institutional Review Boards. Participants reviewed a consent form describing the nature and purpose of the research prior to completion of questionnaires.
## Procedures
All participants were recruited through social media advertisements (Facebook, Instagram, and Twitter, as well as professional listservs (e.g., the Academy for Eating Disorders)) that included a link to the survey (Supplementary Information) hosted on the secure server Qualtrics. Inclusion criteria were being a health professional with experience working with EDs in the United States.
## Participants
Participants were health professionals ($$n = 100$$) with experience working clinically with EDs. Four participants were excluded from analyses because they had not worked clinically with EDs before, resulting in a final sample size of 96 (mean age = 35.54 years, SD = 9.78, range: 21–68 years; $92.70\%$1 female; $93.80\%$ White). The sample included PhD level psychologists ($32.30\%$), graduate and post-baccalaureate trainees ($25.00\%$), social workers/mental health counselors ($24.00\%$), and medical health professionals (Psychiatrists and other Physicians., Registered Dieticians, Registered Nurses, Physician Assistants,, and Nurse Practitioners; $18.80\%$). Participants reported an average of 8.83 years practicing in their professions (SD = 8.19, range 1–36 years).
## Measures
The questionnaire administered in the present study was adapted from Ryman et al. [ 4] and Syurina et al. [ 29] to facilitate direct comparisons between the current and prior samples. Minor modifications or additions were made as detailed below.
## Clinical utility of ON
To begin the survey, participants were presented with the proposed diagnostic criteria for ON [10] and asked if they had met clients who fulfilled these criteria. We added, “What diagnosis/diagnoses did you give to clients who fulfilled these criteria?” to the original survey questions, in order to clarify how clinicians are currently categorizing this disordered eating pattern in their practice. Participants were then asked how prevalent they believed the condition was in the general population in the U.S., and if they believed ON should have its own diagnosis in the upcoming version of the DSM (via “yes/no” responses).
Most participants ($78.1\%$) endorsed previously working with individuals whom they considered to fulfill the proposed ON criteria. Participants indicated that they typically diagnosed these clients with AN ($47.9\%$), Other Specified Feeding or Eating Disorder (OSFED)/Eating Disorder Not Otherwise Specified (EDNOS) ($28.1\%$), and Generalized Anxiety Disorder ($22.9\%$). Participants indicated moderate perceived prevalence of ON in the U.S. general population ($M = 3.05$, SD = 1.02 on a scale from 1 = not at all prevalent to 5 = extremely prevalent). Most participants agreed ON should be included as a diagnosis in future versions of the DSM ($71.9\%$). Of respondents who reported that ON should not be considered a separate diagnosis ($28.1\%$), the majority believed the disordered eating pattern fit within AN ($18.8\%$), Obsessive Compulsive Disorder (OCD) ($10.4\%$), OSFED/EDNOS ($7.1\%$), or Avoidant Restrictive Food Intake Disorder ($7.1\%$). Compared to those who reported ON should be recognized as a unique disorder, the health professionals that disagreed reported spending significantly more time on research and significantly less time on clinical work (Table 1). No significant differences in views of ON emerged when examining results by participants’ professions. Table 1Percentage of time spent on research and clinical work for participants who did and did not endorse ON as a discrete diagnosisON-NoON-YesdftpCohen’s dMSDMSDClinical work41.6928.5163.3134.37822.85 < 0.050.68Research54.1229.6836.6930.59822.44 < 0.050.59ON-No describes the participants who disagreed that ON should have a formal diagnosis in the upcoming DSM. ON-Yes describes the participants that believed ON should have a formal diagnosis in the upcoming DSM. M represent the mean percentage (out of $100\%$)
## Diagnosis and measurement of ON
Participants were asked which diagnostic category/ies they believed ON would fall under in the DSM and were asked to rate, on a scale from 1 (not at all a contributor) to 5 (a vital contributor), the extent to which exercise and weight loss contribute to or are a part of ON. They also responded to the following yes/no questions: “Do you think exercise related symptoms should be part of the diagnostic criteria?” and “Are there any additional components that are missing from the diagnostic criteria?” followed by an open-ended question to describe the components they proposed. These questions were added to the original study based on recent literature critiquing the proposed criteria [20], and proposing additional relevant criteria for the ON diagnosis [16].
Almost all respondents ($94.8\%$) reported that ON fit within the DSM category of “Eating and Feeding Disorders;” fewer believed it should fall under “Obsessive Compulsive” ($36.5\%$) or “Anxiety Disorders” ($14.6\%$). Weight loss ($M = 3.42$, SD = 1.14, on a scale from 1 = no influence at all to 5 = great influence) and exercise ($M = 3.49$, SD = 1.17) were thought to be moderately influential contributing factors to ON; $62.1\%$ of participants agreed that exercise-related symptoms should be part of the ON diagnostic criteria. Twenty-five participants ($26.6\%$) believed additional components should be added to Dunn and Bratman’s [10] proposed ON diagnostic criteria. Besides the exercise-related symptoms, participants ($$n = 4$$) identified body shape/weight concerns as an important additional component that needs to be highlighted in the criteria. For example, one participant noted:“*Weight is* often a concern (such as avoiding weight gain) even if not the primary concern. Folks are rarely neutral about weight.” Relation to disease prevention or general health concerns was noted as worth emphasizing in diagnostic criteria ($$n = 5$$):“Perhaps mention that the restriction in eating may have originally stemmed from a medical concern/health condition (e.g., diabetes), but over time has progressed such that the food choices become much more restrictive than is medically necessary for the condition.” Additional open-ended responses included information on how to differentiate ON from other disorders such as AN and OCD, as well as rejection of scientific or medical information that contradicts patients’ beliefs and fears of eating certain foods (Supplementary Information).
## Demographics
The last section of the survey included questions on gender, age, race/ethnicity, country of residence, highest level of education, profession, experience working clinically with EDs (indicated via yes/no response), and the percentage of professional time spent on research and clinical work.
## Statistical analyses
Descriptive analyses and independent samples t-tests were conducted to determine average prevalence estimates for ON and to examine differences in the opinions of ON as a discrete diagnosis (yes/no) by participants’ professional time (as a percentage of total) allocated to research versus clinical work. Participants’ descriptions of additional components they believed should be incorporated into the ON criteria were categorized by two independent raters with ED knowledge ($90.48\%$ initial inter-rater agreement; a third rater resolved the remaining two discrepancies). Total scores on the sociocultural factors related to ON scale were calculated for each participant, with higher scores indicating more agreement that sociocultural factors have a major influence on ON (Cronbach’s α = 0.88). Because we added an additional item assessing perceived influence of social media, total score percentages were calculated to allow for comparisons across samples. Bivariate correlations were used to examine any differences in perceived sociocultural influence by participants’ professional time allocated to research versus clinical work. Due to the nature of the community sample and lack of compensation for participation, missing data are found throughout, and valid percent is always reported.
## Discussion
This study was the first, to our knowledge, to assess U.S. health professionals’ perspectives on ON as a disordered eating pattern. Recent studies reported health professionals’ opinions of ON outside of the U.S, specifically in the Netherlands [4], Australia and New Zealand [5], and Belgium [6]. Findings from the current study give insight into the current state of knowledge and beliefs among practicing health professionals in the ongoing debate about ON.
Similar to studies previously conducted in the Netherlands, Australia and New Zealand, a majority of the U.S. health professionals surveyed here ($72\%$) indicated that ON should be included as a diagnosis in future versions of the DSM. Contrary to the original study [4], we did not find differences in perspectives about ON by profession, potentially due to small subsample sizes (e.g., ns = 31 or less in each category). However, differences did emerge when we examined types of work within professions, namely professional time allocated to research and clinical work. Health professionals who spend more time conducting clinical work were more likely to endorse ON as a discrete diagnosis compared to participants who reported spending more time on research. The disparate opinions of those who engage in more clinical work compared to research raises an important consideration of how the ON diagnosis debate translates from academia to clinical work and by association, to the individuals suffering from this disordered eating pattern. Specifically, the current study highlights a contrast between professionals who see many ED patients but whose opinions and experiences may not be as visible and professionals who shape the narrative around ON more saliently through research and publications.
Consistent with prior work [4], a majority of participants in the current study reported that ON would fit under the DSM category Eating and Feeding Disorders and many agreed that exercise-related symptoms should be part of the diagnostic criteria for ON. Qualitative data provided several interesting considerations for additional components to the proposed ON criteria. Participants in the current study noted that weight in some sense is often a concern for the ON patients they've encountered, and suggested body shape/weight concerns should be considered more explicitly in the criteria. Some health professionals in the current study also believed that the fear of eating certain foods warranted placement in the criteria. Lastly, participants emphasized the importance of recognizing the disease prevention and general health concerns often present for individuals with ON symptoms. With the exception of exercise-related symptoms, perspectives of the health professionals in the current study largely align with those of a multidisciplinary expert panel that recently published a consensus document on the definition and diagnostic criteria for ON [35].
Finally, the current study provides both quantitative and qualitative data to support the significant influence of sociocultural factors in the emergence of ON, according to the opinions of health professionals with experience working with EDs. Our findings are near direct replications of the original study [29] emphasizing the two perceptions that biological/organic/vegan food and low fat/low carb/gluten free food are the healthiest as the most influential factors in the emergence of ON. Our findings add to prior work by highlighting the significant perceived role of social media in the etiology of ON, an area of study that merits further attention in future work. More professional time spent on clinical work was associated with greater perceived sociocultural influence on the emergence of ON, providing further rationale for researchers to listen to practitioners’ experiences in applied settings in order to work toward a consensus regarding risk and maintaining factors for ON.
## Limitations and future directions
This is the first study, to our knowledge, to qualitatively investigate health professionals' perspectives on the specific components of the diagnostic criteria for ON proposed by Dunn and Bratman [10]. Although the information provided by participants contribute to the ongoing discussion about ON, some important limitations must be noted. Participants were recruited via social media channels and may represent a subgroup of health professionals who use social media regularly, thus limiting generalizability of findings. Though we were able to recruit a diverse sample in terms of profession, age, and years of experience, generalizability of findings remains limited due to the fact that the sample was majority White and female.
Our findings support recent efforts to conceptualize ON within a broader category of EDs dimensionally, rather than a distinct category. Indeed, much of the ON debate reflects the continued struggle with discrete diagnostic categories of overlapping eating pathology that are still necessary for research and insurance purposes, yet less meaningful in applied clinical work. In some ways, the validity of ON as a unique disorder relative to other EDs may be less relevant than its usefulness to clinicians attempting to conceptualize an individual’s current presentation as accurately as possible. Kendell and Jablensky [7] conceptualize usefulness of psychiatric disorders in two parts: first, the quantity and quality of the information in the literature, which depend on adequate diagnostic criteria not currently present for ON; second, whether the implications of the etiological, prognosis, and treatment information are different from the implications of other related syndromes. In the case of ON, perhaps sociocultural factors are more primary considerations for preventing and treating this presentation compared to other EDs (e.g., with social media literacy training). Further, Kendell and Jablensky [7] describe psychiatric diagnoses as working concepts that are invaluable to clinicians because of their utility rather than their validity. The aforementioned review of the ON literature and data provided by the current study support the notion that ON is a working concept with explicit support from health professionals internationally.
## Conclusions
Overwhelmingly, U.S. health professionals with experience working with EDs endorsed the opinion that ON should have its own diagnosis in the upcoming version of the DSM, and many participants provided qualitative responses suggesting this opinion was based on their clinical experience with individuals suffering with ON-like symptoms. Although no differences emerged by profession as in prior studies, the type of work performed as a health professional was associated with whether participants agreed or disagreed that ON should be a distinct disorder. The ultimate goal of any health professional working with ED patients is to alleviate the suffering caused by the disorder. Therefore, understanding why differences in opinions emerged based on professional time allocated to research and clinical work is important in order to best discern how to work together as health professionals in the ED field toward this mutual goal. Given the majority opinion that an ON diagnosis would be useful for health professionals in the current sample and prior studies, continued work toward reliable measurement and assessment of the condition is warranted.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (PDF 173 KB)Supplementary file2 (PDF 108 KB)
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|
---
title: 'Insulin pump therapy with and without continuous glucose monitoring in pregnant
women with type 1 diabetes: a prospective observational Orchestra Foundation study
in Poland'
authors:
- Katarzyna Cypryk
- Ewa Wender-Ozegowska
- Katarzyna Cyganek
- Jacek Sieradzki
- Kinga Skoczylas
- Xiaoxiao Chen
- Toni L. Cordero
- John Shin
- Ohad Cohen
journal: Acta Diabetologica
year: 2023
pmcid: PMC10033617
doi: 10.1007/s00592-022-02020-9
license: CC BY 4.0
---
# Insulin pump therapy with and without continuous glucose monitoring in pregnant women with type 1 diabetes: a prospective observational Orchestra Foundation study in Poland
## Abstract
### Aims
The effects of continuous subcutaneous insulin infusion (CSII) therapy with or without continuous glucose monitoring (CGM) on neonatal outcomes and glycemic outcomes of pregnant women with type 1 diabetes (T1D), living in Poland, were assessed.
### Methods
This prospective observational study enrolled women with T1D ($$n = 481$$, aged 18–45 years) who were pregnant or planned pregnancy. All used CSII therapy and a subset used CGM with CSII (CSII + CGM). Neonatal outcomes (e.g., rate of large for gestational age [LGA] delivery [birth weight > 90th percentile]) and maternal glycemia (e.g., HbA1c and percentage of time at sensor glucose ranges) were evaluated.
### Results
Overall HbA1c at trimesters 1, 2, and 3 was 6.8 ± $1.1\%$ (50.9 ± 12.3 mmol/mol, $$n = 354$$), 5.8 ± $0.7\%$ (40.1 ± 8.0 mmol/mol, $$n = 318$$), and 5.9 ± $0.7\%$ (41.4 ± 8.0 mmol/mol, $$n = 255$$), respectively. A HbA1c target of < $6.0\%$ (42 mmol/mol) at each trimester was achieved by $20.9\%$ ($\frac{74}{354}$), $65.1\%$ ($\frac{207}{318}$), and $58.0\%$ ($\frac{148}{255}$), respectively. For women using CSII + CGM versus CSII only, HbA1c levels at trimesters 1, 2, and 3 were 6.5 ± $0.9\%$ versus 7.1 ± $1.3\%$ (47.8 ± 9.7 mmol/mol versus 54.3 ± 14.0 mmol/mol, $p \leq 0.0001$), 5.7 ± $0.6\%$ versus 6.0 ± $0.9\%$ (38.9 ± 6.5 mmol/mol versus 41.6 ± 9.3 mmol/mol, $$p \leq 0.0122$$), and 5.8 ± $0.6\%$ versus 6.1 ± $0.8\%$ (40.3 ± 6.9 mmol/mol versus 42.9 ± 9.1 mmol/mol, $$p \leq 0.0117$$), respectively. For the overall, CSII only, and CSII + CGM groups, rates of LGA delivery were $22.7\%$ ($\frac{74}{326}$), $24.6\%$ ($\frac{34}{138}$), and $21.3\%$ ($\frac{40}{188}$), respectively.
### Conclusions
Observational assessment of women with T1D using CSII therapy demonstrated low HbA1c throughout pregnancy and low rates of LGA. The addition of CGM to CSII therapy compared to CSII therapy alone was associated with some improved maternal glycemic and neonatal outcomes.
### Clinicaltrials.gov identifier
NCT01779141 (January 2013).
### Supplementary Information
The online version contains supplementary material available at 10.1007/s00592-022-02020-9.
## Introduction
Diabetes management technology (i.e., continuous subcutaneous insulin infusion [CSII] therapy or continuous glucose monitoring [CGM]) has been shown to improve outcomes in non-pregnant individuals living with T1D [1–5]. Pregnancies complicated by T1D warrant particularly careful management, given the risks of adverse outcomes for both mother and baby [6]. Compared to pregnant women without diabetes, women with T1D are at a particularly increased risk of hypoglycemia (especially in early pregnancy) [7], diabetic ketoacidosis [8], preeclampsia [9], miscarriage [10, 11], preterm birth [12], and other complications [10, 13]. Compared to neonates of women without diabetes, babies of mothers with T1D are at increased risk of negative neonatal composite outcomes including being large for gestational age (LGA, birth weight > 90th percentile for the population) [11, 14], macrosomia (neonate birth weight > 4000 g, regardless of gestational age) [10, 15] or congenital malformation [12, 16], and having increased rates of perinatal mortality [11, 12, 16] and neonatal hypoglycemia [10, 11]. A majority of these negative neonatal outcomes are primarily associated with increased fetal hyperglycemia exposure [17–21]. In consequence, macrosomia and LGA affect about half of all babies of mothers with T1D [14], and notably these children go on to live with increased risk of future obesity [19], diabetes [17], and cardiovascular disease [22].
Fortunately, pre-pregnancy planning that includes either CSII or MDI therapy has been reported to improve HbA1c and pregnancy outcomes [23, 24] and, significantly, compared to unplanned pregnancy [23]. Recent prospective observational study of maternal and neonatal outcomes in pregnant women receiving comprehensive preconception planning with either CSII or MDI demonstrated significantly reduced severe adverse pregnancy outcomes when compared with women who received routine care [25]. While good or improved glycemic control (a low or lowered HbA1c level) is observed with intensive insulin treatment and/or diabetes technology management therapies, high rates of LGA or macrosomia [21, 26] and other complications can still persist.
Given the reported impact of pre-pregnancy planning and CGM therapy in pregnancies complicated by T1D [21, 27–29], we report on the maternal glycemic and neonatal composite outcomes from an observational study of pregnant women with T1D who used CSII with or without CGM during the Orchestra Foundation (Fundacja Wielka Orkiestra Swiatecznej Pomocy) insulin pump donation program in Poland.
## Methods
This prospective, observational study assessed Polish women (18–45 years of age) with pregestational T1D, who were pregnant or who planned pregnancy, and who were on MDI therapy for at least three months, before study start. Detailed inclusion and exclusion criteria of participants enrolled across 22 investigational centers throughout Poland have been published elsewhere [30]. Briefly, women with T1D were eligible to take part in the study if their physician recommended and prescribed CSII therapy or sensor-augmented insulin pump (SAP) therapy due to pregnancy or a plan to become pregnant. The decision to start CSII only or CSII with CGM (CSII + CGM) was made by the physician and study participant, independent of the study. Prior enrollment in and/or withdrawal from the Orchestra Foundation registry, current participation or participation in any other interventional clinical trial within the prior 3 months, in vitro fertilization assistance, pregnancy beyond 16 weeks of amenorrhea, any diabetes other than T1D, and inability to read or write were study exclusion criteria.
This study was conducted in compliance with the Declaration of Helsinki of 2008 by means of an informed consent process, Medical Ethics Committee approval (15/KBL/OIL/2013), study training, and clinical trial registration (CinicalTrials.gov identifier NCT01779141). The summary of study visits and points of data collection, including pump and blood glucose meter data uploads to CareLink™ clinical software (Medtronic, Northridge, California, USA), are listed in Supplementary Information S1. The informed consent form was signed at either the first (preconception) visit or first visit within the first trimester of pregnancy. The study protocol was approved by a Central Ethics Committee (Regional Chamber of Physicians at Krakow) for all investigational sites. Competent Authority approval was not required for observational studies in Poland. A STROBE cohort checklist [31] was completed for this report.
The Orchestra Foundation funded the CSII therapy for study participants. Approximately $60\%$ were provided a MiniMed™ Paradigm™ REAL-Time 722 insulin pump (Medtronic) with or without a MiniMed™ Sof-sensor™ senor (Medtronic), and the remainder were provided a MiniMed™ Paradigm™ Veo™ system with the Enlite™ sensor (Medtronic). Participants provided the SAP system were not required to use the low-glucose suspend function.
Women participating in this study received intensive diabetes management that involved education and/or training on diet and carbohydrate counting, physical activity, folic acid supplementation, glycemic goals, self-monitoring of blood glucose (SMBG), and insulin dose adjustment, pump and CGM management (e.g., system calibrations and glucose sensor and infusion set changes). Care included frequent outpatient visits, and hospitalization if necessary. Participants underwent regular routine follow-up, without special requirements or regimens, which involved standard medical information collection into electronic clinical report forms and occasional data upload from devices (pumps and blood glucose meters) into CareLink™ clinical software (Medtronic). The study duration for each participant was up to 22 months (i.e., up to 12 months pre-conception and, if applicable, up to six weeks after delivery).
## Statistical and safety outcomes analyses
The primary objective was to assess the effect of CSII therapy with or without CGM on the HbA1c of overall women and those who enrolled before and during pregnancy, in general. Secondary objectives included assessment of maternal glycemic outcomes (i.e., sensor glucose [SG], standard deviation [SD] of SG, coefficient of variation [CV] of SG, and the percentage of time spent at SG ranges [32]) during CSII CSII + CGM use. Exploratory post hoc analysis of HbA1c was performed during CSII only therapy versus CSII + CGM. Analyses were conducted with two-sample t test or Wilcoxon rank-sum test (for categorical data), and a $p \leq 0.05$ was considered statistically significant. Missing data may have not been available due to a study participant forgetting to upload to CareLink™ clinical or not uploading successfully due to a technical issue. However, no imputation was applied for missing data in this study. Data were not adjusted for confounding.
The prevalence of pregnancy complications (i.e., large for gestational age [LGA], macrosomia, jaundice, congenital malformation, and neonatal death]) and admissions to the neonatal intensive care unit were recorded. LGA was based on Polish percentile grids that calculated neonatal weight in relation to a newborn’s sex [33]. Pregnancy dating was calculated according to first trimester ultrasound scan (i.e., crown-rump length between 30 and 84 mm, which corresponds with the 9 + 5–14 + 1 weeks of pregnancy [according to https://fetalmedicine.org/research/pregnancyDating]).
Exploratory post hoc analysis assessed the association of maternal SG, SD of SG, percentage of time spent at target SG range (63–140 mg/dL [3.5–7.8 mmol/L]) and 1-h postprandial SG [34, 35] with non-LGA versus LGA delivery and positive versus negative composite neonatal outcomes. An odds ratio of mean ($95\%$ confidence interval) was used to determine the strength of association and was analyzed with logistic regression. A $p \leq 0.05$ was considered statistically significant. Positive neonatal composite outcomes were defined as deliveries that were without congenital malformation, did not require mechanical ventilation, and were not LGA. Negative neonatal outcomes deliveries involved congenital malformation, mechanical ventilation requirement, LGA delivery or admittance to the neonatal care unit.
Due to the observational study design, methods to reduce bias were limited and analyses were conducted with respect to intention to treat. Safety outcomes were summarized as the incidence of maternal severe hypoglycemia (hypoglycemia requiring third-party assistance); diabetic ketoacidosis; unanticipated adverse device effects (UADEs, any serious adverse device effect which by its nature, incidence, severity or outcome had not been identified in the current version of the risk analysis report); serious adverse events (SAEs, defined as an adverse event that led to death, serious deterioration in the health of the subject that either resulted in a life-threatening illness or injury, or a permanent impairment of a body structure or a body function, or hospitalization); a medical or surgical intervention to prevent life-threatening illness or injury or permanent impairment to a body structure or a body function; or fetal distress, fetal death, congenital abnormality or birth defect. In the present study, severe hypoglycemia and diabetic ketoacidosis were treated as severe adverse events. The SAE and UADE information was collected throughout the study and reported immediately to Medtronic Poland (Sp.z.o.o) via an SAE/UADE form incorporated in the electronic clinical record.
## Results
A flow diagram (Supplementary Information S2) of participant disposition shows that a total of 481 women (mean age of 30.4 ± 4.0 years and diabetes duration of 12.3 ± 7.7 years) enrolled in the study; 216 of whom were not pregnant. There were 121 who dropped out or were withdrawn before pregnancy and 60 who dropped out or were withdrawn during pregnancy: the latter included 28 who miscarried, 10 early withdrawals prior to delivery, and 22 who gave birth but were lost to follow-up. Of the total 360 pregnant women, 190 ($52.8\%$) used CGM with CSII therapy.
Table 1 summarizes the age, glycemic metrics, total daily insulin, and medical history of the overall group, at baseline. The same characteristics are shown for those who used CSII only or CSII + CGM. The rate of complications (macroangiopathy, nephropathy, neuropathy, and retinopathy) is also provided for each of the aforementioned groups. Table 1Summary of age, glycemic metrics, total daily insulin, and medical history at baseline of study participantsOverall ($$n = 360$$)CSII only ($$n = 167$$)CSII + CGM ($$n = 193$$)dAge, years30.1 ± 4.029.9 ± 4.330.3 ± 3.8HbA1ca($$n = 354$$)($$n = 166$$)($$n = 188$$) %6.8 ± 1.17.1 ± 1.36.5 ± 0.9 mmol/mol50.9 ± 12.354.3 ± 14.047.8 ± 9.7SMBGb($$n = 202$$)($$n = 60$$)($$n = 142$$) mg/dL115.5 ± 16.6115.7 ± 17.3115.5 ± 16.3 mmol/L6.4 ± 0.96.4 ± 1.06.4 ± 0.9TDD, units43.5 ± 15.444.5 ± 14.142.6 ± 16.4Weight, kg65.3 ± 11.566.0 ± 12.664.7 ± 10.5BMI, kg/m223.7 ± 3.723.9 ± 3.823.5 ± 3.6Diabetes history, years12.0 ± 7.812.1 ± 7.811.9 ± 7.9Previous severe hypoglycemia, Nc0.1 ± 0.50.1 ± 0.50.1 ± 0.5Previous DKA, Nc0.0 ± 0.20.0 ± 0.20.0 ± 0.2Macroangiopathy, %1.7 ($\frac{6}{360}$)2.4 ($\frac{4}{167}$)1.0 ($\frac{2}{193}$)Nephropathy, %3.9 ($\frac{14}{360}$)6.0 ($\frac{10}{167}$)2.1 ($\frac{4}{193}$)Neuropathy, %5.6 ($\frac{20}{360}$)6.6 ($\frac{11}{167}$)4.7 ($\frac{9}{193}$)Retinopathy, %15.3 ($\frac{55}{360}$)17.4 ($\frac{29}{167}$)13.5 ($\frac{26}{193}$)Values are shown as mean ± SD or mean. Numbers within parentheses indicate number of participants or participants over total number of participants.aThe first trimester HbA1c of women who enrolled during pregnancy ($$n = 262$$) was a mixture of HbA1c levels collected before enrollment ($$n = 206$$) and on or after enrollment ($$n = 56$$).bThe first trimester SMBG of women who enrolled during pregnancy was only collected after study enrollment and may not represent the averaged metric for the whole trimester.cUp to 12 months prior to study start.dThree women did not become pregnant but provided baseline enrollment data. HbA1c glycated hemoglobin, SMBG self-monitoring of blood glucose, TDD total daily dose of insulin, DKA diabetic ketoacidosis
## Maternal glucose outcomes
Mean HbA1c at each trimester is shown for the overall group and those who enrolled before or during pregnancy, stratified by therapy use (Table 2). Women who enrolled during pregnancy made up a significant proportion of the overall group and their mean HbA1c at T1 trended higher ($$n = 262$$, 7.0 ± $1.2\%$ [53.1 ± 12.9 mmol/mol]) than that of women who enrolled before pregnancy ($$n = 92$$, 6.2 ± $0.7\%$ [44.5 ± 7.7 mmol/mol]). By T3, however, mean HbA1c for both groups (5.9 ± $0.7\%$ [41.2 ± 8.1 mmol/mol] and 6.0 ± $0.7\%$ [41.9 ± 7.7 mmol/mol], respectively) appeared relatively comparable. The use of CGM with CSII therapy by women who enrolled during pregnancy, compared with CSII alone, resulted in a statistically significant reduction in HbA1c over the course of pregnancy (Table 2). This trend was not observed in women who enrolled before pregnancy, although HbA1c reduced with time. Table 2HbA1c of groups who used CSII therapy only or CGM with CSII therapyOverall (N)Enrolled before pregnancy (N)Enrolled during pregnancy (N)T1*T2T3T1T2T3T1*T2T3TotalHbA1c($$n = 354$$)($$n = 318$$)($$n = 255$$)($$n = 92$$)($$n = 79$$)($$n = 61$$)($$n = 262$$)($$n = 239$$)($$n = 194$$) %6.8 ± 1.15.8 ± 0.75.9 ± 0.76.2 ± 0.75.8 ± 0.66.0 ± 0.77.0 ± 1.25.8 ± 0.85.9 ± 0.7 mmol/mol50.9 ± 12.340.1 ± 8.041.4 ± 8.044.5 ± 7.739.6 ± 6.641.9 ± 7.753.1 ± 12.940.2 ± 8.441.2 ± 8.1CSII onlyHbA1c($$n = 166$$)($$n = 140$$)($$n = 108$$)($$n = 34$$)($$n = 27$$)($$n = 22$$)($$n = 132$$)($$n = 113$$)($$n = 86$$) %7.1 ± 1.36.0 ± 0.96.1 ± 0.86.4 ± 0.85.9 ± 0.66.1 ± 0.87.3 ± 1.36.0 ± 0.96.1 ± 0.9 mmol/mol54.3 ± 14.041.6 ± 9.342.9 ± 9.146.2 ± 8.441.6 ± 6.843.0 ± 8.356.4 ± 14.441.7 ± 9.842.8 ± 9.3CSII + CGMHbA1c($$n = 188$$)($$n = 178$$)($$n = 147$$)($$n = 58$$)($$n = 52$$)($$n = 39$$)($$n = 130$$)($$n = 126$$)($$n = 108$$) %6.5 ± 0.95.7 ± 0.65.8 ± 0.66.1 ± 0.75.7 ± 0.65.9 ± 0.76.7 ± 0.95.7 ± 0.65.8 ± 0.6 mmol/mol47.8 ± 9.738.9 ± 6.540.3 ± 6.943.5 ± 7.138.6 ± 6.341.3 ± 7.449.7 ± 10.139.0 ± 6.639.9 ± 6.7p-value < 0.0001b0.0122b0.0117b0.0471b0.0619a0.6355b < 0.0001b0.0399b0.0097bValues are shown as mean ± SD.P-values indicate difference between CSII only and CSII + CGM within each given trimester.*The first trimester HbA1c of women who enrolled during pregnancy ($$n = 262$$) was a mixture of HbA1c levels collected before enrollment ($$n = 206$$) and on or after enrollment ($$n = 56$$).T1, T2, T3 = Trimester 1, 2, and 3, respectively; HbA1C = glycated hemoglobin; CSII continuous subcutaneous insulin infusion; CGM continuous glucose monitoring.aTwo-sample t-test.bWilcoxon rank-sum test.
The proportion of women from all groups achieving mean HbA1c ranges throughout pregnancy, including the mean target HbA1c of < $6.0\%$ (< 42.0 mmol/mol) [34, 35], is stratified by therapy use and shown in Supplementary Information S3. The percentage achieving target HbA1c tended to increase from trimesters 1 to 3 and were $20.9\%$ ($\frac{74}{354}$), $65.1\%$ ($\frac{207}{318}$), and $58.0\%$ ($\frac{148}{255}$), respectively. By trimester 3, the proportion who achieved target HbA1c with CSII only was $49.1\%$ ($$n = 53$$/108), while that for women who used CGM with CSII was 64.6 ($$n = 95$$/147).
Table 3 shows the CGM outcomes of all women who used CGM with CSII therapy. Average SG for all groups was < 120 mg/dL (< 6.7 mmol/L), CV of SG averaged < $33\%$, and a trending increase in time spent in target range from the first to the third trimester was observed. While time in hypoglycemic ranges (< 63 mg/dL [< 3.5 mmol/mol] and < 54 mg/dL [< 3.0 mmol/mol]) still averaged more than the targeted < 1 h/day and < 0.25 h/day, respectively, time in target range reached > $70\%$ and the proportion of time spent at > 140 mg/dL (> 7.8 mmol/mol) remained below $25\%$, for all groups. Table 3Summary of glycemic outcomes throughout pregnancy in women who used CGM with CSII therapyOverall (N)Enrolled before pregnancy (N)Enrolled during pregnancy (N)T1* ($$n = 109$$)T2 ($$n = 167$$)T3 ($$n = 151$$)T1 ($$n = 50$$)T2 ($$n = 49$$)T3 ($$n = 47$$)T1* ($$n = 59$$)T2 ($$n = 118$$)T3 ($$n = 104$$)SG, mg/dL113.0 ± 16.5111.7 ± 14.7113.7 ± 13.7116.6 ± 17.5113.1 ± 14.3115.9 ± 11.6109.9 ± 15.1111.1 ± 14.9112.7 ± 14.5SG, mmol/L6.3 ± 0.96.2 ± 0.86.3 ± 0.86.5 ± 1.06.3 ± 0.86.4 ± 0.66.1 ± 0.86.2 ± 0.86.3 ± 0.8CV of SG, %35.1 ± 7.633.6 ± 7.431.2 ± 5.634.5 ± 6.732.5 ± 5.530.1 ± 5.335.7 ± 8.234.1 ± 8.031.7 ± 5.6Percentage of time spent at sensor glucose ranges, mg/dL < 543.1 ± 4.12.9 ± 3.42.1 ± 2.82.3 ± 2.42.3 ± 2.51.4 ± 1.63.7 ± 5.03.2 ± 3.62.3 ± 3.2 < 637.6 ± 6.57.2 ± 5.65.2 ± 5.06.3 ± 5.26.0 ± 4.63.8 ± 3.08.7 ± 7.37.7 ± 6.05.9 ± 5.563–14070.4 ± 12.472.2 ± 11.773.9 ± 11.069.2 ± 13.272.4 ± 10.774.3 ± 10.571.3 ± 11.772.2 ± 12.173.7 ± 11.2 > 14022.1 ± 13.020.6 ± 12.020.9 ± 11.624.5 ± 14.121.5 ± 11.921.9 ± 10.820.1 ± 11.820.1 ± 12.020.4 ± 12.0 > 2501.2 ± 2.00.8 ± 2.40.6 ± 1.11.4 ± 2.50.6 ± 1.10.5 ± 0.71.0 ± 1.40.9 ± 2.70.6 ± 1.2Values are shown as mean ± SD.*The first trimester SG of women who enrolled during pregnancy was only collected after study enrollment and may not represent the averaged metric for the whole trimester. T1, T2, T3 = Trimester 1, 2, and 3, respectively; SG sensor glucose; CV coefficient of variation.
## Neonatal status and outcomes
For the overall group and those who enrolled before or during pregnancy, a summary of neonatal status (i.e., sex, gestational delivery, mean birth length and weight), rates of blood glucose levels ≤ 40 mg/dL, and prevalence of complications ([e.g., large for gestational age, macrosomia, jaundice, congenital malformation, and death]) are detailed in Supplementary Information S4. Briefly, the rates of LGA (birth weight > 90th percentile) were $22.7\%$ ($\frac{74}{326}$), $28.0\%$ ($\frac{23}{82}$), and $20.9\%$ ($\frac{51}{244}$) for each respective group. For women using only CSII, the rate was $24.6\%$ ($\frac{34}{138}$), and for those using CGM with CSII it was $21.3\%$ ($\frac{40}{188}$). Rates of neonatal hypoglycemia (BG ≤ 40 mg/dL) were also low across groups and were $20.1\%$ ($\frac{51}{254}$), $17.9\%$ ($\frac{10}{56}$), and $20.7\%$ ($\frac{41}{198}$), respectively, and appeared relatively comparable between women who used CSII only ($21.0\%$ [$\frac{21}{100}$]) and CGM with CSII ($19.5\%$ [$\frac{30}{154}$]). This trend was also observed for Caesarean births, where women who used CGM demonstrated a rate of $22\%$ ($\frac{42}{189}$) and those who used only CSII demonstrated a rate of $24.5\%$ ($\frac{34}{139}$). The exploratory analysis of non-LGA versus LGA delivery and positive versus negative neonatal composite outcomes with respect to maternal CGM metrics demonstrated strong association with most of the recommended glycemic targets for pregnancy (Table 4).Table 4Association of maternal glycemic and neonatal outcomes during CGM use with CSII therapyNon-LGA deliverySG, mg/dLSD of SG, mg/dLTime in target range, %1-hour postprandial SG,mg/dLT1T2T3Non-LGA delivery111.1 ± 14.5 ($$n = 140$$)37.6 ± 11.4 ($$n = 140$$)73.0 ± 11.3 ($$n = 140$$)113.8 ± 21.3 ($$n = 46$$)112.1 ± 19.6 ($$n = 93$$)110.5 ± 17.5 ($$n = 81$$)LGA delivery119.9 ± 11.7 ($$n = 40$$)40.3 ± 8.5 ($$n = 40$$)67.9 ± 9.4 ($$n = 40$$)125.9 ± 17.5 ($$n = 14$$)123.6 ± 16.8 ($$n = 26$$)120.3 ± 13.0 ($$n = 24$$)Odds ratio1.04 [1.02, 1.07]1.02 [0.99, 1.05]0.96 [0.93, 0.99]1.03 [1.00, 1.06]1.03 [1.01, 1.05]1.04 [1.01, 1.07] p-value0.0002b0.0456b0.0045b0.0578a0.0014b0.0119aPositive composite neonatal outcomes*110.3 ± 14.2($$n = 126$$)36.7 ± 8.7 ($$n = 126$$)73.7 ± 10.8 ($$n = 126$$)113.6 ± 21.5 ($$n = 42$$)112.3 ± 19.6 ($$n = 86$$)109.8 ± 17.6 ($$n = 77$$)Negative composite neonatal outcomes†119.5 ± 12.6($$n = 54$$)41.9 ± 14.0 ($$n = 54$$)67.5 ± 10.7 ($$n = 54$$)123.5 ± 18.3 ($$n = 18$$)120.8 ± 18.3 ($$n = 33$$)120.6 ± 12.4 ($$n = 28$$)Odds ratio1.05[1.02, 1.08]1.05[1.01, 1.09]0.95[0.92, 0.98]1.02[1.00, 1.05]1.02[1.00, 1.04]1.04[1.01, 1.07] p-value<0.0001b0.0096b0.0005a0.0946a0.0078b0.0008aPositive total composite neonatal outcomes**109.9 ± 13.3($$n = 120$$)36.6 ± 8.5($$n = 120$$)73.8 ± 10.4 ($$n = 120$$)113.5 ± 20.9 ($$n = 40$$)111.9 ± 19.3 ($$n = 83$$)109.6 ± 16.7 ($$n = 73$$)Negative total composite neonatal outcomes††119.5 ± 14.3($$n = 60$$)41.6 ± 13.9($$n = 60$$)67.9 ± 11.4 ($$n = 60$$)122.8 ± 20.1 ($$n = 20$$)121.0 ± 19.0 ($$n = 36$$)119.7 ± 15.7 ($$n = 32$$)Odds ratio1.05 [1.03, 1.08]1.05 [1.01, 1.09]0.95 [0.92, 0.98]1.02 [1.00, 1.05]1.02 [1.00, 1.05]1.04 [1.01, 1.07]p-value < 0.0001a0.0140b0.0006a0.0557b0.0048b0.0046aValues are shown as mean ± SD.Time in target range = 63–140 mg/dL (3.5–7.8 mmol/L).Odds ratios are shown with $95\%$ confidence intervals.*Deliveries that involved no congenital malformation, did not require mechanical ventilation, and were not LGA.**Deliveries that involved no congenital malformation, did not require mechanical ventilation, were not LGA, and required no admittance to neonatal care unit.†Deliveries that involved congenital malformation, required mechanical ventilation, or were LGA.††Deliveries that involved congenital malformation, required mechanical ventilation, were LGA, or required admittance to neonatal care unit. T1,T 2, T3 = Trimester 1, 2, and 3, respectively; LGA large for gestational age; SG sensor glucose.aTwo-sample t-test.bWilcoxon rank-sum test.
## Safety analyses
Regarding overall safety outcomes, there were 377 adverse events and 364 SAEs (Supplementary Information S5). Of the 364 SAEs, 97 were in the group that enrolled before pregnancy and 267 were in the group that enrolled during pregnancy. There were 357 non-device-related events that included three episodes of DKA and seven device-related events that included three episodes of DKA. There were eight episodes of severe hypoglycemia; three were in the group that enrolled before pregnancy and five were in the group that enrolled during pregnancy. All episodes of severe hypoglycemia occurred in participants who used CGM with CSII therapy. There were no reports of UADEs. There was one report of maternal death due to chronic kidney disease, 6 months after delivery.
## Discussion
This observational study shows that CSII therapy helped women with T1D who were pregnant or planning pregnancy achieve glycemic targets throughout pregnancy. Specifically, HbA1c levels of the overall group were reduced from 6.8 ± $1.1\%$ (50.9 ± 12.3 mmol/mol) at trimester 1 to 5.8 ± $0.7\%$ (40.1 ± 8.0 mmol/mol) and 5.9 ± $0.7\%$ (41.4 ± 8.0 mmol/mol) at trimesters 2 and 3, respectively. There was also a trending increase in the proportion of women achieving target HbA1c by the third trimester. For the group who enrolled during pregnancy and used CGM with CSII, mean HbA1c was significantly reduced over trimesters, when compared to the HbA1c of women using only CSII. The significant reduction in HbA1c, with time, may not have been observed in women who enrolled before pregnancy due to the smaller sample size, as well as a smaller HbA1c difference between the CSII only and CGM + CSII therapies, at study start. For instance, this group demonstrated near-target HbA1c at T1, compared with the HbA1c of women who enrolled during pregnancy. While they also spent a lower percentage of time in hypoglycemia, all women using CGM with CSII spent time in target range that exceeded > $70\%$ by the third trimester.
Exploratory analyses showed that achieving lower HbA1c and increased time in target glycemic range was associated with better neonatal outcomes. For the overall group of women, those who used CSII only and those who used CGM with CSII, the LGA delivery incidence rates were lower than other previously reported rates that reached ≥ $50\%$, in pregnant women with pregestational TD [21, 36]. When evaluated against the World Health *Organization criteria* [37], the LGA rates for each aforementioned group were $35.3\%$, $39.9\%$, and $31.9\%$, respectively.
Several organizations have recommended targeted diabetes management for pregnancies complicated by T1D: an HbA1c target of < $6.0\%$ (< 42 mmol/mol) [34, 35], a fasting glucose level of < 90 mg/dL (< 5.0 mmol/L) [35] or < 95 mg/dL (< 5.3 mmol/L) [34], and a 1-h postprandial glucose concentration of < 140 mg/dL (< 7.8 mmol/L) [34, 35]. These metrics worsen in the later trimesters of pregnancy where challenges with gastric emptying and insulin kinetics [38] and impaired glucose metabolism [39] result in increased time above SG target range [20, 21, 28]. In the present study, however, these metrics met or aligned closely with consensus recommendations (e.g., average SG, 1-h postprandial SG of < 120 mg/dL, and CV of SG < $36\%$) [34, 40].
Prospectively randomized [28, 29, 41] and retrospective [21, 42] studies of CSII and/or CGM use versus MDI and SMBG measurements have demonstrated clinical benefits for mothers and their babies. Although the proportion of time spent in target glucose range increased and HbA1c improved (or did not change) with CGM technology relative to control [21, 28], only a small percentage of study participants reached the international consensus recommended TIR of > $70\%$ (~ 17 h of the day). In addition, the overall rates of LGA deliveries in some of these trials remained rather high, averaging close to $50\%$ in the intervention groups and up to $70\%$ in the control groups. A recent observational study of pregnant women with T1D ($$n = 81$$, 11 on MDI and 70 on CSII [28 used CGM]), some of whom underwent pregnancy planning, demonstrated significantly improved HbA1c in those who used CGM with CSII compared to CSII or MDI therapy only (time spent at sensor glucose ranges was not analyzed) [27]. However, there was no difference in macrosomia risk between groups and the rate observed for women using CGM + CSII was $21.4\%$, but $16.7\%$ for women using CSII only. In the present study, rates of macrosomia (weight of ≥ 4000 g) trended similarly and were $22.8\%$ ($\frac{43}{189}$) for women using CGM + CSII and $18.0\%$ ($\frac{25}{139}$) for those using only CSII.
Limitations of the current study include its observational and non-randomized design, and the relatively good glycemia management in study participants before and/or during the first trimester, which may preclude generalizability of findings. In addition, a majority of participants received multidisciplinary clinical support from gynecologists/obstetricians, diabetologists, nurses, and dieticians during the study. Similar findings of significantly reduced adverse pregnancy outcomes during prospective observational [25] and retrospective [43] studies have been reported in women with ideally managed T1D receiving comprehensive preconception-to-pregnancy planning or healthcare management. Thus, a clear distinction between the contribution of technological devices and a comprehensive multidisciplinary approach to diabetes management during pregnancy cannot be easily delineated. Indeed, the observational design was intended to bench mark the achievements of care in diabetes pregnancy clinics that used diabetes technology, not provide specific comparative outcomes. Strengths of this study include its report of outcomes observed in over 300 pregnancies complicated by T1D in women who demonstrated good compliance, enrolled pre- and postconception, and for whom HbA1c and CGM data could be analyzed.
## Conclusions
This observational investigation determined that good maternal glycemia and positive neonatal outcomes can be achieved in a majority of women who were pregnant or planned pregnancy and used CSII only or CGM with CSII. Maternal HbA1c and glucose levels improved throughout the course of pregnancy and positive neonatal composite outcomes were associated with an increased duration of maternal time spent within target glucose range. Although current insulin delivery therapies have advanced to include predictive insulin suspension and/or automated insulin delivery based on CGM, present study findings serve as a benchmark for pregnancy outcomes in women with T1D using only CSII or CGM with CSII.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 429 kb)
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|
---
title: Pre-pregnancy body surface area and risk for gestational diabetes mellitus
authors:
- Lotta S. Holopainen
- Hanna H. Tähtinen
- Mika Gissler
- Päivi E. Korhonen
- Mikael O. Ekblad
journal: Acta Diabetologica
year: 2023
pmcid: PMC10033622
doi: 10.1007/s00592-022-02029-0
license: CC BY 4.0
---
# Pre-pregnancy body surface area and risk for gestational diabetes mellitus
## Abstract
### Aims
To evaluate the effect of the pre-pregnancy body surface area (BSA) on the risk of gestational diabetes mellitus (GDM).
### Methods
The study population consisted of all primiparous women with singleton pregnancies ($$n = 328$$,892) without previously diagnosed diabetes or chronic hypertension in Finland between 2006 and 2019. The information on GDM, oral glucose tolerance test (OGTT) results, and maternal backgrounds was derived from the Finnish Medical Birth Register. The pre-pregnancy BSA was calculated by using the Mosteller formula. Logistic regression models were used to estimate the association between BSA and GDM/ OGTT separately by the body mass index groups.
### Results
A lower BSA predicted an increased risk for GDM and pathological OGTT among the underweight (b = − 2.69, SE = 0.25, $p \leq 0.001$; b = − 2.66, SE = 0.23, $p \leq 0.001$, respectively) pregnant women, and normal weight (b = − 0.30, SE = 0.10, $$p \leq 0.002$$; b = − 0.67, SE = 0.09, $p \leq 0.001$, respectively) pregnant women; and pathological OGTT among the overweight (b = − 0.31, SE = 0.10, $$p \leq 0.001$$) pregnant women. Within the obese class II or greater, a higher BSA predicted a higher risk for GDM ($b = 0.74$, SE = 0.12, $p \leq 0.001$) and pathological OGTT ($b = 0.79$, SE = 0.13, $p \leq 0.001$). Maternal smoking predicted a significantly higher risk of GDM and pathological OGTTs in almost all body mass index groups.
### Conclusion
This study showed that in comparison with women with a higher BSA, underweight, and normal weight pregnant women with a smaller BSA may be more susceptible to GDM and have a pathological OGTT.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s00592-022-02029-0.
## Introduction
Gestational diabetes mellitus (GDM) is a pregnancy complication, in which glucose metabolism is impaired due to the pregnancy-induced pancreatic B-cell dysfunction and insulin resistance. Altered glucose metabolism occurs for the first time during pregnancy, and in most cases, it disappears after childbirth. The risk of fetal macrosomia increases with GDM, predisposing the pregnant woman to complications during labor. People with GDM have a greater risk of gestational hypertension and pre-eclampsia than healthy parturients [1]. GDM also substantially increases the risk for future maternal health problems, e.g., cardiovascular events and type 2 diabetes [2]. The main risks for GDM are maternal obesity, previously diagnosed non-alcoholic liver disease, older age, and family history of any type of diabetes [3, 4]. The percentage of pre-pregnancy overweight (BMI ≥ 25) women was $41.9\%$ and obese (BMI ≥ 30) women were $17.0\%$ in Finland in 2020 [5]. GDM is an increasingly common condition during pregnancy. In the year 2019, GDM was diagnosed in $19.1\%$ of Finnish mothers during their pregnancy. The prevalence and the diagnostic criteria for GDM varies globally [6]. In Finland, gestational diabetes is diagnosed if one of the following events occurs: fasting plasma glucose level > 5.3 mmol/l, one hour after a 75 g glucose dose > 10.0 mmol/l, or two hours after a glucose dose > 8.6 mmol/l [1].
The national Finnish Current Care Guidelines for GDM recommends screening almost all pregnant women, except for those with a low risk for GDM, during their first pregnancy for GDM [1]. Gestational diabetes is diagnosed with an oral glucose tolerance test (OGTT). The OGTT consists of a uniform glucose dose of 75 g to all pregnant women regardless of their body size [7]. A reverse association between the post load 2-h plasma glucose and body height has been demonstrated in several studies in the general population, as well as among pregnant women [8–10]. It has also been found that shorter pregnant women are more susceptible to a GDM diagnosis [10].
Body surface area (BSA) is a total surface area of the human body, and it is commonly used to evaluate drug doses and medical indicators or estimates [11]. It has been found that in the non-pregnant population, the body size has a significant effect on the OGTT results. Impaired glucose tolerance was more frequently observed in smaller individuals compared to relatively larger individuals in the general population [8]. The question remains if maternal BSA affects the risk for having a GDM diagnosis.
The aim of this study was to investigate the effect of pre-pregnancy BSA on the risk of GDM among *Finnish primiparous* women during the years of 2006–2019 by using the population-based register data. We hypothesized that women with a smaller BSA are more susceptible to be diagnosed with GDM compared to women with a higher BSA.
## Data sources
The study data sets were drawn from the Finnish Medical Register and the Finnish Hospital Discharge Register. The Finnish Institute for Health and Welfare (THL) performed the ethical review and granted permission to use its confidential register data. To combine all the register data, pregnant women’s personal identity codes were used. Statistical authorities performed the data linkages, and only the unidentifiable data were provided for the researchers outside the Finnish Institute for Health and Welfare.
The Medical Birth *Register is* considered to be a complete record of all births and newborns in Finland. The register data contain all live births and stillbirths of fetuses with a gestational age of 22 weeks or more or with a birth weight of at least 500 g or more. The register data are collected from all delivery hospitals, and in the case of home births, from the assisting health care personnel. The register contains information on the mother’s and the child’s identity codes; maternal personal data, health care, previous pregnancies and deliveries, and interventions during the pregnancy and delivery; and the newborn’s outcome until it reaches 7 days of age. According to data quality studies, most of the content of the register data corresponds well or satisfactorily with the hospital record data [12].
Information on all episodes of inpatient care, including all hospitalizations requiring an overnight stay in public and private hospitals since 1969 and outpatient visits in public hospitals since 1998, are included in the Hospital Discharge Register. The register includes information on the patient’s background, procedures, hospitalization periods, and the main diagnosis, as well as up to two other diagnoses by the International Classification of Diseases (ICD) code (Eight Revision [ICD-8] in 1969–1986, Ninth Revision [ICD-9] in 1987–1995, and Tenth Revision [ICD-10] since 1996). A systematic review revealed that the completeness and accuracy of the register range from satisfactory to very good [13].
The study data were drawn from the Finnish Medical Register and the Finnish Hospital Discharge Register. The Finnish Institute for Health and Welfare (THL) performed the ethical review and granted the permission to use its confidential register data. As this study a study based on data from a register, informed consent statements for patient enrollment is not applicable. To combine all register data, pregnant women’s personal identity code was used. Statistical authorities performed the data linkages and only unidentifiable data were provided for the researchers outside the Finnish Institute for Health and Welfare.
## Study sample
The study population consisted of all pregnant women with singleton pregnancies ($$n = 835$$,551) in Finland during the years of 2006–2019. Multiparous women were excluded ($$n = 487$$,399). The ICD-10 classification was used during the entire study period, and pregnant women with a diagnosis of pre-pregnancy diabetes (ICD-10 codes: O24.0, O24.1, O24.2 and O24.3) were excluded ($$n = 2691$$). Women with a pre-pregnancy diagnosis of chronic hypertension ($$n = 529$$) and women without weight and height data ($$n = 16$$,040) were also excluded. The final study population consisted of 328,892 pregnant women ($94.5\%$ of all primiparous women with singleton pregnancies during the study period).
The information on maternal background factors was derived from the Finnish Medical Birth Register. The pre-pregnancy BSA was calculated by using the Mosteller formula; BSA (m2) = square root of [(weight (kg) x height (cm))/3600] [14], which utilizes the maternal pre-pregnancy weight and height. Smoking was categorized in three classes: no, quitted in the first trimester, and continued throughout the pregnancy. The BMI was categorized into five groups: < 20 (underweight), 20.0–24.9 (normal weight), 25.0–29.9 (overweight), 30.0–34.9 (obese, class I), and 35 kg/m2 or more (obese class II or greater).
## GDM diagnoses
The GDM screening consists of a globally standardized 2-h 75 g OGTT. The national Finnish Current Care Guidelines for GDM recommends screening between 24 and 28 gestational weeks, except for low-risk women with no abnormal glucose tolerance, under 25 years of age, and a pre-pregnancy BMI between 18.5 and 25 kg/m2, and women with no family history of diabetes. In women with a higher risk for diabetes, the test is performed earlier between 12 and 16 weeks; and if the test result is normal, the test is repeated between 24 and 28 weeks. If the plasma 12 h fasting glucose level is > 5.3 mmol/l, or after one hour > 10.0 mmol/l, or two hours is > 8.6 mmol/l, the mother is diagnosed with GDM [1].
In this study, GDM diagnoses and information regarding the OGTT were obtained from the Finnish Medical Birth Register. GDM was defined by the ICD-10 codes O24.4 and O24.9. The information regarding OGTT results (normal/abnormal) was found for 177,119 women; for 151,773 women, an OGTT was not performed due to a low risk for GDM or the lack of information.
## Statistics
Logistic regression models were used to estimate the association between BSA and the outcomes. GDM and OGTT were added separately as the independent variable, and BSA as the dependent variable into the model. Maternal age was added as a continuous covariate, and maternal smoking and marital status as a binomial covariate into the model. The analyses were performed separately for the BMI group because the previous studies [4, 8, 10] have shown that shorter or smaller-sized persons may have an increased risk for GDM.
The number of GDM diagnoses was 32,564 ($9.9\%$), and there were more pathological OGTTs, 38,261 ($11.6\%$). We assumed that there was an absence of information on GDM diagnoses in the register data. Thus, we performed sensitivity analyses with the combined information on GDM and pathological OGTTs ($$n = 43$$,779, $13.3\%$).
The data analysis was performed with commercially available software (SAS, version 9.4; SAS Institute Inc, Cary, North Carolina). Differences in the results were evaluated by using $95\%$ confidence intervals and p values. Non-overlapping confidence intervals and P values < 0.05 were considered to be significant.
## Results
The study included 328,892 *Finnish primiparous* women with a mean age of 28 years. The mean pre-pregnancy BSA of the study population was 1.73 m2 (SD = 0.19). Table 1 presents the characteristics of the participants according to GDM diagnoses and pathological OGTT results. The overall prevalence of GDM in this study cohort was $9.9\%$ ($$n = 32$$,564). The OGTT was performed for $53.9\%$ ($$n = 177$$,119) participants, and the pathological OGTT result was observed in $21.6\%$ of those tested. The majority ($80.6\%$) of pregnant women did not smoke during their pregnancy, $7.9\%$ quit during the first trimester, and $9.8\%$ continued smoking thereafter. Table 1Characteristics of the study population by gestational diabetes mellitus (GDM) and oral glucose tolerance test (OGTT)GDM, n (%)*Pathological OGTT, n (%)*Total, n (%)*YesNopYesNopTotal32,564 (9.90)296,328 (90.10)38,261 (11.63)138,858 (42.22)328.892BSA, mean (SD)1.86 (0.23)1.72 (0.17) < 0.011.85 (0.23)1.77 (0.19) < 0.011.73 (0.19)Maternal age, mean (SD)29.52 (5.50)27.66 (5.19) < 0.0129.51 (5.46)28.80 (4.99) < 0.0127.84 (5.25)*Marital status* < 0.01 < 0.01Single28619493479202235 (0.68)Cohabiting14.877133.11217.61064.143147,989 (45.00)Married17.264159.92120.12173.136177,185 (53.87)Unknown13713461836591483 (0.45)Maternal smoking < 0.01 < 0.01No smoking25.65123.94530.090115.491265,108 (80.61)Early pregnancy312022.695369611.21525,815 (7.85)Throughout pregnancy322529.005386710.09432,230 (9.80)Unknown568517160820585739 (1.74)BMI < 0.01 < 0.01Underweight, < 20192548.854239814.51150,779 (15.44)Normal weight, 20–24.910.443167.64312.66666.804178,086 (54.15)Overweight, 25–29.9979556.03011.69239.36065,825 (20.01)Obese, class I, 30–34.9594017.301675413.20023,241 (7.07)Obese, class II or higher, 35 + 446165004751498310,961 (3.33)*if not stated otherwise. BMI, Body-mass indexBSA Body surface area
## Pre-pregnancy BSA and GDM
The association between the pre-pregnancy BSA and GDM was analyzed according to maternal BMI (Table 2, Fig. 1). A lower pre-pregnancy BSA predicted an increased risk for GDM among the underweight (b = − 2.69, SE = 0.25, OR = 0.07, $95\%$ CI 0.04–0.11, p = < 0.001) pregnant women and normal weight (b = − 0.30, SE = 0.10, OR = 0.74, $95\%$ CI 0.61–0.89, $$p \leq 0.002$$), pregnant women. A similar association was not observed among the overweight ($b = 0.11$, SE = 0.10, OR = 1.12, $95\%$ CI 0.92–1.36, $$p \leq 0.25$$) and obese class I women (b = − 0.06, SE = 0.13, OR = 0.94, $95\%$ CI 0.73–1.21, $$p \leq 0.64$$). In contrast, among the obese class II or greater, a higher pre-pregnancy BSA predicted a higher risk for GDM ($b = 0.74$, SE = 0.12, OR = 2.10, $95\%$ CI 1.66–2.65, $p \leq 0.001$). Smoking during the first trimester and throughout the pregnancy was a significant predictor for GDM in almost all BMI classes. Table 2Results from the logistic regression models estimating the association between the body surface area (BSA) and the risk for gestational diabetes mellitus (GDM) according to maternal body mass index (BMI)BMI < 20BMI 20–24.9BMI 25–29.9BMI 30–34.9BMI 35 + bSEbSEbSEbSEbSEIntercept − 1.15*0.41 − 4.39*0.19 − 3.80*0.22 − 2.80*0.31 − 3.72*0.34BSA − 2.69*0.25 − 0.30*0.100.110.10 − 0.060.130.74*0.12Maternal age0.09* < 0.010.08* < 0.010.06* < 0.010.06* < 0.010.06* < 0.01Marital statusSingleRefRefRefRefRefCohabiting − 0.41*0.17 − 0.170.090.180.110.170.150.050.19Married − 0.42*0.17 − 0.17*0.090.200.110.160.150.010.19Maternal smokingNo smokingRefRefRefRefRefEarly pregnancy0.22*0.090.34*0.040.30*0.040.28*0.050.100.06Throughout pregnancy − 0.050.090.10*0.040.16*0.040.14*0.050.15*0.06*$p \leq 0.05.$ b, Beta; SE Standard errorFig. 1Adjusted odds ratios and $95\%$ confidence intervals of pre-pregnancy BSA and the risk for GDM according to BMI groups
## Pre-pregnancy BSA and OGTT
The association between the pre-pregnancy BSA and OGTT was also analyzed according to maternal BMI (Table 3, Fig. 2). A lower pre-pregnancy BSA predicted an increased risk for pathological OGTTs among the underweight (b = − 2.66, SE = 0.23, OR = 0.07, $95\%$ CI 0.04–0.11, $p \leq 0.001$), normal weight (b = − 0.67, SE = 0.09, OR = 0.51, $95\%$ CI 0.43–0.61, $p \leq 0.001$), and overweight (b = − 0.31, SE = 0.10, OR = 0.74, $95\%$ CI 0.61–0.88, $$p \leq 0.001$$) pregnant women. Among the obese class I women, such an association was not observed ($b = 0.03$, SE = 0.13, OR = 1.03, $95\%$ CI 0.80–1.33, $$p \leq 0.79$$). In contrary, among the obese class II or greater, a higher pre-pregnancy BSA predicted a higher risk for pathological OGTTs ($b = 0.79$, SE = 0.13, OR = 2.19, $95\%$ CI 1.71–2.81, $p \leq 0.001$). Maternal smoking was a significant predictor for pathological OGTTs in all BMI classes. Table 3Results from the logistic regression models estimating the association between the body surface area (BSA), and the risk for a pathological oral glucose tolerance test (OGTT) according to maternal body mass index (BMI)BMI < 20BMI 20–24.9BMI 25–29.9BMI 30–34.9BMI 35 + bSEbSEbSEbSEbSEIntercept2.41*0.40 − 1.15*0.19 − 2.20*0.21 − 2.36*0.30 − 3.25*0.36BSA − 2.66*0.23 − 0.67*0.09 − 0.31*0.100.030.130.79*0.13Maternal age0.01* < 0.010.03* < 0.010.05* < 0.010.06* < 0.010.06* < 0.01Marital statusSingleRefRefRefRefRefCohabiting − 0.46*0.17− 0.21*0.080.030.10 − 0.070.14 − 0.150.20Married − 0.46*0.17− 0.21*0.080.070.10 − 0.120.14 − 0.220.20SmokingNo smokingRefRefRefRefRefEarly pregnancy0.24*0.080.27*0.040.22*0.040.28*0.050.120.07Throughout pregnancy0.38*0.090.47*0.040.37* < 0.010.24*0.050.30*0.06*$p \leq 0.05.$ b, beta; SE, Standard errorFig. 2Adjusted odds ratios and $95\%$ confidence intervals of pre-pregnancy BSA and the risk for a pathological OGTT according to BMI groups
## Sensitivity analyses
Supplementary table 1 presents the characteristics of the participants according to the combined information on GDM diagnoses and pathological OGTTs. The pattern of results in the sensitivity analyses remained significant (Supplementary Table 2).
## Discussion
To our knowledge, this is the first study to investigate the association between the maternal pre-pregnancy BSA and GDM, as well as findings of OGTTs by the BMI class. Our result showed that a lower pre-pregnancy BSA predicted an increased risk for GDM among the underweight and normal weight pregnant women, as well as an increased risk for pathological OGTTs among underweight, normal weight, and overweight pregnant women. Contrarily, a higher BSA predicted a higher risk for GDM among the women in the obese class II or greater, and a higher risk for pathological OGTTs among the women in the obese class I and obese class II or greater.
The previous studies in the general population have questioned the accurate interpretation of OGTT [8, 9]. Rehunen et al. [ 9] found among the 2659 participants aged 45–70 years with at least one cardiovascular risk factor but no previously diagnosed diabetes or manifested cardiovascular disease, that the height of the person was inversely associated with 2-h plasma glucose in the three lowest BMI groups, but not in the highest BMI group. Palmu et al. [ 8] found in the same population that the body size (assessed with BSA) had an inverse linear impact on the findings from a standardized OGTT in all categories of glucose tolerance, such as that smaller persons were more likely to be glucose intolerant than relatively larger-sized individuals. Regarding pregnant women, a meta-analysis, including ten studies by Arafa et al. [ 15], found that short stature was associated with a higher risk for GDM. They found that each 5-cm increase in height was associated with an approximately $20\%$ reduction in the risk of GDM. These previous studies support the results of our study that smaller-sized women may be more susceptible to GDM.
The important question is whether these pregnant women, who may be more susceptible to a diagnosis of GDM due to their smaller body size, show more GDM-related complications than their peers who were not diagnosed with GDM or not. Chu et. al. [ 10] also found the inverse association between maternal height and the risk for GDM. They further analyzed the risk for GDM-related complications, including preterm births and higher birth weights. They found that the complication risk was at the same level as non-GDM women of similar height, i.e., only taller women had an increased risk of GDM-related pregnancy complications [10]. In the future, the risk for GDM-complications should be investigated in accordance with the BSA. Chu et al. [ 10] speculate that an artifactual GDM diagnosis due to glucose-overload among shorter women is plausible. Based on our results, we suggest that the reason behind the previously observed inverse association between the maternal height and GDM is actually the body size, i.e., body surface area, of the pregnant women, which is used to evaluate drug doses and medical indicators or estimates [11].
The question remains: What should be done to increase the accuracy of diagnosing GDM, and to prevent possible artifactual GDM diagnoses? Thus, there is a need for future studies to investigate the amount of glucose doses in the OGTT based on the size of the pregnant woman, which could provide a more specific and reliable GDM diagnosis. Furthermore, this would be important because the GDM diagnosis has a negative echo. The diagnosis is easily associated with unfavorable lifestyle factors and being overweight [16]. Pregnant women are vulnerable to weight stigma, which causes a lot of stress and negative emotions. Guilt and shame adversely affect maternal hormone levels, and impair the health of the mother and offspring in many ways. [ 17, 18]. Even the pregnancy-related weight stigma can potentially increase the risk of GDM and other complications during pregnancy [16, 19].
Among the general population, smoking has been found to be associated with blood glucose intolerance, impaired fasting glucose, and type two diabetes [20]. Smoking was found to be a predictor for GDM in our study. Studies on the association between smoking and GDM are conflicting [20–22]. According to a meta-analysis [22], smoking is not associated with an increased risk of GDM. However, it has been noted in the recent population-based register study [21] and prospective study [23] that smoking during pregnancy is associated with an increased risk for GDM.
## Strengths and limitations
The strength of this study includes the use of large and comprehensive national register data that included $94.5\%$ of all primiparous women with singleton pregnancies during the study years between 2006 and 2019. Our data included information on maternal background, as well as the diagnoses of pre-pregnancy diabetes and pre-pregnancy chronic hypertension.
The main limitation of this study was that the results of OGTT were dichotomous; thus, we did not have access to specific OGTT results. Another limitation is the possibility of missing data regarding the GDM diagnoses. The prevalence of GDM/OGTT ($13\%$) in our study population of primiparous women is lower compared to the GDM prevalence of $19\%$ from the year 2019 in Finland [5]. The reason for this is that the prevalence of GDM has been rapidly increasing during the last few decades, and our study population includes primiparous women during the years of 2006–2019. We performed sensitivity analyses with the combined information on GDM and pathological OGTT, where the results remained the same compared to our main analyses. In addition, the Finnish health registers are shown to be reliable for research purposes [12, 13], and we used only the variables with known good quality.
## Conclusion
In conclusion, our study showed that underweight and normal weight pregnant women with a relatively smaller BSA, who otherwise had no risk factors for GDM other than, e.g., age or a close relative with type 2 diabetes, were more likely to have a pathological OGTT and receive a diagnosis of GDM. Since most pregnant women undergo the OGTT, it is particularly important that the test methods and diagnostic criteria are specific and appropriate in order to avoid false positive test results and unnecessary stigmas. Our results imply that the size of pregnant women needs to be better taken into account when diagnosing GDM.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (XLSX 10 kb)Supplementary file2 (XLSX 9 kb)
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|
---
title: Predictors and Adverse Outcomes of Acute Kidney Injury in Hospitalized Renal
Transplant Recipients
authors:
- Tammy Hod
- Bernice Oberman
- Noa Scott
- Liran Levy
- Gadi Shlomai
- Pazit Beckerman
- Keren Cohen-Hagai
- Eytan Mor
- Ehud Grossman
- Eyal Zimlichman
- Moshe Shashar
journal: Transplant International
year: 2023
pmcid: PMC10033630
doi: 10.3389/ti.2023.11141
license: CC BY 4.0
---
# Predictors and Adverse Outcomes of Acute Kidney Injury in Hospitalized Renal Transplant Recipients
## Abstract
Data about in-hospital AKI in RTRs is lacking. We conducted a retrospective study of 292 RTRs, with 807 hospital admissions, to reveal predictors and outcomes of AKI during admission. In-hospital AKI developed in 149 patients ($51\%$). AKI in a previous admission was associated with a more than twofold increased risk of AKI in subsequent admissions (OR 2.13, $p \leq 0.001$). Other major significant predictors for in-hospital AKI included an infection as the major admission diagnosis (OR 2.93, $$p \leq 0.015$$), a medical history of hypertension (OR 1.91, $$p \leq 0.027$$), minimum systolic blood pressure (OR 0.98, $$p \leq 0.002$$), maximum tacrolimus trough level (OR 1.08, $$p \leq 0.005$$), hemoglobin level (OR 0.9, $$p \leq 0.016$$) and albumin level (OR 0.51, $$p \leq 0.025$$) during admission. Compared to admissions with no AKI, admissions with AKI were associated with longer length of stay (median time of 3.83 vs. 7.01 days, $p \leq 0.001$). In-hospital AKI was associated with higher rates of mortality during admission, almost doubled odds for rehospitalization within 90 days from discharge and increased the risk of overall mortality in multivariable mixed effect models. In-hospital AKI is common and is associated with poor short- and long-term outcomes. Strategies to prevent AKI during admission in RTRs should be implemented to reduce re-admission rates and improve patient survival.
## Graphical Abstract
## Introduction
The prevalence of chronic kidney disease is increasing, accounting for more than $10\%$ of hospital admissions in the adult population. The parallel increase in the rates of in-hospital acute kidney injury (AKI) may reach as much as $50\%$ of intensive care unit (ICU) admissions [1, 2]. The consequences of AKI during hospitalization are dismal [3, 4]: Even modest changes in serum creatinine (Scr) (an increase >0.5 mg/dL) have been associated with a 6.5-fold increase in the odds of death and a 3.5-day increase in the length of stay (LOS) [5]. Small changes in Scr have been also associated with increased mortality and prolonged hospitalizations in elderly patients admitted with congestive heart failure [6].
With the aim of preventing this serious complication, different studies have sought to establish predictors for AKI during hospitalization, both in the general population (7–11) and particularly for renal transplant recipients (RTRs). Unfortunately, however, information on predictors for AKI during hospitalization of RTRs is still lacking, although $11\%$ of RTRs develop in-hospital AKI during the first three post-transplant years, which is associated with transplant failure and death [12]. For RTRs, studies to date have focused mostly on delayed graft function, which is a form of AKI in the immediate peri-transplant period [13, 14].
RTRs constitute a unique population with an inherent increased risk vs. the general population for in-hospital AKI secondary to different etiologies related to subclinical and chronic rejection, higher risk of infections and immunosuppressive therapy. As a result, strategies to prevent or minimize the occurrence and consequences of AKI during hospitalization in this population would necessarily be more complex than those for the general population.
Renal allograft survival has improved significantly in the short term, with one-year graft survival rates reaching $98.4\%$ [15]. However, ensuring long-term graft survival still poses a very significant challenge in renal transplantation. For RTRs, a better understanding of the risk factors for AKI during admission would form the basis for developing preventive therapeutic measures aimed at reducing the rate of in-hospital AKI, resulting in improved long-term renal allograft survival.
In this study, we sought to pinpoint the risk factors for AKI during hospitalization of RTRs in a non-intensive care setting. In addition, we examined the implications of in-patient AKI for in-hospital mortality, duration of hospitalization, subsequent in-hospital AKI, re-hospitalizations, and overall mortality in this vulnerable population.
## Study Population and Design
Clinical and biochemical parameters were collected retrospectively from the MdClone system, the data acquisition tool at Sheba Medical Center. Additional data was collected from clinical records, as needed. The study was approved by the local ethics committee (IRB approval number: SMC-70-5320). Data was collected for up to 12 admissions post “Renal Transplant” diagnosis for admission dates falling between July 2007 and November 2020.
The initial dataset included 1405 hospitalizations for 399 transplant recipients. We then excluded from the analysis admissions post graft loss (baseline eGFR<15 mL/min per 1.73 m2), post chronic dialysis initiation, and/or admissions ≤30 days from transplant to eliminate the effect of changes in immunosuppressive medications or in renal allograft function early post-transplant secondary to slow and/or delayed graft function, infections and early rejections. Hospitalizations with no Scr or only one Scr measured during admission were also excluded (Figure 1). The final study cohort included 292 RTRs who had undergone kidney transplantation between June 1982 and June 2000, with a total of 807 non-ICU admissions.
**FIGURE 1:** *Consort diagram. RTR, renal transplant recipients; Scr, serum creatinine.*
## Primary Outcome
The primary outcome was AKI during admission, which was defined as a difference of ≥$50\%$ between peak Scr during admission and baseline Scr, according to the Kidney Disease Improving Global Outcomes (KDIGO) definition. AKI staging was based on the KDIGO definitions for SCr increases, i.e., a difference between peak Scr during admission and baseline Scr that was: Stage 1, ≥1.5–1.9 times the baseline Scr; Stage 2, ≥2–2.9 times the baseline Scr; and Stage 3, >3 times the baseline Scr or a peak Scr during admission ≥4.0 mg/dL or initiation of renal replacement therapy.
To determine baseline eGFR, we chose the minimum Scr in the 120 days to 1 day prior to admission. For admissions without Scr measurements in the 1- to 120-day period, we used minimum Scr during admission. To avoid misjudgment of baseline renal allograft function affected by frequent changes in Scr early post-transplant, we used minimum Scr during admission for baseline eGFR assessment in admissions of less than 150 days from transplant.
## Data Extraction and Study Assessments
The following information was extracted from electronic patient records: age, gender, etiology of end stage renal disease (ESRD), dialysis pre-transplant (yes/no), transplant number, donor type, transplant date and relevant medical history, specifically hypertension, congestive heart failure (CHF), ischemic heart disease (IHD) and pre-transplant diabetes. All diagnoses during admissions were obtained from MDClone. After a manual review of in-patient diagnoses, the main hospitalization etiology was selected, and diagnoses were grouped into five categories: infectious disease, cardiovascular disease, disease of the gastrointestinal system, neoplasm, and all others.
The following biochemical parameters during hospitalization were retrieved in an automated fashion from MDClone: average and maximum tacrolimus trough level, average total white blood cell count, average and minimum absolute lymphocyte count, average hemoglobin, average albumin, maximum and minimum glucose, maximum globulins, and average C-reactive protein. The following additional clinical and biochemical information during admission was also retrieved from MDClone: average and minimum systolic and diastolic blood pressures, average weight and body mass index (BMI), fever, maximum pulse, and minimum oxygen saturation. Use and average dose administered during admission for the following medications was automatically obtained from MDClone: tacrolimus, cyclosporine, mycophenolic acid (MPA) (for 238 admissions, mycophenolate dose was converted to the equivalent MPA dose by dividing mycophenolate dose by 1.388) and steroids (steroid derivatives used during admission, such as hydrocortisone, dexamethasone and methylprednisolone, were converted to the equivalent prednisone dose). Other medications administered during admission were also recorded.
## Statistical Analysis
All demographic, clinical and biochemical covariates of interest were tabulated and compared between patients for AKI during admission (yes/no) and between admissions (with and without AKI, and partitioned into AKI stage for AKI patients). Categorical variables were compared using the Chi-squared test (or Fisher’s test where the numbers were small), while continuous variables were first tested for normality using the Shapiro-Wilks Test (and for equality of variances), and were then compared using a t-test (or Anova) for normally distributed variables or a-parametric tests for non-normally distributed variables. An FDR [false discovery rate (Benjamini and Hochberg)] procedure was then carried out to correct for multiple comparisons.
For the primary outcome of AKI during admission, logistic mixed models were used, with RTR being a random effect, and other variables being fixed effects. Univariate models were considered first; variables that were significant ($$p \leq 0.05$$) and/or those with clinical importance were entered into multivariate models.
For the secondary outcomes, LOS was examined using a linear mixed model, with RTR as a random effect and the other variables being fixed effects. LOS (which is inherently right-tailed) was log-transformed to normalize it. Mortality during admission was modeled using the Cox proportional hazards model, which modeled the time from transplant to death or end of follow-up, taking into account the number of admissions per person. Overall mortality was modeled using Kaplan-Meier estimation, with AKI in different staging groups. Thereafter, the Cox proportional hazards model was used to model the time from transplant to death or end of follow-up. Readmission within 90 days was calculated. Logistic mixed models were used to predict the cause of readmission within 90 days, with RTR being a random effect, and other variables being mixed effects. For all secondary outcomes, univariate models were considered first. Significant variables and/or those with clinical importance were entered into multivariate models.
All statistical analyses were carried out using R-3.4.1 [R Core Team [2017]. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/].
## Characteristics of the RTRs Cohort
A total of 292 RTRs comprised our study cohort (Table 1). Median transplant age was 54.9 (IQR, 46.2–64.2); 98 ($33.6\%$) were females. Median time from transplant to first admission was 6.33 years (IQR, 2.5–11.8). Of the RTR cohort, 64 ($21.9\%$), 39 ($13.4\%$), 59 ($20.2\%$) and 26 ($8.9\%$) patients had ESRD secondary to diabetic nephropathy, autosomal dominant polycystic kidney disease (APCKD), glomerulonephritis and nephrosclerosis, respectively; 158 ($54.1\%$) were on renal replacement therapy before the transplant; and 140 ($47.9\%$) had a living donor renal transplant. Forty RTRs ($13.7\%$) died during admission, and the overall mortality rate for the duration of the study was $41.1\%$. The total number of admissions ranged between 1 and 10.
**TABLE 1**
| Variable | Total cohort (n = 292) | Without AKI (n = 143) | With any AKI (n = 149) | p |
| --- | --- | --- | --- | --- |
| RTR characteristics | RTR characteristics | RTR characteristics | RTR characteristics | RTR characteristics |
| Transplant age, years [median (IQR)] | 54.98 (46.2, 64.2) | 55.53 (47.5, 64.51) | 54.21 (45.2, 64.2) | 0.89 |
| Female sex, n (%) | 98 (33.56) | 48 (33.57) | 50 (33.56) | 1 |
| Transplant to 1st admission, years [median (IQR)] | 6.33 (2.45, 11.81) | 6.7 (1.9, 11.2) | 6.3 (3.2, 12.2) | 0.35 |
| ESRD etiology, n (%) | ESRD etiology, n (%) | ESRD etiology, n (%) | ESRD etiology, n (%) | ESRD etiology, n (%) |
| ADPCKD | 39 (13.36) | 28 (19.58) | 11 (7.38) | 0.07 |
| Diabetic nephropathy | 64 (21.92) | 31 (21.68) | 33 (22.15) | |
| Glomerulonephritis | 59 (20.21) | 26 (18.18) | 33 (22.15) | |
| Nephrosclerosis | 26 (8.9) | 12 (8.39) | 14 (9.4) | |
| Other | 69 (23.63) | 29 (20.28) | 40 (26.85) | |
| Unknown | 35 (11.99) | 17 (11.89) | 18 (12.08) | |
| Pre-transplant dialysis | Pre-transplant dialysis | Pre-transplant dialysis | Pre-transplant dialysis | Pre-transplant dialysis |
| Yes | 158 (54.11) | 72 (50.35) | 86 (57.72) | 0.11 |
| No | 46 (15.75) | 29 (20.28) | 17 (11.41) | |
| Unknown | 88 (30.14) | 42 (29.37) | 46 (30.87) | |
| Transplant type, n (%) | Transplant type, n (%) | Transplant type, n (%) | Transplant type, n (%) | Transplant type, n (%) |
| Kidney only | 280 (95.89) | 138 (96.5) | 142 (95.3) | 1 |
| Liver kidney | 4 (1.37) | 2 (1.4) | 2 (1.34) | |
| Heart kidney | 7 (2.4) | 3 (2.1) | 4 (2.68) | |
| Pancreas kidney | 1 (0.34) | | 1 (0.67) | |
| Transplant number, n (%) | Transplant number, n (%) | Transplant number, n (%) | Transplant number, n (%) | Transplant number, n (%) |
| 1 | 262 (89.73) | 127 (88.81) | 135 (90.6) | 0.66 |
| 2 | 26 (8.9) | 13 (9.09) | 13 (8.72) | |
| 3 | 4 (1.37) | 3 (2.1) | 1 (0.67) | |
| Donor type, n (%) | Donor type, n (%) | Donor type, n (%) | Donor type, n (%) | Donor type, n (%) |
| Living | 140 (47.9) | 82 (57.34) | 66 (44.3) | 0.08 |
| Deceased | 85 (29.11) | 36 (25.17) | 49 (32.89) | |
| Unknown | 59 (20.21) | 25 (17.48) | 34 (22.82) | |
| Number of admissions, n (%) | Number of admissions, n (%) | Number of admissions, n (%) | Number of admissions, n (%) | Number of admissions, n (%) |
| 1 | 116 (39.73) | 86 (60.14) | 30 (20.13) | <0.001** |
| 2 | 55 (18.84) | 29 (20.28) | 26 (17.45) | |
| 3 | 33 (11.3) | 10 (6.99) | 23 (15.44) | |
| 4 | 31 (10.62) | 9 (6.29) | 22 (14.77) | |
| 5 | 22 (7.53) | 3 (2.1) | 19 (12.75) | |
| 6 | 15 (5.14) | 3 (2.1) | 12 (8.05) | |
| 7 | 7 (2.4) | 2 (1.4) | 5 (3.36) | |
| 8 | 9 (3.08) | | 9 (6.04) | |
| 9 | 3 (1.03) | 1 (0.7) | 2 (1.34) | |
| 10 | 1 (0.34) | | 1 (0.67) | |
| Number of AKI (per person), n (%) | Number of AKI (per person), n (%) | Number of AKI (per person), n (%) | Number of AKI (per person), n (%) | Number of AKI (per person), n (%) |
| 0 | | 143 (100) | | |
| 1 | | | 76 (26.03) | |
| 2 | | | 30 (10.27) | |
| 3 | | | 25 (8.56) | |
| 4 | | | 11 (3.77) | |
| 5 | | | 2 (0.68) | |
| 6 | | | 4 (1.37) | |
| 8 | | | 1 (0.34) | |
| Death during admission, n (%) | 40 (13.7) | 5 (3.5) | 35 (23.49) | <0.001** |
| Overall mortality, n (%) | 120 (41.1) | 40 (27.97) | 80 (53.69) | <0.001** |
## Univariate Comparison of RTRs Without Any AKI vs. With Any AKI During Admission
Of the 292 RTRs, 149 ($51\%$) had 1 to 8 AKI admission events. For patients with any AKI during admission, the number of admissions per person, the death rate during admission, and the overall mortality were all higher ($p \leq 0.001$). ESRD secondary to APCKD and renal living donor transplant were more common in RTRs without vs. with any AKI during admission as the difference between the groups approached statistical significance. All other comparisons of characteristics, including age, sex, time from transplant to first admission, transplant number and dialysis pretransplant, are shown in Table 1.
## Characteristics of Total Admissions
Our cohort of 292 RTR had a total of 807 non-ICU admissions. Median age during admission was 66.75 (IQR 57.16–73.12); 266 ($33\%$) were females. Median time from transplant to admission was 7.65 years (IQR 4.22–12.75). The most prevalent admission etiology [312 ($38.7\%$) admissions] was an infection. Forty ($4.96\%$) admissions resulted in death during admission. Median LOS was 4.65 days (IQR 2.67–9). For 302 ($37.4\%$) admissions, patients were readmitted within 90 days (Table 2).
**TABLE 2**
| Variable | Total admissions (n = 807) | Admissions without AKI (n = 510) | Admissions with AKI (n = 297) | p a |
| --- | --- | --- | --- | --- |
| RTR characteristics | RTR characteristics | RTR characteristics | RTR characteristics | RTR characteristics |
| Admission age, years [median (IQR)] | 66.75 [57.2, 73.1] | 66.9 [58, 73.2] | 66.5 [56, 73] | 0.65 |
| Female sex, n (%) | 266 (33) | 169 (33.1) | 97 (32.7) | 0.95 |
| Transplant to admission, years [median (IQR)] | 7.65 [4.2, 12.8] | 7.6 [3.8, 12.6] | 7.7 [4.8, 13.5] | 0.43 |
| ESRD etiology, n (%) | ESRD etiology, n (%) | ESRD etiology, n (%) | ESRD etiology, n (%) | ESRD etiology, n (%) |
| ADPCKD | 81 (10) | 63 (12.4) | 18 (6.1) | 0.06 |
| Diabetic nephropathy | 187 (23.2) | 119 (23.3) | 68 (22.9) | |
| Glomerulonephritis | 154 (19.1) | 102 (20.0) | 52 (17.5) | |
| Nephrosclerosis | 73 (9) | 40 (7.8) | 33 (11.1) | |
| Other | 212 (26.3) | 124 (24.3) | 88 (29.6) | |
| Unknown | 100 (12.4) | 62 (12.2) | 38 (12.8) | |
| Pre-transplant dialysis | Pre-transplant dialysis | Pre-transplant dialysis | Pre-transplant dialysis | Pre-transplant dialysis |
| Yes | 449 (55.6) | 289 (56.7) | 160 (53.9) | 0.75 |
| No | 125 (15.5) | 79 (15.5) | 46 (15.5) | |
| Unknown | 233 (28.9) | 142 (27.8) | 91 (30.6) | |
| Transplant type, n (%) | Transplant type, n (%) | Transplant type, n (%) | Transplant type, n (%) | Transplant type, n (%) |
| Kidney only | 775 (96) | 487 (95.5) | 288 (97.0) | 0.76 |
| Liver kidney | 7 (0.9) | 5 (1.0) | 2 (0.7) | |
| Heart kidney | 23 (2.8) | 17 (3.3) | 6 (2.0) | |
| Pancreas kidney | 2 (0.2) | 1 (0.2) | 1 (0.3) | |
| Transplant number, n (%) | Transplant number, n (%) | Transplant number, n (%) | Transplant number, n (%) | Transplant number, n (%) |
| 1 | 735 (91.1) | 461 (90.4) | 274 (92.3) | 0.71 |
| 2 | 67 (8.3) | 45 (8.8) | 22 (7.4) | |
| 3 | 5 (0.6) | 4 (0.8) | 1 (0.3) | |
| Donor type, n (%) | Donor type, n (%) | Donor type, n (%) | Donor type, n (%) | Donor type, n (%) |
| Living | 426 (52.8) | 267 (52.4) | 159 (53.5) | 0.96 |
| Deceased | 239 (29.6) | 152 (29.8) | 87 (29.3) | |
| Unknown | 142 (17.6) | 91 (17.8) | 51 (17.2) | |
| Medical history, n (%) | Medical history, n (%) | Medical history, n (%) | Medical history, n (%) | Medical history, n (%) |
| Diabetes mellitus | 273 (33.8) | 169 (33.1) | 104 (35.0) | 0.7 |
| Hypertension | 496 (61.5) | 297 (58.2) | 199 (67.0) | 0.037* |
| IHD | 292 (36.2) | 164 (32.2) | 128 (43.1) | 0.006** |
| CHF | 170 (21.1) | 98 (19.2) | 72 (24.2) | 0.19 |
| Admission etiology, n (%) | Admission etiology, n (%) | Admission etiology, n (%) | Admission etiology, n (%) | Admission etiology, n (%) |
| ID | 312 (38.7) | 170 (33.3) | 142 (47.8) | <0.001** |
| CV | 174 (21.6) | 132 (25.9) | 42 (14.1) | |
| GI | 64 (7.9) | 48 (9.4) | 16 (5.4) | |
| CA | 38 (4.7) | 23 (4.5) | 15 (5.1) | |
| Others | 219 (27.1) | 137 (26.9) | 82 (27.6) | |
| Vital signs and other clinical parameters during admission, [median (IQR)] | Vital signs and other clinical parameters during admission, [median (IQR)] | Vital signs and other clinical parameters during admission, [median (IQR)] | Vital signs and other clinical parameters during admission, [median (IQR)] | Vital signs and other clinical parameters during admission, [median (IQR)] |
| Fever max, °C | 37.2 [36.9, 37.9] | 37.2 [36.9, 37.6] | 37.4 [37, 38.4] | <0.001** |
| Pulse max | 96 [84, 110] | 93 [81, 102] | 103 [89, 120] | <0.001** |
| Pulse min | 61 [54, 69] | 61 [55, 70] | 60 [53, 67] | 0.04* |
| Pulse average | 75.9 [68.1, 83.7] | 75 [67, 82.5] | 78 [70.1, 85.8] | <0.001** |
| SBP min mmHg | 103 [89, 117] | 108 [96, 120] | 95 [80, 108] | <0.001** |
| SBP average mmHg | 131.2 [118.8, 145.8] | 132.6 [121.2, 147.1] | 128.8 [115.5, 144.1] | 0.008* |
| DBP min mmHg | 54 [46, 62] | 56 [50, 63] | 50 [40, 59] | <0.001** |
| DBP average mmHg | 70.7 [65.2, 76.6] | 71 [66.4, 76.6] | 69.8 [63.1, 76.6] | 0.04* |
| O2 saturation min | 93 [89, 95] | 94 [90, 95] | 91 [85, 94] | <0.001** |
| Weight average | 75 [64.5, 87.4] | 75 [65, 90] | 73.8 [64, 85.1] | 0.34 |
| BMI average | 26.4 [23.1, 30.5] | 26.5 [23.4, 30.8] | 26.1 [22.8, 30.3] | 0.46 |
| Medications during admission | Medications during admission | Medications during admission | Medications during admission | Medications during admission |
| Tacrolimus, n (%) | 386 (47.8) | 232 (45.5) | 154 (51.9) | 0.17 |
| Tacrolimus average dose mg [mean (SD)] | 1.49 (0.96) | 1.46 (0.92) | 1.53 (1.03) | 0.7 |
| Cyclosporine, n (%) | 97 (12.0) | 63 (12.4) | 34 (11.4) | 0.79 |
| Cyclosporine average dose, mg [mean (SD)] | 63.51 (30.15) | 60.60 (23.68) | 68.90 (39.30) | 0.3 |
| MPA, n (%) | 321 (39.8) | 212 (41.6) | 109 (36.7) | 0.28 |
| MPA average dose, mg [mean (SD)] | 157.8 (217.4) | 164.1 (218.7) | 146.9 (215.1) | 0.4 |
| Steroids, n (%) | 549 (68.0) | 333 (65.3) | 216 (72.7) | 0.06 |
| Steroids average dose, mg [mean (SD)] | 16.01 (33.13) | 12.27 (26.52) | 22.10 (41.01) | <0.001** |
| mTOR inhibitors, n (%) | 66 (8.2) | 50 (9.8) | 16 (5.4) | 0.06 |
| Azathioprine, n (%) | 45 (5.6) | 31 (6.1) | 14 (4.7) | 0.66 |
| Loop diuretics, n (%) | 339 (42) | 193 (37.8) | 146 (49.2) | 0.005* |
| Thiazides, n (%) | 44 (5.4) | 29 (5.7) | 15 (5.1) | 0.79 |
| Calcium channel blockers, n (%) | 314 (38.9) | 209 (41.0) | 105 (35.4) | 0.2 |
| Beta blockers, n (%) | 530 (65.7) | 338 (66.3) | 192 (64.6) | 0.75 |
| RAAS inhibitors, n (%) | 247 (30.6) | 176 (34.5) | 71 (23.9) | 0.005* |
| Aldosterone antagonists, n (%) | 37 (4.6) | 28 (5.5) | 9 (3.0) | 0.2 |
| Statins, n (%) | 387 (47.9) | 249 (48.8) | 138 (46.5) | 0.69 |
| NSAIDs, n (%) | 5 (0.6) | 2 (0.4) | 3 (1.0) | 0.48 |
| PPIs, n (%) | 550 (68.1) | 327 (64.1) | 223 (75.1) | 0.004* |
| Laboratory results during admission [median (IQR)] | Laboratory results during admission [median (IQR)] | Laboratory results during admission [median (IQR)] | Laboratory results during admission [median (IQR)] | Laboratory results during admission [median (IQR)] |
| White blood cell average (K/μL) | 8.8 [6.5, 11.9] | 8.3 [6.2, 10.8] | 9.9 [7.3, 14.6] | <0.001** |
| Lymphocyte absolute average (K/μL) | 1.1 [0.7, 1.6] | 1.1 [0.8, 1.7] | 1.1 [0.7, 1.5] | 0.05 |
| Lymphocyte absolute min (K/μL) | 0.7 [0.4, 1.2] | 0.8 [0.5, 1.3] | 0.6 [0.3, 0.9] | <0.001** |
| Hemoglobin average (g/dL) | 10.5 [9.3, 12] | 11 [9.7, 12.3] | 9.9 [9, 10.98] | <0.001** |
| Hemoglobin min (g/dL) | 9.9 [8.3, 11.4] | 10.5 [8.9, 11.9] | 8.96 [7.66, 10.24] | <0.001** |
| Creatinine (mg/dL) | 1.4 [1.0, 1.96] | 1.3 [1.0, 1.8] | 1.63 [1.13, 2.37] | <0.001 |
| eGFR baseline (CKD-EPI)** | 59.9 [41.3, 80.98] | 61.2 [43.7, 81.1] | 58.18 [35.81, 80.34] | 0.34 |
| Glucose max (mg/dL) | 205 [137, 321.5] | 188 [130, 282] | 244 [152, 380] | <0.001** |
| Glucose min (mg/dL) | 86 [71, 106] | 90 [76, 112] | 78 [64, 93] | <0.001** |
| Albumin average (g/dL) | 3.2 [2.8, 3.6] | 3.4 [3.0, 3.8] | 2.9 [2.6, 3.3] | <0.001** |
| Albumin min (g/dL) | 2.9 [2.5, 3.3] | 3.1 [2.7, 3.5] | 2.6 [2.3, 3] | <0.001** |
| Globulins max (g/dL) | 2.9 [2.6, 3.3] | 2.9 [2.5, 3.2] | 3 [2.6, 3.6] | <0.001** |
| Globulins min (g/dL) | 2.5 [2.1, 2.9] | 2.5 [2.2, 2.9] | 2.5 [2, 2.9] | 0.47 |
| Tacrolimus trough level average (μg/L) | 5.5 [3.7, 8.2] | 5.1 [3.6, 7.9] | 6.0 [3.8, 8.6] | 0.25 |
| Tacrolimus trough level max (μg/L) | 6.2 [4, 9.6] | 5.7 [4.1, 8.5] | 7.1 [4.1, 10.9] | 0.028* |
| C-reactive protein average (mg/L) | 61.3 [19.1, 113.3] | 50.1 [14.8, 101.5] | 79.8 [36.1, 137.8] | <0.001** |
| Death during admission, n (%) | 40 (4.96) | 8 (1.6) | 32 (10.8) | <0.001** |
| LOS, days [median (IQR)] | 4.6 [2.7, 9.0] | 3.8 [2.1, 7.0] | 7.01 [3.62, 15.34] | <0.001** |
| Readmission in 90 days, n (%) | 302 (37.4) | 148 (29.0) | 154 (51.9) | <0.001** |
## Univariate Comparison of Admissions Without vs. With AKI During Admission
An AKI during admission was recorded for 297 of 807 ($36.8\%$) admissions. In admissions with AKI vs. admissions without AKI, ESRD secondary to APCKD was less prevalent, while nephrosclerosis was more common ($$p \leq 0.06$$). RTRs with at least one AKI had higher rates of hypertension and IHD ($67\%$ and $43.1\%$ compared to $58.2\%$ and $32.2\%$ of admissions without AKI, $$p \leq 0.037$$ and 0.006, respectively). The main admission diagnosis was an infection in 142 ($47.8\%$) of admissions with AKI vs. 170 ($33.3\%$) in admissions without AKI ($p \leq 0.001$). Significant differences in the various vital signs monitored during admission [maximum temperature, maximum, minimum and average pulse, minimum and average systolic blood pressure (SBP) and diastolic blood pressure (DBP), and O2 saturation] were observed between admissions with and without AKI (Table 2). Steroids use and average dose during admission were significantly higher in admissions with vs. without AKI [216 ($72.2\%$) vs. 333 ($65.3\%$), $$p \leq 0.06$$ (after adjustment for multiple comparisons) and 22.1 (SD 41.01) vs. 12.27 (SD 26.52) mg, $p \leq 0.001$ respectively]. The use of loop diuretics and of proton pump inhibitors was also significantly higher in admissions with vs. without AKI, as opposed to the use of renin-angiotensin-aldosterone system (RAAS) inhibitors, which was lower in admissions with vs. without in-hospital AKI. Laboratory results also differed significantly between admissions with and without AKI. Total white blood cell count, maximum glucose, maximum globulins and C-reactive protein levels were higher in admissions with vs. without AKI, whereas lymphocytes (absolute minimum), average and minimum hemoglobin, minimum glucose and average and minimum albumin were lower in admissions with vs. without AKI. Maximum tacrolimus 12-h trough level was significantly higher in admissions with vs. without AKI [7.1 (IQR 4.05–10.9) vs. 5.7 (4.05–8.5), $$p \leq 0.028$$]. Rates of death during admission and readmission within 90 days as well as LOS were significantly higher for those with vs. without AKI (Table 2).
## Univariate Comparison of AKI Stages 1, 2 and 3 During Admission
Of 297 admissions with AKI during admission, 134 ($45.1\%$), 70 ($23.6\%$) and 93 ($31.3\%$) presented with AKI Stages 1, 2 and 3, respectively. The rate of female RTRs fell as AKI progressed ($39.6\%$, $31.4\%$ and $23.7\%$ in AKI stages 1, 2 and 3 respectively, $$p \leq 0.13$$). Time from transplant to admission increased from 6.95 (IQR 3.8–11.5) to 8.05 (IQR 4.65–12.3) to 8.7 years (IQR 5.9–16.5) as in-hospital AKI stage increased from 1 to 2 to 3, respectively ($$p \leq 0.04$$). Vital signs monitored during admission, such as maximum pulse, increased, whereas minimum SBP and minimum oxygen saturation decreased with worsening AKI stage. MPA use and average dose decreased, whereas average steroid dose increased with progression of AKI. Minimum glucose and average albumin decreased as in-hospital AKI progressed. LOS increased as AKI stage increased, but this was not statistically significant, possibly due to the low number of patients in each group. Rates of death during admission and readmission within 90 days increased as AKI worsened (Table 3).
**TABLE 3**
| Variable | Admissions with AKI (n = 297) | AKI stage 1 (n = 134) | AKI stage 2 (n = 70) | AKI stage 3 (n = 93) | p a |
| --- | --- | --- | --- | --- | --- |
| RTR characteristics | | | | | |
| Admission age, years, [median (IQR)] | 66.5 [55.98, 73] | 67.3 [58.6, 73.7] | 66.1 [55.5, 75.5] | 64.9 [53.4, 72] | 0.4 |
| Female sex, n (%) | 97 (32.7) | 53 (39.6) | 22 (31.4) | 22 (23.7) | 0.13 |
| Transplant to admission, years [median (IQR)] | 7.7 [4.77, 13.48] | 6.95 [3.8, 11.5] | 8.05 [4.65, 12.30] | 8.7 [5.9, 16.5] | 0.04* |
| ESRD etiology, n (%) | ESRD etiology, n (%) | ESRD etiology, n (%) | ESRD etiology, n (%) | ESRD etiology, n (%) | ESRD etiology, n (%) |
| ADPCKD | 18 (6.1) | 10 (7.5) | 5 (7.1) | 3 (3.2) | 0.29 |
| Diabetic nephropathy | 68 (22.9) | 37 (27.6) | 17 (24.3) | 14 (15.1) | |
| Glomerulonephritis | 52 (17.5) | 25 (18.7) | 9 (12.9) | 18 (19.4) | |
| Nephrosclerosis | 33 (11.1) | 18 (13.4) | 5 (7.1) | 10 (10.8) | |
| Other | 88 (29.6) | 29 (21.6) | 27 (38.6) | 32 (34.4) | |
| Unknown | 38 (12.8) | 15 (11.2) | 7 (10.0) | 16 (17.2) | |
| Pre-transplant dialysis | Pre-transplant dialysis | Pre-transplant dialysis | Pre-transplant dialysis | Pre-transplant dialysis | Pre-transplant dialysis |
| Yes | 160 (53.9) | 77 (57.5) | 34 (48.6) | 49 (52.7) | 0.82 |
| No | 46 (15.5) | 18 (13.4) | 14 (20.0) | 14 (15.1) | |
| Unknown | 91 (30.6) | 39 (29.1) | 22 (31.4) | 30 (32.3) | |
| Transplant type, n (%) | Transplant type, n (%) | Transplant type, n (%) | Transplant type, n (%) | Transplant type, n (%) | Transplant type, n (%) |
| Kidney only | 288 (97.0) | 130 (97.0) | 68 (97.1) | 90 (96.8) | 0.86 |
| Liver kidney | 2 (0.7) | 1 (0.7) | 1 (1.4) | 0 (0.0) | |
| Heart kidney | 6 (2.0) | 2 (1.5) | 1 (1.4) | 3 (3.2) | |
| Pancreas kidney | 1 (0.3) | 1 (0.7) | 0 (0.0) | 0 (0.0) | |
| Transplant number, n (%) | Transplant number, n (%) | Transplant number, n (%) | Transplant number, n (%) | Transplant number, n (%) | Transplant number, n (%) |
| 1 | 274 (92.3) | 124 (92.5) | 67 (95.7) | 83 (89.2) | 0.6 |
| 2 | 22 (7.4) | 9 (6.7) | 3 (4.3) | 10 (10.8) | |
| 3 | 1 (0.3) | 1 (0.7) | 0 (0.0) | 0 (0.0) | |
| Donor type, n (%) | Donor type, n (%) | Donor type, n (%) | Donor type, n (%) | Donor type, n (%) | Donor type, n (%) |
| Living | 159 (53.5) | 72 (53.7) | 36 (51.4) | 51 (54.8) | 0.55 |
| Deceased | 87 (29.3) | 45 (33.6) | 19 (27.1) | 23 (24.7) | |
| Unknown | 51 (17.2) | 17 (12.7) | 15 (21.4) | 19 (20.4) | |
| Medical history, n (%) | Medical history, n (%) | Medical history, n (%) | Medical history, n (%) | Medical history, n (%) | Medical history, n (%) |
| Diabetes mellitus | 104 (35.0) | 49 (36.6) | 26 (37.1) | 29 (31.2) | 0.78 |
| Hypertension | 199 (67.0) | 90 (67.2) | 44 (62.9) | 65 (69.9) | 0.78 |
| IHD | 128 (43.1) | 54 (40.3) | 34 (48.6) | 40 (43.0) | 0.71 |
| CHF | 72 (24.2) | 26 (19.4) | 16 (22.9) | 30 (32.3) | 0.24 |
| Admission etiology, n (%) | Admission etiology, n (%) | Admission etiology, n (%) | Admission etiology, n (%) | Admission etiology, n (%) | Admission etiology, n (%) |
| ID | 142 (47.8) | 68 (50.7) | 37 (52.9) | 37 (39.8) | 0.37 |
| CV | 42 (14.1) | 22 (16.4) | 5 (7.1) | 15 (16.1) | |
| GI | 16 (5.4) | 7 (5.2) | 6 (8.6) | 3 (3.2) | |
| CA | 15 (5.1) | 5 (3.7) | 2 (2.9) | 8 (8.6) | |
| Other | 82 (27.6) | 32 (23.9) | 20 (28.6) | 30 (32.3) | |
| Vital signs and other clinical parameters during admission, [median (IQR)] | Vital signs and other clinical parameters during admission, [median (IQR)] | Vital signs and other clinical parameters during admission, [median (IQR)] | Vital signs and other clinical parameters during admission, [median (IQR)] | Vital signs and other clinical parameters during admission, [median (IQR)] | Vital signs and other clinical parameters during admission, [median (IQR)] |
| Fever max, °C | 37.4 [37, 38.40] | 37.3 [37, 38.4] | 37.4 [37, 38.25] | 37.5 [37.1, 38.5] | 0.49 |
| Pulse max | 103 [89, 120] | 98 [87, 111] | 103.5 [90.8, 128.5] | 110 [94, 130] | 0.01* |
| Pulse min | 60 [53, 67] | 60 [53.2, 67] | 59.5 [53, 65] | 61 [52, 68] | 0.8 |
| Pulse average | 78 [70, 85.84] | 76.6 [68, 84.6] | 77.9 [71.5, 84.6] | 79.2 [72.9, 87.1] | 0.24 |
| SBP min mmHg | 95 [80, 108] | 99 [86, 110.7] | 91.5 [72, 102.8] | 89 [70, 110] | 0.01* |
| SBP average mmHg | 128.8 [115.5, 144] | 130 [118.7, 146.4] | 128.4 [114.1, 137.5] | 126.5 [112, 145.8] | 0.04* |
| DBP min mmHg | 50 [40, 59]8 | 50 [45, 58] | 44 [39, 56.5]7 | 49 [36, 63] | 0.24 |
| DBP average mmHg | 69.8 [63.1, 76.6] | 70.5 [64.8, 75.3] | 68.7 [62.6, 75.1] | 70.6 [61.8, 81.1] | 0.6 |
| O2 saturation min | 91 [85, 94] | 93 [88, 95] | 91 [84.3, 93] | 90 [81, 94] | 0.04* |
| Weight average, kg | 73.8 [64, 85.1] | 72.9 [63.2, 85] | 72.7 [63.2, 91.2] | 76.4 [65, 85.6] | 0.78 |
| BMI average | 26.1 [22.8, 30.3] | 26.1 [23.3, 29.5] | 25.7 [22.1, 30.8] | 26.6 [22.8, 30.5] | 0.91 |
| Medications during admission | Medications during admission | Medications during admission | Medications during admission | Medications during admission | Medications during admission |
| Tacrolimus, n (%) | 154 (51.9) | 68 (50.7) | 47 (67.1) | 39 (41.9) | 0.03* |
| Tacrolimus average dose [mean (SD)] | 1.53 (1.03) | 1.49 (0.91) | 1.50 (0.97) | 1.62 (1.30) | 0.88 |
| Cyclosporine, n (%) | 34 (11.4) | 17 (12.4) | 7 (10.0) | 10 (10.8) | 0.94 |
| Cyclosporine average dose [mean (SD)] | 68.90 (39.30) | 71.81 (40.30) | 64.03 (41.24) | 67.35 (40.04) | 0.92 |
| MPA, n (%) | 109 (36.7) | 61 (45.5) | 26 (37.1) | 22 (23.7) | 0.02* |
| MPA average dose [mean (SD)] | 146.86 (215.11) | 186.40 (237.78) | 140.91 (192.53) | 94.35 (185.05) | 0.039* |
| Steroids, n (%) | 216 (72.7) | 92 (68.7) | 53 (75.7) | 71 (76.3) | 0.59 |
| Steroids average dose [mean (SD)] | 22.10 (41.01) | 14.47 (20.03) | 24.98 (38.47) | 30.72 (59.23) | 0.06 |
| mTOR inhibitors, n (%) | 16 (5.4) | 8 (6.0) | 3 (4.3) | 5 (5.4) | 0.91 |
| Azathioprine, n (%) | 14 (4.7) | 7 (5.2) | 3 (4.3) | 4 (4.3) | 0.93 |
| Loop diuretics, n (%) | 146 (49.2) | 61 (45.5) | 34 (48.6) | 51 (54.8) | 0.59 |
| Thiazides, n (%) | 15 (5.1) | 9 (6.7) | 1 (1.4) | 5 (5.4) | 0.47 |
| Calcium channel blockers, n (%) | 105 (35.4) | 41 (30.6) | 29 (41.4) | 35 (37.6) | 0.47 |
| Beta blockers, n (%) | 192 (64.6) | 83 (61.9) | 43 (61.4) | 66 (71.0) | 0.53 |
| RAAS inhibition, n (%) | 71 (23.9) | 35 (26.1) | 15 (21.4) | 21 (22.6) | 0.82 |
| Aldosterone antagonists, n (%) | 9 (3.0) | 4 (3.0) | 1 (1.4) | 4 (4.3) | 0.72 |
| Statins, n (%) | 138 (46.5) | 64 (47.8) | 35 (50.0) | 39 (41.9) | 0.72 |
| NSAIDs, n (%) | 3 (1.0) | 0 (0.0) | 0 (0.0) | 3 (3.2) | 0.16 |
| PPIs, n (%) | 223 (75.1) | 96 (71.6) | 54 (77.1) | 73 (78.5) | 0.65 |
| Laboratory results during admission [median (IQR)] | Laboratory results during admission [median (IQR)] | Laboratory results during admission [median (IQR)] | Laboratory results during admission [median (IQR)] | Laboratory results during admission [median (IQR)] | Laboratory results during admission [median (IQR)] |
| White blood cell average (K/μL) | 9.92 [7.27, 14.57] | 9.5 [6.9, 14.1] | 11.9 [7.7, 17.1] | 10.2 [7.3, 13.5] | 0.42 |
| Lymphocyte absolute average (K/μL) | 1.09 [0.67, 1.52] | 1.1 [0.71, 1.5] | 1.1 [0.7, 1.6] | 1 [0.6, 1.35] | 0.42 |
| Lymphocyte absolute min (K/μL) | 0.58 [0.28, 0.94] | 0.6 [0.3, 1.1] | 0.65 [0.3, 0.96] | 0.5 [0.2, 0.8] | 0.24 |
| Hemoglobin average (g/dL) | 9.91 [9.00, 10.98] | 9.9 [8.98, 11] | 10.1 [9.2, 11.1] | 9.7 [8.8, 10.8] | 0.59 |
| Creatinine (mg/dL) | 1.63 [1.13, 2.37] | 1.4 [1.1, 1.9] | 1.4 [1.1, 2] | 2.5 [1.6, 3.5] | <0.001** |
| eGFR baseline (CKD-EPI)** | 58.2 [35.8, 80.3] | 59.6 [44, 77.8] | 72.1 [54.5, 88.7] | 34.3 [23.6, 72.6] | <0.001** |
| Glucose max (mg/dL) | 244 [152, 380] | 231 [152, 380] | 268 [150, 405] | 225 [168, 350] | 0.78 |
| Glucose min (mg/dL) | 78 [64, 93] | 81 [68, 103] | 77 [65.25, 90] | 74 [57, 85] | 0.018* |
| Albumin average (g/dL) | 2.9 [2.6, 3.3] | 3.1 [2.7, 3.35] | 2.8 [2.4, 3.1] | 2.7 [2.4, 3.8] | 0.003* |
| Globulins max (g/dL) | 3 [2.6, 3.6] | 3 [2.7, 3.6] | 3.2 [2.8, 3.5] | 3 [2.5, 3.6] | 0.61 |
| Tacrolimus trough level average (μg/L) | 5.97 [3.8, 8.6] | 6.5 [4.4, 9.1] | 5.5 [3.5, 7.1] | 4.95 [3.6, 8.1] | 0.11 |
| Tacrolimus trough level max (μg/L) | 7.1 [4.1, 10.9] | 8.1 [4.6, 11.2]7 | 6.2 [3.7, 8.5] | 6.95 [3.9, 11.7] | 0.39 |
| C-reactive protein average (mg/L) | 79.8 [36.1, 137.8] | 62.7 [31.7, 119] | 104.2 [42.9, 158.3] | 85.5 [43.7, 140.5] | 0.11 |
| Death during admission, n (%) | 32 (10.8) | 3 (2.2) | 9 (12.9) | 20 (21.5) | <0.001** |
| LOS, days, [median (IQR)] | 7 [3.6, 15.3] | 6.31 [3.28, 13.11] | 7.34 [4.3, 12.5] | 8.17 [3.62, 19.95] | 0.42 |
| Readmission in 90 days, n (%) | 154 (51.9) | 65 (48.5) | 31 (44.3) | 58 (62.4) | 0.042* |
## Multivariable Analysis for AKI During Admission in RTRs
A mixed-effect logistic model (including admission age, sex, time from transplant to admission, ESRD etiology, medical history of diabetes, hypertension, IHD, CHF and AKI in a previous admission, admission etiology, vital signs, medications during admission, maximum tacrolimus trough level and other laboratory results during admission) revealed that the odds for an AKI during admission increased by $93\%$ (OR 1.93, $95\%$ CI 1.13–3.32, $$p \leq 0.017$$) for AKI in a previous admission and by $91\%$ (OR 1.91, $95\%$ CI 1.07–3.41, $$p \leq 0.028$$) for a medical history of hypertension. In addition, for every increase in minimum SBP of 1 mm Hg, the odds for in-hospital AKI decreased by $2\%$ (OR 0.98, $95\%$ CI 0.97–0.99, $$p \leq 0.002$$). Tacrolimus maximum trough level and albumin level during admission were also found to be associated with AKI during admission (OR 1.08, $95\%$ CI 1.02–1.13, $$p \leq 0.005$$ and OR 0.51, $95\%$ CI 0.29–0.92, $$p \leq 0.025$$, respectively). When tacrolimus maximum trough level was excluded to increase the number of patients and admissions included in the analysis, the odds for in-hospital AKI was more than twofold higher in the case of AKI in a previous admission (OR 2.13, $95\%$ CI 1.44–3.14, $p \leq 0.001$) and almost three times higher when the major diagnosis upon admission was an infection (OR 2.93, $95\%$ CI 1.23–6.98, $$p \leq 0.015$$). For every increase in minimum hemoglobin of 1 g/dL, the odds for AKI during admission decreased by $10\%$ (OR 0.9, $95\%$ CI 0.82–0.98, $$p \leq 0.016$$). Minimum SBP and albumin level during admission were also found to be independent predictors for in-hospital AKI (Table 4).
**TABLE 4**
| Effect | Univariate logistic regression | Univariate logistic regression.1 | Univariate logistic regression.2 | Logistic regression (n = 193 patients, 385 admissions) | Logistic regression (n = 193 patients, 385 admissions).1 | Logistic regression (n = 193 patients, 385 admissions).2 | Logistic regression (n = 283 patients, 767 admissions) | Logistic regression (n = 283 patients, 767 admissions).1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Effect | Odds ratio (95% CI) | p | p | Odds ratio (95% CI) | p | p | Odds ratio (95% CI) | p |
| RTR characteristics | RTR characteristics | RTR characteristics | RTR characteristics | RTR characteristics | RTR characteristics | RTR characteristics | RTR characteristics | RTR characteristics |
| Admission age, per 1 year increase | 0.99 (0.98, 1.01) | 0.85 | 0.85 | 0.99 (0.97, 1.02) | 0.45 | 0.45 | 0.99 (0.97, 1.01) | 0.21 |
| Female vs. male | 0.93 (0.59, 1.48) | 0.77 | 0.77 | 1.17 (0.67, 2.04) | 0.59 | 0.59 | 0.99 (0.65, 1.49) | 0.95 |
| Transplant to admission, years | 1.02 (0.99, 1.06) | 0.11 | 0.11 | 1.02 (0.97, 1.07) | 0.47 | 0.47 | 1.00 (0.97, 1.03) | 0.78 |
| ESRD etiology | ESRD etiology | ESRD etiology | ESRD etiology | ESRD etiology | ESRD etiology | ESRD etiology | ESRD etiology | ESRD etiology |
| APCKD | 1 | | | 1 | | | 1 | |
| Diabetic nephropathy | 2.33 (1.00, 5.41) | 0.049* | 0.049* | 1.09 (0.34, 3.51) | 0.88 | 0.88 | 1.06 (0.43, 2.57) | 0.91 |
| Glomerulonephritis | 2.22 (0.94, 5.24) | 0.07 | 0.07 | 1.7 (0.53, 5.51) | 0.37 | 0.37 | 1.6 (0.7, 3.69) | 0.27 |
| Nephrosclerosis | 3.41 (1.26, 9.22) | 0.016* | 0.016* | 1.87 (0.52, 6.73) | 0.34 | 0.34 | 2.05 (0.77, 5.44) | 0.15 |
| Other | 2.89 (1.26, 6.59) | 0.012* | 0.012* | 1.41 (0.48, 4.15) | 0.53 | 0.53 | 1.72 (0.77, 3.82) | 0.19 |
| Unknown | 2.36 (0.93, 6.00) | 0.07 | 0.07 | 1.54 (0.43, 5.52) | 0.51 | 0.51 | 1.7 (0.68, 4.23) | 0.26 |
| Medical history | Medical history | Medical history | Medical history | Medical history | Medical history | Medical history | Medical history | Medical history |
| Diabetes mellitus | 1.3 (0.82, 2.05) | 0.27 | 0.27 | | | | | |
| Hypertension | 2.02 (1.27, 3.22) | 0.003* | 0.003* | 1.91 (1.07, 3.41) | 0.028* | 0.028* | 1.36 (0.88, 2.1) | 0.16 |
| IHD | 1.95 (1.25, 3.05) | 0.003* | 0.003* | 1.23 (0.67, 2.23) | 0.51 | 0.51 | 1.17 (0.75, 1.81) | 0.49 |
| CHF | 1.59 (0.98, 2.57) | 0.05 | 0.05 | 1.28 (0.64, 2.54) | 0.49 | 0.49 | 1.35 (0.81, 2.24) | 0.24 |
| Previous AKI | 2.88 (2.06, 4.03) | <0.001** | <0.001** | 1.93 (1.13, 3.32) | 0.017** | 0.017** | 2.13 (1.44, 3.14) | <0.001** |
| Admission etiology | Admission etiology | Admission etiology | Admission etiology | Admission etiology | Admission etiology | Admission etiology | Admission etiology | Admission etiology |
| CA | 1 | | | 1 | | | 1 | |
| CV | 0.47 (0.19,1.14) | 0.1 | 0.1 | 1.03 (0.26, 4.02) | 0.97 | 0.97 | 1.5 (0.58, 3.9) | 0.4 |
| GI | 0.51 (0.18, 1.42) | 0.19 | 0.19 | 0.68 (0.16, 3.2.98) | 0.61 | 0.61 | 0.82 (0.28, 2.39) | 0.72 |
| ID | 1.34 (0.59, 3.05) | 0.49 | 0.49 | 1.9 (0.55, 6.61) | 0.31 | 0.31 | 2.93 (1.23, 6.98) | 0.015* |
| Others | 0.9 (0.39, 2.1) | 0.81 | 0.81 | 2.2 (0.6, 8.14) | 0.24 | 0.24 | 2.73 (1.11, 6.68) | 0.03* |
| Vital signs and other clinical parameters during admission | Vital signs and other clinical parameters during admission | Vital signs and other clinical parameters during admission | Vital signs and other clinical parameters during admission | Vital signs and other clinical parameters during admission | Vital signs and other clinical parameters during admission | Vital signs and other clinical parameters during admission | Vital signs and other clinical parameters during admission | Vital signs and other clinical parameters during admission |
| Pulse max, per 1/min increase | 1.028 (1.02, 1.037) | 1.028 (1.02, 1.037) | <0.001** | 1.00 (0.99–1.02) | 1.00 (0.99–1.02) | 0.64 | 1.01 (1.00, 1.02) | 0.03* |
| SBP min, per 1 mm Hg increase | 0.97 (0.96, 0.98) | 0.97 (0.96, 0.98) | <0.001** | 0.98 (0.97, 0.99) | 0.98 (0.97, 0.99) | 0.002* | 0.98 (0.97, 0.99) | <0.001** |
| DBP min, per 1 mm Hg increase | 0.95 (0.94, 0.97) | 0.95 (0.94, 0.97) | <0.001** | | | | | |
| Sat O2 min per 1% increase | 0.97 (0.95, 0.99) | 0.97 (0.95, 0.99) | <0.001** | 1.00 (0.98, 1.02) | 1.00 (0.98, 1.02) | 0.95 | 1.01 (0.99, 1.02) | 0.55 |
| Medications during admission | Medications during admission | Medications during admission | Medications during admission | Medications during admission | Medications during admission | Medications during admission | Medications during admission | Medications during admission |
| Steroids average dose, per 1 mg of prednisone increase | 1.011 (1.005, 1.016) | 1.011 (1.005, 1.016) | <0.001** | | | | | |
| Loop diuretics use | 1.52 (1.04, 2.22) | 1.52 (1.04, 2.22) | 0.03* | 1.22 (0.66, 2.26) | 1.22 (0.66, 2.26) | 0.53 | 1.1 (0.71, 1.7) | 0.66 |
| PPI use | 1.83 (1.2, 2.77) | 1.83 (1.2, 2.77) | 0.0047** | 1.05 (0.56, 1.94) | 1.05 (0.56, 1.94) | 0.89 | 0.99 (0.64, 1.51) | 0.95 |
| Laboratory results during admission | Laboratory results during admission | Laboratory results during admission | Laboratory results during admission | Laboratory results during admission | Laboratory results during admission | Laboratory results during admission | Laboratory results during admission | Laboratory results during admission |
| Lymphocyte absolute average per 1 K/μL increase | 0.94 (0.82, 1.07) | 0.94 (0.82, 1.07) | 0.36 | | | | | |
| Lymphocyte absolute min per 1 K/μL increase | 0.76 (0.6, 0.95) | 0.76 (0.6, 0.95) | 0.015* | 1.02 (0.87, 1.19) | 1.02 (0.87, 1.19) | 0.79 | 1.0 (0.87, 1.15) | 0.99 |
| Hemoglobin average per 1g/dL increase | 0.74 (0.67, 0.82) | 0.74 (0.67, 0.82) | <0.001** | | | | | |
| Hemoglobin min per 1g/dL increase | 0.75 (0.69, 0.81) | 0.75 (0.69, 0.81) | <0.001** | 0.95 (0.84, 1.08) | 0.95 (0.84, 1.08) | 0.41 | 0.9 (0.82, 0.98) | 0.016 |
| eGFR baseline (CKD-EPI)** per 1 mL/min increase | 0.999 (0.99, 1.01) | 0.999 (0.99, 1.01) | 0.8 | 1.01 (0.99, 1.02) | 1.01 (0.99, 1.02) | 0.44 | 1.00 (0.99, 1.01) | 0.42 |
| Glucose max per 1 mg/dL increase | 1.005 (1.00, 1.01) | 1.005 (1.00, 1.01) | <0.001** | 1.00 (1.00, 1.01) | 1.00 (1.00, 1.01) | 0.028 | 1.00 (1.00, 1.01) | <0.001** |
| Glucose min per 1 mg/dL increase | 0.98 (0.98, 0.99) | 0.98 (0.98, 0.99) | <0.001** | | | | | |
| Albumin average per 1 g/dL increase | 0.59 (0.45, 0.8) | 0.59 (0.45, 0.8) | <0.001** | | | | | |
| Albumin min per 1 g/dL increase | 0.2 (0.14, 0.28) | 0.2 (0.14, 0.28) | <0.001** | 0.51 (0.29, 0.92) | 0.51 (0.29, 0.92) | 0.025 | 0.42 (0.27, 0.64) | <0.001** |
| Globulins max per 1 g/dL increase | 1.87 (1.36, 2.56) | 1.87 (1.36, 2.56) | <0.001** | | | | | |
| Globulins min per 1 g/dL increase | 0.72 (0.5, 1.04) | 0.72 (0.5, 1.04) | 0.08 | 1.02 (0.62, 1.69) | 1.02 (0.62, 1.69) | 0.94 | 1.05 (0.72, 1.51) | 0.81 |
| Tacrolimus trough level max per 1 μg/L increase | 1.065 (1.02, 1.11) | 1.065 (1.02, 1.11) | 0.0065* | 1.08 (1.02, 1.13) | 1.08 (1.02, 1.13) | 0.005* | | |
## Outcomes of AKI During Admission in RTRs
We examined four outcomes of AKI during admission: readmission within 90 days, mortality during admission, overall mortality, and LOS.
## Readmission in 90 Days
In a mixed effect logistic regression analysis, in-hospital AKI increased the odds for readmission within 90 days by $95\%$ (OR 1.95, $95\%$ CI 1.35–2.81, $p \leq 0.001$). For every increase in minimum hemoglobin of 1 g/dL, the odds for readmission in 90 days decreased by $8\%$ (OR 0.92, $95\%$ CI 0.85–0.99, $$p \leq 0.02$$; Table 5).
**TABLE 5**
| Effect | Odds ratio (95% CI) | p |
| --- | --- | --- |
| Admission age | 1.01 (0.99–1.03) | 0.17 |
| Gender, F vs. M | 0.93 (0.62–1.38) | 0.72 |
| Hypertension, yes vs. no | 1.34 (0.91–1.97) | 0.14 |
| In-hospital AKI, yes vs.no | 1.95 (1.35–2.81) | <0.001** |
| SBP min (for every increase of 1 mm Hg) | 1.0 (0.99–1.01) | 0.93 |
| Albumin min per 1g/dL increase | 0.76 (0.53–1.1) | 0.15 |
| Glucose max per 1 mg/dL increase | 1.0 (0.99–1.0) | 0.94 |
| Hemoglobin min per 1 g/dL increase | 0.92 (0.85–0.99) | 0.02* |
## Mortality During Admission
In a mixed effect logistic regression analysis, admission age, AKI stage 3 vs. no AKI, minimum SBP and minimum albumin during admission were found to be independent predictors for in-hospital mortality. The odds of mortality during admission were four times higher in RTRs with AKI stage 3 vs. RTRs with no AKI during admission (OR 4.0, $95\%$ CI 1.38–11.6, $$p \leq 0.01$$; Table 6).
**TABLE 6**
| Effect | Odds ratio (95% CI) | p |
| --- | --- | --- |
| Admission age (for every increase in 1 year) | 1.05 (1.01–1.09) | 0.01* |
| Gender, F vs. M | 0.88 (0.35–2.18) | 0.77 |
| Reference- no AKI during admission | Reference- no AKI during admission | Reference- no AKI during admission |
| AKI stage 1 | 0.65 (0.16–2.67) | 0.55 |
| AKI stage 2 | 2.76 (0.86–9.98) | 0.09 |
| AKI stage 3 | 4.00 (1.38–11.6) | 0.01* |
| SBP min (for every increase of 1 mm Hg) | 0.97 (0.95–0.98) | <0.001** |
| Albumin min per 1 g/dL increase | 0.21 (0.08–0.55) | 0.001** |
| Glucose max per 1 mg/dL increase | 1.0 (0.99–1.0) | 0.56 |
| Hemoglobin min per 1 g/dL increase | 0.99 (0.85–1.17) | 0.94 |
## Overall Mortality
Figure 2 shows Kaplan–Meier curves for time to death according to the presence and severity of AKI in the last admission for each patient. In a multivariable Cox proportional hazards regression model for long-term mortality, transplant age, diabetic nephropathy vs. all other ESRD etiologies, and presence of AKI vs. no AKI in any admission were associated with a 1.08-fold ($95\%$ CI 1.06–1.1), 1.95-fold ($95\%$ CI 1.27–2.99) and 1.51-fold ($95\%$ CI 1.01–2.25) increased risk of death, respectively (Table 7).
**FIGURE 2:** *Long-term mortality based on the occurrence and severity of in-hospital AKI in the last admission for each patient.* TABLE_PLACEHOLDER:TABLE 7
## Length of Stay
Figure 3 shows a box-plot diagram for in-hospital LOS according to the presence and severity of in-hospital AKI. In a multivariable linear mixed model for LOS, a major admission diagnosis of a cancer significantly prolonged the LOS compared to all other admission etiologies. For every 1 mm Hg increase in minimum SBP, LOS was shortened by $1\%$ (0.99, $95\%$ CI 0.98–0.99, $p \leq 0.001$). Use of a calcineurin inhibitor (tacrolimus or cyclosporine) during admission increased the LOS by $41\%$ (1.41, $95\%$ CI 1.26–1.58, $p \leq 0.001$). Loop diuretic use, minimum hemoglobin, maximum glucose and minimum albumin during admission were independently associated with LOS. AKI during admission was not found to be an independent predictor for hospital LOS (Table 8).
**FIGURE 3:** *Box-plot diagram for in-hospital length of stay (days) in RTR with no AKI, and AKI stages 1,2 and 3.* TABLE_PLACEHOLDER:TABLE 8
## Discussion
In this study of 292 RTRs, with a total of 807 non-ICU admissions, we found a $51\%$ rate ($\frac{149}{292}$) of any AKI over multiple hospital admissions. AKI during admission was observed in $36.8\%$ ($\frac{297}{807}$) of total admissions. Of 297 admissions with AKI, stages 1, 2 and 3 were recorded for 134 ($45.1\%$), 70 ($23.6\%$) and 93 ($31.3\%$) admissions, respectively. Multivariable mixed effect models for AKI during admission revealed that an AKI in a previous admission doubled the odds for AKI in the subsequent admission. The odds for AKI during an admission were almost three times higher with major diagnosis of infectious etiology during admission. In addition, a medical history of hypertension, minimum SBP, minimum hemoglobin, albumin and tacrolimus maximum trough level were significantly associated with AKI during admission. AKI during admission was associated with adverse events, i.e., for patients who developed AKI, LOS and mortality during admission rose and rates of readmission within 90 days increased with worsening outcomes as AKI severity increased. The overall mortality risk was also higher in RTRs with any AKI vs. no AKI during admission.
The overall incidence of AKI developing 3 months or later after kidney transplantation, excluding RTRs with deceased donor transplants and recipients of second or third transplants, was $20.4\%$ [16]. In seeking to compare this finding with values in the literature, we found that there are only very few studies dealing with the subject. In pediatric kidney transplant recipients, the incidence of AKI was $37\%$ over a study period of 12 years [17]. A very much lower value – 3,066 of 27,232 transplant recipients ($11.3\%$) – was reported in the only study focused on in-hospital AKI (4181 hospitalizations) during the first three post-transplant years. In that study, AKI was identified by the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), which has limited sensitivity, and therefore the overall incidence of AKI was probably underestimated [12]. Based on Scr levels pre and post admission, we detected AKI in a median time from transplant of 7.7 years (IQR 4.77–13.48) in $\frac{149}{292}$ ($51\%$) of RTRs, with a total of 513 admissions (1-10 admissions per person). Our analysis provides a more accurate assessment of the higher incidence of in-hospital AKI in RTRs compared to the non-transplant population, in which AKI occurs in $4\%$–$20\%$ of hospitalized patients [16, 18, 19]. Similarly to our findings, a higher rate of AKI following cardiac surgery ($46\%$ vs. $28\%$) was observed in RTRs compared with non-RTRs [2].
In a study of 11,683 patients developing in-hospital AKI, 2954 ($25\%$) were re-hospitalized with recurrent AKI within 12 months of discharge [20], with each episode of recurrence conferring an increased risk for progression to chronic kidney disease [21]. Analysis of a large database of about 150,000 patients revealed that approximately $20\%$ were readmitted with AKI and about $10\%$ were seen in an emergency room within 30 days of discharge [22]. We found the readmission rate within 90 days to be $51.9\%$ vs. $29\%$ in admissions without in-hospital AKI ($p \leq 0.001$). Moreover, the severity of AKI also affected the 90-day readmission rate, which reached $62.4\%$ in stage 3 as opposed to $48.5\%$ in stage 1 AKI. Our study is the first to show the negative effects of in-hospital AKI on the readmission rate and subsequent AKI events in RTRs.
It is not surprising that a major diagnosis of an infection was associated with in-hospital AKI; for example, in a study conducted in Italy the incidence of in-hospital AKI was $31.7\%$ during the COVID-19 pandemic compared to $25.9\%$ during the pre-COVID-19 period [23]. RTRs are prone to infections and complications of infections, given the immunosuppressive agents they receive to prevent rejection. Infections are commonly complicated by AKI secondary to sepsis associated with hemodynamic instability, volume depletion, and the nephrotoxicity of antibiotics, among other factors. A medical history of hypertension, associated with oxidative stress and endothelial dysfunction [24], was also found to be an independent predictor for in-hospital AKI in our population, as previously described in patients with AKI following surgical resection of malignant pleural mesothelioma [25].
Given the large number and extensive variety of the components of our dataset (different vital signs, clinical parameters, medications and laboratory results during admission) that were retrieved as possible confounders, we were able to demonstrate associations of SBP, hemoglobin, albumin and maximum tacrolimus trough level with in-hospital AKI. CNI nephrotoxicity, a well-known complication of CNI use [26, 27], remains the leading cause of renal failure after transplantation of a non-renal organ [28, 29]. Similar abnormalities have been found when CNIs are used in other settings, for example, in patients with psoriasis [30]. The pathophysiology of acute CNI nephrotoxicity is related to profound alterations in renal vascular resistance and blood flow in the afferent and efferent arterioles and even a reduced diameter of the afferent arterioles [27]. In line with our findings, higher vs. lower preoperative CNI trough levels ($73\%$ vs. $36\%$) have been associated with higher rates of AKI following cardiac surgery in RTRs [2].
The pathophysiology of low SBP and a low hemoglobin level associated AKI is related to the reduction in perfusion pressure and oxygenation, leading to ischemic injury. Renal ischemia-reperfusion injury in kidney transplantation leads to AKI, delayed graft function, and even graft loss [31]. Kidney transplantation involves implantation of denervated kidneys, with impairment of blood flow autoregulation, rendering the renal allograft highly susceptible to ischemic injury and subsequent inflammation and cell death. A reduced nephron mass in RTRs may also increase their susceptibility to ischemic injury. Furthermore, renal allograft ischemia may exacerbate immune-related mechanisms of allograft injury, as manifested by the effect of cold and warm ischemia times on graft function and rejection [32].
AKI is common in RTRs and confers a high risk for graft failure and death [12, 17, 33]. In populations other than RTRs, AKI has been associated with increased LOS and higher mortality [25, 34]. We are the first to describe the association of in-hospital AKI in RTRs with increased LOS and mortality during admission. Our study is probably underpowered to detect an association between in-hospital mortality and milder AKI, found in studies of non-transplant patients [25]. In addition, we found a strong association of AKI with overall mortality over a period of more than 30 years.
Several limitations should be mentioned, including the retrospective study design. In addition, minimum Scr during admission used as baseline Scr in recipients with no Scr within 120 days prior to admission or within 150 days from transplant may not reflect baseline Scr as it could be elevated due to AKI prior to admission, there is no information about the exact timing of maximum Scr during admission, rejection, use of erythropoietin stimulating agents, admissions to other hospitals, transplant loss, renal replacement therapy or recovery from AKI, mortality (death with a functioning graft) and death-censored graft loss. Urine output criteria were not used. In addition, the use of serum creatinine levels to estimate GFR has limitations in assessing kidney function. The strengths of this study include the large size of the cohort, creatinine-based definitions to capture index and recurrent AKI, and the power to examine multiple potential confounders. Nonetheless, we cannot exclude potential residual confounding as in-hospital AKI may be a surrogate for disease severity.
In conclusion, in-hospital AKI in RTRs is an independent risk factor associated with poor short- and long-term outcomes. RTRs with an AKI during admission should be followed up closely, with specific monitoring after discharge to reduce the risk of rehospitalization and death. Efforts should be made to identify patients at high risk for AKI, to develop strategies to prevent AKI during admission, and to minimize adverse outcomes. RTRs should be closely monitored during admission to prevent hypotension, anemia, and hypoalbuminemia. The association of CNI with in-hospital AKI further emphasizes the importance of individualized tailoring of immunosuppressive therapy based on rejection vs. infection risk to prevent complications associated with over immunosuppression, including infections, which are independently associated with in-hospital AKI and AKI itself.
## Data Availability Statement
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.
## Ethics Statement
The study was approved by the local ethics committee in Sheba medical center (IRB approval number: SMC-70-5320). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author Contributions
TH: Conception and design, data acquisition, data interpretation, writing, revising; BO: Data analysis; NS: Data acquisition; LL: Data interpretation; GS: Conception and design; PB: Data acquisition; KC-H: Data interpretation; EM: Revising; EG: Revising; EZ: Conception and design, data interpretation; MS: conception and design, data interpretation, revising. All authors contributed to the article and approved the submitted version.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Abbreviations
BMI, body mass index; CHF, congestive heart failure; CI, confidence interval; CNI, calcineurin inhibitor; DBP, diastolic blood pressure; DM, diabetes; eGFR, estimated glomerular filtration rate; ESRD, end stage renal disease; HTN, hypertension; ICU, intensive care unit; IHD, ischemic heart disease; KDIGO, Kidney Disease Improving Global Outcomes; LOS, length of stay; MPA, mycophenolic acid; OR, odds ratio; RTR, renal transplant recipients; SBP, systolic blood pressure; Scr, serum creatinine; SD, standard deviation.
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|
---
title: Obesity, antioxidants and negative symptom improvement in first-episode schizophrenia
patients treated with risperidone
authors:
- Zhiyong Gao
- Meihong Xiu
- Jiahong Liu
- Fengchun Wu
- Xiang-Yang Zhang
journal: Schizophrenia
year: 2023
pmcid: PMC10033648
doi: 10.1038/s41537-023-00346-z
license: CC BY 4.0
---
# Obesity, antioxidants and negative symptom improvement in first-episode schizophrenia patients treated with risperidone
## Abstract
Negative symptoms remain a main therapeutic challenge in patients with schizophrenia (SZ). Obesity is associated with more severe negative symptoms after the first episode of psychosis. Oxidative stress caused by an impaired antioxidant defense system is involved in the pathophysiology of SZ. Yet, it is unclear regarding the role of obesity and antioxidants in negative symptom improvements in SZ. Therefore, this longitudinal study was designed to assess the impact of obesity on antioxidant defenses and negative symptom improvements in first-episode SZ patients. A total of 241 medication-naive and first-episode patients with SZ were treated with risperidone for 3 months. Outcome measures including symptoms, body weight, and total antioxidant status (TAS) levels were measured at baseline and the end of the third month. We found that after 12 weeks of treatment with risperidone, the body weight increased and clinical symptoms significantly improved. Baseline body mass index (BMI) was negatively correlated with negative symptom improvement after treatment and an increase in TAS was negatively associated with an increase in BMI only in the high BMI group. More importantly, the TAS × BMI interaction at baseline was an independent predictor of negative symptom improvement. Our longitudinal study indicates that the improvement in negative symptoms by risperidone was associated with baseline BMI and TAS levels in patients with SZ. Baseline BMI and TAS may be a predictor for negative improvement in SZ patients after risperidone treatment.
## Introduction
Schizophrenia (SZ) is a severe mental illness characterized by persistent or relapsing episodes of positive symptoms and negative symptoms1. Antipsychotics are recommended as the first-line medication for treating SZ and have been reported in clinical trials to be effective in improving symptoms and behaviors related to SZ. Positive symptoms can be effectively treated by available antipsychotics, modern therapy, and psychiatric support2. It has been shown that about $60\%$ of SZ patients can return home after recovery from their first episode and ~$50\%$ can return to work3,4. However, ~$30\%$ remains severely disabled by their condition and $10\%$ must be hospitalized5,6.
The percentage of obesity in patients with SZ is significantly higher than in the general population7,8. According to recent literature, 40–$60\%$ of individuals with SZ are reported to be overweight or obese9,10. Recent meta-analyses of patients with medication-naïve and first-episode psychosis (MNFE) have revealed increased insulin resistance and impaired glucose tolerance relative to healthy controls11, although earlier studies have shown that patients with first-episode psychosis were diagnosed with much less diabetes than chronic patients on antipsychotics12. Moreover, studies reported rapid weight gain (>$7\%$), usually within 6–8 weeks after treatments with antipsychotics13. Presumably, patients with SZ may be prone to manifest pre-diabetes, which emphasizes the need for early monitoring of overweight/obesity in first-episode SZ.
There is accumulating evidence to reveal an association between obesity, metabolic parameters, or the changes and clinical symptoms in patients with MNFE14,15. For example, studies have reported that body mass index (BMI) and other metabolic parameters, including the homeostasis model assessment of insulin resistance (HOMA-IR), and hemoglobin A1C (HbA1c) were associated with negative symptoms in SZ patients16,17. Even multiple studies showed that weight gain is related to decreased general psychopathology in patients with SZ treated with second-generation antipsychotics18,19. Some recent evidence revealed that weight gain is an important prognostic marker of treatment response to antipsychotics20,21, suggesting that body weight and weight gain are important issues in therapeutic benefits in SZ.
On the other hand, studies have also focused on the essential role of oxidative stress in the pathogenesis of obesity, metabolic disorders, and SZ22,23. Glutathione peroxidase, catalase, and superoxide dismutase are the main enzymatic antioxidants in the cells24,25. Vitamins A and C, tocopherol, glutathione, uric acid, albumin, and bilirubin are important non-enzymatic antioxidants26,27. These antioxidants help detoxify harmful reactive oxygen species to protect from ROS-induced damage to proteins, DNA, and mitochondrial membranes27. Alterations in serum or plasma levels or activities of antioxidants have been reported in patients with obesity or SZ28–33. Total antioxidant status is an important indicator of the additive antioxidant effect in vivo, which is measured via ferric reducing antioxidant potential (FRAP).
Atypical and typical antipsychotics have an impact on antioxidant defense system in patients with SZ34–37. Antipsychotic medication may induce oxidative stress, which further influences the turnover of catecholamines and suppresses the activity of antioxidant enzymes. There is clear evidence for the different effects of typical and atypical antipsychotics in regulating oxidative stress and antioxidant defense systems38–40. Risperidone is one of the most widely prescribed atypical antipsychotics used in the treatment of SZ, for both acute and long-term medication41. It is a benzisoxazole derivative and has a strong binding affinity for D2 and 5-HT receptors42. Animal studies provide strong evidence for the regulation of risperidone in the redox system. It has antioxidant effects by increasing the antioxidant defenses, such as regulating glutathione levels in C6 astroglial cell model43. Administration of risperidone can restore the brain glutathione levels and decrease total antioxidant capacity in rats induced by perinatal phencyclidine44,45. In addition, risperidone treatment significantly upregulates antioxidant enzyme activities in patients with SZ46–48.
Atypical antipsychotics have been reported to be linked to rapid weight gains and obesity in the initial period after antipsychotic medications in SZ patients49. Based on the close relationship between antipsychotic treatment, obesity, and redox regulation in patients with SZ, the aim of this study was to investigate the effect of antioxidants and BMI on the clinical outcome of risperidone in patients with SZ. We hypothesized that the association between TAS levels and clinical symptom improvement following risperidone treatment would be different between the low and high BMI groups and that the interaction between TAS and BMI would predict the response to risperidone in SZ patients. Notably, to minimize the potential effects of types and accumulative doses of antipsychotics, physical-health comorbidity, and duration of illness, only MNFE patients with SZ were recruited in the present study.
## Participants
A total of 241 MNFE patients (128 males/113 females) diagnosed with SZ by SCID-IV, between the age of 16 and 45 years were recruited from Beijing Huilongguan Hospital and Henan Zhumadian psychiatric hospitals in China. Eligibility inclusion/exclusion criteria were described in our prior studies30,50, and detailed criteria were described in the supplementary materials. In brief, inclusion criteria included both sexes, illness duration ≤5 years, no previous treatment with psychotropic medicines or cumulative use of antipsychotics ≤14 days and the clinical global impression (GCI) of 4 or over. The patients had a mean ± SD age of 27.6 ± 9.2 years, a mean BMI of 21.3 ± 3.4 kg/m2, a mean onset age of 26.1 ± 9.2 years and an average illness duration of 1.5 ± 1.3 years. Of the 241 patients, sixty-six patients were smokers ($\frac{66}{241}$, $27.4\%$) and there was no difference in smoking rates between BMI subgroups. The average severity of the illness assessed by the Positive and Negative Syndrome Scale (PANSS) was 76.0 ± 17.4.
The study protocol was approved by the Ethics Committees of Beijing Huilongguan Hospital, and written informed consent was obtained from each patient.
## Study overview
All recruited SZ patients received a flexible dose (4–6 mg/day) of oral risperidone for 3 months. The study consisted of three visits conducted by experienced psychiatrists including a questionnaire survey, clinical assessment scales, and blood sampling on day 1 (visit 1, screening), day 1 (visit 2, baseline assessment), and at the third month or after early discontinuation of risperidone (visit 3, post-treatment assessment).
## Clinical evaluation and TAS measurements
The clinical symptom was assessed by six experienced clinicians using the PANSS51. Before the PANSS rating, the raters participated in comprehensive training. After training, the inter-rater reliability was assessed by comparing the rating of PANSS total score for the same patient assessed by six raters and was analyzed using intraclass correlation coefficients (ICC)52. A high ICC of PANSS total score was achieved (PANSS-ICC > 0.8). The outcome measures were respectively assessed at baseline and 3-month follow-up. The reduction in PANSS total score or its subscores was calculated by the changes in the total score or subscores from baseline to 3-month follow-up.
According to the obesity criteria53, our patients were classified into the low BMI group (BMI ≤ 24 kg/m2) and the high BMI group (BMI > 24 kg/m2). Overweight/obese participants were included in the high BMI group and the remaining participants constituted the low BMI group. Underweight participants also belonged to the low BMI group. The increase in BMI was calculated by the change in the BMI from baseline to 3-month follow-up.
Plasma TAS levels of all patients were measured at baseline and follow-up. Details of the methods were described in the supplementary materials.
## Statistical analysis
For patients who discontinued the medication after 2 months, the last observation carried forward (LOCF) was used. For patients who discontinued the medication before 2 months, their clinical data were not included in the following analyses. Baseline demographic characteristics were compared between the low-BMI group and high-BMI group using analysis of variance (ANOVA) and X2 tests. A repeated-measure ANOVA was used to analyze the different changes in PANSS total score and its subscale scores and TAS levels between the low-BMI group and high-BMI group from baseline to the LOCF endpoint at the third month. Pearson’s product moment correlation was performed to investigate the association between symptom improvements and baseline BMI or changes in BMI in the low-BMI group and high-BMI group, respectively. Linear regression analyses were performed to determine the relative contribution of BMI, TAS levels, and other demographic variables to the variance in clinical symptom improvement after treatment.
The data were analyzed using IBM SPSS software (version 22.0, Chicago, IL), and statistical significance thresholds were determined at P-value < 0.05.
## Demographic data and clinical data in patients at baseline
At baseline, patients were classified into two groups according to their BMI values: the low BMI group ($$n = 207$$) and the high BMI group ($$n = 34$$). There was a significant difference in age between the low BMI and high BMI groups ($p \leq 0.05$) (Table 1). Correlation analysis showed that BMI at baseline was associated with age ($r = 0.25$, $p \leq 0.001$), age at onset ($r = 0.21$, $$p \leq 0.001$$), and negative symptoms in SZ patients (r = −0.13, $$p \leq 0.039$$).Table 1Demographic and clinical characteristics of patients in the high BMI and low BMI groups. VariablePatientsp valueHigh BMI group ($$n = 34$$)Low BMI group ($$n = 207$$)Sex (M/F)$\frac{19}{15109}$/980.85Age (ys)30.8 ± 8.327.1 ± 9.30.03Education (ys)10.0 ± 4.08.9 ± 3.90.15Onset age (ys)28.7 ± 8.725.6 ± 9.20.07TAS (U/ml)194.9 ± 64.3227.9 ± 67.80.008BMI body mass index; ys years.
## BMI and clinical symptom improvements after treatment
After treatment with risperidone for 12 weeks, weight and BMI were significantly increased compared to baseline values (weight: 2.7 ± 3.8 kg; BMI: 1.0 ± 1.3 kg/m2, all $p \leq 0.05$). Moreover, PANSS total score or its subscores were also significantly lower after treatment (all $p \leq 0.01$) (Table 2). Repeated-measure ANOVA showed no significant difference in the improvements in clinical symptoms after treatment with risperidone between high and low BMI subgroups (all $p \leq 0.05$) (Table 2). A significant difference in the increase in weight was observed between low BMI and high BMI groups (3.1 ± 3.8 vs 0.4 ± 3.0, t = −4.2, $p \leq 0.001$).Table 2Comparisons of clinical symptoms before and after 12 weeks of risperidone monotherapy between the low BMI group and high BMI group. Baseline12-week follow-upEffectLow BMI groupHigh BMI groupLow BMI groupHigh BMI groupTime F(p)Group F(p)Interaction F(p)Clinical symptoms P score22.0 ± 6.822.8 ± 5.311.9 ± 4.710.9 ± 3.2298.6 (<0.001)0.01 (0.91)2.0 (0.16) N score18.9 ± 7.218.1 ± 6.214.1 ± 5.614.6 ± 5.644.1 (<0.001)0.01 (0.91)1.1 (0.30) G score35.7 ± 10.337.0 ± 8.924.9 ± 6.024.5 ± 6.8149.4 (<0.001)0.1 (0.75)0.8 (0.36) Total score76.4 ± 18.778.0 ± 14.950.9 ± 13.249.9 ± 12.2207.2 (<0.001)0.02 (0.90)0.5 (0.50) In addition, we found that the baseline BMI was negatively associated with the increase in BMI (r = −0.38, $p \leq 0.001$). Further subgroup analysis by baseline BMI showed that there was no significant association between baseline BMI and improvements in positive and negative symptoms and general psychology in the low BMI group. Whereas in the high BMI group, a significant inverse association between baseline BMI and improvement in negative symptoms was observed (r = −0.44, $$p \leq 0.016$$). Moreover, in the high BMI group, an increase in BMI correlated with improvements in general psychopathology ($r = 0.44$, $$p \leq 0.021$$) and PANSS total score ($r = 0.38$, $$p \leq 0.046$$), but not in the low BMI group (all $p \leq 0.05$).
## TAS levels and clinical symptom improvements after treatment
After treatment, TAS levels were significantly increased relative to baseline levels (215.3 ± 66.6 vs 266.4 ± 105.5, $p \leq 0.05$) (increase: 51.1, $95\%$ confidence interval [CI]: 38.0–64.2). Repeated-measure ANOVA showed that there was no significant difference in the increases in TAS levels after treatment between high and low BMI subgroups ($F = 0.5$, $p \leq 0.05$).
Correlation analyses showed that there was no significant association between the increase in TAS levels and the improvements in clinical symptoms in the low BMI and high BMI groups (all $p \leq 0.05$). However, we found that in the high BMI group, the increases in TAS levels were negatively associated with the increases in BMI (r = −0.46, $$p \leq 0.014$$), but not in the low BMI group ($p \leq 0.05$).
## Interaction effect of TAS levels and BMI on the clinical symptom improvements after treatment
We also found significant associations between TAS levels and BMI in patients at baseline (r = −0.18, $$p \leq 0.006$$) and 12-week follow-up (r = −0.21, $$p \leq 0.004$$). Multiple regression analysis found that an interaction effect of BMI and TAS levels was significantly associated with negative symptoms at baseline ($p \leq 0.05$). Furthermore, multiple regression analysis showed that the TAS levels × BMI interaction was an independent predictor for the improvement in the negative symptoms in the patients with an adjusted R2 = 0.04 (β = −0.15, t = −2.1, $$p \leq 0.035$$), after controlling for age, gender, and education years.
## Discussion
We found that [1] baseline BMI was correlated with the improvement in negative symptoms after 12 weeks of treatment with risperidone only in the high BMI group, [2] an increase in TAS was negatively correlated with an increase in BMI after treatment only in high BMI group, and [3] the TAS × BMI interaction at baseline was an independent predictor of negative symptom improvement.
This study found that baseline BMI was inversely related to negative symptom improvements only in the overweight/obesity MDFE patients with SZ, but not in patients with normal weight. MDFE patients with higher baseline BMI showed less improvement in negative symptoms after risperidone treatment. Negative symptoms of SZ refer to deficits in certain functions common to most people, such as facial expressions, emotional responses, and joy or motivation54. It is a core component of SZ and is linked with poor outcomes in patients with SZ55–57. Negative symptoms remain an unmet treatment challenge. In the CATIE study, one of the largest clinical trials in SZ, negative symptoms were found to be common in SZ patients ($40\%$)58. In addition, in a meta-analysis of placebo controlled clinical trials of atypical antipsychotic drugs ($$n = 7450$$), it was reported that negative symptoms presented after 6-week treatment in $\frac{1}{3}$ of patients actively participating in treatment59. Our findings show that for those patients who were overweight or obese, weight gain needed to be effectively managed to obtain greater negative symptom improvements. The burden of overweight/obesity is both a physical and a psychological problem. An obesity intervention program for MNFE patients with SZ may improve the psychological status of patients. Whereas for the patients with normal weight, fluctuations in body weight may not be correlated with improvements in negative symptoms.
Another finding of this study was that risperidone treatment significantly increased BMI and TAS levels in patients with SZ. In addition, BMI gain after treatment with risperidone was negatively associated with TAS increase in overweight/obese SZ patients. Namely, the more the patient gained weight, the less the increase in TAS levels, which is consistent with our expectations. In line with our findings, animal model studies have also shown that antioxidative enzyme activities were reduced in obese mice following long-term administration of antipsychotics and weight gain60. Many previous clinical studies have investigated the association between overweight or obesity and antioxidants, and recent evidence supports an inverse intrinsic relationship between obesity and antioxidant defense system parameters61–63. In SZ, there is also some evidence supporting that overweight/obesity induced by antipsychotics is related to redox system biomarkers. Furthermore, it has been reported that the combination of antipsychotics with antioxidants, e.g., extraction of ginkgo biloba (EGb47) improved clinical symptoms in SZ patients with higher baseline BMI64. However, we cannot draw a conclusion on the causal relationship between risperidone-induced weight gain and TAS changes based on the current design.
We further found that the interrelationship between baseline BMI and TAS levels was an important predictor of improvements in negative symptoms after treatment with risperidone in SZ. Redox dysregulation, sequent oxidative stress, and metabolic abnormalities have been investigated for many years and are well-established in SZ65–71. Cumulative studies have shown that negative symptoms also were associated with antioxidant activities and overweight/obesity72. Furthermore, substantial evidence supports that risperidone has an impact on antioxidant enzyme activities and affects body weight73,74, which may be further involved in mild to moderate improvement of negative symptoms. Thus, our findings were in line with previously published literature but were incremental with respect to antioxidants and obesity in the mechanism of symptom amelioration with risperidone. Altogether, the most interesting finding of this study was that the interrelationship between BMI and antioxidant defenses at the onset stage of SZ may be a valuable predictor of treatment response to risperidone.
Several limitations should be noted in this study. First, this was a non-experimental study, and as such we were unable to arrive at causal conclusions between overweight/obesity, antioxidant defenses and symptom improvements from prospective observational data using this approach. Second, the follow-up time points for symptom assessment and blood sampling after risperidone monotherapy were limited. Outcome measures were obtained at only two time points (baseline and three months). Third, in this study, the dose of oral risperidone administered to each patient depended on the symptoms judged by the doctor, however, we did not record the detailed dose for each patient. Therefore, we did not add the dose of risperidone as a covariate to eliminate its potential influences on antioxidants and body weight. Forth, the negative symptomatology was not assessed with a specific scale as the Brief Negative Symptom Scale (BNSS) or the Clinical Assessment Interview for Negative Symptoms (CAINS) in this study.
In summary, there was a negative correlation between increases in BMI and TAS levels after risperidone treatment in the high-BMI group. In addition, improvement in negative symptoms in SZ patients was associated with BMI at baseline, suggesting that patients with overweight/obesity during the onset phase of SZ need more attention. For non-obese patients at the onset, there was no correlation between BMI, TAS, and improvements in clinical symptoms. However, considering the small number of SZ patients with overweight or obesity, further studies with a large sample size of SZ patients and a randomized double-blind clinical trial design are necessary to elucidate the mechanisms underlying this interrelationship between obesity, antioxidant defense system, and negative symptoms of SZ patients.
## Supplementary information
SUPPLEMENTAL MATERIAL The online version contains supplementary material available at 10.1038/s41537-023-00346-z.
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|
---
title: Carbon nanotubes as a nitric oxide nano-reservoir improved the controlled release
profile in 3D printed biodegradable vascular grafts
authors:
- Fatemeh Kabirian
- Pieter Baatsen
- Mario Smet
- Amin Shavandi
- Petra Mela
- Ruth Heying
journal: Scientific Reports
year: 2023
pmcid: PMC10033655
doi: 10.1038/s41598-023-31619-3
license: CC BY 4.0
---
# Carbon nanotubes as a nitric oxide nano-reservoir improved the controlled release profile in 3D printed biodegradable vascular grafts
## Abstract
Small diameter vascular grafts (SDVGs) are associated with a high failure rate due to poor endothelialization. The incorporation of a nitric oxide (NO) releasing system improves biocompatibility by using the NO effect to promote endothelial cell (EC) migration and proliferation while preventing bacterial infection. To circumvent the instability of NO donors and to prolong NO releasing, S-nitroso-N-acetyl-d-penicillamine (SNAP) as a NO donor was loaded in multi-walled carbon nanotubes (MWCNTs). Successful loading was confirmed with a maximum SNAP amount of ~ $5\%$ (w/w) by TEM, CHNS analysis and FTIR spectra. SDVGs were 3D printed from polycaprolactone (PCL) and coated with a 1:1 ratio of polyethylene glycol and PCL dopped with different concentrations of SNAP-loaded matrix and combinations of MWCNTs-OH. Coating with $10\%$ (w/w) SNAP-matrix-$10\%$ (w/w) SNAP-MWCNT-OH showed a diminished burst release and 18 days of NO release in the range of 0.5–4 × 10–10 mol cm−2 min−1 similar to the NO release from healthy endothelium. NO-releasing SDVGs were cytocompatible, significantly enhanced EC proliferation and migration and diminished bacterial viability. The newly developed SNAP-loaded MWCNT-OH has a great potential to develop NO releasing biomaterials with a prolonged, controlled NO release promoting in-situ endothelialization and tissue integration in vivo, even as an approach towards personalized medicine.
## Introduction
Nitric oxide (NO) is an endogenous diatomic molecule that regulates many physiological responses such as acceleration of endothelialization and tissue integration, inhibition of platelet adhesion/aggregation and neutralization of pathogenic bacteria1. The combination of NO in the design and development of biomaterials may improve the biological functionality of these materials intended for tissue regeneration and healing applications.
Therefore, an important research goal in biomedical cardiovascular engineering is to design NO-releasing biodegradable small diameter vascular grafts (SDVGs, < 6 mm) that can release NO for several weeks, required for complete endothelialization2, in the physiological range of 0.5 to 4 × 10–10 mol cm−2 min−11.
S-Nitrosothiols (RSNOs) such as S-nitroso-N-acetyl-d-penicillamine (SNAP) are a class of biocompatible NO donors that in response to heat, moisture, or light radiation release NO by cleavage of the S–N bond3. However, S-nitrosothiols have low in vitro or in vivo stability and therefore RSNOs have been incorporated within polymeric reservoirs such as polyurethane, polyethylene glycol (PEG)-polycaprolactone (PCL)4, silicone5,6, silk fibroin7,8.
Multi-walled carbon nanotubes (MWCNTs), are a class of nanoparticles with excellent thermal and chemical stability and a large cavity area suitable for loading of drug molecules including NO donors9. Aggregation due to van der Waals interactions and subsequent reduction of the effective loading capacity is a major limitation of MWCNTs for drug delivery applications. Unmodified MWCNTs form non-dispersible nanotube bundles that induce apoptosis of human cells10. However, MWCNTs functionalized with hydrophilic hydroxyl groups (MWCNT-OH) allow multiple hydrogen bonding and dipole–dipole interactions resulting in reduced aggregation and better dispersibility in polar solvents than non-modified MWCNTs9,11.
In the in-situ tissue engineering approach, an implanted acellular graft should provide mechanical support for tissue regeneration, be degraded gradually while being replaced by native tissue in vivo and ideally contain molecules to encourage host cells’ migration into the graft.
In our previous studies, we showed that incorporation of $10\%$ (w/w) SNAP in a PEG-PCL matrix with a PCL topcoat resulted in a physiological release of NO (14 days) which was associated with the limitation of a high burst release4,12. Although a longer physiological release of NO can potentially support the early adaptation of grafts in vivo, loading of a higher concentration of SNAP in this polymeric matrix leads to a longer burst release. Therefore, the advantages of nanocarriers in controlled release systems as a high carrier capacity have been considered. In this study, we hypothesize that MWCNTs loaded with SNAP and incorporated within the same polymeric matrix and coated in the lumen of 3D printed SDVGs could prolong the NO release in the physiological range without enhancement of the burst release. This NO-releasing SDVGs could then improve endothelial cell migration and proliferation while creating an antibacterial environment. In this work, we designed and developed a novel controlled release system of NO by loading of SNAP in MWCNTs. The effect of parameters as solvent polarity, concentration of MWCNTs and MWCNT functionalization were evaluated by CHNS elemental analysis and Fourier transform infrared spectroscopy (FTIR). The optimized loaded MWCNTs were incorporated into a polymeric matrix and coated on biodegradable 3D printed SDVGs for further characterizations of the NO release profile. Biological studies investigating antibacterial properties, cytocompatibility, endothelial cell (EC) morphology, proliferation and migration and platelet adhesion were performed.
## Results and discussion
In this study, for the first time, the loading of SNAP on MWCNTs-OH and its combination into a polymeric matrix as a coating is reported. Although this innovative NO controlled release system is applicable for different medical devices, we elaborated it in SDVGs (Fig. 1). SDVGs display a high incidence of failure due to poor endothelialization and a subsequent risk for bacterial infection4. Therefore, an NO-releasing coating can promote the next generation of SDVGs with improved clinical performance. Figure 1Schematic representation of the preparation of NO-releasing vascular grafts (SDVGs) and their biological properties in vitro. The upper panel shows (a,b) the loading of SNAP in MWCNTs-OH, (c) coating of them inside the lumen of 3D printed SDVG and (d) NO release from the coated SDVG. The lower panel indicates the biological properties of the NO releasing graft such as increase in EC proliferation (e) and migration (f) and antibacterial properties (g).
## 3D-printing and coating of SDVGs
3D printed conduits fabricated from PCL, using an FFF printer were shown in Fig. 2a. The SEM images reveal the homogeneous layer morphology of the layer-by-layer printed grafts (Fig. 2b). The intraluminal morphology changed after coating with $10\%$ (w/w) SNAP-matrix-$10\%$ (w/w) SNAP-MWCNTs-OH to a porous structure (Fig. 2c) due to the evaporation of solvent, remaining the voids which can encourage cell infiltration while still the outer layer is non-porous to prevent blood leakage. The advantages of the 3D printing approach such as the potential for preparation of customized implants, reproducibility, accuracy and cost-effectiveness has been previously applied for fabrication of SDVGs from polylactic acid (PLA)12, PCL13 and poly(propylene fumarate)14.Figure 2Morphology visualization of SDVGs with and without the coating. Macroscopic image of 3D printed tubes (a) and SEM images of 3D printed tube (b) and the coated tube with $10\%$ (w/w) SNAP-matrix—$10\%$ (w/w) SNAP-MWCNTs-OH (c).
## SNAP loading of MWCNTs-OH
The results demonstrate that with 25 mg MWCNT or MWCNTs-OH and 10 mg SNAP, MWCNTs-OH can store a higher amount of SNAP (5.00 ± $0.67\%$N, Supplementary Table) compared to non-functionalized MWCNTs (2.07 ± $0.84\%$N). A plausible explanation for this result is the lower tendency of aggregation in MWCNTs-OH which can increase the loading capacity. Therefore, MWCNTs-OH was used for further investigations in the current study.
The effect of solvent polarity was investigated by SNAP loading in polar and non-polar solvents. The amount of SNAP loading in MWCNTs-OH in polar solvents were significantly lower (0.37 ± 0.01 and 0.30 ± $0.05\%$N for THF and MeOH) than in non-polar solvent (5.00 ± $0.68\%$N for toluene). This finding as expected is due to the polarity of SNAP molecule and a subsequent great affinity for polar solvents. Therefore, in the non-polar solvents such as toluene, SNAP molecules have a greater affinity to MWCNTs-OH than solvent resulting in a higher loading of SNAP in MWCNTs-OH.
The SNAP loading capacity of MWCNTs-OH was also studied by loading of a constant amount of SNAP (10 mg) in various amounts of MWCNTs-OH (25, 50 and 100 mg). The results indicate that with 10 mg SNAP and 25 mg MWCNTs-OH, the maximum amount of $5\%$ (w/w) SNAP would be loaded. This condition was selected for all the follow-up experiments.
To explain the loading process of SNAP in MWCNTs-OH, as it was shown by Khalifi et al., there is an energy barrier at the edges of the CNT, which hinders the adsorption of drug molecule. Once the drug molecule passes through this energy barrier, it displaces toward the middle parts, as the adsorption energy at the middle of the CNT is much lower than the edges15. Stirring the drug molecules with MWCNTs-OH can overcome the required energy barrier at the nanotube edges. It will also leads to a slower release profile, as it allows for a stable adsorption of drug molecules by the middle parts of the MWCNTs16.
Various nanoparticles are used to develop NO controlled release systems. For example, N-diazeniumdiolates as a NO donor was encapsulated in the liposomes and NO release was prolonged up to 24 h17. In another study, SNAP was encapsulated in silk fibroin nanoparticles and NO release was prolonged over 24 h8. In this study, MWCNTs-OH were used as nanocarriers for SNAP to develop the controlled release system of NO.
## Characterization of the SNAP loaded MWCNTs
The FTIR spectrum of pure MWCNTs-OH, SNAP loaded MWCNTs-OH and SNAP are presented in Fig. 3a. According to these results, while there is no significant peak at the MWCNTs-OH spectrum, SNAP and SNAP loaded MWCNTs-OH represent similar peaks attributed to N–O stretching at 1497 cm−1, N–H stretching at 3350 cm−1 and N–S stretching at 665 cm−118,19. These observations confirm the loading of SNAP in MWCNTs-OH. For the pure MWCNTs-OH, there are small peaks between 3500 to 3800 cm−1 which could be due to the presence of an OH group on the surface of nanotubes. Similar peaks are observed in SNAP loaded MWCNTs-OH, which reveals that OH bond on nanotubes didn't change by loading of SNAP. Therefore, it could be concluded that loading of SNAP in MWCNTs-OH are due to physical bonds rather than a chemical reaction. Figure 3Characterization of ~ 5 w/w % SNAP loaded in MWCNTs. ( a) FTIR spectra of SNAP, SNAP loaded MWCNTs-OH and MWCNTs-OH. TEM inspection of (b) MWCNTs and (c) SNAP loaded MWCNTs. White arrows indicate loaded SNAP. Scale bars: 100 nm.
TEM images show the intertwined, long and cylindrical morphology of MWCNTs with a visible wall structure (Fig. 3b). In contrast to this non-loaded MWCNTs, the entrapment of some particles (SNAP crystals) inside the lumen of MWCNTs is visible in Fig. 3c, which is consistent with previous studies20. In addition, it can be concluded that the loaded SNAP are stored inside the lumen of the MWCNTs since MWCNTs were washed after loading with SNAP to remove any SNAP residue between MWCNTs.
## Improved NO controlled release from 3D printed SDVGs
Previous studies indicated that doping of $10\%$ (w/w) SNAP into the polymeric matrices such as silicone-polycarbonate-urethane is the optimum percentage allowing a slow dissolution of SNAP and NO release21. Doping of more than 5 and less than $10\%$ (w/w) SNAP in the polymeric matrix slows down the release profile by storage of SNAP in its crystalline form stabilized by a strong hydrogen bonding22.
It has also been demonstrated that loading of 15 and $20\%$ (w/w) SNAP in a PEG-PCL matrix results in an increased initial burst release4. Therefore, in this study the NO release profile from 10, 20 and $30\%$ (w/w) of SNAP doped into a PEG-PCL matrix with a PCL topcoat was measured. In this polymeric matrix, PEG facilitates the water absorption for the dissolution of SNAP while the hydrophobic PCL portion prevents the rapid degradation of PEG. Together with the PCL topcoat, this composite coating can regulate the NO release kinetics.
The NO flux from the samples with 10, 20 and $30\%$ (w/w) SNAP-matrix, $10\%$ (w/w) SNAP-MWCNTs-OH and $10\%$ (w/w) SNAP-matrix—$10\%$ (w/w) SNAP-MWCNTs-OH are presented in Fig. 4.Figure 4NO release profile from 3D printed grafts after coating. NO flux in PBS with 100 µM EDTA at different time points from $10\%$, $20\%$ and $30\%$ (w/w) SNAP- matrix, $10\%$ (w/w) SNAP-MWCNT-OH and $10\%$ (w/w) SNAP-matrix-$10\%$ (w/w) SNAP-MWCNT-OH. Part a shows the NO flux after 1 h, part b for day 1–27. The zone between black dashes represents the physiological range of NO flux (0.5–4 × 10–10 mol cm−2 min−1). Data are expressed as mean ± SD ($$n = 6$$). * $P \leq 0.05.$ ** $P \leq 0.01.$ *** $P \leq 0.001.$ **** $P \leq 0.0001.$
Figure 4a,b show the NO release from the $10\%$ (w/w) SNAP-matrix which is in the desired physiological range from 24 h until day 13. Since the prolongation of a physiological NO release, even for a few days, can be crucial for the graft function after implantation, the extension of the NO release without prolongation and/or enhancement of a burst release is an important challenge. The increase of SNAP in the polymeric matrix to $20\%$ and $30\%$ (w/w) prolonged the physiological NO release until day 15 and 16 in our experiments (Fig. 4b), but significantly increased the initial burst release after 1 h (Fig. 4a). Increasing the amount of SNAP in the matrix enhanced the NO release with only significantly different values in the beginning of the release period. The $10\%$ (w/w) SNAP-matrix condition shows a significantly lower NO release compared to $20\%$ (w/w) SNAP-matrix at 1 h and on day 1 and 3. The NO release from the $20\%$ (w/w) SNAP-matrix condition was significantly lower on day 1, 2 and 6 when compared to the $30\%$ (w/w) SNAP-matrix condition. These results indicate that enhancement of the SNAP concentration in the matrix prolongs the physiological NO release for a few days, but is unfortunately associated with a significant increase of the burst release.
Based on these findings and to improve the release profile, the NO release from $10\%$ (w/w) SNAP-MWCNTs-OH doped into PEG-PCL polymeric matrix was studied. The results demonstrate a significant reduction of burst release compared with $10\%$ (w/w) SNAP-matrix after 1 h even though the same amount of SNAP was loaded (Fig. 4a) indicating a controlling role of nanocarriers in the release kinetic. These results are consistent with previous studies showing a slowed down drug release using CNTs as a carrier23,24. The release of rifampicin has been prolonged by modifying the surface of a TiAl6V4 titanium alloy disc by MWCNTs, 10–200 nm in diameter, via plasma enhanced chemical vapor deposition. The antibiotic loading capacity was maximized when MWCNTs were impregnated with rifampicin showing a slow release for more than 5 days25. In another approach, resveratrol was loaded in CNTs and then incorporated to vascular grafts which showed a prolongation of drug release from 8 up to 30 days compared to the same grafts without CNTs23.
Although a significant reduction of the initial burst release could be achieved in our study by loading SNAP in MWCNTs-OH, the NO release profile remained still below the therapeutic range. Based on the results of the $20\%$ and $30\%$ (w/w) SNAP-matrix conditions it can be concluded that SNAP concentrations of higher than $10\%$ (w/w) SNAP-matrix enhance and prolong the burst release. Values for $10\%$ (w/w) SNAP-matrix remained constant in the combination experiments. Indeed, the results of $10\%$ (w/w) SNAP-matrix in combination with $10\%$ (w/w) SNAP-MWCNTs-OH demonstrated a controlled NO release in the physiological range for 18 days with a significant reduction of the initial burst release. The addition of $10\%$ (w/w) SNAP-MWCNT-OH to the $10\%$ (w/w) SNAP-matrix condition significantly enhanced the NO flux at 1 h and in the early period between day 1 to 7 and from day 13 to 20 (except day 14 and 16) compared to the $10\%$ (w/w) SNAP-matrix condition. Loading a total amount of $20\%$ (w/w) SNAP in matrix and MWCNTs-OH ($10\%$ (w/w) SNAP-matrix—$10\%$ (w/w) SNAP-MWCNT-OH) showed a remarkable improved NO release profile compared to $20\%$ (w/w) SNAP in the matrix alone. A significant higher cumulative NO flux in the physiological range from day 2 until 18 was seen when $10\%$ of the SNAP was loaded on MWCNT-OH. Comparing single days, values were significantly different at 1 h, from day 1 until 7 and on day 15 and 18 between these 2 conditions. The values for NO flux remained above the physiological range until day 18 in the $10\%$ (w/w) SNAP-matrix—$10\%$ (w/w) SNAP-MWCNT-OH condition. It can be concluded that MWCNTs-OH led to an improvement of the NO release in the physiological range. This can be seen as a proof of concept for the role of MWCNTs-OH in improvement of release profile with the same SNAP content (Fig. 4).
Currently, there are a few studies incorporating the NO-releasing system into 3D printed medical devices. In these studies, medical devices have been coated with polymeric matrices or impregnated with the NO donor4,26,27. In the current study, the innovative approach refers to the incorporation of the nanocarrier-based NO releasing system into a biodegradable 3D printed graft. This state-of-the-art nanoparticle-based release system has a high potential for optimization compared to the coating and impregnation method.
## Cytocompatibility of NO releasing vascular grafts
The cytocompatibility of NO-releasing ($10\%$ (w/w) SNAP-matrix—$10\%$ (w/w) SNAP-MWCNTs-OH) and control grafts (non-loaded matrix—non-loaded MWCNTs-OH) was evaluated by live/dead assay after direct contact with human umbilical vein endothelial cells (HUVECs). ECs presented as a layer of live cells with only single dead cells (orange arrows) in all conditions suggesting that both NO release and the presence of MWCNTs-OH in the coating layer are cytocompatible and no further residue from the solvent (toluene) present in the final grafts. As shown in Fig. 5a, HUVECs proliferated in all conditions after 3 days. This result is in line with previous studies showing no cytotoxicity for physiological NO release12,28 and CNTs29,30. For example, cardiac fibroblasts demonstrated normal growth and metabolic activity after 3 days on incubation with CNTs. FTIR analysis showed that in the NO-releasing samples, 3 representative peaks for SNAP have been visualized. They could be attributed to N–O stretching at 1497 cm−1, N–H stretching at 3350 cm−1 and N–S stretching at 665 cm−1 are visible while these peaks are absent in the control spectrum (Fig. 5b).Figure 5EC viability and characteristics of NO-releasing and control samples. ( a) Live/dead fluorescence images of HUVECs incubated in direct contact with NO-releasing grafts, control grafts or culture medium. The upper row represents the cellular viability after 24 h and the lower row displays the viability after 3 days of incubation. Live cells are stained green by calcein-AM and dead cells in red by ethidium homodimer-1, respectively. Orange arrows indicate dead cells. ( b) FTIR spectra of NO-releasing ($10\%$ (w/w) SNAP-matrix—$10\%$ (w/w) SNAP-MWCNTs-OH) and control samples (non-loaded matrix—non-loaded MWCNTs-OH) which used for biological experiments. Scale bar = 200 µm.
## EC proliferation, morphology and migration in response to NO releasing grafts
In situ endothelialization with EC coverage of the implanted surface is a key factor for the clinical success of SDVGs and their integration into the native tissue. NO accelerates EC proliferation and migration31,32. Therefore, graft surfaces with controlled NO release have the advantage of promoting initial EC migration after implantation while repopulated ECs will gradually release NO themselves. To prove this, the cellular proliferation and migration in presence of the NO-releasing grafts, control grafts and culture medium were investigated.
The results of the Ki 67 staining (Fig. 6a) indicate a significant enhancement ($P \leq 0.001$) of EC proliferation in the NO releasing condition (48.91 ± 1.54) compared to control grafts (30.96 ± 0.75) and culture medium (32.66 ± 0.94). The MTT assay shows that the NO release from the grafts significantly enhanced the proliferation of cells compared to the control grafts, which showed similar values as incubation with culture medium (Fig. 6b). The results of cell proliferation are in accordance with the Live/Dead assay results and confirm the cytocompatibility of the samples. Figure 6Evaluation of the NO-releasing effect on HUVEC proliferation, morphology, and migration. ( a,b) Proliferation. ( a) Immunofluorescence Ki67 staining (pink) and nucleus DAPI staining (blue) ECs incubated with NO releasing graft, control graft and culture medium after 7 days. ( b) The obtained absorbances by MTT assay indicated a significant increase in the proliferation of HUVECs in response to NO-releasing samples while the proliferation rate was not significantly different in the presence of control samples or culture medium. ( c) Morphology. Immunofluorescent imaging of HUVECs after 1 and 7 days of cultivation in direct contact with NO-releasing grafts, control grafts and culture medium. F-actin was stained in red (phalloidin) and nuclei were stained in blue (DAPI). ( d) Migration. Scratch assay of HUVECs in presence of NO-releasing graft, control graft and culture medium. The monolayer of HUVECs was scratched with a 1000 µL pipette tip indicating orange dots at the beginning of the experiment (upper row) and after 20 h (lower row) of incubation. Data are shown as mean ± SD ($$n = 6$$). * $P \leq 0.05.$
To study the effect of NO-releasing SDVGs on cell morphology, HUVECs were incubated with the NO-releasing and control grafts for 7 days. The cells showed a spreading out morphology representing the proper condition for cell growth in the NO-releasing and the control conditions. Moreover, the same cell size and nuclei size in all conditions confirm a normal growth of HUVECs (Fig. 6c).
Migration of ECs from the adjacent vessel towards the site of implantation is crucial in healing of vascular grafts in situ. Figure 6d represents the scratch cell-free area at the beginning of the experiment and the migration of cells towards the wounded site after 20 h. The images display a significant enhancement ($P \leq 0.001$) of HUVEC migration after incubation with NO-releasing samples (79.43 ± 6.17). However, in presence of control grafts there is no significant difference in the motility of HUVECs (49.06 ± 5.25) compared to the migration rate in culture medium (51.47 ± 4.2) confirming the effect of NO release on EC migration. A plausible explanation for this finding is the influence of exogenous NO in the physiological range on the actin filament elongation. It has been reported that in response to NO release the vasodilator-stimulated phosphoprotein (VASP) protein concentration in the filopodia and VASP phosphorylation is increased33.
From the results on cell proliferation and migration it can be concluded that NO releasing samples can potentially accelerate the EC migration from the neighbor vessel towards the wounded site of the graft. This potential benefit in endothelial coverage of the implant in vivo could effectively result in an increased healing and tissue integration, which consequently improves the clinical success.
## Antibacterial properties of NO-releasing grafts
One of the physiological roles of NO is the antimicrobial properties by deamination of DNA, lipid oxygenation in the bacterial matrix and denaturation of bacterial enzymes34,35. SDVGs manifest a high incidence of failure due to a high risk of bacterial infection which can be potentially reduced by NO. As a proof of concept, antibacterial properties of the NO-releasing and control grafts were studied using the most common Gram-positive and Gram-negative pathogens responsible for medical device associated infections. The results demonstrate that in presence of NO-releasing samples, the viability of both strains was significantly reduced after 5 h and 24 h of incubation. For S. epidermidis and E. coli, all bacteria were eradicated after 24 h and 5 h, respectively, while the control strains proliferated until the steady-state level (Fig. 7). However, for S. aureus the number of bacteria was reduced significantly from 107 CFU mL−1 to 10 CFU mL−1. This slightly higher resistance of S. aureus to NO release is explained by its higher pathogenicity in agreement with prior results4.Figure 7Bacterial viability. Bacterial growth after incubation with NO-releasing and control grafts of Gram positive strains (S. aureus and S. epidermidis) and the Gram negative strain (E. coli). The data were collected at different time points of incubation in PBS with $10\%$ culture media. Data are shown as mean ± SD ($$n = 6$$). **** $P \leq 0.0001.$
In conclusion, this study demonstrated that SNAP was successfully loaded on MWCNTs-OH confirmed by stretching bonds in the FTIR analysis. The incorporation of loaded MWCNTs-OH into a 3D printed SDVGs enabled a significantly improved physiological NO release from the nano-composite coating with reduced initial burst release. According to the in vitro results, this coating potentially accelerates re-endothelialization and tissue regeneration in vivo while reducing the risk of implant failure by inhibiting the risk of infection. This controlled NO release system presents new opportunities for a broad range of implantable medical devices and tissue-engineered scaffolds and forms the basis for the development of future controlled NO release systems.
## Materials
N-Acetyl-d-penicillamine (NAP), sodium nitrate, vanadium (III) chloride (VCl3), sulfanilamide, N-(1-naphthyl) ethylene diamine dihydrochloride (NEDD), PCL (Mn = 80,000), BioUltra grade PEG (Mn = 4000), dimethyl sulfoxide (DMSO), and thiazolyl blue tetrazolium were purchased from Sigma-Aldrich (St. Louis, MO). Poly caprolactone (PCL) filament (Mn ≈ 100,000 g/mol, white, diameter 1.75 mm) under the commercial name Resomer® C was supplied by Evonik Corp. (USA). Hydroxyl (OH) functionalized multiwalled carbon nanotubes (MWCNTs, OD: 8–15 nm, L: 10–50 µm) were purchased from IoLiTec (Germany). Sulfuric acid (H2SO4), hydrochloric acid (HCl), ortho-phosphoric acid (H3PO4), methanol (MeOH), tetrahydrofuran (THF), toluene, tryptic soy broth (TSB), Luria broth (LB), and sodium nitrite (NaNO2) were obtained from Merck (Darmstadt, Germany). Endothelial cell basal medium, FCS and supplement pack endothelial cell obtained from PromoCell (Heidelberg, Germany). Calcium and magnesium free Dulbecco’s phosphate buffered saline (PBS, pH 7.4), Dulbecco’s modified Eagle’s medium (DMED), fetal bovine serum (FBS) and penicillin/streptomycin were purchased from Gibco (Scotland, UK). Live/Dead viability/cytotoxicity kit, Alexa Fluor™ 594 Phalloidin, and DAPI were supplied by Thermo Fisher, Invitrogen™ and BD Pharmingen, respectively.
## 3D-printing of SDVGs
The stereolithography (STL) file of the tubular SDVG were designed in Autodesk fusion 360 software to be 10 mm in height and 4.5 mm in internal diameter with 0.1 mm wall thickness. Next, the STL file was converted to a G-code by slicing the CAD model in the Cura 4.6.1 software (Ultimaker, Netherlands) and imported to the Fused Filament Fabrication (FFF) 3D printer, Zaribo 420 MK3s (Caribou3d Research & Development, Germany). The PCL filament was extruded through a 80 °C nozzle with 100 μm internal diameter.
## Synthesis of SNAP
SNAP was synthesized according to a previously established protocol12,28. Briefly, 1 g of NAP was dissolved in 25 mL of methanol. Then, 15 mL of 1 M HCl, 500 μL of H2SO4 ($98\%$), and 724.5 mg of NaNO2 were added to the NAP solution. The solution was stirred in the dark for 15 min and kept on ice without stirring for 45 min for precipitation of SNAP green crystals. Next, the SNAP crystals were filtered and rinsed with ice-cooled DI water and dried by lyophilization. The final dried crystals were kept in the darkness at − 20 °C.
## Effect of solvent polarity and MWCNT concentration
To study the effect of solvent types and MWCNTs concentration on the SNAP loading capacity, 10 mg of SNAP was incubated with THF, MeOH and toluene and 25, 50 and 100 mg of MWCNTs. The SNAP loaded MWCNTs were washed three times with iced cooled DI water to remove the excess of non-loaded SNAP and then lyophilized. Finally, the SNAP loading capacity of MWCNTs was compared with MWCNTs-OH.
## Characterization of SNAP loaded MWCNTs
To investigate the loading of SNAP in the MWCNTs FTIR was performed. Before the analysis, SNAP loaded MWCNTs-OH were rinsed with ice cooled milli-Q water three times and lyophilized, to remove any non-loaded SNAP. The FTIR spectra of SNAP, SNAP loaded WMCNTs and pure MWCNTs were measured from 400 to 4000 cm−1 with a Bruker Vertex 70 spectrometer (Bruker Optics, Evere, Belgium). Since MWCNTs-OH have no nitrogen and SNAP molecules have two nitrogen atoms in their structure, CHNS elemental analysis by an EA1108 Elemental Analyzer (Carlo Erba Instruments, Italy) was performed in this study to verify the SNAP loading in MWCNTs-OH. To study SNAP loaded MWCNTs and MWCNTs by transmission electron microscopy (TEM), the nanotubes were attached to (300 mesh TEM) support grids (Ted Pella, PA, US) by first adhering a tiny piece (~ 1 mm) of a carbon SEM-support sticker to the grid, and subsequently touching the dry nanotubes with the carbon sticker. By this approach, when observing nanotubes in the TEM, the imaged parts of the nanotubes were actually ‘free floating’ in the column of the TEM. In this way nanotubes were imaged with a transmission electron microscope (JEOL Ltd., Tokyo, Japan), operated at an accelerating voltage of 80 kV and equipped with a 11MPxl *Quemesa camera* (EMSIS GmbH, Münster, Germany). Images were taken at pixel sizes of 0.61 and 0.27 nm.
## Coating of 3D printed SDVGs
Different coating solutions were prepared by 10, 20 or 30 (w/w)% SNAP-loaded matrix, 10 (w/w) % SNAP-loaded MWCNTs-OH and $10\%$ (w/w) SNAP-matrix—$10\%$ (w/w) SNAP-MWCNT-OH. According to the results of our previous study, the matrix was made from a 1:1 mass ratio of a PEG-PCL solution which was covered with a layer of PCL topcoat12. The control grafts were coated with the same coating solution with non-loaded MWCNTs-OH together with a PCL topcoat layer. For coating, 200 mL of coating solution was injected inside the rotating grafts in two steps. After solvent evaporation from the coating layer, tubes were dip coated in a PCL solution of 100 mg dissolved PCL in 1 mL toluene to form a topcoat layer.
## FTIR and SEM characterization of SDVGs
FTIR spectra of selected NO releasing ($10\%$ (w/w) SNAP-matrix—$10\%$ (w/w) SNAP-MWCNTs-OH) and control (non-loaded matrix—non-loaded MWCNTs-OH) grafts for biological studies was measured from 400 to 4000 cm−1 with a Bruker Vertex 70 spectrometer (Bruker Optics, Evere, Belgium). Coated and non-coated 3D printed tubes were mounted on SEM-supporting stubs with silver paint, and sputter-coated with 10 nm chromium with a Leica ACE600 coating machine (Leica-Microsystems GmbH, Vienna, Austria). The specimens’ morphology was visualized using a SE-detector of a Zeiss Sigma scanning electron microscope (Carl Zeiss Microscopy GmbH, Jena, Germany) at the acceleration voltage of 2 kV.
## NO release measurement
The grafts were immersed in 1 mL PBS with 100 µM EDTA as releasing medium and incubated at 37 °C in darkness ($$n = 6$$). At each time-point of 1 h and every 24 h, the solution was collected for NO quantification and replaced with 1 mL of fresh media. The modified Griess assay was used to measure the NO release from the samples12,36. Briefly, 50 µL sample and 50 µL of 1:1 mixture of 2 g sulfanilamide in 100 mL DI water with 3.44 mL H3PO4 ($85\%$) and 0.2 g NEDD in 100 mL DI water were mixed, incubated at RT for 15 min. Next, by further incubation of the samples with 50 µL of the VCl3 solution, 400 mg VCl3 in 50 mL 1 M HCl, the nitrate content was reduced to nitrite. The absorbance was measured at 550 nm and the NO concentration was calculated using a nitrite calibration curve.
## Human umbilical vein endothelial cell (HUVEC) culture
HUVECs (C-12208, PromoCell, Germany) originating from a cell line in passage (P) 4–6 were cultivated in EC growth medium MV2 with $1\%$ penicillin–streptomycin solution in humidified air containing $5\%$ CO2 at 37 °C. The culture medium was replaced with fresh medium every two days and cells were monitored for proliferation and absence of contamination every day.
## Cytotoxicity
To visualize the viability of HUVECs after incubation with the NO-releasing and control samples, a LIVE/DEAD Viability/Cytotoxicity Kit was used (Invitrogen, USA). This staining visualizes simultaneously vital cells (green) with calcein-AM and dead cells (red) by ethidium homodimer-1, indicating interacellular esterase activity and loss of plasma membrane integrity, respectively. 1 × 104 cells were seeded on gelatin coated glass coverslips ($D = 10$ mm) and cultivated in 24-well plates in the presence of the $\frac{1}{20}$ of NO-releasing graft, control graft and culture medium. At both time points, cells were stained with 1 µL calcein-AM and 2 µL ethidium homodimer-1 in 1 mL PBS after washing. After 15 min incubation at 37 °C, the cells were observed with an Axiovert 200 M microscope (Zeiss, Oberkochen, Germany). All images (15–20 per sample) were acquired using the Zeiss proprietary software Axiovision (Rel. 4.8.2).
## Cell proliferation
To study the proliferation of cells in contact with NO-releasing and control grafts, the Ki67 staining and MTT assay were performed.
1 × 104 cells at P5 were seeded on gelatin coated glass coverslips ($D = 10$ mm) and incubated in culture medium with either $\frac{1}{20}$ of NO releasing or $\frac{1}{20}$ of control grafts or without sample. Cells were incubated in 24 well plates at 37 °C and the culture medium was changed every two days. After 7 days, cells were rinsed with PBS, fixed with $4\%$ paraformaldehyde (10 min, RT), washed with PBS and permeabilized with $0.3\%$ triton X-100 (20 min, RT). After blocking with $3\%$ bovine serum albumin (BSA) solution (1 h, RT), Ki67 antibody (Anti-Mo/Rt Ki-67, Invitrogen, USA) ($\frac{1}{100}$ diluted in BSA) was incubated with cells for 1 h at RT. Subsequently, cells were washed with PBS and incubated with the secondary antibody (Alexa flour 568 goat anti-rat IgG, Invitrogen, USA) ($\frac{1}{400}$ diluted in BSA) for 1 h at RT. After PBS washing, cells were incubated with DAPI (5 min, RT), washed with PBS and mounted. Images were obtained at RT with an fluorescence Axiovert 200 M microscope (Zeiss, Oberkochen, Germany) with LD Plan-Neofluar objective lenses at 20 × (NA 0.4) magnification equipped with a camera (AxioCam MRc 5; Carl Zeiss). The acquisition was performed with the AxioVision software version 4.8 (Carl Zeiss).
For the MTT assay, HUVECs at P5 were seeded in 12 well plates with the density of 6 × 104 cells per well. Cells were cultivated in 1 mL medium, medium in presence of the $\frac{1}{20}$ of NO releasing sample or medium and the control specimen. Cell population was assessed at 24 h and 7 days by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide in PBS. At each time point, the culture medium was replaced with 300 µL of 5 mg/mL MTT solution and incubated for 4 h at 37 °C in darkness. After aspirating the MTT solution, 150 µL of DMSO were added to each well and shaken for 30 min. The absorbance of the solution was measured at 570 nm and plotted.
## Cell morphology
1 × 105 HUVECs were seeded on gelatin coated glass coverslips and incubated with $\frac{1}{20}$ of the NO-releasing and control grafts. At days 1 and 7, cells were rinsed with PBS, fixed with $4\%$ paraformaldehyde, washed with PBS and permeabilized with $0.5\%$ Triton X-100 in PBS. Then, specimens were rinsed with $3\%$ BSA solution and incubated in Alexa Fluor™ 594 Phalloidin solution (25 µL/1 mL PBS) for 20 min and DAPI solution (25 µL/1 mL PBS) for 5 min to stain cytoskeleton and nucleus, respectively. The stained cytoskeleton in red and nucleus in blue were visualized with an Axiovert 200 M microscope (Zeiss, Oberkochen, Germany) equipped with a camera (AxioCam MRc 5; Carl Zeiss). Image acquisition was performed with the AxioVision software version 4.8 (Carl Zeiss).
## Cell migration
As encouragement of host EC migration into the implant surface can enhance the clinical success, the wound healing assay was performed to study the effect of NO-release on the EC migration rate. HUVECs were seeded in 24-well plates ($$n = 12$$) at a density of 1 × 104 cells/well and incubated at 37 °C. After confluence, a scratch was made with a 1000 μL pipette tip, the media was aspirated, cells were rinsed twice with PBS and fed with fresh medium in the presence of $\frac{1}{20}$ of the NO-releasing graft, control graft and culture medium. The scratch, as a model for the graft’s edges, was monitored with an Axiovert 25 inverted microscope (Carl Zeiss NV-SA, Belgium) at the beginning and after 20 h of scarification.
## In-vitro antimicrobial investigation
To study the antibacterial properties of the specimens, single colonies of Gram-positive (S. aureus, ATCC 8325-4 and S. epidermidis, ATCC 149900) and Gram-negative (E. coli, ATCC 25922) bacteria were inoculated in TSB and LB, respectively. After an overnight culture at 37 °C, the bacteria were washed 3 times with PBS and the concentration was adjusted to 1 × 107 CFU mL−1 by OD measurement (600 nm). Each sample was incubated in 2 mL of PBS + $10\%$ growth media under shaking at 37 °C. At each time points of 0, 5 h and 24 h, 100 µL from each NO-releasing sample or control sample containing tube of bacteria was collected, serially diluted and plated for counting of colony forming units (CFUs).
## Statistical analysis
All data were expressed as mean ± standard deviation (SD). One-way or two-way analysis of variance (ANOVA) was performed followed by Tukey post-tests. The t-test was used in addition when comparing conditions. The NO release over time in the physiological range was evaluated by comparing the values of each condition on each day and by calculating the area under the curve. $P \leq 0.05$ was considered significant.
## Supplementary Information
Supplementary Table 1. The online version contains supplementary material available at 10.1038/s41598-023-31619-3.
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|
---
title: Berberine regulates glucose metabolism in largemouth bass by modulating intestinal
microbiota
authors:
- Yun Xia
- Hui-Ci Yang
- Kai Zhang
- Jing-Jing Tian
- Zhi-Fei Li
- Er-Meng Yu
- Hong-Yan Li
- Wang-Bao Gong
- Wen-Ping Xie
- Guang-Jun Wang
- Jun Xie
journal: Frontiers in Physiology
year: 2023
pmcid: PMC10033662
doi: 10.3389/fphys.2023.1147001
license: CC BY 4.0
---
# Berberine regulates glucose metabolism in largemouth bass by modulating intestinal microbiota
## Abstract
This study examined the role of intestinal microbiota in berberine (BBR)-mediated glucose (GLU) metabolism regulation in largemouth bass. Four groups of largemouth bass (133.7 ± 1.43 g) were fed with control diet, BBR (1 g/kg feed) supplemented diet, antibiotic (ATB, 0.9 g/kg feed) supplemented diet and BBR + ATB (1g/kg feed +0.9 g/kg feed) supplemented diet for 50 days. BBR improved growth, decreased the hepatosomatic and visceral weight indices, significantly downregulated the serum total cholesterol and GLU levels, and significantly upregulated the serum total bile acid (TBA) levels. The hepatic hexokinase, pyruvate kinase, GLU-6-phosphatase and glutamic oxalacetic transaminase activities in the largemouth bass were significantly upregulated when compared with those in the control group. The ATB group exhibited significantly decreased final bodyweight, weight gain, specific growth rates and serum TBA levels, and significantly increased hepatosomatic and viscera weight indices, hepatic phosphoenolpyruvate carboxykinase, phosphofructokinase, and pyruvate carboxylase activities, and serum GLU levels. Meanwhile, the BBR + ATB group exhibited significantly decreased final weight, weight gain and specific growth rates, and TBA levels and significantly increased hepatosomatic and viscera weight indices and GLU levels. High-throughput sequencing revealed that compared with those in the control group, the Chao one index and Bacteroidota contents were significantly upregulated and the Firmicutes contents were downregulated in the BBR group. Additionally, the Shannon and Simpson indices and Bacteroidota levels were significantly downregulated, whereas the Firmicutes levels were significantly upregulated in ATB and BBR + ATB groups. The results of in-vitro culture of intestinal microbiota revealed that BBR significantly increased the number of culturable bacteria. The characteristic bacterium in the BBR group was Enterobacter cloacae. Biochemical identification analysis revealed that E. cloacae metabolizes carbohydrates. The size and degree of vacuolation of the hepatocytes in the control, ATB, and ATB + BBR groups were higher than those in the BBR group. Additionally, BBR decreased the number of nuclei at the edges and the distribution of lipids in the liver tissue. Collectively, BBR reduced the blood GLU level and improved GLU metabolism in largemouth bass. Comparative analysis of experiments with ATB and BBR supplementation revealed that BBR regulated GLU metabolism in largemouth bass by modulating intestinal microbiota.
## 1 Introduction
Largemouth bass (Micropterus salmoides; also known as California bass), a carnivorous fish belonging to the order Perciformes and the family Centrarchidae, is a freshwater fish native to North America. Additionally, largemouth bass has a high nutritional value with high protein and fat contents and strong disease resistance properties, which contribute to their high economic value. Furthermore, largemouth bass is a freshwater aquaculture species with development potential. Recently, the method for largemouth bass culture has changed from traditional feeding of chilled bait to feeding with feed. Carbohydrates in feeds are one of the main energy sources for animals and are inexpensive and widely available. The supplementation of feeds with the right proportion of carbohydrates can promote the growth of aquatic animals, save protein, and reduce feed costs. However, the supplementation of feed with high levels of carbohydrates can markedly increase the blood sugar content of fish, leading to metabolic disorders, liver hypertrophy and necrosis, reduction in fish feeding activity, growth inhibition, and death, especially in carnivorous fish, such as largemouth bass. Largemouth bass has a low capacity for sugar loading and removal (Ma et al., 2019) and can exhibit physiological growth if the feed sugar level is maintained at approximately $10\%$ (Xu et al., 2016). However, increasing the feed sugar levels will lead to the development of a severe “hepatobiliary syndrome,” which is characterised by persistent hyperglycaemia, hepatomegaly, hepatic glycogen accumulation, slowed growth rates, and reduced feed efficiency, and can cause mass mortality in severe cases, in largemouth bass (Lin et al., 2018).
Berberine (BBR), the active ingredient of the Chinese herbal medicine *Coptis chinensis* Franch., is an isoquinoline alkaloid found in various medicinal plants. In clinical medicine, BBR has been widely used for the treatment of diabetes. Some studies have demonstrated significant regulatory effects of BBR on glucolipid metabolism (Li et al., 2019). The use of BBR as a functional feed additive in aquaculture can alleviate oxidative stress caused by high-fat feeds in blunt snout bream and exert regulatory effects on lipid metabolism (Lu et al., 2017; Zhou et al., 2019). The addition of BBR to feed significantly reduced serum glucose (GLU), total cholesterol (TC), and triglyceride (TG) levels in grass carp and largemouth bass (Pan et al., 2019; Xia et al., 2022a). Pharmacological studies have reported that the bioavailability of BBR is poor with a large proportion (>$90\%$) retained in the intestine after oral administration, resulting in low blood levels in humans and mammals (Ma et al., 2013; Tan et al., 2013). Mammalian studies have demonstrated that BBR regulates host blood GLU levels by modulating intestinal microbiota (Wang et al., 2018; Guo et al., 2019). Previously, we reported that BBR intake altered the gut microbial composition of largemouth bass and hypothesised that the blood GLU-lowering effect of BBR in largemouth bass may be related to the regulation of intestinal microbiota composition (Xia et al., 2022b).
Based on the results of the previous study, which indicated that dietary supplementation with BBR at 1 g/kg was beneficial for promoting growth and health in largemouth bass (Xia et al., 2022b). BBR (1 g/kg feed) was supplemented to the feed of largemouth bass in this study. Largemouth bass was divided into the following four groups by adding BBR (1 g/kg feed) and/or compound antibiotics (ATBs; metronidazole (200 mg/kg feed), gentamicin sulfate (200 mg/kg feed), neomycin sulfate (200 mg/kg feed), ampicillin sodium (200 mg/kg feed) and vancomycin (100 mg/kg feed) (Jin, 2020) to the feed: control, BBR, ATB, and ATB + BBR groups. This study aimed to determine if BBR downregulated blood GLU in largemouth bass only by regulating intestinal flora. The direct effect of BBR on intestinal microbiota was also verified using in-vitro culture.
## 2 Materials and methods
All animal studies in this paper were performed according to the relevant national and international guidelines. All animal care and experimental procedures were approved by the Chinese Academy of Fishery Science of the Pearl River Fisheries Research Institute (LAEC-PRFRI-2022-03-01).
## 2.1 Experimental materials and experimental design
Largemouth bass was obtained from “Superior Bass No. Three”, which was bred by the Pearl River Fisheries Research Institute. The fish were domesticated and fed on a standardised diet for 2 weeks before the experiment. Before the start of the experiment, the fish were allowed to fast for 24 h and weighed after anesthetization with MS-222 (Sigma; 0.1 g/kg bodyweight). Largemouth bass ($$n = 360$$; initial weight = 133.7 ± 1.43 g) were randomly assigned to 12 cage nets (capacity: 280L, 30 fish per tank) in a concrete pond and classified into the following four dietary treatment groups (Table 1): control, fed on a standardised commercial diet; BBR, fed on BBR (1 g/kg feed, Solarbio)-supplemented feed; mixed ATB group, fed on mixed ATB (0.9 g/kg feed: metronidazole (200 mg/kg feed, Sangon), gentamicin sulfate (200 mg/kg feed, Solarbio), neomycin sulfate (200 mg/kg feed, Solarbio), ampicillin sodium (200 mg/kg feed, Solarbio) and vancomycin (100 mg/kg feed, Macklin))-supplemented feed; ATB + BBR group, fed on BBR (1 g/kg feed) and mixed ATB (0.9 g/kg feed)-supplemented feed. Largemouth bass in each dietary treatment group was randomly assigned to three cage nets. Feeding was performed twice a day (8:00–9:00 a.m.; 5:00–6:00 p.m.) with full feeding. All uneaten feed pellets were collected, dried, and weighed at 65°C. Aerated tap water served as the source of water for aquaculture. The experimental conditions were as follows: water temperature, 17C–23°C, dissolved oxygen, 6–9 mg/L; total phosphorus, 0.19 ± 0.07 mg/L; total nitrogen, 2.45 ± 0.39 mg/L; ammonia nitrogen, 0.23 ± 0.06 mg/L; nitrite nitrogen, 0.52 ± 0.08 mg/L; nitrate nitrogen, 0.56 ± 0.12 mg/L; pH, approximately 8.53; culture cycle, 50 days; photoperiod, natural.
**TABLE 1**
| Dietary ingredient (%) | Control | BBR | ATB | BBR + ATB |
| --- | --- | --- | --- | --- |
| Fish meal (Peru; 67 cp%) | 250.0 | 250.0 | 250.0 | 250.0 |
| Fish meal (63 cp%) | 220.0 | 220.0 | 220.0 | 220.0 |
| Chicken meal (United States; 65 cp%) | 140.0 | 140.0 | 140.0 | 140.0 |
| Soy protein concentrate | 95.0 | 95.0 | 95.0 | 95.0 |
| Soybean meal (46 cp%) | 40.0 | 40.0 | 40.0 | 40.0 |
| Fermented soybean meal | 50.0 | 50.0 | 50.0 | 50.0 |
| High-gluten flour (14 cp%) | 50.0 | 49.0 | 49.1 | 48.1 |
| Cassava starch | 50.0 | 50.0 | 50.0 | 50.0 |
| Soybean oil | 60.0 | 60.0 | 60.0 | 60.0 |
| Largemouth Bass Premix 1 (2%) | 20.0 | 20.0 | 20.0 | 20.0 |
| l-Lysine hydrochloride (98%) | 3.0 | 3.0 | 3.0 | 3.0 |
| dl-Methionine (99%) | 2.0 | 2.0 | 2.0 | 2.0 |
| Ca(H2 PO4)2 | 15.0 | 15.0 | 15.0 | 15.0 |
| Choline chloride (50%) | 0.5 | 0.5 | 0.5 | 0.5 |
| BBR (98%, Ruitaibio) | 0.0 | 1.0 | 0.0 | 1.0 |
| ATB | 0.0 | 0.0 | 0.9 | 0.9 |
| Total | 1000.0 | 1000.0 | 1000.0 | 1000.0 |
| Crude protein (%) | 51.43 | 51.43 | 51.42 | 51.4 |
| Crude lipid (%) | 12.33 | 12.33 | 12.33 | 12.33 |
| Calcium (%) | 3.08 | 3.08 | 3.08 | 3.08 |
| Phosphorus (%) | 2.15 | 2.15 | 2.15 | 2.15 |
| Lysine (%) | 3.73 | 3.73 | 3.73 | 3.73 |
| Methionine (%) | 1.24 | 1.24 | 1.24 | 1.24 |
## 2.2 Sample collection
At the end of the 50-day feeding period, largemouth bass was allowed to fast for 24 h before sampling. From each group, 12 fish were randomly selected and anesthetised with MS-222. The bodyweight and length of the fish were measured. The blood samples were quickly collected from the tail vein into Eppendorf tubes, incubated overnight at 4°C, and centrifuged at 4°C and 3,000 g for 10 min. The supernatant was transferred into new Eppendorf tubes and stored at −80°C for subsequent analysis. Specimens were dissected on a sterile bench and weighed. The visceral weight and hepatopancreas weight were recorded. Approximately 0.4 cm × 0.4 cm × 0.4 cm of the liver tissue mass from each fish was soaked in $4\%$ formaldehyde solution and sectioned. The sections were subjected to haematoxylin and eosin (HE) staining and Oil Red O staining. Tissues, such as the remaining liver, midgut and hindgut, and all intestinal contents were collected, snap-frozen in liquid nitrogen, and stored at −80°C for subsequent testing and analysis.
Growth indicator calculation formulas: WGR=m2–m1/m1×$100\%$ SG=Ln m2–Ln m1/t × $100\%$ Wv=mv/mb×$100\%$ Wh=mh/mb×$100\%$ where WGR is the weight gain rate (%), SG is the specific growth rate (%/d), *Wv is* the viscera weight index (%), *Wh is* the hepatosomatic index (%), m1 is the initial mean weight of fish (g), m2 is the final mean weight of fish (g), t is the number of feeding days, mh is the final liver weight per fish (g), mv is final viscera weight per fish (g), mb is the final bodyweight per fish (g).
## 2.3 Measurement of serum and liver biochemical parameters
The serum levels of TC, total bile acid (TBA), GLU, TG, high-density lipoprotein-cholesterol (HDL-C), low-density lipoprotein-cholesterol (LDL-C), glutamic oxalacetic transaminase (GOT), alkaline phosphatase (AKP), acid phosphatase (ACP) and liver GOT were measured using Nanjing Jiancheng Biotechnic Institute kits. Enzyme-linked immunosorbent assay kits (Shanghai Enzyme-linked Biotechnology Co., Ltd., China) were used to determine the liver GOT, phosphoenolpyruvate carboxykinase (PEPCK), phosphofructokinase (PFK), hexokinase (HK), pyruvate kinase (PK), pyruvate carboxylase (PC), and GLU-6-phosphatase (G6P) activities in largemouth bass.
## 2.4 In-vitro culture of intestinal microorganisms
In-vitro culture of intestinal microorganisms of largemouth bass was performed according to a previously reported method with some modifications (Krajmalnik-Brown et al., 2012; Hanning and Diaz-Sanchez 2015; Liu et al., 2019; Aranda-Díaz et al., 2022). Briefly, the gut contents of six largemouth bass were collected, and the faecal slurry was prepared by mixing fresh faecal samples with autoclaved saline into a $10\%$ (w/v) suspension. The faecal slurry was mixed with autoclaved basal nutrient growth medium and preincubated for 60 min in an anaerobic incubator (Shanghai Yuejin Medical Optical Instrument Factory, China) at 37°C under N2 gas. The basal nutrient medium (pH 7.0) composition was as follows: peptone (2 g/L), soybean peptone (3.0 g/L), yeast extract (2 g/L), beef meal (2.2 g/L), digested serum powder (13.5 g/L), beef liver extract (1.2 g/L), GLU (3.0 g/L), KH2PO3 (2.5 g/L), sodium chloride (3.0 g/L), soluble starch (5.0 g/L), L-cysteine (0.3 g/L), sodium thioglycolate (0.3 g/L), vitamin K1 ($0.1\%$), haeme chloride (5 mg/ml), and distilled water.
Different concentrations of BBR (10 μL) and mixed ATB (10 μL) were added to the intestinal bacterial cultures (990 μL). Saline (10 μL) was used as a negative control (control). The preincubated intestinal microbes were divided into the following nine groups: control (saline), B1 (final concentration of BBR in the culture system = 10 μg/ml), B2 (final concentration of BBR in the culture system = 20 μg/ml), A1 (final concentration of mixed ATB in the culture system = 1 μg/ml), A2 (final concentration of mixed ATB in the culture system = 10 μg/ml), B1 + A1, B1 + A2, B2 + A1, and B2 + A2 groups. The groups were laid in a grid of three each and incubated in 6-well plates (Krajmalnik-Brown et al., 2012; Hanning and Diaz-Sanchez 2015; Aranda-Díaz et al., 2022) at 37°C for 24 h. Next, 100 μL of the bacterial suspension was diluted by a factor of 103, 104, and 105. The diluted broth was spread on a solid medium and incubated anaerobically at 37°C for 24 h. Specific single colonies from each treatment group were picked for amplification and sequencing using the universal primers 27F and 1492R (Dees and Ghiorse 2001; Kim et al., 2011).
Additionally, the bacterial broth was subjected to low-speed centrifugation, and most of the medium was removed. The samples were rinsed 1–3 times with sterile phosphate-buffered saline (pH = 7.4). Finally, the collected microbial samples were placed in 1.5 ml EP tubes, sealed with sealing film, stored at −80°C, and subjected to DNA extraction for subsequent analysis.
## 2.5 Intestinal microbiota DNA extraction, amplification, and sequencing
Bacterial DNA was extracted from 200 mg of intestinal contents of each largemouth bass, as well as from isolated gut microbial culture broth using a bacterial DNA extraction kit (Omega Bio-Kit, Norcross, United States). The intestinal contents of largemouth bass were subjected to 16S rRNA gene (V3 + V4 region) sequencing using high-throughput sequencing technology (Novogene Co., Beijing, China). For Solexa polymerase chain reaction (PCR), a 20-µL reaction mixture comprising 5 µL of PCR purification product, 2.5 µL of 2 μM forward primers, 2.5 µL of 2 μM reverse primers, and 10 µL of 2× Q5 HF MM was prepared. The PCR conditions were as follows: 98°C for 30 s, followed by 10 cycles of 98°C for 10 s, 65°C for 30 s, and 72°C for 30 s, and 72°C for 5 min. PCR products were mixed with the same volume of 1× loading buffer and subjected to agarose gel electrophoresis using a $1.5\%$ gel. Amplicons with a size of 400–450 bp were selected for the next step. PCR products were mixed at an equal density ratio and purified using the OMEGA Gel Extraction Kit (Thermo Fisher Scientific, Waltham, MA, United States). Fragments were sequenced after gel cutting and recovery. The sequence data are deposited in the NCBI Sequence Read Archive (SRA) database under the bioproject id PRJNA931008 (https://www.ncbi.nlm.nih.gov/sra/PRJNA931008).
## 2.6 Biochemical identification analysis of isolated cultures of characteristic bacteria
Based on the sequencing results of the isolated culture of the characteristic bacteria in Section 2.4, the identification kit (colorimetric method) for Enterobacteriaceae and other non-fastidious Gram-negative bacilli (API 20E kit, Shanghai bioMérieux Co., Ltd.) was used. The target bacteria were inoculated into the API 20E reagent strips according to the instructions, and the results were observed at 37°C for 18–24 h. The functions of bacteria were identified by colour reactions between bacteria and different substrates or colour changes after adding additional reagents. The identification results were obtained by checking the search table.
## 2.7 Data analysis
The experimental results are shown as the mean ± SEM. One-way ANOVA was used to statistically analyze the data for the differently treated fish groups with SPSS 25.0 software, and Duncan’s multiple range tests were applied as well to detect any anticipated significant differences between treated fish groups at a significant level of $95\%$. The Spearman correlation coefficient of intestinal bacteria and physiological and biochemical indexes was calculated in R 3.6.3 ($p \leq 0.05$: statistically significant within $95\%$ confidence intervals).
## 3.1 Largemouth bass growth indicators
As shown in Table 2, compared with that in the control group, the weight gain rate of largemouth bass was significantly higher in the BBR group ($p \leq 0.05$). Meanwhile, compared with those in the control group, the final body weight and specific growth rate were higher and the viscera weight and hepatosomatic indices were lower in the BBR group, but the changes were not significant ($p \leq 0.05$). Compared with that in the ATB and BBR + ATB groups, the final bodyweight, the weight gain and specific growth rates of largemouth bass were significantly higher in the BBR group ($p \leq 0.05$). Furthermore, compared with those in the control group, the final bodyweight and weight gain and specific growth rates were significantly lower ($p \leq 0.05$) and the viscera weight and hepatosomatic indices were significantly higher in the ATB group ($p \leq 0.05$). The final bodyweight and weight gain and specific growth rates were non-significantly downregulated in the BBR + ATB group ($p \leq 0.05$), whereas the viscera weight and hepatosomatic indices were non-significantly upregulated ($p \leq 0.05$).
**TABLE 2**
| Groups | Control | BBR | ATB | BBR + ATB |
| --- | --- | --- | --- | --- |
| Initial bodyweight (g) | 131.56 ± 9.53 | 134.73 ± 10.37 | 135.7 ± 8.42 | 134.8 ± 11.30 |
| Final body weight (g) | 287.93 ± 6.42bc | 306.05 ± 6.94c | 257.46 ± 10.74a | 278.1 ± 7.87ab |
| Relative weight gain (%) | 118.87 ± 4.88b | 127.15 ± 5.15c | 89.72 ± 7.92a | 106.31 ± 5.84ab |
| Specific growth rate (%/d) | 1.56 ± 0.04bc | 1.64 ± 0.05c | 1.26 ± 0.09a | 1.44 ± 0.06b |
| Viscera weight index | 7.53 ± 0.20ab | 7.08 ± 0.19a | 8.29 ± 0.32c | 7.81 ± 0.21bc |
| Hepatosomatic index | 2.60 ± 0.10ab | 2.32 ± 0.08a | 3.43 ± 0.19c | 2.91 ± 0.18b |
## 3.2 Serum biochemical indicators and enzyme activities of largemouth bass
As shown in Table 3, the serum TC and GLU levels in the BBR group were significantly lower than those in the control, ATB, and BBR + ATB groups ($p \leq 0.05$). Meanwhile, the serum TBA levels in the BBR group were significantly higher than those in the control, ATB, and BBR + ATB groups ($p \leq 0.05$). The serum TG, LDL-C, GOT, AKP, and ACP levels in the BBR group were non-significantly lower than those in the control group ($p \leq 0.05$). Compared with those in the ATB and the BBR + ATB groups, the serum LDL-C, GOT, AKP, and ACP levels were significantly lower in the BBR group ($p \leq 0.05$). The serum GLU, GOT, AKP, and ACP levels in the ATB group were significantly higher than those in the control group ($p \leq 0.05$). Compared with those in the control group, the serum TBA levels were significantly lower in the ATB group ($p \leq 0.05$). The serum TC, TG, LDL-C, and HDL-C levels were not significantly different between the ATB and control groups ($p \leq 0.05$). Compared with those in the control group, the serum GLU and LDL-C levels were significantly higher in the BBR + ATB group ($p \leq 0.05$). The HDL-C and TBA levels in the BBR + ATB group were significantly lower than those in the control group ($p \leq 0.05$). The TC, TG, GOT, AKP and ACP levels were not significantly different between the BBR + ATB and control groups ($p \leq 0.05$).
**TABLE 3**
| Groups | Control | BBR | ATB | BBR + ATB |
| --- | --- | --- | --- | --- |
| TC (mmol/L) | 8.75 ± 0.56b | 6.13 ± 0.21a | 10.36 ± 1.16b | 9.73 ± 0.93b |
| TG (mmol/L) | 6.38 ± 0.60ab | 5.80 ± 0.55a | 7.68 ± 0.49b | 7.10 ± 0.39ab |
| GLU (mmol/L) | 40.16 ± 1.94b | 27.27 ± 2.11a | 53.51 ± 1.63c | 48.70 ± 1.57c |
| HDL-C (mmol/L) | 2.57 ± 0.13bc | 3.19 ± 0.41c | 1.96 ± 0.15ab | 1.67 ± 0.12a |
| LDL-C (mmol/L) | 9.55 ± 0.38ab | 7.50 ± 0.47a | 12.09 ± 1.64b | 13.89 ± 0.68c |
| TBA (µmol/L) | 9.20 ± 0.34b | 13.49 ± 0.59c | 5.64 ± 0.23a | 6.15 ± 0.52a |
| GOT (U/g protein) | 16.44 ± 1.36ab | 13.54 ± 1.59a | 26.68 ± 2.08c | 21.40 ± 1.18b |
| AKP (U/100 ml) | 5.61 ± 0.63ab | 4.37 ± 0.21a | 7.21 ± 0.52c | 6.63 ± 0.50bc |
| ACP (U/100 ml) | 7.04 ± 0.67ab | 6.37 ± 0.43a | 10.13 ± 0.50c | 8.33 ± 0.52b |
## 3.3 Activity of liver-related enzymes in largemouth bass
As shown in Table 4, the hepatic HK, PK, G6P and GOT activities in the BBR group were significantly higher than those in the control, ATB, and BBR + ATB groups ($p \leq 0.05$). The PEPCK, PFK and PC activities in the BBR group were not significantly different from those in the control group ($p \leq 0.05$) but were significantly lower than those in the ATB group. Meanwhile, the PEPCK and PC activities in the BBR group were significantly lower than those in the BBR + ATB group. Compared with those in the control group, the PEPCK, PFK and PC activities were significantly higher in the ATB group ($p \leq 0.05$). However, the HK, PK, G6P and GOT activities were not significantly different between the ATB and control groups ($p \leq 0.05$). Additionally, the PEPCK, PFK, HK, PK, PC, G6P and GOT activities were not significantly different between the BBR + ATB and control groups ($p \leq 0.05$).
**TABLE 4**
| Groups | Control | BBR | ATB | BBR + ATB |
| --- | --- | --- | --- | --- |
| PEPCK (ng/L) | 471.40 ± 18.18ab | 426.24 ± 24.10a | 750.35 ± 38.16c | 518.32 ± 16.13b |
| PFK (ng/L) | 21.73 ± 2.10a | 19.42 ± 0.82a | 26.89 ± 1.37b | 20.23 ± 1.18a |
| HK (ng/L) | 4.41 ± 0.40a | 5.70 ± 0.17b | 4.29 ± 0.48a | 4.39 ± 0.31a |
| PK (ng/L) | 30.43 ± 1.96a | 46.61 ± 4.43b | 30.18 ± 1.41a | 32.88 ± 1.70a |
| PC (ng/L) | 16.80 ± 0.71ab | 15.83 ± 0.49a | 21.11 ± 0.95c | 18.09 ± 0.50b |
| G6P (ng/L) | 26.15 ± 2.27a | 32.13 ± 1.05b | 22.54 ± 1.34a | 25.97 ± 0.71a |
| GOT (ng/L) | 2.10 ± 0.21a | 5.19 ± 1.11b | 1.53 ± 0.18a | 1.36 ± 0.17a |
## 3.4 Analysis of intestinal microbiota α-diversity
High-throughput sequencing analysis revealed at least 53251.33 ± 8208.51 high-quality bacterial 16S rRNA gene reads for each of the 24 samples in the four groups. After clustering with $97\%$ similarity, at least 403.67 ± 187.11 operational taxonomic units were obtained for each group. The coverage index of all samples was higher than 0.99, indicating that the sequences covered almost all types and that the sequencing results were reliable and representative. The α-diversity of intestinal microbiota of each group of samples is shown in Table 5. Compared with that in the control, ATB, and BBR + ATB groups, the Chao one index was higher in the BBR group, indicating that BBR increased species community richness. In contrast, the Shannon and Simpson indices in the ATB and BBR + ATB groups were lower than those in the control group, indicating that BBR decreased community diversity.
**TABLE 5**
| Groups | Control | BBR | ATB | BBR + ATB |
| --- | --- | --- | --- | --- |
| Chao1 | 718.66 ± 165.07 | 908.00 ± 211.50 | 406.47 ± 75.64 | 844.03 ± 189.53 |
| Dominance | 0.14 ± 0.04ab | 0.09 ± 0.02a | 0.29 ± 0.10ab | 0.33 ± 0.09b |
| Observed otus | 713.50 ± 164.08 | 902.33 ± 209.73 | 403.67 ± 76.39 | 842.67 ± 189.80 |
| Pielou e | 0.56 ± 0.05ab | 0.64 ± 0.05b | 0.47 ± 0.08ab | 0.43 ± 0.07a |
| Shannon | 5.32 ± 0.67 | 6.19 ± 0.58 | 4.09 ± 0.72 | 4.22 ± 0.79 |
| Simpson | 0.86 ± 0.04ab | 0.91 ± 0.02b | 0.71 ± 0.10ab | 0.67 ± 0.09a |
| Reads | 53251.33 ± 3,351.11 | 57233.33 ± 4810.38 | 54559.00 ± 4146.23 | 65159.33 ± 2815.17 |
## 3.5 Changes in the intestinal microbiota community of largemouth bass
As shown in Figure 1, at the phylum level, the major microbial composition of each treatment group included Firmicutes, Cyanobacteria, Proteobacteria, Bacteroidota and Actinobacteria. Compared with that in the control group ($17.93\%$), the intestinal Firmicutes content in the BBR group ($7.26\%$) was significantly lower, but was significantly higher in the ATB ($48.14\%$) and ATB + BBR group ($34.17\%$). Compared with that in the control group ($6.12\%$), the Bacteroidota content was significantly higher in the BBR group ($19.48\%$) and significantly lower in the ATB ($1.06\%$) and ATB + BBR groups ($0.76\%$). The Proteobacteria contents in the BBR ($25.62\%$), ATB ($19.16\%$), and ATB + BBR ($6.43\%$) groups were significantly lower than that in the control group ($36.85\%$). The composition of Cyanobacteria was rich in all treatment groups (control: $26.83\%$; BBR: $24.81\%$; ATB: $21.29\%$; ATB + BBR: $39.82\%$).
**FIGURE 1:** *Composition of gut microbiota at the phylum level in largemouth bass. BBR, berberine; ATB, antibiotics.*
As shown in Figure 2, at the genus level, the Mycoplasma content in the ATB ($5.85\%$) and ATB + BBR groups ($24.78\%$) was higher than that in the control group ($0.1\%$). Compared with that in the control group ($28.54\%$), the Chloroplast content was lower in the BBR group ($19.00\%$) and higher in the ATB ($37.28\%$) and ATB + BBR groups ($31.61\%$). The *Pseudomonas content* in the BBR ($8.69\%$), ATB ($10.55\%$), and ATB + BBR groups ($6.20\%$) was lower than that in the control group ($18.50\%$). The Candidatus Paenicardinium content in the BBR group ($1.37\%$) was higher than that in the control group ($0.35\%$). However, Candidatus Paenicardinium was not detected in the ATB and ATB + BBR groups. Compared with that in the control group ($2.05\%$), the Bacteroides content was higher in the BBR group ($5.80\%$) and lower in the ATB ($0.17\%$) and ATB + BBR groups ($0.16\%$). The contents of Faecalibacterium, Lactobacillus, and Lactococcus in the control group were $0.53\%$, $0.65\%$, and $0.39\%$, respectively, while those in the BBR group were $3.62\%$, $2.56\%$, and $1.23\%$, respectively. The *Streptococcus content* in the BBR group ($0.42\%$) was lower than that in the control ($1.14\%$), ATB ($5.84\%$), and ATB + BBR groups ($3.63\%$).
**FIGURE 2:** *Composition of gut microbiota at the genus level in largemouth bass. BBR, berberine; ATB, antibiotics.*
The Spearman correlation coefficient of bacteria and physiological and biochemical indexes indicated that, at genus level, the Pseudomonas, Candidatus Paenicardinium, Bacteroides and Faecalibacterium were positively correlated with liver glycolytic rate-limiting enzymes (PK, G6P, HK) and serum HDL-C; Mycoplasma, Chloroplast, Streptococcus, Lactobacillus and Lactococcus are positively correlated with the liver rate limiting enzymes (PFK, PEPCK, PC) related to gluconeogenesis, serum LDL-C and GLU (Figure 3).
**FIGURE 3:** *Spearman correlation coefficient heatmap of bacteria and physiological and biochemical indexes.*
## 3.6 Histomorphological observations of the liver of largemouth bass
As shown in Figure 4, HE staining analysis of largemouth bass liver revealed that compared with those in the BBR group, the hepatocytes exhibited larger size, increased vacuolization, and enhanced number of nuclei at the edge in the control, ATB, and ATB + BBR groups. Oil Red O staining of liver revealed increased lipid distribution in the liver tissue of the control, ATB, and ATB + BBR groups (Figure 5).
**FIGURE 4:** *Hematoxylin and eosin staining of largemouth bass liver (×10). The red arrows in the picture represent the nucleus pushed to the edge of the cell; the black arrows represent the disappearance of the cell nucleus and cytoplasmic vacuolization. BBR, berberine; ATB, antibiotics.* **FIGURE 5:** *Oil red staining of largemouth bass liver (×10). The red colour in the picture represents lipid drops. BBR, berberine; ATB, antibiotics.*
## 3.7 Results of in-vitro culture of intestinal microbiota
The isolated culture of intestinal microbiota of largemouth bass is shown in Figure 6. The number of intestinal flora in the control, B1, and B2 groups was 4.48 × 108, 1.93 × 109, and 2.28 × 109 CFU, respectively. Compared with that in the control group, the number of intestinal microbiota significantly increased in the BBR group (low concentration B1, high concentration B2) and decreased in the mixed ATB group (low concentration A1, high concentration A2). The number of intestinal microbiota decreased significantly and was almost 0. Additionally, the number of intestinal microbes was almost 0 in groups treated with both mixed ATB and BBR (A1 + B1, A1 + B2, A2 + B1, and A2 + B2).
**FIGURE 6:** *In-vitro culture of gut microbiota of largemouth bass. CONTROL: control; B1: low concentration of berberine; B2: high concentration of berberine; A1: low concentration of mixed antibiotics; A2: high concentration of mixed antibiotics.*
The single colony sequencing results revealed that the characteristic bacteria in the control group were Bacillus sp., Aeromonas hydrophila, and Citrobacter freundii. The dominant characteristic bacteria in the B group were Enterobacter cloacae.
As shown in Table 6, the API test results of the characteristic strains of group B were as follows: positive reactions with arginine (ADH), ornithine (ODC), citric acid (CIT), pyruvate (VP), GLU, rhamnose (RHA), sucrose (SAC), mydrose (MEL), and arabinose (ARA); negative reactions with tryptophan deamination, gelatin, and cytochrome oxidase (OX) reactions.
**TABLE 6**
| Test | Active ingredient | Reactions/Enzymes | Result |
| --- | --- | --- | --- |
| ONPG | o-Nitrophenyl-galactoside | beta-galactosidase | Positive |
| ADH | arginine | Arginine dihydrolase | Positive |
| LDC | lysine (Lys), an essential amino acid | lysine decarboxylase | Negative |
| ODC | ornithine (Ornithine), an essential amino acid | Ornithine decarboxylation | Positive |
| CIT | sodium citrate | Citric acid utilization | Positive |
| H2S | sodium thiosulfate Na2 S2 O3 | H2 S generated | Negative |
| URE | urea (NH2)2CO | urease (enzyme) | Negative |
| TDA | tryptophan (Trp), an essential amino acid | tryptophan deaminase (TSLD) | Negative |
| IND | tryptophan (Trp), an essential amino acid | indole production | Negative |
| VP | pyruvate | 3-Hydroxybutanone produces acetylmethylmethanol | Positive |
| GEL | Kohn Gelatin | gelatinase | Negative |
| GLU | glucose | Fermentation/oxidation | Positive |
| MAN | mannitol | Fermentation/oxidation | Positive |
| INO | inositol | Fermentation/oxidation | Weakly positive |
| SOR | sorbitol C6H14O6 (sugar substitute and mild laxative) | Fermentation/oxidation | Positive |
| RHA | rhamnose | Fermentation/oxidation | Positive |
| SAC | fructose | Fermentation/oxidation | Positive |
| MEL | disaccharide | Fermentation/oxidation | Positive |
| AMY | amygdalin | Fermentation/oxidation | Positive |
| ARA | arabinose (type of sugar) | Fermentation/oxidation | Positive |
| OX | Tetramethyl-p-phenylenediamine vitamin C | cytochrome oxidase | Negative |
## 4 Discussion
In this study, the supplementation of BBR improved the growth of largemouth bass and decreased the viscera weight and hepatosomatic indices. However, the supplementation of ATB exerted contrasting effects, and the effect of ATB + BBR was similar to that of ATB alone. A previous study (Zhang et al., 2013) demonstrated that the moderate addition of BBR improved the growth of largemouth bass, which can be attributed to the ability of BBR to regulate GLU metabolism in largemouth bass and promote intestinal health. Additionally, the supplementation of ATB can adversely affect growth and gut health (Limbu et al., 2018; Sun et al., 2020) and reduce the diversity of gut microbial community composition (Ramirez et al., 2020). In this study, when ATB and BBR were simultaneously added, the BBR could not mitigate the adverse effects of ATB on largemouth bass. This indicates that the use of ATB may disrupt the key pathway through which BBR exerts its regulatory effects. Further studies are needed to elucidate the exact mechanisms.
GOT and GPT are commonly known as transaminases, which are indicators of liver function and are mainly expressed in hepatocytes. Hepatocyte damage induced by inflammation, necrosis, and toxicity promotes the release of GOT and GPT into the blood (Wróblewski 1958). Compared with those in the control group, the serum GOT level were significantly downregulated in the BBR group and significantly upregulated in the ATB group at the end of the experimental period. This indicates that the use of ATB promoted liver damage in largemouth bass. BBR effectively regulated the health of the liver. HE and Oil Red O staining of liver tissues revealed that hepatocytes in the BBR group were small with decreased vacuolisation, centred nuclei, and decreased lipid distribution in the liver tissue. The liver was evaluated by paying special attention to hypertrophy and nuclei position of the hepatocytes (Betancor et al., 2017). Previous studies indicated that feeds high-carbohydrates levels caused liver damage of fish, which were manifested in increased vacuolization and movement of nuclei to the margins (Magalheãs et al., 2021; Zhao et al., 2022). This suggests that the use of BBR is beneficial to the liver health of largemouth bass and that antibiotics are detrimental to liver health. These findings are consistent with those of Björnsson and Serranti et al., who reported the adverse effects of ATB on the liver (Serranti et al., 2013; Björnsson 2017).
Enzymes are specific and catalyze specific reactions, the increase or decrease of enzyme activity, which we believe may indirectly prove that this reaction is promoted or inhibited (Koutedakis et al., 1993; Mahboob et al., 2005), and the change of GLU level further proves that enzyme activities may be appropriate. The serum GLU, TC, TG, HDL-C, and LDL-C levels are key indicators of glucolipid metabolism (Turnbaugh et al., 2006; Luo et al., 2017). The studies demonstrated that GLU metabolism is closely related and complementary to lipid metabolism and positively correlated with adiposity, fasting insulin, and TG and negatively correlated with HDL-C (Lawlor et al., 2012; Khan et al., 2018; Dahal et al., 2022). Compared with those in the control group, the serum TC, TG, and LDL-C levels were downregulated and the serum TBA and HDL-C levels were significantly upregulated in the BBR group. This may indicate that BBR positively regulated glucolipid metabolism in largemouth bass. The TC, TG, LDL-C, and HDL-C levels were not significantly different between the ATB and control groups. The serum TBA level in the ATB group were significantly lower than those in the control group, indicating that the addition of ATB may not promote serum GLU metabolism and lipid metabolism in largemouth bass. In contrast, the simultaneous addition of ATB and BBR significantly decreased the HDL-C and TBA levels but did not affect the TC, TG, GOT, AKP, and ACP levels in largemouth bass. So it is possible to draw a conclusion that the addition of ATB suppressed the positive regulatory effects of BBR on glucolipid metabolism. This further demonstrated that the use of ATB inhibited the key pathway involved in the regulatory effects of BBR.
The serum GLU level in the BBR group were significantly lower than those in the control group, indicating that the addition of BBR decreased the blood GLU level in largemouth bass, which is consistent with the results of Pan et al. [ 2019]. The hepatic activities of HK, PK, and G6P, which are the three key enzymes of glycolysis, in the BBR group were significantly higher than those in the control group. This may indicate that the addition of BBR promoted glycolysis in largemouth bass, which is consistent with the suppressive effects of BBR on blood GLU levels in largemouth bass. The serum GLU level in the ATB group were significantly higher than those in the control group. The HK, PK, and G6P activities were not significantly different between the ATB and control groups. Previous studies have reported that ATB upregulates blood GLU in the organism, which may be associated with a reduction in the overall diversity of the intestinal microbial community (Carvalho et al., 2012; Vrieze et al., 2014; Zarrinpar et al., 2018). This is consistent with the lack of changes in the levels of TC, TG, LDL-C, and HDL-C in largemouth bass after the addition of ATB. This study most likely demonstrated that the addition of both ATB and BBR significantly increased blood GLU level in largemouth bass but did not significantly affect the HK, PK, and G6P activities when compared with the control. The use of ATB may have disrupted key targets of BBR, which may be related to the community diversity of intestinal microbes.
The addition of ATB disrupted the intestinal microbial community, decreased bacterial diversity, and induced dysbiosis of the intestinal flora (Modi et al., 2014; Fröhlich et al., 2016). Compared with that in the control group, the intestinal microbial community richness was upregulated in the BBR group and the community diversity was downregulated in the ATB and BBR + ATB groups. These results were consistent with those of Modi et al. [ 2014] and Fröhlich et al. [ 2016]. The findings of the ATB + BBR group indicated that BBR did not mitigate the adverse effects of ATB. Previous studies demonstrated that the role of BBR in regulating host glycolipid metabolism is related to the interaction between BBR and intestinal microflora (Chen et al., 2022; Gong et al., 2022). By changing the diversity and composition of intestinal microflora and then changing its metabolites, which could further achieve the goal of regulating downstream key targets and signaling pathways (for example the FXR-FGF $\frac{15}{19}$, P-AKT SER473,GLP-1 and insulin secretion, etc.), the BBR ultimately improved host metabolism (He et al., 2022). The bactericidal action of ATB in this research destroyed the main pathway (the intestinal microbiota) that BBR to play the role of reducing fish serum glucose and improving liver health. The above conclusions were further confirmed by the in-vitro test, which revealed that the addition of BBR significantly increased the number of intestinal culturable microbiota, but ATB decreased it, and the addition of both BBR and ATB at the same time still inhibited the growth of intestinal microbiota seriously.
At the phylum level, the intestinal Firmicutes contents were significantly downregulated in the BBR group but upregulated in the ATB and ATB + BBR groups. Meanwhile, the Bacteroidota content was significantly upregulated in the BBR group and downregulated in the ATB and ATB + BBR groups. The intestinal Firmicutes/Bacteroidota relative ratio was downregulated in the BBR group and upregulated in the ATB and ATB + BBR groups. High proportions of Firmicutes/Bacteroidota are associated with a carbohydrate diet (Fukuda et al., 2011). Firmicutes/Bacteroidota are often used as indicators to measure obesity status (Li et al., 2015; Rizzatti et al., 2017). The supplementation of BBR mitigated “obesity” in largemouth bass, whereas ATB had no effect. Furthermore, the inability of BBR to function in the presence of ATB is consistent with the results of gut microbial community diversity analysis. This suggests that the supplementation of ATB suppressed the positive regulatory effects of BBR in glycolipid metabolism in largemouth bass. Furthermore, the use of ATB may inhibit a key pathway, that is, to reduce the gut microbial community diversity, through which BBR exerts its regulatory role.
Proteobacteria are gram-negative bacteria and a major clade of bacteria that include several pathogenic bacteria, such as Escherichia coli, Salmonella, Vibrio cholerae, Helicobacter pylori, and other well-known species. In mammals, intestinal dysbiosis is usually accompanied by increased contents of Proteobacteria. In some intestinal settings, the content of *Proteobacteria is* a potential microbial signature of intestinal disease and inflammation and an identification criterion for intestinal health (Kholil et al., 2015; Rizzatti et al., 2017). Proteobacteria content was significantly downregulated in the BBR, ATB, and ATB + BBR groups. This indicated that the supplementation of BBR and ATB decreased the relative abundance of enteropathogenic bacteria. At the genus level, the *Pseudomonas content* was downregulated in the BBR, ATB, and ATB + BBR groups. Several species in the genus *Pseudomonas are* fish pathogenic bacteria (Altinok et al., 2006; Wiklund 2016), such as *Pseudomonas fluorescens* and Pseudomonas aeruginosa, which are considered pathogenic microorganisms in aquaculture (Bodey et al., 1983; Miquel et al., 2013). The results of this study suggest that the supplementation of BBR and ATB decreased the relative abundance of pathogenic bacteria in the gut flora. The BBR group exhibited increased levels of Faecalibacterium, Lactobacillus, and Lactococcus. Faecalibacterium, a major member of the intestinal microbiota in healthy humans, produces butyric acid, which exerts anti-inflammatory effects and can alleviate obesity in humans. The downregulation of Faecalibacterium promotes an inflammatory response (Reid and Burton 2002; Ferreira-Halder et al., 2017; Lopez-Siles et al., 2017). Previous researches indicated that Faecalibacterium as probiotic improved the immunity and changed intestinal microbiota composition of shrimp and turbot (Guo et al., 2022; Butucel et al., 2023) The supplementation of BBR increased the contents of Faecalibacterium. This suggests that Faecalibacterium promotes intestinal health in largemouth bass, reduces the associated inflammatory response, and regulates body metabolism. Lactobacillus prevents intestinal infections. The incorporation of Lactobacillus strains ingested into the intestine modulates the vaginal flora to a healthy state (van Hylckama Vlieg et al., 2006; Petrova et al., 2017). Lactococcus lactis is a globular gram-positive anaerobic bacterium widely used in the production of fermented dairy products and occupies a key position in the manufacturing of fermented foods with beneficial effects on human health (van de Guchte et al., 1992; Modi et al., 2014). Lactobacillus was also proved to be a kind of probiotics commonly used in aquatic production, which can improve the growth performance, regulate intestinal health, and improve disease resistance of aquatic animals (Suzer et al., 2008; Ramachandran et al., 2014). These results indicate that the use of BBR reduces the relative abundance of pathogenic bacteria in the intestinal flora and increases the relative abundance of beneficial bacteria, promoting intestinal health. Meanwhile, the use of ATB kills harmful bacteria and beneficial bacteria in the intestinal tract. Thus, the use of BBR for the treatment of bacterial diseases in aquatic animals will achieve twice the result with half the effort when compared with the ATB.
The results of the in-vitro culture assays with intestinal microbiota further validated the direct effect of BBR on intestinal microbiota composition. In the group treated with BBR, the dominant characteristic bacteria was E. cloacae, and this bacteria also was found (only <$0.1\%$) within molecular methods in vivo. The difference of the above results are due to the advantages and limitations of each method, and combination of the two methods reflected the actual situation more comprehensively (Kellenberger, 2001; Di Bella et al., 2013). And this strain reacted positively with arginine, ornithine, citrate, and pyruvate, which are important intermediate metabolites involved in glycolysis, tricarboxylic acid cycle and ornithine cycle (Benuck et al., 1971; Akram 2014; Zangari et al., 2020). The positive reactions to GLU, RHA, SAC, MEL, and ARA indicated the carbohydrate metabolism ability of the characteristic bacteria obtained from the BBR group.
## 5 Conclusion
The results of this study demonstrated that the supplementation of BBR decreased the blood GLU level and improved GLU metabolism in largemouth bass. Comparative analysis of the results of experiments with ATB supplementation revealed that BBR regulated GLU metabolism in largemouth bass might be attributable to the modulation of BBR on intestinal microbiota.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/sra/PRJNA931008.
## Ethics statement
The animal study was reviewed and approved by the Chinese Academy of Fishery Science of the Pearl River Fisheries Research Institute, which affiliated to the Ministry of Agriculture and Rural Affairs (MOA), China.
## Author contributions
YX: Methodology, Validation, Formal analysis, Investigation, Data curation, Writing—original draft, Funding acquisition, Writing—review and editing. H-CY: Conceptualization, Software, Writing—original draft, Formal analysis, Methodology. KZ: Resources. J-JT: Review and editing. Z-FL: Investigation. E-MY: Investigation. H-YL: Review and editing. W-BG: Supervision. W-PX: Methodology. G-JW and JX: Conceptualization, Data curation, Supervision, Funding acquisition.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Ketamine study: Protocol for naturalistic prospective multicenter study on
subcutaneous ketamine infusion in depressed patients with active suicidal ideation'
authors:
- Ana Paula Anzolin
- Jeferson Ferraz Goularte
- Jairo Vinícius Pinto
- Paulo Belmonte-de-Abreu
- Luciane Nascimento Cruz
- Victor Hugo Schaly Cordova
- Lucas Sueti Magalhaes
- Adriane R. Rosa
- Keila Maria Cereser
- Márcia Kauer-Sant’Anna
journal: Frontiers in Psychiatry
year: 2023
pmcid: PMC10033666
doi: 10.3389/fpsyt.2023.1147298
license: CC BY 4.0
---
# Ketamine study: Protocol for naturalistic prospective multicenter study on subcutaneous ketamine infusion in depressed patients with active suicidal ideation
## Abstract
### Background
Psychiatric disorders are associated with more than $90\%$ of reported suicide attempts worldwide, but few treatments have demonstrated a direct effect in reducing suicide risk. Ketamine, originally an anesthetic, has been shown anti-suicide effects in clinical trials designed to treat depression. However, changes at the biochemical level were assessed only in protocols of ketamine with very limited sample sizes, particularly when the subcutaneous route was considered. In addition, the inflammatory changes associated with ketamine effects and their correlation with response to treatment, dose-effect, and suicide risk warrant further investigation. Therefore, we aimed to assess whether ketamine results in better control of suicidal ideation and/or behavior in patients with depressive episodes and whether ketamine affects psychopathology and inflammatory biomarkers.
### Materials and methods
We report here the design of a naturalistic prospective multicenter study protocol of ketamine in depressive episodes carried out at Hospital de Clínicas de Porto Alegre (HCPA) and Hospital Moinhos de Vento (HMV). The study was planned to recruit adult patients with Major depressive disorder (MDD) or Bipolar disorder (BD) types 1 or 2, who are currently in a depressive episode and show symptoms of suicidal ideation and/or behavior according to the Columbia-Suicide Severity Rating Scale (C-SSRS) and have been prescribed ketamine by their assistant psychiatrist. Patients receive ketamine subcutaneously (SC) twice a week for 1 month, but the frequency can be changed or the dose decreased according to the assistant physician’s decision. After the last ketamine session, patients are followed-up via telephone once a month for up to 6 months. The data will be analyzed using repeated measures statistics to evaluate the reduction in suicide risk as a primary outcome, as per C-SSRS.
### Discussion
We discuss the need for studies with longer follow-ups designed to measure a direct impact on suicide risk and that additional information about the safety and tolerability of ketamine in particular subset of patients such as those with depression and ideation suicide. In line, the mechanism behind the immunomodulatory effects of ketamine is still poorly understood.
### Trial registration
https://clinicaltrials.gov/, identifier NCT05249309.
## Introduction
Depression is a chronic, recurrent, and highly prevalent condition associated with functional disability and compromised physical health [1]. Its etioloy is multifactorial and combines endogenous susceptibility with exposure to environmental stressors [1, 2]. The association between depression and suicidal behavior has been widely described in literature; for example, according to the WHO [3], psychiatric illnesses are associated with more than $90\%$ of suicide ideation cases and are responsible for $90\%$ of deaths by suicide reported cases worldwide [3, 4]. Furthermore, studies across different populations have confirmed the relationship between depression and suicide (5–7): population-based investigations in the United States [5, 8], Canada [6], and China [7] indicate that depression is the main nosological entity associated with suicidal ideation, suicidal plans, and suicide attempts. Despite these facts, treatments for depression that also impact suicidal behavior are scarce; thus, drugs with anti-suicide effects are highly desirable and one of the main research needs in psychiatry [4].
There is an increasing interest in the benefits of ketamine, its racemic compound, and its enantiomers [i.e., S-ketamine (esketamine) [9] and R-ketamine (arketamine) [10] for the treatment of psychiatric disorders. Indeed, intranasal esketamine for treating depression was recently approved by the Food and Drug Administration (FDA) and European Regulatory Authorities [11, 12] for treating Major depressive disorder (MDD). Ketamine acts on glutamate, the principal excitatory neurotransmitter, as a non-competitive antagonist on the N-methyl-D-aspartate (NMDA) receptor; the effectiveness of glutamate modulating agents in the treatment of mood disorders regulates of glutamatergic neurotransmission, contributing to the pathophysiology of depression, as well as to the mechanisms of antidepressants [13].
Glutamate acts pre- and post-synaptically through the activation of several receptors. Ionotropic glutamate receptors-NMDA, alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and kainate (KA)–are channels that allow the influx of ions into the cell, regulating polarization of the neuronal surface, which activates intracellular signaling cascades. It is believed that NMDA and AMPA are directly involved in the antidepressant actions of ketamine [14]. The pharmacokinetic characteristics of ketamine allow its administration by various routes, including intravenous (IV) [15, 16], subcutaneously (SC) [17, 18], intranasal [11, 12], oral [19, 20], sublingual [21], and intramuscular [22]. The SC route of administration has comparable efficacy to conventional IV infusion but fewer side effects [23, 24]. In a recent systematic review [18] that included 12 studies (two randomized clinical trials, five case reports and five retrospective studies), the authors observed that racemic ketamine and its enantiomer esketamine, via SC, seems to be a promising treatment in depression, given its efficacy and tolerability. The literature has already verified that doses of 0.5 mg/kg are unsuitable for patients with chronic depression [25]. Repeated and staggered doses of ketamine (0.5 mg/kg for the first three infusions to 0.75 mg/kg for the last infusions) reinforced the antidepressant and antisuicidal properties of ketamine in a sample of patients with severe depression (25–27).
Some studies have associated proinflammatory cytokines with the severity of depressive symptoms [28, 29]. Innate immune cells present in the central nervous system (CNS), such as microglia, participate in the process of neuroinflammation; when this process is activated, the production of cytokines that affect synaptic plasticity in regions important for mood regulation increases [19]. Based on pharmacological properties and animal studies [30], it is hypothesized that depressed patients with higher levels of inflammation is more responsive to ketamine treatment [31]. Furthermore, in an animal model of treatment-refractory depression with chronic administration of adrenocorticotropic hormone (ACTH), animals that responded to ketamine exhibited higher baseline plasma concentrations of C-reactive protein (CRP) and tumoral necrosis factor-α (TNF-α) [32, 33]. Blood biomarkers [TNF-α, Interleukin (IL)-6] predicted a favorable antidepressant response to Ketamine administration in a small sample of depressed patients. Other studies report that increased body mass index (BMI) and plasma concentrations of adipokine (both associated with inflammation) correlated with the ketamine response, in other words, lower baseline adiponectin levels correlated with superior antidepressant response to ketamine (percent change from baseline) at 230 min post-infusion [Montgomery-Åsberg Depression Rating Scale (MADRS): $r = 0.25$, $$p \leq 0.03$$; Hamilton Depression Rating Scale (HAM-D): $r = 0.22$, $$p \leq 0.051$$] and at day 1 (MADRS: $r = 0.28$, $$p \leq 0.01$$; HAM-D: $r = 0.34$, $$p \leq 0.002$$) [13, 34].
Patients with depression are also at increased risk for developing metabolic and cardiovascular diseases, being, on average, 1.58 times more likely to have metabolic syndrome (MS) compared to the general population [35]. Among the hormones secreted by adipose tissue, leptin seems to be involved with depressive disorders (36–38). In addition, some works have investigated the involvement of central and peripheral leptin as a potential biomarker for suicide risk [39].
New biomarkers are also essential to predict the outcome of treatment in the future with the application of conventional antidepressants and anti-inflammatory drugs. For example, alterations in the expression of sirtuin 3 (SIRT3) are associated with the pathophysiology of depressive disorders [40]. Similarly, serum levels of soluble urokinase plasminogen activator receptor (suPAR) are positively correlated with inflammatory proteins previous reported in mood disorders, such as tumor necrosis factor-alpha [TNF-α_ and ultra-sensitive C-reactive protein (us-CRP) [41]]. Moreover, studies have observed that high levels of suPAR were associated with a higher probability of depression diagnosis and recent suicide attempts [42, 43].
Therefore, given the potential challenges of conducting a definitive randomized control trial (RCT) of ketamine as a rapid-onset antidepressant in suicidal ideation, a feasibility study is needed to inform tolerability, acceptability, safety, effect, as well as the better understanding of the biochemical changes of ketamine in the treatment of suicide risk in patients with depression. In addition, these data could serve as the basis for the larger RCT using an individualized dose ketamine approach.
## Aims
This article aims to describe the protocol of a multicenter prospective naturalistic study, which allows an analysis of the response to ketamine via SC in relation to the treatment of suicidal ideation and behavior. We hypothesize that ketamine, through its mechanisms of action on NMDA and neuroplasticity, would reduce suicidal ideation or/and behavior in patients with a depressive episode, according to the Columbia Suicide Severity Rating Scale (C-SSRS) and other rating scales.
## Objectives
Our main objective is to evaluate whether ketamine can reduce the frequency and intensity of suicidal ideation or behavior and improve depressive symptoms in patients with depressive episodes. Our secondary aims are the investigation of the impact of ketamine on other psychoathology symptoms, clinical factors, inflammatory biomarkers, and metabolic factors.
## Study design and setting
This is an observational naturalistic, prospective multicenter study performed in two reference centers in ketamine treatment in Porto Alegre, Brazil. The participants were recruited in our reference due to their depression and active suicide ideation or behavior. The ongoing ketamine study started data collection on July 2021 with a target of 45 participants. Data include clinical and psychiatric assessment, blood sampling, and diet on site with a follow-up assessment at 6 months performed by phone call. In addition, an exploratory analysis assesses the risk of suicide throughout ketamine treatment based on subgroups of interest.
## Sample size calculation
The sample size of 45 participants over 2 years is projected to be an appropriate number to inform study feasibility; the sample size calculation was performed using the WINPEPI program, version 11.65 to detect a difference of 1 point on the C-SSRS scale, considering results from previous studies [44] with a power of $80\%$ at a significance level of 0.05.
## Population and eligibility criteria
The target population are adult patients diagnosed with Major depressive disorder (MDD) currently in depressive episodes, were diagnosed using the Diagnostic and Statistical Manual of Mental Disorders–fifth edition, DSM-5 [45], and confirmed using the Mini International Neuropsychiatric Interview (MINI; updated Version 7 for DSM-5) [46].
All patients are treated with subcutaneous (SC) ketamine and continue to use psychiatric medications prescribed by their attending physician. Information on medications (single or in combination) as well as dosage were collected using a structured questionnaire (Appendix 1). Patients did not receive psychotherapy and/or physiotherapy.
The inclusion criteria are [1] adult patients (≥ 18 years); [2] that meet the DSM-5 diagnostic criteria for MDD, BD-1, or BD-2 currently in a depressive episode; [3] with a total score on the Montgomery-Åsberg Depression Rating Scale (MADRS) ≥ 12 and score on items 1 (apparent sadness) and 2 (expressed sadness) ≥ 2 during the triage period (baseline); [4] and a total Young Mania Rating Scale (YMRS) score ≤ 11 during baseline; [5] having current symptoms of suicidal ideation or suicidal behavior, according to the Columbia Suicide Severity Rating Scale (C-SSRS) score ≥ 1; [6] indication/prescription of their assistant physician for the use of SC ketamine; [7] use effective contraceptive methods for heterosexual women of childbearing age; [8] patients with BD-1 is taking lithium, valproic acid, or an atypical antipsychotic at therapeutic doses for at least 4 weeks before the initial assessment; [9] patients with BD – 2 is taking lithium, valproic acid, lamotrigine, or an atypical antipsychotic at therapeutic doses for at least 4 weeks before the initial assessment; [10] can provide consent and comply with study procedures.
The exclusion criteria are [1] Patients with an unstable, defined, or suspected systemic medical condition; [2] Women who are pregnant, breastfeeding or planning to become pregnant within the next year; [3] Patients who do not tolerate the use of ketamine or with previous side effects associated with medications; [4] Inability to comply with informed consent or treatment protocol needs; [5] Patients with psychotic symptoms (according to DSM-5 criteria); [6] Patients with a current diagnosis of any substance use disorder according to the DSM-5 Criteria, except for smoking; [7] Patients with immune, inflammatory, cancer or infections.
Withdrawal Criteria: [1]Patients not using the medication or being considered non-adherent by the responsible clinician; [2] Patients who stop taking contraceptives or become pregnant; [3] In case of modifying doses or adding/deleting medication, patients be kept in the study, but the changes be counted as a primary endpoint; [4] Serious adverse reactions; [5] Withdrawal of consent by the patient; [6] Patients with manic or psychotic episodes as clinically assessed and according to DSM-5 criteria.
## Comparator
The recruitment of the control group occurs in the form of an invitation to blood donors at the HCPA Blood Bank and blood collection performed in the routine collection performed during the blood donation. This group included only in biochemical analyzes to compare the levels of interleukin 6 (IL-6), IL-10, IL-1β, TNFα, SIRT3, suPAR, us-CRP, and leptin of depressed patients with levels of healthy subjects.
The control group consists of 45 healthy volunteers (age ≥ 18). The inclusion criteria for the healthy controls are: [1] Not having a history of psychiatric or neurological diseases; [2] Do not present unstable clinical illnesses or autoimmune diseases; [3] Not being pregnant or breastfeeding.
## Interventions
The study procedure is illustrated in Figures 1, 2. The application of ketamine for the study follows the routine related to the care in force applied to patients for treatment with ketamine at HCPA and HMV.
**FIGURE 1:** *Study protocol. The protocol may have its care attention time changed/decreased, due to the amounts of ketamine sessions (determined by the attending physician), after the end of these sessions the patient is monitored once a month to 6 months, via telephone.* **FIGURE 2:** *Graph scheme of ketamine protocol.*
The psychiatrist reassesses the patient to exclude current criteria that contraindicate treatment with ketamine. In addition, it is verified whether the patient has ingested solids for at least 6 h and at least 2 h before the procedure. Finally, the patients are comfortably accommodated sitting in a reclining chair or lying on a stretcher, and vital signs are checked (blood pressure measurement, digital oximetry, and heart rate).
The medication is administered at an initial dose of 0.5 mg/kg. The nursing staff with undiluted ketamine prepare the syringe for SC administration; the psychiatrist administers the SC injection, preferably in the abdominal wall. The tolerability of the patient treated for the first time with ketamine is evaluated by dividing the dose administered at least in the first two infusions and whenever the dose is increased. In this case, the medication is injected using half of the planned dose and the other half 30 min after or when there is remission of adverse events. Blood pressure and pulse oximetry are checked during this period.
The initial SC infusion is 0.5 mg/kg ketamine; if there is no adequate response with the dose of 0.5 mg/kg, a second infusion is performed at least 2 days after the first, using 0.75 mg/kg and the subsequent 1 mg/kg. If the patient responds adequately to those doses (0.5 or 0.75 mg/kg), it is repeated throughout the course of treatment; these eight sessions, twice a week. Then, after a consolidation phase of 8 more sessions, once a week. *The* general and clinical data of the patients is obtained in person, along with the care procedure. After the end of the ketamine sessions, the psychiatric scales are applied via telephone once a month until the 6th month. In the first and last session of Ketamine (beginning and end of treatment), peripheral blood is collected from patients (15 mL) by a technician trained in blood collection.
## Blood sampling
Blood samples are collected from each patient and allowed to clot in blood collection tubes with no additive. Subsequently, whole blood is centrifuged for 10 min at 1,000 xg and serum is removed, aliquoted and stored at −80°C until assayed. Blood samples are collected from each patient in an anticoagulant tube. Subsequently, the blood is centrifuged for 10 min at 1,000 mg. and the plasma is removed, aliquoted and stored at −80°C until the time of the assay.
## C-SSRS
To measure the risk of suicide and the improvement in suicidal ideation with ketamine, we used the Brazilian version of the C-SSRS (translated and validated to Brazilina Portuguese). The C-SSRS has different versions that assess symptoms in different periods, depending on the characteristics of the study. In the present study, we use the baseline/screening version at the beginning of the first session, which assesses the worst period of suicidal ideation during life and in the last month. For later measurements (before each ketamine application), we use the modified version for use in serial assessment, which tracks symptoms since the last assessment.
The C-SSRS is applied by the researcher through a semi-structured interview and is divided into four subscales: (a) severity of suicidal ideation (5-point ordinal scale); (b) intensity of ideation (5-point ordinal scale); (c) suicidal behavior [nominal scale with binary response (yes/no)]; (d) lethality of effective attempts. The researchers performed the necessary training to apply the C-SSRS scale.
## Secondary outcome measures
These questionnaires below are applied before all ketamine sessions and, after that, via telephone once a month until the 6th month. In addition, the Childhood Trauma Assessment Questionnaire (CTQ) is applied before the first ketamine session.
## Childhood Trauma Assessment Questionnaire (CTQ)
The CTQ [47] is a self-assessment instrument for exposure to abuse situations up to fifteen years of age. It consists of 28 items, classifiable on a 5-point Likert scale, originating from the 70-item long version developed by Bernstein et al. [ 47]. Items that describe childhood experiences are classified according to how often they occurred: 1–never, 2–a few times, 3–sometimes, 4–often or 5–always, being formulated with experiences of abuse or adequate care during childhood.
## Young Mania Rating Scale (YMRS)
The YMRS [48], translated and adapted to Brazilian Portuguese [44], is used to evaluate the appearance of manic symptoms as an adverse effect of the use of ketamine. The YMRS is a scale the researcher applies through direct observation and unstructured interviews. This scale contains eleven items, and the score ranges from 0 to 62. A score less than or equal to 12 indicates no manic episode.
## Montgomery-Åsberg Depression Rating Scale (MADRS)
In its final version, the MADRS Scale [49] consists of ten items that do not include somatic or psychomotor symptoms. This characteristic makes it a more suitable scale to assess patients with general medical comorbidities, as it reduces the risk that symptoms resulting from somatic illness are counted as depressive symptoms. The evaluator can score defined scale grades [0, 2, 4, 6] or intermediate categories [1, 3, 5].
## Hamilton Depression Rating Scale (HAM-D)
To measure ketamine effectiveness on the severity of depressive symptoms, we also use the seventeen-item version of the HAM-D [49] translated and adapted to Brazilian Portuguese, with a structured interview guide. The HAM-D is a scale whose total score ranges from 0 to 52. Although the author has not proposed a standard cutoff point, in practice, scores above 24 are considered to identify a severe depressive episode; between 18 and 24, moderate depressive episode; between 7 and 17, mild depressive episode and below 7, no depressive episode or remission [50].
## Brief psychiatric rating scale (BPRS)
This scale assesses the presence and level of severity of psychotic symptoms, emotional states, and psychomotricity disorders, among other symptoms [51]. The BPRS score adopted is based on the criteria suggested by Elkis et al. [ 51], with scores ranging from 0 to 6 for each item. Thus, 0 (zero) means absence or non-observation of the symptom and six corresponds to the most severe level. Intermediate values correspond, in turn, to intermediate levels of severity. For the evaluation of mental alterations compatible with disorders of a psychotic nature, the total BPRS score is considered.
## Functional assessment short test (FAST)
We use the translated and adapted version for Brazil [52]. It is a hetero-applied instrument for the objective and multidimensional assessment of functionality related to the last fifteen days. It consists of 24 items, divided into six specific subscales. Autonomy refers to the subject’s ability to perform actions alone or to make their own decisions. Occupational functioning refers to the subject’s ability to maintain a regular job, to have a stable performance and to work in an area compatible with their qualification and position at work. Cognitive functioning concerns the subject’s ability to concentrate, make simple mental calculations, solve routine problems, learn new information and remember this learned information. Financial skills involve the subject’s ability to manage their finances in a balanced way; the item “interpersonal relationships” refers to the quality of relationships with friends and family, the ability to participate in social activities and sexual relationships, and the ability to defend personal ideas and opinions. Leisure activities relate to performance in physical activities (sports, exercise) and having activities. The score is determined by the sum of the items, which range from 0 (indicating no limitation) to 3 (indicating severe limitation) [53].
## a) Body weight
Body weight is evaluated before the first application of ketamine, in the fourth, eighth and twelfth weeks after the first application of ketamine. Body weight is measured with individuals barefoot, wearing as little clothing as possible and positioned in the center of the platform during the reading. An electronic scale with a maximum capacity of 150 kg and a precision of 0.1 kg is used.
## b) Stature
Height is evaluated before the first application of ketamine. To perform the measurement, an anthropometric ruler fixed to the wall is used. Height is considered as the distance from the sole of the bare feet to the top of the head, compressing the hair, with the patient in a vertical position, on the flat surface, looking fixed on the horizon [54].
## c) Body mass index (BMI)
Body mass index (BMI) is calculated from weight (kg) and height (m) data using the following formula: For the classification of BMI, the cutoff points established by the WHO is used, where: BMI < 18.5 Kg/m2 is classified as low weight; BMI 18.5–24.9 Kg/m2 is classified as adequate weight; BMI 25–29.9 Kg/m2 is classified as overweight; BMI 30–34.9 Kg/m2 is classified as class I obesity; BMI 35–39.9 Kg/m2 is classified as class II obesity; BMI > 40 Kg/m2 is classified as class III obesity.
## d) Waist circumference (WC)
Waist circumference is evaluated before the first application of ketamine, in the fourth, eighth and twelfth weeks after the first application of ketamine. WC is measured with the aid of an inelastic measuring tape 1.5 m long and accurate to 0.1 cm. The measurement is performed with the patient standing in an upright position, abdomen relaxed, arms extended along the body and feet separated at 25–30 cm. The midpoint between the iliac crest and the lower edge of the last rib, in an orthostatic position, without clothes on the chest and at the end of expiration [54]. The reference value used is the one proposed by the IDF [55].
## e) Blood pressure
Blood pressure is measured before the first application of ketamine, in the fourth, eighth and twelfth weeks after the first application of ketamine. The measurement is performed according to the HCPA and HMV nursing protocol.
## f) Inflammatory profile of the diet
The Dietary Inflammatory Index (DII) is calculated according to previous studies [56, 57] and from the average of food recalls of the last 24 h (R24h) to the interview with intervals between them according to the ketamine applications. The pro-inflammatory nutritional parameters included in the DII score is total calories (kcal); carbohydrates (g); fat (g); protein (g); cholesterol (mg); total saturated fatty acids (g); iron (mg); and vitamin B12 (μg). The anti-inflammatory dietary parameters included in the DII score is: alcohol (g); caffeine (g); fiber (g); total monounsaturated and polyunsaturated fatty acids (g); n-3 and n-6 polyunsaturated fatty acids (g); niacin (mg); riboflavin (mg); thiamine (mg); vitamins A (retinol equivalents), B6 (mg), C (mg), D (μg), E (mg); β-carotene (μg); magnesium (mg); selenium (μg); zinc (mg); and folate (μg). Scores are centered at 0, with positive scores indicating a pro-inflammatory diet and negative scores indicating an anti-inflammatory diet. Continuous DII scores is standardized (mean = 0, standard deviation = 1) for better interpretation of results.
## Outcome measures
Primary and secondary outcomes and endpoints that correspond to the secondary objectives are listed according to the various assessment time points in Table 1. As a primary outcome, this work is expected to prove the benefits of ketamine in the treatment for suicidal ideation in patients with MDD or BD, as well as the durability of the antidepressant effect, and the transdiagnostic comparison of the effect of ketamine. The C-SSRS scale score over 6 months in relation to the initial score is used to assess this primary outcome.
**TABLE 1**
| Assessments | Eligibility | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | Follow up (1 month) | Follow up (2 months) | Follow up (3 months) | Follow up (4 months) | Follow up (5 months) | Follow up (6 months) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Informed consent | X (Re-affirm) | X | | | | | | | | | | | | | |
| General data information | | X | | | | | | | | | | | | | |
| Clinical information | | X | | | | | | | | | | | | | |
| Nutritional measures | | X | | | | | | | X | | | | | | |
| Inflammatory profile of the diet | | X | | X | | X | | X | | | | | | | |
| CTQ | | X | | | | | | | | | | | | | |
| Bloods (IL-6, IL-10, IL-1β, TNFα, SIRT3, suPAR, us-CRP and leptin) | | X | | | | | | | X | | | | | | |
| C-SSRS | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| MADRS | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| BPRS | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| YMRS | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| HAMD | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| FAST | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
As a secondary outcome, we are verifying the occurrence of changes before, during and after treatment on psychiatric scales (e.g., BPRS, MADRS, HAMD, YMRS, FAST) and serum concentration of IL-6, IL-10, IL-1β, TNFα, suPAR, us-CRP, and leptin as well as gene expression and immunocontent of SIRT3. In addition, the presence of MS is evaluated as a potential moderator of treatment response together with the predictors mentioned above.
The standard tools BPRS, MADRS, and HAM-D are used for comparison with other ketamine literature available in psychiatry [23, 24, 58, 59]. Clinical response is defined as MADRS score reduction of ≥ $50\%$ from baseline and remission as MADRS score ≤ 9 [22], and relapse is defined as MADRS ≥ 16 after an initial remission. The time points for measurements of the scales used were chosen to verify the initial and maximum response time (during treatment) and the duration of response (up to 6 months).
## Assessment integrity
Under the guidance of PBA, LNC and MKS (staff psychiatrists), the APA researcher participated in training to perform psychiatric assessments. APA then provides on-site initiation and training for the rest of the research team members (nutritionist, undergraduate students, and other researchers).
Eligible participants undergoing ketamine treatment are monitored regularly in each treatment application. Upon completion of treatment, participants go through the follow-up phase, in which they are monitored monthly by telephone (months 1–6) (Table 1). Each participant receives a unique identification number. All study data is recorded on the study case report forms and entered by study researchers into REDCap, a sophisticated platform for collecting and managing research data protected by Secure Sockets Layer encryption. All source documents and the master list linking participant identification information and identification numbers are stored in a locked cabinet at HCPA. All information is accessible only to those directly involved in the study. There is no advance sharing of data beyond the group of investigators. Study records are maintained for 5 years after study completion in secure archival facilities per the National Council for Health and Medical Research and Good Clinical Practice guidelines.
## Data analysis
We will use the Shapiro-Wilk test to assess the normality of the variables. Clinical and demographic data with normal distribution will be assessed with parametric tests (e.g., t-test for independent samples) and those with non-asymmetric data will be analyzed with non-parametric tests (e.g., Mann-Whitney test). Categorical variables will be compared through the chi-square test or Fisher’s exact test, as appropriate.
For the analysis of the C-SSRS suicidal ideation severity scale, as it is an ordinal scale, we plan to use the Wilcoxon test to detect differences between the scores for each evaluation point throughout the study. In addition, the generalized estimation equations will be used to evaluate the durability of the antidepressant effect and tolerability of SC ketamine.
The association between the variables is evaluated by the Pearson correlation test or Spearman, as per the distribution pattern. The margin of error used is $5\%$.
## Trial duration
July 2021–December 2023.
## Ethics and dissemination
This study is supervised by the Research Ethics Committee of the Hospital de Clínicas de Porto Alegre (CEP-HCPA) and Research Ethics Committee of the Hospital Moinhos de Vento (CEP-HMV). The same is a collegiate instance, of a consultative, deliberative, and educational nature, whose objective is to assess the ethical and methodological aspects (through the issuance of an opinion) and to monitor research projects involving human beings, carried out or proposed by the institution. The instance is registered with OHRP/USA (Office for Human Research Protections): IORG0000588, CEP Registration (IRB – Institutional Review Board) with OHRP/USA (Office for Human Research Protections): IRB 00000921 and Federal wide Assurance (FWA), certificate of commitment that the Institution undertakes to follow the requirements established by the HHS (U.S. Department of Health and Human Services) Protection of Human Subjects: FWA00002409.
This study was approved by Research Ethics Committee the HCPA (CAAE: 33589320300005327) on the 18 June 2020 and Research Ethics Committee the HMV (CAAE: 33589320.3.2001.5330). This trial has been registered in U.S. National Library of Medicine–Clinical Trial Registry (Reference Number: NCT05249309), with recruitment commenced on the May 2021.
The results of this study will be submitted for publication in peer-reviewed journals and presented at relevant conferences.
## Discussion
To the best of our knowledge, this study is the first naturalistic investigation designed to verify the therapeutic effects of SC ketamine in reducing suicide risk in patients with mood disorders. Furthermore, it is the first study to assess the impact of ketamine on serum levels of SIRT3, IL-6, IL-10, TNFα, leptin, us-CRP, suPAR, metabolic parameters, and dietary inflammatory index.
This study includes a period of ketamine administration (treatment) and a 6-months follow-up to determine not only the acute effects of ketamine but also its impacts in the long term. This duration was chosen to obtain adequate data on effect and durability in the short, medium, and long term, maintaining the feasibility of the study. Therefore, this study may provide relevant information for a future definitive study exploring the safety, tolerability, and effects of ketamine for suicidal ideation and/or behavior.
A study carried out in Denmark, observed that recent psychiatric hospitalization was the factor most strongly associated with suicide [60]. This finding reinforces the idea that severe mental disorders is one of the leading indicators of suicide risk. Later, in 2010, mental disorders and substance use disorders were found to be responsible for two-thirds of suicides. Considering the additional burden of mental and substance use disorders as a risk factor for suicide, increased mental and substance use disorders have risen from the fifth most common disease category in the global burden to the third most common disease category [61].
The risk of suicide increases more than twenty-fold in individuals with DDM and is even greater in subjects with comorbidity with other psychiatric disorders or medical conditions [62]. Psychological autopsy data show that approximately half of the individuals who died by suicide were suffering from depression. Lee et al. [ 7] observed that, compared to anxiety disorders, the diagnosis of MDD was associated with an odds ratio about ten times higher.
The causes of suicidal behavior are multiple and complex. Although the presence of MDD is an important predisposing factor, the existence of this pathology alone is not enough to fully explain suicidal behavior, without the interaction with other factors, such as the presence of hopelessness, impulsiveness, and aggression, among others. Furthermore, clinical predictors of suicidal behavior are generally not robust, meaning they are not reproducible for different patient samples, since suicidal behavior results from a combination of individual risk factors [63].
A systematic review recently verified that ketamine and esketamine are promising treatments for MDD, given their efficacy and tolerability [18]. The authors analyzed 12 articles (two randomized controlled trials, five case reports and five retrospective studies). SC ketamine was administered to unipolar and bipolar patients in single or multiple doses, weekly or twice a week; the dose ranged from 0.1 to 0.5 mg/kg. In all studies, SC Ketamine showed a rapid and robust antidepressant effect, with remission rates of 50 to $100\%$ after single or multiple doses, with transient side effects.
Neurobiological studies with adult suicide patients have found reduced levels of serotonin metabolites in central nervous system (CNS) fluid. Deficiency of this and other neurotransmitters (such as norepinephrine) has been observed in cases of suicide since the deficiency of these neurotransmitters in critical places in the brain results in depressive states. Such a deficiency can occur due to insufficient production, excessive neurotransmitter reuptake in the synaptic cleft or failure of the receptor system [64].
Serotonin and norepinephrine are the most studied neurotransmitters when it comes to suicide. Studies have also described the association of a decrease in the level of 5-HT in the brain of the deceased who had a diagnosis of depression. In the case of those who died by suicide, there was a decrease in 5-hydroxyindoleacetic acid (5-HIAA), the main metabolite of 5-HT. Depressed individuals who committed suicide or serious attempts had reduced levels of 5-HT, when compared to patients with depression, but who did not commit suicide or serious attempts [65].
Sirtuin 3 (SIRT3) is the main NAD + deacetylase dependent mitochondria that acts as a regulator of mitochondrial protein function, being essential for maintaining mitochondrial integrity. Abe et al. [ 66] analyzed whether there were alterations in sirtuin messenger RNA (mRNA) expression in peripheral white blood cells of BD patients and also examined whether altered sirtuin mRNA expression is state or characteristic dependent in BD patients who were in a remissive state. As a result, they observed that mRNA sirtuin levels in BD patients significantly decreased in those who were in a depressed state, compared to healthy controls. Therefore, altered sirtuin expression is state-dependent and is associated with the pathogenesis or pathophysiology of bipolar depression. A study correlated SIRT3 with depression, using semiquantitative Western blotting methods, associating the pathogenesis of depression with the expression of SIRT3 [67].
The urokinase plasminogen activating receptor (uPAR) is part of the plasminogen activation system. It is also involved in cell adhesion and migration and is important for the recruitment of immune cells [68]. The soluble form of the receptor, suPAR, results from cleavage and release of membrane-bound uPAR into the blood and reflects activation of the immune system. In most cases, serum levels of suPAR positively correlate with inflammatory proteins such as TNFα and C-reactive protein (CRP) [41]. It was also observed that high levels of suPAR were associated with a higher probability of diagnosis of depression [42]. Ventorp et al. [ 43] evaluated plasma levels of suPAR as a biomarker of low-grade inflammation in patients with DDM and in patients who had recently attempted suicide. It was observed that both depressed patients and those who attempted suicide increased plasma suPAR, which may in the future be a prognostic in relation to the outcome of treatment with the application of conventional antidepressants in conjunction with anti-inflammatory drugs.
Patients with MDD are at increased risk for the development of metabolic and cardiovascular diseases, being, on average, 1.58 times more likely to have MS compared to the general population [35]. On a global scale, it is estimated that $31\%$ of patients diagnosed with BD have MS [35]. MS is an umbrella term for clinical and biochemical changes, which include central obesity (waist circumference or body mass index), dyslipidemia (elevated triacylglycerols and reduced high-density lipoprotein-HDL), hyperglycemia, and hypertension [55]. The presence of metabolic syndrome components in BD, mainly excess adiposity, is associated with reduced neurocognition, in addition to an association with executive function deficits and global cognitive deterioration [69]. Excess central adiposity, assessed by waist circumference, is an important clinical marker in MS, because adipose tissue is recognized as an endocrine tissue that secretes hormones involved in several biological responses, including inflammation [70].
Among the hormones secreted by adipose tissue, leptin appears to be involved with depressive disorders, including MDD and depression in BD (36–39, 71–73). Leptin was described in 1995 as a hormone responsible for the feeling of satiety, which acts mainly on the CNS and inhibits the action of orexigenic neurons and stimulates the action of anorectic neurons, in addition to increasing basal energy expenditure and being secreted in proportion to the adipose tissue stock [74].
In addition to the metabolic aspects involved in mood disorders and suicide, nutritional aspects and diet quality have recently come to be considered in mental disorders [75], mainly as process-stimulating inflammatory agents. In a meta-analysis of prospective cohort studies, individuals grouped in extracts with better quality dietary patterns had a lower odds ratio for incidence of depression or depressive symptoms compared to individuals in extracts with lower quality dietary patterns [76]. In the same study, those individuals who were in the lowest quintiles of the Dietary Inflammatory Index (DII) also had a lower odds ratio of incidence of depression and depressive symptoms [76], suggesting that nutritional factors may influence the development of mood disorders via stimulation of the inflammatory process. The dietary inflammatory index (IBD) is a global measure of the inflammatory potential of the foods consumed, considering macronutrients and micronutrients and their association with inflammatory markers such as IL-1b, IL-4, IL-6, IL-10, TNF –α, and CRP [57]. In the case of MDD and suicidal ideation, the positive index of IBD, indicating a pro-inflammatory eating pattern, was associated with suicidal ideation and MDD in a cross-sectional study with a representative sample of the American adult population [56]. In this sense, a systematic review of cross-sectional studies showed a positive association between a dietary pattern consisting of a high intake of red meat and derivatives and a low intake of fruits and vegetables with the concentration of CRP, IL-6 and IL-18 [77], all inflammatory markers involved in mood disorders. However, the clinical potential of IBD as a reference for the inflammatory profile of the diet still needs to be better characterized in patients with MDD and using ketamine.
The main limitation of this study is its inability to report the definitive efficacy of ketamine, since a multicenter, randomized, controlled clinical trial is required for assess these outcome. Secondly, severely depressed or psychotic patients who cannot consent will be excluded. Thirdly, patients remained on antidepressant medications. Therefore, we cannot rule out the possibility that the improvements in suicidal ideation and depressive symptoms are due to the intensifying effects of ketamine and not just ketamine which would require a RCT. However, this problem can be partially solved based on the well-known antidepressant effect of rapid rise and fall of ketamine compared to gradual changes of antidepressants that take weeks to months. Fourth: have not evaluate criteria for Treatment-Resistant Depression (TRD) and, given the naturalistic design, we cannot exclude that part of the sample could be TRD. Lastly, the lack of blinding and placebo group to analyze the study primary outcomes (clinical outcomes), as the control group will be used only to analyze the biochemical markers.
The use of standard psychiatry research instruments (e.g., MADRS, YMRS, FAST, HAM-D, and BPRS) allows direct comparison of this study with other psychiatric studies. In particular, using YMRS and BPRS allow for a better characterization of the side effect of ketamine confusion on various psychotomimetic and dissociative symptoms as well as manic symptoms. The C-SSRS is widely used in research that evaluates other drugs for suicidal ideation and has high impact power (43–45). The Centers for Disease Control and Prevention (CDC) and the FDA also recommend using the C-SSRS as a reference predictive measure in the analysis of suicide-related issues.
This protocol provides important information for a future definitive study exploring the safety, tolerability, and effects of ketamine for suicidal ideation and/or behavior in addition to investigating the specific molecular mechanism behind the immunomodulatory effects of ketamine.
## Ethics statement
This study was approved by Research Ethics Committee the HCPA (CAAE: 33589320300005327) and Research Ethics Committee the HMV (CAAE: 33589320.3.2001.5330). Participants consent by signing the consent form.
## Author contributions
MK-S, PB-D-A, and AA prepared the body of the protocol. AA, MK-S, PB-D-A, KC, and JVP prepared all information regarding sample size and statistical analysis. MK-S, JVP, PB-D-A, and LC contributed to the preparation of the entire body of the protocol. AA, JG, VC, and KC prepared study flow and assessment description, table, diagram, references, and formatted the manuscript. All authors read and approved the final manuscript.
## Conflict of interest
In the last 5 years, MK-S has received grant or research support from CAPES, CNPq, CNPq-Produtividade FIPE-HCPA and INCT-TM-CNPq; has been a speaker for Daiichi-Sankyo. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Prenatal environment impacts telomere length in newborn dairy heifers
authors:
- Maya Meesters
- Mieke Van Eetvelde
- Dries S. Martens
- Tim S. Nawrot
- Manon Dewulf
- Jan Govaere
- Geert Opsomer
journal: Scientific Reports
year: 2023
pmcid: PMC10033676
doi: 10.1038/s41598-023-31943-8
license: CC BY 4.0
---
# Prenatal environment impacts telomere length in newborn dairy heifers
## Abstract
Telomere length is associated with longevity and survival in multiple species. In human population-based studies, multiple prenatal factors have been described to be associated with a newborn’s telomere length. In the present study, we measured relative leukocyte telomere length in 210 Holstein Friesian heifers, within the first ten days of life. The dam’s age, parity, and milk production parameters, as well as environmental factors during gestation were assessed for their potential effect on telomere length. We found that for both primi- and multiparous dams, the telomere length was $1.16\%$ shorter for each day increase in the calf’s age at sampling ($$P \leq 0.017$$). The dam’s age at parturition ($$P \leq 0.045$$), and the median temperature-humidity index (THI) during the third trimester of gestation ($$P \leq 0.006$$) were also negatively associated with the calves’ TL. Investigating multiparous dams separately, only the calf’s age at sampling was significantly and negatively associated with the calves’ TL ($$P \leq 0.025$$). Results of the present study support the hypothesis that in cattle, early life telomere length is influenced by prenatal factors. Furthermore, the results suggest that selecting heifers born in winter out of young dams might contribute to increased longevity in dairy cattle.
## Introduction
In Holstein Friesian (HF) cows, high milk yield has been associated with a reduced longevity1. As modern dairy cows are criticised for their short productive lifespan and their adverse environmental footprint, longevity is currently encouraged by public opinion1,2. For the dairy industry, longevity is an economically important trait resulting in reduced culling rates, and thus in a lower need of replacement heifers3.
In multiple species including humans4, cattle5, sheep6, birds7, and badgers8, longevity has been linked to leukocyte telomere length. In humans, telomere length (TL) is considered to be a biological marker of aging9. Telomeres are nucleoprotein structures found at the ends of linear eukaryotic chromosomes, consisting of TTAGGG repeats10,11. They protect chromosomal integrity, inhibit aberrant fusions and rearrangements occurring on broken chromosomes and are crucial for the complete replication of genomic DNA11. Telomeres shorten with every cell division10,11 and in response to inflammation and oxidative stress12–14, but can be maintained through telomerase activity10,14. When TL declines to a critical threshold, cellular replicative senescence or cell death is induced15,16. As such, telomere dynamics are closely linked to cell function and cellular aging, and also seem to be associated with organismal aging12,17.
In human population-based studies, TL has been associated with an individual’s lifespan and disease risk18,19, as it is described to be a predictor of disease onset for cardiovascular disease, diabetes, and mortality risk20–22. Similar to what has been described in humans, longevity or productive lifespan23,24, as well as a cows’ future health status25 have been associated with leukocyte telomere length (LTL) and telomere attrition rate in dairy cattle23–25.
Factors influencing TL in humans, such as sex26, oxidative stress27, genetics28, and acquired disease29, are well described. Sex30 and breeding status16 have been described to influence LTL in sheep, whilst in cattle, age and herd5, genetics23, and high ambient temperatures24 have been shown to exert an influence.
Human TL at birth shows a high variability, comparable with the age-adjusted TL variability in adults31. *Addressing* gene-environmental factors that contribute to an individual’s initial TL may provide important insight in the developmental origins of health and disease conditions related to TL. This is in line with ‘The fetal programming of telomere biology hypothesis’, which states that TL and telomerase expression may be programmed by maternal states and stress conditions during pregnancy in women32.
Developmental programming, also referred to as prenatal or fetal programming, refers to the concept that adverse conditions in utero or during infancy can increase the risk of disease and premature aging in later postnatal life33–36. Prenatal programming has been well described in both humans37,38 and livestock33,39–42.
Prenatal programming of TL is an interesting observation, as TL can be tracked throughout time and the ranking of TL appears to be stable over time, implying that the initial setting of newborn TL contributes significantly to TL later in life43–45. In humans, prenatal maternal stress46, maternal pre-pregnancy BMI9, paternal age47, maternal pro-inflammatory state48, ambient temperatures during pregnancy49 and maternal vitamin D intake50, amongst others, are reported to influence newborn TL. A birth cohort effect, which may reflect environmental factors, has been described in sheep6 and badgers8, whilst lameness during pregnancy has been shown to shorten TL in newborn dairy calves25. To the best of our knowledge, prenatal programming of TL has only been scarcely described in non-human species.
Previous work by our research group has shown that prenatal factors contribute in determining a cow’s intrinsic ability to achieve a long and productive life51. Identifying a biological indicator to confirm this research would enable farmers to use this underlying biological mechanism to select animals with a predicted longer life to increase longevity of their livestock.
Therefore, the aim of the present study was to identify prenatal factors that are associated with LTL in newborn dairy heifers. We hypothesised that maternal age and parity, parameters associated with milk yield, and heat stress during gestation could be important in programming the early-life LTL in dairy cattle.
## Calf, dam and TL characteristics
The 210 calves included in this study were all purebred singleton female HF calves, which were born at four Flemish (Belgium) herds, between the summer of 2017 and the fall of 2018. Blood sampling was performed within 10 days after birth.
*The* general characteristics of the studied population ($$n = 210$$) are provided in Table 1. A detailed distribution of these characteristics within each herd can be found in the Supplementary file (S1). The calves had a mean [± standard deviation (SD)] gestational age of 278 ± 4.7 days and most were born during the fall months ($44.8\%$). Their mean age at sampling was 4 ± 2.2 days, and the mean bodyweight at sampling was 40.5 ± 4.72 kg. The mean dam age at parturition was 39.9 ± 18.3 months, and more than half of the dams was multiparous ($58\%$). The mean milk yield during gestation (from conception until drying off) was 7.105 ± 1.627.3 kg, of which 3.094 ± 685.2 kg was produced during the first trimester of gestation. Table 1Calf and dam characteristics of the studied population. CharacteristicMean ± SD or n (%)Calves Number of calves per herd* 146 (21.9) 282 (39.0) 344 (21.0) 438 (18.1) Birth season Spring20 (9.5) Summer68 (32.4) Fall94 (44.8) Winter28 (13.3) Gestation length (days)278 ± 4.7 Age at sampling (days)4 ± 2.2 Body weight at sampling (kg)40.5 ± 4.72 Heart girth at sampling (cm)79.1 ± 3.21 Withers height at sampling (cm)76.1 ± 2.90 Diagonal length at sampling (cm)72.4 ± 3.27Dams Dam age at parturition (months)39.9 ± 18.3 Age primiparous23.4 ± 1.8 Age multiparous50.0 ± 16.7 Paritya (range)1 to 9 Primiparous dams88 [42] Multiparous dams122 [58] Calving interval (days)379 ± 60.2 Length of the dry period (days)44 ± 12.0 Milk yield during gestation (kg)7.105 ± 1627.3 During first trimester3.094 ± 685.2 During second trimester2.605 ± 597.2 During third trimester1.417 ± 438.1aParity 1 = after the first parturition.*A detailed distribution of all characteristics across herds is given in Supplementary file S1.
The calves’ TL was on average 1.01 ± 0.17, ranging from 0.66 to 1.66. The median of the TL was 0.99 and the 25th and 75th percentile were 0.90 and 1.12, respectively. Distributions of the untransformed and log10-transformed TL are given in Supplementary file S2.
## Univariable analysis
In the univariable analysis (Table 2), 23 of the evaluated variables showed an association with the log10 transformed TL ($P \leq 0.15$). These variables included those related to the calf: season of conception ($$P \leq 0.074$$), month of conception ($$P \leq 0.018$$), season of birth ($$P \leq 0.070$$), month of birth ($$P \leq 0.002$$), calf age at sampling ($$P \leq 0.013$$), and calf heart girth ($$P \leq 0.038$$).Table 2Results of the univariable model showing variables associated with the log10 transformed TL ($P \leq 0.15$).VariablesCategoriesEstimatesaP-valueCalf Season of conceptionSpringRef.0.074Summer− 0.842Fall− 7.069Winter− 7.218 Month of conception0.018 Season of birthSpringRef.0.070Summer− 6.816Fall− 7.490Winter− 0.895 Month of birth0.002 Calf age at sampling− 1.2330.013 Calf heart girth− 0.7080.038Dam Dam parity (continuous)− 1.4570.062 Dam parity (categorical)1Ref.0.1332− 0.618≥3− 4.755 Dam age at calving (months)− 0.1220.045 MilkBot® scale− 0.2000.121 MilkBot® milk at peak− 0.2760.129 MilkBot® milk at 60 DIM− 0.0050.121THI Mean THI first trimester0.455< 0.001 Days THI > 65 first trimester0.1370.002 Days THI > 70 first trimester0.2210.004 Mean THI third trimester− 0.4140.002 Days THI > 65 third trimester− 0.1330.002 Days THI > 70 third trimester− 0.1120.003 Median weekly THI− 0.4360.054 Median weekly THI first trimester0.451< 0.0017 Median weekly THI third trimester− 0.3880.003 Weekly THI’s week 25 until week 38bBetween <0.001 and 0.071 Days THI > 70 (whole gestation)− 0.1260.020aEstimates presented as a % difference in TL for a 1-unit increase in the explanatory variable.bDetailed weekly THI’s between week 20 and 39, see Supplementary file S3.
Dam related variables that were tested in the univariable analysis were: dam parity (continuous, $$P \leq 0.062$$), dam parity (categorical, $$P \leq 0.133$$), dam age at calving ($$P \leq 0.045$$), MilkBot® scale ($$P \leq 0.121$$), MilkBot® peak ($$P \leq 0.129$$), and MilkBot® 60 DIM ($$P \leq 0.121$$).
Finally, the THI variables selected in the univariable analysis were: mean THI first trimester ($P \leq 0.001$), days THI > 65 first trimester ($$P \leq 0.002$$), days THI > 70 first trimester ($$P \leq 0.004$$), mean THI third trimester ($$P \leq 0.002$$), days THI > 65 third trimester ($$P \leq 0.002$$), days THI > 70 third trimester ($$P \leq 0.003$$), median weekly THI ($$P \leq 0.054$$), median weekly THI first trimester ($P \leq 0.001$), median weekly THI third trimester ($$P \leq 0.003$$), weekly THI’s between week 25 until week 38 (between resp. $P \leq 0.001$ and $$P \leq 0.071$$), and lastly days THI > 70 during the whole gestation ($$P \leq 0.020$$).
## Multivariable analysis
The results of the multivariable model with the best fit for all animals (primi- and multiparous) and for the multiparous animals alone are shown in Tables 3 and 4, respectively. The R2 for both models revealed that a limited proportion of variance is explained by the variables in the model. The R2 for the model including all animals was $19\%$, of which $8\%$ was explained by the fixed factors in the model. The R2 for the model for multiparous dams was $21\%$, of which $9\%$ was explained by the fixed factors. Hence, in both models, $11\%$ of the variation in TL is explained by the random factor, herd. Table 3Multivariable model (both primi- and multiparous dams included) adjusted from the variables that obtained $P \leq 0.15$ in the univariable analyses. Fixed effectEstimate ($95\%$ CI)aP-valueCalf age at sampling (days)− 1.16 (− 2.089, − 0.205)0.017Dam age at calving (months)− 0.12 (− 0.233, − 0.002)0.045Median THI third trimester of gestation− 0.35 (− 0.597, − 0.104)0.006aEstimates, with $95\%$ confidence interval, presented as a % difference in TL for a 1-unit increase in the explanatory variable. Herd was included as a random factor and the log10 transformed TL was used as the outcome variable. Table 4Multivariable model for the multiparous dams ($$n = 122$$), adjusted from the variables that obtained $P \leq 0.15$ in the univariable analyses. Fixed effectEstimate ($95\%$ CI)aP-valueCalf age at sampling (days)− 1.57 (− 2.882, − 0.190)0.025MilkBot® scale (kg/day)− 0.22 (− 0.465, − 0.022)0.078Days THI >65 during third trimester of gestation− 0.08 (− 0.188, -0.012)0.098aEstimates, with $95\%$ confidence interval, presented as a % difference in TL for a 1-unit increase in the explanatory variable. Herd was included as a random factor and the log10 transformed TL was used as the outcome variable.
Calf age at sampling, dam age at calving and median THI during the third trimester of gestation were significantly and negatively associated with TL in newborn calves. Cows that were older at parturition birthed calves with shorter TL ($$P \leq 0.045$$), and a higher median THI during the third trimester of gestation resulted in calves born with shorter TL ($$P \leq 0.006$$). However, the largest effect was seen in calf age at sampling, with significantly shorter TL ($$P \leq 0.017$$) in calves that were older at sampling.
For the model including multiparous dams, only calf age at sampling was significantly and negatively associated with the newborns TL. The MilkBot® scale, and the days with a THI >65 during the third trimester of gestation tended to be negatively associated with the TL of newborn calves. Calves that were older at sampling had significantly shorter TL ($$P \leq 0.025$$). The higher the MilkBot® scale of the dam, the shorter the TL of newborn calves tended to be ($$P \leq 0.078$$), and more days with a THI >65 during the third trimester of pregnancy, tended ($$P \leq 0.098$$) to amount to shorter TL in newborn calves.
## Discussion
Telomere length at birth has a significant association with survival, length of productive life, and the future health status in cattle25. Thus, in the context of developmental programming of health and disease, it is important to explore parental and environmental factors that are associated with TL at birth.
To the best of our knowledge, this study is the first to examine prenatal programming of bovine telomere biology. We found that for primi- and multiparous dams, calf age at sampling, dam age at calving and median THI during the third trimester of gestation were associated with shorter TL at birth. Looking at the multiparous animals separately, only calf age at sampling showed a significant effect on TL of the newborn calves. Our findings cast a light on prenatal influences on TL, which may contribute to longevity after birth.
There is a significant, negative effect of calf age at sampling on the TL in both of our models. This is in accordance with the study of Seeker et al. [ 2019] that describes a clear decline in LTL in the first year of life23. A faster telomere attrition rate shortly after birth has not only been described in cattle23, but also in humans52 and Soay sheep6. It has been proposed that faster attrition shortly after birth is due to the high number of cell divisions necessary for quick growth. Furthermore, postnatal maturation of the immune system and sudden pathogenic challenges might cause fast telomere depletion during the first months of life23. A point of improvement would be to sample calves within the first 24 hours of birth, to have minimal variation in age at sampling as well as to minimise the telomere attrition happening shortly after birth.
In our study, TL differed significantly between herds. A similar variation in TL has been described in previous research and could be attributed to herd environment5, which may be due to differences in genetics and management. As our samples were placed on the qPCR plates sorted by herd, plate and herd effects might reflect the same thing. However, we technically adjusted qPCR plate effects by including inter-run calibrators. Including both effects in the statistical models would proclaim too much weight on one or both, thus, it was decided to include only herd as a random effect.
Another finding was that for all animals (both primi- and multiparous), TL was negatively associated with dam age at parturition, in other words, the older the dam at calving, the shorter the calf’s TL. In a separate analysis including only multiparous animals, no such effect was observed, suggesting that this effect comes mainly from the primiparous animals. One might speculate that this might be due to the fact that during pregnancy, these adolescent dams are still growing substantially themselves as opposed to multiparous animals53. However, this finding is in contrast to what has been described in the great reed warbler, where maternal age was strongly and positively correlated with TL54. Conversely, no parental age effects on offspring TL where found in free-living Soay sheep and badgers14,55. González-Recio et al. clearly established that being born out of heifers leads to offspring with a longer functional lifespan, compared to animals born out of multiparous dams56. Also, previous research by our group found similar results, comparing dam parity with the odds of the calf producing at least 100,000 kg of milk during its productive life. Producing 100 tonnes of milk is an achievement that is indicative of longevity combined with a high lifetime production. It was shown that the odds of becoming a 100 tonne cow was highest in cows born out of heifers (parity 1 vs. parity 2, OR = 1.58), in other words, born out of young animals51. Consequently, it might be interesting to improve longevity by selecting replacement heifers born out of younger dams.
Interestingly, none of the milk yield parameters were associated with TL in the multivariable model for all animals or multiparous dams separately. Neither dry period length nor total milk yield during gestation were significantly associated with TL. MilkBot®-parameters milk yield at peak production and milk yield at 60 DIM were associated with TL in the univariable analyses, but not in the multivariable models. In multiparous animals, only the MilkBot® scale showed a weak negative association with the TL of newly born calves. The MilkBot® scale signifies the overall magnitude of the milk production, and is the theoretical maximum daily milk yield (lb/day or kg/day). It rises with parity and varies between breeds, with Holstein Friesians having the greatest scale values57. Seeker et al.23 proposed in their study that two genetic groups considerably different in milk yield, did not differ in their mean TL, suggesting it is likely that there is no unfavourable genetic correlation between TL and productivity23. This would be desirable, as it implies that selection for longevity, based on TL, would have no effect on milk production of cows. However, our results show there is a tendency to shorter telomeres in newborn calves born out of dams with a larger scale according to the MilkBot® model. This could imply that the magnitude of the milk production of the dam might influence the calves’ TL negatively, thus have an impact its possible longevity. This tendency might be more pronounced when more multiparous dams, significantly differing in genetic potential for milk production, would be included in the study, since the multiparous dams in our study had similar milk yields. Further research in a larger number of lactating animals might elucidate an effect of milk yield parameters on TL at birth. However, the global population of Holstein Friesians can be considered as one single population unit in terms of genetic divergence58, thus these effects might only be demonstrated at the extremes of the population.
Other than human studies, this is the first study investigating the influence of heat stress during pregnancy on TL in newborn animals. Seeker et al.24 hypothesised that the year of sampling was associated with telomere attrition rates, and that these changes in TL might be partially explained by weather variables. This led us to investigate these weather variables more deeply by taking into account the maximum daily temperatures and daily relative humidity during the entire gestation of the dam. We found that the median THI during the third trimester of gestation was negatively associated with the calves’ TL. Such that every one-unit increase of the median third trimester THI, lead to a $0.35\%$ decrease in TL. In the multiparous dams alone, there was a tendency that during the third trimester of gestation, the amount of days with a THI above 65 was negatively associated with the TL at birth. These results are in agreement with what has been described recently in humans, by Martens et al.49. They describe a clear negative effect of prenatal temperature exposure above the heat threshold (19.5 °C) on the TL of umbilical cord blood in newborn babies. The effect of a 1 °C increase in ambient temperature was strongest at week 36 of pregnancy, and led to a $3.29\%$ decrease of the cord TL at this timepoint. This study also described an interesting protective effect of cold temperatures on TL, with the cold threshold set at 5 °C49. The protective effect of cold exposure may be due to lower metabolic rates and altered oxidative stress states59. Additionally, an immune-stimulating effect of acute cold exposure has been described in humans60. Because of this protective effect, one might speculate that ventilation and/or cooling measures during periods of heat stress might reduce the loss of TL in newborn calves. The potential protective effect of cold temperatures or other factors related to seasonality, remain to be investigated.
A limitation in our research might be that the health status of the dam during pregnancy was unknown. It has been previously described that lameness status of the dam during gestation had a significant effect on TL at birth, with calves of lame cows having shorter TL at birth25. In humans, it has recently been shown that a maternal pro-inflammatory state during pregnancy was significantly associated with shorter TL in newborn babies48. Thus, the effects of lameness or other disease events during gestation could be interesting to investigate further. The absence of paternal data might be another shortcoming in our study. Although sire identification was available for all calves, the use of sires showed great variance both within as well as between herds. As such, sire effects were considered included in the herd effects. Also, sire age at semen collection was impossible to ascertain, thus further investigation of paternal age was not possible. Strong paternal effects have been described in humans, in a study by Nordfjäll et al., where paternal age was positively correlated with the newborn’s TL47. This positive paternal age effect was not demonstrated in other species like sheep and badgers14,55, although investigating paternal data further could be worthwhile.
It is important to note that the R2 of our statistical models is low. In our study, only about $20\%$ of the variance in TL is explained by the variables in our models, of which $11\%$ can be explained by herd. Thus, herd-related factors (e.g. management and genetics) might be considered as important influences on TL variance. Accordingly, about $80\%$ of the variance in TL cannot be explained by the variables in our models but might largely be due to inheritance. Heritability of TL has been described to be moderate ($44.9\%$) across vertebrate species, although there is considerable heterogeneity in heritability estimates between these species61. In sheep heritability was estimated to be $23.3\%$13, whereas in dairy cattle heritability was described to be $36\%$ at birth to $46\%$ at first lactation25. In humans, heritability estimates have been shown to be in the order of $60\%$, although lower and higher estimates have been demonstrated62. Further studies are needed to investigate other prenatal effects on TL in newborn calves. Also, more longitudinal studies are needed to assess these prenatal effects on the newborn’s TL, and the consequences later in life. While heat stress is a hot topic in modern dairy cattle research, its effects on TL as well as the effects of preventative measures warrant further investigation.
## Animal population and data collection
Animals included in the present study were Holstein Friesian dairy cows and their respective calves, belonging to 4 dairy farms in Flanders (Belgium). Herds were selected based on their willingness to collaborate and the availability of necessary data. Informed consent was obtained from all dairy farmers. Herd sizes ranged from 100 to 250 lactating cows, with an average 305-days milk yield between ~9000 and ~11,000 kg. All herds participated in official milk recording. In three herds, cows were milked twice a day while in the fourth herd cows were milked by an automated milking system, which recorded an average of 2.6 milkings a day.
Animals were housed in free-stall barns and were fed according to their requirements for maintenance and production, based on results of the monthly production tests. Rations consisted of high-quality roughages (maize and grass silage, sugar beet pulp, and fodder beets), supplemented with concentrates. Cows were dried off between six to eight weeks prior to their expected calving date. When the animals approached parturition, they were separated in a maternity pen and closely monitored by the farmer. After calving, all calves were immediately moved to individual calf pens with straw bedding and were given 2L colostrum. Care was taken that all calves received 4 L of colostrum within 12 hours after birth.
All purebred singleton female HF calves born at the participating herds between August 2017 and November 2018, were enrolled in the study. Newborn calves were weighed (Seca® flat scale, Seca Benelux, Naarden, the Netherlands), and blood samples were taken within 10 days after birth. Apart from blood sampling and weighing, body measurements of the calves were measured including heart girth, withers height, and diagonal length, as described by Kamal et al.63. Season of birth was grouped as follows: winter (21 December to 20 March), spring (21 March to 20 June), summer (21 June to 20 September), and fall (21 September to 20 December).
Data selection comprised of correct identification of the calves, gestation length, sex, and age at blood sampling. Only calves born after a gestation length of between 265 and 295 days were included in the study. Blood samples had to be taken within ten days after birth. As such, data of 210 calves were included for further analyses.
Prenatal environment was interpreted both in the narrow sense of the word (as uterine environment, thus maternal factors), as well as in the broader sense, meaning climatic conditions during the whole gestation.
Dam information was extracted from the herd databases and included parity, dam age, and monthly milk production via official milk recording. Monthly milk records of the dams were fitted to the MilkBot® model to summarize the magnitude and shape of each lactation curve. MilkBot® parameters such as the ‘scale’ as a measure of the magnitude of the lactation curve, the ‘ramp’ or steepness of the post-parturient rise in milk production, and the ‘decay’ or the rate of late lactation decline were included for analysis. Using the MilkBot® model, milk yield at 60 days of lactation and milk yield at the peak of the lactation curve were calculated57. The dry-off date was also included to calculate the length of the dry period.
Sire information was limited to bull identification. For the 210 calves, 90 different sires were recorded. Most of them ($\frac{59}{90}$) sired only one or two calves. Eight bulls were used on more than five dams, with a maximum of seven calves born out of one sire. Only three bulls were used in more than one herd.
Weather data were obtained from the Royal Observatory of Belgium (Brussels, Belgium) and the Belgian Royal Meteorological Institute (Brussels, Belgium). Weather data included average and maximum temperature, relative humidity, hours of daylight, and hours of total sunlight for each day between 1 October 2016 and 31 December 2018. This timeframe includes weather data starting from the month prior to conception of the first born calf, until one month after birth of the last born calf.
Based on the weather data, a daily temperature-humidity index (THI) was calculated using the relative humidity (RH) and the daily maximum temperature (T), using the following formula: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$THI=\mathrm{0,8}*T+RH*\left(T-\mathrm{14,4}\right)+\mathrm{46,4}$$\end{document}THI=0,8∗T+RH∗T-14,4+46,464. Daily THI’s and median weekly THI’s were calculated. Gestation was divided into trimesters and for each trimester, average THI, number of days with THI greater than 65 and number of days with THI greater than 70 were computed.
## Blood sampling
Whole blood samples were collected, using 10.0 ml BD Vacutainer® tubes (Becton Dickinson, Belliver Industrial Estate, Plymouth PL6 7BP, United Kingdom), with EDTA as an anticoagulant. Blood samples were taken by venepuncture (Vena jugularis), within the first 10 days of life and were stored at – 30 °C until further analysis.
## Ethics statement
All experimental procedures were approved by the Ethical Committee (EC) of the Faculty of Veterinary Medicine (Ghent University, Belgium) under the EC number $\frac{2017}{87.}$ Samples were taken in accordance with the relevant guidelines and regulations and all authors complied with the ARRIVE guidelines65.
## DNA extraction and TL measurement
Telomere length was measured at the Centre for Environmental Sciences, at Hasselt University in Belgium.
Leukocyte DNA was extracted from the whole blood using the QIAamp DNA Mini Kit (Qiagen, Inc., Venlo, the Netherlands). DNA yield and purity were assessed for each sample on a NanoDrop 1000 spectrophotometer (Isogen, Life Sciences, Belgium) and DNA integrity was evaluated with agarose gel-electrophoresis.
Relative average leukocyte TL was measured and assessed in triplicate, as previously described by Martens et al.9, by the use of a modified quantitative real-time PCR (qPCR) protocol. Briefly, the telomeric region was amplified with the use of telomere specific primers (telg and telc), and one single-copy gene (beta-globulin) was amplified on a QS5 Fast Real-Time PCR System (Applied Biosystems, Hasselt, Belgium) in a 384-well format. Cycle thresholds after the amplification of the telomere specific region were normalized relative to the cycle thresholds after the amplification of the single-copy gene using the QBase+2 software (Biogazelle, Zwijnaarde, Belgium)9.
Relative average leukocyte telomere lengths were expressed as the ratio of telomere copy number to single-copy gene number (T/S), relative to the average T/S ratio of the entire sample set. Reaction efficiency was assessed on each reaction plate (using a 6-point serial dilution of pooled buffy coat DNA) and two inter-run calibrators were used to account for inter-run variability.
Coefficients of variation (CVs) of $0.48\%$, $0.29\%$ and $5.97\%$ for telomere runs, single-copy gene runs and T/S ratios, respectively, were achieved. The reliability of our assay was evaluated by calculating the intraclass coefficient (ICC) with $95\%$ CI of triplicate measures (T/S ratios). The intra-assay ICC was 0.849 ($95\%$ CI 0.812 to 0.879).
## Statistical analysis
All statistical analysis were performed in R 3.6.166.
As TL was not normally distributed, all TL measurements were log10 transformed to achieve normal distribution (Shapiro-Wilkinson normality test: $W = 0.99381$, $$P \leq 0.5338$$). To assess factors associated with log10 transformed TL, linear mixed models were built using the lmer() function of the ‘lme4’ package67.
The outcome variable for all models was the log10 transformed TL, and herd was included as a random effect. The fixed effects of interest were related to the calf, its dam and the environment. Calf related variables were conception month and season, birth month and season, age at sampling, body weight as well as the different body measurements. The examined dam related variables were dam parity (both as a continuous and categorical variable), dam age, gestation length, age at conception for the primiparous animals, and parturition-to-conception interval, calving interval, dry period length, and MilkBot® parameters of the previous lactation for multiparous animals. Daily THI’s, weekly THI’s, and mean THI per trimester of gestation were investigated as well.
First, univariable associations between the outcome variable and independent factors were examined with statistical significance assessed at $P \leq 0.15.$ Second, correlation coefficients were calculated between the significant variables to avoid multicollinearity in the next step. A correlation coefficient > 0.60 among two factors led to the selection of one of the two variables for further analysis, based on significance and physiological relevance. The selected fixed effects and their 2-way interactions were combined into a multivariable model, but removed if found non-significant, after which the model was refitted. Modelling was performed using a backward stepwise elimination method with a selection criterion based on the Akaike information criterion (AIC). Statistical significance and tendency were declared at $P \leq 0.05$ and 0.05 < $P \leq 0.1$, respectively.
First a multivariable model was built for all animals. A second model was built for multiparous dams, since milk yield parameters were only available for calves born out of lactating cows. Coefficients of determination (R2 analogs) for both models were calculated using the r.squaredGLMM() function68. Regression estimates with their $95\%$ CI are presented as a % difference in TL for each unit increase in explanatory variable. Figures were generated with the library ‘ggplot2’69.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-31943-8.
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|
---
title: A genome-wide association study identifies a possible role for cannabinoid
signalling in the pathogenesis of diabetic kidney disease
authors:
- Wael Osman
- Mira Mousa
- Mohammed Albreiki
- Zahrah Baalfaqih
- Hinda Daggag
- Claire Hill
- Amy Jayne McKnight
- Alexander P. Maxwell
- Habiba Al Safar
journal: Scientific Reports
year: 2023
pmcid: PMC10033677
doi: 10.1038/s41598-023-31701-w
license: CC BY 4.0
---
# A genome-wide association study identifies a possible role for cannabinoid signalling in the pathogenesis of diabetic kidney disease
## Abstract
Diabetic kidney disease (DKD), also known as diabetic nephropathy, is the leading cause of renal impairment and end-stage renal disease. Patients with diabetes are at risk for DKD because of poor control of their blood glucose, as well as nonmodifiable risk factors including age, ethnicity, and genetics. This genome-wide association study (GWAS) was conducted for the first time in the Emirati population to investigate possible genetic factors associated with the development and progression of DKD. We included data on 7,921,925 single nucleotide polymorphism (SNPs) in 258 cases of type 2 diabetes mellitus (T2DM) who developed DKD and 938 control subjects with T2DM who did not develop DKD. GWAS suggestive results ($P \leq 1$ × 10–5) were further replicated using summary statistics from three cohorts with T2DM-induced DKD (Bio Bank Japan data, UK Biobank, and FinnGen Project data) and T1DM-induced DKD (UK-ROI cohort data from Belfast, UK). When conducting a multiple linear regression model for gene-set analyses, the CNR2 gene demonstrated genome-wide significance at 1.46 × 10–6. SNPs in CNR2 gene, encodes cannabinoid receptor 2 or CB2, were replicated in Japanese samples with the leading SNP rs2501391 showing a Pcombined = 9.3 × 10–7, and odds ratio = 0.67 in association with DKD associated with T2DM, but not with T1DM, without any significant association with T2DM itself. The allele frequencies of our cohort and those of the replication cohorts were in most cases markedly different. In addition, we replicated the association between rs1564939 in the GLRA3 gene and DKD in T2DM ($$P \leq 0.016$$, odds ratio = 0.54 per allele C). Our findings suggest evidence that cannabinoid signalling may be involved in the development of DKD through CB2, which is expressed in different kidney regions and known to be involved in insulin resistance, inflammation, and the development of kidney fibrosis.
## Introduction
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder associated with hyperglycemia due to a combination of insulin resistance and β-cell impairment, influenced by genetic and environmental factors1. Among the different complications that arise with T2DM, progressive loss of renal function represents one of the most serious complications, characterized as diabetic kidney disease (DKD), with an estimated prevalence of 30–$50\%$2. DKD is a multifactorial, heterogeneous condition that is considered as the leading cause of progressive renal impairment and end-stage renal disease3. This condition results in pathologic changes associated with glomerular structure and function, albuminuria, excessive extracellular matrix deposition resulting in mesangial matrix expansion, inflammation, and fibrosis4,5. In addition to poor glycemic control, which can be controlled by pharmaceutical and clinical interventions, there also are several nonmodifiable risk factors associated with DKD, such as age, sex, ethnicity, and genetics6. Familial aggregation of DKD has been demonstrated in different ethnic groups, demonstrating the importance of understanding how environmental and genetic factors interact resulting in distinct disease predispositions and mechanisms in different populations7–13.
The Middle East region has some of the highest prevalence of T2DM worldwide, with a disproportionally high prevalence of DKD, ranging between 10.8 and $61.2\%$, rendering this particular cohort interesting for genetic studies of this trait14,15. Genetic and epigenetic studies of DKD, using candidate gene approaches and genome-wide studies, have been undertaken to identify genetic loci conferring susceptibility or resistance to DKD. Currently, more than 150 genes have been associated with DKD using candidate gene approaches, while about 42 genetic loci have been associated with DKD susceptibility or for DKD indicators, such as albuminuria, through genome-wide association studies (GWAS)12,16–23. There is, however, a few robustly replicated loci that have emerged, with a knowledge gap on the functional role of the newly identified genes in DKD, which would require further replication studies and detailed analysis in experimental models. Furthermore, there are a limited number of GWAS studies that have been conducted in the Middle Eastern populations related to T2DM-associated DKD.
Though DKD demonstrates familial aggregation and clustering, and SNP heritability, the genetic factors influencing DKD remain largely unknown24–27. To further explore the genetic association of DKD in the Middle Eastern population with T2DM, we performed a GWAS to identify possible genetic risk factors and uncover mechanisms that contribute to the development and progression of DKD among an Emirati population. Additionally, we aimed to replicate the strongest signals from the current analysis in different populations to confirm this association, as well as in the GWAS Catalog (European ancestry cohorts). We also aimed to replicate previously identified genome-wide significant associations in the Middle Eastern population.
## Recruitment and ethical consideration
This cross-sectional study recruited 1223 unrelated Emirati participants from Imperial College of London Diabetic Centre (ICLDC), Abu Dhabi, United Arab Emirates, between the period of April 2018 to January 2020. The participants were divided into two clinical groups: T2DM with DKD ($$n = 261$$) and T2DM without DKD ($$n = 962$$). The diagnosis of T2DM was made by a qualified physician based on WHO guidelines and criteria, with an a HbA1c ≥ 6.5 and were receiving treatment for their condition, whereas DKD was diagnosed as outlined previously28. In summary, DKD was defined as either decreased estimated glomerular filtration rate (eGFR < 60 mL/min/1.73 m2) with or without renal damage over a period of at least 3 months, or based on an albumin-to-creatinine ratio ≥ 30 mg/g, or proteinuria > 500 mg over a 24-h period in the setting of T2DM and/or abnormalities, as assessed by imaging or histology29. The inclusion criteria were: [1] UAE nationals, [2] clinically diagnosed with T2DM with or without DKD, [3] above 18 years old and [4] able to provide informed consent and complete the survey. Participants were excluded if they were diagnosed with a critical condition (e.g., cancer) or pregnancy.
An informed consent was obtained from all participants. The study was conducted in accordance with the Declaration of Helsinki and has been approved by the Imperial College London Diabetes Centre (ICLDC) ethical committee (IREC 036).
## Pre-laboratory measurements
All participants completed a validated screening questionnaire that includes details on the demographic characterization, risk factors, symptomatology, and family history of diseases. Medical history, medication and management methods were provided by ICLDC. Anthropometric parameters and blood pressure were collected and assessed during the hospital visit by trained nurses using calibrated stadiometer, weight scale and sphygmomanometer. Biochemical data such as chemistry and hematology parameters, to assess the presence of diabetic complications, were obtained from ICLDC.
## DNA extraction, genotyping, imputation, and quality control
Total of 4 ml of blood sample was collected from each participant into EDTA anti-coagulant tubes. Genomic DNA was extracted using the automated MagPurix 24 system (LTF Labortechnik, GmbH&Co. KG, Hattnaeur, Wasserburg, Germany) according to the manufacturer’s instructions. DNA was quantified using DS-11 Series of Spectrophotometer/Fluorometer (DeNovix Inc. Wilmington, USA).
Genotyping was done using Infinium Global Screening Array (Illumina, Inc., San Diego, CA, USA), which contained 654,027 genetic markers and developed by Avera Institute for Human Genetics (South Dakota, USA). The Infinium Global Screening Array-24 v3.0 BeadChip would be best suited for this ethnically diverse cohort because it features a broad spectrum of diverse exonic content, including both cross population and population specific markers (EUR: 52,980 markers; EAS: 31,375 markers; AMR: 45,977 markers; AFR: 43,122 markers; SAS: 40,298 markers). Genotypes were exported, in Genome Reference Consortium human build 37 (GRCHb37) and Illumina ‘source’ strand orientation. The raw data was processed using Genome Studio (Version 2, Illumina, Inc.) genotyping module to generate the data for further analysis.
Stringent quality control measures were performed for samples and SNPs to ensure subsequent robust association tests. Samples that failed to reach a $98.0\%$ call rate were reanalyzed. Samples were excluded if they had discordant sex information ($$n = 0$$) or outlying heterozygosity rate ($$n = 0$$). We also applied identity by state (IBS) and identity by descent (IBD) to exclude duplicated individuals or those who are first degree relatives (pairwise identity-by-state (PI_HAT) > 0.5, and accordingly, we excluded 27 individuals: three cases and 24 controls. Quality control measures for SNP callings were missing per individual (> $10\%$), missingness per marker (> $5\%$), minor allele frequency (MAF > $1\%$), Hardy–Weinberg Equilibrium (HWE > 0.00001). After quality control, a total of 443,465 variants passed filters.
Genotypes were prephased and imputed with untyped markers (∼39 M) using the Phase 3 multi-ethnic 1000 Genomes Projects panel, as the reference based on the human genome assembly hg19 (https://mathgen.stats.ox.ac.uk/impute/1000GP_Phase3.html), and was carried out by using BEAGLE, using standard protocols and recommended settings. Imputation was performed on both autosomal and sex chromosomes. Post imputation quality control measures (genotyping rate < $95\%$, call rate < $98\%$, MAF > $1\%$, HWE > 0.00001) were conducted, and a total of 7,921,925 variants passed filters.
## Statistical analysis
Continuous variables were presented as means and standard deviations, and categorical data were calculated as proportions and percentages. When needed, Kolmogorov–Smirnov test was used to test the normality of the data. student’s t-test (unpaired t-test) was used to test the significant differences of the pre-laboratory measurements between cases and controls. The significance level for pre-laboratory measurements was set at $p \leq 0.05.$ These analyses were done using SPSS 22 (SPSS Inc., IBM Company, Chicago, Illinois, USA).
In the GWAS and replication analyses, we used a multivariate logistic regression model, assuming additive allelic effects for genotyped and imputed SNPs, to assess the statistical significance of each SNP with adjustment for age, gender, log BMI, and the first ten principal components as covariates. The significance levels for the GWAS were 6.3 × 10−9 ($\frac{0.05}{7}$,921,925) in the analysis following the Bonferroni correction for multiple testing. Odds ratios and $95\%$ confidence intervals were calculated using the minor allele genotype as a reference. Principal component analysis (PCA) was performed using the case–control samples in this GWAS and a reference panel of populations in the 1000 Genome project (Fig. 1). Meta-analysis studies were performed using PLINK 1.09. Haploview v4·1 was used to conduct subsequent association analysis. Functional Mapping and Annotation of genetic associations (FUMA) was used to perform visualization Manhattan plots30. To assess the association of a given SNP with T2DM and DKD, the allelic frequencies were compared by means of a χ2 statistic, which yielded an individual p-value for each combination of SNP and allelic model. A quantile–quantile (Q-Q) plot analysis was carried out to check whether the distribution of the inflation p-values deviated from the expected distribution under the null hypothesis of no genetic association and investigate if the overall significance of the genome-wide associations is due to potential impact of population stratification. Systematic bias and the impact of population stratification was evaluated by calculating the genomic control inflation factor [λ GC] and noted for each analysis. A Manhattan plot was generated with −log10 p-values. Figure 1Q-Q plots and Manhattan plots of the DKD GWAS and gene-based test using FUMA and MAGMA. ( A) Quantile–Quantile plot of the GWAS data. The observed p-values, on a log-10 scale, are plotted against their expected values under the null hypothesis assuming none of the sites have an effect. ( B) Quantile–Quantile plot of the gene-based data. The observed p-values, on a log-10 scale, are plotted against their expected values under the null hypothesis assuming none of the sites have an effect. ( C) Manhattan plot of the DKD GWAS. The plot shows the p-values on a log-10 scale (y-axis) by their chromosomal location (x-axis). ( D) Manhattan plot of the DKD gene-based test as computed by MAGMA. The plot shows the p-values on a log-10 scale (y-axis) by their chromosomal location (x-axis). Input SNPs were mapped to 18,124 protein coding genes. Genome wide significance (Red dashed line in the plot) was defined at $$p \leq 0.05$$/18,124 = 2.759 × 10–6, labelling one gene (CNR2) that reached genome wide significance.
*The* gene-set analysis was conducted using the FUMA software, an online platform for functional mapping of genetic variants from GWAS summary30. *The* gene-set analyses was produced in Multi-marker Analysis of GenoMic Annotation (MAGMA), in which it examines sets of biologically related genes that are strongly associated to the disease of interest31. The MAGMA tool uses multiple linear regression models to assess whether genes in each gene set are associated to a polygenic trait, after correcting for linkage disequilibrium between variants and gene size.
## Replication analysis
Based on the top identified SNP in this cohort, further replication data was obtained from three datasets: DKD summary results of Biobank Japan32, the FinnGen Project33 (cohort name: finngen_R5_DM_NEPHROPATHY_EXMORE), and the UK Biobank34 (cohort name: E1122). The GWAS study for the Japanese population contains 220 cases with DKD vs. 132,764 controls. The Finnish samples included 3283 cases with DKD vs. 181,704 controls, and the UK Biobank contains 58 cases vs. 8444 controls. Replication analysis in type 1 diabetes-induced DKD was done in the UK-ROI cohort (Belfast, UK), white individuals with T1D, diagnosed before 31 years of age, whose parents and grandparents were born in the UK and Ireland19. The UK-ROI cohort case group comprised individuals with persistent proteinuria (> 500 mg/24 h) developing more than 10 years after the diagnosis of diabetes, hypertension (> $\frac{135}{85}$ mmHg and/or treatment with antihypertensive medication), and retinopathy; ESRD ($27.2\%$) was defined as individuals requiring renal replacement therapy or having received a kidney transplant. Absence of DKD was defined as persistent normal urine albumin excretion rate (AER; 2 out of 3 urine albumin to creatinine ratio [ACR] measurements, 20 mg of albumin/mg of creatinine) despite duration of T1D for at least 15 years, while not taking an antihypertensive medication, and having no history of treatment with ACE inhibitors. In the analysis in this study, there were 823 DKD/ESRD cases and 903 controls. DNA from individuals in the UK-ROI collection were genotyped using the Omni1-Quad array (Illumina, San Diego, CA, USA).
A replication analysis was conducted in this cohort on previously identified variants (SNPs and candidate genes) associated with DKD, totaling 49 SNPs in 42 gene loci. The direction of effect and strength of association was measured in this cohort.
## Study cohort characteristics
All study participants had T2DM, and both cases and controls (with or without DKD) demonstrated poor glycemic control indices (mean HbA1c > $7\%$ for both groups). Moreover, those with DKD were mostly men ($p \leq 0.001$), older in age ($p \leq 0.001$), and had higher rates of associated comorbidities, such as hypertension ($p \leq 0.001$) and dyslipidemia ($p \leq 0.001$) (Table 1). The renal function (eGFR, $p \leq 0.001$; creatinine, $p \leq 0.001$), urea, $p \leq 0.001$; vitamin D, $$p \leq 0.002$$) and lipid profile (cholesterol total, $p \leq 0.001$; HDL, $$p \leq 0.002$$; LDL, $p \leq 0.001$) were significantly lower in participants with DKD than non-DKD patients. BMI demonstrated no significant association ($$p \leq 0.392$$) between cases and controls. Table 1Demographic data of the study participants. VariableCase ($$n = 258$$)Control ($$n = 938$$)P-valueGender Male188 ($72.9\%$)547 ($58.3\%$) < 0.001 Female70 ($27.1\%$)391 ($41.7\%$)Age (mean, SD)64.81 (11.48)53.95 (11.38) < 0.001Age (categories) < 4619 ($7.4\%$)238 ($25.4\%$) < 0.001 47–5430 ($11.6\%$)227 ($24.2\%$) 55–6033 ($12.8\%$)211 ($22.5\%$) 61–6767 ($26.0\%$)166 ($17.7\%$) > 67109 ($42.2\%$)96 ($10.2\%$)BMI (mean, SD)31.72 (6.40)31.35 (5.92)0.392BMI (categories) < 18.501 ($0.4\%$)1 ($0.1\%$)0.480 18.51–24.4924 ($9.4\%$)79 ($8.5\%$) 24.50–29.9984 ($32.9\%$)346 ($37.2\%$) > 30.00146 ($57.3\%$)505 ($54.2\%$)Hypertension No36 ($14.5\%$)453 ($52.0\%$) < 0.001 Yes212 ($85.5\%$)418 ($48.0\%$)Dyslipidemia No38 ($15.2\%$)234 ($26.4\%$) < 0.001 Yes212 ($84.8\%$)654 ($73.6\%$)Key glycemic indices Random glucose8.18 (4.72)7.93 (4.32)0.433 HbA1c7.31 (1.93)7.01 (1.97)0.039Key renal function measures eGFR60.14 (31.49)109.93 (27.20) < 0.001 Creatinine139.70 (121.54)64.81 (26.53) < 0.001 Urea9.44 (7.39)4.23 (1.96) < 0.001 Vitamin D66.85 (44.79)76.20 (43.25)0.002Lipid profile Cholesterol total3.58 (1.17)4.01 (1.25) < 0.001 TG1.49 (0.87)1.51 (0.88)0.702 HDL1.13 (0.54)1.24 (0.44)0.002 LDL1.94 (0.89)2.43 (1.01) < 0.001eGFR: estimated glomerular filtration rate; HDL: high-density lipoprotein; LDL low-density lipoprotein; SD: standard deviation; TG: triglyceride.
## GWAS of DKD
To identify genetic variants that contribute to the susceptibility of DKD in the UAE population, we conducted a genome-wide association study (GWAS) using 258 cases with T2DM who developed DKD, and 938 control subjects who had T2DM but did not develop DKD. The samples were genotyped using the Illumina Infinium Global Screening Array (“Methods”). We selected 7,921,925 SNPs for further association analysis with DKD after applying stringent quality control (QC) filtering and imputation analyses (“Methods”). As shown in Supplementary Fig. 1, all cases and controls were representative with the structure of the UAE population as determined by PCA35,36. The full GWAS results of all suggestive loci are presented in the Supplementary Table 1. A Quantile–quantile (Q-Q) plot with the genomic control inflation factor [λ GC] of 1 indicated a low possibility of population stratification (Fig. 1A).
Based on the association analysis, twelve loci showed suggestive associations with DKD ($$p \leq 10$$–5, Fig. 1C and Supplementary Table 1). The leading SNPs of PPP1R9A gene, located in locus 7q21.3, was the most significant SNP at rs78595611 ($$p \leq 9.73$$ × 10–7) and SNP rs76361654 ($$p \leq 7.43$$ × 10–6). PPP1R9A gene is involved in neurite formation, is associated to coronary artery disease, and is a prognostic marker in renal cancer37,38. Two leading SNPs of the cytochrome P450 enzymes (CYP2B7P, rs4001941, $$p \leq 1.12$$ × 10–6; CYP4F24P, rs117074522, $$p \leq 2.27$$ × 10–6) that are involved in drug pharmacokinetics and response have also been associated with DKD in T2DM patients39. Gene OR10H2 located in locus 19p13.12 and gene STX18-AS1 in locus 4p16.2 are associated with acute myeloid leukemia and congenital heart disease, and the SNPs that have been associated to DKD are rs11664515 ($$p \leq 2.27$$ × 10–6) and rs16836018 ($$p \leq 2.27$$ × 10–6), respectively40–42. The RB1 gene located at 13q14.2 has been associated with diabetic cardiomyopathy, chronic kidney disease, and proteinuria, all phenotypes that are associated with kidney function and DKD. The leading SNP of RB1 gene is ($$p \leq 5.49$$ × 10–6)43–45. The expression of gene MOXD1 located in locus 6q23.2 for SNP rs9493286 ($$p \leq 7.25$$ × 10–6) has been identified to be associated with diabetic nephropathy and obesity-related traits46–48.Gene MACROD2 located in locus 20p12.1 have been associated with chronic kidney disease, hypertension, glomerular filtration rate, with the leading SNP rs11696648 demonstrating an association ($$p \leq 9.05$$ × 10–6)41,49–51. The CNR2 gene in locus 1p36.1 (SNP rs542405361, $$p \leq 9.43$$ × 10–6) has been associated with diabetic nephropathy, kidney fibrosis and diabetes-induced cardiac dysfunction, all phenotypes associated with DKD52–54. Additionally, CNR2 have been identified to alleviate inflammation, oxidative stress and fibrosis, suggesting an important role in kidney function and cardiovascular complications of diabetes55.
A gene-set analysis was conducted to identify genes that are associated with DKD, after implementing multiple regression model and multi-marker association. A Quantile–quantile (Q-Q) plot indicated low possibility of population stratification (Fig. 1B). When conducting a multiple linear regression model on the 18,124 protein coding genes, after correcting for linkage disequilibrium between the SNPs and gene size, the CNR2 gene demonstrated genome-wide significance at 1.46 × 10–6, as demonstrated in Fig. 1D.
## Replication analyses—T2DM-induced DKD cohorts
SNPs with suggestive associations were selected for replication in three datasets: the DKD summary results for Biobank Japan32, the UK Biobank34, and FinnGen Project33. Consequently, SNPs in one locus, CNR2, were replicated in the Japanese samples, but not in the Finnish samples, with the leading SNP in rs2501391 in CNR2 gene which we could replicate the Japanese samples with Pcombined = 9.3 × 10–7, and OR = 0.67 (Table 2). The samples from FinnGen project showed very significant differences in allele frequencies in comparison to our samples as well as the Japanese samples (Table 2). The full replication analyses are presented in Supplementary Tables 1 to 4. It is interesting to note that rs2501391 was not the most prominent SNP associated with DKD in the CNR2 gene. However, the more significant SNPs, such as rs542405361, were not found in the Japanese study to replicate them (Supplementary Tables 1 to 3). Due to the different ancestral background of the populations, this variant may be monomorphic in different ethnic groups. However, independent variants associated with DKD may be present in the same region for different ethnicities. Table 2GWAS and replication results of the SNP rs2501391 in CRN2 gene with DKD in the UAE cohort and replication cohorts from Biobank Japan, UK Biobank, and FinnGen project. SNPChr:BPA1/A2Cohort*N (Case vs. Cont)MAFOR ($95\%$ CI)SEPMeta-analysisCaseContPORPh**rs25013911:24,216,118A/GUAE$\frac{258}{9380.2120.2310.58}$ (0.32–0.84)0.1355.34 × 10–5BBJ$\frac{220}{132}$,7640.3930.3210.73 (0.53–0.93)0.1011.92 × 10–3UK-BB$\frac{58}{8444}$––0.84 (0.48–1.47)0.54FinnGen$\frac{3283}{181}$,7040.0020.0010.99 (0.12–1.86)0.4430.75UAE + BBJ9.3 × 10–70.670.17UAE + UK-BB7 × 10–41.510.022UAE + FinnGen1.2 × 10–41.640.22All cohorts7.6 × 10–61.410.099A1: effective allele; BP: base pair, indicating chromosomal location; Chr: chromosome; CI: confidence intervals; Cont: controls; MAF: minor allele frequency; OR: odds ratio, SE: standard error.*Cohorts: UAE: United Arab Emirates cohort, BBJ: Biobank Japan, UK-BB: UK Biobank, FinnGen: Finnish cohort.**Ph: p-value for heterogeneity of Cochrane's Q statistic.
## Replication analyses—T1DM-induced DKD cohorts
To test for the existence of a shared pathway for DKD in both type 1 and type 2 diabetes, SNPs with suggestive associations were also selected for replication in replication analysis in type 1 diabetes-induced DKD in the UK-ROI cohort from Belfast, UK. This is a cohort of 823 DKD/ESRD cases and 903 controls of white individuals from Ireland and the UK (see “Methods”). As shown in Table 3, none of the SNPs showed significant associations with GWAS SNPs. The lead SNP in the CNR2 gene, rs2501391, was not available in the UK-ROI cohort; however, it is highly linked to SNP rs2502959 (r2 = 0.98, D′ = 1), suggesting no significant association with T1DM-induced DKD.Table 3Replication analysis of the suggestive GWAS loci with T2DM in UK-ROI cohort from Belfast, UK.SNPChrGeneA1SEOR ($95\%$ CI)P-valuers400194119CYP2B7PA0.0910.97 (0.81–1.16)0.73rs1169664820MACROD2A0.0981.17 (0.97–1.42)0.11rs25029591CNR2A0.1080.98 (0.8–1.22)0.89rs20339584STX18-AS1C0.0831.0 (0.85–1.17)0.96rs68536734STX18-AS1T0.0840.99 (0.84–1.16)0.88rs15107984STX18-AS1T0.0841.0 (0.85–1.18)0.98rs1880115RP11-332J15.1T0.0981.0 (0.82–1.21)0.98rs15483957PPP1R3AT0.0860.93 (0.79–1.1)0.41rs29688447PPP1R3AT0.0840.91 (0.77–1.07)0.25rs20236917PPP1R3AC0.0850.95 (0.81–1.12)0.56rs11196607PPP1R3AC0.0950.86 (0.71–1.03)0.11rs115340047PPP1R3AG0.0950.86 (0.71–1.04)0.11rs1260571118LOC107985179T0.1021.02 (0.83–1.24)0.88rs613566020MACROD2T0.0891.1 (0.92–1.30)0.31rs157544320MACROD2G0.0981.15 (0.95–1.4)0.15A1: effective allele; Chr: chromosome; CI: confidence intervals; Cont: controls; MAF: minor allele frequency; OR: odds ratio, SE: standard error.
## Association of CNR2-rs2501391 with T2DM
We further investigated whether the CNR2-rs2501391 polymorphism is associated with T2DM itself. A genotype analysis was performed on this SNP in a cohort of 364 patients with T2DM and 103 healthy controls in an independent cohort. In the association analysis, we found that this SNP had no association with T2DM (Table 4), which suggests that CNR2 may be contributing to DKD development, and not T2DM per se. Table 4Association of CNR2-rs2501391 with T2DM in the UAE population. Descriptive statisticsRegression analysisGenotypeDiabetes (%)Non-diabetes (%)P-valueCrude OR ($95\%$ CI)P-valueAdjusted OR* ($95\%$ CI)P-valueAA285 ($78.3\%$)86 ($83.5\%$)0.3321.001.00AG67 ($18.4\%$)16 ($15.5\%$)0.27 (0.04–2.15)0.2201.14 (0.42–3.14)0.792GG12 ($3.3\%$)1 ($1.0\%$)0.35 (0.04–2.88)0.3493.81 (0.32–46.10)0.293*Multivariate logistic regression adjusted for age, gender and logBMI.
## Replication analysis of previous studies on DKD
We also replicated previous associations with DKD or kidney function indicators of DKD from previous GWAS that collectively reported 49 SNPs in 42 gene loci12,17,19–23,56. Based on our dataset, we evaluated the associations of 16 SNPs, and we were only able to replicate the association of rs1564939 in the GLRA3 gene with DKD ($$p \leq 0.016$$, OR = 0.54 per allele C, Table 5). In most cases, there were marked differences between the allele frequencies of our cohort and those of the original cohorts at these SNPs. Table 5Association of previous loci reported with DKD and kidney function in diabetic patients in the UAE cohort. SNPChr:BPGeneA1MAFMAF*POR ($95\%$ CI)OR*Refrs126159702:3,745,215COLEC11, ALLCG0.0660.1330.3551.21 (1.2–1.23)0.7717,18rs75838772:100,460,654AFF3C0.4530.2890.1091.2 (1.2–1.21)1.2919rs49725932:174,462,854Sp3A0.0940.140.6471.08 (1.07–1.1)1.8121rs75885502:213,168,768ERBB4G0.0110.0520.1321.92 (1.86–1.97)0.6619rs557037672:228,121,101COL4A3, COL4A4T0.0570.2060.1501.38 (1.36–1.4)0.7817,18rs64366882:228,259,302COL4A3A0.3970.560.3980.91 (0.9–0.92)1.1318rs15649394:175,651,499GLRA3, GLRA4C0.0460.180.0160.54 (0.53–0.55)–20,22rs100110254:175,654,223GLRA3G0.0450.160.0730.64 (0.63–0.65)–20,22rs125097294:175,655,143GLRA3A0.0310.160.1360.65 (0.63–0.66)–20rs1181248436:30,887,465DDR1, VARS2T0.0510.0110.0650.52 (0.51–0.53)3.9917rs99424716:89,948,232GABRR1C0.1670.640.1941.2 (1.19–1.21)1.1512rs24106018:18,922,577PSD3, SH2D4AC0.473–0.1760.86 (0.86–0.87)22rs5511917078:128,100,029PRNCR1CA0.0360.1220.9501.02 (1–1.04)1.7117,18rs1243785415:94,141,833RGMA, MCTP2G0.0290.0380.3610.76 (0.75–0.77)1.819rs5609464116:53,806,453FTOG0.3560.2520.081.22 (1.21–1.23)1.2323rs220613620:9,351,150PLCB4A0.3520.420.1980.86 (0.85–0.86)1.1212A1: effective allele; BP: base pair, indicating chromosomal location; Chr: chromosome; CI: confidence intervals; MAF: minor allele frequency of the UAE cohort; MAF*: minor alleles frequency according to the reference study; OR: odds ratio; OR*: odds ratio according to the reference study; Ref: references.
## Discussions
We identified a gene-based genome-wide significant variant in CNR2 gene, located in 1p36.11, which encodes the cannabinoid receptor 2 (CB2), associated with DKD in an Emirati cohort. After a replication and meta-analysis of GWAS variants across ancestry groups, we identified a similar signal in the Japanese population, but no association in the Finnish or UK-ROI population. We have discovered an additional eleven variants (rs78595611 in PPP1R9A gene; rs4001941 inCYP2B7P; rs117074522 inCYP4F24P; rs275475 in RP11-332J15.1; rs11664515 in OR10H2; rs16836018 inSTX18-AS1; rs17349061 inRB1; rs9493286 inMOXD1; rs76361654 in PPP1R3A; rs141291445 in LINC00693; and rs11696648 inMACROD2) with suggestive association of DKD that will need to be replicated in a larger cohort, across multiple ancestral ethnic groups from Middle East populations.
When investigating non-genetic effects, several different factors contribute to the development of DKD in patients with T2DM, including hypertension and dyslipidemia with elevated cholesterol level, HDL, and LDL. These factors are reflected in this study and are independently associated with progressive DKD through lipid accumulation in tubular epithelial cells of diabetic kidneys57. A decline in renal function, urea, and vitamin D were also evident in those with DKD. Those observations are in line with previous study findings28,58–61. The presence of hyperglycemia initiates and sustains pathogenic pathways in the kidney that lead to the development and progression of DKD, but several other factors enhance this process even further. This is enhanced by several different factors that will facilitate the progression of the condition. Through optimized diabetes care, implementing pragmatic guidelines for therapeutic treatment, and appropriate targeting of education and support, the prognosis of patients with DKD may be dramatically improved62,63.
The CNR2 gene, encoded by CB2, demonstrated genome-wide significance ($$p \leq 1.46$$ × 10–6) at a gene-set level, and has been implicated in mediating anti-inflammatory effects and immunomodulatory potential64,65. GWAS have associated variations in the CNR2 gene with blood inflammatory cells, especially eosinophils and lymphocytes54,66,67. CB2 receptors may also regulate insulin secretion, insulin resistance, obesity-related inflammation, and metabolic changes such as nonalcoholic fatty liver disease68,69. In mouse and rat models, endocannabinoids generated in renal cells have demonstrated oxidative stress, inflammation, and renal fibrosis70. While there are conflicting reports regarding CB2's role in DKD development71,72, recent studies suggest that CB2 is upregulated by ischemia–reperfusion injury in mice and patients, which is associated to progressive kidney fibrosis73. Additionally, Zhou et al. demonstrated that genetic ablation and selective antagonists inhibiting CB2 were protective against kidney fibrosis, suggesting that CB2 plays an important role in kidney fibrosis pathogenesis74. This suggests that endocannabinoids through CB2 may actually play a dual conflicting role in fibrosis in T2DM. CB2 receptor-mediated responses contribute to both age-related and diet-induced insulin resistance, suggesting that these receptors may be useful therapeutic targets not only for kidney fibrosis, but also for obesity and diabetes as well75. Several factors, such as downstream cellular signaling, activation of b-catenin pathway, type and timing of kidney injury, and genetic variations in CNR2 gene, particularly among different ethnic groups, may influence the receptor function and lead to the various pathological changes observed in the kidney during DKD.
Replication results of previously reported loci that were associated with DKD in patients with T2DM was conducted. SNP rs1564939 in GLRA3 was significant ($$p \leq 0.016$$). This SNP has been associated with albuminuria, an early sign of DKD, in several publications conducted in European populations20. The GLRA3 plays a major role in the central nervous system and has also been linked to ischemic damage. Glycine may also increase effective renal plasma flow and GFR, as well as decrease proximal and distal tubular sodium reabsorption by increasing renal interstitial hydrostatic pressure76. The remaining genetic locus were not replicated in the Middle Eastern cohort. This may be due to ascertainment and selection bias across the studies, or the differences in genetic structure, as indicated by the marked differences in allele frequencies, in which the study was conducted. Emerging evidence suggests that individuals of Middle Eastern descent have a different genome structure due to consanguineous marriage, endogamous unions, tribal structure, and larger family sizes77; however, genome studies from ethnic groups from the region are scarce, representing only $0.08\%$ of genome data in the public domain78. Therefore, a larger cohort study of Middle Eastern descent must replicate these previously reported loci to attribute the lack of association.
There are several limitations in our study. The power analysis indicated that the sample size is barely sufficient to identify genome-wide significant variants. However, regardless of this, after conducting a gene-set analyses on the GWAS data, we were able to identify a genome-wide significant marker associated to DKD. The association of CNR2 gene should be replicated in a larger, multi-ethnic cohort that would be more suited to investigate the genetic association, functional enrichment analysis and functional validation. Due to the lack of information available in this study to adjust for duration of diabetes, future studies should adjust for this confounding variable as it may serve as a genetic risk factor. While presentation, phenotypic profile and clinical course of DKD may be heterogeneous, the recruitment was conducted from one center to avoid ascertainment bias and limit misclassification in the case group. However, the heterogeneous nature of DKD, as well as differences in research design, method, ethnicity and gender compositions of the cohorts in the included studies, may have impacted the replication analysis. Future studies must be conducted in a larger cohort, with subgroup analysis and sensitivity analysis of the different phenotypic stages of DKD. Additionally, due to the lack of comparable studies and datasets available for DKD, we were unable to replicate our GWAS results in independent cohorts from the region; furthermore, we were unable to provide functional studies for the polymorphisms described in this study due to the absence of tissues and biopsies from patients. It is important to note that the replication analysis was performed on samples obtained from biobanks that employ different types of controls than were used in this study. According to the FinnGen or BBJ flow of studies, individuals with a specific phenotype or endpoint are treated as cases, while all other individuals without the phenotype are treated as controls, explaining the large number of controls compared to cases. Our definition of controls was therefore much narrower than in these cases, which may have resulted in a failure of replication of some signals in addition to the differences in genetic structure. Another limitation of this study is that DKD markers such as rs2501391 did not show an association with T2DM risk, or with a previously identified DKD loci. Due to the limited sample size and power, even though this might suggest that genes such as CB2 could contribute to DKD development, it does not exclude the possibility that they may also confer risk for T2DM itself. Lastly, the use of a Euro-centric GWAS array limits the possibility of detected targeted SNPs in the genome that is ethnic-specific79–83. However, to limit this error, the imputation of the genotypes increases the detection of genetic markers that were not directly genotypes and provides further information on the association of these markers, and their respective haplotypes, to the condition.
In summary, we have discovered eleven variants that may be associated with DKD pathogenesis in T2DM patients. However, a highly plausible and significant association was identified in CNR2 gene, suggesting that endocannabinoids signalling through CB2 receptor may be associated with DKD pathogenesis. Further functional studies are warranted to establish the role of CNR2 gene, to improve fundamental knowledge and underlying biological pathway of DKD heterogeneous phenotypes. Performing GWAS studies across different racial and ethnic populations allow the identification of genes and haplotypes associated with different clinical outcomes of DKD. Further studies must be conducted on a large-scale, multi-ethnic cohort to substantiate our current knowledge on DKD pathogenesis and facilitate the development of population-specific therapeutic advances.
## Supplementary Information
Supplementary Figure 1.Supplementary Tables. The online version contains supplementary material available at 10.1038/s41598-023-31701-w.
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|
---
title: Toward explainable AI-empowered cognitive health assessment
authors:
- Abdul Rehman Javed
- Habib Ullah Khan
- Mohammad Kamel Bader Alomari
- Muhammad Usman Sarwar
- Muhammad Asim
- Ahmad S. Almadhor
- Muhammad Zahid Khan
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10033697
doi: 10.3389/fpubh.2023.1024195
license: CC BY 4.0
---
# Toward explainable AI-empowered cognitive health assessment
## Abstract
Explainable artificial intelligence (XAI) is of paramount importance to various domains, including healthcare, fitness, skill assessment, and personal assistants, to understand and explain the decision-making process of the artificial intelligence (AI) model. Smart homes embedded with smart devices and sensors enabled many context-aware applications to recognize physical activities. This study presents XAI-HAR, a novel XAI-empowered human activity recognition (HAR) approach based on key features identified from the data collected from sensors located at different places in a smart home. XAI-HAR identifies a set of new features (i.e., the total number of sensors used in a specific activity), as physical key features selection (PKFS) based on weighting criteria. Next, it presents statistical key features selection (SKFS) (i.e., mean, standard deviation) to handle the outliers and higher class variance. The proposed XAI-HAR is evaluated using machine learning models, namely, random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB) and deep learning models such as deep neural network (DNN), convolution neural network (CNN), and CNN-based long short-term memory (CNN-LSTM). Experiments demonstrate the superior performance of XAI-HAR using RF classifier over all other machine learning and deep learning models. For explainability, XAI-HAR uses Local Interpretable Model Agnostic (LIME) with an RF classifier. XAI-HAR achieves $0.96\%$ of F-score for health and dementia classification and 0.95 and $0.97\%$ for activity recognition of dementia and healthy individuals, respectively.
## 1. Introduction
Smart home and artificial intelligence (AI)-based healthcare systems are appreciated as an excellent paradigm to solve privacy issues in smart homes (1–7). Explainable artificial intelligence (XAI) is the explainable category of AI (black box) in which humans can understand the solution results [8]. Smart homes support automated sustainability to encourage smart cities, smart communities, and high technology-driven solutions [9, 10]. Smart homes provide sustainable health solutions such as supporting cognitively impaired individuals by assessing their daily life routine, remote monitoring of home devices for activity recognition, emotion analysis, and depression estimation (11–17).
Currently, neuropsychologists and clinicians are interested in insight into an individual's functional ability to detect diseases early (18–20). There are many solutions to track and monitor an individual's functional ability, such as wearable sensors, vision-based recognition, Wi-Fi-based activity recognition, smartphone-based human activity recognition (HAR), and intelligent homes utilizing the Internet of Things (IoT) (21–26). To assess the functional ability of cognitively impaired individuals, IoT-oriented smart home infrastructures are the most suitable (27–30). Smart homes play an important role in driving the smart cities' revolution by incorporating IoT that connects several devices, systems, and technologies to achieve health-related tasks. A smart home infrastructure is equipped with robust and autonomous smart sensors, for instance, motion, temperature, pressure, and electricity usage sensors, to provide assisted living solutions [31]. Actions done in a smart home includes eating, sleeping, cooking, medications, task support parallelism, sequence, and interruption, such as listening to a phone call and writing cards while cooking. There is much discussion about the validity of using an IoT-oriented smart home infrastructure for smart home residents' functional ability assessment. For example, the work of Stavrotheodoros et al. [ 23] suggested that daily life functional activity assessment is a reasonable way to measure the decline in perceptions. The authors in Wilson et al. [ 32] argue that data collection is more subtle in a smart home habitat than in a dedicated laboratory environment.
This study presents an activity recognition approach, XAI-HAR, to identify key features from a high dimensional feature matrix and augments statistical features to generalize the process of smart home recognizing activities. It is important to retain better the original meaning and representation of a feature matrix to understand a cognitively impaired individual's functional ability. This study makes the following contributions: The rest of the study is organized as follows: The literature review is presented in Section 2. The selected smart home dataset is discussed and presented in Section 3.1. Section 3 details the proposed approach. The experimental setup and results are presented in Section 4. Finally, in Section 5, the conclusion and future work are presented.
## 2. Background
This section presents the related work on the fusion of activity monitoring and XAI.
## 2.1. Activity monitoring
A smart home is embedded with a diversity of smart devices and sensors. A smart home is equipped with temperature, motion, heat, and light sensors that human-specific devices such as smartphones and computers can remotely control. These sensors are intelligent enough to reason about and decide our smart home environment setting (33–35). Recently, IT organizations have offered some frameworks for smart homes in an endeavor to capitalize on the market and facilitate the customers in their service-based smart environment so that the market competition and industrial advancement will return as financial advantages to the general public of the smart urban areas [36].
Recent studies highlight that remote monitoring and assisted living could provide patients with real-time assistance and significantly minimize all risks while performing different daily living actions [29, 30, 37]. The authors in Dawadi et al. [ 28, 30] use smart homes for activity assessment of a resident and reported that it is the optimal way to monitor and assist the patient living in it. The data gathered from the interactive sensors deployed in the surrounding can be utilized to recognize activities of daily living (ADLs) carried out inside a smart home, such as food preparation, drinking water, and medication. ADLs automated recognition is crucial in observing a smart home resident's functional health. According to a survey on assistive technologies, the top priority of caregivers of patients with Alzheimer's disease is to identify and track their activity. In Cook [35], the authors survey a generalized activity model that combines sensor actions from all testbeds into one uniform labeled dataset. They applied three basic machine learning algorithms, such as naive bayes (NB), hidden Markov model (HMM), and conditional random field (CRF), over annotated activities. The research of Sarwar and Javed [38] and Javed et al. [ 39] is designed to make a helping mechanism that assists individuals to live healthfully. After recognizing the physical activities and consent of guardians, doctors, and intelligent agent rankers, a good healthcare plan is suggested.
The authors in Fong et al. [ 40] proposed a feature-based mechanism for training classifiers that recognizes human activities. They extracted the spatial features called shadow features, which describe current sensor data positions by modeling the performed activities' momentum. The shadow features also highlight the additional information dimensions for nominating activities in the recognition process. Furthermore, they evaluate the devised approach using a wearable and Kinect-based remote sensor. The authors in Eastwood et al. [ 41] design a set of physical features representing human motion to augment the statistical features. For activity recognition, the authors in Lu et al. [ 42] extracted latent features from data acquired from sensors with Beta Process hidden Markov model [43]. To do that, first, they used the dependent beta process and later integrated sensors' state constraints into sampling. The trained support vector machine (SVM) recognizes the activities from these latent features.
The approach proposed by Cook [35] aims to learn a generalized activity model by combining sensor events from different age groups, such as younger adults, healthy older adults, older adults with dementia, and pets. They used CRF, NB, and HMM for recognition. To improve activity recognition, a segmental pattern mining approach is proposed, in which the segment is a consecutive time event of the same activity [44]. In Dawadi et al. [ 27], the authors present a study for health assessment of cognitively impaired individuals to track the health status in the early stages of the individuals moving toward the critical stage such as dementia. Their focus was to classify healthy individuals and individuals with dementia.
## 2.2. Explainable artificial intelligence
Reducing healthcare costs and sustaining a healthier life are important driving factors for governments to invest in smart cities. The authors in Chen et al. [ 45] discuss using machine learning (ML) algorithms to mitigate healthcare anomalies. They propose a 5G-Smart Diabetes system for patients with diabetes using sensors and patient vital analysis. in Eastwood et al. [ 41] designed a set of physical features representing human motion to augment the statistical features. First, a single-layer feature selection framework is applied to analyze the impact on recognition performance. They analyzed that different feature selection mechanisms extract qualitative features that may, in turn, increase the accuracy of recognition. An analysis is conducted on recognizing activities using quick propagation, Levenberg Marquardt, and batch back propagation algorithms [46]. Several features are presented that can be used for activity recognition in Chinellato et al. [ 47]. These features are based on time-related measures (i.e., time of occurrence, duration, and repetition), space-related measures (i.e., location of occurrence, movement), complexity-related measures (i.e., event analysis, person analysis, and object analysis), and inter activity-related measures. They used linear discriminative analysis (LDA), random forest (RF), NB, and SVM for recognition.
In summary, the current studies of feature selection lack in selecting a significant feature subset from the whole dataset as the best representative of all features [48]. Some drawbacks of the feature selection methods discussed in the literature [40, 49] are: [1] In the case of a smart home, the location of a sensor can be the best feature to represent the whole feature matrix, but it may not correctly the activities performed at other locations or interleaved locations, [2] A feature considered best for one activity can be worst for some other activities such as the location feature, [3] A feature representing the activities of healthy individuals may not correctly represent the activities performed by individuals with dementia, and [4] A feature consisting of frequencies of corrupt or damaged sensors.
By considering the above analysis, the following research questions (RQ) are presented:
## 3. Methodology
In this section, we discuss the suggested approach for activity recognition named XAI-HAR for the activities performed by the healthy individuals and individuals with dementia residing in smart homes. The proposed approach provides a privacy-preserved environment to the resident as the data are collected from motion, pressure, and similar binary state sensors. This approach is being used and recommended by state-of-the-art studies (50–54). XAI-HAR consists of two steps: physical key features selection (PKFS) and statistical key features selection (SKFS) to form a feature matrix corresponding to different well-established contemporary methods used for recognizing activities. XAI-HAR presents the concept of selecting vital local features within the dataset. These selected local key features are then transformed for activity recognition. Figure 1 summarizes XAI-HAR for data collection and analysis.
**Figure 1:** *Complete flow of the proposed framework.*
## 3.1. Dataset selection
The XAI-HAR approach is evaluated the publicly available Cognitive Assessment Activity (Kyoto) dataset [27] from the Center for Advanced Studies in Adaptive Systems (CASAS)1. The dataset contains passive and automatic sensing data collected from 79 participants from an on-campus smart home testbed at Washington State University. The smart home consists of a living room, kitchen, and dining room on the first floor. The second floor consists of an office, a bathroom, and two bedrooms. The participant's interaction with the smart home is recorded with binary, digital, and analog sensors.
Figure 2 provides an overview of the raw dataset. For example, motion sensors (Mxx) are deployed on the ceiling, door sensing devices (Dxx) on cabinets and doors, temperature-sensitive devices (Txx) in each room, light sensors (Lxx), burner sensors (AD1-A), hot water sensors (AD1-B), cold water sensors (AD1-C), whole apartment electricity usage (P001), and item sensors (Ixx) placed on specific items. Sensor events are generated and recorded, whereas the participants perform the activities. Each sensor event comprises a date, time, id, and state (value). Such events are used to make instances for different activities. Sensor events are combined for each activity into a period (starting and ending) as a single sample (instance), representing each participant's activity progress. The sensor events are extracted from the state feature based on the starting and ending activities shown as 19−start and 19−end. Each sensor event in this activity is counted based on that sensor's triggering and added as an instance in the dataset.
**Figure 2:** *Raw dataset illustration.*
The dataset contains instances of simple daily life activities. Simple daily life activities are defined as those performed in daily routine and are not interwoven, for instance, taking medicine while doing the dishes. However, in the CASAS dataset, the activities reported by the same sensors and performed in the exact location are difficult to discriminate, such as preparing breakfast, preparing soup, and sweeping the kitchen. Table 1 summarizes the dataset's characteristics used in this study. The ground truth about the personals is generated by comprehensive clinical assessments, which include a review of medical records, neuropsychological testing data, telephone interview of cognitive status (TICS), clinical dementia rating, and some other ways [27].
**Table 1**
| Parameter | Value |
| --- | --- |
| Participants | 79 |
| Mean Age | 66 |
| Healthy | 65 |
| Dementia | 14 |
| Activities | 8 |
| Action 1 | Moping the scullery and tidying up the sitting room. |
| Action 2 | Acquiring medicament box along with a dispenser per week, and instruction based fill up of the dispenser. |
| Action 3 | Calligraphy of a birthday card, of address on an envelope, en-wrapping a check. |
| Action 4 | Searching a suitable DVD to listen and watch a news clip. |
| Action 5 | Grabbing a watering can and sprinkling water on each plant in the living area. |
| Action 6 | Replying to a phone call and answering the questions. |
| Action 7 | Cooking soup with the help of the microwave oven. |
| Action 8 | Selection of an appropriate dress from a collection of clothes, for an interview. |
## 3.2. Feature extraction
A count of 254 features is retrieved from the sensing data. These features help to identify how well an activity is performed. For example, if a person gets stuck or slows in performing an activity, his/her activity duration time would increase. A participant with dementia would not complete an activity on time due to multiple reasons, such as mistakes wandering and confusion in performing an activity. The following features are those extracted from Dawadi et al. [ 27]:
## 3.3. Feature design
Feature design or feature engineering selects the best features and then constructs generic features from the feature matrix capable of efficiently differentiating activities. Feature selection simplifies the model for better understanding and a more straightforward interpretation for users or researchers. A significant benefit of feature selection is that it reduces the number of features the model will train, eventually reducing the training time. In many cases, the feature matrix consists of either dissociated or repeating features that result in overfitting a model, increasing the model's complexity. Usually, the dataset with high dimensions, such as the CASAS dataset which has hundreds of features, may contain a large number of irrelevant and redundant information, which eventually reduces the performance of the learning algorithm [55]. Feature selection enhances the model's generalization and accuracy, reducing the chances of overfitting if the right subset of features is selected. To select the dataset's best features, it is necessary to excerpt features set from the raw dataset. The below sections explain two sets of features extracted from the raw dataset.
## 3.4. Physical key feature selection
Physical features are interpreted by human activities performed in a smart home. To systematically identify and assess the usefulness of the most important features for correctly categorizing various activities, many sophisticated techniques can be used to search the compact feature subsets from the dataset. The below equations present the complete process of selecting optimal features from the entire dataset for a smart home resident's cognitive health assessment.
To select the features for PKFS, the CASAS dataset [27] is considered well known for cognitive impaired classification. It consists of different activity classes and several activity instances where D = D1, D2, …, Dx represent the different classes and I = i1, i2, …, ikx represent the instance belonging to each class Dx, and features of dataset D are the unique sensors S = s1, s2, …, sn that were triggered as on/off while performing activities instances Ikx in a smart home and temporal information Ti. Each feature consists of total frequency, Fs=∑FsiI, in the numeric form of the activated sensor during the progression of activity. The sensors not triggered while performing activities were assigned zero, fkxs=0. In this way, the feature matrix Fkx consisting of activity instances Ikx can be represented by the following Equation 1: Since the values vary widely in ranges of raw data because healthy individuals and individuals with dementia performed the activities, there are high chances of abnormality in sensor frequency. In some machine learning algorithms, objective functions will not work correctly and efficiently. The feature matrix is shifted to a scaled version of a feature matrix to eliminate specific gross influences to address this problem. The Rescaling method has been used to normalize the range of features using Equation [2] as follows: Scaling works better for ML models where the distance between the data points varies widely. In Equation [2], x is the real value of the instance, and x′ is the normalized value. The scaled feature matrix for all activities can be described by Equation [3], according to the proposed approach: Currently, the feature matrix is in shape to select the key feature. A set denoted by *Sk is* initially initialized with an empty set ϕ. Best key features Bf are extracted from activities Dx by counting the features of {fkxs}. In Equation [4], Pxr returns the number of features in a list containing all features fkxs where each feature in fkxs has a frequency greater than 0.
The value of *Uf is* user-defined, as shown in Equation [5], which allows the user to choose the number of best features from the feature matrix, and similarly, Ui in Equation [6] allows the user to select the number of instances. It provides full authority to the user to control the feature selection process, which could be sufficient for deciding the feature as a key feature to perform the health assessment of a smart home resident.
The cross-validation technique is applied to assess the value of Ui and Uf. The best-selected features are then appended to the empty set ϕ. If the selected feature is already in the set, it is discarded; else, it is appended. This process is repeated until each class's features are added or discarded.
The precedence is given to each feature Fs in a certain activity based on its occurrence. This process is repeated based on the maximum frequency in an activity to obtain an overall generic feature matrix. The features having low occurrence inactivity are discarded. Later, the feature matrix is formed based on Equation [6] for the best key features. The feature matrix features have maximum precedence in set ϕ as shown in Equation [7].
## 3.5. Statistical key feature selection
Statistical features are a dataset's features which can be defined and calculated through statistical analysis. Statistical models are generic, increasing the capability of any model to recognize different activities at a fine-grained level. The common statistical features are bias, variance, mean, median, percentiles, standard deviation, etc. Researchers investigated that it is useful to use the statistical feature for human activity recognition [41]. For example, it is proved that variance helps to achieve higher accuracy for different activities, such as walking, jogging, and hopping. The extracted statistical features are root mean square, standard deviation, mean, median, variance, averaged derivatives, zero-crossing rate, interquartile range, mean crossing rate, kurtosis, skewness, pairwise correlation, and spectral entropy from feature matrix generated by PKFS. After successfully extracting statistical features, these features are appended in the previous matrix made by PKFS. The statistical features are represented by Sfkx, and the whole key feature matrix is represented by Equation [8].
## 3.6. XAI-HAR
Various traditional well-known feature selection techniques, namely, principal component analysis (PCA), minimum redundancy maximum relevance (mRMR), information gain (IG), and the proposed technique XAI-HAR is applied along with the machine learning algorithms random forest (RF), K-nearest neighbor (KNN), decision tree (C4.5), support vector machine (SVM), Heoffding tree (HT), multilayer perceptron (MLP), and naive Bayes (NB) for activity recognition. For further comparison, we also apply deep learning algorithms, such as deep neural network (DNN), convolution neural network (CNN), and CNN-based long short-term memory (CNN-LSTM). These methods for selecting feature selection are very effective in selecting the best features. These feature selection techniques are selected to compare and evaluate the proposed feature selection approach XAI-HAR for activity recognition. For PCA, we set the variance to $95\%$. We use local interpretable model agnostic (LIME) and apply it to RF (default parameters) to analyze the main components and explain essential features. LIME provides the model interpretability by producing meaningful and vital information. For KNN, the batch size is set to 100, the nearest neighbors are set to 1, the nearest neighbor searching algorithm is set to LinearNNSearch, and distance weighting is set to False. For the decision tree, the batch size is set to 100, the confidence factor is set to 0.25, subtreeRaising is set to True, and reducErrorPruning is set to False. For SVM, the batch size is set to 100, the complexity parameter is set to 1.0, the kernel is set to PolyKernel, and the tolerance parameter is set to 0.001. For NB, the batch size is set to 100, and useKernalEstimator is set to false. For DNN, the activation is relu in hidden layers and softmax in the output layer along with the optimizer as adam. For CNN and CNN-LSTM, the same parameters are set with the kernel_size as 3.
## 4. Experimental analysis and results
The proposed approach XAI-HAR is fundamentally different from other approaches in the way that XAI-HAR defines K subsets of features for K activity classes. In contrast, feature selection methods such as IG, mRMR, and PCA return a single subset of features from the existing feature set, given as input to selected classifiers. Furthermore, XAI-HAR uses the LIME-based RF model to analyze the main components and explain essential features. This section discusses the different valuation metrics for experimentation and evaluation. For experimentation, CASAS-Cognitive Assessment Activity (Kyoto) [27] dataset is used which is well known for cognitively impaired individuals research. Different experimental analyses are performed with different criteria on the dataset. Three-fold cross-validation [56] is applied for all experiments. This test leaves 1:3 part of the dataset for testing and 2:3 part for training. The KNN, SVM, DT C4.5, HT, MLP, and NB algorithms are used to evaluate the recognition results. For further comparison, we also apply deep learning algorithms, such as deep neural network (DNN), convolution neural network (CNN), and CNN-based long short-term memory (CNN-LSTM).
## 4.1. Evaluation metrics
The selection of evaluation metrics depends on the essence of the data. Accuracy is mainly considered a key evaluation metric when the data are balanced (i.e., an equal number of observations) [57]. However, accuracy alone can be misleading if a dataset contains imbalanced observations in each category. To overcome this limitation, recall, precision, and f-score evaluation metrics are a rationale for the performance computation of DFCII. Given as under are the practical terms to help in evaluation and analysis. TP (i.e., true positive rate representing correctly categorized instances) calculates the accuracy by dividing it by N (all the samples of all activities). The recall measure is computed by TP divided by TP+FN (where FN is the false negative rate that provides wrongly recognized samples). We divide TP by TP+FP (false positive rate: samples of other activities wrongly recognized as one activity sample), and we obtain the precision of a technique. F-score shows the harmonic mean of recall and precision. The experiment's computing environment is set as Intel(R) Corei5, 8th Generation with 16 GB RAM, Windows 10 OS, and Python version 3.7.6 as shown in Table 2.
For cognitive health assessment, the f-score is used as a critical evaluation measure because the f-score is the most appropriate for the imbalanced data [57].
**Table 2**
| Type | Specification |
| --- | --- |
| OS | Windows 10 |
| CPU | Intel(R) Corei5, 8th Generation |
| RAM | 16 GB |
| Python | 3.7.6 |
Figure 3 illustrates the f-score of each activity on activities of individuals with dementia and healthy individuals when XAI-HAR, PCA, IG, and mRMR are applied with the RF learning method. For kitchen activity, the XAI-HAR achieves $96\%$ f-score, while PCA, IG, and mRMR achieve 87, 81, and $83\%$ f-score, respectively. For medicine activity, the XAI-HAR achieves $97\%$ f-score, while PCA, IG, and mRMR achieve 91, 81, and $84\%$ f-score, respectively. For birthday card activity, the XAI-HAR achieves $94\%$ f-score, while PCA, IG, and mRMR achieve 80, 77, and $81\%$ f-score, respectively. In the case of DVD activity, the XAI-HAR achieves $98\%$ f-score, while PCA, IG, and mRMR achieve 84, 80, and $81\%$ f-score, respectively. For watering activity, the XAI-HAR, PCA, IG, and mRMR achieve 98, 91, 82, and $90\%$ f-score, respectively. For phone activity, the XAI-HAR, PCA, IG, and mRMR achieve 94, 82, 80, and $74\%$ f-score, respectively. In the case of soup activity, the XAI-HAR achieves $98\%$ f-score, while PCA, IG, and mRMR achieve 84, 87, and $83\%$ f-score, respectively. For outfit activity, the XAI-HAR achieves $98\%$ f-score, while PCA, IG, and mRMR achieve 90, 82, and $80\%$ f-score, respectively. It is seen that all feature selection methods achieved less accurate results than XAI-HAR when the activities performed by individuals with dementia and healthy individuals were classified collectively.
**Figure 3:** *The comparison of the proposed XAI-HAR with feature selection methods such as PCA, mRMR, and IG in combination with RF for each activity of individuals with dementia and healthy individuals.*
Figure 4 presents the f-score of each activity on the healthy individual's activities when XAI-HAR, PCA, IG, and mRMR are applied with the RF learning method. For kitchen activity, the XAI-HAR achieves $96\%$ f-score, while PCA, IG, and mRMR achieve 70, 82, and $85\%$ f-score, respectively. For medicine activity, the XAI-HAR achieves $98\%$ f-score, while PCA, IG, and mRMR achieve 92, 85, and $75\%$ f-score, respectively. For birthday card activity, the XAI-HAR achieves $95\%$ f-score, while PCA, IG, and mRMR achieve 84, 76, and $95\%$ f-score, respectively. In the case of DVD activity, the XAI-HAR achieves $99\%$ f-score, while PCA, IG, and mRMR achieve 77, 82, and $85\%$ f-score, respectively. For watering activity, the XAI-HAR, PCA, IG, and mRMR achieve 96, 76, 60, and $76\%$ f-score, respectively. For phone activity, the XAI-HAR, PCA, IG and mRMR achieve 93, 67, 90, and $83\%$ f-score, respectively. In the case of soup activity, the XAI-HAR achieves $98\%$ f-score, while PCA, IG, and mRMR achieve 77, 95, and $92\%$ f-score, respectively. For outfit activity, the XAI-HAR achieves $95\%$ f-score, while PCA, IG, and mRMR achieve 85, 92, and $90\%$ f-score, respectively. The results conclude that all feature selection methods achieve less accuracy than XAI-HAR when only the activities performed by healthy individuals are classified.
**Figure 4:** *The comparison of the proposed XAI-HAR with feature selection methods such as PCA, mRMR, and IG in combination with RF for each activity of healthy individuals.*
Figure 5 presents the f-score of each activity on the activities of individuals with dementia when XAI-HAR, PCA, IG, and mRMR are applied with the RF learning method. For kitchen activity, the XAI-HAR achieves $95\%$ f-score, while PCA, IG, and mRMR achieve 89, 85, and $82\%$ f-score, respectively. For medicine activity, the XAI-HAR achieves $98\%$ f-score, while PCA, IG, and mRMR achieve 90, 85, and $79\%$ f-score, respectively. For birthday card activity, the XAI-HAR achieves $96\%$ f-score, while PCA, IG, and mRMR achieve 86, 79, and $90\%$ f-score, respectively. In the case of DVD activity, the XAI-HAR achieves $99\%$ f-score, while PCA, IG, and mRMR achieve 87, 89, and $85\%$ f-score, respectively. For watering activity, the XAI-HAR, PCA, IG, and mRMR achieve 96, 89, 90, and $86\%$ f-score, respectively. For phone activity, the XAI-HAR, PCA, IG, and mRMR achieve 94, 79, 81, and $83\%$ f-score, respectively. In the case of soup activity, the XAI-HAR achieves $98\%$ f-score, while PCA, IG, and mRMR achieve 90, 84, and $92\%$ f-score, respectively. For outfit activity, the XAI-HAR achieves $99\%$ f-score, while PCA, IG, and mRMR achieve 88, 90, and $90\%$ f-score, respectively. It is shown that all feature selection methods achieved less accurate results than XAI-HAR when only the activities performed by individuals with dementia are classified.
**Figure 5:** *Comparison of proposed XAI-HAR with feature selection methods such as PCA, mRMR, and IG in combination with RF for each activity of individuals with dementia.*
Table 3 presents a comparison of XAI-HAR with PCA, IG, and mRMR using the performance evaluation metrics on the CASAS dataset [27]. We use KNN, SVM, DT, NB, HT, MLP, RF, DNN, CNN, and CNN-LSTM learning models for comparison. XAI-HAR improves recognition performance compared with all other models. XAI-HAR achieves the best accuracy of $96.4\%$ in combination with RF compared with KNN, SVM, DT, HT, MLP, and NB. While analyzing the XAI-HAR with existing feature selection approaches, i.e., PCA, IG, and mRMR, XAI-HAR achieves better results. The XAI-HAR with RF achieved a $5\%$ high f-score compared with PCA-based learning models on activities of healthy individuals and individuals with dementia. Similarly, XAI-HAR with RF achieved a $5\%$ high f-score compared with IG-based learning models on activities of healthy individuals and individuals with dementia. The XAI-HAR with RF achieved a $12\%$ high f-score compared with mRMR-based learning models on activities of healthy individuals and individuals with dementia While on activities of individuals with dementia, XAI-HAR with RF achieved a $13\%$ high f-score compared with PCA-based learning models. Similarly, XAI-HAR with RF achieved an $11\%$ high f-score compared with IG-based learning models on activities of healthy individuals and individuals with dementia. XAI-HAR with RF achieved a $3\%$ high f-score compared with mRMR-based learning models on activities of healthy individuals and individuals with dementia. Finally, in healthy individuals' activities, XAI-HAR with RF achieved a $6\%$ high f-score compared with PCA-based learning models. Similarly, XAI-HAR with RF achieved a $9\%$ high f-score compared with IG-based learning models on activities of healthy individuals and individuals with dementia. XAI-HAR with RF achieved an $8\%$ high f-score compared with mRMR-based learning models on activities of healthy individuals and individuals with dementia.
**Table 3**
| Approach | Participants | K-NN | SVM | DT | NB | RF | MLP | HT | DNN | CNN | CNN-LSTM |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| XAI-HAR | Healthy & Dementia | 0.96 | 0.92 | 0.9 | 0.89 | 0.96 | 0.83 | 0.75 | 0.95 | 0.95 | 0.95 |
| PCA | Healthy & Dementia | 0.86 | 0.87 | 0.87 | 0.89 | 0.91 | 0.82 | 0.75 | 0.9 | 0.81 | 0.81 |
| IG | Healthy & Dementia | 0.81 | 0.83 | 0.82 | 0.8 | 0.91 | 0.96 | 0.84 | 0.96 | 0.95 | 0.96 |
| mRMR | Healthy & Dementia | 0.82 | 0.82 | 0.84 | 0.83 | 0.84 | 0.83 | 0.75 | 0.95 | 0.95 | 0.95 |
| XAI-HAR | Dementia | 0.94 | 0.89 | 0.86 | 0.85 | 0.95 | 0.89 | 0.62 | 0.77 | 0.8 | 0.8 |
| PCA | Dementia | 0.79 | 0.82 | 0.79 | 0.8 | 0.82 | 0.88 | 0.78 | 0.91 | 0.87 | 0.87 |
| IG | Dementia | 0.83 | 0.83 | 0.82 | 0.81 | 0.84 | 0.88 | 0.78 | 0.91 | 0.88 | 0.88 |
| mRMR | Dementia | 0.85 | 0.86 | 0.84 | 0.84 | 0.92 | 0.89 | 0.76 | 0.97 | 0.86 | 0.86 |
| XAI-HAR | Healthy | 0.95 | 0.91 | 0.89 | 0.88 | 0.97 | 0.96 | 0.79 | 0.96 | 0.95 | 0.95 |
| PCA | Healthy | 0.87 | 0.86 | 0.87 | 0.85 | 0.91 | 0.96 | 0.79 | 0.96 | 0.96 | 0.96 |
| IG | Healthy | 0.85 | 0.84 | 0.83 | 0.82 | 0.88 | 0.96 | 0.83 | 0.96 | 0.96 | 0.96 |
| mRMR | Healthy | 0.86 | 0.87 | 0.85 | 0.81 | 0.89 | 0.95 | 0.86 | 0.96 | 0.94 | 0.94 |
It is noticed in most cases that the RF outperforms all other classifiers in terms of accuracy, and the HT classifier has the lowest accuracy.
Table 4 presents the time complexity of all models. Experiments reveal that the least model compiling time of the XAI-HAR approach on healthy and dementia individuals dataset is 0.01 s using KNN, and the highest model compiling time is 246 s using the CNN-LSTM model. Next, the least model compiling time of the PCA feature selection approach on the healthy and dementia individuals dataset is 0.11 s using NB, and the highest model compiling time is 195 s using the MLP classifier. Furthermore, the least model compiling time of the IG feature selection approach on healthy and dementia individuals dataset is 0.01 s using KNN, and the highest model compiling time is 55 s using the CNN-LSTM classifier. Furthermore, the least model compiling time of the mRMR feature selection approach on the healthy and dementia individuals dataset is 0.01 s using KNN, and the highest model compiling time is 324 s using the CNN-LSTM classifier. Next, the least model compiling time of the XAI-HAR approach on the dementia individuals dataset is 0.01 s using KNN, and the highest model compiling time is 21 s using the CNN-LSTM model. Furthermore, the least model compiling time of the PCA feature selection approach on the dementia individuals dataset is 0.01 s using KNN, and the highest model compiling time is 24 s using the CNN-LSTM model. Furthermore, the least model compiling time of the IG feature selection approach on the dementia individuals dataset is 0.01 s using KNN, and the highest model compiling time is 10 s using the CNN-LSTM model. Furthermore, the least model compiling time of the mRMR feature selection approach on the dementia individuals dataset is 0.01 s using KNN, and the highest model compiling time is 3 s using the CNN-LSTM model. Next, the least model compiling time of the XAI-HAR approach on the healthy individuals dataset is 0.01 s using KNN, and the highest model compiling time is 144 s using the CNN-LSTM model. Furthermore, the least model compiling time of the PCA feature selection approach on the healthy individuals dataset is 0.01 s using KNN, and the highest model compiling time is 144 s using the CNN-LSTM model. Furthermore, the lowest model compiling time of the IG feature selection approach on the healthy individuals dataset is 0.01 s using KNN, and the highest model compiling time is 82 s using the CNN model. Furthermore, the least model compiling time of the mRMR feature selection approach on the healthy individuals dataset is 0.01 s using KNN, and the highest model compiling time is 42 s using the CNN model.
**Table 4**
| Approach | Participants | K-NN | SVM | DT | NB | RF | MLP | HT | DNN | CNN | CNN-LSTM |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| XAI-HAR | Healthy & Dementia | 0.01 | 0.41 | 0.59 | 0.1 | 2.5 | 194.0 | 0.7 | 42.0 | 77.0 | 246.0 |
| PCA | Healthy & Dementia | 0.8 | 0.44 | 0.49 | 0.11 | 2.6 | 195.0 | 0.55 | 5.0 | 9.0 | 26.0 |
| IG | Healthy & Dementia | 0.01 | 0.2 | 0.2 | 0.03 | 1.37 | 11.6 | 0.09 | 10.9 | 24.0 | 55.0 |
| mRMR | Healthy & Dementia | 0.01 | 0.39 | 0.59 | 0.11 | 2.72 | 2.41 | 0.07 | 12.0 | 89.0 | 324.0 |
| XAI-HAR | Dementia | 0.01 | 0.14 | 0.01 | 0.01 | 0.2 | 5.41 | 0.01 | 3.0 | 5.0 | 21.0 |
| PCA | Dementia | 0.01 | 0.07 | 0.01 | 0.01 | 0.2 | 5.21 | 0.02 | 3.0 | 5.0 | 24.0 |
| IG | Dementia | 0.01 | 0.25 | 0.01 | 0.01 | 0.13 | 1.41 | 0.01 | 2.9 | 7.0 | 10.4 |
| mRMR | Dementia | 0.01 | 0.1 | 0.01 | 0.01 | 0.12 | 0.41 | 0.01 | 3.0 | 3.0 | 3.0 |
| XAI-HAR | Healthy | 0.01 | 0.21 | 0.27 | 0.06 | 1.61 | 56.0 | 0.37 | 21.0 | 61.0 | 144.0 |
| PCA | Healthy | 0.01 | 0.24 | 0.36 | 0.05 | 1.78 | 56.2 | 0.2 | 12.0 | 45.0 | 144.0 |
| IG | Healthy | 0.01 | 0.3 | 0.15 | 0.03 | 1.17 | 14.2 | 0.1 | 20.5 | 82.0 | 77.0 |
| mRMR | Healthy | 0.01 | 0.24 | 0.18 | 0.02 | 1.45 | 5.33 | 0.05 | 15.0 | 42.0 | 36.0 |
Table 5 presents the confusion matrix of the proposed approach. It shows how many instances of one activity get confused with instances of other activities. The kitchen activity is getting confused with the phone activity. The birthday card activity and phone activity are getting confused with each other. In comparison, the remaining five activities are recognized accurately. Overall, XAI-HAR achieved better results than other approaches.
**Table 5**
| Activities | Kit | Med | BC | DVD | Wat | Phone | Soup | Outfit |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Kit | 321 | 0 | 0 | 0 | 1 | 14 | 1 | 1 |
| Med | 0 | 329 | 2 | 0 | 0 | 0 | 4 | 0 |
| BC | 0 | 0 | 311 | 0 | 0 | 24 | 0 | 0 |
| DVD | 0 | 0 | 1 | 328 | 0 | 1 | 0 | 1 |
| Wat | 5 | 0 | 0 | 0 | 335 | 0 | 0 | 0 |
| Phone | 3 | 1 | 24 | 3 | 0 | 272 | 0 | 4 |
| Soup | 1 | 6 | 0 | 0 | 0 | 0 | 326 | 0 |
| Outfit | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 342 |
## 4.2. Explainable RF with local interpretable model agnostic for healthy individuals
We use local interpretable model agnostic (LIME) and apply it to RF to analyze the main components and explain essential features. LIME provides the model interpretability by producing meaningful and vital information. We also use ELI5 to inspect machine learning classifiers and explain their predictions. ELI5 extracts the top 10 features with their corresponding weights.
## 4.2.1. Interpretation of healthy individuals and individuals with dementia
The RF model achieves an accuracy score of $96.25\%$. The result of the LIME model gives a list of essential features and explains each feature's contribution to the dataset's prediction. Figure 6 shows the output of the LIME model and explains the top 10 features. The leftmost sections present the prediction probabilities with $0.96\%$ healthy and $0.04\%$ dementia probability values. The second section represents the 10 most important features. We use binary classification and that is why it is in two colors, blue and orange. Attributes in orange color support the healthy class, and the blue color supports the dementia class. Floating-point numbers on the horizontal bar show the importance of the features. M047, M030, and T101 are the Top three features belonging to the healthy class. The rightmost section contains the actual values of the top 10 variables.
**Figure 6:** *Feature explainability.*
Figure 7 demonstrates that M047, M030, T101, MO32, and M031 are the top most important features of the model belonging to the healthy class. While M005 and LL007 are the top most important features of the model belonging to the dementia class, Figure 8 provides the weights against to top 10 features participating most in the prediction process.
**Figure 7:** *Local importance.* **Figure 8:** *ELI5-based feature Inspection.*
## 4.2.2. Interpretation of healthy individuals
The RF achieves an accuracy score of $97.40\%$. The result of the LIME model gives a list of essential features and explains each feature's contribution to the dataset's prediction. Figure 9 shows the output of the LIME model and explains the top 10 features. The leftmost sections present the prediction probabilities with $0.94\%$ for medicine and $0.05\%$ for phone probability values. For medicine, it is noticed that M013, M017, M017, MO08, M002, I010, M018, D007, I006, and M016 are the top most important features of the model belonging to the healthy class.
**Figure 9:** *Feature explainability.*
The second section represents the 10 most important features. Attributes in orange color support the medicine class and the purple color supports the phone class. Floating-point numbers on the horizontal bar show the importance of the features. M013, M017, and M008 are the top three features belonging to the medicine class. The rightmost section contains the actual values of the top 10 variables.
Figure 10 shows that M013, M017, M008, M051, I006, I010, M023, M015, and D007 are the top three most essential features of the model belonging to the medicine class. Figure 11 provides the weights against to top 10 features participating most in the prediction process.
**Figure 10:** *Local importance.* **Figure 11:** *ELI5-based feature Inspection.*
## 4.2.3. Interpretation of individuals with dementia
The RF model achieves an accuracy score of $93.93\%$. The result of the LIME model gives a list of essential features and explains each feature's contribution to the dataset's prediction. Figure 12 shows the output of the LIME model and explains the top 10 features. The leftmost sections present the prediction probabilities with $0.76\%$ for the medicine class, $0.15\%$ for the phone class, 0.04 for kitchen, 0.02 for DVD, and 0.03 for other probability values. The second section represents the 10 most important features–attributes in orange color support the medicine class and others support not medicine class. Floating-point numbers on the horizontal bar show the importance of the features. M013, M017, and M018 are the Top three features belonging to the healthy class. The rightmost section contains the actual values of the top 10 variables.
**Figure 12:** *Feature explainability.*
Figure 13 shows that M013, M017, and M018 are the top three most essential features of the model belonging to the healthy class. Figure 14 provides the weights against to top 10 features participating most in the prediction process.
**Figure 13:** *Local importance.* **Figure 14:** *ELI5-based feature Inspection.*
## 4.3. Discussion
Currently, clinicians are interested in insight into an individual's functional ability to detect diseases early. This study presented an XAI-empowered human activity recognition approach for individuals with dementia and healthy individuals to monitor their health. RF achieves the best results by using the XAI-HAR feature matrix. The other learning models, such as KNN, SVM, HT, MLP, NB, DNN, CNN, and CNN-LSTM, achieve better f-score using XAI-HAR-based feature matrix than PCA, IG, and mRMR-based feature matrix. However, DT showed relative degradation in dementia individuals' activities compared with others. The rationale behind this degradation is due to the fact that the data collected for dementia individuals have non-normal distribution. In addition, the number of dementia individuals performing activities is also fewer than that of healthy individuals. The KNN looks for the nearest neighbors in the dementia individual's activities for assigning labels. The SVM looks for the boundaries of the target variable in the dataset's search space for assigning labels.
In contrast, DT looks for promising interactions between features representing activities of an individual with dementia. We also provide the explainability of the prediction made by the RF model. We use local interpretable model agnostic (LIME) and apply it to RF to analyze the main components and explain the most important features. LIME provides the model interpretability by producing meaningful and vital information. We use ELI5 to inspect machine learning classifiers and explain their predictions. ELI5 extracts the top 10 essential features with their corresponding weights. As shown in Table 3, it is noticed that the RF achieves a $2\%$ higher f-score than the deep learning models such as DNN, CNN, and CNN-LSTM while using XAI-HAR-based feature matrix. In addition, the deep learning models take more time in model building than RF, as shown in Table 4. The deep learning models not only achieve better f-score on PCA, IG, and mRMR-based feature matrices than RF but also take a long time in model training. So, the RF model works more robustly and efficiently on the XAI-HAR feature matrix than all other learning models. Below, we answer the research questions articulated in this study.
Answer to RQ1: The XAI-HAR consists of two steps: physical key features selection (PKFS) and statistical key features selection (SKFS) to form a feature matrix corresponding to different well-established contemporary methods used for recognizing activities. Further, we use local interpretable model agnostic (LIME) to interpret the decision-making process by classifiers. Answer to RQ2: XAI-HAR presents the concept of selecting local key features within the dataset while maintaining the original meaning of the features. Answer to RQ3: The weighting criteria are set as explained in equations 1, 2, 3, 4, 5, 6, 7, and 8.Answer to RQ4: The results reveal that the proposed approach will help neuropsychologists and clinicians to gain insight into an individual's functional ability to detect diseases and recognize their daily activities. Furthermore, the proposed approach help to understand the reason behind decision-making since detecting cognitive impairment is critical. Finally, it helps to provide interpretability to individuals with dementia.
## 5. Conclusion and future work
This study presented an XAI-empowered human activity recognition approach to enhance the recognition accuracy of cognitively impaired individuals' activities in a smart home. This approach helps to monitor the activities of cognitively impaired individuals and individuals having chronic impairments. The proposed approach improved the recognition accuracy of the intra-class variations. Moreover, XAI-HAR is compared with other commonly used feature selection techniques (PCA, mRMR, and IG) from the literature and other machine learning techniques. The results showed that the XAI-HAR achieved an f-score of $96\%$ using RF, which is higher than other feature selection approaches. In addition, these results demonstrated the further help provided by the proposed XAI-HAR to achieve healthier patients over available patients. In future, we aim to experiment with the proposed approach on the dataset having complex activities. We also intend to develop a dataset with multiple participants of different ages and pets. It will be challenging to detect activities and cognitive conditions in the presence of pets. Furthermore, we intend to extend this study by providing essential features and early detection for other domains, particularly for Parkinson's and Alzheimer's diseases.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.
## Author contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1024195/full#supplementary-material
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|
---
title: 'Demonstrating a link between diet, gut microbiota and brain: 14C radioactivity
identified in the brain following gut microbial fermentation of 14C-radiolabeled
tyrosine in a pig model'
authors:
- Margaret Murray
- Christopher K. Barlow
- Scott Blundell
- Mark Buecking
- Anne Gibbon
- Bernd Goeckener
- Lisa M. Kaminskas
- Patricia Leitner
- Sophie Selby-Pham
- Andrew Sinclair
- Habtewold D. Waktola
- Gary Williamson
- Louise E. Bennett
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10033698
doi: 10.3389/fnut.2023.1127729
license: CC BY 4.0
---
# Demonstrating a link between diet, gut microbiota and brain: 14C radioactivity identified in the brain following gut microbial fermentation of 14C-radiolabeled tyrosine in a pig model
## Abstract
### Background
There is a need to better understand the relationship between the diet, the gut microbiota and mental health. Metabolites produced when the human gut microbiota metabolize amino acids may enter the bloodstream and have systemic effects. We hypothesize that fermentation of amino acids by a resistant protein-primed gut microbiota could yield potentially toxic metabolites and disturb the availability of neurotransmitter precursors to the brain. However, these mechanisms are challenging to investigate via typical in vitro and clinical methods.
### Methods
We developed a novel workflow using 14C radiolabeling to investigate complex nutrient-disease relationships. The first three steps of the workflow are reported here. α-Linolenic acid (ALA) was used as a model nutrient to confirm the efficacy of the workflow, and tyrosine (Tyr) was the test nutrient. 14C-Tyr was administered to male weanling pigs fed a high resistant protein diet, which primed the gut microbiota for fermenting protein. The hypotheses were; [1] that expected biodistribution of 14C-ALA would be observed, and [2] that radioactivity from 14C-Tyr, representing Tyr and other amino acids released from resistant protein following gut microbial fermentation, would be bioavailable to the brain.
### Results
Radioactivity from the 14C-ALA was detected in tissues reflecting normal utilization of this essential fatty acid. Radioactivity from the 14C-Tyr was detected in the brain ($0.15\%$ of original dose).
### Conclusion
Metabolites of gut-fermented protein and specifically amino acid precursors to neurotransmitters such as tyrosine, are potentially able to affect brain function. By extension, resistant proteins in the diet reaching the gut microbiota, also have potential to release metabolites that can potentially affect brain function. The high specificity of detection of 14C radioactivity demonstrates that the proposed workflow can similarly be applied to understand other key diet and health paradigms.
## 1. Introduction
Nutrition directly influences human health, with either positive or negative impacts reflecting the balance of macro- and micro-nutrients, and other factors. It is important to understand the potential impacts of specific nutrients, foods and dietary patterns on human health, in order to inform accurate and scientifically sound nutritional advice. For example, it is important that the bioactivity of components in functional foods, claiming to enhance human health [1], be fully substantiated and based on sound scientific evidence. Likewise, there is growing interest in the impacts of ultra-processed foods on human health and it is important to understand the various mechanisms through which these products influence health [2].
It is a significant challenge to track the chemical fate of nutrients in the body due to the progressive biochemical transformations of food components associated with digestion, absorption, metabolism and excretion [3, 4]. Because of this, it is difficult to demonstrate the mechanisms within the human body by which specific compounds affect physiological functions and health. While in vitro and rodent models can provide valuable mechanistic information, these methods may fail to replicate relevant dosage, nutrient digestion, absorption and metabolism, or the potential influence of other dietary components [5]. Gold standard human intervention studies, relying on sampling of blood, feces, and urine, can demonstrate effects of nutrients on a health outcome, but are limited in demonstrating full mechanistic pathways and tissues involved.
In recent years, there has been much interest in the relationship between dietary intake, the gut microbiota and mental health (6–11). However, the mechanisms that link diet, gut microbiota and mental health are challenging to investigate using the above-described methods, as this is a complex metabolic pathway. Understanding how dietary components influence the gut microbiota and the bioactivities of resulting microbial metabolites as they contribute to the “gut-brain axis” represents a key step in characterizing the relationship between nutrition and mental health [10, 11].
Metabolites produced by the human gut microbiota, including neurotransmitters derived from amino acids (such as tryptamine, dopamine and serotonin), can enter the bloodstream and have systemic effects (10, 12–15). By extension, resistant proteins produced through high-heat processing of foods that are transported and fermented in the colon, may account for elevated release of amino acids in the colon (10, 16–18). Gut microbial fermentation of amino acids could perturb amino acid metabolism and influence brain function. For example, dysbiosis of the gut microbiota can cause an increase in tryptophan metabolic pathways that produce kynurenic acid and other metabolites, and a reduction in pathways involved in serotonin synthesis [19]. Such changes in tryptophan metabolism play a role in the onset and progression of depression [19].
We hypothesize that gut microbial metabolism of amino acids from a high-resistant protein diet [$50\%$ in vitro protein digestibility compared to the standard diet [16]] could yield potentially toxic metabolites arising from fermented protein and additionally disturb the availability of neurotransmitter precursors to the brain. To investigate this, we developed a workflow (Figure 1) using 14C stable isotope radiolabeling of the target nutrient/bioactive compound [20]. The use of 14C-labeling to trace the metabolic fate of nutrients is well-established, with examples including fatty acids [21], triglycerides [22], vitamins [23, 24], and toxins [25]. The proposed workflow involves oral intake of the 14C-labeled nutrient and following a short period allowed for digestion and metabolism, subsequent collection of blood, urine, feces, organs and tissues to determine the biodistribution of 14C. Pigs were chosen as the pre-clinical model, since the digestive system of pigs is most closely matched to that of humans compared to other non-primate species, making them a highly clinically relevant model [26]. The biodistribution of 14C-labeled metabolites is then characterized by measuring radioactivity in samples via liquid scintillation counting (LSC). Following this, specific metabolites may be identified using ultra-high performance liquid chromatography and high resolution mass spectrometry (UHPLC/HR- MS) analysis. Following identification of key metabolites, they can be chemically synthesized to closely study bioactivity in cells and functional assays. Finally, clinical research studies can investigate the impact of target nutrients on health and disease, using pre-identified biomarkers. This workflow is designed to address the challenge of demonstrating direct causal relationships between intake of dietary components and the resulting physiological outcomes, via the actions of bioactive compounds and their metabolites, and could be broadly applied to study diet-disease relationships.
**FIGURE 1:** *Schematic of the 14C-workflow for tracking the metabolic fate of bioactives/nutrients in food, showing the segments demonstrated in this study (*).*
This study aimed to [1] validate the workflow using the essential omega-3 fatty acid, α-linolenic acid (ALA), [2] investigate the biodistribution of tyrosine (Tyr), an aromatic amino acid and neurotransmitter precursor, and its metabolites following fermentation by a resistant protein-primed gut microbiota, and whether these metabolites could enter the brain, and [3] determine whether there was a difference in the metabolism of aromatic amino acid, tryptophan (Trp), between the standard diet and the resistant protein diet, demonstrating gut microbial fermentation. ALA was selected as a model compound to validate the workflow because the biodistribution of ALA is well documented (27–29), allowing for comparison between findings from the workflow and the literature to confirm that the workflow is effective at measuring biodistribution of nutrients in tissues and organs. The research reports on three steps of the overall workflow (Figure 1), including the protocol for feeding selected 14C-labeled nutrients to pigs, followed by tissue recovery and measuring biodistribution of total radioactivity by LSC. The results demonstrate the feasibility of the workflow for characterizing biodistribution of nutrients following both upper gut and colon fermentation-mediated absorption.
## 2.1. Materials
The 14C-labeled nutrients: α-linolenic acid (14C-ALA) and L-tyrosine (14C-Tyr), were provided by Hartmann Analytic GmbH (Braunschweig, Germany). The 14C-ALA contained a single 14C-labeled atom at C1: [1-14C]-α-linolenic acid. In the tyrosine molecule, all 9 carbon atoms were 14C labeled: [14C(U)]-L-tyrosine. The radioactive compounds were formulated into capsules for consumption. The radio-labeled ALA was prepared in two size 0 capsules, each containing 1.36 mg [1-14C]-ALA, with an activity of 10 MBq, and 75 mg methocel (Hartmann Analytic GmbH, Germany). Identical non-labeled capsules were also prepared containing 1.36 mg ALA and 75 mg methocel (Hartmann Analytic GmbH). The 14C-Tyr was prepared in two capsules (size 0), each containing 0.1 mg 14C-Tyr, with an activity of 10 MBq, 64 mg citric acid and 50 mg methocel. Identical non-labeled capsules were also prepared containing 0.1 mg L-Tyrosine, 64 mg citric acid and 50 mg Methocel (Hartmann Analytic GmbH, Germany). The unlabeled and 14C-labeled tyrosine capsules were then enclosed in size 00 acid-resistant capsules to prevent digestion in the stomach and promote release in the colon. The strategy to deliver 14C-Tyr into the colon was intended to mimic amino acids contained within resistant proteins that, due to digestive resistance, are transported and released during fermentation in the large intestine [16].
Reagents for liquid scintillation counting included solvable, soluene-350, Ultima Gold, Hionic-Fluor and scintillation vials and were purchased from Perkin Elmer Pty. Ltd. (Glen Waverley, VIC, Australia). GentleMACS™ M tubes were obtained from Miltenyi Biotec Pty. Ltd. (Macquarie Park, NSW, Australia). Hydrogen peroxide was obtained from Thermo Fisher Scientific (Scoresby, VIC, Australia).
## 2.2. Experimental animals and study design
Ethical approval for the research was granted by the Monash Animal Research Platform-1 Animal Ethics Committee (reference: 17533) and the study was monitored according to the ARRIVE guidelines for animal research [30]. The study protocol preceding administration of 14C-capsules was previously reported [16]. The overall study design and details for administering the 14C capsules is described below. Landrace cross Large White male weanling pigs commenced the standard diet ($$n = 4$$) or the resistant protein diet ($$n = 4$$) 4 weeks prior to 14C-nutrient administration. Feed intake was controlled to provide $100\%$ of energy requirements. Feed intake of individual pigs was monitored daily and water was provided ad libitum.
The animals that received the ALA capsules were fed the standard pig weaner diet (SF18-148 Specialty Feeds, Glen Forrest, WA) containing wheat, barley, lupines, soya meal, calcium carbonate, salt, dicalcium phosphate, lysine, and a vitamin and trace mineral premix. The animals that received the Tyr capsules were fed the high resistant protein diet (SF18-147 Specialty Feeds, Glen Forrest, WA, Australia) substituted with a protein source of high-heat-treated skim milk powder, also containing: barley, skim milk, soya meal, canola meal, calcium carbonate, salt, dicalcium phosphate, and a vitamin and trace mineral premix. The resistant protein diet was treated by autoclaving (15 h at 70°C followed by 20 min at 121°C and cooling for 2 h before vacuum packing) to model high heat/low moisture processing, and contained a high proportion of indigestible protein, that primed the colonic microbiota for protein fermentation [16].
Within each cohort, one animal received the 14C-labeled encapsulated nutrient and three animals received equivalent non-labeled encapsulated nutrients. Sample size of $$n = 1$$ for the 14C-labeled nutrient was deemed sufficient to identify the biodistribution nutrient metabolites due to the specificity of detecting radioactivity. This was also considered appropriate to reduce occupational health and safety risks associated with radioactivity, and ethically acceptable to minimize animal use.
On the day of capsule administration, each pig was moved into an individual metabolic cage and remained in the metabolic cage until euthanasing (3 days), and was accompanied at all times by another pig in an adjacent metabolic cage to minimize stress. Three pigs from each group were administered the unlabeled capsules, and one pig from each group was administered the 14C radio-labeled capsules. The capsules were administered with the morning feed (approximately 9:00 A.M.). The ALA capsules were included with the pig feed. The Tyr capsules were administered using a pill popper to ensure the acid-resistance capsules were delivered to the intestine intact. A 3-day window between capsule administration and euthanasia allowed time for metabolism and distribution of the 14C-nutrients into tissues and for sampling of blood and excreta to track absorption and metabolism (Figure 2).
**FIGURE 2:** *Study design showing the capsule administration and sampling protocol. Blood droplet icon indicates blood sampling, feces icon indicates excreta sampling, capsule icon indicates administration of capsules, brain icon indicates terminal sampling of tissues and organs.*
## 2.3. Sample collection
On the day of capsule administration (day 0) and each of the following three days (day 1, day 2, and final day F), blood and combined urine and fecal samples (excreta) were collected (Figure 2). On day 0, blood samples were collected in the morning (∼11:00 a.m.) and afternoon (∼ 3:00 p.m.) for the ALA group. For the Tyr group, blood samples were only collected in the afternoon (∼ 3:00 p.m.) on day 0, due to ethical concerns of taking two blood samples in a day because of their smaller size. Blood samples were then each day thereafter (∼11:00 a.m.) until euthanasia.
Blood samples were obtained with the pigs under light anesthesia ($8\%$ sevoflurane in $100\%$ oxygen delivered by snout mask, <1 min). Blood samples were immediately centrifuged (10 min at 1,500 × g, 4°C). Aliquots of plasma and blood cells were isolated and snap frozen on dry ice. Excreta samples were collected on days 0, 1, 2, and F and snap frozen on dry ice. Blood and fecal samples were also taken three days before capsule administration. All samples were stored at −80°C until analysis.
Following euthanasia by intravenous overdose of barbiturate pentobarbitone and subsequent confirmation of death, bleeding was performed to minimize blood content of tissue samples and cross-contamination of 14C radioactivity between blood and tissues post-mortem. Sample collection was carried out by a veterinarian with the help of trained researchers. The weight of the pig and each organ was recorded. Organ samples collected were colon, small intestine, stomach, spleen, heart, liver, kidney, lung, pancreas and brain (lobes, cerebellum and mid-brain). Tissue samples collected were skeletal muscle (quadricep and tricep) and subcutaneous abdominal adipose tissue. The contents of the stomach, small and large intestines (digesta) were also collected. All samples were snap frozen on dry ice before storage at −80°C.
## 2.4.1. Determination of radioactivity in plasma and blood cells
The biodistribution 14C-ALA and 14C-Tyr were assessed by LSC. Plasma aliquots were thawed and centrifuged (3,500 rpm, 5 min). Samples from a single “cold” pig were also analyzed for background correction. Plasma supernatant (250–500 μL) was mixed with 2 mL Hionic-Fluor cocktail in 6 ml scintillation vials prior to vortex mixing and scintillation counting on a Tri-Carb liquid scintillation counter (Perkin Elmer, Waltham, MA, United States). To quantify 14C in blood cells, samples were thawed and vortex mixed to obtain a homogenous solution. Blood cell samples (and also tissue and excreta samples described below) were solubilized and analyzed as described previously [31] with some modification. Blood cell samples from 14C dosed pigs or one cold-dosed pig (100 μL) were added to 20 mL scintillation vials and mixed with 2 mL of Solvable tissue solubilizer before being placed at 60°C overnight. Samples were subsequently cooled to room temperature and bleached with 400 μL of hydrogen peroxide ($30\%$ w/v) before the addition of Ultima Gold scintillant (10 mL). Samples were left in the dark at room temperature overnight prior to storage at 4°C for 4 days to suppress chemiluminescent reactions. The samples were then analyzed by LSC in sets of twelve to minimize warming and subsequent increases in chemiluminescent background during counting. Data are reported as the percentage of the original dose of radioactivity (20 MBq) quantified in each sample, or as the level of radioactivity (Bq) per volume of sample.
## 2.4.2. Determination of radioactivity in tissues
Tissues samples were weighed, minced into approx. 3 mm pieces and dispersed in MQ water (three-fold dilution, w/w). Tissues were homogenized in gentleMACS™ M tubes using a gentleMACS™ Dissociator (Miltenyi Biotec, Macquarie Park, NSW, Australia). An initial “screening” stage was used to determine the approximate levels of radioactivity in the samples. For this stage, a 450 μL aliquot of tissue homogenate was mixed with 2 mL Solvable in a 20 ml scintillation vial and incubated at 60°C overnight. After cooling to room temperature, 200 μL of hydrogen peroxide ($30\%$ w/v) was added to bleach the samples before Ultima Gold (10 mL) was added and the solution vortex mixed. The samples were then sequentially stored in the dark at room temperature overnight, then at 4°C for 4 days before scintillation counting as described above. Corresponding tissue samples were also analyzed from a cold pig to provide background correction.
In the second “analytical” stage, all tissue samples were analyzed in sets of 3 or 5. Homogenized tissues from 14C-dosed pigs were processed as described above in the “screening” stage, except that additional steps were taken to correct for any reductions in radioactivity counting efficacy that occurs in some tissue samples. Tissue homogenate from the pig receiving 14C-ALA (100 to 450 μL, depending on radioactive content) was aliquoted in triplicate into 20 mL scintillation vials. Tissue homogenate from the pig receiving 14C-Tyr (50 to 450 μL, depending on radioactive content) was weighted into 5 × 20 ml scintillation vials. From the P2 set, 3 vials were solubilized and analyzed to determine 14C content, while the remaining 2 vials were spiked with remaining pooled plasma from 14C dosed pigs at an appropriate volume to provide approximately the same 14C activity as the aliquoted tissue samples. Tissues were then solubilized in Solvable and treated as described above for whole blood prior to scintillation counting. The 14C content of tissues on a per mass and per organ basis were then analyzed (accounting for counting efficiency in each organ) as described previously [31].
## 2.4.3. Determination of radioactivity in excreta and intestinal contents
Excreta and intestinal contents (colon digesta, stomach digesta and small intestinal digesta) were each aliquoted into 20 ml vials and prepared into a homogenous slurry by mixing in MilliQ water (three-fold dilution, w/w). An initial “screening” stage was used to determine the approximate levels of radioactivity in the samples as described above.
In the second “analytical” stage, all tissue samples were analyzed in sets of 3 or 5 as described above for tissue samples. Pooled excreta and intestinal contents from 14C-dosed pigs were processed as described above in the “screening” stage, except that additional steps were taken to correct for any reductions in radioactivity counting efficacy that occurs in some samples as described above for tissues. Briefly, excreta or intestinal contents with the 14C-ALA (100 to 450 μL, depending on radioactive content) were weighed in triplicate into 20 ml scintillation vials, while samples with the 14C-Tyr (50 to 450 μL, depending on radioactive content) were weighted into 5 × 20 ml scintillation vials. From the 14C-Tyr set, 3 vials were solubilized and analyzed to determine 14C content, while the remaining 2 vials were spiked with remaining pooled plasma from 14C dosed pigs at an appropriate volume to provide approximately the same 14C activity as the aliquoted samples (determined during the screening stage). The samples were then processed and analyzed for radioactivity content as described above.
## 2.5. Analysis of selected metabolites in plasma and feces by LC-MS
Plasma and fecal samples taken from both diet groups at day -3 (Figure 2), were analyzed for selected metabolites of tryptophan [tryptophan (Trp), kynurenine (Kyn), tryptamine (TA)]. Tryptophan is an aromatic amino acid and neurotransmitter precursor, similar to tyrosine. This analysis was to determine whether there was a difference in the metabolites produced from aromatic amino acids between the standard diet (normal amino acid metabolism) and the resistant protein diet (gut microbial metabolism), representing disturbed amino acid metabolism.
## 2.5.1. Plasma sample preparation
The plasma preparation was based on a previously published method by Zhu et al. [ 32] with slight modification. The plasma was thawed on ice before transferring 25 μL to a 1.5 mL polypropylene tube and adding 250 μL of extraction solvent ($80\%$ methanol with $0.02\%$ formic acid and 0.5 μM 3-methyl-2-oxindole as an internal standard). The samples were agitated using a vortex mixer at 4°C before standing at −20°C for 1 h and subsequent centrifugation (20,000 × g, 4°C, 10 min). The supernatant was transferred to a new tube and evaporated to dryness under vacuum. The residue was then reconstituted in 100 μL of $0.1\%$ formic acid and subjected to centrifugation (20,000 × g at 4°C, 10 min) before transferring 80 μL of the supernatant to a sample vial for LC-MS analysis.
## 2.5.2. Fecal sample preparation
Fecal samples were prepared using a similar method to the plasma samples with the following modifications. The fecal samples were allowed to thaw on ice before transferring approximately 30 to 60 mg to a 2 mL polypropylene tube. The samples were weighed and 20 μL of ice-cold extraction solvent (containing 1.25 μM of internal standard) per mg of feces was added. Samples were then agitated using a vortex mixer at 4°C before standing at −20°C for an hour and subsequent centrifugation (20,000 × g, 4°C, 10 min). A total of 500 μL of the supernatant was transferred to a 1.5 mL tube and evaporated to dryness under vacuum. The sample was then reconstituted in 250 μL of $0.1\%$ formic acid, followed by centrifugation (20,000 × g at 4°C, 10 min) before transferring approximately 180 μL of the supernatant into a sample vial for LC-MS analysis.
## 2.5.3. LC-MS analysis
In separate Eppendorf tubes, plasma and fecal samples were extracted using extraction solvent without added internal standard, as described above, before pooling and using as the diluent for calibration solutions (separate sets for plasma and fecal samples). Calibration standards for Trp, TA, Kyn and the internal standard (3-methyl-2-oxindole) were each prepared individually in methanol. Mixed calibration solutions containing all standards and internal standard (1.25 μM) were subsequently prepared, each at 8 levels, over the following concentration ranges: Trp (0.625–160 μM); TA (3.125–400 nM); Kyn (31.25–4,000 nM), using the pooled extract as a diluent, in order to standardize for ionization suppression. The calibration solutions were prepared by mixing 5 μL of the standard mix solution with 95 μL of plasma extract. Endogenous concentrations of each metabolite were determined by extrapolation to the y-axis intercept of the calibration curve (zero added standard).
LC-MS analysis was performed using a Dionex RSLC3000 ultra high-performance liquid chromatograph coupled to a Q-Exactive Plus Orbitrap mass spectrometer (Thermo Fisher Scientific Australia Pty Ltd., Scorseby, VIC, Australia). Samples were analyzed using reverse phase chromatography with a C18 2.1 × 100 mm, 1.8 μm, Zorbax Eclipse Plus and equivalent 5 mm guard column (Agilent Technologies Australia Pty Ltd., Mulgrave, VIC, Australia). A gradient elution of $0.1\%$ formic acid (A) and acetonitrile $0.1\%$ formic acid (B) (linear gradient time-%B: 0 min-$0\%$, 9.5 min-$36\%$, 10.5 min-$95\%$, 12.5 min-$95\%$, 13.0 min-$0\%$, 16 min-$0\%$) was utilized at 40°C. The flow rate was maintained at 300 μL/min. Samples were maintained at 6°C pending injection (10 μL). Mass spectrometry was performed using electrospray ionization in positive ion mode (4 kV, capillary temperature 300°C; sheath gas flow rate 50; auxiliary gas flow rate 20; spare gas 0; probe temp 120°C) cycling between full scan MS at resolution 70,000 and parallel reaction monitoring mode targeting the protonated ions of Trp, Kyn, TA and 3-methyl-2-oxindole (Resolution = 17,500, automatic gain control target 2e5, Max injection time = 100 ms, Isolation window 1.2 m/z and (N)CE/stepped nce: 35). Data analysis was performed using TraceFinder 4.1 software (Thermo Fisher Scientific Inc., Scoresby, VIC, Australia). The concentration of each metabolite was determined by comparison with its corresponding calibration curve and correction for the background level of metabolite introduced from the pooled extract.
## 2.6. Statistical analysis
Datasets including body weight, feed intake, blood and fecal metabolites were tested for normality using the Shapiro–Wilk test and then analyzed using the non-parametric Mann–Whitney U test. Significance differences were reported at $p \leq 0.05.$
## 3. Results
The median weight of pigs on arrival was 8.0 kg (standard diet) and 6.5 kg (resistant protein diet) and at the time of euthansia were 18.8 kg (standard diet) and 8.0 kg (resistant protein diet) (Table 1). The significantly lower body mass gain of the resistant protein diet animals most likely reflected the significantly lower intake of the feed (Table 1) and its significantly lower protein digestibility, as shown previously [16].
**TABLE 1**
| Measure | 14C-ALA | 14C-Tyr |
| --- | --- | --- |
| Starting weight (kg) | 8.0 (7.5, 9.0) | 6.5 (6.0, 7.0) |
| Final weight at day F (kg) | 18.8 (15.0, 19.0) | 8.0 (8.0, 8.5) |
| Average feed intake (g/day) | 613.1 (555.2, 683.9) | 287.8 (280.3, 298.0) |
| Average energy intake (kJ/day) | 8,154 (7,385, 9,095) | 3,828 (3,728, 3,963) |
## 3.1. Biodistribution of 14C-ALA and metabolites
A large proportion of 14C-ALA radioactivity ($52.4\%$) was excreted in the urine and feces, peaking on day 1 and declining significantly at day 2 and day F (Figure 3A). Very low levels of radioactivity were detected in digesta from the stomach, small intestine or colon on day F (Figure 3A and Table 2). 14C-ALA radioactivity was detected in plasma from day 0 and peaked in both plasma and blood cells on day 1 (Figure 3B). Only $1.45\%$ of the original dose of radioactivity was recovered in the organs (stomach, small intestine, colon, liver, pancreas, kidney, spleen, heart, lungs, and brain) (Table 2). The highest amount of total radioactivity in the organs was detected in the liver ($0.62\%$), followed by the small intestine ($0.21\%$). The highest concentrations of 14C-ALA radioactivity were found in the kidney ($0.002\%$ dose/g), liver ($0.001\%$ dose/g) and fat tissue ($0.001\%$ dose/g). The levels of 14C-ALA radioactivity were very low in the brain (<$0.01\%$).
**FIGURE 3:** *Biodistribution of radioactivity from 14C-ALA ($$n = 1$$) and 14C-Tyr ($$n = 1$$) at a total dose of 20 MBq. The results are presented for (A) excreta samples (urine plus feces, taken on days 0, 1, 2, and F) and digesta samples, taken from the stomach, small intestine and colon on day F; (B) *Blood plasma* and blood cells (RBCs) on days 0, 1, 2, and F; and (C) Organ and tissue samples taken on day F.* TABLE_PLACEHOLDER:TABLE 2
## 3.2. Biodistribution of 14C-Tyr and metabolites
A large proportion of 14C-Tyr radioactivity ($48.6\%$) was excreted in the urine and feces, peaking on day 1 and declining significantly at day 2 and day F (Figure 3A). The detection of relatively higher levels of radioactivity in the colon digesta, compared with the stomach and small intestine, on Df supports the idea that the tyrosine was delivered to colon for microbial fermentation, as intended (Figure 3A). Radioactivity was detected in plasma from day 0 to day F, with a peak observed day 1. Radioactivity was also observed in blood cells from day 0 to day F, with a peak on day F (Figure 3B).
Of the original dose, $6.9\%$ was collectively recovered in all the organs sampled (stomach, small intestine, colon, liver, pancreas, kidney, spleen, heart, lungs, and brain) (Table 2). The highest amount of total radioactivity in the organs was detected in the small intestine ($3.1\%$), followed by the liver ($2.2\%$), The highest concentrations of radioactivity were detected in the liver ($0.008\%$ dose/g) and pancreas ($0.006\%$ dose/g). Radioactivity of $0.007\%$ dose/g was detected in the colon digesta. Significantly, of the initial 20 MBq, $0.14\%$ of the radioactivity was detected in the brain, with the highest quantity detected in the brain lobes ($0.088\%$) and highest concentration detected in the cerebellum ($0.003\%$ dose/g). The results support that 14C-Tyr or its metabolites are bioavailable to the brain.
## 3.3. Analysis of tryptophan metabolites
Metabolites related to the tryptophan metabolism pathway (Trp, Kyn, TA) were analyzed in plasma and feces. In this context, the samples from the standard diet group act as a control for the resistant protein diet group in examining the effects of reduced protein digestibility and gut microbial fermentation on the metabolites of an aromatic amino acid.
The fecal concentration of tryptophan was greater in animals on the standard diet compared to the resistant protein diet (Figure 4A). Conversely, the average fecal concentration of kynurenine and tryptamine was lower on the standard diet although this failed to reach statistical significance. In contrast, there was no difference in the plasma concentration of these metabolites (Figure 4B). The variations in fecal metabolite concentrations did not appear to be correlated with level of tryptophan in the diet as it was very similar in both the standard and resistant feeds (Figure 5). Amino acid profiles of the feeds from the two diets were similar for the majority of the amino acids with the exception of lysine and arginine which were significantly lower in the resistant protein diet consistent with their susceptibility to modification by the Maillard reaction.
**FIGURE 4:** *Comparison of levels of tryptophan and its metabolites, kynurenine and tryptamine in (A) feces and (B) plasma, on day -3. Data represent means and standard deviation (n = 3). Statistical comparisons were conducted using the non-parametric Mann–Whitney U test with significant differences shown at p < 0.05.* **FIGURE 5:** *Total amino acid composition of the two formulated diets.*
## 4. Discussion
This research investigates the biodistribution of microbially fermented Tyr, and its metabolites, and the concentration of microbially and normally metabolized Trp to contribute to our understanding of the relationship between gut microbiota, fermentation of dietary protein, and brain function. This represents a key step in characterizing the relationship between nutrition and mental health, and understanding the “food-gut-brain axis” [10, 11]. This study investigated the biodistribution of 14C-labeled Tyr (as a test compound) and ALA (as a model compound), as well as the concentration of Trp, Kyn and TA in plasma and feces. The study was conducted in weanling pigs, in order to maximize the concentration of radioactive metabolites in tissues. The feed characteristics and health impacts of the different feeding treatments were reported previously [16]. The focus of the data reported here is to highlight the biodistribution of 14C-nutrients and their metabolites, with a particular interest in biodistribution of gut microbial fermented tyrosine and its localization in brain tissues, and to highlight alternations in amino acid metabolism from a high resistant protein diet.
## 4.1. Biodistribution of 14C-ALA accompanying a standard diet
Omega-3 fatty acids are involved in many important normal functions in the human body, are linked with the intrinsic nutritional value of fish and also incorporated as ingredients to increase the healthiness of other food products [33]. In particular, ALA is an essential fatty acid in the human diet and a biosynthetic precursor for the long-chain eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) [27], both of which are important for human health (34–36). In the current study, ALA was chosen as a reference compound whose metabolism and biodistribution is well defined [27], for validating the mapping of biodistribution using the workflow.
The accepted metabolism of ALA in mammals following digestion and absorption in the upper GI tract is transport to liver in chylomicrons, followed by incorporation of the ALA into plasma lipoproteins and distribution and incorporation of the ALA to muscle, adipose, skin and other organs [28]. ALA does not accumulate to any extent in either the red blood cells or the brain [37]. A major route of disposal of ingested ALA is via beta-oxidation to CO2, presumably occurring in muscle [28]. It has been shown that between 30 and $60\%$ of 14C-ALA is expired as CO2 within the first 24 h [28, 38]. In the liver, ALA is also metabolized to longer chain polyunsaturated fatty acids (PUFA) via a sequence of desaturation and chain elongations to yield EPA, docosapentaenoic acid and DHA [39]. These longer chain PUFAs are also transported around the body from the liver in plasma lipoproteins. Tissues which accumulate DHA include brain and retina [37, 40]. Similar to observations in the present study, studies in suckling rat pups, and guinea pigs following the metabolic fate of orally dosed 14C-ALA, have shown significantly higher levels of 14C in the liver than the brain [29, 41]. Several studies have revealed substantial deposition of ALA in the skin, in rats and guinea pigs [37, 42] and extensive 14C-labeling from oral doses of 14C-ALA in the skin and fur of guinea pigs [29].
In this study, the remarkable observation of the biodistribution of the orally administered 14C-ALA in a pig was that the majority of the label was excreted in feces and urine ($46.8\%$ on day 2), with less than $9\%$ recovered in other tissues (Figure 3C and Table 2). The high level of excretion of the 14C from ALA in feces and urine has not been reported previously. Excretion of ALA via this route has been reported to be less than $1\%$ of the fed ALA [43]. Previous studies have provided the oral 14C-ALA mixed into a vegetable oil [29, 41], while in the current study, the 14C-ALA was mixed with methocel and contained in a capsule. It is possible that the capsule or the methocel or both limited the digestion and absorption of the 14C-ALA, by delaying uptake until later in the GI tract after the capsule was fully dissolved.
Despite the high level of excretion of the 14C-label from ALA via the feces and urine, there was extensive labeling of muscle [estimated at $6.6\%$ of administered dose, based on body composition data [44]], adipose (estimated at $0.7\%$) and liver ($0.6\%$) with lower levels of uptake of 14C-ALA in kidney, lungs, small intestine and colon (between 0.1 and $0.2\%$ of dose), and very low levels of 14C detected in brain (<$0.002\%$ of dose). The signal of 14C-ALA was highest in kidney tissue ($0.002\%$ dose/g). The 14C labeling of the skin was not collected in this study. This tissue labeling pattern is consistent with studies in rats and guinea pigs [29, 41]. The total mass recovery of the 14C-ALA was approximately $61\%$ of the dose administered. It is presumed that the remaining $39\%$ of the dose was eliminated via metabolism of the 14C-ALA to CO2. The 14C-ALA biodistribution data provided an independent reference nutrient that confirmed expected biodistribution and validated the research methodology.
## 4.2. Biodistribution of 14C-Tyr accompanying a resistant protein diet
The link between poor diet (specifically a Western-style diet), gut microbiota and mental health has been previously highlighted (11, 45–47), however, a clear biological pathway for the link between poor diet and poor mental health remains unproven. We hypothesize that high levels of resistant protein may be a key dietary factor that contributes to poor mental health. In relation to characterizing the “missing link” between diet and mental health, we have previously demonstrated the “Maillard reaction” and “reduced digestibility” elements of the pathway (Figure 6) [16]. High-heat processing of food, driving Maillard chemistry between proteins and reducing carbohydrates, resulted in dietary proteins that were poorly digestible (resistant proteins) and passed through to the large intestine where they were fermented by the gut microbiota and influenced microbial composition [16]. The “fermentation” element has been previously reported, detailing the production of neuroactive and neurotransmitter compounds from the microbial fermentation of amino acids [15, 48, 49]. The results from this study demonstrate that tyrosine or tyrosine-derived metabolites, digested by a resistant protein-primed gut microbiota, can access the brain (Figure 3C and Table 2). Therefore, there is potential for these compounds to have neuroactive effects, contributing toward the “brain signaling” element in understanding the link between poor diet and poor mental health.
**FIGURE 6:** *Schematic showing the proposed mechanisms linking intake of ultra-processed, resistant protein-containing foods with outcomes relating to depression and mood disorders.*
Tyrosine is an aromatic amino acid and precursor to key neurotransmitters, dopamine, adrenaline and noradrenaline, which have profound effects on mood, reward behavior, wakefulness and motor activity [46]. Dietary tyrosine depletion has been implicated in an increased risk of clinical depression [50]. When amino acids, like tyrosine, are fermented by gut microbes, they may be converted to potentially toxic compounds, such as ammonia, amines, N-nitroso compounds, phenols, cresols, indoles and hydrogen supplied (18, 51–53), rendering them unavailable as neurotransmitter precursors and mimicking dietary depletion. However, gut microbes also have the potential to convert amino acids to neurotransmitters (e.g., dopamine, serotonin) that function as signaling molecules in the enteric nervous system, with systemic and brain accessibility, known as the “gut-brain axis” [15, 48, 54]. This aligns with the growing understanding of regulation of brain functions and mood via the gut-brain axis [55]. Significantly, this study has demonstrated the bioavailability to brain of gut microbially fermented 14C-Tyr and/or its metabolites.
This study also investigated whether feeding a resistant protein diet may alter the concentration of aromatic amino acid and neurotransmitter precursor, Trp, and its metabolites, in plasma and feces. Trp is an essential amino acid that is the precursor to serotonin, a neuromodulator involved in mood, appetite and gastrointestinal function [56]. Dietary Trp depletion, resulting in low brain serotonin levels, is associated depressed mood [57]. Trp is present in the body in relatively low quantities and its metabolism is vulnerable to changes in physiological status [56] and can be moderated by the gut microbiota [57]. The reduction in Trp concentration in the feces observed in the present study may be due to destruction of Trp during the high heat processing of the diet or due to the lower feed intake in the resistant protein diet group, both of which could lead to reduced intake of Trp and the role of either pathway cannot be confirmed. Under normal conditions, 90–$95\%$ of ingested *Trp is* converted to Kyn and enters the kynurenine pathway [57, 58]. However, Kyn metabolism can be altered by inflammation and, in turn, changes in Kyn metabolism can influence immune-inflammatory processes [58]. In the present study, no differences in Kyn concentration between the resistant protein diet group and standard diet group were detected in the plasma or feces. Given the reduced protein digestibility of the resistant protein diet [16], we had expected to see a reduction in Kyn due to increased microbial fermentation of Trp to produce other metabolites. However, the small sample size presented here suggests that these findings should be interpreted with caution.
Tryptamine (TA) is a microbial fermentation product of Trp, and indicates alterations in Trp metabolism as a result of protein indigestibility and fermentation by the gut microbiota [48, 49]. Though no significant differences between the diet groups were identified, the non-significant trend toward elevated TA in the feces and plasma of animals on the resistant protein diet may reflect increased microbial fermentation of Trp into TA from the resistant protein diet compared with the standard diet [15, 16, 18]. We have previously reported that the resistant protein diet was accompanied by an altered microbiome composition compared with the standard diet, suggestive of an altered fermentation substrate [16]. This finding suggests that some Trp from the resistant protein diet bypassed normal protein digestion and was fermented by the gut microbiota to form TA, which may limit the amount of Trp available to the brain to produce neurotransmitters such as serotonin and dopamine [50, 59, 60]. Depletion of amino acids, such as tyrosine and tryptophan, has been linked with neuropsychological changes and increased risk of depression and anxiety in healthy adults (46, 50, 59–62). Meanwhile, overproduction of indole, another Trp metabolite produced by the gut microbiota, was associated with increased anxiety-like behavior in rats [63]. This suggests that either the production of toxic metabolites (e.g., indole) and/or the reduced availability of amino acid precursors to the brain may be implicated in neuropsychological conditions [52, 64].
## 4.3. Applications of the proposed workflow
In the absence of radio-labeled nutrient research there are many mechanistic relationships in nutrition and health that remain ambiguous, particularly when activity occurs in tissues and organs that cannot be easily sampled, such as in the brain. Further understanding of how diet increases the risk of poor mental health is required in order to establish ways of reversing this effect [11]. These represent worthy research targets for application of the proposed 14C workflow (Figure 1). For example, aromatic amino acids, Tyr and Trp, are precursors to neurotransmitters dopamine (Tyr), epinephrine (Tyr), norepinephrine (Tyr) and serotonin (Trp) [46, 65]. The digestibility and bioavailability of these amino acids is compromised in resistant proteins [16, 17, 66]. This may reduce the supply of neurotransmitter precursors available to the brain and result in the production of other potentially neuroactive or toxic compounds (e.g., indole) that may impact brain function [10, 15, 18, 19, 48, 51]. The detection and identification of 14C labeled metabolites of amino acids found in the brain would allow further mechanistic work to be done, in established brain cell models, to determine their effects on brain function. This may have important implications for the dietary management of mental health conditions.
The proposed workflow provides a framework for determining the mechanistic functions of bioactive compounds from food by understanding the biodistribution of metabolites within tissues/organs, studying the mechanisms of those metabolites in cell assays, and measuring those same metabolites along with health markers in clinical studies. This allows deep understanding of the bioactivity of key nutrients in the tissues in which they are active. This process cannot be achieved without radio-isotope labeling. Radio-isotope labeling of molecules is a well-established tool used in pre-clinical and other research fields, typically focusing on small molecules with synthetic pathways that permit control of the localization of the isotope atom(s). The use of 14C-labeling to trace the metabolic fate of nutrients is also well-established (21–25), but typically focusses only on biodistribution. The proposed workflow for characterizing biodistribution of nutrients and then identifying their functional activity in relevant tissues is relatively new [20]. With the costs of radio-labeled compounds and specialized infrastructure being relatively high, the cost-benefit lies in the highly conclusive data and outcomes from well-designed research targets, as demonstrated in the current study. Likewise, by identifying suitable compounds active within a target pathway, it may be possible to study proposed nutritional origins of other conditions, such as epilepsy [67]. We are seeking collaboration and partnerships to extend the applications of this workflow.
## 4.4. Strengths and limitations
A strength of the study was the use of the 14C-ALA as a “reference” nutrient (given on a standard diet and for standard digestion and metabolism) to confirm the concepts of the workflow and demonstrate biodistribution of a standard nutrient. This validates the findings reported for “test” nutrient, 14C-Tyr, encapsulated for targeted delivery to and microbial fermentation in a gut microbiome primed on a resistant protein diet. However, the prohibitive costs of administering 14C-labeled nutrients to clinically relevant large animal models (such as the pig) meant the inclusion of controls for 14C-Tyr in which the nutrient was delivered following a standard diet (as compared to the resistant protein diet) or delivered in a standard capsule (as compared to an acid-resistant capsule) were not possible. Therefore, the results of this study can only demonstrate the tissue distribution of tyrosine and its metabolites following metabolism by the resistant protein diet-primed gut microbiota, and cannot indicate how this may have changed from tyrosine metabolism on a standard diet. We have previously compared the effects of the standard and resistant protein diets on the microbiome and other health markers in pigs [16]. The specificity of the 14C radiolabel is an important strength of this research as it allows for accurate measurement of the biodistribution of a compound. However, while a pill popper (to prevent chewing) and acid-resistant capsule (to prevent gastric digestion) were used together to deliver the 14C-Tyr to the gut microbiota, there is the possibility that some 14C-Tyr was released and absorbed in the small intestine. Therefore the biodistribution of 14C radioactivity, without chemical identification of metabolites and without comparison to metabolism following a standard diet, should be interpreted with caution.
## 4.5. Conclusion
Two different 14C-labeled nutrients were used to demonstrate a workflow representing a new and powerful research approach to understand in vivo metabolism and biodistribution of nutrients. 14C-ALA was given as a model nutrient to validate the workflow. Apart from the high loss of 14C-ALA radioactivity (∼$52\%$) in urine and feces (likely due to the encapsulation technique), the remaining biodistribution of radioactivity was as expected based on existing literature, with the highest levels in liver, kidney and fat, and lowest levels in brain tissues. 14C-Tyr was given as a test nutrient to investigate the biodistribution of metabolites of the amino acid following gut microbial fermentation (as a model of dietary resistant protein digestion and metabolism). There was also a high loss of 14C-Tyr radioactivity (∼$49\%$) in urine and feces. The key finding was that 14C-Tyr radioactivity was detected in the brain, suggesting that microbially fermented tyrosine or its metabolites may be able to influence brain function. We previously reported that the resistant protein diet produced an altered microbiome [16]. Here, the trends toward increased production of tryptamine, a microbial metabolite of tryptophan, reflects the delivery of amino acids to the colon for microbial fermentation.
The research has, for the first time, demonstrated a step-wise pathway between dietary intake, gut microbiota and brain. Building on our previous findings [16], this research suggests that gut microbial fermentation of dietary resistant protein might impact brain function through affecting the supply of amino acid-derived neurotransmitters or altering amino acid metabolism toward production of potentially toxic metabolites and pro-inflammatory processes. These findings add to our understanding of the missing link between diet and mental health. However, further research is required to identify the compounds that gain access to the brain and define their activity on brain function. Followed by clinical research to validate the association of high resistant protein intake and production of amino acid fermentation metabolites with outcomes related to depression and anxiety.
## Data availability statement
The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
The animal study was reviewed and approved by the Monash Animal Research Platform-1 Animal Ethics Committee (Reference: 17533).
## Author contributions
MM: methodology, formal analysis, investigation, writing—original draft, visualization, data curation, project administration, and writing—review and editing. CB and SB: investigation, data curation, and writing—review and editing. MB: conceptualization, funding acquisition, and writing—review and editing. AG: methodology, investigation, and writing—review and editing. BG, PL, and AS: writing—review and editing. LK: investigation, formal analysis, data curation, and writing—review and editing. SS-P: conceptualization, methodology, and writing—review and editing. HW: investigation, data curation, and writing—review and editing. GW: supervision, methodology, and writing—review and editing. LB: conceptualization, methodology, investigation, resources, writing—original draft, visualization, supervision, writing—review and editing, and funding acquisition. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: A defect in mitochondrial protein translation influences mitonuclear communication
in the heart
authors:
- Feng Gao
- Tian Liang
- Yao Wei Lu
- Xuyang Fu
- Xiaoxuan Dong
- Linbin Pu
- Tingting Hong
- Yuxia Zhou
- Yu Zhang
- Ning Liu
- Feng Zhang
- Jianming Liu
- Andrea P. Malizia
- Hong Yu
- Wei Zhu
- Douglas B. Cowan
- Hong Chen
- Xinyang Hu
- John D. Mably
- Jian’an Wang
- Da-Zhi Wang
- Jinghai Chen
journal: Nature Communications
year: 2023
pmcid: PMC10033703
doi: 10.1038/s41467-023-37291-5
license: CC BY 4.0
---
# A defect in mitochondrial protein translation influences mitonuclear communication in the heart
## Abstract
The regulation of the informational flow from the mitochondria to the nucleus (mitonuclear communication) is not fully characterized in the heart. We have determined that mitochondrial ribosomal protein S5 (MRPS5/uS5m) can regulate cardiac function and key pathways to coordinate this process during cardiac stress. We demonstrate that loss of Mrps5 in the developing heart leads to cardiac defects and embryonic lethality while postnatal loss induces cardiac hypertrophy and heart failure. The structure and function of mitochondria is disrupted in Mrps5 mutant cardiomyocytes, impairing mitochondrial protein translation and OXPHOS. We identify Klf15 as a Mrps5 downstream target and demonstrate that exogenous Klf15 is able to rescue the overt defects and re-balance the cardiac metabolome. We further show that Mrps5 represses Klf15 expression through c-myc, together with the metabolite L-phenylalanine. This critical role for Mrps5 in cardiac metabolism and mitonuclear communication highlights its potential as a target for heart failure therapies.
The heart requires high levels of mitochondria to sustain function, and mitochondrial stressors can be transmitted to the nucleus and reprogram metabolism. Here, the authors show that a mitochondrial ribosomal protein is important for heart development in mice by increasing nuclear Klf15 expression.
## Introduction
Mitochondria are known as the powerhouse of the cell; in mature cardiomyocytes, continuous ATP production via oxidative metabolism in the mitochondria is essential for maintaining normal heart function. The mammalian mitochondrion is a semi-autonomous organelle constructed from proteins encoded by both nuclear DNA (nDNA) and mitochondrial DNA (mtDNA); over 1000 nDNA-encoded proteins are localized to mitochondria1, while mtDNA encodes 13 proteins that form essential components of the electron transport chain (ETC) involved in the oxidative phosphorylation (OXPHOS) system for ATP generation2. Mitochondrial ribosomal proteins (MRPs), required for mammalian mitochondrial translation, are encoded by nDNA3, translated by the cytosolic ribosome complex, and then translocated to mitochondria. Deficiency in human MRPs have been linked to forms of cardiovascular disease, cancer, developmental and neurodegenerative disorders, mitochondrial respiratory chain diseases, obesity, and inflammatory disorders4,5; specifically, previous reports have established that decreased levels of MRPS6, MRPS10, and MRPS22 are associated with cardiac disorders6,7. Mitoribosomal protein MRPS5, also known as uS5m8, is a key component of the mitochondrial translational machinery that is highly conserved across species. Mutations in Mrps5 affect the accuracy of mitochondrial ribosomal translation9, establishing a role for MRPS5 in mitochondrial protein translation10,11; a decrease in the levels of the mitochondrial protein mt-CO1 were detected in this analysis. In addition, the function of Mrps5 is linked to cellular stress responses and the reduction of MRPS5 protein levels is also associated with increased longevity in worms and mice10,11. Loss of Mrps5 results in developmental defects that are proposed to result from decreased energy (ATP) production3; the decreased ATP pool would be particularly detrimental to the development of organs such as the heart and skeletal muscle, which typically have high energy demands. It is not yet clear whether the translation of mtDNA genes is affected during hypertrophy, but this seems likely given that MRPS5 had been demonstrated to be an important component of the mRNA entry channel in the 28 S subunit of the mammalian mitochondrial ribosome12,13; mutations in MRPS5 has been shown to decrease mitochondrial ribosomal translational accuracy in vivo9. However, no mechanism has been defined to explain how Mrps5-dependent protein translation in the mitochondria is able to transduce changes in cardiac gene expression and metabolism in response to stress.
More than $95\%$ of ATP consumed in the heart is derived from OXPHOS in the mitochondria14. Mitochondrial dysfunction triggers a wide variety of pathological processes involved in cardiovascular disease and it is estimated that more than $50\%$ of individuals with mutations in genes encoding mitochondrial proteins progress to some form of cardiomyopathy15. Previous studies have demonstrated that cardiac hypertrophy impacts energy generation by mitochondria; it has been found that during cardiac hypertrophy, both nDNA and mtDNA-encoded mitochondrial genes show changes in transcript levels; this results in decreased mitochondrial biogenesis, increased reactive oxygen species (ROS) production, and impaired mitochondrial function16.
In this study, we report a critical role for Mrps5 in heart development, pathological cardiac hypertrophy, cardiac metabolism, and mitonuclear communication. We found that cardiac-specific deletion of Mprs5 resulted in stalling of mitochondrial ribosomal translation, cristae structure collapse, and subsequent mitochondrial dysfunction. Mrps5 deficiency links the mitochondrial cristae defect to abnormal cardiac development, pathological cardiac hypertrophy, and heart failure. The use of an AAV9 system to express downstream target genes of MRPS5 in Mrps5 null hearts resulted in a functional rescue. We further identified the transcription factor Klf15 as a key downstream target in the heart, and that overexpression of Klf15 was sufficient to rescue the Mrps5 loss of function phenotypes in the heart. Klf15 expression was also able to restore the metabolic profile of Mrps5 null hearts and reverse the observed pathological elevation in glycolysis/gluconeogenesis and decreased expression levels of mitochondrially encoded genes. These observations suggest a paradigm underlying cardiac hypertrophy, one in which pathological remodeling is driven by reprogramming of the cardiac metabolic profile, resulting from defects in mitochondrial translation.
## Cardiomyocyte-specific Mrps5 deletion results in cardiac hypertrophy and heart failure
To characterize the expression and function of Mrps5 in cardiomyopathy, we first examined its expression during cardiac remodeling under stress conditions. The expression of Mrps5, together with the mitochondrially encoded gene regulators and nuclear-encoded ETC genes (Atp5e, Cox6b2, Cox7a1, Ndufa3, Ndufv3, and Uqcr11), were all downregulated in a mouse model of pressure overload-induced cardiac hypertrophy and heart failure (transverse aortic constriction, TAC model); the hypertrophic marker genes (Nppa, Nppb, and Myh7) were upregulated (Fig. 1a–c). Similarly, Mrps5 expression was significantly downregulated in isolated neonatal mouse cardiomyocytes (NMCMs) in response to the hypertrophic agonizts phenylephrine (PE), isoproterenol (ISO), and fetal bovine serum (FBS), respectively (Fig. 1d). Interestingly, Mrps5 level was not significantly altered in the heart one month after myocardial infarction (MI); however, its expression was dramatically reduced in the heart six months after MI (Supplementary Fig. 1a, b). Consistent with the above findings in mice, the expression of human MRPS5 was decreased in heart tissue samples from patients with dilated cardiomyopathy (DCM) (Fig. 1e, f, Supplementary Fig. 1c, and Supplementary Table 1).Fig. 1Cardiomyocyte-specific Mrps5 deletion results in cardiac hypertrophy and heart failure.a Gene expression level of Mrps5 and other regulators of mitochondrial gene expression and ETC genes are downregulated in hypertrophic hearts. b, c Western blot of protein levels of MRPS5 and mitochondrial encoded protein mt-ATPase 6 in control (Sham) and hypertrophic mouse hearts (TAC). d Gene expression level of Mrps5 and other regulators of mitochondrial gene expression and ETC genes in isolated neonatal mouse cardiomyocytes (NMCMs) in response to hypertrophic agonizts phenylephrine (PE), isoproterenol (ISO) and fetal bovine serum (FBS) in vitro e, f Analysis of protein levels of MRPS5 and mt-ATPase 6 in human hearts from control or patients diagnosed with dilated cardiomyopathy (DCM). g Gene expression level of Mrps5 is significantly decreased at 2 weeks to 18 weeks after tamoxifen injection. h Gross heart morphology from control Mrps5fl/fl and Mrps5cKO mice at 12 weeks after tamoxifen injection. Scale bar = 500 µm. i Heart sections stained with hematoxylin and eosin (top panel) or Sirius Red and Fast Green (bottom panel) from control Mrps5fl/fl and Mrps5cKO mice at 12 weeks after tamoxifen injection. Scale bar = 500 µm. j Heart weight to body weight ratio of Mrps5fl/fl and Mrps5cKO mice at 2 to 22 weeks after tamoxifen injection. k Representative images of heart cross sections from Mrps5fl/fl and Mrps5cKO mice at 8, 12, and 18 weeks after tamoxifen injection (immunostained with Wheat germ agglutinin (WGA) in red and DAPI in blue). Scale bar = 100 μm. l Quantification of the cross-sectional area of cardiomyocytes and m *Cardiac fibrosis* from Mrps5fl/fl and Mrps5cKO mice at 4 to 18 weeks after tamoxifen injection. n Survival curve of Mrps5fl/fl and Mrps5cKO mice post tamoxifen injection. o Echocardiographic measurement of cardiac function in Mrps5fl/fl and Mrps5cKO mice at 5 to 23 weeks after tamoxifen injection. p qRT-PCR analysis of cardiomyopathy marker genes from control Mrps5fl/fl and Mrps5cKO mouse hearts at 6 to 18 weeks after tamoxifen injection. N numbers are indicated in each panel. All data were presented as mean ± SEM. P values were determined by a two-tailed unpaired Students’ t-test.
Next, we sought to determine the functional role of Mrps5 in the heart in vivo. We used an inducible system to conditionally knockout Mrps5 (Mrps5cKO) in adult mouse cardiomyocytes by breeding Mrps5fl/fl mice with αMHC-MerCreMer transgenic mice (Supplementary Fig. 1d). We confirmed Cre-mediated cardiac-specific deletion of Mrps5 in the hearts of αMHC-MerCreMer; Mrps5fl/fl mice (Mrps5cKO) following tamoxifen administration (Fig. 1g and Supplementary Fig. 1e); no appreciable change in Mrps5 expression was detected in liver and lung (Supplementary Fig. 1f). Loss of Mrps5 in the heart results in cardiac hypertrophy, exhibiting as overt enlargement of the heart itself (Fig. 1h, i and Supplementary Fig. g–i) and increased heart weight to body weight ratio (Fig. 1j). Histological examination and quantitative analysis reveal an increase in cardiomyocyte size in Mrps5cKO hearts (Fig. 1k, l). Cardiac fibrosis was also substantially increased in the hearts of Mrps5cKO mice (Fig. 1m and Supplementary Fig. 1h, i). As a result, Mrps5cKO mice began to die after 20 weeks of Mrps5 ablation, and all mutant mice died within 26 weeks after Mrps5 ablation (Fig. 1n). Echocardiography was used to carefully monitor the cardiac function of the Mrps5cKO and control mice between 5 and 23 weeks after tamoxifen treatment (Supplementary Fig. 1j), and we found that Mrps5cKO mice exhibit a decreased fractional shortening (FS%) and ejection fraction (EF%) and increased left ventricular internal diameter (LVID;s) around 8 weeks after Mrps5 ablation (Fig. 1o and Supplementary Fig. 1j–m). Further evidence of the development of cardiac hypertrophy and cardiomyopathy in Mrps5cKO mice includes the upregulation of the hypertrophy and disease marker genes (Nppa, Nppb, and Myh7) and fibrosis marker genes (Col1a1, Col1a2, and Col1a3) in Mrps5cKO hearts (Fig. 1p).
## Mrps5 is required for cardiac development
To gain insight into the function of Mrps5 during cardiac development, we crossed the Mrps5fl/fl mice with cTnTCre mice, in which the expression of Cre recombinase is under the control of the cardiac promoter of the troponin T2 gene, to generate an early cardiomyocyte specific-targeted Mrps5 mutant. We analyzed embryos between E10.5 and birth and found that cTnTCre; Mrps5fl/fl embryos presented a normal Mendelian ratio at E11.5, but no viable null embryos were found at E12.5 or birth (Supplementary Fig. 2a, b). We examined the embryonic heart at E12.5, prior to the onset of mutant embryo lethality and observed a decrease in myocardial wall thickness (Supplementary Fig. 2c). Transmission electron microscope (TEM) analysis revealed impaired mitochondrial cristae structure in Mrps5 mutant cardiomyocytes; in addition, the sarcomere structure was affected (Supplementary Fig. 2d). We performed transcriptome sequencing on the Mrps5 mutant hearts and littermate controls at E12.5 ($$n = 3$$ biological replicates per group, 5 hearts per replicate) (Supplementary Fig. 2e). Among the dysregulated genes, 1228 genes were upregulated and 930 genes were downregulated (Supplementary Fig. 2f). The enriched pathways based on the KEGG database indicated that cardiac disease associated pathways, including “hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM) and arrhythmogenic right ventricular cardiomyopathy (ARVC)” were enriched in the Mrps5 mutant embryonic hearts (Supplementary Fig. 2g). The pathway “regulation of actin cytoskeleton” emerged in the top 20 pathways enriched in the mutant hearts (Supplementary Fig. 2g), consistent with the abnormal sarcomere structure observed via TEM and suggesting that upstream signals contribute to this defect. In addition, enhanced Hippo signaling decreased myogenesis and mitotic spindle genes, which are involved in cardiomyocyte proliferation and myocardial development, likely contributed to the thinner myocardial walls in Mrps5 mutant hearts (Supplementary Fig. 2g, h). Consistent with the observation by TEM that mitochondrial cristae largely collapsed in the Mrps5 mutant hearts, gene set enrichment analysis (GSEA) also showed that oxidative phosphorylation and fatty acid metabolism were significantly reduced. In contrast, glycolysis and reactive oxygen species were enhanced (Supplementary Fig. 2h). These data indicate that Mrps5 ablation in embryonic hearts results in heart developmental defects that lead to embryonic lethality at E12.5.
## Mitochondrial defects in Mrps5 mutant hearts
Organs with a high level of energy consumption, such as the heart, require highly efficient mitochondrial function, which is crucially dependent on mitochondrial OXPHOS. MRPS5 is an important component of the mammalian mitochondrial ribosome12,13, and mutant MRPS5 affects mitochondrial ribosomal translational accuracy in vivo9; therefore, we sought to define the role of MRPS5 in the mitochondria of cardiomyocytes. We performed a series of the transmission electron microscope (TEM) examinations of heart tissue from Mrps5cKO and control Mrps5fl/fl mouse cardiomyocytes from 8 weeks to 18 weeks post Mrps5 ablation (Fig. 2a). No obvious difference in mitochondrial morphology was observed between Mrps5cKO and control Mrps5fl/fl samples 8–10 weeks after tamoxifen administration (Fig. 2a, b). However, we observed that mitochondrial cristae length drastically decreased after 12 weeks post Mrps5 ablation; most of the mitochondrial cristae collapsed within 18 weeks of Mrps5 ablation (Fig. 2a, b).Fig. 2Mitochondrial defects in Mrps5 mutant hearts.a Transmission electronic microscopic (TEM) images of Mrps5fl/fl and Mrps5cKO hearts at 8 to 18 weeks after tamoxifen injection showing mitochondria (M) and sarcomeres (S). Scale bar as indicated in the bottom of the figure panels. b Quantification of mitochondrial cristae length of Mrps5fl/fl and Mrps5cKO cardiomyocytes from 8 to 18 weeks after tamoxifen injection. c Quantification of ATP content in heart tissue samples from Mrps5fl/fl and Mrps5cKO mice, 8 to 18 weeks after tamoxifen injection. d Oxygen consumption rate of Mrps5fl/fl and Mrps5cKO hearts at 8 to 18 weeks after tamoxifen injection. e Quantification of activities of the ETC complexes of Mrps5fl/fl and Mrps5cKO hearts at 8 to 18 weeks after tamoxifen injection. f Immunoblot of mitochondrial electron transport chain protein complexes isolated from Mrps5fl/fl and Mrps5cKO mouse heart mitochondria at 12 weeks after tamoxifen injection. g Quantification of mitochondrial genome encoded protein expression level from Mrps5fl/fl and Mrps5cKO mouse heart mitochondria via parallel reaction monitoring (PRM) mass spectrometry at 10 weeks after tamoxifen injection. h Representative immunoblot and quantification of mitochondrial genome encoded proteins from lysates of Mrps5fl/fl and Mrps5cKO mouse hearts at 12 weeks after tamoxifen injection. VDAC serves as a loading control. N numbers are indicated in each panel. All data were presented as mean ± SEM. P values were determined by a two-tailed unpaired Students’ t-test.
We performed ATP content assessment in Mrps5cKO and Mrps5fl/fl hearts. ATP content sharply decreased from 12 weeks post Mrps5 ablation and was almost undetectable at 18 weeks (Fig. 2c). We measured O2 consumption of myofibers isolated from both Mrps5cKO and Mrps5fl/fl hearts and found a dramatic decrease in O2 consumption in Mrps5cKO hearts (Fig. 2d). We monitored the activities of mitochondrial ETC complexes and found that they started to decrease 12 weeks post Mrps5 ablation, decreasing even further by 18 weeks. This is especially the case for complexes I, II, and III (Fig. 2e). Blue native polyacrylamide gel electrophoresis (BNPAGE) analysis confirmed this observation (Fig. 2f). Given the ETC complexes are made up of nDNA- and mtDNA-encoded proteins, we performed parallel reaction monitoring (PRM) to assess the levels of mtDNA-encoded proteins; this analysis revealed a marked reduction in the levels of mtDNA-encoded proteins in the hearts of Mrps5cKO mice compared to the controls (Fig. 2g). Western blotting demonstrated that mt-ND1 and mt-CO1 protein levels were substantially decreased in Mrps5cKO hearts, confirming that Mrps5 ablation resulted in impaired mitochondrial translation (Fig. 2h). Together, these studies suggest that loss of Mrps5 in the heart leads to structural and functional defects in mitochondria.
## Loss of Mrps5 alters translational and metabolic programs in the heart
To understand the molecular mechanisms underlying Mrps5 function in the heart, we performed RNA sequencing of Mrps5cKO and Mrps5fl/fl hearts 12 weeks after tamoxifen induction. We chose this stage because the mutant hearts have just started to exhibit detectable defects, and the changes in the transcriptome state would more accurately correlate with the onset of the phenotype. Volcano plot, heatmap, and principal component analysis (PCA) of the RNA sequencing confirmed the consistency between biological repeats for each condition (Fig. 3a–c). We identified a set of 6014 genes that were differentially expressed in Mrps5cKO hearts; 3088 genes are upregulated, and 2926 genes are downregulated (Mrps5 itself is among the most noticeably downregulated genes) (Fig. 3a). As expected, DAVID tools (Database for Annotation, Visualization, and Integrated Discovery) and KEGG database analyses revealed that oxidative phosphorylation, thermogenesis, branched-chain amino acids (BCAAs) degradation, cardiac muscle contraction, citrate cycle (TCA cycle), and fatty acid metabolism were among the most affected pathways associated with downregulated genes in Mrps5cKO hearts (Fig. 3d), consistent with the findings of our phenotypic analyses showing that Mrps5cKO hearts display defects in mitochondria and metabolism. Conversely, genes associated with pathways related to the ribosome, ribosome biogenesis, and protein processing in the endoplasmic reticulum are among the most enriched in the hearts of Mrps5cKO mice (Fig. 3e). Given the known essential function of MRPS5 in mitochondrial protein translation, these data suggest a compensatory mechanism in Mrps5cKO cardiomyocytes to activate the translational program. These findings are further supported by the results of our analysis of differentially expressed genes via the reactome and the gene ontology (GO) biological processes databases (Supplementary Fig. 3a–d). Additional analysis using GSEA confirmed that “ribosome” and “ribosome biogenesis” are among the top pathways associated with the upregulated genes, while “oxidative phosphorylation” and “cardiac muscle contraction” are associated with downregulated genes in Mrps5cKO hearts (Fig. 3f, g and supplementary Fig. 3e–g). A heatmap to visualize the expression of representative genes for the significantly altered signaling pathways further confirmed these observations (Fig. 3h). These results support an important role for MRPS5 as a regulator of translation and metabolism in the heart. Fig. 3Loss of Mrps5 alters translational and metabolic programs in the heart.a Volcano plot of dysregulated transcripts in Mrps5cKO hearts by comparison with Mrps5fl/fl hearts at 12 weeks following tamoxifen injection. b Hierarchical clustering heatmap of dysregulated transcripts in Mrps5cKO hearts. $$n = 3$.$ c Principal component analysis (PCA) of gene expression in Mrps5fl/fl and Mrps5cKO hearts. $$n = 3$.$ d Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis of downregulated genes in Mrps5cKO hearts. e KEGG functional enrichment analysis of the upregulated genes in Mrps5cKO hearts. f Gene set enrichment analysis (GSEA) showing dysregulated signaling pathways in Mrps5cKO hearts. g Enrichment plots of key pathways in Mrps5cKO hearts. h Heatmaps of the relative expression of the differentially expressed genes in dysregulated pathways identified in Mrps5cKO hearts. P values were determined by two-tailed unpaired Students’ t-test in (a, d, e).
## Functional screening identifies Klf15 as an Mrps5 downstream target in the heart
We reasoned that genes downregulated in Mrps5cKO hearts would be good candidates for mediators of the translational machinery collapse observed upon Mrps5 ablation. We used multiple criteria to prioritize genes for investigation from 2926 that were downregulated in Mrps5cKO hearts (Fig. 4a). *The* gene expression data derived from the RNA-sequencing dataset was subjected to further analysis and experimentation, then sorted as follows; [1] expression decrease greater than $50\%$ (283 genes), [2] genes exhibiting cardiac enriched expression patterns ($\frac{60}{283}$), [3] genes downregulated in a continuous pattern following Mrps5 ablation ($\frac{18}{60}$), [4] genes consistently downregulated upon hypertrophic stimuli in vitro and in vivo, established through experimentation ($\frac{12}{18}$), (5a) downregulation upon knockdown of Mrps5 in neonatal mouse cardiomyocytes (NMCMs), and (5b) genes consistently downregulated in the hearts of patients with dilated cardiomyopathy (DCM) ($\frac{6}{12}$). From this stepwise screening, we prioritized six genes for analysis: Klf15 (Kruppel-like factor 15, Klf15), Adra1a (Adrenergic receptor, alpha 1α, and Adra1a), Angpt1 (Angiopoietin 1 and Angpt1), Ces1d (Carboxylesterase 1D and Ces1d), Enpp2 (ectonucleotide pyrophosphatase/phosphodiesterase 2, Enpp2), and Pik3r1 (phosphoinositide-3-kinase regulatory subunit 1, Pik3r1).Fig. 4Functional screening identifies KLF15 as an Mrps5 downstream target in the heart.a Diagram of workflow for the selection of six Mrps5 downstream target genes from the 2926 downregulated genes identified in Mrps5cKO hearts. b Expression of Klf15 and other top candidate genes in the hearts of Mrps5fl/fl control and Mrps5cKO hearts. c Decreased expression of Klf15 and other top candidate genes in Mrps5 knockdown cardiomyocytes. d Heatmap showing expression of Mrps5, Klf15, and other top candidate genes in TAC-induced hypertrophic mouse hearts. e Dysregulated expression of Mrps5, Klf15, and other top candidate genes in hearts of human dilated cardiomyopathy patients. f Heatmap of the relative gene expression for Mrps5, Klf15, and other top candidate genes in NMCMs after 12, 24, 48 h stimulation with ISO, PE, and FBS, respectively. g–l Pearson’ r correlation coefficient with corresponding P values for the covariation between Mrps5, Klf15, and other top candidate genes. m Experimental procedure for the use of AAV9-mediated expression of potential targets for functional screening in Mrps5cKO mice. n M-mode echocardiography of Mrps5cKO mice, 7 weeks after injection of indicated AAV9-construct. o Fractional shortening (FS%) of Mrps5cKO mice, 7 weeks after injection with indicated AAV9-construct. p Ejection fraction (EF) of Mrps5cKO mice, 7 weeks after injection with indicated AAV9-construct. q Immunohistology of heart sections of Mrps5cKO mice, 7 weeks after injection with indicated AAV9-construct. DAPI labels the nucleus, WGA marks the cell membrane, and ACTN1 marks cardiomyocytes. Scale bars = 20 µm. r Quantification of sizes of cardiomyocytes from the previous experiment (Fig. 4q). s Sirius Red and Fast Green staining of heart sections of Mrps5cKO mice, 7 weeks after injection of indicated AAV9-construct. Scale bars = 50 µm. The boxed area in the upper panel is shown magnified in the lower panel. t Quantification of fibrosis from the previous experiment (Fig. 4s). N numbers are indicated in each panel. All data were presented as mean ± SEM. P values were determined by one-way ANOVA with the Brown–Forsythe and Welch multiple comparisons test.
The expression of these six genes is significantly reduced in heart tissue at 6, 9, 12, and 18 weeks post Mrps5 ablation (Fig. 4b); these observations were confirmed in NMCMs using siRNA to knockdown Mrps5 (Fig. 4c). The expression of Mrps5 and these six genes is similarly reduced in TAC-induced hypertrophic mouse hearts (Fig. 4d). Interestingly, all but one of these genes are also decreased in the hearts of human patients with DCM; the exception is Pik3r1, which displayed an increased expression pattern when compared to control patient samples (Fig. 4e). We further examined the expression pattern of these six candidate genes in models of cardiomyocyte stress in vitro; NMCMs were treated with isoproterenol (ISO), phenylephrine (PE) and fetal bovine serum (FBS) stimulation for 12, 24, and 48 h (Fig. 4f). Expression of all six genes was reduced and correlated with decreased expression of Mrps5 (Fig. 4g–l). The shared expression responses of these six candidate genes upon decreased levels of Mrps5 suggest that they may mediate the function of Mrps5 in the heart.
Next, we asked whether the introduction of each of these candidate genes into Mrps5cKO mice could rescue the associated cardiac defects. We also included Pik3r1 in these experiments to determine if it had any influence, since the expression of this gene was increased in DCM patients (instead of decreased, like the others). We employed the well-established AAV9 gene delivery system for neonatal and adult mice with the noted modifications and found that GFP was easily detected in mouse hearts after AAV9-Gfp injection, as we have previously reported (Supplementary Fig. 4a–f).
We constructed cardiac-specific AAV9-Klf15, AAV9-Adra1a, AAV9-Angpt1, AAV9-Ces1d, AAV9-Enpp2, AAV9-Pik3r1, and control AAV9-Luci viruses, and injected them into Mrps5cKO or Mrps5fl/fl mice 7 weeks after the final tamoxifen injection. Expression of these target genes in the heart was confirmed (Supplementary Fig. 4g–l). We evaluated cardiac function in these mice using echocardiography 2, 4, and 7 weeks after the AAV injection (Fig. 4m and Supplementary Fig. 5a). Seven weeks after Mrps5 ablation and before the AAV9 treatment, there were no differences in heart function between the groups of Mrps5cKO mice, though they showed a slight reduction in cardiac function when compared with control Mrps5fl/fl mice (Supplementary Fig. 5b–e). Nine weeks post Mrps5 ablation (2 weeks post AAV9 infection), the cardiac function of Mrps5cKO mice treated with the control AAV9-*Luci virus* significantly decreased compared to that of the Mrps5fl/fl mice. However, AAV9-Klf15 and AAV9-Adra1a treatment preserved cardiac function in Mrps5cKO mice compared to control AAV9-Luci-treated animals. The introduction of Angpt1, Ces1d, and Enpp2 expression displayed modest cardiac protection in Mrps5cKO mice. In contrast, AAV-Pik3r1 failed to rescue cardiac function in Mrps5cKO mice (Supplementary Fig. 5a, f–i). Eleven weeks post Mrps5 ablation (4 weeks post AAV9 infection), AAV9-delivered Klf15 and Adra1a continued to rescue heart function in Mrps5cKO mice while Angpt1, Ces1d, and Enpp2 showed diminished protection in Mrps5cKO mice; AAV9-Pik3rl treatment worsened the cardiac function of Mrps5cKO mice compared to AAV9-Luci control (Supplementary Fig. 5a, j–m). Finally, 14 weeks post Mrps5 ablation (7 weeks post AAV9 infection), only AAV9-Klf15 treatment continued to ameliorate the cardiac phenotype of Mrps5cKO mice (Fig. 4n–p and Supplementary Fig. 5n, o). The other candidate genes (Adra1a, Angpt1, Ces1d, and Enpp2) did not maintain their beneficial effects in the heart of Mrps5cKO mice and, as predicted, AAV9-Pik3r1 furtherly impaired heart function in Mrps5cKO mice (Fig. 4o, p and Supplementary Fig. 5n).
We further investigated whether the reintroduction of the identified candidate genes could attenuate the massive cardiac hypertrophy that develops in Mrps5cKO mice. While AAV9-Klf15 treatment significantly reduced hypertrophy in Mrps5cKO mouse hearts, overexpression of Adra1a, Angpt1, Ces1d, and Enpp2 failed to do so; consistent with the previous observations, AAV9-Pik3r1 treatment resulted in a further increase in hypertrophy in Mrps5cKO mice (Fig. 4q, r). Similarly, only overexpression of AAV9-Klf15 was able to prevent cardiac fibrosis in the hearts of Mrps5cKO mice (Fig. 4s, t). Finally, we examined the molecular signatures for cardiac hypertrophy and fibrosis in Mrps5cKO hearts after overexpression of these 6 candidate genes. Our results revealed that only AAV9-Klf15 treatment reduced the expression of the hypertrophic marker gene Nppa and fibrotic marker gene Col1a1 in Mrps5cKO hearts (Supplementary Fig. 5p–r). These data strongly support Klf15 as a gene that can mediate the rescue of cardiac defects in Mrps5cKO mice.
## Overexpression of Klf15 prevents and rescues cardiac defects and the progression of cardiac hypertrophy in Mrps5 mutant mice
We asked whether neonatal overexpression of Klf15 in Mrps5cKO mice, before any phenotypic observations could prevent the onset of cardiac defects in adulthood. AAV9-Klf15 or control AAV9-Luci were injected into Mrps5fl/fl;αMHC-MerCreMer mice at postnatal day 1 (P1), tamoxifen was administrated 4 weeks later to induce the deletion of the *Mrps5* gene, and cardiac function was monitored at set time points after Mrps5 ablation (Fig. 5a). We confirmed the efficacy of AAV9-mediated overexpression of Klf15 in the hearts of Mrps5cKO mice (Fig. 5b). No obvious differences in cardiac function between AAV9-Klf15 and AAV9-Luci treated Mrps5cKO groups were observed at 6 weeks (Fig. 5c–f). By 12 weeks post Mrps5 ablation, AAV9-Luci injected control Mrps5cKO mice exhibited a dramatic decrease in cardiac function, similar to what was observed in the untreated mice with deletion of the *Mrps5* gene in the heart; however, AAV9-Klf15 treatment substantially improved cardiac function as indicated by the improved ejection fraction (EF%), fractional shortening (FS%), decreased left ventricular internal diameter at the end of systole (LVID;s), and left ventricular volume at the end of systole (LV Vol;s) (Fig. 5c–f). AAV9-Klf15 treatment also repressed cardiac hypertrophy, as demonstrated by the reduction in heart weight/body weight ratio and cardiomyocyte size (Fig. 5f–i). Consistently, the expression of the hypertrophic marker genes, Nppa, Nppb, and Acta1 was suppressed in AAV9-Klf15 treated Mrps5cKO mice hearts compared to the control group (Fig. 5j–m). Furthermore, Klf15 overexpression in Mrps5cKO mice hearts significantly suppressed cardiac fibrosis (Fig. 5n, o). One of the most dramatic defects in the Mrps5cKO hearts is the disruption of the mitochondria (Fig. 2a). Therefore, we investigated whether Klf15 overexpression was sufficient to preserve the structure of mitochondria in Mrps5cKO hearts. Indeed, mitochondrial cristae structure was improved, with most remaining intact, in AAV9-Klf15 treated hearts while control AAV-Luci treated hearts displayed mitochondrial cristae disruption (Fig. 5p). This observation is further supported by quantification of the length of cristae, the mitochondria number, and area. We found that re-expression of Klf15 results in an increase in mitochondrial cristae and mitochondrial numbers, whereas mitochondrial area decreased (Fig. 5q–s). In addition to these improvements in mitochondrial morphology and number, we observed a restoration in the levels of mitochondrial ETC proteins in the Mrps5cKO hearts after the re-expression of Klf15 (Supplementary Fig. 6).Fig. 5Overexpression of KLF15 prevents cardiac defects in Mrps5 mutant mice.a Experimental procedure to test the function of Klf15 in preventing the development of cardiomyopathy in Mrps5cKO mice. Neonatal Mrps5cKO mice were injected with control AAV9-Luci or AAV9-Klf15. Tamoxifen was injected at 4 weeks to induce the deletion of Mrps5 in the heart. Echocardiography was performed at indicated time points (M-mode data shown). b Gene expression of Klf15 in Mrps5cKO mice injected with AAV-Luci/Klf15, respectively. c–f Ejection fraction (EF), fractional shortening (FS), left ventricular systolic diameter (LVID;s), and left ventricular volume (LV Vol;s) were recorded in AAV9-Luci or AAV9-Klf15 injected Mrps5cKO mice at indicated time points. g Immunohistology of heart sections of Mrps5cKO mice after neonatal injection of AAV9-Luci or AAV9-Klf15. DAPI (nucleus), WGA (cell membrane), and ACTN1 (cardiomyocytes). Scale bars = 20 µm. h Quantification of cardiomyocyte size from Fig. 5g. i Heart weight to body weight ratio of AAV9-Luci or AAV9-Klf15 injected Mrps5cKO mice. j–m qRT-PCR analysis of Nppa, Nppb, Acta1, and *Mrps5* genes in 19-week-old Mrps5cKO hearts after neonatal injection of AAV9-Luci or AAV9-Klf15. n Sirius Red and Fast Green staining of heart sections from Mrps5cKO mice at 19 weeks of age following neonatal injection of AAV9-Luci or AAV9-Klf15. Scale bars = 50 µm. o Quantification of fibrosis from hearts described in Fig. 5n. p TEM images of heart tissue from 19-week-old Mrps5cKO mice injected at the neonatal stage with AAV9-Luci or AAV9-Klf15. Scale bars as indicated. q Quantification of mitochondrial cristae length from hearts of mice described in Fig. 5p. r Quantification of mitochondrial numbers per µm2 in heart tissue from mice described in Fig. 5p. s Quantification of the mitochondrial area in heart tissue from mice described in Fig. 5p. h–o, q–s Comparisons between Mrps5cKO mice injected with AAV9-Luci (green) or AAV9-Klf15 (red), as indicated in the legend for (Fig. 5r). N numbers are indicated in each panel. All data were presented as mean ± SEM. P values were determined by a two-tailed unpaired Students’ t-test.
Encouraged by the findings that overexpression of Klf15 in neonatal Mrps5cKO mice was sufficient to prevent the development of cardiac hypertrophy, we explored the potential for Klf15 in the treatment of cardiac defects in adult Mrps5 mutant mice. Mrps5fl/fl mice were included as a control to ensure that overexpression of Klf15 alone did not result in an irregular phenotype in normal mice (Fig. 6a). Similar to what we reported previously, AAV9-mediated overexpression of Klf15 preserved cardiac function (Fig. 6b–f) and repressed cardiac hypertrophy (Fig. 6g–i). In AAV9-Klf15 treated Mrps5cKO hearts, mitochondrial cristae structure was well preserved, and we also observed increased mitochondrial numbers and decreased mitochondrial area (Fig. 6j–m). In contrast, control AAV9-Luci failed to rescue the cardiac defects in Mrps5 mutant mice (Fig. 6b–k). Significantly, overexpression of Klf15 did not seem to cause any defect in control Mrps5fl/fl mice (Fig. 6b–i).Fig. 6Restoration of cardiac Klf15 expression rescues heart defects in Mrps5cKO mutant mice.a Schematic depiction of approach used for delivery of AAV9- Luci/Klf15 to the adult Mrps5fl/fl/Mrps5cKO mouse hearts. b Representative examples of M-mode echocardiography recorded at 10 weeks after tamoxifen injection of Mrps5fl/fl and Mrps5cKO mice injected with either AAV9-Luci or AAV9-Klf15 (as indicated). c–f Quantification of EF, FS, LVID;s, and LV Vol;s from each group as indicated in legend for f, at 10 weeks after tamoxifen injection. g Representative images of H&E stained cross sections of heart tissue from each group as indicated. h Representative images of cross sections of heart tissue from each group as indicated. The heart sections were immunostained with WGA in purple and DAPI in blue. Scale bar = 25 μm. i Quantification of the cross-sectional area of cardiomyocytes from hearts of mice at 10 weeks after tamoxifen injection for each group as indicated in legend for (Fig. 6f). j TEM images of AAV9-Luci or AAV9-Klf15 injected Mrps5cKO mice at 14 weeks post last tamoxifen injection. Scale bar as indicated in panels. k Quantification of mitochondrial cristae length from AAV9-Luci or AAV9-Klf15 injected Mrps5cKO mice described in Fig. 6j. l Quantification of mitochondrial numbers per μm2 from AAV9-Luci or AAV9-Klf15 injected Mrps5cKO mice described in Fig. 6j. m Quantification of the mitochondrial area from AAV9-Luci or AAV9-Klf15 injected Mrps5cKO mice described in Fig. 6j. N numbers are indicated in each panel. All data are presented as mean ± SEM. P values were determined by one-way ANOVA with the Brown–Forsythe and Welch multiple comparisons test.
## Klf15 regulates cardiac metabolism and rescues the imbalanced metabolome in Mrps5 mutant hearts
To understand the molecular mechanisms by which Klf15 overexpression rescues the Mrps5 loss of function phenotype in the heart, we conducted RNA sequencing to profile the transcriptomes of Mrps5cKO mice infected with AAV9-Klf15 or control AAV9-Luci. A total of 761 genes were significantly dysregulated, consisting of 421 genes that were upregulated and 340 genes that were downregulated (Fig. 7a, b).Fig. 7Klf15 regulates cardiac metabolism and corrects imbalanced metabolome in Mrps5 mutant hearts.a Volcano plot of the dysregulated genes in Mrps5cKO/AAV-Klf15 by comparison with Mrps5cKO/AAV-Luci hearts. b Hierarchical clustering heatmap of dysregulated genes in Mrps5cKO/AAV-Luci and Mrps5cKO/AAV-Klf15 hearts. c Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis of the downregulated genes in Mrps5cKO/AAV-Klf15 hearts. d KEGG functional enrichment analysis of the upregulated genes in Mrps5cKO/AAV-Klf15 hearts. e Gene Ontology (GO) analysis of the glucose metabolic biological processes affected in Mrps5cKO/AAV-Klf15 hearts. f Heatmaps of the relative expression of the differentially expressed genes in dysregulated pathways identified in Mrps5cKO/AAV-Luci and Mrps5cKO/AAV-Klf15 hearts. g Gene set enrichment analysis (GSEA) showing dysregulated signaling pathways in Mrps5cKO/AAV-Klf15 hearts. h qRT-PCR analysis of the mRNA expression level of glycolysis/gluconeogenesis, OXPHOS, and BCAAs catabolic associated genes in Mrps5cKO/AAV-Luci ($$n = 5$$) and Mrps5cKO/AAV-Klf15 hearts ($$n = 5$$). i Scatter diagram of dysregulated genes in Mrps5cKO versus Mrps5fl/fl hearts and Mrps5cKO/AAV-Klf15 versus Mrps5cKO/AAV-Luci hearts. *The* gene expression level of *Aldob is* reduced in Mrps5cKO but enhanced in Mrps5cKO/AAV-Klf15 hearts. j Diagram illustrating the position and role of Aldob in the glycolysis/gluconeogenesis pathway. All data were presented as mean ± SEM. P values were determined by a two-tailed unpaired Students’ t-test in (a, c, d, e, h, i).
The functional annotation of these differentially expressed genes was compared with our earlier analysis of the Mrps5cKO mice and revealed some noteworthy features. For example, genes associated with the “oxidative phosphorylation” and “thermogenesis” pathways were significantly downregulated in both the uninjected and AAV-Klf15 injected Mrps5cKO hearts (Figs. 3d, f, 7c), indicating that Klf15 overexpression did not restore the regulation of these pathways in Mrps5cKO hearts. Meanwhile, the “glycolysis/gluconeogenesis” pathway genes are highly upregulated in AAV-Klf15 injected Mrps5cKO hearts, suggesting that Klf15 overexpression induced a switch in the metabolic profile from oxidative phosphorylation to glycolysis (Fig. 7d). Additional analyses supported a role for expression of Klf15 in Mrps5cKO hearts in cardiac metabolism, endoplasmic reticulum stress and the unfolded protein response, as well as cardiac remodeling. Of particular importance was the restoration in the expression of genes related to “cardiac muscle contraction”, which was downregulated in the hearts of Mrps5cKO mice but not in the hearts of AAV9-Klf15 injected Mrps5cKO mice. This observation is consistent with the findings showing Klf15 overexpression rescued cardiac function in Mrps5cKO mice.
We further investigated the expression profile for the “glucose metabolic biological processes” in Mrps5cKO mice infected with AAV9-Klf15 or AAV9-Luci by cross-referencing with the Gene Ontology (GO) database (Fig. 7e). These analyses revealed that many glucose metabolic biological processes were significantly enhanced in the hearts of Mrsp5cKO mice upon Klf15 re-expression. Conversely, we observed a significant reduction of the glycolysis-associated genes and proteins in Mrps5cKO hearts (Supplementary Fig. 7a, b). A heatmap of the expression of representative genes and gene set enrichment analysis (GSEA) of important linked pathways further confirmed that expression of Klf15 in Mrps5cKO hearts induced a metabolic shift; these observations illustrate a switch from oxidative phosphorylation to glycolysis/gluconeogenesis (Fig. 7f–h). As a specific example, aldolase fructose-bisphosphate B (Aldob), a glycolytic enzyme that catalyzes the conversion of fructose-1,6-bisphosphate to glyceraldehyde 3-phosphate and dihydroxyacetone phosphate, was restored to normal levels in the hearts of AAV9-Klf15 infected Mrps5cKO mice (Fig. 7i, j). In addition, Klf15 also rescues the decreased BCAAs catabolism in Mrps5cKO hearts (Fig. 7f, h and Supplementary Fig. 8a–c). Overall, these data support a model in which Klf15 overexpression promotes a switch to glycolysis and gluconeogenesis for energy production to compensate for impaired oxidative phosphorylation and enhanced BCAAs catabolism in the hearts of Mrps5cKO mice.
## The mitochondrial metabolite l-phenylalanine and transcription factor c-myc mediate mitonuclear communication to repress Klf15
We hypothesize that mitochondrial stresses and defects in mitochondrial protein translation resulting from the loss of Mrps5 trigger the transcriptional repression of Klf15; this highlights the delicate balance, preserved through mitonuclear communication, which is required for the maintenance of the normal metabolic profile. We hypothesized that the decrease in Klf15 expression in Mrsp5cKO hearts was a result of transcriptional repression. We analyzed the Mrps5cKO RNA-sequencing data in the context of the ENCODE ChIP-Sequence Peaks and conserved transcription factor binding sites over the Klf15 promoter; this approach identified eight transcriptional regulators that putatively regulate Klf15 (Supplementary Table 2). Among them, c-Myc and Max are differentially expressed in Mrsp5cKO hearts (Fig. 8a–c), suggesting that they could participate in the regulation of the *Klf15* gene. Fig. 8The metabolite l-phenylalanine and transcription factor myc mediate mitonuclear communication to repress KLF15.a Rationale used to identify potential upstream signals regulating Klf15 expression in Mrps5cKO hearts. b Correlation of the heatmap and c relative expression of the candidate genes from the Mrps5cKO RNA-sequencing data correlated with the ENCODE ChIP-Seq peaks over the *Klf15* gene promoter and/or the conserved transcription factor binding sites over the Klf15 promoter. d *Dysregulated* gene and metabolite interaction networks after Mrps5 deletion. e Heatmap of the DE metabolites that interact with myc after Mrps5 deletion. f Scatter diagram of dysregulated genes in Mrps5cKO versus Mrps5fl/fl hearts and Mrps5cKO/AAV-Klf15 versus Mrps5cKO/AAV-Luci hearts highlighting Pah expression. *The* gene expression level of *Pah is* decreased in Mrps5cKO hearts while it is elevated in Mrps5cKO/AAV-Klf15 hearts. g qRT-PCR analysis of the expression level of Mrps5, Klf15, Pah, and Myc in Mrps5fl/fl and Mrps5cKO hearts and h Mrps5cKO/AAV-Luci and Mrps5cKO/AAV-Klf15 hearts. i, j Representative immunoblot and quantification of protein levels of MRPS5, mt-CO1, KLF15, MYC, and ACTIN in the whole heart lysates of Mrps5fl/fl and Mrps5cKO mice. k, l Representative immunoblot and quantification of the protein level of MRPS5, KLF15, MYC, and ACTIN in whole cell lysates derived from NMCM cells from Mrps5fl/fl mice after adenoviral Cre/GFP treatment for 5 days and m, n H9C2 cells after doxycycline treatment for 4 days (m, n also includes analysis of mt-CO1). o, p Representative immunoblot and quantification of protein levels of MYC and KLF15 in whole cell lysates from H9C2 cells after treatment with the MYC inhibitor APTO-253, q, r APTO-253 and/or doxycycline or s, t APTO-253 and/or l-Phenylalanine. u, v Representative images and cell size quantification of NMCMs treated with doxycycline and/or phenylalanine, tetrahydrobiopterin (BH4). w Schematic depiction of l-phenylalanine/MYC signaling axis demonstrating mechanism for repression of Klf15 expression generated by a defect in mitochondrial translation. Created with BioRender.com. N numbers are indicated in each panel. All data were presented as mean ± SEM. P values were determined by two-tailed unpaired Students’ t-test in (a, c, g, h, j, l, n). P values were determined by one-way ANOVA with Brown–Forsythe and Welch multiple comparisons test in (Fig. 8p, r, t, v).
Next, we performed untargeted metabolome screening to determine the metabolite changes in Mrsp5cKO hearts and integrated this information with the previously described expression dataset to identify the upstream metabolites (Fig. 8a–c). Both the volcano plot and the heatmap of dysregulated metabolites in Mrps5cKO hearts showed a very tight grouping of the biological replicates (Supplementary Fig. 9a–d, $$n = 5$$). Approximately 70 metabolites were annotated as significantly changed. Examination of differentially expressed metabolites and pathway enrichment analysis revealed that “ABC transporters”, “protein digestion and absorption”, “aminoacyl-tRNA biosynthesis”, and “mineral absorption” pathways were significantly increased; in contrast, “purine metabolism”, “cGMP-PKG signaling pathway”, “renin secretion”, “Foxo signaling pathway” and “regulation of lipolysis in adipocytes” pathways were markedly reduced in Mrsp5cKO hearts (Supplementary Fig. 9e, f). Further analysis of these top dysregulated pathways identified l-leucine, l-isoleucine, l-glutamine, and l-phenylalanine as the most commonly upregulated metabolites in the four most upregulated pathways (Supplementary Fig. 9g). Conversely, adenosine monophosphate (AMP) is the only downregulated metabolite commonly involved in the five most downregulated pathways (Supplementary Fig. 9h). These observations support a role for these metabolites in the defects identified in Mrps5cKO hearts.
To better understand how dysregulated metabolites regulate Klf15, we performed integrative transcriptome and metabolome analysis using MetaboAnalyst. These analyses revealed that “aminoacyl-tRNA biosynthesis” and “protein digestion and absorption” are among the most dysregulated transcriptome and metabolome pathways in Mrsp5cKO hearts (Supplementary Table 3). This is not surprising, given that loss of Mrps5 led to mitochondrial protein translation defects. Interestingly, c-myc, which is a potential regulator of Klf15 and differentially expressed in Mrsp5cKO hearts, is associated with several metabolites, including L-phenylalanine (Fig. 8d, e and Supplementary Fig. 10), suggesting that they may mediate mitonuclear communication to repress Klf15. Intriguingly, transcriptome analysis also revealed that loss of Mrps5 results in a dramatic increase in the expression of phenylalanine hydroxylase (PAH), which encodes an enzyme that catalyzes the hydroxylation of the aromatic side-chain of phenylalanine to generate tyrosine (Fig. 8f). This results in decreased levels of L-phenylalanine in Mrsp5cKO hearts and generates a negative feedback loop that attempts to hold the system in check; overexpression of Klf15 also increased the expression of PAH, disrupting the balance of l-phenylalanine (Fig. 8g, h).
We examined the protein levels of c-myc and KLF15 in the hearts of Mrps5 cardiac knockout mice and found that c-myc expression was increased while KLF15 was decreased (Fig. 8i, j). Similarly, increased c-myc and decreased KLF15 levels were found in cardiomyocytes derived from the hearts of Ad-Cre-treated Mrps5fl/fl neonatal mice compared with those treated with control Ad-GFP (Fig. 8k, l). Doxycycline (Dox) treatment has been shown to induce mitonuclear protein imbalance and extend longevity by inhibiting mitochondrial protein translation in mice10,11. Therefore, we treated cardiomyocytes with Dox and found that the expression of both MRPS5 and KLF15, together with that of mtDNA-encoded oxidative phosphorylation proteins (MTCO1), was reduced (Fig. 8m, n). In contrast, Dox treatment increased protein levels of c-myc (Fig. 8m, n), suggesting that c-myc is likely a downstream mediator of Dox-induced stress in cardiomyocytes. Next, we directly treated cardiomyocytes with APTO-253, a widely used myc inhibitor, and found that inhibition of c-myc resulted in increased expression of KLF15 (Fig. 8o, p). Furthermore, myc inhibitor treatment overrides the Dox-treatment c-myc protein level induction and restored KLF15 protein levels (Fig. 8q, r). Finally, we treated cells with l-phenylalanine and found that this produced an increase in the expression of c-myc, resulting in a decrease of KLF15 (Fig. 8s, t). As expected, chemical inhibition of c-myc expression could reduce c-myc protein levels and partially restore KLF15 levels reduced upon l-phenylalanine treatment (Fig. 8s, t).
In support of our hypothesis, a recent study demonstrated that dysregulated l-phenylalanine catabolism also played a key role in the processing of cardiac aging; pharmacological restoration of phenylalanine catabolism with tetrahydrobiopterin (BH4) could reverse age-associated cardiac impairment17. Given that loss of Mrps5 resulted in cardiac hypertrophy and increased L-phenylalanine, we tested whether tetrahydrobiopterin (BH4) treatment could suppress hypertrophy in cardiomyocytes. Indeed, L-phenylalanine treatment furtherly increased neonatal mouse cardiomyocyte size induced by doxycycline treatment. BH4 treatment suppressed doxycycline-induced cardiomyocyte hypertrophy (Fig. 8u, v). Therefore, these observations serve as an important indication of the therapeutic potential of augmenting l-phenylalanine catabolism to treat mitochondrial translation defect-associated cardiac diseases. Together, we propose a mechanism for L-phenylalanine-mediated mitonuclear communication in which c-myc protein levels are enhanced by this metabolite, resulting in the transcriptional repression of Klf15 in the heart (Fig. 8w).
In addition to the l-phenylalanine/c-myc signaling axis, phosphorylation of CREB had also been identified as an upstream signal in the regulation of Klf1518. When we analyzed the gene subset of ENCODE ChIP-Seq peaks and conserved transcription factor binding sites in the Klf15 promoter, we found sites for eight transcription factors (c-myc, Creb1, Max, Arnt, Nr3c1, Gata1, Usf1, and Usf2). When compared with the DE metabolites identified in the hearts of Mrps5cKO mice, only Myc and Creb1 were present in both groups (Supplementary Fig. 11a). Indeed, the protein levels of phosphorylated CREB (p-CREB), but not that of total CREB is substantially lower in Mrps5cKO hearts (Supplementary Fig. 11b, c). Consistent with a prior report, we found that the protein level for branched-chain amino acid transaminase 2 (Bcat2) is also downregulated in Mrps5cKO hearts (Supplementary Fig. 11b, c). These findings suggest that the p-CREB/CREB signaling is involved in the regulation of Klf15 upon Mrps5 deletion. The DE metabolites that interact with Creb1 include AMP (adenosine monophosphate, AMP), adenosine, ADP (adenosine 5′-diphosphate, ADP), l-glutamine, and l-glutamic acid. Of these, AMP decreased most dramatically in Mrps5cKO hearts (Supplementary Figs. 11d, e, 12). Both p-CREB and KLF15 were downregulated in doxycycline-stimulated cardiomyocytes as well (Supplementary Fig. 11f). Furthermore, AMP supplementation restored KLF15 expression, as well as that of p-CREB (Supplementary Fig. 11f). Taken together, our results demonstrate that both l-phenylalanine/MYC and AMP/p-CREB are key signaling nodes in the response of Klf15 to mitochondrial translational stress (Supplementary Fig. 11g).
## Discussion
In this study, we report that Mrps5 plays a vital role in maintaining normal mitochondrial function in the heart. We show that cardiac-specific loss of Mrps5 in adult mouse causes severe pathological cardiac hypertrophy and heart failure, and cardiac-specific loss of Mrps5 in mouse embryos causes abnormal heart development and embryonic lethality. Loss of function studies focused on other mitochondrial ribosomal proteins will be important to verify the generality and specificity of our data on mitochondrial ribosomal translation. In this study, we identified Klf15 as an Mrps5 target and demonstrated that Klf15 restoration is able to rescue the cardiac defects in Mrps5 mutant mice. Our study reveals a mitonuclear communication axis mediated by Mrps5 and Klf15 in cardiomyocytes that signals the OXPHOS and glycolysis metabolism programs in response to mitochondrial stress. The identification of Mrps5 as a marker of pathological cardiac hypertrophy provides important insights into the links between the mitochondrial ribosomal translational machinery and normal cardiac function. Our studies further indicate that metabolic reprogramming by increasing glycolysis/gluconeogenesis in response to decreased OXPHOS could promote cell survival and benefit failing hearts.
Pathological cardiac hypertrophy is a complex biological process, which involves both transcriptional and post-transcriptional regulation of cardiac gene expression. In the hearts of Mrps5cKO mice, cardiac hypertrophy manifested before we observed any defects in mitochondrial function. The cardiac hypertrophy rapidly progressed to heart failure once disruption of mitochondrial structure and function became detectable. Analyses of both the transcriptome and metabolome confirmed the identity of dysregulated genes in Mrps5cKO hearts; these genes included many that are involved in the development of cardiac hypertrophy and cardiomyocyte contraction. As expected, pathways related to OXPHOS and metabolism were also linked to the onset of cardiac defects in Mrps5cKO hearts. We propose that loss of Mrps5 in the heart impairs mitochondrial protein translation. This produces an imbalanced metabolism that leads to the initial cardiac hypertrophy and the eventual heart failure observed in these animals.
KLF15 is a well-characterized transcription factor, and previous studies have demonstrated that KLF15 regulates cell differentiation19,20, circadian rhythm21,22, and cellular metabolism23. Klf15 deficiency occurs in human cardiomyopathy and aortic aneurysms, and its deletion leads to cardiomyopathy and aortopathy in mice24. KLF15 was shown to repress cardiac hypertrophy, while Klf15 knockout mice develop cardiac hypertrophy in response to pressure overload25. We described a decrease in Klf15 expression in response to the loss of Mrps5 in the heart. Therefore, we propose that loss of Klf15 mediates the development of cardiac hypertrophy and heart failure in Mrps5cKO hearts in response to mitochondrial stress. Interestingly, KLF15 strongly promotes metabolic reprogramming in the Mrps5 null heart even after the collapse of most mitochondrial cristae; there is an increase in glycolysis and a decrease in mitochondrial OXPHOS. While studies show preservation of fatty acids utilization is cardioprotective26,27, others show enhanced glucose utilization is a compensatory process, which may be beneficial to the stressed hearts28–30. In addition to its function in regulating cardiac hypertrophy, previous research also demonstrated that Klf15 plays an important role in the regulation of gluconeogenesis31. Our results revealed that the observed metabolic reprogramming (enhanced glycolysis and reduced OXPHOS) substantially repressed cardiac hypertrophy and pathological remodeling of the heart. Our study provides further evidence of the fine balance that exists in the heart between the two major energy-producing systems and how they are tuned to ensure normal cardiac function under stress conditions. As a compensatory mechanism in response to mitochondrial stress, Mrps5 mutant cardiomyocytes turn on the glycolysis pathway mediated by Klf15-dependent signaling. We also found that protein processing in the endoplasmic reticulum and the unfolded protein response (also known as a cytoprotective signaling pathway upon cellular stress) were enhanced after Klf15 was reintroduced into Mrps5cKO hearts. These results are consistent with prior findings that report an increase in the expression of genes involved in glycolysis during mitochondrial stress. As we have also proposed, these studies further support enhanced glycolysis/gluconeogenesis as a means to provide more energy to sustain normal cardiac function32,33.
Interestingly, the rescue experiments using the top candidate genes identified from both the transcriptome and proteome screening (Adra1a, Angpt1, Ces1d, Enpp2, Klf15, and Pik3r1) produced degrees of rescue in Mrps5cKO mice, indicating a differential requirement for these molecular pathways in the maintenance of cardiomyocyte metabolism and cardiac function. Using echocardiography at set intervals, we demonstrate that cardiac function is essentially preserved during the first 10 weeks post Mrps5 deletion. Intervention during this period using cardiac expression of the six prioritized genes identified as downregulated in the Mrps5cKO mouse hearts demonstrated rescue of the later-stage heart phenotype for all treatments except Pik3r1 (the one gene whose expression in DCM patients was not consistent with our observations).
There is then a transition between 10–12 weeks post Mrps5 deletion in which the mitochondrial cristae start to collapse and mitochondrial function becomes impaired; the data illustrates that Klf15 and Adra1a still maintain an ability to rescue the cardiac phenotype. However, at later stages following Mrps5 ablation (after 12 weeks post Mrps5 deletion), mitochondrial cristae have largely collapsed, resulting in the loss of essentially all mitochondrial function in cardiomyocytes; while expression of Adra1a is no longer able to rescue the heart defects at these late stages, Klf15 expression in Mrps5cKO hearts still demonstrates limited improvement in heart function. Interestingly, the loss in Adra1a effectiveness coincides with the stage at which we observe the destruction of the mitochondrial cristae; after the mitochondrial cristae were destroyed, mitochondrial ribosomal translation stalled, and almost all mitochondrial function was lost. This loss of mitochondrial function would result in a decrease in the energy pool required for cardiac function and this would constrain the ability of Adra1a to enhance cardiac contractility as previously described in refs. 34,35. Therefore, we propose that some mitochondrial function is required for Adra1a treatment to be effective in the Mrps5cKO heart. Consistent with previous reports, we observed that re-expression of Adra1a in Mrps5cKO hearts did not induce cardiac hypertrophy.
We propose that Klf15 rescues the Mrps5cKO cardiac defects by distinct mechanisms before and after the collapse of mitochondrial cristae and loss of mitochondrial function. In the early stages during which mitochondrial function is retained, the primary role of Klf15 is to inhibit cardiac hypertrophy by regulating hypertrophic gene expression. Once mitochondrial function becomes severely impaired as a result of Mrps5 deletion, Klf15 is able to mediate an enhancement of the glycolysis pathway to supplement the energy demands of the Mrps5cKO hearts and enable the animals to survive. The twofold effect of this later Klf15 action is to enhance energy production by glycolysis and gluconeogenesis and reduce the dependence on mitochondrial OXPHOS; the result is a slowing of the deterioration of the Mrps5cKO hearts and preservation of cardiac function.
Klf15 has also been shown to be a key regulator of BCAAs catabolism; it is sharply decreased upon glucose stimulation which downregulates Bcat2, resulting in the accumulation of BCAAs. This process also activates mTOR signaling and metabolic reprogramming, which occurs during cardiomyocyte hypertrophy and heart failure18,36. Additionally, high concentrations of BCAAs have been shown to suppress the expression of KLF15. Our transcriptomic profiling of Mrps5cKO hearts showed that BCAAs degradation-associated signaling pathways were significantly reduced. After Klf15 re-expression in Mrps5cKO hearts, we found that expression of BCAA catabolism-associated genes was enhanced via the AMP-CREB pathway, consistent with a previous report18. These findings suggest that the Klf15-BCAAs catabolism-metabolism reprogramming may also play an important role in rescuing the Mrps5 null hearts.
The purpose of mitonuclear communication (both anterograde, from nucleus to mitochondria, and retrograde, from mitochondria to nucleus) is to maintain homeostasis of a cell both under basal conditions and in response to a variety of stresses. Mutations in genes involved in mitonuclear communication, as well as imbalanced signals derived from impaired mitonuclear responses, are often linked to developmental defects and human diseases. Our study provides important insights into this poorly characterized communication nexus, particularly in the retrograde direction. We provide evidence for Klf15 as an essential mediator of mitonuclear communication in response to a loss of Mrps5 in the heart. We further describe how Klf15 is able to relay signals via l-phenylalanine/c-myc and AMP/p-CREB from mitochondria to nuclei to activate a gene expression program that is able to modulate cardiac metabolism and hypertrophy. In conclusion, our data shed light on how mitochondrial ribosomal translational defects in mammalian cardiomyocytes are able to have profound biological implications for cardiac function. These observations also support a role for metabolic reprogramming as a potential strategy for reducing cardiac hypertrophy and pathological remodeling driven by defects in mitochondrial translation.
## Methods
Data, analytic methods, and study materials will be made available to other researchers for purposes of reproducing the results or replicating procedures, on request, by direct communication. Analytic assays on tissue samples were performed by laboratory staff in a blinded fashion. A detailed description of materials and methods is available in the Supplementary Information.
## Human tissue sampling study protocol
Left ventricular (LV) tissues were taken from patients with terminal-stage heart failure indicated for heart transplantation. In brief, the patient’s heart was removed at the time of transplantation, and LV tissue was subsequently dissected and snap-frozen. We used LV samples from healthy hearts that were not implanted to serve as controls. All experimental protocols involving patients were approved by the Ethics Committee of the Second Affiliated Hospital Zhejiang University School of Medicine.
## Animal studies
All protocols concerning animal studies were approved by the Institutional Animal Care and Use Committees at Zhejiang University, Boston Children’s Hospital, and the University of South Florida. The human study protocol using heart tissue from DCM patients and control patients was approved by the Ethics Committee of the Second Affiliated Hospital of Zhejiang University. Patients provided written informed consent.
## AAV9 preparation and injection
The cDNA fragments encoding Luciferase, Gfp, Klf15, Adra1a, Angpt1, Ces1d, Enpp2, or Pik3r1 were separately cloned into the ITR (inverted terminal repeats)-containing AAV9 plasmid harboring the chicken cardiac TNT promoter, to yield AAV9-cTnT-Luciferase (AAV9-Luci), AAV9-cTnT-Gfp (AAV9-Gfp), AAV9-cTnT-Klf15 (AAV9-Klf15), AAV9-cTnT-Adra1a (AAV9-Adra1a), AAV9-cTnT-Angpt1 (AAV9-Angpt1), AAV9-cTnT-Ces1d (AAV9-Ces1d), AAV9-cTnT-Enpp2 (AAV9-Enpp2), and AAV9-cTnT-Pik3r1 (AAV9-Pik3r1). AAV9 was packaged in 293 T cells with AAV9: Rep-Cap and pHelper (pAd deltaF6, Penn Vector Core) and purified and concentrated by gradient centrifugation. AAV9 titer was determined by quantitative PCR. Adult mice were treated with AAV9 1–2 × 1012 particles/heart, adult mice were anesthetized, and thoracotomy was performed through the fourth intercostal space. The ascending aortic artery and the main pulmonary artery were clamped. The AAV9 was injected at a volume of 100 µl through the tip of the heart into the left ventricular cavity. The arteries were occluded for 10 s after the AAV9 injection. Neonatal mice (postnatal day 1) were treated with AAV9 2–5 × 10 particles/pup by subcutaneous injection according to our prior reports37. These mice were then treated with tamoxifen at the adult stage (4 weeks old for 1-week treatment) to induce KO of Mrps5. Hearts were then collected 14 weeks post tamoxifen injection (at 19 weeks of age).
## Mouse models of pathological hypertrophy/cardiac disease
*To* generate a mouse model of pathological cardiac hypertrophy/disease, transverse aortic constriction (TAC) surgery was performed on 8–10-week-old mice. In brief, mice were anesthetized with $4\%$ chloral hydrate. Then the transverse aorta was constricted against a 27-gauge needle. The sham group animals underwent mock surgery without aortic constriction.
Myocardial infarction (MI) surgery was performed on 8–10-week-old mice by permanent ligation of the left anterior descending (LAD) branch of the coronary artery. In brief, mice were anesthetized with $4\%$ chloral hydrate and the chest was shaved and cleaned with alcohol. Ventilation was performed with a tidal volume of 225 µl for a 25 g mouse and a respiratory rate of 130 breaths per minute. $100\%$ oxygen was provided to the inflow of the ventilator. The chest was opened through a left parasternal incision, and the heart was exposed at the left 3rd–4th intercostal space (a chest retractor was applied to facilitate access). The pericardium was opened, and the LAD coronary artery was ligated using 8-0 silk sutures. The lungs were slightly overinflated to assist in the removal of air from the pleural cavity. The dissected intercostal space and chest skin were closed using a 6-0 silk suture.
## In vivo echocardiography
Echocardiographic measurements were performed on Mrps5fl/fl and Mrps5cKO mice using a Visual Sonics Vevo 2100 Imaging System (Visual Sonics, Toronto, Canada) with a 40 MHz MicroScan transducer. Heart rate and LV dimensions, including diastolic and systolic wall thicknesses, LV end-diastolic and end-systolic chamber dimensions, were measured from the 2D short-axis under M-mode tracings at the level of the papillary muscle. LV mass and functional parameters such as percentage of fractional shortening (FS %) and left ventricular volume were calculated using the above primary measurements and accompanying software.
## Neonatal cardiomyocyte isolation and culture
Hearts from postnatal day 1 C57BL/6 J pups were harvested and washed with PBS to remove blood cells. Ventricular tissue was incubated with enzymes and buffers supplied by the Miltenyi neonatal mice cardiomyocyte isolation kit as indicated. Then cardiac tissue was digested and triturated by pipetting and filtered through a 70 µm cell strainer. NMCMs were then collected by centrifuging at 1000 rpm for 5 min and resuspended in DMEM medium supplemented with $10\%$ fetal bovine serum (FBS) and $1\%$ penicillin/streptomycin. Cells were allowed to attach for 20 min, then NMCMs were collected and maintained in DMEM containing $10\%$ FBS and $1\%$ penicillin/streptomycin. NMCMs were deprived of serum for 24 h prior to neurohumoral treatment to induce cardiomyocyte hypertrophy.
## Quantification of cell size
In vitro: Neonatal cardiomyocytes were stimulated with PE/ISO/FBS for 48 h, stained with ACTN1 and DAPI. Immunofluorescence images were taken with a Nikon N1 microscope, cell size was quantified using Image J software. In vivo: Heart sample slides were stained with Wheat Germ Agglutinin (WGA), ACTN1, and DAPI; fluorescence images were taken with a Nikon N1 microscope, and cell size were quantified via Image J software.
## Quantification of cardiac fibrosis
Heart sample slides were stained with Sirius Red/Fast Green. In brief, slides were stained with Sirius Red for 2 h and Fast Green for 15 min; bright field images of the slides were taken, and areas of fibrosis were quantified with Image J software.
## Measurement of ATP content
An ATP assay kit (Beyotime, cat #S0026) was used to determine the ATP content of mouse hearts. Tissue from fresh mouse heart tissue blocks was lysed with 100 µl lysis buffer ATP lysis buffer per 20 mg heart tissue with thorough grinding (IKA, T10 basic ULTRA-TURRAX). The tissue was then centrifuged at 12,000 × g at 4 °C for 5 min. The sample supernatant was transferred to a new tube for ATP detection. ATP standard solutions were prepared at 7 micromolar concentrations (0.01, 0.03, 0.1, 0.3, 1, 3, and 10). ATP assay buffer was diluted with ATP dilution buffer at a ratio of 1:9 to prepare the ATP assay working buffer. About 100 µL ATP assay working buffer was added to each well of a 96-well plate and allowed to stand for 5 min, then 20 µL of sample supernatant or ATP standard solution was added. RUM value was detected using a Molecular Devices, SpectraMax M5 multi-detection microplate reader system. The ATP relative count content was also determined according to the ATP standard solution RUM value.
## Measurement of cardiac muscle fiber oxygen consumption rate (OCR)
The OCR of cardiac muscle fibers were detected with Oxygraphy-2k from OROBOROs instruments using standard protocols38,39. In brief, heart muscle tissue was cut into small samples of 10–20 mg and put into a 50 ml falcon tube with 10 ml of ice-cold BIOPS. Fiber bundles were then separated mechanically with two very sharp forceps in a small petri dish, on ice. The degree of separation was evaluated by observing a change from red to pink in the separated fiber bundles. After tissue separation, the fiber bundles were placed sequentially into 2 mL of ice-cold BIOPS containing 20 µL of saponin stock solution (5 mg/mL; final concentration 50 µg/mL) into individual wells of a falcon 12-well tissue culture plate, and shaken using gentle agitation on ice for 30 min. Then all samples were quickly transferred from the saponin solution into 2 ml of MiR06 (Mitochondrial respiration medium) buffer and shaken further with gentle agitation for 10 min on ice. Weight measurements were made after permeabilization and before adding the tissue into the O2k chamber; samples were selected in size to comprise several loosely connected fiber bundles of 0.5–2 mg wet weight. The bundles were carefully blotted on filter paper (samples were taken using sharp forceps (angular tip) and placed onto the filter paper). Immediately after reading the wet weight, the sample was transferred into a well with 2 ml ice-cold MiR05 buffer. At the same time, baseline respiration was measured (at 37 °C for mammalian tissues) in MiR06 designed for optimal protection of mitochondrial function, then the fiber bundles were put into each chamber. Oxygen was allowed to flow into each chamber and then closed. Once the respiration curves stabilized, substrates and inhibitors of each electron transfer chain (ETC) complex were put into the chambers in order. Pyruvate, malate, glutamate, and ADP as the substrates of complex I; Rotenone as the inhibitor of complex I; Succinate as the substrate of complex II, antimycin A as the inhibitor of complex III; Ascorbate and TMPD as the substrates of complex IV, and sodium azide as the inhibitor of complex IV.
## Quantification of mitochondrial cristae length
Murine heart tissue or NMCMs were fixed immediately after harvest using $2.5\%$ glutaraldehyde. Mitochondrial morphology was observed using transmission electron microscopy, and images of mitochondria were then analyzed with Image J software; the mitochondrial cristae length and mitochondrial area were further quantified from these images.
## Isolation of mitochondria from heart tissue
Mitochondria were isolated from murine heart tissue as previously described with some modifications40. In brief, mice were euthanized by decapitation and heart tissue was immediately washed in ice-cold IB-1 buffer (three to four times with ice-cold IB-3 to remove the blood), then tissue was cut into small pieces using scissors. The used IB-3 solution was discarded and the tissue was washed once again with 10 ml of fresh, ice-cold IB-1. The heart tissue was transferred to an ice-cold glass/Teflon Potter Elvehjem homogenizer. IB-1 was added in the ratio of 4 ml of buffer per gram of heart tissue. Homogenization, as well as the following steps, were carried out at 4 °C to minimize the activation of proteases and phospholipases. After homogenization was completed, tissue was transferred to a centrifuge and spun at 740 × g for 5 min at 4 °C (repeated 2–3 times). The supernatant was collected and centrifuged at 9000 g for 10 min at 4 °C. The supernatant (representing the cytosolic fraction containing lysosomes and microsomes) was then discarded, and the pellet (containing the mitochondria) was gently resuspended in 20 ml of ice-cold IB-2. The mitochondrial suspension was then centrifuged at 10,000 × g for 10 min at 4 °C (repeated two times). The pellet representing the crude mitochondrial fraction was used for further experimentation.
## RNA sequencing
Cardiac tissues were harvested from 12-week-old littermate control Mrps5fl/fl and Mrps5cKO mice. Ages and genotypes of AAV9 injected mice were recorded. RNA-seq experiments were performed by Novogene (Beijing, China). Briefly, total RNA was isolated from fresh ventricular tissue using TRIzol (Invitrogen). RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA). A total amount of 1 μg RNA per sample was used as input material for the RNA sample preparations. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in First-Strand Synthesis Reaction Buffer (5X). First-strand cDNA was synthesized using a random hexamer primer and M-MuLV Reverse Transcriptase (RNase H-). Second-strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After polyadenylation of 3′ ends of DNA fragments, adapters with hairpin loop structures were ligated to prepare for hybridization. In order to select cDNA fragments of the preferred length (~370–420 bp), the library fragments were purified with the AMPure XP system (Beckman Coulter, Beverly, USA). Then PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers, and Index (X) Primer. PCR products were then purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumia) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina Novaseq platform and 150 bp paired-end reads were generated. Raw data (raw reads) of fastq format were first processed through in-house perl scripts. In this step, clean data (clean reads) were obtained by removing reads containing adapter, reads containing ploy-N, and low-quality reads from raw data. At the same time, Q20, Q30, and GC content of the clean data were calculated. All the downstream analyses were based on clean data with high quality. Reference genome and gene model annotation files were downloaded from the genome website directly. The index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. We selected Hisat2 as the mapping tool for that Hisat2 can generate a database of splice junctions based on the gene model annotation file and, thus, a better mapping result than other non-splice mapping tools. FeatureCounts v1.5.0-p3 was used to count the read numbers mapped to each gene. The FPKM of each gene was calculated based on the length of the gene and the reads count mapped to this gene. FPKM, the expected number of fragments per Kilobase of transcript sequence per millions of base pairs sequenced, considers the effect of sequencing depth and gene length for the reads count at the same time, and is currently the most commonly used method for estimating gene expression levels. Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq2 R package (1.20.0). DESeq2 provides statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The resulting P values were adjusted using Benjamini and Hochberg’s approach for controlling the false discovery rate. Genes with an adjusted P value <0.05 found by DESeq2 were assigned as differentially expressed. Gene Ontology (GO) enrichment analysis of differentially expressed genes was implemented by the ClusterProfiler R package, in which gene length bias was corrected. GO terms with corrected P value less than 0.05 were considered significantly enriched by differentially expressed genes. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism, and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies (http://www.genome.jp/kegg/). We used ClusterProfiler R package to test the statistical enrichment of differential expression genes in KEGG pathways.
## Metabolomics sequencing
Metabolomics sequencing and analyses were performed using an UHPLC (1290 Infinity LC, Agilent Technologies) coupled to a quadrupole time-of-flight (AB Sciex TripleTOF 6600) using services provided by Shanghai Applied Protein Technology. Hearts from 12-week-old Mrps5fl/fl and Mrps5cKO mice were collected in 5 mL vacutainer tubes containing the chelating agent ethylene diamine tetraacetic acid (EDTA), then samples were centrifuged for 15 min (1500 × g, 4 °C). Each aliquot (150 μL) of the plasma sample was stored at −80 °C until analysis by UPLC-Q-TOF/MS. The plasma samples were thawed at 4 °C and 100 μL aliquots were mixed with 400 μL of cold methanol/acetonitrile (1:1, v/v) to remove the protein. The mixture was centrifuged for 15 min (14,000 × g, 4 °C), then the supernatant was dried in a vacuum centrifuge. For LC-MS analysis, the samples were re-dissolved in 100 μL acetonitrile/water (1:1, v/v) solvent. To monitor instrument stability and repeatability, quality control (QC) samples were prepared by pooling 10 μL of each sample and analyzed together with the other samples. The QC samples were inserted regularly and analyzed every five samples. In both ESI positive and negative modes, the mobile phase contained $A = 25$ mM ammonium acetate and 25 mM ammonium hydroxide in water and B = acetonitrile. The gradient was $85\%$ B for 1 min and was linearly reduced to $65\%$ in 11 min. It was then reduced to $40\%$ in 0.1 min and maintained for 4 min. It was then increased to $85\%$ in 0.1 min, with a 5 min re-equilibration period. The ESI source conditions were set as follows: Ion Source Gas1 (Gas1) as 60, Ion Source Gas2 (Gas2) as 60, curtain gas (CUR) as 30, source temperature: 600°C, IonSpray Voltage Floating (ISVF) ± 5500 V. In MS-only acquisition, the instrument was set to acquire over the m/z range 60–1000 Da, and the accumulation time for TOF MS scan was set at 0.20 s/spectra. In auto MS/MS acquisition, the instrument was set to acquire over the m/z range 25–1000 Da, and the accumulation time for product ion scan was set at 0.05 s/spectra. The product ion scan is acquired using information-dependent acquisition (IDA) with high sensitivity mode selected. The parameters were set as follows: the collision energy (CE) was fixed at 35 V with ±15 eV; declustering potential (DP), 60 V (+) and −60 V (−); exclude isotopes within 4 Da, candidate ions to monitor per cycle: 10.
For data processing, the raw MS data (wiff.scan files) were converted to MzXML files using ProteoWizard MSConvert before importing into XCMS (freeware). For peak picking, the following parameters were used: centWave m/$z = 25$ ppm, peak width = c [10, 60], prefilter = c [10, 100]. For peak grouping, parameters were set as follows: bw = 5, mzwid = 0.025, minfrac = 0.5. CAMERA (Collection of Algorithms of MEtabolite pRofile Annotation) was used for the annotation of isotopes and adducts. In the extracted ion features, only the variables having more than $50\%$ of the nonzero measurement values in at least one group were kept. Compound identification of metabolites was performed by comparing the accuracy m/z value (<25 ppm) and MS/MS spectra with an in-house database established with available authenticated standards.
## Western blot
Protein lysate samples were prepared from cultured cells and heart tissues using cell and tissue extraction reagents (Invitrogen, FNN0011 and FNN0071) supplemented with proteinase inhibitors. Lysate samples (30 μg total protein for each) were separated by 6 or $10\%$ SDS–PAGE and electrophoretically transferred to PVDF membranes. MRPS5 protein was detected with rabbit antibody to MRPS5 (Gene Tex, GTX103930; 1:1000 dilution). mt-ATP6 protein was detected with mouse antibody to mt-ATP6 (Abcam, ab219825; 1:1000 dilution). mt-CO1 protein was detected with rabbit antibody to mt-CO1 (Abcam, ab203912; 1:1000 dilution). mt-ND1 protein was detected with rabbit antibody to mt-ND1 (Abcam, ab181848; 1:1000 dilution). COX IV protein was detected with mouse antibody to COX IV (CST, 4844 S; 1:1000 dilution). VDAC protein was detected with rabbit antibody to VDAC (CST, 4661 S; 1:1000 dilution). p-CREB protein was detected with rabbit antibody to p-CREB (CST, 9198 S; 1:1000 dilution). CREB protein was detected with rabbit antibody to CREB (CST, 9197 S; 1:1000 dilution). KLF15 protein was detected with rabbit antibody to KLF15 (Abcam, ab2647; 1:1000 dilution). BCAT2 protein was detected with rabbit antibody to BCAT2 (Abcam, ab95976; 1:1000 dilution). MYC protein was detected with rabbit antibody to MYC (CST, 9402 S; 1:1000 dilution). ALDOB protein was detected with rabbit antibody to ALDOB (HUABIO, ER62642; 1:1000 dilution). HK1 protein was detected with rabbit antibody to HK1 (HUABIO, ST47-05; 1:1000 dilution). GLUT1 protein was detected with rabbit antibody to GLUT1 (HUABIO, ET1601-10; 1:1000 dilution). GLUT4 protein was detected with rabbit antibody to GLUT4 (HUABIO, R1402-3; 1:1000 dilution). GAPDH protein was detected with rabbit antibody to GAPDH (HUABIO, R1210-1; 1:5000 dilution). β-Actin protein was detected with mouse antibody to β-Actin (HUABIO, EM2001-07; 1:5000 dilution). a-tubulin protein was detected with a mouse antibody to a-tubulin (CST, 2144 S; 1:5000 dilution). Protein bands were visualized with the Bio-Rad ChemiDoc imaging system.
## Quantitative reverse transcription polymerase chain reaction (qRT-PCR)
Heart tissue samples or mice cardiomyocytes were homogenized, and then total RNA was extracted with Trizol reagent (Thermo Fisher Scientific) as per the manufacturer’s instructions. Reverse transcription was performed using the HiScript Reverse Transcriptase kit (Vazyme). qRT-PCR was performed using ChamQ SYBR Color qPCR Master Mix (Vazyme) according to the manufacturer’s protocol. qRT-PCR was performed on a Vii7 qPCR machine (Thermo Fisher Scientific) with cycle threshold (CT values) normalized to an endogenous control (18 S RNA or actin) and relative expression was calculated comparing the average change in CT between samples. PCR primers for qRT-PCR were provided in the associated figure legends.
## Statistical analysis
All data were presented as scatter dot plots or bar plots with mean ± standard error of mean (SEM), and $p \leq 0.05$ was considered significant. As to comparison between the two groups, a two-tailed unpaired Students’ t-test was used as indicated. For more than two groups, one-way ANOVA with the Brown–Forsythe and Welch multiple comparisons test was used. Statistical analysis and plotting were performed using GraphPad Prism 9.0 (GraphPad Software, San Diego, CA, USA).
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-37291-5.
## Source data
Source Data
## Peer review information
Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work.
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|
---
title: Coffee consumption and associations with blood pressure, LDL-cholesterol and
echocardiographic measures in the general population
authors:
- Juliana Senftinger
- Julius Nikorowitsch
- Katrin Borof
- Francisco Ojeda
- Ghazal Aarabi
- Thomas Beikler
- Carola Mayer
- Christian-Alexander Behrendt
- Carolin Walther
- Birgit-Christiane Zyriax
- Raphael Twerenbold
- Stefan Blankenberg
- Jan-Per Wenzel
journal: Scientific Reports
year: 2023
pmcid: PMC10033706
doi: 10.1038/s41598-023-31857-5
license: CC BY 4.0
---
# Coffee consumption and associations with blood pressure, LDL-cholesterol and echocardiographic measures in the general population
## Abstract
Coffee, next to water the most widespread beverage, is attributed both harmful and protective characteristics concerning cardiovascular health. This study aimed to evaluate associations of coffee consumption with cardiac biomarkers, echocardiographic, electrocardiographic parameters and major cardiovascular diseases. We performed a cross-sectional analysis of 9009 participants of the population-based Hamburg City Health Study (HCHS), enrolled between 2016 and 2018 median age 63 [IQR: 55; 69] years. Coffee consumption was classified into three groups: < 3 cups/day (low), 3–4 cups/day (moderate), > 4 cups/day (high). In linear regression analyses adjusted for age, sex, body mass index, diabetes, hypertension, smoking, and additives, high coffee consumption correlated with higher LDL-cholesterol (β = 5.92; $95\%$ CI 2.95, 8.89; $p \leq 0.001$). Moderate and high coffee consumption correlated with lower systolic (β = − 1.91; $95\%$ CI − 3.04, − 0.78; $$p \leq 0.001$$; high: β = − 3.06; $95\%$ CI − 4.69, − 1.44; $p \leq 0.001$) and diastolic blood pressure (β = − 1.05; $95\%$ CI − 1.67, − 0.43; $$p \leq 0.001$$; high: β = − 1.85; $95\%$ CI − 2.74, − 0.96; $p \leq 0.001$). Different levels of coffee consumption did neither correlate with any investigated electrocardiographic or echocardiographic parameter nor with prevalent major cardiovascular diseases, including prior myocardial infarction and heart failure. In this cross-sectional analysis, high coffee consumption correlated with raised LDL-cholesterol levels and lower systolic and diastolic blood pressure. However, major cardiovascular diseases including heart failure and its diagnostic precursors were not associated with coffee consumption, connoting a neutral role of coffee in the context of cardiovascular health.
## Introduction
Coffee is one of the most widely consumed beverages around the world. Ever since consumption of coffee became vastly popular, the interest of its implications on health, and specifically the cardiovascular system, grew. First studies on coffee consumption and the risk of coronary artery disease (CAD) were already conducted in the 1960s leading to conflicting results1. Many studies have been published, attributing both protective and harmful characteristics to coffee in the context of the cardiovascular system2–5. Coffee is a complex liquid consisting of more than 1000 bioactive substances6. Most commonly, caffeine is regarded as the main driving component of mediating cardiovascular effects. Nevertheless, narrowing it down to a certain substance oversimplifies the versatile composition of coffee. Looking at coffee as a whole, several studies postulated a dose-dependent relationship of coffee consumption and cardiovascular diseases, e.g. low to moderate coffee consumption was shown to be associated with a reduced risk of heart failure whereas high coffee consumption reversed this trend7–9. However, an in-depth analysis of coffee consumption and its associations with cardiovascular diseases, especially heart failure and its possible precursors is lacking. Only few studies have evaluated the associations of coffee with cardiac functional parameters measured by echocardiography or electrocardiography10,11.
Trying to fill this gap, in the present study we analyze the association of coffee consumption and the cardiovascular system as a whole, integrating lifestyle-related behaviour, comorbidities, biomarkers, electrocardiographic and echocardiographic data, and finally major cardiovascular diseases in a large sample of the general population.
## Study setting
Data from the first 10,000 participants from the Hamburg City Health Study (HCHS, www.hchs.hamburg) served as the base for this analysis. The HCHS (clinicaltrials.gov: NCT03934957), located in Hamburg, Germany, is an ongoing, prospective, single-centre, long-term, and randomly selected population-based cohort study which aims at investigating the interactions of socioeconomic risk factors, modern imaging techniques, physiological measurements, and clinical variables12. Our study population included a subset of the first 10,000 HCHS participants. Subjects with missing data on coffee consumption were excluded. Our final cohort comprised 9009 subjects (Fig. 1).Figure 1Study PRISMA. From a total of 10,000 subjects 9009 provided data on coffee consumption. The 9009 subjects were then stratified by their coffee consumption measured as cups per day.
The research protocol of the study was approved by the HCHS steering board and the local ethics committee (PV5131, State of Hamburg Chamber of Medical Practitioners). All participants gave a written informed consent. The investigation conforms with the principles outlined in the Declaration of Helsinki.
## Laboratory, clinical and questionnaire data
All measurements were conducted between 2016 and 2018 at a baseline visit at the HCHS Epidemiological Study Centre Hamburg-Eppendorf, Hamburg, following the published HCHS protocol12. Cholesterol levels were directly measured in blood samples drawn at the day of examination under fasting conditions. N-terminal pro-B-type natriuretic peptide (NT-proBNP) was measured in serum samples drawn at the day of examination and stored at − 80 °C in a dedicated blood biobank (immunoassay by Alere NT-proBNP for ARCHITECT, Abbott Diagnostics, measurement ranges between 8.2 and 35,000 ng/l). A digital 12-lead electrocardiogram (ECG) combined with a 2-min rhythm strip was acquired from each participant. The durations of wave intervals were calculated electronically and double-checked manually. Further ECG analysis, i.e., on conduction disturbances and underlying rhythm, was conducted by a trained physician. Arterial hypertension was defined as a systolic blood pressure > 140 mmHg, a diastolic blood pressure > 90 mmHg, or the use of antihypertensive drugs. For the assessment of medication, subjects were asked to bring their medication or a list of prescribed drugs to the day of their baseline visit. Before, during and after the baseline visit extensive self-completion questionnaires concerning nutrition, lifestyle, medical, and family history as well as health care research patterns, occupational history and environmental data were completed. Information on dietary intake was collected by validated questionnaires developed for the European Prospective Investigation into Cancer and Nutrition (EPIC) study. The participants were asked how many cups of coffee they drink regarding the last 12 months (1 cup equals 150 ml). Coffee consumption was categorized in the following categories: never, 1 per month, 2 per month, 1 per week, 4–6 per week, 1–2 per day, 3–4 per day, 5–6 per day, 7–8 per day, 9–10 per day, 11 or more per day. We then summarized the categories into 3 groups: < 3 cups/day, 3–4 cups/day, > 4 cups/day. Further questions concerning coffee consumption included additives like milk, sugar, and honey.
Medical history, smoking status, tea, and carbonated drinks consumption were detected by standardized, self-reported questionnaires. Atrial fibrillation was considered present if reported by questionnaire or 12-lead electrocardiogram or both. Diabetes mellitus was determined by a fasting glucose level of ≥ 126 mg/dl, or the use of antidiabetic drugs. CAD was defined as suffering from one or more of the following conditions: history of myocardial infarction, percutaneous coronary intervention (PCI) or coronary bypass surgery. The dichotomized variable PAD was derived from structured anamnesis data, self-based questionnaire, and baseline examination. All participants were asked if they had experienced any history of intermittent claudication, ischemic rest pain, or ischemic wound healing disorders. At the study center, the ankle-brachial-index (ABI) was measured in both legs and cut off for diagnosis were values < 0.9.
## Transthoracic echocardiography
Transthoracic echocardiogram (TTE) examinations were systematically performed at the baseline visit using state-of-the-art cardiac ultrasound equipment (Siemens Acuson SC2000 Prime, Siemens Healthineers, Erlangen, Germany). Images were acquired and analysed by trained and internally certified medical professionals (cardiologists, sonographers) as previously published by our group. For continuous quality assessment, every 100th TTE exam was analysed twice. Left sided volumes and ejection fraction (LVEF) were calculated from the apical four- and two-chamber view using the method of disks summation. Left-sided diameters were measured in parasternal long-axis view. Mitral inflow pattern was assessed in apical four-chamber view by placing pulsed-wave (PW) Doppler sample volume between mitral leaflet tips. PW tissue Doppler imaging (TDI) e’ velocity was measured in apical four-chamber view by placing the sample volume at the lateral and septal basal regions. Tricuspid annular plane systolic excursion (TAPSE) was obtained by M-mode echocardiography in the apical four-chamber view.
## Definition of heart failure
For the classification of subjects Heart Failure (HF) the 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic HF were applied and modified13. HF was classified in two groups: heart failure with mildly-reduced and reduced ejection fraction (HF(m)rEF) as well as heart failure with preserved ejection fraction (HFpEF). Subjects had to show a combination of signs/symptoms, laboratory data, and echocardiographic criteria. Self-reported history of HF and/or the following medication were seen as equivalent if no symptoms or signs were detectable: betablockers, ACE-inhibitors (ACEi), angiotensin receptor blockers (ARB), angiotensin receptor neprilysin inhibitors (ARNI) mineralocorticoid receptor antagonists (MRA), Sodium-glucose Cotransporter-2 (SGLT2) inhibitors, and loop diuretics for HF(m)rEF and only loop diuretics for HFpEF. Oedema were evaluated by physical examination by medical professionals. Dyspnoea, history of HF, and medication were assessed by standardized self-reported questionnaires. All subjects with LVEF < $50\%$ and symptoms or signs of HF were classified as HF with reduced and mildly reduced ejection fraction (HF(m)rEF), instead of differing between heart failure with mildly reduced ejection fraction (HFmrEF, LVEF 41–$49\%$) and heart failure with reduced ejection fraction (HFrEF, LVEF < $40\%$). Subjects were classified in the HFpEF group if they showed LVEF ≥ $50\%$, symptoms or signs of HF, and either at least two or more echocardiographic signs of cardiac structural of functional abnormalities or the combination of NT-proBNP levels exceeding 125 ng/l (sinus rhythm) or 365 ng/l (atrial fibrillation) and at least one or more echocardiographic signs of cardiac structural of functional abnormalities. Echocardiographic signs of cardiac structural or functional abnormalities were defined as: left ventricular hypertrophy: LV mass indexed to BSA ≥ 95 g/m2 for women, ≥ 115 g/m2 for men, left atrial enlargement: left atrial volume index (LAVI) > 34 ml/m2 (sinus rhythm) and > 40 ml/m2 (atrial fibrillation), E/e’ ratio > 9, and tricuspid regurgitation velocity (Vmax) > 2.8 m/s. HF in general describes all subjects with either HF(m)rEF or HFpEF.
## Statistical analyses
Continuous variables are presented as median and interquartile range, and categorical variables are presented as absolute numbers and percentages. Comparisons between the different coffee groups were performed using Kruskal–Wallis test or chi-squared test. For the analysis of the association between coffee consumption and continuous laboratory, echocardiographic, electrocardiographic outcome parameters as well as blood pressure we used multivariable linear regression models. Adjustment was performed for age, sex, BMI, diabetes, arterial hypertension, and smoking. For systolic and diastolic blood pressure, in line with Tobin et al.14, no adjustment for arterial hypertension was performed, instead values for treated individuals were imputed by adding 15 mmHg and 10 mmHg respectively to the measured blood pressure. Furthermore, logistic models were used for binary echocardiographic and electrocardiographic parameters as well as cardiovascular diseases. No correction for multiple testing was applied15. A p-value of < 0.05 was considered as statistically significant. All tests were two tailed. Data analysis was performed using R version 3.5.1.
## Study population
The included 9009 subjects (Fig. 1) of the first 10,000 HCHS participants showed the expected characteristics of a middle-aged European population. 4610 ($51.2\%$) were women with a median age of 63 [IQR: 55; 69] years, and a median BMI of 26.01 [IQR: 23.5; 29.1] kg/m2.
Arterial hypertension was present in 5637 ($62.6\%$) subjects, diabetes in 694 ($7.7\%$) subjects. 1731 ($19.3\%$) subjects were smokers. The median LVEF was $58.5\%$ [IQR: 55.5, 61.8]. 8552 ($94.9\%$) subjects consumed coffee. Of those, 5699 ($63.3\%$) subjects consumed less than three cups of coffee per day (low), 2333 ($25.9\%$) 3–4 cups per day (moderate) and 977 ($10.8\%$) more than four cups per day (high). With a rising amount of coffee consumption, subjects were more likely to be men, younger, smokers, and showed higher LDL-levels and BMIs compared to those with lower coffee consumption. Moderate coffee consumers demonstrated the lowest prevalence of diabetes while no relevant interclass differences were observed for prior myocardial infarction, prevalent coronary artery disease (CAD), and peripheral artery disease (PAD) (Table 1).Table 1Baseline characteristics of the study population. Coffee consumptionOverallLow < 3 cups/dayModerate 3–4 cups/dayHigh > 4 cups/dayp-valueN (%)900956992333977Demographics + biological data Age63.0 {55.0, 69.0}64.0 {57.0, 70.0}60.0 {54.0, 67.0}59.0 {53.0, 66.0} < 0.001 Female4610 {51.2}3099 {54.4}1148 {49.2}363 {37.2} < 0.001 BMI kg/m226.1 {23.5, 29.1}26.0 {23.4, 29.1}26.0 {23.5, 29.1}26.7 {24.7, 29.5} < 0.001 Obesity (BMI > 30 kg/m2)1694 (19.9)1047 (19.4)445 (20.1)202 (22.0)0.193 Smoking current1731 {19.3}827 {14.6}558 {24.0}346 {35.5} < 0.001Cardiovascular diseases Arterial hypertension5637 {65.6}3732 {68.3}1346 {61.0}559 {60.8} < 0.001 Diabetes mellitus694 {8.4}478 {9.1}146 {6.8}70 {7.8}0.006 Heart failure203 {5.1}59 {3.6}31 {4.5}0.047 Myocardial infarction266 {3.0}176 {3.1}56 {2.4}34 {3.5}0.147 CAD582 {8.7}395 {9,4}125 {7.1}62 {8.4}0.016 PAD827 {91.7}528 {20.0}213 {19.3}86 {19.1}0.833Laboratories Cholesterol, mg/dl208.0 {181.0, 237.0}208.0 {181.0, 237.0}208.0 {182.0, 237.0}207.0 {182.0, 237.0}0.947 LDL, mg/dl121.0 {96.0, 146.0}119.0 {95.0, 144.0}122.0 {97.0, 146.0}124.0 {100.5, 149.0} < 0.001 HDL, mg/dl62.0 {50.0, 76.0}63.0 {51.0, 77.0}63.0 {50.0, 76.0}57.0 {47.0, 70.0} < 0.001 NT-proBNP, ng/l80.0 {44.0, 145.0}88.0 {49.0, 159.0}70.0 {38.0, 126.0}62.0 {34.0, 116.0} < 0.001 Hemoglobin, g/dl14.3 {13.6, 15.1}14.3 {13.5, 15.1}14.3 {13.6, 15.1}14.6 {13.9, 15.3} < 0.001Medication ACEi/ARBs1820 {21.2}1207 {22.1}434 {19.6}179 {19.4}0.023 Beta blockers1475 {17.5}1034 {18.9}316 {14.3}125 {13.5} < 0.001 Diuretics173 {2.0}122 {2.2}35 {1.6}16 {1.7}0.153 Lipid lowering drugs1542 {17.9}1059 {19.4}340 {15.4}143 {15.5.} < 0.001Additives Milk5966 {69.8}3803 {72.4}1549 {66.7}614 {63.0} < 0.001 Sugar1126 {13.2}738 {14.0}281 {12.1}107 {11.0}0.007 Honey76 {0.9}60 {1.1}15 {0.6}1 {0.1}0.002 Sweetener419 {4.9}261 {5.0}104 {4.5}54 {5.5}0.411 No additives2842 {33.2}1566 {29.8}855 {36.8}421 {43.2} < 0.001Black/green tea Never1480 {16.6}872 {15.4}423 {18.3}185 {19.1} < 0.001 1–3/week4150 {46.4}2326 {41.1}1261 {54.5}563 {58.0} < 0.001 ≥ 4/week3311 {37.0}2457 {43.4}631 {27.3}223 {23.0} < 0.001Carbonated drinks Never4338 {48.5}2886 {51.0}1060 {45.7}392 {40.6} < 0.001 1–3/week4031 {45.1}2439 {43.1}1098 {47.3}494 {51.1} < 0.001 ≥ 4/week576 {6.4}333 {5.9}163 {7.0}80 {8.3} < 0.001 Decaffeinated coffee0.131 < 3/week776 {97.5}464 {98.1}231 {96.7}81 {96.4} 3–4/week12 {1.5}4 {0.8}7 {2.9}1 {1.2} > 4/week8 {1.0}5 {1.1}1 {0.4}2 {2.4}Continuous variables are presented as median and interquartile range, and categorical variables are presented as absolute numbers and percentages. ACEi angiotensin-converting enzyme inhibitor, ARB angiotensin receptor blocker, BMI body mass index, ACE angiotensin-converting enzyme inhibitor, CAD coronary artery disease, HDL high-density lipoprotein, LDL Low-density lipoprotein, NT-proBNP N-terminal pro-B-type natriuretic peptide, PAD peripheral artery disease.
## Coffee consumption and biomarkers and common cardiovascular risk factors
In multivariable linear regression analysis adjusted for age, sex, BMI, diabetes, arterial hypertension, smoking, additives, and lipid-lowering drugs, high coffee consumption was associated with raised LDL-cholesterol levels indicated by a beta of 5.92 ($95\%$ CI 2.95, 8.89, $p \leq 0.001$) (Table 2, Supplementary Table 2).Table 2Linear regression analysis of laboratories as well as echocardiographic and electrocardiographic parameters with moderate and high coffee consumption. Coffee consumptionModerate (3–4 cups/day)High (> 4 cups/day)beta ($95\%$ CI)p-valuebeta ($95\%$ CI)p-valueLaboratory Total cholesterol, mg/dl1.09 (− 1.14, 3.33)0.3374.78 (1.56, 8.0)0.004 LDL, mg/dl1.63 (− 0.42, 3.68)0.1195.92 (2.95, 8.89) < 0.001 HDL, mg/dl0.57 (− 0.32, 1.46)0.207− 0.83 (− 2.11, 0.45)0.206 NT-proBNP, ng/l− 0.06 (− 0.11, − 0.02)0.005− 0.09 (− 0.15, − 0.02)0.013Vital signs Heart rate, bpm− 0.62 (− 1.24, 0.01)0.052− 0.02 (− 0.92, 0.89)0.969 Systolic blood pressure, mmHg− 1.91 (− 3.04, − 0.780.001− 3.06 (− 4.69, − 1.44) < 0.001 Diastolic blood pressure, mmHg− 1.05 {− 1.67, − 0.43}0.001− 1.85 {− 2.74, − 0.96} < 0.001Electrocardiography PQ interval, ms0.42 (− 1.12, 1.96)0.592− 0.68 (− 2.89, 1.53)0.548 QRS interval, ms0.80 (0.01, 1.59)0.048− 0.43 (− 1.58, 0.71)0.458 QTc interval, ms− 3.18 (− 6.41, 0.05)0.053− 3.81 (− 8.49, 0.88)0.111Echocardiography LVEF, %0.20 (− 0.12, 0.53)0.2380.12 (− 0.37, 0.60)0.634 LV mass index, g/m2− 0.06 (− 1.30, 1.17)0.9190.13 (− 1.65, 1.90)0.887 E/e' mean ratio− 0.10 (− 0.23, 0.03)0.138− 0.09 (− 0.27, 0.10)0.347 TR Vmax, m/s− 0.01 (− 0.03, 0.02)0.623− 0.01 (− 0.06, 0.03)0.527 TAPSE, mm0.16 (− 0.15, 0.470.3220.27 (− 0.18, 0.71)0.239 LASV, ml− 0.06 (− 1.30, 1.17)0.9190.13 (− 1.65, 1.90)0.887Mild coffee consumption (< 3 cups/day) served as a reference. Adjustment was performed for age, sex, BMI, diabetes, arterial hypertension, smoking, and additives. For systolic and diastolic blood pressure no adjustment for arterial hypertension was performed, instead values for treated individuals were imputed by adding 15 mmHg and 10 mmHg respectively to the measured blood pressure. For cholesterol, LDL, and HDL additional adjustment for lipid-lowering drugs was performed. NT-proBNP was transformed with the natural logarithm. Abbreviations as in Table 1: LASV left atrial systolic volume, LVEF left ventricular ejection fraction, TR Vmax peak tricuspid regurgitation velocity; TAPSE tricuspid annular plane systolic excursion, LASV left atrial systolic volume.
Additionally, high coffee consumption demonstrated associations with total cholesterol with a beta of 4.78 ($95\%$ CI 1.56, 8.0; $$p \leq 0.004$$) and obesity (BMI ≥ 30 kg/m2) with an odds ratio (OR) of 1.32 ($95\%$ CI 1.08, 1.62; $$p \leq 0.008$$) (Tables 2 and 3, Supplementary Table 1).Table 3Logistic regression analysis of electrocardiographic findings, comorbidities, and cardiovascular diseases with moderate and high coffee consumption. Moderate (3–4 cups/day)High (> 4 cups/day)OR ($95\%$ CI)p-valueOR ($95\%$ CI)p-valueElectrocardiography LBBB0.91 (0.68, 1.21)0.5270.73 (0.46, 1.11)0.152 AV block1.13 (0.88, 1.44)0.3450.82 (0.54, 1.19)0.310 Atrial fibrillation0.95 (0.73, 1.22)0.6990.69 (0.45, 1.04)0.088Comorbidities and cardiovascular diseases Diabetes mellitus0.85 (0.67, 1.07)0.1660.91 (0.66, 1.23)0.544 Obesity (BMI ≥ 30 kg/m2)1.13 (0.97, 1.30)0.1151.32 (1.08, 1.62)0.008 Coronary artery disease0.93 (0.72, 1.19)0.5641.04 (0.72, 1.46)0.832 Peripheral artery disease1.20 (0.97, 1.48)0.0881.06 (0.78, 1.44)0.693 Heart failure0.85 (0.60, 1.20)0.3641.03 (0.62, 1.64)0.912 HFpEF0.77 (0.45, 1.250.3080.91 (0.41, 1.80)0.796 HF(m)rEF0.95 (0.59–1.49)0.8371.14 (0.58–2.05)0.690Mild coffee consumption (< 3 cups/day) served as a reference. Adjustment was performed for age, sex, BMI, diabetes, arterial hypertension, smoking, and additives. AF atrial fibrillation, LBBB left bundle branch block, RBBB right bundle branch block, AV block atrioventricular block, HFpEF heart failure with preserved ejection fraction, HF(m)rEF heart failure with (mildly) reduced ejection fraction.
## Coffee consumption and ECG/TTE variables
No relevant associations of coffee consumption with ECG parameters were detected in regression analysis. In line, neither morphological nor functional echocardiographic parameters correlated with coffee consumption (Table 2).
## Coffee consumption and blood pressure and cardiovascular diseases
In linear regression analysis, adjusted for age, sex, BMI, diabetes, smoking, and additives, moderate and high coffee consumption correlated with lower systolic (moderate: beta = − 1.91; $95\%$ CI − 3.04, − 0.78; $$p \leq 0.001$$; high: beta = − 3.06; $95\%$ CI − 4.69, − 1.44; $p \leq 0.001$) and diastolic blood pressure (moderate: beta = − 1.05; $95\%$ CI − 1.67, − 0.43; $$p \leq 0.001$$; high: beta − 1.85; $95\%$ CI − 2.74, − 0.96; $p \leq 0.001$) (Table 2, Supplementary Tables 5 and 6).
In contrast, coffee consumption showed no associations with CAD, and PAD. In our population, a total of 605 subjects were identified with the diagnosis of heart failure (Table 1). Nevertheless, neither heart failure in general nor differentiating into HFpEF and HF(m)rEF demonstrated significant associations with coffee consumption. In contrast, NT-proBNP was inversely associated with moderate (beta = − 0.06; $95\%$ CI − 0.11, − 0.02; $$p \leq 0.005$$) and high (beta = − 0.09; $95\%$ CI − 0.15, − 0.02; $$p \leq 0.013$$) coffee consumption (Table 2, Supplementary Table 4).
## Simultaneous consumption of caffeine-containing drinks, dietary patterns, and sex-specific differences
In order to address potential confounding by black and green tea as well as caffeinated soft-drinks we performed sensitivity analyses, excluding all subjects with simultaneous consumption of coffee and green and black tea. Since the coincidence of coffee and tea consumption is extremely high, this led to significant reduction of sample size and statistical power ($$n = 1480$$). High coffee consumption still demonstrated a trend towards associations with LDL and an inverse trend towards associations with systolic and diastolic bp lacking statistical significance. ( Supplementary Tables 132–156). To exclude potential bias caused be the consumption of certain food components, additional adjustment for specific diets (vegetarian diet, vegan diet) was performed revealing no changes in the detected associations of coffee consumption (Supplementary Tables 157–180).
Sex-specific stratification of all our multivariable regression analyses as well as sensitivity analyses separated by sex showed no differences regarding our key findings (Supplementary Tables 80–131).
## Decaffeinated coffee consumption
From the overall cohort 807 subjects consumed decaffeinated coffee. Of those, 481 subjects consumed less than 3 cups/day, 241 subjects 3–4 cups/day, and 85 more than 4 cups/day. In linear regression analysis, adjusted for age, sex, BMI, diabetes, smoking, and additives, moderate and high decaffeinated coffee consumption correlated with lower diastolic (moderate: beta = − 2.05; $95\%$ CI − 4.05, − 0.05; $$p \leq 0.045$$; high: beta − 3.79; $95\%$ CI − 6.87, − 0.71; $p \leq 0.001$) and moderate decaffeinated coffee consumption with lower systolic blood pressure (moderate: beta = − 4.17; $95\%$ CI − 7.88, − 0.45; $$p \leq 0.028$$; high: beta = − 5.01; $95\%$ CI − 10.72, 0.69; $$p \leq 0.085$$) (Supplementary Tables 57–79). No further associations between decaffeinated coffee consumption and the assessed biomarkers, cardiovascular diseases, and ECG/TTE variables were detected.
## Discussion
In this study we demonstrate that coffee consumption was not associated with altered cardiac function and morphology, heart failure, and most of its risk factors. However, we observed an association with higher LDL-cholesterol levels and an inverse association with systolic and diastolic blood pressure.
Coffee is a complex liquid containing a multitude of compounds that could affect cardiovascular health including caffeine and polyphenols16,17. Whereas in earlier studies, detrimental effects of coffee consumption on cardiovascular health were promoted, recent studies favor a neutral or positive effect of moderate coffee consumption2–5.
The number of studies investigating associations of coffee consumption with echocardiographic parameters are scarce. Acute coffee intake seems to have no impact on cardiac function measured by echocardiography in healthy subjects18. Yet, in patients with CAD, coffee intake led to a decrease in left ventricular function, as well as a mild diastolic dysfunction possibly mediated by vasoconstriction and missing cardiac reserve in these patients10. In our community-based study, we did not depict relevant correlations of systolic or diastolic function with coffee consumption. In contrast, the CARDIA study indicated that low-to-moderate daily coffee consumption from early adulthood to middle age was associated with better LV systolic and diastolic function11. Additionally, several studies have suggested a favorable cardiovascular outcome and less heart failure for low- to moderate coffee consumption7,9,19. Accordingly, we observed a weak inverse association of coffee consumption with NT-proBNP. However, in our cross-sectional study there were no relevant associations of coffee consumption with heart failure or echocardiographic and electrocardiographic detectable HF precursors.
In line with most studies, we did not detect associations of coffee consumption with neither atrial fibrillation nor any other measured ECG time interval20. Only few studies addressed the topic of coffee consumption and ECG changes. In young healthy adults, moderate caffeine consumption showed no effect on the PR, QRS, QT and QTc intervals21,22. Supportive of these findings, we were not able to depict any associations between coffee consumption and ECG variables. Nevertheless, some studies reported beneficial effects of coffee consumption on atrial fibrillation23. Although caffeine induces the release of metanephrines and raises calcium sensitivity of the myocardium, our study showed no association of coffee consumption and atrial fibrillation24,25.
In line with previous observations, moderate and high coffee consumption was associated with increasing LDL-cholesterol levels26. Several studies on coffee consumption and lipids proposed that diterpenes, which are highly prevalent in unfiltered coffee, are the main drivers of a coffee-mediated increase in cholesterol levels27,28. In vitro, diterpenes mediated a reduction of LDL receptor activity29. Since the LDL receptor is responsible for the endocytic process of Apo B- and Apo E-containing lipoproteins, its suppression consequently leads to an extracellular accumulation of cholesterol. However, possible coffee-induced elevations of LDL-cholesterol were not accompanied by a rise in the prevalence of cardiovascular diseases such as coronary artery disease or peripheral artery disease.
Data on the effect of coffee on blood pressure are inconsistent30. Whereas several studies demonstrated an association of coffee consumption with elevated blood pressure, other studies were not able to reproduce any influence of coffee consumption on blood pressure31,32. Another meta-analysis even demonstrated a linear association between increasing coffee consumption and decreased risk of hypertension33. Possible explanations for these contradicting results might be attributed to differences in population genetics. Caffeine is mainly metabolized by Cytochrome P450 1A2. Variations in the CYP1A2 allele lead to a slower metabolization of caffeine and are associated with an increased risk for hypertension34. However, even the consumption of decaffeinated caffeine showed the same negative association with systolic and diastolic blood pressure, challenging the role of caffeine as the main driver of the described associations. The positive association with LDL-cholesterol and negative association with blood pressure might support the hypothesis of counterbalancing effects of coffee consumption on cardiovascular health.
## Limitations
The HCHS includes a sample from the middle-aged population of the German city of Hamburg with subjects mainly of Caucasian ascend. Accordingly, translations of our results to other ethnic groups should be done with caution. Since the amount of subjects suffering from heart failure was limited ($$n = 293$$ subjects), we decided to alter ESC HF Guidelines and consider HFrEF and HFmrEF as a joint HF(m)rEF group. This brings up the need for further studies, with larger sample sizes of subjects suffering from HF, allowing the distinction of HFmrEF and HFrEF.
As our study design is cross-sectional, only descriptions of associations but no causal claims can be made. Furthermore, all subjects have to answer the questionnaires by memory. Being asked about the last 12 months' behaviors and habits can always be distorted by either wrong recollection or deliberate misinformation.
Coffee is a highly complex beverage containing more than 1000 compounds acting as myriad bioactive substances. Conclusions about which substance, e.g. caffeine, derived antioxidants or diterpene alcohols, is responsible for the investigated effects, cannot be made.
Finally, coffee consumption might be associated with certain dietary patterns. While our regression analysis accounted for major dietary factors, the possibility of undetected confounding by additional nutritive and non-nutritive components cannot be completely ruled out.
## Conclusion
Our study provides new data on the associations of coffee consumption with cardiovascular health: LDL-cholesterol was positively, systolic and diastolic blood pressure inversely associated. Coffee consumption was not associated with cardiovascular diseases or altered cardiac structure or function suggesting possibly counterbalancing, neutral effects of coffee on cardiovascular health.
## Supplementary Information
Supplementary Tables. The online version contains supplementary material available at 10.1038/s41598-023-31857-5.
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|
---
title: A novel 3D culture model for human primary mammary adipocytes to study their
metabolic crosstalk with breast cancer in lean and obese conditions
authors:
- Marie Rebeaud
- Caroline Bouche
- Stéphanie Dauvillier
- Camille Attané
- Carlo Arellano
- Charlotte Vaysse
- Frédérique Fallone
- Catherine Muller
journal: Scientific Reports
year: 2023
pmcid: PMC10033714
doi: 10.1038/s41598-023-31673-x
license: CC BY 4.0
---
# A novel 3D culture model for human primary mammary adipocytes to study their metabolic crosstalk with breast cancer in lean and obese conditions
## Abstract
Obesity is a negative prognosis factor for breast cancer. Yet, the biological mechanisms underlying this effect are still largely unknown. An emerging hypothesis is that the transfer of free fatty acids (FFA) between adipocytes and tumor cells might be altered under obese conditions, contributing to tumor progression. Currently there is a paucity of models to study human mammary adipocytes (M-Ads)-cancer crosstalk. As for other types of isolated white adipocytes, herein, we showed that human M-Ads die within 2–3 days by necrosis when grown in 2D. As an alternative, M-Ads were grown in a fibrin matrix, a 3D model that preserve their distribution, integrity and metabolic function for up to 5 days at physiological glucose concentrations (5 mM). Higher glucose concentrations frequently used in in vitro models promote lipogenesis during M-Ads culture, impairing their lipolytic function. Using transwell inserts, the matrix embedded adipocytes were cocultured with breast cancer cells. FFA transfer between M-Ads and cancer cells was observed, and this event was amplified by obesity. Together these data show that our 3D model is a new tool for studying the effect of M-Ads on tumor cells and beyond with all the components of the tumor microenvironment including the immune cells.
## Introduction
Breast cancer (BC) is the most common cancer and the second leading cause of cancer-associated death among women worldwide1. Mammary adipose tissue (MAT) represents a major component of the BC microenvironment, and numerous studies now indicate that MAT adjacent to tumors supports BC development and progression2. A large number of studies supports the existence of a bidirectional crosstalk between BC cells and mammary adipocytes (M-Ads), the major cellular component of MAT, at the tumor invasive front (for review3–5). Coculture models using adipocytes (obtained from in vitro differentiation of murine pre-adipocyte cell lines or ex vivo differentiated adipose progenitors) have been used to provide mechanistic insights of this deleterious dialog (for review3–5). Adipocytes affect cancer aggressiveness through soluble factors such as pro-inflammatory cytokines6, extra-cellular matrix (ECM) proteins and proteins involved in ECM remodeling7,8 or through lipid transfer affecting tumor metabolism9 (for review3–5). One of the most specific and emerging mechanism regarding the role of adipocytes involves the ability of cancer cells to hijack the nurturing role of adipocytes, the largest reservoirs of lipids in the tumor microenvironment (TME), to their advantage4.
Epidemiological arguments suggest that this dialog might be amplified in obesity, where the secretory and metabolic profiles of adipocytes are affected10,11. Regardless of menopausal status, obesity worsens the prognosis of BC12,13. BC mortality increases by $18\%$ for every 5 kg/m2 increase in body mass index (BMI), and the risk of relapse at 5 years in obese patients is of $40\%$ compared to 5–$10\%$ in unselected populations12. Understanding the biology and mechanisms of this effect will provide a timely opportunity for improving the treatment and outcomes of obese patients with BC.
To date, most studies have focused on studying changes in adipokines secretion and/or chronic inflammation in MAT of obese subjects with BC14. Increased lipid transfer between M-Ads and cancer cells might be a key event promoting BC aggressiveness in obesity, an hypothesis that remains largely unexplored4. In obesity, mature adipocytes are larger in size due to an increased need to store lipids as triglyceride (TG) in their lipid droplet (LD)10. As for other fat depots, we and others have shown that M-Ads also increase in size in overweight and obese patients, and that there is a positive correlation between M-Ads size and BMI15–17. Lipolysis, a process corresponding to the hydrolysis of TG into free fatty acids (FFA), that are then released in the extra-cellular medium, is also affected by obesity18. An increase in basal and a decrease in catecholamine-stimulated lipolysis have been described in various fat depots18, although these modifications have never been studied in M-Ads. Recent lipidomic profiling of human subcutaneous and visceral adipose tissue (AT) of lean and obese individuals has revealed qualitative and quantitative differences of the lipid content with obesity, and also between different adipose depots19. Accordingly, using M-Ads isolated from lean and obese patients is fundamental to study the metabolic crosstalk between BC cells and mature adipocytes in the context of obesity under the most suitable conditions.
To our knowledge, no validated experimental model exists to answer this question. Although mature adipocytes can be easily isolated by flotation after collagenase digestion, they are not able to survive for more than 24 to 48 h in culture medium20,21. Due to their high lipid content, adipocytes float on the top of the culture medium resulting in dedifferentiation (leading to the appearance of elongated fibroblast-like cells), delipidation and ultimately, cell death20–22. A recently published protocol described a 2D culture model of primary M-Ads over several days23 suggesting that these cells behave differently than other adipocytes. However, we found in our current study that in these conditions, M-Ads, like sub-cutaneous adipocytes (SC-Ads) undergo rapid loss in cell viability as shown in the results section. A more appropriated approach could be to culture isolated mature adipocytes in semi-solid matrices. Several types of 3D models have been reported using mostly SC-Ads embedded in collagen, Matrigel or hyaluronan-based hydrogel20. These approaches support adipocyte viability for several days but some pitfalls can be pointed out. In collagen matrices, that are semi-liquid at 37 °C, a significant proportion of adipocytes undergo an almost general delipidation after 1 week24. Culture in Matrigel, that is extracted from the basement membrane from an Engelbreth–Holm–Swarm mouse sarcoma25, maintains isolated adipocytes intact for up to 6 days26, but soluble factors contained in the ECM of murine tumors could affect the behavior of cocultured BC cells and introduce variability in the experimental results25. Finally, a model of adipocyte aggregates cultured under a membrane (Membrane Adipocytes Aggregates Culture or MAAC) has been recently proposed to maintain adipocyte morphology and function for up to 7 days27. To our knowledge, few of these culture systems have been used to maintain adipocytes isolated from obese patients. Studies have proposed to “recreate” the obesity setting by differentiating adipose progenitors in 3D culture systems and to expose them to exogenous FFA to mimic caloric overload28,29. However, these models might not appropriately reflect the quantitative and qualitative nature of the accumulated lipids present in M-Ads during obesity, since their nature vary depending on the adipose depot30.
To circumvent these problems, we set-up a 3D culture system of M-Ads embedded in a fibrin matrix that has been previously used to culture SC-Ads over a short time31. Fibrin has the advantage of polymerizing quickly, which allows a homogeneous distribution of adipocytes in the gel, preventing them from rising to the upper part of the gel during the polymerization phase, an event that favors delipidation, as observed with collagen matrices24. By optimizing the glucose concentration in the culture medium, we described here a novel 3D culture system of M-Ads obtained from lean and obese patients, enabling maintenance of their cell integrity, size and stimulated-lipolytic function for up to 5 days. In coculture with BC cells, we validated the use of this model to study the transfer of FFA between M-Ads and cancer cells, and our preliminary results showed that this process is increased by obesity. This model represents a new tool to investigate the role of increased lipid transfer between surrounding M-Ads and cancer cells in BC aggressiveness in obese patients.
## Tissue collection
Mammary adipose tissue (MAT) samples were collected from patients undergoing mastectomy for BC at the Toulouse-Oncopole University Cancer Institute (IUCT-O) between January 2020 and September 2022 (Toulouse, France). The study was conducted in accordance with the guidelines and with the full approval of the national CODECOH committee (authorization AC-2016-2658, DC-2016-2656). Written informed consent was received from participants before inclusion in the study, which was conducted in accordance with the Declaration of Helsinki principles as revised in 2000. MAT samples were collected in the quadrant opposite to the tumor, at a distance of at least 3 cm from the tumor. The patients with a history of homolateral breast surgery, chemotherapy, breast and/or axillary radiotherapy, hormone therapy were excluded from the study. Samples were either obtained from normal weight (NW) (BMI between 18.5 and 25 kg/m2) or obese patients (BMI greater than 30 kg/m2). For 2D coculture, subcutaneous adipose tissue (SC-AT) were obtained from NW women undergoing hip surgery at the Orthopedic Surgery and Traumatology Department of the Hospital Pierre Paul Riquet (Toulouse, France). All patients gave informed consent, and the samples were obtained according to national CODECOH committee (authorization DC-2017-2914). Samples were immediately put in 50 mL tubes containing 10 mL of Dulbecco’s Modified Eagle’s Medium (DMEM) (Thermo Fisher Scientific,) and carried out to the research lab within 1 h.
## Adipocyte isolation
Adipocytes were isolated as previously described32. Briefly, after cutting them into small pieces, MATs (5 to 15 g) were digested with type I collagenase from Sigma-Aldrich at 250 U/mL in PBS containing $2\%$ bovine serum albumin (BSA) for 30 min at 37 °C, under shaking. After digestion, the cell suspension was filtered through a 200 µm strainer to remove cell debris and undigested fragments. Floating adipocytes were collected and washed several times with KRBHA (Krebs–Ringer Bicarbonate buffer from Sigma-Aldrich supplemented with 10 mM HEPES and $0.5\%$ BSA pH 7.4) to obtain pure isolated adipocytes.
## BC cell line and culture
The human BC cell line MDA-MB-231 (provided by C. Dumontet, CRCL, Lyon, France) was cultured in RPMI medium without glucose (Thermo Fischer Scientific) supplemented with 5 mM glucose (Sigma Aldrich), $5\%$ fetal calf serum (FCS) and $1\%$ Penicillin–Streptomycin (P/S). Cells were grown at 37 °C in a humidified atmosphere with $5\%$ CO2 and used within 2 months after resuscitation of frozen aliquots. The cells were tested every month by polymerase chain reaction for mycoplasma contamination.
## 2D culture of adipocytes
2D culture was performed according to the protocol of Picon-Ruiz et al.22. Briefly, after isolation, 1 mL of adipocytes were resuspended in 5 mL of DMEM supplemented with $10\%$ FCS and $1\%$ P/S, and were distributed in a 6-well plate (2 mL per well) (corresponding to approximately 6 × 105 cells per well). Adipocytes were maintained in culture for 7 days at 37 °C in a humidified atmosphere with $5\%$ CO2. Every 2 days, 500µL of DMEM was added to each well. To monitor their viability, pictures of the adipocytes were taken at D0, D3 and D7 with a light microscope (Olympus CKX53) and refringent cells were numbered using a Malassez counting chamber. Viability was also assessed after staining with BODIPY® $\frac{493}{503}$ and measure of the lactatate deshydrogenase (LDH) concentration in the supernatants (see below).
## 3D culture of adipocytes
To prepare the 3D fibrin matrix, 100 µL of isolated adipocytes (corresponding to approximately 2 × 105 cells) were first put in 24-well plates. Then, for “undiluted” or “diluted” gels, respectively 100 µL or 70 µL of fibrinogen at 18 mg/mL (Sigma Aldrich) in NaCl solution were added to the wells (respectively 6 and 4.2 mg/mL final concentration) and were gently homogenized. For "diluted" gels, 30 µL of culture medium was added to obtain the same culture volume. One hundred µL of thrombin (Sigma Aldrich) at 25 UI/mL in PBS-CaCl2 were added and homogenized quickly. Finally, gels were placed at 37 °C for a few min to polymerize. One mL of culture medium (DMEM containing 25 mM glucose with $10\%$ FCS and $1\%$ P/S or RPMI 1640 containing either 5 or 11 mM glucose with $5\%$ FCS and $1\%$ P/S) was then added to each well. All the media were purchased from Thermofisher. Gels were kept for 5 days at 37 °C in a humidified atmosphere with $5\%$ CO2.
## MDA-MB-231 coculture with adipocytes embedded in 3D matrix
Tumor cells and adipocytes were cocultured using a transwell culture system (0.4 µM pore size; Dutscher). MDA-MB-231 BC cells were seeded on glass coverslips at 1 × 104 cells per well in the lower chamber of 24-well plates, and isolated adipocytes embedded in 3D fibrin matrix were seeded in the upper chamber of the 24-well transwell inserts. Five hundred µL of RPMI supplemented with 5 mM glucose, $5\%$ FCS and $1\%$ P/S were added to both lower and upper chambers. After 3 days of coculture, cancer cells were stained with DAPI, rhodamine phalloidin and BODIPY® $\frac{493}{503}$ as indicated below. In indicated experiments, adipocytes were labeled with BODIPY FLC16 as previously described33. Briefly, isolated adipocytes were incubated with BODIPY FLC16 at 5 µM in RPMI supplemented with 5 mM glucose and $5\%$ FCS, then washed with pre-warmed PBS, prior to coculture with cancer cells as described above.
## Measure of LDH in the culture medium
The viability of adipocytes cultured in either 2D or 3D was determined by measuring the amount of LDH released into the culture medium using the CyQUANT LDH Cytotoxicity Assay kit (Thermo Fisher Scientific) according to the manufacturer's instructions. The absorbance was measured at 490 nm using a spectrophotometer (MicroQuant, BioTek Instrument Inc). The resulting absorbance was expressed as the percentage of a positive control (set at 100) corresponding to same initial number of adipocytes undergoing 3 cycles of freezing (in liquid nitrogen)/thawing followed by sonication for 5 s.
## Staining of adipocytes and tumor cells
The neutral lipid content of cells was determined by BODIPY® $\frac{493}{503}$ staining (Thermo Fisher Scientific). For adipocytes in 2D culture, after recovery of the well content, adipocytes were washed once with PBS and then labeled with BODIPY® $\frac{493}{503}$ for 30 min under gentle agitation at room temperature (RT). After staining, adipocytes were washed three times with PBS and then placed in Lab-Tek culture chambers (Dutscher) and analyzed immediately using a confocal microscope. For adipocytes in 3D culture, gels were fixed with $3.7\%$ paraformaldehyde for 1 h at RT, then incubated with 10 µg/mL of BODIPY® $\frac{493}{503}$ for 45 min at RT. Single plane (2D) and z-stack fluorescence images were acquired with a confocal laser microscopy system (Confocal TIRF FV1000 Olympus, 10×/NA 0.40 objective, Olympus). Maximum intensity projection was performed using Fiji software (Image J, Bethesda, MD, USA). At least 300 adipocytes from each sample were manually measured with ImageJ. For tumor cells cocultured with adipocytes, cancer cells were labeled with BODIPY® $\frac{493}{503}$ (to stain neutral lipids) at 2.5 ng/mL, with rhodamine-phalloidin (to stain actin cytoskeleton) (Abcam) and with DAPI (to stain nucleus) at 1 µM (Thermo Fisher Scientific) as previously described34. For tumor cells cocultured with BODIPY FLC16-labeled adipocytes, only rhodamine-phalloidin and DAPI were used. Fluorescence images were acquired with a confocal laser microscopy system (Confocal TIRF FV1000 Olympus, PLAN-APO 60X/NA 1.40 objective, Olympus) and analyzed with Fiji/Image J software.
## Measure of adipocyte lipolytic activity
Lipolytic activity of adipocytes was measured as previously described32. Briefly, 100 µL of isolated adipocytes were incubated in 250 µL KRBHA for 3 h at 37 °C with or without 1 µmol/L isoprenaline (Sigma Aldrich) to evaluate stimulated and basal lipolysis. At the end of incubation period, incubation media were collected to quantify the amount of glycerol released using the Free Glycerol Reagent kit (Sigma Aldrich) according to manufacturer’s instructions. For adipocytes embedded in matrix, after medium removal gels were incubated in 250 µL KRBHA with or without isoprenaline and treated as described above.
## Statistics
Statistical analyses were performed by using GraphPad Prism (v8.01). Normal distribution of the data was assessed using the Shapiro Wilk test. For data with normal distribution, Paired t-test or 2-way ANOVA were performed and for data without a normal distribution, Dunn's multiple comparison test was used. P values < 0.05 (*), < 0.01 (**) and < 0.001 (***) were considered significant.
## Like SC-Ads, M-Ads rapidly die when cultured in 2D
As a recently published protocol suggested that primary M-Ads can be cultured in 2D for up to 7 days23, we tested this method with M-Ads isolated from lean patients (BMI: 23.4 ± 2.2 kg/m2). We used SC-Ads from lean women (BMI: 24.7 ± 3.9 kg/m2) as a control since several studies have shown that they cannot be cultured in 2D in a prolonged manner20–22. Thus, SC-Ads or M-Ads were put in 6-well plates containing culture medium and kept at 37 °C for 7 days. As shown in Fig. 1a, the number of both M-Ads and SC-Ads decreased sharply in the wells between D0 (when isolated adipocytes were put in culture medium) and D3, where few refringent cells were found under light microscopy. These refringent cells almost disappeared at D7 (Fig. 1a). In order to evaluate their viability, adipocytes were stained using BODIPY® $\frac{493}{503}$, a fluorescent dye for neutral lipids (Fig. 1b). As seen at D0, primary adipocytes are spheric cells that contain a unilocular LD considered to be representative of their size. Using this approach, we confirmed the decrease in the number of viable adipocytes for both SC- and M-Ads at D3 and at D7 (Fig. 1b). After counting the unlabeled adipocytes, we found a $70\%$ decrease in the adipocytes number between D0 and D3 for both SC-Ads (Fig. 1c) and M-Ads (Fig. 1d) and a further decrease in cell number was seen between D3 and D7 (Fig. 1c,d). In parallel with this decrease in cell number, oil droplets of increasing size appeared in the wells over time, reflecting lipid release from dying adipocytes into the culture medium, as illustrated by a representative image of cultured M-Ads (Fig. 1e). Finally, to confirm adipocyte death, we quantified LDH in the culture medium, this enzyme being released from cells when plasma membrane integrity is altered. As a positive control, the same number of adipocytes as at the beginning of the culture was subjected to complete lysis by freezing/thawing cycles followed by sonication to evaluate maximal LDH release (value set at $100\%$). As early as D2, high levels of LDH were detected in the culture medium which further increased at D7, confirming the progressive death of SC- and M-Ads in 2D culture (Fig. 1f). Taken together, our results demonstrated that, like SC-Ads20–22, M-Ads were not able to survive in 2D culture and undergo rapid death by necrosis during the first days of culture. Figure 1Like SC-Ads, M-Ads rapidly die when cultured in 2D. (a) Representative phase-contrast images taken under light microscope of human SC-Ads and M-Ads cultured in 2D for the indicated times; Scale bar, 100 µm. ( b) Representative images of BODIPY® $\frac{493}{503}$ (neutral lipids, green) stained primary SC-Ads and M-Ads in 2D culture for the indicated times; Scale bar, 100 µm. ( c,d) Number of SC-Ads (c) and M-Ads (d) cultured in 2D over time ($$n = 3$$). ( e) Representative pictures of the culture wells of M-Ads grown in 2D at indicated times points. ( f) Quantification of LDH release in medium during 2D culture at D2 and D7 for SC-Ads and M-Ads ($$n = 3$$). The results are expressed as the percentage of the value obtained when the whole cell population (same cell number than D0) is lysed. The histograms represent mean ± SEM, ns non-significant, *$P \leq 0.05.$
## 3D fibrin matrix preserves adipocytes viability but not lipolytic function at high glucose concentration
One experimental alternative to maintain viable adipocytes for longer times is the use of a 3D matrix. To cultivate M-Ads, we chose fibrinogen to make the gels because of its rapid polymerization in the presence of thrombin31. Two different culture media were used, either DMEM containing 25 mM glucose, a medium commonly used to support in vitro adipogenesis35, or RPMI 1640 which contains 11 mM glucose. In addition to glucose, other differences in the composition in calcium, phosphate and amino acids between these two media are present that could potentially influence adipocyte viability36. We first evaluated the morphology and size of isolated adipocytes at indicated time points (D0, D3 and D5) through BODIPY® $\frac{493}{503}$ staining (Fig. 2a,b). Through confocal microscopy, we observed that M-Ads were homogeneously distributed within the matrix, and that their spheric form was maintained for up to 5 days regardless of the medium used (Fig. 2a). During this period, we did not observe any elongated fibroblast-like cells that could result from a process of adipocyte dedifferentiation, attesting that our conditions allowed the maintenance of mature adipocytes in culture (Fig. 2a). At D5, a significant increase in adipocyte size was observed in both media (mean size: 101.7 ± 19.7 µm and 94.9 ± 16.5 µm in DMEM and RPMI respectively) as compared to those freshly embedded in the matrix (D0) (mean size: 86.8 ± 14.4 µm) (Fig. 2b). We concluded that the increase in M-Ads size reflects lipogenesis activity occurring in the presence of glucose, which provides carbon source for FFA synthesis and facilitates FFA esterification for lipid storage22,37,38. Absence of adipocyte death was shown by measuring LDH released in the culture medium, which remained very low in 3D compared to 2D culture (Fig. 2c). We then investigated the lipolytic function of adipocytes by using isoproterenol (iso), a β-adrenergic agonist, at doses that ensure maximal lipolytic activation39,40. Using freshly isolated cells in suspension as a control, we obtained a significant 2.8-fold increase in glycerol release treated with iso compared to basal condition. This lipolysis activation also occurred in matrix-embedded M-Ads but to a lesser extent (twofold). Nonetheless, at D3 and D5, adipocytes included in matrix were no longer responsive to iso treatment in both DMEM and RPMI (Fig. 2e). A higher basal lipolysis could be noted for cells cultured in DMEM (25 mM glucose) (Fig. 2e) compared to freshly matrix-embedded adipocytes (Fig. 2d) (twofold and 1.7-fold at D3 and D5, respectively), whereas this increase in basal lipolysis was not observed with RPMI that contained less glucose (11 mM) (1.2-fold and onefold at D3 and D5, respectively) (Fig. 2d,e). Such increase in basal lipolysis in the presence of high glucose concentration has been previously reported41. The impaired stimulated lipolysis we observed is likely due to the progressive hypertrophy of adipocytes during the culture observed in both media, since stimulated lipolysis has been reported to be negatively correlated to fat cell volume42,43. To conclude, we showed that 3D fibrin matrix was able to maintain adipocyte morphology and viability for up to 5 days, however high glucose concentrations in the medium seemed to alter their lipolytic function36.Figure 2:3D fibrin matrix preserves adipocyte viability but not lipolytic function at high glucose concentration. ( a) Maximum intensity projection of Z-stack acquired through confocal microscopy. Representative images of M-Ads after BODIPY $\frac{493}{503}$ staining, cultured in fibrin matrix for the indicated time. Top, cultured with DMEM 25 mM glucose $10\%$ FCS medium, bottom, cultured with RPMI 11 mM glucose, $10\%$ FCS medium; Scale bar, 100 µm. ( b) Mean diameter of adipocytes according to their culture medium over time. Between 200 and 300 adipocytes were measured per condition ($$n = 6$$). ( c) Quantification of LDH released in medium during culture at the indicated time for M-Ads in 2D ($$n = 3$$) or 3D ($$n = 12$$) culture. ( d) Amount of glycerol released after 3 h in the presence or not (basal lipolysis) of isoprenaline (iso) before gel inclusion (isolated adipocytes) or after 3 h in gel (adipocytes in gel) ($$n = 8$$). ( e) Similar experiments were performed with adipocytes cultured in 3D matrix for the indicated time, in RPMI or DMEM ($$n = 7$$). Histograms represent mean ± SEM, ns non-significant, *$P \leq 0.05$, ***$P \leq 0.001.$
## 3D fibrin matrix preserves adipocyte lipolytic function for up to 5 days at low glucose concentration
As high glucose concentration seemed to alter M-Ads metabolic function, we lowered the glucose concentration of the RPMI medium to 5 mM to be closer to physiological conditions36, and decreased by 1:3 the density of the matrix to improve the release of lipolysis products. As a control, matrix-embedded adipocytes were maintained in parallel in RPMI with 11 mM glucose. As previously observed, the M-Ads were homogeneously distributed within the matrix and maintained their rounded morphology for 5 days regardless of the glucose concentration (Fig. 3a). While M-Ads significantly increased in size when grown in RPMI 11 mM in these set of experiments, no changes in adipocytes size was observed in RPMI 5 mM glucose (Fig. 3b). We then verified the lipolytic function of adipocytes under these culture conditions. With freshly embedded adipocytes in this less dense matrix, the increase in glycerol release after iso stimulation was similar to the one measured with isolated adipocytes (2.8-fold) (Fig. 3c), showing that a less dense matrix improved the release and diffusion of this lipolysis product in the medium. Furthermore, we also evaluated the lipolytic activity of M-Ads at D3 and D5. Significant glycerol release was observed upon iso stimulation at both time points at low glucose condition, contrary to high glucose condition (Fig. 3d), consistent with our hypothesis that the observed progressive hypertrophy (Fig. 3a) could affect lipolytic response. Taken together, our results showed that M-ads embedded in a less dense fibrin matrix and cultured in RPMI with 5 mM glucose retained their size and respond to lipolytic stimulation for up 5 days. These results demonstrated that glucose concentration was a critical parameter for the culture of isolated adipocytes in 3D in order to maintain their metabolic function. Figure 3:3D fibrin matrix preserves adipocyte lipolytic function for up to 5 days at low glucose concentration. ( a) Maximum intensity projection of Z-stack acquired through confocal microscopy. Representative images of M-Ads after BODIPY® $\frac{493}{503}$ staining, cultured in fibrin matrix for the indicated time. Top, cultured with RPMI 11 mM glucose $10\%$ FCS medium, bottom, cultured with RPMI 5 mM glucose, $10\%$ FCS medium; Scale bar, 100 µm. ( b) Mean diameter of adipocytes according to their culture medium over time. Between 200 and 300 adipocytes were measured per condition ($$n = 6$$). ( c) Amount of glycerol released after 3 h in the presence or not (basal lipolysis) of isoprenaline (iso) before gel inclusion (isolated adipocytes) or after 3 h in gel (adipocytes in gel) ($$n = 5$$). ( d) Similar experiments were performed with adipocytes cultured in 3D matrix for the indicated time, in RPMI containing 5 mM or 11 mM glucose ($$n = 5$$). The histograms represent mean ± SEM, ns non-significant, *$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001.$
## Our 3D matrix model maintains intact and functional adipocytes in both lean and obese conditions
The main objective of our model was to be able to maintain in culture M-Ads isolated from both lean and obese patients in order to study the impact of obesity on the metabolic symbiosis between adipocytes and tumor cells. A series of samples were obtained from either normal weight (NW) (BMI 21.8 ± 1.9 kg/m2, $$n = 37$$) or obese (BMI 31.6 ± 2. 8 kg/m2, $$n = 17$$) patients and used for the different experiments. We first evaluated the morphology and the size of isolated adipocytes at indicated time points (D0, D3 and D5) through BODIPY® $\frac{493}{503}$ staining (Fig. 4a,b). Adipocytes isolated from both NW and obese patients showed homogeneous distribution within the matrix and preserved their morphology for up to 5 days (Fig. 4a). Expectedly, size distribution showed that adipocytes from obese patients were significantly larger than those from NW patients (Fig. 4b) and the calculated mean diameter was of 99.2 ± 12.3 µm versus 80.3 ± 8.4 µm respectively at D0 (Fig. 4c). Importantly, these differences in size were maintained during the culture for up to 5 days (Fig. 4c). Both basal and iso-induced lipolysis were similar between NW and obese-isolated adipocytes (Fig. 4d). These results differ from the literature which showed a decreased noradrenaline sensitivity in isolated SC-Ads from obese patients42,43. Our data suggest that the lipolytic function of M-Ads induced by β-adrenergic stimulation might not be regulated by obesity in opposition to what is observed in SC-Ads42,43. However, we cannot formerly exclude that these results are due to the fact that our patients exhibit only a moderate obesity (BMI: 31.6 ± 2. 8 kg/m2) in opposition to studies performed with SC-Ads that included morbidly obese patients (BMI: 43.1 ± 0.7 kg/m2)43. When obese M-Ads were cultured into matrix, they no longer responded to iso-induced lipolysis after 3 h on D3 and D5, and the basal lipolysis did not increase over time as in NW M-Ads (Fig. 4e). We hypothesized that this absence of response might be due to a defect in the diffusion of iso into the matrix due to the presence of hypertrophic adipocytes and/or to the secretion of excess of ECM molecules by obese adipocytes44. Indeed, it has been shown in a 3D culture system that the mechanical constraints induced by a modified ECM obtained from obese AT reduced adipocyte lipolytic function45. To circumvent this issue, we exposed obese adipocytes to iso for longer time (6 h instead of 3 h). In these experimental conditions, iso was able to induce glycerol release in obese adipocytes (respectively 1.9- and 1.8-fold at D3 and D5) (Fig. 4f). In conclusion, these results demonstrated that our culture model was able to preserve obese adipocytes integrity and function during 5 days. In addition, our preliminary results showed that adiponectin secretion was decreased by about twofold in samples from obese compared to lean patients and this difference was maintained after 3 days of culture (not shown). These results, although they need to be extended, suggested that this model could also be used to study the endocrine role of adipocytes in their tumor promoting effect. Figure 4Our 3D matrix model maintains intact and functional adipocytes in both lean and obese conditions. ( a) Maximum intensity projection of Z-stack acquired through confocal microscopy. Representative images of M-Ads obtained from normal weight or obese patients, after BODIPY® $\frac{493}{503}$ staining, cultured in fibrin matrix for the indicated time; Scale bar, 100 µm. ( b) Distribution of adipocyte diameter (µm) according to their culture condition over time in relative frequency in samples obtained from normal weight (NW) ($$n = 6$$) or obese (OB) patients ($$n = 3$$). ( c) Mean diameter of adipocytes (µm) obtained from NW ($$n = 6$$) or OB patients ($$n = 3$$) at different time point. ( d) Amount of glycerol released after 3 h in the presence or not (basal lipolysis) of isoprenaline (iso) in adipocytes embedded in gels for NW ($$n = 9$$) or OB ($$n = 6$$) patients. ( e) Similar experiments were performed with adipocytes cultured in 3D matrix for the indicated time isolated from NW ($$n = 16$$) or OB ($$n = 10$$) patients. ( f) Amount of glycerol released after 3 or 6 h in the presence or not (basal) of isoprenaline (iso) in adipocytes isolated from obese patients ($$n = 3$$). Histograms represent mean ± SEM, ns non- significant, *$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001.$
## The transfer of FFA between adipocytes and BC cells is increased in obesity
We then investigated if a transfer of FFA between adipocytes and tumor cells was observed using our 3D culture model. To demonstrate a direct transfer of FFA between M-Ads and breast tumor cells, we used a pulse-chase assay previously developed in our team33. We loaded M-Ads obtained from NW patients for 2 h with BODIPY FLC16 in suspension, and after being embedded in fibrin matrix, these adipocytes were cocultured for 2 days with cancer cells to monitor the transfer of this fluorescent FFA (Fig. 5a). Large fluorescent LD were detected in cancer cells cocultured with labeled adipocytes as compared to cancer cells grown alone (Fig. 5b). These results demonstrated that cancer cells were able to induce the release of FFA from these mature adipocytes grown in 3D as previously demonstrated using in vitro differentiated models4,9. Of note, the fibrin matrix remained macroscopically intact during the coculture. The integrity of adipocytes was also verified at the end of the coculture (data not shown). Fibrin matrices also have the advantage of being less sensitive to numerous proteases secreted by tumor cells compared to Matrigel or collagen-based matrix46.Figure 5The transfer of FFA between adipocytes and BC cells is increased in obesity. ( a) Experimental design: Isolated adipocytes (in yellow) were loaded for 2 h with BODIPY FLC16 (in green) and cocultured with cancer cells (in brown) for 2 days. ( b) Representative images of MDA-MB-231 cells cocultured or not (NC) with M-Ads previously loaded with BODIPY FLC16 (in green). Actin was labeled with rhodamine-phalloidin (red) and nuclei were labeled with DAPI (blue). The white box in left panels indicates a zoomed crop of this area that is presented in the middle panel. The right panel shows the staining for BODIPY FLC16 alone. ( c) Experimental design used for the 3D coculture followed by BODIPY® $\frac{493}{503}$ staining. ( d) Representative images of MDA-MB-231 cells cocultured or not (NC) for 3 days with adipocytes obtained from normal weight (Coc NW) or obese (Coc Ob) patients after staining with BODIPY® $\frac{493}{503}$, (neutral lipids, green), rhodamine-phalloidin (actin, red) and DAPI (nuclei, blue). The white box in left panels indicates a zoomed crop of this area that is presented in the middle panels. The right panel shows the staining for BODIPY® $\frac{493}{503}$ alone. ( e) Quantification of the droplet area per nucleus using ImageJ software in MDA-MB-231 cells cocultured (Coc) or not (NC) for 3 days with adipocytes obtained from NW ($$n = 10$$) or obese (OB) patients ($$n = 4$$). The histogram represents mean ± SEM *$P \leq 0.05$, **$P \leq 0.01.$
Since we previously demonstrated that hypertrophic adipocytes internalized less lipids than their smaller counterparts33, we could not use this pulse-chase assay to compare FFA transfer between M-Ads isolated from NW and obese patients. We therefore used a coculture system between cancer cells and M-Ads isolated from NW and obese patients during 3 days (Fig. 5c). After coculture, tumor cells accumulated numerous LD containing neutral lipids unlike non-cocultured cells and this effect was amplified with obesity (Fig. 5d). The surface area of LD was twice larger when tumor cells were cocultured with adipocytes from obese patients than NW (respectively 6.5 µm2/cell and 2.9 µm2/cell) (Fig. 5d,e). Increased lipid transfer in obesity did not result in an increase in the number of co-cultured compared to non-cocultured tumor cells (not shown). The effect on other aspects of tumor progression such as survival, migratory and invasive properties are under investigation. Taken together, our results showed that M-Ads cultured in a fibrin matrix released FFA that were taken up by tumor cells and that this effect was amplified with obesity.
## Conclusion
Obesity has been shown to negatively affect BC prognosis11,12. Yet, biological mechanisms underlying this effect are still largely unknown. An emerging hypothesis is that the transferred FFA between adipocytes and tumor cells may be quantitatively and qualitatively altered under obese conditions, therefore contributing to tumor progression4. To answer these questions, establishing new culture methods adapted to M-Ads obtained from both lean and obese patients is fundamental, as changes in lipid content (qualitative and quantitative) under obesity across different adipose tissues has been highlighted by recent studies19,30. Here, we showed that, like other isolated adipocytes such as SC-Ads, M-Ads rapidly die when grown in 2D, in contradiction with a recently published protocol23. To circumvent this issue, we set up a 3D culture model using fibrin matrix which allows homogeneous distribution of the embedded adipocytes and the preservation of their integrity for up to 5 days in both lean and obese conditions. One of the key findings of our study is that culturing M-Ads in physiological glucose concentration is mandatory to prevent lipogenesis during the time of culture, and preserve their lipolytic function. To date, most of the proposed systems have been using media containing at least 11 mM glucose24,26,27. By adding matrix embedded adipocytes into transwell inserts, we demonstrate the ability to coculture them with cancer cells (without altering the matrix) and highlight the presence of a metabolic crosstalk between these cells. In addition, our preliminary results indicate that this crosstalk is amplified by obesity. This coculture system will further allow us to study the nature of lipids liberated by M-Ads upon tumor secretions, and their impact on tumor aggressiveness in both lean and obese conditions. Given the important questions remaining about the role of the immune system in the obesity-driven BC progression14, this model could also be useful to study the metabolic crosstalk between adipocytes and immune cells including macrophages. As a conclusion, our model is a valuable translational new tool to study mechanisms underlying obesity–BC relationship that remains unclear.
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|
---
title: A sub-ppbv-level Acetone and Ethanol Quantum Cascade Laser Based Photoacoustic
Sensor – Characterization and Multi-Component Spectra Recording in Synthetic Breath
authors:
- Jonas Pangerl
- Elisabeth Moser
- Max Müller
- Stefan Weigl
- Simon Jobst
- Thomas Rück
- Rudolf Bierl
- Frank-Michael Matysik
journal: Photoacoustics
year: 2023
pmcid: PMC10033733
doi: 10.1016/j.pacs.2023.100473
license: CC BY 4.0
---
# A sub-ppbv-level Acetone and Ethanol Quantum Cascade Laser Based Photoacoustic Sensor – Characterization and Multi-Component Spectra Recording in Synthetic Breath
## Abstract
Trace gas analysis in breath is challenging due to the vast number of different components. We present a highly sensitive quantum cascade laser based photoacoustic setup for breath analysis. Scanning the range between 8263 and 8270 nm with a spectral resolution of 48 pm, we are able to quantify acetone and ethanol within a typical breath matrix containing water and CO2. We photoacoustically acquired spectra within this region of mid-infra-red light and prove that those spectra do not suffer from non-spectral interferences. The purely additive behavior of a breath sample spectrum was verified by comparing it with the independently acquired single component spectra using Pearson and Spearman correlation coefficients. A previously presented simulation approach is improved and an error attribution study is presented. With a 3σ detection limit of 6.5 ppbv in terms of ethanol and 250 pptv regarding acetone, our system is among the best performing presented so far.
## Introduction
The application of human breath analysis as a diagnostic tool is considered a major challenge. The ease of breath sampling and its non-invasive implementation is expected to reduce patient discomfort and thus enable higher patient compliance [1], [2], [3]. While there is a wide range of analytical techniques that have already been tested [1], [2], this work focuses on the laser-based sensing technique of photoacoustic spectroscopy (PAS). Compared to many other techniques, laser-based breath analysis has several advantages as it is very specific and enables fast or even real-time detection [4]. The monitoring of acetone in human breath is very desirable since acetone is a biomarker related with ketogenic diet [5], [6], [7] or acute decompensated heart failure [8]. Nevertheless, additional large-scale studies are needed to investigate the informative value of the respiratory gas composition for possible disease patterns as well as to further study the origin of acetone accumulation in human breath, as the current study situation is not conclusive [9], [10], [11], [12], [13]. A low-cost and sensitive acetone detection system could enable broad application of measurement systems and therefore large-scale studies.
PAS is a very sensitive trace gas analysis method and has great potential for miniaturization as no long absorption pathway is required [14], [15], [16]. Its application in breath analysis has been reviewed in [17], and with a focus on the near infra-red in [18], and on the mid infra-red (MIR) range in [19]. There are several wavelength regions which were explored for acetone detection covering the near infra-red (NIR) [20], [21], MIR [11], [22], [23], long-wavelength infrared [24], and even the ultra violet (UV) range [25], [26]. Viola et al. [ 27] and Holthoff et al. [ 28], for example, achieved an acetone limit of detection (LoD) of 110 ppbv (1σ) with 1 s averaging time and 555 ppbv (3σ) averaging over 3 s using an external cavity quantum cascade laser (EC-QCL) and a tunable quantum cascade laser (QCL) between 1015 and 1240 cm−1, respectively. Measuring acetone in breath of lung cancer patients, Mitrayana et al. [ 29] reported a LoD of 11 ppbv with a chopped CO2 laser. With a 1σ LoD of only 3 ppbv employing an EC-QCL Dunayevskiy et al. [ 30] obtained an excellent sensitivity. Within this work, we employed a QCL centered at 8266 nm (1209.8 cm−1). Centeno et al. [ 22] already identified ethanol to be a major interferant in this MIR region. We also investigated the influence of ethanol on our system, not only compensating for it but realizing a two-component monitoring apparatus within this spectral range of the QCL. Ethanol constitutes part of the human exhale matrix and can originate from the metabolism or through external factors like food and alcohol consumption or cleaning agents. Breath ethanol linearly correlates with blood ethanol concentration and is therefore used to assess driving ability. Together with other biomarkers, endogenous ethanol indicates the presence of the bacterium Clostridia, which is associated with pathogenic species that cause blood poisoning (e.g. tetanus) or liver diseases [31].
When a complex bulk matrix like human breath has to be analyzed, using a single specific wavelength is not sufficient, as spectral interference must be expected. To avoid incorrect concentration readings of a photoacoustic sensor, spectral PAS measurements at multiple wavelengths can be employed [11], [22], [32]. While Borisov et al. [ 32], [33] and Reyes-Reyes et al. [ 11] scanned over a large spectral range of hundreds of wavenumbers, we only investigated a very small range of 7 nm (< 1 cm−1), rather comparable to the work of Ciaffoni et al. [ 23].
A large number of biomarkers in the exhaled breath do not directly point to a distinct diagnosis of certain diseases. The occurrence of individual biomarkers rarely indicates a specific physical malfunction or disease. Most likely, several components in the exhaled air must be monitored for diagnosis. We present a QCL-PAS system to detect acetone and ethanol concentrations in a synthetic human breath2 matrix which is among the best performing photoacoustic acetone detection systems presented so far with a LoD (3σ) of 250 pptv with a Lock-In time constant of 10 s. The LoD (3σ) regarding ethanol is 6.5 ppbv. All measurements are carried out in the wavelength range of 8263–8270 nm with a spectral resolution of 48 pm. The QCL target wavelength was chosen upon the findings and simulattions in [38]. We further investigated typical acetone interferants, i.e., H2O, CO2 and ethanol, and verified the additive behavior of normalized single component spectra continuing our investigations presented in [34]. In addition, we improved the approach of spectra simulation published in [35].
## Photoacoustic Spectroscopy
PAS is based on the photoacoustic (PA) effect and was first discovered by Bell in 1880 [36]. It is based on classical absorption spectroscopy, i.e., molecules absorb photons of a certain wavelength. We apply amplitude modulated PAS with a 50 % laser duty cycle allowing relaxation of excited molecules during the QCL’s off-phase. The stored energy is released as a periodic heat input causing pressure fluctuations within the medium that can be recorded as a sound signal. Through matching the laser modulation frequency with the resonance frequency of the acoustic resonator integrated into the measuring cell, a resonance-enhanced photoacoustic signal pa is generated. Here, the photoacoustic pressure maximum of the first longitudinal mode fres of a tube-shaped resonator is detected by a microphone that is centered in the middle of the resonator r⃑mic as described in [37]. The signal can be calculated after[1]par⇀mic=γ-1Qfres2LRVR⏟CellconstantCcellεrelaxαP0 which is a product of cell constant Ccell, relaxation efficiency ϵrelax, absorption coefficient of the gas matrix α, i.e. the product of absorption cross section and analyte concentration, and the optical power P0 of the laser at the wavelength of analyte absorption. The cell constant in turn is a linear combination of decremented heat capacity ratio γ−1, the ratio of quality factor Q and resonance frequency fres as well as the ratio of resonator length LR and its volume VR normalized by 2 in terms of the first longitudinal mode of the photoacoustically generated acoustic wave. The interested reader can find a detailed derivation of the photoacoustic signal in [37], [38], [39].
## Measures of spectral similarity
By recording the photoacoustic signal at increasing high-level currents, i.e. different discrete wavelengths due to the QCL’s tuneability, a photoacoustic spectrum can be generated. A multitude of possible quantitative similarity measures have been used. Among the most popular are the root mean squared error (RMSE), the mean absolute error (MAE) or the mean squared error (MSE) [40]. But those measures highly depend on deviations in the intensity-axis and are therefore very sensitive to a shift in the background signal. The similarity measures employed in this work are the Pearson and Spearman Correlation Coefficient (PCC and SCC). The usage of those metrics in infrared and vibrational spectroscopy is rather uncommon, albeit not new [41], [42], [43], [44], [45]. For their computation each spectrum is regarded as an intensity vector, disregarding the wavenumber information.
The PCC is computed as given in Eq. [ 2]. It measures the linear relationship between two spectra. xi corresponds to the values of the intensity vector of the first spectrum, x® to the intensity mean, yi and y® correspond to the intensities and mean intensity of the second spectrum, respectively. A PCC value of +1 indicates a strong linear relationship, −1 an inverse linear relationship, a score of 0 no relationship. A p-value indicating the significance of the PCC can also be computed. The SCC’s computation is provided in Eq. [ 3], where di is the difference between the ranks of xi and yi in each intensity vector. n corresponds to the number of datapoints per vector. In comparison to the PCC, the SCC is non-parametric and computed between rank variables. This makes it in general more robust towards outliers. In this work we used SciPy’s implementation of PCC and SCC (version 1.9.0) [46].[2]PCC=∑ixi−x®yi−y®∑ixi−x®2∑iyi−y®2[3]SCC=1−6∑idi2nn2−1 The influence of different shapes and properties of spectra on PCC and SCC has been studied by Henschel et al. [ 41], [47] who conclude that the PCC is especially useful for spectra with only one very prominent feature, whereas the SCC generally achieves a higher score for multiple smaller spectral features. In addition, SCC profits from the removal of uninformative spectral regions.
Each metric has its own advantages and disadvantages. Here, the combination of SCC and PCC as a quantitative measure is preferred to typical RMSE as we expect a comparatively high systematic error in the background signal, which overly affects the RMSE measure. In addition, we underline our hypothesis with a qualitative visual analysis of our results.
## Non-spectral interferences
Non-spectral interferences, e.g. relaxation and attenuation effects impair the simulation and evaluation of photoacoustic spectra, as they are non-linear in nature. The photoacoustic signal generation is dependent on the photoacoustic relaxation efficiency (see Eq. [ 1]), which can differ for every transition and depends on the bulk matrix composition [48]. This is caused by the different molecular relaxation pathways available. The inclined reader can find additional information on this challenge in [17]. To tackle the influence of changes in relaxation efficiency three different approaches exist. One is to characterize and compensate the gas matrix variations via measurements [37], [49], the other is a fully data driven approach, which applies machine learning to counter those effects building on a vast number of measurements [50], and at last a purely analytical modeling backed up by precise measurements [48]. All those approaches have their unique advantages and disadvantages. For purely analytical approaches vast literature on the gas composition in question is required, which is not available in the case of acetone and ethanol. A purely data driven approach on the other hand requires a large number of measurements to create a machine learning model. With our measurement setup the acquisition of a single spectrum takes around 3 h which renders this approach hardly feasible. To cope with those disadvantages, we apply a combination of a precise system characterization and simulation. Since the characterization of our system suggests a linear, additive behavior of the PAS signal, we apply a simplified modeling approach. We assume the photoacoustic relaxation efficiency of each infra-red (IR)-active molecule within our laser emission range to be constant and not affected by changes in the typical breath gas matrix.
Additionally, changes in the system’s resonance frequency due to bulk matrix changes can pose a problem. The system is typically specified at its resonance frequency, which changes with the speed of sound, which in turn is dependent on gas pressure, temperature, and gas matrix composition [51]. The system proposed within this work accounts for those effects by verifying the resonance frequency before and after each measurement, which will be further explained in Section 3.
## Synthetic spectra
Synthetic spectra are very desirable in spectroscopy as they allow a low-cost data generation, which can be used to train and develop machine learning based algorithms. The feasibility of a gas detection system trained solely on synthetic data was shown by Goldschmidt et al. [ 52] for dual-comb spectroscopy of N2O and CO. Zifarelli et al. [ 53] use a measured dataset enriched with synthetic data to detect N2O and CO with an addition of C2H2 using a quartz-enhanced PAS (QEPAS) system. They apply a linear combination of their wavelength modulated measurements to augment their dataset as their spectra do not suffer from a strong background. The simulation approach reported earlier [35] and used within this work allows for the simulation of amplitude modulated synthetic PAS spectra.
## Experimental
A distributed feedback (DFB) QCL from AdTech (AdTech Photonics Inc., US) together with a laser driver LDC 3736 from ILX Lightwave (Newport Corporation, US) was used for excitation between 8263 and 8270 nm (1210–1209 cm−1). A square modulated step signal (fmod≈5 kHz) provided by a frequency generator (model 33522B, Keysight Technologies, US) with a high-level amplitude between 350 and 495 mA (step width 1 mA) and a low-level current slightly below the QCL’s threshold at 250 mA was supplied to the LDC. The laser temperature was maintained constant at 23 °C. By increasing the high-level current the wavelength increased, yielding a spectral resolution of approx. 48 pm within the tuned range of approx. 7 nm (1 cm−1).3 Along with this wavelength shift, increasing the QCL current affects the optical power P0 and, thus, the photoacoustic amplitude (see Eq. [ 1]) showing a second-order polynomial behaviour. Hence, it is necessary to compensate for this influence in view of the recorded spectra measurement series. As the photoacoustic background signal4 linearly correlates with the optical power, the scaling was carried out by taking the ratio of measured values to the respective background signal values, which were normalized to 1, i.e.,[4]UPA,scaleI=UPA,measIbscaleIwithbscaleI=UPA,BSIUPA,BS495mAandbscaleI∈(0;1] UPA,scale(I) is the scaled photoacoustic magnitude in µV, UPA,meas(I) the measured photoacoustic magnitude and bscale(I) the scaling coefficient calculated by the ratio of photoacoustic background signal at any high level current UPA,BSI and the background signal of the maximum high level current UPA,BS495mA (see blue dashed line, Fig. 1a). After this scaling process, every measurement point within the recorded spectrum is decoupled from the optical power. Nevertheless, the sensitivity drops with the laser current, as the PA signal voltage is simply multiplied by an individual scaling factor. Fig. 1a exemplary shows a measured water spectrum diluted in synthetic air (SA) illustrated by the black solid line. This spectrum follows the blue dashed scaling curve, which is nothing else than the background signal. Its shape is caused by the change in optical power. Dividing each point of the measured spectra with bscaleI yields the scaled spectrum (red solid line) which is now uncoupled from the optical power and the background signal, respectively. Fig. 1b compares the scaled H2O-sepctrum from (a) with the water vapor absorption cross-section provided by HITRAN verifying our measurement and scaling methods. Fig. 1Scaling of measured spectra employing the background signal shape. In (a) the dashed lines correspond to the right y-axis. In (b) the scaled spectra of 1.24 %v H2O and the HITRAN absorption cross-section is visualized showing spectral concordance. Deviations in the valleys may result from fitting errors as HITRAN applies a Voigt-profile. Fig. 1 The photoacoustic signal UPA,meas(I) detected by a microphone ICS-40720 (ICS-40720, InvenSens Inc., US) was lock-in-amplified (LIA) by an Ametek 7270 (Ametek, US) and then recorded by an in-house developed LabVIEW-based software. The photoacoutic cell (PAC) was temperature controlled at 35 °C to prevent condensation. The laboratory setup including gas delivery system as well as the PAC design have already been described in [37]. All test gases were provided by Westfahlen AG (Münster, Germany). The purity of the dilution gas synthetic air was specified with less than 0.1 ppmv of hydrocarbons. The test gases are summarized in Table 1.Table 1All gas mixtures used during the experiments excluding pure synthetic air. Westfahlen AG provided the analyte concentrations and accuracies. Table 1Analyte gasBuffer gasConcentration of analyte in ppmvSpecified Accuracy in %AcetoneSA9.8±5AcetoneSA1.2±10EthanolSA30.5±5CO2SA200,000±2 The QCL measurement setup is shown in Fig. 2. Here, the QCL was mounted and cooled by the combination of a heat sink and an external fan to prevent the active zone from thermal destruction. Since the mounting package of the QCL already integrates a lens, no further optics are required to illuminate the PAC. The PAC was mounted on adjustable stages to optimally align the beam through the resonator, which has a diameter of 4 mm at a length of 31 mm. Using four linear stages and one rotational stage, the measuring cell and hence the resonator could be positioned and adjusted to any degree of freedom. By means of D1 and D2 the movement in the y-direction, as well as a rotation around the z-axis, could be achieved through identical or asymmetrical turning of the adjusters. This setup allows optimal alignment of the QCL-beam through the PAC without reflections at resonator walls yielding a low and stable background signal and a high sensitivity, respectively. Fig. 2Measurement setup: The QCL combined with an active cooling system is fixed, whereas the photoacoustic measuring cell is adjustable in three translational (x, y, z) (Letters B, C, D1, D2) and two rotational directions. ( β, γ) (Letter A, D1, D2).Fig. 2
Additionally, we applied an acoustic resonance monitoring system (ARMS) to the PAC as explained in [54]. This ARMS is based on a speaker sweep approach yielding resonance frequency and the Q-factor determination. Such a speaker sweep was applied before and after each recorded spectrum to ensure that each measurement is performed at resonance frequency.
## Results and discussion
In this work, we present the performance of a QCL photoacoustic setup for the detection of low concentrations of acetone and ethanol diluted in SA. Besides, we discuss collecting single and sum spectra with and without the analytes acetone and ethanol in the region between 8263 and 8270 nm in a typical breath matrix at ambient pressure, i.e. SA, water and carbon dioxide. The temperature of the PAC was controlled at 35 °C, the mass flow was set to 500 ml/min and the LIA roll off to 18 dB/octave, respectively. Unless mentioned otherwise, the recorded microphone signal was post-processed by (i) demodulation and low pass filtering with a time constant of τLIA=5s, (ii) data sampling with 5 Hz and (iii) averaging of the recorded points over 16 s, i.e., 80 measuring points. Finally, we report on the synthetic data generation. Here we present a comprehensive model analysis, investigating the error contribution within our system and report more on the deviations with respect to ethanol.
## Characterization in synthetic air
The presented calibration characteristics are based on decremental analyte concentration sweeps, i.e. prediluted analyte concentrations are further diluted in SA, while the PA signal corresponding to a particular analyte concentration results from 20 s data averaging with a sampling rate of 5 Hz. The acetone concentration was varied between 9 ppmv and 1 ppmv (higher concentrated gas tank) and from 1000 ppbv to 200 ppbv (lower concentrated gas tank). The ethanol was swept from 25 ppmv to 2.5 ppmv. The measurements were carried out at ambient pressure. After the lowest set analyte concentration pure SA was applied for verifying the background signal amplitude and for determining the noise level. According to theory (refer to Eq. [ 1]), the photoacoustic signal is directly proportional to the number of excited molecules and thus the concentration, yielding a linear calibration characteristic. As we applied amplitude-modulated PAS, there is also a background signal in the absence of any analyte due to losses and reflections at windows and tolerances in resonator geometry and its surface quality. Table 2 summarizes some calibration and performance characteristics of our photoacoustic sensor for the analytes acetone and ethanol measured at a high-level current of 490 mA (1209.22 cm−1), namely sensitivity, linearity (R²), detection limit LoD (3σ), noise level 3σ, as well as the normalized noise equivalent absorption coefficient (NNEA), which is determined after [55].[5]NNEA=LoDNAσ(ν∼Ph)P0SNR∆fVmolTable 2Summary of calibration characteristics for acetone and ethanol at a LIA time constant of τLIA= 5 s and 10 s.Table 2AnalyteτLIA in sSensitivity in µV/ppmvR2LoD (3σ) in ppbvNoise level (3σ) in nVNNEA in W cm−1Hz−0.5Acetone564.10.999961.4593.21.31E-9100.2515.93.27E-10Ethanol57.80.9999611.9133.21.28E-9106.4750.59.77E-10 In Eq. [ 5], LoD is the detection limit determined at SNR-times the standard deviation, NA is the Avogadro constant, *Vmol is* the molar volume at the prevailing temperature and pressure, σ(ν~Ph)is the absorption cross-section of the analyte at wavenumber ν~Ph and ∆f is the equivalent noise bandwidth (ENBW) of the LIA. The SNR equals 3 since we used three times the standard deviation for calculating the LoD. The Allan Deviation plot in Fig. 3 illustrates the LoD improvements for both analytes and two different time constants of 5 s and 10 s if the time for averaging the data is extended. The 1f characteristic of LoD improvement indicates white noise to dominate the background signal. With an LoD(3σ) of 250 pptv and an NNEA of 3.3E-10 Wcm−1Hz−0.5 the presented photoacoustic acetone detection system is among the best performing presented so far. Table 3 summarizes the characteristics and the performance of photoacoustic acetone sensors. The table indicates that we even outperform powerful CO2-Lasers. Note that this representative selection does not claim completeness. Fig. 3Allan Deviation plots for LIA time constant 5 s and 10 s regarding the progress of the 3σ detection limits of acetone and ethanol. The dash-dot lines indicate the measured LoDs at 20 s averaging time with a sample rate of 5 Hz. Fig. 3Table 3Summary of different photoacoustic acetone sensor approaches with their respective LoD. n.a. = not available, QEPAS = Quartz-Enhanced PhotoAcoustic Spectroscopy, CEPAS = Cantilever-Enhanced PhotoAcoustic Spectroscopy. Table 3ReferenceMethodLightsource @ Wavelength, mean optical powerLoD in ppbvIntegration time in sThis workPASQCL @ 8266 nm, 80 mW0.2510Tyas et al. [ 56]PASCO2-Laser @ 9000 to 11,000 nm, 50 W30 (n.a.)(n.a.)Mitrayana et al. [ 29]PASCO2-Laser @ 9000 to 11,000 nm, (n.a)11 (1σ)(n.a.)Dunayevskiy et al. [ 30]PASEC-QCL @ 7150–7500 nm, > 200 mW3 (1σ)(n.a)Viola et al. [ 27]QEPASEC-QCL @ 7100 to 8500 nm, > 400 mW110 (1σ)1Holthoff et al. [ 28]PASQCL @ 8064 to 9852 nm, 1.3 mW (1.1 % duty cycle)555 (3σ)3Suchánek et al. [ 57]CEPASCO2-Laser @ 9000 to 11,000 nm, 0.17 – 1 W24,800 (3σ)0.3Weigl et al. [ 58]PASUV-LED @ 278 nm, 50 mW19.6 (3σ)10
## Sum spectra generation (with and without analyte)
The characteristics of different photoacoustically recorded spectra are investigated with respect to their behavior compared to classical absorption spectra. This involves recording spectra of only a single IR-active component (single spectra) as well as cumulative spectra consisting of various components, so-called sum spectra. Therefore, several spectra measurements were carried out based on different gas configurations of exhaled breath components. These cover measurement series with various concentrations of the analytes acetone and ethanol diluted in SA, CO2, and H2O focusing on the analyte acetone. Fig. 4 shows the scaled photoacoustic spectra of the analytes, i.e. 1.9 ppmv acetone (a) and 7.9 ppmv ethanol (b). Regarding acetone, an almost constant but slightly decreasing absorption can be observed over the full laser range, whereas a clear peak is observed in the range between 1209.7 and 1210.2 cm−1 in the case of ethanol measurements. Fig. 4Photoacoustic spectra of 1.9 ppmv acetone (a) and 7.9 ppmv ethanol (b) together with the respective HITRAN absorption cross-section. The spectra include a constant photoacoustic background signal which is 36 µV.Fig. 4
## Spectra recording with water and acetone mixtures diluted in synthetic air
This section deals with the spectra recording of humidified gas samples with and without the analyte acetone. An abridged version of this topic was already published in [34]. As water is IR-active in a broad spectral range, we also found two peaks within our QCL emission range, i.e., 1209.25 cm−1 and 1209.77 cm−1. Fig. 5a presents a six spectra measurement series. The single spectra for two water concentrations, 0.9 %v and 1.2 %v, and for the acetone concentration of 3.9 ppmv are shown by the graphs IV, V, and I, respectively. Besides, the sum spectra for the mixtures of acetone and both water concentrations are illustrated by the graphs II and III as well as the background signal (graph VI) which equals a straight line due to normalization. Fig. 5b shows again a detailed view of the graphs I, II and III from 5a and furthermore the resulting graphs obtained from the manual addition of the respective single spectra, i.e. I + IV and I + V. The comparison of the measured and calculated sum spectra shows an almost congruent progression with only marginal deviations, which can be attributed to measurement inaccuracies and artifacts. To quantitatively analyse this additive characteristic of spectra, the linear correlations after Pearson and Spearman (PCC/SCC) were calculated, which reached values of $\frac{0.9831}{0.9620}$ and $\frac{0.9855}{0.9823}$ for 0.9 % and 1.2 %v-H2O, respectively. Those values are considered to indicate very good correlation. Since the multiplicative property of acetone concentration was already proven within the concentration sweep in section 3.1, the same behaviour in terms of water was verified by measuring two different water concentrations. Linearity was identified, investigating the respective spectra IV and V with respect to the background signal VI. This yielded a sensitivity of 42.7 µV/%v-H2O and an R2 of 0.9999 calculated at a high-level current of 485 mA, i.e., the local maximum of the water peak. Hence, both additive and multiplicative characteristics of single and sum spectra could be verified among measurements with acetone and water diluted in SA. This implies a constant relaxation efficiency for both single and multicomponent PAS, allowing the measured curves to be considered as real absorption spectra. Fig. 5Photoacoustic spectra for different acetone and water concentrations diluted in synthetic air. ( a) illustrates single spectra of one acetone (I) and two water concentrations (IV, V) as well as the measured sum spectra of two humidified acetone matrices (II, III) and the background signal (VI). In (b) the manual spectra addition (VII, VIII) of the single spectra of (a) are represented by the solid lines demonstrating an almost congruent progression to the measured sum spectra. Fig. 5 This finding allows for a straightforward separation of the two components when applying linear regression. For this approach two measurement points were taken from the constructed spectrum, one placed at a water absorption peak at 1209.31 cm−1 and one at 1209.52 cm−1 where only weak absorption from water was present. A regression fitted on the three single component measurements and the constant background (Fig. 5: I, IV, V, VI) was used to estimate the concentration of the two multi-component measurements. The regression predicted 0.96 %v and 1.21 %v-H2O (set values were 0.89 %v and 1.24 %v-H2O) and 3.92 and 3.91 ppmv acetone (set values were 3.92 ppmv acetone each) for the two measurements (Fig. 5: II, III). This prediction from only two measurement points shows how the spectral interference of water can be compensated for while at the same time quantifying the water concentration of the sample. Hence, the analyte detection is very precise with overlapping water absorption bands with a mean average percentage error (MAPE) of only 0.26 %.
## The impact of CO2 on spectra recordings of acetone and humidified acetone mixtures
Besides water, carbon dioxide is also present in the exhaled breath in the percentage range. This section therefore discusses the influence of CO2 on the photoacoustic spectra recordings originating from gas mixtures with and without humidity and analyte, respectively. In accordance with HITRAN database, no CO2 absorption was found experimentally within our region of laser emission. Accordingly, the shape of the spectrum does not change due to CO2 absorption. However, CO2 influences the speed of sound and therefore the resonance frequency as well as the Q-factor and the adiabatic exponent, which cause the cell constant and therefore the photoacoustic amplitude to decrease with increasing CO2 concentration in dry mixtures. Once water is added to a mixture containing CO2, this effect disappears, i.e. the amplitude is no longer decreased by CO2 addition.5 Table 4 lists measured and calculated values affecting the cell constant for different mixtures of CO2, H2O, and acetone. Since CO2 causes the resonance frequency to decrease by approx. 15.0 Hz/ %v-CO2 and water to increase fres only by approx. 5.4 Hz/%v-H2O, mixtures containing both species show a resonance frequency below that of mixtures without CO2. Nevertheless, the influence of the Q-factor predominatess (1 %/%v-CO2 in dry mixtures) compared to the change in resonance frequency. The change in the adiabatic exponent is also less significant. The relative change in cell constant calculated in the right column of Table 4 is referred to the background signal cell constant without analyte, CO2, and water (first line in Table 4) and provides a compensation factor with respect to the influence of water and CO2. Therefore, the photoacoustic amplitudes of dry measurements containing CO2 were compensated after[6]UPA,comp=UPA11-XCO2bCO2with UPA,comp being the compensated photoacoustic amplitude, XCO2 the CO2 concentration in %v and bCO2 the compensation factor for dry CO2-measruements which was determined to 0.83 %/%v-CO2 with R2 = 0.9971. Acetone as well as ethanol do not affect fres, Q or γ as these analytes are only present in trace concentrations. Table 4Impact of CO2 and H2O on fres, Q and γ (with and without acetone). Values labelled in bold correspond to calculations, the others are based on experiments. The line marked with an (*) belongs to a typical synthetic breath mixture containing 500 ppbv acetone. Table 4CO2 in %vH2O in %vAcetone in ppmvfres in HzQγΔCcellin %000504937.81.3995reference00.890505337.91.39890.0301.240505638.01.39860.01003.92504937.81.3995-0.0500.893.92505337.91.3989-0.0401.243.92505738.01.39870.04300500337.01.3961-2.3330.890500538.21.39540.7731.240500938.21.39520.69301.96500437.01.3961-2.2730.891.96500538.01.39540.2831.241.96500838.01.39520.29500497136.21.3937-4.1950.890497238.21.39310.9851.240497538.11.39280.62501.96497236.21.3938-4.1150.891.96497538.21.39310.7951.241.96497738.11.39290.40* 41.50.5500137.81.39390.08
## Spectra recording of synthetic breath
This chapter finally deals with spectra recording of all components we are currently considering with regard to breath analysis, i.e., the two analytes, acetone and ethanol, and the main components of exhaled breath, namely water, carbon dioxide, nitrogen and oxygen. Although a typical breath sample contains less analyte, we set the ethanol concentration to 7.9 ppmv, as otherwise no noticeable peak could apparently be seen in Fig. 6. Fig. 6 displays single and sum spectra of 7.9 ppmv ethanol, 2.0 ppmv acetone, 0.9 %v-H2O, and 3.0 %v-CO2 diluted in SA, respectively. As CO2 does not absorb within the laser emission spectrum, it does not have any spectral influence. The ethanol spectrum (I) shows a baseline of about 100 µV with a visible peak at approx. 1210.0 cm−1. Manual addition of (I) and the water spectrum (II) yields the sum spectra (V) of analyte and water, which was experimentally validated in (IV). The addition of the analyte spectra (I) and (III) results in the sum spectra achieved by manual addition (VII), experimentally validated in (VI). Finally, the empirically obtained sum spectrum of synthetic breath as a whole is represented by (VIII), while manual addition of individual spectra is illustrated by (IX). Pearson and Spearman correlation coefficients of measured and calculated sum spectra are summarized in Table 5. With scores well above 0.8 they are considered to match very well according to [45]. The comparatively high difference between PCC and SCC in case of the spectra VI and VII is comprehensible considering the visualized spectrum. It is still within expected bounds and the difference stems from the different spectral features both scores represent. Fig. 6Photoacoustic spectra for ethanol and acetone together with water and CO2 diluted in synthetic air. Single spectra of ethanol, water and acetone are illustrated by I, II and III, the others are sum spectra and manual additions. Measured spectra are represented by solid lines, manual spectra addition with identical gas matrices are visualized by the dashed lines. Fig. 6Table 5Pearson and Spearman correlation values of measured and calculated sum spectra. Table 5Gas mixturePCCSCCIV, V0.9130.906VI, VII0.9520.841VIII, IX0.9690.968 Again, both additive and multiplicative characteristics of single and sum spectra could be verified among these measurements with synthetic breath which reinforces the assumption of constant relaxation efficiency. In terms of spectral reconstruction, i.e., both a quantification and separation of a measured sum spectrum into its individual components, the CO2 content can be determined via the measured resonance frequency fres,meas obtained by the ARMS (refer to [54]). As the present humidity content is continuously monitored by a pTH-sensor (BME 680, Robert Bosch GmbH, Germany) within the measuring cell and the resonance shift coefficients of water and CO2 were specified above, the present CO2 content [XCO2] can be recalculated via[7]XCO2=fres,BS−fres,meas−XH2OκH2OκH2Owhere fres,BS is the resonance frequency of the background signal (5049 Hz), [XH2O] the measured water concentration, κH2O the change in resonance frequency through water addition (5.4 Hz/%v- H2O) and κCO2 the frequency coefficient regarding CO2 (15.0 Hz/%v-CO2), respectively. This method yields a LoD(3σ) of 1.19 %v-CO2.
Altogether, about 30 spectra with different gas configurations were recorded. Combined with the findings that the addition of bulk components affects the photoacoustic spectra linearly, we proved our spectra not being influenced by relaxation effects. All this data, together with literature and simulation inputs, provide a data basis for a machine learning model that can quantify the individual components from the measured sum spectra similar to the linear regression presented in section 3.2.1. Additional research and effort extending from linear regression is needed, as the differentiation between ethanol and acetone in this spectral region proves more difficult due to their flat absorption profiles.
## Spectral simulation
The analysis of our system shows a linear, additive behaviour of the investigated spectral components during characterization. This allows for the use of a simple simulation system to further investigate our results. The simulation amounts to applying an intricate instrument function, as well as scaling and an additive background signal to a summation of the absorption spectra of the components, which are available in spectral databases. A basic version of this simulation has already been presented in [35] and is visualized in Fig. 7. The basis of the simulation are absorption spectra from literature, either simulated from the HITRAN line database [59], [60] as for CO2 and H2O, or measured spectra from the PNNL database [61] as for acetone and ethanol. Those are adapted for the prospective concentration and subsequently summed. As an instrument function the laser output spectrum is convoluted with the absorption spectrum. This process is repeated for every high-level current value, the current supplied to the laser during the on phase. The resulting PAS spectrum is scaled, and the background signal magnitude is added.[8]paI=qIPν~I*∑cAν~c+bIFig. 7Partial workflow of the simulation algorithm. The scaled absorption spectra from HITRAN or PNNL databases (a) are summed (b). The QCL output spectrum of a certain high-level current (c) is multiplied with the summed spectra (d) and the area under the curve is computed (e). This process is repeated for each high-level current and scaled with the factor q(I); the background b(I) is added to create the actual output spectrum of the system (not shown).Fig. 7 The model equation is presented in Eq. [ 8]. Upper-case letters indicate vectors. pa(I) is the measured photoacoustic magnitude at high-level current I, qI is the scaling factor which represents a combination of photoacoustic parameters like optical power and the Q-factor. Pν~(I) represents the optical output power of the laser at high-level current I. This vector is convoluted with the summed and scaled absorption spectra. Aν~c is again a vector representing the simulated absorption spectrum of each component for the expected concentration. Finally, bI constitutes the background signal magnitude for each high-level current I.
Only two single molecule spectra for acetone, ethanol, carbon dioxide, water, and two background measurements were used to create this simulation, which amounts to a total of 10 measurements. Within this work, a more intricate analysis of the system than the one presented in [35] is provided and suggests improvements to the simulation algorithm to account for systematic errors we discovered regarding ethanol.
## Model analysis - Error contribution
The pursued modelling approach allows for a detailed error analysis and error attribution. We present an in-depth analysis of the model presented in [35] with a quantitative error analysis and attribution study. We selected three quantities of interest for further analysis: The background signal, the amplification factor and the QCL transfer function. For those three quantities we investigated a constant component and a component that scaled linearly with the supplied high-level current. Those correspond to the original fitting parameters c2, c1, d1, d0, b5, and b4 [27, [1], [2], [3]]. There were more parameters applicable to the simulation, i.e. a quadratic background component, which were not analysed further. We computed the variance of those parameters from the covariance matrix and confirmed the feasibility of those estimates using an affine-invariant ensemble sampler for Markov Chain Monte Carlo (EMCEE) [62]. For this computation 28 measurements were used, including the 10 measurements for model creation. The results are presented in Table 6.Table 6Relative error of selected model parameters. All other parameters remained fixed during computation. Table 6ParameterRelative VarianceTransfer function (linear)0.18016Transfer function (constant)2.0677E-05Background signal (linear)0.015121Background signal (constant)0.028315Amplification factor (linear)0.021535Amplification factor (constant)0.010959 The resulting relative errors were mostly between 1 % and 3 % which corresponds to a well-defined model. Only the parameter associated with the linear part of the QCL transfer function showed a very high error of 18 %. The constant background function parameter is constant for every high-level current within one simulation and thus corresponded to a horizontal baseline signal for a non-normalized signal. Whereas the linear background function parameter corresponded to a parameter which scaled linearly with the high-level current within one simulation and thus corresponds to a linear baseline for a non-normalized signal. The transfer function parameters on the other hand can be understood as the position of the output peak at a certain high-level current. It was modelled including a quadratic component, but the contribution was only computed for the constant and linear part. The EMCEE showed a high uncertainty of the linear parameter and high correlation with the other transfer function parameter, which probably caused the high relative error. This was expected as the optical QCL output spectra acquired with a spectrum analyzer (BRI-771-B, Bristol Instruments Inc., US) and used to generate the model had an unsatisfying resolution. Even though the other relative errors were small, the constant background error of 2.8 % corresponds to a variation in the background baseline of +/- 1.6 µV, which justified our choice of SCC and PCC over the RMSE as error measure. The low error of the amplification parameter confirmed that measurements were performed at resonance frequency. The visible changes can be linked to the influence of CO2 and water on the quality factor of the cell. The model is deemed acceptable for further investigation with the highest improvement potential in the laser transfer function.
## Model analysis - Systematic error with ethanol
One main advantage of a system to model photoacoustic spectra from theoretical absorption spectra is that it allows for an intricate analysis of the system, especially in the case of discrepancies between theory and measurement. We discovered some deviations with the modelling of ethanol in our case, regarding the signal height as well as its shape and have analysed their origin.
We noted a systematic discrepancy between our modelled ethanol spectra and the measured spectra. This observation was made in the validation spectra as well as in the spectra used for model creation. In Fig. 8 the residual error is visualized dependent on the supplied high-level current (red line). We noted a systematic error in the overall simulated signal height which shows in the shift on the y-axis over all high-level currents. We traced this error back to a comparatively high concentration error in the supplied ethanol gas tank, which only contained 96.5 % of the assigned concentration. This was still in the bounds of the margins specified by the supplier (+/−5 %) but led to noticeable errors. Within the model this can be resolved either by readapting the model creation process with corrected concentration values, or as was done within this work by applying a factor of 0.965 to the ethanol base spectrum Aν~c.Fig. 8Residual error (simulation-measurement) of the absorption spectra of ethanol. Three ethanol measurements were normalized to 1 ppmv ethanol concentration, and their mean was taken. Fig. 8 *After this* correction a discrepancy in the shape remained, which shows in Fig. 8 as a pronounced valley from 360 to 420 mA. This error can be mainly attributed to differences of the ethanol spectral shape between our measurement and PNNL spectra in the region from 8261 to 8263 nm, which influences the signal in this current range. We additionally verified that the errors did not stem from QCL output simulation and the background signal. We decided to correct this error via an adaption of the underlying ethanol base spectrum, which has been taken from PNNL [61]. We numerically adapted the PNNL base spectrum between 8260 and 8264 nm. These two adaptions reduced the MAE of the normalized ethanol spectra from 46 nV to 1.5 nV, which improved the model substantially. This results in an improved modelling of the measurements compared to the original simulation presented in [35].
## Conclusion
We developed a highly sensitive photoacoustic system for detecting the biomarkers acetone and ethanol in SA and synthetic breath bulk mixtures, respectively. With a 3σ detection limit of 250 pptv for photoacoustic acetone detection we achieved, to the best of our knowledge, the lowest LoD reported so far. The ethanol limit of detection was 6.5 ppbv. Besides, we measured approx. 30 photoacoustic spectra with different gas matrixes to compare additive and multiplicative properties of those spectra to classical absorption spectra. We found our system did not suffer from any non-linear or non-spectral interferences. The PCC and SCC values for the addition of water to an acetone mixture diluted in SA proved a very good consistency of measured sum spectra and manual addition of the single spectra. The exact shape of the water peaks at 1209.25 cm−1 and 1209.77 cm−1 can be incorporated to compensate the recorded sum spectra and correctly quantify a humidified sample. Adding CO2 to a synthetic breath sample does not affect the absorption spectra and hence not the photoacoustic spectra within our QCL emission range. Therefore, the actual CO2 content can be calculated on behalf of measured coefficients for frequency detuning resulting from major buffer gas changes induced by water and CO2. Knowing the water concentration from an integrated humidity sensor to the photoacoustic measuring cell, the CO2 concentration can easily be calculated via the frequency detuning coefficients for water (+5.4 Hz/%v-H2O) and CO2 (−15.0 Hz/%-CO2). A photoacoustic signal deterioration in terms of a dry CO2 containing breath sample is not relevant for practice since already a minor humidified sample completely compensates this effect. Mixing all components of our in-lab synthetic breath, i.e., the two analytes acetone and ethanol combined with the main components of exhaled breath, namely water, carbon dioxide, nitrogen, and oxygen we again identified a linear behaviour of measured sum spectra versus the manual addition of measured single spectra. PCC and SCC verified our photoacoustic spectra recordings and computations. In consequence, these data can be used as a basis for a simulation approach. The previously proposed simulation approach was verified towards those measurements and an error attribution study was presented. The relative variance of the investigated parameters used during simulation was below 3 %. Only a parameter corresponding to laser transfer function showed a higher variance. This higher variance can be attributed to insufficient resolution in the data used to generate the model and high correlation between the three parameters used to simulate the transfer function. The proposed modelling approach was adapted with respect to major discrepancies of the ethanol base spectrum. The simulation approach verified by this study can serve as the basis towards a machine learning approach to identify and quantify an unknown breath sample of our analytes as well as CO2 and water concentration.
## Funding
Essential financial support for this work has been provided within the scope of the project BreathSens funded by the German Ministry of Education and Research (BMBF) with founding code 13GW0325C as well as the project PreSEDA funded by the German Federal Ministry for Economics and Climate Action (BMWK) with funding code 03EN2028A. Besides, funding was received by the BayWISS-Health and BayWISS Digitalization network financed by the Bavarian Ministry of Research and Arts. Additionally, two of the authors are funded by a PhD scholarship of the Studienstiftung des Deutschen Volkes and the Hanns-Seidel Stiftung. One author was furthermore funded by the Marianne-Plehn-Programm of the Elitenetzwerk Bayern funded by the Bavarian Ministry of Research and Arts.
## CRediT authorship contribution statement
Jonas Pangerl: Conceptualization, Data curation, Investigation, Methodology, Visualization, Writing original draft, Project administration, Writing – review & editing. Elisabeth Moser: Conceptualization, Data curation, Investigation, Methodology, Software, Writing original draft, Writing – review & editing. Max Müller: Conceptualization, Methodology, Validation, Writing – review & editing. Stefan Weigl: Conceptualization, Methodology, Validation, Funding Acquisition, Writing – review & editing. Simon Jobst: Conceptualization, Methodology, Validation, Writing – review & editing. Thomas Rück: Conceptualization, Methodology, Validation, Writing – review & editing. Rudolf Bierl: Funding acquisition, Project administration, Supervision, Validation, Writing – review & editing. Frank-Michael Matysik: Conceptualization, Methodology, Supervision, Validation, Writing – review & editing.
## Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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|
---
title: 'Second-hand smoke exposure in adolescents in Latin America and the Caribbean:
a pooled analysis'
authors:
- Antonio Bernabe-Ortiz
- Rodrigo M. Carrillo-Larco
journal: Lancet Regional Health - Americas
year: 2023
pmcid: PMC10033735
doi: 10.1016/j.lana.2023.100478
license: CC BY 4.0
---
# Second-hand smoke exposure in adolescents in Latin America and the Caribbean: a pooled analysis
## Body
Research in contextEvidence before this studyCurrent worldwide evidence based on a literature search in PubMed (August 1st, 2022) with the following search strategy: “second-hand smoking” AND “survey” and “adolescents”, signals that, according to pooled data of the Global Youth Tobacco Survey, $62.9\%$ of adolescents, aged between 12 and 16 years, has had any exposure to second-hand smoking during last seven days, whereas $32.5\%$ reported a daily exposure. Second-hand smoking has been steadily growing in the Latin America and the Caribbean region. As a result, a more detailed analysis is needed in our region to appropriately implement and improve existing strategies. Added value of this studyOur study expands existing literature in the region by updating prevalence results of second-hand smoking by country and by sex, but also among never smoker adolescents. Despite of the results variability and the implementation of the Framework Convention on Tobacco Control in several countries of the region, the prevalence of second-hand smoke exposure is high among adolescents, especially among females and never smokers. Implications of all the available evidenceOur results highlight the need to strengthen policies to tackle the problem of second-hand smoking. Future research in the region should focus on defining the origin of such exposure (home, school, elsewhere) to implement appropriate strategies.
## Summary
### Background
Second-hand smoke exposure is prevalent amongst adolescents, despite of being a preventable risk factor associated with unfavourable outcomes. The distribution of this risk factor varies by underlying determinants and public health officers need contemporary evidence to update policies. Using the most recent data available from adolescents in Latin America and the Caribbean (LAC), we described the prevalence of second-hand smoking.
### Methods
Pooled analysis of Global School-based Student Health (GSHS) surveys conducted from 2010 to 2018 was conducted. Two indicators were analysed based on information from the 7 days prior to the survey: a) any exposure to second-hand smoking (0 vs ≥1 days of exposure); and b) daily exposure (<7 vs 7 days). Prevalence estimates were carried out accounting for the complex survey design, and reported overall, by country, by sex, and by subregion.
### Findings
GSHS surveys were administered in 18 countries, yielding a total of 95,805 subjects. Pooled age-standardised prevalence of second-hand smoking was $60.9\%$ ($95\%$ CI: $59.9\%$–$62.0\%$) with no substantial differences between boys and girls. The age-standardised prevalence of any second-hand smoking varied from $40.2\%$ in Anguilla to $68.2\%$ in Jamaica, and the highest prevalence was in the Southern Latin America subregion ($65.9\%$). Pooled age-standardised prevalence of daily second-hand smoking was $15.1\%$ ($95\%$ CI: $14.2\%$–$16.1\%$), and was higher in girls than boys ($16.5\%$ vs $13.7\%$; $p \leq 0.001$). The age-standardised prevalence of daily second-hand smoking ranged between $4.8\%$ in Peru to $28.7\%$ in Jamaica, and the highest age-standardised prevalence was in Southern Latin America ($19.7\%$).
### Interpretation
The prevalence of any second-hand smoking is high among adolescents in LAC, though estimates changed substantially by country. While policies and interventions to reduce/stop smoking are implemented, attention should also be paid to avoid second-hand smoke exposure.
### Funding
Wellcome Trust International Training Fellowship (214185/Z/18/Z).
## Evidence before this study
Current worldwide evidence based on a literature search in PubMed (August 1st, 2022) with the following search strategy: “second-hand smoking” AND “survey” and “adolescents”, signals that, according to pooled data of the Global Youth Tobacco Survey, $62.9\%$ of adolescents, aged between 12 and 16 years, has had any exposure to second-hand smoking during last seven days, whereas $32.5\%$ reported a daily exposure. Second-hand smoking has been steadily growing in the Latin America and the Caribbean region. As a result, a more detailed analysis is needed in our region to appropriately implement and improve existing strategies.
## Added value of this study
Our study expands existing literature in the region by updating prevalence results of second-hand smoking by country and by sex, but also among never smoker adolescents. Despite of the results variability and the implementation of the Framework Convention on Tobacco Control in several countries of the region, the prevalence of second-hand smoke exposure is high among adolescents, especially among females and never smokers.
## Implications of all the available evidence
Our results highlight the need to strengthen policies to tackle the problem of second-hand smoking. Future research in the region should focus on defining the origin of such exposure (home, school, elsewhere) to implement appropriate strategies.
## Introduction
Whilst the estimated absolute number of subjects who died of second-hand smoke exposure decreased between 1990 and 2006, this number has gradually increased after that.1 Moreover, second-hand smoke exposure was the cause of about 603,000 premature deaths in 2004, and out of all deaths attributable to second-hand smoking, $28\%$ occur in children and adolescents.2 In addition, second-hand smoking has slowly but steadily grown between 1990 and 2016 in Latin America and the Caribbean (LAC) region.1,3 Based on the most recent Global Youth Tobacco Surveys (GYTS), and with information of 142 countries, it was reported that $62.9\%$ of adolescents, aged between 12 and 16 years, has had any exposure to second-hand smoking during last seven days, whereas $32.5\%$ reported a daily exposure.4 In the same study and using data from 29 LAC countries, second-hand smoking and daily second-hand smoking were present in $53.6\%$ and $22.5\%$ of adolescents, respectively. Although the prevalence of second-hand smoking was considered high among adolescents, no difference between sexes was reported.5 Moreover, second-hand smoking may have a greater impact on never smokers, a group that has not been evaluated since 2008.6 In addition, second-hand smoking has been associated with high probability of being smoker, susceptibility to smoking, smoking initiation, and greater nicotine dependence.7,8 *As a* result, the information herein provided may give current local information useful for appropriate interventions.
Second hand smoking is a relevant modifiable (and preventable) risk factor,9 and it has been associated with increased risk of tobacco use, especially among young adolescents,10 with the subsequent impact on health outcomes.11, 12, 13, 14 *As a* result, the Framework Convention on Tobacco Control (FCTC) was developed by the World Health Organization (WHO) to reduce the increase of tobacco consumption, the escalation in smoking by children and adolescents, and the impact of all forms of advertising, promotion and sponsorship aimed at encouraging tobacco use.15,16 Many countries have signed this framework, but only one (Dominican Republic) has not done so, and some other (Argentina, Cuba and Haiti) did not ratify it. Moreover, strategies implemented by countries to tackle this public health concern are also variable,17 making the profile of second-hand smoking exposure potentially heterogeneous in LAC region, deserving a more contemporary assessment.
Therefore, the present study aimed to describe the prevalence of second-hand smoke exposure and daily second-hand smoking in the LAC region, overall and by sex. In addition, subregion analyses were also performed to better understand the epidemiology of second-hand smoking in the LAC.
## Study design
We utilised information from the Global School-based Student Health (GSHS), a group of surveys built to assess behavioural factors among adolescents between 13 and 17 years. Data from 2010 to 2018, from countries of the Latin American and the Caribbean region, were pooled for analysis.
## Survey characteristics
The GSHS was developed by the World Health Organization (WHO), with help of the Centers for Disease Control and Prevention (CDC) of the United States, and other partners, and data is freely accessible.18 The GSHS is conducted independently in each participating country and includes a core questionnaire to assess 10 key areas (alcohol use, dietary behaviours, drug use, hygiene, mental health, physical activity, protective factors, sexual behaviours, tobacco use, and violence and unintentional injury), core-expanded questions, and country-specific questions.19 For the selection of participants in each country, the survey utilises a bietapic sampling technique. Thus, in the first phase, the selection of schools is proportional to the sample size; whilst in the second phase, classrooms within each of the selected schools are randomly chosen. All the students of that selected classroom are eligible to participate.18
## Definition of variables
Two variables were the outcome of interest based on a specific question from the tobacco use core module: “During the past 7 days, on how many days have people smoked in your presence?”. Response options were 0 days, 1 or 2 days, 3 or 4 days, 5 or 6 days, and all 7 days. For analysis purposes, two different variables were created to assess exposure to second-hand smoke: the first one was based on any exposure, and split subjects into two groups, one with no exposure (i.e., 0 days) against those with any exposure to second-hand smoke (i.e., ≥1 day). The second variable was based on daily exposure to second-hand smoke, i.e., those <7 days vs those with all the 7 days of exposure. This approach was based on previous literature regarding this topic.4,6,20 Additionally, another question of the tobacco use module was utilised: “How old were you when first tried a cigarette?” and those who responded “I have never smoked cigarettes” were considered as those without history of smoking (i.e., never smokers) for sensitivity analyses purposes. Other variables included in the analysis for description purposes were: sex (female vs male), age (in years), country, and survey year.
Finally, the participating countries were grouped into four subregions within LAC using an adapted version of the NCD Risk Factor Collaboration approach21,22: Andean Latin America, Caribbean, Central Latin America, and Southern Latin America (Supplementary Table S1).
## Statistical analysis
STATA 16 for Windows (StataCorp, College Station, TX, US) was used for statistical analysis. All the analyses were done considering the multistage design of each survey by using the denormalized individual GSHS survey weight, and taking into account the sampling design and non-response rates. Analyses by sex were conducted using the appropriate subpopulation option in STATA.23 Age-standardised prevalence of the outcomes of interest was estimated using the WHO population as standard. Such estimations were carried out overall, by sex, by country, and by subregion. Additionally, a sensitivity analysis was pursued by including only those with no history of previous smoking.
Estimates were compared using the Chi-squared test with the second-order correction of Rao and Scott for categorical variables.24 A p-value <0.05 was considered as statistically significant.
## Ethics
Data of the survey is freely available without personal identifiers. As a result ethical review was not considered mandatory.
## Role of the funding source
The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
## Overall description of the study population
Surveys were carried out between 2010 (Guyana and Peru) and 2018 (Argentina, Panama, St. Lucia, and St. Vincent & Grenadines). Sample sizes varied from 813 in Anguilla [2016] to 56,981 in Argentina [2018], adding up to a total of 95,805 records in 18 LAC countries (See Table 1).Table 1Data available for statistical analyses ($$n = 95$$,805).CountryStudy yearSample sizeMissing values (%)Age, mean (SD)Female (%)Anguilla$20168137.5\%$14.6 (1.2)$52.5\%$Argentina$2018569814.4\%$14.6 (1.1)$52.3\%$Bahamas$201313577.1\%$13.5 (1.0)$52.4\%$Barbados$201116295.6\%$14.3 (0.9)$50.8\%$Bolivia$2012369611.7\%$14.5 (1.1)$49.5\%$Chile$2013204910.8\%$14.6 (1.4)$51.1\%$Curacao$2015276523.7\%$14.7 (1.3)$50.7\%$Dominican Republic$2016148115.4\%$15.0 (1.1)$50.5\%$Guyana$201023929.2\%$14.4 (1.0)$52.1\%$Honduras$201217798.5\%$13.9 (1.3)$53.4\%$Jamaica$2017166710.6\%$15.0 (1.1)$52.7\%$Panama$2018294813.3\%$14.9 (1.1)$52.6\%$Peru$201028826.7\%$14.4 (1.0)$50.2\%$St Lucia$2018197011.9\%$14.3 (1.4)$52.8\%$St Vincent & Grenadines$2018187714.4\%$15.0 (1.0)$51.9\%$Suriname$2016212612.8\%$14.3 (1.3)$51.2\%$Trinidad & Tobago$2017386913.1\%$14.2 (1.4)$52.3\%$Uruguay$201235244.2\%$14.4 (1.0)$54.7\%$Missing values included only key variables for analysis.
Missing values in key variables for analyses were present in $7.2\%$ of the records, ranging from $4.2\%$ in Uruguay [2012] to $23.7\%$ in Curacao [2015]. Pooled mean age was 14.6 (SD: 1.2) years, varying from 13.5 in Bahamas to 15.0 years in Dominican Republic, Jamaica, and St. Vincent and Grenadines. The overall proportion of girls was $51.5\%$, varying from $49.5\%$ in Bolivia to $54.7\%$ in Uruguay.
## Second-hand smoke exposure
Pooled age-standardised prevalence of second-hand smoke exposure was $60.9\%$ ($95\%$ CI: $59.9\%$–$62.0\%$); however, prevalence estimates varied from $40.2\%$ ($95\%$ CI: $35.2\%$–$45.5\%$) in Anguilla to $68.2\%$ ($95\%$ CI: $64.5\%$–$71.6\%$) in Jamaica (See details in Fig. 1 and Supplementary Table S2). When analyses were done by subregion, the highest prevalence of second-hand smoke exposure was in the Southern Latin America subregion ($65.9\%$), followed by the Caribbean ($56.0\%$), Andean Latin America ($55.7\%$), and finally Central Latin America ($48.4\%$, $p \leq 0.001$).Fig. 1Age-standardised prevalence of any exposure of second-hand smoking: Results by country and sex. Pooled age-standardised prevalence is shown as continuous line (point estimate) and dashed lines ($95\%$ confidence intervals).
Pooled estimates were similar between males ($60.3\%$; $95\%$ CI: $59.0\%$–$61.6\%$) compared to females ($61.5\%$; $95\%$ CI: $60.2\%$–$62.8\%$, $$p \leq 0.12$$). Second-hand smoke exposure was greater among males compared to females in Andean Latin America ($57.5\%$ vs $53.9\%$, $$p \leq 0.02$$) and Central Latin America ($51.0\%$ vs $46.0\%$, $$p \leq 0.01$$); whereas the situation was inverse in Southern Latin America ($63.4\%$ vs $68.2\%$, $p \leq 0.001$). There was no difference in second-hand smoke exposure in the Caribbean subregion ($57.2\%$ in males vs $54.9\%$ in females, $$p \leq 0.28$$).
When the analysis was conducted among never smokers, the prevalence of second-hand smoke exposure was $51.5\%$ ($95\%$ CI: $50.4\%$–$52.6\%$). The lowest prevalence was in Anguilla ($36.3\%$; $95\%$ CI: $31.5\%$–$41.4\%$) and Panama ($36.3\%$; $95\%$ CI: $31.6\%$–$41.3\%$), whereas the highest prevalence was in Jamaica ($62.2\%$; $95\%$ CI: $57.7\%$–$66.4\%$). By sex, second-hand smoke was slightly lower among males ($50.6\%$; $95\%$ CI: $49.1\%$–$52.1\%$) than females ($52.3\%$; $95\%$ CI: $50.8\%$–$53.8\%$), but this difference was not significant ($$p \leq 0.09$$). By subregion, the prevalence of second-hand smoke exposure was $54.9\%$ in Southern Latin America, followed by the Caribbean ($50.3\%$), Andean Latin America ($48.4\%$) and Central Latin America ($42.5\%$). Detailed information by country and sex can be seen in Supplementary Figure S1 and Supplementary Table S3.
## Daily second-hand smoke exposure
Pooled age-standardised prevalence of daily second-hand smoke exposure was $15.1\%$ ($95\%$ CI: $14.2\%$–$16.1\%$); however, prevalence estimates varied from $4.8\%$ ($95\%$ CI: $3.5\%$–$6.4\%$) in Peru to $28.7\%$ ($95\%$ CI: $24.1\%$–$33.8\%$) in Jamaica. When analyses were done by subregion, the highest prevalence of daily second-hand smoke exposure was in Southern Latin America ($19.7\%$), followed by the Caribbean ($17.2\%$), Central Latin America ($11.1\%$), and Andean Latin America ($4.9\%$, $p \leq 0.001$).
Pooled estimates were lower among males ($13.7\%$; $95\%$ CI: $12.7\%$–$14.8\%$) compared to females ($16.5\%$; $95\%$ CI: $15.3\%$–$17.7\%$, $p \leq 0.001$). See details in Fig. 2 and Supplementary Table S4. Daily second-hand smoke exposure was lower among males compared to females only in the Southern Latin America ($17.1\%$ vs $22.1\%$, $p \leq 0.001$), whereas there was no difference in the other subregions: $18.1\%$ vs $16.4\%$ in the Caribbean ($$p \leq 0.21$$), $11.9\%$ vs $10.4\%$ ($$p \leq 0.23$$) in Central Latin America, and $4.7\%$ vs $5.0\%$ ($$p \leq 0.71$$) in Andean Latin America, when males were compared to females, respectively. Fig. 2Age-standardised prevalence of daily second-hand smoke exposure: Results by country and sex. Pooled age-standardised prevalence is shown as continuous line (point estimate) and dashed lines ($95\%$ confidence intervals).
When the analysis was conducted among never smokers, the prevalence of daily second-hand smoke exposure was $9.8\%$ ($95\%$ CI: $9.1\%$–$10.7\%$). The lowest prevalence was in Peru ($3.4\%$; $95\%$ CI: $2.1\%$–$5.3\%$), whereas the highest prevalence was in Jamaica ($21.1\%$; $95\%$ CI: $16.8\%$–$21.2\%$). By sex, daily second-hand smoke was lower among males ($9.1\%$; $95\%$ CI: $8.1\%$–$10.2\%$) than females ($10.5\%$; $95\%$ CI: $9.6\%$–$11.5\%$, $$p \leq 0.02$$). By subregion, the prevalence of daily second-hand smoke exposure was $12.7\%$ in Southern Latin America, followed by the Caribbean ($12.4\%$), Central Latin America ($7.9\%$), and Andean Latin America ($3.6\%$). Information by country and sex can be seen in Supplementary Figure S2 and Supplementary Table S5.
## Main findings
Our results confirm that about $60\%$ of adolescents between 13 and 17 years reported any second-hand smoke exposure, whereas $15\%$ were daily exposed to second-hand smoking. These rates dropped, but are still high ($52\%$ and $10\%$ for any and daily second-hand smoking, respectively), when only never smokers were analysed. Although adolescents of both sexes were similarly exposed, females had greater daily second-hand smoking rates compared to males. Finally, second-hand smoke exposure was greater among adolescents from the Southern Latin America subregion.
## Comparison with previous studies
Our estimates of any second-hand smoking prevalence using the GSHS are similar to those obtained in a global analysis using the Global Youth Tobacco Survey (GYTS) in the same period (2010–2018), but lower to that obtained for the Americas region.4 Similarly, estimates by sex were almost the same, and no difference between sexes was found.4,5 Nevertheless, the overall prevalence of daily second-hand smoking was half of the estimate reported in the global analysis using the GYTS, several percentual points below the estimate in the Americas region,4 and greater among females than males. Although the question utilised for estimating second-hand smoking prevalence was similar, differences may be attributed to the fact that our prevalence estimates were age-standardised whereas previous results were not. In addition, countries involved in the GSHS are not the same from those included in the GYTS, highlighting the existing heterogeneity in the region.
Our study expands on previous findings by estimating the prevalence of second-hand smoke exposure among never smokers. A previous global study, using information of the GYTS from 1999 to 2008, reported a prevalence of second-hand smoking of $23\%$ both inside and outside home,6 a value higher compared to our estimate. According to this latter study, the estimate in the LAC region was $22\%$, which was also higher compared to our findings. So, a potential reduction in rates of daily second-hand smoking may have been occurred over time, but the differences may be also attributed to the surveys’ characteristics as previously mentioned. A different study,20 analysing information from 31 countries, reported that approximately half of children (<11 years) were exposed to second-hand smoking outside the household, and this estimate was $48\%$ for the North and South America region. Moreover, this study found that second-hand smoking was almost the rule in household with smokers, which usually tend to smoke around their children with little restraint.
## Public health relevance
More than half of never-smoker adolescents were exposed to second-hand smoking during the last seven days, and one in ten were daily exposed, with higher exposure among females. These findings are relevant as second-hand smoke exposure has been linked to an increased probability of being a smoker,7 increased susceptibility to smoking,25 increased probability of smoking initiation,26 greater nicotine dependence symptoms,8 reduced success in smoking cessation,10 and deleterious health outcomes.27 Whereas tobacco control policies have improved in many LAC countries, especially those related to protection against tobacco smoke at home and selling cigarettes to adolescents, only small reductions were seen in second-hand smoking outside home.3 Thus, airborne nicotine has been detected in more than $90\%$ of indoor public environments across different locations surveyed in cities in the LAC region.17,28 In addition to that, exposure to media and advertising remained largely unchanged.3 As the spread of tobacco is facilitated by a variety of complex factors with cross-border effects, including trade liberalisation and globalisation,29 the heterogeneity observed in second-hand smoking rates across countries suggest that efforts to control tobacco exposure may be advancing at different pace in the region. According to our findings, a more continuous and consistent surveillance of second-hand smoking in LAC countries is needed, as well as strengthening efforts to better control tobacco use.
## Strengths and limitations
This study has several strengths. We used the most updated data of the GSHS (i.e., from 2010 to 2018) for 18 countries of the LAC region. Moreover, the same questions regarding second-hand smoking and smoke exposure were utilised in all the settings, making feasible the comparison between countries. In addition, we described second-hand smoke exposure among those who reported never smoking, which can be more relevant from the public health perspective.
However, several limitations should be highlighted. First, the GSHS uses a self-report tool to collect behavioural information, which can lead to bias, particularly recall and social desirability bias. Second, only a subgroup of adolescents (i.e., those present at the school during the survey) was evaluated using this strategy, therefore results should be interpreted cautiously, especially as related to generalizability. Third, not all the countries of the LAC region conduct the GSHS. Fourth, we could not assess whether the exposure occurs at home or elsewhere as done in previous works.4,6 Fifth, the surveys were administered in different years (from 2010 to 2018) across countries. On one hand, this may make between-country comparisons difficult, whereas in the other hand, for each country, the prevalence estimates should be interpreted alongside the year when the survey was conducted to better understand the local context. Finally, prevalence estimates were highly heterogeneous. However, we provided pooled estimated by subregions to gain a better epidemiology perspective of this public health problem.
## Conclusions
Although heterogeneous, the prevalence of second-hand smoke exposure is high among adolescents in the LAC region, including among those who never smoked. Adolescents of both sexes were similarly exposed, but females had greater daily second-hand smoking rates compared to males. There is a need to strengthen tobacco control policies in the region.
## Contributors
AB-O and RMC-L conceived the idea of the manuscript. AB-O conducted the statistical analysis with support of RMC-L. AB-O drafted the first version of the manuscript with critical input of RMC-L. Both authors approved the final version submitted for publication. AB-O and RMC-L had full access to the data and conducted the statistical analyses, and they are the guarantors of the study and vouch for the accuracy of the results. AB-O had final responsibility for the decision to submit for publication.
## Data sharing statement
Data of the GSHS is freely available at the WHO NCD Microdata Repository website: https://extranet.who.int/ncdsmicrodata/index.php/catalog/GSHS/about.
## Declaration of interests
The authors declare that no conflicts of interest exist.
## Supplementary data
Supplementary Figures and Tables Translated Abstract (Spanish)
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|
---
title: An updated prevalence of asthma, its phenotypes, and the identification of
the potential asthma risk factors among young Chinese adults recruited in Singapore
authors:
- Qi Yi Ambrose Wong
- Jun Jie Lim
- Jun Yan Ng
- Praneeth Malipeddi
- Yi Ying Eliza Lim
- Yang Yie Sio
- Fook Tim Chew
journal: The World Allergy Organization Journal
year: 2023
pmcid: PMC10033744
doi: 10.1016/j.waojou.2023.100757
license: CC BY 4.0
---
# An updated prevalence of asthma, its phenotypes, and the identification of the potential asthma risk factors among young Chinese adults recruited in Singapore
## Abstract
### Background
Asthma is a chronic inflammatory disease of the airway characterized by respiratory symptoms: wheezing, shortness of breath, coughing, and chest tightness. Globally, asthma affects over 300 million individuals and carries high morbidity and mortality burden. Previous studies have estimated the prevalence of asthma; however, prevalence estimates have been changing over time. Here, in a population of young Chinese adults from Singapore, we aimed to obtain an updated prevalence of asthma and its phenotypes, and identify potential associated risk factors.
### Methods
The Singapore/Malaysia Cross-Sectional Genetics Epidemiology Study (SMCGES) is an ongoing study which uses established ISAAC guidelines to collect epidemiological data and information pertaining to allergic diseases such as asthma. Responses from young Chinese adults recruited in the National University of Singapore were analyzed.
### Results
Lifetime asthma prevalence rate was estimated at $19.1\%$ ($\frac{2049}{10}$,736), while current asthma prevalence rate was estimated at $6.3\%$ ($\frac{679}{10}$,736). For ever asthma, the most important risk factor was a parental history of asthma. Increased consumption of pulses (aOR: 0.822, $95\%$ CI: 0.706–0.958) was associated with a lowered odds of ever asthma, but cereals (aOR: 1.256, $95\%$ CI: 1.006–1.580), pasta (aOR: 1.265, $95\%$ CI: 1.027–1.553), butter (aOR: 1.350, $95\%$ CI: 1.113–1.632), and margarine (aOR: 1.343, $95\%$ CI: 1.081–1.660) were associated with a higher risk of ever asthma. Increased television/computer usage was associated with a decreased risk of ever asthma (aOR: 0.448, $95\%$ CI: 0.367–0.545). Conversely, genetic factors had a lower strength of effect on current asthma (parental history of asthma - OR: 1.465, $95\%$ CI: 1.135–1.888) as compared to ever asthma. Only increased potato consumption was significantly associated with an increased risk of current asthma (most or all days per week vs never or only occasionally - aOR: 1.577, $95\%$ CI: 1.145–2.180). Physical activity (aOR: 0.693, $95\%$ CI: 0.542–0.885) was associated with a lower odds of asthma, while second-hand smoke exposure was associated with an increased risk for current asthma (aOR: 1.435, $95\%$ CI: 1.001–2.047).
### Conclusion
Overall, the prevalence of lifetime asthma and current asthma among young Chinese adults was $19.1\%$ and $6.3\%$, higher than that of previous studies. Our results suggested a stronger association between genetic factors and ever asthma as compared to current asthma. Parental asthma was the most important intrinsic epidemiological factor for asthma manifestation, while various foods, physical activity levels, and television or computer usage were also significantly associated with asthma. Future studies should consider risk factors in conjunction with other accompanying variables given the potential interactions between them, to discern the effects of environment and lifestyle on asthma more distinctly.
## Background
Asthma is a chronic inflammatory disease of the airway characterized by respiratory symptoms: wheezing, shortness of breath, coughing, and chest tightness.1 Over 300 million individuals suffer from asthma worldwide, with an additional 100 million individuals projected to be at risk.2 Furthermore, asthma carries a significant morbidity and mortality burden, adversely affecting the quality of living and causing premature death, rendering a global health issue which cannot be ignored.2 Monitoring initiatives have been established and adopted worldwide, including the European Community Respiratory Health Survey (ECRHS), International Study of Asthma and Allergies in Childhood (ISAAC), and World Health Survey (WHS), providing constant worldwide updates on the prevalence and epidemiology of asthma.3, 4, 5 However, data from studies using standardized methods are often heterogeneous due variability between study populations, owing to demographic and geographic differences.6
## Objectives
Although the prevalence of asthma in Singapore has previously been reported, it has been established that asthma prevalence has been changing across time.7, 8, 9, 10, 11, 12 Presently, we investigate a sample of young Chinese adults in Singapore with 2 aims: (i) obtaining an updated prevalence of asthma and its phenotypes; and (ii) identifying the epidemiological factors associated with asthma using the ISAAC questionnaire.
## Participants, outcome definition, classification, and characterization
The Singapore/Malaysia Cohort Genetic Epidemiology Study (SMCGES) is an ongoing cross-sectional study conducted in Singapore and Malaysia. Since August 2005, participants were recruited via email and poster advertisements across the campuses of National University of Singapore, Sunway University, and Universiti Tunku Abdul Rahman for the SMCGES. Participants below the age of 18 were excluded. The current paper reports the data obtained from Singapore only; data from Malaysia will be analyzed and published separately.
Atopy status was determined via a Skin Prick Test (SPT), which tested for sensitization to allergens from the house dust mite species *Blomia tropicalis* and Dermatophagoides pteronyssinus. A positive histamine control and a negative saline control were included in the SPT. Subjects who developed a wheal of at least 3 mm in diameter in response to a given allergen were considered SPT positive or atopic. The SPT protocol was consistent with previous descriptions.13 Data for asthma were collected according to established and validated ISAAC guidelines which have been expanded for utility in both children and adults.14 *Ever asthma* cases comprised subjects who indicated ever having had asthma for the question: “Have you ever had asthma?”. Out of the ever asthma cases, those exhibiting a positive SPT result were further categorized as atopic asthma cases. Ever asthma cases were further classified as current asthma cases when any asthma symptoms within the past 12 months were reported – these included wheezing, dry coughing in the absence of a cold or flu, or any asthma exacerbations. Ever asthma subjects who manifested both a positive SPT result and any asthma symptoms within the past 12 months were hence considered current atopic asthma cases. Among current asthma cases, we further distinguished between exercise-induced asthma (experienced wheezy-sounding chest in the last 12 months) and cough-variant asthma (experienced a dry cough at night, which was not associated with a cold or chest infection in the last 12 months).
## Collection of epidemiological data
Epidemiological data collection was performed according to ISAAC protocol, yielding information of pertinence to demographics, familiar background, lifestyle, and diet.14 Basic demographic information concerning age, gender, income category, and housing type were collected. Additionally, the country of origin, years spent in Singapore for non-locals, and personal history of drug allergy were determined. To elucidate familiar and thus genetic predisposition, we identified those with a maternal, paternal, or sibling history of allergic diseases: atopic dermatitis, allergic rhinitis, and asthma. A general overview of participants’ lifestyles was obtained with regard to their physical activity levels (performed never or only occasionally, once or twice per week, or on most or all days), duration of television or computer usage (time spent per day was less than 1 h, 1 to 3 h, more than 3 but less than 5 h, or more than 5 h), alcohol consumption (never, occasionally, or frequently), and smoking status (non-smoker, ex-smoker, or current smoker). As an indicator of animal exposure, subjects indicated whether they had ever kept pets. Lastly, our dietary gathered information on 16 food groups – meat, seafood, fruits, vegetables, pulses, cereals, pasta, rice, butter, margarine, nuts, potato, milk, eggs, fast food (including burgers), and probiotic drinks. To each of these food groups, the frequency of their consumption by each subject was categorized within one of 3 options: never or only occasionally, once or twice per week, and most or all days.
## Scoring of overall glycemic index (GI) level of diet using the quality of diet based on Glycemic Index Score (QDGIS)
Using the Quality of Diet based on Glycemic Index Score (QDGIS), overall dietary glycemic index (GI) quality was assessed by scoring food consumption according to their glycemic index and intake frequency. " High-GI" foods had a GI value of 55 and above, and comprised burgers/fast food, cereals, rice, and potatoes; "low-GI" foods had a GI value of less than 55, and comprised fruits, vegetables, pulses, nuts, milk, and probiotic drinks.15,16 Next, we adapted a previously used rubric to quantify dietary GI.17 For each food, each category of consumption frequency was assigned scores accordingly: most or all days – score 7, once or twice per week – score 2, and never or only occasionally – score 0. Negative signs were prepended to scores for "high-GI" foods and positive signs were prepended to scores for "low-GI" foods, wherein increased consumption of "high-GI" foods resulted in a more negative score, while increased consumption of "low-GI" foods resulted in a higher positive score. The summation of all scores yielded the QDGIS which we then grouped into poor (QDGIS >2), moderate (2 ≤ QDGIS <10), and good (QDGIS ≥10) categories.
## Statistical analyses
Statistical analyses were conducted using R version 4.0.3.18 For analyses of epidemiological to identify associated factors, unadjusted odds ratios (OR) and their corresponding $95\%$ confidence intervals ($95\%$ CI) were first calculated via simple logistic regression where the outcome of interest was ever having had asthma. Next, multiple logistic regression was conducted to adjust for important confounding variables, yielding adjusted odds ratios (aOR) and their respective $95\%$ CI. To further elucidate the relationship between environmental variables and asthma, the logistic regression analyses were repeated, with the outcome being current asthma compared against non-current asthma cases. Effect sizes with a corresponding p-value of less than 0.05 were considered statistically significant.
## Sample demographics
Data from 10 736 participants of Chinese ethnicity from the Singapore cohort recruited from the National University of Singapore were analyzed. The mean age of sample subjects was 22.5 years (standard deviation (SD) = 5.2 years) and there was a preponderance of female subjects ($57.6\%$). The most common total monthly family income per capita was SGD2000 to SGD4000 ($34.0\%$), with many residing in public housing ($67.5\%$). Majority were local Singaporean Chinese ($63.1\%$) and within the non-local subgroup, the mean duration spent in Singapore was 5.9 years (SD = 6.8 years). A summary of sample demographics can be found in Table 1.Table 1Summary table for demographics of sample drawn from a population of young Chinese adult SingaporeansTable 1Demographic factorSummaryAge (mean years ± standard deviation)22.5 ± 5.2NA60GenderFemale6169 ($57.6\%$)Male4545 ($42.4\%$)NA22Total monthly family income per capita< SGD20002350 ($22.7\%$)SGD2000 to < SGD40003528 ($34.0\%$)SGD 4000 to < SGD60002026 ($19.5\%$)≥ SGD60002466 ($23.8\%$)NA366Housing typeHDB (Public housing)6848 ($67.5\%$)Condominium/Private apartments1928 ($19.0\%$)Landed property1372 ($13.5\%$)NA588Born in SingaporeYes7385 ($69.1\%$)No3297 ($30.9\%$)NA54Years spent in Singapore among non-locals (mean years ± standard deviation)5.9 ± 6.8NA262History of drug allergyNo8847 ($88.3\%$)Yes1170 ($11.7\%$)NA719
## Prevalence of asthma and asthma phenotypes
The prevalence of ever asthma was estimated at $19.1\%$ ($\frac{2049}{10}$,736). Of the ever asthma cases, $75.2\%$ ($\frac{1541}{2049}$) were atopic asthma cases and $33.1\%$ ($\frac{679}{2049}$) were current asthma cases (Fig. 1A). Current atopic asthma cases constituted $25.0\%$ ($\frac{512}{2049}$) of ever asthma cases. Among current asthma cases, $69.4\%$ ($\frac{408}{588}$) exhibited symptoms of wheeze-variant asthma (WVA), and $47.1\%$ ($\frac{192}{408}$) of WVA cases had exercise-induced asthma (EIA). $60.7\%$ ($\frac{357}{588}$) of current asthma cases manifested cough-variant asthma. The asthma variants were non-mutually exclusive – $30.1\%$ ($\frac{177}{588}$) had both CVA and WVA, while $18.0\%$ ($\frac{106}{588}$) had all of CVA, EIA, and WVA. A breakdown of asthma variants is summarized in Fig. 1B.Fig. 1Summary of asthma phenotypes. ( A) Flowchart summarizing participants' disease status; (B) Asthma variant distribution among current asthma cases with complete data for asthma variant symptoms ($$n = 588$$).Fig. 1
## Demographic factors
Demographic characteristics significantly associated with ever asthma included male gender was significantly associated with an increased odds of asthma (OR: 1.387, $95\%$ CI: 1.255–1.532, p-value <0.001), a higher total monthly family income per capita which increased the odds of asthma in a dose-effect manner (≥SGD6000 vs < SGD2000 - OR: 1.536, $95\%$ CI: 1.322–1.786, p-value <0.001), being born in Singapore (OR: 2.337, $95\%$ CI: 2.071–2.643, p-value <0.001), and having a history of drug allergy (OR: 1.675, $95\%$ CI: 1.448–1.935, p-value <0.001). Conversely, increased age (p-value = 0.2) and housing type (p-value = 0.864) were non-significantly associated with ever asthma.
Adjustment for gender and parental history of asthma showed that an increased odds of asthma was significantly associated with increased income levels in a dose-effect manner (aOR: 1.558, $95\%$ CI: 1.299, 1.872, p-value <0.001), being born in Singapore (aOR: 2.334, $95\%$ CI: 2.020, 2.706, p-value <0.001), and a history of drug allergy (aOR: 1.692, $95\%$ CI: 1.419, 2.012, p-value <0.001). Increased age (p-value = 0.15) and housing type (p-value = 0.072) were non-significantly associated with ever asthma.
A forest plot summarizing the associations between ever asthma and demographic factors can be found in Fig. 2A.Fig. 2Forest plots for unadjusted (red text) and adjusted (blue text) odds ratios for ever asthma and potential risk factors. ( A) Demographic characteristics; (B) Familiar history of allergic diseases; (C) Lifestyle habits. Fig. 2 Increased age (OR: 1.022, $95\%$ CI: 1.002–1.041, p-value = 0.028) and female gender (OR: 0.764, $95\%$ CI: 0.635–0.919, p-value = 0.004) were significantly associated with an increased odds of current asthma. Conversely, current asthma was not significantly associated with total monthly family income per capita (p-value = 0.5), housing type (p-value = 0.13), being born in Singapore (p-value = 0.4), and any history of drug allergy (p-value = 0.3).
Adjustment for gender and parental history of asthma showed that only age was significantly associated with and increased odds of current asthma (aOR: 1.023, $95\%$ CI: 1.001–1.046, p-value = 0.039). There was no significant association between the odds of current asthma and total monthly family income per capita (p-value = 0.3), housing type (p-value = 0.092), being born in Singapore (p-value = 0.5), and any history of drug allergy (p-value = 0.3).
The associations between demographic factors and current asthma are summarized in a forest plot in Fig. 3A.Fig. 3Forest plots for unadjusted (red text) and adjusted (blue text) odds ratios for current asthma and potential risk factors. ( A) Demographic characteristics; (B) Familiar history of allergic diseases; (C) Lifestyle habits. Fig. 3
## Familiar background
The odds of ever asthma were significantly increased given a maternal history of atopic dermatitis (OR: 1.239, $95\%$ CI: 1.027–1.489, p-value = 0.023), allergic rhinitis (OR: 2.378, $95\%$ CI: 1.748–3.216, p-value <0.001), and asthma (OR: 4.500, $95\%$ CI: 3.659–5.538, p-value <0.001). Likewise, the odds of ever asthma were significantly increased given a paternal history of atopic dermatitis (OR: 2.101, $95\%$ CI: 1.695–2.595, p-value <0.001), allergic rhinitis (OR: 2.189, $95\%$ CI: 1.535–3.091, p-value <0.001), and asthma (OR: 4.503, $95\%$ CI: 3.511–5.781, p-value <0.001). Overall, having any parental history of asthma significantly increased the odds of ever asthma (OR: 4.784, $95\%$ CI: 4.038–5.670, p-value <0.001). Among those with siblings, the odds of ever asthma were significantly increased in those with a sibling history of atopic dermatitis (OR: 1.368, $95\%$ CI: 1.224–1.527, p-value <0.001), allergic rhinitis (OR: 1.472, $95\%$ CI: 1.310–1.653, p-value <0.001), or asthma (OR: 2.100, $95\%$ CI: 1.882–2.343, p-value 0.017).
Following adjustment for gender and parental history of asthma, an increased odds of ever asthma was associated with maternal allergic rhinitis (aOR: 1.840, $95\%$ CI: 1.274–2.634, p-value <0.001), paternal atopic dermatitis (aOR: 1.537, $95\%$ CI: 1.179–1.989, p-value = 0.001), and paternal allergic rhinitis (aOR: 1.559, $95\%$ CI: 1.022–2.340, p-value = 0.035), but not maternal atopic dermatitis (p-value = 0.8). An increased odds of ever asthma was significantly associated a sibling history of atopic dermatitis (aOR: 1.329, $95\%$ CI: 1.147–1.538, p-value <0.001), allergic rhinitis (aOR: 1.365, $95\%$ CI: 1.157–1.606, p-value <0.001), and asthma (aOR: 1.897, $95\%$ CI: 1.639–2.193, p-value <0.001).
A forest plot summarizing the associations between ever asthma and familiar history of allergic diseases can be found in Fig. 2B.
While a maternal history of atopic dermatitis (p-value = 0.089) was non-significantly associated with current asthma, a maternal history of allergic rhinitis (OR: 1.672, $95\%$ CI: 1.027–2.703, p-value = 0.036) and asthma (OR: 1.544, $95\%$ CI: 1.144–2.078, p-value = 0.004) were both associated with an increased odds of current asthma. In contrast, a paternal history of atopic dermatitis (p-value = 0.2), allergic rhinitis (p-value = 0.6), and asthma (p-value = 0.078) were all non-significantly associated with current asthma. Overall, any parental history of asthma was significantly associated with an increased odds of current asthma (OR: 1.465, $95\%$ CI: 1.135–1.888, p-value = 0.003). A sibling history of allergic rhinitis (p-value = 0.6), and a sibling history of asthma (p-value = 0.8) were not significantly associated with current asthma. However, a sibling history of atopic dermatitis (OR: 1.267, $95\%$ CI: 1.038–1.546, p-value = 0.020) was significantly associated with an increased odds of current asthma.
Upon adjustment for gender and parental history of asthma, a maternal history of atopic dermatitis (p-value = 0.8) and allergic rhinitis (p-value = 0.5), and a paternal history of atopic dermatitis (p-value = 0.4) and allergic rhinitis (p-value = 0.8) all were all non-significantly associated with the odds of current asthma. A sibling history of atopic dermatitis was significantly associated with an increased odds of current asthma (aOR: 1.385, $95\%$ CI: 1.074–1.783, p-value = 0.012). Having siblings (p-value = 0.8), a sibling history of allergic rhinitis (p-value = 0.4), and a sibling history of asthma (p-value >0.9) were all non-significantly associated with current asthma.
The associations between current asthma and familiar history are summarized in Fig. 3B.
## Lifestyle
Statistically significant unadjusted associations between physical activity, television or computer usage, and alcohol consumption were found. An increased odds of ever asthma was associated with increased frequency of physical activity (most or all days per week vs never or only occasionally - OR: 1.300, $95\%$ CI: 1.101–1.532, p-value = 0.002), frequent alcohol consumption (frequent vs never - OR: 1.423, $95\%$ CI: 1.005–1.987, p-value = 0.042), and ever keeping pets (OR: 1.134, $95\%$ CI: 1.016–1.267, p-value = 0.025). Increased computer usage was associated with a reduced odds of ever asthma (more than 5 h vs less than 1 h – OR: 0.521, $95\%$ CI: 0.443–0.613, p-value <0.001), and there was dose-effect trend. The association between smoking and ever asthma was non-significant (p-value = 0.8).
Inclusion of gender and parental history of asthma in the logistic model showed that television or computer usage was significantly associated with a reduced odds of ever asthma (aOR: 0.448, $95\%$ CI: 0.367–0.545, p-value <0.001). The associations between asthma and physical activity (p-value = 0.11), alcohol consumption (p-value = 0.2), smoking status (p-value = 0.9), and ever keeping pets (p-value = 0.4) were all non-significant.
A forest plot summarizing the associations between lifestyle habits and ever asthma is presented in Fig. 2C.
## Diet
Unadjusted analyses showed that an increased odds of asthma was associated with increased seafood consumption (most or all days per week vs never or only occasionally - OR: 1.341, $95\%$ CI: 1.094–1.655, p-value = 0.005), pasta (most or all days per week vs never or only occasionally - OR: 1.324, $95\%$ CI: 1.119–1.564, p-value = 0.001), butter (most or all days per week vs never or only occasionally - OR: 1.356, $95\%$ CI: 1.157–1.588, p-value <0.001), margarine (most or all days per week vs never or only occasionally - OR: 1.287, $95\%$ CI: 1.075–1.536, p-value = 0.005), nuts (most or all days per week vs never or only occasionally - OR: 1.230, $95\%$ CI: 1.023–1.475, p-value = 0.026), and potatoes (most or all days per week vs never or only occasionally - OR: 1.257, $95\%$ CI: 1.058–1.494, p-value = 0.009).
Conversely, foods associated with a lower odds asthma included fruits (most or all days per week vs never or only occasionally - OR: 0.769, $95\%$ CI: 0.609–0.977, p-value = 0.029), vegetables (most or all days per week vs never or only occasionally - OR: 0.732, $95\%$ CI: 0.545–0.993, p-value = 0.041), pulses (once or twice per week vs never or only occasionally - OR: 0.803, $95\%$ CI: 0.710–0.908, p-value = 0.001), and probiotic drinks (once or twice per week vs never or only occasionally - OR: 0.873, $95\%$ CI: 0.784–0.972, p-value = 0.013). In consideration of overall diet, a moderate GI level compared to poor GI level associated with a lowered risk of asthma (OR: 0.831, $95\%$ CI: 0.740–0.933, p-value = 0.002). However, while a similar protective association was observed in good GI level as compared to poor GI level, this was statistically non-significant (p-value = 0.2).
Upon adjustment for age and gender, we found that an increased odds of ever asthma was significantly associated with increased consumption of cereals (aOR: 1.256, $95\%$ CI: 1.006–1.580, p-value = 0.047), pasta (aOR: 1.265, $95\%$ CI: 1.027–1.553, p-value = 0.026), butter (aOR: 1.350, $95\%$ CI: 1.113–1.632, p-value = 0.002), and margarine (aOR: 1.343, $95\%$ CI: 1.081–1.660, p-value = 0.007). In contrast, pulses (aOR: 0.822, $95\%$ CI: 0.706–0.958, p-value = 0.012) and probiotic drinks (aOR: 0.861, $95\%$ CI: 0.756–0.980, p-value = 0.024) were associated with a lower odds of asthma. Overall, a moderate GI score compared to a poor GI score was significantly associated with an odds of ever asthma (aOR: 0.820, $95\%$ CI: 0.712–0.944, p-value = 0.006).
The associations between ever asthma and demographic factors are summarized in Table 2.Table 2Summary of associations between dietary habits and ever asthma, including contingency tables with percentage for each response by ever asthma, and odds ratios, unadjusted and adjusted with their corresponding $95\%$ confidence intervals as given by logistic regression models. Table 2Dietary factorNEver asthmaUnivariate logistic regressionMultiple logistic regressionbNo,$$n = 6$$,633aYes,$$n = 2$$,049aUnadjusted ORc$95\%$ CIcp-valueAdjusted ORc$95\%$ CIcp-valueMeat8656Never or only occasionally162 ($2.4\%$)56 ($2.7\%$)––––Once or twice per week662 ($10.0\%$)181 ($8.9\%$)0.7910.563, 1.1240.20.7450.492, 1.1470.2Most or all days per week5791 ($87.5\%$)1804 ($88.4\%$)0.9010.666, 1.2360.50.8640.599, 1.2750.4NA188Seafood8647Never or only occasionally499 ($7.6\%$)129 ($6.3\%$)––––Once or twice per week3296 ($49.9\%$)936 ($45.9\%$)1.0980.896, 1.3550.40.9980.785, 1.280>0.9Most or all days per week2812 ($42.6\%$)975 ($47.8\%$)1.3411.094, 1.6550.0051.2380.974, 1.5880.086NA269Fruits8660Never or only occasionally262 ($4.0\%$)104 ($5.1\%$)––––Once or twice per week2217 ($33.5\%$)679 ($33.2\%$)0.7720.607, 0.9870.0360.8050.593, 1.1050.2Most or all days per week4136 ($62.5\%$)1262 ($61.7\%$)0.7690.609, 0.9770.0290.8030.597, 1.0930.2NA184Vegetables8628Never or only occasionally149 ($2.3\%$)63 ($3.1\%$)––––Once or twice per week812 ($12.3\%$)235 ($11.5\%$)0.6840.495, 0.9550.0240.7480.496, 1.1480.2Most or all days per week5628 ($85.4\%$)1741 ($85.4\%$)0.7320.545, 0.9930.0410.7980.549, 1.1860.3NA4410Pulses8605Never or only occasionally1397 ($21.2\%$)486 ($23.9\%$)––––Once or twice per week3929 ($59.8\%$)1097 ($54.0\%$)0.8030.710, 0.908<0.0010.8220.706, 0.9580.012Most or all days per week1249 ($19.0\%$)447 ($22.0\%$)1.0290.886, 1.1940.71.0370.862, 1.2460.7NA5819Cereals8619Never or only occasionally607 ($9.2\%$)170 ($8.3\%$)––––Once or twice per week2582 ($39.2\%$)759 ($37.3\%$)1.0500.871, 1.2700.61.1220.892, 1.4190.3Most or all days per week3393 ($51.5\%$)1108 ($54.4\%$)1.1660.973, 1.4040.101.2561.006, 1.5800.047NA5112Pasta8629Never or only occasionally2430 ($36.9\%$)676 ($33.2\%$)––––Once or twice per week3473 ($52.7\%$)1110 ($54.4\%$)1.1491.031, 1.2810.0121.2171.067, 1.3890.003Most or all days per week687 ($10.4\%$)253 ($12.4\%$)1.3241.119, 1.5640.0011.2651.027, 1.5530.026NA4310Rice8624Never or only occasionally141 ($2.1\%$)42 ($2.1\%$)––––Once or twice per week633 ($9.6\%$)207 ($10.2\%$)1.0980.758, 1.6180.60.9860.631, 1.583>0.9Most or all days per week5812 ($88.2\%$)1789 ($87.8\%$)1.0330.736, 1.4810.90.9210.612, 1.4310.7NA4711Butter8607Never or only occasionally2895 ($44.0\%$)795 ($39.2\%$)––––Once or twice per week2937 ($44.6\%$)952 ($47.0\%$)1.1801.060, 1.3140.0021.1310.993, 1.2890.064Most or all days per week749 ($11.4\%$)279 ($13.8\%$)1.3561.157, 1.588<0.0011.3501.113, 1.6320.002NA5223Margarine8608Never or only occasionally3621 ($55.1\%$)1033 ($50.9\%$)––––Once or twice per week2425 ($36.9\%$)803 ($39.5\%$)1.1611.044, 1.2900.0061.1551.015, 1.3130.028Most or all days per week531 ($8.1\%$)195 ($9.6\%$)1.2871.075, 1.5360.0051.3431.081, 1.6600.007NA5618Nuts8637Never or only occasionally2837 ($43.0\%$)842 ($41.3\%$)––––Once or twice per week3238 ($49.1\%$)1006 ($49.3\%$)1.0470.943, 1.1620.41.0470.922, 1.1890.5Most or all days per week523 ($7.9\%$)191 ($9.4\%$)1.2301.023, 1.4750.0261.2350.988, 1.5360.061NA3510Potatoes8632Never or only occasionally1119 ($17.0\%$)325 ($15.9\%$)––––Once or twice per week4480 ($68.0\%$)1351 ($66.3\%$)1.0380.906, 1.1930.61.0550.892, 1.2530.5Most or all days per week994 ($15.1\%$)363 ($17.8\%$)1.2571.058, 1.4940.0091.2350.999, 1.5290.051NA4010Milk8634Never or only occasionally1349 ($20.5\%$)438 ($21.5\%$)––––Once or twice per week3072 ($46.6\%$)910 ($44.6\%$)0.9120.801, 1.0400.20.8960.766, 1.0500.2Most or all days per week2174 ($33.0\%$)691 ($33.9\%$)0.9790.853, 1.1240.80.9330.790, 1.1020.4NA3810Eggs8624Never or only occasionally206 ($3.1\%$)67 ($3.3\%$)––––Once or twice per week2716 ($41.2\%$)840 ($41.3\%$)0.9510.718, 1.2740.71.0840.761, 1.5770.7Most or all days per week3666 ($55.6\%$)1129 ($55.5\%$)0.9470.717, 1.2660.71.0110.711, 1.466>0.9NA4513Burgers/fast food8625Never or only occasionally2510 ($38.1\%$)765 ($37.6\%$)––––Once or twice per week3662 ($55.6\%$)1120 ($55.0\%$)1.0030.904, 1.115>0.91.0050.885, 1.141>0.9Most or all days per week418 ($6.3\%$)150 ($7.4\%$)1.1770.959, 1.4400.121.1150.863, 1.4300.4NA4314Probiotic drinks8624Never or only occasionally2664 ($40.4\%$)889 ($43.6\%$)––––Once or twice per week2938 ($44.6\%$)856 ($42.0\%$)0.8730.784, 0.9720.0130.8610.756, 0.9800.024Most or all days per week985 ($15.0\%$)292 ($14.3\%$)0.8880.763, 1.0320.120.8770.729, 1.0520.2NA4612GI level score (categorized)8411Poor2135 ($33.3\%$)731 ($36.7\%$)––––Moderate2731 ($42.6\%$)777 ($39.0\%$)0.8310.740, 0.9330.0020.8200.712, 0.9440.006Good1552 ($24.2\%$)485 ($24.3\%$)0.9130.799, 1.0410.20.9130.777, 1.0710.3NA21556an (%); Percentages were calculated column-wise.bAdjusted for gender and parental history of asthma.cOR: odds ratio; $95\%$ CI: $95\%$ confidence interval
## Lifestyle and dietary habits
Increased physical activity was significantly associated with a decreased odds of current asthma (once or twice per week vs never or only occasionally - OR: 0.741 $95\%$ CI: 0.605–0.908, p-value = 0.004), but this association was not observed at higher frequencies of physical activity (most or all days per week vs never or only occasionally - OR: 0.749, $95\%$ CI: 0.551–1.013, p-value = 0.063). An average television or computer usage time of more than 3 h was significantly associated with a decreased odds of current asthma (more than 3 h–5 h vs less than 1 h – OR: 0.699, $95\%$ CI: 0.529–0.923, p-value = 0.012; more than 5 h vs less than 1 h – OR: 0.732, $95\%$ CI: 0.542–0.986, p-value = 0.041). Conversely, current smoking (OR: 3.194, $95\%$ CI: 1.314–8.190, p-value = 0.011) and exposure to second-hand smoke (OR: 1.530, $95\%$ CI: 1.130–2.064, p-value = 0.006) were significantly associated with an increased odds of current asthma. However, frequent alcohol consumption (p-value = 0.061), and ever keeping pets (p-value = 0.14) were non-significantly associated with current asthma. Fig. 3C summarizes the associations between lifestyle habits and current asthma.
Of 16 food items, only increased potato consumption was significantly associated with an increased odds of current asthma (once or twice per week vs never or only occasionally - aOR: 1.428, $95\%$ CI: 1.034–1.993, p-value = 0.033), wherein a dose-effect trend was also observed (most or all days per week vs never or only occasionally - aOR: 1.577, $95\%$ CI: 1.145–2.180, p-value = 0.006). All other foods were non-significantly associated with current asthma (see Table 3).Table 3Summary of associations between dietary habits and current asthma, including contingency tables with percentage for each response by ever asthma, and odds ratios, unadjusted and adjusted with their corresponding $95\%$ confidence intervals as given by logistic regression models. Table 3CharacteristicNCurrent asthmaUnivariate logistic regressionMultiple logistic regressionbNo,$$n = 1$$,370aYes,$$n = 679$$aUnadjusted ORc$95\%$ CIcp-valueAdjusted ORc$95\%$ CIcp-valueMeat2041Never or only occasionally39 ($2.9\%$)17 ($2.5\%$)––––Once or twice per week130 ($9.5\%$)51 ($7.5\%$)0.9000.473, 1.7640.80.9690.449, 2.177>0.9Most or all days per week1196 ($87.6\%$)608 ($89.9\%$)1.1660.665, 2.1310.61.1680.594, 2.4280.7NA53Seafood2040Never or only occasionally88 ($6.5\%$)41 ($6.1\%$)––––Once or twice per week623 ($45.7\%$)313 ($46.3\%$)1.0780.731, 1.6130.71.0560.677, 1.6740.8Most or all days per week653 ($47.9\%$)322 ($47.6\%$)1.0580.718, 1.5820.81.0290.660, 1.630>0.9NA63Fruits2045Never or only occasionally73 ($5.3\%$)31 ($4.6\%$)––––Once or twice per week456 ($33.3\%$)223 ($32.9\%$)1.1520.741, 1.8270.51.3240.747, 2.4490.4Most or all days per week839 ($61.3\%$)423 ($62.5\%$)1.1870.775, 1.8600.41.4590.837, 2.6590.2NA22Vegetables2039Never or only occasionally43 ($3.2\%$)20 ($3.0\%$)––––Once or twice per week153 ($11.2\%$)82 ($12.1\%$)1.1520.643, 2.1210.61.5120.698, 3.5000.3Most or all days per week1167 ($85.6\%$)574 ($84.9\%$)1.0570.625, 1.8510.81.4910.738, 3.2660.3NA73Pulses2030Never or only occasionally323 ($23.8\%$)163 ($24.3\%$)––––Once or twice per week741 ($54.5\%$)356 ($53.1\%$)0.9520.760, 1.1960.70.9610.731, 1.2670.8Most or all days per week295 ($21.7\%$)152 ($22.7\%$)1.0210.778, 1.3400.90.9160.657, 1.2760.6NA118Cereals2037Never or only occasionally107 ($7.9\%$)63 ($9.3\%$)––––Once or twice per week512 ($37.6\%$)247 ($36.5\%$)0.8190.581, 1.1630.30.9710.641, 1.4850.9Most or all days per week742 ($54.5\%$)366 ($54.1\%$)0.8380.601, 1.1760.30.8790.587, 1.3300.5NA93Pasta2039Never or only occasionally453 ($33.2\%$)223 ($33.1\%$)––––Once or twice per week746 ($54.7\%$)364 ($54.0\%$)0.9910.809, 1.216>0.90.9470.744, 1.2070.7Most or all days per week166 ($12.2\%$)87 ($12.9\%$)1.0650.783, 1.4420.70.9860.677, 1.426>0.9NA55Rice2038Never or only occasionally26 ($1.9\%$)16 ($2.4\%$)––––Once or twice per week132 ($9.7\%$)75 ($11.1\%$)0.9230.470, 1.8600.80.9760.433, 2.283>0.9Most or all days per week1206 ($88.4\%$)583 ($86.5\%$)0.7860.422, 1.5060.50.8700.411, 1.9270.7NA65Butter2026Never or only occasionally526 ($38.7\%$)269 ($40.3\%$)––––Once or twice per week645 ($47.5\%$)307 ($46.0\%$)0.9310.762, 1.1370.50.8690.685, 1.1020.2Most or all days per week187 ($13.8\%$)92 ($13.8\%$)0.9620.718, 1.2820.80.9170.648, 1.2890.6NA1211Margarine2031Never or only occasionally675 ($49.7\%$)358 ($53.3\%$)––––Once or twice per week550 ($40.5\%$)253 ($37.6\%$)0.8670.712, 1.0550.20.8710.689, 1.1010.2Most or all days per week134 ($9.9\%$)61 ($9.1\%$)0.8580.615, 1.1870.40.8680.587, 1.2700.5NA117Nuts2039Never or only occasionally558 ($40.9\%$)284 ($42.0\%$)––––Once or twice per week672 ($49.3\%$)334 ($49.4\%$)0.9770.804, 1.1860.80.9540.758, 1.2030.7Most or all days per week133 ($9.8\%$)58 ($8.6\%$)0.8570.607, 1.1980.40.7730.512, 1.1520.2NA73Potatoes2039Never or only occasionally234 ($17.2\%$)91 ($13.4\%$)––––Once or twice per week903 ($66.3\%$)448 ($66.2\%$)1.2760.980, 1.6730.0741.4281.034, 1.9930.033Most or all days per week225 ($16.5\%$)138 ($20.4\%$)1.5771.145, 2.1800.0061.8261.233, 2.7180.003NA82Milk2039Never or only occasionally289 ($21.2\%$)149 ($22.0\%$)––––Once or twice per week632 ($46.4\%$)278 ($41.1\%$)0.8530.670, 1.0890.20.7980.600, 1.0630.12Most or all days per week442 ($32.4\%$)249 ($36.8\%$)1.0930.850, 1.4060.51.0010.745, 1.348>0.9NA73Eggs2036Never or only occasionally45 ($3.3\%$)22 ($3.3\%$)––––Once or twice per week559 ($41.0\%$)281 ($41.7\%$)1.0280.612, 1.775>0.91.9350.961, 4.2360.078Most or all days per week758 ($55.7\%$)371 ($55.0\%$)1.0010.599, 1.721>0.91.8070.900, 3.9470.11NA85Burgers/fast food2035Never or only occasionally519 ($38.2\%$)246 ($36.3\%$)––––Once or twice per week736 ($54.2\%$)384 ($56.7\%$)1.1010.906, 1.3390.31.1940.947, 1.5090.14Most or all days per week103 ($7.6\%$)47 ($6.9\%$)0.9630.656, 1.3960.80.9610.598, 1.5150.9NA122Probiotic drinks2037Never or only occasionally588 ($43.2\%$)301 ($44.6\%$)––––Once or twice per week581 ($42.7\%$)275 ($40.7\%$)0.9250.757, 1.1290.40.9710.766, 1.2300.8Most or all days per week193 ($14.2\%$)99 ($14.7\%$)1.0020.756, 1.322>0.91.0730.768, 1.4930.7NA84GI level score (categorized)1993Poor494 ($37.1\%$)237 ($35.9\%$)––––Moderate526 ($39.5\%$)251 ($38.0\%$)0.9950.802, 1.234>0.91.1440.884, 1.4820.3Good313 ($23.5\%$)172 ($26.1\%$)1.1450.899, 1.4590.31.1820.883, 1.5800.3NA3719an (%); Percentages were calculated column-wise.bAdjusted for gender and parental history of asthma.cOR: odds ratio; $95\%$ CI: $95\%$ confidence interval Odds ratios adjusted for gender and parental history of asthma showed that physical activity was a significantly associated with a reduced likelihood of current asthma (aOR: 0.693, $95\%$ CI: 0.542–0.885, p-value = 0.003), while exposure to second-hand smoke was significantly associated with current asthma (aOR: 1.435, $95\%$ CI: 1.001–2.047, p-value = 0.047). Lifestyle habits non-significantly associated with the odds of current asthma included a longer duration of television or computer usage (p-value = 0.069), frequent alcohol consumption (p-value = 0.055), current smoking (p-value = 0.4), and ever keeping pets (p-value = 0.8).
## Discussion
From our cross-sectional study, we estimated a lifetime asthma prevalence rate of $19.1\%$, and a current asthma prevalence rate of $6.3\%$. In comparison, estimates for prevalence rate of lifetime asthma and current asthma were given by the Singapore Mental Health Study in 2016 as $11.9\%$ and $2.6\%$, respectively, and by the National Health Survey as $10.5\%$ and $3.9\%$, respectively.11,19 The present findings indicate a possible rise in lifetime asthma prevalence rate, as has been highlighted previously.11 The upward trend in asthma prevalence mirrors that which has been observed and reported worldwide, and reasoning for such patterns have been the subject of speculation.10 Nonetheless, hypotheses for the change in prevalence rate include a possible improvement in awareness of asthma, improvements in diagnosis, and better access to healthcare – all of which do not warrant immediate concern.8,20 Notwithstanding, changes in environment and increased exposure to potential risk factors present a distinct possibility for the cause of increasing asthma prevalence rates. Herein, we examined the environmental factors associated with both ever asthma and current asthma.
Overall, a comparison of factors affecting ever asthma and current asthma indicated that demographics played a significant role in the manifestation of lifetime asthma, but not in current asthma. Notably, age was non-significantly associated with ever asthma but significantly associated with a higher likelihood of current asthma; male gender, while associated with an increased odds of ever asthma, was contrastively associated with a lowered risk of current asthma. Age and gender differences in asthma have been recognized in the literature, where findings revealed that asthma diagnosis peaked in young males but in older women, while severe asthma and asthma exacerbations affected younger boys but older women.21,22 Potential reasons given for the observed discrepancy include the influence of sex hormones and difference in perception of asthma among women, who may respond distinctly to asthma manifestation.22,23 Concordantly, from our cohort of young Chinese adults, we provide further evidence of an age-gender interaction effect on asthma.
Additionally, the demographic characteristics of increased income, being born in Singapore, and a history of drug allergy were significantly associated with ever asthma, suggesting a role for these non-modifiable factors in the manifestation of lifetime asthma. Of interest, the association of income with asthma has been reported in the literature.6 While our findings for increased income as a significant factor for the likelihood of asthma corresponds to that of previous scientific literature, we found that the direction of association was discordant with some earlier reports – the cross-sectional National Health and Nutrition Examination Survey reported that family income below the poverty threshold was associated with an increased likelihood of asthma, and the longitudinal Western Australian Pregnancy Cohort (Raine) Study found that children from low-income households had a two-fold increased risk of asthma while increasing income levels was associated with a decreased risk of asthma.24,25 We posit that higher familiar income affords greater access to healthcare, leading to a higher rate of doctor visits and thus diagnosis of asthma, resulting in a greater prevalence of asthma among individuals from higher income families.26 A familiar history of atopic diseases (ie, atopic dermatitis, allergic rhinitis, and asthma) was significantly associated with an increased likelihood of ever asthma, but to a much lesser extent with current asthma. Notably, both unadjusted and adjusted odds ratios indicated that maternal and paternal allergic rhinitis, maternal and paternal asthma, and sibling diagnoses of atopic diseases were associated with an increased risk of asthma. Moreover, the crude effect sizes for ever asthma and the respective parental risk factors of maternal and paternal asthma were significantly high, with an odds ratio of at least 4.5. In contrast, the odds ratios for current asthma and the respective risk factors of maternal and paternal asthma were relatively low (OR < 2.0). Upon adjustment, a familiar history of allergic diseases was significantly associated with ever asthma, but not with current asthma. Findings from our earlier meta-analysis were consistent with our current findings, wherein familiar medical history was frequently associated with asthma manifestation, of which familiar history of asthma showed the significance in association.6 Indeed, the genetic influence of asthma has long since been identified, and heritability of asthma has been estimated to be as high as $95\%$; numerous candidate genes have been hitherto identified for asthma.27, 28, 29 Here, we further suggest that while genetics constitute an important risk factor in the manifestation of asthma, the persistence of ever asthma into current asthma sees their importance as risk factors diminish.
We have also examined the associations of selected lifestyle habits with both ever asthma, and current asthma. Adjusted odds ratios showed that only increased television or computer usage was significantly associated a decreased odds of ever asthma, while increased physical activity and exposure to second-hand smoke were significant risk factors for current asthma. Concerning physical activity, our findings once again contribute to a repository of inconsistent associations in the literature – higher physical activity has been variously associated with increased risk of asthma in some reports, and a decreased risk of asthma in others.30 However recent findings have indicated a potential link between sedentary lifestyle and asthma risk: a high screen time and low frequency of physical activity increased the risk of central obesity, which in turn correlated highly to the manifestation of asthma.31 We theorize that physical activity is merely a constituent to a multifactorial lifestyle habit component and interacts with other variables including screen time and duration to sleep, influencing a downstream manifestation of asthma.30,32 Moreover, the intensity and energy expenditure of physical activity need to be considered as well.32 With regards to second-hand smoke exposure, we note that while second-hand smoke exposure increased the odds of current asthma, smoking status itself was not significantly associated with asthma, likely due to the low proportion of smokers in our sample. Nonetheless, our findings for second-hand tobacco smoke exposure were concordant with that of previous studies, and reinforced the role of tobacco in increasing the risk of current asthma manifestation.6 Finally, we hypothesized that in addition to environmental factors, dietary habits play some role in the extension of ever asthma into current asthma: increased consumption of selected food groups - pulses, probiotic drinks, and a lower overall GI of diet were associated with a lower likelihood of ever asthma; cereals, pasta, butter, margarine, and potatoes were associated with an increased odds of ever asthma, while only potatoes were identified as a significant risk factor for current asthma. Interestingly, increased consumption of potatoes was associated with an increased likelihood of both ever asthma and current asthma in our cohort – to our knowledge an association hardly reported before. Our current results adds on to the numerous conflicting associations between food and asthma identified hitherto in the scientific literature.33,34 Among these, fruits, vegetables, meat, fish, and fast-food have been the most frequent food-asthma associations highlighted from ISAAC studies.34 Additional grouping of food types into dietary habits, such as "Mediterranean diet" or "Western diet" patterns, yielded no further conclusive associations.34 Moreover, our novel assessment of overall dietary GI, while associated with lowered likelihood of ever asthma in moderate GI level vs poor GI levels groups, showed no dose-effect relationship, and was non-significantly associated with current asthma. Nonetheless, the possible effects of foods synergies make dietary habits are a complex entity worth continued exploration.35 Since dietary habits have potentially distinct effects from food types in isolation, further investigations into food-asthma associations might find sagacity in the usage of principal component analysis or factor analysis methods to discern dietary patterns.36, 37, 38
## Limitations and conclusions
The cross-sectional nature of our study entailed the limitations inherent in cross-sectional studies. Findings for the relationships between potential risk factors and asthma were restricted to associations, while time-trend data could not be gathered from this study. In particular, we acknowledge the temporal factor in the relationship between dietary intake and asthma manifestation, which this study yielded insufficient data on. A longitudinal study design would better account for the possible influence of time in the association between diet and asthma. notwithstanding, the current study provides a starting point for further investigation, highlighting the important risk factors for ever asthma and current asthma. Upcoming genome-wide association studies (GWAS), expression quantitative trait loci (eQTL) analyses, and functional characterization studies would further elucidate the mechanisms involved in asthma manifestation and its associated risk factors.
In conclusion, we have provided an update to the prevalence of ever asthma, current asthma, and asthma phenotypes in a sample of young Chinese adults. As has been the case internationally, the prevalence of asthma has seen an increase in Singapore. Moreover, we have identified several risk factors of interest, such as familiar history of atopic diseases for ever asthma, and age, gender, dietary and lifestyle factors for current asthma. Overall, genetic factors appeared to have more importance in influencing ever asthma, while lifestyle and environmental factors may play a more dominant role in current asthma manifestation. Importantly, we realized that the risk factors should also be analyzed in combinations with other risk factors to account for potential interactions, and future studies including GWAS, eQTL analyses, and functional characterization could better elucidate mechanisms involved.
## Abbreviations
$95\%$ CI, $95\%$ confidence interval; aOR, adjusted odds ratio; eQTL:, expression quantitative trait loci; GWAS, genome-wide association studies; ISAAC, International Study of Asthma and Allergies in Childhood; OR, odds ratio; SPT, skin prick test; TV, television.
## Acknowledgements
We extend our sincerest gratitude to all participants for their contributions to this study.
## Funding
F.T.C. received grants from the $\frac{10.13039}{501100001352}$National University of Singapore (N-154-000-038-001), Singapore Ministry of Education Academic Research Fund (R-154-000-191-112; R-154-000-404-112; R-154-000-553-112; R-154-000-565-112; R-154-000-630-112; R-154-000-A08-592; R-154-000-A27-597; R-154-000-A91-592; R-154-000-A95-592; R154-000-B99-114), $\frac{10.13039}{501100012415}$Biomedical Research Council ($\frac{10.13039}{501100012415}$BMRC) (Singapore) ($\frac{10.13039}{501100012415}$BMRC/$\frac{01}{1}$/$\frac{21}{18}$/077; $\frac{10.13039}{501100012415}$BMRC/$\frac{04}{1}$/$\frac{21}{19}$/315; $\frac{10.13039}{501100012415}$BMRC/APG$\frac{2013}{108}$), Singapore Immunology Network (SIgN-06-006; SIgN-08-020), $\frac{10.13039}{501100001349}$National Medical Research Council ($\frac{10.13039}{501100001349}$NMRC) (Singapore) ($\frac{10.13039}{501100001349}$NMRC/$\frac{1150}{2008}$), $\frac{10.13039}{501100001321}$National Research Foundation ($\frac{10.13039}{501100001381}$NRF) (Singapore) (NRF-MP-2020-0004), Singapore Food Agency (SFA) (SFS_RND_SUFP_001_04; W22W3D0006), and the Agency for Science Technology and Research (A∗STAR) (Singapore) (H$\frac{17}{01}$/a$\frac{0}{008}$; and APG$\frac{2013}{108}$). The funding agencies had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
## Availability of data and materials
All data used and included in this study are available from the corresponding author (F.T.C.).
## Author contributions
F.T.C. conceived and supervised the current research study. Q.Y.A.W. conducted the literature review, analyzed and interpreted the data, and wrote the manuscript. Q.Y.A.W., J.J.L., J.Y.N., P.M., Y.Y.E.L., and Y.Y.S assisted in recruiting study participants and data collation. All authors read and approved the final manuscript.
## Authors’ consent for publication
All authors have read and consented to the publication of this manuscript.
## Ethics approval and consent
Ethical approval for this study was granted by the $\frac{10.13039}{501100001352}$NUS Institutional Review Board (IRB reference code: NUS-07-023, NUS-09-256, NUS-10-445, NUS-13-075, NUS-14-150, and NUS-18-036). This study was performed in compliance with the Declaration of Helsinki, Good Clinical Practice, and local regulatory guidelines. Before participation, each subject was informed of this study's details via a Participant Information Sheet and provided written informed consent to participation through the signature of a Consent Form.
## Declaration of competing interest
F.T.C. reports grants from Singapore Ministry of Education Academic Research Fund, Singapore Immunology Network, National Medical Research Council (Singapore), Biomedical Research Council (Singapore), National Research Foundation (NRF) (Singapore), Singapore Food Agency (SFA), and the Agency for Science Technology and Research (Singapore), during the conduct of the study; and has received consultancy fees from Sime Darby Technology Centre, First Resources Ltd, Genting Plantation, Olam International, and Syngenta Crop Protection, outside the submitted work. The other authors declare no other competing interests.
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|
---
title: The construction and analysis of tricarboxylic acid cycle related prognostic
model for cervical cancer
authors:
- Guanqiao Chen
- Xiaoshan Hong
- Wanshan He
- Lingling Ou
- Bin Chen
- Weitao Zhong
- Yu Lin
- Xiping Luo
journal: Frontiers in Genetics
year: 2023
pmcid: PMC10033772
doi: 10.3389/fgene.2023.1092276
license: CC BY 4.0
---
# The construction and analysis of tricarboxylic acid cycle related prognostic model for cervical cancer
## Abstract
Introduction: Cervical cancer (CC) is the fourth most common malignant tumor in term of in incidence and mortality among women worldwide. The tricarboxylic acid (TCA) cycle is an important hub of energy metabolism, networking one-carbon metabolism, fatty acyl metabolism and glycolysis. It can be seen that the reprogramming of cell metabolism including TCA cycle plays an indispensable role in tumorigenesis and development. We aimed to identify genes related to the TCA cycle as prognostic markers in CC.
Methods: Firstly, we performed the differential expressed analysis the gene expression profiles associated with TCA cycle obtained from The Cancer Genome Atlas (TCGA) database. *Differential* gene list was generated and cluster analysis was performed using genes with detected fold changes >1.5. Based on the subclusters of CC, we analysed the relationship between different clusters and clinical information. Next, Cox univariate and multivariate regression analysis were used to screen genes with prognostic characteristics, and risk scores were calculated according to the genes with prognostic characteristics. Additionally, we analyzed the correlation between the predictive signature and the treatment response of CC patients. Finally, we detected the expression of ench prognostic gene in clinical CC samples by quantitative polymerase chain reaction (RT-qPCR).
Results: We constructed a prognostic model consist of seven TCA cycle associated gene (ACSL1, ALDOA, FOXK2, GPI, MDH1B, MDH2, and MTHFD1). Patients with CC were separated into two groups according to median risk score, and high-risk group had a worse prognosis compared to the low-risk group. High risk group had lower level of sensitivity to the conventional chemotherapy drugs including cisplatin, paclitaxel, sunitinib and docetaxel. The expression of ench prognostic signature in clinical CC samples was verified by qRT-PCR.
Conclusion: There are several differentially expressed genes (DEGs) related to TCA cycle in CC. The risk score model based on these genes can effectively predict the prognosis of patients and provide tumor markers for predicting the prognosis of CC.
## Introduction
Cervical cancer (CC) is the fourth most common cancer and also the fourth leading cause of cancer related deaths. According to a report released by the International Agency for Research on Cancer (IARC) in 2018, there are 570,000 new cases and 310,000 deaths in the world in this year (CA Cancer J Clin, 2020). In spite of the promotion of the HPV-related vaccine and screening programs, many patients with CC are already advanced or have locally advanced cancer at diagnosis, which leads to a poor prognosis. Previous studies found that 5-year survival rate of CC patients detected at an early stage is $92\%$ (Bray et al., 2018), whereas the 5-year survival rate for advanced CC patients, especially for metastatic CC patients, whose survival rates range from $5\%$ to $15\%$, is still low (Moore, 2006). In order to improve survival rates, primary screening and early detection of CC are high priorities. The appropriate biomarkers for clinical diagnosis and prognosis have not been identified yet. Thus, better prognostic biomarkers for CC development are urgently required to increase patient survival.
As a central pathway of cellular oxidative phosphorylation, the TCA cycle participates in physiological processes such as cellular bioenergetics, biosynthesis, and REDOX balance. Most cancers, including CC, are a disease characterized by the accumulation of genetic alterations and genetic dysregulation, leading to uncontrolled cell proliferation requiring increased energy production and macromolecular synthesis (DeBerardinis and Chandel, 2016). In response to increased metabolic stress, malignant cells often reprogram their biochemical pathways so that nutrients can be rapidly absorbed and broken down, thereby promoting disease transformation, maintenance, and progression. As it is universally accepted that cancer cells primarily use aerobic glycolysis for respiration, the TCA cycle has been overlooked until recently in cancer metabolism and tumorigenesis. With modern technological advances such as unbiased and targeted metabolomics along with high-throughput sequencing technology, there are a wealth of new discoveries in the field of tumor metabolism. Recent studies have found that gankyrin positively regulates TIGAR transcription to promotes hepatocellular carcinoma progression by accelerating the conversion of glucose metabolism to PPP and TCA cycle (Yang et al., 2022). Furthermore, glutamine has been shown to be an indispensable nutrient source in many cancer types, particularly MYC-driven cancers (DeBerardinis and Cheng, 2010). Researchers pay increasing attention to lipid metabolism in tumorigenesis recent years. To sum up, these studies have provided compelling evidence that the TCA cycle serves as a significant role in cancer metabolism and tumorigenesis (Sajnani et al., 2017).
In this study, we conducted a series of analysis including Cox regression, LASSO regression and multivariate Cox regression based on TCA cycle-associated genes in CC. A prognostic risk model based on 7 gene signatures was constructed via TCGA database and externally validated by Gene Expression Omnibus (GEO) database. In the meanwhile, the model provided an indication of prognosis, diagnostic value and predicting response to chemotherapy for CC.
## Data collection and preprocessing
From TCGA database (https://portal.gdc.cancer.gov/), we obtained RNA sequence transcriptome data and relevant clinical information of 304 patients with CC and 3 normal adjacent tissue samples. From the GEO database (https://www.ncbi.nlm.nih.gov/geo/, GSE44001), we downloaded RNA sequencing data and clinical information of 300 patients for external validation.
## Identification of differentially expressed TCA cycle-related genes
The list of TCA cycle-related genes was obtained through literature mining (Arnold et al., 2022). Their mRNA expression levels between CC and normal adjacent tissue samples were compared according to TCGA cohort. The limma software package was used to identify the differentially expressed TCA cycle-related genes with the significance threshold ($p \leq 0.05$ and |log2FC|>1.5), which were presented as a heatmap. The “corrplot” package was used to reveal correlations between DEGs associated with the TCA cycle. An interaction network of proteins among TCA cycle-related DEGs was constructed using STRING and visualized using Cytoscape 3.8.0.
## Consensus clustering
The “ConsensuClusterPlus” R package was used for the analysis the comprehensive expression of the 18 differentially expressed TCA cycle-related genes to identify distinct subgroups of 302 CC samples. It was repeated 1,000 times to ensure classification stability (parameters: clustering algorithm, k-means; distance, Euclidean). The optimal k value was determined based on cumulative distribution function and delta area values. Principal Component Analysis (PCA) were performed by “Rtsne” R package to reduce the dimension of the 18 DEGs. The Kaplan-Meier method and log-rank test were used to evaluate the overall survival (OS) rate of patients with different subtypes. Chi-square test was used to analyze the distribution of age, tumor grade, tumor stage and histological type among different clusters.
## Construction of TCA cycle related prognostic signature
To sort out TCA cycle related genes with potential prognostic value ($p \leq 0.05$), univariate *Cox analysis* was performed for OS. Next, using a least absolute shrinkage and selection operator (LASSO) regression model, the optimal value was determined to build a prognostic gene signature. We used R’s glment package to perform Cox regression analysis and LASSO. On the basis of the following formula: Risk score = ∑Coefgene × Expgenes, risk scores for every single patient was calculated, where Coefgene represents the coefficient of each prognostic gene and Expgenes represents the expression level of each gene. According to the median risk scores, patients were divided into high-risk and low-risk group. In addiction, we plotted the receiver operating characteristic (ROC) curves and Kaplan–Meier plots. To perform the validation of the prognostic model, GEO dataset (GSE44001) was analyzed the prognostic value with similar methods.
## Functional Enrichment Analysis and cuproptosis-related gene analysis
Gene set enrichment analysis (GSEA) (https://www.broadlnstitute.org/gsea/) was used to identify differential expressions of genes (gene sets) that were functionally related and whose enrichment in CC patient subgroups was significant. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases were downloaded from the molecular signatures database (MSigDB) as the functional enrichment reference set (http://www.gsea-msigdb.org). Finally, the significantly enriched KEGG pathways are shown centrally. Gene set variation analysis (GSVA) was implemented in the “GSVA” R package to investigate potential molecular characteristics that differed between high- and low-risk groups. Access to the MSigDB, the *Hallmark* gene set “c2. cp.kegg.v2022.1. Hs.symbols.gmt” was gotten to applied in GSVA. According to a threshold of |log2FC| > 0.1 and $p \leq 0.05$, DEGs between high- and low-risk groups were screened and undergone to gene ontology (GO) and KEGG analyses using the “clusterProfiler” R package. Furthermore, we made a comparation the expression levels of cuproptosis-related genes between the high-risk group and the low-risk group, and visualized them by box plots.
## Immune checkpoint analysis and the role of the predictive signature in predicting the clinical treatment response
Spearman correlation analysis was performed using “cor.test” in R to analyze the correlation between immune checkpoint expression with $p \leq 0.05$ as the cutoff for significance. The pRRophetic R package was used to predict chemosensitivity based on data from the Genomics of Drug Sensitivity in Cancer pharmacogenomics database. The half maximal inhibitory concentration (IC50) of clinically commonly used chemotherapy drugs was calculated to evaluate the role of predictive signatures in predicting the treatment response of CC. We compared the IC50 values between the high- and low-risk groups via Wilcoxon signed-rank test.
## Analysis of quantitative reverse TranscriptionPolymerase chain reaction (qRT-PCR)
Both cervical cancer and adjacent non-cancerous tissues used in this study were obtained from postoperative patients with cervical cancer from 2019 to 2022 in Department of gynecology, Guangdong Women and Children Medical Hospital. All samples were obtained through review by the ethics committee, and the informed consent of CC patient was acquired. We extracted RNA from specimens by utilizing the TRIzol reagent (Ambion, United States), followed by reverse transcription into cDNA utilizing the QuantiTect Reverse Transcription Kit (Promega, United States). Quantitative PCR (qPCR) is a technique for measuring the amount of DNA present in a sampl in real time. With the aid of SYBR-Green (Vazyme, China), real-time qPCR assays were carried out, and expression levels were standardized to beta-actin levels. The sequences of primers are listed in Table 1.
**TABLE 1**
| Primer | Sequence (5′to 3′) |
| --- | --- |
| ACSL1-F | CTTATGGGCTTCGGAGCTTTT |
| ACSL1-R | CAAGTAGTGCGGATCTTCGTG |
| ALDOA-F | CAGGGACAAATGGCGAGACTA |
| ALDOA-R | GGGGTGTGTTCCCCAATCTT |
| FOXK2-F | GGAGGCGTCTGAGTCTCCA |
| FOXK2-F | CCCACCTTGTACCCTGAAGA |
| GPI-F | CCGCGTCTGGTATGTCTCC |
| GPI-R | CCTGGGTAGTAAAGGTCTTGGA |
| MDH1B-F | CTAGCATGACGACTGAACTGATG |
| MDH1B-R | AGAGGCACTGGTGATCCAGA |
| MDH2-F | TCGGCCCAGAACAATGCTAAA |
| MDH2-R | GCGGCTTTGGTCTCGATGT |
| MTHFD1-F | GCGCCAGCAGAAATCCTGA |
| MTHFD1-R | AGGTACTTGCTCCTTCAACTGA |
| Beta-actin-F | GTGAAGGTGACAGCAGTCGGT |
| Beta-actin-R | AAGTGGGGTGGCTTTTAGGAT |
## Statistical analysis
All statistical analyses were performed with the use of R software (Version 4.2.1). Wilcoxon test was used to analyze the difference in the expression of TCA-related genes between normal and tumor tissues. Cox regression model was used for univariate and multivariate survival analysis to screen independent prognostic signature. The OS of patients in the high and low risk groups was analyzed by the Kaplan-Meier method and log-rank test. The ROC curve was drawn and the area under the curve (AUC) was determined by using the “survivalROC” software package. GraphPad Prism 9 program was used to draw scatter plots, and paired t-test was used to detect the differences in the expression of prognostic related genes between cervical cancer tissues and adjacent tissues. A p-value of less than 0.05 was considered to be statistically significant ($p \leq 0.001$ = ***, $p \leq 0.01$ = **, and $p \leq 0.05$ = *).
## Identification of TCA cycle-related DEGs between normal and CC tissues
A list of 117 TCA cycle-related genes was identified (Supplementary Table S1), based on published data, and their RNA expression levels compared in TCGA data from 304 CC and 3 normal adjacent tissue samples. There were 18 differentially expressed TCA cycle-related genes identified, with a threshold of $p \leq 0.05$ and |log2FC > 1.5|, of which 17 (PKM, GPI, IDH1, SHMT1, MTHFD1, SHMT2, ENO1, IDH2, ALDOA, DHFR, ELOVL3, SCD, TYMS, HK2, ALDOB, and PKLR) were upregulated and only one gene, ACAT1, was downregulated in tumor tissues (Figure 1A). Correlations among the mRNA expression levels of TCA cycle-related DEGs were analyzed by Pearson correlation analysis (Figure 1B). The results showed that all the TCA cycle-related DEGs had a positive correction with each other. In particular, FASN was significantly correlated with SCD ($r = 0.66$, $p \leq 0.001$) and PKM was significantly correlated with ENO1 ($r = 0.63$, $p \leq 0.001$). Construction of a PPI network revealed that the top 5 genes including PKM, IDH1, ENO1, PKLR and IDH2 were selected, based on their values of closeness, to be the hub nodes in the PPI network (Figure 1C).
**FIGURE 1:** *Differential expressions of 18 TCA cycle-related genes and tumor subclusters based on the TCA cycle-related DEGs. (A) The heatmap showed the 18 TCA cycle-related genes in tumor and normal adjacent tissues. (B) Display of the relationship between the TCA cycle-related DEGs. (C) PPI network indicated the interactions of the TCA cycle-related genes. (Red and green colors represent >0.65 and < = 0.65 closeness respectively).*
## Consensus clustering based on TCA cycle-related DEGs
To explore the relationships between CC subtypes and expression of the 18 TCA cycle-Related DEGs, consensus clustering analysis was performed to classify tumors according to expression levels of TCA cycle -related DEGs. Clustering variable (k) values from 2 to 9 were applied; when $k = 2$, intragroup correlations were low. Hence, patients with CC could be divided into two different subtypes, including 215 cases in cluster 1 and 87 cases in cluster 2 (Figures 2A, B). PCA was conducted to verify the ability of the model to group patients in the entire set and observed that patients in different clusters were dispersed in two directions (Figure 2C). There was a significant difference in OS time between the two clusters ($$p \leq 0.041$$) (Figure 2D). Further, the associations between the clustering and clinicopathological parameters were examined. The significant difference was found between cluster 1 and cluster 2, for the survival state ($p \leq 0.05$) and pathological type ($p \leq 0.01$). In contrast, other parameters such as age, tumor grade and clinical stage were no significant different (Figure 2E).
**FIGURE 2:** *Tumor subclusters based on the TCA cycle-related DEGs. (A) Consensus clustering matrix for k = 2. (B) Delta area value for k = 2, 3, 4, 5, 6, 7, 8, and 9. (C) PCA analysis between different subclusters (red: cluster 1; green: cluster 2). (D) Kaplan–Meier curves of OS in two clusters. (E) Distribution heat map of seven prognostic TCA cycle-related genes and clinicopathological variables in the cluster1 and cluster 2 (p < 0.01 = **, and p < 0.05 = *).*
## Construction of Prognostic Signature for TCGA CC
The TCA cycle-related genes were all chosen for the univariate Cox regression analysis, and we found that 12 genes were significantly associated with OS in TCGA CC (Figure 3A). A LASSO regression analysis was applied to establish a prognostic gene signature using the 12 genes mentioned above. Following LASSO analysis to minimize overfitting (Figures 3B,C), seven genes involving ACSL1, ALDOA, FOXK2, GPI, MDH1B, MDH2 and MTHFD1 were identified (Figure 3D).
**FIGURE 3:** *Establishment of the TCA cycle-related signature in the TCGA dataset. (A) Univariate cox regression analysis screened prognostic TCA cycle-related genes (p < 0.05). (B) LASSO regression of the 12 prognostic genes. (C) Cross-validation for tuning the parameter selection in the LASSO regression. (D) Stepwise multivariate cox regression analysis showed 7 independent prognostic genes.*
The risk score of seven genes was also calculated for further univariate and multivariate Cox regression analyses. The risk score formula to predict OS was developed as follows: risk score = (−0.080 × ACSL1) + (−0.036 × ALDOA)+ (0.014 × FOXK2) + (−0.068 × GPI) + (0.102 × MDH1B) + (−0.216 × MDH2) + (−0.089 × MTHFD1). It is well-known that survival times vary among patients with different pathological types of CC. Thus, prognosis analysis of the seven genes in different pathological types of CC including cervical squamous cell carcinoma and cervical adenocarcinoma. Using this signature, patients were further classified into equal high- and low-risk groups, based on the median risk value (Figures 4A, E). As illustrated in the scatter diagram in Figures 4B, F, individuals in the high-risk score group had worse outcomes than those in the low-risk group. In addition, a significant difference in OS time was detected between the two groups by Kaplan-*Meier analysis* ($p \leq 0.01$, $p \leq 0.05$) (Figures 4C, G). ROC curve analysis was conducted to evaluate the sensitivity and specificity of the prognostic model, resulting in AUC values of the models for predicting 1-, 3-, and 5-year OS of 0.613, 0.663, and 0.736 in cervical squamous cell carcinoma, while 1-, 3-, and 5-year OS of 0.699, 0.663, and 0.633 in cervical adenocarcinoma respectively (Figures 4D,H).
**FIGURE 4:** *Prognostic value of the risk patterns of the signature in the TCGA dataset and prognosis analysis in different pathological types of cervical cancer: cervical squamous cell carcinoma in (A–D) and cervical adenocarcinoma in (E,F). (A,E) Distribution of risk score. (B,F) Survival status plot and survival time. (C,G) Kaplan-Meier analysis of OS. (D,H) ROC curves analysis between high- and low-risk groups.*
## External validation of the seven-gene signature
To test the robustness of the gene signature model built from the TCGA data, data from 300 patients with CC in the GEO cohort, the GSE44001 dataset were also divided into high- and low-risk groups using a similar formula to TCGA data (Figure 5A). According to the uniform formula, the survival analyses found that patients with higher risk scores had poorer OS ($$p \leq 0.001$$) (Figures 5B, C). In the GSE44001 dataset, the AUC was 0.705 at one year, 0.701 at three years and 0.68 at five years (Figure 5D).
**FIGURE 5:** *Validation of the risk model in the GEO cohort. (A) Distribution of risk score. (B) Survival status plot and survival time. (C) Kaplan-Meier analysis of OS. (D) ROC curves analysis between high- and low-risk groups.*
## Functional enrichment analysis
To explore the potential biological processes in high- and low-risk groups, we performed a GSEA. The KEGG pathway analysis showed that phototransduction, RNA polymerase, and steroid biosynthesis were mainly enriched in the low-risk group (Figure 6A), while allograft rejection, glycosaminoglycan biosynthesis—keratan sulfate and other glycan degradation were principally enriched in the high-risk group (Figure 6B). To further identify the expression difference of these two groups, the GSVA enrichment analysis revealed that cancer pathways, including Wnt, Notch and mTOR signaling pathway were highly expressed in low-risk group, compared with high-risk group (Figure 6C). These results suggest that Metabolic reprogramming modulates tumor proliferation, apoptosis, and cell cycle via these pathways. The box plot of Cuproptosis-related gene analysis illustrated that in the low-risk group, CDKN2A DLAT DLD GLS LIAS MTF1, and PDHA1 were significantly downregulated in the high-risk group (Figure 6D). The finding echoed the definition of the novel cell deathmodality “Cuproptosis” which is featured by disturbing specific mitochondrial metabolic enzymes (Tsvetkov et al., 2022).
**FIGURE 6:** *Functional Analysis DEGs between low- and high-risk subgroups. GSEA KEGG pathway enrichment in low-risk group (A), and high-risk group (B). (C) The heatmap of KEGG pathways between low- and high-risk subgroups analyzed by GSVA. (D) Box plot of cuproptosis-related genes analysis based on low- and high-risk subgroups; *p < 0.05, **p < 0.01, ***p < 0.001.*
## Correlation between the predictive signature and CC therapy
Correlation assessment of the association between risk score and immune checkpoint-related genes found that PD-L1, 4-1BBL, OX40L, GITR, B7.1, and B7.2 had a negative correction with the risk score (Figure 7A). In other words, CC patients with higher risk scores had lower expression levels of these immune checkpoints. Our data suggest that patients in low-risk group may be more sensitive to immunotherapy. In addition to immunotherapy, we also analyzed the association between the predictive signature and the efficacy of general chemotherapy for CC. The results found that the IC50 of sunitinib, paclitaxel, cisplatin, and docetaxel was lower in the low-risk group (Figure 7B), which is helpful for exploring individualized treatment schemes suitable for high- and low-risk group patients.
**FIGURE 7:** *Comparison of treatment drugs sensitivity between high- and low-risk groups. (A) Correlation analysis of the expressions of six immune checkpoints with riskscore. (B) IC50 of cisplatin, sunitinib, docetaxel and paclitaxel in high and low risk groups.*
## Real-time quantitative reverse transcription PCR (qRT-PCR).
To determine whether the seven prognostic genes were differentially expressed in CC tissues, a total of 19 paired clinical CC tissues and adjacent normal tissues were analyzed each gene expression using qRT-PCR. The findings illustrated that the expression levels of ACSL1, ALDOA, FOXK2, MDH2, and MTHFD1 in cervical cancer specimens were differentially expressed in contrast with those in normal specimens, whereas there was no significant difference of the expression levels of GPI and MDH1B (Figure 8).
**FIGURE 8:** *qRT-PCR The results showed that the expression of ACSL1, ALDOA, FOXK2, MDH2, and MTHFD1 in cervical cancer was significantly expressed compared with normal group. ns p > 0.05; ∗p < 0.05; ∗∗p < 0.01.*
## Discussion
CC with high incidence and mortality rate remains a considerable health burden in females worldwide. The occurrence and development of CC is a complex, multi-step and multi-gene process, among which high-risk human papillomavirus persistent infection is the main factor (Crosbie et al., 2013). Previous studies stress the importance of TCA cycle in cancer because its products influence cell viability and proliferation (DeBerardinis and Chandel, 2016; Kim and DeBerardinis, 2019). Further, accumulating evidence to illustrate that metabolic heterogeneity influences therapeutic vulnerabilities and may predict clinical outcomes (Eniafe and Jiang, 2021). It used to be thought that cancer progression bypass TCA cycle which is in accord with Warburg effect. However, such concept has been challenged and may be revised with the increasing studies demonstrated that TCA cycle is of great importance in cancers. TCA cycle also generates energy and building blocks to meet the need of cancer cells growth, but hyperactivation of TCA cycle was previously considered to produce excess reaction oxygen species that is otherwise toxic to cells.
One study showed that through AMPK-mediated PDHA phosphorylation, the TCA cycle drives cancer cells to adapt to the metastatic microenvironment for metastasis (Cai et al., 2020). Besides, recent reports also demonstrated that certain TCA intermediates, such as oxaloacetate (OAA) and ketoglutarate (a-KG), play an important role in ROS detoxification (Sawa et al., 2017). Altogether, TCA cycle play a non-negligible role in tumorigenesis, metastasis and therapy. Therefore, we constructed an innovative signature based on TCA cycle associated genes. The results suggested that the TCA cycle related signature have substantial value for predicting OS and the drug sensitivity in CC.
The signature was comprised of seven core genes involving ACSL1, ALDOA, FOXK2, GPI, MDH1B, MDH2 and MTHFD1. FOXK2 was targeted by TP53TG1 via regulating miR-33a-5p and with the involvement of PI3K/AKT/mTOR signaling pathway to accelerates the CC development (Liao et al., 2022). The mechanism study confirmed that circ-ITCH regulated the expression of FOXK2 by adsorbing microrRNA-93-5p (miR-93-5p) to inhibit tumor growth (Li et al., 2020). Notably, FOXK2 was upregulated in the high-risk group in our model. Except for FOXK2, other genes involved in the model have not previously been studied in the context of CC. ACSL1, encoding an isozyme of the long-chain fatty-acid-coenzyme in a ligase family, is downregulate by MiR-27a-3p and MiR-205 to increase the risk of liver cancer and hepatocellular carcinoma respectively (Cui et al., 2014; Sun et al., 2020; Quan et al., 2021). Similarly, ACSL1 acted as a protective factor in our prognostic model. ALDOA increased most markedly in response to TGF-β and further the results of in vitro and in vivo experiments show that ALDOA is associated with the proliferation and metastasis of pancreatic cancer cells (Ji et al., 2016). GPI, a member of the glucose phosphate isomerase protein family, can be used as a potential biomarker for predicting OS of hepatocellular carcinoma (Lyu et al., 2016). Numerous studies have shown that the overexpression of GPI/AMF is connected with poor prognosis, such as tumor invasion and the increased mortality in many cancer types, including gastrointestinal (Gong et al., 2005), kidney, lung and breast cancers (Baumann et al., 1990; Nabi et al., 1991; Jiang et al., 2006). MDH1B, (Malate Dehydrogenase 1B) is one of alleles encoding MDH isozymes. Carm1-mediated arginine methylation of MDH1 inhibits glutamine metabolism, thereby inhibiting the growth of pancreatic cancer (Wang et al., 2016). The tumor-suppressive effects of methyl 3-(3-(4-(2,4,4-trimethylpentan-2-yl)phenoxy)propanamido)benzoate were investigated and demonstrated that dual inhibition of MDH1 and MDH2 is an effective approach to target tumor metabolism (Naik et al., 2017). As a metabolism-related enzyme, MDH2 is overexpressed in endometrial carcinoma tissues and correlated with its grade. These results demonstrated that MDH2 promoted cancer progression of endometrial cancer (Zhuang et al., 2017). Studies have found that MTHFD1 deficiency can significantly inhibit the antioxidant defense ability of cells and inhibit the distant metastasis of tumors, which indicates that the high expression of MTHFD1 in liver cancer tissues indicates a poor prognosis.
In this study, we calculated a risk score based on the constructed prognostic model, and classified patients with cervical cancer into high-risk and low-risk groups according to the median of this risk score. The Kaplan-Meier survival curve showed that the OS of the high-risk group was significantly lower than that of the low-risk group. The calculation of the AUC value showed the value of the risk signature in predicting survival prognosis. Validation set based on the GEO database was analyzed with similar methods to verify the stability of the predictive model. It is important to note that chemotherapy is the main treatment approach for CC, but it often causes a number of side effects, and cancer cells can become resistant to chemotherapy (Grasso et al., 2017; Choi et al., 2021). Cancer treatment failure in CC can be attributed to drug resistance. Thus, assessment of individual drug response is crucial in the treatment of CC. Accumulating evidence suggest that this chemoresistance is strongly associated with specific metabolic abnormalities in cancer cells, particularly increased use of glucose and the amino acid glutamine that promotes anabolic processes. ( Luo et al., 2009; Vivanco, 2014; Belizario et al., 2016). In fact, Reprogramming of metabolic pathways in cancer cells is a complicated and confusing process. A popular view holds that a key function of oncogenes is to reprogram cellular metabolism back to the building blocks that maintain unrestrained tumor growth (Yao et al., 2008). In our study, we developed an integrated computational approach to identify metabolic reprogramming of multiple drugs based on TCA cycle related genes.
Finally, we carried out qRT-PCR on the seven TCA cycle associated genes linked to the prognoses of CC patients. These results demonstrated the accuracy of our first step of difference analysis, improved the credibility of subsequent studies, and also confirmed the association of risk genes with TCA cycle, further validating the predictive power of our model.
Although this study found that TCA cycle related pathways affect the progression and treatment of CC, there remain some limitations. Firstly, the small number of normal samples in TCGA database may lead to a certain bias in the analysis. Secondly, in order to explore the direct mechanisms additional in vitro and in vivo studies are necessary. Finally, this study is designed as a retrospective study, and a large number of experimental data are needed to confirm the study results.
## Conclusion
In conclusion, our study revealed that the prognostic model based on TCA cycle associated genes are significantly correlated with the survival and clinicopathological characteristics in CC. TCA cycle related signature are effective biomarkers for predicting the prognosis of CC patients.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.
## Author contributions
GC: conceptualization, methodology, software, investigation, formal analysis, writing—original draft; XH: methodology, investigation, formal analysis, writing—original draft; WH: data curation, investigation; LO: visualization, investigation; BC: resources, supervision; WZ: software, validation; YL: conceptualization, visualization, supervision, writing—review and editing; XL: conceptualization, funding acquisition, resources, supervision, writing—review, and editing.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1092276/full#supplementary-material
## Abbreviations
CC, cervical cancer; TCA cycle, tricarboxylic acid cycle; DEGs, differentially expressed genes; GSVA, gene set variation analysis; GSEA, gene set enrichment analysis; TCGA, The Cancer Genome Atlas; GEO, Gene Expression Omnibus; OS, overall survival; AUC, area under the curve; ROC, receiver operating characteristic; GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; IC50, half-maximal inhibitory concentration; MSigDB, Molecular Signatures Database; PCA, Principal Component Analysis.
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|
---
title: 'Using electronically delivered therapy and brain imaging to understand OCD
pathology: A pilot feasibility study'
authors:
- Callum Stephenson
- Niloufar Malakouti
- Joseph Y. Nashed
- Tim Salomons
- Douglas J. Cook
- Roumen Milev
- Nazanin Alavi
journal: Frontiers in Psychiatry
year: 2023
pmcid: PMC10033802
doi: 10.3389/fpsyt.2023.1050530
license: CC BY 4.0
---
# Using electronically delivered therapy and brain imaging to understand OCD pathology: A pilot feasibility study
## Abstract
### Background
Obsessive–compulsive disorder (OCD) is a debilitating mental health disorder with current psychotherapeutic treatments, while somewhat effective, yielding low accessibility and scalability. A lack of knowledge regarding the neural pathology of OCD may be hindering the development of innovative treatments. Previous research has observed baseline brain activation patterns in OCD patients, elucidating some understanding of the implications. However, by using neuroimaging to observe the effects of treatment on brain activation, a more complete picture of OCD can be drawn. Currently, the gold standard treatment is cognitive behavioral therapy (CBT). However, CBT is often inaccessible, time-consuming, and costly. Fortunately, it can be effectively delivered electronically (e-CBT).
### Objectives
This pilot study implemented an e-CBT program for OCD and observed its effects on cortical activation levels during a symptom provocation task. It was hypothesized that abnormal activations could be attenuated following treatment.
### Methods
OCD patients completed a 16-week e-CBT program administered through an online platform, mirroring in-person content. Treatment efficacy was evaluated using behavioral questionnaires and neuroimaging. Activation levels were assessed at the resting state and during the symptom provocation task.
### Results
In this pilot, seven participants completed the program, with significant improvements ($p \leq 0.05$) observed between baseline and post-treatment for symptom severity and levels of functioning. No statistically significant ($$p \leq 0.07$$) improvement was observed in the quality of life. Participants had mostly positive qualitative feedback, citing accessibility benefits, comprehensive formatting, and relatable content. No significant changes in cortical activation were observed between baseline and post-treatment.
### Conclusion
This project sheds light on the application of e-CBT as a tool to evaluate the effects of treatment on cortical activation, setting the stage for a larger-scale study. The program showed great promise in feasibility and effectiveness. While there were no significant findings regarding changes in cortical activation, the trends were in agreeance with previous literature, suggesting future work could provide insight into whether e-CBT offers comparable cortical effects to in-person psychotherapy. Applying a greater knowledge of the neural mechanisms of action in OCD can help develop novel treatment plans in the future.
## Introduction
Obsessive–compulsive disorder (OCD) is a debilitating disorder with a lifetime prevalence globally of $3\%$ [1]. Moreover, only half of the patients achieve remission [2]. A lack of understanding regarding the pathology of OCD could be contributing to the lack of treatment effectiveness. By understanding the pathology, more targeted treatments could be developed in the future, leading to innovative solutions with better treatment outcomes. One way to accomplish this could be to observe the effects of treatment using neuroimaging.
Currently, cognitive behavioral therapy (CBT) with exposure and response prevention (ERP) is the gold standard treatment for OCD (3–5). The structure of CBT for OCD mirrors many aspects of its counterpart for depression and anxiety, with the main differentiator being the incorporation of ERP. Although CBT is a gold standard, its in-person delivery comes with high costs, and a large time commitment from healthcare providers which reduces scalability, long wait lists, and geographic and temporal inaccessibility concerns for patients. Treatment is costly, time-consuming, and often inaccessible. Fortunately, due to the structured nature of CBT, it can be effectively delivered electronically, through the internet [e-CBT; (6–11)].
The exact pathology of OCD is currently unknown. However, one way to better understand neural pathology is to use functional MRI (fMRI) to assess brain activation levels in OCD patients. During fMRI, changes in blood flow to different cortical regions are measured while a subject performs a specific task. These tasks can include an image viewing scan (i.e., viewing a neutral image on a screen), a cognitive task (i.e., Stroop task), or a symptom provocation task (i.e., viewing images related to the patient’s anxieties). In patients with OCD, these tasks can help outline brain function during resting state, decision-making, or anxiety processing. The changes in cortical activation are measured as blood-oxygen-level-dependent (BOLD) changes [12]. These BOLD changes can then be mapped to an expected hemodynamic response function (HRF), helping to identify cortical regions activated during different portions of the task (i.e., resting vs. symptom provocation) and time points (i.e., baseline vs. post-treatment).
Previous fMRI research on OCD patients has identified several cortical regions and circuits with abnormal activations [13]. A review by Shephard et al. [ 13] compared baseline scans of healthy controls (HCs) and OCD patients and found that the fronto-limbic circuit (responsible for emotional response), which includes the orbitofrontal cortex, frontal gyrus, anterior cingulate cortex, and amygdala was hyperactive. This could contribute to the increased response to negative emotions commonly seen in OCD patients. Next, the sensorimotor circuitry (responsible for motor behavior and sensory integration), which includes the thalamus, putamen, precentral gyrus, and insula was found to be hyperactive. This could contribute to physical compulsions and abnormal sensitivity to sensory stimuli. The ventral cognitive circuit (responsible for behavioral control), which includes the thalamus, ventral caudate, inferior frontal gyrus, and ventrolateral prefrontal cortex was found to be hypoactive. This could help explain the inability to control compulsive behaviors seen in OCD patients. Moreover, the ventral affective circuit (responsible for reward processing), which includes the thalamus, nucleus accumbens, and orbitofrontal cortex, was also found to be hypoactive compared to HCs. This could present in OCD patients by using compulsions to avoid anxieties and increased fear of punishment. Finally, the dorsal cognitive circuit (responsible for executive function), which includes the thalamus, dorsal caudate, and the dorsolateral and dorsomedial prefrontal cortex, was found to be hypoactive. This could contribute to the maladaptive executive functioning commonly seen in OCD [13]. Knowing which regions are abnormally activating, and what the roles and responsibilities of these regions are can help us develop a deeper understanding of OCD. Moreover, applying this knowledge to treatment development through psychotherapeutics targeting the behaviors connected to these abnormally functioning neural circuits may result in improved neuroplasticity and ultimately, better treatment outcomes [14]. While the aforementioned circuits have been mainly observed at baseline in OCD patients, determining if and how treatment affects these circuits can provide further insight into OCD pathology [15, 16].
There is some previous research evaluating the effects of in-person psychotherapeutics on brain activation in OCD patients. *In* general, in-person treatment resulted in decreased activation in the frontal (orbitofrontal cortex, prefrontal cortex, and anterior cingulate cortex), parietal (precuneus, parietal cortex, supramarginal gyrus), and temporal lobe (caudate nucleus, nucleus accumbens, insula, parahippocampal gyrus), cerebellum, and vermis (12, 17–23). However, to the author’s knowledge, there is no previous knowledge on whether virtually-delivered therapy.
Using fMRI to evaluate the effects of CBT and ERP on brain activation during neural anxiety processing could provide further insight into the cortical pathology of OCD. Functional neuroimaging is not required to discern whether treatment was successful but is required to determine the mechanism of action [24]. By having a clearer understanding of the neural mechanisms of action, more informed development of future treatments can be made for a population that is in urgent need of innovative interventions [25, 26].
## Materials and methods
A non-randomized pilot study design was employed with all participants receiving 16 weekly sessions of e-CBT. fMRI was conducted at baseline and post-treatment to evaluate activation level changes. Clinically validated symptomology questionnaires were used to evaluate treatment efficacy. Additionally, qualitative interviews were conducted to gather personal demographic information as well as information regarding participant experience while using the online psychotherapy platform. The pilot study was registered on the ClinicalTrials.gov Protocol Registration and Results System (NCT04630197). Additionally, ethics approval was obtained from the Queen’s University Health Sciences and Affiliated Teaching Hospitals Research Ethics Board (HSREB; File Number 6031276).
## Participants
Participants were recruited from family medicine and psychiatric clinics at Queen’s University and Kingston Health Sciences Centre sites (Hotel Dieu Hospital and Kingston General Hospital) in Kingston, Ontario, Canada. Additionally, local, and social media advertisements were utilized. Participants were enrolled in the study based on referrals from outpatient clinics, family doctors, as well as self-referrals. Those invited and interested in participating had the study protocol explained along with an evaluation by a psychiatrist on the research team through a secured video appointment. Participants were evaluated for a diagnosis of OCD based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [DSM-5; [27]]. Once a diagnosis of OCD was confirmed and the participant was given written and verbal instructions on how to participate in the study, informed consent was obtained.
Inclusion criteria included the following: over the age of 18 years at the start of the study, a diagnosis of OCD according to DSM-5 criteria, competence to consent to participate, ability to speak and read English, and consistent and reliable access to the internet. Exclusion criteria included the following: having any metal implants or additional factors deemed not safe for an MRI scan, active psychosis, acute mania, severe alcohol, or substance use disorder, and/or active suicidal or homicidal ideation. Additionally, if a participant was currently receiving another form of psychotherapy, they were excluded from the study. If a participant was on medication, they had to have been on that dosage for at least the past 6 weeks and the dosage had to remain unchanged for the duration of the study.
Eleven Participants were deemed eligible and enrolled between September 2021 and February 2022, all receiving baseline fMRI scans. Four of these participants did not complete the program, with two of them never beginning treatment. These two patients were removed from the analysis. The other two patients dropped out after completing the fourth week of therapy feeling that the online format was not a right fit for them. This left seven ($$n = 7$$) patients that completed the 16-week e-psychotherapy program. All but one was able to complete both baseline and post-treatment scans, with one no longer being eligible as they became pregnant. However, this participant was able to complete the post-treatment symptomatology questionnaires, providing data on the efficacy of the program.
At baseline, eight of the nine patients who began treatment were female ($89\%$) with a mean age of 30.78 (SD = 14.18; Table 2). Participants were screened initially by a trained professional to confirm a diagnosis and assess the dominant obsessive thoughts of the patient, which was used to develop the imaging bank for stimulating photos in the fMRI procedure. Patients presented with obsessions including contamination ($$n = 2$$), negative events to family ($$n = 2$$), order ($$n = 3$$), checking ($$n = 4$$), religion ($$n = 1$$), general sexual intrusive thoughts ($$n = 1$$), and child-related sexual intrusive thoughts ($$n = 1$$). Personalized image banks were constructed accordingly in consultation with a psychiatrist on the research team.
**Table 2**
| ID | Sex | Age | Status | Y-BOCS | Y-BOCS.1 | Y-BOCS.2 | OCI-R | OCI-R.1 | OCI-R.2 | Q-LES-Q-SF | Q-LES-Q-SF.1 | Q-LES-Q-SF.2 | SDS | SDS.1 | SDS.2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | | | T0 | T1 | T2 | T0 | T1 | T2 | T0 | T1 | T2 | T0 | T1 | T2 |
| P_1 | F | 66 | Completed | 37 | 28 | 23 | 70 | 70 | 63 | 33 | 40 | 38 | 30 | 30 | 22 |
| P_2 | F | 32 | Completed | 23 | 25 | 22 | 27 | 28 | 27 | 44 | 34 | 39 | 16 | 16 | 11 |
| P_3 | F | 24 | Completed | 21 | 21 | 14 | 40 | 39 | 24 | 27 | 38 | 53 | 19 | 13 | 12 |
| P_4 | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter |
| P_5 | M | 27 | Completed | 15 | 14 | 9 | 17 | 11 | 9 | 40 | 52 | 51 | 16 | 9 | 7 |
| P_6 | F | 28 | Dropout W5 | 18 | | | 29 | | | 51 | | | 10 | | |
| P_7 | F | 28 | Dropout W5 | 21 | | | 37 | | | 23 | | | 9 | | |
| P_8 | F | 34 | Completed | 11 | 8 | 9 | 13 | 8 | 9 | 52 | 54 | 59 | 16 | 9 | 5 |
| P_9 | F | 18 | Completed | 27 | 27 | 19 | 37 | 37 | 29 | 51 | 48 | 51 | 24 | 23 | 24 |
| P_10 | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter | Non-Starter |
| P_11 | F | 20 | Completed | 14 | 11 | 10 | 23 | 25 | 11 | 37 | 52 | 64 | 15 | 13 | 12 |
## Therapy
Weekly sessions of e-CBT were conducted through the Online Psychotherapy Tool (OPTT; OPTT Inc.), a secure, online, cloud-based mental health care delivery platform. These online sessions consisted of approximately 30 slides and interactive therapist videos, with 16 modules in total (1 module per week). The content and format of these online sessions mirrored in-person CBT for OCD (Table 1) [28]. The connection between thoughts, behaviors, emotions, physical reactions, and the environment was a focus of module content. Moreover, mindfulness, body scanning, self-care, goal setting, thinking errors, the 5-part model, and thought records were employed as techniques for participants. ERP was incorporated into the e-CBT program as this is the first-line route of treatment. Slides highlighted different topics each week and included general information, an overview of skills, and homework on that topic. The homework included in each session was submitted through OPTT and reviewed by therapists with personalized feedback provided within 3 days of submission. Weekly homework submission for feedback was mandatory before being eligible for the next session. After each completion of the e-CBT program, participants provided feedback through OPTT on their perception of how the treatment went, and any pros and/or cons of the online format that they found. OPTT can be accessed from a variety of devices (i.e., desktop computer, laptop, cellphone, tablet, etc.) and internet browsers.
**Table 1**
| Session # | Session title |
| --- | --- |
| 1 | What is cognitive behavioral therapy? |
| 2 | Obsessions and compulsions and how to recognize them |
| 3 | Measuring our anxiety |
| 4 | Why you cannot escape your obsessions and compulsions |
| 5 | Common misinterpretations in OCD |
| 6 | Introduction to exposure and response prevention |
| 7 | Customizing a personalized treatment plan |
| 8 | Exploring the treatment plan |
| 9 | Pros and cons of CBT for OCD |
| 10 | Setting goals for working on your OCD |
| 11 | Strategies you can use |
| 12 | Exposure therapy |
| 13 | Situational exposure menu |
| 14 | Imaginal exposure practice |
| 15 | Response prevention |
| 16 | Review |
## Imaging
All neuroimaging occurred at the Queen’s University MRI Facility in Kingston, Ontario, Canada using a Siemens PRISMA Fit 3.0 Tesla whole-body MRI scanner with a 32-channel standard coil. Scans occurred at baseline (pre-treatment) and after week 16 (post-treatment). During scanning, participants were instructed to lay still on the scanning table on their backs, with their heads resting on a foam pad to reduce movement. Scanning appointments took approximately 1 h per session.
Anatomical reference images were captured initially. Following this, fMRI scans occurred while participants were shown neutral images and anxiety-inducing images (i.e., dirty dishes if cleanliness was an anxiety-inducing concept for a specific participant). Participants were shown all images through a mirror back projection. Changes in activation during neural anxiety processing were analyzed. The images participants were shown came from a standardized photo bank provided by the International Affective Picture System [IAPS; [29]]. Each set of pictures was individually tailored to each participant’s obsessions and compulsions. These images were selected ahead of time by the primary investigator in collaboration with a psychiatrist on the research team. Participants were shown a total of 40 images (20 neutral, 20 anxiety-inducing; $R = 0.5$) during the fMRI sessions. There were 4 fMRI runs (Figure 1).
**Figure 1:** *Breakdown of the sequence of IAPS images shown during the fMRI acquisition procedure.*
The images were shown in sets (groups of 5 images) as opposed to intermingled in the hopes of producing a more sustained emotional state and allowing for more distinct readings. The ordering of the image sets repeated halfway through (back-to-back of the anxiety-inducing images in the example above) is to control for participants becoming accustomed to image ordering. The images appeared on a projected screen that was reflected into the scanner for participants to view. $0.5\%$ BOLD signal difference between conditions ($p \leq 10$–6) will be considered a detectable change [effect = 0.005; [30]].
Anatomical reference images were captured with the phase encoding direction collected sagittally from anterior to posterior. These images were captured with T1-weighted high-resolution magnetization prepared rapid acquisition gradient echo (MPRAGE) images with 0.8 × 0.8 × 0.8 mm3 isotropic voxels. A 256 mm field of view (FOV), 2,500 ms repetition time (TR), 2.22 ms echo time (TE), 8-degree flip angle, and a 320 × 320 mm matrix resolution. Following this, T2*-weighted gradient-echo echo-planar imaging (GE-EPI) with 3.0 mm 3.0 × 3.0 × 3.0 mm3 isotropic voxels was used for the stimuli-exposed image acquisitions in an anterior to posterior direction. A 192 mm FOV, 2500 ms TR, 28.4 ms TE, 90-degree flip angle, and a 64 × 64 mm matrix resolution. A multi-band acceleration factor of 2 was employed with 170 volumes being captured. Following the GE-EPI imaging, 2 short spin-echo field map scans were captured from anterior to posterior, then posterior to anterior. These images used a 192 mm FOV, 8000 ms TR, 66.0 ms TE, 90-degree flip angle, 180-degree refocus flip angle and a 64 × 64 mm matrix resolution. All images used a bandwidth of 1,500 Hz.
To un-distort images, the GE-EPI fMRI data was mapped to a non-distorted set of GE images from the same participant. Next, the non-distorted GE images were mapped to the T1-weighted MPRAGE image. Finally, the T1-weighted MPRAGE was mapped to the MNI standardized brain template [31]. In doing this, the GE-EPI fMRI data were mapped to the MNI template with maximum accuracy.
## Training
The therapists involved in care delivery were all graduate students and research assistants trained in psychotherapy delivery and supervised by a psychiatrist on the research team who has extensive experience in electronically delivered psychotherapy. All therapists were taught the standard care pathway, the aim, and the content of each therapeutic session. Moreover, they were provided sample homework from previous patients and were asked to provide feedback as practice. Feedback templates were developed by the primary therapist and reviewed by a psychiatrist on the research team throughout. This feedback varied between sessions and was personalized for each patient’s homework. Before feedback was submitted to the participant, it was read, edited, and approved by a therapist supervisor on the research team. Training occurred through video calls and exercises with feedback.
## Outcomes
The primary outcome measure was changed in neural activation levels between baseline and post-treatment. This was collected through detectable changes in BOLD values from the fMRI scans at baseline and post-treatment (week 16). The secondary outcomes were changes in symptom severity, quality of life, and functioning. Changes in symptom severity were evaluated using clinical symptomatology questionnaires (Y-BOCS; OCI-R; [23], [24]). Changes in quality of life were measured using the Q-LES-Q-SF [32]. Changes in levels of functioning will be measured using the SDS [33]. All questionnaires will be collected directly through OPTT at baseline, after session 8, and post-treatment (week 16). Additionally, participant perception and experience of the therapy program and the online platform were evaluated.
## Compliance
As with all mental health disorders, treatment compliance is always an area of focus when designing interventions. Participants had the importance of treatment compliance explained to them during the informed consent process along with participants needing to submit their homework assignments through OPTT before gaining access to their next treatment session. From a previous meta-analysis conducted, the estimated completion from in-person psychotherapy is approximately $75\%$ [34]. Additional meta-analyses found treatment adherence for online psychotherapy to be between 61 and $66\%$ with no significant difference from the in-person psychotherapy (35–39). A study investigating the efficacy of a 10-session e-CBT program for OCD had a mean completion of 7.28 sessions [40]. From previous research using OPTT, participants completed over 8 sessions on average, with over half of participants completing all sessions. In a previous project using e-CBT for patients with generalized anxiety disorder, $90\%$ of participants completed 10–12 weeks of the 12-week program, with over $75\%$ of participants being retained for a 12-month follow-up [41].
## Analysis
DELETED “*Neuroimaging analysis* was conducted with a whole-brain approach.” All participant raw DICOM images and BOLD data were preprocessed with steps including motion correction, slice-timing correction, smoothing, registration, and normalization, mapped to the MNI T1 2 mm Human Template. Preprocessing For the fMRI data (primary outcome), a $0.5\%$ (effect = 0.005) change in BOLD hemodynamic response function was considered a detectable signal variation between conditions ($p \leq 10$–6). An estimated paradigm of expected BOLD response was created to calculate the correlation between real signal and expected signal to detect noise using a general linear model (Figure 2; S = βX + e; S = time-series data, β = value for each pattern, X = set of time-series patterns, e = residual). *The* general linear model provided a β value for each term in the basis set and a T-value for each β. A first-level analysis computing the BOLD contrast between conditions (i.e., neutral and stimulating images) of each run (i.e., 1, 2, 3, 4) was conducted, followed by a higher-level analysis, evaluating the mean BOLD contrast of all runs at each timepoint for each participant. The individual participant means were then concatenated into an overall BOLD contrast over the runs and at both time points. The overall BOLD contrasts were then compared between scanning periods (i.e., baseline, post-treatment) using one-sample paired t-tests, assuming a normal distribution. Mean BOLD values for the left and right sides of each region of interest (ROI; precuneus, cingulate, thalamus, occipital fusiform gyrus, lingual gyrus, and orbitofrontal cortex) were averaged to find a bilateral value. These bilateral values were calculated for each timepoint and condition and then contrasted against each other. Realignment parameter regressors for the testing conditions were implemented [42, 43]. Effects at each condition entered a group analysis using a random-effects model [44]. Missing data points were accounted for in the analysis with usable questionnaires and fMRI data using the linear model. Preprocessing, first-level analysis, and higher-level analysis were conducted with a combination of packages in Nipype (FSL, AFNI, ANTs, Nilearn) and Brain Voyager (45–50). Motion correction was done through AFNI, slice timing was done through FSL, registration was done through ANTs, and smoothing and filtering were completed through Nilearn. Registration was completed through both FSL and ANTS.
**Figure 2:** *Stimulus onset timing matched the expected hemodynamic response.*
For questionnaire scores (secondary outcomes), mean scores were calculated at each time point (baseline, mid-point, post-treatment) and averaged to find the overall mean, SD, and SE. 2-way paired t-tests were conducted with $$p \leq 0.05$$ for each questionnaire with comparisons between baseline and mid-point, mid-point and post-treatment, and baseline and post-treatment to test for significance. Additionally, the means of each time point were used to calculate the effect size between the previously mentioned time points, with Cohen’s d used as the primary effect size, with significance defined as d < 0.20 = very small effect, d > 0.20 = small effect, d > 0.50 = moderate effect, d > 0.80 = large effect. Using a Pearson Correlation Coefficient, the correlation between the questionnaire score and the BOLD response was evaluated. Data outliers will be defined as 3.29 SD away from the mean on scores.
Skew and kurtosis were analyzed assuming a normal distribution in the questionnaire and fMRI data at all collection time points. Age and sex variables were considered in knowledge creation and translation. Thematic analysis of participant perception and experience of the online program and platform was used.
## Ethics and privacy
The pilot study received approval from the Queen’s University HSREB. Only the care providers involved in the care of the participant had access to their information. Participants were only identifiable by an identification number on the OPTT platform for analysis and hard copies of consent forms with participant identities were stored securely on-site and will be destroyed 5 years after study completion. Only anonymized data was provided to the analysis team members. OPTT is compliant with the Health Insurance Portability and Accountability Act, Personal Information Protection and Electronic Documents Act, and Service Organization Control – 2. Additionally, all servers and databases are hosted in Amazon Web Service Canada cloud infrastructure which is managed by Medstack to assure all provincial and federal privacy and security regulations are met. OPTT only collects anonymized metadata to improve its service quality and provide advanced analytics to the research team. OPTT encrypts all data, and no employee has direct access to patient data. All encrypted backups are kept in the S3 storage dedicated to Queen’s University.
## Behavioral data
For individual participant scores, refer to Table 2. Effect size measurements between baseline and post-treatment can be found in Table 3. Regarding symptom severity, at baseline, participants ($$n = 9$$) presented with a mean Y-BOCS of 20.78 (SD = 7.82). Midway through treatment (week 8), the mean Y-BOCS score decreased to 19.14 ($$n = 7$$; SD = 8.11; $t = 1.36$; $$p \leq 0.22$$). At post-treatment (week 16), these symptom severity scores significantly decreased from the mid-point; to 15.14 ($$n = 7$$; SD = 6.15; t = −3.29; $$p \leq 0.02$$). Overall, the Y-BOCS score decreased by an average of $28.88\%$ through the course of treatment, a significant improvement and large effect size (t = −3.54; $$p \leq 0.01$$; $d = 0.80$; Figure 3). For OCI-R, patients ($$n = 10$$) presented with a mean baseline score of 31.9 (SD = 15.97), followed by a mid-point (week 8) score of 31.14 ($$n = 7$$; SD = 20.80; t = −1.12; $$p \leq 0.31$$). At post-treatment collection, participants ($$n = 7$$) reported a significant difference in mean score from the mid-point of 24.57 with a small effect size (SD = 19.03; t = −2.76; $$p \leq 0.03$$; $d = 0.42$), a $24.63\%$ decrease from baseline (t = −4.01; $$p \leq 0.01$$; Figure 3).
Regarding changes in quality of life, no significant changes were seen across time points. Participants ($$n = 10$$) presented with a mean Q-LES-Q-SF score of 40.0 (SD = 10.12). This quality-of-life rating increased to a mean of 45.43 ($$n = 7$$; SD = 7.98; $t = 1.42$; $$p \leq 0.20$$) at mid-point (week 8). At post-treatment, participant ($$n = 7$$) quality of life further improved from mid-point to a mean score of 50.71 with a large effect size (SD = 9.57; $t = 2.22$; $$p \leq 0.07$$; $d = 1.09$), equating to a $25.36\%$ improvement from baseline ($t = 2.18$; $$p \leq 0.07$$; Figure 3).
Finally, regarding the level of functioning and disability, participants presented with a mean SDS score of 17.22 (SD = 6.53) at baseline ($$n = 9$$). At the mid-point (week 8), this means significantly decreased to 16.14 ($$n = 7$$; SD = 7.76; t = −2.67; $$p \leq 0.04$$). This was followed by a significant improvement from mid-point to post-treatment ($$n = 7$$; mean = 13.29; SD = 7.16), a significant $22.86\%$ improvement from baseline with a moderate effect size (t = −4.32; $p \leq 0.01$; $d = 0.57$; Figure 3).
## Imaging data
Anatomical imaging and BOLD data were collected for all patients ($$n = 11$$) at baseline. Following the removal of non-starters ($$n = 2$$), dropouts ($$n = 2$$), and those no longer eligible for scans ($$n = 1$$), post-treatment scans occurred. For analyzing the change in BOLD data across time points, only patients who completed data collection at baseline and post-treatment ($$n = 6$$) could be included. Across time points, there were no statistically significant differences found in the BOLD contrast between baseline and post-treatment in any cortical regions using a two-way single-sample paired t-test (Table 4). However, as expected, there were significant differences seen between conditions (i.e., neutral and stimulating photos shown during scans). Figure 4 provides a cortical heat map showing the areas of significant differences found in BOLD changes when contrasting mean activation of neutral and anxiety-inducing stimuli through the extracted time course signal of each area (Figure 4).
While there were no statistically significant differences found in changes between periods, there were interesting changes noted in bilateral activations. In the precuneus, a 39.57 and $11.86\%$ decrease in BOLD activation were noted from baseline to post-treatment for neutral and stimulating conditions, respectively. In the cingulate, a 3.60 and $292.07\%$ increase were noted post-treatment for neutral and stimulating conditions. However, in the cingulate, no activation on the right side was noted, allowing for analysis of the right cingulate only. In the thalamus, a 23.38 and $0.47\%$ decrease occurred after the e-CBT program for the neutral and stimulating conditions, respectively. A large decrease of $178.55\%$ and a $4.72\%$ decrease were found in the occipital fusiform gyrus following treatment for the neutral and stimulating conditions. Within the lingual gyrus, similar decreases of 23.12 and $18.48\%$ were observed for the neutral and stimulating conditions, respectively, following therapy. Finally, in the orbitofrontal cortex, an increase of $21.77\%$ during the neutral condition and a decrease of $14.62\%$ during the stimulating condition were observed at post-treatment scans, compared to baseline mean bilateral BOLD activation values. For a breakdown of changes in BOLD activation between time points and conditions in individual left and right sections of each of the previously mentioned regions, refer to Table 5. For specific mean activation values and cluster sizes at all time points for each condition and each ROI, please refer to Table 6.
## Therapy feedback
In the final session (week 16) of the e-psychotherapy program, the last question asked for feedback on the course, both positive and negative, as well as specific strategies that the patient found helpful. *In* general, the positive feedback far outweighed the negative feedback. Patients found the layout of the material easy to follow, as well as the ratio of pictures to text practical and made the content less overwhelming. The systematic course-based structure of care was also a point of emphasis in many of the patient’s positive feedback. Moreover, having specific examples and characters in the modules helped patients relate more to the concepts being taught, providing context and real-world applicability. Having the chat box throughout the week with the care provider was also a welcomed feature, allowing questions and concerns to be addressed.
One patient disliked the online format compared to in-person as they felt a disconnect with the therapist, and found it was easier for them to verbally communicate the more debilitating side of their OCD and intrusive thoughts in person. However, they also pointed out the benefit of having access from home during the COVID-19 pandemic and living in a more remote area, making it difficult to access in-person care. Another patient found the online asynchronous format while more convenient also could make it more difficult sometimes to hold themselves accountable. Another found that some of the content was a bit repetitive, although this is done purposefully to reinforce key concepts. *In* general, patients found the response-prevention, while the most anxiety-inducing aspect of the course, also the most beneficial to them.
## Feasibility
This pilot project was conducted to evaluate the feasibility of examining the effects of e-CBT and ERP on cortical activation in OCD patients. Upon completion of this pilot project, the methodology and protocol developed were found to be feasible in a small sample size as a method to evaluate the effects of psychotherapeutic treatment on cortical activation in OCD patients. Using the pilot data collected along with understanding the feasibility of the described protocol, this can be used as a stepping stone for a larger-scaled randomized-controlled trial (RCT) in the future. The electronic delivery method for the psychotherapy program (OPTT) has been proven feasible in previous work [28, 41, 51] and was again found to be easy to implement and use as an online psychotherapy delivery platform. Regarding functional neuroimaging, the block design with the previously described imaging parameters was effective at capturing high-quality imaging with sensitivity to changes in neural activity. The appointments were able to be conducted within a 1-h time slot, allowing for efficient data collection with larger population size in the future. Within this small sample, of the four participants who did not complete, two were deemed non-starters, with the other two completing a month of e-CBT before deciding the online format was not a good fit for them.
## Online psychotherapy intervention
The e-CBT and ERP programs were, in general, well-received by patients, shown by overall positive qualitative feedback, as well as clinical symptom severity improvements and levels of functioning upon completion. There is growing evidence supporting the implementation of online psychotherapy interventions for the treatment of various mental health disorders, citing the increased accessibility these remote programs can provide to patients while increasing care capacity. Patients found the module-based format engaging, intuitive, interactive, and easy to follow while visually appealing. Moreover, patients mentioned appreciating the chat feature, giving them the ability to contact their therapist throughout the week with content-related questions, as well as having access to $\frac{24}{7}$ technological support provided by OPTT. These modules and feedback templates will continue to be fine-tuned and adapted over time, allowing for more accessible formats and higher quality of care for patients.
## Symptom severity, quality of life, and levels of functioning
Regarding symptom severity, both clinically validated questionnaires (Y-BOCS & OCI-R) provided similar results. While neither of these questionnaires indicated statistically significant symptom improvements from baseline (week 0) to mid-point (week 8), they did report statistically significant improvements from mid-point to post-treatment (week 16) and between baseline and post-treatment. This suggests that while there may not be immediate results seen in the online psychotherapy program, at some point after 8 weeks, significant improvements in symptoms are seen. In future work, additional time points for questionnaire completions should be added (i.e., week 4 and week 12) to elucidate a more specific timepoint where symptom improvement is typically seen. Moreover, long-term follow-ups of 6 months and 1 year would be welcomed additions to the collection protocol, providing insight into the possibility of maintaining benefits from program completion.
Interestingly, while there were statistically significant symptom severity improvements, the analysis failed to reveal any statistically significant changes in quality of life (Q-LES-Q-SF) across any time points. It is important to note that while not statistically significant, there was an approximate $25\%$ increase in the questionnaire score. This finding should be considered when treating patients, understanding that a holistic approach should be taken, finding ways to not just improve symptoms, but also understand how we can ensure an improved quality of life, as they should be treated as separate issues.
Level of functioning and disability showed statistically significant improvements across all time points, suggesting that the reduction in symptom severity could be tied to an improvement in functioning. This is a reasonable thought, as reducing the severity of symptoms in a mental health disorder as debilitating as OCD should result in an overall improvement in the patient’s level of functioning.
It is important to recognize that while these changes in symptom severity, quality of life, level of functioning and disability have all been evaluated regarding statistical significance, clinically significant conclusions have not been made. Moreover, the significant findings in this study should be overstated, as the major limitation in the rigor of these results is the small sample size. As a proof of concept and test of feasibility, the methodology for this study was tested and is promising. Particularly with the neuroimaging results, these investigations typically require much larger samples to be able to make more sound conclusions and should be interpreted as a guide for future work rather than conclusive results. With such a small sample, more work should be done in a large-scale RCT aimed at validating the modules used in this pilot project and investigating the clinical significance of the change in symptoms, quality of life, and levels of functioning. Clinical significance tests can provide insight into whether this method of intervention has a practical, real-world effect that can be felt by this population who deserves the highest quality of care.
## Changes in cortical activation
As expected with a sample ($$n = 6$$) of this size available for post-treatment cortical follow-up, the lack of statistically significant findings is expected. However, the trends seen in changes in mean BOLD activation across tasks and time points are in line with previous work. Generally, the front-limbic circuit, and within this, the orbitofrontal cortex, is hyperactive in OCD patients [13]. This is the same for panic disorder patients, with one study showing that CBT could normalize these irregular activation patterns [52]. In agreement with this, the pilot project showed a $14.62\%$ bilateral decrease in BOLD activation during neural anxiety processing post-CBT program. Other studies using fMRI to assess the effects of treatment on OCD patients have shown decreases in orbitofrontal cortex activation post-treatment (17–21). While this present pilot did not find a correlation between symptom improvement and change in activation, Yang and colleagues found one between Y-BOCS improvement and OFC activation decrease [21]. Additional work using similar symptom provocation tasks as the one used in this pilot showed decreases in the orbitofrontal cortex post-treatment [17]. Moreover, similar studies with an HC comparator showed that CBT resulted in the orbitofrontal cortex having a more similar activation to the HC [19, 20, 23].
Within the thalamus, the present study found decreases of 23.38 and $0.48\%$ in BOLD activation during neutral and stimulating conditions post-treatment. The thalamus has been cited as being hyperactive at rest in OCD patients [13], with other symptom provocation studies finding decreased activation post-CBT intervention [17]. The precuneus has also been shown to be hyperactive in OCD patients, with studies citing decreases following treatment with psychotherapy in resting-state and symptom provocation fMRI tasks [12, 17, 19, 22]. Similarly, decreases were observed in the occipital fusiform gyrus in this pilot, similar to this previous work [12, 17, 19, 22, 23]. In the cingulate, the findings from this study, increases in neutral and anxiety-processing activation post-treatment are in agreeance with some work [19, 23] but non-conforming to other studies [17]. In the lingual gyrus, this pilot found decreases in both neutral and anxiety-inducing conditions, contrary to previous work [12, 21]. While the study was limited in findings of statistical significance, the changes observed, while small, are generally in line with previous work from in-person CBT interventions for OCD patients.
## Conclusion
The present pilot project provides insight into the feasibility and effectiveness of a protocol that uses functional neuroimaging to evaluate the effects of an online psychotherapy program on neural anxiety processing in OCD patients. The protocol provides a proof of concept for a large-scale RCT delivered in the future. From qualitative patient feedback, the online psychotherapy program is intuitive, well-designed, and engaging from a patient perspective, and provides improved efficiency and access to care. Significant improvements in symptom severity and levels of functioning and disability were seen following the completion of treatment, suggesting this is a promising solution to meet the increasing demand for accessible and timely high-quality mental health care. The clinical significance of the online modules and intervention format should be validated in the future through a clinical trial.
While the findings did not provide statistical significance, they were in agreeance with much of the literature surrounding the effects of psychotherapy on cortical activation in OCD patients. To the author’s knowledge, this is the first study to implement an online psychotherapy program to observe the effects of treatment with fMRI on OCD patients. With preliminary findings suggesting similar results to in-person psychotherapy programs, a larger-scale trial could offer more insight into whether this online intervention offers a comparable impact on cortical activation to traditional treatments.
This pilot project sets the stage for a clinical trial further investigating the pathology of OCD through a treatment-centered lens with functional neuroimaging. By understanding the mechanisms of action associated with online psychotherapeutic interventions, innovative treatment plans with high-quality care and improved outcomes can be developed for OCD patients in need of cutting-edge solutions to this debilitating mental health disorder.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Queen’s University Health Sciences and Affiliated Teaching Hospitals Research Ethics Board. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
CS conducted the research described in this manuscript under the co-supervision of NA and RM, conducted all data collection, was responsible for the cognitive behavioral therapy delivery and patient feedback, under the supervision of NA, and additionally, he was responsible for conducting analysis and writing the manuscript of this thesis. NA and RM provided guidance and expertise on the development of the trial and methods, data collection and analysis, and composition of the manuscript. NM was responsible for the development of the electronically delivered cognitive behavioral therapy modules used in this treatment program. JN assisted in the analysis of functional magnetic resonance imaging data. TS and DC assisted in protocol development and data analysis. NA is a co-founder of the care delivery platform used in this study (OPTT) and has shares in the company. All authors contributed to the article and approved the submitted version.
## Funding
CS is a recipient of the Canada Graduate Scholarship – Masters (CGS-M) from the Canadian Institutes for Health Research.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Molecular characterization of extracellular vesicles derived from follicular
fluid of women with and without PCOS: integrating analysis of differential miRNAs
and proteins reveals vital molecules involving in PCOS'
authors:
- Yuqin Yang
- Peng Lang
- Xiaolan Zhang
- Xun Wu
- Shanren Cao
- Chun Zhao
- Rong Shen
- Xiufeng Ling
- Ye Yang
- Junqiang Zhang
journal: Journal of Assisted Reproduction and Genetics
year: 2023
pmcid: PMC10033803
doi: 10.1007/s10815-023-02724-z
license: CC BY 4.0
---
# Molecular characterization of extracellular vesicles derived from follicular fluid of women with and without PCOS: integrating analysis of differential miRNAs and proteins reveals vital molecules involving in PCOS
## Abstract
### Purpose
To elucidate the characterization of extracellular vesicles (EVs) in the follicular fluid-derived extracellular vesicles (FF-EVs) and discover critical molecules and signaling pathways associating with the etiology and pathobiology of PCOS, the differentially expressed miRNAs (DEmiRNAs) and differentially expressed proteins profiles (DEPs) were initially explored and combinedly analyzed.
### Methods
First, the miRNA and protein expression profiles of FF-EVs in PCOS patients and control patients were compared by RNA-sequencing and tandem mass tagging (TMT) proteomic methods. Subsequently, Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes were used to analyze the biological function of target genes of DEmiRNAs and DEPs. Finally, to discover the functional miRNA-target gene-protein interaction pairs involved in PCOS, DEmiRs target gene datasets and DEPs datasets were used integratedly.
### Results
A total of 6 DEmiRNAs and 32 DEPs were identified in FF-EVs in patients with PCOS. Bioinformatics analysis revealed that DEmiRNAs target genes are mainly involved in thiamine metabolism, insulin secretion, GnRH, and Apelin signaling pathway, which are closely related to the occurrence of PCOS. DEPs also closely related to hormone metabolism processes such as steroid hormone biosynthesis. In the analysis integrating DEmiRNAs target genes and DEPs, two molecules, GRAMD1B and STPLC2, attracted our attention that are closely associated with cholesterol transport and ceramide biosynthesis, respectively.
### Conclusion
Dysregulated miRNAs and proteins in FF-EVs, mainly involving in hormone metabolism, insulin secretion, neurotransmitters regulation, adipokine expression, and secretion, may be closely related to PCOS. The effects of GRAMD1B and STPLC2 on PCOS deserve further study.
## Introduction
Polycystic ovary syndrome (PCOS) is regarded as the most common and complex endocrine disorder, affecting ∼$6\%$ to $20\%$ of reproductive aged women [1, 2]. *It* generally manifests with ovulatory dysfunction, fertility decline, clinical and biochemical androgen excess, and polycystic ovaries [3–6].
Assisted reproductive technology (ART) has become an integral part of modern medicine that brings hope for PCOS infertility patients [7]. By using medications that work on super-ovulating to obtain more eggs and inducing multiple follicles to mature, PCOS infertility patients can get pregnant through clinical in vitro fertilization technology. Although ART has improved the pregnancy rate of PCOS patients, many studies have confirmed that PCOS patients have lower egg quality and eggs with lower pregnancy potential, which seriously affects the outcome of assisted reproductive technology [8]. Nevertheless, what is needed to be soberly aware is that the molecular details of PCOS remains unclear.
Follicular development involves a complex network of interacting cellular signals. Follicular fluid (FF) that is mainly formed by the secretion of granulosa cells, follicular membrane cells, and oocytes; and the diffusion of plasma components from capillaries to antrum [9, 10] provides an important microenvironment for oocyte development and maturation.
Exosomes, released from cells, are spherical or cup-shaped vesicles with a double membrane, with a diameter of approximately 40–100 nm. Studies have shown that exosomes are widely found in blood, human milk, placenta, and amniotic fluid [11, 12]. Exosomes contain a variety of regulatory molecules, such as nucleic acids, mRNA, microRNAs (miRNA), proteins, and lipids. These components of exosomes can transfer between different types of cells to regulate corresponding biological processes and signaling pathways, e.g., affecting immunity, intercellular communication, cell proliferation, cell differentiation, and metabolic diseases [13–15].
A few of studies have shown that exosomes exist in FF [16] and act as information transmitters in somatic cells and oocyte communication by transferring a variety of proteins, lipids, miRNAs, and circRNAs [17–20]. For instant, in 2012, da Silveira et al. described the exosomes containing miRNA and protein in horse FF for the first time and further discussed the role of miRNAs related to hormone regulation in different ovarian follicular cells [21]; later, some scholars successively isolated exosomal miRNA from bovine FF and further proved evidences that it can support cumulus expansion [17, 22]. Recently, a study found that the reduction of circLDLR in follicular fluid-derived exosomes (FF-EVs) derepresses the function of miR-1294 and inhibits estradiol production via CYP19A1 in PCOS [18]. Another study found that the exosomal miR-424-5p derived from PCOS FF inhibits granulosa cell proliferation and induces their senescence by targeting CDCA4-mediated Rb/E2F1 signaling [19]. Wang et al. found abnormal expression of long non-coding RNA (LncRNA) in the FF-EVs of PCOS patients [20]. A proteomics study on FF-EVs found that the S100-A9 protein in exosomes from FF promotes inflammation by activating the NF-κB pathway in PCOS [23]. Taken together, these reports reflect that FFEs and their components play an important regulatory role in the growth and development of oocytes.
It is well known that miRNAs subtly influence a vast number of proteins involved in most key biological processes via binding to the 3′-untranslated region (3′UTR) of target genes for cleavage or translational repression [24, 25]. Nevertheless, although, in theory, a single miRNA can indeed dampen levels of hundreds of proteins [26], the effects of miRNAs on proteins are usually quite modest, changing their expression levels by less than twofold.
Integrated analysis of miRNA and protein expression profiles can still be helpful to identify the functional miRNA-target gene-protein interaction pairs involved in regulating specific biological processes. Previously, although there were a few studies on expression profiles analysis of FF-EVs derived from PCOS patients such as, miRNA, lncRNA, or protein, there are very few studies on the above specimens by performing combined analysis of miRNA and protein expression profiles.
In this study, we performed combined analysis of the miRNAs and protein expression profiles of follicular fluid-derived extracellular vesicles (FF-EVs; we called exosomes and microvesicles as “extracellular vesicles” in this study because of the overlapping size range of different types of extracellular vesicles as well as lack of specific marker for distinguishing them) collected from PCOS patients and healthy women in order to accurately seize the key miRNAs that can cause the changes of protein levels in the FF-EVs of PCOS patients and to further discover molecular details led to PCOS. We found that 6 miRNAs and 32 proteins were significantly differentially expressed between PCOS and the control group. We noticed that the hormone-related metabolic and steroid hormone biosynthesis processes were enriched in both differentially expressed miRNA (DEmiRNAs) and differentially expressed proteins (DEPs) data sets. Especially, we noticed two critical molecules, GRAMD1B (GRAM Domain Containing 1B) and SPTLC2 (Serine Palmitoyltransferase Long Chain Base Subunit 2), that are closely associated with PCOS. Our study will help in amendment and improvement of understanding of FF-EVs in patients with PCOS, thus laying a foundation for future research on the role of miRNAs and proteins in the pathogenesis of PCOS, which may be used as potential biomarkers for the diagnosis and treatment of PCOS.
## Characterization and validation of FF-EVs
The clinical characteristics of PCOS patients and control women were listed in Table 1. Based on the results of statistical difference analysis, the levels of LH, E2, T, PRL, and AMH in PCOS group were statistically higher than that in control group. By contrast, the mean infertility duration was shorter in PCOS group than that in control group. The route and methods of this study were shown in Fig. 1A. To characterize FF-EVs, due to their nanosize, transmission electron microscope (TEM) was performed to show and assess their morphology. As shown in Fig. 1B, FF-EVs were round or oval vesicles. The density of FF-EVs was varied from individual to individual. The nanoparticle tracking analysis (NTA) results showed that the diameters of FF-EVs obtained from the 6 samples sized ranging from 30 to 150 nm (Fig. 1C), which was consistent with the characteristic sizes (30–120 nm) of extracellular vesicles. Furthermore, to validate the purity of the extracted FF-EVs, the exosomal markers, CD63 and TSG101, as well as non-exosomal markers, β-actin, were detected by western blot. In the meantime, follicular fluid-derived granulosa samples were used as negative control. First, we compared the expression of CD63 and β-actin between FF-EVs and granulosa samples. The results disclosed that all expressions of β-actin were clearly probed in granulosa samples and were below the level of detection in FF-EVs samples, oppositely, CD63 exhibited clear bands in FF-EVs samples, but not probed in granulosa samples (Fig. 1D), indicating that the extracellular vesicles isolated from follicular fluid were not contaminated by follicular fluid-derived cells, such as granulosa. Next, all six samples were further validated for subsequent study. The results uncovered that TSG101 and CD63 presented obvious bands in all six samples (Fig. 1E), suggesting that FF-EVs were extracted successfully from 6 samples. Taken together, these data display that we successfully extracted FF-EVs that were not significantly different from other sources-derived extracellular vesicles in morphology. Table 1Clinical characteristics of PCOS and control subjectsProjectC1C2C3P1P2P3p valueAge (years)28.527.329.225.326. 528.60.240209BMI (kg/m2)23.324.922.019.923.024.70.624407LH (IU/L)6.18.15.110.112.314.90.0217718FSH (IU/L)7.06.07.86.46.05.10.164435E2 (pmol/L)119.7106.3132.5168.6158.3159.60.00658136T (nmol/L)1.00.51.33.52.92.30.0092485FBG (mmol/L)5.15.25.05.05.25.30.561437Infertility (years)3.93.64.12.92.63.50.0454128PRL (ng/mL)19.825.315.431.636.542.90.0179693AMH (ng/mL)4.15.66.89.211.913.50.0150721Number of follicles121112222221<0.0001BMI, body mass index; LH, luteinizing hormone; FSH, follicle-stimulating hormone; E2, estradiol; T, testosterone; FBG, fasting blood glucose; PRL, prolactin; AMH, anti-Müllerian hormone. The bold numbers indicate statistically significant valuesFig. 1Isolation and identification of FF-EVs. ( A) The route and methods of this research. ( B) TEM results of extracellular vesicles derived from FF of PCOS patients and healthy women. ( C) NTA results profile of extracellular vesicles from ovarian FF of PCOS patients and healthy women. ( D) Western blotting is used to verify isolated FF-EVs without granulosa contamination. CD63 and β-actin were used as primary antibodies, respectively. ( E) The extracellular vesicles isolated from ovarian FF of PCOS patients and healthy women were verified by western blot analysis
## Analysis of miRNAs in FF-EVs
To ascertain the main miRNAs contained in FF-EVs and involved in PCOS, high-throughput sequencing analysis was conducted. The results of sequencing revealed that many miRNAs were differentially expressed in FF-EVs of PCOS compared with control. A total of 1350 differentially expressed miRNAs (DEmiRNAs) from 6 samples of FF-EVs were identified. Of these DEmiRNAs, 747 were upregulated and 603 were downregulated. Moreover, based on the criteria of |log2FC| >1 and p ≤ 0.05, 514 DEmiRNAs were found to have significant changes, in which, 267 were upregulated and 247 were downregulated, and the top 20 most significantly upregulated and downregulated DEmiRNAs presented in a histogram (Fig. 2A) and Table 2. The 40 DEmiRNAs were selected for further clustering analysis. As seen in Fig. 2B, PCOS samples appeared distinctly separated from control samples. Fig. 2Comparison of expression levels of the up-regulated and down-regulated of top 20 DEmiRNAs in 6 FF-EVs samples. ( A) Histogram of upregulated and downregulated of top 20 miRNAs. ( B) *Clustering analysis* of upregulated and downregulated of top 20 miRNAsTable 2The top 20 up-and down-regulated miRNAs (the miRNA with statistical differences are bolded)miRNADirectionLog2FoldchangeFDRp valuehsa-miR-3131up4.5010.0470.001hsa-miR-206up2.8130.950.015hsa-miR-204-5pup2.6450.550.005hsa-miR-383-5pup2.34610.106hsa-miR-100-5pup1.9550.930.014hsa-miR-4433b-5pup1.80410.103hsa-miR-193a-5pup1.79710.046hsa-miR-3960up1.78010.120hsa-miR-29c-5pup1.73110.116hsa-miR-486-3pup1.71210.201hsa-miR-150-5pup1.69510.072hsa-miR-4732-5pup1.67110.261hsa-miR-10a-5pup1.65510.237hsa-miR-146a-3pup1.56510.211hsa-miR-548ap-5pup1.56110.227hsa-miR-548j-5pup1.56110.227hsa-miR-4454up1.55610.227hsa-miR-582-3pup1.54110.281hsa-miR-874-3pup1.49510.120hsa-miR-532-3pup1.46410.239hsa-miR-889-3pdown-1.25010.183hsa-miR-1197down-1.25710.179hsa-miR-329-3pdown-1.30610.168hsa-miR-1185-2-3pdown-1.33210.226hsa-miR-136-5pdown-1.34710.220hsa-miR-509-3-5pdown-1.35910.328hsa-miR-412-5pdown-1.38510.642hsa-miR-380-5pdown-1.39710.205hsa-miR-365a-5pdown-1.39810.346hsa-miR-33a-3pdown-1.41210.258hsa-miR-1278down-1.46210.337hsa-miR-193b-5pdown-1.51010.196hsa-miR-4286down-1.53110.262hsa-miR-6877-5pdown-1.62610.401hsa-miR-6866-5pdown-1.81210.328hsa-miR-585-3pdown-1.83610.212hsa-miR-11400down-1.88210.147hsa-miR-655-3pdown-2.00410.065hsa-miR-379-3pdown-2.01510.327hsa-miR-539-5pdown-2.31910.050FDR, false discovery rate Then, the total 514 DEmiRNAs were analyzed for GO terms and KEGG pathways. As shown in (Fig. 3A and B), the biological process domain mainly focused on negative regulation of gene expression and regulation of metabolic process. In the cellular component domain, it was mainly concentrated in the extracellular space and closely related to the membrane composition; in the molecular function domain, the mRNA binding and organic cyclic compound binding processes were enriched. In the enriched KEGG pathways (Fig. 3C), some pathways, such as metabolism of xenobiotics by cytochrome P450 and oxidative phosphorylation, were very significant, which has long been proven to play a very important role in the female reproductive system, especially in the occurrence of PCOS [27–31]. In the KEGG pathway, we also found that the pathogenic *Escherichia coli* infection pathway was significantly enriched in co-expressed miRNA target genes, indicating that extracellular vesicles may play a key role in the bacteriostatic process in follicular fluid. Fig. 3GO function annotation and KEGG pathways analysis of DEmiRNAs in FF-EVs. ( A) The GO terms of 514 DEmiRNAs, including BP on the left, CC in the middle, and MF on the right. ( B) Multi-class scatter plot of GO terms analysis of 514 DEmiRNAs. ( C) Scatter plot of KEGG enrichment analysis of 514 DEmiRNAs. ( D) The GO terms of 628 candidate predicted target genes of 6 DEmiRNAs, including BP on the left, CC in the middle, and MF on the right. ( E) Multi-class scatter plot of GO terms analysis of 628 candidate predicted target genes of 6 DEmiRNAs. ( F) Scatter plot of KEGG enrichment analysis of 628 candidate predicted target genes of 6 DEmiRNAs Next, in order to further explore the physiological roles and regulatory mechanisms of DEmiRNAs in FF-EVs possibly involved in PCOS, 514 DEmiRNAs were narrowed down according to the highly significant p value. 6 DEmiRNAs, e.g., hsa-miR-3131(up), hsa-miR-206 (up), hsa-miR-204-5p (up), hsa-miR-100-5p (up), hsa-miR-193a-5p (up), and hsa-miR-539-5p (down), were picked out for further predicting their target genes. A total of 628 candidate target genes were predicted by miRWalk and subjected to GO annotation as well as KEGG analysis through GeneTrail 3.2. As shown in Fig. 3D, GO terms derived from the biological process mainly focused on metabolic processes, such as nitrogen compound metabolic process, RNA metabolic process, and some processes related to transcription. In the cellular component domain, the GO terms for the target genes included nucleoplasm, cell projection, and some pathways related to synapse. Molecular function mainly devoted to some processes such as protein binding and transferase and kinase activities. Multi-class scatter plot presented the results of GO terms more intuitively (Fig. 3E). In these enrichment terms, in addition to being closely related to some metabolic processes, many terms were enriched in signal transduction process, especially in some processes related to synapses, suggesting that DEmiRNAs in FF-EVs might be closely related to synapse-related signal transduction processes that is an important process of hormonal changes related to PCOS patients [32, 33]. Among the pathways enriched by KEGG (Fig. 3F), some metabolic pathways like insulin secretion and thiamine metabolism were enriched, and two hormone-related pathways—estrogen signaling pathway and GnRH signaling pathway—were also significantly enriched. In addition, Apelin signaling pathway was also captured. These differential miRNAs in FF-EVs of PCOS patients may have an impact on receptor cell-related metabolic processes, especially hormone metabolism-related processes.
Ingenuity pathway analysis (IPA) enables us to analyze and clarify the possible upstream regulator and downstream effects on cell and organism biology, as well as their interactions [34, 35]. Using ingenuity pathway assessment to analyze 6 DEmiRs, the results showed that, at the disease level, they are mainly related to body damage and abnormalities, reproductive system diseases, and cancer, and at the molecular and cellular functions level, they are mainly related to cell death and survival, cellular development, bio functions information for IPA analysis is in Supplementary file. Furthermore, based on the IPA network shown in Fig. 4, we could see that mir204-5p and mir-206 are closely related to BCL2, one of the anti-apoptotic genes. Therefore, it can be inferred that FEE-derived mir204-5p and mir-206 may be the key factors leading to the increase of follicles in patients with PCOS and by affecting the expression of BCL2 gene. Fig. 4Ingenuity pathway analysis of DEmiRNAs in FF-EVs. Upregulated miRNAs are shown in red, and downregulated miRNAs are shown in green
## Characterization and analysis of DEPs in FF-EVs
The protein components in FF-EVs from normal and PCOS patients were analyzed using TMT based quantitative proteomics system. A total of 3104 proteins were identified, in which, 2487 were quantifiable proteins (1051 upregulated and 1436 downregulated). 10 proteins (three upregulated proteins and seven downregulated proteins) were picked out to validate the TMT results by performing western blotting. Of ten proteins, two upregulated proteins (ENPP2 and TNXB) and downregulated proteins (SPTLC2, MVP, NSDLH, and DHCR7) were definitely confirmed by western blotting (Fig. 5A), and other four proteins, e.g., SAMD9L, SERINCE3, VPS8, and COMT, presented ambiguous trends, indicating that the TMT results were basically reliable. Subsequently, the identified 2487 quantifiable proteins were subjected to bioinformation analysis. GO terms showed that the biological process mainly involved in developmental process, protein metabolic process, signal transduction, and immune system process. In cellular component domain, cytoplasm, organelle membrane, extracellular space, and nucleoplasm were mainly enriched. Additionally, metal ion binding, carbohydrate derivative binding, hydrolase activity, and nucleotide binding were enriched in molecular function (Fig. 5B). GO terms were also intuitively showed by multi-class scatter plot (Fig. 5C). As shown in Fig. 5D, KEGG results showed that all quantifiable proteins were mainly enriched in metabolic pathways, pathways in cancer, PI3K-Akt signaling pathway, and regulation of actin cytoskeleton. And HPV infection and the pathogenic E. coli infection pathway were also significantly enriched, further indicating that FF-EVs may play an anti-inflammatory and bacteriostatic effect in follicles. Fig. 5TMT assay validation and preliminary GO and KEGG analyses of 2487 DEPs in FF-EVs. ( A) TMT assay validation by western blotting. ( B and C) GO function annotation of 2487 DEPs. ( D) Scatter plot of KEGG enrichment analysis of all quantifiable proteins To further narrow down the target proteins, according to the protein ratio, the t-test was performed, and the p value was calculated as a significant index. Finally, we found that there were 32 significant differentially expressed proteins (DEPs) between PCOS and normal controls. Among them, 9 proteins in PCOS patients were significantly higher than that in control group, and 23 proteins were lower than that in control group. The cluster heat map of the above 32 DEPs was shown in Fig. 6A, and the histogram showed the expression levels of these proteins (Fig. 6B). The information of DEPs was listed in Table 3. The subcellular localization analysis of 32 DEPs indicated that most proteins were located in cytoplasm, extracellular, and membrane organelles (Fig. 6C). The identified 32 DEPs were performed GO functional annotation and KEGG pathway analysis using the Gene Trail website tool. In the biological process, we could see that the DEPs mainly participated in various metabolic processes, i.e., lipid metabolic process and nitrogen compound metabolic process, and these metabolic processes have long been proved to be closely related to PCOS [36–39]. In the cellular component domain, the differential proteins were mainly related to the processes of intracellular organelle, organelle membrane, and vesicle. In the terms of molecular function, the oxidoreductase activity pathway was significantly enriched (Fig. 6D and E), which has long been proved to be closely related to the pathology and pathophysiology of PCOS [27, 30, 31, 40].Fig. 6Comparison of the expression levels of the highly enriched 32 DEPs in FF-EVs in 6 FF-EVs samples and their subcellular localization. ( A) Heatmap and Clustering of 32 DEPs in FF-EVs. ( B) Histogram of 32 DEPs in FF-EVs. ( C) Subcellular localization of 32 DEPs in FF-EVs. ( D and E) The GO terms of DEPs include BP, CC, and MF. ( F) Scatter plot of KEGG enrichment analysis of 32 DEPs in FF-EVsTable 3DEPs in FF-EVs of PCOS and normal controlsUniProtKB-ACProtein nameFoldchangeFDRp valueP28161GSTM2Glutathione S-transferase Mu 22.2580.960.011Q8IVG5SAMD9LSterile alpha motif domain-containing protein 9-like2.2090.960.011Q9BYE9CDHR2Cadherin-related family member 21.9770.960.004P35542SAA4Serum amyloid A-4 protein1.7340.990.047Q13822ENPP2Ectonucleotide pyrophosphatase/phosphodiesterase family member 21.5160.960.019O95932TGM3LProtein-glutamine gamma-glutamyltransferase 61.5140.470.000P22105TENXTenascin-X1.5030.990.048P00748FA12Coagulation factor XII1.4830.960.009O95497VNN1Pantetheinase1.4710.960.023Q15043S39AEZinc transporter ZIP140.6510.970.035O15270SPTC2Serine palmitoyltransferase 20.6490.960.026O75976CBPDCarboxypeptidase D0.6410.960.006O75396SC22BVesicle-trafficking protein SEC22b0.6370.960.013P0DOY2IGLC2Immunoglobulin lambda constant 20.6300.960.011Q16850CP51ALanosterol 14-alpha demethylase0.6270.980.045Q96K37S35E1Solute carrier family 35 member E10.6160.960.021P37058DHB3Testosterone 17-beta-dehydrogenase 30.6160.960.023P07099HYEPEpoxide hydrolase 10.6140.960.018Q3KR37GRAMD1BGRAM domain-containing protein 1B0.6120.900.003Q9UBV2SE1L1Protein sel-1 homolog 1 or Suppressor of lin-12-like protein0.6110.980.044P21817RYR1Ryanodine receptor 1 or Skeletal muscle calcium release channel0.6080.980.041Q15392DHC24Delta[24]-sterol reductase0.6070.960.004Q13530SERC3Serine incorporator 30.6060.960.014P51648AL3A2Fatty aldehyde dehydrogenase0.6040.960.007P21964COMTCatechol O-methyltransferase0.5950.960.026A0M8Q6IGLC7Immunoglobulin lambda constant 70.5940.970.028Q14764MVPMajor vault protein0.5870.960.019Q8N3P4VPS8Vacuolar protein sorting-associated protein 8 homolog0.5860.970.033P02792FRILFerritin light chain0.5790.970.030P01834IGKCImmunoglobulin kappa constant0.5600.980.039Q15738NSDHLSterol-4-alpha-carboxylate 3-dehydrogenase, decarboxylating0.5180.960.020 Q9UBM7DHCR77-Dehydrocholesterol reductase0.430.960.024FDR, false discovery rate KEGG pathway analysis also revealed 6 pathways with high enrichment of DEPs, and these pathways were mainly involved in metabolic pathway, metabolism of xenobiotics by cytochrome P450, steroid hormone biosynthesis, steroid biosynthesis, and ferroptosis (Fig. 6F), which have been proven to be related to the occurrence of PCOS [27–29].
## Possible interaction of DEPs in FF-EVs
As shown in Fig. 7, the correlation diagrams were made by integrating the 6 DEmiRNA target gene data sets and DEPs data sets. We found that there was a corresponding relationship between the predicted target genes of 6 differential miRNAs and DEPs. Has-miR-3131’s target genes were related to 17 differential proteins, the target genes of has-miR-204-5p matched 9 DEPs, has-miR-206 matched 8 DEPs, has-miR-193a-5p matched 12 DEPs, and has-miR-100-5p involved in 3 DEPs. In addition, there were 4 DEPs associated with has-miR-539-5p. The red and green color represented upregulation and downregulation DEPs, respectively. Especially, GRAMD1B protein corresponded to all 5 upregulated DEmiRNAs, and SPTLC2 protein corresponded to 4 upregulated and one downregulated miRNAs. Collectively, by integrating DEmiRNAs and DEPs analyses, GRAMD1B and SPTLC2 were considered to be critical molecules involving in PCOS.Fig. 7Correlation diagram of DEmiRs target genes and DEPs. There was a corresponding relationship between the target gens of 6 differential miRNAs and DEPs. The red and green color represented upregulation and downregulation DEPs, respectively
## Discussion
The characteristics of extracellular vesicles are as described previously. The extracellular vesicles in ovarian follicular fluid are involved in the exchange of genetic information between cells. Thus, extracellular vesicles are often used as indicators of oocyte quality and competence [41]. Although some studies reported the related substances in the FF-EVs of PCOS patients, such as miRNA, lncRNA, or protein, there were few studies on both miRNA and protein levels in FF-EVs in PCOS patients. Moreover, their potential interactions are not yet clarified.
In the present study, we obtained the miRNA and protein expression profiles of FF-EVs and further comprehensively analyzed the potential role of miRNAs and proteins in PCOS. Through further biometric analysis, we found that 6 miRNAs and 32 proteins were significantly differentially expressed between PCOS and the control group.
In analyzing the DEmiRNA target genes, GnRH signaling pathway was noted. In line with our analysis, previous research has verified GnRH is closely related to PCOS by regulating neurotransmitters [42].
Interestingly, Apelin signaling pathway was caught in our analysis. In keeping with our results, Apelin has been closely associated with PCOS based on the following facts. Studies have shown that Apelin/apelin-receptor also expressed in ovary such as follicles and granulosa cells, indicating that the Apelin/apelin-receptor plays an important role in the development of follicle. Furthermore, Apelin/apelin-receptor plays roles in vascular establishment and hormone metabolism in ovary. Increased Apelin/apelin-receptor expression has been found in ovary of PCOS, which are associated with abnormal ovarian hormones and function [43, 44].
Considering the fact that determining the vital genes or molecular details involving in PCOS by using miRNAs or proteins expression data alone is very limited and may not be sufficient [45], we therefore integrated miRNA and protein expression data in order to identify PCOS-related miRNAs and investigated relationships between miRNAs and the regulatory networks in PCOS. By integrating the corresponding relationship between the target gene results predicted by the miRWalk database and the differential proteins, it was found that some differential proteins can correspond to the target gens of differential miRNAs. In integrating analyses, we noticed that the enriched pathways in DEmiRNAs target genes and DEPs data sets are very similar, e.g., hormone-related metabolic processes.
Two important molecules, GRAMD1B and SPTLC2, should be emphasized. First, to the best of our knowledge, hyperandrogenism which involves high overall levels of circulating testosterone is one of the important criteria for diagnosing PCOS, and testosterone is a steroid hormone which is directly metabolized from cholesterol. Specifically, the latest study revealed that GRAMD1s belong to endoplasmic reticulum (ER)-anchored protein that is expressed in eukaryotic cells. GRAMD1s comprise an N-terminal GRAM domain and a StART-like domain, which is followed by a C-terminal transmembrane domain that anchors the protein to the ER [46, 47]. They move to ER-PM (plasma membrane) contacts by sensing accessible PM cholesterol via the GRAM domain and can transport accessible cholesterol from the PM to the ER via the StART-like domain. Therefore, the members of GRAMD1s family are important for cholesterol homeostasis. The GRAMD1b GRAM domain possesses distinct sites to detect accessible cholesterol and anionic lipids within the PM [48]. Up to now, there is no relevant study on the involvement of GRAMD1B in PCOS. In present study, we have noted that GRAMD1B protein presented a downregulated level in FF-EVs of PCOS patients. Simultaneously, very importantly, gramd1b gene is the predicted targets of 5 DEmiRNAs that were found to be upregulated in FF-EVs of PCOS patients. We therefore imagined a work scenario, in which decreased GRAMD1 expression or loss of GRAMD1 function caused by upregulated miRNAs (including has-mir-3131, has-mir-206, has-mir-204-5p, has-mir-100-5p, and has-mir-193a-5p) led to sustained accumulation of accessible cholesterol in the PM and possible dysregulation of cellular cholesterol homeostasis as well as altered steroid hormone production and activity, eventually resulting in POCS (Fig. 8A).Fig. 8Possible molecular mechanisms of GRAMD1B and SPTLC2 involving in PCOS. ( A) GRAMD1s comprise an N-terminal GRAM domain and a StART-like domain, which is followed by a C-terminal transmembrane domain that anchors the protein to the ER. GRAMD1s move to ER-PM (plasma membrane) contacts by sensing accessible PM cholesterol via the GRAM domain and can transport accessible cholesterol from the PM to the ER via the StART-like domain. GRAMD1s family are important for cholesterol homeostasis. GRAMD1B protein presented a downregulated level in FF-EVs of PCOS patients. Simultaneously, gramd1b gene is the predicted targets of 5 DEmiRNAs, including has-mir-3131, has-mir-206, has-mir-204-5p, has-mir-100-5p, and has-mir-193a-5p, found to be upregulated in FF-EVs of PCOS patients. An imagined work scenario should be decreased GRAMD1 expression or loss of GRAMD1 function caused by upregulated miRNAs led to sustained accumulation of accessible cholesterol in the PM and possible dysregulation of cellular cholesterol homeostasis as well as altered steroid hormone production and activity, eventually resulting in POCS. ( B) SPT is a rate-limiting step in the de novo ceramide biosynthesis pathway and is composed of two main subunits, namely, Sptlc1 and Sptlc2. Increased ceramide subclasses have also been identified as novel lipidomic biomarkers in PCOS. High ceramide levels have been consistently linked with insulin resistance and the development of diabetes, suggesting that SPTLC2 is the key hub in PCOS Second, ceramide is a sphingolipid metabolite and a major component of the plasma membrane and lipoproteins. De novo synthesis of ceramide starts from the condensation of serine and palmitoyl CoA. Fatty acids (FAs) are converted to ceramide via a series of reactions by serine palmitoyltransferase (SPT), 3-ketosphinganine reductase, ceramide synthase, and dihydroceramide desaturase. SPT is a rate-limiting step in the de novo ceramide biosynthesis pathway and is composed of two main subunits, namely, Sptlc1 and Sptlc2 [49]. The latter encodes a long chain base subunit of serine palmitoyltransferase. Each subunit is stabilized by forming a dimer or multimer in the endoplasmic reticulum to produce ceramide [50]. Increased ceramide subclasses have also been identified as novel lipidomic biomarkers in PCOS [51]. In addition, PCOS is also closely associated with insulin resistance and is linked to an increased risk of developing type 2 diabetes [52]; meanwhile, high ceramide levels have been consistently linked with insulin resistance and the development of diabetes [53], suggesting that SPTLC2 is the key hub in PCOS (Fig. 8B). Our results showed the decreased expression of SPTLC2 in FF-EVs of PCOS patients. These evidences suggest that GRAMD1B and SPTLC2 play vital roles in PCOS. More in-depth molecular mechanisms need to be further explored.
It should be also addressed that recent research found that downregulation of miR-206 can promote the human granulosa-like tumor cell line, KGN cells, proliferation, inhibit cell apoptosis, and further promote the development of PCOS [54, 55]. Jiang et al. found that high expression of miR-204 can improve insulin resistance (IR) of PCOS via the inactivation of TLR4/NF-κB pathway [56]. In our results, miR-206 and miR-204-5p were also downregulated, and by IPA analysis, it was further found that these two miRNAs were both positively related to the anti-apoptotic factor BCL2 gene. Previous study showed that an increase in ovarian apoptosis caused by an imbalance among the Bcl-2 family members may be involved in the transformation of growing follicles in cystic follicles in the ovaries from DHEA-induced PCOS rats [57]. Thereby, miR-206 and miR-204-5p play important roles in PCOS. Several DEPs found in our study have also been reported in previous studies. For instance, Trine Maxel recently found that zip14 (SLC39A14) may affect zinc homeostasis in adipose tissue in PCOS patients [58]. Insulin receptor substrate-related serine phosphorylation affects the metabolism of classical insulin target tissues, which may be the mechanism of PCOS defect after the characteristic combination of insulin action [59, 60]. Wehr and colleagues found that related polymorphisms of the DHCR7 gene are associated with insulin resistance and vitamin D deficiency in PCOS [61]. Our results showed that the expression status of these key proteins in the FF-EVs of PCOS patients is also different, indicating that these proteins may play relevant roles through FF-EVs in vivo.
Herein, it deserves to be addressed that having hemorrhage is very common after puncturing the first follicles and due to the high number of follicles in PCOS patients. The samples used in this study did not contain blood because FFs were only collected when they were clear with the naked eye. In this way, even if the samples were contaminated with blood, we believed that its amount was very little, and its impact on the whole follicular fluid should be generally ignored.
In summary, the results give us a better understanding of intra-follicular abnormalities in PCOS. And our research shows that, compared with the control group, various miRNAs and proteins in PCOS female FF-EVs are differentially expressed, some of which are important in hormone metabolism pathways. In addition, the pathways of miRNA and protein enrichment in FF-EVs are similar, and there is a corresponding relationship between DEmiR and DEPs, illustrating that there may be regulatory relationships between miRNAs and proteins in FF-EVs. However, regarding the specific interactions between miRNAs and proteins in FEEs and the mechanism of action of different miRNAs and proteins in driving the occurrence of PCOS, many questions have not yet been answered, so further research is needed.
## Follicular fluid (FF) sample collection
The patients who donated the FF samples used in this study were undergoing routine in vitro fertilization (IVF) treatment. In addition, all experiments were approved by the ethics committee of Nanjing Maternity and Child Health Care Hospital (NJFY-2019KY-020). An informed consent form has been obtained from each couple regarding the use of FF samples obtained during IVF treatment for this study. The current diagnostic criterion for PCOS is based on the revised 2003 criteria (two out of three is enough for positive diagnosis) as follows: (a) oligo-ovulation and/or anovulation, (b) clinical and/or biochemical signs of hyperandrogenism;, and (c) polycystic ovaries. The control group contained patients undergoing IVF due to male factor infertility or tubal factors. The exclusion criteria for the two groups included women with endometriosis, cancer, primary ovarian insufficiency (POI), or other medical diseases that may affect follicular development.
Control and PCOS patients (Table 1 contains all basic information of patients) received recombinant follicle-stimulating hormone (FSH) injection after treatment with GnRH agonists according to the standard regimen of IVF treatment. FSH stimulation was initiated once downregulation was confirmed by ultrasound and measurements of serum estradiol, luteinizing hormone, and progesterone. From the 5th day of FSH treatment to the day of egg retrieval, real-time ultrasound scans were used every two days to assess the growth of the follicles. When at least one follicle grew to 18–20 mm in diameter, the egg was punctured 34–38 h after hCG was triggered under the guidance of vaginal ultrasound. The sample was centrifuged at 3000 g for 15 minutes to remove cell debris and other particles. The supernatant was stored at −80 °C for future use.
## Exosome isolation
Follicular fluid extracellular vesicles (FF-EVs) were purified and characterized according to previously published protocols with some modifications [62, 63]. In detail, a total of 15 mL of pooled follicular fluid from the patient was obtained and was centrifuged at 3,500 rpm for 15 minutes at 4 °C to settle the debris. Then, the supernatants were transferred into a 15 mL ultracentrifuge tube, ultracentrifuged at 16,500 g for 30 minutes at 4 °C, and then filtered through a 0.2 mm syringe filter to obtain medium containing extracellular vesicles. Finally, the extracellular vesicles were pelleted by ultracentrifugation at 120,000 g for 70 minutes at 4 °C and stored at −80 °C for further analysis.
## Transmission electron microscopy
Extracellular vesicles were analyzed by transmission electron microscopy (TEM) as previously described [64, 65]. A total of 20 μL of exosome suspension (5 μg/μL) was fixed on a continuous grid and then negatively stained with $2\%$ uranyl acetate solution for 1 minute and air-dried. The samples were observed by FEI Tecnai G2 spirit transmission electron microscope (FEITM) at an acceleration voltage of 120 kV.
## Nanoparticle tracking analysis
Nanoparticle tracking analysis (NTA) measurements were performed using a NanoSight NS300 instrument (Malvern Panalytical) with a 488-nm laser and sCMOS camera module (Malvern Panalytical). Measurements in flow mode were performed with a flow rate of 50, these flow measurements consisted of 3 measurements of 60 seconds, and the captured data were analyzed using NTA 3.2 software.
## Extraction of miRNA and protein from extracellular vesicles
Extracellular vesicle miRNAs were extracted from the aforementioned follicular fluid extracellular vesicles pellets using TruSeq Small RNA Sample Preparation kit (Illumina). The concentration of extracted protein was determined by Bradford method, iTRAQ® Reagent - 8PLEX Multiplex Kit (Sgima) was used for proteolysis, and iTRAQ® Reagent - Multiplex Buffer Kit (Sgima) was used for iTRAQ labeling.
## Western blot
Western blot assay was carried out as follows: extracellular vesicles were lysed with RIPA buffer (Santa Cruz, USA) and cleared lysate was collected by centrifugation for protein separation on $10\%$ SDS polyacrylamide gel. The proteins were transferred onto PVDF membranes (Millipore) and detected with respective antibodies at 4 °C overnight, followed by incubation with IRDye Fluor 680-labeled IgG secondary antibody (Li-Cor Bioscience). The images were scanned and quantified by densitometric analysis by Li-COR Odyssey Infrared Imager. Primary antibodies against CD63 (Abcam) and TSG101(Proteintech) were used.
## miRNA analysis method
*Reference* genes and genome annotation files were downloaded from the ENSEMBL website (http://www.ensembl.org/ index.html). Bowtie was used to build a reference genome. Then, comparison of the clean data to the reference genome was carried out through Bowtie. TPM (Transcripts Per Million) represents the expression level of miRNA [66], based on the reads (not fully mature or degraded) that are compared to the miRNA precursor and slide in a certain area of the mature body.
## Protein analysis method
The protein samples were specifically labeled with TMT technology. This technology uses 6 or 10 isotopic labels to label the amino groups of polypeptides specifically. Then, to compare the relative or absolute content of proteins, tandem mass spectrometry analysis was performed. The protein sequences corresponding to the Homo sapiens protein library were identified in uniport (https://www.uniprot.org/proteomes/UP000005640) [67]. The pQuantMS2 in pFind Studio was used to perform quantitative calculations for TMT/iTRAQ. First, the ratio of each marker pair of each PSM was calculated, then the median of the ratio of the peptides contained in each protein was took, and the result was the corresponding protein. The ratio of marker was paired. Finally, the median of the ratio of all the biological replicates of the different samples was compared and the median was to be as the multiple of the difference between the final samples [68].
## Comprehensive bioinformatics analysis
The target genes of these six miRNAs were predicted by using the miRWalk database (http://mirwalk.umm.uni-heidelberg.de/) according to the base-pairing complementarity between the critical “seed” region of the mature miRNA (nt 2–7) and the 3′-UTR of a target genes mRNA, and the genes with high scores were further validated in the miRDB database and were selected. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed by using the online software (https://genetrail.bioinf.uni-sb.de/). The relevant results were visualized by using R language related package. The IPA system (version 62089861, Ingenuity Systems; Qiagen China Co., Ltd.) was used for subsequent bioinformatics analysis. GENEMANIA (http://genemania.org/search/) was used to construct a protein–protein interaction network for DEPs to evaluate the functions of these proteins. The associations between DEmiRNAs and DEPs were preliminary explored by integrating the intersection of miRNAs target genes predicted by miRWalk and differentially expressed proteins.
## Statistical analyses
Differential expression analysis of miRNAs in the two groups of samples was performed using DESeq in the R language package. miRNAs with $p \leq 0.05$ and |log2_ratio| ≥ 1 are identified as differentially expressed miRNAs [69]. The t-test was performed by using the protein ratio, and the p value was calculated as a significant index. The criteria for a significant increase were log2(Foldchange) > median + 2*s.iqr, and p value <0.05 was upregulated differential protein. The standard for significant downregulation was log2(Foldchange) < median-2*s.iqr, and p value <0.05 is the downregulation of differential protein [70].
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|
---
title: NSUN3-mediated mitochondrial tRNA 5-formylcytidine modification is essential
for embryonic development and respiratory complexes in mice
authors:
- Yoshitaka Murakami
- Fan-Yan Wei
- Yoshimi Kawamura
- Haruki Horiguchi
- Tsuyoshi Kadomatsu
- Keishi Miyata
- Kyoko Miura
- Yuichi Oike
- Yukio Ando
- Mitsuharu Ueda
- Kazuhito Tomizawa
- Takeshi Chujo
journal: Communications Biology
year: 2023
pmcid: PMC10033821
doi: 10.1038/s42003-023-04680-x
license: CC BY 4.0
---
# NSUN3-mediated mitochondrial tRNA 5-formylcytidine modification is essential for embryonic development and respiratory complexes in mice
## Abstract
In mammalian mitochondria, translation of the AUA codon is supported by 5-formylcytidine (f5C) modification in the mitochondrial methionine tRNA anticodon. The 5-formylation is initiated by NSUN3 methylase. Human NSUN3 mutations are associated with mitochondrial diseases. Here we show that Nsun3 is essential for embryonic development in mice with whole-body Nsun3 knockout embryos dying between E10.5 and E12.5. To determine the functions of NSUN3 in adult tissue, we generated heart-specific Nsun3 knockout (Nsun3HKO) mice. Nsun3HKO heart mitochondria were enlarged and contained fragmented cristae. Nsun3HKO resulted in enhanced heart contraction and age-associated mild heart enlargement. In the Nsun3HKO hearts, mitochondrial mRNAs that encode respiratory complex subunits were not down regulated, but the enzymatic activities of the respiratory complexes decreased, especially in older mice. Our study emphasizes that mitochondrial tRNA anticodon modification is essential for mammalian embryonic development and shows that tissue-specific loss of a single mitochondrial tRNA modification can induce tissue aberration that worsens in later adulthood.
A mitochondrial tRNA modification enzyme, NSUN3, is found to be essential for embryonic development in mice, with whole-body knockout causing embryonic lethality and heart-specific Nsun3 knockout leading to heart aberration in older mice.
## Introduction
tRNA molecules function as adapters that convert genetic information transcribed in the form of mRNA into proteins1,2. tRNAs contain a variety of modified nucleosides that are post-transcriptionally incorporated by specific enzymes. These tRNA modifications play pivotal roles in maintaining tRNA structural integrity, biochemical stability, and codon-anticodon interactions3,4. The physiological importance of tRNA modifications is shown by the presence of more than 50 human tRNA modification enzymes whose mutations or expressional aberrations are associated with diseases that frequently manifest as brain dysfunction, cancer, diabetes, or mitochondrial diseases3–5.
In humans, protein synthesis takes place not only in the cytoplasm, but also within mitochondria, where 13 respiratory complex proteins are synthesized by translation of mRNAs using 22 tRNAs and two ribosomal RNAs (rRNAs) transcribed from mitochondrial DNA (mtDNA)6. The 22 human mt-tRNAs contain 18 kinds of modifications at 137 positions7, many of which are important for health. Mitochondrial disease collectively refers to a group of diseases caused by mitochondrial dysfunction. Mitochondrial disease-associated mutations have been reported in several nucleus-encoded mt-tRNA modification enzyme genes8–14, suggesting that mt-tRNA modifications play pivotal roles in intra-mitochondrial protein synthesis.
Moreover, whole-body knockouts (KO) of the mt-tRNA modification enzyme genes Mto1 or Mtu1 are embryonic lethal in mice15,16. Mto1 encodes an mt-tRNA modification enzyme required for the synthesis of 5-taurinomethyluridine (τm5U) at the anticodon first nucleotide in five mt-tRNAs (mt-tRNALeu1, mt-tRNATrp, mt-tRNAGln, mt-tRNALys, and mt-tRNAGlu). Mtu1 encodes an mt-tRNA modification enzyme that introduces thiolation to three τm5U-containing mt-tRNAs, resulting in τm5s2U modification at the anticodon first nucleotide of three mt-tRNAs (mt-tRNAGln, mt-tRNALys, and mt-tRNAGlu). In contrast to embryonic lethality in mice lacking Mto1 or Mtu1, which encode enzymes that target the first nucleotide of mt-tRNA anticodon, reported mice lacking enzymes that target other regions of mt-tRNAs are viable. For example, mice lacking Cdk5rap1, which encodes an enzyme that methyl-thiolates the nucleotide adjacent to the mt-tRNA anticodon are viable17. Additionally, mice lacking NOL1/NOP2/Sun domain family member 2 (Nsun2), which encodes a methyltransferase that targets the variable loop of mitochondrial and cytoplasmic tRNAs18,19, are viable and do not display an apparent mitochondria-related phenotype18,20.
The human mitochondrial genetic code deviates from the canonical cytoplasmic genetic code. For example, the AUA codon, which encodes isoleucine in cytoplasmic translation, encodes methionine in mitochondria. To decode AUA as methionine, mt-tRNAMet contains a 5-formylcytidine (f5C) modification in the anticodon first nucleotide21 (Fig. 1a, b). f5C enables the mt-tRNAMet anticodon (CAU) to base pair with not only the AUG codon but also with the AUA codon22. f5C enables f5C-A pairing via imino-oxo tautomerization of the cytosine base, which is stabilized by the 5-formyl group23. f5C is synthesized by two mitochondrial matrix-localized enzymes, NSUN3 and AlkB homolog 1 (ALKBH1). After mt-tRNAMet is transcribed, NSUN3 first methylates cytidine to form 5-methylcytidine, and ALKBH1 then oxidizes the methyl group to form a formyl group24–27. Due to the importance of f5C in mitochondrial translation, knockout of NSUN3 or ALKBH1 in cultured human cells, as well as mutation of Nsun3 in mouse embryonic stem cells, result in a strong reduction of mitochondrial protein synthesis25,26,28.Fig. 1Embryonic lethality of whole-body Nsun3 KO mice.a Secondary structure of the mouse mitochondrial (mt-) tRNAMet with modified nucleosides: pseudouridine (Ψ) and 5-formylcytidine (f5C). The modifications are depicted based on human and bovine mt-tRNAMet modifications7,50. The nucleoside position is numbered following conventional guidelines51. Note that f5CAU anticodon can base pair with two mitochondrial methionine-encoding mRNA codons AUG and AUA. b Chemical structure of f5C. The formyl modification at the cytidine C5 position is shown in red. c Numbers of animals obtained by crossing parental heterozygous (Nsun3+/−) mice. P value was calculated by the chi-square test. d *Genotyping analysis* of embryos at stage E12.5. e, f Morphology of WT (+/+), heterozygous (+/−), and KO (−/−) embryos at stages E12.5 (e) and E10.5 (f) removed from the uterus of a heterozygous mother mouse. Scale bars, 5 mm (e) and 1 mm (f).
Mitochondrial disease-associated mutations have been found in several nucleus-encoded mt-tRNA modification enzyme genes, such as MTO1, GTPBP3, MTU1, TRMT10C, PUS1, and TRMT58–12,14. The mutations result in dysfunctions and developmental disorders in highly energy-consuming organs, including the heart, skeletal muscle, liver, and brain. Similar to the cases of other important mt-tRNA modification enzymes, mutations in the NSUN3 gene are associated with mitochondrial diseases. One mitochondrial disease patient, who had compound heterozygous NSUN3 mutations, developed symptoms of the disease at the age of 3 months, including muscle weakness, ophthalmoplegia, convergence nystagmus, increased plasma lactate level, microcephaly, and developmental delay13. Another mitochondrial disease patient with different compound heterozygous NSUN3 mutations presented at the age of four months with muscle weakness, hypotonia, lactic acidosis, global developmental delay, and seizures29. In addition, a hypertension patient harboring a point mutation in the mt-tRNAMet (A4435G in mtDNA) had thickening of his heart’s left ventricle posterior wall during his 60s and 70s30. This mutation corresponds to the 3′ adjacent nucleotide to the anticodon of mt-tRNAMet (position 37 in the conventional tRNA position numbering) and has been found to decrease the efficiency of NSUN3-mediated mt-tRNAMet modification in vitro26.
To investigate the physiological functions of NSUN3-mediated f5C modification, we generated Nsun3 KO mice. Whole-body Nsun3 KO mice were embryonic lethal, highlighting the importance of NSUN3 along with MTO1 and MTU1 as essential mt-tRNA anticodon modification enzymes for mouse embryonic development. These results establish that mt-tRNA anticodon modifications are crucial for mammalian embryonic development. Moreover, we showed that heart-specific Nsun3 KO resulted in impaired heart respiratory complex activities and mild heart aberration, especially at an older age, indicating that tissue-specific loss of a single tRNA modification species in a single mt-tRNA can cause tissue aberration, especially in later adulthood.
## Nsun3 is essential for embryonic development in mice
To investigate the physiological importance of NSUN3, we first attempted to generate whole-body Nsun3 KO mice by crossing transgenic mice having exon 4 of the *Nsun3* gene floxed by LoxP sequence (Nsun3Flox/Flox) with transgenic mice carrying Cre recombinase under the control of cytomegalovirus enhancer and chicken β-actin (CAG) promoter. This resulted in the permanent deletion of targeted exons in the germ cells. The resulting Nsun3(Flox/−);CAGcre mice were further crossed to C57BL/6 J mice to yield Nsun3 heterozygous mice (Nsun3+/−). By mating Nsun3+/− mice, we obtained five wild-type mice and 13 heterozygous mice, with no homozygous Nsun3 KO mice obtained after multiple generations of breeding (Fig. 1c). We examined the morphology of embryos at embryonic day (E) 12.5 (Fig. 1d, e and Supplementary Fig. 1a). While the morphology of Nsun3 heterozygous embryos did not differ from wild-type embryos, Nsun3 KO embryos were small and appeared to start to become absorbed into mother’s uterus. At E10.5, while Nsun3 KO embryos were smaller in comparison to wild-type or heterozygous embryos (Fig. 1f), heartbeats were observed in all Nsun3 KO embryos. Thus, Nsun3 KO embryos are alive at E10.5 but die before E12.5. These results clearly indicate that constitutive Nsun3 deficiency leads to embryonic lethality in mice.
## Phenotypes in heart-specific Nsun3 knockout mice
To clarify the possible roles of NSUN3-mediated tRNA f5C modification in adult tissue, we generated heart-specific Nsun3 knockout (Nsun3HKO) mice. We chose to ablate Nsun3 in the heart because the heart and skeletal muscle are the most susceptible tissues to mitochondrial dysfunction31. Another reason for choosing heart is that a hypertension patient having a mt-tRNAMet mutation that can reduce NSUN3-mediated modification of mt-tRNAMet, showed left ventricle posterior wall thickening during his 60s and 70s26,30.
Nsun3HKO mice were generated by crossing transgenic mice harboring exon four of the *Nsun3* gene floxed by LoxP sequences (Nsun3 Flox mice) with transgenic mice expressing Cre recombinase under the control of heart-specific Myosin heavy chain promoter (Myh6-Cre mice) (Fig. 2a). The Nsun3HKO mice grew up without any obvious morphological defects, and adult Nsun3HKO mice had equivalent body weights compared to the Flox mice (Fig. 2b). Heart muscle cell-specific Cre expression from the Myh6 promoter resulted in the removal of most of *Nsun3* gene exon 4 in the heart, as confirmed by reverse-transcription quantitative PCR (RT-qPCR) (Fig. 2c). A small fraction of the remaining exon 4 in Nsun3HKO heart may derive from non-heart muscle cells (e.g., blood vessel cells). Mass spectrometry analysis of heart total RNA nucleosides confirmed that f5C was absent in Nsun3HKO hearts (Fig. 2d).Fig. 2Generation of heart-specific Nsun3 knockout (Nsun3HKO) mice.a Schematic illustration of the strategy to generate Nsun3HKO mice. b Body weight of Flox mice and Nsun3HKO mice at the time of sacrifice (13–20 weeks). Means ± s.e.m. from $$n = 4$$ mice. n.s. not significant by Welch’s t-test. c RT-qPCR of Nsun3 mRNA exon 3–exon 4 using heart total RNA of 50-week-old Flox mice and Nsun3HKO mice. The values were normalized by Actb mRNA levels. a.u. arbitrary units. Means ± s.e.m. from $$n = 4$$ mice. d LC-MS analysis of total RNA nucleosides made by nuclease P1 digestion of total RNA from mouse heart. Mass chromatograms detecting multiple reaction monitoring of f5C (Q1/Q3 = $\frac{272.20}{140.20}$) or 2′-O-methylcytidine (Cm, a loading control, Q1/Q3 = $\frac{258.25}{112.05}$) are shown. Q1/Q3: the mass of the single-protonated precursor ion and product ion.
To investigate the impact of Nsun3 deficiency on the heart, we first measured the mass of dissected hearts in 14-week-old young adult mice and 50-week-old mice (Fig. 3a). Although the Nsun3HKO hearts showed equivalent weight as the control Flox mice at 14 weeks of age, Nsun3HKO hearts were $31\%$ heavier than Flox mice hearts at 50 weeks of age. Thus, at an older age, Nsun3HKO hearts show mild enlargement, which often occurs as a compensatory response to compromised heart function. Fig. 3Heart aberrations in Nsun3HKO mice.a The mass of 14- and 50-week-old mice hearts that were dissected and measured after echocardiography. b Representative M-mode echocardiography images of 50-week-old Flox mice and Nsun3HKO mice. The upper images show the axis view of the left ventricle. Lower panels show the M-mode tracing of the left ventricle. c Schematic of diastolic stage and systolic stage of heart. d Left ventricle relative mass estimated by the echocardiography image analysis. e Left ventricle volume at diastolic stage (left panel) and systolic stage (right panel). f Calculated ejection fraction (%) of the hearts. g Left ventricle posterior wall thickness at diastolic stage (left panel) and systolic stage (right panel). Means ± s.e.m. from $$n = 3$$ mice (14-week-old Flox) or 4 mice (14-week-old Nsun3HKO, 50-week-old Flox and Nsun3HKO mice). ** $P \leq 0.01$ and *$P \leq 0.05$ by two-way ANOVA followed by Tukey’s test.
To monitor heart function, we performed cardiac ultrasonography (Fig. 3b, c). The relative masses of the left ventricles, estimated by ultrasonography, were normal in 14-week-old, young adult Nsun3HKO mice, but showed a slightly larger tendency in 50-week-old Nsun3HKO mice, although the difference was statistically insignificant (Fig. 3d). On the other hand, left ventricle volume decreased in the systolic phase of Nsun3HKO hearts at 14 weeks (Fig. 3e). Accordingly, although statistically insignificant, the ejection fraction showed an increasing tendency in Nsun3HKO mice hearts (Fig. 3f). In addition, the left ventricle thickness increased in the systolic phase of 50-week-old Nsun3HKO heart (Fig. 3g), suggesting enhanced heart contraction. Collectively, our results demonstrate that heart Nsun3 knockout causes the development of mild heart abnormalities that become more apparent at an older age.
## Aberrant mitochondrial morphology in Nsun3HKO mouse heart
Abnormal mitochondrial morphology is a hallmark of mitochondrial dysfunction. Since NSUN3 is a mt-tRNAMet modification enzyme required for efficient mitochondrial translation13,24,26, we next examined mitochondrial morphology using transmission electron microscopy. Mitochondria in the cardiac muscle of Flox control mice were filled with well-organized, elongated cristae structures (Fig. 4a). By contrast, the Nsun3HKO heart mitochondria had fragmented cristae structures (Fig. 4b). Metabolic needs due to impairment of mitochondrial function can promote mitochondrial remodeling as a compensation mechanism32,33. Indeed, quantification of the mitochondrial size revealed that the mean size of Nsun3HKO heart mitochondria (1.011 μm2) was 1.5 times larger than the mean size of Flox heart mitochondria (0.690 μm2) at 14 weeks of age and 1.7 times larger at 50 weeks of age (Flox: 0.685 μm2, Nsun3HKO: 1.174 μm2) (Fig. 4a–d). In addition, 50-week-old Nsun3HKO heart mitochondria were $17\%$ larger than 14-week-old Nsun3HKO heart mitochondria (Fig. 4d). These aberrant mitochondrial morphologies indicated that Nsun3HKO mice may have dysfunctional heart mitochondria. Fig. 4Morphological abnormalities of Nsun3HKO mouse heart mitochondria.a, b Representative images of mitochondria in cardiac muscles of 50-week-old Flox mice (a) and Nsun3HKO mice (b). Scale bar, 1 μm. c Histogram showing the size distribution of cardiac mitochondria from 14- or 50-week-old, Flox, or Nsun3HKO mice. $$n = 300$$ mitochondria in each group were analyzed. d Violin plot of the same data as shown in the histogram. The mean mitochondrial areas (Flox 14-wk, 0.690 μm2; HKO 14-wk, 1.011 μm2; Flox 50-wk, 0.685 μm2; HKO 50-wk, 1174 μm2) are indicated by horizontal lines. **** $P \leq 0.0001$ and *$P \leq 0.05$ by Mann–Whitney test.
## Nsun3HKO does not decrease the steady-state levels of heart mitochondrial tRNAs and mRNAs
Mitochondrial RNAs are transcribed as polycistronic precursors and then processed into each RNA species6,34 (Fig. 5a), and the stability of mature mt-RNAs is post-transcriptionally regulated by RNA-binding proteins and RNases in mitochondria35. To evaluate the effects of Nsun3 loss on mitochondrial RNA steady-state levels, we conducted northern blots of heart mt-tRNAs and mt-mRNAs. As a result, we observed a slight increase in the steady-state levels of all monitored mt-tRNAs and mt-mRNAs, including mt-tRNAMet (Fig. 5b–e and Supplementary Fig. 2). This result indicates that mt-tRNAMet steady-state level increased likely due to increased mitochondrial volume (Fig. 4) and/or mitochondria-wide RNA upregulation, rather than an event specific to mt-tRNAMet. The mt-Nd2 mRNA is directly connected to mt-tRNAMet within the polycistronic precursor (Fig. 5a). To assess whether the loss of f5C modification in mt-tRNAMet affects processing at the mt-tRNAMet-Nd2 boundary, the entire membrane of mt-Nd2 northern blot is shown in Fig. 5d. We observed only some increase of the precursor RNA (faint bands observed above mature mt-Nd2) at a comparable level to the increase in mature mt-Nd2 mRNA level, which suggests that the loss of f5C modification in mt-tRNAMet has a minimal or no effect on mt-tRNAMet-Nd2 boundary processing. Overall, these results indicate that Nsun3HKO does not decrease the steady-state levels of observed mature mt-tRNAs and mt-mRNAs. Fig. 5The steady-state levels of mt-tRNAs and mt-mRNAs in Nsun3HKO mouse heart.a Schematic of the linearized mtDNA structure consisting of tRNA genes (yellow), protein-coding genes (blue), rRNA genes (orange), and noncoding regions (gray). Polycistronic precursor RNAs are transcribed from the heavy-strand promoter (HSP) and light-strand promoter (LSP), followed by cleavages at the 5′ and 3′ sides of tRNAs to produce respective RNAs. The mt-tRNAMet gene is indicated in red, and genes encoding northern blotted RNAs are indicated in bold letters. b Northern blot analysis of heart tRNAs from 14-week-old, $$n = 4$$ Flox and Nsun3HKO mice. Cytoplasmic 5.8 S rRNA is shown as a loading control, and cytoplasmic tRNALeuCAA is shown as a comparison to mt-tRNAs. c Quantification of tRNAs in (b) and mt-mRNAs in (d) and (e). tRNA was normalized by 5.8 S rRNA, and mRNA was normalized by 28 S rRNA. Means ± s.e.m. from $$n = 4$$ Flox and Nsun3HKO mice. **** $P \leq 0.0001$, ***$P \leq 0.001$, **$P \leq 0.01$, and *$P \leq 0.05$ by Welch’s t-test. d Northern blot analysis of heart mt-Nd2 mRNA from the same mice used in tRNA analysis. Mature mt-Nd2 mRNA is 1038 nt plus poly(A) tail of up to 50 nt. The methylene blue-stained membrane used for the mt-Nd2 mRNA northern blot is shown on the right to monitor RNA transfer. M indicates size marker. e Northern blot analysis of heart mt-mRNAs from the same mice as in the above analyses. All of the mtDNA-encoded complex IV subunit mRNAs (mt-Co1, mt-Co2, and mt-Co3) were monitored.
## Nsun3HKO causes mitochondrial respiratory complex dysfunction exacerbated at an older age
We next evaluated the quantity and activities of mitochondrial respiratory complexes in 14- and 50-week-old mice hearts. To quantify respiratory complexes, mitochondria were fractionated from 14- and 50-week-old mice hearts and whole respiratory complexes were detected by blue native-PAGE. In Nsun3HKO heart mitochondria, we observed a decrease of complex IV in 14-week-old or 50-week-old heart mitochondria (Fig. 6a, b and Supplementary Fig. 1b). Accordingly, the steady-state level of MT-CO1 protein, a mtDNA-encoded complex IV protein, was markedly decreased in Nsun3HKO mice (Fig. 6c and Supplementary Fig. 1c, d). By contrast, the steady-state levels of mt-mRNAs, including mRNAs of all of the mtDNA-encoded complex IV proteins (mt-Co1, mt-Co2, and mt-Co3 mRNAs), were not decreased (Fig. 5c), consistent with the role of NSUN3-mediated tRNAMet modification in the translation of mt-mRNAs rather than their stability. In the Nsun3HKO hearts, we observed a mild increase in lactate levels (Fig. 6d), which may indicate that glycolysis activity was enhanced, possibly in response to decreased respiratory complex activity in the Nsun3HKO hearts. Thus, finally, we measured the respiratory complex activities of 14- and 50-week-old heart mitochondria. The 14-week-old, young adult Nsun3HKO heart mitochondria showed a decrease in complex IV activity (Fig. 6e). Moreover, 50-week-old Nsun3HKO heart mitochondria showed an additional decrease in complex I activity compared to 14-week-old Nsun3HKO (as seen by comparing Fig. 6e, f, $$P \leq 0.037$$, Welch’s t-test). Thus, Nsun3HKO causes dysfunction of specific mitochondrial respiratory complexes, and the dysfunction exacerbates at an older age. Fig. 6Dysfunction of specific respiratory complexes in Nsun3HKO mouse heart.a, b Blue native-PAGE of respiratory complexes of 14-week-old (a) and 50-week-old (b) mouse heart mitochondria. c Western blot analysis of complexes I–V proteins in 50-week-old mice hearts. mtDNA-encoded MT-CO1 is shown in green and nuclear DNA-encoded proteins are in blue. VDAC1 is a loading control of mitochondrial lysate. d Relative lactate levels in the hearts of 14-week-old mice. Means ± s.e.m. from $$n = 5$$ mice each. *** $P \leq 0.001$ by Welch’s t-test. e, f Relative activities of respiratory complexes I-IV in 14-week-old (e) and 50-week-old (f) mice heart mitochondria. CS citrate synthase activity, measured as a loading control. Means ± s.e.m. from $$n = 3$$ mice (14-week-old Flox) or 4 mice (14-week-old Nsun3HKO, 50-week-old, Flox or Nsun3HKO mice). ** $P \leq 0.01$ by Welch’s t-test.
## Discussion
In this study, we first demonstrated that NSUN3, the enzyme required for f5C modification of the mammalian mt-tRNAMet anticodon first nucleotide, is essential for embryonic development in mice (Fig. 1). The first nucleotide of tRNA anticodon is responsible for proper recognition of the mRNA codon third nucleotide, and loss of NSUN3-mediated f5C disables efficient decoding of AUA codons in mt-mRNAs22. Embryonic lethality of KO mice of other mt-tRNA anticodon modification enzymes, MTO1 (for τm5U modification) and MTU1 (for 2-thiolation in τm5s2U modification), emphasizes the pivotal roles of mt-tRNA anticodon first nucleotide modifications in mammalian embryonic development.
Our study demonstrates that the loss of Nsun3 leads to abnormality in the heart and confirms the importance of Nsun3 in mitochondrial function (Figs. 3, 4, 6). Heart-specific Nsun3 KO resulted in decreased mitochondrial respiratory complex activities, fragmented mitochondrial cristae structures, and mitochondrial enlargement. Interestingly, although the Nsun3HKO heart displayed some abnormalities in young adulthood (14 weeks of age), aberrant heart phenotypes were more apparent in later adulthood (50 weeks of age). This age-exacerbated phenotype is similar to a mitochondrial tRNAMet mutant patient who was diagnosed with hypertension at the age of 44 and experienced thickening of left ventricle posterior wall at the age of 60s and 70s30. In a later report, this tRNAMet mutation was shown to reduce the efficiency of NSUN3-mediated tRNAMet modification in vitro26. Our work clearly indicates that deficiency of NSUN3-mediated f5C modification in the heart is associated with heart aberrations, especially at an older age.
Notably, the phenotypes of Nsun3HKO are weaker compared to the heart-specific Mto1 KO (Mto1HKO) mice that were previously reported15. Mto1HKO mice were born, but did not survive longer than 24 h, whereas Nsun3HKO mice grew up to adults. The enlargement of heart mitochondria in Mto1HKO is more pronounced than in Nsun3HKO. Mto1 knockout causes cytoplasmic unfolded protein responses, due to the accumulation of protein aggregates in the cytoplasm caused by impaired mitochondrial protein import from the cytoplasm15. By contrast, the Nsun3HKO mouse heart did not show upregulation of unfolded protein response marker mRNAs Xbp1 or Chop (Supplementary Fig. 3). Furthermore, while E9 embryos of whole-body Mto1 KO or Mtu1 KO are drastically smaller than wild-type and show aberrant morphologies15,16, whole-body Nsun3 KO embryos at E9.5 exhibited relatively milder phenotype with moderately smaller body size than wild-type (as shown in Supplementary Fig. 4) and continued growing at least until E10.5 (Fig. 1f). The differences in the severity of phenotypes between Nsun3, Mto1, and Mtu1 knockouts may be partially attributed to the numbers of mt-tRNAs that the corresponding tRNA modifications are introduced to. NSUN3 modifies only mt-tRNAMet, whereas MTO1 modifies five mt-tRNAs (mt-tRNALeu1, mt-tRNATrp, mt-tRNAGln, mt-tRNALys, and mt-tRNAGlu) and MTU1 modifies three mt-tRNAs (mt-tRNAGln, mt-tRNALys, and mt-tRNAGlu)26,36. Genetically inherited disorders caused by mt-tRNA modification deficiency are generally regarded to occur during embryonic development or at a young age3,5. The smaller number of NSUN3-modified tRNAs compared to MTO1-modified tRNAs may be the cause of relatively mild Nsun3HKO heart aberrations, which became more apparent in late adulthood, in contrast to the strong disorders from young ages in Mto1HKO.
The reported human patients who have compound heterozygous mutations in the NSUN3 gene were diagnosed to develop the mitochondrial disease at several months of age13,29. These infants presented with symptoms of mitochondrial diseases, such as lactic acidosis and skeletal muscle weakness, but heart failure was not reported. On the other hand, a mt-tRNAMet mutation (A4435G mutation in mtDNA) was associated with hypertension and progressive thickening of the posterior wall of the left ventricle during his 60s and 70s, but was not associated with other clinical features30. This mutation site is located next to the mt-tRNAMet anticodon (position 37 according to the conventional tRNA position numbering), and in vitro experiments have shown that it reduces the efficiency of NSUN3-mediated methylation to about $40\%$26. The patient with the mt-tRNAMet mutation had a relatively mild phenotype compared to patients with NSUN3 mutations, possibly due to the presence of some f5C in mt-tRNAMet. These previous studies and our results collectively suggest that patients with NSUN3 mutations should be closely monitored for a potential decline in heart function as they age.
Upon Nsun3HKO, among the five respiratory complexes, the strongest phenotypes were seen in complexes IV and I; Nsun3HKO resulted in a decreased complex IV steady-state level and decreased complex I and IV activities in older mice, and did not substantially affect other complexes (Fig. 6). One possible cause could be due to the number of AUA codons in mt-mRNAs; the numbers of mouse mt-mRNA AUA codons for each respiratory complex are 140 (complex I), 0 (complex II), 18 (complex III), 46 (complex IV), and 13 (complex V). In previous studies, similar to our Nsun3HKO mice, knockout of mt-tRNA anticodon modification enzymes such as human ALKBH1, mouse Mto1, or mouse Mtu1 all resulted in a marked decrease in activities of respiratory complexes I and/or IV, and lesser extent or no effects on complexes II and III15,16,37 (complex V activity cannot be measured by conventional methods). The biased effects of mt-tRNA anticodon modification enzyme knockouts to complexes I and IV may be due to the number of subunits that mtDNA encodes; mtDNA encodes seven subunits of complex I, no subunit of complex II, one subunit of complex III, three subunits of complex IV, and two subunits of complex V.
This study does not reveal the specific mechanisms between respiratory complex dysfunction and heart abnormalities. However, previous studies have shown various mechanisms by which respiratory complex dysfunctions can lead to progressive heart deficiencies38. For example, dysfunction of complex IV can halt the flow of electrons from NADH via complexes I and III, inducing leakage of electrons and production of reactive oxygen species (ROS)39. ROS overload can directly damage tissue and also open holes in the mitochondrial inner membrane, releasing cytochrome c and triggering cell death40. Furthermore, deficiency in oxidative phosphorylation can cause the heart to increase glycolysis for ATP generation, leading to elevated glucose uptake into the cells. A recent study suggests that high intracellular glucose uptake can lead to the accumulation of branched-chain amino acids via transcriptional rewiring, activating mTOR and causing cardiomyocyte hypertrophy41. It is possible that the reduced function of complex IV (Fig. 6e, f) and increased glycolysis in Nsun3HKO hearts (as suggested by the increased heart lactate level in Fig. 6d) to slightly activate these pathways and result in some thickening of the left ventricular posterior wall.
Previous studies have shown that the f5C modification of mt-tRNAMet mediated by NSUN3 plays a crucial role in maintaining the level of mitochondrial translation in human and mouse cells13,24,26,28. Translation levels are determined at both the initiation and elongation steps, and mt-tRNAMet is used in both. An in vitro study has suggested a role for mt-tRNAMet f5C modification in the initiation step of translation at the AUA codon and not the AUG codon42. Additionally, f5C modification was shown to enhance the efficiency of the elongation step of AUA codon translation, but had little effect on the AUG codon translation in vitro22. In Nsun3HKO hearts, complex IV was the most affected respiratory complex (Fig. 6), although all of the mtDNA-encoded complex IV subunit mRNAs (mt-Co1, mt-Co2, and mt-Co3) use AUG as their initiation codons (Supplementary Table 1). Thus, in the Nsun3HKO heart, the translation elongation step, rather than the initiation step, may be involved in the reduction of the complex IV level.
The role of f5C in the initiation of mitochondrial translation requires further studies. This is because, in addition to AUG and AUA, mammalian complex I and V mt-mRNAs also use AUU, AUC, and GUG codons as initiation codons (Supplementary Table 1). Additionally, the loss of Nsun3 loss leads to a decrease in complex I activity in the Nsun3HKO heart at an older age (Fig. 6f) and a decrease in the translation of mtDNA-encoded complex I, III, and V proteins in human and mouse cells26,28. The initiation step of mitochondrial translation is different from that of bacterial or cytoplasmic translation in various ways43. Therefore, to understand the potential role of mt-tRNAMet f5C modification in translational initiation at AUU, AUC, and GUG codons, it would be necessary to conduct an in vitro translation experiment that uses mitochondrial ribosomes (rather than E. coli ribosomes) and other mitochondrial factors.
Regarding the physiological roles of NSUN3, a lack of understanding remains of the embryonic lethal phenotype of whole-body Nsun3 KO and the relatively weak phenotype of Nsun3HKO. Although the heart is regarded as one of the most susceptible organs to mitochondrial dysfunction at postnatal stages31, the role of other tissues or cells for which mt-tRNA anticodon modifications play critical roles during the embryonic stage remains unclear. This question also arises with respect to the embryonic lethality of whole-body Mto1 KO or Mtu1 KO and viability of previously generated heart- or liver-specific Mto1 or Mtu1 KO mice15,16. Therefore, identifying the specific tissue(s) and stage(s) at which mt-tRNA modifications is critical during embryonic development will be a crucial question for mitochondrial biology and RNA biology.
## Animals
Whole-body Nsun3 knockout mice were generated by crossing transgenic mice having exon 4 of the *Nsun3* gene floxed by the LoxP sequence (Nsun3Flox/Flox) with transgenic mice carrying Cre recombinase under the control of cytomegalovirus enhancer and chicken β-actin (CAG) promoter. This crossing resulted in the permanent deletion of targeted exons in the germ cells. The resulting Nsun3(Flox/−);CAGcre mice were further crossed to C57BL/6 J mice to yield Nsun3 heterozygous mice (Nsun3+/−).
Heart-specific Nsun3 knockout mice were generated by crossing transgenic mice in which the *Nsun3* gene exon 4 was floxed by LoxP sequences (Nsun3 Flox mice), with transgenic mice expressing Cre recombinase under the control of Myh6 promoter (Myh6-Cre mice). Nsun3 Flox mice were backcrossed with C57BL/6 J mice for at least five generations to control for genetic background. Myh6-Cre mice were acquired previously15. Male mice were utilized for experiments, while female mice were primarily used for breeding purposes. Experiments were performed at 14 or 50 weeks of age. Mice were housed at 25 °C in a 12-h light and 12-h dark cycle. All animal procedures were approved by the Animal Ethics Committee of Kumamoto University (Approval ID: A2021-012R2).
## Genotyping
Genomic DNA was extracted from a 3–5 mm piece of tissue clipped from the end of the tail of 4-week-old mice. Approximately 50 ng of genomic DNA was subjected to PCR to detect the WT and KO alleles using KAPA 2 G Robust HotStart ReadyMix (KAPA Biosystems, Boston, USA), or floxed allele and Myh6-Cre alleles using KOD FX DNA polymerase (TOYOBO Life Science, Tokyo, Japan) following the manufacturer’s instructions. The primers are listed in Supplementary Table 2.
## Observation of embryos
Whole-body Nsun3+/− males and females were paired overnight. The next morning, males were removed from the cages. The weight of females was checked on the day before observing embryos to estimate pregnancy. To observe E12.5, E10.5, or E9.5 embryos, the female mice were euthanized by isoflurane or cervical dislocation. The uterus was quickly opened and embryos were observed in phosphate-buffered saline (PBS) under a Stemi305 stereomicroscope (Zeiss, Oberkochen, Germany).
## RNA extraction
Mouse hearts were dissected and homogenized in 3 mL of TRI Reagent (MRC, Cincinnati, USA) using TissueRuptor (Qiagen, Hilden, Germany). The heart lysate in TRI Reagent was then centrifuged at 10,000 × g for 10 min, and the supernatant was used for total RNA extraction according to the manufacturer’s protocol.
## Reverse-transcription quantitative PCR (RT-qPCR)
RT-qPCR was performed as described previously44. cDNA was synthesized using 500 ng of total RNA and Prime-Script RT Master Mix (Takara, Kusatsu, Japan) according to the manufacturer’s protocol. Quantitative real-time PCR was then performed using the Rotor-Gene Q MDx 5plex HRM machine (Qiagen, Hilden, Germany) and TB Green Premix Ex Taq II (Takara) according to the manufacturer’s instructions. The primer sequences are listed in Supplementary Table 2.
## RNA nucleoside mass spectrometry
RNA nucleoside mass spectrometry was performed as previously described in refs. 45–47. A 25 μL solution containing 3 μg of heart total RNA, 20 mM Hepes-KOH (pH 7.6), 2 units of Nuclease P1 (Fujifilm, Tokyo, Japan), and 0.25 units of bacterial alkaline phosphatase (Takara, Kusatsu, Japan) was incubated at 37 °C for 3 h. About 3 μL of the nucleoside solution was then injected into the LC-MS-8050 system (Shimadzu, Kyoto, Japan). The nucleosides were first separated by an Inertsil ODS-3 column (GL Science, Tokyo, Japan) using a mobile phase that continuously changed from $100\%$ of solution A (5 mM ammonium acetate in water, pH 5.3) to 100 % of solution B ($60\%$ acetonitrile in water) in 17 min at a flow rate of 0.4 mL min−1, followed by electrospray ionization and a triple quadrupole mass spectrometry in the multiple reaction monitoring modes.
## Echocardiography
Mice were preconditioned by chest hair removal using a topical depilatory (FujiFilm VisualSonics, Toronto, Canada), anesthetized with 1.5–$2.5\%$ isoflurane administered via inhalation, and maintained in a supine position on a platform with limbs attached for electrocardiogram gating during imaging. Body temperature was kept constant by feeding the signal of a rectal probe back to a heating pad, while heart and respiratory rates were continuously monitored. Transthoracic echocardiography was performed using a high-frequency ultrasound system for small animal imaging (VisualSonics Vevo 2100, FujiFilm VisualSonics, Toronto, Canada) using an MS 400 linear array transducer (18–38 MHz). M-mode recording was performed at the midventricular level. All images were analyzed using Vevo 2100 version 1.4 software. Left ventricle wall thickness and internal cavity diameters at diastole and systole were measured. Left ventricle volumes in diastolic phases (LV Vol d) and systolic phases (LV Vol s) were measured. The ejection fraction (%) was calculated as [(LV Vol d) - (LV Vol s)] (LV Vol d)−1 × 100. All procedures were performed under double-blind conditions with regard to genotype or treatment.
## Electron microscopy
Transmission electron microscopy examination was performed essentially as described previously in ref. 48. Briefly, heart tissues were first fixed in a solution containing $2\%$ paraformaldehyde and $2\%$ glutaraldehyde, cut in the fixative, and then additionally fixed at 4 °C for more than 2 h. The tissues were then washed, post-fixed in $1\%$ OsO4 at 4 °C for 1 hour, washed and stained with $1.5\%$ uranyl acetate at 4 °C for 1 h. After dehydration in ethanol and propylene oxide, the tissues were embedded in epoxy resin for 3 h and then polymerized at 60 °C for more than 48 h. The tissues were trimmed, cut into ~60 to 70 nm sections, and stained with $1.5\%$ uranyl acetate for 10 min and with lead citrate for 10 min. Random sections were obtained from three hearts per group. Images were acquired at 80 kV on a HITACHI 7700 transmission electron microscope (Hitachi, Tokyo, Japan). The mitochondrial areas in images taken at 2500 × magnification were quantified using ImageJ software.
## Northern blot
For the tRNA northern blot, total heart RNA (1.5 µg) was separated using 7 M urea/TBE/$10\%$ PAGE at 150 V. The gel was then stained with SYBR Gold (Invitrogen, Carlsbad, USA) to assess the RNA quality and then transferred to a nylon membrane (Merck Millipore, Billerica, USA) using a wet transfer blotting system (Bio-Rad, Hercules, USA) on the ice at 50 V for 80 min. For mRNA northern blot, 1.8 µg of total heart RNA or 1.5 µg of RNA ladder (Nippon Gene, Tokyo, Japan) was separated using $6.7\%$ formaldehyde/1xMOPS/$1.2\%$ agarose gel at 100 V. The RNA was then transferred to a nylon membrane (Merck Millipore, Billerica, USA) by an overnight, conventional sponging method using 20 × SSC. The next day, the membrane was briefly washed with MilliQ water, stained with methylene blue (MRC, Cincinnati, USA), and photographed. For both tRNA and mRNA northern blot, membranes were crosslinked with UV light at 1200 × 100 µJ cm−2 using HL-2000 Hybrilinker (Funakoshi, Tokyo, Japan) and incubated in prehybridization buffer (6 × SSC, $0.1\%$ SDS, and 1 × Denhardt’s solution) at 42 °C for 1 h. The membranes were then hybridized with DIG-labeled (Roche, Basel, Switzerland) probe DNA in hybridization buffer (900 mM NaCl, 90 mM Tris-HCl pH 8, 6 mM EDTA, and $0.3\%$ SDS) overnight at 50 °C. The membranes were washed with 1 × SSC, blocked using DIG wash and block buffer set (Roche), and probed with anti-DIG alkaline phosphatase Fab fragments (Roche) and CDP-Star (Roche). Images were taken by ImageQuant (GE Healthcare, Chicago, USA). Probes DNA sequences are listed in Supplementary Table 2.
## Lactate level measurement
Lactate levels in mouse hearts were measured using the Lactate Colorimetric Assay Kit II (BioVision, Milpitas, USA). Each heart was homogenated in 1 mL of ice-cold lactate assay buffer in the kit using TissueRuptor (Qiagen, Hilden, Germany). The lysate was centrifuged at 10,000 × g for 5 min, and the supernatant was used for lactate measurement according to the manufacturer’s protocol.
## Mitochondrial fractionation
Mitochondria were isolated from fresh mouse heart tissues essentially as previously described in refs. 15,16. Briefly, dissected heart tissue was cut into small pieces on ice with scissors and then homogenized in 5 mL of extraction buffer [225 mM mannitol, 75 mM sucrose, 10 mM HEPES-KOH (pH 7.6), 2 mM EDTA, Protease inhibitor cocktail (Roche), and $0.0025\%$ 2-mercaptoethanol] using a Teflon homogenizer with 15 strokes at 700 rpm, maintaining cooling on ice. The homogenate was centrifuged at 600 × g for 10 min at 4 °C. Subsequently, the supernatant was transferred to a new tube and centrifuged at 7000 × g for 10 min to acquire the mitochondrial fraction pellet. The mitochondrial fraction pellet was resuspended in the extraction buffer and adjusted to 1 mg mL−1 using a protein assay kit (Bio-Rad, Hercules, USA). The mitochondrial fraction was used for subsequent blue native-PAGE and respiratory complex activity measurements.
## Blue native-PAGE
Blue native-PAGE was performed as previously described in ref. 15. The mitochondrial fraction containing 125 μg of protein was suspended in 40 μL of solubilizing buffer containing 50 μM bis-Tris (pH 7.0), 1 M aminocaproic acid, and 1.5 % DDM (n-dodecyl β-d-maltoside). Samples were cleared by centrifuging at 100,000 × g for 15 min at 4 °C. The supernatant was mixed with 3 μL of brilliant blue G (dissolved in 1 M aminocaproic acid). About 20 μL of the sample was subjected to blue native-PAGE using a 3–$12\%$ Bis-Tris native gel (Invitrogen, Carlsbad, USA). Once the dye traveled one-third of the gel length, the first cathode buffer was replaced with the second cathode buffer (10−1 dilution of the first cathode buffer).
## Western blot
Western blot was performed essentially as previously described in ref. 44. Tissues were homogenized in lysis buffer (150 mM NaCl,100 mM Tris-HCl pH 8, $0.5\%$ NP-40, and protease inhibitor cocktail (Roche, Basel, Switzerland)) and sonicated for 10 s. The protein concentration was determined using a BCA protein assay kit (Thermo Fisher Scientific, Waltham, USA). Samples were electrophoresed in SDS polyacrylamide gel and transferred to an Immobilon-P membrane (Merck Millipore, Billerica, USA). The membrane was blocked with $5\%$ skim milk in TBST buffer (150 mM NaCl, 25 mM Tris-HCl pH 7.4, 2.7 mM KCl, and $0.05\%$ Tween-20) and probed for respective proteins using the primary antibodies diluted in $5\%$ skim milk in TBST buffer at 4 °C, overnight. The membrane was washed in TBST and was probed using the secondary antibody at room temperature for 1 h, followed by washing in TBST. The signals were detected using ECL Prime Western Blotting Detection Reagent (GE Healthcare, Chicago, USA) and an ImageQuant 400 imager (GE Healthcare). The antibodies and their conditions for use are listed in Supplementary Table 3.
## Respiratory complex activity
The mitochondrial fraction (1 mg mL−1) was briefly sonicated before use and the activities of complexes I, II, III, and IV were measured essentially as previously described in refs. 15,49. For complex I activity measurement, 980 μL of the solution containing 50 mM potassium phosphate (pH 7.4), 2 mM KCN, 75 μM NADH (Nicotinamide adenine dinucleotide reduced disodium salt), and 50 μM Coenzyme Q1, was mixed and incubated at 30 °C for 3 min. Subsequently, 20 μL (20 μg) of mitochondrial protein was added and absorbance at 340 nm was measured for 200 s. Enzymatic activity was calculated using the extinction coefficient of NADH (6.22 mM−1 cm−1).
For complex II activity measurement, 965 μL of reaction solution containing 50 mM potassium phosphate (pH 7.4), 20 mM succinate, and 20 μg of mitochondrial protein was mixed and incubated at 30 °C for 10 min. Subsequently, final concentrations of 2 μg mL−1 of Antimycin A, 2 μg mL−1 of rotenone, 2 mM KCN, 50 μM DCPIP (2,6-*Dichloroindophenol sodium* salt hydrate), and DB (decylubiquinone) were added and absorbance at 600 nm was measured for 200 s. Enzymatic activity was calculated using the extinction coefficient of DCPIP (19.1 mM−1 cm−1).
Prior to complex III activity measurement, we prepared DBH2 solution by mixing 100 μL of DB with 10 mg of potassium borohydride and 10 μL of 100 mM HCl. The supernatant was transferred to a new tube and 5 μL of 1 M HCl was added. For complex III activity measurement, 984 μL of reaction solution containing 10 mM potassium phosphate (pH 7.4), 50 μM cytochrome C, 1 mM EDTA, 2 mM KCN, and 4 μM rotenone was mixed and incubated at 30 °C for 10 min. Subsequently, 10 μg (10 μL) of mitochondrial protein and 6 μL of DBH2 solution were added and absorbance at 550 nm was measured for 200 s. Enzymatic activity was calculated using the extinction coefficient of cytochrome c (19.0 mM−1 cm−1).
Prior to complex IV activity measurement, 2.7 mg of cytochrome c was dissolved in MilliQ water and 5 μL of 100 mM dithiothreitol was added and incubated for >15 min at room temperature in the dark. For complex IV activity measurement, 1 mL of reaction solution containing 10 mM potassium phosphate (pH 7.4), 50 μL of cytochrome c, and 10 μL (10 μg) of mitochondrial proteins was mixed and absorbance at 550 nm was measured for 200 s. Enzymatic activity was calculated using the extinction coefficient of cytochrome c (19.0 mM−1 cm−1).
For citrate synthase activity measurement, 1 mL of reaction solution containing 100 mM Tris-HCl (pH 8.0), 300 mM acetyl-coA, 0.1 mM DTNB (5,5′-dithiobis 2-nitrobenzoic acid), 0.5 mM oxaloacetate, and 10 μL (10 μg) of mitochondrial proteins were mixed and absorbance at 412 nm measured for 200 s. Enzymatic activity was calculated using the extinction coefficient of TNB (thionitrobenzoic acid) (13.6 mM−1 cm−1).
## Statistics and reproducibility
All numerical data were analyzed by GraphPad Prism 9 software. All the “n” corresponds to individual animals. Three to five animals were used for each group to confirm reproducibility and minimize animal sacrifice. No data were excluded. Control and KO animals were tested in the order of Control 1, KO1, Control 2, KO2, Control 3, KO3,… unless otherwise noted to minimize time bias in experiments. Blinding was not performed unless otherwise noted, due to constraints of time and personnel. To assess differences between the two groups, Welch’s t-test was used unless otherwise noted. A two-tailed P value of 0.05 was considered significant. To assess differences between four groups with two variables, a two-way analysis of variance (ANOVA) followed by Tukey’s test was used. Data were presented as means ± standard error of means (s.e.m.).
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Description of Additional Supplementary Files Supplementary Data Reporting Summary The online version contains supplementary material available at 10.1038/s42003-023-04680-x.
## Peer review information
Communications Biology thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Eve Rogers.
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|
---
title: A tip-coupled, two-cantilever, non-resonant microsystem for direct measurement
of liquid viscosity
authors:
- Sudhanshu Tiwari
- Ajay Dangi
- Rudra Pratap
journal: Microsystems & Nanoengineering
year: 2023
pmcid: PMC10033823
doi: 10.1038/s41378-023-00483-6
license: CC BY 4.0
---
# A tip-coupled, two-cantilever, non-resonant microsystem for direct measurement of liquid viscosity
## Abstract
We report a non-resonant piezoelectric microelectromechanical cantilever system for the measurement of liquid viscosity. The system consists of two PiezoMEMS cantilevers in-line, with their free ends facing each other. The system is immersed in the fluid under test for viscosity measurement. One of the cantilevers is actuated using the embedded piezoelectric thin film to oscillate at a pre-selected non-resonant frequency. The second cantilever, the passive one, starts to oscillate due to the fluid-mediated energy transfer. The relative response of the passive cantilever is used as the metric for the fluid’s kinematic viscosity. The fabricated cantilevers are tested as viscosity sensors by carrying out experiments in fluids with different viscosities. The viscometer can measure viscosity at a single frequency of choice, and hence some important considerations for frequency selection are discussed. A discussion on the energy coupling between the active and the passive cantilevers is presented. The novel PiezoMEMS viscometer architecture proposed in this work will overcome several challenges faced by state-of-the-art resonance MEMS viscometers, by enabling faster and direct measurement, straightforward calibration, and the possibility of shear rate-dependent viscosity measurement.
## Introduction
At a fixed temperature and pressure, the mass density and the viscosity have unique values for a given fluid. Accurate characterisation of fluids using these properties is of immense importance in healthcare, processing industries, and liquid (lubricant) health monitoring. For example, in healthcare applications, the viscosity of blood plasma can be a marker for several conditions such as cardiovascular diseases and autoimmune disorders like rheumatoid arthritis1,2. Elevated viscosity of whole blood is also associated with diminishing renal functions3,4. The estimation of viscosity is also essential for monitoring the quality of lubricants during operation5. In chemical processing industries, the viscosity of the byproducts and the end product is used as a quality control measure. Currently, most of these applications utilise a rotating cylinder-based bulky instrument for viscosity sensing which is expensive, time-consuming, and incompatible with the paradigm of internet-of-things (IOT) based distributed and online sensing. The use of bulky equipment for fluid-property measurement are expensive, require large operating power, and are slower. There is a need for cheaper, low cost and in-line measurement techniques for accurate viscosity measurement without any post-processing. Moreover, there is much emphasis on the fourth industrial revolution (Industry 4.0) led by a large number of interconnected smart devices for self-monitoring6.
Micro-scale solutions have emerged in the last decade to overcome the above challenges because they offer low cost, small footprint, and on-site installation for monitoring many properties and parameters. Piezoelectric MEMS resonators have become popular devices for the measurement of fluid properties because of their excellent ability to operate at a reasonable quality factor in a fluidic environment as evident from several publications over the last few years7–12. Moreover, PiezoMEMS devices offer low-voltage operation and high electromechanical coupling and hence are suitable for such applications.
Most MEMS-based fluid-property sensors use a single resonator (such as a cantilever) and track its resonance frequency, fr, and quality factor, Q, to estimate the density and viscosity of the fluid7–12. The obtained values depend on the fluid density and viscosity through secondary parameters13,1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{array}{ll}{f}_{r}&=\frac{{f}_{vac}\sqrt{1-\frac{1}{2{Q}^{2}}}}{\sqrt{1+\frac{L{g}_{2}}{m}}}\\ Q&=\frac{2\pi \sqrt{1+{g}_{2}/m}}{L{g}_{1}/m}\end{array}$$\end{document}fr=fvac1−12Q21+Lg2mQ=2π1+g2/mLg1/m In Eq. [ 1], g1 and g2 are the secondary parameters which are a series expansion in terms of density, viscosity, and their products. For example, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${g}_{1}={C}_{1}\sqrt{{f}_{r}}\sqrt{\rho \mu }+{C}_{2}$$\end{document}g1=C1frρμ+C2 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${g}_{2}={C}_{3}\rho +{C}_{4}\frac{\sqrt{\rho \mu }}{\sqrt{{f}_{r}}}$$\end{document}g2=C3ρ+C4ρμfr were used by the previous reports13. This method is time-consuming and demands extensive post-processing of the experimental data. These single-cantilever systems suffer from large errors, particularly for viscosity measurement13, because changes in quality factor can be due to a complex combination of several factors including density, viscosity, acoustic damping, substrate damping, and temperature14. Moreover, the resonators used in the current viscometers are operated in higher-order modes to achieve a better Q-factor15, since higher modes have less energy dissipation16. However, at very high frequencies, viscoelasticity can affect the measurement results.
Another popular category of sensors for liquid property measurement is acoustic wave sensors. However, the response of the SAW-based sensors depends on the square root of the product of density and viscosity (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{\mu \rho }$$\end{document}μρ). Moreover, the high-frequency operation of these sensors brings in the effect of the viscoelasticity of fluids. This, in turn, results in further complications in the measurements. Furthermore, the shear horizontal (SH) mode SAW devices saturate at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{\mu \rho }=2$$\end{document}μρ=2 making the design more complicated4. To overcome these challenges associated with SAW-based sensors, flexural modes of cantilever devices are actively being explored17.
We report a unique tip-coupled two-cantilever (TCTC) sensing system where the kinematic viscosity of any fluid can be obtained directly from the output parameters (velocity/displacement) of the device. Such a measurement technique offers a quick and direct measurement of viscosity. The use of two cantilevers in the proposed tip-coupled configuration allows us to eliminate the need for measurement of the Q-factor and associated complexity in the estimation of viscosity.
## The design of TCTC micro-viscometer
The design of the viscometer mimics a conventional rheometer/viscometer where the test fluid is subjected to shear between a fixed and a rotating object (plates or cylinders). In the conventional rheometer, the relationship between the applied torque and the velocity of the moving object is used as a measure of the liquid viscosity. The sensor design used in this study consists of two micro-cantilevers integrated with piezoelectric, lead zirconate titanate (PZT) thin film as the active element. Figure 1a shows the schematic representation of the design of the TCTC viscosity sensor. Figure 1b–d shows the top view of the fabricated TCTC structure at different magnifications. The cantilevers were fabricated on a 25-μm-thick silicon-on-insulator (SOI) wafer. Both the cantilevers are 200 μm wide. The active and passive cantilevers are 900 μm and 800 μm, respectively. The free ends of these cantilevers face each other with 20 μm between their faces. When the TCTC structure is placed in a fluid, the fluid takes the space between the cantilevers and acts as a coupling agent. In this case, the fluid is subjected to shear between the free ends of the two cantilevers. When an alternating electric field is applied across the piezoelectric film on the active cantilever, the cantilever starts vibrating at the frequency of the applied voltage. This sets the surrounding fluid in motion, which, in turn, applies a time-varying force on the passive cantilever and sets it in oscillatory motion. Since the passive cantilever is coupled to the active cantilever through the fluid medium, the amplitude of the vibration of the passive cantilever depends on the properties of the fluid and the vibration amplitude of the active cantilever. It is important to note that the fluid is subject to shear between the two cantilevers, and hence the effect of fluid viscosity dominates the response of the passive cantilever. Fig. 1Viscosity measurement system using tow microcantilevers in a tip coupled arrangement.a Schematic representation of the design of the TCTC viscometer. b Optical image of the TCTC viscometer. c Optical image of the same device on a printed circuit board. d Magnified optical image of the system showing the gap between the two cantilevers
## Results
Although split electrodes were designed and fabricated on the cantilevers for simultaneous actuation and sensing of the response, the sensor output was corrupted due to the capacitive feedthrough between actuation and sensing electrodes. Due to this difficulty, the cantilevers’ vibrational responses were measured using a laser Doppler vibrometer (LDV), Polytec MSA-400. The active cantilever was actuated by applying a unipolar electric potential of 0.75 V across the top (all three segments) and bottom electrodes of the PZT thin film while the sample was placed in a liquid (test fluid) filled petri-dish. The responses of the cantilevers (shown in Fig. 2c) were measured by placing the measurement laser beam on the tip of the cantilevers successively. The measurement setup is schematically shown in Fig. 2. The deflection profile of the cantilevers was also measured by scanning the whole cantilever system; the obtained profile is shown in Fig. 2b. Test fluid for which results are shown in Fig. 2 were obtained by preparing a $10\%$ solution of glycerol in water. The solution’s corresponding density and kinematic viscosity values are 1026.9 kg/m3 and 2.59 cSt, respectively. The frequency response of each cantilever shows two peaks corresponding to the cantilevers’ resonance frequencies. We observe two peaks for each of the cantilevers because, at the respective resonance frequencies of the individual cantilevers, there is enhanced fluid movement, resulting in more fluidic coupling between the two cantilevers. Fig. 2Measurement method and results of frequency responses of active and passive cantilevers.a A schematic representation of measurement of the frequency response of the cantilevers. b Measured deflection profile of active and passive cantilevers. c Frequency response of the active and the passive cantilever measured in $10\%$ solution of glycerol and water. d The amplitude ratio (passive to active) in a $10\%$ (by volume) solution of glycerol in water The responses of both cantilevers are affected by the properties of the fluid medium. When the device operates in a liquid of higher viscosity, the vibration amplitude of the cantilever reduces given the same actuation voltage. This variable response of the active cantilever results in variable coupling to the passive cantilever. In order to eliminate the variability of the coupling, we utilise the ratio of the amplitude of the passive cantilever to that of the active cantilever as the metric for viscosity measurement. The ratio of the responses is plotted in Fig. 2d for $10\%$ solution of glycerol in water. This parameter is termed as “amplitude ratio, R” in rest of the manuscript. The amplitude ratio response has a peak at the resonance frequency of the passive cantilever, allowing the measurement of the resonance frequency of the passive cantilever immersed in the fluid using the same metric. This in turn can facilitate the estimation of the density of the fluid. The inset shown in Fig. 2d is the magnified view of the same plot in a narrower, 10–15 kHz frequency range. The red dots are mean values from three different measurements, and the green envelope bounds the error bar.
The in-fluid measurements were repeated for different concentrations of glycerol–water (G–W) solutions. Figure 3a shows the ratio of the corresponding responses of the two cantilevers. Figure 3c shows the amplitude ratio vs frequency curve away from the resonance, between 16 and 17 kHz. This frequency range is selected based on the “Frequency Selection” logic discussed in the section “Frequency selection” of this paper. The increase in glycerol content is clearly discernible from the increasing amplitude ratio. In the very low-frequency region, the obtained data is noisy. The low-frequency noise can be attributed to Brownian noise18 and the low-frequency acoustic actuation of the cantilevers. Fig. 3Effect of liquid viscosity on the amplitude ratio.a The amplitude ratios for varying concentrations of glycerol in water. b Zoomed-in view of the amplitude ratio vs frequency plot in the 16–17 kHz frequency range. The increase glycerol content is clearly discernible from the increase in the amplitude ratio. c Amplitude ratio vs kinematic viscosity plot for different concentrations of G–W solution. The kinematic viscosity values were obtained from the literature for the volumetric proportions used in the test samples. d Amplitude ratio vs kinematic viscosity plot for different concentrations of G–W solution at three different frequencies Figure 3b shows a plot of the amplitude ratio against the kinematic viscosity of the test fluids at 15.8 kHz. The values of the kinematic viscosity of G–W solution were taken from the literature19–21. A fit model, aηb with the exponent $b = 0.12$, fits the experimental results. Such power law dependence results allow for a straightforward calibration of the sensor. This is considerably easier than the current resonant sensors where a Taylor series expansion of the hydrodynamic function is utilised for calibration requiring three or more experimental points13.
This method enables the viscosity measurement without the frequency sweep and the requirement of any peak fitting for Q-measurement, thereby enabling faster measurements. Moreover, a frequency sweep away from the resonance may provide information on the shear rate dependence of the viscosity of the fluid. Figure 3d shows the amplitude ratio vs kinematic viscosity plots at three different operating frequencies. Since the G–W mixture is a Newtonian fluid, the curves at higher frequencies have a higher amplitude ratio because of the variable sensitivity which is discussed in the next section.
## Frequency selection
Since the viscosity measurement can be carried out at a single actuation frequency, it is important to discuss the critical factors for the selection of the actuation frequency. The two most important considerations for frequency selection that are discussed in this section are, (i) sensitivity, and (ii) the penetration depth of the shear waves generated by the oscillating active cantilever.
## Sensitivity analysis
The cantilever system can be modelled as a two-degree-of-freedom spring mass-damper system, as shown in Fig. 4a. The masses, m1 and m2 represent the effective masses of the active and passive cantilevers, respectively. The springs, k1 and k2 represent effective spring constants for the active and passive cantilevers, respectively. The dampers, c1 and c2 represent the damping coefficients associated with the vibration energy dissipation of each of the cantilevers. The fluid coupling can be incorporated in the model by introducing a coupling spring, kf and a coupling damper, cf. For an incompressible flow of fluid, the presence of the coupling spring, kf can be ignored. The damping coefficient cf for the coupling damper depends on the fluid properties. Please refer to the Supplementary Materials for further details on the simplified model of this system. Fig. 4Modelling and sensitivity analysis of the cantilever system for viscosity measurement.a An illustrative model of the TCTC viscometer. The two cantilevers can be modelled as spring mass-damper systems and the fluid coupling can be modelled with a spring-damper system. For an incompressible flow of fluid, kf, can be ignored. b Sensitivity plots for added mass (black curve), coupling damper (red curve) and fluid damping (green curve). The plots suggest that the amplitude ratio in the low-frequency region is more sensitive to the change in the damping coefficient of the coupling damper in comparison to the changes in added mass and fluid damping. Resonance frequency is marked with the blue dashed line. c Experimental observation in a very low-frequency range. Small variations in viscosity are not discernible. d *At a* slightly higher frequency, the small variations in the viscosity could be observed through the amplitude ratio To understand the effects of fluid density and viscosity on the amplitude ratio, the sensitivities of the amplitude ratio (R) were calculated and plotted for different parameters. Predominantly, fluid density contributes to the added mass and viscosity contributes to the damping. The mass sensitivity, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{\partial }{\partial m}\left(R\right)$$\end{document}∂∂mR provides information about the effect of the density of the fluid on the amplitude ratio. The coupling damper sensitivity, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{\partial }{\partial {c}_{f}}\left(R\right)$$\end{document}∂∂cfR and the sensing cantilever’s damping coefficient sensitivity, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{\partial }{\partial {c}_{2}}\left(R\right)$$\end{document}∂∂c2R provide some idea about the effect of the viscosity of the fluid on the measured amplitude ratio. Although the added mass sensitivity is calculated by differentiating the expression of, R with respect to m2, this sensitivity is valid for added mass also (the differential operator here is a linear operator). Specifically, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{\partial }{\partial {c}_{f}}\left(R\right)$$\end{document}∂∂cfR provides information about the effect of fluid coupling between the active and the passive cantilevers. The green curve in Fig. 4b shows the sensitivity of the amplitude ratio with respect to change in the damping coefficient of the passive cantilever, (c2). The sensitivity is calculated for a cantilever exhibiting a resonance frequency of 34 kHz. It is well known that the response of the vibrating system is dominated by the damper only near the resonance frequency and hence we see that the amplitude ratio also follows the same trend.
Figure 4b shows a comparison between the sensitivities of the added mass and the coupling damper. The fluid density (added mass of the fluid) has a considerable effect on the frequency response of the cantilever near its resonance. In the low-frequency region, the effect of the added mass is negligible. The coupling damper sensitivity, first increases with frequency, then decreases near the resonance and increases again post the resonance. It is important to note that the response is sensitive to all three variables near the resonance. However, in the low-frequency region, the amplitude ratio is only sensitive to changes in the coupling damper. In the very low-frequency region, however, the sensitivity due to the coupling damper is also minimal. This behaviour is also visible in the experimental data, where the amplitude ratio is not discernible for small changes ($40\%$ to $50\%$) in the concentration of glycerol in water (see Fig. 4c), for the 10–11 kHz frequency range.
The clearly separated plots for 40–$50\%$ solutions (shown in Fig. 4d) in the slightly higher frequency range (15–16 kHz) also suggest that the sensitivity increases as we move to the higher frequencies. It is important to note that the low-frequency measurements also suffer from different noise sources such as acoustic noise and Brownian noise. The noise coupled with low sensitivity may result in undetectable changes (small) in kinematic viscosity.
Since we are interested in the measurement of viscosity through the coupling damper, we must choose the frequency in the region where the amplitude ratio is highly sensitive to the coupling damper, and is not sensitive to the added mass and fluid damping. Based on the sensitivity analysis presented above, we should be using the measurement results away from the resonance frequency (low-frequency region) for fluid-property measurement. However, the lowest frequency that can be used will be dictated by the noise-required sensitivity for that particular measurement. Fortunately, the sensitivity of the device can be varied (if the application demands) by just changing the operating frequency of the device and designing the resonance frequency of the cantilever.
## The penetration depth of shear waves
The transfer of energy from the active cantilever to the passive cantilever is through the shear waves generated by the active cantilever. The flow of shear waves created by an oscillating plate is governed by the characteristic length δ22,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta =\sqrt{\frac{2\eta }{\omega }}$$\end{document}δ=2ηωwhere, ω is the frequency of oscillations and η is the kinematic viscosity of the fluid. The penetration depth of the shear waves follows an increasing trend with kinematic viscosity and a decreasing trend with frequency. Since the gap between the cantilevers is always fixed for a previously fabricated system, there can be an upper limit on the frequency and a lower limit on the viscosity.
## Gap between the cantilevers
The concept of penetration depth also governs the gap between the cantilevers. Since there is an inverse relationship between the actuation frequency and the depth of penetration, as we increase the actuation frequency, the gap has to be reduced, to ensure effective coupling. The gap should be decided based on the minimum desired viscosity and maximum operational frequency.
## Sensitivity and range
The amplitude ratio, which is the metric for viscosity measurement for TCTC viscometer, follows an axb (with b < 1) dependence on kinematic viscosity. Such nonlinear dependence results in variable sensitivity in different measurement ranges. Since the exponent term in viscosity dependence is always smaller than unity, the sensitivity will follow a strongly decaying \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left(ba\times \frac{1}{{x}^{1-b}}\right)$$\end{document}ba×1x1−b trend with viscosity. However, it is important to note that the amplitude ratio is a quantity calculated from the measured amplitudes of the active and passive cantilevers. Hence, the minimum discernible change in the viscosity will also be governed by the precision of the amplitude measurement and associated noise. The range of viscosity for several target applications is shown in Table 1. The viscosity range for these applications falls in the high sensitivity range of the current viscometer. The range of these targeted applications is also highlighted on the experimental curve of the viscometer operation as shown in Fig. 5a. Table 1Range of viscosity values for different applicationsTarget applicationViscosity range (mPa)ReferenceStudy of bio-fluids0.5-1023,24Monitoring of wine fermentation1.4-2.47,25Oils, paints, and varnishes0.1-1024Fig. 5Target viscosity range and calibration error in the TCTC viscosity measurement system.a Amplitude ratio vs viscosity plot with the highlighted region of high sensitivity for target applications as shown in Table 1. b Expected curve of viscosity dependence when taking the two extreme experimental points as calibration points. The same plot also shows other experimental data points following the calibration curve. c Calibration error if the sensor is calibrated only two data points
## Calibration error and precision
Since the experimental curve follows a power law axb, the sensor can be calibrated by performing two calibration experiments to obtain the two coefficients “a” and “b”. The precision of the sensor will be governed by the ability of these coefficients to capture the true viscosity values for other experiments. The expected curve for the dependence of the amplitude ratio on the kinematic viscosity of the fluid is shown in Fig. 5b (solid black curve). This curve was generated by selecting two significantly different viscosities as calibration points. The same plot also shows the experimental data points and the deviation of these data points from the calibrated curve. The maximum error due to calibration between different experimental points is ~$5\%$ (see Fig. 5c). As explained previously, the resonant sensors are calibrated by evaluating the constants of a series expansion of the hydrodynamic function. The best reported maximum error for such a resonant sensor, calibrated using four constants at three different temperatures (effectively running 12 calibration experiments) is $7\%$13.
## Coupling mechanisms
There are three modes of energy transfer from the active cantilever to the passive cantilever, namely structural coupling, coupling due to fluid flow, and acoustic coupling. The structural coupling has no effect on the viscosity measurement because it just adds a constant bias in the measured response. Both the acoustic coupling and the viscous coupling, affect the response of the passive cantilever. Moreover, both of these coupling mechanisms are affected by the kinematic viscosity of the fluid medium.
From a basic Stokes-flow assumption, the force on the passive cantilever due to the oscillations of the active cantilever should follow a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$a\sqrt{\eta }$$\end{document}aη dependence22. The acoustic pressure wave, on the other hand, follows an exponentially decaying curve. The origin of these expressions is explained in the Supplementary Material. In order to verify this behaviour, the experimental data were fitted to the following curve,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R=a\sqrt{\eta }+b{e}^{-c\times \eta }+d$$\end{document}R=aη+be−c×η+d The corresponding fit along with the experimental data points are plotted in Fig. 6. The fit parameters and the goodness of fit are also given in the same plot. The near-perfect fit is evidence of the presence of all three modes of energy coupling, described above. Fig. 6The experimental data fitted with the expression \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R=a\sqrt{\eta }+b{e}^{-c\times \eta }+d$$\end{document}R=aη+be−c×η+d. The square-toot term is the result of viscous flow, the exponentially decaying term comes from the acoustic coupling and the constant term signifies the structural coupling
## Conclusions
The TCTC viscometer design presented in this paper utilises a novel two-cantilever architecture, where the fluid coupling between the tips of the cantilevers is exploited for viscosity sensing. This unique approach is a significant improvement over the current MEMS viscosity sensors, since it enables a direct, faster, and potentially, more sensitive measurement. The relative response of the passive cantilever follows a power law, aηb dependence on the kinematic viscosity of the fluid. Such a dependence allows for calibrations of the sensor using only two data points as opposed to four or more in the current MEMS viscometer. Another significant advantage of the TCTC viscometer is its ability to measure the viscosity of the liquid at different operating frequencies thereby allowing shear rate-dependent measurement. Although the test fluid used in this paper is a Newtonian fluid, the viscometer’s ability to measure viscosity at different frequencies is explained. We believe these initial results are encouraging for the MEMS community to explore the avenues of viscometry using TCTC design.
In this paper, we have also presented an understanding of the energy coupling mechanism between the two cantilevers. It is proposed that in addition to the shear flow of fluid, the energy transfer between the cantilevers can also be due to the structural coupling (originating because the two cantilevers share a common frame) and due to the flow of acoustic waves. This hypothesis has been validated by fitting the experimental data to the appropriate expression which includes all three coupling mechanisms.
## Supplementary information
Supplementary Material The online version contains supplementary material available at 10.1038/s41378-023-00483-6.
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---
title: Bitter taste cells in the ventricular walls of the murine brain regulate glucose
homeostasis
authors:
- Qiang Yu
- Igor Gamayun
- Philipp Wartenberg
- Qian Zhang
- Sen Qiao
- Soumya Kusumakshi
- Sarah Candlish
- Viktoria Götz
- Shuping Wen
- Debajyoti Das
- Amanda Wyatt
- Vanessa Wahl
- Fabien Ectors
- Kathrin Kattler
- Daniela Yildiz
- Vincent Prevot
- Markus Schwaninger
- Gaetan Ternier
- Paolo Giacobini
- Philippe Ciofi
- Timo D. Müller
- Ulrich Boehm
journal: Nature Communications
year: 2023
pmcid: PMC10033832
doi: 10.1038/s41467-023-37099-3
license: CC BY 4.0
---
# Bitter taste cells in the ventricular walls of the murine brain regulate glucose homeostasis
## Abstract
The median eminence (ME) is a circumventricular organ at the base of the brain that controls body homeostasis. Tanycytes are its specialized glial cells that constitute the ventricular walls and regulate different physiological states, however individual signaling pathways in these cells are incompletely understood. Here, we identify a functional tanycyte subpopulation that expresses key taste transduction genes including bitter taste receptors, the G protein gustducin and the gustatory ion channel TRPM5 (M5). M5 tanycytes have access to blood-borne cues via processes extended towards diaphragmed endothelial fenestrations in the ME and mediate bidirectional communication between the cerebrospinal fluid and blood. This subpopulation responds to metabolic signals including leptin and other hormonal cues and is transcriptionally reprogrammed upon fasting. Acute M5 tanycyte activation induces insulin secretion and acute diphtheria toxin-mediated M5 tanycyte depletion results in impaired glucose tolerance in diet-induced obese mice. We provide a cellular and molecular framework that defines how bitter taste cells in the ME integrate chemosensation with metabolism.
The median eminence (ME) at the base of the brain controls body homeostasis. Here, the authors describe a functional tanycyte subpopulation at the ME which tastes the surrounding milieu, responds to metabolic signals and regulates glucose homeostasis.
## Introduction
The median eminence (ME) at the base of the brain is a central hub controlling body homeostasis1,2. Within this circumventricular organ, releasing hormones produced by neuroendocrine cells are secreted from axon terminals into the portal circulation and impinge onto hormone-secreting cells in the pituitary gland to regulate major hormonal body axes. Vice versa, peripheral hormones and other circulating cues can enter the brain by passive diffusion through the fenestrated capillaries within the ME3,4. This allows transduction of important signals to neighboring hypothalamic nuclei responsible for controlling body functions such as energy metabolism5 and reproduction6.
Tanycytes are specialized glial cells that line the walls of the third ventricle in the ME and are classified into four groups based on the position of their cell bodies along the ventricular wall7–9. α1 and α2 tanycytes are located along the ventricular surface of the ventromedial and arcuate nuclei, respectively. β1 tanycytes reside in the lateral extensions of the infundibular recess whereas β2 tanycytes line the floor of the ventricle10. Tanycytes thus have access to signals from multiple body compartments including the cerebrospinal fluid and the blood and have been implicated in regulating various physiological states7. However, while tanycytes have long been known to be essential for body homeostasis, individual signal transduction pathways in these cells are incompletely understood. Recent studies suggest that tanycytes respond to chemical stimuli including glucose, ATP, histamine, and acetylcholine11, probing the internal milieu of the organism and raising the possibility that these cells can be considered as taste cells within the brain. Taste cells within taste buds on the tongue express G protein-coupled receptors for bitter, sweet, and umami taste as well as TRPM5 (M5), a gustatory Ca2+-activated monovalent cation channel12,13. Upon tastant binding, taste receptors turn on a signaling cascade depending on the G protein gustducin which leads to PLCβ breakdown, production of DAG and IP3 and release of Ca2+ ions from the endoplasmic reticulum14. The cytosolic Ca2+ ions in turn open the M5 channel, leading to Na+ entry and depolarization of the plasma membrane14. M5-expressing cells were previously found in taste and chemosensory systems including the digestive tract15 and respiratory system16.
Here we identify a gustatory tanycyte subpopulation in the floor of the third ventricle. These cells express M5, bitter taste receptors and other key taste transduction genes such as the gustatory G protein gustducin. M5 tanycytes are functional taste cells and mediate bidirectional communication between the cerebrospinal fluid and the blood via processes extended towards diaphragmed endothelial fenestrations in the ME. M5 tanycytes also integrate metabolic signals and other hormonal cues and fasting leads to enhanced leptin signaling in these cells. Acute chemogenetic M5 tanycyte activation induces insulin secretion and acute diphtheria toxin-mediated M5 tanycyte depletion results in impaired glucose tolerance in mice. Our results provide a cellular and molecular framework for the integration of chemosensation with metabolism in the brain.
## Distinct tanycytes in the ME express the gustatory ion channel TRPM5
We first screened for cells expressing the TRPM5 ion channel, a key taste transduction gene, in the central nervous system (CNS) capitalizing on a recently established M5-GFP reporter mouse strain15. We found that M5 expression in the CNS is largely restricted to a small area in the hypothalamus, encompassing the arcuate nucleus and ME (Fig. 1a). Within this area, M5 cells are mainly confined to the walls of the third ventricle, indicating that these cells are tanycytes. Fig. 1iDISCO-cleared and 3D-reconstructed images of the median eminence (ME) from M5-GFP mice immunolabeled for GFP (green, $$n = 3$$), the vascular endothelial marker CD31 (red), the endothelial permeability marker PV-1 (red), or the tanycyte marker vimentin (magenta).a M5 tanycytes are located in the floor of the third ventricle (3 V) at the level of the ventromedial arcuate nucleus (Arc) and ME. Tanycytic subpopulations based on previous classifications are indicated. b Enlarged from the box in a, shows the elongated morphology of the M5 tanycytes projecting to the ME external zone (exz) near the capillary vessels. c Not all vimentin-positive profiles (magenta) are simultaneously GFP-labeled (filled arrowheads), demonstrating that TRMP5 expression delineates a specific subpopulation of tanycytes (unfilled arrowheads). d-g Fine architecture of the M5 tanycytes. ( d, detailed in f) Two individual M5 tanycytes (marked by solid and dotted lines) form branched processes with endfeet in close apposition (arrowheads, yellow overlay) to capillary vessels, visualized by CD-31 staining (red). ( e, detailed in g) Other M5 tanycytes terminate at the parenchymal surface away from capillary vessels (arrowheads). h–j M5 tanycytes terminate near permeable microvessels (marked by PV-1 staining (red)), either with endfeet covering the microvessels (h, detailed in i) or seen at a distance using SIM microscopy (j and enlarged box). Scale bars: 50 µm (a‒c, e); 30 µm (d, h); 10 µm (f, g); 5 µm (i, j).
Based on their distribution, laterally facing the ventromedial arcuate nucleus and infundibular sulcus and medially the ME, most M5 tanycytes are of the β1- and β2-subtypes with some labeling also observed in α tanycytes (Fig. 1a). Their cell bodies constitute parts of the wall of the third ventricle and ventral projections from M5 β tanycytes reach the surface of the ME surrounding the blood vessels of the hypothalamo-hypophyseal portal plexus (Fig. 1a, b). Co-labeling for the tanycytic marker vimentin revealed that the M5 cells constitute a specific tanycyte subpopulation as many vimentin-positive profiles remained single-labeled (Fig. 1c). M5 tanycytes seem to be concentrated at the rostrocaudal mid-part of the ME (Supplementary Fig. 1a, b). They constitute ~$5\%$ of the total tanycyte population (4.060 M5 tanycytes out of a total of 73.590 tanycytes analyzed on sections prepared from 4 M5-GFP mice). We also observed M5 expression in the pars tuberalis (Supplementary Fig. 1c) and the choroid plexus (Supplementary Fig. 2).
## M5 β tanycytes have access to blood-borne cues
To trace the projections of individual M5 tanycytes, we used iDISCO tissue clearing with 3D reconstruction. We observed the classic morphology of successive branching into smaller processes forming brush-like distal profiles for the M5 β tanycytes (Fig. 1d-g, Supplementary Movie 1). Most of these projections form endfeet in close vicinity to capillary vessels (Fig. 1d, f). We also found M5 β tanycyte processes branching and terminating in capillary-free portions of the ME parenchymal surface (Fig. 1e, g). We next determined whether M5 β tanycytes terminate around permeable, fenestrated capillary vessels responsible for blood-to-brain exchanges in blood-brain barrier (BBB)-free areas, like the ME. Because endothelial fenestrations in the ME are diaphragmed, we generated an antiserum against the integral diaphragm protein PV-1, responsible for forming the fenestrations. We then performed immunostaining for PV-1 on brain sections prepared from the M5-GFP reporter mice and found that M5 tanycytic processes terminate at a variable distance from PV-1-positive capillaries (Fig. 1h, i) and as close as 0.5 µm based on structured illumination microscopy (SIM) measurements (Fig. 1j, Supplementary Movie 2). Taken together, these data suggest that the genetically defined M5 tanycytes transcend the previous classification of these cells and constitute a tanycyte subgroup. Because of the M5 β tanycyte access to blood-borne cues via the vessel fenestrations, where potential ligands might be sensed, we next investigated these cells using functional calcium imaging.
## Bidirectional calcium signal propagation in M5 β tanycytes
To functionally characterize the M5 tanycytes, we next analyzed acute ME slices prepared from mice expressing the calcium indicator GCaMP3 specifically in these cells. Using live cell confocal imaging, we observed spontaneous calcium signals in the M5 β tanycytes in both M5-GCaMP males and females. We detected a local increase in intracellular calcium in the cell bodies as well as in their emanating processes (Fig. 2a, Supplementary Movie 3). Within single M5 tanycytes, spontaneous calcium signals arose in sophisticated bidirectional waves (Fig. 2a, b). Specifically, within the M5 tanycytes found in one imaging plane (Fig. 2a, white rectangle), some calcium waves propagated from the cell bodies in the wall of the third ventricle towards the external zone of the ME spreading between tanycytic processes. Vice versa, other calcium waves in these cells propagated backwards towards the cell bodies (Fig. 2, time frames corresponding to the time points indicated as gray bars in the normalized intensity graph shown in b). During the spontaneous activity, we frequently observed a change in the direction of the calcium wave within the M5 tanycytes, as well as a rise of the intracellular calcium concentration in only one part of the cell (Fig. 2b, red and blue traces for the cell body and cell process, respectively, black arrows show the wave direction). Notably, the spontaneous activity of individual M5 tanycytes did not correlate with the activity of adjacent M5 tanycytes, since none of these exhibited a simultaneous calcium signal spreading from the cell bodies to the external zone of the ME (Fig. 2, time frames in a).Fig. 2Bidirectional propagation of calcium transients within M5 tanycytes.a Images of a representative coronal section of the median eminence (ME) from a M5-GCaMP mouse showing spontaneous activity. Tanycytic cell bodies protrude from the third ventricle (3 V) wall to the external zone of the ME. Time frame series of the marked area (a, white rectangle) shows the propagation of the waves (see white arrows). b Normalized intensities of the GCaMP3 fluorescence from the cell body and the process (red trace and blue trace for ROI1 and ROI2 in a, respectively) measured as time course, demonstrate the calcium wave along the length of the tanycyte and its projection. Gray time marks in the graph correspond to the time frames in A in ascending order. The calcium wave propagates from the cell body to the end of the ME and back (black arrows) (spontaneous activity was observed during baseline activity in Ca2+ measurements, see for example Fig. 4b, $$n = 6$$ mice). Source data are provided as a Source Data file.
## M5 tanycytes are bitter taste cells
The anatomical position of the M5 tanycytes close to the ME would poise them to contribute to the control of energy metabolism by mediating communication between the blood, the CSF and neighboring hypothalamic areas including the arcuate nucleus. To test whether they adapt to extreme metabolic conditions such as fasting and further characterize these cells, we next analyzed the transcriptome of the M5 tanycytes isolated from both fed and fasted mice. To enrich for the M5 tanycytes and not co-purify the M5 pars tuberalis cells during fluorescence activated cell sorting (FACS) in M5-GFP reporter mice, we generated and injected AAV$\frac{2}{1}$ + 2-CAGS-mCherry virus into the lateral ventricle to mark the tanycytes three weeks before sacrifice. In this strategy, the M5 tanycytes -but not the M5 pars tuberalis cells- are labeled by both green and red fluorescence, allowing for dual fluorescence sorting (Fig. 3a–c). The basal hypothalamus was dissected from these adult male M5-GFP reporter mice, dissociated and then FACS sorting was performed to isolate GFP+ /mCherry+ cells, resulting in an enriched population of ~350 cells per mouse hypothalamus. Subsequent RNA-sequencing (RNA-seq) using an Illumina NovaSeq 6000 platform allowed detection of ~14.000 genes with fragments per kilobase of transcript per million mapped reads (FPKM) values >1 in the M5 tanycytes. Fig. 3Expression profiling of M5 tanycytes.a–c AAV$\frac{2}{1}$ + 2-CAGS-mCherry virus was injected three weeks before FACS sorting of M5 tanycytes ($$n = 3$$). Expression of GFP in M5 tanycytes and pars tuberalis cells a and mCherry in tanycytes b before FACS sorting. c Note the colocalization of GFP and mCherry in the M5 tanycytes but not in the pars tuberalis. d Genes expressed in M5 tanycytes involved in different signaling pathways by RNA-seq (bar plot represents mean ± SEM from $$n = 4$$ different samples, individual log values shown for each gene). e Model indicates M5 activation after TasR activation. VGCC: voltage-gated calcium channels. f Venn diagram showing exclusively expressed genes. g Volcano plots showing the differentially expressed genes between feeding and fasting in M5 tanycytes (p-values were obtained via Wald test with DESeq2 package). h Heatmap shows expressed Tas receptors. i Heatmap of differentially expressed genes in fasting and feeding conditions. *Highlighted* genes are involved in cellular signaling and metabolism. Scale bars: 50 µm a–c. Error bars represent the standard error of the mean. Source data are provided as a Source Data file.
Classical tanycyte markers such as vimentin (Vim), transcription factors Sox2 and Rax as well as protein phosphatase 1 regulatory subunit 1B (Ppp1r1b)17 were highly expressed in our sequencing data sets (Fig. 3d). While we found the glial marker GFAP in these cells, consistent with published data10, the neuronal marker NeuN was not detected, confirming the purity of the sorted cells. Several glucose and lactate transporters were detected (Slc16a1, Slc2a9, Slc2a1, Slc2a10, Slc1a4), consistent with a potential role of these cells in glucose sensing and metabolic status18,19. Slc2a1 was previously reported to be highly expressed in β1 tanycytes and to a lesser extent in β2 tanycytes20. The M5 tanycytes expressed the purinergic P2y1 receptor, suggesting that they may have a glucose-sensing mechanism similar to β-cells in the pancreas. We also identified key components of the taste signal transduction pathway (Fig. 3d), in particular taste receptors Tas2r113 and Tas2r125 (Fig. 3d), in the M5 tanycytes, suggesting that they represent ME taste cells. The observed absence or low expression for Tas1r2 and Tas1r3 respectively (Fig. 3d) would indicate that these cells seem to primarily detect bitter rather than umami or sweet taste. Activation of taste receptors would then be transduced via gustducin (Gnb1, Gng13) and propagated via the M5 channel and voltage-gated sodium and calcium channels (Scn2a, Scn9a, Cacna1a, Cacna2d1) (Fig. 3d), as previously established for tongue taste cells (Fig. 3e).
We also found metabolic hormone receptors such as those for leptin and insulin (Lepr, Insr) as well as receptors for other hormones including thyrotropin-releasing hormone and oxytocin (Trhr, Oxtr). Dopamine (Drd), muscarinic (Chrm1, Chrm2) and purinergic (P2x, P2y) receptor genes were also expressed (Fig. 3d). Furthermore, the M5 tanycytes express several genes implicated in BBB formation and the regulation of blood vessel permeability including vascular endothelial growth factors and angiotensinogen (Vegfa, Vegfb, Agt)21. β-tanycytes express claudin 1 (Cldn1), which is involved in the formation of a tight blood-CSF barrier22, and this was also expressed in the M5 subpopulation. Cadherin-2 (Cdh2) and caveolin-1 (Cav1), both involved in endocytosis and recycling, were also expressed. We also detected expression of genes underlying cell growth, adhesion, and proliferation including mitogen-activated protein kinase 1 (Mapk1), fibroblast growth factor 1 (Fgf1), fibroblast growth factor receptors (Fgfr) and ciliary neurotrophic factor receptor (Cntfr), possibly involved in the formation of the long protrusions and the blood vessel-touching endfeet extended by these cells, along with known factors of endfeet motility regulation such as transforming growth factors (TGFs) and semaphorins7.
## Fasting-induced reprogramming of M5 tanycytes
When comparing the gene expression patterns in cells isolated from fed and fasted animals, respectively, we found that while the majority of the genes displayed similar expression levels in these two conditions, a few genes, however, showed distinct expression patterns. Specifically, we found 11,121 genes commonly expressed in Venn analyses comparing both conditions (Fig. 3f). 563 genes were exclusively expressed in M5 tanycytes from fed mice, and 693 genes upon fasting. We identified 292 up-regulated genes and 178 genes that were down-regulated upon fasting (Fig. 3g). Functional analysis revealed that GO terms and pathways such as “receptor ligand activity”, “ATPase regulator activity” and “neuroactive ligand-receptor interaction” were more highly represented among these differentially expressed genes (Supplementary Fig. 3 and 4).
We found specific taste receptors to be differentially expressed under fed or fasted conditions. While Tas1r3 and Tas2r113 did not show expression differences between the two conditions, expression of both Tas1r1 and Tas2r125 increased upon fasting, with Tas2r125 being completely undetectable in fed mice. Tas2r116 was not detected in either condition (Fig. 3h). We identified several categories of genes which were differentially regulated between feeding and fasting (Fig. 3i). Genes participating in cellular signaling and signal transduction (Gabrb2, Gabra5, Gnb4, Cacna2d3, Pde1c) and several ABC transporters (Abca6, Abca9) were upregulated, as well as solute transporters (Slc6a12, Slc41a3), indicating an increased tanycytic activity upon fasting. Interestingly, genes participating in oxytocin signaling (Kcnj14, Adcy8)23 and also steroid transport (Slc10a6)24 were also upregulated upon fasting. In addition, genes related to lipid homeostasis including the receptor for low density lipoprotein (Apobr) were also differentially expressed between these two extreme nutritional conditions. Finally, genes controlling seasonal change sensation and hibernation such as thioredoxin-interacting protein (Txnip) were also regulated depending on energy status25, consistent with previous data suggesting that tanycytes may also play an important role in photoperiod acclimation26.
## Bitter taste signaling in the median eminence in vitro and in vivo
Since we identified key taste transduction genes such as Tas2R bitter taste GPCRs and gustducin subunits in the M5 tanycytes, we next studied functional bitter taste receptor signaling in these cells. To do this, we used confocal calcium imaging on acute coronal slices of the ME prepared from M5-GCaMP mice and bath-applied the Tas2r agonist denatonium (Fig. 4a). We found that denatonium evoked calcium elevations both in the M5 β tanycyte cell bodies and processes. Heat maps of the normalized fluorescence change (Fig. 4b, c) show that nearly all M5 β tanycytes were activated by denatonium. Fig. 4M5 tanycytes respond to bitter substances.a Calcium imaging of M5 tanycytes in the ME. Green and yellow ROIs represent cell bodies and tanycytic fibers respectively. Gray fluorescent pictures show the intensity changes after 10 mM denatonium application (representative observation from six experiments b Time course heatmap of the GCaMP3 normalized fluorescence change from the tanycytic cell bodies and processes, respectively (includes data from $$n = 6$$ mice, 12–24 weeks old, 2 males, 4 females). Black bars indicate the application of 10 mM denatonium and 30 mM K+ (high K+). c Area under the curve (AUC) of the normalized fluorescence intensities of the tanycytic cell bodies and processes. Every observed cell or process were collected for measurements: 3 min before denatonium (baseline) and 3 min after denatonium application (10 mM Den). Red lines are median, black lines are means for each group of measurements. Means were compared via two-sided t-test (****$$p \leq 1.23$$ 10−18 for cell bodies and $$p \leq 2.06$$ 10−15 for cell processes). d Heatmap showing fluorescence change through the ME (coronal sections from a, $$n = 6$$) from the third ventricle (3 V) wall (upper part) down to the fenestrated capillaries (lower part). Divisions represent apparent sections belonging to cell bodies, processes and end feet. Fluorescent intensity is color coded (color bar on the right, a.u.). Denatonium (10 mM) application is indicated by a black bar on the top, time axis direction from left to right (time scale 3 min, black bar). e Local application of denatonium via a pipette. Denatonium (250 mM) was applied to a constantly perfused coronal section (left panel) by bath solution (far right image panel), to the cell bodies (second image panel) and to the end feet (third image panel) (imaging was done two times per location on two different brain slices). Source data are provided as a Source Data file.
We observed a time course of the calcium increase throughout the ME and found that the M5 cell bodies have higher increases in calcium compared to the processes and the M5 tanycytic endfeet (Fig. 4d). However, we did not see a clear time dependency between activation of the cell bodies and processes after the denatonium application. Furthermore, local application of denatonium via a patch pipette showed both activation of the cell bodies and the M5 tanycytic endfeet and processes (Fig. 4e). This suggests that the bitter taste receptors in the M5 β tanycytes are located both on the CSF and the capillary fenestration sides.
According to the classical taste transduction pathway12, TRPM5 activation occurs subsequent to binding of the bitter tastant to its receptor, depolarizing M5 tanycytes via Na+ entry, which in turn allows more calcium to enter the cell. To show the acute expression of TRPM5, we used stevioside, a potentiator of TRPM527. We observed robust increases in calcium signaling in the M5 β tanycytes after stevioside (10 µM) application (Supplementary Fig. 5a, b). Similarly, intracerebroventricular injection of stevioside induced the activity marker c-Fos in M5 tanycytes ($30.00\%$ ± $6.27\%$, Supplementary Fig. 5c–f).
Consistent with this, responses to denatonium in the ME were significantly reduced in a TRPM5 knock-out background13 (Supplementary Fig. 6). Taken together, these data demonstrate TRPM5-dependent taste signaling in the M5 tanycytes in vitro and in vivo and confirm that these cells are functional bitter taste cells, capable of information exchange through the tanycytic barrier.
## Leptin activation of the floor of the third ventricle depends on M5 tanycytes
We next tested, whether metabolic and other hormonal cues activate M5 tanycytes in vitro. Upon application of leptin, M5 tanycytes robustly responded with an increase in intracellular calcium (Fig. 5a). In comparison, glucagon-like peptide 1 (GLP-1), another satiety hormone, was much less potent in triggering calcium responses in these cells. The M5 tanycytes also showed a strong and continuous reaction to the food supplement arachidonic acid. Furthermore, releasing hormones such as TRH as well as oxytocin triggered calcium signals (Fig. 5b), demonstrating that the M5 tanycytes respond to different hormonal cues. Fig. 5Hormonal and metabolic cues activate M5 tanycytes.a Representative heatmaps of the normalized fluorescence responses from the tanycytic cell bodies to metabolic blood-borne molecules. Application of a substance shown as an empty rectangle. The black bar indicates application of 30 mM K+. From upper to lower: Control application of DMSO ($0.001\%$); Weak activation and moderate responses to GLP-1 (500 nM) and leptin (1 µg/ml), respectively, with comparison to the activation with Arachidonic acid (100 µM). Shown as cells pooled from 1 to 2 brain slices from 10 to 14 week old mice. b Representative heatmaps of Ca2+ responses, normalized as in a, upper: direct response to TRH (10 µM), lower: increase of cellular activity in response to Oxytocin (100 µM). Shown as cells pooled from 2-3 brain slices from 10 to 14 week old mice. c-h Phosphorylation of STAT5 in tanycytes is impaired in M5-DTA mice. p-STAT5 staining in overnight fasted M5-GFP mice injected with saline c or leptin d, quantification of p-STAT5 positive tanycytes and ratio of tanycytes double positive for both p-STAT5 and GFP e, f, WT-DTA (DTA-) g, and M5-DTA (DTA +) h mice injected with leptin (WT saline $$n = 5$$ e, WT-DTA leptin $$n = 9$$ e, g, M5-DTA leptin $$n = 6$$ h, e, M5-GFP saline $$n = 5$$ c, f, M5-GFP leptin $$n = 9$$ d, f, data analyzed with 2-tailed t-test). WT saline vs WT-DTA leptin $$p \leq 0.0346$$, WT-DTA leptin vs M5-DTA leptin $$p \leq 0.0328$$, saline vs leptin $$p \leq 0.0067.$$ Note the p-STAT5 signal in tanycytes in the M5-GFP and WT-DTA mice which was lost in M5-DTA mice. Scalebars: 50 µm (overview), 10 µm (inset). Error bars represent the standard error of the mean. Source data are provided as a Source Data file.
To identify the signaling pathway activated by leptin in M5 tanycytes, we injected leptin into the cerebral ventricle and then immunostained for signaling components downstream of the leptin receptor in the arcuate nucleus and in nearby tanycytes. We detected nuclear phosphorylated signal transducer and activator of transcription 5 (p-STAT5) both in cells in the arcuate nucleus and in tanycytes 30 min after leptin injection, which was essentially absent in saline-injected controls (Fig. 5c, d). Some of the p-STAT5-immunostained tanycytes were M5 cells ($21.4\%$ ± $4.7\%$), raising the possibility that these cells are transcriptionally reprogrammed upon leptin stimulation via p-STAT5 signaling (Fig. 5e, f). Next, we tested leptin signaling across the ME in animals lacking M5 tanycytes. To do this, we generated mice which express diphtheria toxin A chain (DTA) selectively in M5 cells. DTA inhibits translation by catalyzing the ADP ribosylation of the eukaryotic elongation factor 2, resulting in cell death and thus depleting the M5 cells28. Notably, loss of phosphorylated STAT5 in M5-DTA mice was not restricted to the M5 tanycyte population. Instead, the p-STAT5 immunosignal was absent in all tanycytes in these animals (Fig. 5g, h). Ablation of M5 tanycytes did not lead to a leaky ME barrier as we did not observe a statistically significant difference in transvascular Evans Blue diffusion or vimentin staining between WT and ablated animals (Supplementary Fig. 7 and 8). We also observed a calcium response to 100 µM ATP both in WT and ablated animals as a functional control (Supplementary Fig. 7). Taken together, these data suggest that leptin activation of p-STAT5 signaling in tanycytes depends on the M5 subpopulation. Since these cells only constitute a small subpopulation of all tanycytes (Fig. 5), our data indicate complex interactions between different tanycyte subpopulations possibly depending on TRPM5 activation by an increase in intracellular calcium and raises the possibility that these cells influence metabolism in vivo.
## Impaired glucose tolerance upon acute diphtheria toxin-mediated M5 tanycyte depletion
To test this hypothesis and unravel the physiological function of the M5 tanycyte population, we acutely ablated these cells using diphtheria toxin (DT). The diphtheria toxin receptor (DTR) promotes translocation of DT into the cytoplasm resulting in the inhibition of protein synthesis and subsequently cell death. We found that 0.5 ng DT injected bilaterally into the arcuate nucleus of male mice, in which DTR expression is restricted to M5 cells (M5-DTR), efficiently depleted the M5 tanycyte population (Fig. 6a), but not the M5 pars tuberalis or choroid plexus cells (Supplementary Fig. 9). Interestingly, when chronically fed with a HFD (but not when on normal chow, Supplementary Fig. 10a, b), mice with DT-mediated M5 tanycyte depletion showed impaired glucose sensitivity (Fig. 6b) despite improved sensitivity to insulin 18 weeks later (Fig. 6c) with non-statistically lower baseline glucose (Fig. 6d) but decreased levels of insulin (Fig. 6e). Mice with DT-mediated M5 tanycyte depletion also showed a lower HOMA-IR value after M5 tanycyte depletion (Fig. 6f). Food intake was unchanged between M5 tanycyte-depleted mice and controls (Fig. 6g). The animals did not show alterations in body weight (Fig. 6h) or body composition (lean and fat tissue mass, Fig. 6i, j). No difference was seen in respiratory exchange ratio (Fig. 6k), locomotor activity (Fig. 6l) or energy expenditure (Fig. 6m, Supplementary Fig. 10c, d). Taken together, these data demonstrate that acute M5 tanycyte ablation impairs glucose tolerance after high fat diet feeding but increases insulin sensitivity, reduces insulin secretion and produces a non-statistically significant lower baseline glucose. Fig. 6Acute M5 tanycyte ablation impairs glucose tolerance.a M5-GFP mice and M5-GFP-iDTR mice were bilaterally intra-hypothalamically injected with 0.5 ng diphtheria toxin and perfused 7 days later. Note the ablation of the M5 tanycytes ($$n = 3$$). Scale bar: 20 µm. b Glucose tolerance test (GTT) at age of 30 weeks ($$n = 10$$, $$p \leq 0.0004$$). c–e Insulin tolerance test (ITT) ($p \leq 0.01$ for 30 and 60 min), fasting glucose ($$p \leq 0.1096$$), and fasting insulin ($$p \leq 0.0181$$) in 48 weeks old WT mice and mice with DT-mediated M5 tanycyte depletion (Cre +) (Cre- $$n = 7$$, Cre+ $$n = 10$$). HOMA-IR f at the age of 48 weeks (Cre- $$n = 7$$, Cre+ $$n = 10$$, $$p \leq 0.0123$$), food intake g (Cre- $$n = 4$$, Cre+ $$n = 7$$), body weight (h) (Cre- $$n = 11$$, Cre+ $$n = 10$$), body composition (i, j) at the age of 33 weeks (Cre- $$n = 9$$, Cre+ $$n = 10$$), respiratory exchange ratio (RER), locomotor activity and total energy expenditure k–m in 33 weeks old WT and KO mice (Cre- $$n = 10$$, Cre+ $$n = 9$$). Data represent means ± SEM. Asterisks indicate ** $p \leq 0.01.$ *Longitudinal data* b, c, g, h were analyzed using 2-way ANOVA with time and genotype as co-variables and Bonferroni post-hoc analysis for individual time-points. Bar graphs d–f, i–l were analyzed using 2-tailed t-test. Data in m were analyzed using ANCOVA with body weight as co-variate. Source data are provided as a Source Data file.
## Chemogenetic M5 tanycyte activation promotes insulin release
To unravel the mechanism behind the decreased levels of insulin and impaired glucose tolerance observed in M5 tanycyte-ablated mice, we used a complementary experimental strategy and acutely activated the M5 tanycyte population in mice which express HA-tagged designer receptors exclusively activated by designer drugs (DREADD) selectively in M5 cells (M5-DREADD). To validate these mice, 2 μg CNO, a specific DREADD activator, was injected into the third ventricle and these were perfused 1 h later. Co-staining of HA-tag and c-Fos immunoreactivity verified selective activation of the M5 tanycytes (Fig. 7a–c). Blood was collected at different time points and the hormone levels were tested via Luminex. After CNO injection, we did not observe differences in circulating leptin levels from 10 to 60 min. However, while insulin levels showed no differences between WT and M5-DREADD mice at 10 and 20 min after injection, M5-DREADD mice displayed increased insulin release between 30 min and 60 min after CNO administration (Fig. 7d). Over the same time period, we did not observe fluctuation of pituitary hormones following activation of M5 tanycytes (Supplementary Fig. 11), indicating that activation of these cells specifically promotes insulin release. To further corroborate these findings, we injected an AAV-DIO2-*Cre virus* (which expresses Cre recombinase under transcriptional control of type II iodothyronine deiodinase (Dio2) promoter in all tanycytes29), into the cerebral ventricle of WT-DREADD/GFP mice. Three weeks after injection, expression of GFP and thus DREADD was found in the tanycytes but not in the pars tuberalis (Fig. 7e). Intraperitoneal injection of CNO induced c-Fos expression in tanycytes and not in the pars tuberalis (Fig. 7e–g). We then repeated the glucose tolerance test following i.p. CNO injection and found an improvement in glucose tolerance in virus-injected WT-DREADD animals when compared to virus-injected control animals lacking the DREADD allele (Fig. 7e–h). Taken together, these data strongly suggest, that the M5 tanycytes are responsible for the observed GTT phenotype and promote insulin release. Fig. 7Acute M5 tanycyte activation promotes insulin release.a–c M5-DREADD mice were injected with 2 μg CNO into the third ventricle and perfused 1 h later. M5 tanycytes were detected via an anti-HA tag antibody. Note the colocalization of c-Fos and HA tag signal. Scalebar: 50 µm (overview), 10 µm (inset) (result replicated in $$n = 5$$ mice). d Insulin release after M5 tanycyte activation. Mice cheek blood obtained at various time points after third ventricle injection of CNO both in WT and M5-DREADD mice. Serum levels of insulin and leptin were tested via Luminex MAGPIX System ($$n = 4$$). Note that insulin release increased significantly in M5-DREADD mice compared with WT, data analyzed with 2-tailed t-test. Asterisks indicate *** $p \leq 0.001.$ e–g Tanycyte activation via CNO i.p. injection three weeks after AAV-DIO2-*Cre virus* injected into WT-GFP/DREADD mice. Note the colocalization of GFP and c-Fos signal. Inset: GFP was not detected in the pancreatic islet. Scalebars: 50 µm, ($$n = 3$$). h Glucose tolerance test between WT-DREADD and WT mice i.c.v. injected with AAV-DIO2-*Cre virus* (wild type $$n = 8$$, WT-DREADD $$n = 5$$). Data were analyzed using 2-tailed t-test. Asterisks indicate * $p \leq 0.05$, ** $p \leq 0.01.$ Error bars represent the standard error of the mean. Source data are provided as a Source Data file.
To understand this phenomenon, we performed transcriptomic analysis on the activated M5 tanycytes (Fig. 8). We found that 438 genes were significantly upregulated and 406 genes were downregulated in chemogenetically activated M5 tanycytes when compared with controls (Fig. 8a Volcano plot). After M5 tanycyte activation, we found up-regulation of several genes related to metabolism including Orm1, which was reported to activate the JAK2-STAT3 pathway upon binding to the leptin receptor and improve glucose and insulin tolerance30. *Some* genes related to cholesterol metabolism were also up-regulated in activated M5 tanycytes such as Hmgcr, Stard4, Slc10a6, Msmo1, Insig1 and Rbfox2, indicating a function of M5 tanycytes in maintaining lipid homeostasis31–36. In addition, membrane proteins including Emp1 which colocalize with tight junction proteins and regulate blood brain barrier function were also up-regulated following M5 tanycyte activation37. Interestingly, we also identified increased expression of a selection of genes related to insulin production and glucose regulation upon stimulation of the M5 tanycytes, such as Pdp1, Rgs2 and Fem1b38–40. Taken together, these sequencing results are consistent with a role of M5 tanycytes in the regulation of metabolism within the mouse. Fig. 8M5 tanycyte activation leads to differential regulation of metabolism-related genes.a (right) Venn diagram showing exclusively expressed genes and (left) Volcano plot illustrating genes differentially regulated in activated M5 tanycytes compared with controls by RNA-seq (p-values were obtained via Wald test with DESeq2 package, $$n = 3$$ samples). b Scatter Plot of GO terms enriched from upregulated genes in activated TRPM5 tanycytes. The color and size of the dots are scaled with respect to padj value and the number of the differentially expressed genes, respectively. For a p-values were obtained via Wald test with DESeq2 package. For b padj were obtained via hypergeometric test, and FDR correction was done by Benjamini and Hochberg method. c Heatmap of differentially expressed genes in activated vs control M5 tanycytes. *Highlighted* genes are predominantly involved in metabolism. Source data are provided as a Source Data file.
## Discussion
We have identified a gustatory cell type in the floor of the third ventricle that integrates chemosensory and hormonal cues and provides a selective means of bidirectional communication between the CSF and capillary system. Based on their polarized morphology and location, the chemosensory M5 cells are poised to mediate communication between central and peripheral environments and subsequently influence energy metabolism7. Despite previous studies suggesting important roles of tanycytes in the control of body homeostasis, functional signaling pathways in these cells are, however, only now beginning to emerge. The M5 cells in the ME constitute a small subpopulation of the tanycytes, demonstrating functional specialization within the tanycyte population and transcending the previous classification of these cells based on anatomical markers.
We found spontaneously active M5 tanycytes with some cells displaying repetitive signaling patterns during the 30 min measurements, likely reflecting nerve terminal activity preserved in the acutely prepared ME slices. We rarely observed calcium waves spreading between adjacent tanycytes, in contrast to a previous report41. Bidirectional propagation of the calcium signals between the soma and the tanycytic processes indicates communication between the CSF and the capillary portal plexus. Calcium responses to specific hormones, such as TRH, may reflect a modulatory role by M5 tanycytes on fenestration accessibility based on hormone status42.
While first described on the tongue43, taste cells have also been identified in extraoral tissues including the intestinal tract, stomach, and lung44 demonstrating that taste receptor expression is not restricted to the gustatory system. Tas2r taste receptors differ widely in their tuning breadth, ranging from broadly tuned receptors that recognize numerous compounds to receptors that are narrowly tuned and detect only single or very few compounds45. We identified the narrowly tuned bitter taste Tas2r113 and Tas2r125 receptors as well as other key components of the classical taste transduction pathway in M5 tanycytes and confirmed the responses of these cells to bitter substances using functional imaging. In a heterologous cell culture expression setup, Tas2r113 could be activated with phenylbutazone, a synthetic drug compound and Tas2r125 with phenyl-β-d-glucopyranoside and umbelliferone, both of which are natural plant substances without obvious murine counterparts45. The identification of the endogenous ligands of these two and other Tas2 receptors in the M5 tanycytes remains a task for future studies. It is however tempting to speculate that bitter sensing by the chemosensory M5 tanycytes as part of the tanycytic barrier in the ME may protect against the access of toxic molecules from the capillary portal plexus.
Metabolic phenotyping revealed impaired glucose tolerance following M5 tanycyte ablation coupled with longtime (>20 weeks) high fat diet feeding, as well as increased insulin sensitivity and reduced insulin concentrations after 18 weeks, despite unchanged body weight and fat mass. In stark contrast, two previous studies reported unchanged (or even somewhat improved) glucose tolerance and increased body weight and visceral fat mass after ablation of all β1 and β2 tanycytes46 or knocking out of glucokinase expression in tanycytes which subsequently induces apoptosis in these cells47. One obvious difference between the two studies is that here, we selectively remove the M5 tanycyte subpopulation. Ablation of all (VEGFa-expressing) β1 and β2 tanycytes both in the floor as well as in the ventral lateral walls of the ME and third ventricle seems sufficient to open up the CSF barrier towards the arcuate nucleus46, possibly explaining the differences in body weight in the two distinct experimental approaches. Taken together, these data highlight the necessity to continue to identify and characterize functionally distinct tanycyte subpopulations beyond the classifications based on anatomical markers.
How can impaired glucose tolerance in the absence of M5 tanycytes be explained? The brain senses blood glucose levels through vein sensors located in close proximity to the capillary portal plexus in the ME. We found that the M5 tanycytes have a close connection to the fenestrated capillaries and cover the latter with their endfeet. As the functional plasticity of endfeet is a Gq-dependent process42, the M5 channel might underlie opening of voltage-gated channels increasing the intracellular calcium concentration to initiate endfoot plasticity. An absence of M5 tanycytes may thus impair hypothalamic barrier functioning and feedback integration. Consistent with this, we found the M5 tanycytes to express genes important in BBB formation. An alternative, but not exclusive, hypothesis is that because M5 tanycytes are highly sensitive to leptin, they may be tightly involved in the shuttling of circulating leptin into the hypothalamus, a process that we have recently shown to play a key role in the regulation of pancreatic β-cell function48, certainly via the melanocortin system49.
M5 tanycytes do not express the Tas1r2 sweet taste receptor, suggesting that these cells do not sense glucose via this mechanism. The M5 tanycytes mainly express the glucose transporter Glut1 (Slc2a1), but not Glut 2 and very little Glut3. These transporters have different Km values (Glut1 6.9 mM, Glut2 11-17 mM, Glut3 1.4 − 1.8 mM)50. Glucose levels in the interstitial fluid of the brain are lower (~0.7 mM in fasting condition and 1.4 mM after feeding)51 than those in peripheral blood (5.8 mM in fasting condition and 7.7 mM after feeding), indicating that the M5 tanycytes likely do not sense glucose from interstitial fluid. Instead, their endfeet close to the fenestrated blood vessels in the ME possibly detect high glucose levels in the peripheral blood and transmit this information to the brain19. The M5 tanycytes could also contribute to increased cerebral blood flow in response to hypoglycemia52.
We demonstrate that M5 tanycytes express the leptin receptor (LepR) and respond to leptin. Furthermore, leptin activation of p-STAT5 signaling in the floor of the third ventricle (which typically activates all tanycytes) seemed to depend on the M5 subpopulation, possibly through tanycyte intercommunication via connexin 4319,53. The lack of leptin signaling in the ME could also explain the impaired glucose tolerance phenotype in the acute M5 tanycyte ablation model. Consistent with this, previous studies showed that mice with a deletion of the LepR in tanycytes developed impaired glucose tolerance associated with an alteration in insulin release48. GLP-1 had only mild effects on calcium responses in M5 tanycytes. While GLP1R has recently been shown to be expressed in tanycytes using highly sensitive in situ hybridization approaches54 and immunohistochemistry55, our RNA sequencing data did not detect GLP-1 receptor transcripts, however, the expression could be below our detection threshold. We found signal transduction pathway components resembling other extraoral M5 chemosensory cells such as those in the small intestine and stomach, which emphasizes the functional role of M5 tanycytes in chemosensation12,56. M5 is also expressed in pancreatic β-cells and Trpm5-/- mice display decreased glucose tolerance57–59.
We also identified key components of the hypothalamic-pituitary-thyroid axis including thyrotropin-releasing hormone degrading enzyme (Trhde), thyrotropin-releasing hormone receptor (Trhr), and thyroid stimulating hormone receptor (Tshr) in M5 tanycytes (Fig. 3d), raising the possibility that these cells could modulate glucose homeostasis via this signaling pathway42. Tanycytes have previously been shown to regulate T3 access to the paraventricular nucleus of the hypothalamus (PVN), where elevated T3 increases glucose production via sympathetic connections between the PVN and the liver60. Glut1 and 4 expression is upregulated after T3 treatment in different tissues, increasing glucose uptake61,62.
M5 tanycytes also express 11β-hydroxysteroid dehydrogenase type 1 (Hsd11b1), an enzyme controlling glucocorticoid production and thus gluconeogenesis63. Glucocorticoids have previously been reported to decrease Glut4 expression64.
Impaired glucose tolerance upon M5 tanycyte depletion was accompanied by improved insulin sensitivity but decreased plasma insulin levels. This suggests hypoinsulinemia consistent with lower fasting insulin in these animals (Fig. 6e). The autonomic nervous system plays an important role in controlling pancreatic islet function65. Excessive reactive oxygen species (ROS) production in POMC neurons in the arcuate nucleus of Mitofusin 1 knockout mice enhanced sympathetic activity in the pancreas and impaired glucose-stimulated insulin secretion (GSIS) without changes in body weight, and intracerebroventricular (i.c.v.) administration of ROS scavengers normalized GSIS in these animals66. Recently, tanycytes have been shown to regulate lipid homeostasis by palmitate uptake promoting ROS production, engaging the p38-MAPK pathway and resulting in FGF21 production67. In addition, M5 tanycyte-specific activation promoting insulin secretion in M5-DREADD mice (Fig. 7d) also resembles the acute effect of ROS scavengers on GSIS in the POMCMfn1KO mice66. Whether M5 tanycytes influence ROS level in the arcuate nucleus will need to be addressed in future studies. A recent study has shown, that tanycyte activation can evoke depolarization in both NPY and POMC neurons via ATP release, inducing acute hyperphagia41. Which types of neurons are contacted by M5 tanycytes and the downstream effects of M5 tanycyte stimulation on these cells will need to be addressed in future studies.
Finally, an alternative explanation for the impaired glucose tolerance in the ablated mice may be that this perturbates communication between the brain and pancreatic beta cells. The hypothalamus communicates through the NTS with the pancreatic islets and the islets are sending information to the brain through the vagus nerve NTS connection68 to maintain the glucose homeostasis of the body.
## Generation of the Rosa26-NLSiRFP720-2A-Gq (DREADD) knock-in mice
DREADD mice were generated by homologous recombination in mouse embryonic stem (ES) cells using a targeting construct designed to insert a CAGS promoter (CMV enhancer plus chicken β-actin promoter)-driven NLSiRFP720-2A-Gq receptor (DREADD receptor) within the first intron of the *Rosa26* gene locus. This encodes both an infrared fluorescent protein, which is directed to the cell nucleus, and a Gq-coupled receptor, which can be specifically activated by CNO administration. To ensure that this expression is Cre-dependent, floxed strong transcriptional stop signals (three SV40 polyA signals) are present in such a way that the CAGS promoter can only drive expression following Cre-dependent removal of the stop signals. Correct insertion of the NLSiRFP720-2A-Gq receptor construct was verified using Southern blot analysis as follows. DNA was extracted from tail tip biopsies or ES cells using lysis buffer containing 0.1 mg/mL proteinase K (1 mg/mL was used for extraction from ES cells). Following extraction, genomic DNA was digested overnight with EcoRI and run on a $0.7\%$ agarose gel, then transferred to a nylon membrane by capillary transfer and screened by hybridization of a 491 bp 32P-labeled probe complementary to sequences located 5′ to the 5′ homology arm of the targeting construct. Probe hybridization produces a 15.6-kb band from the wild-type allele, whereas the correctly targeted allele generates a 5.8-kb band. Correctly targeted ES cells were injected into C57BL/6 J blastocysts to generate male chimeras that were backcrossed to C57BL/6 J females to produce heterozygous Rosa26-NLSiRFP720-2A-Gq mice. Mice were then further crossed to produce a homozygous colony. Mice were maintained on a mixed genetic background of 129 S × C57BL/6 J. The genotypes of the Rosa26-NLSiRFP720-2A-Gq mice were confirmed by PCR using the primer sequences: 5-GGAAGCACTTGCTCTCCCAAAG-3′ (common forward primer); 5′-GGGCGTACTTGGCATATGATACAC-3′ (DREADD allele reverse primer) and 5′-CTTTAAGCCTGCCCAGAAGACTC-3′ (wildtype allele reverse primer). WT offspring were confirmed by the presence of a single band of 256 bp. For the Rosa26-NLSiRFP720-2A-Gq allele, heterozygous offspring gave two products of 256 and 495 bp, whereas homozygous offspring were identified by the presence of one band at 495 bp.
## Mice
Animal care and experimental procedures were approved by the animal welfare committee of Saarland University, the Regierung Oberbayern and the French Ministry of National Education, Higher Education and Research (APAFIS#2617-2015110517317420 v5) and were performed in accordance with their established guidelines. Experiments were designed based on accepted standards of animal care and all efforts were made to minimize animal suffering. Mice were kept at an ambient temperature of 22 ± 2 °C with constant humidity (45–$65\%$) and a 12 h/12 h light/dark cycle. For calcium imaging experiments both male and female mice were used. For all other experiments, male mice were used. Mice were kept under a standard light/dark cycle with food and water ad libitum. To label TRPM5-expressing cells, we used the TRPM5-IRES-Cre (M5)15 knock-in mouse strain crossed with eROSA26-τGFP (GFP)69, eROSA26-GCaMP3 (GCaMP)70 (generously provided by Dr. D. Bergles, Johns Hopkins University, Baltimore), ROSA26-DTA (DTA)28 or eROSA26-DREADD (DREADD) animals. In the resulting M5-reporter/effector mice, Cre recombinase is expressed under control of the Trpm5 promotor. Cre-mediated recombination results in the removal of a strong transcriptional stop cassette from the ROSA26 locus and subsequent constitutive reporter expression in TRPM5 cells. Mice were kept in a mixed (129/SvJ and C57BL/6 J) background. All animals used in this study were heterozygous for the TRPM5-IC and the eR26-reporter/effector alleles, respectively. To deplete M5 tanycytes, M5-GFP animals were crossed with ROSA26-iDTR (iDTR)71 mice to generate offspring in which, following Cre-mediated recombination, τGFP and the diphtheria toxin receptor are expressed in TRPM5 cells. TRPM5 knock-out (TRPM5-/-)13 mice were kindly provided by Dr. R. Vennekens, University of Louvain, Belgium.
## iDISCO tissue clearing and immunofluorescence
Adult M5-GFP animals were deeply anesthetized with a mix of ketamine/xylazine and transcardially perfused with PBS followed by $4\%$ ice-cold PFA. Brains were removed and postfixed in $4\%$ PFA on ice for three hours. 250 µm thick sections of the median eminence were cut on a vibratome (Leica). Sections were dehydrated in increasing methanol concentrations, incubated in $66\%$ dichloromethane (DCM)/$33\%$ methanol and bleached with $5\%$ H2O2 in methanol. Sections were then rehydrated in decreasing methanol concentrations and incubated in blocking solution ($0.2\%$ gelatin, $0.5\%$ TX-100 in PBS) for three days at RT followed by staining with primary antisera for 1 week at 37 °C diluted in blocking solution. Primary antisera used were rabbit anti-GFP (1:10000, Invitrogen #A-6455, AB_221570), goat anti-CD31 (1:5000, R&D Systems, #AF3628, AB_2161028), chicken anti-vimentin (1:2500, GeneTex, GTX30668, AB_626086) and chicken anti-mouse PV1 (1:5000, directed and affinity-purified against the C-t sequence (CKK)-LPVVNPAAQPSG, custom-prepared by Caslo, Kongens Lyngby, Denmark; labeling identical to that given by the rat monoclonal clone MECA-32 in Ciofi et al.72, AB_2892196). Thereafter, incubation in secondary antisera diluted in blocking solution was for 4 days at 37 °C. Secondary antisera (all from Jackson Immunoresearch) used were donkey anti-rabbit Cy5 (1:500, # 711-175-152), donkey anti-goat Cy2 (1:500, #705-225-147), donkey anti-goat Cy3 (1:500, #705-165-147), donkey anti-chicken 488 (1:500, #703-545-155) and donkey anti-chicken Cy3 (1:500, #703-165-155). After staining, sections were dehydrated and incubated in $66\%$ DCM/$33\%$ methanol overnight at 4 °C. On the next day, sections were washed once in DCM and then cleared in dibenzylether (DBE). Cleared sections were mounted on a glass slide with DBE and imaged with a confocal or SIM microscope (both from Zeiss). Image stacks were analyzed and videos were prepared with the Imaris software package (Bitplane).
## AAV vector production
An AAV expressing mCherry under the control of the CAGs promoter was generated in a combined 1 and 2 serotype using the triple transfection helper-free method. In brief, HEK293T cells in culture were transfected with 3 plasmids in a 1:(0.5:0.5):1 ratio; the first containing essential viral genes such as E2 and E4 (pAdDeltaF6; Addgene plasmid # 112867), the second which determines the AAV serotype was in this case equimolar amounts of both serotype 1 (AAV$\frac{2}{1}$; Addgene plasmid # 112862) and serotype 2 (AAV$\frac{2}{2}$; Addgene plasmid # 104963) plasmids to generate AAV particles of a mixed 1 and 2 serotype and the third which dictates the packaged contents of the virus particles and contained mCherry under control of the CAGs promoter both flanked by two ITR sites (Addgene plasmid # 91947). Transfection was undertaken when the cells reached 60-$70\%$ confluence using a 4:1 (v:w) ratio of Polyethylenimine (PEI) to plasmid DNA. 60 – 72 hours after transfection, both supernatant and cells were processed to recover the virus. Viral titer was measured by qPCR analysis with primers specific to the ITR region of the packaging plasmid (fwd ITR primer: 5’-GGAACCCCTAGTGATGGAGTT, rev ITR primer: 5’-CGGCCTCAGTGAGCGA). We named this mixed serotype virus AAV$\frac{2}{1}$ + 2-CAGS-mCherry.
## M5 tanycyte enrichment, RNA-seq library building and sequencing
M5-GFP and M5-DREADD-GFP mice were i.c.v. injected with 2 μl AAV$\frac{2}{1}$ + 2-CAGS-mCherry virus three weeks before cell sorting. The basal hypothalamus from adult male M5-GFP mice, which either underwent 14 hours of fasting (8 mice) or normal diet feeding (6 mice) or from 6 adult male M5-DREADD-GFP mice (M5-GFP mice were used as control (6 mice), animals were injected with 2 µg CNO i.c.v. and sacrificed after 3 hours) were dissected and pooled from 2 mice. Dissociation was performed using a Papain dissociation system (Worthington) as described previously73. The dissociated cells were then sorted using FACS (Sony SH800, Software version 2.1.5, Supplementary Fig. 12). Cells were sorted by fluorescence (endogenously expressed GFP and virally expressed mCherry) with excitation at 488 nm and 561 nm and emission detected at FL2 ($\frac{525}{50}$ nm) and FL3 ($\frac{600}{60}$ nm). Approximately 1000 sorted M5 tanycytes for each condition were used to build RNA-seq libraries using the Smart-seq2 method. The sequencing and analyses were performed by Novogene Europe. Briefly, total RNA was purified from sorted cells using the RNeasy plus Micro kit (Qiagen, Hilden, Germany). Total RNA was then amplified using the SMART-Seq v4 Ultra Low Input RNA kit for Sequencing (Takara Bio USA, Mountain View, USA) synthesizing double stranded cDNA (ds-cDNA). The ds-cDNA was then purified with AMPure XP beads and quantified with Qubit (Life Technologies). The library preparation was performed using the NEB Next Ultra RNA Library Prep kit (New England Biolabs, Ipswich, USA) following the manufacturer’s recommendations. Libraries were sequenced on an Illumina NovaSeq 6000 S4 flowcell with PE150. Raw reads were subjected to quality control and then trimmed for library adapters and low-quality tails. Trimmed reads were mapped to the mouse reference genome (mm10) using STAR (version v2.6.1d)74. The quantification of read numbers mapped of each gene was performed by FeatureCounts75, and then differential expression analysis was performed using DESeq276 with screening analysis threshold set to p-value<0.05 and |log2(FoldChange)|>1. The clusterProfiler R package77 was used to perform GO and KEGG pathway enrichment analyses.
## Acute brain slice preparation and calcium imaging
M5-GCaMP mice were sacrificed by rapid cervical dislocation. The brain was extracted from the skull and transferred to ice-cold cutting solution (containing in mM: NaCl 87, KCl 3, NaH2PO4 1.25, NaHCO3 25, glucose 10, sucrose 75, MgCl2 1, CaCl2 0.5). During sectioning, the brain was immersed in the ice-cold cutting solution and continuously aerated with $95\%$ O2/$5\%$ CO2. 200 μm thick coronal sections were obtained using a Leica VT1200S vibratome. The slices were incubated for at least 30 min in artificial cerebrospinal fluid (ACSF, containing in mM: NaCl 120, KCl 3, NaH2PO4 1.25, NaHCO3 25, glucose 10, MgCl2 1, CaCl2 2) at 35-37 °C before calcium imaging.
For the cell loading experiments, coronal sections (200 μm) were loaded with a fluorescent Ca2+ indicator Cal520 (10 μΜ) during 1 h in aerated ACSF ($95\%$ O2/$5\%$ CO2) at RT. Following this, the sections were kept in aerated ACSF for at least 30 minutes before imaging. Imaging of the calcium signals in M5 tanycytes was performed with a Zeiss LSM 710 confocal imaging system operated by the ZEN software (Carl Zeiss ZEN 2012 (black) 64 bits, Version 14). To hold the brain slice, a RC-26G Open Diamond Bath Imaging Chamber (Warner Instruments) was routinely used. For the chamber floor, glass coverslips (22×40 mm, CS-$\frac{22}{40}$, Warner Instruments) were prepared as follows. The coverslips were soaked in nitric acid ($70\%$, Sigma Aldrich) overnight and then washed with distilled water until reaching pH ~7.0. The coverslips were attached to the chamber with vacuum grease silicone (Beckman Coulter, Cat. No. 335148), the brain slice was then transferred into the chamber and attached to the floor by short solution removal followed by replenishment of the ACSF in the chamber. The solution flow rate through the chamber was kept at 2 ml/min using a custom-made tube perfusion system. During imaging, all bath applied solutions were aerated ($95\%$ O2/$5\%$ CO2). In the experiments with local application, solutions were applied via a patch pipette. For this, glass pipettes pulled from borosilicate glass tubes (GB150T-8P, SCIENCE PRODUCTS GmbH) were routinely used. The opening of a patch pipette had electrical resistance of 3-5 MOhm in ACSF. Imaging was done using a 20x water immersion objective (W Plan-Apochromat 20x/1,0 DIC VIS-IR, Carl Zeiss), which allowed observation of the whole median eminence in coronal sections prepared from adult mice. The frequency of frame collection was set to 2 Hz. Calcium imaging corrections and statistical analyses were done with MATLAB_2021b (Mathworks, Natick, MA, USA). Heatmaps for the fluorescence responses throughout the ME were made using MATLAB as follows. On a representative image of a coronal section, a line around the third ventricle border was marked touching tanycytic cell bodies. Pixel intensities above a fluorescent threshold and outside the third ventricle line (i.e., inside the brain slice) were taken for the further analysis. All intensities, each calculated as ΔF/F0, from pixels which were equidistant to the third ventricle line were summed up and normalized by the number of pixels, giving a fluorescence of the ME at a particular distance from the third ventricle. This was done for every time frame giving one vertical line for each time point. Then all the vertical lines were organized in chronological order from left to right. The values in the heatmap were color coded as indicated in the corresponding figure and figure legend.
## Surgery and tissue preparation
Twelve to sixteen-week-old M5-GFP or M5-DREADD mice were anesthetized with $5\%$ isoflurane mixed with oxygen and then transferred to a stereotaxic injection apparatus (Stoelting Co.). Anesthetized mice were maintained under $2\%$ isoflurane. The scalp was shaved and opened with a scalpel along the midline to expose the skull. The tissue was cleaned with $3\%$ H2O2. According to the lateral ventricle coordinates given in the Franklin and Paxinos Atlas78, one hole was drilled into the cranium using a dentist drill. With a Hamilton microliter syringe, 2 μl of 3 mM stevioside, 2 µg leptin, 2 μg CNO in saline or 2 μl saline as control was injected into the lateral ventricle (0.46 mm posterior, 2.50 mm ventral and 1.00 mm lateral to bregma) or the third ventricle (1.79 mm posterior, 5.80 mm ventral to bregma). Mice were then transcardially perfused with PBS followed by $4\%$ paraformaldehyde in PBS. Brains were removed, postfixed for 2 hours in $4\%$ paraformaldehyde in PBS, and then placed in a $30\%$ sucrose solution. Brains were frozen in tissue-freezing medium (Leica, Nussloch, Germany) in a dry ice/ethanol slurry. 14 μm thick coronal sections were cut using a cryostat (Leica) and stored at −80 °C until use.
## Quantification of serum hormone levels
Twelve to sixteen-week-old wild type or M5-DREADD mice were anesthetized with isoflurane mixed with oxygen and injected with 2 μg CNO in saline into the third ventricle. Mice were removed from stereotaxic injection apparatus and cheek blood was taken at various time points. The blood was allowed to clot before centrifugation at 1000 g for 10 min to collect serum. Serum hormone levels were measured via Luminex MAGPIX System. Insulin and leptin levels were measured via mouse metabolic magnetic bead panel (Cat. # MMHMAG-44K). Adrenocorticotropic hormone (ACTH), follicle-stimulating hormone (FSH), prolactin (PRL), thyroid stimulating hormone (TSH) and growth hormone (GH) level were measured via mouse pituitary magnetic bead panel (Cat. # MPTMAG-49K). Briefly, 200 μl assay buffer was added into each well of a 96-well plate and shaken for 10 min. Assay buffer was removed and 10 μl of either matrix solution, assay buffer, standard or control and samples was added into appropriate wells. A total of 25 μl beads were added to each well and this was incubated overnight at 4 °C with shaking. Well content was removed and beads washed with wash buffer. A total of 50 μl detection antibody was added to each well and incubated 30 min at room temperature. 50 μl streptavidin-phycoerythrin was added to each well and incubated for 30 min. Well contents were removed and beads were again washed with wash buffer. 100 μl drive fluid was added to each well and the plate was run on MAGPIX using xPONENT software (version 4.2). Data were analyzed with Prism 5.
## Surgery and metabolic phenotyping
Five-week-old M5-GFP-iDTR, M5-iDTR mice, or WT-iDTR mice were anesthetized then transferred to a stereotaxic injection apparatus as described above. After exposure of the skull, two holes were drilled into the cranium and 0.5 ng diphtheria toxin were injected into both sides of the third ventricle (1.8 mm posterior, 5.8 mm ventral, and 0.25 mm lateral to bregma). Mice were allowed to recover 7 days after surgery. Metabolic phenotyping started 14 days after the surgery. For metabolic phenotyping, mice were kept at an ambient temperature set to 22 ± 2 °C with a constant humidity (45–$65\%$) and a 12 h/12 h light/dark cycle. Mice had free access to water and were fed ad libitum with a high fat diet ($58\%$ kcal fat; Research Diets, New Brunswick, NJ, USA; # D12331) since week 7. Intraperitoneal glucose tolerance (ipGTT) was assessed in 30-week-old mice after 5 h fasting and after stimulation with 1.5 g glucose per kg body weight. Body composition (fat and lean tissue mass) was analyzed in 33-week-old mice using a magnetic resonance whole-body composition analyzer (EchoMRI, Houston, TX). Intraperitoneal insulin tolerance (ipITT) was assessed in 48-week-old mice after 3 h fasting with stimulation of 0.75 units insulin (Recombinant Insulin Human, Novo Nordisk) per kg body weight. Plasma insulin and HOMA-IR were evaluated/calculated post ipITT. Energy expenditure, substrate utilization (respiratory exchange ratio, RER) and home-cage activity were assessed in 33-week-old mice using a climate-controlled indirect calorimetric system (TSE System, Bad Homburg, Germany). After acclimatization for 24 h, levels of O2 and CO2 were measured every 10 min for 4–5 days. Intraperitoneal glucose tolerance of WT-DREADD mice and WT mice not containing the DREADD allele (12–16 week old) were performed 3 weeks after 2 μl AAV-DIO2-*Cre virus* injection into the cerebral ventricle. Mice were fasted 12 h and injected with a mixture of 1 mg CNO-HCl and 2 g glucose per kg body weight.
## Immunohistochemistry
Sections were washed with PBS and blocked in $10\%$ donkey serum/$3\%$ BSA/$0.3\%$ Triton X-100 in PBS for 1 h at room temperature. Sections were incubated with primary antisera overnight at 4 °C followed by secondary antisera at room temperature for 2 hours. Antisera used were as follows: rabbit anti-c-Fos (1:500, Cell Signaling Technology, #2250, AB_2247211), chicken anti-GFP (1:1000, ThermoFisher, #A10262, AB_2534023), chicken anti-HA tag (1:1000, ThermoFisher, #PA5-33243, AB_2550658), chicken anti-vimentin (1:500, GeneTex, GTX30668, AB_626086), goat anti-human HB-EGF (1:1000, R&D Systems, #AF-259-NA), rabbit anti DsRed (1:1000, TaKaRa, #632496), goat anti-chicken Alexa 488 (1:500, Invitrogen, #A11039), goat anti-rabbit cy3 (1:500, Invitrogen, #A10520), donkey anti-goat cy3 (1:1000, Jackson Immuno Research, #705-165-147) and donkey anti-chicken 488 (1:500, Jackson Immuno Research, #703-545-155). For p-STAT5 staining, sections were washed with TBS, antigen retrieval was performed at 95 °C in 0.01 M Tris for 2 min, then blocked in $0.25\%$ BSA/$0.3\%$ Triton X-100 in TBS for 10 min at room temperature. Sections were washed with TBS and incubated with rabbit anti-pSTAT5 (1:600, Cell Signaling Technology, #9359, AB_823649) overnight at 4 °C followed by goat anti-rabbit Cy3 (1:500, Jackson Immunoresearch, #711-165-152) at room temperature for 2 h. Cell nuclei were counterstained with bisbenzimide (Sigma). Images were captured using a Zeiss Axio Imager 2 microscope. Counting procedures for p-STAT5 and c-Fos are described below.
## Evans blue injection
WT-DTA and M5-DTA mice were subjected to tail vein injection of 50 μl $1\%$ Evans blue dissolved in $0.9\%$ saline. Mice were sacrificed 20 min later by decapitation. The brains of the injected mice were removed and quickly frozen in O.C.T. (Leica). 14 μm sections were obtained on a cryostat and stored at −80 °C until analysis. Evans blue signal was observed by fluorescence microscopy. Area covered by Evans blue was measured with ZenBlue software.
## Quantification
p-STAT5 and c-Fos positive cells were manually counted in every tenth section in 14 μm coronal sections obtained serially across the median eminence. Counted numbers for each section were added together and averaged to give an average number of c-fos or p-STAT5 positive tanycytes per section for each mouse.
Areas positive for DTR immunosignal within the pars tuberalis and choroid plexus were calculated for every tenth section in 14 μm coronal sections obtained serially along the median eminence and ventricle using Zen 2.3 software (Zeiss).
## Statistical methods
Statistical analyses of the calcium imaging data were performed using MatLab2021b (Mathworks, Natick, MA, USA). The data were collected and analyzed offline. Single measurements of individual tanycytes were baseline-corrected, grouped and are presented as mean and median. Comparisons between groups were done with N-way ANOVA tests, followed by Tukey’s or Bonferronis post-hoc tests. The effects of substances on the tanycytes were measured as calcium elevations, presented with mean and median, and compared via paired two-sided t-tests. Statistical differences were considered significant at *, $p \leq 0.05$; **, $p \leq 0.01$; ***, $p \leq 0.001$; ****, $p \leq 0.0001.$ Data of energy expenditure were analyzed using ANCOVA with body weight as covariate as previously described79,80.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Description of Additional Supplementary Files Supplementary Movie 1 Supplementary Movie 2 Supplementary Movie 3 Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-37099-3.
## Source data
Source Data
## Peer review information
Nature Communications thanks Fanny Langlet and the other, anonymous, reviewers for their contribution to the peer review of this work.
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|
---
title: Bioelectrical impedance phase angle is associated with physical performance
before but not after simulated multi‐stressor military operations
authors:
- Alyssa N. Varanoske
- Melissa N. Harris
- Callie Hebert
- Neil M. Johannsen
- Steven B. Heymsfield
- Frank L. Greenway
- Arny A. Ferrando
- Jennifer C. Rood
- Stefan M. Pasiakos
journal: Physiological Reports
year: 2023
pmcid: PMC10033850
doi: 10.14814/phy2.15649
license: CC BY 4.0
---
# Bioelectrical impedance phase angle is associated with physical performance before but not after simulated multi‐stressor military operations
## Abstract
Physical performance decrements observed during multi‐stressor military operations may be attributed, in part, to cellular membrane dysfunction, which is quantifiable using phase angle (PhA) derived from bioelectrical impedance analysis (BIA). Positive relationships between PhA and performance have been previously reported in cross‐sectional studies and following longitudinal exercise training programs, but whether changes in PhA are indicative of acute decrements in performance during military operations is unknown. Data from the Optimizing Performance for Soldiers II study, a clinical trial examining the effects of exogenous testosterone administration on body composition and performance during military stress, was used to evaluate changes in PhA and their associations with physical performance. Recreationally active, healthy males ($$n = 34$$; 26.6 ± 4.3 years; 77.9 ± 12.4 kg) were randomized to receive testosterone undecanoate or placebo before a 20‐day simulated military operation, which was followed by a 23‐day recovery period. PhA of the whole‐body (Whole) and legs (Legs) and physical performance were measured before (PRE) and after (POST) the simulated military operation as well as in recovery (REC). Independent of treatment, PhAWhole and PhALegs decreased from PRE to POST ($p \leq 0.001$), and PhALegs, but not PhAWhole, remained lower at REC than PRE. PhAWhole at PRE and REC were associated with vertical jump height and Wingate peak power ($p \leq 0.001$–0.050), and PhAWhole at PRE was also associated with 3‐RM deadlift mass ($$p \leq 0.006$$). However, PhA at POST and changes in PhA from PRE to POST were not correlated with any performance measure ($p \leq 0.05$). Additionally, PhA was not associated with aerobic performance at any timepoint. In conclusion, reduced PhA from PRE to POST provides indirect evidence of cellular membrane disruption. Associations between PhA and strength and power were only evident at PRE and REC, suggesting PhA may be a useful indicator of strength and power, but not aerobic capacity, in non‐stressed conditions, and not a reliable indicator of physical performance during severe physiological stress.
Military operations induce decrements in bioelectrical impedance analysis phase angle, indicating cellular membrane disruption. Phase angle was associated with strength and power performance, but not aerobic capacity, before and in recovery from the operation. However, changes in phase angle measured immediately after the operation were not associated with changes in performance, indicating that phase angle may be related to strength and power in non‐stressed conditions, but not a reliable indicator of performance during severe physiological stress.
## INTRODUCTION
Bioelectrical impedance analysis (BIA) is a non‐invasive, user‐friendly, inexpensive tool often used to estimate body composition changes in response to exercise training paradigms, dietary interventions, aging, and disease (Lukaski et al., 2017; Lukaski & Raymond‐Pope, 2021). BIA assesses the conductivity of the biological components of the human body by emitting a small electrical current into the body and measuring the passage of the current through the tissues (Kyle et al., 2004; Ward, 2019). The opposition to current flow, termed resistance (R), along with the current delay caused by cell membrane capacitance, termed reactance (Xc) (Kyle et al., 2004), are often used in population‐specific prediction equations to estimate fat‐free mass (FFM) and fat mass (Campa et al., 2021; Campa, Gobbo, et al., 2022; Lukaski & Raymond‐Pope, 2021). When expressed trigonometrically, the relationship between Xc and R is called impedance (Z), and the ratio of these variables presented in degrees is the phase angle (PhA). A change in bioelectrical impedance properties, including cellular membrane integrity or intracellular (ICW)/extracellular water (ECW) distribution directly affect Xc and R, resulting in a shift of the PhA (Francisco et al., 2020; Silva et al., 2014; Stobaus et al., 2012; Toso et al., 2000). Specifically, increases in body water result in decreased R (Campa, Colognesi, et al., 2022; Lukaski et al., 2019; Lukaski & Raymond‐Pope, 2021; Piccoli et al., 1994), whereas increases in body cell mass, cellular membrane integrity, and membrane storage capacity increase Xc (Stobaus et al., 2012). The interaction of Xc and R resulting in a high PhA therefore signifies greater body cell mass, cellular membrane integrity, and cellular function (Campa, Colognesi, et al., 2022; Custodio Martins et al., 2022).
PhA in the human body typically ranges from 1° to 12° (Sardinha & Rosa, 2023), and higher values within this range are positively associated with muscle strength and physical function (Cioffi et al., 2020; Custodio Martins et al., 2022; Fukuoka et al., 2022; Giorgi et al., 2018; Langer et al., 2022; Micheli et al., 2014; Norman et al., 2011; Piccoli et al., 2007; Rodriguez‐Rodriguez et al., 2016; Sato et al., 2020). Increases in PhA have been reported following resistance training programs and concurrent exercise training (Campa, Colognesi, et al., 2022; Sardinha & Rosa, 2023). Alternatively, low PhA has been reported in individuals presentingmalnutrition, alcoholism, cancer/cachexia, HIV, sarcopenia, and in older adults (Cardinal et al., 2010; Fukuoka et al., 2022; Genton et al., 2017, 2018; Gupta et al., 2008; Lukaski et al., 2017; Nescolarde et al., 2013; Tanaka et al., 2019; Toso et al., 2000). Taken together, low PhA is generally indicative of diminished cellular membrane integrity, physical fitness, and overall health, whereas high PhA indicates improved cellular function, strength, and quality of life (Custodio Martins et al., 2022; Lukaski et al., 2017; Norman et al., 2012). As such, PhA has been described as a global indicator of muscle cellular health and quality and is often used to predict functional health outcomes (Campa, Colognesi, et al., 2022; Lukaski et al., 2017; Lukaski & Raymond‐Pope, 2021).
Optimization of physical fitness, including muscular strength, power, and aerobic capacity, is an integral component of Warfighter training, as sustained, multi‐stressor military operations comprised of high energy expenditures, inadequate energy intake, and sleep deprivation typically degrade muscle mass and physical performance (Lieberman et al., 2002, 2005; Margolis et al., 2014; Nindl et al., 2002, 2007). It is generally well‐accepted that size is an essential determinant of the force‐generating capacity of muscle (Bamman et al., 2000); therefore, attenuating muscle mass loss during military operations may mitigate performance decline. However, the most accurate quantification of muscle mass often relies on invasive, expensive, technical, large, or radiation‐emitting equipment (i.e., dual‐energy x‐ray absorptiometry, magnetic resonance imaging, D‐3 creatinine dilution, air displacement plethysmography), making muscle mass quantification in the field impractical. Furthermore, even muscle mass estimations derived from less‐invasive, more convenient pieces of equipment, such as BIA, are based off population‐specific prediction equations which rely on inherent assumptions of cellular hydration that may not be appropriate for all individuals (Campa, Colognesi, et al., 2022). Examining changes in raw BIA parameters, such as PhA, R, Xc, and Z, may circumvent these issues by providing a non‐invasive method of elucidating cellular health, function, and membrane disruption to help predict performance during military operations without the use of population‐based prediction equations, costly equipment, or the use of radiation‐emitting devices for muscle mass estimation.
Despite the potential for PhA to elucidate the underlying causes of performance decline, previous research using this metric of muscle quality has been limited to examining relationships between PhA and physical performance in cross‐sectional studies (Fukuoka et al., 2022; Giorgi et al., 2018; Langer et al., 2022; Micheli et al., 2014; Norman et al., 2011; Piccoli et al., 2007; Rodriguez‐Rodriguez et al., 2016; Sato et al., 2020) or following longitudinal exercise training programs (Fukuda et al., 2016; Nunes et al., 2019; Ribeiro et al., 2017; Souza et al., 2017). Research in healthy, male Army cadets reports a positive relationship between PhA and handgrip strength (Langer et al., 2022); however, whether this relationship still exists following sustained military operations has not yet been explored. Furthermore, previous studies have demonstrated that the relationship between muscle mass and strength is not linear (Ahtiainen et al., 2016; Jakobsgaard et al., 2018; Mattocks et al., 2017; Parente et al., 2008), and decreases in muscle strength have been reported during simulated multi‐stressor military operations independent of muscle mass loss (Pasiakos et al., 2019; Varanoske et al., 2022). One such investigation highlighting the discordant relationship between muscle mass and strength was recently reported by our laboratory. Healthy, young males who were administered exogenous testosterone (750 mg testosterone undecanoate) prior to a 20‐day simulated military operation had greater FFM compared to controls at the end of the simulation; however, the preservation of FFM did not translate to physical performance enhancement (Varanoske et al., 2021, 2022). As military operations and training usually elicit physical performance decrements, muscle mass loss, inflammation, and muscle damage (Lieberman et al., 2002, 2005; Margolis et al., 2014; Nindl et al., 2002, 2007), it is possible that examining changes in raw BIA parameters may provide a non‐invasive method of elucidating cellular membrane disruption, which may undermine physical performance capabilities. Therefore, the purpose of this analysis was to examine the associations between changes in raw BIA parameters and physical performance following a simulated, sustained military operation. A secondary purpose was to determine the effects of exogenous testosterone administration (TA) on changes in raw BIA parameters following simulated, sustained military operations.
## MATERIALS AND METHODS
This manuscript reports a secondary analysis of variables measured during a randomized, double‐blind, placebo‐controlled trial, which was designed to assess whether exogenous TA (750 mg undecanoate, administered once) restores eugonadal circulating total and free testosterone concentrations and improves measures of FFM and physical performance during, and in recovery from, a 20‐day simulated sustained military operation (Varanoske et al., 2021, 2022). The study design and methodology relating to primary outcomes have been previously described (Varanoske et al., 2021, 2022) but are summarized below to provide context for the outcomes discussed in this report. All testing occurred at the Pennington Biomedical Research Center (PBRC) in Baton Rouge, LA. The Institutional Review Board (IRB) of the PBRC (protocol 2019‐017) and the U.S. Army Medical Research and Development Command, Human Research Protections Office approved the study protocol and trial documents. All procedures were in accordance with the ethical standards of the 1964 Helsinki Declaration and its later amendments. The study occurred from October 2019 through July 2021. The ClinicalTrials.gov identifier is NCT04120363.
## Participants
Complete inclusion and exclusion criteria and extended details regarding participant recruitment, randomization, and attrition have been reported previously (Varanoske et al., 2021, 2022). Briefly, males aged 18–35 who were healthy, physically active (expended at least 300 kcal/day on average through structured aerobic and strength‐training activities), had normal testosterone concentrations (10.4–34.7 nmol/L), and met age‐specific U.S. Army body composition standards (Department of the Army Headquarters, 2013) were recruited.
Physically active males were enrolled ($$n = 34$$), randomized ($$n = 34$$; TEST: $$n = 16$$; PLA: $$n = 18$$), and completed the study ($$n = 32$$; TEST: $$n = 16$$; PLA: $$n = 16$$; participant flow chart presented previously in Varanoske et al., 2022). No group demographic differences were observed at PRE (all $p \leq 0.05$; Table 1; full details previously reported in Varanoske et al., 2022). Two participants in the PLA group dropped out of the study during the simulated military operations and were therefore not included in the analyses except for correlations between variables at PRE.
**TABLE 1**
| Unnamed: 0 | TEST (n = 16) | PLA (n = 18) | Total (n = 34) | p‐value |
| --- | --- | --- | --- | --- |
| Age (years) | 27.1 ± 4.3 | 26.2 ± 4.5 | 26.6 ± 4.3 | 0.646 |
| Body mass (kg) | 77.7 ± 14.5 | 78.1 ± 10.6 | 77.9 ± 12.4 | 0.924 |
| Height (cm) | 177.1 ± 8.1 | 176.3 ± 5.3 | 176.7 ± 6.7 | 0.761 |
## Experimental design
Participants underwent a 3‐phase, 50‐day study, consisting of 7 days of baseline testing (days 1–7), 20 days of simulated military operations (days 8–27), and 23 days of recovery (days 28–50) (details in Varanoske et al., 2021, 2022). After completing baseline testing (day 8), participants were randomized to receive either a single intramuscular injection of testosterone undecanoate (TEST; AVEED™, 750 mg testosterone undecanoate in 3 mL) or an iso‐volumetric placebo (PLA; sesame oil solution, 3 mL). Details of randomization, treatment allocation, and blinding have been published (Varanoske et al., 2021). The baseline and recovery periods consisted of controlled feeding and daily check‐ins, but participants were permitted to self‐select their physical activity and sleep patterns. The 20‐day sustained military operation was a highly‐controlled diet, physical activity, and sleep intervention (participants lived in the inpatient unit at PBRC), which consisted of four consecutive 5‐day cycles of undulating stress. The first 2 days in each cycle were ‘low stress' days, entailing exercise‐induced energy expenditures equaling 1000 kcal above baseline values and 8 h of sleep per night. The following 3 days in each cycle were ‘high stress' days, entailing exercise‐induced energy expenditures equaling 3000 kcal above baseline values and 4 h of sleep per night. To reach this energy expenditure during the simulated military operation, participants exercised several times per day using a variety of endurance and muscle‐loading modalities to mimic movements typically observed during real‐life sustained military operations. Steady‐state ruck marching was the primary exercise modality, but other activities included walking, running, cycling, elliptical, field‐based operational activities, and stretching (see (Varanoske et al., 2021) for details). Daily energy expenditure was determined for each participant and exercise using the Compendium of Physical Activities (Ainsworth et al., 2011) or according to published equations (American College of Sports Medicine et al., 2018; Glass et al., 2007).
Throughout all phases, total energy intake and macronutrient distribution remained fixed but were individualized for each participant. Individual physical activity patterns and habitual exercise‐induced energy expenditure during and prior to the baseline period were determined using accelerometry and a physical activity questionnaire (PAR‐Q+). Resting metabolic rate was measured by using indirect calorimetry (Deltatrac II Metabolic Cart Sensormedics, Yorba Linda, CA). The food records and resting metabolic rate measurements were used to calculate total daily energy expenditure and prescribe individual dietary intake to maintain energy balance and body mass. The macronutrient distribution of the diet was based on the composition of the Meal, Ready‐to‐Eat (MRE) ($15\%$ protein, $55\%$ carbohydrate, and $30\%$ fat) (Varanoske et al., 2021), the standard US Department of Defense ration. Dietary intake during the simulated military operation was monitored on the in‐patient unit but consisted solely of items derived from MREs (menu 39; Ameriqual, Evansville, IN, USA). For the baseline and recovery phases, Registered Dietitians developed individualized menus consisting of commercial products, and compliance was checked daily.
## Outcome measures
Procedural details of all outcome variables have been previously reported (Varanoske et al., 2021), but those pertaining to this analysis are summarized below.
## Bioelectrical impedance analysis (BIA) procedures
Participants arrived at the body composition testing facility on the morning of days 7 (PRE), 28 (POST), and 49 (REC) after at least an 8 hour (overnight) fast. As the data presented here was a secondary analysis of a larger investigation, day 49 was selected as the REC timepoint to allow for sufficient rest between physical performance testing (days 46–47) and body composition testing and to ensure that body composition testing was conducted prior to muscle and whole‐body protein turnover analyses (days 49–50) (Varanoske et al., 2021). Participants were encouraged to hydrate properly on the day leading up to and the morning of the assessment. Hydration status was evaluated upon arrival via analysis of urine specific gravity (CLINITEK 500, Siemens Healthcare Diagnostics, Malvern, PA, USA) prior to BIA analyses. Upon confirmation, participants were instructed to remove footwear, socks, and jewelry and were placed supine on an examination table for at least 5 minutes prior to examination. Contact sites for electrodes on the fingers and ankles were cleaned before measurement with a sterile antimicrobial tissue provided by the manufacturer. Eight touch type electrodes were used in accordance with standard protocols. Whole‐body segmented multi‐frequency BIA measurements were acquired on an InBody S10 system (InBody Inc., Cerritos, CA). Segmental (right leg, left leg, right arm, left arm, trunk) impedance (Z), reactance (Xc), and phase angle (PhA) values at 50 khz were recorded directly from the device. Segmental resistance (R) values were calculated from segmental Z and Xc according to the following equation: Z=√R2+Xc2.
Whole‐body Z, Xc, and R (ZWhole, XcWhole, RWhole, respectively) were calculated from the sum of segmental right leg, right arm, and trunk values according to manufacturer's instructions. Whole‐body PhA (PhAWhole) was calculated using the following equation: PhAWhole=arctanXcWholeRWhole×180π.
As recent research suggests that PhA of the lower body is a better predictor of physical performance than PhAWhole (Bongiovanni et al., 2021), Z, Xc, R, and PhA of the legs (ZLegs, XcLegs, RLegs, and PhALegs, respectively) were also calculated using the average of the left and right legs for each parameter.
## Physical performance
A battery of physical performance tests was completed during each phase, as previously described (Varanoske et al., 2021). Participants were familiarized with each test before testing in each phase. The order and timing of the tests were standardized, and participants completed a dynamic warm‐up before testing began. For the analysis in this report, one variable was chosen from each of the five tests based on what was most encompassing and commonly reported in the literature. A brief summary of each test and the measures chosen are reported below.
The vertical jump test was used to evaluate lower‐body power (Vertec, Jump USA, Sunnyvale, CA, USA). Participants completed a series of three maximal countermovement jumps by flexing their knees and hips, moving downward, and extending their knees and hips rapidly while swinging up their dominant arm to touch the highest vane on the Vertec. Jump height from the jump in which participants reached the greatest height was used in the analysis.
A three repetition maximum (3‐RM) trap bar deadlift was used to assess total body muscular strength in accordance with the U.S. Army Combat Fitness Test (Department of the Army Headquarters, 2017). Following three warm‐up sets, the bar was loaded with ~$85\%$ of their estimated 1‐RM. Participants stood in the middle of the bar with feet shoulder width apart, bent at the knees and hips, reached down, and grasped the center of the handles. They then stood up and lifted the bar by extending the hips and knees until in an upright stance, paused slightly at the top of the movement, flexed the hips and knees slowly, and lowered the bar to the ground in a controlled manner. If they failed to complete three repetitions, they retested at a lower weight. If successful, additional weight was added, and they retested after 3 min of rest. The maximal amount of mass they could lift during the 3‐RM deadlift was recorded.
The Wingate test was used to measure anaerobic capacity. Participants were positioned on an electronically braked cycle ergometer (Excalibur Sport, Lode, The Netherlands) equipped with software (Lode Ergometry Manager software version 10.11.0, Lode B.V., Lode, The Netherlands) and began pedaling for 5 min at 50 W. On “Go,” participants increased their cadence to 90 rpm, and a fixed resistance based off body mass, cycle cadence, and torque factor was added to the bike. Participants were instructed to pedal maximally for the duration of the 30s test. Absolute peak power was recorded, as the reported decrease in body mass from PRE to POST artificially influenced relative peak power measures.
Peak aerobic capacity (i.e., VO2peak) was measured using a graded exercise test and an indirect open circuit respiratory system (ParvoMedics TrueOne 2400, East Sandy, UT, USA) on a treadmill (Track Master TMX425CP, Full Vision, Inc., Newton, KS, USA). Participants began by completing a 5‐min warm‐up and then ran for 4 min at a pace predetermined during familiarization at a $0\%$ grade. The grade was then increased to $2\%$, followed by an additional $2\%$ every 2 min thereafter until volitional exhaustion. Absolute VO2peak was recorded for the same aforementioned reason of avoiding the use of relative measures.
A 4‐km (2.5 mile) outdoor timed ruck march (e.g., backpack load carriage) was completed while wearing a 31.3 kg rucksack to assess military‐relevant aerobic endurance. Total time to complete the march was recorded.
## Statistical analysis
Sample size was based off the primary outcome, anticipated differences in lower‐body physical performance in TEST relative to PLA, as previously described (Varanoske et al., 2021, 2022). As the present analysis represent secondary outcomes for the larger study, no a priori power analysis was performed. Changes in Xc, R, Z, PhA, and physical performance over time were assessed using a mixed‐effect linear model analysis of variance (ANOVA). Subject was treated as a random effect, and group (TEST and PLA), time (PRE, POST, and REC), and group × time interaction were considered fixed effects in the model. Least squares means from the model were used to estimate interaction effects. Distribution and heterogeneity of residuals were examined, and non‐normal data were log10‐transformed as needed to meet model assumptions. When a significant main effect or interaction was detected, pairwise comparisons across time points were conducted, and p‐values were adjusted using the Bonferroni correction. Outliers were identified (values exceeding 2 × interquartile range above the 75th percentile or below the 25th percentile) for the primary outcome variable (PhA) and were excluded from analyses ($$n = 1$$ at POST). As group × time interactions were not significant for any variable, treatment groups were pooled for correlation analyses at all time points. Mean differences between raw BIA vectors in Whole and Legs at each time point were compared using paired samples t‐tests. Associations between BIA parameters and physical performance at PRE, POST, and REC, as well as changes in these variables from PRE to POST (i.e., POST value – PRE value) were assessed using Pearson's correlations.
All analyses were considered 2‐tailed, with α = 0.05 considered statistically significant and were completed with statistical software (SPSS V.26.0, Chicago, IL, USA). Unless otherwise noted, data following a normal distribution are reported as estimated mean ± estimated standard error, and data that were log10‐transformed are reported as geometric estimated mean ± geometric estimated standard error.
## Longitudinal responses to the simulated military operation intervention and recovery
A main effect of phase was observed for all BIA vectors examined (all $p \leq 0.001$) (Table 2, Figure 1). Raw impedance vectors for the whole‐body and legs (ZWhole, ZLegs, XcWhole, XcLegs, RWhole, RLegs) decreased from PRE to POST (all $p \leq 0.001$) and increased from POST to REC ($p \leq 0.001$ to 0.027) but remained lower than PRE values by the end of the study (all PRE vs. REC $p \leq 0.005$). PhAWhole and PhALegs were decreased from PRE to POST (both $p \leq 0.001$) and increased from POST to REC (both $p \leq 0.001$). PhAWhole was not different from PRE at REC ($$p \leq 0.194$$), but PhALegs remained lower at REC compared to PRE ($$p \leq 0.022$$) (Figure 2). No group differences between groups or phase × group interactions were observed for any BIA parameter (all $p \leq 0.05$).
Changes in physical performance variables throughout this study have previously been reported in the intent‐to‐treat population (a larger sample of participants) (see Figure 5 and Supplementary Tables 2–6 in Varanoske et al., 2022). For context in this report, these metrics have been re‐analyzed in the smaller sample of participants. A main effect of phase was observed for all physical performance parameters ($p \leq 0.001$ to 0.017) (Table 3). Vertical jump height, 3‐RM deadlift mass, Wingate peak power decreased, and ruck march time trial time increased, from PRE to POST (vertical jump height: $p \leq 0.001$; 3‐RM deadlift mass: $$p \leq 0.027$$, Wingate peak power: $p \leq 0.001$; ruck march time trial: $p \leq 0.001$). Wingate peak power increased ($$p \leq 0.010$$), and ruck march time trial decreased ($p \leq 0.001$) from POST to REC and were not different from PRE at REC (Wingate peak power: $$p \leq 0.082$$; ruck march time trial: $$p \leq 0.822$$). Vertical jump height and 3‐RM deadlift mass were not different at REC from POST (vertical jump height: $$p \leq 0.089$$; 3‐RM deadlift mass: $$p \leq 0.066$$) or PRE (vertical jump height: $$p \leq 0.185$$; 3‐RM deadlift mass: $p \leq 0.999$). VO2peak was increased from POST to REC ($p \leq 0.001$), but was not different between PRE and POST ($$p \leq 0.204$$) or PRE and REC ($$p \leq 0.058$$).
**TABLE 3**
| Unnamed: 0 | Group | Phase | Phase.1 | Phase.2 | p‐value | p‐value.1 | p‐value.2 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | Group | PRE | POST | REC | Phase | Group | Phase × group |
| Vertical jump height (cm) | TEST | 53.6 ± 2.4 | 48.4 ± 2.4 | 52.8 ± 2.5 | <0.001 1 | 0.970 | 0.258 |
| Vertical jump height (cm) | PLA | 54.6 ± 2.4 | 49.9 ± 2.5 | 50.7 ± 2.4 | <0.001 1 | 0.970 | 0.258 |
| Vertical jump height (cm) | Total | 54.1 ± 1.7 | 49.1 ± 1.7 | 51.8 ± 1.7 | <0.001 1 | 0.970 | 0.258 |
| 3‐RM deadlift mass (kg) | TEST | 127.4 ± 7.0 | 122.7 ± 7.0 | 125.7 ± 7.2 | 0.017 1 | 0.615 | 0.344 |
| 3‐RM deadlift mass (kg) | PLA | 133.8 ± 7.0 | 122.4 ± 7.1 | 133.9 ± 7.0 | 0.017 1 | 0.615 | 0.344 |
| 3‐RM deadlift mass (kg) | Total | 130.6 ± 5.0 | 122.5 ± 5.0 | 129.8 ± 5.0 | 0.017 1 | 0.615 | 0.344 |
| Wingate peak power, absolute# (W) | TEST | 843.3 ± 48.3 | 731.1 ± 48.3 | 792.5 ± 48.3 | <0.001 1,2 | 0.789 | 0.985 |
| Wingate peak power, absolute# (W) | PLA | 822.2 ± 48.3 | 717.8 ± 48.7 | 778.0 ± 48.3 | <0.001 1,2 | 0.789 | 0.985 |
| Wingate peak power, absolute# (W) | Total | 833.7 ± 34.2 | 724.4 ± 34.3 | 785.2 ± 34.2 | <0.001 1,2 | 0.789 | 0.985 |
| VO2peak, absolute (L/min) | TEST | 3.21 ± 0.15 | 3.13 ± 0.15 | 3.29 ± 0.15 | <0.001 2 | 0.621 | 0.664 |
| VO2peak, absolute (L/min) | PLA | 3.29 ± 0.15 | 3.21 ± 0.16 | 3.45 ± 0.15 | <0.001 2 | 0.621 | 0.664 |
| VO2peak, absolute (L/min) | Total | 3.26 ± 0.11 | 3.17 ± 0.11 | 3.37 ± 0.11 | <0.001 2 | 0.621 | 0.664 |
| Ruck march time trial# (min) | TEST | 44.8 ± 2.1 | 50.0 ± 2.1 | 44.1 ± 2.1 | <0.001 1,2 | 0.066 | 0.680 |
| Ruck march time trial# (min) | PLA | 40.5 ± 2.1 | 45.9 ± 2.1 | 38.5 ± 2.1 | <0.001 1,2 | 0.066 | 0.680 |
| Ruck march time trial# (min) | Total | 42.6 ± 1.5 | 47.9 ± 1.5 | 41.2 ± 1.5 | <0.001 1,2 | 0.066 | 0.680 |
When groups were pooled, PhAWhole was significantly different from PhALegs at POST ($$p \leq 0.036$$), but was not different at PRE ($$p \leq 0.117$$) and REC ($$p \leq 0.951$$). All other raw BIA parameters were significantly lower in Legs than Whole at all timepoints ($p \leq 0.001$).
## PRE, POST, and REC
A heat map of the relationships between BIA parameters and physical performance outcomes are presented in Figures 3a–c, and correlation plots between PhAWhole and physical performance variables at PRE and POST are presented in Figure 4. In all phases, Whole raw BIA parameters were significantly associated with their respective parameters in Legs ($r = 0.784$ to 0.911; all $p \leq 0.001$). ZWhole, RWhole, and XcWhole as well as ZLegs, RLegs, and XcLegs were positively correlated during all phases ($r = 0.518$ to >0.999, $p \leq 0.001$ to 0.004). PhAWhole was positively correlated with XcWhole ($r = 0.398$ to 0.482; $$p \leq 0.004$$ to 0.033), and PhALegs was positively correlated with XcLegs in all phases ($r = 0.684$ to 0.690; all $p \leq 0.001$). PhAWhole was negatively correlated with ZWhole (r = −0.552, $$p \leq 0.002$$) and RWhole (r = −0.561, $$p \leq 0.002$$) in POST, RWhole in PRE (r = −0.344, $$p \leq 0.046$$), and neither in REC. PhALegs was not associated with ZLegs or RLegs in any phase.
**FIGURE 3:** *Heat map of associations between dependent variables at (a) PRE, (b) POST, (c) REC, and (d) changes from PRE to POST. Cells are colored based on the Pearson's correlation coefficient between the two variables. #Data is log10‐transformed for analysis. *p < 0.05, **p < 0.01, ***p < 0.001. Legs, average of left and right legs; PhA, phase angle; POST, after the 20‐day simulated, multi‐stressor military operation; PRE, before the 20‐day simulated, multi‐stressor military operation; R, resistance; REC, after the 23‐day recovery period; Whole, whole‐body; Xc, reactance; Z, impedance.*
PPhAWhole and PhALegs were positively associated with vertical jump height ($r = 0.408$ to 0.682, $p \leq 0.001$ to 0.025) at PRE and REC as well as Wingate peak power at REC ($r = 0.361$ to 0.451, $$p \leq 0.011$$ to 0.046). PhAWhole was also correlated with 3‐RM deadlift mass ($r = 0.459$, $$p \leq 0.006$$) and Wingate peak power ($r = 0.349$, $$p \leq 0.050$$) at PRE. RWhole, RLegs, ZWhole, and ZLegs were negatively correlated with 3‐RM deadlift mass and Wingate peak power (r = −0.571 to −0.359, $p \leq 0.001$ to 0.044), and RLegs and ZLegs were also negatively correlated with VO2peak (r = −0.366 to −0.364, $$p \leq 0.033$$ to 0.034) at PRE. Additionally, XcWhole and XcLegs were positively correlated with vertical jump height ($r = 0.432$, $$p \leq 0.011$$; $r = 0.488$, $$p \leq 0.003$$, respectively) at PRE. ZWhole and RWhole, but not ZLegs or RLegs, were negatively correlated with 3‐RM deadlift mass and Wingate peak power (r = −0.468, $$p \leq 0.012$$ and r = −0.370, $$p \leq 0.040$$, respectively) at REC. No raw BIA vectors were associated with any physical performance variable (all $p \leq 0.05$) at POST.
**FIGURE 4:** *Correlations between PhAWhole and physical performance variables at PRE, POST, and changes from PRE to POST. Circles and squares represent individual data points, solid lines represent the line of best fit, and dotted lines represent 95% confidence intervals of the line of best fit. PhA, phase angle; PLA, participants randomized to 750 mg sesame oil solution on day 8; POST, after the 20‐day simulated, multi‐stressor military operation; PRE, before the 20‐day simulated, multi‐stressor military operation; TEST, participants randomized to 750 mg testosterone undecanoate on day 8.*
## Changes in variables throughout the simulated military operation
A heat map of the relationships between changes in BIA parameters and physical performance outcomes from PRE to POST is presented in Figure 3d, and correlation plots between changes in PhAWhole and physical performance variables from PRE to POST are presented in Figure 4. Changes in all Whole raw BIA parameters were significantly associated with their respective parameters in Legs ($r = 0.809$ to 0.911; all $p \leq 0.001$). ΔZ, ΔR, and ΔXc in Whole and Legs were all positively correlated ($r = 0.542$ to >0.999, $p \leq 0.001$ to 0.002). ΔPhAWhole and ΔPhALegs were positively correlated with ΔXc ($r = 0.823$ and 0.890, respectively; both $p \leq 0.001$). ΔPhALegs was also positively correlated with ΔZLegs and ΔRLegs ($r = 0.556$, $$p \leq 0.002$$ and $r = 0.544$, $$p \leq 0.002$$, respectively).
ΔZWhole and ΔRWhole were negatively correlated with Δ3‐RM deadlift mass (both r = −0.412, $$p \leq 0.026$$). Changes in all other variables, including ΔPhAWhole and ΔPhALegs, were not associated with changes in any physical performance variable (all $p \leq 0.05$).
## DISCUSSION
The current analysis used a 20‐day, simulated sustained military operation as a model for studying changes in raw BIA metrics and the utility of correlating these measures with physical performance during, and in recovery from severe physiological stress in healthy, young males. Additionally, the potential protective effects of administering a single dose of testosterone undecanoate (750 mg) prior to the simulated sustained military operation on non‐invasive measures of muscle cellular integrity were examined. We report significant decreases in all raw BIA parameters measured (R, Xc, Z, and PhA) in both the whole body and legs from PRE to POST, which were not different between TEST and PLA. With the exception of PhAWhole, all of these parameters remained suppressed from PRE values at REC, 23 days after the end of the sustained military operation. Additionally, PhAWhole was positively associated with performance on the vertical jump, 3‐RM deadlift, and Wingate tests, but not on the VO2peak or ruck march time trial time tests at PRE. These associations persisted at REC with the exception the 3‐RM deadlift task. However, PhA was not associated with performance on any test at POST, and decreases in PhA from PRE to POST were not associated with decreases in physical performance. Taken together, these observations suggest that simulated sustained military operations disrupt cellular membrane integrity, and these consequences are not mitigated by TA. Also, PhA is significantly associated with strength and power performance, but not aerobic capacity in healthy, young males, and not during periods of acute stress or muscle damage.
To our knowledge, this is the first study to report changes in raw BIA parameters in response to military operations or training. It is also the first study to examine longitudinal BIA changes in response to TA in healthy, young males. Importantly, R, Xc, Z, and PhA are variables measured directly by the BIA device, and, unlike other BIA‐derived variables that require prediction equations to estimate/quantify [e.g., FFM, fat mass, percent body fat, total body water (TBW), ECW, ICW], they do not rely on inherent assumptions that may be inappropriate for certain individuals, increasing the chance of measurement error (Campa, Colognesi, et al., 2022). The decreases in R, Xc, Z, and PhA from PRE to POST are consistent with the physiologic effects of sustained military operations (Margolis et al., 2014; McClung et al., 2013; Nindl et al., 2002, 2007) and suggest changes in body composition, hydration status, cellular integrity, inflammatory status, muscular health, and injury (Lukaski et al., 2017; Nescolarde et al., 2013; Norman et al., 2012; Stobaus et al., 2012; Toso et al., 2000). Though the magnitude of cellular disruption, muscle damage, and inflammation induced by military operations is influenced by several factors including the extent of energy deficit, the duration of the operation, participant training status, sleep restriction, psychological distress, environmental conditions, gender, and physiological preparedness, our findings align with other studies using more invasive techniques such as muscle biopsies and blood draws to characterize the physiological impacts of military training on cellular health and muscle damage (Henning et al., 2011; Howard et al., 2020; Koury et al., 2016; Margolis et al., 2014; McClung et al., 2013; Pasiakos et al., 2019; Varanoske et al., 2018). Reductions in R generally indicate greater body fluid retention, which can be caused by either positive (increased lean body mass, euhydration) or negative (edema, anasarca, inflammation) stimuli (Campa, Colognesi, et al., 2022; Lukaski et al., 2019; Lukaski & Raymond‐Pope, 2021; Piccoli et al., 1994). However, when decreases in R are accompanied by proportionally greater decreases in Xc, as observed in the current study (RWhole: −$6.5\%$; XcWhole: −$11.4\%$), the bioelectrical impedance vector is shifted, resulting in a decrease in PhA (Campa, Colognesi, et al., 2022) (Figure 1). Previous studies report that low PhA is associated with loss of lean body mass, muscle strength, and quality of life, as well as malnutrition, alcoholism, aging, cancer/cachexia, frailty, and mortality, among others (Cardinal et al., 2010; Fukuoka et al., 2022; Genton et al., 2017, 2018; Gupta et al., 2008; Lukaski et al., 2017; Nescolarde et al., 2013; Tanaka et al., 2019; Toso et al., 2000). Within that context, the reductions in R, Xc, Z, and PhA observed in the current investigation likely indicate an inability of cells to store energy, body cell mass loss, inflammation, cellular membrane disruption, and negative energy balance. That these observations persisted through REC for all variables except PhAWhole highlights the longitudinal implications of sustained military operations, demonstrating that cellular integrity is impaired for several weeks into recovery.
Changes in physical performance have been previously reported in the larger cohort of subjects included in this study (Varanoske et al., 2022) but were reanalyzed here to include only subjects in the BIA analysis. Performance on the vertical jump, 3‐RM deadlift, Wingate, and ruck march time trial tests were impaired at POST compared to PRE. Wingate peak power, VO2peak, and ruck march time trial time improved from POST to REC. At REC, performance on all tests were not different from PRE. However, the reported changes in physical performance were not different between TEST and PLA. Reasons for the lack of a beneficial effect of TA on performance may be due to the short study duration (Saad et al., 2011; Varanoske et al., 2020), the training status of participants, the high musculoskeletal injury rate during the simulated military operation which may not have permitted subjects to perform to the best of their ability, the potential lack of motivation during performance testing, or several other factors detailed extensively in our previous report (Varanoske et al., 2022). Importantly, despite no effect of TA on physical performance, TEST maintained FFM throughout the simulated military operation, suggesting that TA may have additional beneficial effects on muscle before physical performance improvements are observed (Varanoske et al., 2022).
However, contrary to our hypothesis, reductions in R, Xc, Z, and PhA from PRE to POST were not attenuated in TEST compared to PLA. As research suggests that lean body mass is positively associated with PhA (Francisco et al., 2020; Norman et al., 2012; Primo et al., 2022) and circulating testosterone concentrations are positively associated with PhA, body cell mass, ICW, and FFM in males with chronic kidney disease (Cigarran et al., 2013), it is surprising that the greater FFM and total and free testosterone concentrations in TEST (Varanoske et al., 2022) did not translate to improvements in R, Xc, Z, or PhA. Nevertheless, correlation should not imply causation, and it is possible that factors other than androgen status contribute to changes in PhA. Testosterone exerts its anabolic biological effects by binding to the androgen receptor in the cell cytoplasm, triggering a cascade of events to induce expression of genes specific to promote the growth and development of male sex organs and secondary male sexual characteristics, including changes in muscle and fat distribution as a result of increased muscle protein synthesis and reduced breakdown (Ferraldeschi et al., 2015; Handelsman, 2013; Nieschlag & Nieschlag, 2019; Rossetti et al., 2017) that may act independently of cellular membrane health. While other studies suggest that TA may also act as an anti‐inflammatory agent (Altuwaijri et al., 2003; D'Agostino et al., 1999; Urban et al., 2014) which may decrease proteolysis (Bhatnagar et al., 2012; Dogra et al., 2006; Langen et al., 2004) as well as an anti‐apoptotic agent to prolong cell survival in culture models (Erkkila et al., 1997; Kang et al., 2021; Morimoto et al., 2005; Pronsato et al., 2012), we reported observed no effect of TA on cellular membrane integrity. Potential reasons for this discrepancy may be related to the non‐invasive technique of assessing cellular health in the current study versus using more invasive techniques of obtaining biological samples to examine gene and protein expression, mitochondrial transcription, immunohistochemistry, inflammation, and cell morphology that may provide additional insight. It is possible that BIA parameters are not sensitive enough to discern potential effects of TA on cellular membrane integrity in this cohort. Nevertheless, all participants in the current study underwent the same relative physiological stress during the simulated military operations, and TEST and PLA exhibited similar increases in circulating cortisol and insulin‐like growth factor‐1 (IGF‐1) concentrations and decreases in sex‐hormone binding globulin (SHBG) (Varanoske et al., 2022). Therefore, despite maintenance of muscle mass in TEST, sustained military operations appears to elicit a similar extent of muscle damage and disruption to cellular membrane integrity in both TEST and PLA, and the mechanism of testosterone action likely increases muscle mass independently of improvements in cellular health and integrity.
Significant positive associations were observed between PhAWhole and performance on the vertical jump, 3‐RM deadlift, and Wingate tests, as well as between PhALegs and vertical jump height at PRE. These observations align with several cross‐sectional studies demonstrating that higher PhA is related to greater strength and power performance and that elite athletes generally possess greater PhA than control counterparts (Cioffi et al., 2020; Custodio Martins et al., 2022; Fukuoka et al., 2022; Giorgi et al., 2018; Langer et al., 2022; Micheli et al., 2014; Norman et al., 2011; Piccoli et al., 2007; Rodriguez‐Rodriguez et al., 2016; Sato et al., 2020). Although a direct cause‐effect relationship has not been established, positive correlations between PhAWhole and XcWhole and negative correlations between PhAWhole and RWhole are consistent with the notion that strength training interventions elicit increases in FFM and body cell mass and decreases in fat mass, altering the electrical conductivity of tissues (Mulasi et al., 2015). A recent meta‐analysis demonstrated that resistance training elicits a leftward shift in the bioelectrical impedance vector as a result of decreases in R and increases in Xc, whereas physical inactivity induces a rightward shift (Campa, Colognesi, et al., 2022). Tissues with greater water and electrolyte content act as better conductors, decreasing R; simultaneously, increases in FFM stimulate increases in Xc (Mulasi et al., 2015; Ribeiro et al., 2018). Together, increases in Xc and decreases in R increase PhA (Custodio Martins et al., 2022). These inferences are further supported by the significant positive correlations between Xc (in both Whole and Legs) and vertical jump height, and negative correlations between Z and R (in both Whole and Legs) and 3‐RM deadlift mass and Wingate peak power at PRE. These results highlight the contribution of tissue conductive properties to strength and power performance.
Despite these findings, performance on the ruck march time trial task was not significantly associated with any BIA measure, and VO2peak was only significantly associated with ZLegs and RLegs, but not with any parameter in Whole. In contrast, other studies report significant associations between PhA and aerobic capacity in adolescents, older adults, and obese individuals (Langer, da Costa, et al., 2020; Langer, de Fatima, et al., 2020; Matias et al., 2020; Streb et al., 2020). As PhA is associated with the number of cells in the body (Dittmar et al., 2015), it is thought that greater cell mass can provide more metabolic activity to generate greater oxygen consumption and carbon dioxide production for physical activity (Fiaccadori et al., 2014; Moore & Boyden, 1963). However, research in healthy, physically‐active adults is lacking, and we are among the first to report relationships between aerobic capacity and PhA in young, athletic, healthy individuals. It is possible that PhA may be predictive of aerobic performance only in populations where cellular health may already be compromised. Additional research examining relationships between PhA and aerobic capacity in young, healthy individuals is warranted.
Although significant reductions in both physical performance and BIA parameters were observed from PRE to POST, no significant correlations were observed between these metrics at POST, ΔPhA was not correlated with changes in any physical performance variable, and only ΔZWhole and ΔRWhole were associated with Δ3‐RM deadlift mass. These findings contradict longitudinal studies reporting significant relationships between changes in PhA and strength following resistance training interventions (Fukuda et al., 2016; Nunes et al., 2019; Ribeiro et al., 2017; Souza et al., 2017). However, resistance training interventions aimed at improving strength usually incorporate adequate rest, proper nutrition, and progressive overload designed to stimulate muscular adaptations while avoiding overuse and overtraining. In contrast, the current study employed an acute intervention designed to simulate a multi‐stressor military operation, which consisted of high exercise‐induced energy expenditure (up to 12 h of structured physical activity per day), limited energy intake (resulting in an energy deficit), and sleep deprivation (Varanoske et al., 2022). This intervention induced undesirable changes in cellular membrane integrity, consistent with other studies reporting reductions in PhA and Xc following muscle injury (Nescolarde et al., 2013). Despite the persistent suppression of bioelectrical impedance parameters at REC compared to PRE, many of the associations between these parameters and physical performance that were present at PRE once again became significant at REC. Although it is possible that the high incidence of musculoskeletal injury or lack of motivation during the sustained military operation may have affected physical performance testing at POST (Varanoske et al., 2022), these findings suggest that acute impairments in cellular membrane integrity in this cohort are not related to the extent of physical performance decrements. Longitudinal measures of PhA, Xc, R, and Z appear to be more indicative of physical performance rather than acute measures obtained after physiological stress.
We observed greater reductions in BIA parameters measured in the Legs compared to those measured in Whole (RWhole: −$6.5\%$, RLegs: −$10.8\%$; XcWhole: −$11.4\%$, XcLegs: −$18.3\%$; ZWhole: −$6.6\%$, ZLegs: −$10.9\%$; PhAWhole: −$5.0\%$, PhALegs: −$8.6\%$). These findings may be attributed to the implementation of primarily lower‐body exercises to increase energy expenditure during the simulated military operation. These prolonged, low‐intensity, repetitive aerobic activities, which included ruck marching, running, walking, cycling, and elliptical, likely resulted in greater disruption of cellular integrity in the legs than in the trunk or arms. The inability of PhALegs to recover to PRE values by REC is also likely a result of a greater disruption in the legs than in the whole‐body during the simulated military operation, and a longer recovery period is likely necessary for complete recovery of PhALegs. However, the associations between raw BIA parameters and physical performance were stronger, and significant relationships occurred more frequently, in Whole than in Legs at PRE and REC, despite the performance tests also relying heavily on the lower body. These findings contradict those in elite soccer players demonstrating that changes in lower‐body PhA from pre‐season through the first half of a championship were a stronger predictor of vertical jump performance than changes in whole‐body PhA (Bongiovanni et al., 2021). However, this study examined longitudinal changes in PhA over the course of a training season where both PhA of the lower‐body and vertical jump height increased, reflecting improved cellular integrity and muscle mass (Bongiovanni et al., 2021). In contrast, the present study examined changes in performance and PhA in response to acute severe physiological stress, reporting decreases in all of these parameters.
Strengths of this investigation included the use of a controlled environment eliciting significant physiological stress, in which energy intake, energy expenditure, and sleep duration were meticulously designed and monitored. Additionally, this study appears to be the first to examine changes in raw BIA vectors in response to military operations as well as TA in young, healthy males, demonstrating that cellular membrane integrity is impaired during sustained military operations, but these effects are not attenuated by TA. The use of raw BIA parameters, rather than those that are calculated from prediction equations (ex: TBW, FFM, ICW, ECW), enhances our confidence in the accuracy of the measures as these are not based on assumptions of cellular hydration. Nevertheless, several limitations should be noted. The observational study design prevents us from drawing conclusions regarding causality, and we are unable to definitively state that longitudinal changes in PhA contribute to changes in physical performance. Additionally, as this was a secondary investigation of a larger study, this analysis may be underpowered to detect relationships between some physical performance metrics and bioelectrical impedance parameters. Also, the number of correlations included in this analysis increase risk for type II error, and this investigation should therefore be considered preliminary and hypothesis‐generating. Furthermore, this study recruited only male participants, so the findings are likely not generalizable to females, and future research examining the effects of sustained military operations on cellular integrity in females is warranted. Finally, the use of a single timepoint for BIA analyses during REC, where most variables did not return to PRE values by the end of this period, does not allow us to provide a time course of recovery. The smallest relative decrement reported in any BIA variable from PRE to POST was PhAWhole (−$5.0\%$), which was also the only variable to return to PRE values by REC, and it is possible that a longer recovery period is necessary for other BIA parameters with larger decrements to return to baseline values.
In conclusion, this analysis demonstrated that sustained simulated, sustained military operations elicit significant disruptions to cellular membrane integrity, as evidenced by decreases in PhA in both the whole‐body and legs. The shift in PhA was a result of decreases in Xc and increases in R, indicating reduced cellular mass, an inability of cells to store energy, and inflammation. TA (administered once, 750 mg testosterone undecanoate) did not attenuate changes in any of these parameters despite the maintenance of FFM, as previously reported (Varanoske et al., 2022). PhA was positively associated with indices of strength and power (vertical jump height, 3‐RM deadlift mass at PRE, and Wingate peak power), but not aerobic capacity (VO2peak, ruck march time trial time) at PRE and REC. However, PhA was not associated with physical performance at POST, and changes in PhA from PRE to POST were not associated with changes in physical performance. These findings suggest that sustained military operations elicit cellular membrane disruption, but TA does not mitigate these consequences. Additionally, greater PhA is indicative of greater strength and power performance, but is not related to aerobic capacity in healthy, young males. These relationships also do not exist during periods of acute stress or muscle damage.
## AUTHOR CONTRIBUTIONS
Alyssa N. Varanoske, Neil M. Johannsen, Steven B. Heymsfield, Frank L. Greenway, Jennifer C. Rood, Arny A. Ferrando, and Stefan M. Pasiakos conceived and designed research; Alyssa N. Varanoske, Melissa N. Harris, Callie Hebert, Steven B. Heymsfield, Frank L. Greenway, and Jennifer C. Rood performed experiments; Alyssa N. Varanoske and Steven B. Heymsfield analyzed data; Alyssa N. Varanoske interpreted results of experiments; Alyssa N. Varanoske prepared figures; Alyssa N. Varanoske drafted manuscript; Alyssa N. Varanoske, Neil M. Johannsen, Steven B. Heymsfield, Arny A. Ferrando, and Stefan M. Pasiakos edited and revised manuscript; Alyssa N. Varanoske, Melissa N. Harris, Callie Hebert, Neil M. Johannsen, Steven B. Heymsfield, Frank L. Greenway, Arny A. Ferrando, Jennifer C. Rood, and Stefan M. Pasiakos approved final version of manuscript.
## FUNDING INFORMATION
The U.S. Army Medical Research and Development Command, Military Operational Medicine Research Program funded this research. Supported in part by an appointment to the U.S. Army Research Institute of Environmental Medicine administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and the U.S. Army Medical Research and Development Command. The funding sources had no role in the study design; collection, analysis, and interpretation of data; in writing the report; and in the decision to submit this article for publication.
## CONFLICT OF INTEREST STATEMENT
The authors declare that they have no competing interests. The opinions or assertions contained herein are the private views of the authors and are not to be construed as official or as reflecting the views of the Army or the Department of Defense. Any citations of commercial organizations and trade names in this report do not constitute an official Department of the Army endorsement of approval of the products or services of these organizations.
## ETHICS STATEMENT
The Institutional Review Board (IRB) of the PBRC (protocol 2019‐017) and the U.S. Army Medical Research and Development Command, Human Research Protections Office approved the study protocol and trial documents, and all procedures were in accordance with the ethical standards of the 1964 Helsinki Declaration and its later amendments.
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|
---
title: A united model for diagnosing pulmonary tuberculosis with random forest and
artificial neural network
authors:
- Qingqing Zhu
- Jie Liu
journal: Frontiers in Genetics
year: 2023
pmcid: PMC10033863
doi: 10.3389/fgene.2023.1094099
license: CC BY 4.0
---
# A united model for diagnosing pulmonary tuberculosis with random forest and artificial neural network
## Abstract
Background: *Pulmonary tuberculosis* (PTB) is a chronic infectious disease and is the most common type of TB. Although the sputum smear test is a gold standard for diagnosing PTB, the method has numerous limitations, including low sensitivity, low specificity, and insufficient samples.
Methods: The present study aimed to identify specific biomarkers of PTB and construct a model for diagnosing PTB by combining random forest (RF) and artificial neural network (ANN) algorithms. Two publicly available cohorts of TB, namely, the GSE83456 (training) and GSE42834 (validation) cohorts, were retrieved from the Gene Expression Omnibus (GEO) database. A total of 45 and 61 differentially expressed genes (DEGs) were identified between the PTB and control samples, respectively, by screening the GSE83456 cohort. An RF classifier was used for identifying specific biomarkers, following which an ANN-based classification model was constructed for identifying PTB samples. The accuracy of the ANN model was validated using the receiver operating characteristic (ROC) curve. The proportion of 22 types of immunocytes in the PTB samples was measured using the CIBERSORT algorithm, and the correlations between the immunocytes were determined.
Results: *Differential analysis* revealed that 11 and 22 DEGs were upregulated and downregulated, respectively, and 11 biomarkers specific to PTB were identified by the RF classifier. The weights of these biomarkers were determined and an ANN-based classification model was subsequently constructed. The model exhibited outstanding performance, as revealed by the area under the curve (AUC), which was 1.000 for the training cohort. The AUC of the validation cohort was 0.946, which further confirmed the accuracy of the model.
Conclusion: Altogether, the present study successfully identified specific genetic biomarkers of PTB and constructed a highly accurate model for the diagnosis of PTB based on blood samples. The model developed herein can serve as a reliable reference for the early detection of PTB and provide novel perspectives into the pathogenesis of PTB.
## Introduction
Tuberculosis (TB) affects nearly five million adult males, 3.5 million adult females, and 1 million children, and there are approximately 10.4 million cases of TB worldwide (Jeremiah et al., 2022). Owing to the increasing global population, public health departments are continually aiming to improve the diagnostic efficiency of TB and reduce its rate of transmission. Microscopic examination of sputum smears for acid-fast bacilli and sputum cultures are commonly used for diagnosing pulmonary TB (PTB) worldwide. However, these microbiology-based approaches and culture methods are time consuming, and the probability of infection is high (Barac et al., 2019). It is therefore urgently necessary to study and development non-sputum-based, simple, sensitive, and specific tests for diagnosing PTB. The biomarkers of PTB have been increasingly explored in the last three years owing to several studies on the identification of novel diagnostic biomarkers and development of novel diagnostic methods for PTB (Khambati et al., 2021; Morrison and Mcshane, 2021; Khimova et al., 2022). These studies have paved the way for the diagnosis and identification of novel biomarkers of PTB. While there has been success in clinical use of pathogen-based biomarkers in the form of Cepheid GeneXpert and Urine Lipoarabinomannan (LAM), host-based biomarkers are in less advanced stages of development (Nogueira et al., 2022). Based on previous literature, the present study aimed to identify more specific biomarkers of PTB using blood samples.
Blood-based gene expression signatures are the most potential biomarkers for diagnosing PTB. According to the target product profile (TPP) for non-sputum biomarker triage tests published by the World Health Organization in April 2014, TPPs require a minimum diagnostic sensitivity of $90\%$ and specificity of $87\%$ for the diagnosis of PTB in adults (Denkinger et al., 2019). Several recent studies have demonstrated that whole-blood RNA signatures can be used for predicting active TB infections and determining the progression of *Mycobacterium tuberculosis* infections in individuals who are at a risk of developing active TB (Kaforou et al., 2013; Blankley et al., 2016; Sweeney et al., 2016; Zak et al., 2016).
The increasing use of high-throughput sequencing technologies in the last decade has enabled the investigation of various aspects of diverse diseases (Dillies et al., 2013; Sullivan et al., 2017). Large volumes of high-throughput data have been stored in public platforms owing to the rapid development of high-throughput sequencing technology. These data can therefore be used for selecting critical indicators or feature biomarkers, which is a significant challenge for the development of diagnostic models. Machine learning techniques, including random forest (RF) and artificial neural network (ANN), can provide novel insights for solving this problem, and have been widely employed in previous studies for constructing diagnostic models by analyzing sequencing data (Dillies et al., 2013; Sullivan et al., 2017). Random Forest algorithm can perform random sampling to screen the target biomarkers and has high predicted accuracy (Byeon, 2019). Furthermore, the Artificial Neural Network can be used to evaluate the weight of target biomarkers screened by RF and construct the predicted model for PTB with divided training and validation datasets (Curchoe et al., 2020). However, multi-biomarker-based diagnostic models and the combination of RF and ANN have not been employed for the diagnosis of TB to date.
The present study aimed to construct a multi-mRNA diagnostic model for the diagnosis of PTB. To this end, the genes that were differentially expressed between the PTB and control samples in the public datasets in the Gene Expression Omnibus (GEO) database were initially identified. The essential biomarkers for classifying PTB were screened using an RF classifier, and the weight of each biomarker was determined using ANN. A diagnostic model was subsequently developed based on these biomarkers and the accuracy of the model in discriminating between PTB and control samples was verified by receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC) of the training (GSE83456) and validation (GSE42834) cohorts was determined to be 1.000 and 0.946, respectively. The high accuracy indicated that the diagnostic model constructed herein met the necessary requirements for the clinical diagnosis of PTB. The protocol and algorithms used in the present study are depicted in Figure 1.
**FIGURE 1:** *Flow chart of the present study. DEGs, differentially expressed genes; RF, random forest; ANN, artificial neural network.*
## Data processing
In this study, two RNA expression datasets were initially retrieved from the GEO database using the keywords “tuberculosis, normal.” The GSE83456 and GSE42834 datasets were processed using the GPL10558 platform of an Illumina HumanHT-12 V4.0 Expression BeadChip system. Based on the available literature on the use of machine learning for the diagnosis of diseases, we assumed that the sample size of the two datasets was appropriate for developing a machine learning-based diagnostic model. The obtained RNA-seq data were subsequently annotated and normalized using R software (version 4.2.1). The GSE83456 and GSE42834 datasets were selected as the training and validation cohorts, respectively.
## Identification of differentially expressed genes (DEGs)
The DEGs between the PTB and control samples in the training set were identified using the limma package in R, with $p \leq 0.05$ and |log2foldchange (FC)| >1.0. The DEGs were visualized using the pheatmap and ggplot2 packages in R.
## Functional enrichment analysis
The identified DEGs were subjected to Gene Ontology (GO) enrichment analysis for investigating the biological functions of the DEGs, using the clusterProfiler package in R (version 4.1.5). GO terms with $p \leq 0.05$ were considered to be significantly enriched. The Metascape webserver (http://metascape.org) was also used to annotate the enriched biological pathways for comprehensive analysis of the biomarkers. The most enriched functions or pathways were subsequently displayed using bubble and bar plots.
## Screening significantly enriched DEGs using RF
The DEGs were further screened using the randomForest package in R software. The optimal tree number was first identified based on the best stability and lowest error rate by calculating the error rate of each of the 1–500 trees. We established an RF model based on the optimal tree number for screening the specific PTB genes as candidate biomarkers using the mean decrease in Gini coefficient. In the RF algorithm, a gene importance value greater than 2 is considered to be a common screening criterion, and has been used in other studies on machine learning-based diagnostic models.
## Construction and evaluation of an ANN-based diagnostic model
In order to construct an ANN-based diagnostic model, the min-max method was used for normalizing the input data, which were subsequently converted into the “Gene Score” according to the gene expression levels. For instance, the expression of an upregulated gene was denoted as 1 if the expression level was higher than the median expression value across all the samples, or denoted as 0 in other instances. Similarly, the expression of a downregulated gene was generally denoted as 0, or as 1 if the expression level was higher. A neural network-based classification model was subsequently constructed by calculating the weights of the significantly enriched DEGs using the neuralnet package in R (version 4.2.1). A neural network contains an input layer, a hidden layer, and an output layer. In this study, the number of hidden layers was set to 5, and the number of output parameters was set to 2 nodes (contract/segment). Additionally, the AUC value of the training cohort was calculated using the pROC package in R (version 4.2.1). The accuracy of the model was also verified using the independent GSE42834 cohort.
## Analysis of immune infiltration
CIBERSORT is a deconvolution algorithm that is used for quantifying cell types based on the gene expression profiles, and was used to determine the abundance of 22 types of immune cells in the PTB and control tissues. Using the CIBERSORT algorithm, the immune infiltration landscape in the GSE83456 cohort was comprehensively analyzed, and the differences between the control and PTB groups were depicted using waterfall and correlation plots.
## Statistical analyses
The differences in gene expression between the control and PTB samples were compared using Student’s t-tests. The categorization effects of the critical biomarkers on the PTB and control specimens were determined using ROC curves and the AUC using the pROC package in R. Statistical analysis was performed using the R software (version 4.2.1) and GraphPad Prism (GraphPad Prism, USA). $p \leq 0.05$ was considered to be statistically significant, unless otherwise stated.
## Data processing and identification of DEGs
The limma package in R was used for identifying the DEGs between the 45 PTB and 61 control samples using the classical Bayesian algorithm, based on the following criteria: $p \leq 0.05$ and |log2FC| >1. A total of 33 DEGs were finally identified, including 11 and 22 DEGs that were significantly upregulated and downregulated, respectively. As depicted in Figure 2A, the expression of these DEGs differed significantly between the PTB and control groups. The results were graphically represented using a volcano plot, which further revealed the differences in gene expression and statistical significance of the DEGs (Figure 2B).
**FIGURE 2:** *Identification of DEGs in the training cohort. (A) The heatmap of the 33 DEGs, including 11 upregulated and 22 downregulated ones. PTB were represented by red samples, normal were represented by blue samples. Red blocks indicate high-expressed genes, and blue blocks indicate low-expressed genes. Con, control group; PTB, Pulmonary Tuberculosis. (B) Volcano plots of all DEGs in the GSE83456 dataset. Two dotted lines on the X-axis represent the value of log2FC is −1 and 1. The dotted line on the Y-axis represent the adj.p.value is 0.05. Red dots represent high-expressed genes, blue dots represent low-expressed genes and black dots represent not significant changed genes.*
## Functional enrichment analysis of DEGs
The biological significance of the 33 DEGs in the pathogenesis of PTB was investigated by GO pathway enrichment analysis using the clusterProfiler package in R. The findings revealed that the 33 DEGs were primarily involved in immune-related functions, including adaptive immune response based on somatic recombination of immune receptors comprising immunoglobulin superfamily domains, positive regulation of T cell activation, positive regulation of leukocyte cell-cell adhesion, regulation of leukocyte apoptotic process, and leukocyte apoptotic process. The findings are presented in a bubble plot (Figure 3). The Metascape webserver was also used for annotating the enriched GO terms. The results of *Metascape analysis* revealed that the three pathways of DEGs were significantly enriched (Figure 4A, B and C).
**FIGURE 3:** *Functional enrichment analysis results. Top five enriched GO terms in biological process (BP).* **FIGURE 4:** *The results of Metascape analysis. (A) The network of enriched terms. The 3 clusters were identified and rendered network graphics, in which terms with a similarity score > 0.3 are connected by an edge. The thickness of the edge represents the similarity score. (B) Colored by p-value, terms containing more genes tend to have a more significant p-value (C) Bar graph of enriched terms. Values of p determine the color of the bar. The values of p are lower, and the color is more profound.*
## Screening key genes using an RF classifier
In order to identify the reliable diagnostic biomarkers of PTB, the DEGs were classified using an RF classifier. According to Figure 5A, which depicts the relationship between the RF tree number and the error rate of the model, the trees with the lowest error rate ntrees value (ntrees = 31) were selected. Based on the model accuracy and decreased mean square error, the Gini coefficient method was used for measuring the importance of all the variables. The results of MeanDecreaseGini are provided in Figure 5B. Kruppel-like factor 12 (KLF12) was identified as the most important biomarker. A set of 11 specific biomarkers, including KLF12, interleukin 23 subunit alpha (IL23A), neural EGFL-like 2 (NELL2), Family With Sequence Similarity 102 Member A (FAM102A), Calcium Voltage-Gated Channel Subunit Alpha1 E (CACNA1E), Oxysterol Binding Protein like 10 (OSBPL10), complement component C1q (C1QC), Hook Microtubule Tethering Protein 1 (HOOK1), Chromosome 2 open reading frame 89 (C2orf89), inhibitor of DNA binding 3 (ID3), and Kelch Like Family Member 3 (KLHL3), with significance >2 were selected as critical biomarkers for further analysis. The heatmap revealed that CACNA1E and C1QC were upregulated in the PTB group, while the remaining 9 genes were downregulated (Figure 5C).
**FIGURE 5:** *Screening PTB biomarkers by random forest. (A) The relations between the error rate and the number of decision trees. (B) The Gini coefficient method in random forest modeling of the train cohort. The genetic variable is on the y-axis and the importance index is on the x-axis. (C) Heatmap of the 11 specific periodontitis biomarkers.*
## Construction of the ANN model
The weights of each of the biomarkers are provided in Supplementary Table S1. The weights of the 11 biomarkers were analyzed using ANN, based on the gene scores. The ANN model consisted of one input layer, one hidden layer, and one output layer, as depicted in Figure 6A. The input layer included 11 neurons, hidden layer included five neurons and output layer included 2 neurons. The absolute partial derivative of the error function was less than 0.01.
**FIGURE 6:** *Construction and evaluation of ANN diagnostic model. (A) Topology, which include one input layer, one hidden layer and one output layer, the visualization of the artificial neural network. (B) ROC curves of train model in the GSE83456 dataset. (C) ROC curves of test model in the GSE42834 dataset.*
## Validation of the ANN model
The performance of the ANN model was determined using the pROC package in R, and the AUC of the training GSE83456 cohort was 1.000. This indicated that the ANN model performed exceptionally well in diagnosing PTB (Figure 6B). The ANN model also demonstrated outstanding performance with the independent GSE42834 validation cohort, and the AUC of the validation cohort was determined to be 0.946 (Figure 6C).
## Assessment of immune infiltration
The present study further investigated the correlation between the ratios of the 22 types of immunocytes in the PTB and control specimens using the CIBERSORT algorithm. The composition of the immunocytes in the PTB and normal samples and the relationships among the immunocytes are provided in Figure 7A. The findings revealed a positive correlation between the levels of M0 macrophages and monocytes, and between the levels of M0 macrophages and neutrophils. However, there was a negative correlation between the abundance of resting mast cells and activated mast cells, levels of memory B cells and naïve B cells, and the ratio of follicular helper T cells and neutrophils (Figure 7B).
**FIGURE 7:** *Immune infiltration assessment via the CIBERSORT in the GSE83456 dataset. (A) Composition of 22 immunocytes on PTB samples and normal. (B) The relationship among 22 immunocytes are displayed in correlation matrix.*
## Discussion
The early detection and diagnosis of PTB can reduce its chances of transmission; therefore, identifying specific biomarkers for the prediction of PTB is crucial for controlling disease progression. RF and ANN can be combined for developing reliable diagnostic models for certain diseases, including osteoarthritis and hypertrophic cardiomyopathy (Xie et al., 2020; Li S et al., 2022; Li Z B et al., 2022). RF and ANN are advanced tools for diagnosing PTB, but their main limitation is the necessity for trained and qualified personnel for implementing these tools, as the construction of neural networks, which includes training and testing, is a challenging task. Additionally, the use of statistical tools for diagnosing diseases continues to be a matter of difficulty.
The present study identified 33 DEGs between PTB and control samples in the GSE83456 cohort. A total of 11 candidate genes were identified using an RF classifier, and an ANN algorithm was used for computing the weights of these genes. A classification model was constructed for the diagnosis of PTB, and a ROC curve was generated for assessing the efficacy of the classification by the ANN model. An independent GSE42834 cohort was used for determining the reliability of the classification model.
The results of enrichment analysis demonstrated that the majority of DEGs were primarily enriched in immune-related functions. It has been reported that T cells are involved in the development of TB, and the activation of T cells enhance resistance to M. tuberculosis infections (Feruglio et al., 2017). Leukocytes are also implicated in the inflammatory pathogenesis of TB (Ocana-Guzman et al., 2021). However, adaptive immune responses based on somatic recombination of immune receptors comprising immunoglobulin superfamily domains have not been previously reported in TB, and may serve as a novel therapeutic target for PTB. Altogether, the findings revealed that these DEG identified herein are positively involved in the immune processes in PTB.
Of the 11 genes screened using the RF classifier, KLF12 (Natarajan et al., 2022), IL23A (Khader et al., 2011), NELL2 (Yang et al., 2015), OSBPL10 (Li et al., 2022), C1QC (Cai et al., 2014), and ID3 (Han et al., 2021) have been identified as candidate biomarkers of TB in previous studies.
Notably, the present study identified additional five genes that have not been previously shown to be associated with the pathogenesis of PTB. The KLHL3 gene, which is downregulated in PTB, encode proteins that are components of the CullinRING E3 ubiquitin ligase complex and are involved in the ubiquitin-proteasome system. The complex degrades proteins and also plays an essential role in maintaining cellular functions (Zhang et al., 2022). It has been reported that the ubiquitin-proteasome system also plays a role in inducing CD8+ T cells (Shen et al., 2008). Therefore, the downregulation of KLHL3 may suppress the degradation of proteins that regulate the ubiquitin-proteasome system and subsequently induce CD8+ T cells that participate in the pathogenesis of PTB.
The present study revealed that the expression of HOOK1 is downregulated in PTB. A previous study reported that enhancing the interaction between HOOK1 and CD147 may increase the exosomal levels of amyloid-β (Xie et al., 2018). The deposition of amyloid-β has been reported to be associated with tuberculous meningitis (Stroffolini et al., 2021). We therefore speculated that HOOK1 may affect the deposition of amyloid-β to regulate the pathogenesis of PTB. CD147++ Tregs cells, a recently described highly suppressive and activated subset of human Tregs, are capable of producing proinflammatory cytokines in TB (Feruglio et al., 2015). These studies collectively suggest that HOOK1 may participate in the pathological processes of PTB via multiple pathways.
The CACNA1E protein can mediate the entry of calcium ions into excitable cells and regulate various calcium-dependent processes. Numerous studies have reported that calcium channel blockers have anti-tuberculosis potential (Lee et al., 2015; Song et al., 2015; Lee et al., 2021). Therefore, the upregulation of CACNA1E in PTB may result in the activation of calcium channels and lead to the pathogenesis of PTB.
The present study is the first to identify the association between FAM102A and the pathogenesis of TB. The findings revealed that the expression of FAM102A was downregulated in the samples of PTB in this study. Notably, protein-protein interaction (PPI) analysis with STRING (string-db.org) revealed that the FAM102A protein interacts with NELL2, which has been confirmed as a biomarker of TB. It has been additionally reported that NELL2 plays a crucial role in protecting cells from environments that induce cell death (Kim et al., 2015). The deficiency of NELL2 induces mitochondria-dependent cellular apoptosis and inhibits cellular proliferation by phosphorylating and activating extracellular signal-regulated kinase $\frac{1}{2}$ (ERK$\frac{1}{2}$) (Liu et al., 2021). These findings suggest that FAM102A can function as a biomarker of PTB by interacting with NELL2, and subsequently influence cellular apoptosis and regulate the pathogenesis of PTB.
The C2orf89 protein, also referred to as TRABD2A, could be involved in activating resting CD4+ T cells but not activated CD4+ T cells. The TRABD2A protein is located on the plasma membrane of resting CD4+ T cells and disappears following the activation of T cells (Liang et al., 2019). CD4+ T cells produce cytokines, which are vital in controlling M. tuberculosis infections (Ferreira et al., 2021). It is therefore likely that the production of cytokines, including interferon (IFN)-γ, by activated CD4+ T cells suppresses M. tuberculosis infections and downregulates the TRABD2A protein located on the plasma membrane of resting CD4+ T cells.
The particularities of our research are combining RF and ANN methods innovatively, and multiple biomarkers combined diagnosis, which showed outstanding results in the predictive power aspect. The AUC of train model and valid model are both greater than 0.9. Compared with several literatures (Manisha Singh et al., 2022; Yu Dong Zhang, 2020) which utilize the chest radiography images to detect Pulmonary Tuberculosis with the help of machine learning tools (CAD, DL, ICNN), our work is analysing biomarkers from peripheral blood biomarkers and constructing diagnostic model for PTB with the combination of RF and ANN. Although, RF, ANN, or other machine learning had been utilized in diagnosing TB (Dande and Samant, 2018; Orjuela-Canon et al., 2022), combining RF and ANN to diagnose PTB had never been reported. Our samples are both from human blood, we could design the diagnostic kit based on the eleven biomarkers and to detect the blood which sampling from human fingers. It is we choose figure blood sampling rather than sputum smear and X-ray that bring us the diagnostic convenience and safety. However, the present study has certain limitations. Firstly, although our diagnostic model performed well, the number of samples in the training and validation datasets was relatively small. Therefore, independent patient cohorts with a larger sample size are necessary for evaluating the performance of the ANN-based classification model developed herein, and sufficient samples need to be collected from affiliated hospitals for this purpose. Secondly, all the samples were only classified as normal or PTB, which may influence the results of screening; therefore more subtypes of PTB should be considered in future studies. Thirdly, the correlation between the novel biomarkers and the pathogenesis of PTB remain to be determined, and further experimental studies are necessary for elucidating the underlying mechanisms by which the biomarkers regulate the pathogenesis of PTB. Altogether, the model developed herein has high accuracy and excellent diagnostic convenience owing to the use of data obtained from routine blood tests.
## Conclusion
Altogether, the present study successfully constructed a novel diagnostic model for PTB. As the diagnostic method is based on peripheral blood tests, a diagnostic kit can be designed based on the 11 biomarkers identified herein, which is highly convenient for the rapid and accurate diagnosis of PTB. The diagnostic model, biomarkers, and the peripheral blood test method discussed herein provide novel insights into the underlying mechanisms and can aid further studies on the clinical diagnosis of PTB. However, further experimental studies are necessary for determining the underlying mechanisms by which the identified biomarkers regulate the pathogenesis of PTB.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
## Author contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1094099/full#supplementary-material
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|
---
title: A matrigel-free method for culture of pancreatic endocrine-like cells in defined
protein-based hydrogels
authors:
- Mark T. Kozlowski
- Heather N. Zook
- Desnor N. Chigumba
- Christopher P. Johnstone
- Luis F. Caldera
- Hung-Ping Shih
- David A. Tirrell
- Hsun Teresa Ku
journal: Frontiers in Bioengineering and Biotechnology
year: 2023
pmcid: PMC10033864
doi: 10.3389/fbioe.2023.1144209
license: CC BY 4.0
---
# A matrigel-free method for culture of pancreatic endocrine-like cells in defined protein-based hydrogels
## Abstract
The transplantation of pancreatic endocrine islet cells from cadaveric donors is a promising treatment for type 1 diabetes (T1D), which is a chronic autoimmune disease that affects approximately nine million people worldwide. However, the demand for donor islets outstrips supply. This problem could be solved by differentiating stem and progenitor cells to islet cells. However, many current culture methods used to coax stem and progenitor cells to differentiate into pancreatic endocrine islet cells require Matrigel, a matrix composed of many extracellular matrix (ECM) proteins secreted from a mouse sarcoma cell line. The undefined nature of Matrigel makes it difficult to determine which factors drive stem and progenitor cell differentiation and maturation. Additionally, it is difficult to control the mechanical properties of Matrigel without altering its chemical composition. To address these shortcomings of Matrigel, we engineered defined recombinant proteins roughly 41 kDa in size, which contain cell-binding ECM peptides derived from fibronectin (ELYAVTGRGDSPASSAPIA) or laminin alpha 3 (PPFLMLLKGSTR). The engineered proteins form hydrogels through association of terminal leucine zipper domains derived from rat cartilage oligomeric matrix protein. The zipper domains flank elastin-like polypeptides whose lower critical solution temperature (LCST) behavior enables protein purification through thermal cycling. Rheological measurements show that a $2\%$ w/v gel of the engineered proteins display material behavior comparable to a Matrigel/methylcellulose-based culture system previously reported by our group to support the growth of pancreatic ductal progenitor cells. We tested whether our protein hydrogels in 3D culture could derive endocrine and endocrine progenitor cells from dissociated pancreatic cells of young (1-week-old) mice. We found that both protein hydrogels favored growth of endocrine and endocrine progenitor cells, in contrast to Matrigel-based culture. Because the protein hydrogels described here can be further tuned with respect to mechanical and chemical properties, they provide new tools for mechanistic study of endocrine cell differentiation and maturation.
## Introduction
Type 1 diabetes (T1D) is an autoimmune disorder that results in the destruction of a patient’s pancreatic endocrine beta cells, which prevents insulin secretion and endogenous regulation of blood glucose levels. This condition affects approximately nine million people worldwide according to the World Health Organization (Xu et al., 2018), and can lead to serious complications such as coronary artery disease, retinopathy, and diabetic nephropathy resulting in kidney failure (Bjornstad et al., 2015). Currently, T1D is treated by a combination of injections of exogenous insulin and careful regulation of diet and lifestyle. Although life-saving, insulin injection does not completely prevent glucose excursion or the associated long-term complications (Nalysnyk et al., 2010). A promising alternative therapy is the transplantation of cadaveric human islets, which has been used effectively in over 1,500 patients worldwide since the first report of a successful allogenic transplant in the year 2000 (Shapiro et al., 2000; Shapiro et al., 2017). However, clinical demands outpace transplant supplies, as multiple donors are typically required for each recipient (Walker et al., 2022), and repeated transplantations into a single recipient over time may be required due to progressive graft failure caused by allo- and auto-immunity (Moore et al., 2015).
To meet the demand for transplantable beta cells, several groups have developed methods for the production of functional beta-like cells from pluripotent stem cells capable of treating insulin-dependent diabetic mice (Pagliuca et al., 2014; Rezania et al., 2014; Ma et al., 2018a; Ma et al., 2018b; Sui et al., 2018; Nair et al., 2019; Maxwell et al., 2020; Hogrebe et al., 2021; Du et al., 2022; Hebrok et al., 2022; Maxwell et al., 2022; Parent et al., 2022), and clinical trials of similar techniques are underway in humans (de Klerk and Hebrok, 2021; Migliorini et al., 2021; Butler and Gale, 2022). However, in order to produce transplantable cells, progenitor cells require appropriate biochemical and physical cues to promote beta cell formation, a process that is still not fully elucidated. Currently, many methods rely on the use of Matrigel, a secretion of Engelbreth-Holm-Swarm mouse sarcoma cells enriched for extracellular matrix (ECM) proteins. Matrigel is undefined and extremely complex, with one proteomic profile showing over 1,800 unique proteins (Hughes et al., 2010). The undefined nature of Matrigel makes it difficult to determine exactly which factors are responsible for the differentiation of pluripotent stem cells towards a particular lineage. Matrigel also suffers from lot-to-lot variation, which affects cellular fate decisions (Vukicevic et al., 1992; Goldstein et al., 2011). Furthermore, the xenogeneic origin of Matrigel impedes its use in clinical applications (Ludwig et al., 2006; Unger et al., 2008). Transplanted human pluripotent stem cells mixed with Matrigel potentiate the formation of cancer cells, such as teratomas, in mice (Hentze et al., 2009; Prokhorova et al., 2009; Vaillant et al., 2011). The mechanical properties of Matrigel are also difficult to tune independently of its chemical properties, which is a critical shortcoming because the mechanical environment surrounding the developing stem cells can also affect their differentiation (Engler et al., 2006). Another major challenge for stem cell transplantation in T1D is the immaturity of the in-vitro differentiated beta-like cells (Melton, 2021). This is partly due to a lack of suitable assay system that allows the examination of signals important for differentiation of primary, non-stem cell derived, endocrine progenitor cells (Engler et al., 2006; Mamidi et al., 2018).
Both fibronectin (Nakamura et al., 2022) and laminin (Tjin et al., 2014) are known to be important in pancreatic endocrine functions. For these reasons, in this study we engineered two novel recombinant proteins, which we call PEP-FN or PEP-LAMA3, that are well-defined, gel-forming, and equipped with cell-binding motifs. PEP-FN uses an arginine-glycine-aspartic acid (RGD) integrin-binding domain derived from fibronectin (Ruoslahti and Pierschbacher, 1987), and the PEP-LAMA3 protein uses a laminin alpha-3 cell-binding domain previously reported by Tjin and co-workers (Tjin et al., 2014). Previous work characterized recombinant triblock protein, which we called PEP (Dooling and Tirrell, 2016; Rapp et al., 2017; Rapp et al., 2018), a synthetic domain with coiled-coil helix domains that self-associate and give the gel its material properties. These proteins were produced in quantities of hundreds of milligrams in *Escherichia coli* under conventional culture conditions. We tested the ability of the PEP-FN and PEP-LAMA3 proteins to support the growth of primary endocrine and endocrine progenitor cells isolated from young (8-day-old) mice in 3-dimensional (3D) space. Prior studies demonstrated that pancreatic progenitor cells are present in these young mice and are suitable for in vitro modeling of cell differentiation to endocrine lineages (which include beta cells among others) (Ghazalli et al., 2015). Here, we demonstrate that the PEP-FN and PEP-LAMA3 proteins support endocrine lineage and endocrine progenitor cells, compared to the control culture containing Matrigel that favors ductal cell formation (Ghazalli et al., 2015).
## Materials and methods
Protein Expression and Purification. DNA sequences that encode for PEP-FN or PEP-LAMA3 proteins (Supplementary Table S1) were constructed in the pQE80-L plasmid (Qiagen, Hilden, Germany) and expressed in E. coli strain BL21 DE3 in Terrific Broth (TB). From N-terminal to C-terminal, the proteins contain a 6x-histidine tag, a rat cartilage oligomeric matrix protein domain (coiled-coil P) with mutation I58A, an elastin-like polypeptide domain (E), a fibronectin-derived RGD cell-binding domain or a laminin alpha three mimetic, another E domain, and another P domain with mutation I58A. The 6x-histidine tag enabled purification using a Ni-NTA column. Additional details, including steps taken to remove bacterial endotoxin, are presented in supporting information. Both proteins were obtained in yields of ∼100 mg/L of bacterial culture after purification.
Rheology. Rheological studies were conducted on a TA Scientific ARES rheometer with TA Orchestrator software. A plate-and-cone geometry with a diameter of 25 mm, cone angle of 0.0396 radians, and a gap of 0.0483 mm (TA Scientific, New Castle, DE) were used. The sample was warmed to 37°C by a Peltier plate. A thin ring of paraffin oil (JT Baker, VWR, Radnor, PA) was placed around the sample to prevent evaporation. A strain sweep was first conducted to establish the linear viscoelastic region of the material, at a frequency of 10 rad/s. Based on these results, frequency sweeps were conducted at a maximum strain of $1\%$.
Mice and pancreatic cell preparation. Animal experiments were conducted under the supervision of the Institutional Animal Care and Use Committee at City of Hope. Adult C57BL/6J (B6) mice (The Jackson Laboratory, Bar Harbor, ME) were purchased and bred at City of Hope. Pancreases were procured from pooled 8-day-old mice ($$n = 5$$–10) of both sexes, placed into Petri dishes kept on ice which contained approximately 20 ml Phosphate Buffered Saline (PBS) solution supplemented with $0.1\%$ bovine serum albumin (BSA, Sigma-Aldrich, Milwaukee, WI) and 100 units/ml penicillin and 100 μg/ml streptomycin (PS) (Gibco, Grand Island, NY). The spleen and fat surrounding the pancreas were removed with tweezers under a dissecting microscope. The pancreases were washed twice in fresh PBS, and thoroughly minced using spring scissors. Cold PBS/BSA/PS was added, supplemented by 2 units of bovine pancreatic DNase I (EMD Millipore, Temecula, CA) and 2 mg Collagenase B (Sigma-Aldrich) per mL. The pancreases were then pipetted up and down several times to disrupt the tissue, and incubated in a water bath at 37°C for 15 min with additional pipetting every 5 min. The cell suspension was placed in 25 mL cold PBS/BSA/PS/DNase I, and centrifuged at 800 g for 5 min. The supernatant was removed, and the cells were resuspended in 1 ml of PBS/BSA/PS/DNase I. The cell suspension was filtered sequentially through a 100 and 40 μm mesh filter, and once again centrifuged. The cells were finally resuspended in approximately 500 μl of PBS/BSA/PS/DNase I, stained with $0.02\%$ Trypan Blue, and manually counted on a hemacytometer.
Cell plating. The culture medium consisted of either $5\%$ (v/v) Matrigel (Corning, Manassas, VA) + $1\%$ (w/v) methylcellulose (Wedeken et al., 2017) or a $2\%$ (w/v) solution of proteins PEP-FN or PEP-LAMA3. The liquid medium consisted of Dulbecco’s Modified Eagle Medium/Nutrient Mixture F-12 (DMEM/F12) containing L-glutamine and 15 mM HEPES (Corning), $10\%$ KnockOut Serum Replacement (KSR, ThermoFisher Scientific), 10 mM nicotinamide, and 25 ng/ml recombinant human epidermal growth factor (EGF). PEP-FN or PEP-LAMA3 was dissolved in DMEM/F12 at slightly above working concentration (approximately $4\%$–$5\%$), and allowed to dissolve overnight at 4°C prior to use. Dissociated pancreatic cells were added at a concentration of 10,000 cells (for PEP-FN and PEP-LAMA3) or 2,500 cells (for Matrigel-methylcellulose, MM) per 100 uL of medium per well in flat-bottomed 96-well uncoated plates (ThermoFisher, Waltham, MA). Sterile distilled water (150 μl) was added to the wells at the edge of the plate to prevent evaporation. Control MM conditions consisted of a $1\%$ (w/v) methylcellulose and $1\%$ (v/v) *Matrigel medium* in DMEM/F-12 with the same 2-factor growth medium. Cells were plated in triplicate wells, unless specified otherwise, and incubated at 37°C in a $5\%$ CO2 atmosphere in a stationary, humidified, mammalian cell incubator.
Colony counting and analyses. Pancreatic colonies were observed under an Olympus CKX31 phase-contrast microscope with a ×10 objective lens and counted using a manual differential counter. All colonies grown in Matrigel-methylcellulose media were analyzed on day 4 post-plating due to their rapid growth, whereas those grown in PEP-FN and PEP-LAMA3 media were analyzed on day 7.
Whole-mount immunostaining. Pancreatic cell samples were fixed overnight at 4°C in $4\%$ paraformaldehyde (PFA) in PBS, by directly adding the PFA into the culture wells. The samples were pooled, centrifuged at 400 g for 15 min to pellet, and the supernatant removed. The samples were washed in PBS, with three changes of wash buffer, and left on a rocking table overnight at 4°C. The PBS was removed, replaced with blocking buffer, and incubated overnight. Primary antibodies (Supplementary Table S2) were added and incubated overnight at 4°C, followed by three washes of PBS supplemented by $0.15\%$ Tween-20 (PBST), and a fourth overnight wash in PBST. Donkey secondary antibodies (Supplementary Table S2) were then added and incubated overnight at 4°C, followed by washing for three times, with the fourth wash for 3 days at 4°C in PBST with rocking and daily changes of wash buffer. The samples were stained with DAPI to visualize the nucleus prior to imaging using an AxioObserver Z1 microscope with an ApoTome attachment (Carl Zeiss Inc., Oberkochen, Germany) and a ×20 objective lens, with the averaging of three ApoTome images presented.
Micromanipulation of a single pancreatic colony and microfluidic qRT-PCR. Detailed procedures for micromanipulation and microfluidic qRT-PCR were previously described (Tremblay et al., 2016). Colonies of interest in the culture medium were photographed using an Olympus CKX41 optical microscope with a Luminera Infinity2 camera attachment. Individual pancreatic colonies were hand-picked using a pipette tip in a volume <3 uL. The colonies were placed in a mixture consisting of 5.0 μL of 2x reaction mix, 2.5 μl of Taqman probe mix, 0.2 μL of SuperScript III enzyme, and 1.3 μL TE buffer, following manufacturer instructions for the SuperScript III reverse transcription kit (Thermo-Fisher). The colonies were then subjected to amplification in a Veriti 96-well thermal cycler (Applied Biosystems, Foster City, CA) using the following cycles: 15 min at 55°C, then 22 cycles alternating between 95°C for 15 s and 65°C for 4 min, before being lowered to a temperature of 4°C. The pre-amplified samples were frozen at −20°C until further use. The thawed samples were loaded into a Fluidigm Biomark 48.48 IFC microfluidic chip (South San Francisco, CA) according to the manufacturer’s instructions. In brief, the samples were diluted to a volume of 45 μl using TE buffer. 2.7 μl of diluted sample was added to 3.3 μl of a master mix consisting of 3.0 μl universal PCR Master Mix and 0.3 μl 20xGE Sample Loading Reagent. Separately, 3 μl of individual primers were mixed with 3 μl 2xGE Assay Loading Reagent. 5 μl of sample, and 5 μl of primer, were loaded into the appropriate well on the Fluidigm chip. The primer list is provided in the Supplementary Table S3. The microfluidic chip was then run on a Biomark real-time PCR instrument, and the raw *Ct data* were obtained using the Fluidigm software. For data analysis, the qRT-PCR results were normalized against the housekeeping gene B2M to derive ΔCt, where ΔCt = Ct B2MG, and Ct represents the number of PCR cycles required to exceed a certain threshold. Expression level is then calculated as Expression = 2(−ΔCt)* 1,000. The resulting expression level had a constant of one added to it, and was subsequently converted into Log2. When examining the impact of protein hydrogels to cells in culture, Matrigel-methylcellulose medium (MM) was used as the baseline control, whereas colony morphologies used MM ring as the baseline control. Fold change for each individual colony was compared against the baseline using the formula: Log2FC = Log2(A)—Log2(B), or in Microsoft Excel format = log (A, 2)–log (B, 2). For this study, A indicates the expression of a single colony, and B indicates the median expression value of the baseline control. Because of the presence of outliers, the median was used to approximate baseline expression instead of the mean.
Statistical analysis. Statistical significance was determined by unpaired, two-tailed Student’s t-test with Welch’s correction between PEP-FN or PEP-LAMA3 versus Matrigel-methylcellulose, and budding ring, proto-ring, or grape-like versus MM Ring using GraphPad Prism version 9.5.0. p-values <0.05 were considered significant. Data are presented as individual datapoints, with a red bar indicating median. Sample group size (n) is indicated in each figure legend.
Additional materials and methods are described in the Supporting Information.
## Results
Design and expression of PEP-FN and PEP-LAMA3. We previously reported the creation of a recombinant triblock protein (PEP) capable of forming 3D hydrogels (Shen et al., 2006; Olsen et al., 2010). This protein consists of two leucine zippers (P) derived from rat cartilage oligomeric matrix protein (Efimov et al., 1996) separated by an elastin-like polypeptide (E) consisting of repeating units of the sequence VPGXG, where X can be any amino acid other than proline (Varanko et al., 2020). The P domains form pentameric bundles, leading to physical crosslinking of individual strands and formation of a gel. A characteristic of elastin-like polypeptides is lower critical solution temperature (LCST) behavior, which causes separation of protein-rich phases under conditions of high temperature and high salt, allowing enrichment of these proteins at scale by thermal cycling. Although we have examined carefully the mechanical behavior of hydrogels prepared from PEP and several variants (Shen et al., 2006; Olsen et al., 2010; Dooling and Tirrell, 2016), these proteins have not previously been functionalized with cell-binding motifs or tested in cell culture.
Both fibronectin (FN) and laminin (LAM), which engage integrin and other receptors on the cell surface, have been shown previously to support pancreatic islet cell survival (Jiang et al., 2002; Kaido et al., 2004; Pinkse et al., 2006; Hadavi et al., 2018; Llacua et al., 2018; Lan et al., 2020). Therefore, we inserted either a RGD-containing sequence derived from FN or a laminin alpha 3 (LAMA3) mimetic sequence between two elastin-like domains in PEP (Figure 1A); the resulting proteins are designated “PEP-FN” and “PEP-LAMA3”, respectively. An added 6x-histidine tag enabled further purification using a Ni-NTA column after the proteins were expressed in E. coli and enriched by thermal cycling. The resulting protein preparations yielded single bands in Coomassie blue stained SDS-PAGE gels (Figure 1B). The expected masses for PEP-FN and PEP-LAMA3 were 41,202 and 40,700 Da, respectively, based on their amino acid sequences (Supplementary Table S1); electrospray ionization time-of-flight (ESI-TOF) mass spectrometry confirmed the actual masses to be 41,176 and 40,666 Da, respectively, both within $0.1\%$ of the expected masses (Figures 1C,D).
**FIGURE 1:** *Construction and characterization of PEP-FN and PEP-LAMA3 hydrogels. (A) Schematic drawings of PEP-FN and PEP-LAMA3 proteins, which contain the following main elements: 1) leucine zipper (green) from rat cartilage oligomeric matrix protein which forms bundles (pentamers) in solution, leading to a physically cross-linked gel, 2) elastin-like polypeptides (yellow), and 3) the cell binding peptide (blue; either the fibronectin RGD-containing peptide, or laminin three alpha mimetic), which is at the center of the protein. (B) SDS-PAGE gel stained with Coomassie InstantBlue on cell lysate prior to purification (lanes 1 and 3) or after purification (lanes 2 and 4). (C, D) ESI-TOF of both proteins showing that the protein products are at the correct molecular weights (∼41 kDa) (E) Rheological characterization of Matrigel-methylcellulose, PEP-FN, and PEP-LAMA3 media, as used in experimental concentrations.*
Rheological characterization of PEP-FN and PEP-LAMA3 hydrogels. Physical properties such as matrix stiffness are known to change the differentiation behavior of cells (Engler et al., 2006; Murphy et al., 2014; Lv et al., 2015). We therefore characterized the rheological properties of PEP-FN and PEP-LAMA3 hydrogels (Figure 1E) and compared them to the properties of a medium containing $5\%$ (v/v) Matrigel and $1\%$ (w/v) methylcellulose (referred to as “Matrigel-methylcellulose [MM] medium”), which we previously established for pancreatic cell culture (Jin et al., 2013). We knew from our previous studies that MM medium keeps dissociated single cells from re-aggregating (Jin et al., 2013; Jin et al., 2014), but its rheological properties had not been determined. We found that MM medium displayed an elastic modulus (G’) of approximately 20 Pa across a range of frequencies from 0.1 to 100 rad/s. PEP-FN and PEP-LAMA3 hydrogels were slightly stiffer, with elastic moduli of approximately 80 and 120 Pa, respectively, when measured at a concentration of $2\%$ w/v. All three media allow convenient handling of cells in the procedures used in this work.
Protein hydrogels prevent cellular aggregation. To determine whether single cells plated in our protein hydrogels may re-aggregate, we placed dissociated pancreatic cells from young mice in PEP-FN, PEP-LAMA3 or in DMEM-F12 medium that did not contain a gel-forming agent such as PEP-FN, PEP-LAMA3, or Matrigel. Cells were monitored under a wide-field light microscope, and images taken every 10 min over 17 h. We found that in the suspension culture the cells readily aggregated (Supplementary Movie S1), whereas PEP-FN and PEP-LAMA3 hydrogels prevented cell aggregation or movement (Supplementary Movies S2, S3). This result implies that cell clusters or colonies (see below) formed from dissociated pancreatic cells would be the result of growth, rather than re-aggregation, of cells that were seeded.
Novel colony types are found in PEP-FN and PEP-LAMA3 hydrogels. In our previous studies (Jin et al., 2013), cystic colonies, which we name “MM Ring” here, were the most commonly-observed colonies when pancreatic cells were plated in MM medium. MM Ring colonies consist of large, bright cysts with cells that are not granular at day 4 post-plating. When cells were grown in PEP-FN or PEP-LAMA3, we observed several new types of colonies by phase-contrast microscopy after 7 days in culture (Figure 2A). The new morphologies were designated “grape-like”, “budding ring”, or “proto-ring”. Grape-like colonies are composed of small, barely touching cells. Budding rings are ring colonies that appear to have several cells attached to the ring, and either lack lumen formation, or have a “filled” lumen. Proto-rings are similar to budding rings, but lack external cell attachment. The proto-rings also appear to have internal granules, in contrast to MM Rings, which appear smooth. Distributions of each colony type grown in the three types of media were determined (Figure 2B). The raw data are presented in Supplementary Table S4. As expected, MM medium significantly favored the growth of MM Ring colonies. Interestingly, both PEP-FN and PEP-LAMA3 hydrogels supported the growth of other colony types.
**FIGURE 2:** *PEP-FN and PEP-LAMA3 hydrogels support the formation of novel types of colonies from pancreatic progenitor cells from 8-day-old mice. (A) Representative photomicrographs of various colony types. Cartoon rendition and short description of each type is also shown (B) The proportion of each colony type grown in different materials is shown. Supplementary Table S4 (i.e. “Data were derived from a total of three to six replicates from three experiments, see Supplementary Table S4.”*
Gene expression patterns in individual colonies are influenced by the materials in which they grow in culture. To investigate the influence of culture materials on the gene expression of cells within individual colonies, we performed micromanipulation by observing a colony under a light microscope, then using a micro-pipette with a narrow opening to lift individual colonies and subject them to microfluidic qRT-PCR analysis (Figure 3A), which allows for gene expression analysis from as little as 1 cell (Jin et al., 2013). Compared to those grown in the MM medium, colonies grown in both PEP-FN and PEP-LAMA3 expressed lower levels of ductal markers, Sox9 (Kopp et al., 2011), carbonic anhydrase-2 (Ca-II), and cytokeratin-7 (Krt7) (Inada et al., 2006; Pujal et al., 2009). Expression of other ductal markers cytokeratin-19 (Krt19) (Qadir et al., 2020) and Mucin1 (Jonckheere et al., 2010) were also lower in PEP-FN colonies compared to colonies from MM medium (Figure 3B). The endocrine maturation marker, urocortin-3 (Ucn3) (Pechhold et al., 2009), and endocrine progenitor cell marker, neurogenin-3 (Ngn3) (Gradwohl et al., 2000; Gu et al., 2002), were significantly upregulated by PEP-FN and PEP-LAMA3. Other endocrine markers, neurogenic differentiation 1 (NeuroD1), insulin-1 (Ins1), and glucagon (Gcg) trended higher in colonies supported by PEP-LAMA3 compared to MM, but the difference did not reach significance (Figure 3B). Acinar lineage markers, Amylase 2a and Elastase, were not changed (Figure 3B). The proliferation marker Ki67 was downregulated in colonies from both PEP-FN and PEP-LAMA3, compared to MM medium (Figure 3B). The lowered expression of Ki67 is consistent with prior findings that indicate inhibition of proliferation is required for lineage differentiation of progenitor cells during pancreas development (Hart et al., 2003; Murtaugh et al., 2003; Miyatsuka et al., 2011; Shih et al., 2012). The apoptosis marker Puma (Yu and Zhang, 2008) was also not changed in colonies grown in PEP-FN and PEP-LAMA3 compared to MM medium (Figure 3B), suggesting that PEP-FN and PEP-LAMA3 are not toxic to the cells. Together, these results suggest that both PEP-FN and PEP-LAMA3 cultures promote the growth of endocrine lineage cells, whereas MM medium promotes the growth of ductal cells.
**FIGURE 3:** *PEP-FN and PEP-LAMA3, compared to Matrigel-methylcellulose medium, support pancreatic cells that express higher levels of endocrine and endocrine progenitor cell markers, and lower levels of ductal cell markers. (A) Experimental scheme. Pancreases from 8-day-old mice were dissociated into a single cell suspension and plated into colony assays containing Matrigel-methylcellulose (MM), PEP-FN or PEP-LAMA3. The resulting colonies were visualized under a light microscope and individually lifted one-by-one. mRNA from each colony was converted into cDNA in a single-step RT-PCR reaction. Pre-amplified cDNA was run in a microfluidic PCR chip, where multiple genes expressed by a colony were detected simultaneously (B) Gene expression analysis using microfluidic qRT-PCR on individual colonies grown in different cultures. Data were first analyzed relative to the internal control housekeeping gene, B2M. Subsequently, the data were transformed into Log2 scale and normalized to the median of the expression levels from colonies grown in the control MM medium. Student’s t-test with Welch’s correction was used to determine the significance of the values between PEP-FN vs. MM and PEP-LAMA3 vs. MM media. Data were derived from a total of 8–12 colonies grown in various media. Each dot represents a colony and the red line represents the median. Red asterisks indicate statistical significance when compared against the control MM culture: *p < 0.05, **p < 0.005, ***p < 0.005, and ****p < 0.0005.*
qRT-PCR reveals that grape-like, budding ring, and proto-ring colonies are enriched for endocrine lineage markers. Because PEP-FN and PEP-LAMA3 supported higher proportions of grape-like, budding ring, and proto-ring colonies (Figure 2B), and PEP-FN and PEP-LAMA3 supported cells with higher endocrine gene expression (Figure 3), we predicted that grape-like, budding ring, and proto-ring colonies have higher expression levels of endocrine lineage markers. To test this prediction, we separated the colonies analyzed in Figure 3 based on their morphologies and reanalyzed the data (Figure 4A). MM Ring colonies were used as a comparison because this morphology has been well characterized in our previous studies using MM medium (Wedeken et al., 2017). We found that the ductal markers Ca-II and Krt7 were significantly downregulated in budding ring, proto-ring, and grape-like colonies relative to MM Ring colonies. The ductal markers Mucin1, Krt19, and Sox9 were also downregulated in proto-ring colonies compared to MM Ring colonies (Figure 4B). In contrast, the endocrine progenitor marker Ngn3 and endocrine maturation marker Ucn3 were upregulated in budding ring, proto-ring, and grape-like colonies, compared to MM Ring colonies. Interestingly, Ins1 was upregulated in proto-ring colonies, compared to MM Ring colonies (Figure 4B). Compared to MM ring colonies, the proliferation marker Ki67 was downregulated in budding ring and grape-like, but not proto-ring, colonies (Figure 4B). There was no statistically significant difference observed in the expression of the apoptosis marker Puma (Figure 4B). Together, these results suggest that grape-like, budding ring, and proto-ring, colonies are more endocrine in nature, and grape-like and budding ring colonies are post-mitotic compared to MM Ring colonies, which are more duct-like and proliferative.
**FIGURE 4:** *Pancreatic colonies with morphology of “budding ring”, “proto-ring” or “grape-like” express higher levels of endocrine and endocrine progenitor cell markers and lower levels of ductal cell markers (A) Experimental scheme. The procedures were the same as explained in Figure 3A, except that the morphology of each colony was also identified and recorded when visualized under a light microscope before performing pre-amplification and microfluidic PCR (B) Gene expression analysis using microfluidic qRT-PCR on individual colonies with various morphologies. Data were first analyzed relative to the internal control housekeeping gene, B2M. Subsequently, the data were transformed into Log2 scale and normalized to the median expression of the colonies with the MM ring morphology. Student’s t-test with Welch’s correction was used to determine the significance of the values between various morphologies vs. MM ring. Data were derived from a total of six to nine colonies of each morphology. Each dot represents a colony and red line represents the median. Red asterisks indicate statistical significance when compared against the control MM ring morphology: *p < 0.05, **p < 0.005, ***p < 0.005, and ****p < 0.0005.*
Colonies grown in PEP-LAMA3 express neurogenin3 and chromogranin A proteins. During development, Ngn3 is expressed transiently in endocrine progenitor cells, with peak expression at embryonic day 15.5 (Gradwohl et al., 2000; Gu et al., 2002; Van de Casteele et al., 2013). A lower level of Ngn3 expression persists into adulthood. We were particularly interested in the presence of Ngn3 in colonies supported by our protein hydrogels indicated by the qRT-PCR (Figure 3). Ngn3-positive cells have previously been difficult to culture, and having a method to obtain such cells may aid in future studies that aim to understand pancreatic endocrine differentiation and maturation. Due to the more significant effect of PEP-LAMA3 compared to PEP-FN on endocrine lineage marker expression (Figure 3B), we focused our attention on colonies grown using PEP-LAMA3. We performed whole-mount immunofluorescence (IF) staining on pooled colonies using antibodies against Ngn3 and observed that some cells from PEP-LAMA3 stained positive for Ngn3 (Figure 5A). No Ngn3 staining was observed in other cells of the same batch, which were handled and washed in the same way (Figure 5B), suggesting that the red/Ngn3 signal observed in Figure 5A is specific.
**FIGURE 5:** *Whole-mount immunofluorescence staining reveals that some cells grown in PEP-LAMA3 express Ngn3, an endocrine progenitor marker. (A) Photomicrographs of cells in a colony grown from PEP-LAMA3 expressing the endocrine progenitor marker Ngn3 (B) Photomicrographs of other cells grown from PEP-LAMA3 that were handled as in (A) but did not express Ngn3. The positive control marker, EpCAM, was used to identify pancreatic epithelial cells. DAPI nuclear staining was overlaid with the brightfield images of the colonies. Scale bars = 50 µm.*
Pancreatic lineage cells are derived from the definitive endoderm during development and express epithelial cell markers (McCracken and Wells, 2012). To test whether the cells that did not express Ngn3 in Figure 5B were of epithelial cell origin, we co-stained EpCAM, an epithelial cell marker (Trzpis et al., 2007; Keller et al., 2019). Pancreatic cells grown from the PEP-LAMA3 medium expressed EpCAM (Figures 5A,B), suggesting retention of epithelial cell identity.
Finally, we tested the protein expression of a pan-endocrine marker, chromogranin A (ChromA) (Facer et al., 1985; Deftos, 1991; Portela–Gomes and Stridsberg, 2001; Portela-Gomes et al., 2008), which we also co-stained with EpCAM. As expected, EpCAM was expressed by MM ring colony from MM medium (Figure 6A) and grape-like colony from PEP-LAMA3 (Figure 6B). However, ChromA was only detected in grape-like but not in the MM ring colony. These results confirmed the presence of endocrine-like cells grown in PEP-LAMA3 hydrogel.
**FIGURE 6:** *Whole-mount immunofluorescence staining reveals that some cells grown in PEP-LAMA3 express chromogranin A, a pan-endocrine lineage cell marker. (A) Photomicrographs of a MM ring colony stained negative for ChromA, a pan-endocrine marker, but positive for the epithelial cell marker, EpCAM (B) Photomicrographs of a grape-like colony stained positive for both ChromA and EpCAM. DAPI nuclear staining was overlaid with the brightfield images of the colonies. Scale bars = 50 µm.*
## Discussion
In this study, we demonstrate the utility of two novel, well-defined, protein-based hydrogels (i.e., PEP-FN and PEP-LAMA3) in supporting the survival and growth of primary endocrine cells and endocrine progenitor cells in 3D space and in the absence of Matrigel. Although 3D pancreatic organoids have been described using various complex materials, including high concentrations of Matrigel (Huch et al., 2013), decellularized small-intestinal tissues (Giobbe et al., 2019), and decellularized pancreatic tissues (Sackett et al., 2018), our results are consistent with other emerging studies in the stem and progenitor cell field that show that it is possible to grow various progenitor cell types in 3D culture without Matrigel. In particular, synthetic and protein hydrogels can be tuned for their chemical and mechanical properties so that the microenvironment surrounding the cells can be better controlled, manipulated and studied (Kozlowski et al., 2021).
The triblock PEP protein, which may be regarded as the parent of the PEP-FN and PEP-LAMA3 protein hydrogels reported here, has been characterized previously in terms of its mechanical properties and strand exchange dynamics (Olsen et al., 2010; Dooling and Tirrell, 2016; Rapp et al., 2017; Rapp et al., 2018). The material properties of PEP can be modified by introduction of point mutations in the terminal leucine zippers: Dooling and co-workers compared six single-site variants in the P domain, and found I58A to accelerate stress relaxation (Dooling and Tirrell, 2016). In this study, we chose the I58A mutant to match the stiffness of the Matrigel-methylcellulose medium as closely as possible. We found that $2\%$ solutions of PEP-I58A functionalized with FN or LAMA3 domains form semisolid matrices with elastic moduli in the range of 80–120 Pa, comparable to the ideal modulus of approximately 100–300 Pa for human forebrain (Bejoy et al., 2018) and human and mouse intestine (DiMarco et al., 2015; Gjorevski et al., 2016; Cruz-Acuña and Quirós, 2017), and to that of the Matrigel-methylcellulose medium, which is known to support the growth of primary murine pancreatic ductal progenitor cells (Wedeken et al., 2017). Interestingly, the moduli of the materials used here are roughly an order of magnitude lower than that of healthy human pancreas, which is approximately 1,000 Pa (Rubiano et al., 2018). Disease states of the pancreas are associated with stiffer tissues, which can become as high as 5 kPa in tumors (Rubiano et al., 2018). However, these measurements were made on the whole human pancreas, not just the islets of Langerhans or progenitor cells. It may be the case that the islets or the progenitor cell niche and their surrounding ECM are in fact softer than the pancreas as a whole. Future experiments using a gradient of elastic moduli will likely find that cell fate is dependent on matrix stiffness, and proteins of the PEP type with different point mutations in the leucine zipper may be instrumental in understanding these effects.
Human pluripotent stem cell derived pancreatic progenitor (PP) cells and beta like cells (SC-β cells) (Melton, 2021) as well as adult human and rodent islets have been used to model endocrine differentiation and maturation in vitro (Jiang et al., 2022). Although human NGN3+ endocrine progenitor cells can be differentiated from stem cells without exogenous ECM proteins, the subsequently differentiated beta-like cells are still immature in functionality compared to adult human beta cells (Hrvatin et al., 2014; Maxwell et al., 2022). In fact, gene expression profiling on SC-β cells have revealed a gene signature that resembles human fetal beta cells (Hrvatin et al., 2014), which are immature. On the other hand, studies that use primary adult islet cells cannot address the question of functional maturity because these adult cells are already terminally differentiated and matured. The maturation process of beta cells is not initiated until after birth (Blum et al., 2012), when postnatal development is marked by changes in diet, nutrient metabolism and the hormonal milieu (Wortham and Sander, 2021). Accordingly, murine beta cells become functionally mature between day 1 and day 15 after birth (Blum et al., 2012). Therefore, in this work we chose murine postnatal day 8 pancreatic cells to identify biomaterials capable of supporting the survival of the primary Ngn3-expressing endocrine progenitor cells and endocrine cells in a 3D culture platform.
We find that murine postnatal pancreatic cells arising from culture in both PEP-FN and PEP-LAMA3 hydrogels are endocrine in nature, in contrast to cells grown in Matrigel, which favors the ductal cell type. Previously, we functionalized an elastin-like polypeptide (ELP) with an IKVAV-containing sequence derived from laminin alpha 1, which we designated artificial (a)ECM-lam. Because aECM-lam alone did not form a hydrogel, we mixed it at a final concentration of 100 ug/mL in $1\%$ (w/v) methylcellulose for cell culture. Using this culture system, we showed that aECM-lam, but not the control ELP fitted with a scrambled IKVAV sequence, favored the formation of not only endocrine but also acinar cells from pancreatic progenitor cells (Jin et al., 2013; Ghazalli et al., 2015). This result, together with those reported here, highlights the importance of cell-binding domains in cell fate determination, as demonstrated in other progenitor cell types (Leipzig et al., 2011; Brown and Badylak, 2014; Jann et al., 2020). However, whether a PEP functionalized with the laminin alpha one IKVAV sequence may also support the dual fates of endocrine and acinar cells awaits further investigation.
There are several limitations with our current studies. First, we determined that the presence of FN and LAMA3 motifs were sufficient for formation of the endocrine colonies, but we did not show that they were necessary, by using a PEP construct without the presence of a signaling sequence or scrambled sequences. A prior study has shown that, without functionalization, a PEG-based gel crosslinked with amikacin, known as Amikagel, can support stem cell derived PP cells reaggregation and spontaneous differentiation (Candiello et al., 2018). Second, the lessons we have derived from this study are limited to young mice: we did not test using either murine embryonic, or human stem cells. Finally, we did not study mechanical effects on differentiation. Considering that the COMPcc coiled-coil’s binding affinity is tunable (Dooling and Tirrell, 2016; Rapp et al., 2017), the mechanical properties of both PEP constructs could be altered without changing the chemical properties of the culture medium. Considering recent advances connecting stem cell differentiation to changes in mechanical properties (Han et al., 2020; Hayward et al., 2021; Zhang et al., 2022), this may be a fruitful area of further research.
In summary, our study provides a proof-of-concept cell culture matrix for endocrine cell differentiation in young mice without Matrigel. This work sets the stage/enables the further development of hydrogel matrices for other cell models with tunable physical properties for optimization. We have shown that primary endocrine and endocrine progenitor cells isolated from day-8 murine pancreas can be grown in PEP-FN and PEP-LAMA3 recombinant protein hydrogels in 3D space as colonies. Our well-defined protein hydrogel system will be an ideal platform to further define signaling required for maturation of pancreatic beta cells needed for replacement therapy of T1D.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.
## Ethics statement
The animal study was reviewed and approved by City of Hope IACUC (protocol #11017).
## Author contributions
Study Design: MK, HK, DT. Data Collection: MK, H-PS. Key Reagents: DC, CJ, LC, DT. Data Analysis: MK, HZ, HK. Drafting Paper: MK, HZ, HK.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fbioe.2023.1144209/full#supplementary-material
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|
---
title: Bivariate quantitative Bayesian LASSO for detecting association of rare haplotypes
with two correlated continuous phenotypes
authors:
- Ibrahim Hossain Sajal
- Swati Biswas
journal: Frontiers in Genetics
year: 2023
pmcid: PMC10033866
doi: 10.3389/fgene.2023.1104727
license: CC BY 4.0
---
# Bivariate quantitative Bayesian LASSO for detecting association of rare haplotypes with two correlated continuous phenotypes
## Abstract
*In* genetic association studies, the multivariate analysis of correlated phenotypes offers statistical and biological advantages compared to analyzing one phenotype at a time. The joint analysis utilizes additional information contained in the correlation and avoids multiple testing. It also provides an opportunity to investigate and understand shared genetic mechanisms of multiple phenotypes. Bivariate logistic Bayesian LASSO (LBL) was proposed earlier to detect rare haplotypes associated with two binary phenotypes or one binary and one continuous phenotype jointly. There is currently no haplotype association test available that can handle multiple continuous phenotypes. In this study, by employing the framework of bivariate LBL, we propose bivariate quantitative Bayesian LASSO (QBL) to detect rare haplotypes associated with two continuous phenotypes. Bivariate QBL removes unassociated haplotypes by regularizing the regression coefficients and utilizing a latent variable to model correlation between two phenotypes. We carry out extensive simulations to investigate the performance of bivariate QBL and compare it with that of a standard (univariate) haplotype association test, Haplo.score (applied twice to two phenotypes individually). Bivariate QBL performs better than Haplo.score in all simulations with varying degrees of power gain. We analyze Genetic Analysis Workshop 19 exome sequencing data on systolic and diastolic blood pressures and detect several rare haplotypes associated with the two phenotypes.
## 1 Introduction
Information on multiple phenotypes is often collected in health-related studies to obtain a bigger picture of patients’ health conditions (Teixeira-Pinto and Normand, 2009). Studies have found variants at numerous genetic loci to be associated with these phenotypes (Solovieff et al., 2013). Sometimes, a genetic variant is associated with more than one phenotype, a phenomenon known as pleiotropy. Recent studies have confirmed the widespread presence of pleiotropy in the human genome, thus showing the underlying common genetic mechanisms of numerous traits (Solovieff et al., 2013; Gratten and Visscher, 2016; Buniello et al., 2019; Watanabe et al., 2019). Investigating and understanding pleiotropy can uncover additional associations, redefine disease classification, and expand our understanding of the genetic basis of complex diseases with wide-ranging implications for healthcare (Hackinger and Zeggini, 2017; Lee et al., 2019; Lee et al., 2021).
The most common way of the testing trait–variant association is to consider one phenotypic trait at a time and test its association with genotypic variants under study. However, such a univariate statistical approach ignores valuable additional information contained in the joint distribution of the phenotypes. Even more importantly, such an approach amounts to a lost opportunity to investigate potential pleiotropy and shared genetic mechanisms. It may also result in a loss of power, especially with multiplicity adjustment, for performing multiple univariate tests. Therefore, considering a multivariate framework to model the phenotypes jointly is appealing from both biological and statistical perspectives.
Several methods have been proposed that utilize a multivariate framework to jointly model multiple correlated phenotypes, including some recent gene-based approaches (Klei et al., 2008; O’Reilly et al., 2012; Van der Sluis et al., 2015; Ray et al., 2016; Hackinger and Zeggini, 2017; Kaakinen et al., 2017; Lee et al., 2017; Ray and Basu, 2017; Deng et al., 2020). However, most of these studies consider single-nucleotide polymorphisms (SNPs) or variants (SNVs) as a genetic unit obtained from genome-wide association studies (GWAS) or next-generation sequencing (NGS) studies. Thus, when rare variants are of interest, one has to rely on SNVs obtained from NGS as rare SNPs are not usually genotyped in GWAS. Yet, most NGS data lack the adequate sample size required for multivariate analysis of correlated phenotypes. Hence, an alternative approach to multiple trait–rare variant association tests that does not necessarily rely on NGS data is warranted.
Haplotype-based tests are powerful alternatives to SNP-based genetic association tests (Bader, 2001; Wang and Lin, 2015). Haplotypes are more biologically meaningful genetic variants as compared to SNPs, which are not inherited independently. Moreover, common SNPs can make up a rare haplotype in a haplotype block, providing avenues to investigate the common disease rare variant (CDRV) hypothesis. Thus, rare variants can also be investigated using GWAS data through haplotype-based tests, allowing the use of data from much larger sample sizes than those of NGS. Several tests have been proposed to investigate the CDRV hypothesis through haplotype-based tests (Guo and Lin, 2009; Li et al., 2010; Li et al., 2011; Biswas and Lin, 2012; Lin et al., 2013), among which logistic Bayesian LASSO (LBL) is a well-studied and powerful method (Biswas and Lin, 2012; Biswas and Papachristou, 2014; Datta and Biswas, 2016; Papachristou and Biswas, 2020). LBL was extended to incorporate gene–environment interactions (Zhang et al., 2017a; Zhang et al., 2017b; Papachristou and Biswas, 2020), data generated using complex sampling designs (Zhang et al., 2017a), and family data (Wang and Lin, 2014; Datta et al., 2018). LBL was also adapted to accommodate two phenotypes, namely, bivariate LBL-2B for binary phenotypes and bivariate LBL-BC for binary and continuous phenotypes (Yuan and Biswas, 2019; Yuan and Biswas, 2021). LBL and its extensions utilize regularization to decrease the unassociated effects close to zero, which, in turn, helps the effect of an associated haplotype, especially if it is a rare one, to stand out. Bivariate LBL-2B and LBL-BC model the dependency between two phenotypes via a latent variable. Notably, there is another haplotype-based bivariate genetic association test for correlated quantitative traits; it uses the haplotype trend regression approach (Pei et al., 2009). However, it is only applicable for testing associations with common haplotypes and hence cannot be used for the CDRV hypothesis.
There is no haplotype-based association test currently available that can detect rare haplotypes associated with multiple quantitative phenotypes jointly. To fill this gap, we propose a new method, bivariate quantitative Bayesian LASSO (QBL) to jointly model two correlated continuous phenotypes. We borrow the well-studied framework of bivariate LBL and make appropriate modifications to accommodate quantitative traits. The properties of bivariate QBL are investigated using extensive simulations under various association scenarios, sample sizes, and the number of haplotypes. We also compare its performance to a standard univariate haplotype-based association test, Haplo.score (Schaid et al., 2002). Finally, we apply our proposed method to exome sequencing data from Genetic Analysis Workshop (GAW) 19. We analyze haplotype blocks in several genes of interest (as per literature) and detect rare haplotypes associated with systolic and diastolic blood pressures (SBP and DBP) jointly.
## 2.1 Likelihood formulation
We closely follow the framework of bivariate LBL-2B and LBL-BC and accordingly the notations used therein. Consider a sample of n subjects with two continuous correlated (standardized) phenotypes denoted by Yic and Yic′. Let Yc=Y1c,Y2c,…,Ync, Yc′=Y1c′,Y2c′,…,Ync′, and G=G1,G2,…,Gn, where Gi represents the ith individual’s observed genotype on the SNPs, making up the haplotype block under study. Furthermore, let SGi be the set of haplotype pairs compatible with Gi as the haplotype pair for an individual may not be unambiguously determined from the genotype data; Zir denotes the rth element of SGi. We introduce a latent variable ui to model the marginal dependence between Yic and Yic′. Let ui ∼ N0,σu2 for all i and u=u1,u2,…,un. We assume that although Yic and Yic′ are marginally dependent, they are conditionally independent, given ui. In other words, the latent variable induces conditional independence between the two correlated outcomes. We also assume that *Zir is* independent of ui. The likelihood can be written as Lψ=∏$i = 1$n∑ZirϵSGiPYic,Yic′,Zir,ui ∝∏$i = 1$n∑ZirϵSGiPYic,Yic′|Zir,uiPZir,ui ∝∏$i = 1$n∑ZirϵSGiPYic|Zir,uiPYic′|Zir,uiPZirPui, where ψ is the vector of model parameters, which includes regression coefficients, variance parameters, and parameters associated with haplotype frequencies (to be introduced soon). Notably, bivariate QBL does not require specification of the haplotype pair for an individual (which is typically unknown due to phase ambiguity); rather, it averages over all compatible haplotype pairs for a person to incorporate uncertainty in haplotype pair estimation. Suppose there are m possible haplotypes in the haplotype block and population under study. In the following, we model the probabilities in the aforementioned likelihood in terms of the model parameters (the subscripts i and r are suppressed for simplicity).
## 2.1.1 Modeling of
PYc|Z,u
and
PYc′|Z,u
A haplotype pair Z consists of two haplotypes denoted as zk/zk′ k,k′=1,2,…,m. Let Xz=1,x1,x2,…,xm−1 be a (row) design vector with xk equal to the number of times zk appears in the haplotype pair Z; $k = 1$,…,m−1, i.e., zk=0,1, or 2. The mth haplotype is assumed to be the baseline without loss of generality. Let βc and βc′ be the vectors of regression coefficients (including the intercept), i.e., they include the effects of haplotypes on phenotypes Yc and Yc′, respectively. The slope coefficients have the same interpretation as in a usual linear regression model, i.e., the expected change in the quantitative trait if a person carries a copy of a specific haplotype as opposed to the baseline haplotype. As Yc and Yc′ are two continuous phenotypes and u is the latent variable that induces a correlation between them, we use the following linear models: Yc=Xzβc+u+ϵc and Yc′=Xzβc′+u+ϵc′, where ϵc ∼ N0,σc2 and ϵc′∼ N0,σc′2. We assume ϵc,ϵc′, and u to be uncorrelated with each other. The marginal correlation coefficient between Yc and Yc′ can be shown to be equal to σu2σu2+σc2σu2+σc′2 and, thus, must be non-negative. If the two traits are negatively correlated, then the values for one of them should be multiplied by −1 before applying this method.
## 2.1.2 Modeling
PZ
We model PZ in terms of two sets of parameters: f=f1,f2,…,fm, denoting the frequencies of m haplotypes in the population, and d, the within-population inbreeding coefficient (Weir, 1996).
For a given haplotype pair Z=zk/zk′ P(Z)=PZ=zk/zk′f,d)=δkk′dfk+2−δkk′1−dfkfk′ where δkk′=10 if zk=zk′zk≠zk′ and d∈−1,1 capture the excess/reduction of homozygosity. The aforementioned expression of PZ reduces to the assumption of Hardy–*Weinberg equilibrium* (HWE) when $d = 0$, while other values of d allow for the Hardy–Weinberg disequilibrium.
## 2.2 Prior distributions
There are many choices of shrinkage priors to regularize the regression coefficients, such as LASSO, ridge, Student’s t-test, horseshoe, and spike and slab. However, their performances are rather similar when the number of predictors (haplotypes) is smaller than the sample size, as is the case in this study (Van Erp et al., 2019). We choose Bayesian LASSO to regularize the regression coefficients for its ease of implementation, following previous LBL versions. Specifically, the prior for each slope parameter in βc and βc′ is assigned a double exponential distribution with mean 0 and variance 2λc2 and 2λc′2, respectively. We use standard normal priors for the intercepts β0c and β0c′. The amounts of penalty for the slope coefficients are controlled by the hyper-parameters λc and λc′. We let them follow gamma (a, b) distribution with a = $b = 20$, following the original LBL method and its extensions (Biswas and Lin, 2012; Yuan and Biswas, 2019; Yuan and Biswas, 2021).
The prior for the frequency vector f is set to be non-informative Dirichlet (1, …, 1) consisting of m values. We consider a uniform prior for d. However, given that PZ, as shown in Section 2.1.2, must always be non-negative, d and f are not independent. In particular, d must be greater than −fk1−fk for all k values. Thus, the prior for d, given f, is set to be Uniformmaxk−fk1−fk,1. We use a weakly informative half-Cauchy prior for σu with a fixed hyper-parameter A given by πσu∝1+σuA2−1, where σu>0, and set $A = 10$ (Yuan and Biswas, 2019; Yuan and Biswas, 2021). A non-informative uniform prior is used for σc2 and σc′2, whose probability density function is given by pσ2∝σ−1, where σ2>0.
## 2.3 Posterior distributions
The joint posterior distribution of all parameters can be obtained by combining the likelihood and prior distributions as follows: πβc,βc′,λb,λc,f,d,σu,σc2,σc′2,Z|Yc,Yc′,G,u∝LΨ πβc|λc πβc′|λc′ πλc πλc′ πd|f πf πσu πσc2 πσc′2 where Z consists of all possible haplotype pairs for all n subjects. We use Markov chain Monte Carlo (MCMC) methods to estimate the posterior distributions of all parameters. Details of the MCMC algorithm can be found in Supplementary Appendix A1. Notably, we update the latent variable u at every MCMC iteration, and thus, obtain its posterior distribution.
## 2.4 Association testing
We use the posterior distributions of regression coefficients for testing the association of haplotypes with the two phenotypes jointly. In particular, to test the association of the jth haplotype with the two continuous phenotypes jointly, the hypotheses are H0∶βjc≤ϵ andβjc′≤ϵ vs Ha∶βjc >ϵ or βjc′ >ϵ where we set ϵ to be 0.1 (Biswas and Lin, 2012; Yuan and Biswas, 2019; Yuan and Biswas, 2021). Notably, the alternate hypothesis corresponds to the association with at least one phenotype.
To carry out this test, we calculated the Bayes factor (BF), which is the ratio of the posterior odds to the prior odds in favor of the alternative hypothesis. The prior odds can be found in Supplementary Appendix A2.
The posterior odds are obtained from the estimated posterior distributions. Once the BF for each haplotype in a block is obtained, their maximum BF is recorded. If this maximum BF exceeds a certain threshold, we conclude that the haplotype block is associated with at least one of the two phenotypes. We calculated the appropriate threshold following Yuan and Biswas [2019] and Yuan and Biswas [2021]—to be described in detail in the Simulation study and Application sections.
We compare the performance of bivariate QBL with a standard haplotype association test, Haplo.score (Schaid et al., 2002). We use the R package Haplo.stats to apply Haplo.score twice to the two continuous phenotypes individually (Sinnwell and Schaid, 2022).
## 3.1 Data generation
*We* generate data under two haplotype settings and five association scenarios to examine the properties of bivariate QBL and compare with Haplo.score. The two haplotype settings consist of 6 and 12 haplotypes (in a haplotype block under this study), as shown in Table 1. Following the simulation studies conducted previously for investigating univariate and bivariate LBL methods, we formed each haplotype by combining five SNPs (to allow easy comparison across various LBL versions). However, we note that, in principle, bivariate QBL can handle haplotype blocks with a larger number of SNPs at the expense of an increased computational burden (this issue is discussed in the Discussion section). Under each setting, the causal haplotype is 11011, a rare haplotype of frequency $1\%$. This target haplotype can be associated with one or both phenotype(s) and its effect(s), i.e., the corresponding β coefficient(s) can be positive (risk) or negative (protective). This leads to five association scenarios in total with the non-zero β values (for 11011) chosen to ensure that the power of the proposed method or Haplo.score at type I error rates of $0.5\%$–$10\%$ is in a reasonable range. We assume other haplotypes in the block to be null or non-associated, i.e., their β coefficients are equal to 0.
**TABLE 1**
| Setting | Hap | Freq | Scenario 1 | Scenario 1.1 | Scenario 2 | Scenario 2.1 | Scenario 3 | Scenario 3.1 | Scenario 4 | Scenario 4.1 | Scenario 5 | Scenario 5.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Setting | Hap | Freq | βc | βc′ | βc | βc′ | βc | βc′ | βc | βc′ | βc | βc′ |
| 1 | 01100 | 0.300 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 10100 | 0.005 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 11011 | 0.010 | 1 | 1 | −1 | −1 | 1 | −1 | 1 | 0 | −1 | 0 |
| 1 | 11100 | 0.155 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 11111 | 0.110 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 10011 | 0.420 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 00111 | 0.070 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 01000 | 0.020 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 01011 | 0.050 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 01101 | 0.060 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 01110 | 0.140 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 10010 | 0.080 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 10100 | 0.005 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 11011 | 0.010 | 1 | 1 | −1 | −1 | 1 | −1 | 1 | 0 | −1 | 0 |
| 2 | 11101 | 0.090 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 11110 | 0.130 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 11111 | 0.100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 10001 | 0.245 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
*To* generate a haplotype pair for a subject, we use the haplotype frequencies, as shown in Table 1. Using those frequencies and assuming HWE, the probabilities of all possible haplotype pairs can be calculated. Based on those probabilities, we randomly generate one haplotype pair, say Z, for each subject in the sample, which corresponds to a design row vector XZ. After assigning haplotype pairs to all subjects, we generate two continuous phenotypes for each subject using the following bivariate normal (BVN) distribution. YcYc′∼ BVN(XzβcXzβc′),(σc2ρσcσc′ρσcσc′σc′2), where βc and βc′ (excluding intercepts β0c and β0c′) are as shown in Table 1; σc=σc′=1 and ρ are varied to be 0.1,0.5, or 0.9. We set β0c=β0c′=25.
*We* generate samples of sizes 500 and 1,000. For each sample size and simulation setup, resulting from a combination of a haplotype setting, a non-null association scenario, and a fixed ρ -value, 500 samples are generated. We also generate the corresponding null scenarios, i.e., for each combination of sample size, haplotype setting, and ρ -value, all β s are set to be equal to 0 and 1,000 samples are generated. To each sample, we apply bivariate QBL to both phenotypes jointly. The MCMC is run for a total of 3,00,000 iterations with 50,000 burn-in to achieve acceptable convergence (Gelman et al., 2003). To declare significance, we use appropriate cutoffs to the resulting BFs. The determination of the cutoffs for both bivariate QBL and Haplo.score is discussed in the following sub-section.
## 3.2 Calculation of cutoffs
The cutoffs for bivariate QBL are calculated in the following way. For each sample, we obtain one BF value per haplotype. We record the maximum of those BFs. Thus, we obtain 1,000 maximum BF values from the 1,000 null scenario replicates. We sort these 1,000 values in a descending order and obtain the cutoff for a specific type I error rate to be the corresponding percentile. It is to be noted that by taking the maximum overall BF values from a haplotype block, we adjust for multiple testing within that block.
We calculate cutoffs for Haplo.score in a slightly different way because it is applied to each phenotype. For each sample, we obtain two (global) p-values from two Haplo.score analyses. Then, we record the minimum of these two p-values. Similar to bivariate QBL, we obtain 1,000 minimum p-values from the 1,000 null samples. We sort them in an ascending order and obtain the cutoff of Haplo.score for a specific type I error rate by taking the relevant bottom percentile.
Once the cutoffs are obtained in the aforementioned manner, we use these cutoffs to calculate power for the corresponding non-null setups described previously. The type I error rates and power obtained by varying the cutoffs for a p-value (for Haplo.score) and BF (bivariate QBL) are then plotted against each other to obtain receiver operating characteristic (ROC)-type curves. For Haplo.score, the power is shown for detecting associations with at least one of the two phenotypes, as well as with each phenotype separately (in scenarios 1–3, where the target haplotype is associated with both phenotypes).
## 3.3 Results
The results for settings 1 (six haplotypes) and 2 (12 haplotypes), sample sizes 500 and 1,000, and correlation coefficients 0.1, 0.5, and 0.9 are shown in Figures 1–12. Notably, bivariate QBL outperforms Haplo.score in all figures even though the margin of difference varies depending on the combination of association scenarios and ρ -values. Bivariate QBL shows the best performance in scenario 3, where the effect sizes of the target haplotype are in opposite directions (one β positive and another β negative). In this scenario, the power of bivariate QBL exceeds Haplo.score by a substantial margin. This margin increases in favor of QBL as the correlation coefficient increases. Bivariate QBL also maintains this superior performance in scenarios 4 and 5, where the target haplotype is unrelated to one phenotype but has a positive (scenario 4) or negative (scenario 5) association with the other phenotypes. Again, the power gain margin of bivariate QBL increases as the correlation between the two phenotypes increases. This outperformance trend can be seen in all combinations of haplotype settings and sample sizes considered in this study.
**FIGURE 1:** *Simulation results under sample size 500, setting 1 (six haplotypes), and ρ = 0.1. Scenarios are shown in Table 1. HS, Haplo.score; phenotype12, phenotype 1 or 2.* **FIGURE 2:** *Simulation results under sample size 500, setting 1 (six haplotypes), and ρ = 0.5. Scenarios are shown in Table 1. HS, Haplo.score; phenotype12, phenotype 1 or 2.* **FIGURE 3:** *Simulation results under sample size 500, setting 1 (six haplotypes), and ρ = 0.9. Scenarios are shown in Table 1. HS, Haplo.score; phenotype12, phenotype 1 or 2.* **FIGURE 4:** *Simulation results under sample size 1,000, setting 1 (six haplotypes), and ρ = 0.1. Scenarios are shown in Table 1. HS, Haplo.score; phenotype12, phenotype 1 or 2.* **FIGURE 5:** *Simulation results under sample size 1,000, setting 1 (six haplotypes), and ρ = 0.5. Scenarios are shown in Table 1. HS, Haplo.score; phenotype12, phenotype 1 or 2.* **FIGURE 6:** *Simulation results under sample size 1,000, setting 1 (six haplotypes), and ρ = 0.9. Scenarios are shown in Table 1. HS, Haplo.score; phenotype12, phenotype 1 or 2.* **FIGURE 7:** *Simulation results under sample size 500, setting 2 (12 haplotypes), and ρ = 0.1. Scenarios are shown in Table 1. HS, Haplo.score; phenotype12, phenotype 1 or 2.* **FIGURE 8:** *Simulation results under sample size 500, setting 2 (12 haplotypes), and ρ = 0.5. Scenarios are shown in Table 1. HS, Haplo.score; phenotype12, phenotype 1 or 2.* **FIGURE 9:** *Simulation results under sample size 500, setting 2 (12 haplotypes), and ρ = 0.9. Scenarios are shown in Table 1. HS, Haplo.score; phenotype12, phenotype 1 or 2.* **FIGURE 10:** *Simulation results under sample size 1,000, setting 2 (12 haplotypes), and ρ = 0.1. Scenarios are shown in Table 1. HS, Haplo.score; phenotype12: phenotype 1 or 2.* **FIGURE 11:** *Simulation results under sample size 1,000, setting 2 (12 haplotypes), and ρ = 0.5. Scenarios are shown in Table 1. HS, Haplo.score; phenotype12: phenotype 1 or 2.* **FIGURE 12:** *Simulation results under sample size 1,000, setting 2 (12 haplotypes), and ρ = 0.9. Scenarios are shown in Table 1. HS, Haplo.score; phenotype12, phenotype 1 or 2.*
The performances of bivariate QBL and Haplo.score are the closest in the first two scenarios only when the correlation coefficient is high, i.e., 0.9, as shown in Figures 3, 6, 9. However, Figure 12 shows that even with ρ=0.9, bivariate QBL is clearly much more powerful than Haplo.score in these two scenarios. Moreover, when the correlation between the two phenotypes is weak or moderate, bivariate QBL outperforms Haplo.score in these scenarios at any combination of haplotype setting and sample sizes.
## 4 Application to GAW 19 data
We consider two continuous phenotypes, SBP and DBP, available in these data. They are moderately correlated (sample correlation coefficient = 0.55) and likely share a common genetic mechanism (Schillert and Konigorski, 2016). Typically, SBP and DBP are combined to create a single binary phenotype referred to as hypertension. More specifically, clinical thresholds are used for each BP to classify it as high blood pressure (BP); a subject is a case of hypertension if one of them is high (Datta and Biswas, 2016). However, converting a quantitative phenotype to a binary phenotype leads to a loss of information. Furthermore, combining them into one binary phenotype is a lost opportunity to investigate pleiotropy. As bivariate QBL can analyze the two continuous phenotypes jointly, it can potentially provide additional insight into these data.
There are 1,851 subjects in these data after discarding the missing values. Following Yuan and Biswas [2019], we analyze eight genes, namely, FBN3, HRH1, INMT, MAP4, SAT2, SHBG, ULK4, and ZNF280D. There are 28 SNVs in FBN3, 10 in HRH1, 18 in INMT, 18 in MAP4, 7 in SAT2, 15 in SHBG, 70 in ULK4, and 30 in ZNF280D. We combine five successive SNVs, starting from the first SNV, and create sliding haplotype blocks covering the whole gene, that is, on each gene, the first haplotype block consists of SNVs 1–5, second block consists of SNVs 2–6, and so on. For example, ULK4 has 66 haplotype blocks and MAP4 has 14 blocks.
We apply bivariate QBL to each haplotype block with both phenotypes jointly and Haplo.score to the same haplotype block twice with SBP and DBP separately. We calculate appropriate (and more general purpose) cutoffs for bivariate QBL and Haplo.score based on both simulated data and permutating the GAW19 phenotypes, as described in the following. We simulate 1,200 null samples, following setting 2 of Table 1. To match the GAW19 data more closely, we generate sample sizes of 1,851 with the correlation coefficient (between SBP and DBP) set to 0.55. As GAW19 data are exome sequence and have far more rare haplotypes than those considered in our simulations, we complement 1,200 simulated null samples by GAW19 data with permutated phenotype values. In particular, we permute the phenotypes of all subjects while retaining the pairing between SBP and DBP. Then, we combine the permuted phenotypes with genotypes in the ULK4 gene to create a null sample. We repeat this process 10 times to obtain 660 (66 × 10) blocks or null samples. Similarly, the permuted phenotypes are also combined with genotypes from MAP4 gene and repeated 10 times to provide 140 (14 × 10) blocks or null samples. The results from 800 null samples obtained using permutations are combined with those from 1,200 simulated null samples to calculate cutoffs.
The cutoffs based on 2000 null samples are calculated in the same manner, as described in the simulation study section for both bivariate QBL and Haplo.score. The cutoffs for type I error rates of $1\%$ and $2.5\%$ are found to be BFs of 10.91 and 4.65 for bivariate QBL and p-values of 0.0004 and 0.0058 for the Haplo.score global test, respectively.
The haplotype blocks found to be significantly associated at a type I error rate of $2.5\%$ using at least one of the methods are shown in Table 2. Bivariate QBL found a larger number of haplotype blocks to be significant, and the findings are consistent with the literature (Datta et al., 2016; Yuan and Biswas, 2019). For example, Haplo.score could not detect the haplotype in FBN3, whose β^ values for SBP and DBP are in opposite directions. All the haplotype blocks found to be significant using Haplo.score are also detected by bivariate QBL. At the type I error rate of $1\%$, bivariate QBL identifies all haplotype blocks in ULK4, as shown in Table 2, as significant, whereas Haplo.score identifies only one haplotype block (39–43) as significant. Therefore, bivariate QBL appears to perform better than Haplo.score in GAW19 data, which is in agreement with our findings in the simulation study.
**TABLE 2**
| Unnamed: 0 | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Bivariate QBL | Bivariate QBL.1 | Bivariate QBL.2 | Haplo.score | Haplo.score.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Gene | Win | Hap | Freq | β (SBP) | β (DBP) | BF | p-value (SBP) | p-value (DBP) |
| ULK4 | 3–7 | h10101 | 0.0016 | 1.206 | 0.824 | 14.06 | 0.0292 | 0.0913 |
| ULK4 | 4–8 | h01010 | 0.0014 | 1.608 | 0.747 | 54.56 | 0.0056 | 0.1308 |
| ULK4 | 5–9 | h10101 | 0.0014 | 1.619 | 0.767 | 50.52 | 0.0033 | 0.1319 |
| ULK4 | 6–10 | h01010 | 0.0016 | 1.211 | 0.843 | 15.67 | 0.0011 | 0.0405 |
| ULK4 | 7–11 | h10100 | 0.0016 | 1.218 | 0.849 | 16.63 | 0.0007 | 0.0335 |
| ULK4 | 8–12 | h01000 | 0.0016 | 1.207 | 0.836 | 14.82 | 0.0009 | 0.0477 |
| ULK4 | 9–13 | h10000 | 0.0017 | 1.209 | 0.835 | 20.66 | 0.0012 | 0.0384 |
| ULK4 | 39–43 | h11100 | 0.0055 | 0.869 | 0.666 | 41.33 | 0.0001 | 0.2726 |
| ULK4 | 40–44 | h11000 | 0.0052 | 0.854 | 0.801 | 25.26 | 0.0791 | 0.2656 |
| MAP4 | 11–15 | h10000 | 0.0043 | 0.778 | 1.714 | 10.49 | 0.0301 | 0.7634 |
| FBN3 | 24–28 | h00010 | 0.0014 | 0.783 | −0.54 | 10.41 | 0.0313 | 0.2224 |
## 5 Discussion
Health-related studies usually collect multiple outcomes to better assess patients’ health, understand complex diseases/traits, and inter-connection between them, which, in turn, can help in developing effective prevention and treatment strategies. These outcomes are often correlated and may share a common genetic etiology. A commonly used practice in genetic association studies is to analyze these outcomes in a one-at-a-time manner. Such a univariate approach essentially ignores the additional information contained in the joint distribution of the outcomes. Also, it is a missed chance to investigate the possibility of pleiotropy among these outcomes. Therefore, it is statistically and biologically more beneficial to adopt a multivariate approach to analyze the outcomes jointly. Moreover, analyzing haplotypes as genetic variants is advantageous because they are biologically interpretable, and haplotype-based tests can be performed on both NGS and GWAS data. There is no haplotype-based association test available that can detect rare variants associated with multiple continuous phenotypes yet. To fill this void, we propose bivariate QBL to detect the association of two quantitative traits with rare (and common) haplotypes. Our findings from the simulation study show that the method performs better than Haplo.score in all simulation setups that we considered.
Bivariate QBL performs best when the two outcomes have high positive correlation between them, and the target haplotype has discordant effects on the two phenotypes, i.e., one positive β and another negative β. This finding is consistent with the literature (Liu et al., 2009a; Ferreira and Purcell, 2009; Galesloot et al., 2014). In particular, to compare with Galesloot et al. [ 2014], we note that the first two scenarios in our study (both β s of the same sign) correspond to positive genetic correlation in their terminology, scenario 3 (one positive β and another negative β) corresponds to negative genetic correlation, and scenarios 4 and 5 (one β is 0) correspond to no genetic correlation. In scenarios 3–5, with a negative or zero genetic correlation, bivariate QBL outperforms Haplo.score at any combination of haplotype settings, correlation, and sample sizes, and its power increases as the positive residual correlation (i.e., ρ in our context) increases. Bivariate QBL gains substantial power in these scenarios with increasing residual correlation as it not only avoids the burden of multiple testing but also incorporates the additional information provided by the cross-trait correlation. However, even with type I error rates of less than $1\%$, bivariate QBL has power close to or practically 1, whereas Haplo.score has a much lower power in these scenarios.
The performance of Haplo.score is close to that of bivariate QBL only when both outcomes are highly correlated and the target haplotype affects both outcomes in the same direction, i.e., scenarios 1 and 2. In these scenarios, the power of bivariate QBL increases as the correlation decreases. In the terminology of Galesloot et al. [ 2014], this means when both genetic correlation and residual correlation are of the same sign, the power of bivariate QBL decreases as the positive residual correlation increases. This phenomenon of bivariate QBL is also consistent with other multivariate genetic association tests that exist in the literature (Liu et al., 2009a; Ferreira and Purcell, 2009). In practice, it is unlikely that two phenotypes will have a very high correlation. On the other hand, we note that bivariate QBL estimates haplotype frequencies (f) jointly with the haplotype effects and other parameters. Haplotype frequencies are estimated very well by bivariate QBL, especially due to the fact that we set the starting values of f in the MCMC algorithm to its maximum likelihood estimate (obtained from the hapassoc package) (Burkett et al., 2006; Burkett et al., 2015). Thus, there is practically no impact of haplotype frequency estimation on type I error and power of the method.
In GAW19 data, SBP and DBP are moderately correlated (0.55) (Datta et al., 2016; Yuan and Biswas, 2019). As another example, Liu et al. ( 2009b) observed a correlation between the body mass index and bone mineral density of 0.384 and 0.257, respectively, in two datasets. When there is a weak-to-moderate correlation, bivariate QBL outperforms Haplo.score by a substantial margin. In our GAW19 data application, we detected several rare haplotype blocks to be associated with SBP and DBP jointly. Specifically, nine blocks were detected in ULK4, one in MAP4, and another in FBN3. These results agree with the findings from previous studies (Levy et al., 2009; International Consortium for Blood Pressure Genome-Wide Association Studies Ehret et al., 2011; Ehret and Caulfield, 2013). Notably, the correlation between SBP and DBP is moderate and as per our simulation results, bivariate QBL is far more powerful than Haplo.score in this situation. However, many of those haplotype blocks could not be detected by Haplo.score. This indicates that bivariate QBL can help establish multiple trait–variant associations and identify potential pleiotropic effects for further investigation.
Bivariate QBL has a limitation in terms of computing time. In our simulation study, for a sample size of 500, bivariate QBL takes 86 and 166 s to finish 2,00,000 MCMC iterations for 6 and 12 haplotypes, respectively. This is for a machine with 3.50-GHz Milan processor with 128 cores under the Linux operating system and 256 GB RAM. However, it is faster than both bivariate LBL-2B and LBL-BC. Bivariate QBL can handle a larger number of SNPs in a haplotype at the expense of an increased computational burden. The runtime of bivariate QBL almost doubles when we increase the number of SNPs in a haplotype block from 5 (86 s) to 10 (158 s). Another limitation is that the method can only accommodate two continuous phenotypes at a time. We plan to extend the framework of bivariate QBL (and LBL) to accommodate many correlated continuous and/or binary phenotypes jointly. We also plan to extend the framework to investigate gene–environment interactions and develop a computationally efficient version of this method.
Despite these limitations, we believe bivariate QBL is an important addition to the existing genetic association tests, especially because there is currently no rare haplotype association test available that can analyze two correlated continuous phenotypes jointly.
## 6 Software
An R package implementing the proposed bivariate QBL method will be made available at https://www.utdallas.edu/∼swati.biswas/ and https://github.com/ihsajal/ as part of the existing package LBL.
## Data availability statement
The data analyzed in this study are subject to the following licenses/restrictions: *The data* are from Genetic Analysis Workshop 19. Participants of the workshop have access to these de-identified data for secondary analysis. Requests to access these datasets should be directed at https://bmcproc.biomedcentral.com/articles/10.1186/s12919-016-0007-z.
## Author contributions
SB conceived the study. IS and SB developed the methodology. IS carried out all simulations and data analyses under the supervision of SB. Both authors participated in interpreting the results and writing the manuscript. Both authors approved the final version of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1104727/full#supplementary-material
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|
---
title: 'Management of patients with recurrent/metastatic endometrial cancer: Consensus
recommendations from an expert panel from Brazil'
authors:
- Diocésio Alves Pinto de Andrade
- Andréa Paiva Gadelha Guimarães
- Andréia Cristina de Melo
- Angélica Nogueira-Rodrigues
- Larissa Müller Gomes
- Mariana Scaranti
- Joyce Maria Lisboa Maia
- Alessandra Menezes Morelle
- Candice Amorim de Araújo Lima Santos
- Cristiano de Pádua Souza
- Daniela de Freitas
- Donato Callegaro Filho
- Eduardo Paulino
- Elge Werneck Araújo Júnior
- Juliana Martins Pimenta
- Marcela Bonalumi dos Santos
- Michelle Samora de Almeida
- Ronaldo Pereira Souza
- Samantha Cabral
- Fernando Cotait Maluf
journal: Frontiers in Oncology
year: 2023
pmcid: PMC10033867
doi: 10.3389/fonc.2023.1133277
license: CC BY 4.0
---
# Management of patients with recurrent/metastatic endometrial cancer: Consensus recommendations from an expert panel from Brazil
## Abstract
### Background
Endometrial cancer is of increasing concern in several countries, including Brazil, in part because of an ageing population, declines in fertility, and the increasing prevalence of obesity. Although endometrial tumors had lagged behind other cancer types in terms of treatment improvements, molecular characterization of these tumors is paving the way for novel therapies and an expansion of the therapeutic arsenal. We aimed to help medical oncologists who manage patients with recurrent or metastatic endometrial cancer in the Brazilian healthcare setting.
### Methods
The panel, composed of 20 medical oncologists, convened in November 2021 to address 50 multiple-choice questions on molecular testing and treatment choices. We classified the level of agreement among panelists as [1] consensus (≥$75\%$ choosing the same answer), [2] majority vote ($50\%$ to <$75\%$), or [3] less than majority vote (<$50\%$).
### Results
Consensus was present for 25 of the 50 questions, whereas majority vote was present for an additional 23 questions. Key recommendations include molecular testing for every patient with recurrent/metastatic endometrial cancer; choice of first-line treatment according to microsatellite instability and HER2, with the addition of programmed death ligand 1 (PD-L1) and hormone receptors (HRs) for second-line therapy; carboplatin and paclitaxel as the preferred option in first-line treatment of HER2-negative disease, with the addition of trastuzumab in HER2-positive disease; pembrolizumab plus lenvatinib as a key option in second line, regardless of HER2, PD-L1 or HRs; and various recommendations regarding treatment choice for patients with distinct comorbidities.
### Conclusion
Despite the existing gaps in the current literature, the vast majority of issues addressed by the panel provided a level of agreement sufficient to inform clinical practice in Brazil and in other countries with similar healthcare environments.
## Introduction
Cancer of the uterine corpus is currently the most frequent gynecological malignancy in the US and the sixth most commonly diagnosed neoplasm in women worldwide, with 417,000 new cases estimated for 2020 [1, 2]. Even though incidence rates for uterine cancer vary up to 10-fold across countries, and the highest rates are found in North America and Eastern and Northern Europe, incidence rates have been rising worldwide, and countries with historically lower rates have had the largest proportional increase in incidence [3]. Although only 7,840 new cases of tumors of the uterine corpus have been estimated for Brazil in 2023 [4], this country had the third largest average annual percent increase (nearly $5\%$) in incidence in a recent worldwide survey [3, 5, 6]. Reasons for the rising incidence of uterine tumors remain incompletely understood, but an ageing population, declines in the fertility rate and the increasing prevalence of obesity are likely to play a major role [1, 7, 8]. Endometrial cancer, which accounts for most neoplasms of the uterine corpus – since uterine sarcomas account for only approximately 3-$7\%$ of cases [9] – has a median age at diagnosis of 63 years and a strong association with obesity [10, 11]. In fact, the association between obesity and endometrial cancer is stronger than for any other common cancer type, and between $36.5\%$ to $54.9\%$ of all uterine corpus tumors in the US are attributable to obesity across different States in that country [7, 10, 12, 13]. The use of unopposed estrogen and tamoxifen are also recognized risk factors for endometrial cancer, and changes in the prevalence of these factors likely play a role in current trends for this disease [8, 10, 11, 14].
Even though survival has improved since the mid-1970s for most common cancer types, neoplasms of the uterine corpus represent an exception, largely because of the lack of major treatment advances over the last few decades [2]. In recent years, however, molecular characterization of endometrial tumors has become a key component in treatment decisions for patients with recurrent and metastatic disease, and an increased understanding of the molecular basis for different uterine cancers has paved the way for novel therapies for these patients (8, 10, 15–17). As a result, the practicing oncologist has witnessed the recent expansion of the knowledge base and the therapeutic arsenal against recurrent and metastatic endometrial cancer [15, 18]. This is particularly evident in second- and subsequent-line treatment, given recent clinical trials of agents with activity against specific molecular subgroups. To informe decisions in the Brazilian healthcare setting, a panel of experts convened in an attempt to establish consensus recommendations in this country for the management of patients with endometrial cancer that is metastatic at diagnosis or presenting as recurrent disease not amenable to local control. The current article presents the results of that panel.
## Panel composition and methodology
The panel was composed by 20 medical oncologists from Brazil, with expertise in gynecological oncology, and working in institutions representing diverse geographic and socioeconomic settings in this country. The panel was coordinated by a committee composed of three of the current authors (DAPA, APGG and FCM), who prepared the 50 multiple-choice questions addressed by the panel and coordinated its conduct by teleconference in November 2021. To provide their recommendations, panel members were expected to take into account the published scientific literature and their own clinical experience. Recommendations were provided in an anonymous manner using an online system that also allowed tabulation of results after the end of the voting period for each question. The questions aimed to elicit recommendations regarding molecular testing and the choice of treatment for patients with recurrent or metastatic endometrial cancer, with particular emphasis on treatment past the first line.
The results for each of the 50 questions addressed by the panel were analyzed descriptively. The level of agreement among voters was ascertained by classifying responses to each question as [1] consensus, [2] majority vote, or [3] less than majority vote. If at least $75\%$ of the voting panel members provided a particular recommendation, consensus was present. If between $50\%$ and $74.9\%$ of the voting members provided a particular recommendation, this was considered as majority vote. For each question, voters had the option to abstain when they felt impeded to provide a qualified response for any reason; of note, recommendation percentages included the option “abstain” in their denominator. The panel was made possible by an educational grant from Merck, Sharp & Dohme, who had no influence on the creation of the questions, the panel conduct, or the writing of the article, all of which rest under the entire responsibility of the authors. Approval by an ethics committee was not required, given the nature of this specific manuscript that only involved expert contributors (the authors). No human subjects were involved.
## Patient assessment before treatment
Table 1 displays results pertaining to the nine questions related to patient assessment. Consensus was reached for two of those questions: [1] every patient with recurrent/metastatic endometrial cancer should undergo molecular testing before treatment initiation; and [2] computed tomography scan of the chest and abdomen are recommended for baseline assessment before treatment. Moreover, majority vote was present for five of the remaining seven questions: [1] $50\%$ of panelists recommended a more complete histopathological and molecular assessment that includes tumor grade, histological subtype, and the status of microsatellite instability (MSI), HER2, p53, programmed death ligand 1 (PD-L1), polymerase epsilon (POLE), and hormone receptors; [2] $64.7\%$ of panelists recommended that HER2 assessment is necessary in specific cases (and an additional $29.4\%$ recommend it for all cases); [3] $52.9\%$ of panelists recommended against testing for PD-L1; [4] $58.8\%$ did not recommend the assessment of tumor mutational burden (although $41.2\%$ of voters recommended it in specific circumstances); and [5] $52.9\%$ of panelists recommended CA125 assessment. Finally, there was more heterogeneity for the two remaining questions. However, if response options are pooled, $73.4\%$ of voters recommended the assessment of MSI “always”, “mostly” or “in some specific cases”. Likewise, a total of $83.4\%$ of voters recommended the assessment of hormone receptors “always”, “mostly” or “in some specific cases”. Of note, there were no abstentions for questions related to patient assessment.
**Table 1**
| Questions | Recommendations | Recommendations.1 | Recommendations.2 | Recommendations.3 | Recommendations.4 | Recommendations.5 | Recommendations.6 | Recommendations.7 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Should every patient with metastatic endometrial cancer have molecular analysis before treatment initiation? | Yes | No | Abstain | | | | | |
| Should every patient with metastatic endometrial cancer have molecular analysis before treatment initiation? | 88.2% | 11.8% | 0% | | | | | |
| Which information is essential in the histopathological report of a patient with metastatic endometrial cancer? | Grade and histological subtype | Grade, histological subtype, and MSI | Grade, histological subtype, MSI, and HER2 | Grade, histological subtype, MSI, HER2. and p53 | Grade, histological subtype, MSI, HER2, p53, and PD-L1 | Grade, histological subtype, MSI, HER2, p53, PD-L1, and POLE | Grade, histological subtype, MSI, HER2, p53, PD-L1, POLE, and HR | Abstain |
| Which information is essential in the histopathological report of a patient with metastatic endometrial cancer? | 0% | 0% | 33.3% | 16.7% | 0% | 0% | 50% | 0% |
| The assessment of MSI is necessary before starting treatment for metastatic endometrial cancer: | Always | Mostly | In some specific cases | Only after failure of first-line chemotherapy | Never | Abstain | | |
| The assessment of MSI is necessary before starting treatment for metastatic endometrial cancer: | 46.7% | 20% | 6.7% | 26.7% | 0% | 0% | | |
| The assessment of HER2 is necessary before starting treatment for metastatic endometrial cancer: | Always | Mostly | In some specific cases | Only after failure of first-line chemotherapy | Never | Abstain | | |
| The assessment of HER2 is necessary before starting treatment for metastatic endometrial cancer: | 29.4% | 5.9% | 64.7% | 0% | 0% | 0% | | |
| The assessment of PD-L1 is necessary before starting treatment for metastatic endometrial cancer: | Always | Mostly | In some specific cases | Only after failure of first-line chemotherapy | Never | Abstain | | |
| The assessment of PD-L1 is necessary before starting treatment for metastatic endometrial cancer: | 5.9% | 11.8% | 11.8% | 17.6% | 52.9% | 0% | | |
| The assessment of tumor mutational burden is necessary before starting treatment for metastatic endometrial cancer: | Always | Mostly | In some specific cases | Only after failure of first-line chemotherapy | Never | Abstain | | |
| The assessment of tumor mutational burden is necessary before starting treatment for metastatic endometrial cancer: | 0% | 0% | 41.2% | 0% | 58.8% | 0% | | |
| The assessment of HR is necessary before starting treatment for metastatic endometrial cancer: | Always | Mostly | In some specific cases | Only after failure of first-line chemotherapy | Only after failure of second-line chemotherapy and immunotherapy | Never | Abstain | |
| The assessment of HR is necessary before starting treatment for metastatic endometrial cancer: | 27.8% | 27.8% | 27.8% | 0% | 11.1% | 5.6% | 0% | |
| The assessment of CA125 is necessary before starting treatment for metastatic endometrial cancer: | Yes | No | Abstain | | | | | |
| The assessment of CA125 is necessary before starting treatment for metastatic endometrial cancer: | 52.9% | 47.1% | 0% | | | | | |
| What imaging tests are needed to start the treatment of metastatic endometrial cancer? | Chest radiography and abdominal ultrasound | Chest and abdominal CT scan | Chest and abdominal CT scan plus bone scintigraphy | PET-CT scan | Abstain | | | |
| What imaging tests are needed to start the treatment of metastatic endometrial cancer? | 0% | 88.9% | 11.1% | 0% | 0% | | | |
## First-line treatment
As shown in Table 2, consensus was reached for four of six questions pertaining to first-line treatment: [1] first-line treatment should differ according to MSI and HER2 statuses; [2] chemotherapy should be used in first-line treatment of HER2-negative disease; [3] chemotherapy plus trastuzumab should be used in first-line treatment of HER2-positive disease; and [4] the conventional regimen of carboplatin (area under the curve 5-6) and paclitaxel (175 mg/m2 every 3 weeks) should be used in the first-line treatment of HER2-negative disease. Majority vote was reached for the remaining two questions: [1] $56.3\%$ of panelists recommended that chemotherapy should not be used alone in first-line treatment of HER2-positive disease; and [2] $66.6\%$ of voters recommended six as the standard number of cycles for first-line treatment.
**Table 2**
| Questions | Recommendations | Recommendations.1 | Recommendations.2 | Recommendations.3 | Recommendations.4 | Recommendations.5 | Recommendations.6 | Recommendations.7 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Should first-line treatment differ according to MSI and HER2 status? | Yes | No | Abstain | | | | | |
| Should first-line treatment differ according to MSI and HER2 status? | 88.9% | 11.1% | 0% | | | | | |
| In which scenario should isolated chemo not be indicated? | MSI-high | PD-L1 positive | HER2 positive | Isolated chemo should always be indicated | Abstain | | | |
| In which scenario should isolated chemo not be indicated? | 12.5% | 0% | 56.3% | 18.8% | 12.5% | | | |
| What should be the first-line treatment for HER2-negative metastatic disease? | Chemo | Chemo + pembrolizumab | Chemo + TTZ | CPI | Pembrolizumab + lenvatinib | Hormone therapy | Abstain | |
| What should be the first-line treatment for HER2-negative metastatic disease? | 83.3% | 0% | 5.6% | 0% | 5.6% | 5.6% | 0% | |
| What should be the first-line treatment for HER2-positive metastatic disease? | Chemo | Chemo + pembrolizumab | Chemo + TTZ | TTZ monotherapy | CPI | Pembrolizumab + lenvatinib | Hormone therapy | Abstain |
| What should be the first-line treatment for HER2-positive metastatic disease? | 12.5% | 0% | 87.5% | 0% | 0% | 0% | 0% | 0% |
| What chemo regimen should be indicated in the first-line treatment of HER2-negative disease? | Carboplatin, AUC 5-6 + paclitaxel, 175 mg/m2 every 3 weeks | Carboplatin, AUC 5-6 on Day 1 + paclitaxel, 80 mg/m2 D1-D8-D15 every 3 weeks | Carboplatin, AUC 5-6 on Day 1 + paclitaxel, 60 mg/m2 D1-D8-D15 every 3 weeks | Platin doublet with another agent (liposomal doxorubicin, gemcitabine) | Platin doublet + bevacizumab | Abstain | | |
| What chemo regimen should be indicated in the first-line treatment of HER2-negative disease? | 100% | 0% | 0% | 0% | 0% | 0% | | |
| How many cycles of chemo should be given in first-line treatment? | 4 cycles | 6 cycles | 8 cycles | Until disease progression and/or limiting toxicity | Abstain | | | |
| How many cycles of chemo should be given in first-line treatment? | 0% | 66.7% | 0% | 33.3% | 0% | | | |
## Second-line treatment
A total of 33 questions addressed issues related to second-line treatment. The first three of these questions have their corresponding recommendations shown pictorially in Figure 1, whereas the remaining 30 questions are shown in Table 3. Although the three questions depicted in Figure 1 had only majority vote for one single answer, in all three cases two answers can be pooled to establish a consensus minimum duration of progression-free interval of 6 months before patients are re-exposed to carboplatin and paclitaxel after having stable disease or partial response to first-line treatment, and of 3 months after complete response.
**Figure 1:** *Recommendations for re-exposure to carboplatin and paclitaxel according to response to first-line therapy.* TABLE_PLACEHOLDER:Table 3 As shown in Table 3, consensus was reached for 18 of the remaining 30 questions pertaining to second-line treatment: [1] the assessment of MSI is necessary before second-line treatment; [2] the choice of second-line treatment should be guided by MSI, HER2, PD-L1 and hormone receptor status; [3] pembrolizumab plus lenvatinib should be the treatment of choice for HER2-positive disease without MSI after the regimen of carboplatin, paclitaxel and trastuzumab; [4] pembrolizumab, 200 mg every 3 weeks, plus lenvatinib, 20 mg/day, should be the second-line treatment for HER2-negative disease without MSI after carboplatin plus paclitaxel; the choice of second-line treatment with pembrolizumab plus lenvatinib should not be influenced by [5] hormone receptor status, [6] HER2 status or [7] PD-L1 status, but [8] should be influenced by MSI status; neither [9] hypertension nor [10] diabetes mellitus or [11] dyslipidemia are contraindications for second-line treatment with pembrolizumab plus lenvatinib, but [12] poor performance status is; [13] second-line treatment for HER2-negative disease with MSI after carboplatin plus paclitaxel should be an immune checkpoint inhibitor; the choice of second-line treatment with a checkpoint inhibitor should not be influenced by [14] hormone receptor status, [15] HER2 status, [16] PD-L1 status or [17] histological subtype, but [18] should be guided by MSI status.
Majority vote was present for 11 of 30 questions related to second-line treatment, whereas for one question there was less than majority vote (see Table 3). It should be noted, however, that for some of these questions the response options can be pooled in a manner that indicates a clear preference for somewhat similar interventions. This is the case, for example, of the need to assess hormone receptors before second-line treatment: $11.1\%$ of panelists indicated that this should always be done, $33.3\%$ that this should be done in most cases, and $44.4\%$ that this should be done in specific cases. On the other hand, some questions with a majority vote indicated a clear dichotomy of opinions, such as whether the choice of second-line treatment with an immune checkpoint inhibitor should be influenced by tumor mutational burden; in this case, $47.4\%$ of voters believe the choice would depend on that assessment, whereas $52.6\%$ believe that it would not.
## Treatment past two lines
Two questions addressed treatment beyond two lines. For the first, about what should be the choice after first-line treatment with carboplatin plus paclitaxel and second-line treatment with pembrolizumab plus lenvatinib, $82.4\%$ of panelists recommended chemotherapy regimens other than carboplatin plus paclitaxel. For the question of how many lines of chemotherapy should a patient receive, regardless of histological subtype or biomarkers, $73.7\%$ of voters indicated that the number of lines should not be fixed but rather guided by the continued presence of adequate performance status.
## Discussion
To our knowledge, this is the first consensus meeting conducted in Brazil to address the management of advanced endometrial cancer. Previous work from our country has successfully addressed the surgical management of this disease, which is of increasing concern in many countries [3, 19]. As many other middle-income countries, the healthcare system in *Brazil is* subject to accessibility constraints, and local practice guidelines are relatively scarce. We believe the current work can help medical oncologists to make decisions and base their practice on consensus recommendations. Nevertheless, consensus was present for only 25 of the 50 questions, in many cases as a reflection of current doubts in the literature. On the other hand, majority vote was present for an additional 23 questions, and less than majority vote was present for only three questions. Therefore, for the vast majority of issues addressed by the panel, there seems to be a sufficient level of agreement among members to guide clinical practice.
The classification of endometrial tumors has been predominantly based on morphology, with increasing use of ancillary testing, such as immunohistochemistry. Moreover, molecular subtyping increasingly provides prognostic insight and helps the practicing clinician in making treatment decisions [8, 10, 17, 20]. Importantly, randomized trials – such as Postoperative Radiation Therapy in Endometrial Cancer (PORTEC) 4a and the RAINBO trials program – are ongoing to assess the validity of making adjuvant treatment decisions for early-stage disease based on molecular classification [21, 22]. In the advanced-disease setting, molecular tumor features can already be used to guide therapy, particularly in uterine serous cancers and MSI tumors (10, 23–26). The vast majority of panel members consider the assessment of MSI and HER2 as essential for all patients before treatment initiation, with division of opinions about other markers, such as tumor mutational burden, p53, PD-L1, POLE, and hormone receptors (Table 1). Likewise, there is a predominant view that molecular markers, particularly MSI and HER2, should guide second-line therapy (Table 2). It should be noted that POLE mutations are identified with the use of sequencing, which may not be widely available. POLE and p53 are important to define molecular classification, but they do not yet influence the choice of treatment [27]. PD-L1 proved to be a predictor of response in other tumors, such as lung cancer [28], but it did not influence therapeutic responses with immunotherapy in the treatment of endometrial cancer [29]. Furthermore, the assessment of tumor mutational burden may be restricted to large referral centers, and up to now it has not been shown to influence treatment choices or prognostic assessment among patients with endometrial cancer [30].
The current results indicate consensus among panel members that patients with HER2-positive tumors benefit from the addition of trastuzumab to first-line therapy with carboplatin and paclitaxel. Even though the evidence in the literature is restricted to uterine serous tumors [15, 23], and based on a phase 2 trial with 61 patients, the current panel indicates HER2 testing as an essential step in patients who are candidates to first-line therapy and a preference for adding trastuzumab to such treatment, thus increasing progression-free survival (Table 2) [23]. For all other endometrial tumors, the literature suggests, and the panel indicates by consensus, that carboplatin plus paclitaxel remains the standard of care in the first line, with a median progression-free survival of 13 months and overall survival of 37 months [31]. Of note, recent results indicate that the regimen of carboplatin plus paclitaxel is also the standard of care for uterine carcinosarcoma [32].
Re-exposure to carboplatin plus paclitaxel is indicated by the panel – and corroborated by a retrospective study [33] – when the progression-free interval is at least 6 months, but a shorter interval is considered appropriate for patients with a complete response to first-line therapy (Figure 1). Indeed, the panel recommends by consensus that chemotherapy regimens other than carboplatin plus paclitaxel be used after failure of first-line treatment with this regimen and failure of second-line treatment with pembrolizumab plus lenvatinib. As suggested by several of the panel recommendations for second-line therapy, the latter regimen seems to be the currently preferred option in second line for patients without MSI, even for those with HER2-positive disease, regardless of previous treatment with trastuzumab (Table 2). In KEYNOTE-775, a phase 3 trial, the combination of pembrolizumab plus lenvatinib improved the objective response rate, progression-free survival, and overall survival, regardless of MSI status, when compared with chemotherapy of physician’s choice in patients with one or two prior lines of therapy [34], thus confirming results from a previous single-arm trial (KEYNOTE-146) [29, 35]. The latter results led to the approval of this combination in several countries, including Brazil, and to the design of an ongoing phase 3 trial in the first line, in comparison with carboplatin plus paclitaxel [36].
The panel addressed issues related to the toxicity of the combination of pembrolizumab plus lenvatinib and expressed greater concern with its use in patients with poor performance status or with heart disease; on the other hand, there is little concern for patients with hypertension, dyslipidemia, or diabetes mellitus (Table 3). The toxicity associated with this combination includes hypertension, fatigue, nausea/vomiting, diarrhea, decreased appetite, weight loss, hypothyroidism, hand-foot syndrome, musculoskeletal pain, stomatitis, and proteinuria [34, 35, 37]. These adverse reactions may usually be managed with supportive care medications and judicious lenvatinib dose modifications [37].
As in other tumor types, the role of immunotherapy is expanding in endometrial cancer. Single-agent checkpoint inhibitors are also an option among patients with disease progression after the first line. Pembrolizumab was approved in 2017 for patients with mismatch repair deficiency or MSI-high tumors (including endometrial cancer), based on aggregate results from five single-arm trials [38]. Subsequent results from the single-arm trial, KEYNOTE-158, among patients with previously treated, advanced endometrial cancer with mismatch repair deficiency or MSI-high, have shown an objective response rate of $48\%$, median progression-free survival of 13.1 months, and median overall survival that was not reached at the time of reporting [24]. Other checkpoint inhibitors are under investigation for advanced endometrial cancer, and these include dostarlimab, recently approved in the US for recurrent, mismatch repair deficiency tumors based on results from the ongoing GARNET trial [26]. Of note, the panel indicated a preference, at least by majority vote, for the use of a checkpoint inhibitor as the preferred option for second-line treatment of HER2-negative or HER2-positive disease with MSI, whether or not trastuzumab has been used in the first line (Table 3).
There seems to be no strong preference for the use of hormone therapy by the current panel, considering the settings investigated and the questions posed to members. Nevertheless, it should be noted that patients with advanced or recurrent endometrioid endometrial tumors with low-volume disease and a long disease-free interval, especially if they have insufficient conditions for chemotherapy, can be treated with progesterone; this is even more important for tumors that are positive for hormone receptors, particularly if they are grade 1 or 2, even though no randomized trials have compared this approach versus chemotherapy in the first line [10, 15]. Likewise, hormone therapy can be an option for patients with more limited performance status or for treatment past the first line in selected cases.
Despite its findings and potential relevance for practicing clinicians, our consensus has some limitations, including [1] the fact that not all 20 physicians answered the 50 questions asked, [2] the need to rely on scientific literature with lower level of evidence and/or grades of recommendation (e.g., phase 2 trials) to define consensus regarding some of the settings for which no higher level exists, and [3] the amalgamation of recommendations without a distinction between public and private healthcare settings in our country.
## Conclusion
Given that at least majority vote was present for 47 of the 50 questions addressed by the panel, we believe that the current work can help medical oncologists treating patients with endometrial cancer in Brazil and in other countries with similar healthcare environments to make decisions informed by the current recommendations, which are based on the scientific literature and expert opinion. Nevertheless, several questions regarding the management of these patients remain, either because of knowledge gaps in the literature or because some topics have not been addressed by the current panel. Therefore, continued effort is needed to ensure adequate dissemination and implementation of current best practices in this and other fields in oncology.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author.
## Author contributions
All authors contributed equally to the development of the project and the writing of the paper. The panel was coordinated by a committee composed of three of the current authors (DA, AG and FM). All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2023.1133277/full#supplementary-material
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|
---
title: 'Mouse strain-dependent variation in metabolic associated fatty liver disease
(MAFLD): a comprehensive resource tool for pre-clinical studies'
authors:
- Hamzeh Karimkhanloo
- Stacey N. Keenan
- Jacqueline Bayliss
- William De Nardo
- Paula M. Miotto
- Camille J. Devereux
- Shuai Nie
- Nicholas A. Williamson
- Andrew Ryan
- Matthew J. Watt
- Magdalene K. Montgomery
journal: Scientific Reports
year: 2023
pmcid: PMC10033881
doi: 10.1038/s41598-023-32037-1
license: CC BY 4.0
---
# Mouse strain-dependent variation in metabolic associated fatty liver disease (MAFLD): a comprehensive resource tool for pre-clinical studies
## Abstract
Non-alcoholic steatohepatitis (NASH), characterized as the joint presence of steatosis, hepatocellular ballooning and lobular inflammation, and liver fibrosis are strong contributors to liver-related and overall mortality. Despite the high global prevalence of NASH and the substantial healthcare burden, there are currently no FDA-approved therapies for preventing or reversing NASH and/or liver fibrosis. Importantly, despite nearly 200 pharmacotherapies in different phases of pre-clinical and clinical assessment, most therapeutic approaches that succeed from pre-clinical rodent models to the clinical stage fail in subsequent Phase I-III trials. In this respect, one major weakness is the lack of adequate mouse models of NASH that also show metabolic comorbidities commonly observed in NASH patients, including obesity, type 2 diabetes and dyslipidaemia. This study provides an in-depth comparison of NASH pathology and deep metabolic profiling in eight common inbred mouse strains (A/J, BALB/c, C3H/HeJ, C57BL/6J, CBA/CaH, DBA/2J, FVB/N and NOD/ShiLtJ) fed a western-style diet enriched in fat, sucrose, fructose and cholesterol for eight months. Combined analysis of histopathology and hepatic lipid metabolism, as well as measures of obesity, glycaemic control and insulin sensitivity, dyslipidaemia, adipose tissue lipolysis, systemic inflammation and whole-body energy metabolism points to the FVB/N mouse strain as the most adequate diet-induced mouse model for the recapitulation of metabolic (dysfunction) associated fatty liver disease (MAFLD) and NASH. With efforts in the pharmaceutical industry now focussed on developing multi-faceted therapies; that is, therapies that improve NASH and/or liver fibrosis, and concomitantly treat other metabolic comorbidities, this mouse model is ideally suited for such pre-clinical use.
## Introduction
Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver condition in developed countries, with a global prevalence of $24\%$1. Non-alcoholic steatohepatitis (NASH) is a severe form of NAFLD, which is characterized as the presence of steatosis (liver lipid exceeding $5\%$ of liver weight), hepatocellular ballooning and lobular inflammation, in the absence or presence of various degrees of fibrosis1,2. NASH and liver fibrosis can further proceed to end-stage liver diseases, such as cirrhosis and hepatocellular carcinoma (HCC)3,4, and liver fibrosis is a strong contributor to liver-related and overall mortality5. NASH is associated with a variety of metabolic co-morbidities, including obesity ($82\%$ prevalence), type 2 diabetes (T2D, $48\%$) and dyslipidaemia ($82\%$)6–8. These associations have prompted some to view NASH as the hepatic manifestation of advanced metabolic disease, leading to a redefinition of this disease cluster to metabolic (dysfunction) associated fatty liver disease (MAFLD)9.
Despite the substantial global burden on healthcare systems, there are currently no FDA-approved therapeutic strategies for preventing or reversing NASH and/or liver fibrosis10. With nearly 200 pharmacotherapies being in different phases of pre-clinical and clinical trials, to date, only a few have showed the anticipated improvements in NASH and fibrosis (i.e., obeticholic acid, elafibranor, selonsertib, cenicriviroc, and resmetirom)11. Importantly, most therapeutic targets that prove promising in pre-clinical mouse models do not show the same desired effects in humans. This is not surprising considering the current lack of adequate mouse models that fully recapitulate the many aspects of the human disease. Ideally, such a mouse model should be diet-induced to mimic chronic human overnutrition, show all features of histologically defined NASH and liver fibrosis, and display characteristic metabolic comorbidities, including obesity, hyperglycemia, glucose intolerance, insulin resistance, dyslipidaemia, and cardiovascular disease6.
Inbred mice are the most popular animals used for liver research due to their availability and low cost, with the C57BL/6 strain being the workhorse in these studies12. While NASH can be induced through different experimental approaches, each design has its limitations. For example, while feeding mice a methionine choline deficient (MCD) diet leads to NASH with severe fibrosis and bona fide ballooning degeneration, the mice lose rather than gain weight due to hepatotoxicity12. The MCD diet as well as carbon tetrachloride (CCl4) dosing, a hepatotoxin that leads to rapid liver fibrosis, are not representative of human disease progression, with CCl4 leading to genotoxicity and oxidative DNA damage13. In addition, genetic models of obesity/diabetes (e.g., ob/ob) or NASH (e.g., PTEN-/-, PPARα-/- mice) either do not develop NASH or do not show all characteristic metabolic comorbidities13. Typical western diets consumed by humans contain high levels of lipids, sucrose, fructose and cholesterol. However, while mice fed such western-style diets develop obesity, insulin resistance and hepatic steatosis, several studies have shown that features of NASH are not pronounced in this dietary model14, with the degree of steatosis, inflammation and fibrosis impacted by dietary composition, treatment duration, and most importantly, the rodent strain15. Given the vast differences in susceptibility of inbred mouse strains to hepatic steatosis16,17, NASH and liver fibrosis18, this suggest that the genetic background and dietary exposure might be important for NASH induction, however, this remains unexplored in a systematic manner.
Given the lack of mouse models that adequately recapitulate NASH pathology in the presence of NASH-associated metabolic comorbidities, particularly utilizing a western diet to mimic chronic human overnutrition, this study aimed to provide an in-depth comparison of NASH pathology and markers of the metabolic syndrome in eight commonly available inbred mouse strains, offering a comprehensive resource tool for future pre-clinical studies in liver disease.
## Mouse strains show substantial variation in NASH pathology and liver fibrosis
All eight mouse strains were fed the NASH or Control diet for a maximum of 32 weeks. The prevalence of NASH and liver fibrosis varied substantially across mouse strains, as shown by representative histopathological H&E, Masson’s Trichrome and Picrosirius Red staining (Fig. 1A). Mouse strains were classified as ‘NASH resistant’ or ‘NASH susceptible’ based on hepatic scores for steatosis, lobular inflammation, hepatocyte ballooning and fibrosis (Fig. 1B). Representative histopathology for the Control mice is shown in Fig. S1. BALB/c mice were completely resistant to NASH and liver fibrosis, with complete absence of hepatic steatosis and inflammation (Fig. 1A,B), while C3H, NOD and DBA mice only showed mild steatosis, in the absence or presence of low grades of lobular inflammation and hepatocyte ballooning. In contrast, A/J, CBA, BL6 and FVB/N mice (Fig. 1A,B) were susceptible to NASH and liver fibrosis, with co-presence of substantial steatosis (grades 2–3), lobular inflammation (grades 2–3), hepatocyte ballooning (grade 1–2) and modest fibrosis (grade 1a) (Fig. 1C, see Table S3 for the respective NAS scores). NASH-related fibrosis was confirmed in A/J, CBA, BL6 ($$p \leq 0.11$$) and FVB/N mice using quantitative Orbit Image Analysis (Fig. 1D).Figure 1Liver histopathology assessment identifies NASH sensitive and NASH resistant mouse strains. ( A) Representative liver histology (H&E, Masson’s Trichrome and Picrosirius Red staining) in A/J, BALB/c, C3H/HeJ, C57BL/6J, CBA/CaH, DBA/2J, FVB/N and NOD/ShiLtJ mice fed a western-style diet, sorted by increasing prevalence of NASH and liver fibrosis. ( B) Heat map analysis showing hepatic scores for steatosis, lobular inflammation, hepatocyte ballooning and fibrosis, (C) comparison of hepatic steatosis, inflammation, hepatocyte ballooning and fibrosis across all eight mouse strains, (D) percentage hepatic fibrosis ($$n = 6$$/group), (E) liver weight ($$n = 8$$–11/group), (F) liver triglyceride content ($$n = 7$$–10/group, $$n = 4$$ C3H Chow), (G) plasma alanine aminotransferase (ALT) ($$n = 8$$–10/group) and (H) plasma aspartate aminotransferase (AST) ($$n = 8$$–10/group, $$n = 4$$ C3H Chow) activity, all sorted by increasing prevalence of NASH and liver fibrosis (as assessed by histopathological grading). Data are means ± SEM, *$p \leq 0.05$ vs. respective controls, as assessed by two-way unpaired t-test.
As expected, the livers of all mice fed the NASH diets were heavier compared with the respective Control livers, with the most pronounced increase in liver weight observed in CBA, BL6 and FVB/N mice (1.6–2.3-fold increase; Fig. 1E). Similarly, liver triglyceride content was increased in all eight mouse strains (C3H $$p \leq 0.14$$), with the greatest 2.6–8.6-fold increase again observed in CBA, BL6 and FVB/N mice (Fig. 1F). Plasma alanine aminotransferase (ALT) is commonly assessed as a marker of liver injury. ALT levels were increased in the four mouse strains (A/J, BL6 $$p \leq 0.15$$, CBA and FVB/N) that exhibited histopathologically confirmed NASH and liver fibrosis, as well as in NOD mice, but not in the other three NASH-resistant strains (Fig. 1G). In contrast, plasma aspartate aminotransferase (AST) was only elevated in CBA mice fed the NASH diet (Fig. 1H).
## Gene expression analysis corroborates NASH histopathology
Consistent with the histopathology scoring, gene expression of α-1 type I collagen (Col1a1), the major component of fibrillar collagen in the liver, the profibrogenic cytokine transforming-growth factor-1 (Tgfb1) and tissue inhibitor of metalloproteinases 1 (Timp1), which inhibits matrix degradation, was increased in the four NASH susceptible strains (A/J, BL6, CBA and FVB/N–Timp1 in FVB $$p \leq 0.065$$) but not in the NASH resistant mouse strains, except for a significant increase in *Timp1* gene expression in C3H mice with NASH (Fig. 2A–C). In contrast, expression of α-2 actin (Acta2), a marker of hepatic stellate cell (HSC) activation, was increased in C3H mice but reduced in A/J mice, while connective tissue growth factor (Ccn2/Ctgf) expression was unaffected by the NASH diet in all strains (Fig. 2D,E).Figure 2Analysis of genes encoding profibrotic and proinflammatory proteins in the liver. mRNA expression was assessed in the livers of Control (brown) and western diet-fed (blue) A/J, BALB/c, C3H/HeJ, C57BL/6 J, CBA/CaH, DBA/2 J, FVB/N and NOD/ShiLtJ mice, including (A) α-1 type I collagen (Col1a1) ($$n = 8$$–10/group), (B) transforming-growth factor-1 (Tgfb1) ($$n = 7$$–10/group), (C) tissue inhibitor of metalloproteinases 1 (Timp1) ($$n = 6$$–10/group), (D) α-2 actin (Acta2) ($$n = 8$$–10/group), (E) connective tissue growth factor (Ccn2/Ctgf) ($$n = 6$$–10/group), (F) adhesion G protein-coupled receptor E1 (Adgre1 / F$\frac{4}{80}$) ($$n = 8$$–10/group), (G) chemokine (C–C motif) ligand 2 (Ccl2 / Mcp1) ($$n = 7$$–10/group) and (H) tumour necrosis factor α (Tnf) ($$n = 6$$–10/group, $$n = 3$$ Balb/c and NOD Chow (not detected in other mice for these strains)). ( I) Plasma C-reactive protein (CRP) ($$n = 8$$–10/group), with mouse strains in all graphs sorted by increasing prevalence of NASH and liver fibrosis (as assessed by histopathological grading). Data are means ± SEM, *$p \leq 0.05$ vs. respective controls, as assessed by two-way unpaired t-test.
We next assessed gene expression of adhesion G protein-coupled receptor E1 (Adgre1 / F$\frac{4}{80}$), a widely used monocyte-macrophage marker, and found F$\frac{4}{80}$ expression to be increased in NOD, A/J, BL6 and CBA mice (Fig. 2F). Gene expression of chemokine (C–C motif) ligand 2 (Ccl2 / Mcp1) and tumour necrosis factor-α (Tnf) were measured as readouts of hepatic inflammation. Mcp1 expression was increased in the four NASH susceptible mouse strains (A/J, BL6, CBA and FVB/N) as well as in C3H mice (Fig. 2G), while Tnfa expression was increased in NOD, A/J, BL6 and CBA mice (Fig. 2H). We also assessed plasma C-reactive protein (CRP), which is used clinically as a biomarker of systemic inflammation19. Plasma CRP was increased in the four NASH susceptible mouse strains (A/J $$p \leq 0.054$$, BL6, CBA and FVB/N) and one NASH resistant mouse strain (BALB/c) (Fig. 2I).
Taken together, both histopathological grading and gene expression analysis demonstrate that A/J, CBA, BL6 and FVB/N are susceptible to mild liver fibrosis and NASH, with significant presence of hepatic steatosis, inflammation and hepatocyte ballooning. In contrast, BALB/c, C3H, NOD and DBA only show mild (or no) hepatic steatosis, with no evidence of NASH or liver fibrosis.
## NASH-susceptible mouse strains show increased hepatic cholesterol and diacylglycerol synthesis rates in the liver
NAFLD is associated with a multitude of defects in lipid metabolism that ultimately contribute to hepatic steatosis and NAFLD progression. These include, but are not limited to, increased fatty acid uptake and de novo lipogenesis, as well as a compensatory enhancement of fatty acid oxidation, which is insufficient to alleviate the lipid burden20. [ 14C]-tracer assays were used to assess hepatic lipid and glucose metabolism in precision-cut liver slices derived from mice. While fatty acid uptake was not impacted by the NASH diet in any mouse strain (Fig. 3A), cholesterol ester synthesis was significantly increased in the NASH susceptible CBA, BL6 and FVB/N strains (Fig. 3B), and diacylglycerol (DAG) synthesis was also increased in CBA and FVB/N mice (Fig. 3C). In contrast, the NASH diet did not affect fatty acid oxidation (Fig. S2A), triglyceride or ceramide synthesis, except for a significant $40\%$ reduction in triglyceride synthesis in BALB/c mice and reduced ceramide synthesis in NOD and A/J mice (Fig. S2B,C). Phospholipid synthesis was only significantly increased in CBA mice with NASH (Fig. S2D), while neither glucose oxidation nor de novo lipogenesis were impacted by NASH in any mouse strain (Fig. S2E,F). Together, these data indicate modest changes in lipid metabolism in NASH susceptible mice, with increases in cholesterol ester synthesis being the dominant change. Figure 3Assessment of hepatic lipid metabolism. Lipid metabolism was assessed in precision-cut liver slices using [14C]-radiolabelled fatty acids. ( A) Fatty acid uptake ($$n = 8$$–11/group), (B) cholesterol ester synthesis ($$n = 6$$–11/group) and (C) diacylglycerol (DAG) synthesis ($$n = 6$$–10/group), with mouse strains in all graphs sorted by increasing prevalence of NASH and liver fibrosis (as assessed by histopathological grading). Data are means ± SEM, *$p \leq 0.05$ vs. respective controls, as assessed by two-way unpaired t-test.
## NASH-susceptible FVB/N mice show key features of the metabolic syndrome
The histopathology analysis highlights that four of the eight mouse strains (A/J, CBA, BL6 and FVB/N) are susceptible to diet-induced NASH and moderate F1 liver fibrosis. We next assessed the presence of common NASH-associated metabolic comorbidities in mice. While metabolic assessments were conducted in all eight mouse strains, here we focus on the four NASH-susceptible strains. Data for the four NASH resistant strains are provided in Figs. S3 and S4. Of the NASH-susceptible mouse strains CBA, BL6 and FVB/N mice showed increased body weight and fat mass following the dietary intervention, while A/J mice were completely refractory to diet-induced obesity (Fig. 4A–H).Figure 4Metabolic phenotyping of NASH-susceptible mouse strains. Metabolic assessment is shown for the four NASH-susceptible mouse strains, including A/J, CBA/CaH, C57BL/6J and FVB/N, with mouse strains sorted by increasing prevalence of NASH and liver fibrosis (from left to right). ( A–D) Weekly body weight ($$n = 8$$–10/group), (E–H) fat mass ($$n = 8$$–11/group), (I–L) glucose tolerance ($$n = 8$$–10/group), (M–P) plasma insulin assessed during the glucose tolerance test ($$n = 8$$–10/group), and (Q–T) insulin tolerance ($$n = 8$$–10/group). Data are means ± SEM, *$p \leq 0.05$ vs. respective controls, as assessed by two-way unpaired t-test (E–H), or two-way ANOVA and Bonferroni post-hoc analysis (A–D, M–T).
Further metabolic assessment showed that while A/J mice were resistant to diet-induced hyperglycaemia, hyperinsulinaemia (Table S4) and glucose-induced insulin secretion (Fig. 4M), they exhibited mild glucose intolerance (Fig. 4I), and even mild improvements in insulin sensitivity (Fig. 4Q, see Table S4 for glucose disappearance rate for ITT (KITT)). No differences were observed in the HOMA-IR used as insulin resistance index (Table S4). Thus, the A/J mouse strain does not develop metabolic co-morbidities associated with NASH.
Contrary to our expectations, CBA mice showed improvements in glucose tolerance when fed the NASH diet (Fig. 4J, see Table S4 for area-under-curve (AUC)), potentially related to substantial hyperinsulinaemia and in this respect increased HOMA-IR (Table S4), and increased glucose-induced insulin secretion (Fig. 4N), in the absence of fasting hyperglycaemia (Table S4) or insulin resistance (Fig. 4R, Table S4). While BL6 mice were resistant to fasting hyperglycaemia, hyperinsulinaemia (Table S4) and glucose-induced insulin secretion (Fig. 4O), with no change in the HOMA-IR (Table S4), they exhibited mild impairments in glucose tolerance (Fig. 4K, Table S4) and insulin sensitivity (Fig. 4S, Table S4). In contrast, while FVB mice did not develop hyperglycaemia (Table S4), they showed substantial glucose intolerance (Fig. 4L, Table S4), hyperinsulinaemia (Table S4) and increased glucose-induced insulin secretion (Fig. 4P). In addition, assessment of insulin tolerance showed that FVB mice were the only mouse strain that developed substantial insulin resistance (Fig. 4T, Table S4), with a significant increase in the HOMA-IR (Table S4). Taken together, these results indicate that from the four NASH susceptible mouse strains only FVB/N mice develop diet-induced obesity, glucose intolerance, hyperinsulinaemia and marked insulin resistance.
## All NASH-susceptible mouse strains exhibit hypercholesterinaemia
In addition to impairments in glycaemic control and insulin action, NAFLD is associated with dyslipidaemia, including increased plasma triglyceride and low-density lipoprotein (LDL) cholesterol, and decreased high-density lipoprotein (HDL) cholesterol, with all being risk factors for cardiovascular disease21. Dietary cholesterol supplementation led to a significant increase in total plasma cholesterol in all four NASH-susceptible mouse strains. Plasma triglycerides were increased in A/J, were not different in BL6 mice and were surprisingly reduced in CBA and FVB/N mice (Fig. 5A–D). Plasma non-esterified fatty acids (NEFA) were reduced in A/J mice and were not different in the other NASH susceptible strains (Fig. 5A–D).Figure 5Assessment of plasma lipids and adipose tissue lipolysis in NASH-susceptible mouse strains. Metabolic assessment is shown for the four NASH-susceptible mouse strains, including A/J, C57BL/6J, CBA/CaH, and FVB/N, with mouse strains sorted by increasing prevalence of NASH and liver fibrosis (from left to right). ( A–D) Plasma levels of triacylglycerol (TAG) ($$n = 6$$–10/group), total cholesterol (Chol) ($$n = 8$$–10/group) and non-esterified fatty acid (NEFA) ($$n = 8$$–10/group). ( E–H) Assessment of NEFA release from epididymal adipose tissue explants as a readout of basal and isoproterenol-stimulated lipolysis ($$n = 8$$–10/group). Data are means ± SEM, *$p \leq 0.05$ vs. respective controls, as assessed by two-way unpaired t-test (A–D), or two-way ANOVA and Bonferroni post-hoc analysis (E–H).
A major contributor to circulating NEFA, particularly in the fasted state, is the release of fatty acids from adipose tissue. Basal lipolysis is increased, and β-adrenergic stimulated lipolysis is decreased in obesity, which is linked to excessive ectopic lipid deposition and insulin resistance22,23. We therefore assessed lipolysis in epididymal adipose tissue explants in the basal state and following β-adrenergic stimulation (i.e., in the presence of isoproterenol). Basal lipolysis was unaffected by diet in any of the four NASH-susceptible mouse strains (Fig. 5E–H), while BL6 and FVB/N mice fed the NASH diet showed a significant reduction in β-adrenergic stimulated lipolysis (FVB $$p \leq 0.0544$$, Fig. 5E–H). Plasma lipids and lipolysis rates in the four NASH-resistant mouse strains (BALB/c, C3H, DBA and NOD) are shown in Fig. S4. Taken together, this analysis shows that FVB/N mice present with hypercholesterinaemia and mildly reduced catecholamine stimulation of lipolysis, in addition to obesity and impaired glycaemic control.
## NASH leads to inconsistent changes in kidney fibrosis
An increasing body of evidence suggests that NAFLD and chronic kidney disease (CKD) share common pathogenetic mechanisms, and that NAFLD is related to the incidence and stage of CKD24. We therefore assessed changes in renal fibrosis, including glomerular and interstitial fibrosis, in all mouse strains using Picrosirius Red staining of coronal kidney histological sections (Fig. 6A, Fig. S5). While glomerular fibrosis was significantly increased in A/J and CBA mice (Fig. 6B) and CBA mice further showed increased interstitial fibrosis (Fig. 6C), BALB/c mice surprisingly exhibited reduced glomerular and interstitial fibrosis with the NASH diet (Fig. 6B,C). Furthermore, toxic metabolites that are usually eliminated by the kidneys, including urea, accumulate in the blood in CKD, and are useful markers of kidney dysfunction25. However, plasma urea levels were not impacted by the NASH diet in any of the mouse strains (Fig. 6D). Overall, these results highlight that while early-stage NASH is unlikely to be associated with severe impairments in renal function, CBA mice could be a useful model of western diet-induced renal fibrosis. Figure 6Assessment of renal fibrosis and plasma urea. ( A) Representative renal glomerular and cortical interstitial histology (Picrosirius Red staining) in A/J, BALB/c, C3H/HeJ, C57BL/6J, CBA/CaH, DBA/2J, FVB/N and NOD/ShiLtJ mice fed a western-style diet, sorted by increasing prevalence of NASH and liver fibrosis. Percentage of glomerular (B) and interstitial (C) fibrosis ($$n = 5$$–6/group), and (D) plasma urea ($$n = 4$$–10/group) in all mouse strains. Data are means ± SEM, *$p \leq 0.05$ vs. respective controls, as assessed by two-way unpaired t-test.
## NASH increases systemic fat oxidation
Obesity and metabolic disease are associated with changes in systemic energy metabolism and substrate utilization, including increased preference for fatty acid oxidation26. While ANCOVA analysis using (lean body mass + 0.2 × fat mass) as a covariate27 indicated that systemic energy expenditure was not impacted by NASH in any mouse strain (Fig. S6A), all eight mouse strains showed a significant reduction in the respiratory exchange ratio (Fig. S6B), demonstrating increased oxidation of fatty acids. Food intake was increased in C3H mice, but not impacted in any other strain (Fig. S6C), while locomotor activity was not impacted by the NASH diet in any mouse strain (Fig. S6D). Taken together, these data highlight that NASH induces a preferential oxidation of fatty acids in the absence of changes in systemic energy expenditure.
## Discussion
Despite liver fibrosis being a strong contributor to liver-related and overall mortality5, there are currently no approved pharmacotherapies for inhibiting or reversing progression of NASH and liver fibrosis. A major obstacle for successful translation of findings from animal models to humans is the lack of adequate mouse models that recapitulate human NASH pathology and the accompanying metabolic comorbidities. Through deep phenotypic assessment, we show that FVB/N mice fed a western diet recapitulate the key features of human NASH (Fig. 7) and advocate their use as a suitable pre-clinical model to assess therapeutic strategies for NASH with mild liver fibrosis. With efforts in the pharmaceutical industry now focussed on developing multi-faceted therapies; that is, therapies that improve NASH and/or liver fibrosis, and concomitantly treat other metabolic comorbidities, this mouse model is well suited for such pre-clinical use. Figure 7Summary heat map analysis highlighting the primary hepatic and metabolic changes observed in mice fed the NASH diet compared to their respective Controls, with all data points being relative and not representing actual values/data points. This heat map was considered for decision making of a ‘good’ NASH model. There was no formal weighting given to the criteria, with histological features of NASH as the primary consideration, then placing equal weighting on the metabolic comorbidities as a secondary consideration. For pro-fibrotic and pro-inflammatory gene expression, all variables measured through gene expression received equal weighting. Glucose intolerance is shown as total AUC, while insulin resistance is shown as glucose disappearance rate for the ITT (KITT).
Numerous mouse models of NASH have been developed, including various dietary approaches, as well as toxins (e.g., carbon tetrachloride, streptozotocin) or genetic manipulation in the absence/presence of dietary interventions. The majority of such studies utilize the C57BL/6 (BL6) mouse strain, due to their availability and low cost, detailed phenotypic characterization, and overall favoured standing as the background strain for most genetically engineered models12. In these BL6-centered studies, mouse models of NASH can be broadly subdivided into (i) severe NASH and liver fibrosis in the absence of features of the metabolic syndrome (e.g., methionine choline deficient (MCD) diet, SREBP1c TG or PPARα-/-), or (ii) hepatic steatosis with presence of metabolic dysfunction but in the absence of NASH and / or liver fibrosis (e.g., high-fat diet (HFD), ob/ob)13. A relatively new model is the MUP-uPA transgenic mouse line, which expresses transiently high amounts of urokinase plasminogen activator in hepatocytes, and develops both NASH pathology and extensive metabolic dysfunction when fed a HFD. However, the majority of these mice spontaneously progress to developing hepatocellular carcinoma, thereby precluding longer-term examination of NASH / fibrosis13. Here, we show that while BL6 mice fed a western diet enriched in fat, sucrose, fructose and cholesterol develop NASH with mild liver fibrosis, they exhibit very mild glucose intolerance and insulin resistance, with no changes in fasting blood glucose or plasma insulin, or glucose-stimulated insulin secretion. Typically, metabolic impairments in mice are assessed with limited temporal resolution, most commonly following a 8–12 week dietary intervention, pointing to severe hyperinsulinemia and impairments in glycaemic control in the BL6 mouse strain28, as also frequently reported by our group26,29. However, recent studies show that while prolonged exposure to high-fat high-sugar diets leads to morbid obesity and NAFLD, changes in glucose homeostasis can be highly dynamic, with glucose intolerance and insulin sensitivity initially deteriorating but being indistinguishable to that of chow mice following > 24 weeks of high-fat feeding30,31, as is the case in our study. In previous studies, this time-dependent improvement in glucose homeostasis coincided with adaptive β-cell hyperplasia30 or a preservation of peripheral glucose disposal31. While hyperinsulinemia was not affected in BL6 mice fed a western diet, we did not have the capacity to assess the contribution of peripheral tissues to the blood glucose response.
In-depth assessment of NASH histopathology and extensive metabolic phenotyping identified the FVB/N mouse strain as the optimal diet-induced mouse model for NASH, mild F1 liver fibrosis and a broad spectrum of metabolic comorbidities, most prominently presence of obesity, glucose intolerance, hyperinsulinemia, insulin resistance, hypercholesterinemia, systemic inflammation, and a mild impairment in catecholamine regulation of adipose tissue lipolysis. Our histopathological and metabolic assessment of FVB/N mice agrees with previous reports indicating that FVB/N mice are highly susceptible to HFD-induced obesity, glucose intolerance, insulin resistance and hepatic steatosis26,33. Similarly, ob/ob mice bred on an FVB/N genetic background show more severe hyperglycaemia and hepatic insulin resistance when compared to ob/ob on a C57BL/6 J background34. FVB/N mice show high incidence of spontaneous multifocal hepatocellular necrosis35, and exhibit the greatest increase in liver weight, prevalence of periductal fibrosis, highest rates of plasma ALT and substantial hepatocyte ballooning compared to four other mouse strains, when administered an agent that induces Mallory-Denk bodies (i.e., hepatocyte inclusions found in several liver diseases that correlate with hepatocyte ballooning)36, overall indicating that FVB/N mice have a high predisposition to advanced liver disease. Given that ~ $85\%$ of NASH patients are overweight or obese, ~ $50\%$ have impaired glucose tolerance and/or hyperinsulinemia, $98\%$ have insulin resistance and $72\%$ have dyslipidaemia6,37, the metabolic defects in FVB/N mice are representative of the most common metabolic defects observed in human NASH. While FVB/N mice only develop mild F1 fibrosis, their liver phenotype is similar to the vast majority of human NAFLD patients, where on average $12\%$ and $5\%$ of obese patients have NASH and fibrosis, respectively, and importantly, of those with fibrosis, $83\%$ have F1 fibrosis38. Therefore, this mouse model is of relevance to testing therapeutics that would be useful for early prevention and would benefit the majority of NASH patients. However, it should be noted that while ~ $80\%$ of NASH patients also present with hypertriglyceridemia6,37, plasma triglyceride levels were reduced in FVB/N mice fed the NASH diet, potentially related to reduced VLDL secretion, as dysfunctional VLDL secretion has previously been described as a feature of NASH39.
CBA/CaH mice also developed NASH and hepatic F1 fibrosis, as well as renal fibrosis, however these mice did not develop insulin resistance, and exhibited improvements in glucose tolerance, which was likely related to their considerable hyperinsulinemia. These observations contrast previous studies suggesting that 10–$20\%$ of CBA/Ca mice are prone to ‘spontaneous maturity onset diabetes obesity syndrome’, which includes presence of obesity, hyperglycaemia, impaired glucose tolerance and hyperinsulinaemia40,41. In addition, our macroscopic evaluation of CBA livers suggested that $30\%$ of mice developed hepatocellular carcinoma, which is in accordance with previous studies reporting high tumour incidence in CBA mice42. Despite their susceptibility to liver cancer and metabolic disease in a subset of mice, little is known about spontaneous and diet-induced predisposition to NAFLD in this mouse strain. The present results showing the absence of hyperglycaemia, glucose intolerance and insulin resistance, indicate that this strain is limited as a pre-clinical model for metabolic (dysfunction) associated fatty liver disease (MAFLD).
While BL6 and FVB/N mice developed many metabolic defects with western diet feeding, the A/J mouse strain, which also developed NASH and F1 fibrosis, was completely refractory to diet-induced obesity and various metabolic defects, including hyperglycaemia and hyperinsulinemia, and exhibited only very mild glucose intolerance and even mild improvements in insulin sensitivity. While the A/J strain is known to be resistant to diet-induced obesity43,44, the presence of NASH and fibrosis observed in our study was surprising given that previous studies suggested that A/J mice are resistant to NAFLD in the context of diet-induced obesity12. However, it should be noted that hepatic triglyceride levels in A/J NASH mice were ~ threefold lower than in western diet-fed BL6, CBA and FVB/N mice, and were overall more similar to the four NASH-resistant mouse strains, highlighting their relative resistance to hepatic lipid accumulation. Previous studies in A/J mice employed high-fat and high-sucrose dietary interventions, whereas the western diet used in the present study was further enriched in cholesterol and fructose, a carbohydrate known to drive hepatic lipogenesis29.
While this study focussed primarily on the identification of NASH susceptible mice with metabolic comorbidities, we further identified the BALB/c mouse strain as completely refractory to hepatic steatosis, NASH and liver fibrosis, in fact, these mice even showed reduced renal fibrosis despite the presence of obesity, hyperinsulinemia and hypercholesterinaemia. We have previously shown that BALB/c mice are resistant to both lipid- and fructose-induced hepatic steatosis26,29, and are refractory to fructose-induced activation of lipogenic pathways in the liver29, which is likely to contribute to their resistance to the development of metabolic dysfunction when fed a western diet. The complete lack of diet-induced hepatic deterioration is intriguing, and understanding the molecular pathways underlying such resistance to NAFLD may lead to the identification of novel therapeutic strategies for NASH and liver fibrosis.
In addition to detailed hepatic histopathological analysis, we provide in-depth assessment of diet-induced changes in hepatic lipid and glucose metabolism using carbon-14 tracer studies in precision-cut liver slices. We observed increased rates of DAG and cholesterol ester synthesis in the NASH sensitive CBA and FVB/N mice, as well as increased cholesterol ester synthesis in BL6 mice. Disturbances in cholesterol metabolism, particularly an increase in cholesterol synthesis and accumulation of free cholesterol, contribute to the pathophysiology of NASH by driving hepatic inflammation and fibrosis45,46, and are likely to contribute to the susceptibility of BL6, CBA and FVB/N mouse strains to NASH. In addition, the increase in DAG synthesis is likely to further contribute to disease progression in CBA and FVB/N mice being an intermediate in TAG synthesis and a direct inhibitor of hepatic insulin action through activation of protein kinase c signalling47. In contrast, fatty acid uptake, fatty acid and glucose oxidation, and de novo lipogenesis were not impacted by NASH in these strains. This was surprising given that NAFLD/NASH is commonly associated with increased lipogenesis and fatty acid uptake, as well as reduced β-oxidation capacity and mitochondrial dysfunction48,49, particularly with dietary fructose driving lipogenesis and suppressing fat oxidation50. Interestingly, BALB/c mice showed a significant reduction in triglyceride synthesis when fed the NASH diet, which may contribute to their resistance to hepatic steatosis and NASH. While BALB/c mice are known to be completely refractory to fructose-induced activation of lipogenesis29, the overall discrepancies are potentially related to the lack of hormonal regulation in the ex vivo liver slice experimental system, and the lack of an integrated in vivo response, including absence of hyperglycaemia and hyperinsulinemia, with glucose and insulin well known to contribute to excessive lipid accumulation51.
In conclusion, we provide a comprehensive assessment of liver pathology and the metabolic phenotype of eight commonly used inbred mouse strains, and show that FVB/N is the mouse strain that most faithfully recapitulates human NASH pathophysiology, including the most predominant NASH-associated metabolic comorbidities. The mild induction of F1 fibrosis is representative of the vast majority of human NASH patients, therefore providing a useful model for assessment of novel therapies for ‘borderline’ NASH and prevention of disease progression. A longer feeding regime and/or modulation of diet composition might provide a better model for advanced NASH, more severe liver fibrosis and potentially hepatocellular carcinoma, while a more in-depth metabolic analysis on these mice, including measures of adipose tissue inflammation and heart function, would likely provide further insights into the suitability of the FVB/N strain as a useful model for MAFLD. Finally, future studies in female mice are warranted given the equally high incidence of NASH in males and females.
## Animal studies
Eight common in-bred mouse strains, including A/J, BALB/c, C3H/HeJ, C57BL/6J, CBA/CaH, DBA/2J, FVB/N and NOD/ShiLtJ, were sourced from the Animal Resources Centre (Canning Vale, Australia), and will be referred to as A/J, BALB/c, C3H, BL6, CBA, DBA, FVB and NOD throughout the manuscript. Male mice were housed at 22 °C on a 12:12-h light–dark cycle and fed either a rodent chow diet or a western-style diet enriched in fat ($39.8\%$ energy from fat; of this $18\%$ trans-fat), carbohydrates ($40\%$ energy from carbohydrate; of this $30\%$ fructose), protein ($20\%$ energy from protein) and $2\%$ cholesterol (20 g/kg) (SF16-033, Specialty Feeds, Australia), from now on referred to as NASH diet. The chow diet is a cereal grain-base pellet diet provided in the form of 12 mm pellets, containing $5\%$ energy from fat, while the NASH diet is a semi-pure high fat diet formulation based on Research Diets D09100301. The NASH diet contains a fixed formula ration using the following ingredients: wheat, barley, lupins, soya meal, fish meal, mixed vegetable oils, canola oil, salt, calcium carbonate, dicalcium phosphate, magnesium oxide, and a vitamin and trace mineral premix (see Table S1 for full diet composition). Mice were fed the NASH diet from 8–9 weeks of age for a total of 30–32 weeks. Prior to commencing the experimental diet, mice were matched by body weight within their strain and grouped into NASH ($$n = 8$$–10) and control groups ($$n = 8$$–10), and body weight was recorded weekly during the feeding regime. Experimental procedures were approved by the University of Melbourne Anatomy & Neuroscience, Pathology, Pharmacology, and Physiology Animal Ethics Committee (ethics application No. 2015115) and conformed to the National Health and Medical Research Council of Australia guidelines regarding the care and use of experimental animals, and followed the recommendations in the ARRIVE guidelines52.
## Body composition and energy expenditure
Fat and lean mass were measured using the Bruker LF50H Minispec Body Composition Analyser (Coherent Scientific Pty Ltd, Thebarton, Australia), in accordance with the manufacturer’s instructions. Whole-body energy expenditure, the respiratory exchange ratio, food intake and physical activity were assessed using a 16-chamber Promethion Metabolic System (Sable, Nevada, USA). Studies were commenced 24 h after acclimatisation and the above parameters were monitored thereafter for a further 24 h in 30 min intervals.
## Glucose and insulin tolerance
Mice were fasted for 4 h starting at 7:00, gavaged with glucose (2 g/kg body weight) or injected i.p. with insulin (0.75 U/kg body weight, Actrapid), and blood glucose levels were assessed (Accu-Chek II glucometer; Roche Diagnostics, Castle Hill, Australia) before and at selected time points after glucose/insulin administration. During the glucose tolerance test, additional blood was collected for assessment of plasma insulin by ELISA (Ultra-Sensitive Mouse Insulin ELISA; Crystal Chem, Elk Grove Village, IL, USA).
## Liver histopathology
A piece of liver consistently obtained from the same left lobe was fixed in $10\%$ formalin, embedded in paraffin, and 5 μm sections were cut and stained with Masson Trichrome (MT), hematoxylin–eosin (H&E) or Picrosirius red (PR) by Phenomics Australia Histopathology and Slide Scanning Service (University of Melbourne, Australia). Histopathological analysis was conducted and scored by a single pathologist blinded to mouse strain and dietary intervention, in accordance with the Clinical Research Network (CRN) NAFLD activity score (NAS)53 and Kleiner classification of liver fibrosis54. Samples were stratified according to hepatic scores for steatosis (grade 1, 5–$33\%$ of parenchyma; grade 2, > 33–$66\%$ of parenchyma; grade 3, > $66\%$ of parenchyma), inflammation (grade 1, < 2 inflammatory foci per × 200 field; grade 2, 2–4 foci; grade 3, > 4 foci), hepatocyte ballooning (few or many ballooning cells are present per high-power field for grade 1 or 2, respectively) and fibrosis (grading according to Kleiner54). Presence of NASH was determined as joint presence of steatosis, hepatocyte ballooning and lobular inflammation (NAS ≥ 3), as defined by the Clinical Practice Guidelines of European Association for the Study of the Liver (EASL), the European Association for the Study of Diabetes (EASD) and European Association for the Study of Obesity (EASO)55. Percentage fibrosis area was calculated using Orbit Image Analysis56 and five representative PR images across each liver section at 100 × magnification.
## Kidney histopathology
The left kidney from each mouse was fixed in $10\%$ formalin, embedded in paraffin, 5 μm coronal sections were cut and stained with Picrosirius red (PR) by Phenomics Australia Histopathology and Slide Scanning Service (University of Melbourne, Australia). Percentage fibrosis area was calculated using Orbit Image Analysis56, using either four representative glomeruli from each section at 630 × magnification, or two representative PR images of the cortical interstitium at 400 × magnification.
## Hepatic triglyceride content
Total liver triglyceride content was determined by mass spectrometry. Briefly, liver (~ 6 mg) was homogenized in 100 μl 1:1 butanol-methanol (v/v), containing 5 μL of SPLASH® II LIPIDOMIX® Mass Spec Standard (330709W, Avanti Polar Lipids Inc.) using a Precellys Evolution Tissue Homogenizer (Bertin Instruments, USA). Samples were mixed thoroughly for 1 h at room temperature, centrifuged (14,000g, 10 min, 20 °C) and transferred into sample vials with glass inserts for analysis by ultrahigh performance liquid chromatography (UHPLC) coupled to tandem mass spectrometry (MS/MS) employing a Vanquish UHPLC linked to an Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific).
## Assessment of hepatic lipid and glucose metabolism
Lipid metabolism was assessed using freshly prepared 300 μm precision-cut liver slices. Briefly, a piece of liver obtained from the same lobe of the liver for each mouse was embedded in $3\%$ agarose (SeaPlaque™ Agarose, Lonza Bioscience) and 300 µm thick liver slices were generated using a Krumdieck Tissue Slicer (Alabama Research and Development). Liver slices were settled in oxygenated M199 media (Thermo Fisher Scientific, Scoresby, Australia) for 60 min before metabolic assessment. For assessment of fatty acid metabolism, liver slices were then incubated in 1 mL of low glucose DMEM (Thermo Fisher Scientific, Scoresby, Australia) containing $2\%$ BSA (w/v), 500 μmol/L oleate and 1 μCi/mL [1-14C] oleate for 2 h. At the completion of the experiment, the culture medium was acidified in 1 mL 1 mol/L perchloric acid to liberate 14CO2 derived from complete fatty acid oxidation, which was collected in 300 μL 1 mol/L NaOH and counted on a Tri Carb 2810TR liquid scintillation analyser (Perkin Elmer, Massachusetts, USA). The liver slices were washed in PBS and homogenized in 1 mL 2:1 chloroform:methanol (v:v) to extract lipids. The homogenate was centrifuged at 2000g for 10 min to achieve phase separation, the upper aqueous phase containing acid-soluble β-oxidation intermediates (ASM) was counted using the liquid scintillation analyser, the lower organic phase was transferred to a fresh tube, dried under N2 at 40 °C, and reconstituted in 2:1 chloroform: methanol containing the following lipid standards: cholesterol linoleate (C0289, Sigma-Aldrich, St. Louis, MO, USA), glyceryl tripalmitate (T5888, Sigma-Aldrich), oleate (O1008, Sigma-Aldrich), dipalmitin (D2636, Sigma-Aldrich), C24:0 ceramide (43799, Sigma-Aldrich) and l-α-phosphatidylcholine (P2772, Sigma-Aldrich). The lipid mixtures were spotted onto glass-backed Silica Gel 60 plates and the lipids were resolved in a 65:25:4 chloroform:methanol:water (v/v) solution, followed by two runs in 75:35:1 hexane:diethyl ether: acetic acid (v/v). The plates were air-dried, sprayed with dichlorofluorescein ($0.02\%$ w/v in ethanol) dye and the lipid bands were visualized under UV light. The lipid bands were scraped, and radioactivity measured on the liquid scintillation counter. Total fatty acid oxidation was calculated as the sum of radioactivity detected in the CO2 and ASM fractions, while fatty acid uptake was calculated as the sum of total fatty acid oxidation, and radioactivity detected in all lipid fractions. All values were normalized to liver slice weight.
Glucose oxidation and lipogenesis were measured for 2 h in low glucose DMEM (Thermo Fisher Scientific, Scoresby, Australia) containing $2\%$ BSA and 1 μCi/mL U-14C-glucose (Perkin Elmer). Similar to above, the media was acidified to liberate and capture 14CO2 as a readout of glucose oxidation, while liver slices were washed, lipids extracted and radioactivity within the total lipid extract counted for assessment of lipogenesis.
## Adipose tissue lipolysis
Lipolysis was assessed as the release of non-esterified fatty acids (NEFA) from epididymal adipose tissue explants in the basal state and following β-adrenergic stimulation. Briefly, adipose tissue was incubated in phenol red-free low glucose DMEM (Thermo Fisher Scientific, Scoresby, Australia) containing $2\%$ BSA, in the absence or presence of isoproterenol (1 µM) for 2 h. NEFA release into the media was assessed by colorimetric assay (Wako HR series NEFA-HR[2], Osaka, Japan).
## Plasma analysis
Plasma was collected following a 4 h fast and used for assessment of triglycerides (Triglycerides GPOPAP; Roche Diagnostics, Indianapolis, IN), NEFA (Wako Pure Chemical Industries, Osaka, Japan), cholesterol (Abcam, ab65390), C-reactive protein (Mouse C-Reactive Protein (CRP) ELISA Kit; Crystal Chem, Elk Grove Village, IL, USA), urea (Urea Assay Kit, Cell Biolabs Inc, BioAssay Systems), as well as alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activity, as described previously57,58.
## Gene expression analysis
Total RNA was extracted from liver using TRI reagent (Sigma Aldrich, Castle Hill, Australia), treated with DNase (Ambion DNA free kit, Thermo Fisher, VIC, Australia) and reverse transcribed using iScript Reverse Transcriptase (Invitrogen, USA). Real-time PCR was performed using SYBR Green PCR Master Mix (Quantinova® SYBR Green PCR kit, QIAGEN; Germany) on a CFX Connect™ Real-Time PCR Detection System (Bio-Rad Laboratories, Gladesville, Australia). Samples were normalized to Hprt as a housekeeping gene. Primer sequences are provided in Table S2.
## Statistical analysis
Data are presented as mean ± standard error of the mean (SEM), with statistical significance set at the $5\%$ level of significance ($P \leq 0.05$). Data were assessed for normal distribution using the D’Agostino and Pearson test with parametric tests used in cases of normal distribution, and non-parametric tests used in cases of non-normal distribution. Parametric tests included, where appropriate, a two-tailed unpaired Students t-test, one or two-way analysis of variance (ANOVA) followed by Bonferroni multiple comparisons tests. Non-parametric testing included the Wilcoxon matched pairs signed rank test. The energy expenditure ANCOVA analysis done for this work was provided by the NIDDK Mouse Metabolic Phenotyping Centers (MMPC, www.mmpc.org) using their Energy Expenditure Analysis page (http://www.mmpc.org/shared/regression.aspx) and supported by grants DK076169 and DK115255.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-32037-1.
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---
title: In silico and in vivo hepatoprotective activity of the synthesized 5-benzylidene-2-thiohydantoin
against diethylnitrosamine-induced liver injury in a rat model
authors:
- Lana S. Akree
- Zahra A. Amin
- Hiwa O. Ahmad
journal: Scientific Reports
year: 2023
pmcid: PMC10033926
doi: 10.1038/s41598-023-27725-x
license: CC BY 4.0
---
# In silico and in vivo hepatoprotective activity of the synthesized 5-benzylidene-2-thiohydantoin against diethylnitrosamine-induced liver injury in a rat model
## Abstract
In the present study, the hepatoprotective effect of 5-benzylidine-2-thiohydantoin (5B2T), a unique derivative of the thiohydantoin group, on liver injury induced by diethylnitrosamine (DEN) in male rats was investigated. The experimental animals were divided into three groups, each with 14 rats. Rats in group I were considered to be controls and received only $10\%$ Tween 80. Rats in group II were injected with 200 mg/kg DEN intraperitoneally. Rats in group III were injected with a single dose of DEN 200 mg/kg intraperitoneally and received the treatment orally (50 mg/kg, 5B2T) for two durations, 3 and 6 weeks. At the end of the experiment, blood was collected for the analysis of liver function and pro-inflammatory cytokine IL-6 and tumor necrosis factor α (TNF-α) levels. Additionally, liver specimens were used for histopathological examination and immunohistochemistry. The single intraperitoneal injection of 200 mg/kg DEN into rats resulted in significant elevation of serum enzyme levels of AST, ALT and ALP, which are indicators of hepatocellular damage, along with elevation in TNF-α and IL-6 in the DEN group. The results of both LFTs and ELISA in the treatment group showed improvements and a decline in the levels of the markers. Histopathological examination showed fibrosis, necrosis and infiltration of inflammatory cells in the DEN group, with lower intensity in the treatment group. The results of immunohistochemical staining revealed strong positive staining of both HSA and Ki-67 antibodies in the DEN group, with much lower intensity in the treatment group. The results of the docking study indicated that 5B2T has a remarkable interaction with TNF-α (PDB ID: 1TNF) and human IL-6 (PDB ID: 1IL6) with binding site energies of − 7.1 and − 6.1 (kcal/mol), respectively. The correct absorption and binding between the drug and the receptor was evaluated through computerized molecular docking by using the AutoDock program. The conclusion of the results from the current study reflected the interesting hepatoprotective abilities of 5B2T against DEN-induced hepatocellular damage and cancer in experimental rats.
## Introduction
Liver cancer is one of the most common and critical malignancies worldwide, with rapid growth and poor prognosis. Liver cancer may start as normal liver injury that develops into fibrosis and can then progress to cirrhosis and end in a critical case of carcinoma1. Hepatocellular carcinoma (HCC), simply known as liver cancer, is a fatal type of cancer with high morbidity and mortality rates. One reason why the diagnosis of liver cancer is difficult is because of its asymptomatic state until it reaches developed stages2. Hepatocellular carcinoma is composed of malignant neoplastic cells that to a large extent look like hepatocytes, which alter with differentiation3. The liver is the largest internal organ, and it is responsible for the accomplishment of many vital activities, for instance, the eradication of internal and external biological waste materials and metabolic waste (e.g., bile, urea, and lipids) out of the blood circulation. In addition, it has a very important role in many functions of the immune system4. Hepatocellular carcinoma incidences are currently increasing in both developed and developing countries due to the high rates of viral infections (e.g., HBV, HCV and HDV), alcoholism and obesity5.
Nitrosamines are a group of toxic chemical compounds that are considered to be very potent, toxic and carcinogenic for both humans and animals6. N-nitroso alkyl compounds, specifically diethylnitrosamine (DEN), can initiate different types and stages of malignancies in various organs, including the liver, lungs and blood, and are widely used as inducer chemicals to promote cancers in experimental animals (most commonly rats)7. Diethyl nitrosamines were previously well established as preservatives for many foods, such as dairy products, smoked and salted fish and meat, soybeans and alcoholic drinks. In the food industry, some chemicals are added as microbial growth inhibitors, preservative materials, colorants and flavor stabilizers. Most famously, nitrites are used. Nitrites are transformed to nitrosamines under the influence of high temperature and gastric acidic juice, and the net result is that these types of foods are a major source of these toxic materials, which is why these chemicals are no longer utilized as preservatives in food processing8. Hydantoins (also known as glycolylurea, arise from the reaction of glycolic acid and urea) and their derivatives (and some other molecules) are a group of heterocyclic compounds (organic), and they are considered to be very important and vital chemicals, because they play a pivotal role in many biological and pharmacological approaches, as well as in medicinal chemistry and in agrochemical applications, in addition to these, they represent the key precursors for the chemical and enzymatic synthesis of many crucial non-natural alpha amino acids and their conjugates of medical importance. Hydantoin is a colorless solid, and is a derivative of the oxidation of imidazolidine, it carries the formula C3H4N2O29. Thiohydantoins and their derivatives are a focus of interest for scientists and researchers nowadays because of their high bioactive and therapeutic potentials. Thiohydantoin is a sulfur (S, or thio) analog of the compound hydantoin (also known as imidazolidine-2,4-diones), at which one or two carbonyl groups are replaced by thiocarbonyl groups10. One interesting fact about this group of molecules is that their biological activity is changed and affected according to the nature of substituents. Significant numbers of thiohydantoin derivatives can be classically prepared by the condensation reactions of various aldehydes11,12. Among the major biological applications, therapeutic activities and pharmacological purposes of this class of heterocyclic compounds include anti-tumor activity, anti-bacterial activity, anti-parasitic activity, anti-malarial activity, anti-fungal activity, anti-epilepsy activity, anti-melanogenesis activity10. It's interesting to note the emergence of the thiohydantoin ring as an efficient pharmacophoric ingredient in the development of potent inhibitors for EGFR and VEGFR growth factor receptors. Furthermore, thiohydantoin derivatives are androgen receptor and TNF-antagonists, as well as effective inhibitors of several enzymes, including DNA Topoisomerase I, II (TopI, II), NOXs, isocitrate dehydrogenases (IDHs), B-cell lymphoma-2 (Bcl-2) and sirtuins (SIRTs), kinesin spindle protein (KSP), prolyl hydroxylases 1–3 (PHD 1–3), CDK2, and CDK4.11,25. The aim of the present study was to test the hepatoprotective effect of the new thiohydantoin derivative 5-benzylidine-2-thiohyadantoin (5B2T) in a rat model of DEN-induced liver injury.
## Materials and methods
For the present study, a newly synthesized derivative of thiohydantoin, 5B2T, was prepared experimentally in the laboratory and used as a treatment drug for liver injury.
## Chemistry
All melting points of the compounds were determined on Griffin apparatus. Infrared spectra were recorded in the range 4000–600/cm via a SHIMADZU CORP series FTIR instrument. The NMR spectra were run at Bruker DPX 400 (400 MHz) spectrometer using tetramethylsilane (TMS) as the internal standard. Chemical shifts were measured in ppm (δ) related to TMS (0.00) ppm. High-resolution mass spectrometric data were obtained in electrospray (ES) mode unless otherwise reported, on a Waters Q-TOF micro-mass spectrometer using a Gilson 232XL auto-sampler.
## Preparation of the drug
Commercially available 2-thiohydantoin was placed with the required aldehydes in a round bottom flask equipped with a magnetic stirrer and reflux condenser under a nitrogen atmosphere in triethylamine and water. The mixture was stirred overnight at room temperature, and the pH was adjusted to 3 by adding 3 M HCl. The solid product was filtered off and washed with diethyl ether and water. The pure compounds were collected and dried13.
5B2T (the final product) was synthesized by using commercially available hydantoin dissolved in ethanol in the presence of benzaldehyde and triethylamine and in an overnight H2O reflux (this was done following the success of a test reaction).
## Acute toxicity test
The OECD-423 guidelines was followed to determine the safety of the new synthesized compound 5B2T by administration of a single dose of 2 g/kg and 5 g/kg to the experimental mice14. In brief, 36 healthy mice (18 males and 18 females) were divided into three groups labeled as: vehicle (dH2O), low dose (2 g/kg) and high dose (5 g/kg) of 5B2T, respectively. Each mice was made to fast an overnight prior to dosing. Food was withheld for another 3–4 h after the administration. The animals were closely observed for 30 min and at 2, 4, 24 and 48 h after the administration for detection of any signs of acute clinical toxicity, morbidity and mortality. Behavioral observations include: respiration (dyspenea), salivation, skin piloerection, exopthalmus, convulsion and locomotion changes. After keeping alive for 14 days, on day 15, the mice were sacrificed to measure serum biochemical (liver and kidney) parameters following the standard methods15.
The experimental mice treated with high doses (2 g/kg and 5 g/kg) of 5B2T remain living for 14 days with active healthy condition and there were no obvious toxicity signs and no cases of death were recorded. The results obtained from blood biochemical tests did not demonstrate any difference between the treated groups and the control group as shown in Tables 1 and 2 suggesting that 5B2T was safe when administered orally and the lethal dose (LD50) for both genders was greater than 5 g/kg. Table 1Renal function test of experimental mice treated with high doses of 5B2T.GroupsBlood urea (mg/dL)BUN (mg/dL)Creatinine (mg/dL)Uric acid (mg/dL)Serum protein (mg/dL)Serum albumin (mg/dL)Male mice Vehicle20.1 ± 0.824.7 ± 0.011.2 ± 1.86.5 ± 0.46.0 ± 2.33.5 ± 0.3 2 g/kg 5B2T21.5 ± 1.624.3 ± 0.071.3 ± 1.16.4 ± 0.26.1 ± 2.13.6 ± 0.2 5 g/kg 5B2T20.2 ± 0.823.8 ± 0.081.2 ± 1.66.3 ± 0.56.5 ± 0.93.5 ± 1.2Female mice Vehicle20.0 ± 0.124.1 ± 0.11.1 ± 0.66.2 ± 1.36.3 ± 0.13.4 ± 2.7 2 g/kg 5B2T21.0 ± 0.324.8 ± 2.11.1 ± 0.76.4 ± 0.16.1 ± 0.13.5 ± 1.3 5 /kg 5B2T22.2 ± 0.724.2 ± 2.21.1 ± 0.36.5 ± 0.56.1 ± 0.13.3 ± 0.9Values expressed as mean ± S.E.M. There are no significant differences between groups. Significant value at $p \leq 0.05.$Table 2Liver function test of experimental mice treated with high doses of 5B2T.GroupsTotal Billirubin (mg/dL)Direct Billirubin (mg/dL)Indirect Billirubin (mg/dL)AST (U/L)ALT (U/L)ALP (U/L)Male mice Vehicle1.1 ± 0.80.1 ± 0.20.2 ± 0.325.4 ± 0.133.1 ± 0.3100.5 ± 0.2 2 g/kg 5B2T1.0 ± 0.10.1 ± 0.20.3 ± 0.124.9 ± 0.134.1 ± 0.6110.6 ± 0.1 5 g/kg 5B2T1.0 ± 0.20.2 ± 0.10.2 ± 0.225.0 ± 0.333.5 ± 0.9109.3 ± 0.4Female mice Vehicle1.2 ± 0.20.1 ± 0.30.2 ± 0.122.2 ± 1.136.3 ± 0.3110.4 + 0.7 2 g/kg 5B2T1.2 ± 0.30.1 ± 0.10.2 ± 0.323.5 ± 0.836.0 ± 0.0111.5 ± 0.3 5 /kg 5B2T1.2 ± 0.10.2 ± 0.30.2 ± 0.322.5 ± 0.636.1 ± 0.1111.3 ± 0.3Values expressed as mean ± S.E.M. There are no significant differences between groups. Significant value at $p \leq 0.05.$
## In vivo animal model
Forty-two healthy adult male rats weighing between 200 and 250 g with an average age between 3 and 4 months were used in this study. They were obtained from the animal house unit/College of Pharmacy/Hawler Medical University. The animals were given standard food and tap water. The animals were handled and received human care according to the ethical principles of the National Institutes of Health's Guide for the Care and Use of Laboratory Animals16 under the permission of the Ethics committee of College of Pharmacy/Hawler Medical University (no. 2021.25.08-205 HMU. PH. EC). All methods are reported in accordance with ARRIVE guidelines (https://arriveguidelines.org). The animals were maintained at 22 ± 3 °C under a 12 h–12 h light/dark cycle with 50–$60\%$ humidity for at least one week prior to the experiment. Rats were grouped into three groups ($$n = 12$$). The administration of the dosage was done considering the body weight (B.W.) of the rats. Group I was given $10\%$ Tween 80 throughout the experiment and was regarded as the placebo group. Rats in group II received a 200 mg/kg6 single-dose intraperitoneal injection of DEN following the method of Rezaie et al.6 and served as liver injury positive control group, while rats in group III were given 50 mg/kg 5B2T as an oral treatment in two periods: the first period lasted for 3 weeks, and the second period lasted for 6 weeks. At the end of the experiment, all rats were sacrificed, and blood was collected for biochemical analysis and proinflammatory cytokine analysis by ELISA. The tissues of the harvested livers of the rats were fixed with $10\%$ formalin and then subjected to a series of hydration and dehydration reactions. Then, they were embedded, sectioned, processed and stained with hematoxylin and eosin stains and tested for detection of specific antibodies, which were nuclear protein (Ki-67) and hepatocyte specific antigen (HSA).
## Molecular docking
The two-dimensional (2-D) structures of the ligand molecules (Fig. 1) were built using chemdraw professional 16.0 and converted to 3-dimensional (3-D) structures using Chem3D 16.0 module and saved as a pdb format structures (http://www.cambridgesoft.com/). The ligand was optimized by adding Geister charges and hydrogen and the pdbqt format of the ligands were prepared with AutoDock Tools 1.5.7.Figure 15-benzylidine-2-thiohydantoin.
The ligand molecules were then used as input for AutoDock Vina (https://vina.scripps.edu/) to carry out the docking simulation.
The X-ray crystal structure of the target of necrosis factor TNF-α (PDB ID:1TNF) and Human interleukin-6 IL-6 (PDB ID: 1IL6) were retrieved from the RCSB Protein Data Bank web server (http://www.rcsb.org/pdb/). The active binding sites were identified using Discovery Studio visualizer 2021. The grid dimensions were set at 8.41 × 64.05 × 31.20 (PDB ID:1TNF), and 3.49 × − 3.45 × 0.44 (PDB ID: 1IL6) according to the coordinates x, y, and z, for the target active binding sites identified in Discovery Studio visualizer 2021. The water molecules were removed from the receptors and polar hydrogen and Kollman charges were added. The pdbqt format of the receptors were generated by AutoDock Tools 1.5.7. AutoDock Vina was compiled and runs under Windows 10.0 Professional operating system. Discovery Studio 2021 was used to deduce the pictorial representation of the interaction between the ligands and the target protein.
The binding affinity (ki) of ligands for selected targets were calculated were assessed using Eq. [ 1]1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Ki = {\varvec{e}}^{{\left({\Delta G /\left({Rx t} \right)} \right)}}$$\end{document}Ki=eΔG/Rxt where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta G$$\end{document}ΔG is the binding energy in kcal/mol, the universal gas constant $R = 1.987$ kcal/K/mol, at room temperature (25 °C) $T = 273$ + 25 = 298 K. Ki is the inhibition constant where the Ki principally depends on the binding (or association) constant (Kb) having a unit of mM17.
## ADME prediction
Prediction of pharmacokinetics and physicochemical parameters plays a key role in drug design18. The evaluation of drug-likeness properties were evaluated for 5BT using the SwissADME (http://www.swissadme.ch/) and admetSAR (http://lmmd.ecust.edu.cn/admetsar2).19 Drug-like molecule must obey the Lipinski’s rule of five (RO5): the molecular weight MW of the active oral drug should be ≤ 500 Da; the log p should be < 5; the number of hydrogen bond acceptors should be nOH ≤ 10; the number of hydrogen bond donors nOHNH should be ≤ 5; and the number of rotatable bonds should be ≤ 1020.
## Spectral analysis of 5B2T
Utilizing the method of preparation mentioned in the materials and methods section, thiohydantoin (2.0 g, 17.2 mmol), trimethylamine (4.9 ml, 37 mmol) and benzaldehyde (1.9 ml, 19 mmol) were added to 50 ml of water. Yield = $69.9\%$, Mp 270–272 °C, HRMS calculated for C10H8N2OS m/z [M + H] + 204.0358; found 204.0358; 1H-NMR (400 MHz, DMSO-d6): δ 12.41 (s, H, NH), δ 12.19 (s, H, NH), δ7.74 (d, $J = 7.4$ Hz, 2H, phen), δ 7.45–7.37 (m, 3H, phenyl), δ 6.49 (s, H, CH=C). 13C-NMR (101 MHz, d6-DMSO): δ 177.3 (C=S), δ 164.0 (CO), δ 130.5 (C=CH), δ 128.6 (2xCH), δ 127.3 (C), δ 127.0 (2xCH), δ 126.0 (CH), δ 109.7 (CH=C). IR (neat): vmax = 3225/cm (NH), 1723 cm-1 (C=O), 1475/cm (C=S), 1643/cm (C=C).
## The in vivo hepatoprotective effect
The results of the present study (Fig. 2) showed that the administration of DEN caused a significant elevation in the biochemical parameters in the first group (DEN-treated rats), while treatment with 5B2T caused a notable decrease in the same biochemical markers. At the same time, the ELISA results reflected a significant increase in proinflammatory cytokines (TNF-α and IL-6) in rats treated with DEN and particularly lower levels of the same cytokines in 5B2T-treated rats (Fig. 3).Figure 2The effect of 5B2T on Liver biochemical parameters. The X axis present the treatment groups and the Y axis presnt the liver function parameters, DEN: diethylnitrosamine, T: $10\%$tween 80, 5B2T: treatment group. AST: acetate aminotransferase, ALT: alanine aminotransferase, ALP: alkaline phosphatase. Figure 3The effect of 5B2T on the Pro-inflammatory cytokines TNF-α and IL-6 levels. The X axis present the treatment groups and the Y axis presnt the cytokines levels, DEN: diethylnitrosamine, T: $10\%$tween 80, 5B2T: treatment group. TNFα: tumor necrosis factor alpha and IL-6: Interleukin 6.
The histopathological examination of the liver sections in the placebo ($10\%$ Tween 80)-treated rats showed completely normal histological features for both durations (3 weeks and 6 weeks) (Fig. 4A and B). The administration of DEN for 3 weeks showed highly noticeable liver damage, which was represented by massive disfiguration in hepatic structure, dilation in the sinusoids and massive fibrosis around the portal area (Fig. 4C). The administration of DEN for 6 weeks in turn caused massive abnormal histological features of the hepatic sections, accompanied by infiltration of inflammatory cells around the central vein area, which is an indicator of inflammation and advanced stage of liver injury. Furthermore, dilation of bile canaliculi was presented, along with hepatic peliosis and adenoma (Fig. 4D). Rats treated with DEN and 5B2T showed dramatic improvements in the histopathological appearance of the tested liver sections for both durations (3 and 6 weeks) (Fig. 4E and F). Figure 4Liver section, H&E. 400×. (A) Placebo group/3 weeks, shows normal histological features of hepatic cords (black arrow), and central vein (blue arrow). ( B) Placebo group/ 6 weeks, shows normal histological features of blood vessels in portal area (black arrow), and bile cuniculi (blue arrow), hepatocytes (red arrow). ( C) DEN group/3 weeks, shows fibrosis around portal area (black arrow), hyperplasia of Kupffer cells (blue arrow), and dilatation of sinusoids (red arrow). ( D) DEN group/6 weeks, shows hepatocellular adenoma (black arrow), hyperplasia of Kupffer cells (blue arrow), vacuolar degeneration (red arrow), coagulative necrosis (purple arrow), with dilatation of sinusoids (green arrow). ( E) 5B2T group/3 weeks, shows hyperplasia of bile cuniculi (black arrow), necrotic hepatocytes (blue arrow), and infiltration of inflammatory cells (red arrow). ( F) 5B2T group/6 weeks, ShowS mild peliosis (blue arrow), and few hepatocytes showed cellular degeneration (red arrow).
The immunohistochemical changes were evaluated by screening the expression of two liver markers: Ki-67 and HSA. The findings of the placebo group (group I) showed negative staining with Ki-67 antibodies in the nucleus of the hepatocytes at 3 and 6 weeks (Fig. 5A and B). In contrast, the DEN-treated rats (group II) showed positive staining with Ki-67 antibodies in the affected hepatocytes for the first and second durations, and the affected cell nuclei were stained dark brown (Fig. 5C and D). The stained hepatic histological sections from 5B2T-treated rats (group III) showed weak positive staining with Ki-67 antibodies in the cytoplasm of the affected hepatocytes as golden-brown granules for the first duration (3 weeks); on the other hand, rat histological sections for the second duration (6 weeks) showed weak positive staining with Ki-67 antibodies in the cytoplasm of the injured liver cells and a golden-brown granule appearance (Fig. 5E and F). Moreover, the sections of the $10\%$ Tween 80-treated rats (group I) showed negative staining with HSA antibodies in the cytoplasm of the hepatocytes for the first and second duration (Fig. 6A and B). The histological sections of DEN-treated rats (group II) showed strong positive staining with HSA antibodies in the affected hepatocytes, which stained as golden-brown cytoplasmic granules for the first and second durations, and the affected cell nuclei were stained dark brown (Fig. 6C and D). The stained hepatic histological sections of the 5B2T-treated rats (group III) showed weak positive staining with HSA antibodies in the cytoplasm of the affected hepatocytes as golden-brown granules for the first duration of the experiment (3 weeks); on the other hand, for the second duration (6 weeks), histological sections revealed weak positive staining with HSA antibodies in the cytoplasm of the injured liver cells with golden-brown granule appearance (Fig. 6E and F).Figure 5Liver section, IHC Ki67 -ab. 400×. (A) Placebo group/3 weeks, shows negative staining with Ki67 antibodies in the nucleus of hepatocytes (red arrow). ( B) Placebo group/6 weeks, shows negative staining with Ki67 antibodies in the nucleus of hepatocytes (red arrow). ( C) DEN group/ 3 weeks, shows positive staining with Ki67 antibodies in the affected hepatocytes which stained the nucleus as dark brawn color (red arrow), notice unspecific staining in other cellular elements (black arrow). ( D) DEN group/6 weeks, shows positive staining with Ki67 antibodies in the affected hepatocytes which stained the nucleus as dark brawn color (red arrow). ( E) 5B2T group/3 weeks, shows few positively staining nucleus of hepatocytes with Ki67 antibodies (red arrow). ( F) 5B2T group/6 weeks, shows negative staining nucleus of hepatocytes with Ki67 antibodies (red arrow).Figure 6Liver section, IHC HSA -ab. 400×. (A) Placebo group/3 weeks, shows negative staining with HSA antibodies in the cytoplasm of hepatocytes (red arrow). ( B) Placebo group/ 6 weeks, shows negative staining with HSA antibodies in the cytoplasm of hepatocytes (red arrow). ( C) DEN/3 weeks, shows strong positive staining with HSA antibodies in the affected hepatocytes which stained as cytoplasmic golden brawn granule (red arrow), notice unspecific staining in other hepatocytes (black arrow). ( D) DEN group/6 weeks, shows strong positive staining with HSA antibodies in the affected hepatocytes which stained as cytoplasmic golden brawn granule (red arrow). ( E) 5B2T group/3 weeks, shows few weakly positive staining with HSA antibodies in the cytoplasm of hepatocytes as golden brown granules (red arrow). ( F) 5B2T group/6 weeks, shows few weakly positive staining with HSA antibodies in the cytoplasm of hepatocytes as golden brown granules (red arrow).
The correct absorption and binding between the drug and the receptor is referred to as molecular docking. The most significant interaction of a ligand and receptor has the lowest docking energy. AutoDock Vina was used to evaluate the affinity, the conformation of binding, and the best ligand. For both of the ligands, among multiple docking poses, only the highest docking scores were included in the study. All the data regarding the binding force, number of hydrogen bond interactions and amino acid participation in the interactions that have been observed in TNF-α and IL-6 are listed in Table 3 and Figs. 7 and 8.Table 3Molecular docking of 5B2T.NameBinding Energy (kcal/mol)Predicted Inhibition Constant pKi (µM)Interaction positionDistance A°Bonding typeTNF-α (PDB ID:1TNF)− 6.75.9PRO A: 1172.36 (NHCS)Conventional Hydrogen BondTYR C:1192.62 (OCNHCS)LYS C:982.51COTYR A: 1193.04Pi-Doner H-bondPRO A:1174.44Pi-AlkylILE A:1185.41Pi-AlkylALA A:964.99Pi-AlkylPRO B: 1173.91Pi-AlkylILE C:1183.32Carbon-Hydrogen BondIL-6 (PDB ID: 1IL6)− 6.15.4ARG A:1692.91Conventional Hydrogen BondARG A:1692.55Carbon-Hydrogen BondLEU A:1662.47Pi-SigmaLEU A:655.09Pi-AlkylPRO A:664.95Pi-AlkylGLU A: 1733.88Pi-AnionPHE A:1742.42Doner-DonerFigure 72D and 3D representation of the interaction of 5B2T with TNF-α (PDB ID: 1TNF).Figure 82D and 3D representation of the interaction of 5B2T with IL-6 (PDB ID: 1IL6).
5-benzylidine-2-thiohydantoin (5B2T) binds with TNF-α (PDB ID:1TNF) forming; hydrogen bonds with PRO A: 117, TYR C:119: 116, LYS C:98, TYR A: 119, ILE C:118 and with IL-6 (PDB ID: 1IL6) with ARG A:169, respectively. While, the hydrophobic interactions were observed between 5-benzylidine-2-thiohydantoin (5B2T) binds and TNF-α (PDB ID:1TNF) and IL-6 (PDB ID: 1IL6) with PRO A:117, ILE A:118, ALA A:96, PRO B: 117, LEU A:166, LEU A:65 and PRO A:66, respectively. Pi-anion interaction formed with GLU A: 173 and Unfavorable doner-doner bond was observed between 5B2T and PHE A; 174 amino acid in IL-6 (PDB ID: 1IL6) (Figs. 7 and 8).
In silico ADME/T and drug-likeness prediction of 5B2T was theoretically calculated via admetSAR and SwissADME. The molecular weight 204.25 (g/mol) has acceptable ADMET range properties. A significant value of $78\%$ revealed a high chance of crossing blood brain barrier. The percentage of human intestinal drug absorption $98.73\%$ was in the acceptable range (> 80). Octanol–water partition coefficient (Log P) of 1.03 was found to be less than 5 with no more than one violation is allowed. The topological surface areas (TPSA) were found to be in the acceptable range (< 140). In addition, H-bond acceptors (HBA) and donors (HBD) were found to be in the range of 3–6 and 2–4, respectively (Table 4).Table 4List of ADME and physicochemical properties of 5B2T.MW (g/mol)BBB+Caco2+HIA+logpTPSA A2nONnOHNHRBsN ViolationsAMEStoxicityCarcinogenicity180–500− 3 to 1.2 < 25 poor > 500 great < 25 poor > 80 high < 5 ≤ 1402.0–20.00.0–6.0 ≤ 10 < 5NontoxicNon carcinogenic204.250.7893.8198.731.0373.22 Å2210NontoxicNon carcinogenicMW: molecular weight; BBB+: blood–brain barrier; Caco2 +, Caco-2: Permeability; HIA +: %Human Intestinal Absorption; logp: logarithm of partition coefficient between n-octanol and water; TPSA2: topological polar surface area; nON: number of hydrogen bond acceptors; nOHNH: number of hydrogen bond donors; RBs: number of rotatable bond.
## Discussion
In the present study, we synthesized (Z)-5-benzylidene-2-thioxoimidazolidin-4-one under Knoevenagel condensation conditions. 1H-NMR spectrum for 5B2T showed a typical singlet signal for the hydrogen on the double bond (CH=C) at δ 6.49 ppm, and the three aromatic protons were displayed at δ 7.45–7.37 ppm as multiple signals and two aromatic protons were displayed as doublet at δ7.74 ppm (d, $J = 7.4$ Hz, 2H, phen). No overlaps were seen in 1H-NMR spectrum between a typical signal for the hydrogen on the double bond and aromatic ring signals for 5B2T. The 13C-NMR spectrum exhibited compatible signals with numbers of carbons presents in 5B2T. On the other hand, 13C-NMR spectrum indicated the presence of one stereoisomer which was represented by one carbon signal of each carbon atom in 5B2T. Under Knoevenagel condensations, E and Z geometrical isomers were possible during Knoevenagel condensations. While, 13C-NMR spectrum suggested one isomer of 5B2T and the configuration of Z-isomers suggesting that this configuration has greater thermodynamic stability due to less steric hindrance between the carbonyl group and CH = phenyl ring21.
DEN is a well-known carcinogenic chemical and is an acute hepatic toxin to many experimental animals. Prolonged administration of DEN has been proven to initiate hepatic tumors. A single intraperitoneal injection of 200 mg/kg DEN into experimental rodents can effectively induce irreversible liver injury22. The main factor that enables DEN to induce liver injury and cancer is the high possibility of this substance generating reactive oxygen species (ROS) that result in oxidative stress and damage to DNA, lipids and proteins. For DEN to be able to establish these actions, it must be metabolized first by an enzyme inside the body called cytochrome p450, which makes DEN produce high levels of ROS that lead to lipid peroxidation of the cell membrane and DNA adducts via alkylation, resulting in cell damage and injury. The successful administration of DEN in experimental animals results in $100\%$ induction of hepatocellular carcinoma (HCC)23. In this study, a single intraperitoneal injection of 200 mg/kg DEN was employed to induce liver injury and cancer in experimental animals/male rats to evaluate the hepatoprotective effects of 5B2T (a drug-like molecule) in treating hepatic disorders. The results of the biochemical analysis are shown in Fig. 2, in which it is obvious that administration of DEN caused a noted serum elevation in the levels of liver biochemical markers, including aminotransferases (ALT, AST and ALP), as an indication of liver injury and instability in hepatic metabolic activities. This elevation may be attributed to the cytoplasmic release of these enzymes into the blood circulation following the rupture of the plasma membrane due to the cellular damage induced by DEN24. Alkaline aminotransferase (ALT), aspartate aminotransferase (AST) and alkaline phosphatase (ALP) are known to be the most sensitive serum biochemical biomarkers for the diagnosis of any hepatic disability25. The oral supplementation of 5B2T showed a potent decline in the levels of the serum enzymes that were previously induced by DEN. Many studies supported these results and reported the same high levels of biochemical markers of the liver during DEN-induced carcinogenesis, including a study evaluating garlic oil against HCC induced by DEN26. This suggests the ability of 5B2T to inhibit tumor progression in rats induced by DEN, which may be attributed to the capability of the used treatment to preserve the unity and integrity of the cell's plasma membrane, preventing the leakage of these cytoplasmic enzymes from the inside of the cells to the outside through membranes and expressing hepatoprotective activities. This may also be the reason why the biochemical markers restored their activity after the administration of 5B2T also other factors may contributed including the metal type, the ligand type and its donor atoms. Alongside the biochemical evaluation, this study also included the evaluation of the enzyme-linked immune sorbent essay (ELISA) technique for further illustrations of the effects of both the inducer and the treatment on the liver. Figure 3 shows the effect of 5B2T on proinflammatory TNFα and IL-6 cytokine levels. A strong increase was observed in the serum levels of proinflammatory cytokines in rats treated with DEN, and these results were associated with cancer and inflammation (especially TNF-α and IL-6), which have been documented to be increased after the administration of DEN27. In contrast, rats that were treated with 5B2T revealed a notable decrease in the levels of these cytokines. TNF-α mediate cytotoxic effects by expression of p55 and p75 receptors in his molecular mass of 55 kDa, Thus inducing of (ROS) reactive oxygen species in their cell membrane’s nicotinamide adenine dinucleotide phosphate and endothelial mitochondria also TNF-α disrupt the electron transport chain in the mitochondrion complex beside stimulation of nuclear factor-kappa beta activation and up regulation of IL-6 expression28.
These findings were supported by the histopathological and immunohistochemical improvements in the tested liver sections. Following the administration of DEN, the characteristic histological features of the liver were distorted, disorganized and lost. This may occur because of a series of reactions, including oxidative stress, loss of cell membrane integrity, infiltration of inflammatory cells and the eventual transformation of normal hepatocytes into tumor cells29. Figure 4 demonstrates the histopathological results. Microscopic results of the histopathologically tested liver sections taken from DEN-treated rats in the first duration of the experiment revealed massive disfiguration and damage to the liver, mainly represented by portal area fibrosis and dilatation in the sinusoids. On the other hand, the 6-week duration of DEN administration showed a more serious and advanced stage of liver injury indicated by infiltration of inflammatory cells in the central vein area, bile canaliculi dilatation, peliosis and adenoma, supporting the long-term destructive effects of DEN on the liver. The same results were found in a study evaluating the effect of ginger against DEN-induced hepatotoxicity in rats30, supporting the harmful effects of DEN on the liver. In contrast, liver sections of the 3-week-treated rats with 5B2T showed improvements in the liver's general architecture, with fewer fibrotic and inflammatory cells. At the same time, rats that were treated for 6 weeks with 5B2T not only showed inhibition of the destructive effects of DEN but also showed restoration of partial to complete histological features to a greater extent than the first duration of treatment. Additionally, immunohistochemical evaluation of two main liver-specific antibodies, Ki-67 and HSA, is illustrated in Figs. 5 and 6. The placebo group showed negative staining of Ki-67 and HSA in the nucleus of the tested tissue cells. The tissue sections of DEN-treated rats revealed strong positive staining for Ki-67 and HSA antibodies for both experiment durations, which aggressively supports the pernicious effects of DEN on hepatocytes. In a study carried out to evaluate Ki-67 in DEN-induced HCC in rats, a significant increase in the number of Ki-67-positive cells was observed after DEN induction31. Treatment with 5B2T showed interesting results that improved the prognosis, reflecting the high efficacy of the chemical in curing an advanced stage of liver injury. The tested liver sections for HSA from the 3- and 6-week-treated rats demonstrated weak positive staining in the cytoplasm of the affected hepatocytes. Comparing these results to the Ki-67 evaluation, more interesting results were found suggesting that a longer treatment duration has a direct proportional relationship with the increased efficacy of the drug, regardless of the administered dosage (i.e., with the stability of the drug dose). The tested sections of the 3-week-treated rats with 5B2T showed few positively stained nuclei with Ki-67 Abs; in contrast, the 6-week treatment duration results showed total negative staining with Ki-67 Abs. The noted curing abilities of this unique derivative of the thiohydantoin group of drug-like molecules may be attributed to its possession of a stereogenic center in its 5th position32. Nevertheless, it is also believed that these chemical derivatives of thiohydantoin are able to block the liver's inflammation and injury receptors, as well as inhibit their expression. For example, it is believed that 2-thiohydantoin derivatives have a major role in the inhibitory activity of important enzymes, such as nicotinamide adenine dinucleotide phosphate (NAPDH) oxidase (NOX), which has an important role in the Krebs cycle and in ranging host defense to inflammation as well as cell signaling, and isocitrate dehydrogenase (IDH), which has a pivotal role in the tricarboxylic acid cycle10. The thiohydantoin group is widely known for its pharmacological uses as antimicrobial and anticancer agents, especially 2-thiohydantoin derivatives, because of their low toxicity to human cells. As mentioned before, the 5B2T derivative is a valuable and promising option that can be used in the future as a treatment for liver disorders due to its easy preparation and, more importantly, the possession of a stereogenic center at the fifth position33. The highest binding site (− 7.1 kcal/mol) suggests a strong binding site affinity for the 5B2T interaction with TNF-α. 5B2T establishes three hydrogen bonds with GLU B161, GLU C: 116, and PRO100 and one hydrophobic bond with GLU173. The IL-6 (PDB ID: 1IL6) binding site and 5B2T interaction has a significant binding site energy of -6.1 (Kcal/mol) and establishes one hydrogen bond with SER170 and three hydrophobic bonds with LEU 65, 166, and PRO 66. The ADMET analysis for 5B2T was found to be compatible with the rule of five (RO5) for drug-like molecule according to Lipinski and his team34. The molecular weight is < 500/gmol which is obey with the reference range. Blood–brain barrier (BBB) + value describes the ability of the compound to cross the BBB, which is in the permissible ranges. The topological surface areas (TPSA), log p, H-bond acceptors (HBA) and donors (HBD) were found to be in acceptable range. The values show that 5B2T can be absorbed by human intestine, and it is nontoxic and non-carcinogenic compound. All values revealed that 5B2T satisfies with the rules of Lipinski’s rule of five (Ro5), and Veber, 4. The promising results for 5B2T indicate that they can be used as drug candidates. In this study, single intraperitoneal injection of 200 mg/kg of DEN was employed to induce liver injury and cancer in experimental animals/male wistar rats, for the evaluation of the hepatoprotective effects of 5-benzylidine-2-thiohydantoin chemical (drug-like molecule) in treating hepatic disorders, Fig. 9 summarizes the experiment. Administration of $10\%$ of Tween $80\%$ was considered to be the study's control. Rats treated with DEN caused a massive liver injury that’s in particular caused elevation of liver's biochemical markers (total bilirubin, direct bilirubin, indirect bilirubin, alanine aminotransferase, aspartate aminotransferase and alkaline phosphatase) along with pro-inflammatory cytokines elevation (tumor necrosis factor-alpha and interleukine-6) and a detected high levels of HAS and ki-67 antibodies, eventually this resulted in loss of hepatic architecture. Rats treated with 5-benzylidine-2-thiohydantoin notably restored hepatic functions and architecture that was represented in the decrease of the same hepatic markers. These results strongly support the gold therapeutic characteristics of the treatment, and promising hepatoprotective activities. Figure 9The effect of 5-benzylidine-2-thiohydantoin on liver injury induced by diethylnitrosamine. DEN: Diethylnitrosamine, TNFα: tumor necrosis factor alpha and IL-6: Interleukin 6. TB: total bilirubin, DB: direct bilirubin, AST: acetate aminotransferase, ALT: alanine aminotransferase, ALP: alkaline phosphatase.
## Conclusion
Experimentally, the synthesized chemical (5-benzylrdine-2-thiohydantoin) clearly established curative properties against induced liver injury represented by liver biochemical markers, pro-inflammatory cytokines (TNF-α and IL-6), immuno histochemistry findings. Also molecular docking results showed strong binding with high energy at different sites between the 5-benzylidine-2-thiohydantoin and pro-inflammatory cytokines, showing the efficacy of the chemical as liver treatment.
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|
---
title: 'Effect of PCSK9 inhibition in combination with statin therapy on intracranial
atherosclerotic stenosis: A high-resolution MRI study'
authors:
- Lingshan Wu
- Qianqian Kong
- Hao Huang
- Shabei Xu
- Wensheng Qu
- Ping Zhang
- Zhiyuan Yu
- Xiang Luo
journal: Frontiers in Aging Neuroscience
year: 2023
pmcid: PMC10033935
doi: 10.3389/fnagi.2023.1127534
license: CC BY 4.0
---
# Effect of PCSK9 inhibition in combination with statin therapy on intracranial atherosclerotic stenosis: A high-resolution MRI study
## Abstract
### Introduction
Intracranial atherosclerotic stenosis (ICAS) is a common cause of stroke worldwide. Evolocumab, a proprotein convertase subtilisin/kexin type-9 inhibitor (PCSK9i), effectively lowers low-density lipoprotein (LDL) and produces favorable changes in coronary atherosclerosis. This study aimed to determine the effects of PCSK9i on intracranial plaques in moderate-intensity statin-treated individuals with ICAS.
### Methods
This prospective, observational study monitored the imaging and clinical outcomes of individuals with ICAS who were consecutively treated with moderate-intensity statins with or without PCSK9i. Individuals underwent monthly visits and repeat high-resolution MRI (HR-MRI) at week 12. The primary outcome was a change in HR-MRI after 12 weeks of treatment and the secondary outcome was major vascular events during follow-up.
### Results
Forty-nine individuals were studied (PCSK9i group: 26 individuals with 28 abnormal vascular regions; statin group: 23 with 27 regions). The PCSK9i group showed a significant reduction in the normalized wall index (0.83 vs. 0.86, $$p \leq 0.028$$) and stenosis degree (65.5 vs. $74.2\%$, $$p \leq 0.01$$). Similarly, a greater percentage of individuals with a good response to the efficacy of treatment were treated in the PCSK9i group than that in the statin group (75 vs. $44.4\%$, $$p \leq 0.021$$). The incidence of major vascular events was overall similar between the groups. The treatment options (OR = 8.441, $$p \leq 0.01$$) and prior diabetes (OR = 0.061, $$p \leq 0.001$$) were significantly associated with the efficacy of treatment.
### Discussion
Statin and PCSK9i combination treatment stabilized intracranial atherosclerotic plaques more often compared to statins alone, as documented by HR-MRI. Further study is warranted to determine if combination treatment improves clinical outcomes in ICAS.
## Introduction
Intracranial atherosclerotic stenosis (ICAS) is one of the most common causes of cerebrovascular ischemic events worldwide and is associated with stroke recurrence and higher mortality (Wang et al., 2014; Hurford et al., 2020; Gutierrez et al., 2022). The underlying mechanisms by which ICAS causes stroke include artery-to-artery embolism or arterial occlusion due to plaque rupture with in situ thrombosis, hemodynamic impairment due to highly stenotic plaques, and local small arterial branch origin occlusion (Qureshi and Caplan, 2014; Krasteva et al., 2020; Gutierrez et al., 2022). In the treatment of ICAS, data suggest that percutaneous transluminal angioplasty and stenting (PTAS) are inferior to medical therapy (Wang et al., 2020; Hurford and Rothwell, 2021). In the SAMMPRIS trial, a higher 12-month risk of stroke was reported in patients with ICAS who underwent PTAS plus aggressive medical management versus medical management alone. The SAMMPRIS trial was terminated early owing to a high rate of 30-day stroke or death in the PTAS group (Chimowitz et al., 2011). In the recent CASSISS trials, compared with medical therapy alone, the addition of PTAS to medical therapy in individuals with symptomatic ICAS resulted in no significant difference in the risk of stroke or death within 30 days or stroke in the qualifying artery beyond 30 days through 1 year (Gao et al., 2022).
Lipid-lowering treatment is an important part of the medical therapy of ICAS. Statins that work through inhibiting 3-hydroxy3-methylglutaryl coenzyme A reductase, stabilize arterial plaque in ICAS at high doses by lowering the serum low-density lipoprotein (LDL; Chung et al., 2020). Of importance, a $10\%$ reduction in LDL was estimated to reduce the carotid intima-media thickness by $0.73\%$ per year. Carotid intima-media thickness is a strong predictor of the development of carotid plaque and stenosis (Amarenco et al., 2004). However, high-dose statins are associated with side effects, such as muscle pain, abnormal liver dysfunction, and renal insufficiency. Further, there appears to be a dose response relationship between Atorvastatin and decreased liver function (Cai et al., 2021).
Proprotein convertase subtilisin/kexin type-9 inhibitor (PCSK9i) lower LDL levels by approximately $60\%$ (Sabatine et al., 2017; Schwartz et al., 2018). PCSK9i decreased the lipid component and produced favorable anatomic changes in coronary atherosclerosis consistent with plaque stabilization and regression (Nicholls et al., 2022; Ota et al., 2022; Raber et al., 2022). However, the effects of PCSK9i on ICAS remain to be determined.
The STAMINA-MRI trial employed serial high-resolution MRI (HR-MRI) to assess the effect of statin treatment on ICAS. The study results indicated that high-dose statin treatment stabilized symptomatic intracranial atherosclerotic plaques (Chung et al., 2020). However, owing to more adverse effects, high-dose statins are impractical in practice, as confirmed in a recent study that moderate dose statin monotherapy predominated as a mode of lipid-lowering therapy (Ray et al., 2021). We conducted a prospective observational study to determine the effects of PCSK9i on ICAS in moderate-intensity statin-treated individuals.
## Study design
This prospective observational study evaluated individuals with ICAS who were consecutively treated with statins with or without PCSK9i from January 2021 to July 2022. The inclusion criteria were: (a) at least 18 years of age; (b) individuals who had symptomatic or asymptomatic ICAS (>$50\%$), confirmed by computed tomographic angiography (CTA), magnetic resonance angiography (MRA), or digital subtraction angiography (DSA), at the proximal portion of the middle cerebral artery (MCA), basilar artery, or the intracranial portion of the internal carotid artery and vertebral artery; and (c) individuals who agreed to be enrolled in the trial and gave signed written informed consent. The exclusion criteria were: (a) individuals with extracranial artery stenosis >$50\%$; (b) a history of moyamoya disease, vasculitis, arterial dissection, or etiologies other than atherosclerosis; (c) individuals allergic to alirocumab, atorvastatin, or gadolinium; (d) prior or current use of alirocumab; (e) individuals treated with other lipid-lowering drugs or combinations of such drugs; (f) individuals with severe hepatic or renal dysfunction; (g) inability to tolerate MRI or with poor HR-MRI imaging quality; and (h) individuals who lacked baseline HR-MRI or had incomplete follow-up data.
Study participants were divided into two groups according to their lipid-lowering drugs: the PCSK9i group and the statin group. Individuals who received Atorvastatin 10–40 mg/day were assigned to the statin group, while those who received both Atorvastatin at 10–40 mg/day and evolocumab at 140 mg every 2 weeks were assigned to the PCSK9i group. During treatment, the evolocumab dosage was without adjustment, while a reduced dose of statins was allowed if adverse effects were observed, including myalgia, elevated liver enzymes, or other side effects linked to statin therapy. During the treatment period, individuals underwent clinical (on-site) visits at weeks 4, 8, and 12, and repeated HR-MRI imaging at week 12. Laboratory studies were obtained before and during the 12-week treatment interval.
This study was approved by the ethics board of Tongji Hospital (Wuhan, China; No. TJ-IRB20210107). All study participants provided written informed consent.
## Clinical data
Clinical data including demographic information, vascular risk factors, and laboratory metrics were collected. Vascular risk factors included hypertension, diabetes, previous ischemic stroke, coronary artery disease, and smoking. The following laboratory data at admission and follow-up were collected: total cholesterol, triglyceride, high-density lipoprotein (HDL), and LDL.
## HR-MRI protocol
All the subjects underwent a 3.0 T MRI scanner (United Imaging Healthcare, Shanghai) with a 32-channel head coil. The imaging sequences and parameters were as follows: three-dimensional (3D) time-of-flight (TOF) MR angiography: repetition time (TR)/echo time (TE) of $\frac{18.6}{3.4}$ ms, field of view (FOV) of 27.2 cm × 22 cm, flip angle of 16, slice thickness of 0.35, and 336 slices; diffuse-weighted imaging (DWI): repetition time (TR)/echo time (TE) of 4,$\frac{930}{99.20}$ ms, field of view (FOV) of 27.2 cm × 22 cm, flip angle of 90, slice thickness of 5, and 20 slices; precontrast and postcontrast T1-weighted (T1W): repetition time (TR) /echo time (TE) of $\frac{750}{22.4}$ ms, field of view (FOV) of 27.2 cm × 22 cm, flip angle of 65, slice thickness 0.66, and 220 slices. Postcontrast images were acquired after intravenous injection of a contrast agent (Gadobenate Dimeglumine Injection; BRACCO, China; 0.2 mg/kg body weight).
## MRI imaging analysis
The Medical Image Processing, Analysis, and Visualization (MIPAV) application was used to manually segment and extracted the characteristic of regions. First, based on the luminal image with TOF MR angiography, we selected the location of the most narrowed lumen (MNL) on the cross section. Second, we selected the reference site, which is the normal vessels located contralateral or proximal to the stenotic portion. Then, the vessel area (VA) and lumen area (LA) of the MNL and reference site were automatically calculated by the application after being traced and sketched manually. The wall area (WA) = VA−LA. The degree of stenosis was calculated as the stenosis degree = (1-LAMNL/LAreference) × $100\%$ (Chung et al., 2020). The wall area index = WAMNL/WAreference. The normalized wall index = WAMNL/(LAMNL + WAMNL) (Harteveld et al., 2018), which was used to evaluate the plaque burden (Sahota et al., 2021; Xiao et al., 2021). The remodeling index (RI) = VAMNL/VAreference. An RI ≥ 1.05 was defined as positive remodeling, 0.95–1.05 was intermediate remodeling, and an RI ≤ 0.95 was negative remodeling (Ma et al., 2010). The presence or absence and the pattern of enhancement were determined by the pre- and postcontrast T1 fluid-attenuated inversion recovery images. The presence of enhancement was defined as a > $20\%$ increase in the normalized signal intensity of the plaque after contrast agent injection, which was calculated as signal intensity of the plaque/signal intensity of the mid pons. If the enhancement was uniform or circumferential, it was considered concentric; otherwise, it was regarded as eccentric. The enhanced area (EA) was manually drawn. The enhancement degree = EAMNL/WAMNL × $100\%$ (Ryoo et al., 2015). Figure 1 shows an illustration of HR-MRI measurements. The efficacy of treatment was defined as the change of stenosis degree. According to the percentage change of stenosis degree, individuals were categorized into two groups: good responders (<0) and poor responders (>0).
**Figure 1:** *Illustration of HR-MRI arterial measurements.*
Evaluation of HR-MRI was conducted by two experienced observers (Lingshan Wu and Qianqian, Kong) who were blinded to treatment status and sequencing of imaging studies (baseline/follow-up). Disagreements were resolved by discussion, and when necessary, a third reader (Xiang Luo) with expertise in the field were consulted. The intraclass correlation coefficients for the measured HR-MRI parameters were above 0.80, which indicated good reliability.
## Outcomes
The primary endpoint was changes in HR-MRI variables before and after the 12-week treatment. Secondary endpoints were the major vascular events from baseline to 12 weeks, including vascular deaths, myocardial infarction (MI), and cerebrovascular events (ischemic stroke, transient ischemic attack, and hemorrhagic stroke).
## Adverse events
Any adverse events that occurred during the follow-up, such as muscle pain, liver dysfunction as determined by a change in laboratory data, allergy, and renal insufficiency, were reported.
## Statistical analysis
The statistical analysis was performed using SPSS version 26.0 (IBM, SPSS, Chicago, IL, United States). Continuous variables were compared using a t-test or the Mann–Whitney U-test. Categorical variables were compared with χ2 tests. Univariate logistic regression analysis was used to determine the factors associated with the efficacy of treatment. Variables with $p \leq 0.1$ in univariate analyses were included in the subsequent multivariable analysis using the forward stepwise method. Results were given by the odds ratio (OR) and $95\%$ confidence interval (CI). A value of $p \leq 0.05$ was considered significant.
## Patient characteristic
Of the 63 individuals evaluated, 49 with ICAS completed the 12-week treatment and were examined with follow-up HR-MRI (including 26 in the PCSK9i group and 23 in the statin only group). The disposition of individuals enrolled in the study is illustrated in Figure 2. Compared with the PCSK9i group, the statin group had a higher proportion of men (82.6 vs. $50\%$, $$p \leq 0.017$$). During the course of the study, the ratio of individuals who received 20 mg Atorvastatin between two groups was not different (82.6 vs. $92.3\%$, $$p \leq 0.400$$), nor was the ratio of those who received dual antiplatelet treatment (73.9 vs. $80.8\%$, $$p \leq 0.566$$). There was no significant difference in stroke risk factors, the remodeling pattern, or enhancement characteristics between the two groups. The clinical characteristics are summarized in Table 1.
**Figure 2:** *Cohort selection decision tree. PCSK9i, a proprotein convertase subtilisin/kexin type 9 inhibitor; HR-MRI, high-resolution MRI.* TABLE_PLACEHOLDER:Table 1
## Laboratory data
There was no significant difference in laboratory results between the two groups at baseline (Table 2). Compared with the baseline, the total cholesterol (2.04 vs. 3.93, $p \leq 0.001$), triglycerides (0.94 vs. 1.37, $$p \leq 0.035$$), and LDL (0.64 vs. 2.38, $p \leq 0.001$) in the PCSK9i group were significantly decreased, while only the total cholesterol (2.81 vs. 3.36, $$p \leq 0.018$$) and LDL (1.44 vs. 2.03, $$p \leq 0.017$$) were significantly decreased in the statin group. Compared with the statin group, the total cholesterol (2.04 vs. 2.81, $p \leq 0.001$) and LDL (0.64 vs. 1.44, $p \leq 0.001$) on treatment in the PCSK9i group were significantly lower. The percentage change in the total cholesterol (−47.4 vs. −$21.6\%$, $p \leq 0.001$) and LDL (−68.3 vs. −$32.1\%$, $p \leq 0.001$) were significantly greater in the PCSK9i group than in the statin group.
**Table 2**
| Unnamed: 0 | Statin (n = 23) | PCSK9i (n = 26) | Value of p |
| --- | --- | --- | --- |
| Total cholesterol (mmol/L) | | | |
| Baseline | 3.36 (2.87 ~ 3.98) | 3.93 (2.59 ~ 4.36) | 0.385 |
| On treatment | 2.81 (2.33 ~ 3.78) | 2.04 (1.79 ~ 2.21) | <0.001 |
| Percent change | −21.6 (−34.1 ~ 3.2) | −47.4 (−57.2 ~ −24.6) | <0.001 |
| p value from baseline | 0.018 | <0.001 | |
| Triglyceride (mmol/L) | | | |
| Baseline | 1.53 (0.91 ~ 2.04) | 1.37 (1.11 ~ 1.83) | 0.930 |
| On treatment | 1.14 (0.85 ~ 1.53) | 0.94 (0.79 ~ 1.49) | 0.522 |
| Percent change | −22.5 (−40.6 ~ 27.3) | −27.6 (−44.4 ~ −3.1) | 0.272 |
| Value of p from baseline | 0.358 | 0.035 | |
| HDL (mmol/L) | | | |
| Baseline | 0.99 ± 0.26 | 0.95 ± 0.19 | 0.650 |
| On treatment | 1.01 ± 0.22 | 1.06 ± 0.27 | 0.491 |
| Percent change | 2.3 ± 14.4 | 11.8 ± 24.1 | 0.137 |
| Value of p from baseline | 0.773 | 0.129 | |
| LDL (mmol/L) | | | |
| Baseline | 2.03 (1.54 ~ 2.69) | 2.38 (1.25 ~ 2.82) | 0.644 |
| On treatment | 1.44 (1.20 ~ 1.94) | 0.64 (0.43 ~ 0.76) | <0.001 |
| Percent change | −32.1 (−42.7 ~ 4.2) | −68.3 (−81.3 ~ −52.6) | <0.001 |
| Value of p from baseline | 0.017 | <0.001 | |
## Primary and secondary endpoints
There was no significant difference in HR-MRI findings between the two groups at baseline. The stenosis degree decreased from 74.2 to $65.5\%$ in the PCSK9i group ($$p \leq 0.010$$ for comparison from baseline). A significant reduction in the normalized wall index (0.83 vs. 0.86, $$p \leq 0.028$$) was observed in the PCSK9i group but not the statin group. Similarly, a greater percentage of individuals with a good response to the efficacy of treatment were treated in the PCSK9i group than that in the statin group (75 vs. $44.4\%$, $$p \leq 0.021$$). However, the wall area index, remodeling index, enhancement area, and enhancement degree were not significantly different, regardless of PCSK9i administration. Details of the HR-MRI findings before and after treatment are summarized in Table 3.
**Table 3**
| Unnamed: 0 | Statin (number of regions = 27) | PCSK9i (number of regions = 28) | Value of p |
| --- | --- | --- | --- |
| Wall area index | | | |
| Baseline | 1.05 (0.90–1.53) | 1.14 (0.77–1.45) | 0.798 |
| Follow-up at week 12 | 1.10 (0.83–1.67) | 1.103 (0.83–1.44) | 0.481 |
| Percentage change | −1.2 (−7.68–11.6) | −4.9 (−18.1–11.3) | 0.350 |
| p value from baseline | 0.883 | 0.635 | |
| Normalized wall index | | | |
| Baseline | 0.83 ± 0.048 | 0.86 ± 0.06 | 0.062 |
| Follow-up at week 12 | 0.84 ± 0.05 | 0.83 ± 0.06 | 0.496 |
| Percentage change | 1.1 ± 4.4 | −3.8 ± 4.5 | <0.001 |
| p value from baseline | 0.532 | 0.028 | |
| Stenosis degree (%) | | | |
| Baseline | 69.3 ± 12.6 | 74.2 ± 10.4 | 0.141 |
| Follow-up at week 12 | 71.0 ± 12.8 | 65.5 ± 13.8 | 0.103 |
| Percentage change | 3.2 ± 14.2 | −12.0 ± 13.1 | <0.001 |
| p value from baseline | 0.620 | 0.010 | |
| The efficacy of treatment, n (%) | | | 0.021 |
| Good responder | 12 (44.4) | 21 (75) | |
| Poor responder | 15 (55.6) | 7 (25) | |
| Remodeling index | | | |
| Baseline | 0.72 (0.56–0.93) | 0.73 (0.56–0.92) | 0.566 |
| Follow-up at week 12 | 0.73 (0.58–0.89) | 0.79 (0.54–0.93) | 0.768 |
| Percentage change | −2.7 (−7.3 ~ 4.3) | 0.31 (−11.5 ~ 13.7) | 0.614 |
| p value from baseline | 0.849 | 0.787 | |
| Enhancement area (EA) | | | |
| Baseline | 10.91 ± 7.82 | 8.63 ± 6.67 | 0.113 |
| Follow-up at week 12 | 10.71 ± 7.41 | 8.01 ± 6.35 | 0.113 |
| Percentage change | −1.2 ± 29.6 | −8.3 ± 31.9 | 0.421 |
| p value from baseline | 0.929 | 0.722 | |
| Enhancement degree | | | |
| Baseline | 0.61 ± 0.17 | 0.64 ± 0.21 | 0.711 |
| Follow-up at week 12 | 0.58 ± 0.22 | 0.61 ± 0.31 | 0.710 |
| Percentage change | −3.2 ± 29.5 | −1.8 ± 39.7 | 0.894 |
| p value from baseline | 0.573 | 0.697 | |
During the follow-up, one individual in each group experienced a major vascular event (Table 4).
**Table 4**
| Patient No. | Age | Sex | Site | Clinical presentation | Treatment | Outcome |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | 43 | Female | LMCA | symptomatic | PCSK9i + moderate-intensity statins | Within 12 weeks of follow-up, this patient experience TIA twice manifested by right hemiparesis. |
| 2 | 34 | Female | RICA | asymptomatic | moderate-intensity statins alone | The patient experienced a right hemispheric stroke 6 weeks after the baseline HR-MRI. |
## Safety
As shown in Table 5, the ratio of individuals with abnormal liver dysfunction was similar between two groups (4.3 vs. $7.7\%$, $$p \leq 1$$). During the follow-up, no other adverse events occurred.
**Table 5**
| Events | Statin (n = 23) | PCSK9i (n = 26) | Value of p |
| --- | --- | --- | --- |
| Muscle problems, n (%) | 0 | 0 | |
| ALT >3 × ULN, n (%) | 1 (4.3) | 2 (7.7) | 1.0 |
| Renal insufficiency, n (%) | 0 | 0 | |
| General allergic reaction, n (%) | - | 0 | |
| Local injection site reaction, n (%) | - | 0 | |
| Other, n (%) | 0 | 0 | |
## Exploratory analyses
Based on these results, univariate and multiple logistic regression analyses were performed to identify the independent factors associated with the efficacy of treatment Among the clinical and HR-MRI variables, the treatment options (OR = 8.441, $$p \leq 0.01$$) and prior diabetes (OR = 0.061, $$p \leq 0.001$$) were significantly associated with the efficacy of treatment (Table 6).
**Table 6**
| Unnamed: 0 | Univariate analysis | Univariate analysis.1 | Multivariate analysis | Multivariate analysis.1 |
| --- | --- | --- | --- | --- |
| PCSK9i group | 3.75 (1.195–11.768) | 0.023 | 8.441 (1.668–42.708) | 0.01 |
| Age | 0.993 (0.948–1.039) | 0.749 | | |
| Male sex | 0.75 (0.229–2.453) | 0.634 | | |
| Hypertension | 0.342 (0.094–1.241) | 0.103 | | |
| Diabetes | 0.124 (0.035–0.443) | 0.001 | 0.061 (0.011–0.329) | 0.001 |
| Previous ischemic stroke | 1.957 (0.526–7.276) | 0.317 | | |
| Coronary artery disease | - | 0.999 | | |
| Current smoking | 1.062 (0.361–3.126) | 0.912 | | |
| Enhancement | 0.444 (0.121–1.636) | 0.223 | | |
| Wall area index | 2.042 (0.689–6.046) | 0.198 | | |
| Normalized wall index | - | 0.209 | | |
| Remodeling index | 0.705 (0.305–1.629) | 0.413 | | |
| Enhancement area (EA) | 0.961 (0.891–1.035) | 0.291 | | |
| Enhancement degree | 0.054 (0.003–1.107) | 0.058 | | |
## Discussion
This study assessed the effects of PCSK9i on intracranial plaques in moderate-intensity statin-treated individuals with ICAS. Our findings show that the addition of PCSK9i to statins, compared with statins alone, resulted in a greater reduction in both the LDL and stenosis degree after 12 weeks. Treatment with PCSK9i lowered the LDL from 2.38 to 0.64 mmol/L and the mean stenosis degree from 74.2 to $65.5\%$. The plaque burden also decreased than baseline, with NWI from 74.2 to $65.5\%$. Based on the exploratory analyses, our findings indicate that the addition of PCSK9i to statins is a favorable factor for the improvement of the stenosis degree, while diabetes is a risk factor.
Individuals with diabetes might have a higher risk of ICAS (Qureshi and Caplan, 2014; Ma et al., 2019b). We found that diabetes is a risk factor for the improvement of the stenosis degree. Diabetes is associated with increased plaque burden, healed plaque ruptures, and positive remodeling, along with greater calcification in type 2 diabetes (Yahagi et al., 2017). Inflammatory (macrophages and T lymphocytes) infiltrate and necrotic core is greater in diabetes vs. non-diabetics. Moreover, diabetes is associated with increased plaque burden, positive remodeling, and calcification (Yahagi et al., 2017).
Plaque rupture with in situ thrombosis is one of the most important causes of ICAS-associated stroke, which can produce artery-to-artery embolism or occlusion of the artery (Bentzon et al., 2014; Gutierrez et al., 2022). Data indicate that lower LDL levels are associated with regress of atherosclerosis (Nissen et al., 2006). Similarly lower LDL levels are associated with reduce the risk of atherosclerotic cardiovascular disease in individuals with coronary artery disease (Ference et al., 2017). The STAMINA-MRI trial showed that high-dose statin treatment stabilized symptomatic intracranial atherosclerotic plaques (Chung et al., 2020). The lowering of blood lipid levels is especially helpful for stroke risk reduction in individuals with large artery atherosclerosis plaque (Hindy et al., 2018). Compared with less-intensive lipid-lowering statin-based therapies, more intensive therapies may be associated with a reduced risk of recurrent stroke in individuals with atherosclerosis (Lee et al., 2022).
Latest guidelines suggest an LDL reduction of ≥$50\%$ from baseline and an LDL goal of <1.4 mmol/L for secondary prevention are desirable in very-high-risk individuals (Mach et al., 2020). Log-linear dose–response after statin treatment were observed, consistent with the so-called “rule of $6\%$,” which describes the additional percentage reduction in the LDL from pretreatment for each statin dose doubling (Oni-Orisan et al., 2018). While there was also a dose–response relationship between statin and liver dysfunction, this relationship was only determined in Atorvastatin (Cai et al., 2021). Given the side effects and dose–response effect of statin, the target LDL level is hard to achieve with a statin alone. LDL receptors on the hepatocyte cell membrane can decrease the levels of circulating LDL particles by binding to the LDL particles/LDL receptor complex (Gallego-Colon et al., 2020). At the lysosome, LDL particles are recycled into esterified cholesterol and triglycerides to carry various roles within the cell. Similarly, the LDL receptor can be recycled back to the cell surface (Gallego-Colon et al., 2020).
Proprotein convertase subtilisin/kexin type-9 inhibitor can diminish the clearance of LDL from circulation by increasing LDL receptors to catabolism in the hepatocyte and blocking the normal recycling of the LDL receptor to the surface of the hepatocyte (Cohen et al., 2006; Taylor and Thompson, 2016). PCSK9i is a monoclonal antibody that effectively lowers LDL levels by approximately $60\%$ (Sabatine et al., 2017; Schwartz et al., 2018). Similarly, statins inhibit the rate-limiting step of cholesterol biosynthesis up-regulating hepatic LDL receptors expression (Nurmohamed et al., 2021). In the GLAGOV trial, individuals with coronary artery disease treated with statins and PCSK9i had a greater decrease in percentage atheroma compared with those given statins alone (Nicholls et al., 2016). The Fourier trial found that PCSK9i in a background of statin therapy lowered LDL levels to a median of 30 mg/dL and reduced the risk of cardiovascular events (Sabatine et al., 2017). We found that compared with moderate-intensity statins alone, the addition of PCSK9i significantly reduced the stenosis degree in individuals with ICAS, which suggests that PCSK9i can stabilize and regress intracranial atherosclerosis. Increased fibrous cap thickness and decreased macrophage accumulation grade were greater with PCSK9i and statin combination treatment than with the statin treatment alone. Matrix metalloproteinases released from the accumulated macrophages can degrade collagen tissue of the fibrous cap, which is a major determinant of plaque vulnerability (Yano et al., 2020). Similarly, the percentage change in lipid arc was greater in the statin plus PCSK9i group vs. the statin alone (Yano et al., 2020). Note that compared with baseline, the stenosis degree after treatment made no difference among individuals in statin group. This differs from the STAMINA-MRI trial. In the STAMINA-MRI trial, all the subjects included under the 6-month high-dose (40–80 mg Atorvastatin or 20 mg Rosuvastatin) statin treatment, with LDL decreased from 3.25 to 1.58 mmol/L. While in this study, $82.6\%$ of patients received Atorvastatin 20 mg/day for 12 weeks in the statin group and the degree of LDL decrease is lower than in the STAMINA-MRI trial (from 2.03 to 1.44 mmol/L). As mentioned above, the greater the LDL reduction, the greater the carotid intima-media thickness reduction (Amarenco et al., 2004). Therefore, the difference between the two studies perhaps secondary to the lower statin dose and shorter follow-up time of our study. The incidence of major vascular events was similar overall between the groups during the short duration of our study. What is more, there were no hemorrhagic strokes observed in the PCSK9i group, despite LDL levels ranging from 2.38 to 0.64 mmol/L. This is relevant, as others noted that lower LDL levels were associated with a higher risk of hemorrhagic stroke (Wang et al., 2013; Ma et al., 2019a).
## Strengths and limitations
This study had several strengths. To the best of our knowledge, it was the first to evaluate the effects of PCSK9i on intracranial plaques in moderate-intensity statin-treated individuals with ICAS through HR-MRI. The study design permitted an assessment of the effect of PCSK9i on the background of moderate statin therapy. Another plus was the overall rigor of the cohort determination, such that only individuals with ICAS and atherosclerosis were eligible for inclusion.
Several limitations should be noted. First, this was a prospective but observational study. Inherent selection bias may have occurred among the groups. Second, the relatively small number of study subjects and the short interval between baseline and follow-up HR-MRI were other limitations of this study. Data indicate that 12-week-treatment of statin and PCSK9i combination treatment produced incremental growth in fibrous-cap thickness and regression of the lipid-rich plaque in coronary artery disease (Yano et al., 2020), while the short interval predicted to under emphasize vascular changes occurring beyond 12 weeks. Based on the present results, larger and longer-term studies should investigate PCSK9i in individuals with ICAS. Third, all analysis in this study was performed at a single level, the location of the most severe stenosis. Others applied 3D volumetric analysis to assess carotid plaque (Sadat et al., 2010). However, volumetric analysis could not be performed in the present study secondary to the small diameter of the intracranial arteries (Ryoo et al., 2015). Last, treatment with antiplatelet, antihypertensive, and hypoglycemic drugs may alter with the progression of atherosclerotic plaques and the major vascular events in individuals with ICAS.
In conclusion, this study evaluated the efficacy of PCSK9i on intracranial plaques in moderate-intensity statin-treated patients with ICAS. Addition of PCSK9i to moderate statin therapy, compared with statins alone, significantly reduced the stenosis degree. Further research is needed to understand whether this combination improves clinical outcomes in ICAS.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
XL, SX, and WQ: concept and design. LW, QK, HH, and XL: acquisition of data. LW, HH, and PZ: statistical analysis. LW: drafting of the manuscript. LW, QK, HH, SX, WQ, PZ, ZY, and XL: critical revision of the manuscript for important intellectual content. All authors contributed to the article and approved the submitted version.
## Funding
This study was supported by the National Nature Science Foundation of China (82171385 to XL), Key Research and Development Program of Hubei Province (2020BCA070 to XL), the Application Foundation Frontier Special Project of Wuhan Science and Technology Bureau (2020020601012226 to XL), and the Flagship Program of Tongji Hospital (2019CR106 to XL).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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---
title: Neuronal P2X4 receptor may contribute to peripheral inflammatory pain in rat
spinal dorsal horn
authors:
- László Ducza
- Andrea Gajtkó
- Krisztina Hegedűs
- Erzsébet Bakk
- Gréta Kis
- Botond Gaál
- Roland Takács
- Péter Szücs
- Klára Matesz
- Krisztina Holló
journal: Frontiers in Molecular Neuroscience
year: 2023
pmcid: PMC10033954
doi: 10.3389/fnmol.2023.1115685
license: CC BY 4.0
---
# Neuronal P2X4 receptor may contribute to peripheral inflammatory pain in rat spinal dorsal horn
## Abstract
### Objective
Intense inflammation may result in pain, which manifests as spinal central sensitization. There is growing evidence that purinergic signaling plays a pivotal role in the orchestration of pain processing. Over the last decade the ionotropic P2X purino receptor 4 (P2X4) got into spotlight in neuropathic disorders, however its precise spinal expression was scantily characterized during inflammatory pain. Thus, we intended to analyze the receptor distribution within spinal dorsal horn and lumbar dorsal root ganglia (DRG) of rats suffering in inflammatory pain induced by complete Freund adjuvant (CFA).
### Methods
CFA-induced peripheral inflammation was validated by mechanical and thermal behavioral tests. In order to ensure about the putative alteration of spinal P2X4 receptor gene expression qPCR reactions were designed, followed by immunoperoxidase and Western blot experiments to assess changes at a protein level. Colocalization of P2X4 with neuronal and glial markers was investigated by double immunofluorescent labelings, which were subsequently analyzed with IMARIS software. Transmission electronmicroscopy was applied to study the ultrastructural localization of the receptor. Concurrently, in lumbar DRG cells similar methodology has been carried out to complete our observations.
### Results
The figures of mechanical and thermal behavioral tests proved the establishment of CFA-induced inflammatory pain. We observed significant enhancement of P2X4 transcript level within the spinal dorsal horn 3 days upon CFA administration. Elevation of P2X4 immunoreactivity within *Rexed lamina* I-II of the spinal gray matter was synchronous with mRNA expression, and confirmed by protein blotting. According to IMARIS analysis the robust protein increase was mainly detected on primary afferent axonterminals and GFAP-labelled astrocyte membrane compartments, but not on postsynaptic dendrites was also validated ultrastructurally within the spinal dorsal horn. Furthermore, lumbar DRG analysis demonstrated that peptidergic and non-peptidergic nociceptive subsets of ganglia cells were also abundantly positive for P2X4 receptor in CFA model.
### Conclusion
Here we provide novel evidence about involvement of neuronal and glial P2X4 receptor in the establishment of inflammatory pain.
## 1. Introduction
Pain is a striking and devastating symptom of many diseases affecting the physical and mental well-being (Noehren et al., 2015; Raffaeli et al., 2021). Several lines of evidence support that the main causating agents are inflammatory factors, malignant disorders as well as damages to the nervous system (Sica et al., 2019; Troiani et al., 2019). The condition may emerge in the form of allodynia, hyperalgesia or spontaneous pain states, in which the nociceptive threshold is markedly altered resulting in central sensitization (Majedi et al., 2018; Davies et al., 2019; Kuner and Kuner, 2021). Earlier findings retrieved from animal models confirmed the activation of peripheral afferents reacting to chemical stimuli released by immune cells, keratinocytes and endothel cells (Ji et al., 2016; Eitner et al., 2017). Till date much effort has been made to gain a better understanding of the pathological mechanisms including peripheral plasticity, transduction, and propagation of noxious inputs (Julius, 2013; Arcourt and Lechner, 2015; Bennett et al., 2019). In addition, the role of glial cells received growing attention lately in the central sensitisation (Salter and Beggs, 2014; Chen et al., 2018). Owing to the complicated molecular machinery involved in pain conditions, the treatment strategy is still a challenging health issue (Murray and Lopez, 2013). Purinergic P2X ATP sensitive ligand-gated ionotropic receptors are trimeric non-selective cation channels assembled from seven different homomeric and heteromeric subunits (P2X1–P2X7) encoded by different mammalian genes. P2X4 receptor is expressed by several cell types, involved in various physiological processes such as epithelial transport, metabolism, liver regeneration. The receptor is permeable to Na+, K+, and Ca2+ ions, and its activation upon ATP-binding induces cell depolarisation (Suurväli et al., 2017). In accordance with literature P2X4 receptor highly contributes to neuropathic pain. Overexpression of P2X4 is associated with nonadaptive modification of synaptic strength leading to central sensitisation (Suurväli et al., 2017; Long et al., 2018; Zhang et al., 2020). Earlier studies reported that in neuropathic pain P2X4 was mostly expressed by spinal microglial cells. Upon activation microglial cells initiate many signaling pathways via kinase cascades, which result in the secretion of variety of molecules that ultimately aggravate pain (Tsuda, 2016; Teng et al., 2019). Moreover, P2X4 may alter activity via neuron-microglial interactions (Zhang et al., 2020). Despite its unequivocal role in microglial cells, abundant neuronal P2X4 expression was also detected earlier at a spinal level (Lê et al., 1998), however evidence is still lacking on its distribution in DRG neurons and superficial spinal dorsal horn, even if previous works has highlighted the potential of neuronally expressed P2X4 receptor (Ulmann et al., 2010; Lalisse et al., 2018; Duveau et al., 2020). Therefore, the present study aimed to (i) describe the P2X4 expression in lumbar DRG and superficial spinal dorsal horn at a gene-and protein level, (ii) screen the potential receptor expressing sites here upon peripheral inflammation.
## 2.1. Animals
The concept has been approved by the Animal Welfare Committee of the University of Debrecen (licence number: $\frac{212}{2015}$/DEMÁB) in agreement with the national laws and European Union regulations (Directive of 24 November 1986 ($\frac{86}{609}$/EEC, European Communities Council). Animals were kept under standard ad libitum feeding conditions. Experiments were conducted on adult (3–4 months, weighing 250–300 g) male Wistar-Kyoto rats (Gödöllő, Hungary), among which two groups, the control ($$n = 21$$) and CFA treated ($$n = 18$$) animals were utilized. Increased focus was placed on minimizing animal use in our experimental design, therefore care was taken to follow the Three Rs (3Rs) guiding principles of animal experimentation. In CFA treated rats peripheral inflammatory pain was induced by administering injection of 100 μL 1:1 mixture of physiological saline and CFA agent (Sigma-Aldrich, St Louis, USA, catalog no.: F5881) into the right hindpaw described earlier by Hylden et al. [ 1989]. Based on the latest terminology of International Association of the Study of Pain (IASP1) our experimental design is interpreted as one of the nociceptive pain models.
## 2.2. Mechanical allodynia test
Measurement of mechanical allodynia were carried out on 3 control and 3 CFA-treated animals. Rats were exposed to noxious mechanical stimulus to evaluate hindpaw withdrawal reflex. Mechanical threshold was tested by modified von Frey test (Dynamic Plantar Aesthesiometer, Ugo Basile, Gemonio, Italy). Following a 20-min habituation in a cage covered with acrylic sidewalls and mesh floor, a flexible, von Frey-type filament was directed with gradually increasing force on the plantar surface of the animal hindpaw until withdrawal. Mechanical withdrawal threshold (MWT) of both hindpaws was recorded prior to CFA injection, then repeated daily upon CFA administration. The measurement was replicated five times for each paw alternating between the left and right hindpaw.
## 2.3. Thermal allodynia test
Experiments were carried out on 3 control and 3 CFA-treated animals. Rats were placed into a plastic cage with a Perspex enclosure that rendered the animals unrestrained for the duration of the measurement. Temperature threshold was tested by Plantar Test Instrument-Hargreaves Apparatus (Ugo Basile, Genomio, Italy). Following a 20-min habituation the hindpaw of the rats were positioned above an infrared light source, directed onto the plantar surface until the animal withdrew its paw. Thermal withdrawal latency (TWL) of both hindpaws was recorded prior to CFA injection, then repeated daily upon CFA administration. The measurements were repeated five times in each case alternating between the left and right hindpaw.
## 2.4.1. RNA isolation and reverse transcription
Experiments were carried out on 3 control and 3 CFA-treated rats. Animals were euthanized 3 days after CFA administration with intraperitoneally injected sodium pentobarbital (50 mg/kg). Control animals were handled similarly, though without CFA injection. L4-L5 spinal segments and lumbar DRG were removed, then immersed in RNAlater Stabilizing Solution (Thermo Fisher Scientific, Waltham, MA, USA, catalog no.: 00695052). Thereafter samples underwent flash freezing in liquid nitrogen, then storage at −80°C. Samples were mixed with TRIzol Reagent (Applied Biosystems, Foster City, CA, USA), then centrifuged at 10,000 g at 4°C for 15 min with a supplement of $20\%$ RNase-free chloroform. Upon incubation in 1,000 μL RNase-free isopropanol for 1 h at −20°C, total RNA was purified in $70\%$ ethanol. Finally, the RNA precipitate was resuspended in RNase-free water, then stored at −80°C. RNA purity and concentration were measured using a NanoDrop 1,000 spectrophotometer (Thermo Fisher Scientific). Reverse transcription was performed from 1,000 ng of total RNA using the High Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific) according to the instructions provided by the manufacturer. The resulting cDNA was stored at −20°C.
## 2.4.2. Quantitative PCR
RT-qPCR reactions were carried out by using the comparative ∆∆Ct method. As a relative quantification SYBR Green-based system (Promega, Madison, WI, USA) was applied. P2X4 primer pairs were designed by the Primer-BLAST service of the NCBI (National Institutes of Health) and ordered from Integrated DNA Technologies (IDT, Coralville, IA, USA). Nucleotide sequences of the used primer pairs are provided in Table S1 in the Supporting information section. GoTaq qPCR Master Mix (Promega) was applied for the reactions that were performed with a QuantStudio 3 Real-Time PCR System (Thermo Fisher Scientific). As a standard thermal profile, initial denaturation (at 95°C, for 2 min), then 40 cycles of denaturation (at 95°C, for 5 s), annealing and extension (at 60°C, for 30 s), and final extension (at 72°C, for 20 s) were used. Following the 40 cycles of amplification, a melt curve stage was carried out involving 3 steps: denaturation at 95°C for 15 s, annealing at 55°C for 15 s, and a dissociation phase with 0.15°C/s increments between 55°C and 95°C. Acquired data were first analyzed using the QuantStudio Design and Analysis Software (version 1.5.1), then processed with Microsoft Excel programme. The ∆∆Ct method (Livak and Schmittgen, 2001) was used for data analysis, Ct values were normalized onto the most stably expressed reference gene out of four candidates and then treated and respective control values were compared. NormFinder software was used to determine the optimal normalization gene from our selection of four reference genes based on expression stability. In our case, RT-qPCR data were normalized to the expression level of RPL4 (ribosomal protein L4, Supplementary Table S1).
## 2.5.1. Tissue preparation
Immunohistochemical experiments were carried out on 9 control and 9 CFA-treated rats. Animals were sacrificed as described in chapter 2.4 quantitative real-time PCR analysis. Subsequently, transcardial perfusion was conducted with oxygenated physiological saline solution (mixture of $95\%$ O2, $5\%$ CO2) and fixative containing either $4\%$ paraformaldehyde (single-and double imunolabelings) or $2.5\%$ paraformaldehyde and $0.5\%$ glutaraldehyde (electron microscopy). Thereafter, L4-L5 segments of spinal cord and lumbar DRG were collected and postfixed, then cryoprotected in 0.1 M PB solution containing $20\%$ sucrose concentration overnight. Proper penetration of chemicals was granted by immersing spinal cord into liquid nitrogen. Agar-embedded samples were sectioned at 50 μm thickness with vibratome (Leica VT1000S, Leica Biosystems, Deer Park, IL 60010 United States).
## 2.5.2. Single immunolabeling
Immunoperoxidase reactions were carried out on rat spinal dorsal horn sections of 3 control and 3 CFA injected animals. Prior to antibody treatments the sections were gently shaken in 0.01 M Tris-phosphate-buffered saline (TPBS, pH 7.4) solution supplemented with $10\%$ normal goat serum (NGS) (Vector Labs, Burlingame, CA, USA, catalog no.: S-1000) for 50 min. Free-floating sections were incubated with anti-P2X4 antibody (1:1,000; Alomone Labs, Jerusalem BioPark, Israel catalog no.: APR-002) for 72 h at 4°C. Anti-P2X4-antibody was also diluted in TPBS to which $1\%$ NGS was added. Thereafter, the sections were transferred into biotinylated goat anti-rabbit IgG solution (1:200; Vector Labs) for 4 h at room temperature. Afterwards, avidin-biotinylated horseradish peroxidase complex (1:100, Vector Labs) was transferred on the sections for 24 h at 4°C, then chromogen reaction was visualized with 3,3′-diaminobenzidine (DAB) reagent (Sigma-Aldrich, catalog no.: D-5637). Sections were mounted on glass slides and coverslipped with DPX medium (Sigma-Aldrich). Fluorescent micrographs were captured by Olympus CX-31 epifluorescent microscope equipped with Olympus DP-74 camera (both manufactured by Olympus Co. Ltd., Tokyo, Japan). Optical density of images was analyzed by ImageJ software (version 1.8.0, NIH). As a control, rabbit anti-P2X4 antibody (diluted as 1:1,000, Alomone Labs) specificity have been preliminarily verified in knockout animals by the manufacturer, but we similarly tested it on our samples by adding anti-P2X4 antibody to synthetic P2X4 peptide (Alomone Labs catalog no.: BLP-PR002) for antibody depletion. Briefly, synthetic blocking peptide was blended with antibody (equimolar 1 μg peptide/1 μg antibody ratio), stored at 4°C overnight, then centrifuged. Following incubation with the mixture for 72 h at 4°C, spinal cord sections were further transferred into biotinylated rabbit anti-goat secondary antibody dissolved in TPBS (diluted as 1:200, Vector Labs, USA) for 4 h at room temperature. Afterwards, the sections were treated similarly to the single immunostaining method. The preadsorption of blocking peptide to anti-P2X4 antibody abolished the specific immunolabeling (Supplementary Figure S1A). In addition, negative control reaction omitting the primary antibody against P2X4 receptor was also carried out (Supplementary Figure S1B). Negative control reaction omitting the primary antibody was also performed in lumbar DRG (Supplementary Figure S1C). Of note, original DAB chromogen labeled images were converted into monochromatic gray-scale images to enhance contrast.
## 2.5.3. Double immunolabeling
Double immunolabeling protocols were performed on 3 control and 3 CFA treated animals to study the distribution of P2X4 on variety of neuronal and glial labelings including markers of primary afferents (IB4, CGRP), axonterminals of glutamatergic and γ-aminobutyric acid (GABA) ergic interneurons (VGLUT2, VGAT), postsynaptic densities of excitatory and inhibitory synapses (PSD95, Gephyrin), as well as astrocytes (GFAP) and microglial cells (Iba1) within spinal dorsal horn. Prior to antibody treatments the sections were gently shaken in phosphate-buffered saline (PBS, pH 7.4) solution supplemented with $10\%$ NGS serum. Antibodies were also diluted in PBS to which $1\%$ NGS was added. First, free-floating sections were incubated with an antibody mixture that contained rabbit anti-P2X4 antibody (1:1,000; Alomone Labs) and one of the antibodies as follows: [1] guinea pig anti-calcitonin gene-related peptide (CGRP, 1:2,000, Peninsula Labs, San Carlos, California, USA, catalog no.: T5027), [2] biotinylated isolectin B4 (IB4, 1:2000, Invitrogen, Eugene, Oregon, USA, catalog no.: I21414), [3] guinea pig anti-vesicular glutamate transporter 2 (VGLUT2, 1:2,000, Millipore, Temecula California, USA, catalog no.: AB2251), [4] mouse anti-vesicular gamma-amino butyric acid transporter (VGAT, 1:200, Synaptic Systems, Goettingen, Germany, catalog no.: 131011), [5] mouse anti-postsynaptic density protein 95 (PSD95, 1:100, Frontier, Geumcheon-Gu, Seoul, Korea, catalog no.: AB2723), [6] mouse anti-gephyrin (1:100, Synaptic Systems, catalog no.:147021) [7] mouse anti-glial fibrillary acidic protein (GFAP, 1:1,000, Millipore, Temecula, California, USA, catalog no.: MAB3402,), and [8] guinea-pig anti-ionized calcium binding adaptor molecule 1 (Iba1, 1:2,000; Synaptic Systems, Goettingen, Germany catalog no.: 234–004). Following incubation with primary antibody solutions for 72 h at 4°C, sections were transferred into proper mixture of secondary antibodies listed below: (a) goat anti-rabbit IgG-AlexaFluor 488 (1:1,000; Thermo Fisher Scientific, catalog no.: A11034) (b) goat anti-guinea-pig IgG-AlexaFluor 555 (1:1,000; Thermo Fisher Scientific, catalog no.:A21435) (c) streptavidin conjugated with AlexaFluor 555 (1:1,000, Invitrogen, Eugene, Oregon, USA, catalog no.: S21381) or (d) goat anti-mouse IgG-AlexaFluor 555 (1,1,000; Thermo Fisher Scientific, catalog no.:A21422). List of the applied primary and secondary antibodies are provided in Table S2 in the Supporting information section. The protocol was slightly modified in stainings including PSD95 and gephyrin markers, before primary antibody incubation pepsin pretreatment was carried out for antigen retrieval. The sections were incubated for 10 min at 37°C in 0.2 M HCl containing 1 mg/ml pepsin (Watanabe et al., 1998). In control and CFA treated lumbar DRG tissue samples (L4-L5) double immunolabeling was performed by using antibodies raised against P2X4 receptor as well as CGRP peptidergic-and IB4-nonpeptidergic primary afferent markers.
## 2.5.4. Quantification of confocal microscopy
The colocalization of applied markers with P2X4 was quantitatively analyzed on 1-μm thick single optical z-stack sections of the superficial spinal dorsal horn (*Rexed laminae* I–II) captured with Olympus FV3000 confocal laser microscope (60x oil-immersion lens, numerical aperture: 1.4). The confocal parameters (laser power, confocal aperture) were identical while recording each reaction. Scanned images were processed with Olympus Fluoview 2.1 and Adobe Photoshop CS6 softwares. Data were taken from three randomly selected sections of each animal. Identification of *Rexed laminae* I and II was based on the following: (a) The border between the dorsal column and the dorsal horn was detected based on the intensity of immunostaining. ( b) The border between laminae II and III was estimated according to earlier descriptions (McClung and Castro, 1978; Molander et al., 1984). Briefly, double-fluorescent z-stack confocal sections were converted into rendered images by IMARIS (Bitplane ver.7.3) algorithm, with which immunoreactive spot/profile detection and colocalization were calculated. Volumes (μm3) of neuronal and glial profiles were determined from control and CFA treated samples, then P2X4 expression was quantified as the % volume of the marker-labeled structures containing P2X4-positive puncta / total volume of the markers. Our prior sample analysis showed that CFA-induced inflammation did not significantly alter the volumes of the investigated markers based on comparisons between control vs. CFA treated samples. Total number of CGRP and/or IB4 DRG cells was also quantified in sections taken from control and CFA treated samples, then checked whether they colocalize (in % number of the cells) with P2X4 receptor.
## 2.5.5. Preembedding immunoperoxidase reaction for transmission electronmicroscopy
Preembedding immunostaining protocol was performed on sections taken from 3 control animals as described in chapter 2.5.2- single immunolabeling to visualize the ultracellular distribution of P2X4. After washing in 0.1 M PB (pH 7.4) and $1\%$ sodium borohydride treatment for 30 min, free-floating sections were fixed with $2\%$ paraformaldehyde and $0.5\%$ glutaraldehyde. Prior to antibody treatments the sections were immersed in $10\%$ NGS (Vector Labs) for 50 min. Anti-P2X4 antibody was diluted in 0.01 M TPBS to which $1\%$ NGS was added. Upon incubation with rabbit anti-P2X4 antibody (1:1,000, Alomone Labs) for 72 h at 4°C, sections were placed into biotinylated goat anti-rabbit IgG (Vector Labs) for 4 h at 4°C. Following a treatment with avidin biotinylated horseradish peroxidase complex (Vector Labs) for 24 h at 4°C, the immunoreaction was visualized with 3,3′-diaminobenzidine (Sigma-Aldrich). Subsequently the sections were treated with $0.2\%$ osmium-tetroxide for 50 min, dehydrated and then embedded into Durcupan ACM resin (Sigma-Aldrich, catalog no.: 44610) on glass slides. After reembedding, ultrathin sections were made and transferred on Formvar-coated single-slot nickel grids, and counterstained with uranyl acetate and lead citrate.
## 2.5.6. Preembedding nanogold immunolabeling for transmission electronmicroscopy
Preembedding P2X4 nanogold immunostaining was performed on 3 control animals. Free-floating sections were handled identically as described in chapter 2.5.5 Preembedding immunoperoxidase reaction before secondary antibody treatment. Thereafter sections were transferred into a goat anti-rabbit IgG solution coupled with 1,4 nm gold particles (1:100, Aurion, Wageningen, The Netherlands) for 12 h at 4°C. Following washing steps in 0.01 M TPBS the sections were postfixed for 10 min in $2.5\%$ glutaraldehyde, then washed repeatedly in 0.01 M TPBS and 0.1 M PB. Nanogold labeling was further intensified with silver enhancement (Aurion R-GENT, Wageningen, The Netherlands). Sections were treated with $1\%$ osmium-tetroxide for 45 min, then dehydrated and embedded into Durcupan ACM resin (Sigma-Aldrich) on glass slides. Selected sections were reembedded, ultrathin sections were cut and placed on Formvar-coated single-slot nickel grids, and counterstained with uranyl acetate and lead citrate.
## 2.6. Western blotting
L4-L5 spinal segments of 3 control and 3 CFA injected animals were harvested. Detergent compatible BCA assay (Pierce, Rockford, USA) was used to measure protein concentration. Samples were dissolved in reducing buffer (50 μg protein/lane) and run on $12\%$ SDS-polyacrylamide gels previously described by Laemmli [1970]. Following separation proteins were electrophoretically transferred onto PVDF membrane (Millipore, Bedford, USA). The membranes were blocked with $10\%$ normal bovine serum albumin (BSA, Sigma-Aldrich) in2 Tween-Tris-buffered saline (TTBS solution, 20 mM TRIS, 500 mM NaCl, pH 7.5, $0.05\%$ Tween-20). Membranes were incubated with rabbit anti-P2X4 (1:1,000, Alomone Labs) and internal control antibody (mouse anti-β-tubulin, 1:2,000, Sigma-Aldrich) for 2 hours room temperature. Upon washing with TTBS, membranes were treated with goat anti-rabbit secondary antibody conjugated with horse-radish-peroxidase, (HRP, 1:200, DakoCytomation, Glostrup, Denmark) and goat anti-mouse secondary antibody conjugated with HRP (1:200, DakoCytomation). The labelled protein bands were visualized with DAB chromogen reaction (Sigma-Aldrich).
## 2.7. Statistics
Prior to the statistical analysis, sample sizes were not predetermined, but power tests were carried out to confirm that they were sufficient for quantifying the experimental data. Data were analyzed by SigmaStat software. Equal variances between data were presumed. Differences were considered significant when $p \leq 0.05.$Data sets of behavioral tests ($$n = 10$$/day) carried out in control and CFA treated animals were compared with compared with Repeated measures 2-way ANOVA followed by Sidak’s multiple comparison test. Data sets of qPCR analysis were determined by using nine parallel replicates ($$n = 9$$) taken from control and CFA treated animals, respectively. Inter-group statistical differences were determined by Student’s t-test. Mean-and standard deviation (SD) values were also calculated. Data sets of single immunolabeling were determined by using nine randomly selected sections taken from control and CFA treated animals respectively (3–3 animals, 3 sections 2 areas, $$n = 18$$). Between-groups statistical differences were calculated by Student’s t-test. Mean-and standard deviation (SD) values were also calculated. Data sets of double immunofluorescent labelings were determined by using nine randomly selected confocal sections (spinal dorsal horn and DRG samples) taken from control and CFA treated animals, respectively (3–3 animals, 3 sections 2 areas, $$n = 18$$). Statistical differences between control and CFA treated groups were determined by Student’s t-test. Mean-and standard deviation (SD) values were also calculated. Analysis of western blots and determination of the relative amount of P2X4 receptor protein were carried out on parallel replicates ($$n = 3$$) taken from control and CFA treated rats, respectively. Statistical differences between control and CFA treated groups were calculated by using Student’s t-test. Mean-and standard deviation (SD) values were also calculated.
## 3.1. Animals showed significant mechanical nociceptive sensitivity during peripheral inflammation
The mechanical withdrawal threshold (MWT) values observed in control animals were comparable during the experimental duration, no significant differences were found between left and right hindpaws. The mean MWT (collected from the repeated measurements of left-and right hindpaw) was 49.47 ± 1.04 g in control animals. The contralateral (left, non-injected) hindpaw of the CFA treated animals also presented a similar mean MWT value (48.32 ± 1.96 g). However on the ipsilateral (right, CFA injected) hindpaw, provoked inflammation resulted in a significant decrease in MWT figures during the first three experimental days. The highest decline in MWT was detected on day 3 when it dropped to 20.6 ± 1.85 g (***$p \leq 0.001$). The difference between data of the MWT of control and CFA treated rats was strikingly significant during the first 3 days (***$p \leq 0.001$) (Figure 1A).
**Figure 1:** *(A) Modified von Frey allodynia test illustrating mean mechanical paw withdrawal threshold (MWT) values on both hind limbs of control rats (Control, green dotted line with triangle) and rats administered with CFA (day 0) into the right (ipsilateral, CFA ipsi, red line with circle) hindpaw. The left hindpaw (contralateral, CFA contra, blue dotted line with square) was unhandled. Note that MWT values of control animals and the contralateral hind paw of CFA treated animals were parallel throughout the experimental design. CFA injection resulted in the highest decline in MWT on post-injection day 3, when MWT values dropped to 20.6 ± 1.85 g (***p < 0.001). The difference between data of the MWT obtained from control and CFA treated rats was also highly remarkable (***p < 0.001) during experimental day 1–3. Between-groups statistical differences were evaluated by using Repeated measures 2-way ANOVA followed by Sidak’s multiple comparison test Data are shown as mean + SD. (B) Thermal allodynia test indicating mean thermal withdrawal threshold latency (TWL) figures on both hind limbs of control rats (Control, green dotted line with triangle) and rats administered with CFA (day 0) into the right (ipsilateral, CFA ipsi, red line with circle) hindpaw. The left hindpaw (contralateral, CFA contra, blue dotted line with square) was unhandled. Note that TWL of control animals and the contralateral hind paw of treated animals were parallel throughout the experimental design. CFA injection resulted in marked drop to 2.54 ± 0.22 s in TWL on post-injection day 3 [***p = (***p < 0.001)] at the ipsilateral hindpaws. The difference between data of the TWL obtained from control and CFA treated rats was also highly significant during experimental day 1–3 (***p < 0.001). Between-groups statistical differences were evaluated by using Repeated measures 2-way ANOVA followed by Sidak’s multiple comparison test. Data are shown as mean + SD.*
## 3.2. Animals showed significant thermal nociceptive sensitivity during peripheral inflammatory pain
The values of CFA evoked thermal allodynia were quite parallel to that of mechanical allodynia. Control-and contralateral (left, non-injected) hindpaws of CFA injected animals did not produce marked differences in TWL figures. The mean TWL values varied in the range of 5.57 ± 0.65–6.61 ± 0.63 s in control animals, whereas values between 5.92 ± 0.21–6.36 ± 0.77 s were recorded on the contralateral side of CFA treated rats. Regarding the ipsilateral (right, CFA injected) hindpaw, CFA injection also resulted in robust decrease. The highest drop in TWL was detected on day 3, when time values dropped to 2.54 ± 0.22 s (***$p \leq 0.001$). The discrepancy between data of the TWL obtained from control and CFA treated rats was also highly significant (***$p \leq 0.001$) (Figure 1B).
## 3.3. Spinal P2X4 gene-and protein expression were highly enhanced upon CFA injection
Our results showed an abundant immunoreactivity for P2X4 receptor in *Rexed lamina* I and II, whereas the deeper laminae were sparsely stained. Compared with control (Figure 2A) more robust P2X4 immunoreactivity (56 ± $7.42\%$ increase, ***$$p \leq 0.000501$$) was obtained by densitometric analysis of spinal cord sections taken from CFA injected animals (Figures 2B,C).Subsequently, we aimed to elucidate whether the inflammation influenced the P2X4 expression at a transcriptional level. RT-qPCR experiments were designed from spinal dorsal horn tissue extracts of the L4-L5 segments. We found considerable enhancement of P2X4 mRNA transcripts (***$p \leq 0.001$, 37.92 ± $10.42\%$ increase compared to control sample) at the summit of mechanical and thermal nociceptive sensitivity in inflammation (Figure 2D). We hypothesized that the tendency of P2X4 gene expression would be also explicit at a protein level, therefore protein blotting was conducted from spinal dorsal horn extracts of L4–L5 segments 3 days following CFA injection (Figure 2E; Supplementary Figure S2A). Densitometric analysis showed significant increase of the receptor protein in samples of CFA treated animals compared to control (53.18 ± $12.11\%$ increase *$$p \leq 0.013$$) (Figure 2F).
**Figure 2:** *(A,B) Photomicrographs showing immunoperoxidase labelings for P2X4 within the spinal dorsal horn. In comparison with control animals (A) stronger P2X4 immunoreactivity was detected in Rexed laminae stronger P2X4 immunoreactivity was detected in Rexed laminae I and II of spinal cord sections taken from CFA treated animals (B). Note that the intensity of the reaction was more prominent at the lateral aspect of the dorsal horn compared with medial areas. Scale bar: 200 μm. (C) Bar showing relative optical density measured from P2X4 immunoreactivity within the spinal dorsal horn during peripheral inflammation. The values represent the relative ratio of P2X4 expression (%) upon CFA treatment compared with control (100%). Inflammation substantially increased the P2X4 immunoreactivity (***p = 0.000501, 56% increase). Between-groups statistical differences were evaluated by Student’s t-test. Data are shown as mean + SD. (D) Bar showing P2X4 mRNA expression within the spinal dorsal horn in peripheral inflammation. The values represent the relative ratio of P2X4 mRNA (normalized to Rpl4 mRNA) upon CFA injection compared with control (100%). Inflammation substantially increased the amount of P2X4 transcripts (***p < 0.0001, 37.92% increase). Between-groups statistical differences were evaluated by Student’s t-test. Data are shown as mean + SD. (E,F) Representative blotting image and protein densitometry showing significant (*p = 0.013, 53.18% increase) enhancement of P2X4 protein in spinal dorsal horn tissue lysates (L4–L5) of animals 3 days following CFA injection (CFA ipsi) compared to control rats (Ctrl). β-tubulin was used as loading control. Between-groups statistical differences were evaluated by Student’s t-test. Data are shown as mean ± SD.*
## 3.4. P2X4 Receptor is upregulated on primary afferent fibers of spinal dorsal horn and lumbar DRG ganglia cells during peripheral inflammation
In our study we aimed to decipher the fine spinal distribution of P2X4 in peripheral inflammatory pain. Thus, we carried out double immunostainings in which we determined the localization of P2X4 immunoreactive spots on neurons and glial cells of the superficial spinal dorsal horn by IMARIS analysis. Superficial *Rexed laminae* I and II of spinal dorsal horn for colocalization analysis were distinguished by performing double immunostaining against CGRP and IB4, where CGRP labelled *Rexed lamina* I and IIo, and IB4 indicated *Rexed lamina* IIi (Figures 3A,B). To determine whether the receptor was present on central axonterminals including non-peptidergic and peptidergic nociceptive primary afferents as well as axonterminals of glutamatergic and GABAergic interneurons, we analyzed the colocalization of selected markers (IB4, CGRP, VGLUT2 and VGAT) with P2X4. Based on our findings 23.08 ± $3.54\%$ of the IB4 positive non-peptidergic fibers certain to be positive for P2X4 receptor in control animals, which significantly increased (***$$p \leq 0.0011$$) to 38.24 ± $3.67\%$ (Figures 3C, 4A,G) in CFA administered rats. 71.79 ± $4.25\%$ of CGRP positive peptidergic primary afferent fibers showed colocalization with P2X4 in control animals which also significantly (***$p \leq 0.001$) raised to 84.66 ± $6.24\%$ in peripheral inflammatory pain (Figures 3D, 4B,G). VGLUT2 positive excitatory-and VGAT positive inhibitory axonterminals of spinal interneurons showed no significant change in P2X4 expression upon CFA treatment. In control animals 9.67 ± $2.92\%$ of the VGLUT2 positive fibers were colocalized with P2X4, whereas in inflammatory pain it was calculated as 9.34 ± $3.1\%$ (Figures 3E, 4C,G). In case of VGAT positive fibers we observed similar results, between-groups difference was statistically irrelevant. 8.61 ± $1.67\%$ of the axonterminals overlapped with P2X4 receptor in control rats, whereas upon CFA injection 7.47 ± $2.1\%$ was obtained (Figures 3F, 4D,G). In contrast to results obtained from spinal axonterminals, on postsynaptic dendrites lower receptor number was detected. 4.54 ± $1.15\%$ of PSD95 positive excitatory postsynaptic dendrites showed positivity for P2X4 in control conditions, and CFA treatment did not influence the receptor expression (4.99 ± $1.09\%$) (Figures 3G, 4E,G). Inhibitory postsynaptic density marker gephyrin colocalized with P2X4 in the same degree. 5.84 ± $0.95\%$ of gephyrin positive dendrites were also immunoreactive for P2X4 in control animals, which was calculated as 3.68 ± $0.16\%$ in CFA model (3.68 ± $0.16\%$) (Figures 3H, 4F, G). Astrocytes abundantly expressed P2X4 during inflammatory pain. 8.28 ± $0.93\%$ of GFAP-labelled astrocyte membrane compartments were found to express the receptor in control conditions, which were increased to 13.38 ± $3.9\%$ upon CFA administration (*$$p \leq 0.0103$$) (Figures 5A,C,E). Intriguingly, microglial cells in our model did not contribute to P2X4 receptor upregulation in inflammation. 3.5 ± $1.56\%$ of Iba1 positive microglial cells showed positivity for P2X4 in control animals, which was unchanged upon CFA injection (3.99 ± $0.75\%$) (Figures 5B,D,E). In lumbar DRG tissue (L4-L5) the amount of P2X4 gene transcripts was exceptionally increased (***$$p \leq 0.0005$$) upon CFA treatment (1,224.40 ± $19.37\%$, more than 12 times fold change increase compared to control) (Figure 6A). This finding was moderately supported by protein blotting, which verified notable increase (52.77 ± $44.77\%$ increase compared to control, *$$p \leq 0.05$$) (Figures 6B,C; Supplementary Figure S2B). Single immunoperoxidase labeling was also performed to visualize the receptor expression within L4-L5 DRG cells (Figure 6D). Evaluation of P2X4 colocalization with nociceptive markers (CGRP, IB4) of small diameter neurons proposed that in control animals 79.76 ± $6.76\%$ of the peptidergic CGRP positive neuronal profiles coexpressed P2X4 receptor, which was enhanced to 86.24 ± $4.42\%$ upon CFA treatment based on IMARIS calculation (*$$p \leq 0.0263$$) (Figures 6E,H,K). In case of non-peptidergic IB4 positive cells 37.65 ± $10.87\%$ was colocalized with P2X4 receptor, this ratio was also substantially elevated in inflammatory pain (51.42 ± $11.71\%$, p* = 0.0245) (Figures 6F,I,K). We also analyzed the putative overlapping between CGRP and IB4 positive neuronal profiles. 34.27 ± $5.45\%$ of CGRP positive cells showed positivity for IB4, which was insignificantly changed to 41.99 ± $10.50\%$ in peripheral inflammation (Figures 6G,J,L).
**Figure 3:** *(A) Representative immunofluorescent double-labeled 1-μm thick confocal laser image showing CGRP (red color) and IB4 (blue color) immunoreactivity in the superficial spinal dorsal horn. (B) The area indicated by white rectangle with dashed line on panel A was further magnified. LI, LIIo and LIIi captions indicate the respective superficial laminae of Rexed within the spinal gray matter. Scale bars: 200 and 10 μm. (C–H) Representative immunofluorescent double-labeled 1-μm thick confocal laser images showing the colocalization of non-peptidergic and peptidergic primary afferents (IB4 and CGRP, red, C,D), excitatory and inhibitory interneurons- (VGLUT2 and VGAT, red, E,F) and excitatory and inhibitory postsynaptic markers (PSD95 and Gephyrin, red, G,H) with P2X4 receptor (green, C–H) within the superficial spinal dorsal horn of control animals. Scale bar: 10 μm.* **Figure 4:** *(A–F) Representative illustrations of double-labeled 1-μm thick confocal laser images rendered by IMARIS software. The illustrations represent IMARIS transformations (with x-z projections) of confocal images including presynaptic markers of non-peptidergic and peptidergic afferents (IB4 and CGRP, red, A,B), excitatory and inhibitory interneurons- (VGLUT2 and VGAT, red, C,D) and excitatory and inhibitory postsynaptic markers (PSD95 and Gephyrin, red, E,F) with P2X4 receptor (green, A–F) within the superficial spinal dorsal horn of control animals. (G) Bart chart showing the volume (μm3) of neuronal markers, which are immunoreactive for P2X4. Blue columns indicate data obtained from control animals (Control) whereas red columns indicate values calculated from CFA-treated animals (CFA ipsi). Individual datapoints of each column are also shown with colors (control, green dots, CFA ipsi-purple dots). Asterisks indicate that CFA-evoked inflammation significantly increased the volume of non-peptidergic primary afferent IB4 (***p = 0.0011, 15.16% increase) and peptidergic primary afferent CGRP (***p < 0.0001, 12.87% increase) immunoreactive puncta colocalized with P2X4 receptor. Data are shown as mean ± SD. Between-groups statistical differences were evaluated by Student’s t-test.* **Figure 5:** *(A–D) Representative immunofluorescent double-labeled 1-μm thick confocal laser images supplemented by IMARIS analysis (with x-z projections) illustrating the colocalisation between immunoreactivity for markers that are specific to astrocytes (GFAP, red, A,C) or microglial cells (Iba1, red, B,D) and P2X4 receptor (green, A–D) in superficial spinal dorsal horn of control animals. Scale bar: 10 μm. (E) Bart chart showing the volume (μm3) of glial markers, which are immunoreactive for P2X4. Blue columns indicate data obtained from control animals (Control) whereas red columns indicate values calculated from CFA-treated animals (CFA ipsi). Individual datapoints of each column are also shown with colours (control, green dots, CFA ipsi-purple dots). Asterisks indicate that CFA-evoked inflammation moderately increased the volume of GFAP positive astrocytes (*p = 0.0103, 5.1% increase) colocalised with P2X4 receptor. Iba1 positive microglial cells showed significant P2X4 receptor expression neither in control nor upon CFA treatment. Data are shown as mean ± SD. Between-groups statistical differences were evaluated by Student’s t-test.* **Figure 6:** *(A) Bar chart showing P2X4 mRNA expression (%) within lumbar DRG tissue (L4–L5) in peripheral inflammatory pain. Values represent the relative ratio of P2X4 mRNA (normalized to Rpl4 mRNA value) upon CFA treatment compared with control. CFA injection robustly increased the amount of P2X4 mRNA transcripts (***p = 0.0005, 1,224% increase). Between-groups statistical differences were evaluated by Student’s t-test. Data are shown as mean + SD. (B,C) Representative western blot image and protein densitometry showing significant enhancement of P2X4 protein (*p = 0.05, 52.7% increase) in lumbar DRG tissue (L4-L5) lysates of animals 3 days following CFA injection (CFA ipsi) compared to control rats (Ctrl). β-tubulin was used as loading control. Between-groups statistical differences were evaluated by using Student’s t-test. Data are shown as mean + SD. (D) Photomicrograph showing immunoperoxidase labeling for P2X4 within lumbar DRG section (L4) of control animal. Scale bar: 50 μm. (E–J) Representative immunofluorescent double-labeled 1-μm thick confocal laser images of lumbar DRG tissue (L4–L5) showing colocalisation of either peptidergic or non-peptidergic nociceptive neuronal profiles (CGRP, red; E,H, IB4, red; F,I, respectively) with P2X4 receptor (green, E, F, H, I) as well as colocalisation of CGRP with IB4 marker (G–J) in control animals. White rectangles with dashed line of panels E–G were magnified on panel H–J. Scale bars: 200 (G) and 50 μm (J). (K–L) Bar chart showing colocalization values (%) of peptidergic and non-peptidergic lumbar DRG (L4–L5) profiles (CGRP and IB4) with P2X4 receptor as well as the colocalization of CGRP with IB4 marker. Blue columns indicate data obtained from control animals (Control) whereas red columns indicate values from CFA-treated animals (CFA ipsi). Individual datapoints of each column are also shown with colors (control, green dots, CFA ipsi-purple dots). Asterisks indicate that CFA-evoked inflammation significantly increased colocalization of CGRP and IB4 positive ganglion cells (*p = 0.0263 6.48% increase, and *p = 0.0245 13.77% increase respectively) with P2X4 receptor, however CFA injection did not influence overlap between the markers. Between-groups statistical differences were evaluated by Student’s t-test. Data are shown as mean ± SD.*
## 3.5. Ultrastructural analysis clarifies the localization of P2X4 On axonal profiles and neuronal soma
We intended to provide novel experimental evidence regarding the ultrastructural distribution of P2X4 receptor by nanogold labeling within superficial spinal dorsal horn. Both pre-and postsynaptic regulation were earlier suggested for P2X4 based on ultrastructural results of Lê et al. [ 1998], and in fact PSD95 and gephyrin markers colocalized with the receptor in some extent, still our results rather emphasize the presynaptic role of spinal P2X4 receptor. Immunoperoxidase reaction verified clusters of immunoprecipitates at the synaptic vesicles as well as near the presynaptic contacts and mitochondrium (Figure 7A). These observations were confirmed by nanogold labelings, deposits of silver intensified gold nanoparticles were accumulated at the proximity of presynaptic membrane, synaptic vesicles and mitochondria (Figures 7B,C). In addition, in agreement with previous findings of Lê et al. [ 1998] we also detected gold nanoparticles on neuronal soma adjacent to endoplasmatic reticule and mitochondria (Figure 7D).
**Figure 7:** *Transmission electron microscopy images demonstrating the ultrastructure of the superficial spinal dorsal horn with preembedding immunolabeling for P2X4 receptor. (A) Example of an immunoperoxidase reaction for P2X4 on an axon profile (AX). Red arrows show clusters of immunoprecipitates accumulated at the proximity of the presynaptic membrane and mitochondrium (M). No precipitate was detected on dendrite (D). (B,C) Examples of nanogold immunolabelings for P2X4 on axon profiles (AX). Red arrows show deposits of gold nanoparticles intensified with silver on synaptic axonterminal near mitochondrium (M), dendrites (D) were not stained. (D) Nanogold immunolabeling for P2X4 on soma (SM). Red arrows show nanoparticles within the cytoplasm adjacent to endoplasmatic reticule (ER) and mitochondria (M) at the proximity of cell nucleus (CN). Scale bars: 500 nm.*
## 4. Discussion
In this study we elucidated the expression and cellular distribution of P2X4 receptor within the spinal dorsal horn and lumbar DRG cells of L4-L5 segments in control and CFA treated male rats. Validity of CFA model concept as well as mechanical and thermal behavioral tests were previously confirmed by Molander and Grant [1985], Ma and Woolf [1996], Raghavendra et al. [ 2004]. Briefly, according to our results significant increase in the level of P2X4 mRNA was detected within the spinal dorsal horn 3 days following the CFA administration. Immunoperoxidase technique also showed robust enhancement of P2X4 immunoreactivity within *Rexed laminae* I and II of the spinal dorsal horn, which was further supported by Western blot. With IMARIS, a major fraction of P2X4 receptor has been clarified on primary afferent axonterminals and astrocytes, despite its expression on presynaptic axons and neuronal somata, it was absent on postsynaptic dendrites when validated at ultrastructural level. The L4-5 DRG analysis also showed increased expression of P2X4 in peptidergic CGRP-positive and non-peptidergic IB4 nociceptive subsets of ganglia cells after CFA injection. Of note, CGRP and IB4 positive subsets of DRG cells have considerable overlap, which was earlier described in rats by multiple investigations (Petruska et al., 2002; Price et al., 2005; Price and Flores, 2007). It has been well documented that nerve injury upregulates spinal microglial P2X4 expression (Ulmann et al., 2008; Tsuda et al., 2013; Stokes et al., 2017; Inoue, 2019). In inflammatory and neuropathic pain models (Todd, 2010; Aby et al., 2018; Peirs et al., 2020) increased excitability was reported within the superficial spinal dorsal horn. Besides, it has been recently reported that P2X4 receptor contributes to several neuropathological states as it modulates synaptic transmission altering hippocampal memory via downregulation of GABA A receptor (Roshani et al., 2022), but its increased surface expression was also detected in mice suffering from amyotrophic lateral sclerosis (Bertin et al., 2022). In addition, P2X4 deficient mice showed impaired inflammasome function upon spinal cord injury (de Rivero Vaccari et al., 2012). P2X4 inhibition was also found to reduce microglial directed inflammation and apoptosis via Nod-like receptor protein 3 (NLRP3) inflammasome in rat brain trauma model (He et al., 2022). Microglial P2X4 activation elicits brain-derived neurotrophic factor (BDNF) release, which downregulates neuronal K+-Cl−cotransporter 2 (KCC2). As a result, polarity switch of inhibitory GABA-and glycinergic iongradient leads to excitatory activation of the spinal dorsal horn network (Coull et al., 2005; Montilla et al., 2020). Thus, P2X4-BDNF signaling is relatively well understood in neuropathic pain. Genetic depletion of microglial derived BDNF prevents the development of neuropathic allodynia. Interestingly, major gender differences were observed in P2X4 signaling of chronic neuropathic pain, hence microglial cells were found to be dispensable for the establishment of allodynia in female, but not in male animals. In the former, most probably adaptive immune cells contribute to spinal pain signaling (Sorge et al., 2015; Malcangio, 2017; Mapplebeck et al., 2018; Khir et al., 2021). However, in peripherally induced inflammation P2X4 signaling may differ. Lalisse et al. [ 2018] found earlier in CFA model that BDNF evoked KCC2 downregulation did not depend on microglial cells. Supposedly, P2X4 receptor upregulation elicits BDNF production of lumbar DRG cells in order to be transported via primary central axonterminals for secretion within spinal dorsal horn in a neuronal guided manner (Cho et al., 1997; Lever et al., 2001; Lalisse et al., 2018). Moreover, selective genetic deletion of BDNF in Nav1.8 positive sensory neurons also reduced inflammation without influencing neuropathic sensitivity (Zhao et al., 2006). Till date scanty and contradictory information are available about the P2X4 expression in lumbar DRG tissue upon inflammation, hence others also documented that CFA did not alter P2X4 expression in lumbar DRGs, but did in spinal cord (Xu et al., 2018). Earlier studies reported substantial expression of P2X4 receptor in several types of ganglia cells (Bo et al., 2003; Zhang et al., 2020) and macrophages in inflammation (Ulmann et al., 2010). Our results may also shade these postulations. In our samples P2X4 mRNA expression was 12 times larger than that of the control after CFA injection, which was also consistent with data retrieved from studies related to neuropathia (Deng et al., 2018; Kohno and Tsuda, 2021), and western blot experiments, even if the enhancement of P2X4 protein expression proved to be rather moderate in comparison with the mRNA profile. The differences between mRNA and protein amount may be explained by posttranscriptional mechanisms that control protein levels regardless of mRNA amount (McCarthy, 1998; Csárdi et al., 2015; Brion et al., 2020; Buccitelli and Selbach, 2020). In diabetic neuropathic model, immunofluorescent labelings showed predominant P2X4 expression by satellite cells (Teixeira et al., 2019), but in our model satellite cells were not found to be stained with P2X4, instead we rather highlighted that CGRP and IB4 positive subsets of lumbar ganglia cells were substantially colocalized with P2X4 receptor, but change in overlap between CGRP and IB4 markers was found to be insignificant in peripheral inflammation. Interestingly, CFA treatment did not alter the amount of P2X4 receptor within lumbar DRG cells implying that peripheral inflammation may influence exclusively the spinal P2X4 expression (Xu et al., 2018). The controversial data between CFA models may be explained either with different species (mice vs. rats), or the time-course of the experimental design selected by the different research groups. Moreover, the degree of swelling and hypersensitivity may be correlated with the way of CFA preparations and injections administered (McCarson and Fehrenbacher, 2021). P2X4 shows a widespread distribution, throughout the central and peripheral nervous system. Beyond microglial cells, the receptor has been already detected in neurons including but not limited to olfactory bulb, hypothalamus, cerebellum (Collo et al., 1996) substantia nigra (Amadio et al., 2007), retinal ganglion cells (Wheeler-Schilling et al., 2001), and spinal dorsal horn (Bardoni et al., 1997). However, still little is known about the precise neuronal localization of spinal P2X4. Overexpression of P2X4 receptor may directly facilitate the responsiveness of the neuronal network within the spinal dorsal horn resulting in inflammation. In vivo extracellular recordings demonstrated CFA induced hyperexcitability of wide dynamic range (WDR) neurons within murine spinal dorsal horn via decrease of the C-fiber response threshold. C-fibers are connected with WDR neurons of the superficial laminae of spinal dorsal horn, where increase of wind-up amplitude measured in inflammatory pain, is robustly suppressed in P2X4 −/− mice (Aby et al., 2018). P2X4 activation may control gene expression via Ca2+ signaling, and promotes neurotransmitter release to enhance sensory transmission that eventually leads to central sensitization associated with inflammatory pain. Furthermore, in P2X4R deficient mice following peripheral inflammation BDNF signaling as well as extracellular signal-regulated kinase (ERK) regulated phosphorylation of N-metyl-D-aspartate (NMDA) receptor subunit GluN1 and KCC2 downregulation were all attenuated, proposing a significant role for P2X4 receptor in chronic inflammation (Lalisse et al., 2018; Khir et al., 2021). We also detected robust increase of P2X4 mRNA and protein level in the spinal dorsal horn at post-injection day 3. Peripheral inflammation resulted in highly significant P2X4 enrichment on non-peptidergic IB4 and peptidergic CGRP positive primary afferent fibers, concurrently VGLUT2 positive profiles were only weakly colocalized with the receptor. We contemplated that there is a great deal of interest in this matter, hence as reported by Li et al. [ 2003] significant numbers of VGLUT2 labeled axonterminals found in the spinal dorsal horn were of primary afferent origin following dorsal rhizotomy. Thus, quantitative data regarding P2X4 expression of CGRP and IB4 labeled primary afferents should also include data related to VGLUT2 positive terminals. Nevertheless, the background of this question is much more complex than initially thought, hence based on earlier results of Todd et al. [ 2003] peptidergic axonterminals labeled with either CGRP and substance P or CGRP and somatostatin antibodies were either weakly immunoreactive or not immunoreactive for VGLUT2. Furthermore, those nonpeptidergic fibers that were labelled with biotinylated IB4 also showed either weak positivity for VGLUT2 or were not immunostained. In contrast, high VGLUT2 expression was detected in majority of CGRP and IB4 positive somata of DRG cells (Brumovsky, 2013). These findings were corroborated by other authors such as Alvarez et al. [ 2004], who also revealed low level of VGLUT2 expression in central terminals of nociceptors by performing dorsal rhizotomy. They concluded that VGLUT2 immunofluorescence was not substantially reduced upon rhizotomy, therefore the majority of VGLUT2 positive immunoreactivity should originate from intrinsic source of the spinal dorsal horn. Moreover, authors proposed that probably the signal of primary afferents was abundantly outnumbered by strong VGLUT2 immunoreactivity of the intrinsic spinal interneuron terminals, hence the resolution of their methods was not enough to perform quantitative analysis. We also propose as a possible explanation that robust VGLUT2 expression of spinal dorsal horn neurons might mask changes occurring in VGLUT2 positive primary afferent terminals. Differences between the studies may be due to the different VGLUT2 antibodies used, hence Li et al. used their specially manufactured affinity-purified antibodies for VGLUT transporters. Our workgroup applied one of the same antibodies for VGLUT2 (guinea pig anti-vesicular glutamate transporter 2 (VGLUT2, 1:2000, Chemicon/Millipore, Temecula California, USA, catalog no.: AB2251), which was used by Todd and Alvarez. Thus, based on their earlier observations, in our experimental design CGRP or IB4 positive primary afferent fibers were regarded as VGLUT2 negative. The distribution of the P2X4 receptor was also verified on glial cells and neuronal somata. Unfortunately, still little is revealed regarding the specific role of P2X4 in glial cells other than microglia (Montilla et al., 2020). Regarding our findings from CFA model, we suppose that rather astrocytes than microglial cells contribute to P2X4 expression, whereas we do not have experimental data on oligodendrocytes. Accumulating data indicate the putative interaction between P2X4 and P2X7 receptors. Supposedly, P2X4 promotes P2X7 receptor directed NLRP inflammasome activation, resulting in production of IL-1ß and IL-18 (Kanellopoulos et al., 2021). Interestingly, our earlier results (Ducza et al., 2021) showed increased amount of NLRP2 protein in astrocytes of the spinal dorsal horn upon CFA injection, which puts our latest finding about the significant overexpression of P2X4 receptor on astrocytes into a new perspective. Nevertheless, there is still no experimental evidence regarding the correlation between P2X4 receptor and NLRP2 inflammasome. P2X4 receptor expression within neuronal soma was not surprising, hence earlier data suggested the existence of a permanent and dynamic turnover between cell membrane and intracellular organelles (Royle et al., 2005; Qureshi et al., 2007; Cao et al., 2015) thus there may be a constant P2X4 trafficking between neuronal plasmamembrane and intracellular organelles as well, which regulates receptor density as well as sensitisation-desensitization cycles (Robinson and Murrell-Lagnado, 2013; Xu et al., 2014). Interestingly, the receptor fraction is negligible on excitatory and inhibitory postsynaptic dendrites. This notion implies that even if purinergic signaling is identified both at pre-and postsynaptic sites by Lê et al. [ 1998], here we rather emphasize the former, hence at a spinal level rather the presynaptically located P2X receptors had drawn attention earlier by inducing glutamate release from sensory neurons (Gu and MacDermott, 1997; Montilla et al., 2020) and regulating synaptic co-transmission of molecules in cultured rat dorsal horn neurons (Hugel and Schlichter, 2000; Montilla et al., 2020).
## 5. Conclusion
We concluded that, lumbar DRG-and spinal overexpression of P2X4 receptor were tangible at a gene-and protein level in peripheral inflammation, moreover, we propose here that IB4 and CGRP positive DRG neurons and primary afferent terminals may considerably contribute to P2X4 dependent spinal pain signalization.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.
## Ethics statement
The animal study was reviewed and approved by Animal Welfare Committee of the University of Debrecen.
## Author contributions
KHo and LD: conceptualization. LD prepared the manuscript, performed immunohistochemical stainings and quantitative IMARIS analysis. LD and KHe carried out CFA injections and behavioral tests. KHo, BG, KM, and PS edited and reviewed the manuscript. AG and GK photographed the confocal-and electronmicroscopical sections. RT conducted and analyzed qPCR experiments. KHo and EB carried out western blotting and data analysis. All authors contributed to the article and approved the submitted version.
## Funding
This project was supported by the Hungarian National Brain Research Program (nos. KTIA_NAP_13-2-2014-0005 and 2017–1.2.1-NKP-2017-00002).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnmol.2023.1115685/full#supplementary-material
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|
---
title: 'Emerging trends and focus on the link between gut microbiota and type 1 diabetes:
A bibliometric and visualization analysis'
authors:
- Keyu Guo
- Jiaqi Li
- Xia Li
- Juan Huang
- Zhiguang Zhou
journal: Frontiers in Microbiology
year: 2023
pmcid: PMC10033956
doi: 10.3389/fmicb.2023.1137595
license: CC BY 4.0
---
# Emerging trends and focus on the link between gut microbiota and type 1 diabetes: A bibliometric and visualization analysis
## Abstract
### Objective
To conduct the first thorough bibliometric analysis to evaluate and quantify global research regarding to the gut microbiota and type 1 diabetes (T1D).
### Methods
A literature search for research studies on gut microbiota and T1D was conducted using the Web of Science Core Collection (WoSCC) database on 24 September 2022. VOSviewer software and the packages Bibliometrix R and ggplot used in RStudio were applied to perform the bibliometric and visualization analysis.
### Results
A total of 639 publications was extracted using the terms “gut microbiota” and “type 1 diabetes” (and their synonyms in MeSH). Ultimately, 324 articles were included in the bibliometric analysis. The United States and European countries are the main contributors to this field, and the top 10 most influential institutions are all based in the United States, Finland and Denmark. The three most influential researchers in this field are Li Wen, Jorma Ilonen and Mikael Knip. Historical direct citation analysis showed the evolution of the most cited papers in the field of T1D and gut microbiota. Clustering analysis defined seven clusters, covering the current main topics in both basic and clinical research on T1D and gut microbiota. The most commonly found high-frequency keywords in the period from 2018 to 2021 were “metagenomics,” “neutrophils” and “machine learning.”
### Conclusion
The application of multi-omics and machine learning approaches will be a necessary future step for better understanding gut microbiota in T1D. Finally, the future outlook for customized therapy toward reshaping gut microbiota of T1D patients remains promising.
## Introduction
Type 1 diabetes (T1D) is an autoimmune disease characterized by an ongoing destructive process due to aberrant antibodies and autoreactive T cell responses to self-antigens of pancreatic islet beta cells (β-cells), ultimately leading to absolute insulin deficiency (DiMeglio et al., 2018). Recently, an increasing number of studies have helped us understand how gut microbial dysbiosis disrupts immune homeostasis as a consequence of abnormal innate and adaptive immune responses to alterations in the gut microbiota and its metabolites, thereby engaging β-cell autoimmunity (Hu et al., 2015, 2017; Brown et al., 2019; Siljander et al., 2019).
Alterations in gut microbiota composition and intestinal permeability increase prior to T1D diagnosis, as shown by multiple large and well-characterized human T1D cohort studies (Davis-Richardson et al., 2014; Kemppainen et al., 2015; Kostic et al., 2015; Maffeis et al., 2016; Vatanen et al., 2016; Stewart et al., 2018; Vatanen et al., 2018; Harbison et al., 2019). Gut microbiota dysbiosis has also been observed in T1D patients (Brown et al., 2011; Mejía-León et al., 2014; Alkanani et al., 2015; Kostic et al., 2015; Stewart et al., 2018; Vatanen et al., 2018; Harbison et al., 2019; Huang et al., 2020a,b). Evidence from non-obese diabetic (NOD) mice, a widely used animal model of T1D, further confirmed that gut microbiota is involved in the progression of islet autoimmunity and T1D onset (Wen et al., 2008; Brown et al., 2016; Pearson et al., 2016; Huang et al., 2020a; Fuhri Snethlage et al., 2021; Huang et al., 2021; Girdhar et al., 2022; Jia et al., 2022; Marietta et al., 2022).
One of the critical T1D management strategies is to improve glycemic control and reduce complications in patients with T1D. More recently, research has focused on studying the association between gut microbiota and glycemic control and diabetic complications. The results showed that gut microbiota alteration is related to glycemic control, diabetic kidney disease, and macrovascular complications (Winther et al., 2020; van Heck et al., 2022; Shilo et al., 2022a). Based on these findings, therapeutic strategies targeting the gut microbiota were administrated in rodent and human studies to investigate their roles in preventing or reversing T1D progression, making remarkable advances (Mariño et al., 2017; Hänninen et al., 2018; Huang et al., 2020a; de Groot et al., 2021; Zou et al., 2021; Bell et al., 2022; He et al., 2022; Martens et al., 2022). Yet, despite these advances, further validation is required, and many challenges still need to be addressed.
Bibliometrics is a widely employed method to map studies within a specific research field by statistical and quantitative analysis of the academic impact and characteristics of publications. The aim is to complement empirical findings and to highlight frontier research and development trends toward guiding future directions in a rapidly evolving field such as gut microbiota. Global research trends in gut microbiota (Baudoin et al., 2019; Yuan et al., 2021; Zyoud et al., 2022a) and their roles in different diseases, including cancer (Zhang et al., 2020; Yang et al., 2022; Zyoud et al., 2022b), neuropsychiatric (Zyoud et al., 2019; Zhu et al., 2020; Cabanillas-Lazo et al., 2022; Wang H. et al., 2022; Zhao et al., 2022) and gastrointestinal disorders (Zyoud et al., 2021), among others, have been extensively explored and illustrated by bibliometric analysis.
Although previous research has studied the role of gut microbiota in T1D pathogenesis, diagnosis, prognostication, and treatment, to date no quantitative description of gut microbiota and T1D has been reported. In this article, we reviewed the role of gut microbiota in the field of T1D, identified related articles, and analyzed their characteristics. Our present study included research involving both animal models and human beings, allowing researchers to better understand how gut microbiota works in T1D.
## Data source and search strategy
The search strategy was set as TI = (type 1 diabetes and their synonyms) AND TI = (gut microbiota and their synonyms). All of these synonyms mainly refer to Medical Subject Headings (MeSHs) provided by the National Library of Medicine/PubMed, and the wildcard “*” was applied in place of any number of characters to identify as many relevant papers as possible. We searched Science Citation Index Expanded (SCIE) in Web of Science Core Collection (WoSCC) and screened all studies in this field published until 24 September 2022. The following exclusion criteria were used: (A) non-articles, such as conference abstracts and proceedings, corrigendum documents, retracted publication, letters, and edited materials, among others; (B) articles written in a language other than English; (C) articles with a publication date outside the time frame from 1 January 1999 to 31 December 2021. A total of 639 papers were retrieved from SCIE of WoSCC (Figure 1), and 324 articles were included in the following bibliometric and visualization analysis after screening.
**Figure 1:** *Workflow diagram of the literature search and screening of articles about gut microbiota and T1D.*
## Bibliometric and visualize analysis
To perform bibliometric and visualization analysis, we used a Microsoft Excel 2019 (Microsoft, Redmond, WA, United States), the packages ggplot2 and Bibliometrix R in RStudio (version 2022.07.1, RStudio team, Boston, MA, United States), and VOSviewer software (version 1.6.18, Leiden University Science and Technology Research Center, the Netherlands).
## Annual distribution of publication
From WoSCC, we retrieved 639 papers, among which 324 were original articles written in English from 49 countries/regions and published from 1999 to 2021. In the last two decades, the number of papers on gut microbiota and T1D indicates an upward trend (Figure 2A). Before 2008, only sporadic reports were available in the literature, but their numbers sharply increased thereafter, albeit not in the last 5 years, and only 44 articles were published in 2022 by the end of September.
**Figure 2:** *(A) Annual publication numbers and fit of the publication growth curve. (B) number of average citations per year.*
The average annual citations of papers are shown in Figure 2B. The dynamic changes in average annual citations of articles may reflect that research from years with a high citation count had a significant academic impact. The whole distribution in average annual citations of papers showed six different peaks. The peak of annual citations that corresponds to the inflection point in the fitting curve is 2008. The graphical representation showed a dynamic variation of annual citations of papers on gut microbiota and T1D with a small peak every 2–3 years after 2008.
## Major countries/regions and institutions
These studies originated from 49 different countries and regions, most of which ($27.5\%$, $$n = 89$$) were from the United States, in addition to 35 studies from China, 20 from Finland, 20 from Italy, and 17 from Denmark. Most of these studies were designed and conducted by international teams and the frequency of occurrence of each coauthor’s country was visualized in the world map in Figure 3A. International collaboration networks of the top 20 countries were displayed in Figure 3B. The annual publications of the top 10 countries over time were shown in Figure 4. As shown in Figure 3, the United States not only accounts for most outputs but also is the center of international collaborations-most closely with European countries. The detailed quantitative analysis of collaboration papers of the top 10 countries also indicated that the publications of multiple countries/regions account for most of the worldwide research on gut microbiota and T1D (Figure 4). In addition, the top 10 most productive institutions and their annual publications are outlined in Table 1. The top 10 institutions are distributed as follows: six institutions in Finland (University of Helsinki, University of Turku, Tampere University Hospital, University of Oulu, Tampere University, and Turku University Hospital), three institutions in the United States (University of Florida, Yale University, and University of Colorado) and the University of Copenhagen in Denmark.
**Figure 3:** *Distribution of countries conducting research and international collaborations in the field of gut microbiota and T1D. (A) World map showing the distribution of countries conducting research in this field. (B) International collaboration network of the top 20 countries.* **Figure 4:** *(A) Publication partnerships. (B) Annual contributions of the top 10 countries.* TABLE_PLACEHOLDER:Table 1
## Highly cited researchers and studies
The top 10 most influential researchers are almost exclusively found in the top 10 most productive institutions. And the h-index, total citations, affiliations, and countries of the top 10 authors in number of publications were listed in Table 2 and the results showed that the top 10 authors are mainly from the United States and Finland. Table 3 showed that Li Wen had the highest number of publications, that Jorma Ilonen and Mikael Knip had the highest h-index, and that Mikael Knip had the most total citations. The top 10 authors not only are productive but also publish high-quality papers, and have a significant academic influence in the field of gut microbiota and T1D.
Furthermore, the 10 most cited papers outlined in Table 4, showing that Li Wen, who accounted for the largest share of the publications, had the most cited research in original articles on gut microbiota and T1D worldwide. These invaluable studies owned breakthrough discoveries, and the original articles were published in top-tier journals, such as Nature, Science, Cell, Immunity, and New England Journal of Medicine between 2008 and 2018.
**Table 4**
| Title | First author | Journal | Quartile | IF* | Year | TC | TC per year | Normalized TC |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Innate immunity and intestinal microbiota in the development of type 1 diabetes | Li Wen | Nature | Q1 | 69.504 | 2008 | 1375 | 91.67 | 2.16 |
| Sex differences in the gut microbiome drive hormone-dependent regulation of autoimmunity | Janet G. M. Markle | Science | Q1 | 63.714 | 2013 | 1109 | 110.9 | 6.16 |
| How informative is the mouse for human gut microbiota research? | Thi Loan Anh Nguyen | Dis Model Mech | Q1 | 5.732 | 2015 | 687 | 85.88 | 5.85 |
| Temporal development of the gut microbiome in early childhood from the teddy study | Christopher J. Stewart | Nature | Q1 | 69.504 | 2018 | 627 | 125.4 | 11.07 |
| The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes | Aleksandar D. Kostic | Cell Host Microbe | Q1 | 31.316 | 2015 | 612 | 76.5 | 5.21 |
| Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans | Tommi Vatanen | Cell | Q1 | 66.85 | 2016 | 555 | 79.29 | 6.67 |
| Shared and distinct genetic variants in type 1 diabetes and celiac disease | Deborah J. Smyth | New Engl J Med | Q1 | 176.079 | 2008 | 517 | 34.47 | 0.81 |
| Toward defining the autoimmune microbiome for type 1 diabetes | Adriana Giongo | ISME J | Q1 | 11.217 | 2011 | 516 | 43.0 | 3.78 |
| Gender bias in autoimmunity is influenced by microbiota | Leonid Yurkovetskiy | Immunity | Q1 | 43.474 | 2013 | 516 | 51.6 | 2.87 |
| Gut microbiota in children with type 1 diabetes differs from that in healthy children: a case–control study | Mora Murri | BMC Med | Q1 | 11.15 | 2013 | 443 | 44.3 | 2.46 |
Total citation counts usually reflected the significance of the journal. The most cited journals were shown in Figure 5A, with Nature at the top of the list, followed by Diabetes, Science, Diabetologia, and PLoS One. In turn, the h-index expresses the academic influence of the journal, and PLoS One has the highest h-index followed by Diabetes, Diabetologia, Scientific Reports, and Frontiers in immunology (Figure 5B). Figure 5C summarized annual changes in the cumulative number of publications of these top 10 journals.
**Figure 5:** *Top 10 journals by (A) total citations and by (B) h-index and (C) cumulative number of publications of the top 10 journals.*
The historical direct citation network of seminal papers on gut microbiota and T1D was shown in Figure 6, which was generated by historical cited paper visualization analysis. To further evaluate the quality of articles involved in historical direct network in the research, two metrics were applied, including the local citation score (LCS) which represented the sum number of citations of each specific article from the 324 publications included in bibliometric analysis, and the global citation score (GCS) which reflected the times an article has been cited by all documents in the WoSCC database (Table 5). It showed that the paper from Li Wen published in 2008 not only got on the topmost LCS but also won the highest GCS.
**Figure 6:** *Historical direct citation network of seminal papers about gut microbiota and T1D.* TABLE_PLACEHOLDER:Table 5
## Emerging trends and research focus
The keywords that describe the theme of the literature are important for highlighting hotspots in a specific research field. In present study, the top 50 most frequently used of author keywords and keywords plus were shown in a Word Cloud by visualization analysis (Figures 7A,B). The most frequently used author keyword was “autoimmunity” followed by “inflammation,” “children,” “metabolomics,” “obesity,” “dysbiosis,” “intestinal permeability,” “probiotics,” “short chain fatty acids,” “butyrate,” “virome,” “genetic,” and so forth. In addition, the most frequent keywords plus is “children,” followed by “inflammation,” “T-cell,” “autoimmunity,” “permeability,” “short chain fatty acids,” “regulatory T cell,” “innate immunity,” “obesity,” “insulin resistance,” “prevention,” and “diet.”
**Figure 7:** *Distribution of the top 50 (A) Author Keywords and (B) Keywords Plus.*
To further examine internal relationships between publications, clustering analysis was conducted based on all 106 keywords in VOSviewer (Figure 8). By total link strength, the 106 items were divided into seven clusters, and each cluster was highlighted with a specific color. Cluster 1 included 24 items, which were mainly correlated with epidemiology research on risk factors related to T1D onset (red). Cluster 2 consisted of 18 items, which mainly reflected the relationship between factors affecting gut microbiota and T1D (blue). Cluster 3 encompassed 22 items, which were associated with the relationship between alterations of the gut microbiome composition and metabolomics, and the role of molecular mimicry in β-cell autoimmunity (green). Cluster 4 comprised 12 items related to the relationship between the intestinal barrier and T1D onset (yellow). Cluster 5 contained 12 items, which reflected the inflammation and oxidative stress associated with gut microbiota involved in the metabolic disorder underlying T1D and T2D (purple). Cluster 6 covered 10 items, which were mainly related to the effect of the gut microbiome on immune responses in T1D (azure blue). Cluster 7 consisted of the last 10 items, which were mainly associated with therapeutic strategies targeting the gut microbiota in T1D (orange).
**Figure 8:** *Cluster analysis of high-frequency keywords.*
Lastly, we analyzed developing trends of high-frequency keywords of the gut microbiota and T1D research filed as the variation of keywords over time may express the evolution of hot topics and frontier research in this field and may have some significance in guiding future research. The visualization of high-frequency keywords over time was combined with year and frequency of keywords, and each keyword was mapped using different colors depending on the year. The closer the color was to blue, the earlier the keyword appeared, whereas the closer the color was to yellow, the more frequently the keywords appeared in recent published papers (Figure 9). The emergence of these seven clusters did not correlate with time, and the more yellow nodes were not found in a specific cluster, thus suggesting relatively balanced development dynamics in these clusters. The evolution of recent research hotspots can be compared by high-frequency keywords between the last 3 years and earlier stages. As shown in Figure 9, gut microbiota metabolites, such as “bile acids” and “short-chain fatty acids” are becoming increasingly popular. The topic about gut microbiota interaction with host immunity in T1D has been the focus of intense research, and “neutrophils” is an emerging keyword. Similarly, the relationship of gut microbiota with β-cell autoimmunity has remained an important topic for researchers to investigate in depth over time, but recent keywords such as “HbA1c,” “hyperglycemia,” “hypertension,” and “diabetic nephropathy” are increasingly more related to the clinical features and outcome of T1D.
**Figure 9:** *Developing trends of high-frequency keywords.*
Among keywords regarding countries and regions, “Asian” and “Africa” have appeared recently, indicating that more research has been conducted in these two regions. The results also showed that the “metagenomics” is more frequently applied than “16 s RNA sequencing” based on the use of sequencing technology keywords and that research has moved from “children” to “pregnancy” based on the life stage in which gut microbiota plays a role. Lastly, the emerging keyword “machine learning” has been an extension of traditional statistical methods and may become a valuable and increasingly necessary tool for exploring the relationship between gut microbiota and T1D.
## Discussion
The present study is the first bibliometric research about gut microbiota and T1D and includes a total of 324 articles retrieved from SCIE of WoSCC for bibliometric and visualization analysis. Since 2008, the number of publications per year has increased, and the average annual citations of articles also peaked in 2008. Based on the fit of the curve and actual data of 2022, we project that the number of publications per year will remain at relatively constant level for a period.
Four publications in 2008 were included in our analysis, and subsequent analysis of the historical citation network suggested that a study conducted by Li Wen and her colleagues is the most cited original article and a milestone in the field of research on gut microbiota and T1D. In that study, Li Wen et al. found that Myd88 deficiency protects NOD mice from T1D, whereas Myd88−/− NOD mice under germ-free conditions did develop T1D (Wen et al., 2008). Their study revealed for the first time that the interplay between the host innate immune system and gut microbiota is at the root of T1D pathogenesis, thus laying a solid foundation for future research.
European countries and the United States are the leading contributors to this field. Research success in those countries may be linked to their well-characterized cohort studies, the most representatives of which are The Environmental Determinants of Diabetes in the Young (TEDDY), the Pathogenesis of Type 1 Diabetes-Testing the Hygiene Hypothesis (DIABIMMUNE), and All Babies in Southeast Sweden (ABIS) studies. To identify environmental factors related to the onset of T1D, the TEDDY study recruited newborns with high-risk human leukocyte antigen (HLA) alleles from the general population and first-degree relatives (FDRs) of T1D patients, who were prospectively analyzed and followed up at different clinical centers, including centers in Colorado, Washington State, Georgia, Florida in the United States, centers in Finland, Germany and Sweden. DIABIMMUNE is another important multinational longitudinal study for testing the hygiene hypothesis in T1D, which recruited newborns with high-risk HLA haplotypes from Finland, Estonia, and Russian Karelia. In addition, ABIS from *Sweden is* a prospective birth cohort aimed at examining the role of gut microbiota in the etiology of T1D. Diet intervention studies, such as the Primary dietary intervention study to reduce the risk of islet autoimmunity in children at increased risk for type 1 diabetes (BABYDIET), the Finnish Diabetes Prediction and Prevention Project (DIPP), and the Trial to Reduce IDDM in the Genetically at Risk (TRIGR) also directly or indirectly addressed the association between gut microbiota and islet β-cell autoimmunity.
Moreover, we relied on animal models to explore how gut microbiota regulate T1D pathogenesis (Tlaskalová-Hogenová et al., 2011; Pearson et al., 2016). Evidence has shown that NOD mice develop spontaneous β-cell autoimmunity mimicking T1D and the development of this animal model under specific pathogen free and germ free condition enables research on gut microbiota to move forward from bedside to bench. Although the current main researchers and research institutions are located in the United States and European countries, the keywords “Asian” and “Africa” have appeared recently, indicating that research on T1D and gut microbiota is starting to gain widespread attention in these two regions. To compensate for research disadvantages in Asian and Africa in relevant animal studies, such as the later start, the lack of depth of research, the relatively small size of human studies, and most studies are cross-sectional. Therefore, actions to improve research conditions and techniques, in addition to setting up large well-characterized cohorts in Asian and *Africa is* needed.
In the present study, we analyzed publications on gut microbiota and T1D and listed the most influential research, researchers, and institutions. This information may be helpful for those who want to learn from the outstanding studies and seek collaboration opportunities. Another key feature of our study consisted of highlighting the hotspots and change trends of research on T1D and gut microbiota. The high-frequency keywords and clustering analysis indicated the current hotspots and main research directions. Our analysis of animal studies and human research conducted in the past two decades suggested that gut microbiota is closely related to the onset of islet β-cell autoimmunity in T1D from seven perspectives. Several environmental triggers, especially gut microbiota, contribute to islet β-cell autoimmunity based on human research (Cluster 1 and Cluster 2), and the most prevalent stage has advanced from children to early pregnancy although the exact reasons for this shift must be further investigated. Subsequently, the causative link between gut microbiota and T1D has gradually been proved in animal models. From Cluster 3 to Cluster 6, the hot topics explored by researchers are associated with the mechanism underlying the effect of gut microbiota on T1D, including alterations in the gut microbiota and its metabolites, which can cause changes in intestinal permeability, mimic molecules in β-cell islets, and induce oxidative stress, inflammation abnormal innate and adaptive immune responses.
Among the top of the 50 most frequently used author keywords and keywords plus, 16 s RNA sequencing and detecting short chain fatty acids stand out as major methods for exploring alterations in the gut microbiome composition and its metabolites. The terms “metagenomics” and “metabolomics” have been more frequently used in recent research, and the advent of multi-omics profiling technologies has enabled us to identify the gut microbiome composition at species level, to explore the functional potential of the gut microbiome, and to investigate mycobiomes and virome in to the context of T1D (Vehik et al., 2019; Auchtung et al., 2022). These new research technologies are more comprehensive and accurate tools for testing and verifying alterations in the gut microbiota and its effect on T1D.
The keywords cloud has showed that T cells play a key role in T1D progression regulated by gut microbiota, as confirmed by many studies and this research continues. Moreover, other components of the immune system, most recently neutrophils, have also gained considerable research attention. Some of the important goals of these studies are translational applications of gut microbiota toward improving therapeutic strategies for T1D (Cluster 7). Recently, an increasing number of studies have suggested that gut microbiota is involved in the glycemic control and complications of T1D. Therefore, gut microbiota-targeted therapeutic strategies are promising new approaches not only in preventing or halting disease progression but also in improving the clinical outcome of T1D patients. The present gut microbiota-targeted therapeutic strategy in T1D, which consists of diet, probiotics, prebiotics, fecal transplant, among other measures, has made only an initial progress. However, gut microbiota-targeted therapeutic strategies have already brought venture, benefits, challenges, and opportunities for the prevention and cure of T1D.
Machine learning, a newly emerged keyword in the field of gut microbiota in T1D, has promoted the development of research on integrating gut microbiota to predict disease progress and therapeutic response. Current research has revealed that gut microbiota may be able to predict the diabetes risk. Simultaneously, whether in individuals without diabetes or patients with type 2 diabetes (T2D) or T1D, gut microbiota can predict the individual postprandial glucose response (Zeevi et al., 2015; Mendes-Soares et al., 2019; Shilo et al., 2022b). In addition, integrating clinical and gut microbiome characteristics can predict the short-and long-term glucose status and β-cell function of T2D patients (Aasmets et al., 2021). In the field of T1D, the microbial risk score and the genetic risk score model predicted well the impact of interactions between host genetics and gut microbiome on disease progression (Wang C. et al., 2022). We believe that machine learning is a potential research direction in the field of gut microbiota and T1D, which enables the integration of medical information and gut microbiota data to identify predictive microbiota biomarkers and to develop customized therapy toward reshaping the gut microbiota of T1D patients.
## Conclusion
In the present study, we extracted the publications of original research articles on gut microbiota and T1D published in SCIE of WoSCC and conducted bibliometric and visualization analysis. The development and deep research on gut microbiota in NOD mice would bringing T1D to a new translational level in the field of gut microbiota. And the application of multi-omics and machine learning approaches will be a necessary future step for better understanding the gut microbiota in T1D and develop customized therapy toward reshaping the gut microbiota of T1D patients. Based on these, the warming of gut microbiota-targeted therapeutic strategies that would help develop optimal strategies for “precision” gut microbiota modulation in T1D, and progress is being made toward this goal, but there is still a lot to do.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.
## Author contributions
KG, JL, and JH performed the literature search and drafted the manuscript. JH provided ideas and suggestions. KG, JL, XL, JH, and ZZ discussed and revised the manuscript. All authors contributed to the article and approved the submitted version.
## Funding
This work was funded by the National Natural Science Foundation of China (grant nos. 82100899 awarded to JH and 81820108007 awarded to ZZ), the Natural Science Foundation of the Hunan province of China (grant no. 2021JJ40833 awarded to JH) and the Pilot and Feasibility grant of the Yale Diabetes Research Center (grant no. DK 045735 awarded to JH).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Improving adherence to immunosuppression after liver or kidney transplantation
in individuals with impairments in personality functioning – A randomized controlled
single center feasibility study
authors:
- Jolana Wagner-Skacel
- Nadja Fink
- Judith Kahn
- Nina Dalkner
- Emanuel Jauk
- Susanne Bengesser
- Marco Mairinger
- Gerhard Schüssler
- Christoph Pieh
- Vanessa Stadlbauer
- Alexander H. Kirsch
- Sabine Zitta
- Alexander R. Rosenkranz
- Peter Fickert
- Peter Schemmer
journal: Frontiers in Psychology
year: 2023
pmcid: PMC10033957
doi: 10.3389/fpsyg.2023.1150548
license: CC BY 4.0
---
# Improving adherence to immunosuppression after liver or kidney transplantation in individuals with impairments in personality functioning – A randomized controlled single center feasibility study
## Abstract
### Introduction
Although adherence to immunosuppressive medication is the key factor for long-term graft survival today, 20–$70\%$ of transplant recipients are non-adherent to their immunosuppressive medication.
### Objective
A prospective, randomized, controlled single-center feasibility study was designed to evaluate the impact of a step guided multicomponent interprofessional intervention program for patients after kidney or liver transplantation on adherence to their immunosuppressive medication in daily clinical practice.
### Materials and methods
The intervention consisted of group therapy and daily training as well as individual sessions in a step guided approach. The primary endpoint of the study was adherence to immunosuppression as assessed with the “Basel Assessment of Adherence to Immunosuppressive Medications Scale” (BAASIS). The coefficient of variation (CV%) of Tacrolimus (TAC) through levels and the level of personality functioning was a secondary endpoint. We conducted six monthly follow-up visits.
### Results
Forty-one age- and sex-matched patients [19 females, 58.5 (SD = 10.56) years old, 22 kidney- and 19 liver transplantation] were randomized to the intervention- ($$n = 21$$) or control-group ($$n = 20$$). No differences between intervention- and control groups were found in the primary endpoint adherence and CV% of TAC. However, in further exploratory analyses, we observed that individuals with higher impairments in personality functioning showed higher CV% of TAC in the controls. The intervention might compensate personality-related susceptibility to poor adherence as evident in CV% of TAC.
### Discussion
The results of the feasibility study showed that this intervention program was highly accepted in the clinical setting. The Intervention group could compensate higher CV% of TAC after liver or kidney transplantation in individuals with lower levels of personality functioning and non-adherence.
### Clinical trial registration
ClinicalTrials.gov, identifier NCT04207125.
## 1. Introduction
Following solid organ transplantation, non-adherence to immunosuppressant medication is associated with poor clinical outcome including graft rejection, which leads to increased care cost (Vlaminck et al., 2004; Pinsky et al., 2009; De Geest et al., 2020). In 2019, 720 solid organ transplantations were performed in Austria, 108 of them in Graz, which included mostly kidney (KT) and liver transplantations (LT) (ÖBIG-Transplant, 2019). With 87.7 transplanted patients per million inhabitants, Austria has one of the highest transplantation rates in Europe (European Directorate for the Quality of Medicines and Health Care of the Council of Europe, 2018). For patients, transplantation is often a step into a new life after living with a chronic disease for years. However, it should not be forgotten that patients are still chronically ill (Erim et al., 2013). After KT or LT, immunosuppressive medication is required to prevent rejection. Lifelong adherence, the extent to which the patient’s behavior matches prescriber’s recommendations, to immunosuppressive medication is important to prevent graft failure (Pabst et al., 2015; Nöhre et al., 2018). Nonetheless, many transplant recipients have difficulties when it comes to medication intake. Between 20 and $70\%$ of all transplant recipients do not follow therapy recommendations and do not take their medication as prescribed (Massey et al., 2013, 2015; Neuberger et al., 2017; Low et al., 2019). Non-adherence is linked to poor post-transplant outcomes including late acute rejection and graft loss (Dew et al., 2008; De Geest and Dobbels, 2010). Results from a meta-analysis of 147 transplantation studies show that non-adherence was the highest among kidney transplant recipients, reaching 36 cases per 100 patients per year (Dew et al., 2007). Non-adherence can be detected by objective direct measures (observation that medication was taken) or indirect (serum drug levels, biological markers, and electronic monitoring) and subjective measures such as self-reports. Adherence is a dynamic process with the need to be measured repeatedly over time. Monitoring should be incorporated into the routine clinical management of all transplant recipients (Neuberger et al., 2017; Gustavsen et al., 2019).
Risk factors for non-adherence can be categorized into five interrelated areas: socioeconomic, patient-related, disease-related, treatment-related and factors related to the healthcare setting (World Health Organization, 2003). Interventions should target more than one risk factor by combining educational and behavioral interventions over time with a multilevel approach, thereby influencing not only the patient but also the healthcare provider (Neuberger et al., 2017). Improving adherence to the immunosuppressive drug regimen is the most important intervention to improve long-term transplantation outcome (Pinsky et al., 2009; Shemesh et al., 2018).
In recent studies, the main factors influencing adherence were the knowledge about the medication, complexity of the medication, and the side effects (Zhu et al., 2017). Adherence also hinges on the relationship to the caregiver, mental illness, social support, and sociodemographic parameters. These factors are very likely influenced by the level of personality functioning and the attachment style (Mathes et al., 2017). Personality functioning describes enduring maladaptive patterns of emotion, cognition, regulation and behavior including abilities in interpersonal functioning as well as coping strategies and the regulation of affect and stress. The concept of personality structure or personality functioning–also referred to as structural integration or personality organization–describes basic self- and other-related affect-laden processing and regulatory capacities (Hörz-Sagstetter et al., 2018). Structure refers to the availability of mental functions. The concept of personality structure has its origins in psychoanalytic/psychodynamic theory and is traced back to Freud’s structural model of what he called the psychic apparatus (Freud, 1900, 2000). Personality functioning at a well-integrated level is characterized by a coherent sense of self, flexible functioning even under stress from external or internal conflicts, appropriate expression and regulation of impulses and emotions, internalized moral values, and engagement in satisfying relationships (Zimmermann et al., 2012). In the clinical environment, patients with a lower level of personality function are often experienced as “difficult to treat” (Ehrenthal et al., 2019), with the result that these patients often do not receive adequate treatment. Difficulties in the doctor-patient relationship are reflected in non-adherence and a worsening of the outcome.
Most studies that evaluated interventions targeted at adherence in adults combine educational and behavioral components and found larger effects than studies with only one component (Foster et al., 2018). The multicomponent TAKE-IT intervention, which combines electronic adherence monitoring, problem-solving skills training, and technology-based adherence support in adult kidney transplant recipients resulted in a significantly better medication adherence than in the control group (De Bleser et al., 2009). Even better effects were observed in studies, which took an individualized approach or used more frequent interventions (Bender et al., 2011). However, as several studies have shown, final recommendations on a certain adherence intervention cannot be made so far, and further research is urgently needed especially translated into daily clinical practice (Zhu et al., 2017; Duncan et al., 2018; Foster et al., 2018; Kostalova et al., 2022).
Therefore, we developed a step guided multicomponent (combining education, motivational interviewing, and psychodynamic therapy) interprofessional (consisting of psychiatrists, psychotherapists, nursing scientists, nurses) intervention to increase adherence to medical and behavioral recommendations in liver or kidney transplant recipients. The multilevel intervention program is integrated into daily routines using clinically feasible methods of screening and tracking adherence and activities that empower patients in order to improve their self-management.
In the present study, we assessed whether this approach is feasible (Tickle-Degnen, 2013) in a clinical setting and whether it improves adherence as measured by the “Basel Assessment of Adherence to Immunosuppressive Medications Scale” (BAASIS) and the coefficient of variation (CV%) of TAC-through levels. In our experience, there is a strong influence of personality functioning on emotional regulation, the doctor-patient relationship and consequently health management. Thus, we assessed the association between personality functioning and adherence to have a focus on non-adherence.
## 2. Materials and methods
This study was a prospective, single center, non-blinded, randomized controlled psychotherapeutic trial with two parallel groups assessing the potential superiority of a multilevel intervention program. A stratified randomization based on the type of allograft (KT or LT) was used. Depending on the stratification, the patients were randomized in a 1:1 ratio to the group receiving a multilevel intervention program during the time either after transplant or to the control group, receiving standard of care after being transplanted (shown in Figure 1) and the description of the multicomponent interprofessional step guided approach (shown in Figure 2).
**FIGURE 1:** *CONSORT flow diagram of the clinical trial.* **FIGURE 2:** *Description of the multicomponent interprofessional step guided approach.*
A sample of 60 patients was recruited during time on the waiting list. The study was conducted at the University Transplant Center Graz, Medical University of Graz, Austria. The study was approved by the Ethics Committee of Medical University of Graz (protocol No: EK 32-062 ex $\frac{19}{20}$).
Patients were included when they had the alarm for transplantation for LT or KT living donation, were able to understand the character and individual consequences of the trial, were fluent in German language, gave written informed consent before enrolment in the trial and received maintenance immunosuppression with TAC.
Patients < 18 or > 90 years or pregnant or lactating women were excluded. Patients waitlisted for KT or LT were approached by study personnel and were included in the study after having provided oral and written informed consent. A stratified randomization based on the organ the patient got transplanted is used. Depending on the stratification, the patients are randomized at a 1:1 ratio into the group receiving a multilevel step guided intervention program during the time either after liver or kidney transplantation or into the control group receiving standard of care after being transplanted. The online tool Randomizer (randomizer.at) was used for randomization. Clinical data were recorded, the psychological assessment and BAASIS was performed, and laboratory parameters as well as TAC through levels were recorded from the hospital database at each visit.
## 2.1. Primary outcome measure
The primary outcome was the proportion of patients categorized as non-adherent. Medication adherence was assessed at months 1–6 after transplantation, using a validated version of the Basel Assessment of Adherence to Immunosuppressive Medications Scale (BAASIS) questionnaire. The BAASIS was developed to assess adherence to immunosuppressive drugs in adult transplant recipients and followed the newly published taxonomy of medication adherence. This self-reported interview consists of three quantifiable phases: initiation, implementation and persistence. Five items assess the implementation dimension and one item assesses the persistence. An optional item assesses initiation (Dobbels et al., 2010).
## 2.2. Secondary outcome measures
Coefficient of variation (CV%) of TAC was calculated based on its through level, measured during the first 6 months after transplantation (Shuker et al., 2015). Clinical outcomes including incidence of infections, incidence of biopsy proven acute rejection, transplant function (creatinine, estimated glomerular filtration rate), death, graft losses, hospital readmissions, side effects, number of trough level controls, and achievement of TAC target concentrations during 6 months after transplantation were recorded.
## 2.3. Further patient characteristics
Personality functioning was assessed with the short version of the Operationalised Psychodynamic Diagnosis Structure Questionnaire (OPD-SQS) at inclusion of the patient and at months 1–6 after transplantation. Attachment dimensions were assessed with Experiences in Close Relationships-Revised (ECR-RS) at inclusion of the patient and at months 1–6 after transplantation.
## 2.3.1. Intervention group
To improve adherence after transplantation, a multilevel step guided intervention program based on theoretical research including education, motivational interviewing and psychodynamic therapy in an interprofessional setting consisting of psychiatrist, psychotherapist, nursing scientists, nurses was developed and implemented at the University Transplant Center, Medical University of Graz. The first and the second part were conducted during the inpatient stay after transplantation. The third part was conducted during the transplant recipient’s outpatient follow-up appointments. A short, detailed description of the intervention program follows:
## 2.3.1.1. Part 1 individual educational training and mentoring
After being transferred to the intermediate care unit, patients received short training units (5–10 min per day) by the nursing staff depending on the patient’s cognitive abilities. The patients were informed on currently prescribed medications and received written information about the multilevel intervention program, medication names and pictures of the medication, effects and side effects of the medication. The nursing staff at the intermediate care unit was trained in motivational interviewing and teaches back method.
## 2.3.1.2. Part 2 group therapy
The 90 min group session with the focus on a structure based psychodynamic therapy was conducted in the first 2 weeks of the inpatient stay with the nursing scientist and psychiatrist and psychotherapist. Patients were introduced to mindfulness training, existential flourishing, stress coping strategies. Important was also the Introduction to a daily routine and day structure as a cornerstone of adherent behavior. Questions like the following are reflected in the group as a matter of what we think we are doing in our daily lives and interactions: *What is* to live well? What type of effort must we put in? What, when it comes to becoming ourselves, are we working with? How do others factor in? *What is* the role of justice in all of this? Afterward an advanced practice nurse for transplant care explained signs and symptoms of rejection and the importance of timely intake of immunosuppressive medications. Effects and side effects of the current medication were discussed with each patient and patients were instructed in the dispensation of their individual medication.
## 2.3.1.3. Part 3 individual treatment approach
The goal of this session was to promote patient engagement in self-management of their chronic illness, to improve the patients’ ability to manage symptoms, treatments, physical and psychosocial consequences and lifestyle changes.
## 2.3.2. Control group
Patients in the control group were treated according to standard of care and did not receive any additional intervention regarding their adherence behavior. This standard included monthly appointments with the liver or kidney transplant treatment team to assess kidney and liver function and to address any issues raised by the provider or the patient.
## 2.4.1. Basel assessment of adherence to immunosuppressive medications scale (BAASIS)
The BAASIS was used to assess adherence to immunosuppressive medications in adult transplant recipients and is available as questionnaire as well as interview guideline in several languages (Leuven Basel Research Group, 2019). Psychometric properties were tested by Marsicano Ede et al. [ 2013]. The BAASIS consists of five items, four of which assess issues with the implementation and one the non-persistence of immunosuppressive medication use. Three items have a sub-question regarding the frequency of occurrence. Any “yes” on any of the items 1a, 1b, 2, 3, or 4 indicates that the study participant is non-adherent (Leuven Basel Research Group, 2019). Since this dichotomous scoring of the BAASIS resulted in limited variance (with partially only single participants being classified as non-adherent; see Table 2) and discards part of the assessed information, we also used a complemental, metric scoring. For this, we used the sum of items 1–4, which resulted in higher variance [see Table 2; Dobbels et al. [ 2010]].
## 2.4.2. Brief symptom inventory-18 (BSI-18)
The BSI-18 was used to assess psychiatric symptoms and psychological distress in the preceding week. The inventory comprises 18 items and assesses psychological distress on the three subscales depression, anxiety, and somatization. The subscales show an internal consistency with a Cronbach’s alpha of α = 0.79 for the sub-dimensions (Derogatis, 2001).
## 2.4.3. Operationalised psychodynamic diagnosis structure questionnaire short version (OPD-SQS)
The OPD-SQS was used as a screening instrument for supporting therapeutical decision making in treatment planning and therapy focus (Ehrenthal et al., 2015). The OPD-SQS consists of 12 Items with three subscales (self-perception, contact, relationship) explore patient characteristics, which might be of relevance to adherence, such as the level of personality functioning as self-regulatory and interpersonal competencies, would impact the effectiveness of the intervention. The subscale “self-perception” combines aspects of self with structural skills of emotion regulation. The subscale “contact” combines interactional skills with aspects of self-uncertainty. The subscale “relationship” depicts the representation of relationship experiences and connections to expectations of new relationships. The range reaches from 0 (“highest structural level”) to 48 (“lowest structural level”). The internal consistencies range from α = 0.87 to 0.89 (Ehrenthal et al., 2012).
## 2.4.4. Experiences in close relationships-revised (ECR-RS)
The ECR-RS was used to assess differences with respect of attachment-related anxiety. It identifies four types of attachment including secure, preoccupied, detached and fearful attachment, which correspond to the secure, ambivalent, avoidant, and disorganized attachment types described by Ainsworth [1978]. It contains attachment-related anxiety and avoidance features in four kinds of relationships: relationships with mother, father, romantic partners, and friends. The ECR-RS contains nine items assessing attachment in each of those four domains, therefore producing 36 items. Romantic attachment is associated with basic aspects of relationship functioning (Fraley et al., 2011). High scores indicate insecure adult attachment styles, while low scores can be viewed as having a secure adult attachment style (Brennan et al., 1998). It employs a 7-point Likert scale (1 = “absolutely disagree” to 7 = “absolutely agree”).
## 2.4.5. Coefficient of variation of tacrolimus (CV% of TAC)
The CV% of TAC through levels was calculated as the ratio of the standard deviation (o’) to the mean (μ) (CV percentage = o’ /μ × 100). It is a useful method for the quantification of intrapatient variability and it shows the degree of variation (Shuker et al., 2015). High intrapatient variability of tacrolimus has shown to be associated with poor outcome and higher risk for rejection (Shuker et al., 2015; Gueta et al., 2018; Rayar et al., 2018; Rahamimov et al., 2019).
## 3.1. Description of the sample
The final sample with complete data sets consisted of 41 individuals, 21 of whom were randomized to the intervention group, and 20 to the control group (see also Table 1). Overall, 19 women and 22 men with a mean age of 58.49 years (SD = 10.56) took part in the study. The sex ratio did not differ across intervention and control groups (χ21 = 0.30, $$p \leq 0.87$$), and no differences were found in age (t37 = −0.04, $$p \leq 0.97$$). Among the study patients, 22 underwent KT, and 19 underwent LT; this ratio did also not differ between intervention and control groups (χ21 = 0.21, $$p \leq 0.65$$). The patients included in the study had no adverse events. Significant correlation was found between personality functioning (OPD-SQS) and symptom load (BSI-18) a low level of structural integration was accompanied by a higher symptom load (see in Table 2).
## 3.2. Statistical analyses: Main effects of the intervention
In the following, we report tests of intervention effects for our primary and secondary outcome measures. We use univariate statistical tests for the six timepoints rather than multivariate tests because outcome data were not available for each patient and timepoint, and our aim was to preserve the largest possible sample size.
## 3.2.1. Primary outcome: Adherence assessed by the BAASIS
To assess the effectiveness of the intervention with respect to patients’ adherence, we first evaluated differences in the BAASIS scores at each of the six timepoints. As detailed in the methods section, we used (a) the original dichotomous BAASIS scoring and (b) an alternative, metric scoring (given the limited variance in the original scoring). Table 3 and Figure 3 present the results of these tests. We did not observe significant differences between the intervention and control groups at any of the timepoints for either the original or the alternative BAASIS scoring. Note, however, that BAASIS scores were only available for 39–$85\%$ of the sample for the single timepoints (see Table 3). The drop-out rate was $14\%$ ($$n = 3$$) in the intervention group and $15\%$ ($$n = 3$$) in the control group, respectively. Adherence after 6 months (T6) was $78\%$ ($$n = 14$$) at the intervention group, $22\%$ ($$n = 4$$) were categorized as non-adherent. In the control group $76\%$ ($$n = 13$$) were categorized as adherent and $24\%$ ($$n = 4$$) as non-adherent. There was no statistically significance between the two groups (X2 = 0.01; $$p \leq 0.93$$). The same pattern of results was observed across other study time points. In the metric BAASIS scores we found no statistically significant differences between the intervention and control groups (see Table 3). The response rate for the BAASIS scores was low from T1 to T5 (39 to $85\%$, see Table 3).
## 3.2.2. Secondary outcome: Tacrolimus coefficient of variation
To assess the effectiveness of the intervention with respect to variation in TAC levels across the study period, we used the coefficient of variation (across all timepoints, assuming a stable TAC target level in the early post-transplant phase) as secondary outcome measure in a between-groups comparison. The analysis did not yield evidence for a significant intervention effect at a between-groups level [xint = 29.63 (16.08), xcon = 30.99 (11.23); t37 = −0.31, $$p \leq 0.76$$; see Table 3 and Figure 4].
**FIGURE 4:** *TAC coefficient of variation (CV%) by group. Mean differences are displayed for descriptive purposes and not statistically significant (see Table 2).*
## 3.3. Exploratory analyses: Impact of patient characteristics on intervention effectiveness
Our statistical analyses did not yield evidence for a main effect of the intervention on the primary outcome measures; however, it might be the case that individuals benefit differentially from the intervention. As outlined above, particularly those individuals with lower levels of personality functioning might benefit more from the intervention. To investigate the potential impact of personality functioning on the intervention effectiveness, we first inspected correlations of personality functioning (OPD-SQ) and outcome measures separately for treatment and control groups. This might give hints on whether the associations between personality functioning and the outcome measures differs between the groups, or, in other words, whether the intervention effectiveness depends upon patient characteristics. We then tested the significance of differences in those coefficients which displayed notable differences in the first place using a formal moderation analysis (multiple regressions).
We observed a notable difference in correlations between groups in the relation of OPD-SQS and TAC COV between groups (rint = –0.19, $$p \leq 0.46$$; rcon = 0.47, $$p \leq 0.04$$; Δr = 0.66). As Figure 5 shows, there was a strong and significant positive relationship between personality functioning and TAC COV in the control group, which means that those with higher impairment in personality functioning displayed higher variation in TAC. Such an associations was not evident in the intervention group, where the correlation was weak and non-significant. The TAC COV was thus not dependent upon personality functioning in patients in the intervention group. A formal test of moderation showed that the difference in magnitude of these correlations is statistically significant (interaction test; see Table 4). We also observed a difference of Δr = 0.37 between OPD-SQS and average BAASIS scores (rint = 0.47, $$p \leq 0.05$$; rcon = 0.10, $$p \leq 0.69$$), but this interaction was not statistically significant (see Table 4).
**FIGURE 5:** *Associations between personality functioning and TAC coefficient of variation (CV%) within groups. OPD-SQS, Operationalized Psychodynamic Diagnosis Structure Questionnaire Short Version.* TABLE_PLACEHOLDER:TABLE 4 To further explore the nature of the correlation differences in the association of the OPD-SQS and the TAC COV between the control and intervention groups, we repeated the aforementioned correlation comparisons (control vs. interventions group) for the OPD-SQS subscales self-perception, contact, and relationship. This might give hints on which aspects of personality functioning impact the intervention effectiveness. We observed equal differences in correlation of Δr = 0.62 for the contact (rint = −0.05, $$p \leq 0.86$$; rcon = 0.57, $$p \leq 0.01$$) and relationship subscales (rint = −0.33, $$p \leq 0.19$$; rcon = 0.29, $$p \leq 0.21$$), the difference in correlation for the self-perception–subscale was somewhat smaller (rint = −0.06, $$p \leq 0.82$$; rcon = 0.36, $$p \leq 0.12$$; Δr = 0.52). This points to the impact of interpersonal aspects of personality functioning for intervention effectiveness.
## 4. Discussion/Conclusion
This feasibility study presents a randomized controlled single-center trial using a multilevel intervention program for improving medication adherence in patients after LT or KT implemented in a clinical setting. We did not find differences in adherence measured with BAASIS between intervention- and control group. We observed a notable difference in correlations between groups in the relation of level of personality functioning and TAC COV. Without intervention, individuals with impairments in personality functioning had higher TAC COV values. The intervention is able to compensate these individual differences in personal vulnerability. A formal test of moderation showed that this interaction was statistically significant. We found the measurements and interventions well-accepted with high completion rates in a cohort of 41 patients LT or KT, respectively. Our most important finding is a significant correlation of personality functioning PF and CV% of TAC with improvement in individuals that would have difficulties in adherence.
The focus on patients with non-adherence was recently published to be a goal in the management of adherence in a multidisciplinary team with the use of novel therapeutic approaches focus on multimodal therapy for non-adherent population incorporated in a realistic clinical setting (Myaskovsky et al., 2018; Geramita et al., 2020; Kuypers, 2020). Individuals with lower levels of personality functioning might benefit more from the intervention program because of the frequent contact and the training of behavioral changes with the goal to improve health literacy and the attitude toward oneself. Personality functioning levels are thought to vary on a continuum ranging from unimpaired/well-integrated to severely impaired/disintegrated (Clarkin and Huprich, 2011). Personality functioning at a well-integrated level is characterized by a coherent sense of self, flexible functioning even under stress from external or internal conflict, appropriate expression and regulation of impulses and emotions, internalized moral values, and engagement in satisfying relationships (Zimmermann et al., 2012). Individuals at lower levels of personality functioning typically exhibit problems with self-regulation or self-other differentiation. This ability comes with a number of associated challenges and has implications for unhealthy behavior and interpersonal relationships, including the doctor-patient relationship (Stern et al., 2010; Wagner-Skacel et al., 2021). We see this link between low levels of personality functioning and symptom load including depressive, anxiety and somatization symptoms in transplant recipients. These finding underline the increasing importance of assessing personality functioning for diagnosis and treatment planning.
There might be several reasons why we did not observe a difference in adherence between intervention- and control groups. On the one hand the reason could be the small sample size in our study, on the other hand the passing of the measurements as BAASIS, which might be more important to measure the progress than the outcome of an intervention study with focus on non-adherence. A systematic review and the COMMIT group recommended this validated scale as the most appropriate self-report instrument for measuring non-adherence in transplant recipients because of its simplicity and ease of scoring (Dobbels et al., 2010; Neuberger et al., 2017). Another reason might be a selection bias: patients who agreed to participate may have more openness and interest regarding education and therefore higher adherence.
Multilevel intervention programs show a long-lasting effect on improving medication adherence after transplantation (Brennan et al., 1998; Low et al., 2015; Mathes et al., 2017; Schäfer, 2017). Therefore, it is necessary to offer individual educational training, mentoring and group therapy during the inpatient stay and an individual treatment approach during the outpatient follow-up appointments. So far, there are no study findings about intervention programs which start in the immediate post-transplant period. A strength of the present intervention program is its patient-centered approach, which allows influencing factors for non-adherence to be identified and addressed early as the implementation in a real -word setting. Much of the extant literature on adherence barriers has focused on modifiable factors (e.g., knowledge, social support), however, less is known about how barriers may be associated with relatively stable constructs such as personality and attachment. The evaluation of the implementation shows associations between personality functioning and adherence. This may lead to more personalized interventions oriented on the individual needs of the patients. Personality functioning, also referred to as structural integration, describes basic emotion-related perception and regulation capacities directed toward the self and others. Patients with impairments of structural integration are detracted in their psychosocial functioning and experience difficulties in self-regulation and interpersonal relations. Social support and functioning in transplant patients are important variables guaranteeing psychological and social wellbeing (Garcia et al., 2018). The importance of social functioning has been recognized in coping with stress and health treatment adherence (Ordin and Karayurt, 2016) providing better physical and mental health effects (Langenbach et al., 2008). Social and personality functioning describes patterns of emotion, cognition, regulation, and behavior in social interactions. Patients impaired in their social and personality functioning are more skeptic toward the treatment team and have a lack of interpersonal relations. In the clinical setting these patients are often experienced as “difficult to treat” (Ehrenthal et al., 2019). Due to the recent important change of personality disorder classifications, in a dimensional or a composite categorical dimensional approach for personality, the personality functioning and social functioning construct includes a broad range of personality facets (Zimmermann et al., 2012). In particular, the focus on domains beyond symptoms, such as global personality functioning has been accepted as highly important for indication and treatment planning (Doering et al., 2014). Perceived weak social support is an important risk factor for poor commitment to adhere to a treatment regimen (Blumenthal et al., 2006) especially among transplant providers, in determining patients’ suitability for transplantation (Ladin et al., 2018). Improving adherence is fundamentally linked to a stable relationship between physician and patient characterized by trust. This is better managed by the patient through a secure attachment style and a well-integrated personality functioning (Jennissen et al., 2020).
A structured assessment of waitlisted patients’ personality traits may be a valuable addition to routine pre-transplant data gathered. This may allow to more accurately identify patients who are at increased risk for non-adherence after transplantation and potentially provide these patients with interventions that are designed to mitigate this risk (Chan et al., 2013).
The major limitation was the open study design where participants, psychiatrist, advanced practice nurse and nurses who are performing the interventions are aware of the participant’s treatment allocation. Furthermore, the participants received information about the treatment and the intended goal, which may have led to information bias. We note that, due to the small sample size, the main confirmatory hypotheses tests might be underpowered (particularly regarding the dichotomous BAASIS scoring as primary outcome; see Table 3). Also, the exploratory analyses presented here await replication in larger samples, since the within-group sample sizes were small for correlational analyses. Still, the patients’ personality functioning as a variable with impact on adherence interventions may provide a potentially important starting point for future works. A further limitation of this study may be the assessment method of the primary outcome, which is based on a self-report of medication adherence using the BAASIS questionnaire and can lead to a self-reporting bias. Therefore, it was decided to follow recommendations to combine direct and indirect measurement methods to obtain more reliable results (Neuberger et al., 2017).
In conclusion, this study aimed to generate evidence for a clinically feasible multicomponent interprofessional step guided intervention program that fits into daily post-transplant routines with cost, time and personnel effectiveness. The novel therapeutic strategy is also tailored to the individual patient needs. The intervention program was highly accepted in a real-life setting and could compensate higher TAC COV after liver or kidney transplantation in non-adherent individuals with lower levels of personality functioning. Therefore, investigating the bio-psycho-social underpinning of non-adherence and its treatment is crucial to improve live-saving adherence. We also explored whether patient characteristics, which might be of relevance to adherence, such as the level of personality functioning as self-regulatory and interpersonal competencies, would impact the effectiveness of the intervention. The study findings may also have relevance to other patient groups with chronic conditions in whom medication non-adherence contributes to negative outcomes.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Local Ethics Committee of the Medical University of Graz, Austria (EK 32-062 ex $\frac{19}{20}$). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
JW-S: conceptualization, data curation, and writing—original draft. NF and JK: conceptualization and data curation. ND: conceptualization and methodology. EJ: methodology and writing—original draft. SB and MM: funding acquisition. GS and CP: supervision. VS and AK: data curation and supervision. SZ: conceptualization. AR and PF: supervision. PS: conceptualization and editing of manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor declared a past co-authorship with the author JW-S.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Non-invasive hemodynamic diagnosis based on non-linear pulse wave theory applied
to four limbs
authors:
- Xiaorui Song
- Yi Liu
- Sirui Wang
- Honghui Zhang
- Aike Qiao
- Xuezheng Wang
journal: Frontiers in Bioengineering and Biotechnology
year: 2023
pmcid: PMC10033961
doi: 10.3389/fbioe.2023.1081447
license: CC BY 4.0
---
# Non-invasive hemodynamic diagnosis based on non-linear pulse wave theory applied to four limbs
## Abstract
Introduction: *Hemodynamic diagnosis* indexes (HDIs) can comprehensively evaluate the health status of the cardiovascular system (CVS), particularly for people older than 50 years and prone to cardiovascular disease (CVDs). However, the accuracy of non-invasive detection remains unsatisfactory. We propose a non-invasive HDIs model based on the non-linear pulse wave theory (NonPWT) applied to four limbs.
Methods: This algorithm establishes mathematical models, including pulse wave velocity and pressure information of the brachial and ankle arteries, pressure gradient, and blood flow. Blood flow is key to calculating HDIs. Herein, we derive blood flow equation for different times of the cardiac cycle considering the four different distributions of blood pressure and pulse wave of four limbs, then obtain the average blood flow in a cardiac cycle, and finally calculate the HDIs.
Results: The results of the blood flow calculations reveal that the average blood flow in the upper extremity arteries is 10.78 ml/s (clinically: 2.5–12.67 ml/s), and the blood flow in the lower extremity arteries is higher than that in the upper extremity. To verify model accuracy, the consistency between the clinical and calculated values is verified with no statistically significant differences ($p \leq 0.05$). Model IV or higher-order fitting is the closest. To verify the model generalizability, considering the risk factors of cardiovascular diseases, the HDIs are recalculated using model IV, and thus, consistency is verified ($p \leq 0.05$ and Bland-Altman plot).
Conclusion: We conclude our proposed algorithmic model based on NonPWT can facilitate the non-invasive hemodynamic diagnosis with simpler operational procedures and reduced medical costs.
## 1 Introduction
Cardiovascular diseases (CVDs) have been the cause of numerous deaths and a major public health concern worldwide (Moran et al., 2014; McAloon et al., 2016). However, the early clinical symptoms of CVDs in the cardiovascular system (CVS) are not apparent, and medical measures can only be implemented when symptoms or complications are detected. Therefore, this presents a large gap in treating CVDs. Studies have shown that cardiovascular functions are characterized by pulse waves changes in hemodynamic diagnosis indexes (HDIs) are reflected by changes in pulse waveforms, which can be used for the early detection of CVDs symptoms (Chow et al., 2008; O’Rourke, 2009; Celermajer et al., 2012; SONG and QIAO, 2015; Wang et al., 2022). HDIs can be used to comprehensively evaluate the health status of a patient’s CVS, particularly for patients older than 50 years and prone to CVDs, more accurately and directly in terms of total peripheral resistance (TPR), stroke volume (SV), arterial compliance (AC), etc., ( Thenappan et al., 2015; Suzuki et al., 2019; Trammel and Sapra, 2020). However, the direct detection of HDIs requires specialized and expensive equipment along with the guidance of clinical technicians and can be invasive (Meyers et al., 2008; Tachibana et al., 2016), which hinders their clinical application.
Blood pressure and pulse wave have been used as the basis for diagnosing and treating CVDs in clinical practice owing to their convenience and rapid, highly reliable detection (Wilkinson et al., 2002; Santana et al., 2012). However, the CVS is complex, and the pulse wave information of a single limb cannot fully describe the health of a patient’s CVS. Studies (O’Shea and Murphy, 2000; Clark et al., 2012b; Mancia et al., 2013; Sughimoto et al., 2014; Singh et al., 2015; Wu et al., 2020) have further confirmed that the blood pressure and pulse wave of four limbs can be used to comprehensively evaluate the CVS and diagnose CVDs, thus providing more clinical signs of disease and aid doctors in clinical decision-making (van der Hoeven et al., 2013; Singh et al., 2015). The European Society of hypertension and society of Cardiology reported that the systolic blood pressure difference between two arms more than 10 or 15 mmHg can cause peripheral vascular diseases (Mancia et al., 2013; Weber et al., 2014). The International Institute of Clinical Health (Clark et al., 2012b) demonstrated that a blood pressure difference of over 20 mmHg between two arms is dangerous and typically related to potential CVDs for hypertension. Clark et al. ( 2012a) reported that patients with this difference require further vascular evaluation, thereby suggesting that this signal is a useful indicator for vascular diseases and risk of death. Chen, Su, Clark, and others (Clark et al., 2007; 2012b; 2012a; Chen et al., 2012; Sheng et al., 2013; Cao et al., 2014; Su et al., 2014) have reported that the upper limb diastolic pressure difference and lower limb diastolic pressure difference of more than 10 mmHg or 15 mmHg correspond to statistical differences in the statistical analysis of peripheral vascular disease, CVDs mortality, and all-cause mortality. Therefore, the signal data of blood pressure and pulse wave of four limbs must be used to improve the accuracy of the HDIs calculation model.
The non-invasive detection method and application of cardiovascular function based on pulse wave theory, from theoretical description to modeling analysis and from linearization to non-linear theory, have been developed (Li et al., 1981). Studies have found that the oscillometric method and arterial elastic lumen theory used to calculate pulse wave conduction velocity can be used to evaluate cardiovascular health status (Boutouyrie, 2008; Solà et al., 2011). A goat experiment was conducted to compare oscillometric non-invasive and invasive blood pressure measurements. The results revealed that the accuracy of oscillometric blood pressure measurement technology was still insufficient; it was a method suitable for groups rather than individuals. Previous studies developed a new model based on the non-linear pulse wave theory (NonPWT), constructed motion and constitutive equations of the blood vessel wall and peripheral tissues, and introduced animal experiments to estimate the non-linear coefficients of the pressure gradient and pulse wave propagation velocity (Hashizume, 1988; Wu and Lee, 1989; Ma et al., 1992; Dimitrova, 2015; Chen et al., 2017; Bi et al., 2021). This model preserves the waveform characteristics measured for blood vessel walls and peripheral tissues and improves estimation accuracy. To apply NonPWT for HDIs calculations, the blood pressure and pulse waveform data of four limbs are required. These data must be selected to accurately characterize the complexity of cardiovascular features. Simultaneously, NonPWT that can preserve the non-linear characteristics of the pulse wave must also be proposed.
In this study, a hemodynamic diagnosis model for the CVS was developed. Additionally, mathematical models, including the pulse wave velocity and pressure information of the brachial and ankle arteries, pressure gradient, and blood flow based on NonPWT, were established. Corresponding to blood flow, a mathematical model with signal data of blood pressure and pulse waveform of the four limbs was proposed. The consistency between the clinical and calculated values was determined to evaluate the performance of the mathematical models. Evidently, compared with the previous hemodynamic diagnosis method, the proposed hemodynamic diagnosis method is quicker and more effective in calculating HDIs (SV, AC, and TPR), which reflect the blood-pumping function of the heart and the elasticity of blood vessels. Further, its calculations are consistent with clinical measurements, and it can be used to non-invasively evaluate cardiovascular function. Unlike the traditional pulse wave theory used to calculate HDIs our method extracts the pulse waveform data of four limbs, and considers the risk factors of cardiovascular diseases; Hence, it has greater accuracy and can be used to calculate more HDIs more simply.
## 2.1 Data collection
The study, its experimental protocols, and relevant details were approved by the Institutional Ethics Committee of Beijing University of Technology. This study was a retrospective analysis of the “Study on Evaluation Method of Cardiovascular Disease Based on Non-invasive Detection of Blood Pressure and Pulse-Wave of Limbs” (Song et al., 2016), which recruited 412 subjects, where the clinic data collection as shown in Figure 1A. Herein, the Fukuda VS-1500A, manufactured by Beijing Fukuda Electronic Medical Instrument Co., Ltd, obtained the pulse waveform of four limbs, synchronously measures the blood pressure of four limbs, and automatically calculates the pulse wave velocity, ankle-brachial index, cardio-ankle vascular index, etc. CHM-T3002 Cardiac Hemodynamic Monitor, manufactured by Shandong Baolihao Medical Appliances Ltd., were used for measuring SV, AC, TPR, cardiac output, cardiac index, stroke volume index, ejection fraction, function index of left ventricular, index of contractility, etc. All subjects were registered at the Beijing University of Technology Hospital, and information on their diseases was collected from their medical records. The content of the study was explained to the subjects in detail, following which they signed an informed consent form based on this information. All experiments were performed per relevant guidelines and regulations.
**FIGURE 1:** *Construction process of hemodynamic diagnosis indexes model. (A) Data collection, (B) pulse wave processing, and (C) theoretical model. HDIs, hemodynamic diagnosis indexes; SV, stroke volume; TPR, total peripheral resistance; AC, arterial compliance.*
## 2.2 Pulse waveform processing
The pulse waveform processing as shown in Figure 1B. Pulse waveform of four limbs was collected using a blood pressure pulse instrument. The pulse waveform obtained by the instrument was the curve of the blood vessel radius changing with time, and the waveform signal is typically expressed as a voltage value. Therefore, the voltage information was calibrated as the waveform information according to the following equation. Pi=Ps−PdMs−Md∗Mi−Md+Pd mmHg [1] where Ms, Md, and Mi are the peak value of the actual sampled signal, valley value of the actual sampled signal, and value of any point in the actual sampling signal, respectively; and Ps, Pd, and Pi are the systolic blood pressure, diastolic pressure, and any point pressure of the pulse wave, respectively.
The frequency domain information of the pulse wave was obtained using Fourier transform, which loses relatively less information. The physiological and pathological information contained in the waveform was retained as much as possible.
This study collected pulse signals from four limbs of the subjects, and approximately five–six complete waveforms were collected for each sample. To avoid subjectivity in the manual selection, the waveforms were averaged and normalized using a method reported previously (Li et al., 2019). To prevent the distortion of the pulse signals, according to the actual sampling frequency, the sampling points of one cardiac cycle of the pulse wave were set at 200. Because the focus of the model was on the characteristic information of the pulse wave, the amplitude of the pulse wave was normalized to 0–200 in each cycle.
## 2.3 Non-linear pulse wave theory model
The governing equation of blood flow was established based on continuum equations, Navier–Stokes equations and the basic assumptions of the NonPWT. The basic assumptions of NonPWT were as follows. Blood is a viscous and incompressible Newtonian fluid, and its flow in the artery is a symmetrical laminar flow (∂2u∂z2≪∂2u∂r2). The blood vessel wall is a thin-walled axisymmetric cone, that is, locally orthogonal, anisotropic, and elastically incompressible. The radial motion of the blood vessel wall is greater than that of the blood vessel wall, and the radial motion is ∂R∂t. The axial and radial velocities vary with time at different locations in the blood vessel. The governing equation of blood flow is as follows. ∂u∂t+u∂u∂z+ϑ∂u∂r=−1ρ∂P∂z+ν∂2u∂z2+∂2u∂r2+1r∂u∂r [2] ∂ϑ∂t+u∂ϑ∂z+ϑ∂ϑ∂r=−1ρ∂P∂z+ν∂2ϑ∂z2+∂2ϑ∂r2+1r∂ϑ∂r−ϑr [3] ∂u∂z+1r∂∂rrv=0 [4] where u and ϑ are the axial and radial velocities in the blood, respectively; ν is the blood viscosity; ρ is the blood density and ρ=1.05*10−3kg/cm3; and P is the blood pressure.
In this study, aortic blood flow was used as the basis for calculating HDIs. Therefore, the governing equation of blood flow requires further derivation until it can be solved. Based on the basic assumptions of NonPWT, radial coordinate normalization was introduced into the coordinate transformation, as follows. η=rRz,t [5] where Rz,t is the arterial radius; and η is the radial coordinate parameter. The boundary conditions were set as follows. when r=R,η=1,u1,z;$t = 0$,ϑ1,z;t=∂R∂t [6] when $r = 0$,η=0,∂u∂r ∣$r = 0$=ϑ∣$r = 0$=0 [7] Based on these boundary conditions and assumptions, the basic equation for blood flow can be obtained as follows. ∂u∂t=Fz,t+ηR∂R∂t−ϑR∂u∂R+uR∂ϑ∂η+ϑη+νR2η∂∂ηη∂u∂η [8] where Fz,t is the axial pressure gradient; herein, Fz,t=−1ρ∂P∂z The basic equation of blood flow was calculated using a differential algorithm and numerical analysis, and the blood flow equation in the blood vessel in a cardiac cycle was solved as follows. dQdt+λQ+εQ2=AFz,t=−Aρ∂P∂z [9] which was the basis for calculating HDIs. Here, Q is the blood flow corresponding to each sampling point in the cardiac cycle; λ is the primary coefficient of blood flow change with time in a cardiac cycle, with λ=8aνR2−4βR∂R∂t; ε is the quadratic coefficient of blood flow change with time in a cardiac cycle, with ε=−4β+β0πR3∂R∂z; And A is a function of the change in the vessel radius with time during the contraction and expansion of the vessel wall in a cardiac cycle, with A=πRt2.
## 2.4 Establishment of pressure gradient model
This study solved the pressure gradient to calculate blood flow. The pressure gradient can be expressed as a linear superposition of harmonics with different amplitudes and frequencies, as follows. Pz,t=Pm+∑$$n = 1$$∞Anexpiwnt−zcn [10] where *Pm is* the mean arterial blood pressure; *An is* the amplitude of the harmonic; wn is the frequency of the harmonics, with ωn=2nπT; And cn is the propagation velocity of the pressure wave.
In this study, the data sources were the blood pressure and pulse information of the four limbs. the corresponding pressure gradient calculation formula for the four limbs was obtained. The pressure gradient calculation formula at $z = 0$ was derived by differentiating z and t in the formula: Ftf=∂p∂zf=−1c1fdPtdtf1+∑$$n = 1$$∞bnrcosωnt−bnIsinωntf [11] where c1 is the fundamental wave and propagation velocity of the pressure wave, with c1=R2ρ∂P∂R; bnr and bnI are the recurrence coefficients of the arterial pulse wave, with bnr=un+1c1cn+1−1−∑$h = 1$nuhbn−hr−vhbn−hI and bnI=vn+1c1cn+1−1−∑$h = 1$nvhbn−hr+uhbn−hI; and f is the label of the four limbs, with $f = 1$,2,3, and 4 representing the right upper, left upper, right lower, and left lower limbs, respectively.
## 2.5 Calculation and derivation of blood flow
The blood flow equation in the cardiac cycle, which was introduced into the formal variable Q,t=A∙Ft−λQ−εQ2, and the flow estimation and correction values were calculated using the estimation correction method. Qn+1*=Qn+TGQn,τn∆τ [12] Qn+1=Qn+TGQn,τn+GQn+1*,τn+1∆τ/2 [13] where Qn+1* is the flow-estimation value; Qn+1 is the flow correction value; n is the time sequence number, with $$n = 1$$,2,……N; τ is the time unit, with τ=tN,∆τ=1N,τn=n−1∆τ; and N is the number of sampling points in a cardiac cycle, which is equal to the number of sampling times in a cardiac cycle.
According to the blood flow equation and flow estimation and correction values, the blood flow equation of the four limbs in the cardiac cycle was derived and calculated as follows. dQtdtf+λtfQtf+εtfQ2tf=AtfFtf [14] The equation included three important parameters: λtf,εtf,and Atf. Herein, λtf was solved as λtf=8αγR2tf−ββ12α2β2m4−1Pmf−β12α2β2m4−1Ptf−PmfdPtdtf [15] where *Pmf is* the mean arterial pressure of the four limbs in a cardiac cycle, with Pmf=1T∫0TPtfdt; *Ptf is* the pulse pressure value of the four limbs at each sampling point, corresponding to each time in a cardiac cycle; α is the non-linear pulse-wave propagation coefficient of the flow rate with time; β is the non-linear pulse wave propagation coefficient with a time-varying blood-vessel radius; γ is the ratio of the blood dynamic viscosity to the blood density under the action of gravity; β1 is the ratio of the blood vessel’s length in vivo and equilibrium; β2m is the ratio of the radius of the blood vessel to the undeformed radius of the blood vessel at equilibrium; and α2 is the correction coefficient under physiological human conditions.
Herein, εtf was calculated as follows. εtf=4β+β0πR2tftanφRtf+β12α2β2m4−14ctfPmf−β12α2β2m4−1Ptf−Pmf dPtdtf [16] where φ is the half-cone angle of the blood vessel in its natural state; and β0 is the non-linear pulse wave propagation coefficient with a time-varying blood-vessel radius.
Moreover, Atf was calculated as: Atf=πR2tf=πRm∗1+bf∗lnPtfPmf2 [17] where *Rm is* the estimated human blood vessel radius, with Rm=0.042+0.0006251+0.36Ghl.
The blood flow of four limbs is related to the geometric shape of blood vessels. In order to accurately calculate the blood flow of four limbs, necessary animal experiments are carried out to estimate the correction coefficient, which are used to calculate the radius of four limbs.
## 2.6 Establishment of blood flow models based on blood pressure and pulse wave of four limbs
In this study, blood flow was key to the accuracy of the hemodynamic parameters. Due to systolic or diastolic blood pressure, blood pressure of upper or lower limbs shown different clinical applications when evaluating cardiovascular function. As reported in the study (Clark et al., 2012a; Chen et al., 2012; Cao et al., 2014), the differential pressure of upper limb systolic pressure is more than 15 mmHg, reminding patients to conduct further vascular assessment, which is a useful indicator of vascular disease and death risk. Based on the above theoretical model, as shown in the Figure 1C, we derive blood flow equation for different times in one cardiac cycle considering the different distributions of blood pressure and pulse wave of four limbs, then obtain the average blood flow in one cardiac cycle. The blood flow models established in four different conditions are defined as model I, model II, model III and model IV.[1] Model I: Blood flow at different times in a cardiac cycle was calculated according to the blood flow of the four limbs at different times in a cardiac cycle. Qt=aQt1+bQt2+cQt3+dQt4+e [18] where a,b,c, and d are the correction coefficients of the blood flow in the right upper, left upper, right lower, and left lower limbs, respectively; and e is a constant.[2] Model II: Blood flow at different times in a cardiac cycle was calculated according to the high-order fitting of the sum of the four limbs. Qt=aQt1+Qt2+Qt3+Qt42+b(Qt1+Qt2+Qt3+Qt4)+c [19] where a and b are the quadratic and first-order fitting coefficients, respectively; and c is a constant.[3] Model III: Blood flow at different times in a cardiac cycle was calculated according to non-linear regression analysis of the higher systolic blood pressure in the upper and lower limbs. Qt=amaxQt1,Qt2+bmaxQt3,Qt4+c [20] where a and b are correction coefficients of the upper and lower limb blood flows, respectively; and c is a constant.[4] Model IV: Blood flow at different times in a cardiac cycle was calculated according to the high-order fitting of the sum of the higher systolic blood pressure in the upper and lower limbs. Qt=amaxQt1,Qt2+maxQt3,Qt42 +bmaxQt1,Qt2+maxQt3,Qt4+c [21] where a and b are the quadratic and first-order fitting coefficients, respectively; and c is a constant.
## 2.7 Model establishment of cardiovascular hemodynamic indexes
Cardiovascular hemodynamic parameters can comprehensively evaluate the health of the CVS. Due to the early clinical symptoms of CVDs are not obvious, the probability of undetected high-risk patients is still high. The study used three hemodynamic diagnosis indexes that can reflect cardiovascular function easily and quickly. SV directly represented the outcome of heart pumping ability and was a perioperative monitoring indicator during cardiovascular surgery. AC reflects the ability of the artery to passively expand during ventricular systole to accommodate most of the stroke volume while continuing blood flow during diastole. TPR is an important hemodynamic indicator that reflects cardiac afterload. These are the main hydrodynamic factors that determine high and low arterial blood pressures. Herein, SV,TPR,and AC were solved using the following equations. SV=∫0TQtdt=QmT,ml/B [22] TPR=PmCOmmHgml=80PmCO,dyn∙s/cm5 [23] AC=SVPs−Pd,ml/B/mmHg [24]
## 2.8 Data screening
Next, the proposed hemodynamic diagnosis method based on NonPWT was verified for universally applicability to people. The datasets were classified into three groups: A calculated and two validation model groups. Figure 2 illustrates the data-screening procedure. For the validation group, 35 subjects were randomly selected to verify the model accuracy. To further evaluate the capability of the HDIs proposed model, 50 subjects were selected to verify the model accuracy under different disease conditions. These 50 subjects fulfilled the following three criteria. 1) The detection of pressure and the pulse wave of four limbs and the cardiac function parameters were completed. 2) Patients with cancer, heart failure, and other uncommon CVDs that had significant effects on pulse waves and cardiac function parameters were excluded. 3) Inter-arm blood pressure differences (10–15 mmHg and >15 mmHg) were considered risk factors.
**FIGURE 2:** *Subjects’ data screening process. Screening criteria (A–D): (A) calculated model group of 300 subjects, (B) validation model group of 85 subjects; (C) verifying the accuracy of the model using 35 randomly-selected subjects, and (D) another 50 randomly-selected subjects and under different disease conditions.*
The experimental design and investigation of clinical data were based on epidemiology and the clinical data was statistically analyzed. The clinical HDIs results were compared with those of the proposed NonPWT model. Thus, the consistency between the algorithm in this study and clinical measurements was verified. Additionally, complex equations were calculated and solved.
## 3.1 Calculation results of blood flow
Blood flow in the upper and lower arteries was calculated based on the pulse waveform of the four limbs in a cardiac cycle in Figure 3A, and the results are shown in Figure 3B. The pulse wave is a wave generated by a heartbeat and pumping of blood to the extremities and the terminal arteries. It is the waveform of the pressure generated by the blood flow against the walls of the blood vessels, and the waveform reflects the changes in the volume of circulating blood, with the peak of the wave reflecting the moment of maximum blood flow. In this study, data at the end of the extremities were collected under the guidance of the clinician, and the reference values for blood flow at the end of the upper and lower extremities in the clinic were 2.5–12.67 ml/s and 5.33–19.87 ml/s, respectively. The results revealed that the average blood flow in the cardiac cycle of the upper artery was 10.78 ml/s, and the blood flow in the lower artery was higher than that in the upper artery. Compared with the clinically measured values, the calculated value of blood flow was within the clinical reference range.
**FIGURE 3:** *Pulse waveform and blood flow of four limbs during one cardiac cycle; (A) pulse waveform of four limbs during one cardiac cycle, and (B) blood flow of four limbs during one cardiac cycle.*
The blood flow of the four limbs and the blood flow of the body were analyzed with medical statistics using 300 cases; thus, the fitting formulas (models I, II, III, and IV) were obtained. The quadratic fitting formula of the sum of blood flow of the four limbs was $p \leq 0.1.$ The quadric fitting formula of the sum of the higher systolic blood pressure in the upper and lower limbs was $p \leq 0.05.$ Qt=7.147Qt1+3.481Qt2+0.347Qt3+2.161Qt4+82.082 [25] Qt=0.09Qt1+Qt2+Qt3+Qt42−8.522Qt1+Qt2+Qt3+Qt4)+302.177 [26] Qt=2.547maxQt1,Qt2+0.32maxQt3,Qt4+77.029 [27] Qt=0.393maxQt1,Qt2+maxQt3,Qt42−18.817(maxQt1,Qt2 +maxQt3,Qt4)+324.143 [28]
## 3.2 Calculation results of hemodynamic diagnosis indexes
Herein, 35 cases were randomly selected from the sample, and a t-test was performed. The calculation results for SV, TPR, and AC are summarized in Table 1. Compared with the clinical measurement value, SV and TPR exhibited no significant difference between the four calculated models, whereas for AC, only model IV was not significantly different. The SV was the least significant difference in the statistical results for model IV; Hence, the calculated results of SV were closest to the clinical results, followed by TPR and AC. Based on the results of the fit of the computational model, the quadratic fitting formula of the sum of the higher systolic blood pressure in the upper and lower limbs was statistically significant, and the fitted model was closer to the measured values than the simple linear model. After the combined evaluation, model IV was used as a model for cardiovascular disease with different risk factors.
**TABLE 1**
| Datasets | SV(ml) | p | TPR(dyn∙s/cm5) | p.1 | AC(ml/mmHg) | p.2 |
| --- | --- | --- | --- | --- | --- | --- |
| Model I | 73.73 ± 15.90 | 0.955 | 1506.55 ± 138.01 | 0.683 | 2.02 ± 0.27 | 0.009 |
| Model II | 72.83 ± 14.28 | 0.941 | 1467.28 ± 159.19 | 0.864 | 2.12 ± 0.32 | 0.048 |
| Model III | 72.33 ± 15.18 | 0.885 | 1503.65 ± 175.75 | 0.746 | 2.03 ± 0.26 | 0.009 |
| Model IV | 73.63 ± 15.81 | 0.966 | 1470.35 ± 181.29 | 0.906 | 2.12 ± 0.33 | 0.051 |
| Clinical value | 73.33 ± 15.27 | | 1479.40 ± 153.83 | | 2.45 ± 0.37 | |
The consistency test between the calculated and clinical measurement results was further verified through the Bland–Altman method-based analysis based on the model IV values of SV, TPR, and AC. The results are shown in Figure 4; The relationship between the average (horizontal axis) and difference (vertical axis) is illustrated using scatter plot. According to the basic idea of the Bland–Altman method, the two methods were generally consistent when $95\%$ more points existed in the scatter plot within the confidence interval (mean + 1.96 standard deviation; Mean—1.96 standard deviation) that did not exceed the professionally acceptable critical value range. For SV, TPR, and AC, 35 sets fell within the $95\%$ confidence interval, and the results of the two methods were consistent. Thus, these results indicated that the calculated model based on the NonPWT was consistent with the clinical measurements. Note that for clinical applications, for a clinical requirement that the difference between the two methods must fall within a certain range, the bounded range of consistency will be reconsidered, and consistency will be re-evaluated, which is expected to be further improved using larger datasets as our future task.
**FIGURE 4:** *Consistency analysis between calculation and clinical measurements value of (A) SV, (B) AC, and (C) TPR; The relationship between the average (horizontal axis) and the difference (vertical axis) is illustrated using scatter plots.*
## 3.3 Calculation results of hemodynamic diagnosis indexes with cardiovascular diseases
A risk factor for a disease is a factor that increases the incidence of the disease in a population, and when this factor is removed, the incidence of the disease decreases. Hypertension, atherosclerosis, and inter-arm systolic blood pressure differences are major risk factors for cardiovascular disease. To verify the accuracy and applicability of the calculated model, the subjects were divided into four groups and the results with the calculations of model IV were compared. Fifty cases were randomly selected from the sample; they were divided into four groups according to Figure 2; Subsequently, a t-test was performed. As summarized in Table 2, the mean values of the five groups in terms of systolic and diastolic blood pressures were significantly different ($p \leq 0.001$). The risk factors for each disease group significantly differed from those of the control group. However, no significant differences were observed in HDIs (SV, TPR, and AC) between any of the disease and control groups after performing the t-test.
**TABLE 2**
| Characteristics | 300 subjects | Atherosclerosis | Hypertension | IASBPD | IASBPD.1 |
| --- | --- | --- | --- | --- | --- |
| Characteristics | 300 subjects | Atherosclerosis | Hypertension | 10–15 mmHg | >15 mmHg |
| Age | 60.56 ± 8.69 | 61.83 ± 4.88 | 61.75 ± 4.58 | 59.86 ± 4.40 | 57.22 ± 12.51 |
| BMI(kg/m2) | 25.43 ± 3.60 | 22.43 ± 2.78* | 28.05 ± 3.78** | 25.55 ± 3.09 | 25.16 ± 3.28 |
| SBP(mmHg) | 136.08 ± 18.52 | 129.83 ± 7.33* | 162.00 ± 19.58** | 124.86 ± 6.25* | 122.00 ± 8.69* |
| DBP(mmHg) | 81.83 ± 10.59 | 77 ± 6.03 | 95.75 ± 9.02** | 77.07 ± 7.89* | 71.67 ± 10.39** |
| CAVI | 7.76 ± 1.21 | 7.45 ± 0.93 | 7.90 ± 1.52 | 7.42 ± 0.93 | 8.04 ± 0.73** |
| ABI | 1.08 ± 0.11 | 0.77 ± 0.20** | 1.01 ± 0.07** | 1.01 ± 0.19 | 0.94 ± 0.19 |
| SV(ml) | 77.38 ± 18.12 | 88.38 ± 20.25 | 68.54 ± 12.97* | 73.87 ± 14.47 | 87.95 ± 19.33 |
| TPR(dyn∙s/cm5) | 1569.79 ± 420.73 | 1299.75 ± 496.20 | 1739.79 ± 354.68 | 1557.92 ± 399.32 | 1317.49 ± 348.46 |
| AC(ml/mmHg) | 1.73 ± 0.77 | 2.03 ± 0.51 | 1.49 ± 0.62 | 1.73 ± 0.60 | 2.09 ± 0.54 |
After the classification according to risk factors, t-tests were performed on clinical measurements and calculated values. Model IV was used as the computational model because it was closest to the clinical values among the four models. The mean SV, TPR, and AC values are summarized in Table 3. Although no significant differences were observed between the statistical results and the clinical measurement values, a new phenomenon was observed, as follows. The p values of SV in the inter-arm systolic pressure difference >20 mmHg and in the hypertensive group were smaller, thus impacting the accuracy of the SV calculation when the subject had a hypertensive disease or an inter-arm systolic pressure difference >20 mmHg. The p-value of TPR was smallest for inter-arm systolic pressure differences >20 mmHg; therefore, when the inter-arm systolic pressure difference of the subject was greater than 20 mmHg, a greater impact was reflected on the accuracy of the TPR calculation. The p-value of AC for inter-arm systolic pressure difference >20 mmHg was the smallest; therefore, when the inter-arm systolic pressure difference of the subject was greater than 20 mmHg, a greater impact was reflected on the accuracy of the AC calculation. Both TPR and AC reflect the vascular pliability function, which is corroborated by previous studies that suggest a risk of atherosclerosis when the inter-arm systolic pressure difference is greater than 20 mmHg.
**TABLE 3**
| Unnamed: 0 | Datasets | SV(ml) | p | TPR(dyn∙s/cm5) | p.1 | AC(ml/mmHg) | p.2 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Atherosclerosis | Model IV | 85.27 ± 19.43 | 0.792 | 1488.83 ± 514.86 | 0.532 | 1.95 ± 0.53 | 0.792 |
| Atherosclerosis | Clinical value | 88.38 ± 20.25 | 0.792 | 1299.75 ± 496.20 | 0.532 | 2.03 ± 0.51 | 0.792 |
| Hypertension | Model IV | 67.03 ± 10.94 | 0.761 | 1828.25 ± 445.13 | 0.595 | 1.41 ± 0.49 | 0.731 |
| Hypertension | Clinical value | 68.54 ± 12.97 | 0.761 | 1739.79 ± 354.68 | 0.595 | 1.49 ± 0.62 | 0.731 |
| IASBPD 10–15 mmHg | Model IV | 72.46 ± 14.76 | 0.8 | 1663.41 ± 452.32 | 0.519 | 1.63 ± 0.59 | 0.65 |
| IASBPD 10–15 mmHg | Clinical value | 73.87 ± 14.47 | 0.8 | 1557.92 ± 399.32 | | 1.73 ± 0.60 | 0.65 |
| IASBPD >15 mmHg | Model IV | 84.27 ± 17.99 | 0.682 | 1468.60 ± 390.68 | 0.399 | 1.95 ± 0.41 | 0.537 |
| IASBPD >15 mmHg | Clinical value | 87.95 ± 19.33 | 0.682 | 1317.49 ± 348.46 | 0.399 | 2.09 ± 0.54 | 0.537 |
The consistency test between the calculated and clinical measurement results was further verified through the Bland–Altman method-based analysis, and the results are shown in Figure 5. According to the basic idea of the Bland–Altman method, the SV, TPR, and AC of these sets fall within the $95\%$ confidence interval, and extremely rare cases will fall outside of the $95\%$ confidence interval. Thus, our results demonstrated that the calculated model based on the NonPWT was consistent with clinical measurements in subjects with CVDs. However, the validation of disease factors revealed that risk factors led to a reduction in significant differences, which was expected to be further improved by disease classification in our future work.
**FIGURE 5:** *Consistency analysis between calculation and clinical measurements value of SV, TPR, and AC with cardiovascular diseases [(A) Hypertension, (B) Atherosclerosis, (C) IASBPD 10–15 mmHg, and (D) IASBPD >15 mmHg]; The relationship between the average (horizontal axis) and the difference (vertical axis) is illustrated using scatter plots.*
## 4 Discussions
This study proposed a non-invasive algorithm to calculate HDIs based on the blood pressure and pulse wave of four limbs via the NonPWT. Compared with the previous pulse wave theory, the proposed NonPWT retained the non-linear characteristics of physiological information and frequency domain characteristics of the waveform, and realized fast and accurate calculation of HDIs. Compared with the relative error, the calculation error of the sum of the higher systolic blood pressures in the upper and lower limbs was the smallest. Consistency analysis revealed that $95\%$ of the points of the HDIs fell within the limits of agreement. Additionally, it exhibited practical value in the HDIs calculation, which could considerably improve the computational efficiency and simplify the operation process.
The computational models of HDIs are based predominantly on the pulse wave information of a single limb; however, the human body is a complex physiological system, and blood pressure formation and fluctuations are achieved through the dynamic interaction between the heart and vascular system. At the functional level, numerous factors, such as blood flow, output per beat, vessel wall elasticity, and peripheral resistance, influence blood pressure. Studies (Charmoy et al., 2007; Verberk et al., 2011; van der Hoeven et al., 2013) have shown that blood pressure measurement affects the diagnosis of CVDs. Moreover, some studies (O’Shea and Murphy, 2000; Clark et al., 2012b; Mancia et al., 2013; Singh et al., 2015) have also confirmed that simultaneous measurement of blood pressure in all four limbs and evaluation of blood pressure differences in all four limbs can provide additional clinical information that can contribute to clinical decision-making and predict clinical prognosis as a major indicator. In particular, the difference in systolic blood pressure in both upper extremities and the difference in systolic blood pressure in both lower extremities of >15 mmHg can cause peripheral vascular disease and is typically associated with underlying CVDs (Mancia et al., 2013; van der Hoeven et al., 2013; Weber et al., 2014). In this study, we derive blood flow equation for different times of the cardiac cycle considering the four different distributions of blood pressure and pulse wave of four limbs. The higher second fit of the sum of the higher systolic blood pressure in the upper extremities and the higher systolic blood pressure in the lower extremities was statistically significant, and the calculation error of HDIs, as summarized in Table 1, revealed model IV to be the closest to the clinical values among the four models. In a comprehensive analysis, the calculation of HDIs using the information of blood pressure pulses of the four limbs was closer to the realism of cardiovascular health. Therefore, further studies on cardiovascular health assessment, both in mathematical models and hydrodynamic simulations, should consider the information on blood pressure and pulse wave of the four limbs in estimating blood flow.
Blood flow is key to calculating HDIs, and it directly affects the calculation results. This study proposed four models for blood flow considering the four different distributions of blood pressure and pulse wave of four limbs. Blood pressure and pulse wave data of the four limbs were used in models I and II, and blood pressure and pulse wave data of the four limbs that had higher systolic blood pressure among the four limbs were used in models III and IV. The statistical analysis results revealed that the quadratic fit formula of model IV was statistically significant ($p \leq 0.05$). The calculation error of HDIs, model IV, had the smallest error. According to Poiseuille’s law, blood flow is determined by a combination of factors, such as the radius, length, and pressure of blood vessels. The blood pressure pulse data of the lower extremity will be less accurate in calculating blood flow with changes in vessel length and radius, which explains why the calculation error of the HDIs of models I and II was larger. In addition, it also shows that the model has higher accuracy in calculating HDIs, which are closer to the heart. Because the blood vessels were elastic and their radius was variable, relying on the blood flow at a given moment to calculate the vascular compliance and total peripheral resistance led to the largest computational error with the radius of the vessel changes. Despite the error in the model, consistency was confirmed between the model based on NonPWT and actual clinical measurements, which illustrated the feasibility of the model in this study. Based on the blood pressure pulse information of the four limbs, the health detection of cardiovascular function was achieved, and its non-invasive detection method, simplicity, and affordability appeared to have more practical applications. This prevented people from going to the hospital only when they had symptoms of discomfort, which delayed the condition and missed the optimal treatment time.
Non-etheless, this study has several limitations. First, a major limitation lies in the small number of subjects and the related clinical information. The fitting coefficients for the calculation of blood flow were derived primarily from medical statistics. From a statistical perspective, the larger the sample size, the higher the accuracy of the fitting coefficients; the proposed blood flow model can create relatively high-quality calculation and accomplish a high-accurate HDIs calculation. In addition, taking into consideration that some physiological information, such as age, height, weight, and shoulder width, were involved in the blood flow model, which inevitably led to calculation errors due to the individual differences of subjects. In future work, we will classify subjects and build personalized models. Second, it was impossible to clarify whether the model is capable to specific functions which can reflect the systolic and diastolic functions of the heart. A diagnostically important application of the current method may be the calculation of SV, AC and TPR while quantitatively evaluating the severity of specific CVDs, which will be explored in our future work when large-size datasets are available and more cardiac function parameters are increased to be predictable. Third, this study focused on calculating the cardiac function parameters. Four blood flow models were constructed for this purpose. However, according to Poiseuille’s law, the brachial artery of the upper limb was closer to the heart than the ankle artery, and the resistance was smaller, which led to lower blood pressure in the brachial artery than in the ankle artery, thus leading to different blood pressures in the limbs. Although the larger the sample size, the higher the accuracy of statistics, it was hard to estimate how many additional samples are enough to improve the accuracy for the HDIs. Therefore, we should consider the weight of the blood flow of the four limbs and the classification of cardiovascular diseases to propose NonPWT methods to achieve more complex HDIs calculations in future work.
## 5 Conclusion
In this study, a model of pressure gradient and blood flow was built via NonPWT and a hemodynamic diagnosis model was proposed. On this basis, a fast and accurate calculation of HDIs was achieved. Compared with the traditional calculation method, the proposed NonPWT method retained the non-linear characteristics of the physiological information and frequency domain characteristics of the waveform and increased the computational accuracy. Compared with previous cardiovascular function research, the proposed cardiovascular hemodynamic diagnosis method extracted pulse wave data from four limbs and considered the risk factors of cardiovascular diseases, which implies higher accuracy. Thus, it can be applied for calculating other HDIs, in addition to those mentioned in the article, as well as those in other research fields assessing cardiovascular health. In terms of waveform data processing, computational efficiency, and accuracy, the proposed hemodynamic diagnosis method can meet the need for non-invasive calculations for cardiac health. It can be applied to the general health examination of healthcare institutions such as community medical care, rehabilitation and health care, sports and fitness, leisure and recuperation.
## Data availability statement
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by The Institutional Ethics Committee of Beijing University of Technology. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
XS was responsible for literature retrieval, data analysis, and paper preparation. YL was responsible for providing the clinical data. SW screened the literature. AQ was responsible for the language modification. HZ was responsible for the data analysis. XW was responsible for supervision.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Exploration of the core gene signatures and mechanisms between NAFLD and sarcopenia
through transcriptomic level
authors:
- Ziying Xu
- Zihui Yu
- Shang Li
- Ziyan Tian
- Jing Yuan
- Fuping You
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10033966
doi: 10.3389/fendo.2023.1140804
license: CC BY 4.0
---
# Exploration of the core gene signatures and mechanisms between NAFLD and sarcopenia through transcriptomic level
## Abstract
### Introduction
The increased prevalence of non-alcoholic fatty liver disease (NAFLD) and sarcopenia among the elderly are facing a significant challenge to the world’s health systems. Our study aims to identify the coexpressed genes in NAFLD and sarcopenia patients.
### Methods
We downloaded the transcriptome data of NAFLD tissue from patients, as well as muscle tissues from sarcopenia patients, from the GEO database in order to investigate the shared transcriptional regulation mechanisms between these two diseases. Then, focusing on the genes that were frequently expressed in these diseases, together with GSVA and WGCNA, we utilized a range of analysis methods to identify the main co-expressed genes in both diseases by taking intersections. We investigated these changes after learning that they mostly affected lipid metabolism and oxidative stress injury pathways.
### Results
By analyzing these genes and their interactions with transcription factors and proteins, we were able to identify 8 genes that share common patterns. From these 8 genes, we were possible to forecast potential future medicines. Our research raises the possibility of NAFLD and sarcopenia transcriptome regulatory pathways in aging populations.
### Discussion
In conclusion, a complete transcription pattern mapping was carried out in order to identify the core genes, underlying biological mechanisms, and possible therapeutic targets that regulate aging in NAFLD and sarcopenia patients. It provides novel insights and proof in favor of decreasing the increased prevalence of sarcopenia in the elderly caused by NAFLD.
## Introduction
Non-alcoholic fatty liver disease (NAFLD) is characterized by the accumulation of lipids in the liver, which may further lead to the deterioration of liver fibrosis [1], cirrhosis and even liver cancer [2]. According to statistics, about $25\%$ of the world population is suffered from NAFLD, and patients attacked by NAFLD are becoming younger [3]. Therefore, it is imperative to underlying the mechanisms of NAFLD and develop an effective treatment for it. Hepatic steatosis is age-related and associated with metabolic syndromes such as high-fat diet [4], flora disorders[5] and hyperlipidemia, as well as various toxins, drugs, and diseases. The pathogenesis of NAFLD is well established as a two-strike and multiple-strike theory but the molecular mechanism of the occurrence and development of NAFLD remains uncovered. [ 6]. However, other aging diseases interrelated with lipid metabolism and fibrosis can aggravate the exacerbation of NAFLD.
Skeletal muscle accounts for $40\%$ of the body weight, undertake $30\%$ of the basic energy metabolism, and maintains behavioral functions. In adults, muscle loss begins at age 30 and accelerates after age 50. By age 60, muscle loss can reach 30 percent. The degeneration of muscle with the increase of age is defined as sarcopenia, which is accompanied by a series of pathological changes such as decreased muscle mass, fibrosis, and fat infiltration, seriously affecting the functional activities of the elderly and reducing life expectancy[7]. The prevalence of sarcopenia in people over 80 years of age is as high as $50\%$ and becoming a novel condition with direct life-threatening within the developing world [8] [9]. Multiple factors are responsible for muscle glycolipid-metabolism disorder with aging. Reduced oxidative capacity or physical activity with aging both increases the proportion of lipids in body composition, and causes activation of inflammation and insulin resistance[10, 11]. Numerous pro-inflammatory cascades are conjunct within muscle and visceral fat, which approach less muscle mass. Additionally, impaired insulin sensitivity can be further increased by muscle catabolism, resulting in abundant ectopic fat deposition within the muscle. Interstitial fibrosis is the other major histopathological change during the progress of sarcopenia, which contributes to the recession of force generation and enhances muscle stiffness.
Glycolipid-metabolism and fibrogenesis appear to be the intersection joint of NAFLD and sarcopenia. With a high degree of functional sharing, crosstalk and mutual regulation, one’s metabolic disorders can lead to compensatory or even systemic metabolic disorders [12]. Studies have shown that sarcopenia is an important indicator of the severity of NAFLD [13]. Therefore, investigating the association and verifying the shared pathways between them provides a prospective way of creating novel age-related disease treatment strategies. [ 14].
Transcriptome analysis can determine and quantify changes in transcription levels in various states[15]. A large number of applications in the life sciences have made transcriptomics widely used (16; 17). As the needs have changed, new techniques for transcriptome studies targeting low cell numbers and even more accurately targeted sequencing have emerged [18]. In disease research, transcriptome technology can help researchers more accurately understand the pathogenesis of diseases and the relationship between specific RNA and diseases. Based on clarifying the precise regulation of various genes in diseases, transcriptome technology can help in the development of new drugs and has important applications in the prevention and treatment of tumors. The integration and analysis of biological data by various bioinformatics tools are important means of life science research. For example, a network algorithm or Random Forest was used to predict patient-related biomarkers [19]. Transcriptome data combined with dual disease analysis can be used to better understand the pathological molecular mechanisms between diseases and make more accurate drug predictions [20]. The present study aimed to identify hub genes and a hot research topic to the link between NAFLD and Sarcopenia. Therefore, by obtaining transcriptome sequencing data from clinical patients of the two diseases from the GEO database, further joint analysis of their gene expression data was conducted. The differences and commonalities were preliminarily analyzed to clarify the disease characteristics of NAFLD and Sarcopenia. After that, the co-expressed genes of the two diseases were screened. Diversity statistical analysis methods were used to obtain the co-expressed genes and the pathways significantly associated with NAFLD and Sarcopenia. Finally, we integrate the results from the single analysis and intend to provide a basis for subsequent clinical-related research.
## Data processing
Two genome-wide transcriptome profiling using RNA-Seq (GSE167523, GSE167186) of NAFLD and sarcopenia samples were obtained from the GEO database by using Illumina high throughput sequencing platform. 98 NAFLD patients’ gene expression profiles formed the GSE167523 data set. The 72 samples in the GSE167186 data set were patients with sarcopenia. The analytic workflow is shown in Figure 1.
**Figure 1:** *The framework of this study.*
## Top 1000 expressed genes selection and gene set variation analysis
Counts in NAFLD and *Sarcopenia data* sets (GSE167523, GSE167186) were normalized treatment. First, the two data sets were integrated according to gene name. Using the cpm function of package R edgeR (V.3.38.4), Counts per million (CPM) were calculated, and log2 was performed. After the log2 (cpm+1) value is arranged from the largest to the smallest, the first 1000 genes are selected as the top 1000 genes. *These* genes were analyzed by GSVA (Gene set variation analysis) using R package GSVA (V.1.44.5), The reference gene sets were selected from Homo sapiens C5 (ontology gene sets) in the MSigDB database. Use the GSVA function in the GSVA package. The analysis parameters are method=“gsva”, kcdf=“Gaussian”.
## Analysis of inter-sample correlation and differentially expressed genes
When analyzing the correlation between samples, the vst function in package R DEseq2 (V.1.36.0) was first used to standardize the expression matrix. Then dist function was used to calculate the Pearson distance between samples, and the prcomp function was used for Principal Component Analysis (PCA). The DESeq function in using DEseq2 gene counts matrix analysis of differentially expressed genes, DEGs judgment standard for pvalue < 0.05 & (log2FoldChange > = 2 | log2FoldChange < = 2), The common genes are pvalue < 0.05 & (log2FoldChange >= -2 & log2FoldChange <= 2). For volcano mapping, the R package EnhancedVolcano (V.1.14.0) is used.
## Weighted correlation network analysis
The log2 (cpm+1) matrix with an input file as the gene was constructed using an R package called “WGCNA”. The power value was determined by the pickSoftThreshold function. Weight coexpression network uses the blockwiseModules function. The plotDendroAndColors function draws the clustering between samples. The labeledHeatmap function shows the correlation between the disease and gene Modules. The plotEigengeneNetworks function shows the correlation between each gene Module.
## Gene Ontology and pathway enrichment analyses
GO is a database established by the Gene Ontology Consortium that provides simple annotations of gene products in terms of function, the biological pathways involved, and their location in the cell. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway is a database dedicated to storing information about genetic pathways in different species. KEGG’s Orthopedic Annotated System (KOBAS) (http://kobas.cbi.pku.edu.cn) is a gene/protein functional annotation and functional enrichment Web server developed by Peking University, which collected functional annotation information of 4325 species. “ GO terms” and “KEGG pathways” analyses use the “enrichGO” function and “enrichKEGG” function in the R package clusterProfiler (V.4.4.4), respectively, with the p-value cutoff set to 0.05. GO terms-genes network mapping uses the cnetplot function. The R packages GOplot (V.1.0.2) and ggplot2(V.3.3.6) are also used for visualization.
## Determination and functional analysis of hub gene
Search Tool for the Retrieval of Interacting Genes (STRING; http://string-db.org)(11.0 version) Relationships between proteins of interest can be searched, such as direct binding relationships or co-existence of upstream and downstream regulatory pathways, to construct PPI networks with complex regulatory relationships. The TF-genes network is predicted by NetworkAnalyst software. DSigDB database was used to predict possible small-molecule drugs.
## GO and KEGG pathway analyses of NAFLD and sarcopenia
We conducted a preliminary analytic and statistical study on the disease data from NAFLD and sarcopenia. First, we performed enrichment analysis on the top 1000 genes from 98 NAFLD patients. The observations demonstrate a strong relationship between fat metabolism and energy metabolism in both molecular function, cellular component, and biological process (Figure 2A). And when we look at the KEGG data, we can see that these genes are related to some relevant metabolic pathways, such as liver alcoholic glycolysis degradation, fatty beta−alanine acid, cytochrome adducts P450, and other related metabolic pathways shown in Figure 2B. Genetic groups related to NAFLD were described.
**Figure 2:** *GO and KEGG Pathway Analyses of NAFLD and Sarcopenia. (A) The enriched GO terms of the top 1000 genes in NAFLD; (B) The KEGG of the top 1000 genes in NAFLD; (C) The enriched GO terms of the top 1000 genes in Sarcopenia; (D) The KEGG of the top 1000 genes in Sarcopenia.*
Then we obtained transcriptome data from 81 patients with sarcopenia and selected the top 1000 genes for the enrichment analysis of GO and KEGG. *Sarcopenia* genes were significantly correlated with energy metabolism and REDOX pathways in molecular function, cellular components, or biological processes (Figure 2C). Dilated Hypertrophic Cardiomyopathy, Biosynthesis of Amino Acids (TCA Acids), and Alzheimer’s Amyotrophic Chemical Carcinogenesis were all strongly associated with KEGG enrichment (Figure 2D). This indicates that energy metabolism and redox pathways play a significant role in the disease features of NAFLD and sarcopenia, respectively.
## Gene set variation analysis in NAFLD and sarcopenia
To further investigate the similarity between the two diseases, we integrated the genes of NAFLD and Sarcopenia. The enriched GSVA of the two diseases showed the main pathways as follows, according to the clustering analysis of the genes expressed in the two diseases: Purine nucleotide salvage, Fat-soluble vitamin catabolic process, Lipoxygenase pathway, Long-chain fatty acyl CoA biosynthetic process, Long-chain fatty acyl CoA metabolic process (Figure 3). A high similarity between the two diseases was identified by GSVA.
**Figure 3:** *Gene Set Variation Analysis (GSVA) in NAFLD and Sarcopenia. GSVA of top 1000 genes in NAFLD and Sarcopenia.*
## Differential gene expression in NAFLD and sarcopenia
Principal component analysis (PCA), which was used to further evaluate the transcriptome of these two diseases, revealed that the differences between the two diseases were more meaningful than the differences between the two diseases themselves (sFigure1A). The Pearson distance analysis, as shown in Figure 4A, further supported this result. Figure 4B indicates the variations in overall gene expression between the two diseases. The statistics show that there are numerous overlap genes between the two diseases, which will need to be further investigated. Searching at the differences between the two diseases and the differentially expressed genes in sarcopenia versus NAFLD, it is fairly obvious from GO terms that the majority of the genes associated with sarcopenia’s high expression are those that are participated in muscle system processes, muscle contraction, muscle organ development, and other components of muscle development (Figure 4C). However, the metabolism of small molecules, sterols, alcohol, and other fat and disease-related metabolic pathways were all strongly expressed by NAFLD (Figure 4D). Similar results to those in the GO term were shown in the KEGG enrichment (sFigure 1C). Sarcopenia is mostly overexpressed in pathways linked to muscle growth relative to NAFLD; while compared to sarcopenia, NAFLD is overexpressed in lipid metabolism-related pathways.
**Figure 4:** *Differential gene expression in NAFLD and Sarcopenia. (A) The correlation heatmap of NAFLD and Sarcopenia; (B) Volcanic map showing the differentially expressed genes of NAFLD and Sarcopenia; (C) The GO term of the up-regulated pathways in sarcopenia versus NAFLD; (D) The GO term of the up-regulated pathways in NAFLD versus sarcopenia.*
## Common genes analyses in NAFLD and sarcopenia
We further investigate the relationship between the two diseases in consideration of the common genes depicted in Figure 4B. The resulting Go term revealed the common genes of NAFLD and Sarcopenia, regardless of their molecular function, cellular component, or biological process, by clustering the shared genes. These mainly enriched processes involve ribonucleoprotein complex biogenesis, ribosome biogenesis, ncRNA processing, histone modification, rRNA metabolic process, transcription coregulator activity, On DNA binding transcription factor binding, and other pathways, which indicates that these two diseases are strongly connected to epigenetic changes (Figure 5A). After evaluating the KEGG enrichment of common genes, we observed that various relevant pathways were enrichment, as well as metabolic pathways of numerous significant diseases (Figure 5B). Two diseases associated with nucleic acid metabolism and epigenetic modifications by GO enrichment.
**Figure 5:** *Common Genes Analyses in NAFLD and Sarcopenia. (A) The GO term of all common genes in NAFLD and Sarcopenia; (B) The KEGG of all common genes in NAFLD and Sarcopenia.*
## Weighted correlation network analysisof NAFLD and sarcopenia
The scale-free network, adjacency matrix, and topological overlap matrix (sFigure 2A) were all constructed after the two groups of data were clustered using the Pearson correlation coefficient. Removal of the outliers, a sample clustering tree (sFigure 2B) was established. Finally, Figure 6A displays 12 modules based on average hierarchical clustering and dynamic tree pruning (the grey module is often regarded as an undefined module). We found that the blue and turquoise modules, which were selected as clinically significant modules for further analysis, were significantly correlated with NAFLD and sarcopenia. We investigated the connection between characterizing genes. Information regarding the pairing relationships between gene co-expression modules can be obtained from characteristic genes. The characteristic genes were grouped. The results demonstrated that the 11 modules can be grouped into two clusters in Figure 6B and that each of the module combinations (blue and pink, and turquoise and yellow) exhibit a high level of interactive connectedness. We enriched the modules for GO terms by combining them with clinical characteristics (Figure 6C). Blue modules were found to be significantly correlated with histone modification and RNA splicing, whereas brown modules were associated with gastrointestinal diseases, green modules with olfactory dysfunction, gray modules with miRNA regulation, red modules with cofactor 2, and turquoise modules with lipid metabolism. Which, the turquoise module also indicated that the organic acid catabolic process, carboxylic acid catabolic process, small molecule catabolic process, cellular lipid catabolic process, and alcohol metabolic process were significantly correlated (Figure 6D). Blue module revealed that protein methylation, protein alkylation, RNA splicing, and RNA splicing via transesterification processes were all strongly related to both diseases (Figure 6E). WGCNA shows that metabolism-related processes and behaviors such as RNA shearing are closely associated with both diseases.
**Figure 6:** *WGCNA of NAFLD and Sarcopenia. (A) Module–trait associations. Each row corresponds to a module, and each column corresponds to a trait. Each cell contains the corresponding correlation and P value. The table is color-coded by correlation according to the color legend; (B) Eigengene dendrogram and eigengene adjacency plot; (C) Gene Ontology analysis; (D) Gene Ontology analysis of the genes involved in the turquoise module; (E) Gene Ontology analysis of the genes involved in the blue module.*
## Protein-protein interaction network
Therefore, intersection analysis was conducted on the genes in the obtained GSVA, DEG of common genes, and the modules obtained by WGCNA. 126 genes were screened out from these intersection genes for subsequent analysis (Figure 7A). Therefore, The PPI network of the intersection DEGs was constructed using String (Figure 7B). We analyzed the enrichment top GO pathway by looking at the GO of intersection genes and found that these genes and blood vessel remodeling, regulation of transcription involved in G1/S transition of the mitotic cell cycle, regulation of hormone biosynthetic process, and other vascular regulation and hormone anabolic pathways (Figure 7C). Two pairs of genes with high and low expression were filtered out by combining the results of Figures 7B, C. The results showed that the PPI network of these 126 genes correlated with energy, and hormone anabolism.
**Figure 7:** *Protein-Protein Interaction Network (PPI). (A) The Venn diagram showed that seven algorithms have screened out 16 overlapping hub genes in NAFLD and Sarcopenia. (B) PPI network diagram. (C) The GO biological process analyses overlap genes from (A).*
## Pathway–gene functional network to screen hub gene
The expression patterns of NAFLD and Sarcopenia were found to be positively correlated after reviewing the expression values of all genes. This finding implies that the two diseases may be attached by a co-regulatory network and that further research into the co-regulatory mechanisms of the two diseases is essential (Figure 8A). Figure 8B shows the high expression of HIF1A and ATG5, as well as ADM and CST3, in the two diseases. HIF1A and ATG5 were also strongly connected with the reoxidation-reduction of the disease. ADM and CST3 were linked to hormonal disorders. The mutually compatible receptors BMP2 and BMPR2, which are connected to protease hydrolysis, etc., were two pairs of genes with low expression in common. TFDP1 and E2F6 were two genes that also have significant regulatory functions in transcription and translation (Figure 8C). Additionally, we constructed a TF-target regulatory network diagram based on the eight-node genes, and Figure 8D clearly illustrates the correlation between the eight genes. With a high degree of linkage, CST3, TFDP1, ADM, and BMPR2 could be especially noteworthy in the TF-target network. To give thorough treatment for patients who also have NAFLD and sarcopenia, several additional TFs were included, and medicines prediction was also done based on these genes. The top 10 potential medicines were displayed in Table 1. Future treatments for both diseases may be based on these 8 node genes.
**Figure 8:** *Pathway–Gene Functional Network to screen hub gene. (A) Correlation of overlap genes in NAFLD and Sarcopenia. (B) Two pairs of genes that were positive-regulated expressed. (C) Two pairs of genes that were negative-regulated expressed. (D) The TF–target network of periimplantitis. TF, transcription factor.* TABLE_PLACEHOLDER:Table 1
## Discussion
Investigating embryonic development, we learned that mesodermal differentiation is the principal source of muscle formation [21], while the liver consists of endoderm-derived hepatobiliary cell lineage and various mesodermal-derived cells [22], and liver development, from liver specification to liver maturation, requires close interaction with cells of mesodermal origin. This also implies that the progenitor cells of both organs have a strong commonality, and the origin from the same germ layer indicates that they are also functionally very closely related.
To better identify genes that are co-regulated in both diseases, we used the GSVA, common gene in DEGs analysis, and WGCNA analyses to jointly identify genes that are expressed in high abundance in both diseases and involved in disease development. Our research focused on the analysis of the co-expressed genes in both tissues to identify potential therapeutic strategies. We conducted an enrichment analysis for the top 1000 genes associated with each disease after homogenizing the obtained data. While Sarcopenia was more closely associated with energy metabolism and reoxidation reduction, we could see that the principal genes expressed in NAFLD were still associated with lipid metabolism, glycometabolism, and energy metabolism (Figure 2). As the body’s primary metabolic organs, the liver and muscle can also be considered as potentially sharing some of the same functions [23]. After realizing this commonality, we investigated the variations and consistency of the data in more detail. We proceeded by analyzing the differences between the two diseases and the co-expressed genes. We could see that sarcopenia and NAFLD were distinguishable in that sarcopenia had a higher expression of genes mainly related to muscle development. The high gene expression of NAFLD relative to sarcopenia was enriched in the adipose metabolism pathways associated with NAFLD illness itself (Figure 4E), which also reflected the specificities of each disease. This was associated with the gene expression of the muscle itself (Figure 4D). We were particularly interested in the relationship between the two diseases in our research. The major pathways of co-expressed enrichment of these two diseases were identified by GSVA, and the results revealed that the enrichment pathway was not only significantly related to epigenetics but also involved in other metabolic diseases and immune-related pathways (Figure 3). This suggests that the gene expression of most metabolic diseases is very similar, and the cause of metabolic diseases may be related to REDOX-related pathways [24], epigenetic modification, or the mutual regulation of various RNAs [25, 26], as well as activating the immune system [27, 28].
WGCNA was used for further analysis to check the relationship between these two diseases in more depth. In the highly correlated turquoise and blue modules, we found that the key genes for the two diseases were still enriched in the epigenetic modification and lipid metabolism pathways (Figure 6D, E). Additionally, it is consistent with the preliminary results. In addition to checking more relevant genes and more precisely identifying and verifying the core genes of the two diseases, we chose the intersection of genes obtained by various analysis methods. These 126 genes were shown to be significantly enriched for the cell cycle, angiogenesis, and hormone anabolism pathways. We further checked into the co-expression of these genes and observed 4 pairs of genes out of a large number that were concurrently positive- or negative-regulated. HIF-1A activates the transcription of numerous genes, including those involved in energy metabolism, angiogenesis, apoptosis, and other genes whose protein products increase oxygen delivery or facilitate metabolic adaptation to hypoxia, serving as a master regulator of cellular and systemic homeostatic response to hypoxia [29]; ATG5 encoded protein participates in several cellular functions, including the production of autophagic vesicles, mitochondrial quality control following oxidative damage, inhibition of the innate antiviral immune response, and proliferation and development of lymphocytes [30]. We also observed that the preprohormone ADM, which is produced by this gene, can be broken down into two physiologically active peptides: adrenomedullin and pro-adrenomedullin N-terminal 20 peptide. Adrenomedullin is a 52 AA peptide having a variety of activities, such as vasodilation, hormone secretion regulation, angiogenesis stimulation, and antibacterial action. It also plays a significant role in oxidative stress [31]; CST3 inhibitors appear to have preventive properties in a variety of human fluids and secretions, but they also play a crucial regulatory role in the development of cancer and other diseases [32] (Figure 8B). *These* genes have a strong connection to the REDOX of the disease. Therefore, synergistic high expression in NAFLD and sarcopenia is significant.
However, there are fewer studies related to the direct occurrence of RNA splicing in NAFLD, but lipid accumulation, as well as obesity, are closely associated with the development of NAFLD. Which is the main cause of increased alternative RNA splicing in the liver. Gene expression data from the liver and muscle of Pihlajamaki et al. provided that obese patients found substantial downregulation of RNA splicing genes, suggesting that the expression of RNA splicing-related genes is negatively associated with liver lipids accumulation and hyperinsulinemia and that altered expression of RNA splicing factors may contribute to obesity-related phenotypes [33]. Also, NAFLD, especially sarcopenia, as a disease of the elderly, is significantly associated with increased RNA splicing [34]. For example, Li et al. published an article in Cell Metabolism demonstrating that death-associated protein kinase-related apoptosis-inducing kinase-2 (DRAK2) can inhibit the phosphorylation of SRSF6 by the SRSF kinase SRPK1, and regulates selective splicing of mitochondrial function-related genes [35].
The activation of BMP signaling in skeletal muscle is significant in maintaining muscle mass as well as muscle-nerve interaction during cachexia and the aging process [36, 37]. Restoring BMP activity ameliorates cancer-mediated muscle wasting and sarcopenia [36, 37]. The activity of BMP receptors in muscles induced hypertrophy was dependent on Smad$\frac{1}{5}$-mediated activation of mTOR signaling [38]. TFDP-1 is a heterodimerization partner for members of the E2F family of transcription factors and up-regulates E2F-mediated transcriptional activation [39]. E2F/TFDP-1 regulates the expression of various cellular promoters, particularly gene products that are involved in the cell cycle [40]. The combination of TFDP1 with E2Fs can promote liver regeneration by regulating MYCN transcription [41]. Elevated expression of TFDP1 was associated significantly with larger tumor size and down-regulation of TFDP1 inhibited the growth of Hep3B cells. In conclusion, overexpression of TFDP1 may contribute to the progression of some HCCs by promoting the growth of the tumor cells [40]. Murine and human HCC data indicate significant correlations of STMN1 expression with E2F1/TFPD1 and with KPNA2 expression and their association with poor prognosis in HCC patients [42]. These four genes are negatively regulated and there are opposite regulatory patterns, and we checked their roles and found that the mechanisms are also different in the two diseases.
We also examined the TF regulatory network for these 8 genes, and we found that several of the transcription factors among these genes had strong connections to fibrosis, damage, and fat metabolism (Figure 8D). Resveratrol, which ranked top among such genes to predict small molecule medicines, was discovered to have a beneficial preventative impact on obesity-induced diet in NAFLD and NASH patients [43] (Table 1). It can also improve the validation status of skeletal muscles [44]. Resveratrol is also a highly significant healthcare product in daily life, demonstrating the necessity of a daily supplement. In addition, the last few medicines are also widely used. This evidence can support the continued usage of previously prescribed medicines.
When compared to other research, our study still has several limitations. For example, disease development may be regulated at various histological levels, and we have only conducted a preliminary investigation of the co-regulatory mechanisms of NAFLD and sarcopenia at the transcriptome level. For instance, studies on DNA/RNA methylation have been applied to explain how so many diseases develop [39]. Our analysis also revealed a strong correlation between both diseases and lipid metabolism as well as oxidative stress, demonstrating the importance of further metabolomic research [26, 45]. What’s more, the research should really be based on healthy samples to obtain differentially expressed genes and then compare them. However, since healthy human liver and muscle samples are not easy to obtain, we only collected partial liver control datasets, but considering that the liver’s gene expression is affected by sex and age [46] [47], we were unable to find a dataset that could be matched exactly. The dataset was not available for muscle. Therefore, the study was mainly conducted on the expression profile of the disease. Therefore, it is necessary to update the data in healthy subjects if they are available subsequently.
In summary, our work demonstrates the potential transcriptome regulatory mechanisms of NAFLD and sarcopenia. Through a thorough mapping of the transcription pattern, the key genes, molecular processes, and potential therapeutic targets that cause NAFLD and sarcopenia were examined. It offers a new perspective and supporting evidence to decrease the high incidence of NAFLD in sarcopenia patients.
## Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be listed below. Repository/Repositories Accession Number Gene Expression Omnibus GSE167523 Gene Expression Omnibus GSE167186.
## Author contributions
JY and FY designed the study. ZX and ZY performed data analysis. ZX prepared the figures and tables. ZX, ZY, and SL wrote the manuscript and approved the final draft. ZX, ZY, and ZT participated in data interpretation and analysis. ZX, ZY, and SL were involved in proofreading and deep editing and approved the final manuscript. JY and FY devised the main conceptual idea, supervised the project, performed proofreading and deep editing of the manuscript, and approved the final draft. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1140804/full#supplementary-material
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|
---
title: Validation of a scale to assess adherence to oral chemotherapy based on the
experiences of patients and healthcare professionals (EXPAD-ANEO)
authors:
- Amparo Talens
- Elsa LÓpez-Pintor
- Mercedes Guilabert
- Natalia Cantó-Sancho
- María Teresa Aznar
- Blanca Lumbreras
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10033971
doi: 10.3389/fphar.2023.1113898
license: CC BY 4.0
---
# Validation of a scale to assess adherence to oral chemotherapy based on the experiences of patients and healthcare professionals (EXPAD-ANEO)
## Abstract
Background: Lack of adherence to Oral antineoplastic agents (OAAs) treatment has important clinical, social and economic consequences.
Objective: To develop and validate a novel instrument for assessing adherence to OAAs, based on the reported experiences of people with cancer in relation to their treatment and the opinions of the healthcare professionals who care for them.
Methods: We performed a multicenter validation study of a scale designed to assess adherence to OAAs. First, a steering committee developed the items for an initial scale, based on the results of a qualitative study that evaluated patients’ and professionals’ experiences with this treatment. We then assessed the validity and reliability of the initial scale in a sample of 268 outpatients with cancer who received their OAAs from four Spanish hospitals.
Results: The mean age of the sample of 268 outpatients was 64.1 (standard deviation [SD] 12.4) years, and $47\%$ of participants were women. With the results of this analysis, we developed the EXPAD-ANEO scale, which has 2 factors, one for beliefs and expectations and another for behavior. Both factors explain $52\%$ of the explained common variance. Good reliability was obtained, with a McDonald’s omega of 0.7 for the first factor and 0.6 for the second factor. The fit indices were optimal (Root Mean Square Error of Approximation = 0.02, Comparative Fit Index = 0.99, Tucker Lexis Index = 0.99 and Standardized Root Mean Squared Residual = 0.03), which verifies the appropriateness of the items to the model. We measured EXPAD-ANEO criterion validity against pill count, obtaining a specificity of $80\%$. We measured convergent validity with the Morisky-Green test and found a significant association ($p \leq 0.001$). We measured divergent validity with questions on health literacy from the 16-item European Health Literacy Survey and found no correlation ($$p \leq 0.153$$).
Conclusion: EXPAD-ANEO is the first validated instrument for evaluating patients’ experiences with and adherence to OAAs, providing valuable information that can help health professionals to establish individual strategies or collective programs for improving therapeutic results and reducing healthcare costs.
## 1 Introduction
People with cancer are increasingly prescribed oral antineoplastic agents (OAAs), which have several advantages over intravenous chemotherapy (Borner et al., 2001; McCue et al., 2014). However, the patients themselves, or their carers, are responsible for administering this treatment as prescribed, and lack of adherence can have serious clinical, social and economic consequences (McCue et al., 2014). Non-adherence compromises therapeutic efficacy, reducing the health outcomes and quality of life of people with cancer, while increasing associated health costs (Noens et al., 2009; Daouphars et al., 2013).
There is substantial heterogeneity between the instruments currently used to measure adherence to OAAs and the results of different studies are difficult to compare. A systematic review by Greer and colleagues indicated that adherence to oral treatment in people with cancer was between $46\%$ and $100\%$ (Greer et al., 2016). Two studies conducted in Spain reported adherence rates ranging from $72\%$ to $79\%$ (Olivera-Fernandez et al., 2014; Fernández-Ribeiro et al., 2017). Most tools currently in use are validated for other chronic diseases (Silveira et al., 2021). In people with cancer, one of the most frequently used tools is the Morisky-Green test (Huang et al., 2016). There have been initiatives to develop scales for measuring adherence to OAAs, usually in people with specific types of cancer (Bagcivan and Akbayrak, 2015; Gambalunga et al., 2022), or in people using specific drugs, such as imatinib (Daouphars et al., 2013). The definition of adherence among OAA users varies depending on the method of measurement, and there is no consensus in the literature, although any result other than $100\%$ represents an opportunity for improvement.
The recommended option for determining adherence to treatment is to combine different methods, including pill count and personalized interviews with validated instruments (Morisky et al., 2008). This approach has been shown to provide a good approximation of real adherence (Solán, Sorli Redó and García, 2007). However, there are few initiatives in the literature to validate adherence scales or questionnaires in OAA users (Claros et al., 2019; Huang et al., 2016). Some studies have included very small patient samples (Peng and Wu, 2020), and the results of others have shown limited psychometric properties (Silveira et al., 2021). Furthermore, there are currently no references in the Spanish population, although research has shown that social and cultural characteristics may directly influence the barriers to and facilitators of adherence to OAAs (Irwin and Johnson, 2015). Understanding and taking into account how medication affects people’s daily lives is therefore crucial when designing specific tools for people using these drugs.
A previous qualitative study by our research team identified and evaluated the main barriers to and difficulties with correct use of OAAs as perceived by users, and the priorities of professionals who care for them (Talens et al., 2021). Using the results of this previous study as a starting point, we aimed to develop and validate a scale to measure adherence to OAAs based on the pharmacotherapeutic experience of people with cancer and the perspectives of the relevant healthcare professionals.
## 2.1 Study design
We conducted a multicenter validation study of a scale designed to measure OAA adherence in outpatients with cancer, based on their pharmacotherapeutic experience and the perspectives of the professionals who care for them. We named our instrument the EXPAD-ANEO scale after the Spanish abbreviation of EXPeriencia con y ADherencia a AntiNEoplasicos Orales (experience with and adherence to oral antineoplastic agents).
The design process involved three stages, as described by Boateng et al. [ 2018] and colleagues: item development, scale development and scale evaluation (Figure 1). We also considered the existing standards and guidelines for validation practices (Chan, 2014), and the COSMIN recommendations (Mokkink et al., 2010).
**FIGURE 1:** *Study design.*
## 2.2 Scope of the study
We included a sample of people with cancer who collected their oral antineoplastic treatment from the hospital pharmacies of four hospitals in the Valencian Community (Spain). The coordinating center was Elda General University Hospital, which has 548 beds and serves a geographically dispersed population of 189,629 inhabitants. The largest site—Alicante General University Hospital—is the tertiary care hospital of the province, with 841 beds and a catchment population of 280,535 inhabitants; while San Juan General University Hospital has 407 beds and serves 225,153 inhabitants; and Elche General University Hospital has 448 beds and serves 169,599 inhabitants.
## 2.3 Developing items to include in the scale
We organized a steering committee of six healthcare professions (four pharmacists, one physician and one psychologist), whose role was to construct the initial scale and take the necessary decisions throughout the validation process to define a reliable and valid final version. The working method we chose to approve the different versions of the scale was consensus conference (Martín-Delgado et al., 2013), so that the team could develop and reformulate the items after the various rounds of analysis and discussion.
In an initial phase based on qualitative research methods and scientific literature review, the committee analyzed the categorized discourse on the experiences and perspectives of people with cancer and healthcare professionals to identify the main dimensions explored in the qualitative study.
This previous qualitative study aimed to assess the medication experience in cancer patients undergoing ANEO treatment. The results of this study described what the patients perceived as barriers and facilitators to adherence and compared them with the healthcare professionals’ perspectives. Eight dimensions were initially identified: 1) treatment experiences, 2) polymedication, 3) beliefs regarding medication, 4) need for treatment and expectations about effectiveness 5) information and sources relating to the treatment 6) medication errors and forgetting to take medication and how to prevent this; 7) adverse effects and consequences of the treatment with ANEO; and 8) social, family and professional support. The main results of this study showed that the presence of adverse effects, lack of information about treatment, beliefs, needs and expectations regarding medications, social and family support, and the relationship with the health professionals were the most impactful aspects in the medical experience of patients in treatment with ANEO.
With this information, the steering committee established a proposal of dimensions for the initial scale through an iterative process involving deductive methods based on the literature review and inductive methods based on the discussions with patients and professionals.
## 2.4 Designing the scale
For the responses to the items, the steering committee designed a five-point Likert scale (1 = never, 2 = almost never, 3 = sometimes, 4 = almost always, 5 = always), to ensure efficiency in the subsequent validation. The committee also decided to vary the direction of the responses so that respondents would not detect a pattern (i.e., answering ‘never’ could represent high or low adherence, depending on the question).
We also formed an expert panel to evaluate content validity of the dimensions and items of the initial scale (ensuring there were no redundant or missing questions) and face validity (checking whether the questions were clear and easy to answer, whether the scale was adequate and whether respondents would understand the structure and response scale). For this panel, we recruited eight people through purposive sampling (Otzen and Manterola, 2017): four were healthcare professionals (two medical oncologists and two pharmacists) with at least 5 years of experience in cancer management, treatment and research; and four were people with cancer who had been on OAAs for more than 3 months. The results of this qualitative evaluation prompted changes in the reactive items of the scale.
Subsequently, we carried out a pilot study in a group of 22 people with cancer to identify any comprehension difficulties and to evaluate the functioning of our scale. We consecutively included adults (aged 18 years or older) with a cancer diagnosis when they attended or contacted one of the participating hospital pharmacies to collect or request their treatment. Eligible treatments belonged to the subgroups L01 (Antineoplastic agents) and L02 (Endocrine therapy) and L04 (Immunosuppressants) of the Antineoplastic and immunomodulating agents’ group (L) of the Anatomical Therapeutic Chemical (ATC) Classification System. We included only people who had been on the treatment for more than 1 month. We excluded people with communication difficulties or who refused to participate in the study. We measured the time taken to complete the questions and we analyzed whether the items addressed participants’ full experience. After completing the scale, participants were asked whether they had difficulty understanding any of the questions and whether they considered the response scale to be adequate.
## 2.5 Validation of the scale: Tests of reliability and validity
We performed a prospective evaluation in cancer outpatients on OAAs recruited between March and November 2021, to collect data that would help us to determine the validity and reliability of the EXPAD-ANEO scale.
To ensure homogeneous data collection in the four participating hospitals, we provided specific training to the pharmacists who would collect the data.
## 2.5.1 Selection criteria
People eligible for study inclusion were aged 18 or over, had a cancer diagnosis, and had been receiving OAAs (ATC code L01, L02 or L04) for at least 3 months. We excluded people with communication or comprehension difficulties.
## 2.5.2 Sample size calculation
We calculated a sample size of 268 patients from an infinite population, assuming $78\%$ adherence in the Spanish population (Fernández-Ribeiro et al., 2017), and applying a confidence level of $95\%$ and precision of $5\%$. The necessary sample size to validate an instrument varies according to the number of items (10 respondents per item (Boateng et al., 2018)) or dimensions, but the minimum recommended number to ensure stable and generalizable results is 175–200 participants (Hair et al., 2009).
## 2.5.3 Recruitment
The team of pharmacists in each participating hospital consecutively recruited eligible people until reaching the predefined sample size. When a person receiving OAAs contacted the hospital pharmacy to collect their medication, the pharmacist checked whether they met the eligibility criteria and, if so, invited them to participate in the study. Prior to inclusion, the potential participant received information on the study objectives and an informed consent form. If they agreed to the conditions and gave their informed consent, a telephone interview was scheduled. The increased use of telepharmacy in Spanish hospitals since the COVID-19 pandemic facilitated this process. The pharmacists were responsible for conducting the interview and evaluating therapeutic adherence.
## 2.5.4 Study variables
We collected sociodemographic and clinical variables of participants, such as age, sex, educational attainment (no schooling/primary education/secondary education/tertiary education), ECOG score (0–4), living situation (alone/with family/institutionalized), diagnosis, treatment objectives (adjuvant/palliative), treatment duration, line of treatment, adverse effects, and health literacy. We measured health literacy on a small scale of six questions selected by the steering committee from the 16-Item European Health Literacy Survey (HLS-EU-Q16) (Nolasco et al., 2020), to measure the association with adherence.
We evaluated the dependent variable, adherence to OAAs, using the new EXPAD-ANEO scale, the hospital pharmacy dispensing records, the Morisky-Green test and the pill count method, which we used as the benchmark.
All variables were obtained from the hospital electronic medical records of the Valencian Community (Orion Clinic and Abucasis) and the telephone interviews.
## 2.5.5 Validity and reliability
Table 1 lists all the indexes and statistics calculated to assess the reliability and validity of the instrument, with the correspondent cut-off values to be applied to each methodology.• Item reduction and dimensionality analysis: to check the relationship between items, we applied Bartlett’s test of sphericity. We carried out an exploratory factor analysis using the maximum likelihood extraction method, and a principal component analysis with an oblique rotation method. We also performed an optimal implementation of parallel analysis to determine the fit of the items to the model and whether any of them should be eliminated based on the measure of sampling adequacy (MSA) index. MSA values below 0.50 suggest that the item does not measure the same domain as the rest of the items and should therefore be dropped (Lorenzo-Seva and Ferrando, 2021). We did not consider factor loadings of less than 0.3. Subsequently, we carried out a confirmatory factor analysis to evaluate the fit of the model obtained in the exploratory factor analysis. To assess the fit of the model, we included the following robust goodness of fit statistics: 1) Root Mean Square Error of Approximation (RMSEA), considering as admissible adjustment values of 0.06 or less; 2) Comparative Fit Index (CFI), where values above 0.95 would be adequate; 3) Tucker Lexis Index (TLI), where values above 0.97 indicate a good model fit; and 4) Standardized Root Mean Squared Residual (SRMR), where values below 0.10 would be adequate (Cangur and Ercan, 2015). In addition, we calculated the total percentage of variance explained, and whether the set of items that make up the instrument had a given unidimensional or multidimensional structure.• Reliability: degree to which an instrument is able to measure without errors, i.e., to measure accurately and consistently over time. We used McDonald omega coefficient to determine the internal consistency of the items and how they relate to each other, both for the global scale and for the single item. McDonald’s omega coefficient is intended to replace Cronbach’s alpha given that the instrument has a certain multidimensional structure. The instrument demonstrates an acceptable reliability when the omega coefficients is greater than 0.6 (Dunn et al., 2014), (Nájera Catalán, 2019).• Criterion validity: to assess the criterion validity of the questionnaire in the absence of a gold standard for measuring adherence, we used pill count as a surrogate, as it is one of the most commonly used methods in daily practice. Participants with a pill count of $90\%$ or more were considered adherent to treatment (Daouphars et al.). We determined the cut-off point with the highest specificity for classifying participants as adherent or non-adherent.• Construct validity based on known groups: to assess the construct validity of the questionnaire, we followed the strategy of comparing two groups established according to the Morisky-Green test, as it is the most widely used in people with cancer (convergent validity) (Morisky et al., 1986). For this study, we used the four-question version that classifies respondents as adherent or non-adherent; it is validated for different chronic pathologies and is widely used in research. To test divergent validity, we compared the EXPAD-ANEO scores with the health literacy scores. We calculated the chi-squared (X2) statistic and correlation coefficient to evaluate potential heterogeneity between groups.
**TABLE 1**
| Psychometric properties | Psychometric properties.1 | Name of index, statistic, or coefficient | Abbreviation | Adequate value |
| --- | --- | --- | --- | --- |
| Reliability | Internal consistency | McDonald omega coefficient | ω | >0.6 |
| Validity | Redundancy between items | Bartlett’s test of sphericity | — | p-value <0.001 |
| Validity | Adequacy of the items to the model | Measure of Sampling Adequacy | MSA | >0.5 |
| Validity | Model’s fit | Root Mean Square Error of Approximation | RMSEA | ≤0.06 |
| Validity | Model’s fit | Comparative Fit Index | CFI | >0.95 |
| Validity | Model’s fit | Tucker Lexis Index | TLI | >0.97 |
| Validity | Model’s fit | Standardized Root Mean Squared Residual | SRMR | <0.10 |
For the statistical analyses, we used SPSS version 28 and Jamovi 1.6.23.
## 2.6 Ethical considerations
This study received a favorable opinion from the Institutional Review Board of Elda General Hospital on 14 April 2020 (PI $\frac{2020}{12}$), and subsequent amendments in March 2021 were also approved.
It is registered in ClinicalTrials.gov under the Identifier NCT04550533 (clinicaltrials.gov/ct2/show/NCT04550533).
## 3.1 Development of the items
With reference to the eight categories identified in the qualitative study (Talens et al., 2021), and after reviewing the dimensions of other instruments, the steering committee created the following five exploratory dimensions: Experience with the treatment, Beliefs and expectations, Sources of information and support, Errors, Forgetfulness and polypharmacy, and Side effects. Subsequently, the steering committee generated several items for each dimension (30 in total), based on the scientific literature and the productivity measures of ideas provided, spontaneity and consistency in the group discussions with patients and professionals.
In different meetings, after the expert analysis in the consensus conference, the committee eliminated six items that it deemed redundant, and four items with responses that could not be adapted to the Likert scale (although we collected these four variables in the patient interviews because they provided information on participants’ sociodemographic characteristics and health literacy). In this way, we generated the first version of the instrument, which had four main dimensions. The committee initially grouped the eight dimensions described in the qualitative analysis into four categories according to analogy and similarity in the items included in each dimension. Thus, the dimension of beliefs regarding medication was combined with the dimension of need for treatment and expectations of effectiveness. In the same way, the dimension of information about treatment was combined with social, family and professional support, and these aspects were considered as facilitators of adherence; the dimension of polymedication was combined with the category of medication errors, failures and forgetting to take medication as well as the dimension of adverse effects into a single dimension because all of them are problems related to medication. The four resulting categories were 1) ANEO experiences, 2) Beliefs and expectations, 3) Information and support, 4) Problems related to ANEO.
## 3.2.1 Face and content validity
Eight participants (four outpatients and four professionals) evaluated the face and content validity of the instrument. The average age of the outpatients was 59.2 (standard deviation [SD] 7.5) years, and half were women. Two professionals were oncologists and two were pharmacists. The average age of the professionals was 46 (SD 9) years, and one was a woman. Half of respondents thought the scale was understandable, while the rest suggested removing the abbreviations and changing the wording of items 2, 5, 7 and 17. All respondents agreed that the items were relevant and all understood the response scale, although two respondents said they would prefer a simpler scale, with two response choices. Regarding the length of the questionnaire, three respondents found it too long, two though it was too short and the remaining three considered it suitable. Using the respondents’ comments, we reformulated four items, spelled out the abbreviations and changed the order of the questions. Although two participants preferred dichotomous questions, we maintained the five-point Likert scale after consulting with experts, who agreed on the efficiency of rating scales.
## 3.2.2 Pilot study
The pilot study included 22 of 25 outpatients who we had invited to participate. The average age of the group was 68.7 (SD 7.6) years, and $41\%$ were women. Most participants ($86\%$) lived with their family, $50\%$ had no schooling and $73\%$ were retired. Thirteen participants ($59\%$) were treated in the oncology department. The median time from diagnosis was 35 months (interquartile range (IQR) 87 months), and the most common diagnoses were prostate cancer ($18\%$) and multiple myeloma ($18\%$). Nearly two-thirds of participants ($64\%$) used multiple medications, and the most common anticancer drugs were ibrutinib ($18\%$), capecitabine ($18\%$) and abiraterone ($14\%$). Following the pilot study, we reformulated eight questions based on the participants’ recommendations.
## 3.3.1 Included patients
The pharmacists from the four hospitals invited a total of 350 people to participate, of whom 24 ($8\%$) refused, 35 ($11\%$) were unable to respond to the questions or had communication difficulties, and 23 ($9\%$) were ineligible as they had been on the treatment for less than 3 months.
We included 268 patients ($77\%$). Each had a telephone interview with a pharmacist, lasting approximately 15 min. The aim of this initial contact was to build trust between the participants and the investigators. To ensure accuracy in the responses, we limited the recall period to 1 month. The mean age of the participants was 64.1 (SD 12.4) years (range 25–91 years), and $47\%$ were women. Most participants ($88\%$) lived with their family, $18\%$ had no schooling and $57\%$ were retired. Sixty-one per cent of participants were managed in oncology departments and $39\%$ in hematology departments. The median duration of OAA treatment was 12 months (range 5–29 months). The most common diagnoses were multiple myeloma ($16\%$), breast cancer ($14\%$), chronic lymphocytic leukemia ($10\%$) and prostate cancer ($10\%$). In $83\%$ of participants, the goal of treatment was palliative. OAAs constituted the first treatment for $68\%$. Most participants ($59\%$) were receiving more than five drugs, and the most common anticancer agents were lenalidomide ($15\%$), ibrutinib ($11\%$), capecitabine ($9\%$) and abiraterone ($7\%$).
## 3.3.2 Validation of the scale
We collected data prospectively using the 20-item scale (four dimensions) we had created through the process described above (Supplementary Table S1).• Item reduction and dimensionality analysis: after a descriptive analysis of all variables, we observed that Q14 (Have you stopped taking your medication at any point on the recommendation of someone in a similar situation?) was a constant, so we removed it from the scale. In all other questions except Q3, we identified an important floor/ceiling effect (respondents tended to select ‘never’ or ‘always’); we therefore decided to dichotomize the responses (Yes/No). All items had values in both categories. The p-value of Bartlett’s test was below 0.001, indicating a relationship between the items. After carrying out the exploratory factor analysis and the principal component analysis, we found that 12 questions had an MSA index below 0.5, so we eliminated them from the scale. This left seven items in the final scale (Q4, Q5, Q15, Q16, Q17, Q18 and Q19). The factor analysis extracted two factors accounting for $52\%$ of the explained common variance. The factor loading values ranged from 0.34 (Q5) to 0.88 (Q4) as shown in Table 2. We verified appropriateness using the robust goodness of fit statistics, obtaining the following results: RMSEA = 0.02, CFI = 0.99, TLI = 0.99 and SRMR = 0.03.• Reliability: *As this* is a two-dimensional instrument (2 factors), we calculated the omega coefficients for each factor separately and for each item if it were removed. Table 3 presents the result of this analysis. McDonald’s omega coefficient showed a reliability of 0.7 for factor 1 and 0.6 for factor 2. Considering the omega coefficient, the range of correlations of each item with the total score was 0.4 (Q4 and Q17) to 0.8 (Q5).• Criterion validity: Regarding criterion validity, we found that a cut-off of 1 point would optimize the specificity of the questionnaire at $80\%$. This means that a person scoring 1 point or more on the questionnaire was considered non-adherent to treatment.• Construct validity based on known groups: finally, when we assessed convergent validity against the Morisky-Green test, the X2 test showed a significant association ($P \leq 0.001$). Regarding divergent validity, we first verified the normality of the two variables (score on the questionnaire we are validating and score on the literacy questionnaire); as the variables were not normally distributed, we used Spearman’s correlation coefficient rather than Pearson’s correlation coefficient. We found a correlation coefficient of 0.087 ($$P \leq 0.153$$), indicating no correlation between the two questionnaires.
After the validation procedure, the scale finally included 2 dimensions and 7 items.
## 3.4 Interpretation of the new adherence scale
Table 4 presents the results of the validation: the two-dimension, seven-item EXPAD-ANEO scale. The dimension related to beliefs and expectations finally includes three items. We grouped items that described patient’s attitudes affecting adherence in a new dimension denominated behaviour and attitudes. This new dimension includes four items, mainly those describing medication-related problems.
**TABLE 4**
| Beliefs and expectations about treatment |
| --- |
| Q4. Do you sometimes stop taking the antineoplastic because you think it is useless? |
| Q5. Do you sometimes think that another intravenous/transplant drug would produce better results than the current oral drug? |
| Q19. Do you sometimes stop taking the drug when you feel well for fear of feeling ill? |
| BEHAVIOR AND ATTITUDES |
| Q15. Do you sometimes miss a dose of your chemotherapy when you feel sick? |
| Q16. Do you sometimes stop taking the chemotherapy without consulting your doctor because it drains your energy and makes you tired? |
| Q17. Do you sometimes miss a dose of your chemotherapy for fear of reactions like vomiting, cramps, diarrhea or skin problems? |
| Q18. Do you sometimes stop taking your chemotherapy because you are worried it will affect your work or social life? |
Due to the extreme responses (at both ends of the scale) obtained in the validation, the steering committee decided to reduce the response scale to two options.
Possible scores range from 0 to 7 points, with each affirmative answer adding 1 point. Because the instrument is highly specific, a respondent who gives only one affirmative answer is considered non-adherent. According to the results of the EXPAD-ANEO scale, $20\%$ of people interviewed were non-adherent to their oral antineoplastic treatment. The mean of the scale for the whole population was 0.28 (SD 0.66), and scores ranged from 0 to 4. Among the non-adherent participants, the mean score was 1.41 (SD 0.77), and scores ranged from 1 to 4.
## 4 Discussion
By adopting a methodological approach that included qualitative research techniques, we were able to design a tool through orderly discussion, where people with cancer were at the center of the process from the outset and participated in the development, design and validation stages. To the best of our knowledge, this is the first study to incorporate the experiences and opinions of people with cancer and professionals in the development of a scale that measures adherence to OAAs. Most studies of this type are based on literature reviews and expert opinions only (Claros et al., 2019a). Peng and colleagues conducted a study with similar methods in a Chinese population, although their scale did not measure adherence specifically but rather self-management of oral chemotherapy, without considering other anticancer approaches such as hormonal or targeted therapy (Peng and Wu, 2020). One recent publication describes the development of the A-BET questionnaire, which involved a qualitative study in people with breast cancer receiving hormone therapy, and a subsequent validation stage; however, the sample size was very small (Gambalunga et al., 2022). One aspect that sets our study apart is the wide range of treatments included (all oral antineoplastic agents dispensed in public Spanish hospitals).
In addition, the validation process enabled us to select the most appropriate items for evaluating adherence through measurement of their psychometric properties. Face and content validity assessment is important to ensure the items are relevant and represent the construct they are intended to measure. In the literature, we found that the development of these types of tests commonly involves expert opinions (Lessa et al., 2015; Baudot et al., 2016) or pilot studies in people with the same characteristics (Urzua et al., 2010). In our study, the double evaluation (by the group of experts followed by the pilot study in people with cancer) conferred validity and coherence to our instrument. Bagcivan and colleagues used a more sophisticated model to determine the quantitative content validity index (Bagcivan and Akbayrak, 2015).
Compared with other scales, EXPAD-ANEO showed acceptable reliability, which we evaluated with McDonald’s omega coefficient because it is currently considered a more sensitive measure than the commonly used Cronbach’s alpha, and more appropriate for estimating reliability, particularly of multidimensional instruments including different scales of items and factor loads (Dunn et al., 2014; da Silveira and Jorge, 2002).
A systematic review by Claros and colleagues included six validation studies. Only two of the included studies showed acceptable validity and reliability for measuring adherence in people with cancer: the Adherence Determinants Questionnaire (ADQ) (Lessa et al., 2015) and the Oral Chemotherapy Adherence Scale (OCAS) (Bagcivan and Akbayrak, 2015).
In a more recent study, the validation of the Treatment Adherence Measure (TAM) (Silveira et al., 2021) in outpatients with multiple myeloma was unsatisfactory. That study reported very high adherence to treatment and a tendency to extreme responses, as in our study. Evaluation of other adherence measurement tools has shown a ceiling effect: more that $15\%$ of responses to each question represent highest adherence on the scale (da Silva Carvalho et al., 2010).
The factor analyses in our study extracted the seven items that made up the final scale: Q4, Q5 and Q19 grouped in a factor related to beliefs about and expectations of the treatment; and Q15, Q16, Q17 and Q18 included in a factor referring mainly to behaviors of treatment use. Tests like Morisky-Green usually explore only the second factor. The first factor of our instrument distinguishes it from other adherence scales, and provides the opportunity to explore patients’ thoughts, beliefs and expectations, and how these variables correlate to more or less adherent behavior. In addition, we responded to the floor/ceiling effect identified during the validation process by dichotomizing the responses (Yes/No), as in other published studies (Silveira et al., 2021; da Silva Carvalho et al., 2010).
Criterion validity is important because it compares our results with the gold standard. In the absence of an objective validated measure for OAA adherence, we used pill count (the most widely used method in daily practice), considering a value over $90\%$ representative of adherence (Daouphars et al.; Timmers et al., 2014). The $80\%$ specificity enabled us to detect non-adherence in a population that is generally considered very adherent, with only one affirmative response.
We confirmed convergent validity with the Morisky-Green test and divergent validity with the health literacy questions selected by the steering committee, with statistical significance in both cases. The Morisky Green test is the most widely used questionnaire in clinical practice to assess adherence and is validated in different chronic diseases such as hypertension and diabetes (Morisky et al., 2008). However, it has not been validated in cancer patients, despite its use for this pathology (Signorelli et al., 2022), which represents a limitation for its use in clinical practice. In addition, the Morisky-Green test aims to assess patient attitudes, assuming that if patients’ behaviour is good, the patient is adherent. In contrast, the result of our EXPAD-ANEO research, not only considers a patient’s behaviour, but also explores the patient’s beliefs associated with adherence and the need for treatment, as well as what they considered to be barriers to adherence. EXPAD-ANEO is therefore, a scale designed to include the patients’ differential social and cultural characteristics, the patient’s opinion and perspective, as well as the healthcare professionals’ approach. Moreover, given that this scale has been validated specifically for patients with cancer who are undergoing treatment with oral antineoplastic agents, it is a reliable scale for using in clinical practice.
Possible scores on the EXPAD-ANEO scale range from 0 to 7; $80\%$ of our participants scored 0 points and were considered adherent. Respondents who scored just one point on this highly specific scale ($15\%$ of our population) were considered non-adherent. The grouping of the scores towards the two extremes of the scale indicated a lack of discriminating capacity in a population that is considered highly adherent. However, any result other than $100\%$ shows room for improvement and the opportunity to detect isolated cases of non-adherence and avoid therapeutic failure.
## 4.1 Implications for clinical practice
A population of people with cancer in a specific social, cultural and economic context, with access to universal healthcare, showed high levels of adherence ($80\%$), comparable with data from other studies in the literature (Greer et al., 2016), but there is still room for improvement. The systematic use of a simple, valid, reliable and highly specific instrument for detecting non-adherence in this population could help healthcare professionals to establish individualized measures or collective strategies to improve adherence and health outcomes. By helping physicians and pharmacists to better understand the personal aspects that influence patients’ use of medication, a tool like this could contribute to improving healthcare quality (Olivera-Fernandez et al., 2014; Gatwood et al., 2017; Middendorff et al., 2018; Birand et al., 2019; Passey et al., 2021).
Furthermore, the success of treatments and strategies increases when patients are placed at the center of the system and are encouraged to participate in decision-making with healthcare professionals (González-Bueno et al., 2018). Today, it is crucial to consider patients’ perspectives when evaluating and improving healthcare services (Boateng et al., 2018). This approach redirects health systems towards person-centered care (González-Bueno et al., 2018). *In* general, improving the quality and safety of patient care involves evaluating patient-reported experience measures and patient-reported outcomes measures (Valderas and Alonso, 2008), as we have in this study, along with other more objective indicators, with the aim of orienting the system towards increasingly integrated and humanized care, in which patients can take decisions about their own health (Martin-Delgado et al., 2021).
## 4.1.1 Strengths
The EXPAD-ANEO scale is the first tool developed in a Spanish population for measuring adherence to OAAs. We adopted an innovative approach to designing and validating the scale, evaluating adherence in relation to the experiences of the people using the treatment while also incorporating the opinions of healthcare professionals. Throughout the design and validation process, we followed current standards, recommendations and expert consensus (Mokkink et al., 2010; Chan, 2014; Boateng et al., 2018).
In addition, our study included four hospitals and a large sample of outpatients who used a wide range of oral antineoplastic agents. Similar studies in the literature have included few patients and limited medications (Claros et al., 2019a; Huang et al., 2016).
## 4.1.2 Limitations
One of the main limitations of this study was that we had no gold standard against which to validate our instrument. For the criterion validity assessment, we used pill count as a surrogate, for two main reasons: first, it is the method most widely used by hospital pharmacists when evaluating adherence in clinical practice; and second, it is the available method that most closely reflects real adherence, since use of electronic devices is limited to research. Another possible limitation is the variability in data collection resulting from the multicentric nature of the study. We tried to reduce this effect by training the hospital pharmacists before data collection. In addition, as with all scales, there is a risk of overestimating adherence, though we minimized this risk in our study by comparing the results of the EXPAD-ANEO scale with other measures such as pill count.
For practical reasons, we were unable to retest our scale on the same large sample of outpatients; as a result, reliability was limited to internal consistency. We had to delay the study during the COVID-19 pandemic, and the pharmacists were unable to collect data in face-to-face interviews as planned because most patients received their medication at home. In view of the increased implementation of telepharmacy (remote pharmaceutical care) in the Spanish health system, we decided to conduct the interviews over the telephone. This may have led patients to consider the questions less carefully and give extreme responses on the five-point scale.
The qualitative study initially described eight different dimensions which were then grouped into four dimensions and finally, after the validation of the scale through the factor analysis, it was transformed into a two-dimension scale. Although the initial scale included eight categories with more potential information, the factor analysis process eliminated redundant information and converted the more specific thematic units into more general ones. Hence, the final scale was easier to use in practice.
Finally, although we had originally aimed to vary the direction of responses in the scale, all seven questions that remained after item reduction had the same response pattern (Yes = non-adherent, No = adherent).
## 4.2 Future research
EXPAD-ANEO constitutes a starting point for developing this type of practical and sensitive instrument to helps professionals predict adherence based on the experiences of people with different chronic pathologies, including cancer, or even specific cancers or cancer stages. These instruments should be integrated into clinical practice as part of the routine clinical interview so that physicians can propose specific interventions without delay when they detect a possible lack of adherence. The new scale is designed to be collected by health professionals and the Morisky-Green test is a self-reported questionnaire. Further research could be carried out to evaluate the characteristics of this new scale so that it can be applied in a self-reported format to facilitate its incorporation in clinical practice.
As the validation of an instrument is not a static process, future studies should evaluate the EXPAD-ANEO scale’s sensitivity to change, i.e., the ability of the scale to detect changes in adherence to oral medication after an intervention, for example, by the size of the effect.
## 5 Conclusion
EXPAD-ANEO scale is a novel instrument with acceptable validity and reliability for systematically evaluating adherence to OAAs. It can serve as a starting point for future studies. Researchers can use this scale to explore patients’ experiences and adherence to treatment. Healthcare professionals can easily integrate this simple and applicable tool into their care routine during clinical interviews. It can help them to explore, in an unbiased way, the beliefs and behaviors of people with cancer in relation to their medical treatment.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by the Institutional Review Board of Elda General Hospital on 14 April 2020 (PI$\frac{2020}{12}$), and subsequent amendments in March 2021 were also approved. It is registered in ClinicalTrials.gov under the Identifier NCT04550533 (clinicaltrials.gov/ct2/show/NCT04550533). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
Conceptualization, AT, EL-P, MG, and BL; Methodology, AT, MA, EL-P, MG, NC-S, and BL; Software, NC-S; Validation, AT, EL-P, MA, NC-S, MG, and BL; Formal Analysis, NC-S; Investigation, AT and MG; Resources, AT and MG; Data Curation, NC-S; Writing—Original Draft Preparation, AT and EL-P; Writing—Review and Editing, AT, EL-P, MA, NC-S, MG, and BL EL-P and BL share senior authorship. All authors have read and agreed to the published version of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2023.1113898/full#supplementary-material
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|
---
title: Cordyceps militaris extracts and cordycepin ameliorate type 2 diabetes mellitus
by modulating the gut microbiota and metabolites
authors:
- Xinyuan Liu
- Mengqian Dun
- Tongtong Jian
- Yuqing Sun
- Mingyu Wang
- Guoying Zhang
- Jianya Ling
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10033974
doi: 10.3389/fphar.2023.1134429
license: CC BY 4.0
---
# Cordyceps militaris extracts and cordycepin ameliorate type 2 diabetes mellitus by modulating the gut microbiota and metabolites
## Abstract
Introduction: Cordyceps militaris, which has many potential medicinal properties, has rarely been reported to alleviate type 2 diabetes mellitus (T2DM).
Methods: The effects of C. militaris extracts (CE) and cordycepin (CCS) on high-fat diet and streptozotocin (STZ) induced T2DM mice were analysed by gut microbiome and metabolomics methods in this study.
Results: The results demonstrated that glucose and lipid metabolism parameters, oxidative stress biomarkers and inflammation cytokines were down-regulated in the CCS and CE groups. A comparative analysis of the fecal samples from mice in the model and experimental groups showed that experimental groups resulted in a higher abundance of Firmicutes/Bacteroidetes.
Conclusion: This study provides evidence that C. militaris can be used as a food supplement to relieve T2DM, which provides a promising prospect for new functional food in it.
## Highlights
[1] Based on the 16S rDNA gene sequencing and metabolomics analyses, the underlying mechanism of cordycepin (CCS) and C. militaris extracts (CE) on the amelioration of T2DM by modulating the gut microbiota and metabolites was firstly investigated[2] CCS and CE showed beneficial effect on modulating gut microbiota composition and structure.[3] CCS and CE presented beneficial effects on modulating metabolites, including phenols, fatty acids, phenylacetones, pyrimidine nucleosides, carboxylic acids, etc[4] Further analyses of metabolic pathways indicated that the therapeutic effect of CCS and CE might be related predominantly to PI3K/Akt/mTOR pathway.
## 1 Introduction
Diabetes mellitus (DM) is a metabolic disease that exists widely in the world, and its clinical manifestation is that long-term blood sugar is higher than the standard value (Maxwell, K. G. et al., 2020). Diabetes and its complications can cause damage to the eyes, kidneys, nerves, heart, blood vessels and other tissues and organs of patients, and even lead to death. As of 2021, 537 million adults worldwide have diabetes, and about 6.7 million people have died from the disease. T2DM is a common type of diabetes, and it is the most persistent and common metabolic disease as a global public health issue. The pathogenesis of T2DM is considered as impaired insulin secretion and decreased insulin sensitivity, about $90\%$ of diabetic patients are diagnosed with T2DM (Hameed I et al., 2015). Mechanisms of new drugs for treating T2DM include targeting ß cells and the incretin axis. Traditional hypoglycemic drugs can only be used to control the level of blood glucose to suppress complications such as metformin, glibenclamide and miglitol (Chiriacò M et al., 2019). For centuries, edible fungi and natural plants have attracted extensive attention and been used for pharmacological research due to their low toxicity and beneficial to human body. Some edible fungi, such as Ganoderma lucidum, Grifola frondose, Hericium erinaceus, Phellinus linteus, Auricularia auricular, C. militaris have shown potential anti-diabetic effects (Chen M et al., 2020) (Chen Y et al., 2019) (Liu Y et al., 2020) (Khursheed R et al., 2020). Therefore, the exploration of new natural active ingredients with excellent pharmacological effects and long-term safety have become the research direction of new drugs for the treatment of T2DM.
Cordyceps militaris was known as medicinal and edible fungi with significant blood glucose lowering (Wang F et al., 2015) (Dong Y et al., 2014) (Gong X et al., 2021) (Hsu CH et al., 2008). It can be used as new resource food and raw material of health food. Compared with other medicinal fungi, few studies have been focused on the mechanism of action of C. militaris and its active components in the treatment of T2DM. Cordyceps militaris belongs to the entomopathogenic fungi, lavicipitaceae and Ascomycotina, which is widely used in medicines and health products. Cordyceps militaris has been shown to have antidiabetic effects and contains various active ingredients such as polysaccharide, cordycepin, adenosine and various trace elements (Dong Y et al., 2014). Among these active substances, cordycepin, a unique active ingredient of C. militaris, has been shown to decrease blood glucose and regulate dyslipidemia (Wang F et al., 2015), reduce inflammation (Gong X et al., 2021), relieve oxidative stress (Hsu CH et al., 2008), and promote immune regulation (Gong X et al., 2021), anti-tumor (Hsu CH et al., 2008), etc. It is reported that cordycepin can significantly regulate the intestinal microflora (An Y et al., 2018). However, the specific mechanism by which cordycepin and C. militaris extract improves T2DM is not yet fully understood.
Gut microbiota and metabolites are important factors in mediating the development of T2DM, an imbalance in the gut microbiota can affects glucose and lipid metabolism, thereby promoting the occurrence and development of metabolic diseases such as T2DM, non-alcoholic fatty liver disease (Zhou M et al., 2021), etc. Recently, a large number of edible fungi have been proved to alleviate T2DM by regulating the intestinal flora, leading us to explore whether the gut microbiota and gut barrier were involved in the beneficial effect of cordycepin and C. militaris extract on T2DM.
*In* general, microbiomics was used to analyze the structure of gut microbiota community, and metabolomics was used to analyze the metabolites of gut microbiota to explore the relationship between metabolites and disease. The research on intestinal flora mainly focuses on microbiome and metabolomics (Li L et al., 2021). To the best of our knowledge, few studies have combined gut microbiome and metabolomics to elucidate the effects of medicinal and edible fungi on T2DM, and none of them has been reported related to C. militaris. The purpose of this research is to evaluate the mechanism of cordycepin and C. militaris extract on alleviating the symptoms of T2DM from the perspectives of gut microbiome and metabolomics. This work has laid a theoretical foundation for the research on the treatment of T2DM with medicinal and edible fungi.
## 2.1 Microorganism and materials
The anamorph strain JY20 of C. militaris, originally conserved in our lab, was confirmed by means of both morphological and molecular methods. Potato dextrose liquid medium was used as fermentation medium for 5 days. The mycelium was then inoculated to rice medium in glass jars and cultured in the dark at 22°C for 7 days, then at 22°C for 10 days, with a 10:14 h light/dark cycle for conversion of the fungi and forming stromata, with a temperature difference of more than 10°C between day and night for 28 days to form the mature fruiting body (Zhao X et al., 2019). The cultured stroma of C. militaris was lyophilized and ground through a 60-mesh sieve. The extraction of lyophilized fermentation product (60 mesh) was carried out three times, each time with 20-fold volume deionized water under ultrasound at 25°C for 30 min. The extract was centrifuged at 5,000 rpm for 15 min. The combined supernatants were evaporated at 55°C under reduced pressure, lyophilized, and then stored at −20°C for further analysis. Then, 1 g of lyophilized powder dissolved in ultrapure water, filtered through a 0.22 μm membrane to a final volume of 20 mL.
STZ (HPLC≥$98\%$) (Solarbio Science and Technology company, cat. No. S8050), Metformin hydrochloride tablets (Glucophage® 500 mg tablets) (Sino-American Shanghai Squibb Pharmaceutical company, cat. No. H20023371), Cordycepin (HPLC≥$98\%$) (Yuanye Biotechnology company, cat. No. B20196), stored at dark and low temperature, and diluted to the desired concentration prior to use. High-fat feed (Xiaoshuyoutai Biotechnology company). Total cholesterol (TC), Triglyceride (TG), Low-density lipoprotein cholesterol (LDL-C), How-density lipoprotein cholesterol (HDL-C), Alanine aminotransferase (ALT), Superoxide dismutase (SOD), Catalase (CAT) kits (JianchengNanjing, China). The kits of Interleukin-6 (IL-6) and *Tumor necrosis* factor-α (TNF-α) (Boshen Biotechnology company, China).
## 2.2 Animal experiments
Seventy male Kunming mice (8 weeks old, 40 ± 2 g) were purchased from Vital River Laboratory Animal Technology company (Beijing, China) and housed in polypropylene cages ($$n = 5$$ mice/cage). Animals were housed at 22°C ± 2°C on a 12 h light/dark cycle and allowed free access to food and water. All methods and experimental protocols in the research process were approved by the Ethics Committee for Animal Research of School of Life Sciences, Shandong University (NO: SYDWLL-2021-29), and the protocols conformed to the U.S. Public Health Service Policy on Use of Laboratory Animals.
After the 7-day acclimation period, all mice were randomly divided into control group (ND, $$n = 10$$) and model group (T2DM, $$n = 60$$). The control group was fed with a normal diet, while the model group was fed with a $60\%$ high-fat diet. After 4 weeks, all mice were fasted but had free access to water for 18 h. The model group mice were induced by intraperitoneal injection of STZ (70 mg/kg, dissolved in sodium citrate buffer, 0.1 mol/L, pH = 4.5) for 5 days. Meanwhile, the control group were injected with the same volume of citrate buffer. On the fifth day after injection, the level of 12 h fasting blood glucose (FBG) was measured in each group, and mice with FBG ≥11.1 mmol/L were considered as T2DM mice (Lu JM., 2016). Subsequently, all the T2DM mice were randomly divided into six groups ($$n = 10$$): model control group (HFD); metformin group (PC, 350 mg/kg); Cordycepin high-dose group (CCSH, 50 mg/kg); Cordycepin low-dose group (CCSL, 25 mg/kg); C. militaris extracts high-dose group (CEH, 1.5 g/kg); C. militaris extracts low-dose group (CEL, 1 g/kg). All mice were fed their respective diets until the end of the study period. Mice in the CCS groups, CE groups and PC group were gavaged for six consecutive weeks, while those in the ND and HFD groups were given the same volume of physiological saline ($0.9\%$). The body weight of mice and FBG were measured every week. Fecal samples were aseptically collected at the fourth and sixth week of treatment. At the end of the experiment, the mice were fasted overnight, sacrificed under ether anesthesia. The blood samples were collected from the eyes and centrifuged (3000 r/min, 15 min) to obtain serum for biochemical analysis. All fecal and serum samples were cryopreserved at −80°C until analysis.
## 2.3 Metabolic parameters
Biochemical analysis of TC, TG, HDL-C, LDL-C, ALT and oxidative stress analysis of SOD, CAT and enzyme-linked immunosorbent assay of IL-6, TNF-α were executed by a microplate reader (Vlctor-x3, PerkinElmer, Waltham, MA, USA) according to protocols.
## 2.4 Gut microbiome
The special regions (16S V3V4) of 16S rRNA genes were sequenced to study the diversity of gut microbes. DNA was extracted from the fecal samples using a commercial DNA extraction kit (DNeasy PowerSoil Kit, German). DNA concentration and integrity were measured by a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, United States) and agarose gel electrophoresis, respectively. Then DNA was sequenced using NovaSeq6000 platform (Illumina, California, United States) with a universal primer pair (343F: 5′-TACGGRAGGCAGCAG-3'; 798R: 5′-AGGGTATCTAATCCT-3′) by OE Biotech company (Shanghai, China). The raw data were processed using QIIME software (version 1.8.0). Then, clean reads were subjected to primer sequences removal and clustering to generate operational taxonomic units (OTUs) using VSEARCH software with $97\%$ similarity cutoff. All representative reads were annotated and blasted against Silva database (Version 132) using RDP classifier (confidence threshold was $70\%$) (Wang Q et al., 2007). The alpha diversity and beta diversity were analyzed using QIIME software.
## 2.5 Untarget metabolomics
In this experiment, gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry were used to study the metabolomics of fecal samples of mice after drug intervention for 6 weeks. In GC-MS, derivatized samples were analyzed on an Agilent 7890B gas chromatography system coupled to an Agilent 5977A MSD system (Agilent Technologies, CA, United States). Separation was performed on an Agilent DB-5MS fused silica capillary column (30 m × 0.25 mm × 0.25 μm). Mass spectrometry conditions were electron impact ionization (EI) source, ion source temperature 330°C, and transfer line temperature 280°C. The scanning mode is full scan, and the mass scanning range is m/z 50–500.
LC-MS was performed using a Dionex Ultimate 3000 RS UHPLC coupled with a Q-Exactive + quadrupole-orbitrap mass spectrometer equipped with a heated electrospray ionization (ESI) source (Thermo Fisher Scientific, Waltham, MA, USA) to analyze ESI positive Metabolic profiles in ion and negative ion mode. The column was a Waters ACQUITY UPLC HSS T3 (1.8 μm, 2.1 × 100 mm) in positive and negative modes.
## 2.6 Statistical analysis
The study of gut microbiome was evaluated by alpha diversity and beta diversity. The criteria for screening differential metabolites were VIP value greater than 1.0 and p-value less than 0.05. Metabolic pathway enrichment analysis of differential metabolites was performed based on the KEGG database. All statistical data were analyzed using SPSS software (version 25.0; SPSS, Chicago, IL, United States). GraphPad Prism (version 8.0) for creating all drawings. Data are presented as the means ± standard error of mean (SEM) values. Comparisons between multiple groups were evaluated using one-way ANOVA followed by LSD post hoc test. A p-value < 0.05 was considered to indicate statistical significance.
## 3.1 Effects of CCS and CE on FBG and body weight of T2DM
After 4 weeks of intragastric administration, compared to the first week, the level of fasting blood glucose was increased, and the HFD group showed obvious emaciation, uneven coat color and weight loss, urine output and water intake was elevated, as in previous studies (Zhang F et al., 2022). After 4 weeks of gavage, fasting blood glucose gain induced by HFD was lower when CCS and CE were administered ($p \leq 0.01$) (Figures 1A,B).
**FIGURE 1:** *Effects of CCS and CE on physiological and biochemical parameters in T2DM. (A) FBG; (B) Body weight; (C) TG; (D) TC; (E) LDL-C; (F) HDL-C; (G) ALT; (H) SOD; (I) CAT; (J) TNF-α; (K) IL -6. Values are expressed as means ± SEM. Differences were assessed by ANOVA and denoted as follows: **p < 0.01, *p < 0.05 vs. ND (n = 10), ##
p < 0.01, #
p < 0.05 vs. HFD (n = 10).*
## 3.2 Effects of CCS and CE on metabolic parameters in T2DM
It was showed that compared with ND group, the levels of TC, TG and LDL-C in HFD group were significantly increased ($p \leq 0.01$), and HDL-C was significantly decreased, indicating that T2DM had symptoms of dyslipidemia. CCS and CE groups showed increased levels of TC, TG, LDL-C and decreased levels of HDL-C (Figures 1C–F). The expression of ALT, SOD, and CAT in the HFD group was increased compared to ND group (Figures 1G–I), indicating that the T2DM mice had liver damage and presented oxidative stress, while the intervention of CCS and CE significantly reduced the levels of oxidative factors ($p \leq 0.01$). To verify the effects of cordycepin and C. militaris extract on inflammatory factors in T2DM mice, enzyme-related immunosorbent assay was performed. The results showed that the levels of TNF-α and IL-6 in HFD group were higher than those in ND group, indicating a more obvious inflammatory response in T2DM mice. After CCS and CE treatment, the levels of TNF-α and IL-6 in treatment group were significantly lower than those in HFD group (Figures 1J,K).
## 3.3 CCS and CE modulate the community structure of gut microflora
The species diversity of the samples was evaluated at the Operational Taxonomy Units (OTUs) level. At the 4st and 6st week of gavage, mice feces were taken for 16S rRNA gene sequencing. It is observed that 1787 OTUs are jointly owned by each group, while the OTUs unique to HFD group are less than those of the ND group, and the other treatment groups have more OTUs compared to controls, indicating that CCS and CE effectively suppressed HFD-induced intestinal microbial flora diversity reduce (Figure 2A).
**FIGURE 2:** *CCS and CE modulate the community structure of the gut microbiota. (A) OTUs petal images of each treatment group after 4 weeks of gavage. Core OTUs represent OTUs common to all groups, and numbers on petals represent OTUs unique to each group. (B) Sparse curve (C) Shannon index (D) PCA principal component analysis (E) Euclidean-based PcoA.*
The alpha and beta diversity indices were assessed for each group of gut microbiota (Figures 2B,C). Through the Chao1 index analysis and the displayed sparse curve, the trend shows that the sample selection is reasonable (Figure 2B). The Shannon-Weaver curve reflect species diversity for each sample as a sequencing function and the curve tends to be flat, indicating that the amount of sequencing data is large enough to reflect the vast majority of microbial species information in the sample (Figure 2C).
Principal component analysis (PCA) was performed by variance decomposition, and it can be seen that there was significant clustering among samples within the group, and the treatment group was significantly separated from the ND group (Figure 2D). After suffering from T2DM, some changes have occurred in the gut microbiota of mice. Similarly, principal co-ordinates analysis (PCoA) based on euclidean algorithm showed that the composition of gut microflora in each group had obvious aggregation. Multivariate analysis of the variance of the PCoA matrix scores revealed a statistically significant separation between the microflora of each group (Figure 2E).
## 3.4 CCS and CE improve gut microbiota composition in T2DM
Firmicutes is a common indicator of gut microbiota balance, and bacteria of the phyla Firmicutes and Bacteroidetes were dominant in each group. The top 15 phyla of the relative abundance of intestinal microorganisms in each group after 4 weeks treatment is displayed in the form of a column chart. Remarkably, the relative abundance of Firmicutes increased in each treatment group compared with HFD group, and showed a dose-dependent (Figure 3A). We can see that the relative abundance (%) of Firmicutes, Bacteroidetes and the ratio of Firmicutes/Bacteroidetes in each group. Among them, F/B in ND group was 0.2558, HFD group was 0.0985, CCSH, CCSL, CEH and CEL group were 0.4551, 04432,0.5115 and 0.3039, respectively (Table 1). It was found that both CCS and CE improved the gut microbiota community structure in T2DM.
**FIGURE 3:** *Effects of CCS and CE on the community structure of gut microflora in T2DM. (A) The distribution of community structure at the phylum level of each treatment at the fourth week after gavage group (Top 15); (B) Community structure distribution of each treatment group at the fourth and sixth week of gavage at the genus level (Top 15); (C) *Heatmap analysis* of the differential species of gut microbes at the genus level at the fourth week of gavage.* TABLE_PLACEHOLDER:TABLE 1 *In* general, CCS and CE had a profound impact on the composition and abundance of the gut microbiome. From the 4st week to the 6st week of intervention, the relative abundance of Bacteroides increased, while that of Muribaculaceae decreased in CCS and CE groups. In addition, after 4 weeks of intervention, CCS and CE groups increased the relative abundance of Colidextribacter compared to controls; the relative abundance of Eubacterium_xylanophilum_group in each treatment group was higher than that in the ND group (Figure 3A). After 6 weeks of intervention, the relative abundance of Alloprevotella in treatment group except the CEL group was higher than that in ND group, especially in the cordycepin group, CCSH (0.1496), CCSL (0.1251); Compared with ND group, HFD significantly increased the relative abundance of Muribaculum, nevertheless, CCSH and CE treatments down this trend. Roseburia in the HFD group (0.0027) was lower than that in ND group (0.0081), while that in the CCS, CEH, and CEL groups were 0.0202, 0.0204, and 0.0199; Concomitantly, CE reduced the proportion of Lachnoclostridium (Figure 3B). In the heatmap of differential species at the four s t week of gavage, we can see that compared with the ND group, the relative abundance of some genera in the CCS and CE groups showed a downward trend, specifically, Adlercreutzia, Prevotellaceae-UCG-001. The relative abundance of Odoribacter in CCS and CE group declined significantly compared with that in HFD group. Overall, CCSL upregulated the relative abundance of Atopobium, Erysipelotrichaceae, Marvinbryantia, Barnesiella, Dialister, CCSH upregulated the relative abundance of collinsella and Subdoligranulum, Separately, CE could upregulate the relative abundance of Corynebacterium, Prevotellaceae_UCG−003, Dubosiella, and Enterococcus (Figure 3C).
## 3.5 Multivariate statistical analysis of metabolomics
GC-MS, LC-MS was used to detect metabolic information of intestinal contents in positive and negative ion modes. The position of the coordinate point represents the degree of dispersion of each sample in OPLS-DA. GC-MC detection results showed that the samples of CCSH group and HFD group were obviously separated (Figure 4A). CEH group and HFD group showed a separation trend in different quadrants (Figure 4B). Similarly, the comparison between CCSH and CE group and HFD group also showed a similar trend in the detection results of LC-MS (Figures 4C,D). Overall, the intestinal flora of the treatment group (CCSH and CEH) was significantly different from that of the model group (HFD).
**FIGURE 4:** *OPLS-DA analysis. (A) CCSH vs. HFD detected by GCMS; (B) CEH vs. HFD detected by GC-MS; (C) CCSH vs. HFD detected by LC-MS; (D) CEH vs. HFD detected by LC-MS.*
## 3.6 Volcano plots of differential metabolites
We screened the differential metabolites based on the volcano maps, and the screening criteria were that the VIP value of the first principal component of the OPLS-DA model was greater than 1, and the p-value of the t-test was less than 0.05.
Compared with group HFD, 18 differential metabolites were detected in group CCSH by GC-MS detection, among which 15 were upregulated and three were downregulated. When group CEH was compared with HFD, 19 different metabolites were found, among which 5 were upregulated and 14 were downregulated (Figures 5A,B). A total of 363 differential metabolites were detected in group CCSH and group HFD by LC-MS, including positive and negative ion modes. Among them, 198 metabolites (78 upregulated and 120 downregulated) were significantly changed in positive ion mode. Then in negative ion mode, Significant changes were observed in 254 metabolites (89 upregulated and 76 downregulated). Similarly, a total of 699 differential metabolites were detected in the comparison between group CEH and HFD. Among them, 371 metabolites (122 upregulated and 249 downregulated) were significantly changed in positive ion mode, and 328 metabolites (121 upregulated and 207 downregulated) were significantly changed in negative ion mode (Figures 5C,D).
**FIGURE 5:** *(A) Volcano plots of CCSH vs. HFD in GC-MS assay (B) CEH vs HFD in GC-MS assay. (C) Volcano plots of CCSH vs HFD in LC-MS assay. (D) Volcano plots of CEH vs HFD in LC-MS assay.*
## 3.7 Clustering hierarchy of differential metabolites
In order to more visually show the relationship between samples and the differences in metabolite expression between different samples, we performed systematic clustering of significantly different metabolite expression levels. Analysis of GC-MS results showed that compared with group HFD, some components included in group CCSH presented higher levels, such as phenyl propanoic acids, pyrimidine nucleosides, phenols, carboxylic acids and derivatives, prenol lipids, pyridines and derivatives, flavonoids (Figure 6A). Meanwhile, downregulated metabolites include purine nucleotides, fatty acyls. Similarly, carboxylic acids and derivatives were upregulated metabolites in group CEH compared with group HFD, and downregulated metabolites include fatty acyls, amino acids, organooxygen compounds (Figure 6B).
**FIGURE 6:** *Heatmap of differential metabolites in fecal samples. (A) CCSH vs. HFD detected by GC-MS (B) CEH vs. HFD detected by GC-MS (C) CCSH vs HFD detected by LC-MS (D) CEH vs. HFD detected by LC-MS.*
According to LC-MS detection, more altered metabolites were observed. Compared with group HFD, the metabolites upregulated in CCSH group included glycerophospholipids, isoflavonoids, fatty acyls, carboxylic acids and derivatives, prenol lipids, thiocarboxylic acids and derivatives. Several metabolites were downregulated such as steroids and steroid derivatives, benzene and substituted derivatives, glycerophospholipids, pyrroles, fatty acyls, naphthalenes, prenol lipids, benzofurans, azoles, carboxylic acids and derivatives (Figure 6C). In addition, glycerophospholipids, sterol lipids, fatty acyls belonging to the group CEH, occurred at higher levels than group HFD. Meanwhile, downregulated metabolites in group CEH include isoflavonoids, carboxylic acids and derivatives, benzofurans, cinnamic acids and derivatives, prenol lipids, organooxygen compounds, polyketides, sterol Lipids, coumarins and derivatives, fatty acyls, glycerophospholipids, stilbenes (Figure 6D).
## 3.8 Metabolic pathways analysis in KEGG
In the comparison of CCSH and HFD by GC-MS, after the introduction of KEGG, metabolites of CCSH can screen out some metabolic pathways under the condition of $p \leq 0.05$ (Figure 7A). Such as mTOR signaling pathway, PI3K-Akt signaling pathway, FOXO signaling pathway, cGMP-PKG signaling pathway, citrate cycle (TCA cycle) pathway. Similarly, metabolic pathways were screened in CCSH and HFD by LC-MS detection method (Figure 7B). Specifically, including: retinol metabolism pathway, arachidonic acid metabolism pathway, PPAR signaling pathway, adipocytokine signaling pathway.
**FIGURE 7:** *Metabolic pathway bubble diagram. (A) CCSH vs HFD metabolic pathways in stool samples detected by GC-MS (p < 0.05) (B) CEH vs HFD detected by GC-MS (C) CCSH vs HFD detected by LC-MS (D) CEH vs HFD detected by LC-MS (p < 0.05).*
Results shown that, some metabolic pathways were screened by GC-MS in the comparison of CEH with HFD. Specifically, it includes D-glutamine and D-glutamate metabolism, proximal tubule bicarbonate reclamation, etc. ( Figure 7C). The results of LC-MS detection showed that some metabolic pathways were screened out by comparison between CEH and HFD. Specifically, it includes tryptophan metabolism pathway, PI3K-Akt signaling pathway, FOXO signaling pathway (Figure 7D).
## 4 Discussion
The purpose of this study was to explore the potential mechanisms of cordycepin and its aqueous extract in alleviating the symptoms of T2DM. Results showed that high-fat diet led to weight loss, loss of luster of hair and increased the level of FBG. The level of FBG in the metformin group, CCS and CE group was significantly lower than that in the HFD group ($p \leq 0.01$), suggesting that cordycepin and C. militaris extract had potential blood glucose lowering effects in T2DM.
Compared with ND group, the levels of TC, TG, and LDL-C in HFD group were significantly increased ($p \leq 0.01$), and HDL-C was significantly decreased, which indicated that T2DM would lead to dyslipidemia. The levels of TC, TG and LDL-C in the treatment group were lower than those in HFD group, which proves that CCS and CE could regulate the lipid metabolism of T2DM. The content of cordycepin in CCSL was 122 times that of CEL by HPLC. Nevertheless, the downregulation of TG level by CE was more significant, which shows that there were other potential lipid-regulating active substances in C. militaris extract.
HFD group had higher levels of proinflammatory cytokines and excessive oxidative stress Compared to controls, Cordycepin and C. militaris extract may exert beneficial effects by reducing inflammatory factors and attenuating oxidative stress. The levels of ALT and SOD in T2DM were significantly increased, which was alleviated by CCS and CE ($p \leq 0.01$), indicating that cordycepin and C. militaris extract had potential effects on the antioxidant capacity and liver protection of mice. Compared with the ND group, the level of IL-6 in HFD group increased, while CCS and CE significantly downregulated these inflammatory factors, suggesting that CCS and CE had potential effects in regulating the inflammatory response.
Recent studies have highlighted that many ingredients from natural plants exhibited bifunction in model organisms by mediating hormesis (Jiang et al., 2020a). Hormesis is a biphasic dose-response relationship characterized by low dose stimulation and high dose inhibition, and is typically represented as a J-shaped or inverted U-shaped curve (Sun H et al., 2020). Extracts of many herbs, either individually or in combination, can trigger hormetic phenomena in different models in vitro, including animal and human cells.
In the comparison of high and low doses of CCS group, there were no significant differences in biochemical indexes such as CAT, ALT and IL-6, etc. It is explained that at low doses, it may already be in the second half of the “J” curve in the dose-response curve model, approaching the flat part. Low doses can already reduce the index, and the difference in effect between high and low doses is not significant, so there is no obvious dose dependence. In the comparison of high and low doses in the CE group, the biochemical indexes ALT and CAT had a more obvious therapeutic effect at low doses than at high doses, and IL-6 was also more effective at low doses in the CCS group. We speculate that the cause may be similar to the biphasic dose-effect, such as an inverted U-shaped curve: within a certain range, low concentrations have a stimulative effect, while high concentrations relatively diminish this stimulative effect. This suggests that dosing studies for cordycepin and water extracts are important and need to be further explored.
The dose of Chinese herbal medicine has been widely concerned. The active substances in *Coptis chinensis* have a biphasic dose effect on the regulation of detoxifying enzymes GST and CarE, which shows an increasing trend at low dose and a decreasing trend at high dose (Jiang et al., 2020b). Our study suggests that C. militaris as a dietary supplement, the effects of different doses still need attention.
By sequencing the 16S rRNA gene, we can intuitively see the changes of intestinal microflora structure of CCS and CE groups, so as to explore its regulatory effect on intestinal microflora of T2DM. In this study, the rationality of sample selection was determined by evaluating the a-diversity and ß-diversity indices of intestinal flora in each group. In PcoA based on Euclidean, it can be seen that the samples in each group have obvious aggregation. After 6 weeks of intragastric treatment, the intestinal flora structure of CCS and CE groups tends to approach ND, which indicates that CCS and CE can improve the intestinal flora disorder of T2DM. From the OTU Venn diagram after 4 weeks of gavage treatment, it can be seen that T2DM reduced the diversity of microbiota, and both CCS and CE increased the diversity of intestinal microbiota (Figure 2A). After 4 weeks of gavage, at the phylum level, compared with ND group, the relative abundance of Firmicutes in the HFD group decreased significantly, while that in CCS and CE groups increased, showed a dose-dependent trend, combined with the ratio of Firmicutes/Bacteroidetes, indicating that both CCS and CE can ameliorate the composition of gut microbiota in T2DM.
The community structure distribution map at the genus level showed that T2DM changed the gut microbial community structure of mice, and the relative abundances of Alistipes, Muribaculum, and Lachnoclostridium in HFD group were higher than those in ND group. Compared to controls, Parabacteroides elevated in the CCS and CE groups. Studies have shown that *Parabacteroides distasonis* is one of the core floras of the human body, NAFLD, DM and other disease states are significantly negatively correlated, and may play a positive regulatory role in glucose and lipid metabolism (Wang K et al., 2019). The relative abundances of Eubacterium_xylanophilum_group and Colidextribacter in CCS and CE groups were higher than those in ND group. Eubacterium_xylanophilum_group could interfere with the catabolism of Branched-Chain Amino Acid (BCAA) to alleviate high-fat induction the body weight of obese mice (Zhang L et al., 2020); Colidextribacter was proved to be an inosine-producing bacterium, which could change the intestinal microbial structure and improve LPS-induced acute liver injury and inflammation by regulating the TLR4/NF-κB signaling pathway, indicating that CCS, CE has a certain positive effect on preventing liver damage and reducing obesity symptoms (Guo W et al., 2021). After 6 weeks of gavage, the relative abundance of Roseburia in CCSH, CEH, CEL groups was significantly higher than that in HFD group. Roseburia is a butyric acid-producing bacteria that degrades dietary fiber xylan in the intestinal tract, and butyric acid secreted by gut microbiota will Promote postprandial insulin secretion (Leth ML et al., 2018), suggesting that CCS and CE may reduce blood sugar of T2DM by influencing intestinal microflora. Alloprevotella belongs to short-chain fatty acid-producing bacteria and anti-inflammatory bacteria, the amplitude of Alloprevotella in CCS was significantly greater than that in HFD group, indicating that CCS has a positive effect on anti-inflammatory and short-chain fatty acid production (Sanna S et al., 2019); The treatment of CE inhibited the HFD-induced increase in the relative abundance of Lachnoclastic, metagenomics showed that Lachnoclastic could be used as a marker for the diagnosis of colorectal adenoma and colon cancer, indicating that the active components in CE could effectively protect and prevent colorectal cancer (Liang JQ et al., 2020). Notably, Clostridia_UCG-014 showed a high relative abundance only in ND group, and has been studied relatively rarely to date (Figure 4B).
Metabolomics studies of fecal samples show that the differential metabolites of CCSH and HFD are rich in some pathways related to DM, in particular, peroxisome proliferator-activated receptor (PPAR) pathway, arachidonic acid pathway, PI3K/Akt pathway, mTOR signaling pathway, FOXO signaling pathway, oxidative phosphorylation chemical pathway, TCA cycle pathway, adipokine signaling pathway. These pathways may play an important role in the regulation of blood glucose and dyslipidemia by CCS.
PPARs are member of the nuclear receptor transcription factor superfamily that regulate the expression of target genes. Three isoforms were found in different species: PPARα, PPARβ/δ, and PPARγ. PPARs are key regulators of glucose homeostasis and lipid metabolism, and also important targets for the development of modern anti-diabetic drugs to improve insulin sensitivity and blood glucose level by regulating target genes (Jiang et al., 2020a). PPARs regulate gene transcription by initially activating by binding to ligand fatty acids and their derivatives, forming heterodimers with retinoid X receptor, and then binding to DNA sites of specific sequences to induce target gene activation (Chan and Wells, 2009), thereby regulating lipid metabolism, adipocyte differentiation, glycogenesis, ubiquitination, and inflammation (Gross B et al., 2017). In the comparison of CCSH and HFD, PPAR signaling pathway was enriched. According to KEGG, we speculate that CCSH could regulate lipid metabolism, reduce inflammatory response and insulin resistance through PI3K/Akt/PPAR signaling pathway.
Arachidonic acid (AA) is the precursor of many bioactive substances, which can significantly prevent early insulin resistance induced by a high-fat diet (Wang B et al., 2021). Three different enzyme systems in the metabolic pathway of AA, cyclooxygenase, lipoxygenase and cytochrome P450, can produce important unsaturated fatty acids (Carboneau BA et al., 2017). Prostaglandin (PG) produced by AA metabolism plays an important role in the dysfunction of ß-cells and insulin secretion. Among them, PGI2 can promote pancreatic ß-cells to secrete insulin and prevent STZ-induced hyperglycemia in mice by maintaining the mass of pancreatic ß-cell and plasma insulin level (Li X et al., 2021). This approach may be one of the important ways for CCSH to improve diabetes by regulating endocrine and other physiological processes.
The PI3K/Akt pathway is an upstream regulation pathway of mTOR, which is involved in the uptake and utilization of glucose of cells and plays an important role in cell growth and metabolism. In addition, the PI3K/Akt/mTOR pathway plays a key role in glucose homeostasis, Therefore, CCSH can regulate glucose metabolism through this pathway. FOXO is a fork head transcription factor that involved in glucose metabolism and regulating gluconeogenic genes including PEPCK and G6Pase. After dephosphorylation, FOXO is in an activated state, which leads to gluconeogenesis (Agus et al., 2018). It is regulated by upstream PI3K/Akt pathway, and loses its activity after phosphorylation, which inhibits the expression of gluconeogenesis genes, thus lowering blood sugar. Overall, we speculate that CCSH is involved in the regulation of glycolysis and gluconeogenesis through PI3K/Akt/FOXO/PEPCK pathway.
Similarly, the PI3K/Akt/mTOR pathway, the PI3K/Akt/FOXO/PEPCK pathway still existed in CEH group. In addition, through metabolic pathway enrichment analysis, we found that some amino acid metabolic pathways were changed after CEH treatment, specifically, including the metabolic pathway of tryptophan, alanine-aspartate-glutamate metabolic pathway and D-Glutamine and D-glutamate metabolic pathways. Tryptophan has been proved to be converted to serotonin (Karamitri and Jockers, 2019), which in turn is converted to melatonin. Studies have shown that melatonin is related to insulin resistance, which can improve insulin resistance and participate in regulating glucose homeostasis. It can be seen that amino acid metabolism plays a role in the process of CEH regulating blood sugar. Therefore, the mechanism of CEH regulating blood sugar may be related to the metabolism of amino acids.
The differential metabolites of CCSH and HFD included phenols, fatty acids, phenylacetones, pyrimidine nucleosides, carboxylic acids and their derivatives, pyridines and their derivatives. Particularly, Catechin of phenylacetone compound can reduce lipid accumulation and increase the number of islets ß cells in T2DM (Wang TJ et al., 2011), which has a potential role in the treatment of T2DM. Among the differential metabolites of CEH and HFD, there are alcohols, amino acids, fatty acids, etc. Studies have shown that some branched-chain amino acids, aromatic amino acids and their derivatives, and fatty acids are highly correlated with the development of diabetes (Ahola-Olli AV et al., 2019). Erucic acid is a fatty acid that has a significant positive correlation with lipid, lipoprotein synthesis, and elevated plasma glucose (Ghosh A, 2007). High content of erucic acid can easily lead to cardiomyopathy and fat deposition. The erucic acid level in CE group was significantly lower compared with that in HFD group, which indicated that CE could improve blood glucose metabolism and blood lipid metabolism.
According to the metabolomics analysis results of different fecal samples, it is obvious that our mice produce many different metabolites through the interaction between intestinal microbes and the host, which are associated with many metabolic pathways, mainly focusing on PI3K/Akt/PPAR, PI3K/Akt/mTOR, PI3K/Akt/FOXO/PEPCK pathway, tryptophan metabolism, alanine diurnal acid-glutamic acid metabolism pathway, D-glutamine and D-glutamic acid metabolism pathway, unsaturated fatty acid synthesis pathway, TCA cycle pathway. It can be speculated that cordycepin and C. militaris extract may affect insulin resistance, blood sugar level, amino acid metabolism, fatty acid metabolism, anti-inflammatory ability and energy supply in T2DM through these pathways, thus alleviating the symptoms of T2DM.
## 5 Conclusion
In conclusion, our study suggests that the mechanism of C. militaris intervention on T2DM may be to improve the abundance of Firmicutes/Bacteroidetes, promote the growth of beneficial bacteria, and regulate the intestinal flora structure and enhance the metabolites and metabolic pathways associated with T2DM alterations. Thereby improving physiological and biochemical indicators and relieving the symptoms of type 2 diabetes. In addition, this study combined with molecular biology experiments, 16S rDNA sequencing and metabolomics detection, for the first time to explore the potential mechanism of CCS and CE regulating intestinal flora and metabolites to improve T2DM. Furthermore, our results suggest that C. militaris, as a kind of edible fungus, can alleviate T2DM in mice, and provide a scientific foundation for improving human health by using C. militaris.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, PRJNA832098 https://figshare.com/s/0df8d0cc07e297824e7d.
## Ethics statement
The animal study was reviewed and approved by the Ethics Committee for Animal Research of School of Life Sciences, Shandong University (NO: SYDWLL-2021-29).
## Author contributions
XL and MD have the same contribution. MD and XL: conceptualization, methodology, investigation, formal analysis, writing-original draft, data curation. TJ: validation. YS: resources. MW: resources. GZ: conceptualization, resources. JL: funding acquisition, supervision, project administration, writing—review and editing.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Abbreviations
AA, arachidonic acid; ALT, alanine aminotransferase; BCAA, branched-chain amino acid; CAT, catalase; CE, cordyceps militaris extracts; CCS, cordycepin; DM, diabetes mellitus; ESI, electrospray ionization; FBG, fasting blood-glucose; GC-MS, gas chromatograph- mass spectrometer; HDL-C, high-density lipoprotein cholesterol; HPLC, high performance liquid chromatography; IL-6, interleukin-6; LC-MS, liquid chromatography- mass spectrometry; LDL-C, low-density lipoprotein cholesterol; OPLS-DA, orthogonal projections to latent structures; OTUs, operational taxonomy units; PCA, principal component analysis; PCoA, principal coordinates analysis; PBS, phosphate buffered saline; PG, prostaglandin; PPARs, peroxisome proliferators-activated receptors; SOD, superoxide dismutase; STZ, streptozocin; T2DM, type 2 diabetes mellitus; TC, total cholesterol; TCA, tricarboxylic acid; TG, triglyceride; TNF-α, tumor necrosis factor-α.
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|
---
title: Phosphoethanolamine cytidylyltransferase ameliorates mitochondrial function
and apoptosis in hepatocytes in T2DM in vitro
authors:
- Hu Xu
- Weizu Li
- Lei Huang
- Xinyu He
- Bei Xu
- Xueqing He
- Wentong Chen
- Yaoxing Wang
- Wenjun Xu
- Sheng Wang
- Qin Kong
- Youzhi Xu
- Wenjie Lu
journal: Journal of Lipid Research
year: 2023
pmcid: PMC10033998
doi: 10.1016/j.jlr.2023.100337
license: CC BY 4.0
---
# Phosphoethanolamine cytidylyltransferase ameliorates mitochondrial function and apoptosis in hepatocytes in T2DM in vitro
## Body
Type 2 diabetes mellitus (T2DM) is the most common type of diabetes, accounting for approximately $90\%$ of all diabetes cases [1]. Over time, it can cause damage to the heart, blood vessels, eyes, kidneys, and nerves [2]. T2DM is a serious problem worldwide, with 700 million people expected to be affected by the disease by 2045 [3]. Epidemiological statistics show that T2DM is closely related to metabolic syndromes, such as hypertension, hyperlipidemia, and obesity [4]. Nonalcoholic fatty liver disease and nonalcoholic steatohepatitis are common in patients with T2DM but the relationship between liver metabolic disorders and T2DM is complicated [5, 6, 7]. Impaired liver function indicators are often observed in patients with T2DM, with higher aspartate aminotransferase (AST), alanine aminotransferase (ALT), and gamma-glutamyl transferase concentrations in subjects with T2DM than in those without T2DM [8]. In addition, pathological tissue sections showed significant steatosis in the livers of patients with T2DM [9]. The level of liver inflammation is significantly increased in patients with T2DM, and insulin resistance models in HepG2 cell lines show oxidative stress, increased levels of reactive oxygen species (ROS), and increased apoptosis [10]. Mitochondria are the most important energy metabolism sites of cells, and under hyperglycemic conditions, the oxidative phosphorylation ability of mitochondria decreases. An increase in electron transport chain and ROS production is also believed to aggravate pathological processes. Therefore, diabetic patients often have mitochondrial dysfunction, but the specific molecular mechanism of mitochondrial dysfunction caused by T2DM has yet not been fully elucidated [11].
Phospholipids are an integral part of biological membranes and are important signaling molecules involved in many physiological processes, thus, they are vital for maintaining body homeostasis [12]. The second most abundant phospholipid in the human body is phosphatidylethanolamine (PE), which is involved in various cell processes including apoptosis, autophagy, and membrane fusion [13]. There are four synthetic pathways of PE in eukaryotes, of which the two most important are the de novo synthesis of PE by the CDP-ethanolamine (CDP-etn) pathway and phosphatidylserine decarboxylase (PISD) pathway [14]. Some studies have shown that PE levels in the livers of mice with T2DM diabetes are significantly reduced but the specific mechanism of such phospholipid changes remains unclear [15, 16].
Phosphoethanolamine cytidylyltransferase (PCYT2) is generally considered to be a rate-limiting enzyme in the CDP-etn pathway [17]. PCYT2 plays an important role in liver PE synthesis. In fact, most PE is synthesized by the CDP-etn pathway on the endoplasmic reticulum (ER). In CDP-etn pathway, cells first convert the ingest ethanolamine into phosphoethanolamine through ethanolamine kinase, which is converted into CDP-etn under the catalysis of PCYT2, and finally, CDP-etn and diglyceride (DG) form PE under the catalysis of CDP-etn: 1,2-diacylglycerol ethanolamine phosphotransferase [18]. It was confirmed that low expression of Pcyt2 in the liver can lead to severe accumulation of diglycerol ester and hepatic steatosis [19]. In addition, Pcyt2 inhibition results in mitochondrial respiratory dysfunction caused by the accumulation of its substrate, phosphoethanolamine, in MCH58 fibroblasts [20]. These findings indicate that PCYT2 is indispensable to the maintenance of lipid metabolism and mitochondrial homeostasis.
Based on this evidence, we investigated whether PCYT2 is involved in liver injury induced by T2DM and its possible molecular mechanism. This study opens new avenues for the treatment of liver damage in patients with T2DM.
## Abstract
Liver function indicators are often impaired in patients with type 2 diabetes mellitus (T2DM), who present higher concentrations of aspartate aminotransferase, alanine aminotransferase, and gamma-glutamyl transferase than individuals without diabetes. However, the mechanism of liver injury in patients with T2DM has not been clearly elucidated. In this study, we performed a lipidomics analysis on the liver of T2DM mice, and we found that phosphatidylethanolamine (PE) levels were low in T2DM, along with an increase in diglyceride, which may be due to a decrease in the levels of phosphoethanolamine cytidylyltransferase (Pcyt2), thus likely affecting the de novo synthesis of PE. The phosphatidylserine decarboxylase pathway did not change significantly in the T2DM model, although both pathways are critical sources of PE. Supplementation with CDP-ethanolamine (CDP-etn) to increase the production of PE from the CDP-etn pathway reversed high glucose and FFA (HG&FFA)-induced mitochondrial damage including increased apoptosis, decreased ATP synthesis, decreased mitochondrial membrane potential, and increased reactive oxygen species, whereas supplementation with lysophosphatidylethanolamine, which can increase PE production in the phosphatidylserine decarboxylase pathway, did not. Additionally, we found that overexpression of PCYT2 significantly ameliorated ATP synthesis and abnormal mitochondrial morphology induced by HG&FFA. Finally, the BAX/Bcl-2/caspase3 apoptosis pathway was activated in hepatocytes of the T2DM model, which could also be reversed by CDP-etn supplements and PCYT2 overexpression. In summary, in the liver of T2DM mice, Pcyt2 reduction may lead to a decrease in the levels of PE, whereas CDP-etn supplementation and PCYT2 overexpression ameliorate partial mitochondrial function and apoptosis in HG&FFA-stimulated L02 cells.
## Reagents
AST and ALT testing kits were purchased from Nanjing Jiancheng Biotechnology Institute (Nanjing, China). Lyso PE (18:1) (846725P), CDP-etn [90,756], oleic acid (OA; O1008), and palmitic acid (PA; P0500) were purchased from Sigma (Darmstadt, Germany). An ATP assay kit (S0026), BCA protein assay kit (P0010S), mitochondrial membrane potential assay kit (C2006), and ROS assay kit (S0033S) were obtained from Beyotime (Shanghai, China). RPMI 1640 medium (SH30027.01) and FBS (SH30406.02) were acquired from Gibco (Waltham, MA). PCYT2 (ab135290) antibody was supplied by Abcam (Cambridge); BAX (#5023), Bcl-2 (#3498), cleaved-caspase3 (#9661), and β-actin (#3700) were supplied by Cell Signaling Technology (Danvers, MA); and PISD (sc-390070) was supplied by Santa Cruz Biotechnology (Santa Cruz, CA). Elabscience (Shanghai, China) supplied goat anti-rabbit (E-AB-1034) and goat anti-mouse (E-AB-1035) secondary antibodies. TRIzol reagent [15,596,018] and RevertAid RT kit (K1691) were obtained from Thermo Fisher Scientific (Waltham, MA). The primers for qRT-PCR were synthesized by Sangon Biotech (Shanghai, China). A SYBR green fluorescent probe was obtained from Bio-Rad (Richmond, CA).
## Cell culture and experiments
An established human fetal hepatocyte line (L02) was received from the ATCC. RPMI 1640 medium with $10\%$ FBS and $1\%$ penicillin/streptomycin were used to culture the cells. The incubator conditions used were $5\%$ CO2 and 37.0°C. To assess the effect of high glucose (HG) with free fatty acids (FFA) on cell lipid metabolism, cells were exposed to 30 mM D-glucose with 1 mM FFA (OA:PA = 2:1) as described previously [21]. In order to detect the effects of related substances on cells respectively, lysophosphatidylethanolamine (LPE) (100 μM) and CDP-etn (100 μM) were added to the medium within the HG&FFA mix. CDP-etn refers to CDP-etn sodium salt hydrate; it was dissolved in culture medium and supplied to cells. LPE was dissolved in ethanol and then added to the culture medium and provided to the cells.
## Gene transfection
The lentivirus for overexpression of Homo sapiens PISD and PCYT2 was purchased from GENECHEM (Shanghai, China). The vector backbone used was GV492, the sequence was Ubi-MCS-3FLAG-CBh-gcGFP-IRES-puromycin, and the cloning site was BamHI/AgeI. The control cells were infected with an equal quantity of negative control virus named CON335 (Ubi-MCS-3FLAG-CBh-gcGFP-IRES-puromycin). *To* generate stable cell lines, lentiviruses were incubated overnight with cells, and then the cells were sorted based on the presence of GFP. Next, puromycin was used to select stably transfected cells. Stably transfected L02 cells were used for the following experiments.
## Animal model
Establishment of the T2DM diabetes model in mice was achieved as previously described [22]. C57BL/6J mice aged 8 weeks were used in this study. The cages were maintained in a specific pathogen-free, temperature-controlled environment with alternating light and dark 12-h period. The animals had free access to water and food for one week as they acclimated. The animals were then randomly assigned to a normal diet group (CON, Cat. 1010009) or a high-fat diet group (HFD, $60\%$ energy from fat, Cat. XTHF60-1) for the following 8 weeks. Mice in the HFD group were made to fast overnight and then were intraperitoneally injected with streptozotocin (STZ) citric acid buffer (pH 4.0) at 40 mg/kg/d each day for seven consecutive days. Seven days later, the blood glucose level of each animal was detected. Animals with a fasting blood glucose level greater than 11.1 mM were considered as T2DM mice. At the end of the experiment, the mice were anesthetized with sodium pentobarbital at a dose of 100 mg/kg; the mice were fasted overnight prior to sacrifice. The mice were then dissected and the blood and liver were collected. The animal experiments were approved by the Laboratory Animal Ethics Committee of Anhui Medical University (Hefei, China) and carried out in accordance with the Guidelines for the Care and Use of Laboratory Animals.
## RNA isolation and real-time PCR
Total RNA was extracted from mouse liver tissue and cells using TRIzol reagent. RNA was quantified using a nucleic acid concentration detector (Molecular Devices, Sunnyvale, CA) at 260 nm (OD260). The mRNA in total RNA was reversed into cDNA using a RT kit and a gradient PCR instrument (Bio-Rad, Hercules, CA) as per the manufacturer’s protocol. A real-time fluorescence quantitative PCR instrument (Bio-Rad) was used to detect mRNA expression levels and the β-actin gene was used as the reference gene. The results of the relative gene expression levels were normalized, supplemental Table S1 shows the primer sequences used for qRT-PCR analyses.
## Western blot analysis
Total protein was extracted from cells and liver tissue using RIPA buffer supplemented with phosphatase and protease inhibitors. Equal amounts of protein from each sample were electrophoresised in 8–$12\%$ SDS-PAGE and then transferred to PVDF membranes (Merck, Darmstadt Germany). The membrane was blocked with $5\%$ skimmed milk in tris-buffered saline solution and Tween-20 (TBST) and then incubated overnight in the following primary antibodies: anti-PCYT2 (1:250), anti-PISD (1:500), anti-BAX (1:500), anti-Bcl-2 (1:500), anti-cleaved-caspase3 (1:500), and anti-β-actin (1:5000). Then, the blots were incubated with the corresponding secondary antibodies conjugated with horseradish peroxidase. Western blotting was observed using a chemiluminescence solution (Affinity, Changzhou, China) and a protein imager (Bio-Rad).
## Oral glucose tolerance and insulin tolerance tests
After two groups of mice were fasted overnight, an oral glucose tolerance test (OGTT) and an insulin tolerance test (ITT) were performed. In the OGTT experiment, the mice were intragastric administration with glucose solution (2 g/Kg). The ITT experiment was performed one week after the completion of the OGTT experiment. An insulin solution (prepared with 0.5 U/kg physiological saline) was intraperitoneally injected, and glucose test strips were used to measure the blood glucose concentration at 0, 15, 30, 60, and 120 min following injection.
## Preparation of lipidomics samples
Frozen liver tissue was removed from a refrigerator maintained at −80°C. Surgical scissors were used to cut 50 mg of frozen tissue and placed in the eppendorf (EP) tube. Physiological saline ($0.9\%$ NaCl) was added to the tissue at a ratio of 10:1. After grinding with a tissue grinder for 1 min, 600 μl of the homogenate was placed to another EP tube. Then, 800 μl of methyl tert-butyl was added, vortexed, and mixed for 1 min. The mixture was shaken at 1500 rpm for 30 min at 25°C. Then, the mixture was centrifuged at 12,000 g at 4°C for 10 min and then left to stand for 5 min to separate the solution. Five hundred microliters of the upper layer solution was then pipetted into another EP tube and nitrogen was blown in until the solution was completely volatilized. For reconstitution, 200 μl of isopropanol/methanol 1:1 (v/v) was added, vortexed, and mixed for 5 min. This reconstituted solution was then extracted using syringes, passed through a 0.22 μm filter, and then transferred into lighttight sample vials.
## Lipid profiling
Liquid chromatographic conditions were as follows: Acclaim C30 column (3.0 μm, 2.1 mm × 150 mm); column temperature: 45°C; flow rate: 0.26 ml/min; injection volume: 5 μl. Mobile phase A was ethylene glycol/water (60:40, v/v, containing $0.1\%$ formic acid and $0.1\%$ ammonium formate), and B was isopropyl alcohol/ethylene glycol (90:10, v/v, containing $0.1\%$ formic acid and $0.1\%$ ammonium formic acid). Liquid chromatography gradient elution program was as follows: $30\%$ B over 2 min, $43\%$ B from 2 to 2.1 min, $55\%$ B from 2.1 to 12 min, $65\%$ B from 12 to 18 min, $100\%$ B from 18 to 25 min and then $30\%$ B from 25 to 30 min. Mass spectrometry conditions were as follows: ion source: electron spray ionization (ESI) source; spray voltage: $\frac{3800}{3200}$ V (+/−); ionization modes: ESI+, ESI-; sheath gas: 40 arb; auxiliary gas: 10 arb; capillary temperature: 320°C; scanning range: 100–1500 m/z; resolution: 17,500 full width at half-maximum. Nitrogen was used as the carrier gas.
## PE content determination
L02 cells were cultured in Petri dishes with 10 cm diameter. After exposure to different treatments, mitochondria of L02 cells were isolated using a cell mitochondria isolation kit (Beyotime, Shanghai, China) according to the manufacturer’s instructions. The mitochondrial and extramitochondrial contents were homogenized in $5\%$ Triton X-100 and samples were heated at 80°C to solubilize all lipids. The mitochondrial and extramitochondrial PE content was determined using a PE assay kit (MAK361, Merck KGaA, Germany). Thereafter, 10 μl of each sample (or standard) and Working Reagent Mix were added to each well of a 96-well plate. The fluorescence intensity was measured using an automatic microplate reader (Varioskan Flash, Thermo Fisher Scientific) at excitation and emission wavelengths of 535 and 587 nm, respectively.
## H&E staining
The livers were fixed with $4\%$ paraformaldehyde and then embedded in paraffin wax. Kept the thickness of the slice was 4 μm using a histotome. A H&E staining kit (Servicebio, Wuhan, China) was used for the experiments according to the protocol.
## ROS detection
L02 cells were inoculated into 6-well plates at 1 × 106 per well and treated with HG&FFA, CDP-etn, or LPE. The cell culture medium was removed from the plates, and the cells were then incubated in 1 ml of medium containing 2',7'-dichlorodihydrofluorescein diacetate, 10 μM for 20 min in a cell culture box. After incubation, the cells were washed three times with serum-free cell culture medium to fully remove the probe that did not enter the cells. The cells were then observed using a fluorescence microscope (Carl Zeiss AG, Oberkochen, Germany). The fluorescence intensity was analyzed using ImageJ software (National Institutes of Health, Bethesda, https://imagej.net/ij/index.html).
## Mitochondrial membrane potential detection
L02 cells were plated with 1 × 105 cells per well in 24-well plates. After HG&FFA, CDP-etn, or LPE treatment, the cells were incubated with 200 μl serum-free medium with a dissolved JC-1 probe (5 μg/ml) at 37°C for 20 min then washed twice with a staining buffer (1×). After washing, 500 μl of culture medium was added to each well. The cells were observed using a fluorescence microscope and the fluorescence intensity was analyzed using ImageJ software.
## ATP detection
The ATP content in the cells was detected using an ATP kit (Beyotime, Shanghai, China). A sample tube containing 20 μl of sample or standard substance was placed in a luminometer (SuPerMax 3100) and mixed quickly with a micropipette. After a minimum of 2 s interval, the relative lights unit value was measured.
## Apoptosis detection
L02 cells were plated in six-well plates at 2 × 105 cells per well. After treatment with HG&FFA, CDP-etn, or LPE, the cells were collected in EP tubes, stained with 2.5 μl Annexin V-FITC and 2.5 μl propidium iodide, incubated at 25°C for 10–20 min, mixed well, and then detected by flow cytometry.
## Electron microscope
The cells were cultured in Petri dishes each with a diameter of 6 cm. After treatment, the cells were collected and counted. Samples containing 1 × 106 cells were fixed by slowly pouring them into $2.5\%$ glutaraldehyde fixative solution along the tube wall and storing in a refrigerator at 4°C. Ethanol gradient dehydration (30, 50, 70, 80, 90, and $100\%$), critical point drying, and coating were performed followed by electron microscope (EM) observation.
## Statistical analysis
Two-tailed unpaired and paired t-tests were used to determine whether the outcomes were significantly different between the two groups. For statistical analysis of more than two groups, ANOVA was used. Results are mean ± SD. $P \leq 0.05$ was considered a statistically significant.
## Mice with T2DM have impaired liver function and altered lipid composition
The fasting blood glucose level of the T2DM (HFD + STZ) mice was considerably higher than that of the control (CON) group (Fig. 1A). The OGTT showed that the fasting blood glucose level of the T2DM mice was almost four times higher than that of the CON group; meanwhile, the area under the curve for the model group was 53.89 mmol/L·h, while that for the CON group was 16.39 mmol/L·h. The ITT indicated that the effect of insulin in lowering blood glucose on the HFD + STZ group was significantly lower than on the CON group (Fig. 1B). Compared with the CON group, the levels of serum ALT and AST were significantly higher in the HFD + STZ group, and H&E staining showed HFD + STZ mice had significant hepatic steatosis (Fig. 1C, D), indicating that liver function was impaired. The lipids in the liver tissues of the CON and HFD + STZ mice were analyzed and identified by lipid chromatography and mass spectrometry. After the data were normalized by the total peak area, an unsupervised principal component analysis was used to analyze the data, which showed that the model sample was well separated from the control group. On this basis, two supervised separation methods, partial least squares discriminant analysis (PLS-DA), and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were used to improve the separation effect. The OPLS-DA model was verified by permutation analysis (200 times). The results indicated satisfactory predictive ability (Fig. 1E).Fig. 1Impaired liver function and liver lipidomics in diabetic mice. A: Fasting blood glucose level of mice ($$n = 8$$). B: OGTT and ITT ($$n = 8$$). C: Serum ALT and AST level of mice ($$n = 8$$). D: Liver histology determined by H&E. E: OPLS-DA score plots of lipidomics, comparing CON and HFD+STZ groups, as well as the validation of OPLS-DA models by permutation analysis (200 times) ($$n = 9$$). All experiment were repeated three times. ∗$P \leq 0.05$, ∗∗$P \leq 0.01.$ ALT, alanine aminotransferase; AST, aspartate aminotransferase; HFD, high-fat diet; ITT, insulin tolerance test; OGTT, oral glucose tolerance test; OPLS-DA, orthogonal projections to latent structures discriminant analysis; STZ, streptozotocin.
## CDP-etn pathway is impaired in T2DM mice liver
In the lipidomics data, variable important in projection values greater than 1 and values of the t test lower than 0.05 were defined as metabolic differences. Among the many different metabolites, the PE content in the liver of the HFD + STZ group was observed to be lower than that of the CON group (Fig. 2A). Therefore, we tested the key enzymes in two main PE synthesis pathways: PISD in the mitochondria and PCYT2 in the ER, respectively (Fig. 2C). The mRNA and protein levels of Pcyt2 in the livers of T2DM mice were significantly decreased, while the mRNA and protein levels of Pisd did not change significantly (Fig. 2D, E). We found that the DG level in the liver of HFD + STZ mice was significantly high (Fig. 2B). DG is the substrate of PCYT2, which catalyzes phosphoethanolamine to produce CDP-etn. This indicates that the change in DG may be due to the decreased levels of Pcyt2.Fig. 2Metabolites and enzymes related to PE de novo synthesis pathway changed significantly in the liver of T2DM mice. A and B, Heat maps of PE (A) and DG (B) in the liver tissues. C: Two major pathways of PE synthesis in mammalian cells, the PISD pathway and de novo pathway. D: qRT-PCR was used to detect mRNA levels of Pcyt2 and Pisd in mice liver ($$n = 6$$). E: The protein levels of Pcyt2 and Pisd in mice liver were detected by Western blotting ($$n = 3$$). All experiments were repeated three times. ∗$P \leq 0.05$, ∗∗$P \leq 0.01.$ DG, diglyceride; Pcyt2, phosphoethanolamine cytidylytransferase; PE, phosphatidylethanolamine; Pisd, phosphatidylserine decarboxylase; qRT-PCR, quantitative RT-PCR; T2DM, type 2 diabetes mellitus.
## HG&FFA treatment impaired the CDP-etn pathway in hepatocytes in vitro
To further understand the mechanism of liver lipid metabolism disorders in T2DM, we tested the mRNA and protein levels of PCYT2 and PISD in L02 cells under HG&FFA stimulation. The PCYT2 expression was significantly reduced, while the mRNA and protein levels of PISD did not change significantly (Fig. 3A, B). Based on these results, we next tested glucose intake in L02 cells treated with glucosamine (18 mM), which is an insulin resistance inducer (supplemental Fig. S2A). These cells showed a significant reduction in glucose intake when compared with that of control cells. In addition, the protein and mRNA levels of PCYT2 in L02 cells treated with glucosamine decreased significantly, while those of PISD did not change (supplemental Fig. S2B, C). These results were consistent with our observations in the livers of T2DM mice where Pcyt2 levels were lower than that in control mice while the levels of PISD did not change. Fig. 3Related metabolic enzymes in PE de novo synthesis pathway in L02 cells were significantly changed by high glucose and free fatty acids stimulation. A: qRT-PCR was used to detect mRNA levels of PCYT2 and PISD in L02 cells ($$n = 3$$). B: Western blotting was used to detect protein levels of PCYT2 and PISD in L02 cells ($$n = 3$$). All experiments were repeated three times. ∗$P \leq 0.05$, ∗∗$P \leq 0.01.$ PCYT2, phosphoethanolamine cytidylytransferase; PE, phosphatidylethanolamine; PISD, phosphatidylserine decarboxylase; qRT-PCR, quantitative RT-PCR.
## Addition of CDP-etn and overexpression of PCYT2 reversed the changes in lipid metabolites in L02 cells stimulated by HG&FFA
To further confirm the effect of PCYT2 on liver metabolites in T2DM, we tested the lipids of control L02 cells (C), HG&FFA-stimulated L02 cells (H), HG&FFA-stimulated L02 cells with 100 μM CDP-etn (ZL), L02 cells overexpressing PCYT2 (LV), and L02 cells overexpressing PCYT2 under HG&FFA stimulation (LH). CDP-etn is a direct product of PCYT2 catalysis. Addition of CDP-etn can specifically supplement the PE produced by the CDP-etn pathway [23]. After the data were normalized by the total peak area, unsupervised principal component analysis was used to separate the model samples from the control group. On this basis, two supervised separation methods, PLS-DA and OPLS-DA, were used to improve the separation effect. The OPLS-DA model was also verified by a permutation analysis (200 times) (Fig. 4A). The results indicated satisfactory predictive ability. Variables with important in projection values greater than 1 and $P \leq 0.05$, were defined as metabolic differences. The PE and DG were converted into heatmaps. The results showed that under HG&FFA stimulation, the PE content in L02 cells significantly decreased and the DG content increased significantly, but CDP-etn treatment significantly reversed the PE and DG levels caused by HG&FFA. Compared with control L02 cells, the PE and DG levels of PCYT2 overexpression (OE-PCYT2) cells did not change significantly, but OE-PCYT2 cells showed a reversal of the increase in DG levels and the decrease in PE levels caused by HG&FFA (Fig. 4B, C).Fig. 4CDP-etn supplementation or overexpression of PCYT2 reversed the changes of L02 metabolites caused by HG&FFA. A: OPLS-DA score plots of lipidomics as well as the validation of OPLS-DA models by permutation analysis (200 times) in L02 cells ($$n = 5$$–6). B and C, Heat maps of PE (B) and DG (C) in L02 cells. D and E, The mitochondrial and extramitochondrial PE content in different groups of cells (CON, control group; HG&FFA, high glucose and free fatty acids; CDP-etn, CDP-etn supplementation in L02 cells stimulated with HG&FFA; OE-PCYT2, overexpression of PCYT2 group; OE-PCYT2+H&F, overexpression of PCYT2 in L02 cells stimulated with HG&FFA, $$n = 3$$). ∗$P \leq 0.05$, ∗∗$P \leq 0.01.$ DG, diglyceride; CDP-etn, CDP-ethanolamine; HG&FFA, high glucose and free fatty acids; OE PCYT2, PCYT2 overexpression; OPLS-DA, orthogonal projections to latent structures discriminant analysis; PE, phosphatidylethanolamine; PCYT2, phosphoethanolamine cytidylytransferase.
Furthermore, we have demonstrated that PCYT2 levels in the hepatocytes of T2DM model were lower than those in the control group, in vivo and in vitro, and low PCYT2 levels resulted in a decrease in PE levels. To explore whether PE content in the mitochondria was also reduced under diabetic conditions, we extracted the mitochondria and determined the mitochondrial and extramitochondrial PE contents. As a result, the PE content of L02 cells under HG&FFA stimulation was reduced to half of that of the control group both inside and outside mitochondria, however, after supplementation with CDP-etn, the PE content returned to normal levels. Overexpression of PCYT2 in L02 cells increased the PE content significantly, with the higher increase being observed outside the mitochondria. Under HG&FFA stimulation, PCYT2 overexpression ameliorated the PE content and the effect was more significant outside the mitochondria (Fig. 4D, E). This phenomenon may be attributed to the flow of phosphatidylserine (PS) and PE between the mitochondria and ER: PS is transformed into PE in the mitochondria, while PE is transformed into PS in the ER, although PCYT2 is expressed outside the mitochondria. These results indicate that under cellular stimulation by HG&FFA, PCYT2 plays an important role in regulating the mitochondrial and extramitochondrial PE content.
## HG&FFA affect the mitochondrial function of L02 cells through the CDP-etn pathway
Mitochondrial PE is mainly provided by PISD, so supplementation with LPE can alleviate the decrease in PE caused by the decrease in PISD expression [24]. To investigate the role of PCYT2 in maintaining mitochondrial function, the effects of LPE or CDP-etn on the mitochondrial function and apoptosis level of L02 cells stimulated by HG&FFA were compared. The results of flow cytometry showed that after adding 100 μM CDP-etn, the apoptosis rate was changed from $38\%$ in the model group (HG&FFA) to $18\%$, which effectively reduced the rate of apoptosis induced by HG&FFA (Fig. 5A). ATP content detection showed that HG&FFA stimulation reduced the intracellular ATP content by $50\%$, while the intracellular ATP content of the 100 μM CDP-etn group increased by $30\%$ compared with the HG&FFA group (Fig. 5B). The JC-1 results showed that the mitochondrial membrane potential decreased significantly after HG&FFA treatment, and that of the CDP-etn group significantly increased after 100 μM CDP-etn treatment compared with the model group (Fig. 5C). Similarly, the level of ROS was significantly increased under HG&FFA stimulation, and 100 μM CDP-etn reduced the level of cellular ROS caused by HG&FFA (Fig. 5D). However, adding 100 μM LPE did not ameliorate the changes in apoptosis, ROS, ATP content, or membrane potential caused by HG&FFA. In addition, ROS levels of L02 cells under insulin resistance model were also increased (supplemental Fig. S2D). The mitochondrial oxidative capacity of L02 cells was determined using the Seahorse XF Cell Mito Stress Test. Compared to the control cells, the oxygen consumption rate of the mitochondria was significantly decreased by stimulation with HG&FFA, including basal respiration, maximal respiration, spare capacity, and ATP production (Fig. 5E). Supplementation with CDP-etn significantly ameliorated the mitochondrial function, while supplementation with LPE aggravated mitochondrial damage. These results indicate that the decrease in PE caused by the decrease in PCYT2 may be a potential cause of liver mitochondrial dysfunction in T2DM.Fig. 5HG&FFA affect the mitochondrial function of L02 cells through the CDP-ethanolamine pathway. A and B, *The apoptosis* rate (A) and ATP content (B) of L02 cells stimulated by HG&FFA with or without LPE and CDP-etn ($$n = 4$$). C: The changes of mitochondrial membrane potential in each group was detected by JC-1 probe in L02 cells ($$n = 3$$). D: DCFH-DA probe was used to detect ROS levels in L02 cells ($$n = 3$$). E: The mitochondrial oxygen consumption rate of L02 cells was detected using Seahorse (H, L02 cells stimulated by HG&FFA; ZL, CDP-etn supplementation in L02 cells stimulated with HG&FFA; LPE, LPE supplementation in L02 cells stimulated with HG&FFA, $$n = 3$$). All experiments were repeated three times. ∗$P \leq 0.05$, ∗∗$P \leq 0.01.$ DCFH-DA, 2',7'-dichlorodihydrofluorescein diacetate; HG&FFA, high glucose and free fatty acids; LPE, lysophosphatidylethanolamine; ROS, reactive oxygen species.
To determine whether supplementation with CDP-etn could also improve abnormal glucose and lipid metabolism in the hepatocytes in T2DM, we detected glucose intake and relative levels of triglyceride (TG) based on the lipidomic analysis. Our results indicated that CDP-etn-treated group, but not LPE-treated group, exhibited an increased glucose intake and decreased TG levels compared with the HG&FFA group (supplemental Fig. S4A, B).
## Overexpression of PCYT2 can ameliorate the mitochondrial damage caused by HG&FFA in hepatocytes
To further investigate the role of PCYT2 in HG&FFA-induced apoptosis and mitochondrial damage, we tested the ATP content of L02 cells overexpressing PISD or PCYT2. The results showed that overexpression of PCYT2, but not PISD, effectively improved the reduction in ATP content caused by HG&FFA (Fig. 6A). The ultrastructure of L02 cells was observed by EM. The mitochondrial structure of cells in the HG&FFA group showed some abnormalities, including vacuoles, irregular mitochondrial membranes, and cristae breaks. OE-PCYT2 reversed the damage to the mitochondrial structure caused by HG&FFA to a certain extent (Fig. 6B). The mitochondrial oxidative capacity of L02 cells determined using the Seahorse system differed significantly between OE-PCYT2 and OE-PISD under stimulation with HG&FFA. The mitochondrial oxygen consumption rate of OE-PCYT2 cells was significantly increased compared to that of OE-PISD cells (Fig. 6C). The results showed that overexpression of PCYT2, but not PISD, could increase glucose uptake and decrease TG levels when compared with HG&FFA stimulation (supplemental Fig. S4A, B).Fig. 6Overexpression of PCYT2 alleviated mitochondrial damage of L02 cells caused by HG&FFA. A: Changes of ATP content in L02 cells overexpressing PCYT2 or PISD ($$n = 3$$). B: Transmission electron microscopy was used to observe the ultrastructural changes of mitochondria in L02 cells. C: The mitochondrial oxygen consumption rate of L02 cells was detected using Seahorse ($$n = 3$$). All experiments were repeated three times. ∗$P \leq 0.05$, ∗∗$P \leq 0.01.$ HG&FFA, high glucose and free fatty acids; PCYT2, phosphoethanolamine cytidylytransferase; PISD, phosphatidylserine decarboxylase.
## PCYT2 plays an important role in apoptosis caused by HG&FFA via BAX/Bcl2/caspase3 signaling
To further explore the mechanism of apoptosis induced by HG&FFA in L02 cells, or by the decrease in PCYT2 in hepatocytes, we assessed the protein levels of components of apoptosis signaling, BAX, Bcl-2, and cleaved-caspase3. The levels of BAX and cleaved-caspase3 were significantly high while Bcl-2 were significantly low in the liver of HFD + STZ mice and in L02 cells after stimulation with HG&FFA. CDP-etn (100 μM) could protect cells from HG&FFA-induced apoptosis by reducing BAX and cleaved-caspase3 and increasing Bcl-2. However, such protection did not occur under 100 μM LPE treatment, which shows that the product of PCYT2 catalysis plays an important role in protecting liver cells from the effects of HG&FFA (Fig. 7A–C). The protein levels of PISD and PCYT2 in the OE-PISD and OE-PCYT2 groups increased three times and four times, respectively, compared to the controls. Under HG&FFA stimulation, compared with the HG&FFA group, the protein levels of BAX and cleaved-caspase3 in the OE-PCYT2 group decreased and the Bcl-2 protein level increased. However, such a reversal was not observed in the OE-PISD group (Fig. 7D–E). These results taken together indicate that mitochondrial damage and apoptosis of hepatocytes under HG&FFA may be caused by a decrease in PCYT2.Fig. 7PCYT2 plays an important role in apoptosis caused by HG&FFA via BAX/Bcl2/caspase3 signaling. A: Changes of protein levels of BAX, Bcl-2 and cleaved-caspase3 in T2DM group compared with CON group in mice ($$n = 3$$). B and C, Changes of protein levels of BAX, Bcl-2, and cleaved-caspase3 in L02 cells treated with CDP-etn (B) or LPE (C) under stimulation with HG&FFA ($$n = 3$$). D and E, Protein levels of BAX, Bcl-2, and cleaved-caspase3 in L02 cells overexpressing PCYT2 (D) or PISD (E) under stimulation with HG&FFA ($$n = 3$$). All experiments were repeated three times. ∗$P \leq 0.05$, ∗∗$P \leq 0.01.$ CDP-etn, CDP-ethanolamine; HG&FFA, high glucose and free fatty acids; PCYT2, phosphoethanolamine cytidylytransferase; PISD, phosphatidylserine decarboxylase; T2DM, type 2 diabetes mellitus.
## DISCUSSION
Metabolomics and lipidomics studies on T2DM mice and patient serum have revealed a variety of metabolites that are present at altered levels. Among components involved in lipid metabolism, fatty acids such as myristic acid, PA, stearic acid, linoleic acid, oleic acid, and arachidonic acid are significantly increased in patients with diabetes and are thought to be independent predictors of diabetes progression as well as impair the effects of insulin. In contrast, glycerol phospholipids such as phosphatidylcholine (PC) and PE and lipid derivatives such as sheaths dihydroammonia and aminol are reduced in diabetic patients, as some other metabolites are involved in carbohydrate metabolism, amino acid metabolism, and the tricarboxylic acid cycle. These changes are considered important clinical markers [25, 26, 27]. Although previous studies reported changes in these metabolites, they did not explore the molecular mechanisms underlying these changes.
Our study has demonstrated that the PE content in the livers of T2DM mice is significantly reduced and the mechanism of this reduction may be a decrease in Pcyt2. Our study has further shown that the reduction of PE also causes mitochondrial dysfunction, including decreased mitochondrial membrane potential, decreased ATP content, increased ROS, and increased apoptosis, which may be mediated by activation of the BAX/Bcl-2/caspase3 pathway (Fig. 8).Fig. 8In type 2 diabetes, decreased expression of PCYT2 in liver leads to decreased PE content and increased DG content, resulting in decreased mitochondrial membrane potential, increased production of reactive oxygen species, reduced ATP content and other mitochondrial damage, thereby activating the apoptosis pathway of BAX/Bcl-2/cleaved-caspase3, thus cause apoptosis of liver cells, resulting in liver damage. DG, diglyceride; PCYT2, phosphoethanolamine cytidylytransferase; PE, phosphatidylethanolamine.
PE plays an important regulatory role in neurodegeneration and the occurrence and development of some neurodegenerative diseases; the lack of PE affects the progression of Alzheimer’s disease and Parkinson’s disease [13]. PE is thought to be a target of ophiobolin A, a powerful natural anticancer agent. Therefore, PE is thought to play an important role in killing tumor cells. Moreover, LACTB, a tumor suppressor gene, is downregulated in many tumor cell lines and has been found to downregulate PISD. These results open up a new field of PE research for the future [28]. PCYT2 is a rate-limiting enzyme in the CDP-etn pathway; knocking it out can result in a variety of effects. A systemic knockout of Pcyt2 in mice is embryonic lethal, and knocking out Pcyt2 in the liver causes severe TG deposition. Knocking out Pcyt2 in muscle tissue will not only cause a significant increase in DG and TG content but also an increase in mitochondrial content, both of which will also cause a significant decrease in PE [19, 29, 30]. These findings indicate that PCYT2 plays an important role in regulating lipid and energy homeostasis. However, the role of PCYT2-mediated energy metabolism in liver injury induced by T2DM is not well understood.
T2DM patients often exhibit impaired energy metabolism, which is characterized by mitochondrial dysfunction, high production of ROS, and low levels of ATP [31, 32]. The mitochondrial apoptotic pathway is activated in hepatocytes of patients with T2DM [33]. Our study suggests that the reduction of PE from the CDP-etn pathway in T2DM may also induce apoptosis through the BAX/Bcl-2/caspase3 pathway. After supplementing with CDP-etn, a direct product of PCYT2, or by overexpression of PCYT2, some damage caused by HG&FFA was reversed, providing evidence in support of our hypothesis. Combined with the lipidomics results, these results further prove that PE is an indispensable participant in cell survival and that PCYT2 plays an important role in regulating cell survival. This provides a new area for future research on lipids in T2DM.
However, our study had certain limitations. DG is believed to play a role in promoting insulin resistance [34]. Although we found a significant increase in the content of DG, we have not yet investigated whether the increase in DG can further aggravate insulin resistance and increase liver damage in the diabetic model. Moreover, the specific mechanism underlying the decrease in PCYT2 expression in T2DM is still unknown. Studies have shown that the upstream transcription factors of PCYT2 may include early growth response factor 1 (EGR1), E74 like ETS transcription factor 3 (ELF3), and nuclear transcription factor Y subunit alpha (NFYA) [35, 36, 37]. EGR1 promotes the progression of nonalcoholic fatty liver disease in patients with insulin resistance [38]. NFYA is an important transcriptional regulator of lipid and glucose metabolism and adipokine biosynthesis related to the occurrence of T2DM [39]. However, our qRT-PCR results showed that EGR1 in vitro and in vivo and ELF3 and NFYA in T2DM in vitro did not change significantly (data not shown). To further comprehend the complexity of diabetes, the changes in PCYT2 transcription factors in the liver of T2DM patients need further study.
Interestingly, PE is the most abundant among all phospholipids in mitochondria, and PE in mitochondria is synthesized by the PISD pathway [40]. In our study, no significant changes were observed in the PISD; this might be because the activity of PISD may be prevalent in muscles. The main synthetic pathway of PE in the liver is the CDP-etn pathway, which can also be seen by the difference in the composition of PE between the two in different organs [41, 42]. In addition, because there is a unique PC synthesis pathway in the mammalian liver, PE is catalyzed by PE N-methyl transferase to form PC, which provides $30\%$ of the PC content in the liver. The PC/PE ratio is a determinant of the oxidative capacity and energy production of the liver mitochondria. This may explain why the reduction of PE outside the mitochondria also affects the normal function of mitochondria. Similarly, PE in the phospholipid remodeling pathway can also be transferred to mitochondria through the generation of PS by PSS2, which may also affect the generation of mitochondrial PE pool as the raw material for PE synthesis in PISD, thus affecting mitochondrial oxidative phosphorylation ability [14, 43]. It is worth noting that the decrease in PCYT2 often leads to the accumulation of the upstream product phosphoethanolamine, a metabolite that damages cellular respiration [19]. However, whether the reduction of PCYT2 in T2DM leads to the accumulation of phosphoethanolamine and affects mitochondrial respiration requires verification, but we still believe that the reduction of PE also leads to mitochondrial dysfunction. In addition, other phospholipids may also play a role in T2DM and regulate insulin sensitivity. For example, PC and phosphoinositol are associated with insulin resistance. However, the specific functions of these phospholipids in T2DM and the regulation of phospholipid remodeling in the disease need to be further explored [44, 45, 46].
In summary, we have demonstrated the reduction of PE in the liver in T2DM and its possible mechanism, which emphasizes the importance of PE produced by the PCYT2 pathway and opens new avenues for the treatment of liver injury in patients with T2DM in the future.
## Data availability
All data described are included in the manuscript and the supplementary data.
## Supplemental data
This article contains supplemental data.
Supplemental data
## Conflict of iInterest
The authors declare that they have no conflicts of interest with the contents of this article.
## Author contributions
H. X. obtained and analyzed the data; W. L., L. H., X. H., B. X, X. H., W. C., Y. W., W. X., S. W., and Q. K. conducted parts of the experiments; W. L., L. H., X. H., B. X, X. H., W. C., Y. W., W. X., S. W., and Q. K. contributed to the discussion; W. X. and W. L. wrote and edited the manuscript.
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|
---
title: Bioinformatics-based construction of prognosis-related methylation prediction
model for pancreatic cancer patients and its application value
authors:
- Tiansheng Cao
- Hongsheng Wu
- Tengfei Ji
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10034005
doi: 10.3389/fphar.2023.1086309
license: CC BY 4.0
---
# Bioinformatics-based construction of prognosis-related methylation prediction model for pancreatic cancer patients and its application value
## Abstract
Objective: *Pancreatic adenocarcinoma* (PAAD) is a highly malignant gastrointestinal tumor with almost similar morbidity and mortality. In this study, based on bioinformatics, we investigated the role of gene methylation in PAAD, evaluated relevant factors affecting patient prognosis, screened potential anti-cancer small molecule drugs, and constructed a prediction model to assess the prognosis of PAAD.
Methods: Clinical and genomic data of PAAD were collected from the Tumor Genome Atlas Project (TCGA) database and gene expression profiles were obtained from the GTEX database. Analysis of differentially methylated genes (DMGs) and significantly differentially expressed genes (DEGs) was performed on tumorous samples with KRAS wild-type and normal samples using the “limma” package and combined analysis. We selected factors significantly associated with survival from the significantly differentially methylated and expressed genes (DMEGs), and their fitting into a relatively streamlined prognostic model was validated separately from the internal training and test sets and the external ICGC database to show the robustness of the model.
Results: In the TCGA database, 2,630 DMGs were identified, with the largest gap between DMGs in the gene body and TSS200 region. 318 DEGs were screened, and the enrichment analysis of DMGs and DEGs was taken to intersect DMEGs, showing that the DMEGs were mainly related to Olfactory transduction, natural killer cell mediated cytotoxicity pathway, and Cytokine -cytokine receptor interaction. DMEGs were able to distinguish well between PAAD and paraneoplastic tissues. Through techniques such as drug database and molecular docking, we screened a total of 10 potential oncogenic small molecule compounds, among which felbamate was the most likely target drug for PAAD. We constructed a risk model through combining three DMEGs (S100P, LY6D, and WFDC13) with clinical factors significantly associated with prognosis, and confirmed the model robustness using external and internal validation.
Conclusion: The classification model based on DMEGs was able to accurately separate normal samples from tumor samples and find potential anti-PAAD drugs by performing gene-drug interactions on DrugBank.
## 1 Background
Pancreatic adenocarcinoma (PAAD) is the 14th most common cancer globally (Kocarnik et al., 2022), with an estimated 458,918 confirmed pancreatic cancer cases and 432,242 death cases each year all over the world (Ferlay et al., 2019). The incidence of PAAD varies widely by country, as Europe and North America showed the highest age-standardized incidence, which was the lowest in South-Central Asia and Africa (Ilic and Ilic, 2016). Incidence rate of PAAD is generally higher in developed countries compared to developing countries, with a standardized incidence rate of $\frac{4.9}{100}$,000 and $\frac{3.6}{100}$,000 for men and women, respectively. In the United States, 5-year survival rate of PAAD is $9.3\%$, and it is the fourth leading factor resulting in cancer-related mortality (Gandhi et al., 2018). Apart from smoking, diabetes, alcohol drinking, obesity, occupational exposure and genetic factors, PAAD is as well an epigenetic disease (Goral, 2015; Midha et al., 2016; Hu et al., 2021). Abnormal DNA methylation patterns are a common human tumorous feature (Kulis and Esteller, 2010). From precancerous lesions to PAAD, epigenetic changes play an important role in the multistage carcinogenesis (Xu et al., 2019).
Epigenetics are changes in gene expression but not in DNA sequence, and the major epigenetic alteration leading to PAAD progression is DNA methylation (Wang et al., 2016). To detect epigenetic abnormalities in PAAD, it is necessary to identify genome-wide patterns of DNA methylation. Nones et al. [ 2014] used high-density arrays to capture 167 untreated PAAD sample methylation and compared it with normal tissue adjacent to the cancerous one and identified 3,522 abnormally methylated genes. In addition, partial methylation of CDKN1C promoter CpG islands and reduced expression of protein products are observed when comparing PAAD precursor cells methylation expression to normal pancreatic duct epithelial cells (Sato et al., 2008). Basic studies have shown that in PAAD precursor cells, CDKN1C gene is under-expressed and there is reduced expression of protein products and partial methylation of CDKN1C promoter CpG islands. The above evidence supports that aberrant DNA hypo/hypomethylation occurs in PAAD precursor lesions leading to further progression to PAAD [13].
As research continues, aberrant methylation of DNA CpG islands has become a prominent feature of PAAD and a potential diagnostic marker and therapeutic target for PAAD. However, the results of clinical trials were disappointing, probably due to the low level of epigenetic specificity (Matsubayashi et al., 2006; Marabelle et al., 2020). Therefore, in order to use methylation as a future therapeutic tool for PAAD, an in-depth understanding of the methylation expression profile and supporting pathways of PAAD is needed. According to the mutation and gene expression profile data of PAAD patients and gene expression profiles of normal pancreas from GTEX, this study screened differentially methylated and expressed genes (DMEGs), and confirmed that methylation was a reliable prognostic marker for PAAD and a potential oncogenic drug target for PAAD.
## 2.1 Acquisition of clinical data, gene expression profiles and data processing
Methylation data, clinical follow-up data, and gene expression profiles of PPAD came from TCGA (https://portal.gdc.cancer.gov/) by means of UCSC Xena. *The* gene expression profiles of normal pancreas samples were obtained from the GTEX (http://www.gtexportal.org/home/index.html) databases using UCSC Xena.
For sample data reliability, we set the following inclusion criteria (Kocarnik et al., 2022): only normal samples and primary PAAD samples were retained (Ferlay et al., 2019); PAAD samples with wild-type KRAS gene were retained (Ilic and Ilic, 2016); PAAD samples with complete clinical data were retained. A total of 182 samples were obtained from TCGA, including 178 tumor samples, 70 KRAS wild-type tumor samples and 4 normal samples. A total of 167 normal pancreas samples were obtained from the GTEX database. In order to homogenize the data, the “sva” R package was applied to remove the batch effect from the combined data of the two datasets, and a total of 19,593 protein-coding genes were retained by ENSG conversion of gene symbols using genecode V35.
## 2.2 Analysis of differentially methylated genes (DMGs)
The Illumina HumanMethylation450 BeadChip matrix contained 380,097 probes of around $99\%$ ($$n = 26$$,081) of the RefSeq genes. For each probe, the raw gene methylation intensity was expressed as a beta value. To identify differentially methylated CpG sites (DMS), PAAD tumor samples were compared with paracancer samples using the “limma” R package (Ritchie et al., 2015). The Benjamini and Hochberg (BH) method adjusted p-value of each methylation site to FDR (false discovery rate) (Ghosh, 2012). Statistical thresholds were set for FDR <0.01 and |delta β-value|> 0.1.
The CpG locus to gene match files were downloaded from the Illumina website (https://www.illumina.com/). In different regions (TSS200, TSS1500, Gene body, 5′-UTR, 3′-UTR, transcription start site, integration region), the average β-values of genes were calculated with the correspondence. Using the “limma” R package, the differentially methylated regions were calculated, where FDR <0.01, delta β-values < -0.1 were the demethylated regions, FDR <0.01, delta β > 0.1 were hypermethylated regions.
## 2.3 Analysis of differentially expressed genes, differentially methylated genes and pathways
Differentially expressed genes (DEGs) were analyzed for normal and tumor samples in the TCGA-PAAD cohort using the “limma” R package, and p values were adjusted using the Benjamini and Hochberg (BH) method, where FDR >0.01 and log2FC > 2 were up-regulated genes, and FDR >0.01 and log2FC < −2 were down-regulated genes.
To identify the relationship between gene methylation and gene expression profiles, we took the intersection of differentially methylated genes and DEGs to obtain differentially methylated and expressed genes (DMEGs) and classified them into four groups: HyperDown, HyperUp, HypoDown, HypoUp (Table 1). Then, we used Gene Ontology (GO) functional enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database through the “clusterProfiler,” “org.Hs.eg.db,” “enrichplot” and “ggplot2” R software packages (Wu et al., 2021), and FDR <0.05 was used as the screening condition to perform enrichment analysis of DEGs to discover the main biological characteristics of DEGs and plot the bubble map.
**TABLE 1**
| Groups | Methylation cut-off | Expression cut-off |
| --- | --- | --- |
| HypoUp | FDR <0.01 and delta β-value < −0.1 | FDR <0.01 and log2FC > 2 |
| HypoDown | FDR <0.01 and delta β-value < −0.1 | FDR <0.01 and log2FC < -2 |
| HyperUp | FDR< 0.01 and delta β-value >0.1 | FDR <0.01 and log2FC > 2 |
| HyperDown | FDR <0.01 and delta β-value >0.1 | FDR <0.01 and log2FC < -2 |
## 2.4 Marker evaluation of PAAD methylation and expression profiles
DEGs were proposed as tumor markers for the diagnosis of PAAD, and $50\%$ of the expression profile data of DMEGs and methylation data of DMEGs for PAAD were the training set and $50\%$ as the test set. The training set data were analyzed by principal component analysis (PCA) with the “prcomp” R function (Luu et al., 2017) to clarify the eigenvector weights of the principal components and construct a diagnostic model of PAAD, which was plotted and visualized using the “ggplot2” R software package (Maag, 2018). Finally, to evaluate the diagnostic advantage of PCA model for PAAD, the receiver operating characteristic (ROC) curves of the PCA model were plotted by the “pROC” R software package and the area under curve (AUC) was calculated for the training set and test set (Robin et al., 2011), where AUC showed a low accuracy at 0.5–0.7, higher accuracy at 0.7–0.9, and high accuracy at AUC above 0.9.
## 2.5 The prediction of DMEGs and target drugs
The use of key genes as potential therapeutic targets is a cornerstone in the development of therapeutic agents for sepsis. We determined PAAD and drug proximity based on drug-target pairs from the drugbank database (https://go.drugbank.com/) and the Protein-Protein interaction (PPI) network (threshold score of 400). Here, given distance d (s,t) as the shortest path between node s and node t (where s ∈ S, PAAD-related genes; t ∈ T, drug target genes), D (degree of related gene set nodes in PPI), T (set of drug target genes), S (PAAD-related genes), and the calculation is as follow: dS,$T = 1$T∑t∈Tmins∈Sds,t+ω [1] where ω, the weight of the target gene, was calculated as ω = -ln (D+1) if the target gene was a gene in the PAAD-related gene set, otherwise ω = 0.
Next, between these simulated drug targets and the key gene set, we calculated the distance d (S,R), and generated the simulated reference distributions after performing random repetitions for 10,000 times, at the same time we the observed distances corresponding to the actual were scored using the mean and standard deviation of the μd (S,R) and σd (S,R) reference distributions and converted into a normalized scoring, i.e., the proximity z. zS,T=dS,T−μdS,RσdS,R [2] Finally, a gene set distance density score map was constructed by normalized distance scoring.
## 2.6 Molecular docking
A technique for designing drugs based on receptor features and the way that drug molecules interact with receptors is called molecular docking. In the realm of computer-aided drug development, it is a theoretical modeling technique that primarily investigates the interaction between molecules (such as ligands and receptors) and forecasts their binding mechanism and affinities. ( Lohning et al., 2017; Saikia and Bordoloi, 2019). Autodock Vina software was used in molecular docking (Trott and Olson, 2010). To prepare input files, we applied AutoDockTools 1.5.6. The pdb file of the protein came from Protein Data Bank (Velankar et al., 2021) with PDB ID 6SUK. The Polar hydrogens were added to the solution after all water molecules, potassium ions, and protein B chains had been eliminated. The zinc ion’s charge was modified in the receptor protein’s PDBQT file to +2.0, and the grid’s coordinates in each XYZ direction were −19.5, 74.5, and 34.8 during molecular docking. The lengths were 20 in each XYZ direction. The Lamarckian approach was utilized to determine the ligand molecule’s strongest binding mode. The maximum number of output conformations was set to 10, the exhaustiveness was set to 8, and the allowable energy range was set to a maximum of 3 kcal/mol. With the aid of Pymol, the output maps were processed.
## 2.7 Dynamics simulation
In this study, the binding stability of the receptor-ligand complex was assessed by performing molecular dynamics simulations of 100 ns (Zhou et al., 2022) using the Gromacs2019 package. In the molecular dynamics simulations the CHARMm36 force field was employed. With the aid of the CHARMM Common Force Field (CGenFF) software, the str files for the ligands were acquired. The system was dissolved in TIP3P water molecules in a dodecahedral box. At a concentration of 0.154 M, sodium and chloride ions were introduced to the system to neutralize its charge. Using a cutoff of 5,000 steps and the steepest descent algorithm, the solventized system’s energy was minimized. The LINCS method was used to restrict the length of covalent bonds. Using the PME technique, the total electrostatic interactions were determined. At constant temperature (300 K) and pressure (1 bar), NVT and NPT simulations were then run for 100 ps, with the compound’s confined atoms re-establishing the system’s equilibrium at its initial coordinates. Finally, a 100 ns long Pruduct MD run with a 2 fs time step was completed. The Gromacs built-in tool was used to determine the ligands’ root mean square deviation (RMSD) values.
## 2.8 Development and verification of the prognostic gene signature model associated with DMEG
In the TCGA-PAAD dataset, we first randomly and equally divided 241 KRAS wild samples into training (Train) and validation (Test) groups according to the ratio of 1:1, and then reduced the associated genes (Tibshirani, 1997) by Least absolute shrinkage and selection operator (Lasso) regression method. In regression analysis, by compressing some coefficients at the same time setting some coefficients to zero, Lasso regression can better solve multicollinearity. We choose the number of factors when the coefficients of independent variables tended to zero with the gradual increase of lambda. Then, we used the AIC deficit pool information criterion through stepwise regression that takes the statistical fit of the model and parameter numbers into account. A better model of smaller value indicated a sufficient fit of the model with fewer number of parameters (Zhang, 2016). After that, the “survminer” package was used to find the best cutoff (Niu et al., 2021) of the gene signature in the Train dataset of TCGA, and the PAAD was divided into two groups based on the cutoff value, and finally the log-rank test was used to compare the survival differences between the two groups.
To verify the robustness of the gene signature model, we first used the same model and the same coefficients as the training set in the validation set, and then compared the survival differences between the two groups by log-rank test. After that, we downloaded the expression profiles of PAAD from the ICGC database as well as clinical information, and then used the model constructed above to calculate each score separately and obtain the best cutoff, and then performed the survival curve analysis in the external dataset for the high- and low-risk groups.
## 2.9 Statistical analysis
All statistical analyses were operated in R software (version 4.1.2, https://cran.r-project.org/doc/manuals/R-lang.html). The optimal threshold of gene expression or score was selected for risk grouping of PAAD using the surv_cutpoint function of the “survminer” package. The Kaplan-Meier assessment method was used to assess the survival differences between the low-risk and high-risk groups, and the Log-rank test was used for comparison. Unless otherwise stated, all statistical tests were two-sided and $p \leq 0.05$ was considered statistically significant. Comparisons between multiple groups were performed and plotted using the “ggpubr” and “ggplot2” packages, and the statistical significance of box plots was assessed using the Mann-WhitneyU or Kruskal–Wallis tests.
## 3.1 Analysis of differentially methylated genes in PAAD
To identify differential gene methylation in PAAD, we first performed a comparative analysis of methylation data from 185 KRAS wild-type PAAD samples and 10 normal samples, and identified a total of 2,630 differentially methylated genes (FDR <0.01, |delta β-values| > 0.1, Figure 1A), within the Gene body region, 758 genes were hypermethylated and 418 genes were demethylated. 834 genes were hypermethylated and 462 genes were demethylated in TSS20; 748 genes were hypermethylated and 498 genes were demethylated in TSS1500. We found that the number of hypermethylation in the three regions was slightly larger than that of hypermethylation overall (Figure 1B). In the Gene body and TSS200 regions, the difference between hypermethylation and demethylation was the largest, with a ratio of about 1.8:1. Among the hypermethylated genes, 244 genes appeared in all three regions of Gene body, TSS20 and TSS1500, 369 genes appeared in two of them, and the remaining 870 genes appeared in only one region (Figure 1C). Among the demethylated genes, only 32 genes appeared in all three regions, and 163 genes appeared in two of them. These differentially methylated genes were mainly associated with GABAergic synapse, Neuroactive ligand-receptor interaction, Nicotine addiction, and other pathways, as shown by GO and KEGG functional enrichment analysis (Figure 1D). ( Figure 1E). The above results confirmed that PAAD methylation was region-specific.
**FIGURE 1:** *Analysis of PAAD differentially methylated genes. (A) Volcano plot of differentially methylated within the gene body, TSS200 and TSS1500 regions. (B) Histograms of differentially methylated genes within the three regions. (C) Venn diagram of hypermethylation within three different regions. (D) Venn diagrams of demethylated genes within three different regions. (E) KEGG and GO functional enrichment analysis of differentially methylated genes, where the color from blue to red indicates that the FDR is from large to small, and the dots from small to large represent the increasing number of enriched genes, left: hypermethylation, right: demethylation.*
## 3.2 Analysis of differential genes in PAAD and combined analysis of differentially metaylated genes
To screen the differential genes between normal and KRAS wild-type PAAD samples, we analyzed the differential genes between 171 normal samples and 70 KRAS wild-type tumor samples using the “limma” package, and obtained a total of 2,928 significantly DEGs, of which 2029 were down-regulated and 1,163 were up-regulated in tumors (FDR <0.01, |log2FC| > 2, Figure 2A). A total of 2,928 significantly DEGs were obtained, of which 1,163 were up-regulated and 2029 were down-regulated in tumors (FDR <0.01, |log2FC| > 2, Figure 2A). Then, unsupervised hierarchical clustering of these significantly differentially expressed genes revealed that the differential genes could clearly screen tumor samples from the normal ones (Figure 2B). KEGG study showed that the significant differential genes were mainly related to Fat digestion and absorption (Figure 2C). Biological process (BP) enrichment study demonstrated that the differential genes were largely correlated with Lipid transport, Lipid localization and other pathways; cellular components (CC) showed that the differential genes were associated with neural cell body, trans-Golgi. The results of Molecular Function (MF) showed that the differential genes were related to Cytokine-cytokine receptor interaction, natural killer cell-mediated cytotoxicity, Olfactory transduction, and other such pathways that have been previously reported to be associated with PAAD occurrence Figures 2D–F (Malchiodi and Weiner, 2021; Hu et al., 2022).
**FIGURE 2:** *Analysis of PAAD differential genes. (A) Volcano plot of differentially expressed genes in expression profile. (B) Heat map of differentially expressed genes. (C) Results of differential gene KEGG enrichment. (D) Results of differential gene GO BP enrichment. (E) Differential gene GO CC enrichment results. (F) Differential gene GO MF enrichment results, the color from blue to red in CDEF represents FDR from large to small, the size of the dot represents the number of enriched to genes, a larger dot indicates more enriched genes.*
To search for genes more critical for PAAD occurrence, differentially methylated and expressed genes (DEMGs) were obtained by intersection analysis of DMGs and DEGs. In Gene body, TSS200 and TSS1500, 141, 187 and 154 DEMGs were obtained, respectively (Figures 3A–C). The methylation ploidy and expression difference ploidy of these DMEGs are shown in Figure 3D, and each graph shows the 22 genes with the largest expression difference ploidy. Next, we counted DMEGs in the three regions and identified a total of 318 DMEGs, including 56 in HyperUp, 112 in HyperDown, 69 in HypoUp, and 81 in HypoDwon (Figure 3E).
**FIGURE 3:** *Joint analysis of differentially expressed genes and differentially methylated genes. (A) Venn diagram of differentially expressed genes with differentially methylated genes in the GeneBody region. (B) Venn diagram of differentially expressed genes with differentially methylated genes in the TSS200 region. (C) Venn diagrams of differentially expressed genes versus differentially methylated genes within the TSS1500 region. (D) Quadrant plots of differentially expressed genes versus differentially methylated genes within the TSS200, TSS1500, and GeneBody regions. (E) Histogram of four regulatory patterns of differentially expressed genes and differentially methylated genes in TSS200, TS1500, and GeneBody.*
## 3.3 Analysis of DMEGs genes in PPAD
To further investigate the role of DMEGs in PAAD, we first used the “circlize” package to map the distribution of 318 DMEGs on chromosomes, with chromosomes chr11 and chr12 having the largest number of 26 DMEGs, chr10, chr12, chr17, chr16, chr2, chr19, chr3, chr20, chr5, chr4, chr7, chr6, and chr6. Chr17, chr16, chr2, chr19, chr3, chr20, chr5, chr4, chr8, chr7, chr6 chromosomes also possessed more than 10 DMEGs each (Figure 4A). We constructed a linear judgment classification model using the gene expression profiles of DMEGs and methylation data from GeneBody, TSS200 and TSS1500, respectively, to evaluate the difference of DNA methylation patterns and gene expression between PAAD tumors and normal samples, and also performed PCA and ROC analyses. The results of PCA showed that DMEGs could classify PAAD and normal samples effectively (Figure 4B), and the AUC values were all 1, suggesting an excellent performance in classification (Figure 4C). GO and KEGG enrichment analysis showed that DMEGs were mainly associated with cell differentiation in spinal cord, neuron fate commitment, calcium signaling pathway, phospholipase C-activating G protein-coupled receptor signaling pathway, digestion, central nervous system neuron differentiation, neuroactive ligand-receptor interation, cell fate commitment, regionalization, and pattern specification process (Figure 4D).
**FIGURE 4:** *Analysis of DMEGs. (A) Distribution of DMEGs on the genome. From inside to outside, there are DMGs in the TSS1500 region, DMGs in the TSS200 region, DMGs in the genebody region, DEGs, and corresponding values. The outermost circle is the corresponding chromosome position. (B) PCA analysis could distinguish tumor from normal samples based on the gene expression and methylation of DMEGs. (C) ROC curves of tumor and normal samples based on a linear discriminant model using the expression profiles and methylation of DMEGs. (D) Results of KEGG and GO enrichment analysis of DMEGs, where different colors represent different pathways and connecting lines represent the existence of association between genes and pathways.*
## 3.4 DMEGs and potential target therapeutic agents
As mentioned previously, DMEGs may be the key genes causing PAAD, and therefore targeting DMEGs is a potential target for the treatment of PAAD. To this end, we calculated the proximity of DMEGs to PAAD according to Formula 1 and converted the observed distances into normalized scores according to Formula 2. We found that either with our randomly selected gene set as a sample or DEMGs as a sample, using the random data acquired for multiple hypothesis testing and selecting drugs with a distance set distributed around 0 to 3 and FDR <0.01, a total of 78 potential target drugs were obtained, and Figure 5 shows the distance density fraction of drugs to DMEGs.
**FIGURE 5:** *DMEGs and potential target therapeutic drugs. Density fractionation plot of drug to DMEGs gene set distance.*
## 3.5 Molecular docking and pharmacokinetic simulation
Currently, the ADRA1A protein does not have any resolved crystal structure. We used the AlphaFold Protein Structure Database website (https://www.alphafold.ebi.ac.uk/) website for ADRA1A homology modeling to obtain the 3D structure of the ADRA1A protein and the Deepsite (https://www.playmolecule.com/deepsite/) website to predict the protein activity of ADRA1A [32]. In addition, the Gromacs2019 software package was used to predict potential small molecule compounds, and a total of 10 small molecule compounds were identified by calculating RMSD values, namely DB06201, DB12733, DB00610, DB00450, DB00699, DB06706, DB06711, DB06764 DB00949, and DB08954 (see Table 2).
**TABLE 2**
| Compound | Compound.1 | Target | Docking score | H-bond interactions |
| --- | --- | --- | --- | --- |
| DB06201 | Rufinamide | GRM5 | −4.874 | SER143, SER145, THR168 |
| DB12733 | Dipraglurant | GRM5 | −4.348 | SER145, THR168 |
| DB00610 | Metaraminol | ADRA1A | −5.158 | MET1, GLU87 |
| DB00450 | Droperidol | ADRA1A | −5.819 | GLU87 |
| DB00699 | Nicergoline | ADRA1A | −2.137 | MET1 |
| DB06706 | Isometheptene | ADRA1A | −2.752 | GLU87 |
| DB06711 | Naphazoline | ADRA1A | −5.167 | — |
| DB06764 | Tetryzoline | ADRA1A | −5.246 | — |
| DB00949 | Felbamate | GRIN2B | −10.586 | GLU106, SER132 |
| DB08954 | Ifenprodil | GRIN2B | −6.821 | GLU106, ARG115, ALA135 |
Taken together, DB0094 (Felbamate) 9 had the highest molecular docking score and therefore had a higher potential to be a potential inhibitor of GRIN2B protein. Compound DB0094 interacted with GRIN2B protein, and the RMSD value of compound DB0094 was relatively stable overall (basically stable at around 3 Å) (Figure 5). The compound was able to produce hydrogen bonding interactions with SER132 and GLU106 of GRIN2B protein, and favorable hydrophobic interactions with ILE111, PRO78, ALA107, PRO177 and ALA135, as well as with TYR109, PHE114 and PHE176. Compound DB00949 (Felbamate) showed a high molecular docking score that many favorable interactions with GRIN2B protein were produced.
Figure 6A shows the changes of RMSD values of the D-protein backbone of GRIN2B protein bound to compound DB00949 (Felbamate) during the molecular dynamics simulation at 80 ns As can be seen from the figure, the conformation of the GRIN2B protein was very stable during the molecular dynamics simulation at 80 ns, which also indicated to some extent that the protein structure generated based on homology modeling was relatively reasonable (Figure 6B). In addition, Figure 6C gives the RMSD values of the molecular backbone of compound DB00949 (Felbamate) binding to GRIN2B protein during molecular dynamics (MD) simulation of 80 ns The results demonstrated that compound DB00949(Felbamate)’s RMSD value fluctuated relatively large by an obvious increasing trend during the first 20 ns The stability was basically achieved when it reached 20 ns It remained comparatively constant in the subsequent 60 ns Since the molecular docking was semi-flexible in this experiment, it is understandable that the RMSD values of the ligand’s molecular backbone fluctuated moderately in the initial stage of the dynamics simulation. Overall, compound DB00949 (Felbamate) was relatively stable when binding to GRIN2B protein, which further suggested that compound DB00949 (Felbamate) had a high potential to be a potential inhibitor of GRIN2B protein.
**FIGURE 6:** *Binding mode plot of GRIN2B protein with compound DB00949(Felbamate). (A) RMSD diagram of GRIN2B protein during 80 ns molecular dynamics simulation. (B) RMSD values of compound DB00949 (Felbamate) during 80 ns molecular dynamics simulation. (C) Plot of the dynamic binding pattern of GRIN2B protein with compound DB00949 (Felbamate) during 80 ns molecular dynamics simulation.*
## 3.6 Establishment of prognostic gene signature associated with DMEG
To explore the role of DMEG gene expression in PAAD prognosis, we first randomly divided 241 KRAS wild samples into two groups, one as the training set ($$n = 121$$) and one as the validation set ($$n = 120$$). We used 10-fold cross-validation to execute 1,000 Lasso regression analysis on the expression and clinical survival data of these 318 DMEGs genes, and we counted the appearances of each probe 100 times (Figure 7A). 3 probes (S100P, LY6D, and WFDC13) appeared the most frequently, and these 3 genes showed the highest frequency with different coefficient of variation trajectories of lambda as Figure 7B, standard deviation distributions of different lambda as Figure 7C. K-M survival curve results indicated that these three genes were able to distinguish more significantly between the two risk groups (Figures 7D–F). Finally, the risk score formula was obtained as follow: RiskScore=0.44*S100P+0.147*LY6D+0.29*WFDC13
**FIGURE 7:** *Establishment of prognostic gene signature associated with DMEG. (A) Frequency of individual gene combinations for one thousand lasso regressions. (B) Coefficient change trajectories of individual genes under different lambda. (C) Standard deviation distribution of the models under different lambda. (D) Prognostic KM curves of S100P in high and low expression groups. (E) Prognostic KM curves of LY6D in high and low expression groups. (F) Prognostic KM curves of WFDC13 in high and low expression groups.*
According to the expression level of the sample, we calculated the risk score for PAAD samples, and the RiskScore distribution is shown in Figure 8A. From the results of survival analysis, samples with high risk scores showed a significantly worse overall survival (OS) ($p \leq 0.001$). Then, we used the “timeROC” package to perform ROC analysis for prognostic classification of RiskScore, and the AUCs of predictive classification efficiency were 0.82, 0.89, and 0.77 for one-, three-, and five-year, respectively (Figure 8B), suggesting a good predictive performance. Finally, we performed zscore for Riskscore and determined the cut-off value, divided the sample into high-risk and low-risk groups, and plotted K-M curves. The low-risk group showed significantly better prognosis than that in the high-risk group (Figure 8C, log rank $p \leq 0.0001$).
**FIGURE 8:** *Performance of the prognostic gene signature in training set. (A) Risk score, survival time and survival status and expression of the 3 genes in training set. (B) ROC curve and AUC of the 3-gene signature classification. (C) Distribution of KM survival curves of the 3-gene signature in training set.*
## 3.7 Validation of the prognostic gene signature associated with DMEG
The model was validated further by using the same coefficients and model in the training set as in the validation set. The risk score of each sample was calculated using the same method, and the RiskScore distribution is shown in Figure 9A. Similarly, the AUCs of the classification efficiency of the one-year, three-year, and five-year prognostic predictions were 0.53, 0.86, and 0.85, respectively (Figure 9B), and the OS of the high-risk-score samples was significantly worse than that of the low-risk-score samples (Figure 9C, log rank $$p \leq 5$$e x10-4, HR = 2.42). Next, we used the same coefficients and model in the TCGA-PAAD cohort KRAS wild-type group samples as in the training set. We also calculated risk scores for each sample separately based on the expression level of the samples, and the RiskScore distribution is shown in Figure 10A, with AUCs of 0.72, 0.88, and 0.85 for the prognostic predictive classification efficiency at one, three, and 5 years, respectively (Figure 10B). Survival analysis showed that the OS of the high-risk score samples was significantly smaller than that of the low-risk score samples (Figure 10C, log-rank $$p \leq 0.00039$$, HR = 3.78). Finally, we performed the same validation in the ICGC-PAAD external data cohort, and the RiskScore distributions for each sample are shown in Figure 11A. The AUCs for prognostic predictive classification efficiency at one, three, and 5 years were 0.85, 0.85, and 0.91, respectively (Figure 11B), and survival analysis showed that the OS of the high-risk score sample was significantly worse than that of the low-risk score sample (Figure 11C, log rank $$p \leq 0.00024$$, HR = 1.68).
**FIGURE 9:** *Validation of the prognostic gene signature in validation set. (A) Risk score, survival time and survival status and expression of the 3 genes. (B) ROC curve and AUC of the 3-gene signature classification. (C) Distribution of KM survival curves of the 3-gene signature in the validation set.* **FIGURE 10:** *Validation of prognostic gene signatures in KRAS wild-type PAAD samples. (A) Risk score, survival time and survival status and expression of the 3 genes in KRAS wild-type samples; (B) ROC curves and AUC of the 3-gene signature classification; (C) Distribution of KM survival curves of 3-gene signature in TCGA KRAS wild-type samples.* **FIGURE 11:** *Validation of prognostic gene signatures in external datasets. (A) Risk score, survival time vs. survival status and expression of the 3 genes; (B) ROC curve and AUC for the 3-gene signature classification; (C) Distribution of KM survival curves of 3-gene signature in ICGC-PAAD samples.*
## 4 Discussion
PAAD as one of the most lethal and aggressive malignancies has a 5-year survival rate of less than $10\%$, (Jiménez et al., 2017), and is now among the top four leading causes resulting in tumor-associated death (Kleeff et al., 2016). The median age of onset of PAAD is 71 years, and with the aging of the population, its morbidity and mortality will increase rapidly. By 2030, PAAD is estimated as a second cause to tumor mortality (Rahib et al., 2014). The cause of pancreatic cancer is still unclear, and only $5\%$–$10\%$ of pancreatic cancer patients can be attributed to genetic factors (Siegel et al., 2022), although the mutation rate of KRAS reaches $95\%$, but a single KRAS gene mutation does not lead to the development of pancreatic cancer. Epigenetic alterations are more closely related to environmental and age factors than genetics. Past studies have found that epigenetic alterations occur in the early stages of tumor and are cumulative with tumor development (Nebbioso et al., 2018). In this study, we first DEGs and DMGs in normal samples versus tumor samples without KRAS wild-type based on expression profiling data of pancreatic cancer, and performed functional analysis. Then a classification model was constructed, which can accurately separate normal samples from tumor samples. Finally, we used DMEGs to perform gene-drug interactions on DrugBank to find some potential anti-PAAD drugs, which provides new ideas and potential targets for understanding the role of methylation in PAAD and treating PAAD.
In the early 20th century, Fukushima N and other scholars extensively studied the methylation of different genes in PAAD and its precancerous lesions (intra-epithelial neoplasia (PanIN), and found abnormal methylation of ppENK and p16 [13]. Next, it was shown that the incidence of aberrant methylation was $7.3\%$–$7.7\%$ in PanIN-1 patients, $22.7\%$ in PanIN-2 patients, and $46.2\%$ in PanIN-3 patients, a phenomenon that suggests that the incidence of aberrant methylation increases with a more advanced PanIN grade, but the exact mechanism is not clear (Fukushima et al., 2002). Our study, by screening for differentially methylated genes, initially it was found that methylation genes were mediated through Cytokine-cytokine receptor interaction, Natural killer cell-mediated cytotoxicity, Olfactory transduction, and some other pathways leading to the development of PAAD. To further confirm the pathway correlation between PAAD and gene methylation, the intersection of differentially genes and differentially methylated genes was taken and performed enrichment analysis again, and the results demonstrated that methylation led to PAAD by affecting cytokine receptor, NK cell-mediated cytotoxicity pathway.
Illumina human methylation 450 k bead array provides a better technical platform for further study of DNA methylation, therefore, we focused on methylation genes within the three regions of Gene body, TSS1500, and TSS200. A total of 758 hypermethylated genes and 418 demethylated genes were identified within the Gene body region, which was consistent with the incidence of PAAD hypomethylation reported in previous studies, and hypomethylation was mainly associated with cell cycle cycling, cell differentiation, and cell surface antigen/cell adhesion (Pedersen et al., 2011; Schäfer et al., 2021; Zhu et al., 2021). TSS1500 is a functional element belonging to differential methylation and is located between 1.5 kb and 200 bp upstream of the transcription start site. Previous studies identified the TSS1500 region as an oncogenic cofactor variable in lung adenocarcinoma and squamous carcinoma by differential methylation probes, and extensive analysis showed that gene probes outside the TSS1500 region could act as potential pathogenic players by affecting the activity of phosphatidylinositol-3,4,5-trisphosphate (Cao et al., 2022). Our study likewise demonstrated an expression imbalance between hypermethylation and hypomethylation in the TSS1500 region, and by using genes in the TSS1500 region, we were able to construct a classification model to distinguish PAAD from normal tissue, providing a useful tool to identify PAAD. tSS200 also belongs to the transcription factor repressor functional element, and methylation in the TSS200 region is not only related to tumor development, but also involved in the acceleration of epigenetic mutational load and epigenetic age, providing a new perspective for our understanding of the age of DNA methylation (Yan et al., 2020).
In 2005, the European Palliative Care Research Collaborative (EPCRC) network working group screened important clinical markers for survival prediction in patients with end-stage cancer based on decades of clinical evidence and recommended a variety of prognostic tools. On this basis, researchers have successively validated and derived several relevant prediction models according to cancer types, and PAAD prognostic models have emerged, which can be broadly classified into traditional manual prediction and statistical-based bioinformatics modeling, with the latter being the majority at present, but they all share common problems such as small sample size, low specificity, and poor predictive performance (Yuan et al., 2021) (Wang et al., 2021; Zhao et al., 2021). Compared with previous PAAD models, we performed model improvement by combining methylation genes (S100P, LY6D, and WFDC13) with clinical factors in prognostic factors and confirmed the model robustness by external and internal validation. S100P is a member of the S100 protein family containing 2 EF-hand calcium-binding motifs. s100 is localized in the cytoplasm and/or nucleus of a variety of cells and is involved in cell cycle progression and cell differentiation. Meta-analysis showed that S100P is a highly sensitive and highly specific tool for the diagnosis of PAAD (AUC = 0.93) (Hu et al., 2014; Camara et al., 2020). LY6D is mainly involved in lymphoid differentiation and cell surface activity, and the study showed that LY6D is significantly highly expressed in PAAD and is a valid predictor of PAAD, a result consistent with our study (Wang et al., 2020; Xu et al., 2021). WFDC13 belongs to the telomere cluster family of genes, and there are relatively few studies on WFDC13 in PAAD. Our data indicated that WFDC13 was a potential prognostic gene for PAAD and was implicated in the methylation process of PAAD, which provided new ideas for future basic experiments. However, our study was still inadequate and further basic experiments to elucidate the mechanism of the role of this methylation gene in PAAD are required.
There are some limitations in this study. Although the results showed that 3-DMEGs-based signature could distinguish tumor samples and normal samples, the model reliability should be improved with long-term clinical application. Additionally, we downloaded expression profiles and methylation data of PAAD from public databases. Thus, further prospective data should be collected to validate the results. Besides, experimental studies and clinical trials should be performed to verify the results of molecular docking in this study.
## 5 Conclusion
With the gene expression profile data of PAAD, we identified DEGs and DMGs between normal samples and tumor samples with KRAS wild type; the classification model based on DMEGs was able to accurately separate normal samples from tumor samples, and the gene-drug interactions were performed on DrugBank to find some potential anti PAAD drugs.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.
## Author contributions
All authors contributed to this present work: TJ designed the study and revised the manuscript, HW acquired the data. TC drafted the manuscript. All authors read and approved the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: The assessment of cortical hemodynamic responses induced by tubuloglomerular
feedback using in vivo imaging
authors:
- Blaire Lee
- Dmitry D. Postnov
- Charlotte M. Sørensen
- Olga Sosnovtseva
journal: Physiological Reports
year: 2023
pmcid: PMC10034006
doi: 10.14814/phy2.15648
license: CC BY 4.0
---
# The assessment of cortical hemodynamic responses induced by tubuloglomerular feedback using in vivo imaging
## Abstract
The tubuloglomerular feedback (TGF) mechanism modulates renal hemodynamics and glomerular filtration rate in individual nephrons. Our study aimed to evaluate the TGF‐induced vascular responses by inhibiting Na‐K‐2Cl co‐transporters and sodium‐glucose co‐transporters in rats. We assessed cortical hemodynamics with high‐resolution laser speckle contrast imaging, which enabled the evaluation of blood flow in individual microvessels and analysis of their dynamical patterns in the time‐frequency domain. We demonstrated that a systemic administration of furosemide abolishes TGF‐mediated hemodynamic responses. Furthermore, we showed that the local microcirculatory blood flow decreased, and the TGF‐induced hemodynamic oscillations were sustained but weakened after inhibiting sodium‐glucose co‐transporters in Sprague–Dawley rats.
High‐resolution in vivo blood flow imaging was employed to unveil the TGF‐induced hemodynamics in response to systemic administration of furosemide and phlorizin in Sprague–Dawley rats. Furosemide reduced the blood flow and abolished TGF oscillations in observed microvessels as expected. Phlorizin decreased the microcirculatory blood flow and the magnitude of TGF oscillations.
## INTRODUCTION
Despite the constant fluctuation in systemic blood pressure, the kidney maintains a stable filtration rate due to renal autoregulation (Loutzenhiser et al., 2006), which takes place in individual nephrons, or structurally discernible filtration units of the kidney. The two prominent components of renal autoregulation are the myogenic response (MR) and the tubuloglomerular feedback (TGF) mechanism. While both components target the resistance of afferent arterioles, they occur in response to distinct triggers. MR is the constriction of a vessel due to increased transmural pressure. The TGF correlates to the amount of chloride passing into the distal tubule. When the macula densa detects an increase in the tubular fluid Cl− concentration through the Na‐K‐2Cl co‐transporters, it signals the afferent arteriole to constrict (Briggs & Schnermann, 1987; Burke et al., 2014; Just, 2007; Schnermann, 1998). The effect is reduced blood flow and hydrostatic pressure into the glomerulus. Typically, the TGF mechanism modulates the afferent arteriolar resistance at around 0.033 Hz resulting in oscillatory blood flow. Managing glomerular capillary pressure is pertinent for the kidney to sustain function and prevent deterioration.
Since the concentration of tubular sodium chloride can modulate the TGF, pharmacologically altering the reabsorption of the ions by inhibiting various Na+ co‐transporters and its implications in renal autoregulation is of great interest. Furosemide is a loop diuretic for treating patients with heart failure and hypertension. Furosemide inhibits the Na‐K‐2Cl co‐transporters (NKCC2) expressed in the thick ascending limb of the loop of Henle and the macula densa, effectively blocking the reabsorption and the sensing of sodium chloride (Castrop & Schnermann, 2008; Ponto & Schoenwald, 1990). Although it is well‐known that furosemide abolishes the TGF in a single nephron (Just, 2007), the hemodynamics due to TGF inhibition has not been observed in a population of nephrons in vivo due to the lack of appropriate instruments.
The kidney plays an integral role in glucose homeostasis partly through glucose reabsorption. Sodium‐glucose co‐transporter 2 (SGLT2) expressed in the luminal membrane of the proximal convoluted tubules is responsible for $97\%$ of the glucose reabsorption through a sodium‐dependent active transport (Vallon & Thomson, 2017). Phlorizin, an SGLT 1 and 2 inhibitor, lowers the blood glucose level, induces glucosuria, and promotes weight loss in diabetic patients by blocking the sodium‐glucose reabsorption (Vallon, 2015; White Jr, 2010). Recent findings suggest that inhibiting SLGT2 can reduce glomerular hyperfiltration and restore the TGF in a compromised kidney (Sen & Heerspink, 2021). Still, the in vivo effect of SGLT2 inhibition on nephron blood flow and the TGF remains poorly understood.
High‐resolution laser speckle contrast imaging (LSCI) can measure microvascular hemodynamics in a population of nephrons in real‐time (Lee, 2022; Postnov et al., 2022). With LSCI, one can record the microvascular blood flow at the renal surface. These microvessels, or “star vessels”, stem from the efferent arterioles and branch in a stellate fashion underneath the kidney capsule (Beeuwkes III & Bonventre, 1975; Nordsletten, 2006; Yoldas and Orhun Dayan, 2014). The LSCI can robustly detect these microvessels' TGF oscillations and nephron hemodynamics because they are anatomically downstream from the afferent arterioles, where the TGF signals originate. Yet, the approach and analysis pipeline for employing high‐resolution LSCI in acute intervention studies have not been fully established.
We acutely administered systemic infusions of furosemide (NKCC2 inhibitor) or phlorizin in anesthetized Sprague–Dawley rats. We assessed the TGF‐induced hemodynamic changes in a population of nephrons in vivo with a high‐resolution LSCI. We also applied the superlet analysis‐ an improved method for analyzing biosignals‐ to renal hemodynamics for the first time (Moca et al., 2021). We also extracted new analytical metrics to quantify the characteristics of TGF oscillations. Finally, we evaluated the effects of furosemide and phlorizin on cortical hemodynamic responses mediated by the TGF.
## Surgical preparation and experimental protocol
Danish National Animal Experiment Inspectorate approved all rodent experiments. Male normotensive Sprague Dawleys ($$n = 12$$, RjHan:SD, Janvier) weighing 330 ± 23 g were used. The animals were fed ad libitum and housed in a 12‐h light/dark cycle. Detailed animal preparation can be found in Postnov et al. [ 2022] and Holstein‐Rathlou et al. [ 2011]. Here we provide an abridged version. All experiments were performed under sevoflurane anesthesia (Sevorane, Abbvie), $8\%$ at induction, and maintained at around $1.5\%$. The body temperature was maintained at 37C on a servo‐controlled heating table. The animal was laid ventral side up, and a mask delivering anesthesia covered the nose and the mouth during the surgery. A small horizontal incision was made between the cervical and pectoral region to expose and isolate the trachea, the left jugular, and the right common carotid. Two polyethylene tubes (PP10, Smith Medicals) were inserted into the left jugular vein for infusions of saline ($0.5\%$ NaCl) and a muscle relaxant (0.5 mg/mL) (Nimbex, Apsen) ‐to prevent secondary breathing motion‐ at 20 μL/min via syringe pumps (AL‐1000, World Precision Instruments). A slightly larger polyethylene tube (PP50, Smith Medicals) was placed into the right carotid for continuous blood pressure monitoring. The animal was ventilated through tracheotomy with a volume‐controlled respirator (7025, Ugo Basile) at 7 mL per stroke (60 strokes/min). A laparotomy was performed to expose and stabilize the kidney with a custom 3D‐printed well. $1.5\%$ (w/v) agarose solution was poured over the kidney, and a glass coverslip was gently placed on top of the kidney to prevent a parched surface. A myograph wire (40 μm in diameter) bent at an acute angle was placed on top of the coverslip to track visceral and breathing motion for image registration. The left ureter was cannulated with a polyethylene tube (PP10 connected to PP50, Smith Medicals) for free urine flow and sample collection. A renal flow probe (1PRB, connected to T420 Transonic) was fastened around the renal artery for an animal to measure the standard renal blood flow. Each experiment was successful for animals with a stable blood pressure of 100–130 mm Hg.
The animals were separated into two groups: Animals receiving furosemide ($$n = 5$$) and animals receiving phlorizin ($$n = 7$$). The experiments were carried out in an alternating pattern between the animal groups between 9 am and 5 pm. First, baseline saline infusion and image recording took place for 30 min. Then, the saline syringe connecting to the left jugular was replaced with either Phlorizin (10 mg/mL, 274,313, Sigma‐Aldrich) or furosemide (2.5 mg/mL) solution and infused at 20 μg/mL to induce glucosuria or diuresis, respectively (Malatiali et al., 2008). A minimum of 30 min of acclimation period allowed the administered substance to reach its peak systemic concentration. Finally, 30 min of postacclimation recording took place while continuing the infusion to sustain bioavailability and saturate the TGF response. The animals were sacrificed at the end of each experiment. See the experimental overview in Figure 1.
**FIGURE 1:** *Experimental overview assessing the influence of loop diuretic (Furosemide) and Phlorizin on renal hemodynamics. (a) Imaging setup for laser speckle contrast imaging for real‐time high‐resolution tissue blood flow measurements. (b) Time‐averaged blood flow map of the renal surface acquired with the LSCI. The yellow star‐like shaped regions are microvasculatures with relatively high blood flow compared to the surrounding renal tissue. (c) The microvasculatures are segmented (demonstrated by black borders) for blood flow time series extraction. (d) The timeline for the whole experiment. The recording takes place during the drug infusion.*
## Imaging setup
We used a high‐resolution LSCI (Lee, 2022) to record the changes in renal blood flow with the infusion of Phlorizin or Furosemide. Here we briefly describe the imaging setup. A CMOS sensor camera (Basler acA2048‐90umNIR, 5.5 μm pixel size, 8bit mode) was vertically attached to a video zoom lens with 4.5× magnification (VZM 450, Edmund Optics). A volume holographic grating stabilized diode (LP7850‐SAV50, 785 nm Thorlabs) was used to illuminate the tissue surface and passed through a linear polarizing filter before reaching the photo‐sensors to reduce recorded specular reflections and first‐order scattering events. Finally, the apparatus was mounted to a height‐adjustable z‐stage plate to enable vertical translation. The original modality is a multiscale system, and the low zoom arm (larger field‐of‐view) can also be activated to record the whole kidney blood flow. Thirty minutes of raw speckle images were recorded at 50 Hz with 5 ms exposure time for the control and the intervention period. We eliminated all ambient light and kept all settings and system configurations consistent across every experiment.
## Data analysis
Postacquisition data analysis approaches are described in Lee [2022] and Postnov et al. [ 2022]. Here we provide a summary of each step.
## Image registration and speckle contrast analysis
We aligned the images to the first frame to eliminate the motion artifact from breathing in the x–y plane. A thin myograph wire was placed on the glass cover over the kidney as a reference mark during the experiment. The raw frames were converted to binary images, with the myograph wire and the surrounding tissue as the foreground and the background. We then calculated the geometrical transformation for each frame compared to the first frame and aligned the images based on transformation metrics.
We implemented temporal contrast analysis to preserve high spatial resolution to improve image quality and segmentation performance. First, temporal laser speckle contrast was calculated from raw data sets by K=σμ. σ is the standard deviation, and μ the mean of the selected temporal kernel (25 frames). Following temporal contrast analysis, the data was sub‐sampled from 50 to 1 Hz and converted from the contrast images to blood flow index images by BFI=1K2.
A mean spatial blood flow map was created by averaging the frames recorded over the time frame of interest: control and drug. Semiautomatic segmentation was applied to high‐zoom data to segment individual vessels from the surrounding renal tissue according to the approach described in Lee [2022] and Postnov et al. [ 2022].
## Time‐frequency analysis
TGF is an oscillatory signal that operates within a range of frequencies (0.018 and 0.033 Hz), and it can vary over time like many other biological oscillations (Zou et al., 2002). Therefore, time‐frequency super‐resolution (superlet) analysis was implemented to the extracted flow time series to reveal TGF oscillations (Moca et al., 2021). While there are many methods to analyze frequency components of biosignals, superlet was chosen for its exceptional performance in resolving the frequency of a signal that changes over time, a primary objective in detecting the changes in TGF frequencies over an observation period (Moca et al., 2021).
We extracted three metrics from the blood flow time series and the power spectrum. These metrics were compared between the control and the drug period. Metric 1 (BFI): Mean blood flow index was calculated by averaging the blood flow index time series over the control and the drug period. Metric 2 (Sigma). The standard deviation of TGF oscillations. Blood flow index time series were passed through a bandpass to filter of frequency between 0.015 and 0.04 Hz so that signals only contained the frequency range of interest. Then we calculated the standard deviation of filtered signals. We use this metric to measure the amplitude of TGF oscillations. Metric 3 (AUC). The area under the curve of the power spectrum within the TGF frequency. First, we performed trapezoidal numerical integration between 0.015 and 0.04 Hz of the individual power spectrum to find the area under the curve within the TGF frequency band. Then we drew a median line between 0.04 and 0.05 Hz for each power spectrum and calculated the area below the line within the TGF frequency band. Then the area below the median line was subtracted from the area under the curve. We use this metric to measure the significance of TGF band frequencies.
## Statistical analysis
Paired sample t‐test was used to compare the urine samples collected before and after administering either furosemide or phlorizin. In addition, the linear mixed‐effects model was used to compare the TGF metrics between the control and their respective furosemide or phlorizin data, adjusting for between‐subject variability. Results with $p \leq 0.001$ were declared significant. The central mark of box and whisker plots indicate the median, and the top and the box edges indicate the 75th and the 25th percentiles of the data.
## Characterizing the TGF‐induced hemodynamics
To accurately capture the transient dynamics of TGF, we took an exemplary microvessel blood flow time series obtained from a random experiment. We decomposed the signal from the time domain to frequency‐power components using time‐frequency superlet analysis shown in Figure 2. It has been demonstrated that the superlet provides a superior time‐frequency resolution compared to the Fourier and the Wavelet transform. The time‐averaged power spectrum shows a sharp and narrow peak near 0.03 Hz, demonstrating that the superlet analysis can sharply localize the TGF frequency as shown in Figure 2e. Figure 2f shows the TGF signal over time for the control period, corresponding to the oscillation shown in Figure 2c. The line at 0.03 Hz disappears on the right panel of Figure 2f because the TGF oscillation no longer exists, demonstrated by the noisy signal in Figure 2d.
**FIGURE 2:** *Illustration of the analysis from segmentation to a time‐frequency spectrogram. (a) A close‐up image of a star vessel that can be imaged at the renal surface. The images of vessels undergo a segmentation process, from which (b) blood flow index time series can be extracted. (c, d) A closer look at the B reveals that the TGF oscillations during the control period (black) disappear with the furosemide infusion (red). (e) Power spectrum shows a prominent peak at around 0.03 Hz associated with TGF during the control period. (f) The spectrogram of superlet analysis for the control period (left) shows a sharp localization of the TGF frequency around 0.03 Hz sustained for 30 min that disappears completely with the furosemide infusion (right).*
With the implementation of the superlet analysis, we explored how nephron hemodynamics can vary from vessel to vessel. Although TGF is known to be a persistent mechanism in the nephrons, we found three distinct types of hemodynamic behaviors in the power spectrum, shown in Figure 3. In some nephrons, TGF operates at a single frequency stable over the observation period (Figure 3a). In other nephrons, we did not see any TGF peaks in the power spectrum (Figure 3b). Although this does not mean the TGF mechanism does not exist, it may be indistinguishable from other dynamics and noise. In many cases, we found that nephrons can have dynamic TGF frequencies over time, exhibiting multiple peaks between 0.015 and 0.04 Hz (Figure 3c). For example, the time‐frequency spectrogram in Figure 3f shows that the TGF frequency slowly migrates from 0.02 to 0.025 Hz over the observation period of 900 s.
**FIGURE 3:** *Three types of TGF‐induced responses in different vessels. The top panel represents time‐averaged power spectra: One peak around 0.03 Hz corresponds to a single frequency of TGF oscillation (a); a TGF peak cannot be detected (b); Multiple peaks within the narrow frequency band correspond to TGF oscillations with different frequencies (c). The bottom panel represents the time‐frequency spectrogram matching to power spectra on (a–c): A stable TGF frequency over the observation time (d), No observable TGF frequency (e); Multiple TGF frequencies within the observation time (f).*
## Acute effect of Na‐K‐2Cl co‐transporter inhibition on TGF dynamics
Furosemide, a loop diuretic that blocks Na‐K‐2Cl‐ co‐transporters, is known to cause diuresis and increase the excretion of sodium, chloride, and other ions. In animals receiving the systemic infusion of furosemide, urine flow increased from 12.6 ± 2.95 to 63.0 ± 3.63 μL/min ($p \leq 0.01$). The urinary sodium excretion increased from 0.38 ± 0.29 to 5.98 ± 0.57 μEq/min ($p \leq 0.01$). The blood pressure remained unchanged from 112.16 ± 3.21 to 108.49 ± 2.21 mm Hg.
With the systemic infusion of furosemide, we abolished the TGF mechanism and analyzed the hemodynamic changes in 317 microvessels associated with individual nephrons across five Sprague–Dawley rats. Figure 4 shows that animals 1, 2, and 3 exhibited strong TGF‐mediated oscillations during the control period. Interestingly, vessels in animal 1 showed heterogeneous phases across the observed vessels, while animals 2 and 3 showed synchronized oscillations. The synchronizations are especially evident between 300 and 500 s in animal 2 and between 300 and 400 s in animal 3. After the furosemide infusion, the oscillations disappear, evidenced by the lack of oscillatory patterns in the carpet plot. The oscillations do not reach the minimum and maximum color values as they do in the carpet plots for control. Animals 4 and 5 showed weak TGF oscillations during the control period (compared to animals 1–3), and the lack of TGF‐mediated hemodynamics continued during the furosemide infusion period. Nevertheless, all animals show that the TGF oscillations have been eliminated after Na‐K‐2Cl inhibition despite having various amplitudes of TGF oscillations during the control period.
**FIGURE 4:** *Eliminating TGF with furosemide administration in normotensive Sprague–Dawley rats. (a) An example time series taken from one vessel shows that the TGF oscillations present during the control period (black) disappear after furosemide infusion (red). (b) Carpet plots show TGF‐mediated blood flow oscillations across many vessels, with the color range representing filtered blood flow index. Oscillations are strong in animals 1–3 and weak in animals 4 and 5 in control (first column). Regardless, the TGF oscillations are eliminated in all animals, as demonstrated by the lack of oscillatory patterns (second column). The plots only show 600 s of 30‐min recordings to visualize the oscillatory patterns better.*
To quantify the visual changes shown in Figure 4, we selected three metrics to compare between control and furosemide as described in the methods: mean blood flow index (BFI), the standard deviation of the TGF bandpass‐filtered signal (Sigma), and the area under the curve of the power spectrum (AUC) (Figure 5a–c). We found that furosemide had a significant effect on the metrics associated with the TGF: the BFI increased ($F = 12.13$, $p \leq 0.001$), while the Sigma and the AUC decreased significantly ($F = 86.6$, $p \leq 0.0005$; $F = 226.2$, $p \leq 0.0005$). A decrease in Sigma indicates the lack of TGF‐induced oscillations in the filtered signal. The reduced AUC shows that the power of the TGF frequency band in the power spectrum is weak. In Figure 5d, we used a 3‐dimensional scatter plot to show the spatial separation of the three metrics between control and furosemide for every segmented microvessels ($$n = 318$$). Animals 1, 2, and 3 show a similar pattern of separation in the metrics with minimal overlap in data points between the control and after the furosemide infusion. The spatial separation of the data points is less visible in animals 4 and 5 because they had weak TGF oscillations in control (Figure 5). Lastly, it should be noted that animal 5 seems to have a slightly higher AUC after furosemide relative to the control period, but this is a negligible difference. Details of the statistics are provided in Table S1.
**FIGURE 5:** *Increased local blood flow and the TGF dynamic changes induced by furosemide administration. Top panel: Box plots represent the median (central red line), the 25th (bottom edge), and 75th (top edge) percentiles of the analyzed TGF metrics: (a) mean blood flow index (BFI), (b) standard deviation of filtered TGF time series (Sigma), and (c) area under the curve (AUC). Reduced Sigma and AUC metrics represent the elimination of TGF oscillations. (d) Three metrics are plotted for every observed microvessel. Top row: 3 animals show a good separation in metrics between the control and the furosemide condition. Bottom row: Metrics of control and furosemide are clustered together. *p < 0.001; **p < 0.005. The units for BFI, Sigma, and AUC are arbitrary. μMean values for control and furosemide data points.*
## Effect of acute sodium‐glucose co‐transporter 2 inhibition on TGF dynamics
We infused phlorizin, a sodium‐glucose co‐transporter inhibitor known to increase the urine flow and the excretion of sodium and glucose in the urine. We observed a significant increase in urine flow rate from 11.11 ± 2.83 to 24.17 ± 4.83 μL/min ($p \leq 0.05$). The glucose excretion increased from 0.01 to 4.77 ± 1.14 μEq/min ($p \leq 0.05$) along with increased sodium excretion from 0.56 ± 0.27 to 3.50 ± 0.35 μEq/min ($p \leq 0.01$). The blood pressure remained unchanged from 108.29 ± 5.58 to 100.73 ± 4.93 mm Hg.
To explore the changes in TGF hemodynamics induced by inhibiting sodium‐glucose co‐transporter 2, we systemically administered phlorizin in rats and observed the renal hemodynamics. Hemodynamics in 318 microvessels associated with individual nephrons were analyzed across seven Sprague–Dawley rats. As expected, Figure 6a shows that TGF oscillations remain intact after the infusion but operate at a reduced blood flow. An exemplary blood flow time series shows TGF‐mediated oscillations around BFI (a.u.) of 60 during the control period that reduces to 52 during the phlorizin infusion while maintaining the oscillation. During the control period, animals 1–4 show some degree of synchronization in TGF oscillations across vessels, which can be interpreted from the vertical blue‐yellow striations. Animals 5, 6, and 7 show weak synchronicity in TGF oscillations, evidenced by the lack of vertically aligned striations in their respective carpet plots: the phase and frequency of the TGF oscillations vary across the observed vessels. Overall, animals show different levels of oscillatory synchronicity during the control period. After the infusion of phlorizin, the TGF oscillations are maintained, regardless of the degree of synchronization. The phlorizin plots do not reach the maximal color intensity compared to the control plots (Figure 6b), indicating a reduction in the blood flow index.
**FIGURE 6:** *TGF oscillations in segmented vessels under phlorizin administration in normotensive Sprague–Dawley rats. (a) Exemplary blood flow time series demonstrating sustained TGF oscillations for 600 s in both control and phlorizin. Note the different y‐axis in the blood flow index (a.u.). (b) First column (control): TGF band‐filtered signals show consistent TGF oscillations over the observation time. In the second column (phlorizin): TGF oscillations are still well pronounced. The plots only show 600 s of 30‐min recordings to visualize the oscillatory patterns better. μMean values for control and furosemide data points.*
The diversity in TGF‐driven oscillatory patterns across vessels in an animal can be further understood by looking at Figure 7. The vessels show varying dominant TGF frequencies in the collective time‐averaged power spectra. More importantly, the power of the TGF frequencies varies across vessels. It was previously mentioned that animal 3 showed synchronized TGF oscillations (strong vertical striations) while animal 5 showed weak synchronization (no vertical striations) in Figure 6 during the control period. Regardless of the level of synchronicity, Figure 7 shows that the magnitude of TGF‐induced oscillatory hemodynamics can differ from vessel to vessel within an animal. After the phlorizin infusion, the diversity in the power of TGF frequency still exists, although the dominant TGF frequency may vary.
**FIGURE 7:** *Different vessels can exhibit different amplitudes of the TGF frequency peak. (a) The time‐averaged power spectra of 40+ vessels show that vessels can have heterogeneous TGF peak amplitudes during the control period. After the phlorizin infusion, the dominant TGF frequency peak around 0.03 Hz remains well‐visible but exhibits variations in power across vessels. (b) Vessels show a dominant TGF frequency between 0.025 and 0.03 Hz in varying degrees of power in both conditions.*
We quantified the same metrics for the phlorizin group as we did for the furosemide group (Figure 5) to measure the altered TGF‐mediated hemodynamics in individual vessels induced by phlorizin. We observed a significant decrease in the BFI ($F = 48.4$, $p \leq 0.0005$), presumably associated with increased afferent arteriolar resistance in observed nephrons (Figure 8a). A significant decrease in Sigma ($F = 274.6$, $p \leq 0.0005$) indicates a smaller amplitude for TGF oscillations. The AUC was also reduced ($F = 132.3$, $p \leq 0.0005$), indicating a weaker contribution of the TGF oscillation to the nephron hemodynamic partially caused by a smaller sigma. Figure 8d shows that the data points plotting the metrics per vessel separate the control from the intervention period well. Animals 1–4 show an apparent reduction in the AUC and the sigma across all imaged microvessels, indicating a change in the TGF‐mediated hemodynamics induced by phlorizin. Although animal 5 shows a minimal change to the signal and the AUC in vessels, there is a prevalent decrease in the BFI. Animals that originally had weak TGF oscillations (animals 6 and 7) show considerable overlap in data points. Details of the statistics are provided in Table S1.
**FIGURE 8:** *Decreased nephron blood flow and TGF dynamic changes induced with phlorizin administration. Top panel: Box plots represent the median (central red line), the 25th (bottom edge), and 75th (top edge) percentiles of the analyzed TGF metrics: (a) mean blood flow index (BFI), (b) standard deviation of filtered TGF time series (Sigma), and (c) area under the curve (AUC). (d) Three‐dimensional scatter plot of the three metrics for each microvessels. (d) Top row: 3 animals show a good spatial separation across all three dimensions. Bottom row: control and phlorizin clusters show similar metrics in 2 animals. **p < 0.0005. The units for BFI, Sigma, and AUC are arbitrary. μMean values for control and phlorizin data points.*
## DISCUSSION
This study reveals altered TGF‐driven hemodynamics induced by furosemide and phlorizin in a large population of nephrons for the first time. The current paradigm considers TGF as a single‐nephron event, and the micropuncture technique remains a gold standard for answering questions in renal pathophysiology. Micropuncture methods can access the TGF‐induced dynamics at a single‐nephron resolution, which is suitable for establishing the connection between tubular NaCl load and tubular pressure (Vallon, 2009). However, this study contends that single‐nephron measurements are insufficient in understanding nephron hemodynamics by demonstrating the inherent diversity in TGF‐driven hemodynamics across vessels, time, and animals, highlighting the need for real‐time access to hemodynamics in a population of nephrons.
Here, we propose to look at TGF‐induced cortical hemodynamic responses through high‐resolution LSCI. This technique allows us to simultaneously access the TGF‐mediated vascular responses in many vessels on the kidney cortex. We show that administering furosemide increases the local microcirculatory blood flow by $11.3\%$, and the TGF oscillations become eliminated. There is a strong link between the changes in the predistal tubular reabsorption of solutes like sodium and glucose and the sequential perturbation of the TGF mechanism, which may alter the glomerular filtration pressure. An expected result of this study is increased microvascular blood flow by abolishing the TGF mechanism with furosemide. This paper's results are consistent with single‐nephron studies, which found that an intra‐luminal microperfusion of furosemide can abolish TGF‐mediated oscillations in Sprague–Dawley rats (Leyssac, 1986; Leyssac & Holstein‐Rathlou, 1986). The effect of furosemide on the TGF‐induced hemodynamics found in this study is obvious. But novelty lies in demonstrating the hemodynamical response associated with a population of nephrons with the superlet analysis‐ with improved sensitivity to the transient signals and SNR compared to other time‐frequency analyses (Moca et al., 2021).
To discuss the results of the phlorizin experiment, we distinguish between the TGF mechanism and the TGF‐mediated oscillations. While we expect the TGF mechanism to remain operable at all times, the TGF‐induced oscillations are temporally dynamic. Contrary to the effects of furosemide, phlorizin administration induces a relative drop in local microcirculatory blood flow by $6.42\%$ and weakens (but sustains) TGF‐mediated oscillations in microvessels associated with nephrons. Previously, it was shown that SGLT2 inhibitors could reduce the glomerular capillary pressure via the afferent arteriolar constriction by eliciting the TGF (Ehrenkranz et al., 2005; Ghezzi et al., 2018; Sen & Heerspink, 2021; White Jr, 2010). It was also shown that dapagliflozin saturates the TGF response in early diabetic rats (Thomson, 2012). Plus, inhibiting proximal HCO3 reabsorption‐ similar to SGLT2 inhibition can reduce overall proximal reabsorption. Sequentially, the TGF can reset to a lower operating point when the proximal reabsorption is reduced, demonstrating the adaptability of TGF in response to fluid‐content changes (Deng et al., 2002; Scott et al., 1997). Furthermore, Kidokoro et al. [ 2019] showed that administering empagliflozin for 30 min in mice reduced the afferent arteriolar diameter in vivo. These accumulating studies provide a plausible explanation for the smaller oscillatory amplitudes (sigma) and the AUC seen in our results. Interestingly, there is an increased expression and activity of SGLT2 in diabetic kidneys, leading to a higher potency of SGLT2 inhibitors (Vallon & Thomson, 2017). Expanding this study to diabetic rat kidneys may unveil novel TGF‐driven hemodynamical patterns in response to SGLT2 inhibitors: We could potentially understand its real‐time nephroprotective influence on a compromised kidney.
This study also presents the observed variability in the dominant TGF dynamic among animals. Five out of 12 animals showed weak TGF oscillations in control (two from the furosemide group and three from the phlorizin group). These animals underwent the same surgical and experimental protocol, yet the TGF‐mediated hemodynamic diversity persisted. Similar observations were made in a study by Scully et al. [ 2014] in which a strong MR signal was detected in three out of six long Evans rats. The size of the imaged region is approximately 1700 × 1700 μm of the renal surface, and the weak TGF signal of segmented microvessels does not represent the whole kidney hemodynamics. Figure S1 demonstrates that the TGF signal exists during control in all animals in varying dominant frequency and power when the wavelet transform is applied to the blood flow time series of the whole renal surface using the low zoom data.
Several factors can be addressed for future studies. First, the group size could be expanded to make more robust statistical conclusions about hemodynamical changes induced by various tubular transporter inhibitors. Plus, including female rats in subsequent studies would mitigate the current limitation of this study. Second, a chronic study of an analog of phlorizin with fewer side effects would be more clinically relevant. Third, while this study does not quantify the network behavior of the nephrons, a phase‐frequency cluster analysis could be implemented to quantify the synchronicity level among nephron‐associated microvessels. This could reveal the collective behavior of nephron ensembles adapting to altered TGF‐induced hemodynamics. Finally, the pressure‐induced (myogenic) vasomotion and the TGF contribute to efficient autoregulation by modulating the afferent arteriolar tone (Carlström et al., 2015; Chon, 2005; Just, 2007). In spontaneously fluctuating single‐nephron blood flow obtained from Sprague–Dawley rats, there exists a slow oscillation (20–30 mHz) mediated by the TGF and a fast oscillation (100 mHz) connected to the myogenic activity (Chon, 2005). It was also shown that the TGF modulates the myogenic activity (Marsh, 2005), and myogenic oscillations are enhanced when TGF was inhibited with furosemide (Yip et al., 1993). Although this focuses on the TGF hemodynamics, it would be interesting to see if the myogenic mechanism can compensate for the reduced TGF oscillations observed after the inhibition of SGLT2. The ramifications of reduced TGF fluctuations are unclear: *Is this* a sign of renal decline or a mechanism the kidney uses to respond to a physiological imbalance?
Recent experiments showed that many nephrons coordinate their TGF‐induced hemodynamic responses, which can also be seen in the control data of our results (Figures 4 and 6). The presence of TGF oscillations links to the synchronization of vascular responses of neighboring nephrons (Brazhe et al., 2014; Holstein‐Rathlou et al., 2011; Mitrou et al., 2015). Recently, Postnov et al. revealed that renal microcirculatory blood flow tends to demonstrate clustered, frequency‐locked activity (Postnov et al., 2022). One could speculate that renal autoregulation provides better protection when nephrons act together. Cooperating nephrons can increase the efficiency of renal autoregulation by collectively engaging in more preglomerular resistance. Yet, the efficacy of such cooperative behavior on overall renal autoregulation remains an open question, and this paper's approach opens doors to answering these questions.
## CONFLICT OF INTEREST STATEMENT
The authors declare no competing interest.
## ETHICAL STATEMENT
The study was conducted in accordance with the Danish National Animal Experiments Inspectorate (protocol #: 2020‐15‐0201‐00547).
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|
---
title: The increased antioxidant action of HDL is independent of HDL cholesterol plasma
levels in triple-negative breast cancer
authors:
- Amarilis de Lima Campos
- Maria Isabela Bloise Alves Caldas Sawada
- Monique Fátima de Mello Santana
- Rodrigo Tallada Iborra
- Sayonara Ivana Santos de Assis
- Mozania Reis
- Jacira Xavier de Carvalho
- Luiz Henrique Gebrim
- Marisa Passarelli
journal: Frontiers in Oncology
year: 2023
pmcid: PMC10034011
doi: 10.3389/fonc.2023.1111094
license: CC BY 4.0
---
# The increased antioxidant action of HDL is independent of HDL cholesterol plasma levels in triple-negative breast cancer
## Abstract
### Introduction
The association between high-density lipoprotein cholesterol (HDLc) with the incidence and progression of breast cancer (BC) is controversial. HDL removes excess cholesterol from cells and acts as an antioxidant and anti-inflammatory. BC is a heterogeneous disease, and its molecular classification is important in the prediction of clinical and therapeutic evolution. Triple-negative breast cancer (TNBC) presents higher malignancy, lower therapeutic response, and survival rate. In the present investigation, the composition and antioxidant activity of isolated HDL was assessed in women with TNBC compared to controls.
### Methods
Twenty-seven women with a recent diagnosis of TNBC, without prior treatment, and 27 healthy women (control group) paired by age and body mass index (BMI) were included in the study. HDL and low-density lipoprotein (LDL) were isolated from plasma by discontinuous density gradient ultracentrifugation. Plasma lipid profile and HDL composition (total cholesterol, TC; triglycerides, TG; HDLc; phospholipids, PL) were determined by enzymatic colorimetric methods. ApoB and apo A-I were quantified by immunoturbidimetry. The antioxidant activity of HDL was determined by measuring the lag time phase for LDL oxidation and the maximal rate of conjugated dienes formation in LDL incubated with copper sulfate solution. The absorbance (234 nm) was monitored at 37°C, for 4 h, at 3 min intervals.
### Results
The control group was similar to the TNBC concerning menopausal status, concentrations, and ratios of plasma lipids. The composition of the HDL particle in TC, TG, PL, and apo A-I was also similar between the groups. The ability of HDL to retard LDL oxidation was $22\%$ greater in the TNBC group as compared to the control and positively correlated with apoA-I in HDL. Moreover, the antioxidant activity of HDL was greater in the advanced stages of TNBC (stages III and IV) compared to the control group. The maximum rate of formation of conjugated dienes was similar between groups and the clinical stages of the disease.
### Discussion
The results highlight the role of HDL as an antioxidant defense in TNBC independently of HDLc plasma levels. The improved antioxidant activity of HDL, reflected by retardation in LDL oxidation, could contribute to limiting oxidative and inflammatory stress in advanced stages of TNBC.
## Introduction
Breast cancer (BC) is the most commonly diagnosed malignant tumor in women and contributes to $69\%$ of deaths associated with cancer. Considering its heterogeneous nature, the histological classification of BC based on the expression of receptors for estrogen, progesterone, and HER-2 helps to predict therapy and prognosis [1]. Triple-negative BC (TNBC) is negative for hormone receptors and HER2 accounting for about 10-$20\%$ of all BC. It differs from other types of invasive cancer by being more prevalent in women younger than age 40, growing and spreading faster with fewer oncological therapeutic options, and tending to have a worse prognosis and survival [2].
Alterations in plasma lipid and lipoprotein profiles are contributors to BC, considering the role of lipids, particularly cholesterol, in tumor proliferation and metastasis [3, 4]. Solid tumors accumulate cholesterol by increasing its synthesis and the uptake of native and modified forms of low-density lipoproteins (LDL). In TNBC, plasma lipids are reported as increased, probably helping to supply lipids to the tumor and supporting its aggressiveness. Potential lipid biomarkers (ceramides, phosphatidylcholine, lysophosphatidylcholine, and diacylglycerol) were detected in TNBC [5], reinforcing the change in lipid profile in this more aggressive tumor [2].
On the other hand, high-density lipoproteins (HDL) are considered protectors since they remove excess cholesterol from cells and minimize oxidation and inflammation [6]. Nonetheless, the association of HDL cholesterol (HDLc) with BC development and progression is still controversial, largely due to the concomitant presence of metabolic comorbidities that influence the levels of HDLc in plasma, ongoing oncological therapies and intrinsic differences among hormone-sensitive tumors, and TNBC [7]. Moreover, it is conceivable that, at the resemblance of the prediction of cardiovascular disease, HDLc measurement utilized as a protection metric is not sufficiently discriminating, considering the interplay of functions promoted by HDL. Markers of lipid peroxidation are enhanced in BC pointing to a role in HDL dysfunction [8]. In the present investigation, the composition and the antioxidant role of HDL particles isolated from TNBC women´s plasma were measured in comparison to healthy control women.
## Material and methods
Two-hundred and one women newly diagnosed with BC between 18 and 80 years old in stages I to IV of the disease, not receiving any treatment were recruited at Hospital Pérola Byington. The molecular classification of tumors was performed according to the American College of Pathologists [9, 10] in breast samples obtained by percutaneous biopsy or surgery submitted to immunohistochemistry. From this large casuistic, a convenience sample of TNBC was obtained corresponding to the $16\%$ frequency of TNBC in population. Then, 27 TNBC women were included in the protocol. Twenty-seven health women paired by age and body mass index (BMI) were recruited at Unidade Básica de Saúde Dra. Ilza Weltman Hutzler and included as a control group. Women with diabetes mellitus, autoimmune diseases, hypothyroidism, chronic kidney disease (estimated glomerular filtration rate < 60mL/min/1.73m2), smokers, alcoholics, in use of antioxidants, anti-inflammatory drugs, hormone replacement therapy or contraceptives, and with a previous history of cancer, and in situ breast disease or actual pregnancy were not included in the study. All participants have signed an informed written consent approved by institutional Ethics Committees in accordance with the Declaration of Helsinki.
## Isolation of HDL from TNBC and control women
Blood was obtained by a venous puncture after 12h fasting and the plasma was immediately isolated by refrigerated centrifugation (3,000 rpm, 4°C, 15 min). HDL ($D = 1.063$-1.21 g/mL) was isolated from BC and control women´s plasma by discontinuous density ultracentrifugation and immediately frozen at -80°C in a $5\%$ saccharose solution. Plasma and HDL composition in lipids [total cholesterol (TC), triglycerides (TG), and phospholipids (PL)] was determined by enzymatic techniques. ApoB (plasma) and apo A-I (isolated HDL) were quantified by immunoturbidimetry (Randox Lab. Ltd. Crumlin, UK). HDLc plasma levels were determined after precipitation of apoB in plasma treated with dextran sulfate/magnesium chloride. Low-density lipoprotein cholesterol (LDLc) was determined by the *Friedewald formula* [11]. Isolated HDL was extensively dialyzed against phosphate buffer saline (PBS) without EDTA immediately prior to the experiments of LDL oxidation.
## Isolation of LDL from a healthy donor
LDL ($D = 1.019$-1.063 g/mL) was obtained by sequential ultracentrifugation of plasma from a unique healthy volunteer and was purified by discontinuous density ultracentrifugation. After dialysis against PBS with EDTA and sterilization, protein quantification was performed by the Lowry technique [12] and LDL kept at 4°C was utilized for experiments within 2 weeks. Samples were dialyzed against PBS without EDTA immediately before the experiments of LDL oxidation.
## Measurement of cooper-induced LDL oxidation in the presence of HDL isolated from TNBC and control women
The antioxidant role of HDL was accessed by determining the lag time phase for LDL oxidation and the maximal rate of conjugated dienes formation induced by copper sulfate (CuSO4) as previously described [13]. Briefly, 40 µg of LDL protein (diluted in 500 µL of water) obtained from a single donor were incubated with 1mL of 10 μmol/L CuSO4 alone (final concentration) as a control incubation or in the presence of 80 µg of HDL protein from control or TNBC women at 37 °C. The absorbance at 234 nm was continuously monitored in 3 min intervals for 4 h. The time (min) of LDL resistance against oxidation (lag time phase) was calculated between the beginning of the reaction and the time interval with the extrapolated line of the propagation phase, and the maximum ratio of formation of conjugated dienes, determined by the absorbance maximum/minute (Δ absorbance/Δ min between the initiation phase and the maximal absorbance phase). The inter-assay coefficient of variation was $7.8\%$.
## Statistical analysis
Non-parametric data were represented by the median with lower and upper quartiles The Mann-Whitney test was used for comparisons between two groups, and the Kruskal-Wallis test with the Bonferroni posttest for more than two groups. Correlation analysis was done by the Spearman test. A value of $P \leq 0.05$ was considered statistically significant. IBM® SPSS Statistics (version 27.0), GraphPad Prisma (version 5.04) for Windows, and Microsoft® Excel for Mac (version 16.52) software were used for data tabulation and analysis.
## Results
Age, BMI, and menopausal status were similar between control and TNBC groups. Moreover, no differences were observed in plasma lipid profile and lipid ratios between groups (Table 1).
**Table 1**
| Unnamed: 0 | Control | TNBC |
| --- | --- | --- |
| n | 27 | 27 |
| age (years)* | 52 (43.5 – 58.5) | 54 (41.0 – 61.0) |
| BMI (kg/m2)* | 28 (24.4 – 29.7) | 28 (24.2 – 31.6) |
| Menopause (%) | 66.7 | 66.7 |
| TC (mg/dL) | 184 (142 – 201) | 202 (175 – 218) |
| TG (mg/dL) | 87 (58 – 122) | 104 (96 – 144) |
| HDLc (mg/dL) | 43 (34 – 46) | 42 (37 – 48) |
| VLDLc (mg/dL) | 17 (12 – 24) | 21 (19 – 28) |
| LDLc (mg/dL) | 117 (93 – 138) | 132 (97 – 152) |
| non HDLc (mg/dL) | 136 (104 – 160) | 161 (135 – 177) |
| apoB (mg/dL) | 98 (83 – 143) | 135 (106 – 165) |
| TC/apoB | 1.52 (1.37 – 2.06) | 1.46 (1.24 – 2.02) |
| TG/HDLc | 1.76 (1.24 – 3.12) | 2.54 (2.07 – 3.46) |
Women with BC were divided according to the clinical stages of the disease, as localized (stages I and II) and advanced disease (stages III and IV). There was no difference in age and BMI and plasma lipids by comparing these groups with the control group (Table 2).
**Table 2**
| Unnamed: 0 | Control | TNBCStages I and II | TNBCStages III and IV |
| --- | --- | --- | --- |
| n | 27 | 13 | 14 |
| age (years) | 52 (43.5 – 58.5) | 54 (40 – 62) | 54 (45 – 61) |
| BMI (kg/m2) | 28 (24.4 – 29.7) | 29 (24 – 31) | 27 (24 – 31) |
| TC (mg/dL) | 184(142 – 201) | 191(183 – 216) | 207(172 – 226) |
| TG (mg/dL) | 87(58 – 122) | 118(97 – 192) | 101(92 – 132) |
| HDLc (mg/dL) | 43(34 – 46) | 44(32 – 63) | 48(32 – 56) |
| VLDLc (mg/dL) | 17(12 – 24) | 24(19 – 38) | 20(18 – 26) |
| LDLc (mg/dL) | 117(93 – 138) | 114(95 – 141) | 138(118 – 155) |
| non-HDLc (mg/dL) | 136(104 – 160) | 159(131 – 174) | 164(144 – 188) |
| apoB (mg/dL) | 98(83-143) | 139(110 – 171) | 136(101 – 156) |
| TG/HDLc | 1.76(1.24 – 3.12) | 2.16(1.94 – 2.35) | 2.62(2.33 – 2.91) |
| TC/apoB | 1.52(1.37 – 2.06) | 1.35(1.17 – 1.92) | 1.81(1.40 – 2.08) |
The composition of the HDL particle in TC, TG, PL, and apo A-I was similar between the control and BC groups (Table 3) and among clinical stages of BC (data not shown).
**Table 3**
| Unnamed: 0 | Control(n= 27) | TNBC(n = 27) |
| --- | --- | --- |
| TC (mg/dL) | 46.8 (38.0 – 57.0) | 43.7 (30.2 – 55.8) |
| TG (mg/dL) | 15.3 (11.5 – 24.3) | 22.5 (14.0 – 83.7) |
| PL (mg/dL) | 86.4 (69.7 – 102.4) | 88.7 (76.5 – 115.2) |
| apo A-I (mg/dL) | 104.4 (73.4 – 143.7) | 101.2 (82.4 – 120.0) |
As shown in Figure 1A, the lag time phase for the LDL oxidation was $22\%$ higher in the presence of HDL from TNBC women as compared to HDL from control subjects, reflecting a better antioxidant of HDL in TNBC. The lag time phase for LDL oxidation was similar between localized (stages I and II) and advanced disease (stages III and IV) but was higher in advanced disease as compared to the control group (Figure 1B).
**Figure 1:** *Lag time phase for CuSO4-induced LDL oxidation in the presence of HDL isolated from control and TNBC women. The lag time phase for LDL oxidation was determined by incubating LDL (from a unique plasma donor) with CuSO4 in the presence of HDL from control (n = 27) and TNBC (n = 27) women. Absorbance was monitored at 234 nm, every 3 min for 4h Comparisons were done between control and TNBC groups by the Mann-Whitney test (A), and among control and the stages of BC (B) by the Kruskal-Wallis test with the Bonferroni posttest.*
The maximal rate of conjugated dienes formation was similar between control and TNBC groups and among control and BC cases according to the stage of the disease (Figures 2A, B).
**Figure 2:** *Maximal rate of conjugated dienes formation in CuSO4-induced LDL oxidation in the presence of HDL isolated from control and TNBC women. The maximal rate of LDL oxidation was determined by incubating LDL (from a unique plasma donor) with CuSO4 in the presence of HDL from control (n = 27) and TNBC (n = 27) women. Absorbance was monitored at 234 nm, every 3 min for 4 h Comparisons were done between control and TNBC groups by the Mann-Whitney test (A), and among control and the stages of BC (B) by the Kruskal-Wallis test with the Bonferroni posttest.*
The antioxidant role of HDL inferred by the lag time phase for LDL oxidation positively correlated with the concentration of apo A-I in the HDL particle (Figure 3).
**Figure 3:** *Correlation between apoA-I in HDL and the lag time phase for LDL oxidation in TNBC women. The antioxidant role of HDL isolated from TNBC women (n =27) was determined by the lag time phase for LDL oxidation induced by CuSO4. The correlation was done by the Spearman test.*
## Discussion
In the present investigation, it was demonstrated that the antioxidant capacity of the HDL particle isolated from women with TNBC is greater when compared to that of control women, particularly in the more advanced stages of the disease. This event was observed despite similar concentration of HDLc, plasma lipids, and apo A-I between groups.
Alterations in the plasma lipid and lipoprotein profile are identified as independent contributors to the risk of BC in women, regardless of menopausal status [14]. Findings from several major clinical studies suggest a direct association between LDLc and BC risk and an inverse relationship between circulating HDLc and the risk of developing BC. However, these results have not been replicated in some epidemiological studies [15]. Despite the lack of epidemiological evidence, the growth of benign and malignant tumor tissues has been associated with changes in plasma concentrations of lipids and lipoproteins in patients with BC [16]. Several studies have found that many cancer-causing signaling pathways affect cholesterol production, meaning that cholesterol plays a role in tumor formation [4].
Although small, the casuistry of the present study showed no difference in the profile of plasma lipids, particularly HDLc, and lipid ratios between women in the control and BC groups, unlike what has been described in other studies [15, 16]. Women in both groups were also similar in terms of age, BMI, and menopausal status. Even when subdivided according to the clinical stage of the disease, no differences were observed in anthropometric characteristics, menopausal status, and plasma lipids. However, components of the metabolic syndrome were not considered in this study due to the logistics of attendance and collection of samples from the participants.
HDL exerts several actions that appear to be protective against the development of many non-degenerative chronic diseases, although their association specifically with the prevention of BC is much discussed [17]. This is due to the fact that the reduction of HDLc is associated with risk factors for cancer, such as menopause, diabetes mellitus, obesity, and insulin resistance (17–19).
Reduced HDLc concentrations are associated with decreased overall survival, worse prognosis and survival for TNBC [8, 20, 21], and higher incidence of BC in postmenopausal women [22]. A follow-up period of 11.5 years found an inverse association between HDLc and BC risk [23], and retrospectively collected clinical data showed that HDLc reduction had a significant association with the overall risk of BC [7]. Furthermore, low HDLc has been associated with more aggressive tumor characteristics [16] although HDLc concentrations lower than 50 mg/dL were modestly associated with BC risk [24]. There is also evidence that the elevation of HDLc, due to genetic or drug causes, is associated with a higher risk of BC [25].
It is important to consider that the metric for inferring protection conferred by HDL - by determining the cholesterol content in the particle (HDLc) - seems to fail in predicting risk, similar to what happens for atherosclerotic macrovascular disease. This event lies in the fact that HDLc does not invariably reflect the functionality of HDL, in particular its antioxidant, anti-inflammatory, and cellular cholesterol removal activities. The controversies regarding the association between HDL and BC also reside in the face of the different studied populations and sample sizes. Disease duration, histological and molecular types, as well as influences imposed by the presence of metabolic comorbidities linked to the risk of breast tumors, oncological therapies, and changes in lifestyle, can also add bias to the analyses [26].
The HDL particle composition in CT, TG, PL, and apo A-I was similar between women in the control and BC groups and between the early and advanced stages of the disease. Thus, the greater antioxidant activity observed in HDL from the BC group ($22\%$ increase in the delay time for LDL oxidation) and, more specifically, in the advanced stages of the disease, cannot be attributed to changes in classic components of its composition. However, it is known that HDL transport a range of proteins and bioactive lipids that make up their proteomics and lipidomics. These are not easily determined by simpler laboratory techniques, but may be determinants of their activity in modulating LDL and cell membranes oxidation.
HDL anchors several enzymes in its structure, particularly paraoxonase (PON-1), which acts in the hydrolysis of lipid peroxides, minimizing LDL oxidation and the consequent supply of cholesterol and oxysterols to tumor cells [27]. PON-1 concentration and activity were not determined in the present study but may contribute to the observed antioxidant response. There are studies reporting a decrease in PON-1 activity in cancer patients [28, 29]. This may indicate impaired defense property against oxidative stress with potential implications for cell proliferation, promotion of gene instability, and changes in cell susceptibility to chemotherapy. There is a consistent correlation between cancer and decreased serum PON1 activity [29].
In this study, the lag time for LDL oxidation was positively correlated with apo A-I content in the HDL particle. Apo A-I is one of the components of HDL that favors the antioxidant activity of this lipoprotein [30], as well as PON-1. However, a high concentration of apo-AI was associated with a high incidence of BC [31]. On the other hand, the incidence of BC was lower among women with higher apoB concentration and higher apoB/apo-AI ratio [32]. These findings were surprising across all regression models. However, until recently, the association between apolipoproteins and BC was not evaluated in larger studies.
Solid tumors contain a large amount of lipids due to their increased synthesis and lipoprotein uptake [33] through scavenger receptors. In particular, the greater expression of the scavenger receptor class B type 1 (SR-B1) is linked to the greater aggressiveness of tumors and their unfavorable prognosis (34–36) while changes in its functionality, due to mutations, are related to the inhibition of tumor proliferation [37].
In a large sample of women with newly diagnosed BC, naïve to treatment, including all molecular types ($$n = 186$$), HDL composition was compared with healthy control women ($$n = 150$$). In BC, HDL was less enriched in TC, FL, and oxysterols (particularly 27-hydroxycholesterol) which may indicate less removal of cellular lipids. However, in vitro analysis of the intrinsic ability of HDL to remove cellular cholesterol demonstrated that cholesterol efflux from macrophages was similar between HDL isolated from BC and controls. On the other hand, in advanced stages of the disease (stages III and IV), despite the similar composition in apoA-I and lipids, HDL showed a lower ability to remove cholesterol from macrophages compared to HDL in the early clinical stages of BC [38].
Similarly to the present study, the anti-inflammatory activity of HDL was higher in BC ($$n = 38$$) compared to the control group ($$n = 9$$), regardless of the molecular type. However, in the more advanced stages of the disease (stages III and IV), the capacity of HDL to inhibit the secretion of inflammatory cytokines by macrophages was greater than in the initial stages (I and II) (unpublished data).
It is not possible, from these findings, to infer the exact contribution of HDL to tumor evolution, since it can also be modified in the tumor microenvironment. Thus, the results observed in HDL isolated from plasma may be due to reverse causation and may not necessarily reflect a causal effect on the genesis and evolution of cancer. The concept of HDL modulation by the tumor by reverse causation can unlink HDL as a direct determinant of tumor risk, being more related as a marker of tumor evolution than exactly protective or inducing its genesis. Inflammation and oxidation accompany the tumor bed and can modify HDL functionality. In this sense, inflammatory markers bind to HDL, detaching the apoA-I, which compromises its functionality [39, 40]. Another limitation of the present investigation is the fact that dietary habits and physical activity were not recorded which may impact HDL generation and metabolization.
In the present study the results showed, for the first time, the role of HDL as an antioxidant defense in TNBC. This occurred independently of changes in HDL particle composition and plasma lipid profile. The greater antioxidant activity in advanced stages of TNBC, reflected by the delay in LDL oxidation even without changing the maximum ratio of conjugated dienes formation, could contribute to limiting oxidative and inflammatory stress in these tumors with worse clinical and therapeutic prognosis. By reducing LDL oxidation, HDL would reduce the supply of cholesterol and oxysterols to the tumor microenvironment, through oxidized LDL. Furthermore, it would limit the propagation of signaling pathways that result in processes of epithelial-mesenchymal transition and metastasis. Results reinforce that the determination of HDLc does not represent the best metric to infer the association of HDL with BC risk and, possibly, the evolution of the disease. Further investigation is required to better understand if the antioxidant function of HDL can contribute to the evolution of other histological types of BC. This is especially important considering the heterogeneous nature of BC, specifically related to the action of steroid hormones (estrogens and progesterone) that drives tumor evolution as well as HDL generation and metabolization.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Universidade Nove de Julho (#3.139.460; February/2019); Centro de Referência da Saúde da Mulher (Hospital Pérola Byington; #3.225.220; March/2019); e Hospital das Clínicas da Faculdade de *Medicina da* Universidade de São Paulo (#3.317.909, March/2019). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
Conceptualization, MP and LHG. Casuistic selection, MIBACS, MR, and JC. Methodology, ALC, MFMS, RTI, and SISA. Formal analysis, ALC, MIBACS, and MP. Investigation and data curation, ALC, MIBACS, and MP. Writing—original draft preparation, ALC and MP. Writing—review and editing, MP. Resources, MP. Project administration, MP. Funding acquisition, MP. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Bioinformatics gene analysis of potential biomarkers and therapeutic targets
of osteoarthritis associated myelodysplastic syndrome
authors:
- Peicheng Xin
- Ming Li
- Jing Dong
- Hongbo Zhu
- Jie Li
journal: Frontiers in Genetics
year: 2023
pmcid: PMC10034022
doi: 10.3389/fgene.2022.1040438
license: CC BY 4.0
---
# Bioinformatics gene analysis of potential biomarkers and therapeutic targets of osteoarthritis associated myelodysplastic syndrome
## Abstract
Objective: Osteoarthritis (OA) and Myelodysplastic syndrome (MDS) are diseases caused by the same immune disorder with unclear etiology and many similarities in clinical manifestations; however, the specific mechanisms between osteoarthritis and myelodysplastic syndrome are unclear.
Methods: The expression profile microarrays of osteoarthritis and myelodysplastic syndrome were searched in the GEO database, the intersection of their differential genes was taken, Venn diagrams were constructed to find common pathogenic genes, bioinformatics analysis signaling pathway analysis was performed on the obtained genes, and protein-protein interaction networks were constructed to find hub genes in order to establish diagnostic models for each disease and explore the immune infiltration of hub genes.
Results: 52 co-pathogenic genes were screened for association with immune regulation, immune response, and inflammation. The mean area under the receiver operating characteristic (ROC) for all 10 genes used for co-causal diagnosis ranged from 0.71–0.81. Immune cell infiltration analysis in the myelodysplastic syndrome subgroup showed that the relative numbers of Macrophages M1, B cells memory, and T cells CD4 memory resting in the myelodysplastic syndrome group were significantly different from the normal group, however, in the osteoarthritis subgroup the relative numbers of Mast cells resting in the osteoarthritis subgroup was significantly different from the normal group.
Conclusion: There are common pathogenic genes in osteoarthritis and myelodysplastic syndrome, which in turn mediate differential alterations in related signaling pathways and immune cells, affecting the high prevalence of osteoarthritis and myelodysplastic syndrome and the two disease phenomena.
## Introduction
Osteoarthritis (OA) is a chronic disease characterized by joint destruction, osteophytes and degenerative lesions of articular cartilage, while the pathogenesis of osteoarthritis is still unclear, and according to relevant studies, osteoarthritis is the result of a combination of factors (Watts et al., 2022; Yang et al., 2022). With the continuous development of modern medicine, it has been found that the immune system plays an important role in the OA. Immune cell infiltration mediates autoimmune response to osteoarthritis, inducing the secretion of chemokines, pro-inflammatory cytokines and proteases, which in turn disrupt the immune balance to accelerate cartilage erosion (Li et al., 2021b; Hao et al., 2021; Franco-Trepat et al., 2022). A larger scale epidemiological data study confirmed that compared with the matched control group, patients with autoimmune diseases have an increased risk of developing Myelodysplastic syndrome (MDS). Osteoarthritis is not caused by mechanical trauma, but caused by immune imbalance (Dalamaga et al., 2002).
Myelodysplastic syndrome (MDS) is a heterogeneous group of clonal diseases originating from hematopoietic myeloid-directed stem cells or pluripotent stem cells, and about $10\%$ of patients with MDS may develop secondary to autoimmune diseases (Dong et al., 2022). It has been shown that some patients with MDS have a higher probability of secondary autoimmune disease than their natural incidence, while others have no clinical manifestations of immune disease but may have abnormal immunologic parameters, providing further evidence of the association between MDS and autoimmune disease (Wang et al., 2022). The pathogenesis of MDS is related to autoimmunity against hematopoietic stem cells, which may lead to abnormal clonal development of hematopoietic stem progenitor cells due to autoimmune reactions triggered by some specific immune stimuli, such as abnormal T cell response to antigen or abnormal T-B cell interaction, which can cause autoimmune diseases in other tissues and organs of the body along with hematopoietic stem cell destruction (Fu et al., 2019; Moskorz et al., 2021; Votavova et al., 2021).
The use of platelet-rich plasma (PRP) in the treatment of patients with OA complicated by MDS has had poor outcomes, and reports suggest that possibly MDS affects trilineage bone marrow dysplasia and influences the prognostic outcome (Li et al., 2022). However, there are few reports on the relational nature of the two. Therefore, it is important to clarify the potential connection between the two for the prevention and treatment of OA and MDS. Bioinformatics is an interdisciplinary discipline based on electronic information technology to conduct relevant research in the field of biomedicine, which can search for potential patterns between diseases from the perspective of gene molecules. Therefore, this study was conducted to screen and identify the common disease genes of OA and MDS based on bioinformatics, and to provide theoretical support for the prevention and treatment of the combined disease of OA and MDS.
## Data collection
Gene chips of MDS and OA patients were queried from the GEO public database and the MDS gene chip GSE19429 was downloaded. The GSE19429 chip contained 183 samples from MDS patients and 17 samples from healthy individuals. Also downloaded OA gene chip GSE55235 containing 10 samples from OA patients and 10 samples from healthy individuals, where see Table 1. Normalized the expression matrix and analyzed the differential genes between patients and controls using the limma package, where the screening conditions were $p \leq 0.05$ and |logFC|>0.5, to obtain the DEG of MDS versus OA, Venn diagram to determine the co-expression of differential genes. Using the “ggplot2” and “pheatmap” R packages, gene expression in the normal and MDS groups could be visualized.
**TABLE 1**
| Dataset id | Contributors | Disease type | Platform | Sample size (cases) | Sample size (cases).1 |
| --- | --- | --- | --- | --- | --- |
| Dataset id | Contributors | Disease type | Platform | Patient group | Healthy controls |
| GSE19429(Pellagatti et al., 2010) | Pellagatti A, et al. | MDS | GPL570 | 183 | 17 |
| GSE55235(Woetzel et al., 2014) | Woetzel D, et al. | OA | GPL96 | 10 | 10 |
## Functional enrichment of DEG
The GO/KEGG Analysis tool is a functional annotation tool based on the clusterprofiler package in R language (https:/hiplot.com.cn/advance/clusterprofiler-go-kegg) (Wu et al., 2021), which can independently select the latest GO and KEGG libraries for functional annotation. The conventional gene function enrichment analysis includes gene ontology (GO) and signaling pathwaykyoto encyclopedia of genes and genomes (KEGG), where GO enrichment analysis can be divided into molecular function (MF), GO enrichment analysis can roughly compare and classify DEGs to understand their biological properties, while KEGG analysis helps to understand the position and function of genes in the overall network of signaling pathways.
## Protein-protein interaction (PPI) analysis
The PPI network was constructed using the STRING database (Szklarczyk et al., 2019) by incorporating differential gene-encoded proteins and their directly related proteins, setting the interaction score >0.4 as the screening criterion, and then performing network topology analysis, and using the “cytoHubba” plug-in included in Cytoscape software. The top 10 genes with the highest scores were mined using the MNC calculation method in Cytoscape software, and the PPI modules were filtered by using the “MCODE.”
## cBioPortal database analysis
The cBioPortal (Brunner et al., 2022) (http://cbioportal.org) is an open web resource for querying, analyzing and visualizing multidimensional cancer genomic data from multiple databases, selecting MDS-related datasets and applying the Oncoprint module to analyze *Hub* gene variants.
## Validation of diagnostic markers
The value of the obtained genes as diagnostic markers was validated in two independent datasets, GSE19429 and GSE55235, by plotting receiver operating characteristic (ROC) curves for the characteristic genes obtained by taking the intersection set above and evaluating their diagnostic value with a threshold value of $p \leq 0.05$ to be determined. The Area Under Curve (AUC) was calculated, and the AUC was taken to be in the range of 0–1. The larger the AUC, the better the predictive performance.
## Immune-infiltration analysis
The expression matrices of immune cell subtypes were deconvoluted using the CIBERSORT to calculate the relative proportions of 22 immune cell types (Lu et al., 2021), and a p value was obtained for each sample. The barplot function of the “graphics” package of R was used to plot the histogram of the composition ratio of each immune cell in two groups of samples; The “vioplot” package of R was used to correlate the immune cells of patients. “ Vioplot” package in R language was used to compare the ratio of immune cells in the healthy control group and patient groups and to draw violin plots, we analyzed the difference of population immune cells through the rank sum test, and took the comparison of $p \leq 0.05$ as a meaningful judgment.
## Common disease genes in osteoarthritis and myelodysplastic syndromes
A total of 331 OA-related DEGs (106 upregulated genes and 225 downregulated genes) and 1594 MDS-related DEGs (840 upregulated genes and 754 downregulated genes) were obtained from the analysis of the osteoarthritis gene chip GSE55235 and the myelodysplastic syndrome gene chip GSE19429, as shown in Figures 1A–D. The DEGs obtained were intersected using the R language package “VennDiagram,” and 52 common disease genes were obtained, as shown in Figure 1E.
**FIGURE 1:** *Screening results of DGEs for osteoarthritis and myelodysplastic syndromes. (A) Heat map showing the top 50 OA-associated DEGs.(B) Volcano plot showing 331 OA-associated DEGs. (C) Heatmap showing the top 50 MDS-associated DEGs (D) Volcano plot showing 331 osteoarthritis-associated DEGs. (E) Wayne diagram indicating common disease genes.*
## Co-expression of differential genes for functional enrichment
The biological processes of co-expressed differential genes in osteoarthritis and myelodysplastic syndromes are focused on cardiac muscle tissue development, striated muscle tissue development, immune response-activating cell surface Cellular components are clustered in the cation channel complex, basement membrane and host cellular component. The molecular function component is focused on SH2 domain binding, DNA-binding transcription factor binding, and GTPase activity (Figures 2A, B); KEGG is concentrated in Primary immunodeficiency, FOX signaling pathway and B cell receptor signaling pathway. These pathways reveal a strong link between immune cells and immune activation pathways and the development of both diseases. Activation pathways are closely related to the development of both diseases (Figures 3A, B).
**FIGURE 2:** *Results of GO analysis. (A) Bubble diagram; (B) bar graph.* **FIGURE 3:** *Results of KEGG analysis. (A) Bubble diagram; (B) bar graph.*
## PPI network and modularity analysis
Co-DEGs were analyzed using the String database (Figure 4A). Then, cytoscape to construct a PPI network containing 52 nodes and 101 edges. Subsequently, cluster analysis was performed using the Cytoscape plugin MCODE, and two functional modules were identified from the entire network (Figures 4B, C). We assessed the degree of core and intermediate in the PPI network and screened 10 genes to be screened as key diagnostic genes for osteoarthritis and myelodysplastic syndromes, top 10 hub genes were BLNK, SOCS2, SIK1, RGS1, STK17B, MEF2C, PDE4B, PIM1, RRASE, and PTPN6 (Figure 4D). Next we wanted to further analyze the function of the genes, so we performed GSEA analysis regarding their variation in MDS with OA, GSEA enrichment analysis showed activity in Immune_system_development, negative_regulation_of_response_to_stimulus, and anatomical_structure_formation_involved_in_morphogenesis play a decisive role in the process, and the results are shown in the Supplementary Figure S1.
**FIGURE 4:** *PPI network construction and functional module analysis. (A) PPI network; (B) Module 1; (C) Module 2; (D) 10 hub genes of PPI network.*
## Hub co-expression gene screening and validation
10 hub genes were found in the dataset between the MDS patient group and healthy controls, where BLNK, SOCS2, SIK1, RGS1, STK17B, MEF2C, and PDE4B showed a downregulation trend in MDS, however, PIM1, RRAS, and PTPN6 showed opposite changes (Figures 5A–J). In OA we found that SIK1, PIM1, and PDE4B showed a downregulation trend, while RRAS and BLNK showed an upregulation trend (Figures 6A–J). Thus, we found that SIK1, PDE4B and RRAS are the genes that share the trend of both changes. These findings suggest that all these candidate genes have diagnostic potency and can be of diagnostic value for both diseases. To verify the value of hub genes in clinical applications, the AUCs of the 10 hub genes associated with MDS were therefore 81.00, 72.80, 81.20, 71.80, 77.00, 73.30, 80.40, 78.60, 81.20, and $81.10\%$ (Figures 7A–J).
**FIGURE 5:** *Box plots showing the differences in the expression of hub genes in MDS group.* **FIGURE 6:** *Box plot showing the difference in expression of hub genes in OA group (A) STK17B; (B) SIK1; (C) RRAS; (D) RGS1; (E) PTPN6; (F) SOCS2; (G) PIM1; (H) PDE4B; (I) MEF2C; (J) BLNK.* **FIGURE 7:** *ROC curves of key genes for MDS diagnostics (A) BLNK; (B) STK17B; (C) SOCS2; (D) SIK1; (E) RRAS; (F) RGS1; (G) PTPN6; (H) PIM1; (I) PDE4B; (J) MEF2C.*
## Genetic variation
Since there is no dataset for OA disease to analyze gene mutations, in this section we will co-DEG the *Hub* gene in the MDS. The alteration status of the 10 *Hub* genes was analyzed using data from MDS patients from ecBioPortal Cancer Genomics Database. The frequency of alterations in each hub gene is shown in Figure 8A. The most alterations were found in RRAS, STK17B1 ($0.6\%$, $0.2\%$, respectively), where missense mutations were the predominant type, and 756 ($1\%$) of the other 7554 MDS patients (Figure 8B).
**FIGURE 8:** *Genetic alterations. (A) Representation of genetic alterations showing genetic alterations in 10 key genes, which were altered in 756 (1%) of 7554 MDS patients; (B) illustrates the total frequency of alterations in 10 hub genes.*
## Analysis of immune infiltrating cells
For infiltrating immune cells, Figures 9A, B shows that the percentage of infiltration of 22 immune cells is further demonstrated in the immune cell ratio box plots. The analysis of the differences in immune cell infiltration in MDS and OA samples was visualized by violin plots, with $p \leq 0.05$ as a significant difference. The results showed that there were significantly more Macrophages M1 in the MDS group than in the normal group ($$p \leq 0.022$$), while the B cells memory ($$p \leq 0.020$$) normal group and T cells CD4 memory resting ($$p \leq 0.004$$) of the MDS group were significantly less than in the normal group (Figure 9C), while in the OA disease group Mast cells resting was significantly different from the normal group in terms of relative number and was highly expressed in the disease group (Figure 9D).
**FIGURE 9:** *Immune cell infiltration analysis. (A,B) Relative number of immune cells; (C,D) Analysis of the difference in the relative number of immune cells.*
## Discussion
The relationship between MDS and autoimmune diseases is receiving increasing clinical attention, and MDS, like other malignancies, can present with various paraneoplastic immune phenomena such as vasculitis, arthritis, Sweet syndrome, and immune hemolytic anemia (Grignano et al., 2018; Fozza et al., 2022). Chondrocytes are an important factor in cartilage tissue to maintain articular cartilage homeostasis, and studies have confirmed that massive apoptosis of chondrocytes and abnormal expression of immune factors are one of the important causes of OA (Ishii et al., 2022). A total of two datasets were retrieved from GEO in this study, and 52 common genes in MDS and OA were obtained compared to normal controls. Enrichment analysis was performed to identify some GO terms and KEGG pathways, and 10 genes that were strongly associated with MDS and OA were selected in the PPI network construction. The results showed that the AUC values of the above genes were all greater than 0.7, suggesting their diagnostic ability with high reliability, and all genes, except PDE4B, had correlation with immune infiltrating cells.
We further performed PPI network analysis on 52 co-expressed genes, constructed the PPI network composed of proteins, and screened the 10 most critical hub genes in this network by analysis. B-cell ligand protein (BLNK) is a B-cell-specific bridging protein, and silencing BLNK can slow the progression of OA by regulating NF-κB signaling pathway (Cheng Y. et al., 2021). Bioinformatic analysis revealed that MDS patients with altered BLNK had poorer (Le 2019). Kurata et al. [ 2021] found that an inverse relationship between BLNK and C/EBPβ expression was also noted in pre-B-ALL cases, and that high levels of CEBPB expression were associated with shorter survival in pre-B-ALL patients with BLNK downregulation. MEF2C, a member of the MEF2 family, was originally identified in skeletal muscle cells and plays an important role in myoblast differentiation (Zeng et al., 2022). At the same time, MEF2C has been associated with processes such as differentiation and development of cardiac muscle, neural crest and chondrocytes (Zhang and Zhao, 2022). Studies have indicated that the p38 inhibitor Pamapimod protects chondrocyte hypertrophy by inhibiting the p38/MEF2C pathway (Zhang et al., 2020). Recent studies have shown that MEF2C is aberrantly expressed in tumor progression such as hepatocellular carcinoma (Chen et al., 2022) and leukemia (Cante-Barrett et al., 2022). Suppressor of cytokine signaling protein 2 (SOCS2) is an important negative regulator of the inflammatory signaling pathway JAK/STAT pathway that regulates cascade changes in the inflammatory response and is associated with cancer as well as neurological as well as immune-related diseases (Li et al., 2021a). It has been reported that SOCS2 suppresses the inflammatory response, reducing chondrocyte apoptosis, and inhibiting the progression of osteoarthritis (Zhang et al., 2021). In addition, enhanced GH signaling via SOCS2 deletion accelerated growth plate fusion (Samvelyan et al., 2022).
Non-receptor-type protein tyrosine phosphatase 6 (PTPN6), expressed mainly in hematopoietic stem cells, regulates receptor tyrosine kinases by binding to target proteins and dephosphorylating tyrosine substrates (Ahn et al., 2022; Luo et al., 2022). The m6A methyltransferase METTL14 promotes proliferation and osteogenic differentiation of bone marrow mesenchymal stem cells in steroid-associated femoral head necrosis by upregulating m6A levels of PTPN6 and activating the Wnt signaling pathway (Cheng C. et al., 2021). Similarly, proto-oncogene PIM1, a member of the PIM family of serine/threonine kinases, was identified from lymphoma samples as a frequently activated gene, which is highly conserved evolutionarily (Zhang H. et al., 2022). A recent study suggests that PIM1-quercetin docking may play an important role in the treatment of osteoarthritis (Ma et al., 2019). Phosphodiesterases (PDE) are specific enzymes for intracellular cAMP and cGMP degradation, and the PDE4 family is closely related to the regulation of inflammatory responses, with the PDFAB isoform playing an important role (Zhou et al., 2022). In PsA, dysregulated miR-23a expression promotes synovial inflammation by enhancing synovial fibroblast activation through PDE4B signaling (Wade et al., 2019). In addition, circPDE4B acts as a scaffold to promote RIC8A-MID1 binding, thereby reducing RIC8A-dependent activation of the P38 signaling pathway and thus regulating OA progression (Shen et al., 2021).
RRAS affects malignancies such as gliomas and pancreatic ductal adenocarcinomas (Xiao et al., 2019). Clinical studies point to the occurrence of pediatric myelodysplastic syndromes accompanied by germline RRAS mutations (Catts et al., 2021). RGS1, STK17B, and SIK1 have not yet been reported between OA and MDS disease.
In this study, we found that differences in immune-related or inflammation-related indicators differed in the two types of tumors and could be important indicators for differential diagnosis. Based on this, we compared the presence of immune-related differential genes among the differential genes in the two types of tumors and found that all nine genes play an important role, thus suggesting that there are differences in the immune environment in the two diseases and that such differences may provide a reference for future diagnosis and treatment. Due to the quantitative limitation problem of the OA dataset, only the common hub genes associated with MD were evaluated for diagnostic value in this study, and five genes (BLNK, MEF2C, SOCS2, PTPN6, and PDE4B) were found to have high diagnostic value and could be diagnostic biological markers for MDS. Differences in immune-related indicators exist in OA and MDS and can be important indicators for differential diagnosis. Based on this, we compared the presence of immune-related differential genes in OA and MDS and found that nine hub genes both play an important role, and such differences may provide a reference for future diagnosis and treatment.
This study elucidates the potential relationship between OA and MDS through bioinformatics methods, and provides a more reliable target for the in-depth study of the diagnosis and prediction of the two diseases. However, we further verify these target genes through in vivo and in vitro experiments to understand their specific functions. After the gene level is confirmed, we will explain the correlation between molecular mechanism level and clinical through clinical multi center or single center cohort research, It aims to provide a theoretical basis for clinical treatment and development of targeted drugs.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article. For osteoarthritis, the GEO dataset name: GSE55235, Repository link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55235. For myelodysplastic syndrome, the GEO dataset name: GSE19429, Repository link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19429.
## Author contributions
All authors participated in the design, interpretation of the studies and analysis of the data and review of the manuscript. JL designed the study. PX wrote the original draft. ML collected raw data. JD and HZ performed statistical and bioinformatics analyses. JL supervised the study.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2022.1040438/full#supplementary-material
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|
---
title: Bulk and single-cell transcriptome analyses of islet tissue unravel gene signatures
associated with pyroptosis and immune infiltration in type 2 diabetes
authors:
- Yaxian Song
- Chen He
- Yan Jiang
- Mengshi Yang
- Zhao Xu
- Lingyan Yuan
- Wenhua Zhang
- Yushan Xu
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10034023
doi: 10.3389/fendo.2023.1132194
license: CC BY 4.0
---
# Bulk and single-cell transcriptome analyses of islet tissue unravel gene signatures associated with pyroptosis and immune infiltration in type 2 diabetes
## Abstract
### Introduction
Type 2 diabetes (T2D) is a common chronic heterogeneous metabolic disorder. However, the roles of pyroptosis and infiltrating immune cells in islet dysfunction of patients with T2D have yet to be explored. In this study, we aimed to explore potential crucial genes and pathways associated with pyroptosis and immune infiltration in T2D.
### Methods
To achieve this, we performed a conjoint analysis of three bulk RNA-seq datasets of islets to identify T2D-related differentially expressed genes (DEGs). After grouping the islet samples according to their ESTIMATE immune scores, we identified immune- and T2D-related DEGs. A clinical prediction model based on pyroptosis-related genes for T2D was constructed. *Weighted* gene co-expression network analysis was performed to identify genes positively correlated with pyroptosis-related pathways. A protein–protein interaction network was established to identify pyroptosis-related hub genes. We constructed miRNA and transcriptional networks based on the pyroptosis-related hub genes and performed functional analyses. Single-cell RNA-seq (scRNA-seq) was conducted using the GSE153885 dataset. Dimensionality was reduced using principal component analysis and t-distributed statistical neighbor embedding, and cells were clustered using Seurat. Different cell types were subjected to differential gene expression analysis and gene set enrichment analysis (GSEA). Cell–cell communication and pseudotime trajectory analyses were conducted using the samples from patients with T2D.
### Results
We identified 17 pyroptosis-related hub genes. We determined the abundance of 13 immune cell types in the merged matrix and found that these cell types were correlated with the 17 pyroptosis-related hub genes. Analysis of the scRNA-seq dataset of 1892 islet samples from patients with T2D and controls revealed 11 clusters. INS and IAPP were determined to be pyroptosis-related and candidate hub genes among the 11 clusters. GSEA of the 11 clusters demonstrated that the myc, G2M checkpoint, and E2F pathways were significantly upregulated in clusters with several differentially enriched pathways.
### Discussion
This study elucidates the gene signatures associated with pyroptosis and immune infiltration in T2D and provides a critical resource for understanding of islet dysfunction and T2D pathogenesis.
## Introduction
Diabetes mellitus is a chronic disease with high death and disability rates and large global economic burden. Type 2 diabetes (T2D) accounts for > $90\%$ of all diabetes cases. The prevalence of T2D in China has increased rapidly in recent decades [1]. A key pathological feature of T2D is hyperglycemia, which results from insulin resistance in peripheral tissues and islet beta cell dysfunction. Current therapies for T2D are directed toward reducing elevated blood glucose levels by improving insulin sensitivity in partial peripheral tissue, enhancing insulin secretion from the remaining beta cells, and promoting urinary glucose excretion. However, interventions focused on improving islet beta cell dysfunction are lacking. T2D is often associated with a strong genetic predisposition [2], but its genetics remain poorly understood [3]. Understanding the diverse molecular processes and pathophysiological mechanisms, especially islet beta cell dysfunction, which triggers T2D is crucial to improve the prevention and treatment of this disease.
Islet dysfunction is an important pathophysiological mechanism in T2D [4]. Islet inflammation plays a crucial role in islet dysfunction (5–7). This process is characterized by immune cell infiltration (7–9), cell death [10, 11], fibrosis [12], and amyloid deposition [13, 14]. The link between islet inflammation and dysfunction has been well explored [15]. Multiple studies on T2D have shown that targeting islet inflammation could help maintain normal islet function [9, 16]. In addition, histological changes, including immune cell infiltration [8, 17] and pyroptosis [10], have been observed in the islets of patients with T2D.
Microarray is a promising and widely used method for large-scale gene expression profiling. Several studies have been performed to enhance our understanding of the molecular mechanisms underlying T2D pathogenesis. Recent studies have focused on the relationships between T2D-related genes and immune infiltration [18, 19] as well as between pyroptosis- and diabetes-related genes [20]. Pyroptosis is a type of programmed cell death associated with inflammation and immunity [21]. However, the roles of pyroptosis and infiltrating immune cells in islet dysfunction of patients with T2D have yet to be explored.
T2D is a chronic, heterogeneous, and progressive disease. Elucidating the molecular basis underlying islet dysfunction, which has been implicated in the pathogenesis of T2D, has been a major focus of diabetes research. Conventional bulk RNA sequencing (RNA-seq) measures the average RNA levels in samples. Advances in single-cell RNA sequencing (scRNA-seq) have enabled specific profiling of cell populations [22]. Single-cell transcriptome analysis provides novel insights into cellular functional alterations that contribute to islet dysfunction and T2D pathogenesis [23] and may reveal cellular heterogeneity in T2D. In the present study, we aimed to identify novel biomarkers (genes related to disease phenotypes, pyroptosis, and immune infiltration) in T2D by performing a conjoint analysis of three bulk RNA-seq datasets of islets. We constructed miRNA and transcriptional networks based on the identified genes and performed functional analyses. We then analyzed the pyroptosis-related genes and infiltrating immune cells in different molecular subtypes of T2D. Furthermore, we analyzed single-cell data of islets to reveal the heterogeneity in T2D. This study elucidates the gene signatures associated with pyroptosis and immune infiltration in T2D and provides an important foundation for understanding of islet dysfunction and T2D pathogenesis.
## Data acquisition
Three bulk RNA-seq datasets (GSE118139, GSE25724, and GSE20966) were downloaded from the National Center for Biotechnology Information Gene Expression Omnibus (GEO) database. GSE118139 [24] was obtained using the GPL22120 platform Agilent-078298 human ceRNA array V1.0 4X180K [Probe Name Version] (Homo sapiens). GSE118139 contains data from four human islet samples, of which two were from patients with T2D and two were from patients without diabetes. GSE25724 [25] was obtained using the GPL96 platform [HG-U133A] Affymetrix Human Genome U133A Array. GSE25724 contains data from 13 human islet samples, of which six were from patients with T2D and seven were from patients without diabetes. GSE20966 [26] was obtained using the GPL1352 platform [U133_X3P] Affymetrix Human X3P Array. GSE20966 contains data from 20 human islet samples, of which 10 were from patients with T2D and 10 were from patients without diabetes. We downloaded the raw data from these three datasets and merged them into a matrix file containing 18 samples from patients with T2D and 19 samples from patients without diabetes. Batch effects were eliminated using the removeBatchEffect function of the limma package in R. The quality of the datasets was assessed using boxplots, principal component analysis (PCA), and heatmaps. After removing the batch effects, the merged matrix was used for subsequent analyses.
Additionally, we downloaded the single-cell transcriptomic dataset GSE153855 [27] from the GEO database. This dataset was obtained using the GPL16791 platform Illumina HiSeq 2500 (Homo sapiens). GSE153855 contains data from 11 human islet samples, of which five were from patients with T2D and six were from patients without diabetes.
## Differential gene expression analysis
We performed a differential gene expression analysis of the merged dataset to compare the transcriptomes of the islet samples from patients with and without T2D. Data were analyzed using the limma package in R (version 3.52.2) [28]. Differentially expressed genes (DEGs) related to T2D were defined as upregulated genes with a log fold change (FC) above 0.5 or downregulated genes with a logFC lower than -0.5 at $P \leq 0.05.$
## Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses
GO [29] enrichment analysis including biological process, molecular function, and cellular component categories is a common method for large-scale functional enrichment studies of genes at different dimensions and levels. KEGG provides genomic and molecular information [30]. KEGG pathway analysis is widely used in bioinformatics to annotate and enrich pathways. T2D-related DEGs were subjected to GO and KEGG pathway analyses using the clusterProfiler package in R [31], with $P \leq 0.05$ as a significance threshold. The results of the enrichment analyses were visualized using bubble plots.
## Differential expression analysis according to ESTIMATE immune score
Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression Data (ESTIMATE) [32] is used to infer the proportion of stromal and immune cells via gene expression signatures. This algorithm was used to calculate the immune, stromal, and ESTIMATE scores. The samples were divided into high- and low-immune score groups according to their median immune scores, and the differential genes between the two groups were analyzed using the limma package in R (version 3.52.2) [28]. Significant differential gene expression was defined as $P \leq 0.05$, and absolute values of logFC were > 0.05. Volcano and histogram plots were generated using the ggplot2 package in R (version 3.3.6) [33], and a heatmap was plotted using the pheatmap package in R (version 1.0.12) [34].
## Expression analysis of pyroptosis-related genes
By querying the gene set of pyroptosis-related genes in the MSigDB (http://software.broadinstitute.org/gsea/msigdb) database [35, 36] and reviewing previous studies [37], we obtained 31 pyroptosis-related genes that were expressed in the merged matrix (Table S1). Heatmaps and boxplots were created to display the expression patterns of the 31 pyroptosis-related genes in the merged matrix. We then queried the chromosomal locations of these genes based on the human reference genome (UCSC.HG19.Human. CytoBandIdeogram) from the GENCODE database [38]. The RCircos package in R (version 1.2.2) [39] was used to create a Circos plot for the expression distributions of the genes on the chromosome. A correlation-based heatmap was generated using the corrplot package in R [40], and correlation scatter plots were created using the ggpubr package in R (version 0.4.0) [41].
## Construction of a clinical prediction model
The association between the genes and T2D was assessed through univariate logistic regression analysis and genes with $P \leq 0.5$ were selected for further least absolute shrinkage and selection operator (LASSO) regression. A nomogram was established using the results of LASSO regression to predict risk. A calibration curve was generated to evaluate the relationship between nomogram predictive probability and observed outcome. In addition, we constructed receiver operating characteristic (ROC) curves and calculated the areas under the ROC curve (AUCs) to assess the predictive performance of the model (R package pROC).
## Gene set variation analysis (GSVA)
GSVA [42] is a nonparametric, unsupervised method for estimating variations in gene set enrichments through expression dataset samples. The pyroptosis pathway score in the merged matrix was measured using the GSVA package in R (version 1.42.0). The GSVA algorithm transforms gene expression data into a gene set sample matrix, producing an enrichment score for each sample and pathway. Each pathway gene set was computed using the Kolmogorov–Smirnov rank test statistic.
## Gene set enrichment analysis (GSEA)
GSEA is used to identify classes of genes or proteins that are overrepresented in a large group of samples [43] and are highly correlated with disease phenotypes. The merged matrix was analyzed using GSEA to identify significantly enriched or depleted gene sets. Gene sets (msigdb.v7.0.entrez.gmt) were downloaded from the Molecular Signatures database (MSigDB) [16]. GSEA was performed using the clusterProfiler package in R (version 4.4.4).
## Weighted gene co-expression network analysis (WGCNA)
WGCNA [44] is a widely used data mining method to construct biologically relevant modules based on pairwise correlations between gene expression profiles. Genes with the top $25\%$ variance of gene expression values were screened for cluster analysis following the WGCNA tutorial (https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/). An appropriate soft threshold was selected to calculate the adjacency matrix, which was converted to a topological overlap matrix (TOM). Then, we hierarchically clustered this TOM and used the cutreeDynamic function with method ‘tree’ to identify modules of correlated genes (minimum module size of 30 genes). The pathways enriched by GSVA were fused with pyroptosis-related modules to observe the correlation between each module and pyroptosis. A correlation heatmap was drawn to obtain the gene sets corresponding to the modules positively correlated with pyroptosis.
## Protein–protein interaction (PPI) establishment and identification of hub genes
Considering the important roles of pyroptosis and immunity in diabetes, we analyzed whether any genes of the pyroptosis module overlapped with T2D-related DEGs between the high- and low-immune score groups. A PPI network based on overlapping genes was constructed using the STRING database [45]. PPI pairs were identified using the confidence criterion (0.75). The degree of each node was calculated using the CytoHubba plugin for Cytoscape (version 3.7.1) [46]. A bar chart was drawn according to the reverse order of the degree of each node. Genes with more than 10 nodes were selected as hub genes.
## Construction of miRNA interaction network
Candidate target miRNAs were predicted using the miRTarBase database and analyzed using the multiMiR package in R (version 1.18.0) [47]. Photoactivatable ribonucleoside crosslinking and immunoprecipitation (PAR-CLIP) [48] is used to identify the binding sites of RNA-binding proteins and miRNA-containing ribonucleoprotein complexes. The miRNA validation level was set to “PAR-CLIP” to further screen miRNAs that interact with hub genes. Finally, a miRNA–mRNA interaction network based on the above prediction results was constructed using Cytoscape (version 3.7.1) [46].
## Construction of transcriptional regulatory network
Transcription factor (TF) lists were retrieved from the Cistrome database [49, 50](http://cistrome.org/), and differential gene expression analysis of the merged dataset was performed to identify differentially expressed TFs. A correlation test was performed to identify TFs associated with hub genes, with an absolute value of correlation coefficient > 0.4 and $P \leq 0.001.$ The resulting data were imported into Cytoscape (version 3.7.1) to construct a transcriptional regulatory network.
## Analysis of immune subtypes
The immune subtypes of each sample were predicted from the merged gene expression profiles using the ImmuneSubtypeClassifier package in R (version 0.1.0). A Sankey diagram was generated to show the relationship between hub genes and six immune subtypes using the ggalluvial package in R (version 0.12.3). The six immune subtypes were wound healing (C1), IFN-γ dominant (C2), inflammatory (C3), lymphocyte depleted (C4), immunologically quiet (C5), and transforming growth factor (TGF)-β dominant (C6).
## Analysis of immune cell infiltration
To determine the relative abundance of 22 immune cells in the merged matrix, we analyzed the transcriptomic data using CIBERSORT [51]. For each sample, the sum of all the estimated fractions of immune cells was equal to one. Differences in immune cell abundances between the high-risk and low-risk groups were compared using the t-test, and $P \leq 0.05$ was considered to indicate statistical significance. A correlation-based heatmap was generated using the corrplot package in R. Correlations were calculated using the Pearson’s correlation coefficient. Scatter plots and fitting curves were constructed using the ggplot2 package in R. The merged matrix was subjected to single-sample gene set enrichment analysis (ssGSEA) using the GSVA package in R (version 1.42.0). The infiltration levels of 28 subpopulations of tumor-infiltrating lymphocytes were evaluated based on the cell marker gene CellMarker [52]. Box plots were constructed using pubr.
## Unsupervised clustering of T2D samples
Owing to the prevalence of heterogeneity between patients, unsupervised clustering of T2D samples based on 17 hub genes could resolve this heterogeneity and reclassify the samples. Unsupervised consensus clustering of the samples was performed through aggregation hierarchical clustering using the ConsensusClusterPlus package in R (version 1.60.0). Spearman’s method was used to calculate the distance, and clustering was conducted using K-means. We ascertained the optimal number of clusters by considering a consensus matrix heatmap, consensus cumulative distribution functions (CDFs), and the relative change in area under the CDF curve. Boxplots and heatmaps were drawn to determine the expression differences of hub genes, immune scores, and immune cell infiltration levels among different clusters.
## Quality control, cluster analysis, and major cell type identification of single-cell expression data
The single-cell RNA sequencing dataset GSE153855 was imported into R and converted into a Seurat object using the Seurat package in R (version 4.1.1) [53]. A high proportion of transcript counts derived from mitochondria-encoded genes might indicate low cell quality; therefore, we removed cells with a percentage of mitochondrial transcripts larger than $5\%$. We conducted quality control through the counts and expression of sequencing genes and the percentage of mitochondrial genes. Cells were filtered using nFeature_RNA > 200, nCount_RNA < 6000, and percent.mt < 5 as cutoffs. Violin plots were created to show the number of genes, gene expression values, and percentage of mitochondrial genes. Dimensionality was reduced using PCA. The first 10 principal components were chosen to further reduce dimensionality and visualization using the t-distributed statistical neighbor embedding (t-SNE) algorithm. The cell type information for each cluster was annotated using built-in annotations from the GSE153855 dataset. The reliability of the built-in annotation information was visualized by creating bubble and violin plots, which displayed the expression of marker genes reported in the literature for various cell types of islets (54–57) in the clusters. We used the following markers for cell type identification: beta cells (FXYD2), alpha cells (KANSL3 and SOD2), delta cells (LAPTM4B, TMEM163, and UBR4), macrophages (CD86), endothelial cells (FLT1), and ducts (PROM1).
## Differential analysis of hub genes among different cell types
DEGs were identified among all clusters using the “FindAllMarkers” function, which uses the Wilcoxon rank-sum test. The hub genes among the clusters were screened by taking the intersection DEGs between the clusters and cluster marker genes. The DoHeatmap function was used to generate an expression heatmap for hub gene expression.
DEGs were screened among the 11 cell types. The hub genes in the different cell types were identified by intersecting DEGs with 115 genes and visualized using a heatmap (Figure 13A). The distributions of various cell types in the T2D and non-T2D samples were also visualized (Figure 13B). The cell types distributed in the T2D samples were as follows: alpha (351, $46.184\%$), exocrine (185, $24.342\%$), beta (79, $10.395\%$), delta (63, $8.289\%$), ductal (38, $5\%$), macrophage (22, $2.895\%$), mast (4, $0.526\%$), endothelial (3, $0.395\%$), gamma (3, $0.395\%$), and stellate (1, $0.132\%$).
**Figure 13:** *Differential analyses of hub genes among different cell types and distribution of the various cell types in T2D and non-T2D samples. (A) Heatmap of hub genes among 11 clusters. (B) Distribution of various cell types in T2D and non-T2D samples.*
## Pseudotime trajectory analysis
The differentiation pseudotime of the different cell subtypes was inferred using the Monocle package in R (version.2.22.0) [58]. Highly variable genes were identified using the “VariableFeatures” function, and cells were ranked using the “setOrderingFilter” function. Finally, the “DDRTree” method was used to reduce dimensionality, and the “orderCells” function was used to estimate the arrangement of cells along the trajectory. Plots of cellular trajectories were drawn based on marker genes and clusters. Each trajectory was analyzed using a standard protocol with default parameters.
## Cell–cell communication
The CellChat package in R [54] was used to infer and qualify intercellular communication by combining single-cell expression profiles with known ligands, receptors, and their cofactors. The ligand–receptor interaction probability and perturbation test were used to identify significant ligand–receptor relationship pairs. Cell–cell communication networks were then integrated by adding the number or strength of ligand–receptor pairs with significant interactions between cell types. A heatmap was used to show the contribution of the input and output pathways to the cells. The numbers and weights of the interactions are shown by circular plots.
## Statistical analyses
All data calculations and statistical analyses were performed using R. For the comparison of two groups of continuous variables, the statistical significance of normally distributed variables was estimated using the independent t-test, and differences between non-normally distributed independent variables were analyzed using the Wilcoxon rank-sum test. Chi-square test or Fisher’s exact test was used to compare and analyze the statistical significance between two groups of categorical variables. The correlation coefficients between different genes were calculated using Pearson correlation analysis. All statistical tests were 2-sided, and $P \leq 0.05.$
*The* general idea and methodologies used in this study are shown in a flow chart (Figure 1).
**Figure 1:** *Data analysis flow chart. T2D, type 2 diabetes; non-DM, non-diabetes mellitus, ESTIMATE, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data; scRNA, single-cell RNA; WGCNA, weighted gene co-expression network analysis; DEGs, differentially expressed genes.*
## Functional analysis of T2D-related genes and ESTIMATE immune score
Three bulk RNA-seq datasets (GSE118139, GSE25724, and GSE20966) were combined into a merged matrix, with 18 islet samples from patients with T2D and 19 islet samples from patients without diabetes. Heatmaps (Figures S1A, B), box plots (Figures 2A, B), and PCA plots (Figures S2A, B) indicated the successful removal of batch effects from the merged matrix, which was then used for subsequent analyses. A total of 918 T2D-related DEGs (220 upregulated and 698 downregulated) were identified. GO terms were analyzed, as shown in Figures 2C–E and Table S2, to explore the functions of the DEGs.
**Figure 2:** *GO analyses of T2D-related genes; ESTIMATE immune score, and 550 immune and T2D-related genes in the merged matrix. (A) Box plot of three datasets without batch effect correction. (B) Box plot of three datasets after batch effect correction. Red represents T2D patient, whereas black represents non-DM control. (C–E) Top 10 significantly enriched cellular components, biological processes, and molecular functions. (F) Box plot showing the differences in ESTIMATE, immune, and stromal scores between the T2D and non-DM groups. (G) Heatmap showing the expression differences of 550 immune and T2D-related genes between the low- and high-immune score groups as well as between the T2D and non-DM groups. ns, P ≥ 0.05; *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001.*
The box plot shows the differences in the ESTIMATE, immune, and stromal scores between the T2D and non-diabetes mellitus (non-DM) groups (Figure 2F). The immune score was significantly higher in the T2D group than in the non-DM group. Based on the median value of the immune score, the samples were divided into two groups (high- and low-immune score groups). The volcano plot (Figure S2C) shows that 835 immune-related genes were differentially expressed between the high- and low-immune score groups. The Venn diagram displays 550 immune and T2D-related genes (Figure S2D; Table S3). The heatmap (Figure 2G) shows the expression differences of these genes between the low- and high-immune score groups as well as between the T2D and non-DM groups.
## Panorama of pyroptosis-related genes and correlation analysis of pyroptosis-related genes in T2D
The heatmap (Figure 3A) and boxplot (Figure 3C) display the expression patterns of the 31 pyroptosis-related genes in the T2D and non-DM groups. APIP, DDX3X, DHX9, and TNFRSF21 were significantly downregulated in the T2D group, whereas CASP1, GBP2, GSDMB, GSDMD, NLRP1, NOD2, PYCARD, TREM2, and ZBP1 were significantly upregulated in the T2D group. The Circos plot (Figure 3B) shows the expression distributions of the 31 pyroptosis-related genes on the chromosome.
**Figure 3:** *Panorama of pyroptosis-related genes in T2D. (A) Heatmap displaying the expression patterns of 31 pyroptosis-related genes in the T2D and non-DM groups. (B) Circos plot showing the distribution of 31 pyroptosis-genes on the chromosome. (C) Box plot displaying the expression patterns of 31 pyroptosis-related genes in the T2D and non-DM groups. ns, P ≥ 0.05; *, P < 0.05; **, P < 0.01; ***, P < 0.001; **** , P < 0.0001.*
We analyzed the association between the 31 pyroptosis-related genes and T2D. The correlation heatmap (Figure S3A) shows the correlated expression patterns of the 31 pyroptosis-related genes. The top four significant negative and positive correlations of gene pairs are shown by correlation scatter plots (Figures S3B–I): AIM2-HMGB1 (r = -0.628, $P \leq 0.001$; Figure S3B), DDX3X-TREM2 (r = -0.614, $P \leq 0.001$; Figure S3C), HMGB1-TREM2 ((r = -0.611, $P \leq 0.001$; Figure S3F), NLRX1-DHX9 (r = -0.719, $P \leq 0.001$; Figure S3I), CASP1-CASP8 ($r = 0.844$, $P \leq 0.001$; Figure S3D), NOD2-ZBP1 ($r = 0.843$, $P \leq 0.001$; Figure S3E), NLRP3-CASP1 ($r = 0.794$, $P \leq 0.001$; Figure S3G), and GSDMB-NLRP2 ($r = 0.78$, $P \leq 0.001$; Figure S3H).
## Construction of the clinical prediction model
We evaluated the predictive power of the 31 pyroptosis-related genes for T2D. As shown in the univariate forest plot (Figure 4A), 13 pyroptosis-related genes were significantly associated with the prevalence of T2D: DDX3X, GBP2, GSDMB, GSDMD, NLRP1, NOD2, PYCARD, TNFRSF21, APIP, CASP6, DHX9, TREM2, and ZBP1. LASSO regression was performed on these 13 genes. The lambda value with the minimal average deviance was determined as the optimal lambda (-3.7) through cross-validation (Figures 4C, D). GBP2, NLRP1, and NOD2 were identified, and a nomogram (Figure 4B) was constructed to predict the probability of T2D. ROC analysis revealed that this risk score for T2D had high predictive power, with an AUC of 0.968 (Figure 4E). The calibration curve (Figure 4F) also demonstrated the accuracy of the nomogram-predicted probability.
**Figure 4:** *Construction of a clinical prediction model. (A) Univariate forest plot showing the predictive power of 31 pyroptosis-related genes for T2D. (B) Nomogram integrating GBP2, NLRP1, and NOD2 in T2D. (C) Diagram representing relationships between penalty parameters and binominal deviances. (D) Diagram representing the relationships between penalty parameters and regression coefficients. (E) ROC curve of the nomogram (AUC = 0.968). (F) Calibration curve of the nomogram.*
## WGCNA
Data from patients with T2D in the merged matrix were used as inputs for WGCNA. Meanwhile, we performed GSVA and extracted the pyroptosis-related pathway GO_PYROPTOSIS into WGCNA. We selected β = 14 as the soft thresholding power to ensure a scale-free network (Figures 5A, B). Figure 5C shows the trait and GO_PYROPTOSIS pathway scores for each sample. We constructed a hierarchical clustering tree and identified 12 modules after fusing them (Figure 5D). Among the 12 modules, six (magenta, darked, black, blue, dark turquoise, and grey) had 1193 genes that were positively correlated with the GO_PYROPTOSIS pathway (Figure 5E).
**Figure 5:** *WGCNA. (A) Analysis of scale-free index for various soft-threshold powers. (B) Analysis of mean connectivity for various soft-threshold powers. (C) Dendrogram of samples and heatmap of pyroptosis trait. (D) Cluster dendrogram plots of the 12 coexpressed modules identified by WGCNA in different colors. (E) Heat map describing the relevance of modules (rows) to pyroptosis (columns). Blue represents negative correlations, whereas red represents positive correlations.*
## PPI network establishment and identification of pyroptosis-related hub genes
To further analyze the differentially expressed immune and T2D-related genes in the pyroptosis-related modules, we intersected 1193 genes in the pyroptosis-related modules with 550 immune and T2D-related DEGs and obtained 115 genes (Figure 6A; Table S4). Then, we constructed a PPI network using the STRING database and visualized the network using Cytoscape (Figure 6C). We identified the top 20 genes based on the number of nodes (Figure 6B) and then selected the top 17 genes with > 10 nodes as pyroptosis-related hub genes: INS, CHGA, GCG, GAD2, NEUROD1, PCSK1, ABCC8, GRIA2, CHGB, STMN2, GNAS, IAPP, CPE, KCNQ1, NKX2-2, SCG5, and SLC17A6.
**Figure 6:** *Protein–protein interaction network and identification of pyroptosis-related hub genes. (A) Venn diagram showing the intersection between immune and T2D-related genes with genes in pyroptosis-related modules. (B) Bar graph showing the top 20 genes in the intersection. (C) Protein–protein interaction network of genes in the intersection.*
## Construction of miRNA interaction and transcriptional regulatory network
A miRNA–mRNA interaction network based on the 17 pyroptosis-related hub genes was constructed, and 635 miRNAs were identified. The miRNA–mRNA interaction network is shown in Figure 7A. A transcriptional regulatory network (Figure 7B) based on the 17 pyroptosis-related hub genes was constructed. The transcriptional regulatory network is shown in Figure 7B.
**Figure 7:** *miRNA–mRNA interaction network and transcriptional regulatory network. (A) Interaction network diagram of pyroptosis-related hub genes and miRNAs. (B) Interaction network diagram of pyroptosis-related hub genes and transcription factors.*
## Enrichment analyses of pyroptosis-related hub genes
GO terms were analyzed to explore the functions of pyroptosis-related hub genes (Figures 8A–C). KEGG pathway analysis indicated that the pyroptosis-related hub genes were enriched in maturity-onset diabetes of the young, insulin secretion, type 1 diabetes mellitus, and glutamatergic synapses (Figure 8D). The signaling pathway of maturity-onset diabetes of the young is shown in Figure 8E. Four hub genes (NKX2-2, NEUROD1, INS, and IAPP) were significantly downregulated. The ssGSEA algorithm was used to calculate the pyroptosis score of each sample in the merged matrix based on the 17 pyroptosis-related hub genes, and the samples were divided into high- and low-pyroptosis score groups according to the median pyroptosis score. GSEA (Table S5) and GSVA (Table S6) were performed to compare the high- and low-pyroptosis score groups. The top five pathways in the GSEA results are shown in Figure 8F. The heatmap of the GSVA pathway enrichment results shows the resulting spectrum of the differential enrichment of 177 pathways in the high- and low-pyroptosis score groups (Figure 8G).
**Figure 8:** *Enrichment analyses of pyroptosis-related hub genes. (A) Enriched GO terms in the “cellular component” category. (B) Enriched GO terms in the “biological process” category. (C) Enriched GO terms in the “molecular function” category. (D) Top 10 significantly enriched KEGG pathways of pyroptosis-related hub genes. (E) Abnormal expression of pyroptosis-related hub genes in maturity-onset diabetes of the young signaling pathway. (F) Enrichment map showing the top 5 pathways from pyroptosis score-based GSEA. X-axis represents enrichment score, and Y-axis represents pathway name. (G) Heatmap illustrating the enriched pathways between the low- and high-pyroptosis score groups as well as between the T2D and non-DM groups. Red represents upregulation, whereas green represents downregulation.*
## Analyses of immune subtypes and correlation analyses of infiltrating immune cells
The Sankey diagram demonstrates the association between disease states, pyroptosis states, and immune subtypes (Figure 9A). The disease states were almost uniformly distributed among pyroptosis states and the C1, C3, and C4 immune subtypes.
**Figure 9:** *Correlation analyses among pyroptosis-related hub genes, immune subtypes, and infiltrating immune cells. (A) Sankey diagram demonstrating the association among disease states, pyroptosis states, and immune subtypes. (B) Differential distributions of 28 types of infiltrating immune cells between the high- and low-pyroptosis score groups. (C) Differential distributions of 28 types of infiltrating immune cells between the non-DM and T2D groups. Red represents upregulation, while yellow represents downregulation.ns, P ≥ 0.05; *, P < 0.05; **, P < 0.01; ***, P < 0.001; **** , P < 0.0001.*
The differential distributions of 28 types of infiltrating immune cells were compared between the high- and low- pyroptosis score groups as well as between the T2D and non-DM groups. In the high pyroptosis score group (UP), the abundance of activated dendritic cells, central memory CD4+ T cells, myeloid-derived stem cells (MDSCs), and type 17 T helper cells significantly decreased, whereas the abundance of immature dendritic cells significantly increased (Figure 9B). In the T2D group, the abundance of activated CD4+ T cells, activated dendritic cells, central memory CD8+ T cells, effector memory CD8+ T cells, macrophages, MDSC, natural killer cells, natural killer T cells, neutrophils, type 1 T helper cells, and type 17 T helper cells significantly increased, whereas the abundance of effector memory CD4+ T cells, immature dendritic cells, and memory B cells significantly decreased (Figure 9C).
We evaluated the infiltration of 22 immune cell types in the merged matrix by using the CIBERSORT algorithm and determined the abundance of 13 immune cell types. The correlation heatmap depicted possible correlations between the 13 types of immune cells (Figure S4A). The significant negative and positive correlations between the two types of immune cells are shown in Figures S4B–F.
## Analysis of pyroptosis-related hub genes and immune infiltration in different molecular subtypes
We classified the T2D samples into a merged matrix through unsupervised consensus clustering. The optimal number of clusters was determined to be two after comprehensive consideration of the delta area curve, CDF, and consensus matrix heatmap (Figures S5A–C).
The heatmap (Figure 10A) and box plot (Figure 10C) reveal the expression distributions of the pyroptosis-related hub genes in different molecular subtypes (clusters 1 and 2). The expression of 17 pyroptosis-related hub genes was high in cluster 2 and low in cluster 1. The box plot shows the difference in ESTIMATE, immune, and stromal scores between clusters 1 and 2 (Figure 10B). The immune score was significantly higher in cluster 1 than in cluster 2. Figure 10D shows the differences in the abundance of the 13 types of immune cells between the two clusters. The abundance of monocytes and CD8+ T cells was significantly higher in cluster 2 than in cluster 1.
**Figure 10:** *Analysis of pyroptosis-related hub genes and infiltrating immune cells in different molecular subtypes. (A) Heatmap of the expression distribution of pyroptosis-related hub genes in different molecular subtypes (clusters 1 and 2). (B) Box plot showing the differences in ESTIMATE, immune, and stromal scores between clusters 1 and 2. (C) Box plot of the expression distribution of pyroptosis-related hub genes between clusters 1 and 2. (D) Box plot showing the difference in 13 types of immune cells between clusters 1 and 2. ns, P ≥ 0.05; *, P < 0.05; **, P < 0.01; ***, P < 0.001.*
## Correlation analysis between pyroptosis-related hub genes and infiltrating immune cells
We then analyzed the association of the 17 pyroptosis-related hub genes with the 13 types of infiltrating immune cells. The correlation heatmap depicts the possible correlations between the 17 hub genes and 13 types of immune cells (Figure 11A). The top four significant negative and positive correlations are shown by correlation scatter plots (Figures 11B–I): B cell memory and INS (r = -0.351, $P \leq 0.05$; Figure 11B), B cell memory and GCG (r = -0.385, $P \leq 0.05$; Figure 11C), macrophages M1 and PCSK1 (r = -0.363, $P \leq 0.05$; Figure 11F), macrophages M1 and IAPP (r = -0.395, $P \leq 0.05$; Figure 11I), plasma cells and INS ($r = 0.374$, $P \leq 0.05$; Figure 11D), macrophages M0 and GNAS ($r = 0.397$, $P \leq 0.05$; Figure 11E), macrophages M0 and NEUROD1 ($r = 0.371$, $P \leq 0.05$; Figure 11G), and T cells CD8 and ABCC8 ($r = 0.38$, $P \leq 0.05$; Figure 11H).
**Figure 11:** *Correlation analyses between pyroptosis-related hub genes and infiltrating immune cells. (A) Correlation heatmap depicting possible correlations between 17 hub genes and the 13 types of immune cells. Red represents positive correlation, blue represents negative correlation, and white represents no significant correlation. (B) Correlation of memory B cells and INS (r = -0.351, P < 0.05). (C) Correlation of memory B cells and GCG (r = -0.385, P < 0.05). (D) Correlation of plasma cells and INS (r = 0.374, P < 0.05). (E) Correlation of M0 macrophages and GNAS (r = 0.397, P < 0.05). (F) Correlation of M1 macrophages and PCSK1 (r = -0.363, P < 0.05). (G) Correlation of M0 macrophages and NEUROD1 (r = 0.371, P < 0.05). (H) Correlation of CD8+ T cells and ABCC8 (r = 0.38, P < 0.05). (I) Correlation of M1 macrophages and IAPP (r = -0.395, P < 0.05).*
## Quality control, cluster analysis, and major cell-type identification of single-cell expression data
Single-cell RNA-seq analysis was performed on five T2D and six non-T2D samples from the GSE153855 dataset. Cells were filtered using nFeature_RNA >200, nCount_RNA < 6000, and percent.mt < 5 as cutoffs. The violin plots show the number of genes (nFeature), expression values of genes (nCount), and percentage of mitochondrial genes (percent.mt) (Figures 12A–C). Subsequently, we performed PCA for dimensionality reduction to visualize the overall distribution of the data (Figure S6A) and the relationship between the number of principal components and standard deviation (Figure S6B). In this study, we selected 10 principal components for cell clustering. Eleven distinct clusters were identified among 1892 cells using t-SNE (Figure S6C). Figure S6D shows the clusters for the T2D and non-T2D samples.
**Figure 12:** *Quality control, cluster analysis, and major cell type identification of single-cell expression data GSE153855. (A) Violin plot showing numbers of genes (nFeature) of samples. (B) Violin plot showing expression values of genes (nCount) of samples. (C) Violin plot showing percent of mitochondria genes (percent.mt) of samples. (D) Dimensionality reduction plot using t-SNE showing seven distinct clusters of T2Dsamples. (E) Dimensionality reduction plot using t-SNE showing annotated the cell-type for each cluster of T2D samples. (F) Dimensionality reduction plot using t-SNE showing nine distinct clusters of non-T2D samples. (G) Dimensionality reduction plot using t-SNE showing annotated the cell type for each cluster of non-T2D samples.*
In addition, clustering analysis was performed separately using t-SNE for the T2D and non-T2D samples. The cell types for each cluster were annotated using built-in annotations in the GSE153855 dataset. The bubble and violin plots show the reliability of the built-in annotation information (Figures S2E–F). The respective marker genes were relatively highly expressed in the corresponding cells, which proved that the built-in cell annotations in this dataset were reliable. Seven distinct clusters were identified among 760 cells from the T2D samples through t-SNE (Figure 12D). Figure 13E shows the visualization of t-SNE colored according to the cell type in the T2D samples. As shown in Figures 12D, E, the cell types distributed in the seven clusters were as follows: alpha in cluster 0 (252, $98.824\%$), exocrine in cluster 1 (183, $100\%$), alpha in cluster 2 (99, $94.286\%$), beta in cluster 3 (76, $98.701\%$), delta in cluster 4 (62, $95.385\%$), ductal in cluster 5 (38, $71.698\%$), and macrophage in cluster 6 (22, $100\%$). Meanwhile, nine distinct clusters were identified among 1132 cells from the non-T2D samples through t-SNE (Figure 12F). Figure 12G shows the visualization of t-SNE colored according to the cell type in the non-T2D samples. As shown in Figures 12F, G, the cell types distributed in the nine clusters were as follows: alpha in cluster 0 (330, $100\%$), alpha in cluster 1 (164, $95.906\%$), beta in cluster 2 (126, $99.213\%$), ductal in cluster 3 (94, $78.333\%$), exocrine in cluster 4 (100, $100\%$), delta in cluster 5 (92, $97.872\%$), beta in cluster 6 (87, $98.864\%$), gamma in cluster 7 (66, $83.544\%$), and stellate in cluster 8 (23, $100\%$).
## Cell–cell communication and pseudotime trajectory analysis
CellChat was used to infer and quantify intercellular communication. Figure 14A shows the contribution of the outgoing and incoming pathways to cell types. The outgoing and incoming pathways with the largest contribution were GCG, and the cell type with the strongest correlation was alpha. We then drew circle plots to visualize the numbers (Figure 14B) and weights (Figure 14C) of cell interactions and found that alpha cells were the largest in number and weight.
**Figure 14:** *Cell–cell communication and pseudotime trajectory analysis. (A) Heatmap of the contribution of outgoing and incoming pathways. (B) Circle plot of numbers of cell interactions. (C) Circle plot of weights of cell interactions. (D) Pseudotime trajectory colored according to pseudotime progression. (E) Pseudotime trajectory colored according to cell type. (F) Pseudotime trajectory colored according to state of cell population.*
Pseudotime trajectory analysis was performed on the cell types of the T2D samples by using the Monocle package. The trajectory plots of cells are colored according to pseudotime progression, cell type, and state of cell population (Figures 14D–F). Figure 14E shows that the trajectory plot was divided into five pseudotemporal states. Alpha cells were mainly distributed in states 3, 4, and 5.
## GSEA among different clusters
GSEA was performed among the 11 clusters to illustrate the biological functions associated with these clusters (Figure 15). A relatively large number of differential pathways were enriched in clusters 5(endothelial cells) and 9 (macrophages). In addition, the TGF, myc targets v1, myc targets v2, mitotic spindle, G2M checkpoint, and E2F target signaling pathways were significantly upregulated in cluster 5. Results showed that UV response upregulation, unfolded protein response, tumor necrosis factor α signaling via the NF-κB, reactive oxygen species, P53, myc targets v2, myc targets v1, G2M checkpoint, E2F targets, and DNA repair signaling pathways were significantly upregulated in cluster 9.
**Figure 15:** *GSEA analyses among different clusters. The abscissa represents the cell clusters, and the ordinate denotes rich pathway. Circle size reflects the normalized enrichment score. Red represents upregulation, while blue represents downregulation.*
## Discussion
T2D is a lifelong metabolic disorder with a worldwide prevalence of $10.5\%$ in 2021 [59]. T2D is a genetic disease, but its genetics remains poorly understood [3]. Therefore, the pathophysiological mechanisms that trigger T2D should be elucidated to improve the management of this disease. In this study, we explored the potential crucial genes and pathways associated with pyroptosis and immune infiltration in T2D in a merged matrix from three bulk RNA-seq datasets of islets. We constructed miRNA and transcriptional networks based on these hub genes and performed functional analyses. Furthermore, these pyroptosis-, immune-, and T2D-related genes were analyzed using scRNA-seq data to explain the cellular heterogeneity in T2D. Our study provides insights into the molecular mechanisms underlying islet inflammation and human T2D pathogenesis caused by islet dysfunction.
In this study, we identified 918 T2D-related DEGs in the merged matrix. GO analysis revealed that these genes were highly enriched for the synthesis, secretion, and mode of action of hormones or peptides. The immune scores were significantly higher in the T2D group than in the non-diabetic group. Then, 550 immune- and T2D-related DEGs were obtained. Our results indicated that the immune system plays a crucial role in T2D pathogenesis, consistent with previous findings that immunologic–metabolic crosstalk is involved in T2D development (60–62). Accumulating evidence has shown that pyroptosis, a programmed proinflammatory cell death pathway, is activated during T2D development (63–65). We identified 31 pyroptosis-related genes in the merged matrix.
After LASSO regression, GBP2, NLRP1, and NOD2 were used to construct a clinical prediction model. The AUC value of the model was 0.968, suggesting that this model exhibited excellent accuracy [66] and might be an ideal target for the diagnosis of T2D. GBP2, a member of the GTPase family, triggers pyroptosis by supporting inflammasome activation [67]. GBP2 has been identified as a candidate gene in diabetic retinopathy [68]. Polymorphisms in NLRP1 affect susceptibility to type 1 diabetes in the Chinese Han population [69]. NOD2, a member of the nucleotide oligomerization domain (NOD)-linked receptor family, is associated with immune and chronic inflammatory disorders [70]. NOD2 is upregulated in diabetic cardiomyopathy and silencing this gene could protect against diabetes-induced cardiomyopathy [71]. However, Ozbayer et al. reported that NOD2 is not associated with T2D [72].
We identified 115 genes associated with pyroptosis, immune cell infiltration, and T2D and considered 17 of these genes to be pyroptosis-related hub genes. In the present study, INS was the top gene with the most nodes, and it appeared to be closely related to T2D. Mutations and translation defects in INS have been associated with diabetes [73]. A recent study has reported that INS could be regulated by m6A modification, providing a prediction for the occurrence of T2D [74]. Most of the other hub genes, including GCG [75], NEUROD1 [76], PCSK1 [77], ABCC8 [78], STMN2 [79], IAPP [80], KCNQ1 [81], NKX2-2 [82], SCG5 [83], CPE, and GNAS [84], have been strongly associated with the onset or development of T2D. CHGA, GAD2, GRIA2, CHGB, and SLC17A6 have not been previously reported to be associated with T2D.
To probe the potential upstream and downstream regulators of hub genes, we constructed miRNA–mRNA interaction and transcriptional regulatory networks. Each miRNA can target many mRNAs, and a single mRNA can be regulated by several miRNAs. Growing evidence has indicated that miRNAs, endogenous regulators of gene expression, are involved in T2D pathogenesis [85]. Srividya et al. have summarized miRNAs as biomarkers for T2D diagnosis [86]. A meta-analysis identified 40 miRNAs that are associated with T2D [87]. Transcriptional regulatory networks describe the regulatory interactions between TFs and their target genes. Similar to miRNAs, a single TF usually regulates multiple genes, and a gene is regulated by multiple TFs. In the present study, GO analysis revealed that these genes were also highly enriched for the synthesis, secretion, and mode of action of hormones or peptides. These results were consistent with the GO analysis results of the T2D-related DEGs. For KEGG pathway analysis, the pyroptosis-related hub genes were enriched in maturity-onset diabetes of the young, insulin secretion, type 1 diabetes mellitus, and glutamatergic synapses. The signaling pathway of maturity-onset diabetes of the young showed the greatest impact on T2D. Previous studies have shown that the maturity-onset diabetes of the young pathway plays a significant role in T2D pathogenesis [88, 89]. GSEA and GSVA were performed for the high- and low-pyroptosis score groups, respectively. These results complemented the GO and KEGG pathway analyses.
Abnormal differentiation of components of the immune system is involved in the progression of T2D (90–93). In the present study, disease states were almost uniformly distributed among pyroptosis states and among the wound healing (C1), inflammatory (C3), and lymphocyte-depleted (C4) immune subtypes. We determined the abundance of 13 immune cell types in the merged matrix and found correlations among them. Multiple immune cell types were identified in the islets. Immune cells and inflammatory mediators accumulate in the islets of both animal models and humans [7, 17, 94]. A recent study has confirmed the effect of T cells on T2D [95]. Wang et al. summarized the role of the imbalance between T helper 17 and regulatory T cells in T2D [96]. In the present study, we identified two molecular subtypes (clusters 1 and 2) by performing unsupervised clustering for T2D samples in the merged matrix. Cluster 2 showed a high expression of 17 pyroptosis-related hub genes and a high abundance two types of immune cells (monocytes and CD8+ T cells). Macrophage count increases in the islets of patients with T2D, and islet macrophage infiltration correlates with islet dysfunction [7, 8]. Inflammation triggers the differentiation of monocytes into macrophages. Wu et al. performed an analysis of single-cell data on human pancreas and found that monocytes and CD8+ T cells are enriched in the T2D pancreas [97]. Our results are consistent with previous reports that monocytes and macrophages are the primary immune cell subsets that contribute to islet inflammation during T2D development [98]. We also found correlations between the 17 pyroptosis-related hub genes and 13 immune cell types. The relationship between pyroptosis regulators and immune infiltrate characterization has been discussed in diseases, including cancer and periodontitis [99, 100].
In this study, we identified 11 cell types from the scRNA-seq dataset. The islets of Langerhans are composed of multiple types of endocrine cells (alpha, beta, delta, gamma, and epsilon) with distinct functions and non-endocrine cells (101–103). Maayan et al. found that human pancreatic cells can be divided into 14 cell populations based on the expression of unique transcripts and references [104]. Joshua et al. performed single-nucleus ATAC-seq on human pancreatic islets and identified 12 distinct cell clusters [105]. The location of the respective marker genes was consistent with the distribution of each cluster, suggesting the accuracy of the cluster analysis and major cell-type identification. We found differences in the distribution of various cell types between the T2D and non-T2D samples, revealing the heterogeneity caused by T2D. Our results are in concordance with the results of a previous study that β-cell mass decreases and α-cell volume increases in the pancreatic tissue of patients with T2D [106]. We obtained candidate hub genes for different cell subtypes by intersecting DEGs with 115 genes. INS and IAPP were determined to be pyroptosis-related and candidate hub genes. The relationships of INS and IAPP with T2D have been reported in previous studies [74, 80, 107].
Our intercellular communication analysis showed that the gene with the largest contribution was GCG and the cell type with the strongest correlation with T2D was alpha. GCG has been considered an islet alpha cell type-specific gene to cluster alpha cells [56, 108]. GCG encodes a variety of peptides, of which glucagon and glucagon-like peptide-1 have attracted increasing attention because of their effects on glucose metabolism. In the last few decades, multiple novel drugs for T2D treatment have been developed based on the utilization of the signaling systems of GCG products [109]. Pseudotime trajectory analysis showed that cell types of T2D existed along the trajectory, and alpha cells were located at the end of the trajectory line. Chiou et al. presented a detailed characterization of islet cell types and state regulatory programs, which provided a wide perspective to interpret the genetic mechanisms underlying T2D [104]. In the present study, we found that myc targets v1, myc targets v2, G2M checkpoint, and E2F target pathways were significantly upregulated in clusters 5 and 9. MYC is a signaling pathway capable of regulating apoptotic cell death, proliferation, survival, and differentiation [110]. A previous study found that myc, a member of the Wnt signaling pathway, is upregulated in the islets of patients with T2D [111]. The G2M cell cycle checkpoint plays a critical role in diabetic oxidative stress signaling [112]. The E2F signaling pathway is associated with the proliferation and regeneration of islets from patients with T2D [113]. These results suggest that cell death, proliferation, and regeneration play important roles in islet dysfunction in T2D.
However, this study has some limitations. First, the merged and scRNA-seq datasets used in this study are still relatively small. A larger number of cells in the scRNA-seq dataset are required to identify rare cell subpopulations or detect minor changes in gene expression. Second, the lack of detailed clinical data hindered the evaluation of the relationship between clinical characteristics of T2D and gene expression. Third, further experiments, such as quantitative real-time PCR, western blot, and immunohistochemistry, are warranted to clarify the functions of the hub genes in T2D.
In conclusion, we identified candidate genes associated with pyroptosis, immune infiltration, and disease phenotypes in T2D development. Furthermore, we presented a detailed characterization of islet cell types and their expression patterns in T2D. The combined bulk RNA-seq data and cell type-specific data of islets provided insights into the molecular mechanisms underlying T2D and novel therapeutic targets for T2D treatment. We believe this hypothesis generating study provides a critical resource for understanding of islet dysfunction and T2D pathogenesis.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found within the article/ Supplementary Materials.
## Author contributions
YS and CH conceptualized the study and analyzed the data. YS drafted the manuscript. YS, CH, and YJ drew the figures. MY and ZX performed the data acquisition and collation. YS, CH, YJ, MY, ZX, LY, WZ, and YX collaborated with the interpretation and discussion of the results. YX critically revised the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
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## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1132194/full#supplementary-material
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---
title: Effects of dietary camelina, flaxseed, and canola oil supplementation on inflammatory
and oxidative markers, transepidermal water loss, and coat quality in healthy adult
dogs
authors:
- Taylor L. Richards
- Scarlett Burron
- David W. L. Ma
- Wendy Pearson
- Luciano Trevizan
- Debbie Minikhiem
- Caitlin Grant
- Keely Patterson
- Anna K. Shoveller
journal: Frontiers in Veterinary Science
year: 2023
pmcid: PMC10034026
doi: 10.3389/fvets.2023.1085890
license: CC BY 4.0
---
# Effects of dietary camelina, flaxseed, and canola oil supplementation on inflammatory and oxidative markers, transepidermal water loss, and coat quality in healthy adult dogs
## Abstract
### Introduction
Camelina oil contains a greater concentration of omega-3 (n-3) a-linolenic acid (C18:3n-3; ALA) than omega-6 (n-6) linoleic acid (C18:2n-6; LA), in comparison to alternative fat sources commonly used to formulate canine diets. Omega-3 FAs are frequently used to support canine skin and coat health claims and reduce inflammation and oxidative stress; however, there is a lack of research investigating camelina oil supplementation and its effects on these applications in dogs. The objective of this study was to evaluate the effects of camelina oil supplementation on coat quality, skin barrier function, and circulating inflammatory and oxidative marker concentrations.
### Methods
Thirty healthy [17 females; 13 males; 7.2 ± 3.1 years old; 27.4 ± 14.0 kg body weight (BW)] privately-owned dogs of various breeds were used. After a 4-week wash-in period consuming sunflower oil (n6:n3 = 1:0) and a commercial kibble, dogs were blocked by age, breed, and size, and randomly assigned to one of three treatment oils: camelina (n6:n3 = 1:1.18), canola (n6:n3 = 1:0.59), flaxseed (n6:n3 = 1:4.19) (inclusion level: 8.2 g oil/100 g of total food intake) in a randomized complete block design. Transepidermal water loss (TEWL) was measured using a VapoMeter on the pinna, paw pad, and inner leg. Fasted blood samples were collected to measure serum inflammatory and oxidative marker concentrations using enzyme-linked immunosorbent assay (ELISA) kits and spectrophotometric assays. A 5-point-Likert scale was used to assess coat characteristics. All data were collected on weeks 0, 2, 4, 10, and 16 and analyzed using PROC GLIMMIX in SAS.
### Results
No significant changes occurred in TEWL, or inflammatory and oxidative marker concentrations among treatments, across weeks, or for treatment by week interactions. Softness, shine, softness uniformity, color intensity, and follicle density of the coat increased from baseline in all treatment groups ($P \leq 0.05$).
### Discussion
Outcomes did not differ ($P \leq 0.05$) among treatment groups over 16-weeks, indicating that camelina oil is comparable to existing plant-based canine oil supplements, flaxseed, and canola, at supporting skin and coat health and inflammation in dogs. Future research employing an immune or exercise challenge is warranted, as the dogs in this study were not subjected to either.
## Introduction
Dogs are unable to produce the omega-6 (n-6) linoleic acid (C18:2n-6; LA) and the omega-3 (n-3) α-linolenic acid (C18:3n-3; ALA), endogenously, and as such, these must be obtained in the diet [1]. Omega-3 fatty acids (FAs) in particular have been linked to numerous health benefits, including a reduction in inflammation and oxidative stress, and improved skin and coat health properties, which are directly associated (2–7).
There is a competitive relationship between the n-6 and n-3 FA pathways for the use of the Δ5- and Δ6-desaturase and elongase enzymes needed to convert LA and ALA into longer chain FAs. Consequently, a balanced dietary n-6:n-3 ratio is needed to ensure sufficient conversion to longer chain FAs in both pathways. Specifically, and most notably, LA is converted into arachidonic acid (AA), and ALA is converted into eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) [8]. Both AA and EPA and DHA are parent compounds for the production of pro- and anti-inflammatory eicosanoids, respectively. An increase in endogenous n-6 AA results in a prothrombotic, pro-constructive, and pro-inflammatory state, whereas increased EPA and DHA give rise to resolvins, which are anti-inflammatory and pro-resolving. Greater concentrations of n-6 FAs and a higher n-6:n-3 ratio allow for greater conversion of n-6 FAs to AA and more pro-inflammatory effects. In contrast, greater concentrations of n-3 FAs and a lower n-6:n-3 ratio allow for increased production of EPA and more anti-inflammatory effects [9]. As a result, excessive amounts of n-6 FAs and a high n-6:n-3 ratio promote the pathogenesis of many inflammatory, autoimmune, and dermatological disorders, whereas greater concentrations of n-3 FAs and a low n-6:n-3 ratio exert suppressive effects [10].
In order to formulate canine diets to meet the ideal n-6:n-3 ratio of between 5:1 and 10:1, n-3 rich ingredients are typically required [11]. Two oils commonly used to increase n-3 inclusion in canine diets are fish oil, as a result of its high levels of EPA and DHA (180 mg EPA, 120 mg DHA/1,000 mg of oil provided in the most common fish oil capsules in the United States today, however, doses vary widely between supplements), and flaxseed oil, due to its favorable n-6:n-3 ratio of 1:4.19 (12–15). However, large-scale fish oil production required to meet the demands of the growing pet food industry is not environmentally sustainable long-term, and the high abundance of ALA in flaxseed oil makes it susceptible to oxidation, making its use in commercial diets difficult [12, 15]. Additionally, flaxseed crops are sensitive to various climates, diseases, and pests, making both of these options less than desirable [12, 14, 15]. Alternative animal-based (beef, 1:0.05; milk, 1:0.07; eggs, 1:0.05) and plant-based (canola, 1:0.59; corn, 1:0.01; soybean, 1:0.12; and sunflower oil, 1:0.00) lipid sources commonly used in canine diet formulations all have higher concentrations of n-6 FAs rather than n-3 FAs (15–17). This leaves room in the market for an alternative plant-based oil source that is economically and environmentally sustainable, with good shelf-stability and a favorable concentration of n-3 FAs that could contribute to achieving the ideal n-6:n-3 ratio in canine diets.
The oil seed camelina (Camelina sativa) is considered a low-input, high-yield crop due to its short growing season and resistance to various seasons, climates, and soil types (18–21). The product of this robust crop, camelina oil, provides a rich source of n-3 FAs as a result of its desirable n-6:n-3 ratio of 1:1.8 [22]. Additionally, camelina oil contains high concentrations of tocopherols and polyphenols, which have been associated with improved skin and coat health due to their antioxidant properties [22]. Due to camelina oil being naturally high antioxidants as well as having a slightly lower concentrations of n-3 FAs in contrast to flaxseed oil, it's shelf-stability is better by comparison [23].
Additional data from this study suggests camelina oil to be safe for canine consumption [24]. The inclusion of oil supplements in canine diets is often associated with claims of maintenance or support of skin and coat health, but currently there is no data directly comparing the effects of camelina oil supplementation to the effects of other oils approved for use in pet foods on markers of skin and coat health and inflammation. The objective of this study was to compare the effects of dietary camelina oil supplementation to those of flaxseed oil and canola oil supplementation on skin and coat health and inflammatory and oxidative markers in healthy, adult dogs. Outcomes include changes in oxidative and inflammatory biomarkers and coat quality. Additionally, skin barrier function and integrity was assessed by measuring transepidermal water loss (TEWL). Authors hypothesize that camelina oil (n-3:n-6 = 1:1.8) is comparable, flaxseed (n-3:n-6 = 1:4.19) and canola oil (n-3:n-6 = 1:0.59) in terms of its effects on oxidative and inflammatory markers, coat quality, and TEWL.
## Animals and housing
This experiment was approved by the University of Guelph's Animal Care Committee (AUP #4365) and was carried out in accordance with national and institutional guidelines for the care and use of animals. Thirty client-owned, adult (7.2 ± 3.1 years) dogs of mixed sex (17 females: 16 spayed, one intact; 13 males: 10 neutered, three intact), weight (27.4 ± 14.0 kg) and breed participated in this study (Table 1). All dogs were deemed healthy based on their previous medical history as well as a pre-study physical examination performed by a licensed veterinarian, complete blood count (CBC), and serum biochemistry profile. During the recruitment process, dogs were excluded if they had any skin conditions, received any pro- or anti-inflammatory medications 2-months prior to baseline samples, had abnormalities on their physical examination, CBC, or serum biochemistry, or were younger than 2 years of age. Dogs were housed at their owners' homes for the duration of the study, they followed their usual daily routines. Pet owners were instructed to provide no supplements, medications, antibiotics, antifungals, antiparasitics, or topical creams without notifying the researchers. Prior to week 10, dog #10, consuming FLX, withdrew from the study due to circumstances unrelated to the research trial or treatment diet.
**Table 1**
| Treatment | Mean age (years)a | Mean BW (kg)b | Breeds | Male:female | Neutered:spayed:intact |
| --- | --- | --- | --- | --- | --- |
| | | | Miniature dachshund | | |
| | | | Havanese | | |
| | | | Mix, unknown | | |
| CAM | 7.8 | 25.0 | Mix, Australian shepherd/collie | 2:8 | 2:7:1 |
| CAM | 7.8 | 25.0 | Mix, boxer whippet | 2:8 | 2:7:1 |
| | | | Standard poodle | | |
| | | | Norwegian elkhound | | |
| | | | Labrador retriever (3) | | |
| | | | Miniature dachshund | | |
| | | | Pekingese | | |
| | | | Mix, sled dog/unknown | | |
| | | | Mix, border collie/sheltie | | |
| FLX | 7.7 | 27.0 | Mix, husky/pointer | 6:4 | 5:4:1 |
| | | | Great dane | | |
| | | | Standard poodle | | |
| | | | Bernese | | |
| | | | Labrador retriever (2) | | |
| | | | Mix, mastiff/boxer | | |
| | | | King Charles cavalier spaniel | | |
| | | | Mix, samoyed/collie | | |
| | | | Sheltie | | |
| OLA | 6.05 | 28.0 | German shepherd | 6:4 | 4:4:2 |
| | | | Barbet | | |
| | | | Standard poodle | | |
| | | | Bernese | | |
| | | | Labrador retriever (2) | | |
## Dietary treatments
Over a 4-week wash-in period, all dogs were acclimated to a dry extruded commercial kibble (SUMMIT Three Meat Reduced Calorie Recipe, Petcurean, Chilliwack, BC, Canada; Table 2), sunflower oil (SA Kernel-Trade, Kuiv, Ukraine; Table 3), and beef-based treats (Beef Tendersticks, The Crump Group, Brampton, ON, Canada; proximate analysis: metabolizable energy 3039 kcal/kg; crude protein minimum $65\%$; crude fat minimum $5.1\%$; crude fiber maximum $4.0\%$; moisture max $9.56\%$). Oil was included in the diet at 8.2 grams of oil per 100 grams of total food intake, bringing the total dietary lipid content to $20\%$ on an as-fed basis. Treats were included in the diet up to 2.5 grams per 100 grams total intake, and the remaining proportion of the diet was provided as kibble. During the wash-in period and throughout the study, daily portions of food, oil, and treats were pre-weighed by researchers and provided to the owners in 2-week intervals to be offered to dogs daily at a frequency determined by the owner. To avoid the occurrence of lipid peroxidation, owners were instructed to mix the oil with the food immediately before feeding. Any leftover kibble, oil, and/or treats were returned to researchers and subsequently weighed and recorded. Dogs were initially fed to meet their estimated maintenance energy requirements (110 kcal ME × kg BW0.75), and BW was recorded every 2 weeks starting at baseline. Each dog's food allotment was then adjusted accordingly to maintain baseline BW throughout the study. No abnormal observations were reported by owners throughout the 16-week study period in terms of diet tolerance (i.e., vomiting, stool quality, halitosis, etc.).
## Study design
This study was conducted using a randomized complete block design (RCBD) with repeated measures. Following the 4-week wash-in period, dogs were blocked by breed, age, and BW and groups were randomly assigned to one of 3 treatment oils: camelina oil (CAM) ($$n = 10$$; eight females; two males), flaxseed oil (FLX) ($$n = 10$$; five females; five males), or canola oil (OLA) ($$n = 10$$; four females; six males). The sunflower oil used during the wash-in was replaced with either CAM, FLX, or OLA, and feeding continued as described for 16 weeks. Both OLA and FLX were chosen as control groups for this study as they are commonly used to formulate canine diets and provide a source of n-3 FAs.
## Blood collection
Dogs were fasted for a minimum of 10 h overnight and blood samples were collected via cephalic venipuncture using a syringe (Becton, Dickinson and Company, Franklin Lakes, NJ, USA). Of the collected blood, 5 mL was put into a serum vacutainer (Becton, Dickinson and Company, Franklin Lakes, NJ, USA). Blood was allowed to clot and was centrifuged at 7,200 × g for 15 min using an accuSpin Micro 17 centrifuge (Thermo Fisher Scientific, Waltham, MA, USA). Then, the serum aliquots were frozen at −80°C until later analysis.
## Inflammatory and oxidative markers
Serum samples were analyzed for prostaglandin E2 (PGE2) (Canine Prostaglandin E2 ELISA Kit MBS013017, MyBioSource, Vancouver, BC) and junction plakoglobin (JUP) (Canine Junction Plakoglobin ELISA Kit MBS104997, MyBioSource, Vancouver, BC) using commercially available ELISA (Enzyme-linked immunosorbent assay) kits. Samples were run in duplicate according to the manufacturer's instructions. Serum glycosaminoglycan (GAG) (dimethyl methylene blue) and nitric oxide (NO) (Griess Reaction; Molecular Probes, Eugene, OR) concentrations were determined using spectrophotometric assays [26, 27]. Serum NO and GAG samples were analyzed as previously described by MacNicol et al. [ 28].
In the current study, concentrations of GAG tended to be higher in males compared to females. Studies in humans by [1] Larking [31] and [2] Claassen and Werner [32] found that, similar to the present study, females have lower concentrations of GAG. Claassen and Werner analyzed GAG in thyroid cartilage while Larking measured GAG excretion in the tissue. Since GAG is a marker of cartilage turnover, Claassen and Werner attribute their findings to greater cartilage turnover in males, while Larking accredits their findings to the males in their study having a greater mean height [31, 32]. It is possible that the female dogs in the present experiment had a smaller average height and lower cartilage mineralization than the males, which contributed to the lower concentration of circulating GAGs observed. However, height and cartilage mineralization were not measured in the present study. Furthermore, the observation made in our study was only a tendency; this, combined with the dearth of work carried out in dogs and lack of equal distribution of male/female, intact/neutered/spayed dogs in the current study make it difficult to form any cogent conclusions. Future research should investigate this relationship further using a dog model.
No significant changes were observed in PGE2, JUP, GAG, or NO concentrations over the 16-week study period. It is possible that the stability of these concentrations across time and among treatments is attributed to the lack of exercise or immune challenge experienced by the dogs on the current study. It is well-established that both exercise and immune challenges result in a wide range of physiological and biochemical adaptations, the magnitude of which is directly related to the intensity and duration of the exercise or immune challenge encountered (33–36). This wide range of physiological and biochemical adaptations include changes in inflammatory and oxidative biomarker concentrations [28, 33].
Dogs and horses both experience increased PGE2 concentrations following exercise. In horses, NO and GAG concentrations increase following exercise and compared to baseline, but no change was observed in dogs [28, 33]. Pearson et al. attribute these results, similar to previous findings, to variations in NO production depending on exercise intensity, suggesting that it is possible that the lack of changes observed in NO concentration in the current study is due to the low intensity of the exercise experienced by the dogs [33]. Markers like PGE2, NO, GAG, and JUP are often upregulated during times of immune challenge/disease (37–40). A myriad of studies completed in humans suggest no effects of n-3 PUFA supplementation on inflammatory or immune markers in healthy individuals (41–43). As an example, Pot et al. found that supplementing fish oil and sunflower oil to healthy individuals had no effect on chemokine, cytokine, or cell adhesion molecule concentration compared to baseline [41]. Healthy individuals, similar to the canine subjects of our study, generally have low levels of circulating inflammatory markers. Thus, the chance that low levels of inflammation are reduced even further by an intervention with oil is very small and difficult to measure. The dogs of the present study were healthy upon recruitment and on every sample period based on a veterinary examination, as well as CBC and biochemistry analysis, indicating a lack of immune response that would elicit an inflammatory response. Additionally, the dogs did not participate in any intense exercise prior to or on sample days, and thus had no known reason to elicit any exercise stress induced response impacting markers of inflammatory or oxidative stress. For safety and animal care purposes, no procedures with the potential to cause harm to the animals, like an inflammatory or immune challenge, can be carried out in client-owned dogs. Additionally, the objective of the present study was to determine how these three oils compare to one another in terms of their effects on these biomarkers to gauge their use in dog food formulations for typical pets, not to evaluate their performance following an exercise or immune challenge. Future studies should compare the effects of these three oils and their performance following exercise and immune challenge.
## Skin barrier function
Skin barrier function and integrity were assessed by measuring TEWL, which is defined as the amount of water that passively evaporates through skin to the external environment due to a water vapor pressure gradient on both sides of the skin barrier and is commonly used to characterize skin barrier function and integrity [29, 30]. On weeks 0, 2, 4, 10, and 16, TEWL was measured using a VapoMeter® SWL-3 (Delfin Technologies Ltd, Kuopio, Finland), according to the manufacturer's instructions. Since privately-owned dogs were used, it was not feasible to shave multiple patches for TEWL measurements, and as a result, researchers chose three body sites with little hair to measure TEWL, including: the right paw pad, right pinna, and right inner thigh. Ten measurements were taken per body site and the average was used for analyses. Once the averages were calculated, any values above or below the average by 50 g/m2/h or more were considered outliers and removed. All dogs were brought to the University of Guelph by their owners on collection days to ensure environmental conditions during collections remained consistent. All measurements were carried out by a single operator, in the same order of body sites, and in a climate-controlled room to maintain consistency between samples and to avoid variation in VapoMeter® readings due to temperature and humidity fluctuations [29]. Room conditions were stable at 22–23°C ambient temperature and 44–$50\%$ ambient relative humidity. The evaporation rate value is calculated in grams of water per square meter per hour (g/m2/h). All dogs were behaviorally acclimated to the use of the VapoMeter®, the researchers involved in sample collection, and the collection room, prior to the first sample day to minimize stress, thereby reducing variation in measurements. If dogs were wet due to weather upon arrival they were dried with a towel, to reduce variation further.
## Coat quality
Two researchers blinded to treatment were trained to perform a subjective coat assessment on weeks 0, 2, 4, 10, and 16 using a 5-point Likert scale (under Supplementary material). A Likert scale was used to measure the softness, shedding, dander, shine, spring, softness uniformity, color, color uniformity, and follicle density of the coat. Follicle density was assessed on the center of the back of the dogs by scoring the thickness/amount of hair coming from individual follicles. To increase consistency among dogs given different management practices in each household, all dogs were bathed 2 weeks prior to each assessment and owners were instructed to keep dogs dry and to not brush or groom them during this period.
Spring and follicle density increased significantly from baseline. This is likely due, at least in part, to the growth of winter coats as the study began at the end of summer and went into the winter (September–January). Dogs have a light summer undercoat that is shed before a thick winter undercoat grows in, which could explain the increase in spring and follicle density. This further supports the observation of the present study in that shedding was greater in all dogs at the beginning of the study at weeks 0 and 2, compared to weeks 10 and 16.
Softness, shine, and color of the dogs' coats increased from baseline. This is likely a result of the dogs consuming an increased amount of n-3 FAs following baseline, which can be further metabolized into EPA and DHA, though with limited efficiency. Supplementation of fish oil, a rich source of EPA and DHA, was found to improve skin and hair coat quality in dogs from baseline based on a clinical score, with maximal improvement occurring after 8 weeks [55]. The positive effects on skin and coat health are thought to be due to an increase in EPA and DHA in the erythrocyte membrane, along with increased total lipids in the hair shaft [55]. The same study observed that following supplement withdrawal, skin and coat health clinical scores remained the same for 1 month and began to deteriorate following the second month [55]. Although we did not take measurements on week 8, we did take measurements on week 10, and this is where we saw the largest improvement (i.e., softness, shedding, shine, spring, and color). This is most likely due to the increase in ALA, which is the parent compound of EPA and DHA, the dogs received from their treatment oil (CAM 1:1.8, FLX 1:4.19, OLA 1:0.59) in comparison to the wash-in sunflower oil (1:0). It is important to note that our study had no negative control group, since the absence of an oil supplement would alter all macronutrient intakes and our aim was to compare to existing approved oil supplements. As a result it cannot be ruled out that the observed changes in coat quality may be a result of the placebo effect. Future studies should consider employing a control group fed no oil supplement to rule out the possibility of the placebo effect impacting observations.
All dogs in the current study were considered healthy, with no known dermatological conditions or skin disorders. The coats of these dogs were in relatively good condition at baseline, and future research should investigate these oil supplements and their effects on skin and coat health in dogs with poor skin and coat quality as a result of conditions like atopic dermatitis. It is important to note that ectoparasites, particularly fleas in dogs, can negatively impact skin and coat health [56]. In this study, although complete blood count and biochemistry values were assessed, and physical examinations were performed by a licensed veterinarian prior to study recruitment and throughout the entire trial, diagnostic and preventive control in terms of ectoparasites was not considered, and this is a limitation of this study. Authors recommend future studies consider using more specific techniques as inclusion criteria when recruiting participants in order to ensure the absence and prevention of parasites and their potential impact on skin and coat health.
## Statistical analysis
Data are presented as mean ± SD unless otherwise stated. All statistical analyses were performed using the PROC GLIMMIX of SAS Studio® software (v.9.4., SAS Institute Inc., Cary, NC, USA). Dog was the experimental unit, and treatment, TEWL site, and sex, and age were treated as fixed effects (age and sex data not presented). Week was treated as a repeated measure. An analysis of variance (ANOVA) was performed to assess the effects of treatment on inflammatory and oxidative marker concentrations, TEWL, and coat scores. When the fixed effects were significant, the means were separated using Tukey–Kramer adjustments. Significance was declared at a P ≤ 0.05. Trends were declared at P ≤ 0.10.
## Prostaglandin E2
There were no differences among treatments ($$P \leq 0.973$$), across weeks ($$P \leq 0.397$$), or for treatment by week interactions ($$P \leq 0.987$$) (Table 4). Additionally, no differences were observed due to sex ($$P \leq 0.937$$) or age ($$P \leq 0.274$$).
**Table 4**
| Unnamed: 0 | Week | Week.1 | Week.2 | Week.3 | Week.4 | P -values | P -values.1 | P -values.2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | 0 | 2 | 4 | 10 | 16 | Treatment | Week | Treatment * week |
| Prostaglandin E2 (pg/mL) | Prostaglandin E2 (pg/mL) | Prostaglandin E2 (pg/mL) | Prostaglandin E2 (pg/mL) | Prostaglandin E2 (pg/mL) | Prostaglandin E2 (pg/mL) | Prostaglandin E2 (pg/mL) | Prostaglandin E2 (pg/mL) | Prostaglandin E2 (pg/mL) |
| CAM | 0.88 ± 1.45 | 2.77 ± 1.45 | 3.49 ± 1.45 | 2.35 ± 1.45 | 2.32 ± 2.33 | | | |
| OLA | 3.07 ± 1.34 | 3.07 ± 1.38 | 2.41 ± 1.44 | 2.82 ± 1.44 | 2.80 ± 1.39 | 0.9734 | 0.3965 | 0.9868 |
| FLX | 2.55 ± 1.23 | 4.07 ± 1.28 | 3.07 ± 1.34 | 3.44 ± 1.28 | 3.15 ± 1.33 | | | |
| Junction plakoglobin (ng/mL) | Junction plakoglobin (ng/mL) | Junction plakoglobin (ng/mL) | Junction plakoglobin (ng/mL) | Junction plakoglobin (ng/mL) | Junction plakoglobin (ng/mL) | Junction plakoglobin (ng/mL) | Junction plakoglobin (ng/mL) | Junction plakoglobin (ng/mL) |
| CAM | 8.73 ± 1.08 | 9.38 ± 1.08 | 8.56 ± 1.11 | 8.65 ± 1.08 | 7.82 ± 1.08 | | | |
| OLA | 10.09 ± 1.01 | 9.60 ± 1.01 | 9.51 ± 1.01 | 9.96 ± 1.01 | 7.39 ± 1.09 | 0.9693 | 0.2487 | 0.9133 |
| FLX | 8.94 ± 0.94 | 10.97 ± 0.94 | 10.78 ± 0.94 | 9.34 ± 0.97 | 8.35 ± 1.02 | | | |
| Glycosaminoglycan (μg/mL) | Glycosaminoglycan (μg/mL) | Glycosaminoglycan (μg/mL) | Glycosaminoglycan (μg/mL) | Glycosaminoglycan (μg/mL) | Glycosaminoglycan (μg/mL) | Glycosaminoglycan (μg/mL) | Glycosaminoglycan (μg/mL) | Glycosaminoglycan (μg/mL) |
| CAM | 4.43 ± 0.73 | 4.73 ± 0.73 | 4.23 ± 0.73 | 4.91 ± 0.80 | 3.97 ± 0.76 | | | |
| OLA | 3.03 ± 0.73 | 4.34 ± 0.73 | 4.47 ± 0.72 | 4.17 ± 0.76 | 3.74 ± 0.72 | 0.2083 | 0.9945 | 0.9147 |
| FLX | 4.33 ± 0.66 | 4.50 ± 0.66 | 4.82 ± 0.69 | 4.85 ± 0.69 | 4.04 ± 0.78 | | | |
| Nitric oxide (μM/mL) | Nitric oxide (μM/mL) | Nitric oxide (μM/mL) | Nitric oxide (μM/mL) | Nitric oxide (μM/mL) | Nitric oxide (μM/mL) | Nitric oxide (μM/mL) | Nitric oxide (μM/mL) | Nitric oxide (μM/mL) |
| CAM | 2.20 ± 5.50 | 9.30 ± 5.50 | 4.82 ± 5.62 | 8.34 ± 5.60 | 10.90 ± 5.64 | | | |
| OLA | 4.31 ± 5.05 | 7.19 ± 5.05 | 5.85 ± 5.05 | 9.26 ± 5.05 | 10.15 ± 5.18 | 0.6476 | 0.3587 | 0.7288 |
| FLX | 11.70 ± 4.58 | 12.76 ± 4.58 | 19.56 ± 4.72 | 13.74 ± 4.72 | 16.34 ± 4.72 | | | |
## Junction plakoglobin
There were no differences among treatments ($$P \leq 0.969$$), across weeks ($$P \leq 0.249$$), or for treatment by week interactions ($$P \leq 0.913$$) (Table 4). No differences were observed due to sex ($$P \leq 0.914$$) or age ($$P \leq 0.743$$).
## Glycosaminoglycan
There were no differences among treatments ($$P \leq 0.208$$), across weeks ($$P \leq 0.995$$), or for treatment by week interactions ($$P \leq 0.915$$) (Table 4). Concentrations of GAG tended to be greater in males compared to females ($$P \leq 0.078$$). There were no differences observed due to age ($$P \leq 0.329$$).
## Nitric oxide
There were no differences among treatments ($$P \leq 0.648$$), across weeks ($$P \leq 0.359$$), or for treatment by week interactions ($$P \leq 0.729$$) (Table 4). No differences were observed due to sex ($$P \leq 0.226$$) or age ($$P \leq 0.424$$).
## Transepidermal water loss
Of the 4,440 individual TEWL measurements collected throughout the study period, 18 were considered outliers and removed [D = Dog, W = Week; Paw pad: D6W2(CAM), D8W16(FLX)(2 values), D9W16(FLX), D17W4(CAM), D18W2(FLX), D18W4(FLX)(2 values), D23W10(CAM), D23W16(CAM); Inner ear: D5W4(OLA), D5W10(OLA), D12W10(OLA); Inner leg: D6W2(CAM), D6W10(CAM), D12W0(OLA), D16W0(FLX), D29W0(FLX)]. These outliers could often be attributed to changes in the environment, leading to signs of stress or excitement in the dogs (i.e., researchers entering and leaving the room, noises occurring outside of the sample room, and in the case of some outliers these samples were taken near the end of the collection period and the dogs would become impatient, no longer wanting to remain in the same spot for samples).
There were no differences among treatments ($$P \leq 0.726$$), across weeks ($$P \leq 0.738$$), or for treatment by week interactions ($$P \leq 0.996$$). Additionally, there were no differences for site by week ($$P \leq 0.378$$), or sex ($$P \leq 0.274$$) (Table 5). However, there were differences observed among sites ($P \leq 0.0001$), in that TEWL values for the paw pad were greater than those of the pinna or inner thigh. Additionally, there was a trend observed in age ($$P \leq 0.072$$), in that senior dogs (11–14 years; $$n = 3$$) tended to have lower mean TEWL values compared to young (2–4 years; $$n = 7$$), young adult (5–7 years; $$n = 9$$), and adult dogs (8–10 years; $$n = 9$$).
**Table 5**
| Unnamed: 0 | Unnamed: 1 | Week | Week.1 | Week.2 | Week.3 | Week.4 | P -values | P -values.1 | P -values.2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Treatment | Site | 0 | 2 | 4 | 10 | 16 | Trt | Site | Week |
| CAM | Paw pad | 92.57 ± 8.80 | 98.97 ± 8.80 | 88.28 ± 8.80 | 83.98 ± 8.80 | 92.7 ± 8.80 | | | |
| OLA | Paw pad | 88.27 ± 8.85 | 86.95 ± 8.85 | 76.32 ± 8.85 | 71.38 ± 8.85 | 67.56 ± 8.85 | | | |
| FLAX | Paw pad | 99.43 ± 8.79 | 109.51 ± 8.79 | 100.37 ± 8.79 | 87.38 ± 9.21 | 88.46 ± 9.21 | | | |
| CAM | Pinna | 14.03 ± 8.80 | 12.27 ± 8.80 | 18.78 ± 8.80 | 14.47 ± 8.80 | 16.68 ± 8.80 | | | |
| OLA | Pinna | 14.43 ± 8.85 | 15.84 ± 8.85 | 16.87 ± 8.85 | 24.43 ± 8.85 | 18.40 ± 8.85 | 0.7261 | < 0.0001 | 0.7375 |
| FLAX | Pinna | 9.10 ± 8.79 | 12.69 ± 8.79 | 12.13 ± 8.79 | 13.27 ± 9.21 | 9.92 ± 9.21 | | | |
| CAM | Inner thigh | 23.11 ± 8.80 | 23.56 ± 8.80 | 18.2 ± 8.80 | 17.52 ± 8.80 | 22.93 ± 8.80 | | | |
| OLA | Inner thigh | 16.86 ± 8.85 | 15.72 ± 8.85 | 18.18 ± 8.85 | 17.32 ± 8.85 | 21.23 ± 8.85 | | | |
| FLAX | Inner thigh | 15.7 ± 8.79 | 13.44 ± 8.79 | 16.36 ± 8.79 | 14.30 ± 9.21 | 16.51 ± 9.21 | | | |
In the present study, mean TEWL values were significantly greater when measured on the paw pad compared to the inner leg and inner ear. This is likely the result of the tubular, unbranched eccrine glands that open directly onto the skin of the paw pads and noses of canines. These glands allow sweat to be released from these areas, contributing to the water-loss detected by the VapoMeter, and thereby likely contributing to greater TEWL values compared to the inner leg and pinna [44]. Additionally, TEWL values were found to be lower in senior dogs compared to young, young adult, and adult dogs. Similar findings have been observed in other canine and human studies and although the exact mechanism behind these observations is unclear, there are various theories [45, 46]. The thickness of the stratum corneum and flattening of corneocytes increases with age, while natural moisturizing factors, stratum corneum hydration, and epidermal lipid synthesis are reduced (47–53). Additionally, the density of dermal capillaries decreases with age, which may lower skin temperature and in turn decrease water diffusion [51, 54]. All of these findings provide examples of mechanisms that increase the path length and resistance of a water molecule and subsequently contribute to lower TEWL in older individuals, and in agreement with the present study.
## Softness
There were no differences among treatments ($$P \leq 0.539$$), for treatment by week interactions ($$P \leq 0.757$$), or due to age ($$P \leq 0.479$$), week by age (0.338) or week by sex ($$P \leq 0.738$$) interactions. However, there were differences observed across weeks for pooled data ($$P \leq 0.005$$) in that softness was greater on week 10 and 16 compared to week 0, and greater on week 10 compared to week 2. Week 4 was not different from any other time points (Figure 1). Additionally, softness was greater in females compared to males ($$P \leq 0.026$$).
**Figure 1:** *Mean coat quality assessment scores completed using a 5-point Likert scale on 30 client owned healthy adult dogs fed one of three treatment oils (camelina oil, canola oil, flaxseed oil) and commercial kibble. A, B, C, DBars without a common letter differ significantly (P < 0.05).*
## Shedding
There were no differences among treatments ($$P \leq 0.882$$), due to age (0.894) or sex ($$P \leq 0.760$$), or for treatment by week ($$P \leq 0.444$$), week by age ($$P \leq 0.302$$), or week by sex ($$P \leq 0.514$$) interactions. For pooled data across weeks, shedding was greater on weeks 0 and 2 compared to weeks 10 and 16 ($$P \leq 0.004$$). Week 4 was not different from any other time points (Figure 1).
## Dander
There were no differences among treatments ($$P \leq 0.648$$), due to age ($$P \leq 0.114$$) or sex ($$P \leq 0.349$$), across weeks ($$P \leq 0.129$$), or for treatment by week ($$P \leq 0.869$$), week by age ($$P \leq 0.171$$), or week by sex ($$P \leq 0.163$$) interactions (Figure 1).
## Shine
There were no differences among treatments ($$P \leq 0.815$$), due to age ($$P \leq 0.945$$), or sex ($$P \leq 0.191$$), or treatment by week ($$P \leq 0.998$$), week by age (0.992), or week by sex ($$P \leq 0.375$$) interactions. However, there were differences across weeks for pooled data ($P \leq 0.0001$) in that shine on weeks 2, 4, 10, and 16 was greater than at week 0 (Figure 1).
## Spring
There were no differences among treatments ($$P \leq 0.918$$), due to age ($$P \leq 0.663$$) or sex ($$P \leq 0.401$$), or for treatment by week ($$P \leq 0.397$$), week by age ($$P \leq 0.773$$), or week by sex ($$P \leq 0.997$$) interactions. However, there were differences across weeks for pooled data ($$P \leq 0.014$$) in that spring was greater on week 10 compared to week 4 and 0. There were no differences on weeks 2 and 16 (Figure 1).
## Softness uniformity
There were no differences among treatments ($$P \leq 0.969$$), due to age ($$P \leq 0.860$$) or sex ($$P \leq 0.132$$), or for treatment by week ($$P \leq 0.799$$), week by age ($$P \leq 0.996$$), or week by sex ($$P \leq 0.142$$) interactions. However, a trend was observed across weeks for pooled data ($$P \leq 0.065$$) in that softness uniformity tended to be greater on week 16 compared to week 0. Weeks 2, 4, and 10 were not different from any other time points (Figure 1).
## Fur color
There were no differences among treatments ($$P \leq 0.323$$), due to age ($$P \leq 0.770$$) or sex ($$P \leq 0.546$$), or for treatment by week ($$P \leq 0.567$$), week by age ($$P \leq 0.345$$), or week by sex ($$P \leq 0.954$$) interactions. However, there were differences across weeks for pooled data ($P \leq 0.0001$) in that color was higher on weeks 4, 10, and 16 compared to week 0. Additionally, color was greater on week 10 and 16 compared to week 2. Furthermore, color tended to be higher on week 10 compared to week 4 (Figure 1).
## Fur color uniformity
There were no differences among treatments ($$P \leq 0.541$$), due to age ($$P \leq 0.893$$) or sex ($$P \leq 0.911$$), across weeks ($$P \leq 0.362$$), or for treatment by week ($$P \leq 0.291$$), week by age ($$P \leq 0.787$$), or week by sex ($$P \leq 0.910$$) interactions (Figure 1).
## Follicle density
There were no differences among treatments ($$P \leq 0.873$$), due to age ($$P \leq 0.795$$) or sex ($$P \leq 0.854$$), or for treatment by week ($$P \leq 0.670$$), week by age ($$P \leq 0.846$$), or week by sex ($$P \leq 0.299$$) interactions. However, there were differences across weeks for pooled data ($$P \leq 0.027$$) in that follicle density was greater on week 16 compared to week 0. Weeks 2, 4, and 10 were not different from any other time points (Figure 1).
## Discussion
The purpose of this study was to assess the effects of camelina oil supplementation on skin and coat health compared to canola and flaxseed oil, two oils currently used to formulate canine diets. The results presented herein suggest no differences in TEWL, coat quality, or the inflammatory and oxidative markers assessed due to treatment over the 16-week period.
## Conclusion
In conclusion, camelina oil is comparable to canola and flaxseed oil in terms of its effects on skin barrier function, coat quality, and the circulating inflammatory and oxidative markers measured in the current study when fed to healthy adult dogs, subjected to no physical or immunological challenge, and observed for 16-weeks. Canola and flaxseed oil are commonly used in canine food formulations. Flaxseed oil specifically has the ability to support skin and coat health claims, making camelina oil a potential alternative plant-based oil source with high concentrations of ALA that could contribute to achieving the ideal n-6:n-3 ratio in canine diets, while supporting skin and coat health claims.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The animal study was reviewed and approved by University of Guelph Animal Care Committee. Written informed consent was obtained from the owners for the participation of their animals in this study.
## Author contributions
AS and WP: conceptualization and funding acquisition. AS, WP, and DM: methodology. TR, SB, KP, and CG: study conduct. TR: formal analysis and writing—original draft preparation. TR, SB, DWM, CG, KP, LT, DM, WP, and AS: writing—reviewing and editing. All authors have read and agreed to the published version of the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
AS is the Champion Petfoods Chair in Canine and Feline Nutrition, Physiology and Metabolism and additionally consults for Champion Petfoods. AS has received various honoraria and research funding from various pet food manufacturers and ingredient suppliers and was a former employee of P&G Petcare and Mars Petcare. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fvets.2023.1085890/full#supplementary-material
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|
---
title: 'Causal relationship between human blood omega-3 fatty acids and the risk of
epilepsy: A two-sample Mendelian randomization study'
authors:
- Zhen Liang
- Yingyue Lou
- Zijian Li
- Songyan Liu
journal: Frontiers in Neurology
year: 2023
pmcid: PMC10034028
doi: 10.3389/fneur.2023.1130439
license: CC BY 4.0
---
# Causal relationship between human blood omega-3 fatty acids and the risk of epilepsy: A two-sample Mendelian randomization study
## Abstract
### Background
Though omega-3 fatty acids reduce seizures in several animal models, considerable controversy exists regarding the association between omega-3 fatty acids and epilepsy in human.
### Objective
To assess whether genetically determined human blood omega-3 fatty acids are causally associated with the risk of epilepsy outcomes.
### Methods
We conducted a two-sample Mendelian randomization (MR) analysis by applying summary statistics of genome-wide association study datasets of both exposure and outcomes. Single nucleotide polymorphisms significantly associated with blood omega-3 fatty acids levels were selected as instrumental variables to estimate the causal effects on epilepsy. Five MR analysis methods were conducted to analyze the final results. The inverse-variance weighted (IVW) method was used as the primary outcome. The other MR analysis methods (MR-Egger, weighted median, simple mode, and weighted mode) were conducted as the complement to IVW. Sensitivity analyses were also conducted to evaluate heterogeneity and pleiotropy.
### Results
Genetically predicted the increase of human blood omega-3 fatty acids levels was associated with a higher risk of epilepsy (OR = 1.160, $95\%$CI = 1.051–1.279, $$P \leq 0.003$$).
### Conclusions
This study revealed a causal relationship between blood omega-3 fatty acids and the risk of epilepsy, thus providing novel insights into the development mechanism of epilepsy.
## Introduction
Epilepsy is a common chronic central nervous system (CNS) disorder with substantial morbidity and mortality, which is pathologically characterized by spontaneous, recurrent, and transient CNS dysfunction [1]. As estimated, the current prevalence of epilepsy has achieved $1\%$ in the general population, with $80\%$ of people with epilepsy living in low- and middle-income countries, causing a substantial financial burden [2, 3]. Aiming to increase the life quality of patients with epilepsy, the goal of all epilepsy treatment is abolishing seizures completely while minimizing the side effects [4].
Nowadays, the most classic treatments for epilepsy are still antiepileptic drugs (AEDs) and surgical intervention [5]. Despite the continuous development of AEDs, side effects are still observed and more than $30\%$ of patients with epilepsy progress to refractory epilepsy [5, 6]. In these cases, the option is surgical intervention normally by disconnecting rather than removing some brain tissue [7]. However, due to the high trauma, high cost, and narrow range of adaptation, there is an urgency to develop new adjuvant treatments, such as dietary therapy [8, 9].
Fatty acids are hydrocarbon chains with a methyl group (–CH3) at one end of the molecule and a hydrophilic carboxylic group (–COOH) at the other end. Fatty acids can be divided into polyunsaturated fatty acids (PUFAs), monounsaturated fatty acids (MUFAs), and saturated fatty acids (SFAs), according to the number of carbon-carbon bonds (C=C). PUFAs are fatty acids with multiple double C=C, while MUFAs have one C=C and SFAs have no C=C [10, 11]. The two main families of PUFAs include linoleic acid and its derivatives, the omega-6 class, and a-linolenic acid and its derivatives, the omega-3 class. Omega-3 fatty acids mainly consist of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), which are found in fish oils [12]. As essential fatty acids, omega-3 fatty acids cannot be synthesized in enough amounts by the body, and therefore they must be supplied by the diet [13]. However, there is no consensus on the nutritional requirements of omega-3 fatty acids currently, in large part because their function is not yet fully defined. Previous studies have reported the beneficial effects of omega-3 fatty acids in cardiovascular [14], inflammatory, and autoimmune diseases [15, 16]. Meanwhile, omega-3 fatty acids are considered to play antiepileptic roles by stabilizing the neuronal membrane, reducing inflammation, and resisting oxidative damage as the elements of neuronal membrane phospholipids (17–19). In different animal models of epilepsy, omega-3 fatty acids were found to reduce seizures or raise the seizure threshold (20–24). Nevertheless, the roles of omega-3 fatty acids have been inconsistent in several small sample randomized controlled trials (RCTs) (25–30). Given the controversial results and the small sample size of the current RCTs, it is necessary to carry out a larger sample of RCTs or other research strategies with the aim to clarify the relationship between omega-3 fatty acids and human epilepsy.
Mendelian randomization (MR) is a recently developed analytic method, which used genetic variants [single nucleotide polymorphisms (SNPs)] as instrumental variables to mimic the random allocation in the RCT [31, 32]. In the case of the absence of reliable RCTs or embarking on new RCTs, MR is an ideal alternative research strategy to clarify the causal relationships between exposures and outcomes [33]. In addition, MR can avoid reverse causality as genotype formation precedes disease onset and is unaffected by disease progression [34].
Due to the causal relationships of omega-3 fatty acids on epilepsy being still controversial, we conducted a comprehensive two-sample MR analysis by utilizing genome-wide association study (GWAS) data to clarify the potential causalities in the present study. Findings from this work would not only help to recognize the pathophysiology underlying epilepsy, but also provide reliable evidence for establishing feasible strategies for epilepsy treatment and prevention in clinical practice.
## Data acquisition and instruments variables selection
*The* genetic instrumentation for omega-3 was obtained from the public GWAS dataset from the International Federation of Industrial Universities Project (https://gwas.mrcieu.ac.uk), consisting of 114,999 individuals of European ancestry and containing more than twelve million SNPs. The selected SNPs must satisfy the following three criteria: [1] selection of SNPs that were significantly associated with omega-3 fatty acids with a genome-wide significance threshold of $P \leq 5$ × 10−8; [2] removal of interference from linkage disequilibrium, setting kb = 10,000 and r2 < 0.001, indicating that there is no linkage disequilibrium between SNPs and that the assignment between two SNPs is completely random.; [ 3] there was no interference from other potential risk factors (35–37). In this study, we used “R studio version 4.1.3” and “MR-PRESSO” packages to test outliers. We set a significance threshold of $P \leq 5.0$ × 10−8 after linkage disequilibrium pruning (r2 < 0.001 within a 10,000-kilobase window) to obtain the SNPs of omega-3 fatty acids. In addition, we carried out sensitivity analysis to test whether the results of MR analysis were reliable. In the case of $P \leq 0.05$, it indicated that confounding factors did not affect the outcome, that is, there was no potential bias. Horizontal pleiotropy test was also used to further illustrate this conclusion. The SNPs screening process and the whole workflow of MR analysis are shown in Figure 1.
**Figure 1:** *The flowchart of the study. The whole workflow of MR analysis.*
Genetic datasets for generalized epilepsy (non-focal epilepsy or all types of epilepsy) were obtained from the International League Against Epilepsy Complex Consortium and included all documented cases of generalized epilepsy involving 33,446 people with a total of 3,769 epilepsy patients, containing 480,000 SNPs [38]. Summary statistics for the generalized epilepsy dataset are available at the website: https://gwas.mrcieu.ac.uk/datasets/ieu-b-$\frac{9}{.}$
## Two-sample MR analysis
We systematically assessed the causal relationship between omega-3 fatty acids and the risk of epilepsy using a two-sample MR design. A convincing MR design should comply with three fundamental assumptions: [1] there is a strong association between the IV Z and the exposure factor X; [2] the IV Z is not associated with any confounders of the exposure-outcome association; [3] the IV Z does not affect the outcome Y except possibly by association with exposure [39]. Among them, the second and third assumptions are collectively known as the independence of horizontal pleiotropy, which could be tested using an array of statistical methods [34].
The inverse variance weighted (IVW), MR-Egger regression, and weighted median (WM) were the main methods used for MR analysis in this study. The traditional IVW method was used as the main MR analysis to assess the causal effect between omega-3 fatty acids and epilepsy. The principle of IVW is to weigh the inverse of the variance of each IV as the weight while ensuring that all IVs are valid, the regression does not consider the intercept term, and the final result is the weighted average of the effect values of all IVs. It is worth noting that IVW can only get correct causal estimates if the SNPs are fully consistent with the three principles of MR studies. Therefore, in the absence of heterogeneity and pleiotropy, we preferentially used the IVW estimates [40]. The MR-Egger method differs most from IVW in that the presence of the intercept term is considered when doing regression analysis, and also it uses the inverse of the variance of the outcome as a weight for the fit. We prefer the MR-Egger results when the results are confounded by pleiotropy [41]. WM is defined as the median of the weighted empirical density function of the ratio estimates and can output accurate results when more than $50\%$ of the instrumental variables are invalid. That is, we prefer to use the results of the WM method when there is heterogeneity but not pleiotropy [42].
## Horizontal multiplicity and heterogeneity tests
In this study, outliers were detected by using the MR-PRESSO method. If outliers were present, they were removed and the analysis was repeated. The “leave-one-out” sensitivity analysis was performed by removing individual SNPs one at a time to assess whether the variation drove the association between the exposure and outcome variables [43]. In addition, to clarify whether there was horizontal pleiotropy in this MR analysis, the MR-Egger intercept test was also performed, and if the intercept term in the MR-Egger intercept analysis was statistically significant, the study was shown to have significant horizontal pleiotropy [41]. Finally, this study also used Cochran's Q statistic of MR-Egger and IVW for testing the heterogeneity of 24 independent omega-3 fatty acids genetic IVs in the GWAS dataset for generalized epilepsy. Significant heterogeneity in the analysis was demonstrated if Cochran's Q statistic test was statistically significant [44]. Similar to the meta-analysis, we chose a random effects model to analyze the study process. $P \leq 0.05$ was considered significant in all studies. All statistical analyses were performed using R studio version 4.1.3, and R packages such as “Two sample MR” and “MR-PRESSO” were used.
## Data availability
All data used in this study were obtained from GWAS summary statistics which were publicly released by genetic consortia. The full period of data collection was from November 1, 2022, to December 1, 2022.
## Selection of instrumental variables
After a series of quality control steps (Figure 1), 24 independent omega-3 fatty acids SNPs were selected as IVs ($P \leq 5.0$ × 10−8, r2 < 0.01). Detailed information of selected IVs used in MR analyses is shown in Table 1.
**Table 1**
| SNP | Effect allele | Other allele | Beta | Eaf | Pval | Pos | SE |
| --- | --- | --- | --- | --- | --- | --- | --- |
| rs10096633 | T | C | −0.0387569 | 0.123905 | 1.50E−10 | 19830921 | 0.00615902 |
| rs10162642 | A | G | −0.0488977 | 0.210115 | 4.10E−24 | 58577163 | 0.00501433 |
| rs10184054 | G | C | −0.0361462 | 0.224107 | 5.60E−15 | 21203877 | 0.00486548 |
| rs11242109 | T | G | 0.0240622 | 0.479016 | 2.40E−09 | 131677047 | 0.0040658 |
| rs11563251 | T | C | 0.0349727 | 0.110601 | 3.20E−08 | 234679384 | 0.00647476 |
| rs117143374 | C | T | −0.0370966 | 0.142254 | 2.20E−10 | 40555561 | 0.005847 |
| rs139974673 | C | T | 0.117987 | 0.025918 | 2.30E−21 | 44027885 | 0.0128075 |
| rs1672811 | C | T | 0.0251849 | 0.748488 | 3.00E−08 | 15501099 | 0.0046967 |
| rs2072114 | G | A | −0.319891 | 0.122727 | 1.00E−200 | 61605215 | 0.00615881 |
| rs2131925 | T | G | 0.0713932 | 0.64568 | 1.20E−66 | 63025942 | 0.0042536 |
| rs261291 | C | T | 0.113461 | 0.356068 | 1.70E−161 | 58680178 | 0.00424898 |
| rs35135293 | T | C | −0.0208868 | 0.51675 | 3.90E−08 | 20363666 | 0.00408348 |
| rs4000713 | A | G | −0.0288196 | 0.295408 | 1.00E−11 | 25990597 | 0.00446039 |
| rs4704834 | G | A | 0.0289714 | 0.644056 | 1.70E−13 | 156443066 | 0.00424051 |
| rs58542926 | T | C | −0.171666 | 0.074383 | 1.40E−113 | 19379549 | 0.00775231 |
| rs6129624 | A | G | −0.0257607 | 0.335237 | 5.10E−10 | 39167592 | 0.00437946 |
| rs629301 | T | G | 0.0382887 | 0.778033 | 1.30E−14 | 109818306 | 0.00488385 |
| rs673335 | C | T | −0.0669996 | 0.159762 | 1.10E−34 | 75450576 | 0.00554146 |
| rs7924036 | T | G | 0.0233527 | 0.504205 | 5.50E−10 | 65191645 | 0.00406452 |
| rs7970695 | A | G | −0.0253039 | 0.620549 | 1.20E−10 | 121423376 | 0.00419603 |
| rs964184 | C | G | −0.116637 | 0.867229 | 8.90E−87 | 116648917 | 0.00596503 |
| rs9947684 | G | A | 0.0423222 | 0.654493 | 7.40E−25 | 47166694 | 0.00426979 |
| rs9963974 | A | T | 0.0307343 | 0.31897 | 3.90E−12 | 47280303 | 0.00435821 |
| rs9987289 | G | A | 0.0566995 | 0.909151 | 3.20E−16 | 9183358 | 0.00707191 |
## Causal effects of omega-3 fatty acids on epilepsy
The IVW results (Figure 2) showed a causal effect association between omega-3 fatty acids and generalized epilepsy [odds ratio (OR) = 1.160, $95\%$ confidence interval (CI) = 1.051–1.279, $$P \leq 0.003$$]. Besides, WM (OR = 1.203, $95\%$ CI = 1.079–1.342, $$P \leq 0.001$$) and MR-Egger (OR = 1.181, $95\%$ CI = 1.028–1.357, $$P \leq 0.029$$) also supported the causal relationship, showing the robustness of the results. Given that all beta values in the results were in the same direction, our analysis suggests that increased levels of omega-3 fatty acids may increase the risk of generalized epilepsy (Figure 3, Table 2).
**Figure 2:** *Forest plot of MR analysis of the causal relationship between omega-3 fatty acids and generalized epilepsy. The x-axis shows the MR effect size of omega-3 fatty acids on generalized epilepsy. The y-axis shows the results of the analysis for each SNP.* **Figure 3:** *Scatter plot of MR analysis of the causal relationship between omega-3 fatty acids and generalized epilepsy, the beta value represents the slope of the graph; The regression line for MR-Egger, weighted median, IVW, simple mode, and weighted mode is shown.* TABLE_PLACEHOLDER:Table 2
## Horizontal pleiotropy and heterogeneity analysis
There was no evidence of heterogeneity in IVW analysis ($Q = 34.579$, $$P \leq 0.042$$) and MR-Egger regression ($Q = 34.797$, $$P \leq 0.054$$). MR-Egger regression showed no evidence of directional pleiotropy across genetic variants (Egger intercept = −0.001; $$P \leq 0.713$$). Leave-one-out sensitivity analysis revealed that all black dots were distributed on the right of the dashed line egger intercept = −0.001 (Figure 4). The funnel plot showed that the interpretation of our approach was relatively stable (Figure 5).
**Figure 4:** *Stability of the causal relationship between omega-3 fatty acids and generalized epilepsy assessed by leave-one-out sensitivity analysis. The x-axis shows the MR leave-one-out sensitivity analysis of omega-3 fatty acids on generalized epilepsy. The y-axis shows the analysis of the effect of removing individual SNP on generalized epilepsy.* **Figure 5:** *Funnel plot of the causal relationship between omega-3 fatty acids and generalized epilepsy as assessed by MR analysis.*
## Discussion
To the best of our knowledge, the present study for the first time applied MR analysis to infer the causal relationships between human blood omega-3 fatty acids and the risk of epilepsy in a large population data set of generalized epilepsy. Our results suggested a causal relationship between increased levels of blood omega-3 fatty acids and the risk of epilepsy, which is inconsistent with many previous studies suggesting that omega-3 fatty acids are antiepileptic.
Omega-3 fatty acids are PUFAs with multiple double bonds, mainly including alpha-linolenic (ALA), EPA, and DHA. Most importantly, omega-3 fatty acids are involved in the transmission of nerve impulses and play neuroprotective roles in CNS disorders [45, 46]. Specifically, several studies have suggested that omega-3 fatty acids, elements of neuronal membrane phospholipids, may exert antiepileptic effects by stabilizing neuronal membranes, reducing inflammation, and resisting oxidative damage (17–19). These antiepileptic effects have been verified on different animal models of epilepsy (20–24). Unlike the consistency of results from animal experiments, the functions of omega-3 fatty acids in clinical trials remain controversial. Some clinical studies have concluded that fatty acids are beneficial in humans with epilepsy. For example, the ketogenic diet, which we are familiar with, has been proposed to rise the resistance to seizures by increasing omega-3 fatty acids levels, particularly DHA [25, 26]. One RCT of different dose of fish oil (EPA and DHA mixture) vs. placebo in 24 participants with drug-resistant epilepsy revealed that low-dose fish oil was associated with a $33.6\%$ reduction in seizure frequency compared with placebo after treatment but high-dose fish oil was no different than placebo in reducing seizures [27]. Yuen et al. performed one RCT in which 57 intractable epilepsy patients were randomized to either the omega-3 fatty acids supplement group (30 cases) or the placebo group (27 cases). Results showed that seizure frequency was reduced over the first 6 weeks of treatment in the omega-3 fatty acids supplement group, but this effect was not sustained [28]. Not to be overlooked, some RCTs have also found that omega-3 fatty acids do not reduce seizure frequency and even increased seizure frequency compared to placebo, which supports the results of our MR study. In one RCT, researchers concluded that no positive effect of omega-3 fatty acids on seizure frequency was identified [29]. What is more, in another RCT, 21 adults with uncontrolled epilepsy were randomized to either a placebo group or the PUFAs supplement group (EPA and DHA mixture). After a 12-week treatment period, seizure frequency increased by $6\%$ in the PUFAs group while decreased $12\%$ in the placebo group [30]. The diversity of these RCTs results reveals that the relationship between omega-3 fatty acids and epilepsy is currently unclear. Probably due to the huge difficulty in conducting RCTs, the sample sizes of these studies were small, which also greatly affected the accuracy of the results. In our MR study, relying on the GWAS database, we included a large sample of generalized epilepsy genetic data (n case = 3,769), which also gave us a high degree of accuracy in our results. However, we should also note that the current RCTs included patients with mostly refractory or drug-resistant epilepsy, and the GWAS epilepsy genetic data we used was derived from patients with generalized epilepsy, which is not clear what proportion of these are refractory or drug-resistant epilepsy. These subtle differences in the sample may also have an impact on the results. Therefore, future studies targeting the relationship between omega-3 fatty acids and different subtypes of epilepsy are meaningful.
On the other hand, based on the conflicting results of the above RCTs and our MR study, we assume that it might also be necessary to re-examine the traditional view that fatty acids can be antiepileptic or protective of multiple systems in the body. Interestingly, two previous studies also support our assumptions [47, 48]. In recent years, the American Diabetes Association has recommended that UK patients with type 2 diabetes mellitus intake oily fish and replace SFAs with PUFAs to prevent diabetes, which appears to be a consensus on the anti-diabetic effects of PUFAs [49]. However, in a meta-analysis study, researchers compared the effects of higher intake levels of omega-3 fatty acids, omega-6 fatty acids, and total PUFAs on the incidence of type 2 diabetes mellitus, respectively, and showed no significant reduction in the incidence of type 2 diabetes mellitus. In addition, the investigators also conducted an intervention study in which patients with type 2 diabetes mellitus consuming higher levels of omega-3 fatty acids, omega-6 fatty acids, and total PUFAs. The results also showed no significant reduction in key type 2 diabetes-related indicators (glycated hemoglobin, fasting glucose, fasting insulin, and insulin resistance index) [47]. Another meta-analysis measured the association between omega-3 fatty acids and all-cause mortality by three aspects, including mixed prevention, secondary prevention, and patients with the cardio-aid defibrillators. All the results showed that omega-3 fatty acids supplementation could not reduce the risk of all-cause mortality, sudden death, myocardial infarction, cardiac death, or stroke based on relative and absolute measures of association [48]. Therefore, it is reasonable to assume that omega-3 fatty acids may not be so magical or beneficial to the organism and that future studies should be designed to be more in-depth and questionable.
The most valuable strengths of this study are its relatively large sample size and the idea of applying a two-sample MR analysis, which minimizes the risk of confounding bias and allows us to take advantage of large-scale epilepsy genetic data. Besides, our study was largely free of reverse causality and residual confounders by using the MR analysis. Specifically, we employ a series of methods to verify any violation of MR assumptions in order to ensure the reliability of MR estimates. The robustness of MR estimates is confirmed by the concordant directions and similar magnitude of various MR models. No evidence of horizontal pleiotropy was found using complementary statistical methods.
It is worth noting that there are still several limitations in this study as well. First, the current GWAS database does not contain the summary-level statistics of epilepsy subcategories, such as drug-resistant epilepsy or refractory epilepsy, making it not possible to further infer the potential relationship between blood omega-3 fatty acids level and risk for subtypes of epilepsy. Second, the data of exposure and outcome in this study were both derived from European databases. Therefore, the results may not be suitable for other ethnic populations. Third, giving that the datasets of exposure and outcome were both derived from European population, there may be a degree of sample overlap. However, as far as we know, there is no good way to evaluate overlapping sample sizes. Finally, although this study suggests that blood omega-3 fatty acids level is causally associated with epilepsy, we should recognize that MR analysis is only a predictive result without verification. Therefore, this causality still needs to be further explored and verified in well-powered RCTs to clarify the existence of causality.
## Conclusion
In conclusion, this MR study indicated that omega-3 fatty acids could increase the risk of generalized epilepsy in a causal way.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Author contributions
ZLia and SL designed the study. ZLi and YL performed statistical analyses. ZLia wrote the first version of the draft. SL performed visualization and revised the draft. All authors read and approved the final manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Differential expression of epigenetic modifiers in early and late cardiotoxic
heart failure reveals DNA methylation as a key regulator of cardiotoxicity
authors:
- Emma L. Robinson
- Pietro Ameri
- Leen Delrue
- Marc Vanderheyden
- Jozef Bartunek
- Paola Altieri
- Stephane Heymans
- Ward A. Heggermont
journal: Frontiers in Cardiovascular Medicine
year: 2023
pmcid: PMC10034031
doi: 10.3389/fcvm.2023.884174
license: CC BY 4.0
---
# Differential expression of epigenetic modifiers in early and late cardiotoxic heart failure reveals DNA methylation as a key regulator of cardiotoxicity
## Abstract
### Background
Anthracycline-induced cardiotoxicity is a well-known serious clinical entity. However, detailed mechanistic insights on how short-term administration leads to late and long-lasting cardiotoxicity, are still largely undiscovered. We hypothesize that chemotherapy provokes a memory effect at the level of epigenomic DNA modifications which subsequently lead to cardiotoxicity even years after cessation of chemotherapy.
### Methods
We explored the temporal evolution of epigenetic modifiers in early and late cardiotoxicity due to anthracyclines by means of RNA-sequencing of human endomyocardial left ventricular biopsies and mass spectrometry of genomic DNA. Based on these findings, validation of differentially regulated genes was obtained by performing RT-qPCR. Finally, a proof-of-concept in vitro mechanistic study was performed to dissect some of the mechanistic aspects of epigenetic memory in anthracycline-induced cardiotoxicity.
### Results
Correlation of gene expression between late and early onset cardiotoxicity revealed an R2 value of 0.98, demonstrating a total of 369 differentially expressed genes (DEGs, FDR < 0.05). of which $72\%$ ($$n = 266$$) were upregulated, and $28\%$ of genes, ($$n = 103$$) downregulated in later as compared to earlier onset cardiotoxicity. Gene ontology analysis showed significant enrichment of genes involved in methyl-CpG DNA binding, chromatin remodeling and regulation of transcription and positive regulation of apoptosis. Differential mRNA expression of genes involved in DNA methylation metabolism were confirmed by RT-qPCR in endomyocardial biopsies. In a larger biopsy cohort, it was shown that Tet2 was more abundantly expressed in cardiotoxicity biopsies vs. control biopsies and vs. non-ischemic cardiomyopathy patients. Moreover, an in vitro study was performed: following short-term doxorubicin treatment, H9c2 cells were cultured and passaged once they reached a confluency of $70\%$–$80\%$. When compared to vehicle-only treated cells, in doxorubicin-treated cells, three weeks after short term treatment, Nppa, Nppb, Tet$\frac{1}{2}$ and other genes involved in active DNA demethylation were markedly upregulated. These alterations coincided with a loss of DNA methylation and a gain in hydroxymethylation, reflecting the epigenetic changes seen in the endomyocardial biopsies.
### Conclusions
Short-term administration of anthracyclines provokes long-lasting epigenetic modifications in cardiomyocytes both in vivo and in vitro, which explain in part the time lapse between the use of chemotherapy and the development of cardiotoxicity and, eventually, heart failure.
## Introduction
For decades, anthracycline-induced cardiotoxicity has been known as a clinical entity [1]. Anthracyclines are widely used, potent, and broad-spectrum anti-neoplastic agents, constitute part of standard chemotherapeutical schemas and are lifesaving in hematological and breast cancer. Their utility as an effective anti-cancer therapy must be off-set by the fact that they cause dose-dependent cardiotoxicity [1]. Over half of patients with anthracycline exposure have been reported to have some cardiac abnormalities on echocardiogram or gated nuclear angiography by 20 years after diagnosis (1–3). In the worst assessments of rates of anthracycline-induced cardiotoxicity, cancer survivors have a 15-fold increased risk of developing heart failure (HF) in later life and have a $5\%$ increase in likelihood of having a heart transplantation [1, 3]. However, in current daily clinical practice, likelihood ratios are lower in part since lower cumulative doses of chemotherapy are used nowadays [4].
Two clinical effects induced by anthracyclines can be distinguished: an acute cardiotoxic response occurring within months or years of anthracycline exposure, and a delayed onset cardiomyopathy (CMP) even several years after chemotherapy cessation [3]. Although evolving in a favorable way, present understanding of mechanisms behind cardiotoxic effects of neoplastic treatment is remarkably limited. The suitability of classic circulating diagnostic biomarkers for HF e.g., NT-pro-BNP are largely inadequate [4]. Validated prospective tools to identify patients that will develop acute and delayed CMP following anti-cancer therapy are currently non-existent. Preventive measures, let al.one causal treatments, are scarce [2].
Even though anthracycline-induced HF is documented abundantly, and a two-phased pathophysiological mechanism has been put forward [5], in-depth mechanistic insights on how a relatively short-termed administration of chemotherapy can lead to late and long-lasting cardiotoxicity, are still unknown. Since an important proportion of these patients experience late cardiotoxicity, defined as cardiotoxic effects that are identified one and up to 20 years after the administration of potentially cardiotoxic drugs [1, 4], we hypothesized that chemotherapy provokes a long-lasting “memory effect” at the level of non-genetic DNA modifications (“epigenetics”), which can lead to cardiotoxicity even years after cessation of chemotherapy.
Epigenetic mechanisms are an established and accepted means through which pathological cardiac remodeling occurs (6–8). Therefore, in our study we explored the behavior and temporal evolution of epigenetic mechanisms in early and late cardiotoxicity due to anthracyclines by means of deep RNA-sequencing of human endomyocardial biopsies and mass spectrometry of genomic DNA. Based on these findings, validation of differentially regulated genes was obtained by performing RT-qPCR. Finally, a proof-of-concept in vitro mechanistic study was performed to dissect some of the aspects of epigenetic memory in anthracycline-induced cardiotoxicity. These findings were the basis for the construction of our hypothetical model of chemotherapy-induced cardiotoxicity based on long-term changes in the cardiac epigenome, or epigenetic memory.
## Ethics
Patients provided written informed consent for the procurement of left ventricular endomyocardial biopsy (LV EMB), after dedicated oral and written explanation of the purpose of the procedure by a qualified physician. The Local Medical Ethics Committee (OLV Aalst, Belgium) provided formal approval. The procurement of the LV EMBs respected the principles of the Declaration of Helsinki, locoregional applicable law, and EU-GDPR regulations concerning privacy and data storage. Clinical characteristics for each patient group from which the endomyocardial biopsies were taken can be found in (Table 1). Circulating markers of inflammation and cardiac dysfunction and damage were not significantly different between earlier and later onset toxic cardiomyopathy (Supplementary Figure S1).
**Table 1**
| Unnamed: 0 | Control | Early toxicity | Late toxicity |
| --- | --- | --- | --- |
| Number (n) | 3 | 6 | 7 |
| Age (years) | 71 ± 5 | 67 ± 10 | 67 ± 12 |
| Male/female (ratio) | 2:1 | 1:5 | 1:6 |
| Weight (kg) | 72.0 ± 5.6 | 67.5 ± 13.2 | 67.7 ± 14.8 |
| Length (cm) | 165.5 ± 2.2 | 165.6 ± 11.3 | 166.3 ± 10.5 |
| LVEF (TTE, %) | 59 ± 6 | 27 ± 8 | 26 ± 8 |
| LVEDD (TTE, mm) | 45 ± 3 | 58 ± 4 | 51 ± 7 |
| LVESD (TTE, mm) | 28 ± 3 | 47 ± 3 | 43 ± 9 |
| LVMI (TTE, g/m2) | No data | 95 ± 11 | 76 ± 26 |
| NYHA class | I | III | III |
| Time between first chemo and first signs of heart failure (months) | | 29 ± 16 | 113 ± 86 |
| Concomitant radiotherapy (%) | | 50 | 57 |
| Inflammatory cell count (CD45, n/mm2) | | 5.0 ± 1.4 | 4.4 ± 1.1 |
## Human material
LV EMB were procured during left heart catheterization. A Mullins sheath (MedtronicTM, Switzerland) was introduced in the right or left femoral artery after which LV EMBs were taken by means of a classical biopsy tool (Bipal 7 bioptome, Cordis Corp, Miami Lakes, FL, United States) under fluoroscopic imaging guidance. Biopsies were immediately snap frozen in liquid nitrogen and subsequently stored at −80°C.
## RNA isolation, quantitative RT-PCR
Total RNA was isolated from left ventricular EMBs using the miRVANA isolation kit (Ambion, Warrington, United Kingdom) according to the manufacturer's instructions, without enrichment for small RNAs. Potential genomic DNA contamination was removed using the DNA-free kit (Ambion, Warrington, United Kingdom). To identify and quantify mRNA, cDNA was generated using the BIO-RAD iScript cDNA synthesis kit (#1708891) followed by quantitative real-time PCR using BIO-RAD iQ SYBR Green master mix (#1708882) and a final primer concentration of 200 nM. Primers were ordered from Eurogentec, purified by desalting and sequences can be found in Supplementary Table S1.
## RNA-sequencing
Following total RNA isolation as above, ribosomal RNA (rRNA) was removed using the rRNA Human/Mouse/Rat kit (New England Biolabs, # E6310). Stranded RNA-seq libraries were prepared using the Lexogen SENSE RNA-seq library preparation kit (discontinued). Sequencing was performed at The Babraham Institute, Cambridge, United Kingdom on a HiSeq 2,500 as single end RNA-sequencing. RNA sequencing fastq output sequencing files were passed through fastqc analysis for basic quality checks and alignment to the reference genome (GRCh38/hg38) was performed using hisat2 to reference genome [9, 10]. Reads were trimmed prior to alignment using TrimGalore, using Phred quality score for base calling cutoff of 20, corresponding to a maximum error of 1 in 100 bases and with a maximum trimming error rate of 0.1 (http://www.bioinformatics.babraham.ac.uk/projects/download.html#trim_galore) [11, 12]. Subsequent QC and differential gene expression analysis was performed using SeqMonk v47.0 (The Babraham Institute, United Kingdom. https://www.bioinformatics.babraham.ac.uk/projects/seqmonk/). *Differential* gene expression analysis was performed using DEseq2 through R, with false discovery rate correction performed using Benjamini-Hochberg correction. *Only* genes with an adjusted p value (q value) of less than 0.05 were deemed to be differentially expressed between earlier and later onset cardiotoxic samples [13]. RNA-sequencing data are available from the EBI European Nucleotide Archive (ENA) ELIXIR Data Resource (Project # PRJEB51152, Submission #ERA9297316).
## In vitro experiments
Experiments were carried out with H9c2 cells, as described extensively elsewhere [14]. The rat embryonic cardiac cell line, H9c2, was purchased from the American Type Culture Collection (ATTC CRL-1446, Rockville, MD, United States). Cells were cultured as reported previously [15] and treated at a confluency of $70\%$–$80\%$. The complete culture medium ($10\%$ FBS in Dulbecco's modified Eagle's medium (DMEM)) was replaced with one with $0.5\%$ FBS, added 1 h before starting experimental treatments, which were also carried out in $0.5\%$ FBS. Cells were exposed to 0.1 µmol/l doxorubicin (in DMSO from Adriblastina, Pfizer, United States) for only 3 h. Cells were then maintained in complete medium for 7, 14 or 21 days before harvesting and passaged each time when a confluency of $70\%$–$80\%$ was reached. Cells and medium were harvested at different time points and immediately stored in TRIzolTM (Ambion, #15596026).
## Mass spectrometry
Genomic DNA was isolated from EMB and H9c2 cells using the Zymo Quick-DNA mi-prep kit (#D3025), according to the manufacturer's instructions. 1 μg of genomic DNA was submitted for liquid chromatography triple quadrupole (LC-QQQ) mass spectrometry (Thermo Scientific) for analysis of DNA cytosine, methylcytosine (MeC) and hydroxymethylcytosine (hMeC) at the Leuven VIB Metabolomics core facility (https://vib.be/labs/vib-metabolomics-core-leuven) as previously described [16]. Peak areas for the fragment ions were quantified by external calibration relative to relevant standards.
## Immunoblotting
Total protein lysates were prepared by resuspending H9c2 cell frozen cell pellets in 500 μl RIPA lysis buffer (50 mM Tris HCL pH 8.0, 150 mM NaCl, $1\%$ NP40, $0.5\%$ Na-Deoxycholate, $0.1\%$ SDS) with 2 × Halt™ Protease and Phosphatase Inhibitor Cocktail (Pierce Thermo Scientific #78442). Cells were lysed by incubating for 30 min on ice and then homogenized in a bullet blender for 5 min at setting #9 at 4°C. Protein concentration was measured using a standard BCA Protein assay kit (Pierce™ BCA Protein Assay Kit #23225). 30 µg protein was loaded with 1 × Laemmeli sample buffer (50 mM Tris pH 6.8, $2\%$ SDS w/v, $10\%$ glycerol v/v, $5\%$ β-mercaptoethanol v/v and bromophenol blue onto $4\%$–$15\%$ pre-cast Criterion TGX polyacrylamide gels (BIO-RAD #5671084) and run at 200 V. Transfer was then performed onto 0.45 um nitrocellulose membrane (BIO-RAD #1620115) for 2 h at 500 mA. Membranes were then blocked in $5\%$ non-fat milk powder in 1 × TBS-Tween (20 mM Tris, 150 mM NaCl, $0.1\%$ Tween 20). TET2, DNMT3A and GAPDH were probed for using primary antibodies anti-Tet2 (Protein tech # 21207–1-AP). anti-DNMT3A (Protein tech # 20954-1-AP) and anti-GAPDH (Thermo Fischer Scientific # AM4300) respectively in $2.5\%$ bovine serum albumin (Fischer # AK8905-0100) in 1 × TBS-Tween overnight at 4°C at a final concentration of 1:1000. Washes were performed thrice for 10 min in 1 × TBS-Tween followed by secondary antibody incubation with Horse Radish Peroxidase-conjugated anti-rabbit or anti-mouse antibodies (Southern Biotech #OB4050-05 and #OB1031-05) at a final concentration of 1:2000 in $5\%$ non-fat milk in 1 × TBS-Tween. Final washes were performed in 1 × TBS-Tween thrice for 10 min prior to imaging on an Odyssey® XF digital imaging system (LICOR) using the chemiluminescence program for 30 s for each membrane.
## Statistics
Data represent mean ± SEM unless otherwise stated. Statistical significance was calculated by ANOVA with Tukey's post hoc multiple testing using Bonferroni correction. For comparisons between two groups, a standard Student's t–test was used for normally distributed data, and a Mann-Whitney test was used for non-normally distributed data. Normality testing was performed with the Kolmogorov-Smirnov and Shapiro-Wilk normality test. Data were analyzed statistically using GraphPad Prism v7.0. A two-sided p value of < 0.05 was considered as statistically significant. Statistical analysis of RNA-sequencing data is described in the RNA-sequencing section.
## Early vs. late cardiotoxicity exhibit distinct cardiac transcriptomes
Deep RNA sequencing of EMBs from patients with earlier (less than 5 years after the start of chemotherapy) vs. later cardiotoxicity (later than 5 years after the start of chemotherapy) was performed (Figure 1). Correlation of expression of annotated genes between late and early onset cardiotoxicity revealed a coefficient of determination (R squared, R2) value of 0.98 (Figure 1A), with differential gene expression analysis (DESeq2 [13]) demonstrating a total of 369 differentially expressed genes (DEGs, FDR < 0.05) (Figure 1B). $72\%$ of DEGs ($$n = 266$$) were upregulated in late as compared with early onset cardiotoxicity (Figure 1B), whereas $28\%$ of genes ($$n = 103$$) were downregulated (Figure 1B). Gene ontology analysis showed significant enrichment of genes involved in oxidative phosphorylation, the oxidative stress response, FAS signaling, sarcomere organization, methyl-CpG DNA binding, chromatin remodeling, peptidase activator activity, regulation of transcription and positive regulation of apoptosis, in increasing order (Figure 1C). Due to insufficient availability of control human heart left ventricular tissue, RNA-sequencing was performed in toxic cardiomyopathy EMBs only.
**Figure 1:** *Transcriptome analysis reveals upregulation of epigenetic modifiers in early vs. late chemotherapy-induced heart failure in myocardial patient biopsies. (A) relative upregulation of genes in early vs. late chemotherapy-induced heart failure endomyocardial biopsies; (B) in total 266 genes (72%) were upregulated. Relative downregulation of genes in early vs. late chemotherapy-induced heart failure endomyocardial biopsies, in total 103 genes (28%) were downregulated; (C) gene ontology analysis shows the pathways that are preferentially enriched in early vs. late cardiotoxicity.*
## Mediators of DNA methylation are differentially regulated in myocardial tissue in cardiotoxicity-driven heart failure
Given our hypothesis that epigenetic mechanisms play a role in late onset and long term cardiotoxic effects of anthracycline treatment along with methyl CpG DNA binding being a differentially expressed pathway in the deep sequencing analysis, we focused on expression of epigenetic readers, writers and erasers in EMBs (Figure 2). Heat map clustering showed important upregulation of genes involved in active DNA demethylation, including TET$\frac{1}{2}$, and downregulation of de novo DNA methyltransferase DNMT3B (Figure 2). Differential mRNA expression of genes involved in DNA methylation metabolism was confirmed by performing classical RT-qPCR in EMBs (Figure 3). Moreover, in a larger biopsy cohort, it was shown that Tet2 expression was more abundantly expressed in cardiotoxicity biopsies vs. control biopsies, but also compared with non-cardiotoxic non-ischemic cardiomyopathy patients (Figure 4). For a sub-set of genes identified as differentially expressed from the RNA-sequencing data, methylated or hydroxymethylated DNA immunoprecipitation qPCR ((h)MeDIP-qPCR) experiments were performed at within the gene promoter regions (−1000–0 bp from TSS) (Figures 5A-B). Importantly, these findings are unlikely due to a more cardiac phenotype in later onset cardiotoxicity, as measures of cardiac dysfunction, such as LV ejection fraction (LVEF), end diastolic LV dimensions (LVEDD), and myocardial inflammatory cell infiltration did not differ significantly between earlier and later cardiotoxicity (see Table 1).
**Figure 2:** *Heat map analysis of epigenetic markers revealed distinct differential regulation of DNA methylation modifiers in early vs. late cardiotoxicity in myocardial patient biopsies. Heat map showing upregulation of genes involved in DNA demethylation (e.g., TET1/2) and downregulation of genes involved in DNA methylation (e.g., DNMT3B).* **Figure 3:** *Targeted gene expression analysis of epigenetic markers revealed distinct differential regulation of DNA methylation modifiers in early vs. late cardiotoxicity in myocardial patient biopsies. Confirmation of the upregulation of TET2, and the downregulation of DNMT3A and DNMTB in biopsies of chemotherapy-induced cardiomyopathy patients vs. controls. While COL1A1 was also upregulated in late cardiotoxicity patients, HDAC4/9 and MYH7 were not significantly affected. As expected in heart failure biopsies, NPPA and NPPB genes were upregulated. * Meansp < 0.05 in one-way ANOVA.* **Figure 4:** *TET2 levels are more elevated in the hearts of patients with anthracycline-induced heart failure compared to non-ischemic non-cardiotoxic cardiomyopathy counterparts. Control patients are patients who underwent CABG but had no structural heart disease and normal left ventricular function (biopsy was procured upon cardiac surgery). CCMP patients are patients with non-ischemic idiopathic cardiomyopathy in whom cardiotoxicity and structural disease e.g., amyloidosis was excluded. Cardiotoxic patients are patient in which a clear link between heart failure and the previous use of anthracycline-based chemotherapy was demonstrated. *Means p < 0.05 in one-way ANOVA.* **Figure 5:** *Expression of epigenetic markers is markedly increased in H92c cells 1 and 3 weeks after doxorubicin administration indicative of an epigenetic memory. (A) In this panel, mRNA expression data are shown vs. control cells (CT T0). A clear upregulation after passaging cells for one and three weeks after only short doxorubicin administration is observed for ANF, BNP, APOBEC1, TET1, TET2, HDAC4, MBD3 and SMARCA1. DNMT3A and DNMT3B are not significantly altered; (B) in this panel, a relative comparison is made between the control and doxorubicin-treated cells at similar time points. Here it is clear that the abovementioned epigenetic regulators are significantly upregulated and that this upregulation is more pronounced the longer cells are passaged, indicative of a memory effect. *p < 0.05 in one-way ANOVA. Abbreviations: CT = control; T0 = no doxorubicin; T1 = 1 week time point; T4 = 4 week time point; DOX = doxorubicin. (C) Tet2 and Dnmt3a protein expression in H9C2 cells.*
## Short-term exposure to doxorubicin leads to long-lasting alterations in epigenetic modifiers and modifications in cardiomyocytes
H9c2 cardiomyocytes in culture were treated for three hours with a concentration of doxorubicin known to cause important intracellular changes along with expression of senescence markers, but no cell death. The hallmark of this type of toxicity is the accumulation of SA-β-galactosidase [14, 15, 17]. Following doxorubicin treatment, the medium was washed away and substituted with fresh culture medium. Subsequently, cells were cultured and passaged once they reached a confluency of $70\%$–$80\%$. No other treatments were performed. The expected increase in SA-β-galactosidase levels persisted up to three weeks after exposure to doxorubicin (Figure 6D). Furthermore, when comparing untreated cells to doxorubicin-treated cells, even and especially three weeks after short-term treatment, Nppa, Nppb, Tet/2 and other genes involved in active DNA demethylation were markedly upregulated (Figures 5A-C). Whilst de novo methyltransferases Dnmt3a and Dnmt3b were not significantly changed at the mRNA level, Dnmt3a was downregulated at the protein level in H9c2 cells treated with doxorubicin compared with control cells and more so following three weeks than one week of treatment (Figures 5A-C). Accordingly, and suggesting a functional consequence of these gene expression changes, global DNA methylation levels were decreased in later vs. earlier cardiotoxicity biopsies (Figure 6A). When the three-week vs. one-week time point were compared, epigenetic modifier alterations were still persistent and even more pronounced (Figures 5B-C). These alterations coincided with a loss of DNA methylation and a gain in hydroxymethylation (Figures 6B-C), reflecting the epigenetic changes seen in the endomyocardial biopsies (Figure 6A). The differential methylation and demethylation were associated with changes in expression patterns of epigenetic regulators in the EMBs (Figure 7).
**Figure 6:** *DNA methylation/demethylation patterns in biopsies of patients with chemotherapy-induced heart failure mimic patterns observed in H9c2 cells, which show increased senescence even long after a short-term doxorubicin administration. (A) Relative global levels of methylated cytosine vs. demethylated cytosine levels (MeC/C) in early vs. late cardiotoxicity biopsies shows increased methylation at a later time point after administration of chemotherapy, measured by liquid chromatography mass spectrometry; (B) Relative MeC/C levels trend to decrease over time after doxorubicin administration in H9c2 cells, while (C) hMeC/C levels are also lower; *p < 0.05 in two-way ANOVA. (D) Quantitative beta galactosidase staining – a marker of cell senescence – shows strongly increased senescence in doxorubicin cells, one and three weeks after treatment, indicative of a memory effect.* **Figure 7:** *Differential methylation and demethylation is associated with changes in expression patterns of genes responsible for epigenetic regulation. (A) Chromatin immunoprecipitation analysis revealed a loss of DNA methylation and an increase in hydroxymethylation at promotors of selected differentially expressed genes in later vs. earlier onset toxic cardiomyopathy. *p < 0.05, **p < 0.01.*
## Discussion, conclusion, clinical implications
We found major changes in the gene expression profile of the hearts of patients who suffered from anthracycline-related cardiomyopathy within or after 5 years from treatment. In particular, we highlighted differences in the expression of genes involved in the regulation of DNA methylation, which were associated with a loss of DNA methylation and a gain in DNA hydroxymethylation in EMBs of late- vs. early-onset anthracycline cardiomyopathy. Moreover, the same, persisting epigenetic modifications were observed in a cell model of doxorubicin cardiotoxicity. Overall, these data indicate that anthracyclines cause long-term changes in the cardiac transcriptome and epigenome, possibly contributing to the development of cardiomyopathy and HF (Figure 8).
**Figure 8:** *Working model: late-onset chemotherapy-associated heart failure is induced and maintained by a “epigenetic memory” from anthracycline administration. Hypothetical mechanism explaining the effect of cardiotoxicity even years after short-lived chemotherapy administration. The effect of anthracycline is an acute and long-lasting deregulation of genes involved in epigenetic modifications, especially DNA methylation/demethylation. While methylation of genes (via cytosine) leads to transcriptional repression, hydroxymethylation leads to transcriptional induction. This hMeC/MeC balance shift leads to increased senescence of cardiomyocytes, ultimately contributing to long term anthracycline cardiomyopathy.*
## Is there evidence of a pivotal role for epigenetic memory in other contexts?
Cells, organs and organisms must respond to changes in their environment to adapt and survive. Epigenetic memory plays a crucial role in these alterations. Three types of epigenetic memory are generally distinguished: cellular memory, transcriptional memory and transgenerational memory [18]. The latter concept is held responsible for e.g., altered human physical traits in several generations post the historical Dutch famine crisis (19–21). Also, the placenta is involved in early growth and has been linked to several diseases that develop in later late, notably cardiovascular diseases [22]. In immunology, especially memory T-cells retain a long-term epigenetic imprint that confers constitutive and inducible gene expression associated with a rapid recall response capacity [23]. In the last decade, emerging evidence pointed out an important role for epigenetic memory in different aspects of cardiovascular disease e.g., atherosclerosis [24], post-myocardial infarction remodeling and HF [25]. For example, genome-wide DNA methylation profiling of atherosclerotic vs. normal human aorta revealed altered global DNA hypermethylation status, and the identified locations were mapped to genes known to be involved in atherosclerosis [26]. A multitude of studies with small sample sizes has been carried out to investigate DNA methylation in HF. Interestingly, it was even investigated whether ablation of Dnmt3a and 3b in genetically manipulated mice did affect the phenotype response to pressure overload, which was not the case. However, transcriptional responses altered substantially in these models [27, 28]. We recognize the limitation of using human EMB samples in our study, once cardiac dysfunction has already ensued and progress in developing appropriate pre-clinical models of anthracycline-associated CMP needs to be made. Although some evidence is certainly correlative, a lot of these studies are hypothesis-generating and feed the idea that epigenetic memory must be of importance in HF.
## Contribution of other cardiac cell types in the pathophysiology of toxic cardiomyopathy
Whilst we modeled acute and long-term toxic cardiomyopathy in a cardiomyocyte cell line in this study, we acknowledge that they are outnumbered by other cells types in the heart 1:3. Other cell types have been shown to play a role in the pathophysiology of acute and late-onset cardiotoxicity. Endothelial cells and fibroblasts comprise the major cell types in the heart by number [29].
There is pre-clinical evidence for fibroblast activation, apoptosis and senescence due to exposure to anthracyclines as well as increased myocardial strain in survivors of childhood cancers (30–32). These data provide evidence of fibroblast dysfunction in cardiotoxicity.
A further abundant cell type in the mammalian heart is the endothelial cell. Endothelial dysfunction is a major contributor to cardiac and vascular disease and systolic and diastolic dysfunction [33]. Anthracycline exposure has been linked to changes in endothelial cell biology. In human endothelial cells exposure to anthracyclines in culture, nuclear damage and apoptosis was observed [34]. Chemotherapy treatment has been linked with disordered VEGF signaling in human endothelial cells, which could affect angiogenesis in vivo [35].
A further key mechanism underlying cardiac damage in response to cardiotoxic drugs is that of DNA damage driven by reactive oxygen species (ROS). The heart muscle and cardiomyocytes in particular are prone to ROS damage due to their high mitochondrial content and energy turnover. Anthracycline drugs can also enter the nucleus to cause direct DNA damage [34]. Ensuing molecular and cellular dysfunction as a result of increased elevated ROS includes DNA damage, somatic mutation and nuclear damage, mitochondrial dysfunction, altered calcium handling and reduced protein synthesis and cardiac protein disarray including sarcomeric proteins [36]. These changes can lead to acute and chronic pathological changes in cardiomyocyte function and death.
A key molecular function of doxorubicin is inhibition of topoisomerase II (TopII) and evidence shows that TopII is depleted in cardiomyocytes of mice exposed to doxorubicin [37]. TopII helps to maintains higher order DNA structure and organization. Depletion of TopII can lead to nuclear disarray in cardiomyocytes, contributing to cardiomyocyte death as an acute response to anthracycline treatment, increasing risk of heart failure later.
We acknowledge that through the aforementioned mechanisms, apoptosis of cardiac cells as part of the acute response to chemotherapy exposure, contributes to a decline in cardiac function. Our data, however, uniquely support long-term induction of pro-apoptotic gene expression in the heart, more than half a decade post chemotherapy cessation, that is associated with loss of promoter methylation at these gene loci.
Overall, with cardiomyocytes being the only terminally-differentiated cell type in the heart, long-lasting cellular and molecular effects, including epigenetic changes, in the heart post chemotherapy cessation are most likely to derive from the cardiomyocytes, contributing to long-term cardiac dysfunction.
## What is the potential for epigenetic drugs in the treatment of anthracycline-associated cardiotoxicity?
If epigenetics plays a key role in anthracycline-induced cardiotoxicity, can these processes be therapeutically targeted? Inhibitors of different HDACs are being investigated in numerous clinical trials on metastatic cancers e.g., multiple myeloma, melanoma, gastric cancer, and so on. No such trials are currently being performed in HF patients let al.one cardiotoxic HF patients. As far as DNMT is considered, different products are used for the treatment of hematologic malignancies and solid tissue tumors (e.g., 5-azacytidine and decitabine being DNMT inhibitors), hence they might dramatically influence the epigenome by their direct action on DNMT in non-tumor cells. These off-target effects (in non-tumor cells) might be responsible for the epigenetic alterations we observed in our study. However, inhibiting these pathological effects should not come at the cost of the beneficial effects of chemotherapy on the oncologic process.
One further caveat is the predominant use of human cardiac biopsies from female toxic cardiomyopathy patients. Whilst this means we cannot deduce any sex differences in the data, females outnumber males in clinical cohorts of severe toxic cardiomyopathy [38].
## Future perspectives
Our paper sheds a ray of light on the conundrum of the protracted toxic effect of chemotherapy on the heart. Nevertheless, substantial challenges concerning care for these patients remain. Currently – irrespective of common risk factors for cardiovascular disease e.g., hypercholesterolemia –stratifying low vs. high cardiotoxicity risk remains a challenge for physicians. Also, despite considerable progress in the last two decades, dedicated imaging tools especially for the detection of early cardiac dysfunction are still lacking or insufficiently validated. Finally, more research diving into the mechanism of cardiotoxicity and further development of translationally-relevant pre-clinical models are needed as to develop specific therapeutics to treat this impactful condition.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ebi.ac.uk/ena, ERA9297316.
## Ethics statement
The studies involving human participants were reviewed and approved by The Local Medical Ethics Committee (OLV Aalst, Belgium). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
ELR designed, performed and analyzed most of the experiments and wrote important parts of the paper. PAL performed the cell experiments. PA analyzed some of the in vitro experiments and provided critical reading of the manuscript. LR performed and analyzed some of the in vitro experiments and provided critical reading of the manuscript. MV and JB collected the in vivo human myocardial biopsies and provided critical reading of the manuscript. SH interpreted most of the experiments, provided funding for this manuscript, provided critical reading of the manuscript and wrote important parts of the paper. WAH analyzed most of the experiments, wrote important parts of the paper, and supervised the research leading to this manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2023.884174/full#supplementary-material.
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|
---
title: Analysis of common genetic variation across targets of microRNAs dysregulated
both in ASD and epilepsy reveals negative correlation
authors:
- Carol Stella
- Covadonga M. Díaz-Caneja
- Maria Jose Penzol
- Alicia García-Alcón
- Andrea Solís
- Álvaro Andreu-Bernabeu
- Xaquín Gurriarán
- Celso Arango
- Mara Parellada
- Javier González-Peñas
journal: Frontiers in Genetics
year: 2023
pmcid: PMC10034058
doi: 10.3389/fgene.2023.1072563
license: CC BY 4.0
---
# Analysis of common genetic variation across targets of microRNAs dysregulated both in ASD and epilepsy reveals negative correlation
## Abstract
Genetic overlap involving rare disrupting mutations may contribute to high comorbidity rates between autism spectrum disorders and epilepsy. Despite their polygenic nature, genome-wide association studies have not reported a significant contribution of common genetic variation to comorbidity between both conditions. Analysis of common genetic variation affecting specific shared pathways such as miRNA dysregulation could help to elucidate the polygenic mechanisms underlying comorbidity between autism spectrum disorders and epilepsy. We evaluated here the role of common predisposing variation to autism spectrum disorders and epilepsy across target genes of 14 miRNAs selected through bibliographic research as being dysregulated in both disorders. We considered 4,581 target genes from various in silico sources. We described negative genetic correlation between autism spectrum disorders and epilepsy across variants located within target genes of the 14 miRNAs selected ($$p \leq 0.0228$$). Moreover, polygenic transmission disequilibrium test on an independent cohort of autism spectrum disorders trios ($$n = 233$$) revealed an under-transmission of autism spectrum disorders predisposing alleles within miRNAs’ target genes across autism spectrum disorders trios without comorbid epilepsy, thus reinforcing the negative relationship at the common genetic variation between both traits. Our study provides evidence of a negative relationship between autism spectrum disorders and epilepsy at the common genetic variation level that becomes more evident when focusing on the miRNA regulatory networks, which contrasts with observed clinical comorbidity and results from rare variation studies. Our findings may help to conceptualize the genetic heterogeneity and the comorbidity with epilepsy in autism spectrum disorders.
## Introduction
Autism spectrum disorder (ASD) comprises a group of neurodevelopmental disorders characterized by impairments in social interaction and communication, restricted interests and repetitive behaviors (Bourgeron, 2015) that affect around $1\%$ (Zeidan et al., 2022) and $2.3\%$ (Maenner, 2021) of the population worldwide. ASD displays great clinical heterogeneity and shows high comorbidity rates with neuropsychiatric and other medical conditions (Tye et al., 2019). Epilepsy is a severe neurological condition characterized by recurrent seizures with $0.76\%$ of lifetime prevalence across the worldwide population (Fiest et al., 2017).
The co-occurrence of ASD and epilepsy has been well documented at the epidemiological level: between $11\%$ and $39\%$ of ASD patients have epilepsy (Canitano, 2007; Lukmanji et al., 2019), while around $15\%$–$47\%$ of people with epilepsy also have ASD (Clarke et al., 2005; Lukmanji et al., 2019). This comorbidity is also associated with intellectual disability and greater severity of the ASD symptomatology (Ko et al., 2016; Lee et al., 2020). Beyond clinical comorbidity, a clear pattern of biological overlap between ASD and epilepsy has been demonstrated (Lee et al., 2015). At the cellular level, several studies have suggested that an alteration of the excitatory—inhibitory (E/I) balance could underlie this co-occurrence (Frye and Rossignol, 2016; Bozzi et al., 2018).
From the genetic perspective, both ASD and epilepsy have been described as complex disorders with considerable heritability estimates (around $80\%$ for ASD (Bai et al., 2019) and $32\%$ for epilepsy (Chen et al., 2017)), which denotes a solid genetic base. A consistent polygenic contribution from both common (Abou-Khalil et al., 2018; Grove et al., 2019) and rare protein-disrupting variation (Feng et al., 2019; Satterstrom et al., 2020; Motelow et al., 2021) has been reported for both ASD and epilepsy. Previous studies have evidenced the genetic overlap between both disorders at the rare variant level; ASD patients with rare protein-disrupting variants, especially affecting neurodevelopmental genes, show increased likelihood of comorbid epilepsy (Bishop et al., 2021). This has been observed for some genetic syndromes such as Phelan-McDermid (Bozzi et al., 2018) or the Fragile X syndrome (Chen et al., 2017) but it has also been described at a genome-wide level for de novo disrupting mutations (Heyne et al., 2018; Satterstrom et al., 2020; Mahjani et al., 2021). In fact, the diagnostic yield of reportable pathogenic variants in ASD is increased from $5\%$–$19\%$ to $15\%$–$60\%$ when comorbid epilepsy is present (Bishop et al., 2021).
By contrast, at the common genetic variation level, recent studies have suggested a limited shared genetic risk from common polygenic contributions across both disorders (The Brainstorm Consortium et al., 2018). However, existent genetic correlation between complex traits may reside in specific pathways and remain undetected when analyzing the whole common genetic variation across the genome (Colbert et al., 2021; Perry et al., 2022). In the past few years, new genomic tools have been developed in order to study the shared common variation in specific genome regions or pathways. Methods such as p-HESS (Shi et al., 2017) or GNOVA (Lu et al., 2017) allow to estimate partial genetic correlation and annotation-stratified covariance to unravel potential specific mechanisms involved in the comorbidity between disorders.
Dysregulation of microRNAs (miRNAs), non-coding RNAs that regulate gene expression by repressing translation via destabilization of mRNA species, has been consistently described in both ASD and epilepsy (Issler and Chen, 2015; Toma et al., 2015; Wu et al., 2016; Wang and Zhao, 2021). MiRNAs regulate the expression of key genes during neurodevelopment and adult brain structure and function, by acting on processes such as synaptogenesis, neurogenesis, neuronal differentiation, and neuroplasticity. Some specific miRNAs have been described to be down- or up-regulated both in ASD and epilepsy. For instance, miR-146a has been found to be upregulated in ASD children and patients with epilepsy (Wang et al., 2015; An et al., 2016; Nguyen et al., 2018). Another example is miR-199, which has been found to be upregulated in patients with temporal lobe epilepsy with hippocampal sclerosis (Antônio et al., 2019), but downregulated in ASD (Sarachana et al., 2010). Since many of these miRNAs are dysregulated both in ASD and other neurodevelopmental disorders (Hicks and Middleton, 2016; Hu et al., 2017), a combination of miRNAs may be potentially used as novel biomarkers for ASD diagnosis or even to describe subgroups of ASD (Hicks et al., 2020; Cui et al., 2021; Salloum-Asfar et al., 2021). Similarly, the potential use of miRNAs as biomarkers and therapeutic targets for epilepsy has been suggested (Henshall et al., 2016; Wang and Zhao, 2021).
Collectively, miRNAs are predicted to regulate around the $60\%$ of the protein-coding genes, by establishing complex regulatory pathways for each miRNA (Friedman et al., 2009). The implication of common genetic variation within genes targeted by some miRNAs has been described for psychiatric disorders (Toma et al., 2015; Hauberg et al., 2016) and available evidence supports that the study of common genetic variation across genes targeted by miRNAs dysregulated in different conditions or traits might be useful to disentangle the genetic correlation structure between them. For example, genetic variation within MIR19A/MIR19B has been implicated in the genetic overlap of educational attainment with ASD and attention hyperactivity disorder (ADHD) (Verhoef et al., 2021).
The current study was designed to assess the shared genetic contribution to both ASD and epilepsy within target genes of miRNAs dysregulated in both conditions. To achieve this aim, we first identified the target genes of miRNAs previously described to be altered in both conditions in more than one study. MiRNA target predictions from various in silico sources were used to ensure a high consistency of the selected genes. Then, we evaluated partial genetic covariance between ASD and epilepsy across these miRNAs’ targets and compared it against their covariance across genes not targeted by these miRNAs. Given the available evidence indicating shared miRNAs’ dysregulation in both conditions, we hypothesize that common genetic variation within these miRNAs’ targets will inform about the genetic correlation structure between both phenotypes. Finally, using polygenic transmission disequilibrium test (pTDT), a method previously performed in a larger ASD cohort (Weiner et al., 2017), we analyzed ASD and epilepsy polygenic transmission using an ASD cohort of 233 trios and analyzed differences in these polygenic contributions to ASD with and without comorbid epilepsy.
## Genetic summary data from previous studies
GWAS summary statistics were used for genetic correlation analysis and polygenic score calculations. GWAS data for ASD (Grove et al., 2019) and epilepsy (Abou-Khalil et al., 2018) used in this study were downloaded from the PGC (https://www.med.unc.edu/pgc/download-results/) and the TILAE (http://www.epigad.org/gwas_ilae2018_16loci.html) consortia repositories.
## ASD sample for polygenic score calculation
Genetic data from a Spanish cohort of ASD trios (comprising subjects with and without comorbid epilepsy; Supplementary Data Sheet S1) was used to evaluate polygenic transmission in an independent sample. Individuals aged three or above with a diagnosis of ASD ($$n = 233$$) and their parents were recruited from AMITEA, a specific outpatient ASD program at the Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón in Madrid, Spain. The Research Ethics Committee at Hospital General Universitario Gregorio Marañón reviewed and approved the study. All the participants and/or their legal representatives provided written informed consent after a full explanation of the study procedures. Diagnosis of ASD was established by child psychiatrists with extensive experience in ASD who had completed clinical training in the autism diagnostic interview-revised (ADI-R) and research training in the autism diagnostic observation schedule (ADOS-2). All diagnoses were based on best clinical judgment after full review of all available clinical information following Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition Text Revision or Fifth Edition (DSM-IV-TR or DSM-5) criteria. When necessary (due to inconsistencies found between the sources of information available), the ADOS-2 and/or ADI-R were also administered. ASD trios were later divided in those with (ASD_EPI; $$n = 36$$ trios) and without (ASD_NoEPI; $$n = 197$$ trios) comorbid epilepsy as per medical records. Available phenotypic information was included in Supplementary Data Sheet S1.
## Selection of miRNAs involved in both ASD and epilepsy pathophysiology
We identified altered miRNAs in both ASD and epilepsy after reviewing available literature. Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were followed to ensure the reproducibility of the analyses performed. The first phase consisted in selecting original papers (no reviews or comments) available in PubMed from 2010 to present, published in English, based on human samples, focusing on miRNAs expression levels, and including at least five patients with either ASD or epilepsy and a control group for comparison. The search was performed in January 2022. The search terms are available in Supplementary Data Sheet S2. Two reviewers screened all retrieved papers, independently and in duplicate, in two selection steps: the first consisted of the analysis of the abstracts and the second consisted of the analysis of the whole papers. Any doubt was solved by consensus. Afterward, both reviewers collected independently data from each selected article, extracting information about study characteristics, source of sample (tissue type) and direction of dysregulation (up- or downregulated), as described in Supplementary Data Sheet S3. We did not extract any additional information that goes beyond the aim of the present study. For the subsequent analyses, only those miRNAs whose expression levels significantly differed between cases and controls in both conditions in at least two independent studies were considered ($$n = 14$$), in order to ensure the inclusion of miRNAs whose dysregulation had been replicated at least once.
## Prediction of genes targeted by miRNAs involved in both ASD and epilepsy
*Target* genes of the 14 selected miRNAs were determined with miRNAtap (Pajak and Simpson, 2016) (https://bioconductor.org/packages/release/bioc/html/miRNAtap.html). MiRNAtap is an R package that integrates ranked miRNA target predictions from DIANA, Targetscan, PicTar, Miranda, and miRDB available online and aggregates them with various methods, which improves quality of predictions above any of the single sources (Pajak and Simpson, 2016). A total of 23,476 protein coding genes from ENSEMBL included in HUGO Gene list (HGNC) were considered. We included target genes that appeared as predicted targets in at least four out of the five different sources of in silico prediction of miRNA targets. Genes were considered targets for each source when specific threshold were exceeded (Supplementary Data Sheet S4). 4,581 genes were targeted by at least one of the 14 miRNAs studied here.
Additional filters were also performed to restrict target genes to brain expressed genes and haploinsufficient genes (since haploinsufficiency of ASD (Katayama et al., 2016; Yan et al., 2022) and epilepsy (Chénier et al., 2014; Carvill et al., 2021) related genes has been demonstrated as a risk factor). For the first filter, genes were considered brain expressed as expressed in any GTEx v7 brain tissue ($$n = 18$$,036 genes). 16,573 out of 23,476 genes were brain expressed. For the second filter, Decipher predictions of haploinsufficiency (https://www.deciphergenomics.org/about/downloads/data; (Huang et al., 2010)) across the human genome (hg19) were downloaded, and those genes belonging to the top $25\%$ with the highest haploinsufficiency score were used as haploinsufficient genes ($$n = 4$$,764 genes). 313 out of 23,476 genes belonged to the top $25\%$ genes with the highest haploinsufficiency score. *All* gene lists are provided in Supplementary Data Sheet S5.
## Genome-wide correlation and partial genetic covariance between ASD and epilepsy
Genome-wide genetic correlation between ASD and epilepsy was evaluated by LD-score regression (LDSC) (Bulik-Sullivan et al., 2015), using the recommended settings (https://github.com/bulik/ldsc). This method generates a score for each SNP, based on the level of linkage disequilibrium with the nearby variants. The z-score of each variant for Trait one is multiplied with the z-score for Trait two and then a regression of this value against LD scores is performed. The coefficient obtained (i.e., the slope) represents the genetic correlation; high values indicate high impact of the SNP on both traits.
GNOVA (Lu et al., 2017) (GeNetic cOVariance Analyzer) (https://github.com/xtonyjiang/GNOVA) was used to estimate genetic covariance between ASD and epilepsy across the following partitions created: A) variants within the 4,581 target genes of any of the 14 selected miRNAs (miR genes) B) variants within the 4,543 target genes of any of the 14 selected miRNAs and expressed in the human brain and C) variants within the 313 target genes belonging to the top $25\%$ percent with highest described haploinsufficiency. Annotation files for these genomic partitions (bed files encompassing whole gene bodies) were created following the recommended settings (https://github.com/bulik/ldsc/wiki/LD-Score-Estimation-Tutorial).
On account of the lower number of genes in miR relative to the 18,895 protein coding genes not targeted by any of the 14 selected miRNA (no_miR genes), estimated genetic covariance between ASD and epilepsy across miR was compared to the distribution of covariances across no_miR from 1,000 random selections of the same number of genes [4,581] from the no_miR annotation.
1,000 Genomes European population-derived reference data was used for LD scores calculation both in LDSC and GNOVA.
## Polygenic scores calculation
The 233 Spanish ASD trios (Supplementary Data Sheet S1) were sequenced as part of the Autism Sequencing Consortium (ASC) (Satterstrom et al., 2020). Exome data was used to calculate polygenic risk scores (PRS) in the 233 Spanish ASD trios. Firstly, exome based VCF files were imputed at the Michigan Imputation Server (https://imputationserver.sph.umich.edu/) using the 1,000 Genome Project phase 3v.5 reference panel to capture genomic variants beyond exome. From 654,286 variants, a total of 38.1 million imputed variants were obtained. Only biallelic variants with imputation quality score >0.9 and minor allele frequency (MAF) > $0.1\%$ were considered (72,292 genotyped and 997,210 imputed SNPs were retained). Then, exome based PRS scores were calculated. The GWAS summary statistics for ASD and epilepsy mentioned above were used as the discovery sample. PRS were calculated for each individual from the target sample as the sum of the number of effect alleles weighted by their effect in the discovery sample. Indels were excluded. Clumping was performed using PLINK v1.9 code “--clump-r2 0.1 --clump-kb 500”. We used Flip Strand and removed ambiguous genomic positions.
For each individual, we initially calculated the following PRS scores:- ASD PRS based on variants across target genes from any of the 14 selected miRNAs- Epilepsy PRS based on variants across target genes from any of the 14 selected miRNAs- ASD PRS based on variants across genes not targeted by any of the 14 selected miRNAs- Epilepsy PRS based on variants across genes not targeted by any of the 14 selected miRNAs.
In all cases, different PRS were generated across different P thresholds ($p \leq 0.00005$; 0.001; 0.01; 0.05; 0.1 and 0.2 in the discovery dataset). Then, polygenic transmission disequilibrium tests (pTDT) were performed, using PRS information from the parent-child trios. Briefly, the expected PRS distribution of the offspring is compared with the average PRS distribution of the parents, and their deviations are tested with a one-sample t-test. For each disorder and genomic partition, the P threshold with the most significant p-value from pTDT in the whole sample was selected. Polygenic transmission of ASD and epilepsy predisposing variation within or outside the miRNA target genes (miR and no_miR) was then calculated in ASD_NoEPI and ASD_EPI subsamples of ASD trios, separately, at the most significant P threshold in each case.
## Statistical analyses
Genetic covariance analyses were performed following the recommended settings by the developers of GNOVA and LDSC. For multiple test comparison, Benjamini–Hochberg FDR correction was performed.
To assess for disequilibrium of polygenic transmission of common predisposing variation to ASD and epilepsy in ASD_NoEPI and ASD_EPI subsamples, one-sided t-test were performed in form of pTDT. Full procedure is described in previous work (Weiner et al., 2017). Significant transmissions were confirmed with random permutation of ASD_EPI and ASD_NoEPI subjects.
In case of significant pTDT, comparison of polygenic transmission values between ASD_NoEPI and ASD_EPI groups were performed with two-sample t-test. Data normality was contrasted with Shapiro–Wilk test. Equality of variances across groups was assessed with Bartlett test.
## Target genes of miRNAs dysregulated in ASD and epilepsy
We searched for miRNAs dysregulated in both ASD and epilepsy. Our systematic search of PubMed produced 313 results for epilepsy and 193 for ASD. A total of 143 miRNAs were dysregulated in either ASD or epilepsy. After screening and selecting papers according to our criteria (see Methods), 14 miRNAs involved in both disorders were selected: let-7, miR-21, miR-27, miR-34, miR-92, miR-124, miR-129, miR-145, miR-146, miR-155, miR-181, miR-193, miR-199, miR-223 (Table 1; Figure 1).
We considered a total of 4,581 protein-coding target genes of these 14 miRNAs robustly present in various sources from miRNAtap (mIR genes; Table 1). Around $63\%$ of genes were regulated by only one from the 14 miRNAs selected (Figure 1).
## Genetic correlation and partial covariance between ASD and epilepsy
We used GNOVA to assess the partial genetic covariance between ASD and epilepsy across variants within genes targeted by the 14 selected miRNAs (NGenes = 4,581). Negative covariance within miR genes was observed (rho ($95\%$CI) = -0.006 (-0.012; -0.001); $$p \leq 0.0228$$; Figure 2; Supplementary Data Sheet S6). Genetic covariance between ASD and epilepsy at the whole genome was also negative but not significant (rho ($95\%$CI) = -0.021 (-0.04; 0.003); $$p \leq 0.069$$; Supplementary Data Sheet S6).
**FIGURE 2:** *Genetic covariance between autism (ASD) and epilepsy across miRNA target gene annotations. Partial genetic covariances were calculated with GNOVA (Table 2). Estimated genetic covariance between ASD and epilepsy across genes targeted by the selected 14 miRNAs (miR: 4,581 genes, red dashed line) was compared against 1,000 genetic covariances across genes not targeted by the selected 14 miRNAs (no_miR, blue density distribution) that were estimated after 1,000 random selections of the same number of genes (4,581 genes). Statistical comparison between genetic covariance across miRNA target genes and the distribution of genetic covariances across the remaining genes was performed by one-sided t-test. The derived p-value is displayed (p = 0.040).*
Moreover, negative genetic covariance between ASD and epilepsy was greater across mIR genes than the distribution of 1,000 estimated covariances calculated by using the same number of genes [4,581] randomly selected from the remaining 18,895 not targeted by the miRNAs (no_miR genes). Genetic covariance within miR was significantly smaller than the distribution of covariances based on no_miR genes ($$p \leq 0.039$$; Figure 2).
*The* genetic covariance between ASD and epilepsy after restricting to miR genes expressed in the human brain (NGenes = 4,544) was also significant (rho ($95\%$CI) = -0.005 (-0.009; -0.001); $$p \leq 0.0248$$; Supplementary Data Sheet S6). Interestingly, when restricting to haploinsufficient miR genes (NGenes = 313), positive covariance was observed (rho ($95\%$CI) = 0.009 (0.003; 0.016); $$p \leq 0.0102$$; Supplementary Data Sheet S6. However, the number of miRNA targets across haploinsufficient genes was significantly lower than across non-haploinsufficient genes (Chi-square X2 = 402.95; $p \leq 10$−16), suggesting underrepresentation of haploinsufficient genes across miRNA targets.
## Polygenic score prediction in the ASD sample with and without comorbid epilepsy
We then calculated ASD and epilepsy polygenic risk scores (PRS) in our cohort of ASD complete trios. PRS were calculated for variants across genes targeted and non-targeted by the 14 miRNAs selected. Transmission of predisposing variants in each case (pTDT) was assessed for ASD trios including individuals with (ASD_EPI; $$n = 36$$) and without (ASD_NoEPI; $$n = 197$$) comorbid epilepsy.
Across miRNA target genes, we observed a lower transmission of ASD risk alleles than expected by chance for ASD_EPI trios (pTDT ($95\%$CI) = -0.4549 (0.1060; -0.8039); $$p \leq 0.0121$$; Figure 3A), but not for ASD_NoEPI trios (pTDT ($95\%$CI) = -0.0697 (0.0802; -0.2197); $$p \leq 0.3601$$; Figure 3A). We confirmed these results by conducting 10,000 random permutations of comorbid epilepsy status (ASD_EPI-Pperm = 0.0153; ASD_ NoEPI -Pperm = 0.8060). Difference in ASD polygenic transmission within miRNA target genes between both groups was also found (two sample $t = 2.0496$, $$p \leq 0.0457$$; Supplementary Data Sheet S7).
**FIGURE 3:** *Polygenic transmission disequilibrium test (pTDT) on ASD and epilepsy risk variants in ASD trios with (ASD_EPI trios, N = 36), and without (ASD_NoEPI trios; N = 197) comorbid epilepsy. Two-tailed t-tests were performed to test for over or under-transmission of common predisposing alleles to ASD (A) or epilepsy (B), from parents to children (Supplementary Data Sheet S7). Common predisposing variation across genes targeted (miR; N = 4,581 genes) or not targeted (no_miR; N = 18,895 genes) by the 14 selected miRNAs. Error bars represent 95% confidence intervals. For each disorder and genomic partition, we selected the most significant p-value threshold from the pTDT previously performed in the whole sample of ASD trios (Supplementary Data Sheet S7). (A) pTDT on ASD predisposing variants (B) pTDT on epilepsy predisposing variants. Significant p-values from permutation analyses are marked with an asterisk. two sample t-test was performed to evaluate differences in pTDT between ASD_EPI and ASD_noEPI subsamples.*
*Across* genes not targeted by the selected 14 miRNAs, we observed no significant transmission disequilibrium of ASD risk alleles in either subgroup (ASD_EPI: pTDT ($95\%$CI) = 0.2445 (0.5744; -0.0855) $$p \leq 0.1415$$; ASD_NoEPI: pTDT ($95\%$CI) = 0.0841 (0.2347; -0.0664); $$p \leq 0.2719$$; Figure 3A).
No significant differences were found in transmission of epilepsy risk variants, neither between the ASD_EPI and ASD_NoEPI nor between the miR and no_miR genomic partitions (Figure 3B; Supplementary Data Sheet S7).
## Discussion
This study suggests the presence of a negative genetic relationship between ASD and epilepsy across common genetic variation within target genes of miRNAs involved in both conditions. Moreover, we described a lower transmission of ASD risk alleles from variants within target genes of these miRNAs in ASD patients with comorbid epilepsy than expected by chance. Our results support the idea of a genetic divergence between ASD and epilepsy in terms of common variation, which is more marked when analyzing pathways potentially involved in both conditions such as some miRNA regulatory networks. These results contrast with the direct genetic overlap found between both phenotypes across rare de novo disrupting variation and contribute to conceptualizing the complexity of the genetic relationships between these conditions.
From the genomic perspective, while rare de novo variation affecting neurodevelopmental related genes has been previously described as a predisposing factor to both ASD and epilepsy (Todd and Bassuk, 2018; Hiraide et al., 2019; Mahjani et al., 2021), no clear link between both disorders at the level of common genetic variation has been found (Abou-Khalil et al., 2018; The Brainstorm Consortium et al., 2018). This is important since most ASD genetic risk resides in common polygenic contribution (Gaugler et al., 2014). Here in this study, by restricting our analysis to the regulatory networks of 14 miRNA involved in both ASD and epilepsy, we described a negative genetic correlation between both disorders. Although these results may appear counterintuitive, since these 14 miRNAs have been described to be affected in both phenotypes, several recent studies have described patterns of genetic relationships between ASD and comorbid phenotypes at the level of common genetic variation that differ from those found for disrupting rare variation. For instance, whereas ASD subjects with low IQ have a significantly increased number of disrupting de novo mutations (Iossifov et al., 2014), a positive genetic correlation between ASD and IQ has been described (Zhang et al., 2021). Moreover, ASD polygenic scores predict higher cognitive performance in the global population (Clarke et al., 2016). Another rare-common disparity in genetic relationships was described for schizophrenia; while there is a consistent overlap across genes disrupted by de novo rare variation in schizophrenia and ASD (Kenny et al., 2014; Satterstrom et al., 2020; Singh et al., 2020), shared common variation between both phenotypes has been reported to be chiefly restricted to high functioning autism (González-Peñas et al., 2020).
The negative significant covariance reported here between ASD and epilepsy at the selected miRNA regulatory pathways depict a scenario in which common predisposing variation within miRNA contributes to both conditions in opposite directions. This could be due to the fact that common ASD predisposing variation underpin high functioning autism (González-Peñas et al., 2020) while rare variation is mainly involved in more severe forms of ASD, which are more frequently associated with intellectual disability and epilepsy (Strasser et al., 2018). In this sense, when considering haploinsufficient genes, which have been related with both ASD and epilepsy related genes in terms of rare genetic variation (Katayama et al., 2016; Carvill et al., 2021), we described a clear underrepresentation across target genes of the miRNAs here studied. Also, across the reduced set of haploinsufficient target genes ($$n = 313$$), a positive correlation between ASD and epilepsy was described, reinforcing the existence of different genetic contributions from genes primarily affected by rare or common genetic variation.
The lower transmission of ASD risk variants that we observed in ASD patients with comorbid epilepsy supports the idea of common variation differently contributing to phenotypic variation across the autism spectrum (Heyne et al., 2018; González-Peñas et al., 2020; Zhang et al., 2021).
In a recent study, Antaki et al. [ 2022]. have described a negative correlation between transmitted ASD PRS and de novo protein truncating mutations, consistent with a liability threshold model by which the genetic load needed to meet a diagnostic criterion is reached by the additive contribution of both polygenic background and rare high impact genetic variation, in combination to environmental risk factors (Falconer, 1967). Our results describing a lower ASD PRS transmission in ASD patients with epilepsy than expected by chance suggests this liability threshold model is also observable at a pathway specific level. Therefore, focusing on shared altered pathways between ASD and comorbid conditions such as miRNAs may help to disentangle the genetic variation underlying the observed phenotypic heterogeneity of ASD.
All these findings support that the diagnostic category ASD, as described in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) could encompass distinct genetically divergent subcategories (Li et al., 2019) that reflect phenotypic variation within the spectrum. Our results support the observed heterogeneity of rare and common variation contribution to complex disorders and highlight the likely underlying genetic divergence between ASD with and without comorbid epilepsy (Burgess et al., 2019). These results contribute to a better understanding of the biological characteristics of the ASD clinical subtypes and may guide patient stratification by focusing on specific pathways or mechanisms of gene expression regulation.
Our work was subject to several limitations. First, the discovery sample size of the latest GWAS of epilepsy used here was modest. Since various types of conditions exist under the epilepsy umbrella, larger and stratified case-control cohorts would be required to reach sufficient power and comparable to ASD and to validate the results presented here. Second, our independent cohort for pTDT analyses, although carefully phenotyped, had also a limited sample size. These findings need to be replicated in a larger sample. Finally, we here selected a list of miRNAs for which dysregulation is observed in both conditions through a comprehensive literature search. Although we only considered miRNAs with significant findings in at least two independent studies, there was heterogeneity among studies in terms of the technique performed and tissue used for the analysis and there could also be miRNA selection or reporting biases. In vivo studies incorporating more reliable methods that enable direct detection of miRNA targets could lead to more powerful outcomes.
In summary, our results provide further evidence about the genetic complexity of ASD, suggesting miRNA dysregulation as one important pathway to explain the genetic complexity in the relationship between ASD and epilepsy. A clear understanding of ASD genetic heterogeneity could be useful to identify risk groups and trajectories and pave the way for precision medicine approaches to neurodevelopmental conditions (Table 2).
**TABLE 2**
| Annotation | Genetic covariance (SE) | Genetic correlation (SE) | p-value | FDR-p |
| --- | --- | --- | --- | --- |
| Whole Genome | -0.0216 (0.0121) | -0.1432 (0.0788) | 0.069 | 0.069 |
| mIR genes | -0.00627 (0.00275) | -0.2161 (0.0339) | 0.0228 | 0.0331 |
| mIR genes (brain expressed) | -0.0048 (0.0021) | -0.1984 (0.0384) | 0.0248 | 0.0331 |
| mIR genes (top 25% Haploinsuficient) | 0.0092 (0.0033) | 0.2202 (000,043) | 0.0102 | 0.0331 |
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.
## Ethics statement
The studies involving human participants were reviewed and approved by Research Ethics Committee at Hospital General Universitario Gregorio Marañón. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin. Written informed consent was obtained from the individual(s), and minor(s)’ legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.
## Author contributions
JG-P conceptualized and designed the work. CS performed all the analyses and wrote the manuscript. MP, MJP, AG-A, and AS recruited aurtism cohorts. XG, AA, CA, and CD-C and the rest of authors contributed with the design and the revision of the manuscript.
## Conflict of interest
CA has been a consultant to or has received honoraria or grants from Acadia, Angelini, Gedeon Richter, Janssen Cilag, Lundbeck, Minerva, Otsuka, Roche, Sage, Ser-vier, Shire, Schering Plough, Sumitomo Dainippon Pharma, Sunovion and Takeda. CD-C has received honoraria from Exeltis and Angelini not related to this work.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1072563/full#supplementary-material
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|
---
title: Adipokines as predictive factor of cardiac function in pediatric patients with
chronic kidney disease
authors:
- Miguel Angel Villasis-Keever
- Jessie Nallely Zurita-Cruz
- Claudia Zepeda-Martinez
- Gabriela Alegria-Torres
- Juana Serret-Montoya
- Maria de Jesus Estrada-Loza
- Beatriz Carolina Hernández-Hernández
- Sara Alonso-Flores
- Monica Zavala-Serret
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10034059
doi: 10.3389/fendo.2023.1120445
license: CC BY 4.0
---
# Adipokines as predictive factor of cardiac function in pediatric patients with chronic kidney disease
## Abstract
### Background
Adipokines are associated with cardiovascular disease; in chronic kidney disease (CKD) patients adipokines could be useful prognostic factors.
### Objectives
To explore whether leptin and adiponectin in kidney replacement therapy (KRT) children could have a role on their cardiac function, in the long-term.
### Design
Prospective cohort study was performed with pediatric KRT patients, aged 8 to 17 years who were undergoing hemodialysis or peritoneal dialysis. At enrollment, lipid profile, adipokines (leptin, leptin receptor, free leptin, and adiponectin), anthropometric measurements and cardiological evaluation were determined. At two-year follow-up, a new cardiological evaluation was performed. Statistical analysis: *Quantitative data* are presented as median and interquartile range (IQR). Mann-Whitney U test and Chi-squared were used for the between-group comparison. Multivariate analyzes were performed to determine the association of adipokines levels with ventricular ejection fraction (LEVF).
### Results
We included 56 patients, with a median age of 12.5 years. In the first cardiological evaluation, median LVEF was $70.0\%$ (IQR $61\%$, $76\%$), 20 patients ($35.7\%$) had some cardiovascular condition, and 10 ($17.8\%$) altered LVEF. At 24-month follow-up, the median LVEF was $70.5\%$ (IQR $65.1\%$, $77\%$), while the delta-LVEF values was $3\%$ (IQR -$6.5\%$, $7\%$). Delta-LVEF were correlated with baseline adipokines serum levels, and the only positive correlation found was with free leptin ($r = 0.303$, $$p \leq 0.025$$). In multivariate analysis, levels of free leptin (Coef. 0.12, $p \leq 0.036$) and leptin (coef. 1.72, $$p \leq 0.049$$), as well as baseline LVEF (Coef. -0.65, $p \leq 0.001$) were associated with delta-LVEF.
### Conclusions
Free leptin, leptin and LVEF at the beginning of follow-up were associated with the LVEF decrease at the 24-month follow-up in KRT children.
## Introduction
In recent years, both the prevalence and incidence of chronic kidney disease (CKD) in children have increased [1]. Unlike adult patients, the most common causes of CKD in children are congenital malformations; but the increase in overweight/obesity in children may be a contributing factor as well [2].
In adult patients who had CKD during childhood, cardiovascular disease (CVD) is the leading cause of death, with estimates ranging from 23 to $60\%$. It seems that CVD begins early in CKD children, and arterial hypertension in CKD patients can increase kidney disease progression, due to intraglomerular hypertension, hyperfiltration and increased protein excretion [3, 4]. During end-stage CKD, the exhausted adaptive mechanisms and side effects of renal replacement therapy lead to progressive heart failure and accelerated calcification [5].
Adipose tissue is considered an endocrine organ that produces multiple adipocytokines; leptin and adiponectin stand out because they have been identified as mediators of inflammation and may be important markers of chronic systemic inflammation [6, 7]. Leptin is a peptide hormone produced by adipocytes, and its serum levels are proportionally correlated to body fat stores [8]. As well, leptin exhibits proinflammatory actions, including upregulating the phagocytic function of macrophages, increasing the production of proinflammatory cytokines, and stimulating reactive oxygen species [9]. In contrast, adiponectin is produced by the mitochondria of adipocytes, and acts as an anti-inflammatory factor, inhibiting the production of proinflammatory cytokines [10]. However, high adiponectin concentrations have been associated with adverse cardiovascular outcomes in adult patients with ischemic heart disease, chronic heart failure and CKD, which has been called the “adiponectin paradox” (11–13).
In developing countries, such as Mexico, kidney transplantation is performed at a lower rate than in developed countries, and several years may pass before a pediatric patient with end-stage renal disease undergoes kidney transplantation. Therefore, it is important to maintain optimal cardiometabolic conditions in the long term in these patients. Leptin and adiponectin have been considered as prognostic factors for the progression of cardiometabolic disorders in patients with overweight/obesity, but information is lacking or controversial in CKD patients. This study aims to explore whether leptin and adiponectin in kidney replacement therapy (KRT) children could have a role on their cardiac function, in the long-term.
## Subjects
A prospective cohort study was carried out from January 2018 to December 2020 at two tertiary pediatric care centers in Mexico City: Hospital de Pediatría (Mexican Institute of Social Security) and Hospital Infantil de México Federico Gómez (Mexico Ministry of Health). In both centers, all pediatric KRT patients are usually cared for by a multidisciplinary team that includes pediatric nephrologists, pediatric endocrinologists, pediatric cardiologist, psychologists, and nutritionists.
Children aged between 8 and 17 years with stage V CKD according to the Kidney Disease: Improving Global Outcomes (KDIGO) staging scale [14], and who were receiving peritoneal dialysis or hemodialysis were considered eligible to participate in the study. Patients who were scheduled for kidney transplantation in the next 12 months, with diagnosed with diabetes mellitus, those did not agree to participate, or those who had incomplete clinical and biochemical evaluation data were excluded. The cohort follow-up duration was 24 months. All included patients were selected using a consecutive sampling technique.
According to the Declaration of Helsinki, the protocol was approved by hospitals’ ethics and research committees, under registry numbers: R-2018-3603-075 & HIM-2017-117. A parent or legal guardian signed an informed consent form, and each child provided written assent.
## Anthropometry
The anthropometric indicators of each patient were recorded by a certified nutritionist. Height was measured to the nearest 0.1 cm with a SECA model 769 stadiometer (SECA 769, SECA Corp. Oakland Center Columbia, MD, USA). Weight and body fat percentage measurements were conducted using the bioimpedance method (Tanita BC-568 Segmental Body Composition Monitor, Tokyo, Japan) with the patients barefoot and wearing only underwear. Anthropometric measurements were performed both, at the beginning and at the end of the 24-month follow-up.
## Serum hormones and biochemistry level measurements
Blood samples were obtained from the forearm of each subject via the antecubital vein, between 7:00 and 8:00 a.m. after a minimum of 12 hours of fasting during the baseline visit. Serum aliquots were separated (centrifuged at 4°C; 3000 rpm; 15 min) and frozen at -80°C until biochemical analysis. Leptin and leptin receptor levels were measured using an enzyme-linked immunosorbent assay (ELISA) (Human Leptin Duo Set, DY 398, Human Leptin Receptor, CAT DY 389, R&D Systems, Minneapolis, MN, USA); Human Adiponectin DuoSet (DY1065), R&D Systems, Minneapolis, MN, USA). Plates were read using an ELISA microplate reader (Labsystems Multiskan EX, MTX Labsystems Inc., Vienna, VA) and were determined in duplicate according to the manufacturer’s instructions. The plates were assessed using an ELISA microplate reader (Labsystems Multiskan EX, MTX Labsystems Inc., Vienna, VA) and were assessed in duplicate as per the manufacturer’s instructions. Intra- and interassay coefficients of variation <$7\%$ were considered acceptable. A standard curve was also generated for each assay. Free leptin levels were calculated by dividing the levels of total leptin by that of leptin receptors [15]. Creatinine and urea levels were determined by colorimetric enzymatic methods (Bayer Diagnostics, Puteaux, France). All electrochemiluminescence immunoassays (ECLIAs) were performed using a COBAS 6000 e601 (Roche Diagnostics GmbH, Indianapolis, IN, USA) in duplicate according to the manufacturer’s recommendations. Intra- and interassay coefficients of variation < $7\%$ were considered acceptable. A standard curve was also generated for each assay.
## Cardiology evaluation
Cardiological evaluation was performed by a certified pediatric cardiologist, at baseline and at 24-month follow-up. All patients underwent to a physical examination, chest X-ray, electrocardiogram, as well as echocardiographic evaluation. The latter was performed with Philips iE33 cardiovascular ultrasound machine with xMATRIX 5 MHz, using Pediatric xMATRIX X 2-7 MHz transducers.
## Definitions
Patients with BMI <5th percentile were considered malnourished, obesity with BMI > 95th percentile, and overweight with BMI > 85th percentile, according to the 2000 CDC Growth Charts [16]. Patients with <2 standard deviations of height for age, BMI was calculated considering the age that corresponds to the 50th percentile of actual height.
Hemodialysis and peritoneal dialysis treatment adequacy was calculated by Kt/V (K, dialyzer clearance of urea; t, dialysis time; and V, volume of distribution of urea). In hemodialysis patients, Kt/V > 1.2/week was considered adequate; in the case of peritoneal dialysis, when Kt/V > 1.8/week [17, 18].
There were two criteria for hypertension according to age: in patients < 13 years, when systolic or diastolic blood pressure was ≥95th percentile for age, height, and sex. While for those > 13 years-old, when systolic blood pressure was ≥130 mmHg, or diastolic blood pressure ≥80 mmHg [3].
Based on the cardiology evaluation, patients with hypertensive cardiomyopathy, dilated cardiomyopathy, aortic valve dysfunction were identified. Patients considered to have altered left ventricular ejection fraction (LVEF) had values <$40\%$, as well as those with LVEF >$40\%$ but who also had clinical data of heart failure [19].
## Statistical analyses
Quantitative data are presented with median and interquartile range (IQR) since they did not show normal distribution, according to Shapiro-Wilk test. LVEF delta was calculated by the difference in the LVEF value at the end of follow-up, minus the baseline value, of each patient.
Two groups were formed to carry out the different analyses: with and without altered LVEF; Mann-Whitney U test and Chi-squared were used for the between-group comparison. Baseline cytokine levels were correlated with delta-LVEF values using Pearson’s correlation coefficient. Two models of lineal regression analysis were performed to determine the association between basal cytokines levels with delta-LVEF values, adjusted for nutritional status (overweight/obesity), hypertensive cardiomyopathy, hemodialysis and time on renal replacement therapy.
A p-value < 0.05 was considered statistically significant. All analyzes were performed with STATA v.11.0.
## Results
Table 1 shows the baseline characteristics of the 56 included patients, noting that 10 patients ($17.8\%$) already had altered LVEF. There were patients from 10 to 14 years-old, with similar sex ratio. The majority had a normal nutritional status ($57.1\%$), and 16 patients ($27.9\%$) were overweight or obese. Regarding the CKD etiology, the most frequent was CAKUT in $46.4\%$ ($$n = 26$$), followed by glomerulopathy ($28.6\%$, $$n = 16$$).
**Table 1**
| Characteristic | Totaln = 56 | Altered left ventricular ejection fraction | Altered left ventricular ejection fraction.1 | p2 |
| --- | --- | --- | --- | --- |
| Characteristic | Totaln = 56 | Non = 46 | Yesn = 10 | p2 |
| Age, y | Age, y | Age, y | Age, y | Age, y |
| Median (interquartile range) | 12.5 (10.5, 14.5) | 12 (11, 15) | 13 (10, 13) | 0.827 |
| Sex, % | | | | |
| Female | 28 (50.0) | 24 (52.2) | 4 (40.0) | 0.364 |
| Male | 28 (50.0) | 22 (47.8) | 6 (60.0) | |
| Somatometry, median (interquartile range) | Somatometry, median (interquartile range) | Somatometry, median (interquartile range) | Somatometry, median (interquartile range) | Somatometry, median (interquartile range) |
| Weight, kg | 35.7 (27.5, 41.2) | 35.5 (25.4, 41.3) | 40.5 (23.8, 41.1) | 0.830 |
| Height, cm | 143 (130, 153) | 144 (130, 152) | 140 (128, 154) | 0.089 |
| Height z score | -1.9 (-3.3, -0.9) | -1.5 (-3.2, -0.9) | -3.6 (-4.1, -1.8) | 0.014 |
| Body mass index, kg/m2 | 17.4 (16.0, 20.0) | 17.3 (15.7, 19.1) | 22.9 (18.5, 23.7) | 0.035 |
| Body mass index z score | -0.06 (-1.09, 1.2) | -0.15 (-1.1, 1.1) | 0.09 (-0.9, 2.0) | 0.466 |
| Nutritional status, % | Nutritional status, % | Nutritional status, % | Nutritional status, % | Nutritional status, % |
| Normal | 32 (57.1) | 28 (60.9) | 6 (60.0) | 0.138 |
| Malnutrition | 8 (14.3) | 6 (13.0) | 0 (0.0) | |
| Overweight | 10 (17.9) | 9(19.6) | 0 (10.0) | |
| Obesity | 6 (10.7) | 3 (6.5) | 4 (30.0) | |
| Etiology of chronic kidney disease | Etiology of chronic kidney disease | Etiology of chronic kidney disease | Etiology of chronic kidney disease | Etiology of chronic kidney disease |
| CAKUT | 26 (46.4) | 20.0 (43.5) | 6 (60.0) | 0.794 |
| Glomerulopathy | 16 (28.6) | 14 (30.4) | 2 (20.0) | |
| immunological | 4 (7.1) | 4 (8.7) | 0 (0.0) | |
| Indeterminate | 10 (17.9) | 8 (17.4) | 2 (20.0) | |
| Replacement treatment; % | Replacement treatment; % | Replacement treatment; % | Replacement treatment; % | Replacement treatment; % |
| Hemodialysis | 12 (21.4) | 8 (17.4) | 4 (40.0) | 0.126 |
| Peritoneal dialysis | 44 (78.6) | 38 (82.6) | 6 (60.0) | |
| Age at diagnosis of CKD, y | Age at diagnosis of CKD, y | Age at diagnosis of CKD, y | Age at diagnosis of CKD, y | Age at diagnosis of CKD, y |
| Median (interquartile range) | 9.0 (5.5, 12.0) | 10.0 (4.0, 12.0) | 9.0 (7.0, 9.0) | 0.575 |
| Time of renal replacement, months | Time of renal replacement, months | Time of renal replacement, months | Time of renal replacement, months | Time of renal replacement, months |
| Median (interquartile range) | 18.0 (7.0, 31.0) | 18.0 (7.0, 31.0) | 15.0 (10.0, 24.0) | 0.574 |
| Hypertension, % | Hypertension, % | Hypertension, % | Hypertension, % | Hypertension, % |
| Presence | 34 (60.7) | 28 (60.8) | 6 (60.0) | 0.613 |
| Kt/V, l/week | Kt/V, l/week | Kt/V, l/week | Kt/V, l/week | Kt/V, l/week |
| Median (interquartile range) | 1.88 (1.12, 3.40) | 2.12 (1.51, 3.42) | 1.80 (1.11, 2.65) | 0.751 |
| Altered | 15 (26.8) | 13 (28.3) | 2 (20.0) | 0.461 |
| Left ventricular ejection fraction, % | Left ventricular ejection fraction, % | Left ventricular ejection fraction, % | Left ventricular ejection fraction, % | Left ventricular ejection fraction, % |
| median (interquartile range) | 70 (61, 76) | 72 (61, 80) | 62 (61, 64) | 0.001 |
| Cardiological assessment, % | Cardiological assessment, % | Cardiological assessment, % | Cardiological assessment, % | Cardiological assessment, % |
| Normal | 36 (64.3) | 32 (69.7) | 4 (40.0) | 0.124 |
| Hypertensive cardiomyopathy | 14 (25.0) | 10 (21.7) | 4 (40.0) | |
| Valve dysfunction | 4 (7.1) | 2 (4.3) | 2 (20.0) | |
| Dilated cardiomyopathy | 2 (3.6) | 2 (4.5) | 0 (0.0) | |
| Adipokines, median (interquartile range) | Adipokines, median (interquartile range) | Adipokines, median (interquartile range) | Adipokines, median (interquartile range) | Adipokines, median (interquartile range) |
| Leptin, ng/ml | 4.6 (3.0, 6.2) | 3.8 (2.1, 6.1) | 6.0 (5.3, 6.6) | 0.020 |
| Leptin receptor, ng/ml | 0.5 (0.1, 2.2) | 0.4 (0.1, 1.9) | 1.3 (0.1, 4.4) | 0.314 |
| Free leptin, ng/ml | 6.4 (1.1, 60.5) | 6.6 (1.2, 59.8) | 5.1 (0.7, 60.5) | 0.789 |
| Adiponectin, µg/ml | 6.1 (5.3, 6.3) | 6.0 (5.3, 6.2) | 6.4 (6.3, 6.4) | 0.018 |
Although there were more peritoneal dialysis patients in the normal LVEF group ($82.6\%$ vs $60\%$), the difference was not statistically significant. Both the time on renal replacement therapy, as well as Kt/V and frequency of hypertension were similar between the two groups (Table 1).
Regarding cardiology evaluation, at baseline the median LVEF was $70\%$ (IQR $61\%$ to $76\%$) in the 56 patients. As expected, LVEF was statistically lower in the altered LVEF group compared to the other group ($62\%$ vs $72\%$), $$p \leq 0.001$$ As also shown in Table 1, in the altered LVEF group the proportion of patients with hypertensive cardiomyopathy and valvular dysfunction was higher than the other group, which was not statistically significant.
Cytokine levels were different between the two groups. Compared with the normal LVEF group, leptin ($$p \leq 0.02$$), leptin receptor ($$p \leq 0.31$$), and adiponectin ($$p \leq 0.018$$) levels were higher in the altered LVEF group, whereas free leptin levels were lower ($$p \leq 0.78$$). Furthermore, cytokine levels were compared according to nutritional status; as shown in Table 2, leptin (6.5 ng/ml vs 2.1 ng/ml, $$p \leq 0.002$$) and free leptin (65.1 ng/ml vs 0.6 ng/ml, $$p \leq 0.028$$) were higher in patients with obesity compared to those with malnutrition (Table 1).
**Table 2**
| Unnamed: 0 | Nutritional status | Nutritional status.1 | Overweightn=10 | Obesityn=6 | p2 |
| --- | --- | --- | --- | --- | --- |
| | Normaln=32 | Malnutritionn=8 | Overweightn=10 | Obesityn=6 | p2 |
| Adipokines, median (interquartile range) | Adipokines, median (interquartile range) | Adipokines, median (interquartile range) | Adipokines, median (interquartile range) | Adipokines, median (interquartile range) | Adipokines, median (interquartile range) |
| Leptin, ng/ml | 5.0 (3.5, 6.1) | 2.1 (0.9, 3.3) | 4.1 (1.8, 6.2) | 6.5 (5.3, 6.5) | 0.002 |
| Leptin receptor, ng/ml | 0.4 (0.1, 1.7) | 2.3 (1.9, 3.4) | 0.36 (0.1, 1.9) | 0.1 (0.1, 47.9) | 0.093 |
| Free leptin, ng/ml | 9.5 (2.7, 59.8) | 0.6 (0.4, 1.7) | 5.9 (1.6, 60.5) | 65.1 (0.1, 65.9) | 0.028 |
| Adiponectin, µg/ml | 6.1 (5.6, 6.3) | 5.9 (4.1, 6.3) | 5.8 (4.8, 6.1) | 6.4 (6.0, 6.9) | 0.073 |
When analyzing the levels of adipokines with the baseline patients’ characteristics through logistic regression, leptin levels (OR 3.11; $95\%$IC 1.06, 9.11, $$p \leq 0.038$$), leptin receptor levels (OR 1.06; $95\%$IC 1.001, 1.12, $$p \leq 0.043$$) hypertensive cardiomyopathy (OR 18.37; $95\%$ IC 1.28, 263.1, $$p \leq 0.032$$) were associated with altered LVEF, contrary to adiponectin levels (OR 1.22; $95\%$ IC 0.67, 2.22, $$p \leq 0.503$$).
## End of follow-up
After 24-month follow-up, no change was observed in BMI z-score (median -0.06 vs median -0.18, $$p \leq 0.99$$), in the 56 patients. But the proportion of patients with malnutrition ($21.4\%$) and obesity ($17.8\%$) increased (Figure 1). As cardiac function at the end of follow-up, LVEF median was $70.5\%$ (IQR $65.1\%$, $77\%$), while the delta-LVEF was $3\%$ (IQR -$6.5\%$, $7\%$).
**Figure 1:** *Change in nutritional status from baseline to 24 months of follow-up in chronic kidney disease pediatric patients.*
Figure 2 presents the correlation analyses between baseline cytokine levels and delta-LVEF values. As shown, leptin ($r = 0.259$, $$p \leq 0.058$$) and free leptin levels ($r = 0.303$, $$p \leq 0.025$$) were positively correlated with delta-LVEF. This was not observed for serum adiponectin levels ($r = 0.165$, $$p \leq 0.232$$).
**Figure 2:** *Correlation between delta-LVEF values and baseline serum levels of free leptin (A), leptin (B), adiponectin (C), and leptin receptor (D).*
Finally, according to the linear regression analyses, in the first model free leptin levels (coef. 0.12; $95\%$IC 0.08, 0.24, $$p \leq 0.036$$) and baseline LVEF values (coef. -0.65; $95\%$IC -0.91, -0.38, $p \leq 0.001$) were associated with delta-LEVF, while in the second model leptin levels (coef. 1.72; $95\%$IC 0.01, 3.44, $$p \leq 0.049$$) and baseline LVEF (coef. -0.63; $95\%$IC -0.90, -0.35, $p \leq 0.001$) were associated with delta-LEVF, Tables 3A, B, respectively.
## Discussion
To our knowledge, this is the first study where leptin and adiponectin levels have been evaluated as potential prognostic markers of cardiac function in CKD pediatric on renal replacement therapy. Our results seem to indicate that both elevated serum leptin and free leptin levels are associated with a decrease in LVEF, at two years of follow-up. This information could be relevant, since the most common cause of mortality among CKD patients is cardiovascular disease.
Cardiovascular diseases in pediatric patients can be present in CKD early stages. In our study, it was identified in about a third of the 56 included patients, mainly due to hypertensive cardiomyopathy. According to Groothoff et al, they reported $61.5\%$ of cardiac abnormalities in a cohort of 140 pediatric patients followed for 20 years, since 1972. This high frequency is probably related to the time of the study, since kidney transplantation was not performed in a timely manner as it is today, therefore the time in renal replacement therapy was longer [20]. Moustafa et al. reported $88\%$ of cardiac alterations due to left ventricular hypertrophy and left ventricular dilatation, but the frequency of hypertension was higher ($72\%$) than in our study ($60.7\%$) [20, 21].
In recent years, it has been described that overweight and obesity can cause cardiovascular disorders in CKD patients, which could aggravate the damage caused by renal failure [22]. However, the effect that adipocytokines may have in CKD children is unknown, particularly on heart function.
Adipokines change according to nutritional status; for example, leptin is a good indicator of the amount of adipose tissue in the body [8]. As we observed in this study, patients with overweight or obesity had the highest serum leptin and free leptin levels, while malnourished patients had the lowest concentrations [23, 24]. In patients only with obesity, elevated serum leptin concentrations lead to increase cardiovascular risk [9]; however, in the context of chronic diseases patients, such as CKD, this situation is not clear. CKD patients could be at greater risk of malnutrition and inflammatory-related diseases due to the release of inflammatory cytokine by adipocytes, and the involvement of regulatory molecules, as myostatin, hepatocyte growth factor and soluble Toll-like receptor 4 [25]. Sarcopenia as a chronic proinflammatory state increases the risk of damage to target organ, such as impaired cardiac function (26–28).
We observed that the decrease in delta-LVEF was associated with free leptin levels. This finding is consistent with studies conducted in patients with anorexia, in whom the energy balance is negative because of insufficient caloric intake. In these patients, increased leptin receptor levels may represent a protective mechanism that decreases the bioavailability of free leptin that would further conserve energy [29, 30]. Adult patients on hemodialysis and sarcopenia have a worse prognosis for cardiovascular events and mortality, which has been related to low fat content, as a consequence of the proinflammatory state that occurs in sarcopenia (26–28). As well, it has also been reported that high levels of angiotensin II are negatively associated with skeletal muscle strength [31]. Angiotensin II acts on IGF-I/insulin signaling pathways, by decreasing Akt phosphorylation and activating muscle proteolysis by the ubiquitinproteasome system [32] and caspase-3 apoptotic pathways in muscle [31, 33]. Thus, a mechanism by which angiotensin II induces muscle atrophy is by disrupting the IGF-I system.
Similar to our study, increased adiponectin levels have also been observed in adult patients with heart failure, diabetes mellitus, and CKD. The high levels of circulating adiponectin could be attributed to the counterregulatory upregulation of adiponectin production in response to stress caused by severe chronic diseases [12, 13, 34, 35]. Adiponectin has multiple beneficial phenotypic expression effects that include anti-inflammatory, antiatherogenic or cardioprotective actions (36–38). Therefore, it is likely that the high levels of circulating adiponectin in these patients can be partially explained by the compensatory upregulation of adiponectin production in response to severe chronic stress related to CKD. Furthermore, in patients with heart failure downregulation of adiponectin receptor is associated with decreased downstream signaling, such as inactivation of the PPAR-α/AMPK pathway, and downregulation of several target genes in skeletal muscles, resulting in functional resistance to adiponectin [39, 40]. However, more studies are needed to explain the mechanism of this compensatory adiponectin response in cardiovascular diseases.
On the other hand, myocardial remodeling secondary to hypertension is mainly due to hypertrophy of cardiomyocytes, interstitial fibrosis, and alterations in the wall of the intramyocardial arteries. This is an adaptive response to overload as an attempt to normalize systolic stress, which alters the left ventricle global function [41]. Matteucci et al. reported a regression of left ventricular hypertrophy and improvement of left ventricular systolic function when blood pressure is controlled [42]. Persistent uremia results in thickening of myocardial cells and concentric remodeling of the left ventricle together with activation of the intracardiac renin-angiotensin system, which induces hyperaldosteronemia. This promotes cardiac fibrosis via signals that induce production of profibrotic growth factors, which causes myocardial remodeling. Late renal transplantation causes a longer exposure to uremia, which increase in the probability of developing hypertensive cardiomyopathy, as we observed in our patients [4, 5].
Finally, we must recognize the limitations of the study, mainly due to the small sample size, which may affect the interpretation of multivariate analyses. Therefore, more studies should be carried out to verify whether adipokines can be considered as prognostic markers for the deterioration of cardiac function in CKD pediatric patients. In these studies, it seems appropriate to include measurements of adipokines and body composition at the end of follow-up.
## Conclusions
Baseline leptin, free leptin levels and LVEF were associated with the decrease of LVEF at the 24-month follow-up in CKD pediatric patients.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.
## Ethics statement
According to the Declaration of Helsinki, the protocol was evaluated and approved by the ethics and research committee of the hospital under registry number R-2018-3603-075 & HIM-2017-117. A parent or legal guardian signed an informed consent form, and each child provided written assent according to the recommendations of the Declaration of Helsinki. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.
## Author contributions
Conceptualization Methodology & Formal analysis: MV-K and JZ-C; Investigation: JZ-C, CZ-M, GA-T, JS-M, ME-L, BH-H, SA-F, and MZ-S; Writing, review & editing: MV-K and JZ-C. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Clinical differences between small and large pheochromocytomas and paragangliomas
authors:
- Lin Zhao
- ZhiMao Li
- Xu Meng
- Hua Fan
- ZengLei Zhang
- ZhaoCai Zhang
- YeCheng Liu
- XianLiang Zhou
- HuaDong Zhu
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10034061
doi: 10.3389/fendo.2023.1087506
license: CC BY 4.0
---
# Clinical differences between small and large pheochromocytomas and paragangliomas
## Abstract
### Background
Pheochromocytomas and paragangliomas (PPGLs) are neuroendocrine tumors, most of which are characterized by the release of catecholamine, and range in diameters from less than 1 cm to 10 cm or more. However, knowledge of the differences in clinical features between small and large PPGLs is insufficient.
### Methods
A retrospective analysis of patients with PPGLs treated at our institution between January 2018 and June 2020 was performed. The clinical characteristics of patients were investigated, and comparisons were made between patients with large and small PPGLs. The logistic regression analysis was used to confirm the risk factors, and the receiver operating characteristic curve was used to evaluate the diagnostic performance of the variables.
### Results
Totally 263 patients were included, including 110 patients in small tumor group and 153 patients in large tumor group. There were more male patients in the large tumor group ($$p \leq 0.009$$). More patients had hypertension ($p \leq 0.001$) and diabetes ($$p \leq 0.002$$) in the large tumor group. The 24-h urinary epinephrine (24hU-E) ($p \leq 0.001$) and 24-h urinary norepinephrine (24hU-NE) ($$p \leq 0.002$$) concentrations were higher in the large tumor group. In terms of tumor location, adrenal-PPGLs were more frequent in the large tumor group ($p \leq 0.001$). Multivariate logistic regression analysis showed that male sex [odds ratio (OR): 2.871, $95\%$ confidence interval (CI): 1.444–5.711, $$p \leq 0.003$$], 24hU-E concentrations (OR: 1.025, $95\%$ CI:1.004–1.047, $$p \leq 0.020$$), 24hU-NE concentrations (OR: 1.002, $95\%$CI: 1.001–1.004, $$p \leq 0.045$$), and adrenal-PPGLs (OR: 2.510, $95\%$ CI:1.256–5.018, $$p \leq 0.009$$) were positive risk factors for large tumors. Taking above variables into the same model, the area under the receiver operating characteristic curve of the model for predicting the large tumor was 0.772 ($95\%$ CI: 0.706–0.834). After the short-term follow-up, there was no significant difference in tumor recurrence between the two groups ($$p \leq 0.681$$).
### Conclusions
Significant differences in numerous clinical characteristics exist between large and small PPGLs. The male patients were more likely to be with large tumors, and such tumors were more likely to reside on the adrenal glands. Catecholamine measurements also help predict tumor size of PPGLs. Clinical decision-making will benefit from this information.
## Introduction
Pheochromocytomas and paragangliomas (PPGLs) are neuroendocrine tumors, most of which are characterized by the production of catecholamines. Adrenal PPGLs originate from adrenomedullary chromaffin cells, and extra-adrenal PPGLs arise from extra-adrenal chromaffin cells of the sympathetic paravertebral ganglia located in the thorax, abdomen, pelvis, as well as from parasympathetic ganglia [1]. The combined incidence of PPGLs is approximately 0.57 cases per 100,000 person years [2]. These tumors commonly cause hyperadrenergic symptoms such as hypertension, headaches, palpitations, and sweating [3]. There is a wide range of sizes for PPGLs, ranging from less than 1 to 10 cm or more in diameter [4]. Investigations of the imaging differences between large and small PPGLs have been conducted, for example, Reinig et al. [ 5] and Kim et al. [ 4] found that smaller PPGLs tended to be homogeneous, whereas larger tumors were heterogeneous because of hemorrhage and necrosis. However, insufficient data are available to determine whether small and large PPGLs differ in clinical features. Therefore, this study was aim to evaluate such differences.
## Study population
All consecutive adult patients ($$n = 313$$) with PPGLs who were treated at Peking Union Medical College Hospital, Beijing, China, between January 2018 and June 2020 were enrolled. All patients with PPGLs included in our study were surgically treated and diagnosed by surgical pathology. We excluded 25 patients who were referred because of tumor recurrence or metastasis after treatment and excluded 12 with incomplete clinical information. The study also excluded patients diagnosed with bilateral adrenal PPGLs ($$n = 6$$) or with concurrent adrenal PPGLs and extra-adrenal PPGLs ($$n = 7$$) upon their first visit, because it was not possible to identify which tumors were functional. Therefore, 263 patients were included in the analysis. Figure 1 shows a flowchart describing patient selection. The Ethics Committee of Peking Union Medical College Hospital approved the study, which was also conducted in accordance with the provisions of the Declaration of Helsinki. The requirement for informed consent was waived because of the retrospective nature of the study, and all data were anonymized and deidentified.
**Figure 1:** *Flow chart showing the selection of patients. PPGLs: pheochromocytomas and paragangliomas.*
## Clinical assessment
We collected and analyzed retrospective data on patients’ clinical histories, biochemical test results, and surgical pathological findings. Hypertension was defined [6] as follows: 1) systolic blood pressure (SBP) in the office ≥140 mmHg and/or diastolic BP (DBP) ≥90 mmHg following repeated examinations; or 2) ambulatory BP monitoring results showing the averages of SBP/DBP of 24 h ≥130 and or ≥80 mmHg; or 3) home SBP ≥135 mmHg and/or home DBP ≥85 mmHg; or 4) an existing diagnosis of hypertension with an established antihypertension diet or treatment. A variety of hypertension patterns were present in PPGLs, including sustained, paroxysmal, and mixed patterns [3]. Hypertension was diagnosed according to preoperative BP data. The definition of diabetes was as follows: 1) in repeated tests of asymptomatic patients, during a 75-h oral glucose tolerance test, a fasting plasma glucose was ≥7.0 mmol/L, or a 2-h plasma glucose value ≥11.1 mmol/L; or HbA1c ≥$6.5\%$; or 2) in patients with classic hyperglycemia symptoms, the random plasma glucose level was ≥11.1 mmol/L; or 3) an existing diagnosis of diabetes with an established hypoglycemic diet or treatment [7]. We allocated patients into two groups according to a cutoff value determined according to the typical sizes of malignant adrenal tumors. A lesion over 4 cm in diameter has a $70\%$ chance of becoming malignant [8], and we therefore chose a diameter of 4cm as the cutoff value. Tumor diameters were determined according to the histopathological findings using tissue specimens. Tumors with diameter > 4cm were considered as large tumors and tumors with diameter≤ 4cm were considered as small tumors. Measurements of 24-h urinary catecholamines were conducted using the high-performance liquid chromatography-mass spectrometry. Data for all laboratory indicators were acquired upon the patient’s first visit to our medical institution. The minimum follow-up time was 6 months after surgery. Follow-up results of all patients were obtained through outpatient records and telephone calls.
## Statistical analysis
Continuous values were reported as the mean ± standard or median (25th, 75th percentiles). Student’s t-tests or rank-sum tests were used to compare continuous variables between groups. Categorical variables were represented as numbers (percentages), and Pearson’s chi-square or Fisher’s exact tests were used to evaluate the significance of differences. Multivariate logistic regression included parameters with $p \leq 0.1$ in univariate logistic regressions. SPSS statistical software, version 25.0 (IBM Corp. Armonk, USA) was used to perform all analyses. GraphPad Prism 8.0 (GraphPad Software Corp. CA, USA) was used to analyze receiver operating characteristic (ROC) curves. Statistical significance was defined as two-sided p values < 0.05.
## Characteristics of the whole cohort
The clinical characteristics of patients with PPGLs are shown in Table 1. We included 263 patients in the analysis, including 119 patients with adrenal PPGLs ($45.2\%$) and 144 patients with extra-adrenal PPGLs ($54.8\%$). Among patients with extra-adrenal PPGLs, 72 patients had tumors located in the head and neck, and 72 patients had tumors located in the trunk. The mean age of the subjects was 45.9 ± 12.9 years. Men accounted for $46.8\%$ of the cohort. Sixty-one ($23.2\%$) patients had diabetes, and 142 ($54.0\%$) had hypertension. Eighty-eight patients ($33.5\%$) had sustained hypertension, 48 ($18.3\%$) had paroxysmal hypertension, 6 ($2.3\%$) had mixed hypertension. Thirty-five percent of patients reported dizziness or headache, $33.1\%$ reported palpitations, $25.1\%$ reported excessive sweating, $7.2\%$ reported nausea or vomiting, and $6.5\%$ reported PPGL crisis.
**Table 1**
| variable | All patients (n=263) |
| --- | --- |
| Age, years(n=263) | 45.9 ± 12.9 |
| Male, % (n=263) | 123(46.8) |
| BMI, kg/m2(n=263) | 24.2 ± 3.3 |
| Diabetes, % (n=263) | 61(23.2) |
| Hypertension, %(n=263) | 142(54.0) |
| Patterns of hypertension | |
| Sustained, % | 88(33.5) |
| Paroxysmal, % | 48(18.3) |
| Mixed, % | 6(2.3) |
| Symptoms(n=263) | |
| Dizziness or headache, % | 92(35.0) |
| Palpitations, % | 87(33.1) |
| Excessive sweating, % | 66(25.1) |
| Nausea or vomiting, % | 19(7.2) |
| PPGL crisis, % | 17(6.5) |
| 24hU-E, μg/24h(n=205) | 4.2(2.8, 16.6) |
| 24hU-NE, μg/24h (n=205) | 51.2(32.1, 182.9) |
| 24hU-DA, μg/24h (n=205) | 232.3(186.3,297.8) |
| Adrenal PPGLs, % (n=263) | 119(45.2) |
| Extra-adrenal PPGLs, % (n=263) | 144(54.8) |
| Tumor diameters (cm) (n=263) | 5.0(3.2, 6.0) |
## Characteristics of patients between the two groups
During the whole study, 110 patients were in the small tumor group, and 153 patients were in the large tumor group. The clinical characteristics of patients in the two groups are shown in Table 2. Age and body mass index were not significantly different between the groups. Compared with small tumor group, the proportion of men in the large tumor group was higher ($53.6\%$ vs $37.3\%$, $$p \leq 0.009$$), and more patients in the large tumor group had hypertension and diabetes ($64.1\%$ vs $40.0\%$, $p \leq 0.001$; and $30.1\%$ vs $13.6\%$, $$p \leq 0.002$$, respectively). Palpitations, excessive sweating, and nausea or vomiting were more likely to be experienced in the large tumor group, while the frequencies of dizziness or headache and PPGL crisis were not significantly different between the two groups. The levels of 24-h urinary epinephrine (24hU-E) ($p \leq 0.001$) and 24-h urinary norepinephrine (24hU-NE) ($$p \leq 0.002$$) in the large tumor group were also higher. There was no significant difference in 24-h urinary dopamine levels between groups ($$p \leq 0.063$$). Adrenal PPGLs were more frequent to be found in the large tumor group ($61.4\%$ vs $22.7\%$, $p \leq 0.001$), and extra-adrenal PPGLs were more frequent to be found in the small tumor group ($77.3\%$ vs $38.6\%$, $p \leq 0.001$).
**Table 2**
| variable | Small tumor group (n=110) | Large tumor group (n=153) | P value |
| --- | --- | --- | --- |
| Age, years(n=263) | 45.6 ± 13.3 | 46.1 ± 12.6 | 0.766 |
| Male, % (n=263) | 41(37.3) | 82(53.6) | 0.009 |
| BMI, kg/m2(n=263) | 24.0 ± 3.5 | 24.3 ± 3.2 | 0.505 |
| Diabetes, % (n=263) | 15(13.6) | 46(30.1) | 0.002 |
| Hypertension, %(n=263) | 44(40.0) | 98(64.1) | <0.001 |
| Patterns of hypertension | | | |
| Sustained, % | 24(21.8) | 64(41.8) | 0.001 |
| Paroxysmal, % | 19(17.3) | 29(19.0) | 0.728 |
| Mixed, % | 1(0.9) | 5(3.3) | 0.398 |
| Symptoms(n=263) | | | |
| Dizziness or headache, % | 33(30.0) | 59(38.6) | 0.151 |
| Palpitations, % | 24(21.8) | 63(41.2) | 0.001 |
| Excessive sweating, % | 16(14.5) | 50(32.7) | 0.001 |
| Nausea or vomiting, % | 3(2.7) | 16(10.5) | 0.017 |
| PPGL crisis, % | 5(4.5) | 12(7.8) | 0.283 |
| 24hU-E, μg/24h(n=205) | 3.6(2.5,5.0) | 4.8(3.0,29.0) | <0.001 |
| 24hU-NE, μg/24h (n=205) | 36.1(25.8,149.9) | 70.5(36.2,197.3) | 0.002 |
| 24hU-DA, μg/24h (n=205) | 218.8(180.2,257.2) | 237.0(188.3,306.1) | 0.063 |
| Adrenal PPGLs, % (n=263) | 25(22.7) | 94(61.4) | <0.001 |
| Extra-adrenal PPGLs, % (n=263) | 85(77.3) | 59(38.6) | <0.001 |
Multivariate logistic regression analysis was used to identify risk factors of large tumors in patients with PPGLs. The clinical symptoms in patients with PPGLs were related to the secretion of catecholamines, therefore, interactions among these parameters were possible. Consequently, only catecholamine concentrations were included in the logistic regression analysis. According to the results of the univariate logistic regression analysis (Table 3), sex, diabetes, hypertension, 24hU-E concentrations, 24hU-NE concentrations, 24hU-dopamine concentrations and tumor locations were included in the multivariate logistic regression analysis. The result showed that male sex [odds ratio (OR): 2.871, $95\%$ confidence interval (CI): 1.444–5.711, $$p \leq 0.003$$], 24hU-E concentrations (OR: 1.025, $95\%$ CI: 1.004–1.047, $$p \leq 0.020$$), 24hU-NE concentrations (OR: 1.002, $95\%$ CI: 1.001–1.004, $$p \leq 0.045$$), and adrenal PPGLs (OR: 2.510, $95\%$ CI: 1.256–5.018, $$p \leq 0.009$$) were positive risk factors for large tumors in patients with PPGLs.
**Table 3**
| variable | Univariate logistic regression analysis | Univariate logistic regression analysis.1 | Univariate logistic regression analysis.2 | Multivariate logistic regression analysis | Multivariate logistic regression analysis.1 | Multivariate logistic regression analysis.2 |
| --- | --- | --- | --- | --- | --- | --- |
| | p | OR | 95%CI | p | OR | 95%CI |
| Age* | 0.765 | 1.003 | 0.984-1.022 | | | |
| Male** | 0.009 | 1.944 | 1.179-3.206 | 0.003 | 2.871 | 1.444-5.711 |
| Diabetes** | 0.002 | 2.723 | 1.429-5.189 | 0.528 | 0.741 | 0.291-1.883 |
| Hypertension** | <0.001 | 2.673 | 1.614-4.427 | 0.994 | 0.997 | 0.479-2.078 |
| BMI* | 0.504 | 1.026 | 0.962-1.105 | | | |
| 24hU-E* | 0.005 | 1.033 | 1.01-1.057 | 0.020 | 1.025 | 1.004-1.047 |
| 24hU-NE* | 0.017 | 1.002 | 1.001-1.004 | 0.045 | 1.002 | 1.001-1.004 |
| 24hU-DA* | 0.044 | 1.003 | 1-1.006 | 0.147 | 1.001 | 0.999-1.003 |
| Adrenal PPGLs ** | <0.001 | 5.417 | 3.119-9.409 | 0.009 | 2.510 | 1.256-5.018 |
The ROC curve analysis was used to evaluate the diagnostic performance of the variables. The area under the ROC curve (AUC) of males for predicting the large tumor was 0.582 ($95\%$ CI: 0.512–0.651, $$p \leq 0.024$$); the AUC of the 24hU-E concentrations for predicting the large tumor was 0.656 ($95\%$ CI: 0.581–0.731, $p \leq 0.001$); the AUC of the 24hU-NE concentrations was 0.637 ($95\%$ CI: 0.554–0.720, $$p \leq 0.002$$); the AUC of adrenal PPGLs was 0.694 ($95\%$ CI: 0.629–0.758, $p \leq 0.001$). Taking gender, 24hU-E concentrations 24hU-NE concentrations, and adrenal PPGLs into account in the same model, the AUC of the model for predicting the large tumor was 0.772 ($95\%$ CI: 0.706–0.834, $p \leq 0.001$) (Figure 2). A total of 212 patients in our study underwent regular imaging review after surgery, of which 90 were with the preoperative small tumor group and 122 were with the preoperative large tumor group. After the mean follow-up of 20.2 ± 11.7 months, there was no significant difference in recurrence between the two groups ($4.4\%$ vs. $2.5\%$, $$p \leq 0.681$$).
**Figure 2:** *Receiver operating characteristic curve analysis evaluating the diagnostic performance for tumor diameter in patients with PPGLs. (A)The AUC of the male for predicting the large tumor was 0.582 (95% CI: 0.512–0.651, p=0.024). (B) The AUC of the 24U-E concentrations for predicting the large tumor was 0.656 (95% CI: 0.581–0.731, p<0.001). (C) The AUC of the 24hU-NE concentrations for predicting the large tumor was 0.637 (95% CI: 0.554 – 0.720, p = 0.002). (D) The AUC of the adrenal PPGLs for predicting the large tumor was 0.694 (95% CI: 0.629–0.758, p<0.001). (E) The AUC of the whole model for predicting the large tumor was 0.772 (95% CI: 0.706–0.834, p < 0.001). PPGLs, pheochromocytomas and paragangliomas; AUC, area under the curve; 24hU-E, 24-hour urinary epinephrine; 24hU-NE, 24-hour urinary norepinephrine.*
## Discussion
In this study, we found that the tumor diameters in male patients with PPGLs were more likely to exceed 4 cm, and high concentrations of catecholamines could predict large PPGLs. Furthermore, large tumors were more likely to reside on the adrenal glands. After the short-term follow-up, there was no significant difference in tumor recurrence between the two groups. Our findings provide an important basis for further understanding of the clinical characteristics of PPGLs, the risk stratification of patients with PPGLs, and developing a reasonable clinical screening and follow-up plan.
Published studies have described the relationship between catecholamines and tumor diameter in patients with PPGLs. In Falhammar et al. ’s study [9], urine norepinephrine/plasma normetanephrine levels and tumor size were positively correlated. Additionally, Guerrero et al. [ 10] found a direct, significant correlation between tumor size and catecholamine hormone levels independent of clinical presentation; and when excluding confounding factors, there was a stronger linear correlation between them. Furthermore, hormone levels vary greatly among all tumor sizes, with smaller tumors exhibiting a lower tendency to secrete high levels of catecholamines. The results of Eisenhofer’s study [11] also indicated that tumor diameter correlated positively with summed 24h urinary normetanephrine and metanephrine ($p \leq 0.001$). In addition to the strong correlation between tumor diameter and plasma or urinary deconjugated metanephrines, there was also a significant positive relationship between tumor diameter and urinary or plasma catecholamines ($p \leq 0.001$). Since most patients did not have the results of plasma free metanephrines or urinary fractionated metanephrines in this study, which provide higher sensitivity and specificity [1], we couldn’t get the relationship between them and tumor diameter. The relationship between the 24hU-catecholamines and tumor diameter in the present study is in accordance with the findings of the studies above. This data in the present study indicates that urinary catecholamine concentrations can serve as a predictor of tumor size. Accordingly, we are able to predict the tumor diameters prospectively. A prediction like this, may be helpful during subsequent imaging procedures to confirm the localization of the tumor. It is increasingly necessary for laboratory medicine to be integrated into making diagnose, and particularly important to provide guidance regarding testing procedures, interpretations, and follow-ups. One example where such guidance may be especially useful is the laboratory diagnosis of PPGLs.
As for the tumor diameters of adrenal and extra-adrenal PPGLs, in this study, adrenal PPGLs were more prone to be large tumors, which is consistent with the findings of others [12, 13]. Goffredo et al. compared clinical characteristics between malignant adrenal PPGLs and extra-adrenal PPGLs, they found adrenal PPGLs were larger than extra-adrenal PPGLs (mean size 7.7 vs. 4.5 cm, $$p \leq 0.001$$), and larger tumor size was also associated with greater mortality [12]. Similarly, in a study of describing baseline characteristics of patients with malignant PPGLs, Hamidi et al. [ 13] found that compared with extra-adrenal PPGLs, adrenal PPGLs were larger (median size 9.0 cm vs 5.8 cm, $p \leq 0.0001$) and were more frequently functional ($91\%$ vs $72\%$, $$p \leq 0.0001$$); they also reported that older age at primary diagnosis, larger tumor size, and synchronous metastases were independent factors for the shorter survival. Other studies have also reported that larger or heavier tumors are strongly associated with malignant disease and mortality [14, 15]. In the present study, there was no significant difference in tumor recurrence between the groups after the short-term follow-up. The possible reasons for this maybe that we excluded patients diagnosed with metastatic PPGLs at first visit in our hospital before analysis, and as metastatic PPGLs often become evident several years after initial diagnosis, this may be also due in part to the short-term follow-up.
In this study, men sex was more likely to harbor large tumors, in fact, previous studies have also found that men sex was associated with the possibility of malignancy [13, 16]. In a large retrospective cohort of patients with adrenal tumors, Iñiguez-Ariza et al. [ 16] found that one of the factors that predicted a malignant adrenal mass was male sex. Hamidi et al. [ 13] reported that there was a significant association between male sex and shorter survival ($$p \leq 0.014$$) in patients with malignant PPGLs. This suggests that compared with female patients, male patients may require more attention and close clinical follow-up to reduce the occurrence of adverse outcomes.
There are several limitations in this study. Firstly, this was a single-center retrospective study, and the results should be generalized with caution. Secondly, not all patients’ urinary catecholamines were measured. Thirdly, due to the lack of genetic screening results, we couldn’t get relationship between genotype and tumor diameter. To evaluate the exact clinical features of small and large PPGLs, a prospective cohort study is required.
## Conclusion
Significant differences in numerous clinical characteristics exist between large and small PPGLs. Male patients with PPGLs were more likely to be with large tumors, and large tumors were more likely to reside on the adrenal glands. Furthermore, catecholamine measurements not only provide information for predicting the presence or absence of PPGLs, but also help predict tumor size if PPGLs are present. It may be useful to make clinical decisions according to this information, and clinicians must be aware of the clinical features of PPGLs since early identification of a can be life-saving.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Committee of Peking Union Medical College Hospital. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
LZ, YCL and XLZ conceptualized and designed the study. LZ, ZML and XM provided analyzed and interpreted the data. LZ, ZML, HF, ZLZ and ZCZ provided statistical support, including data collection and assembly. YCL, XLZ and HDZ reviewed the framework and content of the discussion. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
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|
---
title: 'Identifying predictive factors for long-term visual recovery after corneal
endothelial keratoplasty in Fuchs'' dystrophy: Potential interaction between the
corneal dysfunction and retinal status'
authors:
- Charlotte Maffre
- Pierre Fournié
- Eve Durbant
- Carl Arndt
- Zoubir Djerada
- Alexandre Denoyer
journal: Frontiers in Medicine
year: 2023
pmcid: PMC10034073
doi: 10.3389/fmed.2023.1120283
license: CC BY 4.0
---
# Identifying predictive factors for long-term visual recovery after corneal endothelial keratoplasty in Fuchs' dystrophy: Potential interaction between the corneal dysfunction and retinal status
## Abstract
### Introduction
Descemet membrane endothelial keratoplasty (DMEK) is the main treatment for Fuchs' dystrophy (FECD). The outcomes are excellent, but the final visual recovery may vary from patient to patient with sometimes no obvious reason of such a spread.
### Methods
We conducted a clinical prospective multicentric study to identify the predictive factors for the visual result 1 year after surgery. Eighty three patients (83 eyes) were included.
### Results
Postoperative BCVA after 1 year was 0.20 ± 0.18 logMAR. Logistic regression revealed that good visual recovery correlated negatively with preoperative central macular thickness ($p \leq 0.001$) and the need for rebubbling ($$p \leq 0.05$$), and positively with preoperative visual acuity ($$p \leq 0.009$$). Multivariate formula to predict the 1-year BCVA has been suggested.
### Discussion
Preoperative retinal status seems to be the main predictive factor for long-term visual result after DMEK. Our predictive multivariate model could assist in better informing the patient about the prognosis of the surgery, and in adjusting the therapeutic strategy also, further highlighting the essential collaboration between both cornea and retina subspecialists.
## Introduction
Worldwide, Fuchs' endothelial corneal dystrophy (FECD) is the most common corneal dystrophy [1]. It is a bilateral and partially hereditary chronic disease characterized by progressive dysfunction and loss of corneal endothelial cells. FECD may lead to corneal edema, resulting in blurred vision, loss of best corrected visual acuity (BCVA), and, sometimes, painful epithelial damage [2]. Specific surgical procedures have been developed over the past decade based on the targeted replacement of damaged tissue. As a result, penetrating keratoplasty was replaced by Descemet stripping automated endothelial keratoplasty (DSAEK) and, more recently, by Descemet membrane endothelial keratoplasty (DMEK), which targets the patient's membrane and endothelial cell layer [3]. In addition to the low rate of surgical complications and graft rejection of DMEK when compared with other techniques, most previous studies also attributed excellent outcomes to DMEK [4, 5].
In our experience, however, visual recovery after DMEK may sometimes vary from patient to patient, even in cases that are apparently similar. This may lead to a lack of predictability in the final visual outcomes after this procedure. Previous studies have considered the factors influencing the outcomes of DMEK. Brockmann et al. [ 6] investigated BCVA, central corneal thickness, and endothelial cell density after DMEK in 108 eyes with Fuchs' dystrophy. They reported that a corneal thickness, as measured by pachymetry, >625 μm is significantly linked to poor BCVA after the surgery. As a result, a cut-off was defined when giving advice relating to performing DMEK. Schrittenlocher et al. [ 7] found that a preoperative BCVA below $\frac{20}{100}$ indicated poor visual recovery after 12 months, probably due to structural alterations in the cornea occurring in the late stages of the disease. Other factors, including corneal haze and graft–host irregularity, were highlighted by Turnbull et al. [ 8].
Considering the retina, Zwingelber et al. reported that vitreomacular traction may influence the visual prognosis [9], and Steindor et al. studied DMEK outcomes in patients with macular comorbidities [10]. However, poor things are known about relationship between the corneal and the putative retinal changes in this disease. The purpose of this prospective cohort study was to provide a global multivariate model to better understand and predict the long-term visual recovery after DMEK in the treatment of FECD.
## Study design
This prospective observational multicentric study was conducted in the Quinze-Vingts National Ophthalmology Hospital (Paris, France; CIC 503, CPP Île-de-France, agreement number 10793), the department of ophthalmology at the Purpan University Hospital (Toulouse, France), and the department of ophthalmology at the Robert Debré University Hospital (Reims, France), in accordance with the Declaration of Helsinki, Scotland Amendment, 2000. Permission was obtained from the institutional review board, and all patients gave validated and informed consent.
From January 2018 to September 2019, patients scheduled for DMEK were consecutively enrolled. Fuchs' dystrophy requiring DMEK were included according to the main criteria for surgery, i.e., fluctuating vision throughout the day, photophobia, BCVA > 0.4 logarithm of the minimum angle of resolution (logMAR), and clinical corneal edema. The main exclusion criteria were any ocular disease (ocular surface disease, inflammatory or infectious diseases, intraocular pressure >21 mmHg or diagnosed glaucoma, retinal disease) aside from Fuchs' dystrophy and cataract. Patients with macular edema (CMT >300 μm) without any other retinal abnormality were not excluded. Other exclusion criteria were age < 21 years, pregnancy, or an inability to understand the study and give informed consent.
Patients' preoperative data were collected: age; gender; symptoms including photophobia or morning fog; diabetic status; distant BCVA (logMAR scale); keratometry; refractive spherical equivalent, intraocular pressure (IOP); optical coherence tomography (OCT) corneal thickness mapping (RT-Vue™, Optovue, Fremont, USA); OCT foveal thickness; and macular status (Spectralis-OCT™, Sanotek, Heidelberg, Germany). Graft features were also recorded, including endothelial cell density, donor's age and gender, as well as the need for rebubbling.
All patients underwent DMEK under general anesthesia, which was performed by three experienced surgeons, following a standard “no touch” method, as described by G Melles [11, 12]. No iridotomy was performed during the surgery. Postoperatively, patients were placed in a supine position for 48 h, and pharmacological mydriasis has been maintained for the day of the surgery using phenylephrine and tropicamide. Rebubbling was decided in cases with persistent edema and a partially detached graft, as imaged by OCT. Rebubbling was performed within the 7 days after DMEK. All patients received treatment with dexamethasone eye drops postoperatively (tid for 6 months, bid for 3 months, and then once a day) and tear-film substitutes.
The following postoperative data were collected 1 year after the surgery: BCVA, keratometry, OCT corneal thickness mapping, and OCT macular thickness mapping. One-year distant BCVA was the primary endpoint.
## Statistical analysis
All data are given as mean ± SD. Data were controlled for normality and homogeneity of variance to perform adequate tests. The probability level of significance was adjusted according to the post-hoc Bonferroni procedure to maintain an overall type I error of 0.05.
For binomial analysis, the patients were separated into two groups depending on their distant BCVA (long-term visual recovery). To facilitate analysis, we choose a score of 1 or 0 for binomial factors. Visual recovery was considered to be good if it was < 0.4 logMAR or bad if it was ≥0.4 logMAR, accordingly to the inclusion criteria and the results previously published about post-DMEK visual recovery [13]. T-tests, chi-square tests, and scatter plots with the R2 coefficient were performed to assess the univariate associations between pairs of variables. For multivariate analysis, the a priori power was estimated using the number of events. This allowed us to include up to nine covariates in the multivariate analysis, with a power >$80\%$. The post-hoc simulations showed that the power of all multivariate analyses was higher than $90\%$. Variables included in the full model were selected using a step-down model. All the selected variables were checked to ensure that no collinearity existed between them. We used the bootstrap method ($$n = 500$$) to study the uncertainty in the selected variables and to penalize this uncertainty when estimating the predictive performance of the model. The R2 indexes were used as discrimination indicators, and the C statistics and Dxy indexes were used as rank discrimination indicators.
The statistical analysis resulted in two attractive models, one with logistic regression and one with multiple linear regression, allowing us to suggest computation formulae to predict the distant BCVA 1 year postoperatively.
## Population
Of the patients considered, 103 eyes met the inclusion criteria. Of these patients, 7 were excluded from the analysis because of ocular comorbidities during the 1-year follow-up period (2 patients with increasing cataract who had cataract surgery during the follow-up period, 4 patients with long-term ocular hypertension requiring additional medication, one with herpes simplex virus keratitis), and 13 due to missing data. Therefore, a total of 83 eyes were analyzed. The patients' preoperative features are detailed in Table 1. Analysis of correlations between the preoperative data revealed significant relationships between preoperative distant BCVA and mean CCT ($p \leq 0.0001$), and between preoperative distant BCVA and mean CMT ($$p \leq 0.01$$). No other significant correlations were found. One year after surgery, the mean distant BCVA was 0.2 ± 0.18 logMAR, with 63 patients ($75.9\%$) presenting a visual recovery better than 0.4 logMAR (Figure 1). Other postoperative data are detailed and compared with preoperative values in Supplementary Table 1.
## Univariate analysis of correlations
Bad visual recovery was significantly associated with an increase in preoperative macular thickness ($p \leq 0.0001$), and bad preoperative BCVA ($$p \leq 0.003$$). In addition, the presence of epitheliopathy was shown to have a weak association with bad recovery ($$p \leq 0.047$$). Rebubbling appeared to be associated with poor recovery; however, this finding was not significant ($$p \leq 0.096$$). None of the other data recorded were statistically significantly correlated with long-term visual outcomes. These results are summarized in Table 2. Figure 2 details the correlation between preoperative macular thickness and 1-year visual recovery. Using Youden index method, we found a cut-off value of 313 μm for CMT: it could be a pertinent preoperative indicator for poor visual recovery when exceeding this value.
## Logistic regression for visual recovery
Multivariate analysis using the logistic regression model selected three main variables: the preoperative central macular thickness ($$p \leq 0.002$$), rebubbling ($$p \leq 0.041$$), and the preoperative distant BCVA ($$p \leq 0.062$$), as shown in Figure 3 and Supplementary Figure 1. Figure 3 details the odds ratio for these three preoperative parameters, which expresses the effect of each one on the probability of good visual recovery at 1 year. Our model to predict the final outcome was converted into a useable nomogram (Figure 4). Practically, following this nomogram, each variable can be converted into a subscore, and the sum of these three subscores matches the probability to recover a good BCVA after the surgery.
**Figure 3:** *Forest plot of the main effects in the logistic regression model. Preoperative central macular thickness (OR = 0.517; IC 95% [0.342–0.781]; p < 0.002); rebubbling (OR = 0.269; IC 95 % [0.076–0.949]; p = 0.041); and preoperative distant BCVA (OR = 0.622; IC 95 % [0.379–1.023]; p = 0.061).* **Figure 4:** *Nomogram to predict good visual recovery at 1 year. Based on logistic regression model, the nomogram includes preoperative central macular thickness (μM), preoperative distant BCVA (logMAR), and rebubbling as the main factors.*
## Multiple linear regression to predict final BCVA
As shown in Table 3 and Supplementary Figure 2, in our final model, four factors were found to be associated with the one-year-post-DMEK BCVA, namely preoperative central macular thickness ($p \leq 0.001$), preoperative distant BCVA ($$p \leq 0.009$$), diabetes ($$p \leq 0.046$$), and rebubbling ($$p \leq 0.05$$). Gender also influenced slightly the visual recovery ($$p \leq 0.103$$). A formula to predict the 1-year distant BCVA has been suggested in Table 3 and can be written in an Excel spreadsheet.
**Table 3**
| Unnamed: 0 | Coefficient | IC 95% | P |
| --- | --- | --- | --- |
| Gender | 0.068 | [−0.014–0.150] | 0.103 |
| Preoperative central macular thickness | 0.001 | [0.028–0.079] | < 0.001 |
| Diabetes | 0.118 | [0.0024–0.233] | 0.046 |
| Rebubbling | 0.084 | [0.00008–0.168] | 0.05 |
| Preoperative distant BCVA | 0.137 | [0.012–0.083] | 0.009 |
## Preoperative macular thickness as the main predictive factor for long-term visual outcome
We observed a high variability in the central macular thickness before DMEK, which was strongly linked with the visual recovery 1 year after surgery. This finding immediately raises the question of a pre-existing endothelio–macular relationship that still needs to be established. We could hypothesize that adjacent inflammation from the anterior chamber to the retina plays a role in this phenomenon, as it does in contiguous macular edema in acute anterior uveitis. This would imply anterior segment inflammation or stress during FECD. Oxidative stress has been reported to be involved in the pathogenesis of FECD. In Fuchs' dystrophy, as a consequence of a genetic add-on disorder, there is mitochondrial dysfunction leading to the potential alteration of the membrane, which, in turn, leads to excessive mitophagia [14, 15]. As a result of these stress factors, structural changes appear involving fibroblastic transformation of the stromal cells and sub-epithelial fibroblast infiltration, which can be observed as a scar in other tissue.
It has already been proven that the levels of some proinflammatory cytokines, such as interleukin (IL)-1a, IL-6 IL-8, IL-17a, TNF-alpha, and IFN-c, were higher in the aqueous humor of eyes with bullous keratopathy and a low density of ECs [16, 17]. In addition, high levels of cytokines seem to accelerate the loss of ECs, leading to a vicious cycle [18]. As a mechanism for the development of macular edema, in this case, we could speculate that there is an interrelation between the anterior and posterior segments via inflammatory mediators traveling to the vitreous pocket, as could also be the case for macular edema in anterior uveitis. Therefore, in response to the diffusion of proinflammatory mediators, immunity cells could be activated, breaking the hemato-retinal barrier and leading to macular edema [19].
In the literature, some additional studies were undertaken to investigate the influence of macular edema on the outcomes of DSAEK and DMEK. However, these studies only considered the postoperative macular status. Kocaba et al. [ 20] studied the incidence of and risk factors for macular cystoid edema after DMEK on 80 eyes and found that postoperative macular edema was more frequent after DMEK surgery ($8\%$) or the combined procedure, triple DMEK ($18\%$), than phacoemulsification alone (0.1–$2.3\%$), suggesting that DMEK is a self-risk factor. This was consistent with the results found by Heinzelman et al. [ 21] and Flanary et al. [ 22], in 155 eyes and 173 eyes, respectively. They reported macular edema after triple procedure as 13.3 and $8\%$ and DMEK alone as 12.5 and $7.1\%$, respectively.
This is the first time that a study has been carried out to look for a relationship between the preoperative retinal status (central macular thickness), which may be impacted during the natural history of FECD, and subsequent visual outcomes after corneal surgery. This result is of interest in clinical practice because it could modify the indications for DMEK, suggesting that better long-term visual outcomes are achieved when the procedure is performed earlier. In addition, it could enable us to predict the visual prognosis of the eye at the preoperative visit and improve the information given to patients relating to their expected visual outcome. Once again, our results highlight the need for a close collaboration between cornea and retina subspecialists in this disease to improve the therapeutic management. Future studies could look at answering the question of whether it is possible to avoid macular thickening by adjusting the therapeutic arsenal around surgery. For instance, evaluation of the use of corticoids drops or intravitreal injection (before or during the surgery, or more than tid after) or additional non-steroidal anti-inflammatory drops would be of huge interest. Hence, the use of prophylactic anti-inflammatory medication or other drugs against macular edema in patients scheduled for DMEK with abnormal preoperative CMT should be evaluated. Moreover, we herein found a cut-off value of 313 μm for CMT related to the good/bad visual recovery, further questioning the indication of the surgery as well as the need for preoperative medication against macular edema in patients with CMT exceeding this threshold.
## Preoperative visual acuity
Statistical analysis showed that good preoperative visual acuity is strongly correlated with good recovery. This result is supported by the literature [23] and can be explained by many facts. As mentioned previously, in the advanced stages of FECD, and secondary to stress, there is fibroblastic transformation of the stromal cells and the possibility of subepithelial fibroblast infiltration. In addition, endothelial dystrophy produces a stromal haze, which creates an obvious impediment to visual rehabilitation, even after a transplant. Preoperative visual acuity is also related to ophthalmologic comorbidities, in particular, macular comorbidities, including an incipient preexistent endothelio–macular decompensation. All these factors are positively correlated with poor visual outcomes. It has to be noticed, however, that statistical analyses in the present study demonstrated that preoperative visual acuity influences long-term visual outcome in an independent way, i.e., regardless of the preoperative corneal pachymetry or macular status. Preoperative corneal pachymetry was not found to influence the visual recovery significantly.
## Rebubbling
The incidence of rebubbling in our study was $30.12\%$. This result is supported by the literature, 10–$40\%$ [24], $23.1\%$ [25], and $23.8\%$ [26]. In the present study, multivariate analysis showed a significant correlation between rebubbling and visual outcomes. This was a surprising result, as it was not supported by the results reported by Gerber-Hollbach et al. [ 27] who studied the clinical outcomes of rebubbling 6 months after DMEK. In their study, the patients were divided into two groups: a group in which rebubbling was performed ($$n = 25$$) and a control group ($$n = 25$$). The data suggested a better global postoperative BCVA in the control group, but this difference was not statistically significant. In another study, Siebelmann et al. [ 24] reported that rebubbling did not influence the BCVA outcome. However, the study did not compare rebubbled eyes with a control group. Lazaridis et al. [ 28] showed that patients who had rebubbling in one eye and subsequently went on to have DMEK on the other eye also received a rebubbling on the second eye, with an incidence of $58.8\%$. The reason for this phenomenon remained unclear, but it suggests that some unidentified intrinsic factors may increase the risk of rebubbling.
In parallel, there is no consensus in the literature regarding the hypothesis of endothelial cell loss due to rebubbling. The results of Lazirids et al. [ 28] indicate a significant loss, while those of Feng et al. [ 29] do not. The literature also does not agree on whether rebubbling is a risk factor for post-DMEK macular edema. According to Inoda et al., it is recognized as a risk factor [30]; however, the results of other previous studies do not support it as a risk factor for postoperative macular edema [8, 20].
We conducted an intermediate analysis—univariate and multivariate—on the 25 rebubbled eyes of our study, in order to highlight a potential associated preoperative risk factor that could suggest a bias for this result. No variable emerged from this analysis. A conclusion cannot be drawn based on this analysis, as it was obviously lacking power, and the investigation was not the main aim of our research. However, we were able to reach an agreement in recognizing that rebubbling may trigger inflammation and cause eye stress.
## Diabetes
Our results show a mild correlation in our multiple linear regression models between the incidence of diabetes and the one-year postoperative BCVA. This is supported by other studies, which showed that hyperglycemia increases the production of mitochondrial superoxide, a reactive oxygen species, leading to DNA cell damage, depleting the cellular store of antioxidants, accelerating the aging process in the cell process, and accelerating apoptosis, especially in FECD when defense mechanisms are already impaired. In addition, Zhang et al. [ 31] found that corneal thickness is greater in the diabetic population when compared with an aged-matched population, which was supported by other results in the literature suggesting a mild pump dysfunction. Similarly, Price et al. [ 32] reported that patients with diabetes experienced more endothelial cell loss after the graft than non-diabetic patients but with no visual consequences. Price also reported that diabetes status is correlated with an increased risk of rebubbling, as did Janson et al. [ 33]. The latter studied the relationship between DMEK outcomes and diabetes in 41 patients with diabetes not on insulin therapy, 22 patients with diabetes on insulin therapy, and 271 controls. They did not show any statistically significant difference in visual acuity between the three groups at any of the postoperative time points. Last, diabetes was not shown to be a risk factor for post-DMEK macular edema in the previously quoted studies [19, 20, 29], although diabetes is still recognized as a risk factor for Irvine–Gass syndrome in cataract surgery. The reasons for these differences have not yet been clearly explained and merit further studies.
## Other factors studied
We could have speculated that procedures that included cataract surgery or other surgeries would have increased the risk of postoperative inflammation and poor visual outcomes because the procedures are longer and require more manipulation. However, our study did not show any difference in visual recovery between single or combined procedures. This result is supported by the literature, which suggests that, in terms of BCVA, DMEK and triple DMEK are comparable [34]. Chaurasia et al. [ 35] conducted a comparative study between two groups (triple DMEK and single DMEK). They reported that triple DMEK was not associated with a higher risk of complications, i.e., there was no statistically significant difference in the incidence of graft failure, rejection, rebubbling, endothelial cell loss, or post-surgical macular edema, and BCVA improved significantly in both groups.
More interestingly, preoperative pachymetry did not show any significant association between corneal thickness and visual acuity in our study. This result has however to be balanced with the fact that individual pachymetry before Fuch's decompensation was not known. Analyzing the relationship between corneal thickening due to Fuchs' decompensation, i.e., the part of pachymetry due to corneal edema, and the visual recovery could have provided interesting information. Regarding epitheliopathy, the current study showed a marginal univariate association, which was not highly significant. There is no agreement in the literature regarding corneal thickness as a predictive factor for visual rehabilitation. Schrittenlocher et al. [ 7] found no correlation between corneal thickness and BCVA. In contrast, Brockmann et al. [ 6] studied corneal thickness as primary endpoint, and found a correlation ($$p \leq 0.014$$) between preoperative corneal thickness and visual recovery when corneal thickness was >625 μm. This result does not support the results of the current research. However, the standard deviation of the corneal thickness in the population of Brockmann's study was greater than in the current study, and we could speculate that extreme values influenced the results.
The cell density of the graft did not have a statistically significant impact on visual performance after DMEK. This result could be explained, at least in part, by the fact that grafts are carefully selected to provide enough cells, i.e., the graft's endothelial cell density is always >2,500 cells/mm2. Also, the other features of graft such as donor's gender and age were not statistically linked with the outcomes. Heinzelmann et al. [ 36], however, suggested endothelial grafts for older donors may be easier to deploy during the procedure, leading to less surgically-induced cell loss.
Last, according to the study design and exclusion criteria, the influence of other ocular factors, e.g., ocular hypertension, glaucoma, optic nerve or retinal diseases, has not been investigated.
## Limitations of the present study
First, the study could be limited by a lack of power because of the sample size. To mitigate this limitation, we used the bootstrapping method, which allowed us to validate our main result. It should also be noted that a secondary validation of our formula using a posteriori independent patients' sample would have reinforce the validation of our predictive model. Second, when collecting data, we should have taken the differential pachymetry—i.e. the proportion of corneal thickening caused by FECD—into account. However, pre-disease pachymetry measurements are difficult to obtain as patients often only consult in the advanced stages of the disease. Therefore, pre-disease pachymetry results are unknown in this study. Another study that collects differential pachymetry measurements is required to confirm the results of the present study. Finally, we did not measure cell endothelial density before and after the surgery, which could have brought some additional data. However, the difficulty to measure accurately endothelial cell density in eyes with corneal edema often leads to unproductive/biased data.
There is still variability in the long-term visual recovery after DMEK, despite our expertise and the overall excellent outcomes of this procedure. Our study highlights, for the first time, the crucial role of the preoperative macular status, which may be intrinsically linked to the natural history of FECD. Herein, the central macular thickness has been shown to be the most significant predictive factor for long-term visual outcomes, and may explain the poor recovery that is observed in some cases. Finally, this study enabled us to provide an equation to predict visual recovery after DMEK by considering some specific factors. This is of interest to practitioners as it allows them to provide appropriate and individualized information for patients concerning prognosis. Also, it questions the need for adjusting perioperative therapy according to the initial macular status to optimize the outcomes.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by Ethics Committee of the University Hospital of Reims, Reims, France. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
Design of the study: AD, ZD, and PF. Conduct of the study: CM, ED, PF, and AD. Collection and management of the data: CM, ED, and AD. Analysis and interpretation of the data and review and approval of the manuscript: AD, ZD, and CA. Preparation of the manuscript: CM and AD. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed.2023.1120283/full#supplementary-material
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|
---
title: 'Pattern of linear growth and progression of bone maturation for girls with
early-onset puberty: A mixed longitudinal study'
authors:
- Shuangyi Liu
- Zhe Su
- Lili Pan
- Jinfeng Chen
- Xiu Zhao
- Li Wang
- Longjiang Zhang
- Qiru Su
- Huiping Su
journal: Frontiers in Pediatrics
year: 2023
pmcid: PMC10034074
doi: 10.3389/fped.2023.1056035
license: CC BY 4.0
---
# Pattern of linear growth and progression of bone maturation for girls with early-onset puberty: A mixed longitudinal study
## Abstract
### Background and objective
With a worldwide trend to earlier age of onset of puberty, the prevalence of early-onset puberty (EP) among girls has increased. The impact of EP on the pattern of linear growth and bone maturation is unclear. Accordingly, the objective of our study was to describe this pattern for girls with EP in Shenzhen, China.
### Methods
A total of 498 untreated girls diagnosed with EP at Shenzhen Children's Hospital, China, between January 2016 and December 2021. A total of 1,307 anthropometric measurements and 1,307 left-hand radiographs were available for analysis. Artificial intelligence (AI) was used to determine bone age (BA). Participants were classified into groups according to chronological age (CA) and BA. The pattern of linear growth (height) and progression of bone maturation was described between groups using the Lambda-Mu-Sigma (LMS) method. Published height-for-CA and height-for-BA norm references for a healthy Chinese population were used for age-appropriate comparisons.
### Results
The mean CA of appearance of first pubertal signs (breast buds) was 8.1 ± 0.5 years. Compared to norm-referenced data, girls with EP were significantly taller at a CA of 7–10 years. This was followed by a slowing in linear growth after a CA of 10 years, with 71 girls with EP having already achieved their target adult height. From 7 to 10 years of BA, the linear growth was slower in the EP group compared to norm-reference values. This was followed by a period of catch-up growth at 11.2 years of BA, with growth curves approaching norm-referenced values. The BA progressed rapidly from 7 to 8 years of age in about half of the girls with EP (median ΔBA/ΔCA >1.9), slowing, thereafter, until the period of catch-up growth at 11.2 years of BA.
### Conclusions
BA provides a more reliable reference than CA to assess growth parameters among girls with EP. Our limited data set does indicate that EP does not negatively impact final adult height. Therefore, the growth curves from our study are relevant, providing a reference for pediatricians in this clinical population and, thus, preventing over-treatment for EP.
## Introduction
The worldwide secular trend of earlier onset of puberty among girls has been confirmed over the last century (1–3). From 1977 to 2013, the age onset of breast development has, on average, occurred 3 months earlier every decade [4]. However, the overall final adult height (FAH) of children and adolescents has been increasing year-by-year in Europe [5, 6], Africa [7], and Asia (8–10). In China specifically, the FAH for girls has increased from 158.2 cm in 1975 to 160.8 cm in 2019 [11, 12].
Early-onset puberty (EP) is defined as puberty that begins earlier than two standard deviations (2SD) or the 3rd percentile of the median age accepted as “normal” [13, 14]. According to a cross-sectional study [10] on sexual maturation, EP in girls in *China is* defined as breast development that begins between 7.1 years and 9.2 years of age. Multiple research studies (15–17) have shown that most girls with EP can reach their mid-parental height (MPH). For girls with EP who progress from one pubertal stage to the next within 6 months or exhibit a bone age (BA) progression that exceeds 1 year per each chronological year, puberty is considered to be both “early” and “fast” (EFP) [18, 19]. EFP may lead to an accelerated progression of BA and early epiphyseal closure, reducing the FAH. Considering the increasing incidence of EP among girls, periodical assessment and monitoring of FAH and BA would be necessary for individualized management of EP. To date, however, no longitudinal study of the linear growth and progression of bone maturation for girls with EP has been conducted in China to inform practice. Accordingly, our aim in this study was to describe the pattern of linear growth and bone maturation for girls with EP in south China to provide reference values.
## Study participants
Eligible participants for this study were untreated girls diagnosed with EP at Shenzhen Children's Hospital, China, between January 2016 and December 2021. Potential participants were identified from the hospital's database. The inclusion criteria were as follows: onset age of breast development between 7.1 years and 9.2 years of age; no drug therapies for EP; regular follow-up at intervals of 3–6 months; and BA assessments performed at least twice within a 6- to12-month interval. Exclusion criteria were: other causes of breast enlargement, including hamartoma, intracranial tumors, exogenous estrogen intake, or focal proliferative breast diseases; isolated telarche observed during the follow-up period; premature pubarche; birth weight that was small-for-gestational-age (SGA) or large-for-gestational-age (LGA); premature (PM), twin, or triplet birth; presence of other endocrine conditions, such as growth hormone deficiency, abnormal thyroid function, and abnormal adrenal cortex function; bone dysplasia, congenital dysplasia, cognitive impairment, or chronic health condition; long-term use of hormone therapy and use of Chinese herbal soup; psychomotor delay; abnormal nutritional status, with a body mass index (BMI) exceeding >$120\%$ of the 95th age-appropriate percentile (obesity) or <5th percentile (wasting) [20]; family history of precocious puberty; and parental height >2 standard deviation (SD) or <−2 SD of the mean for the general population in China.
## Statement of ethics
The study protocol was approved by the research ethics committee of Shenzhen Children's Hospital, China [20211036]. All parents or guardians of participants provided written informed consent for use of their child's data for research.
## Data variables and their measurements
Standardized anthropometric measurements were obtained, in the morning for all participants, by pediatric endocrinologists at Shenzhen Children's Hospital. Weight (to the nearest 0.01 kg), measured with the child in light clothing, and height (to the nearest 0.1 cm) were measured using calibrated and standardized apparatus following a standard procedure. The height of both parents was also obtained. The pubertal stage of breast development was assessed at every visit using Tanner's Stages of Puberty [21].
Plain radiographs of the left hand and wrist were obtained by radiologists; poor images were excluded. The radiographs were evaluated using an automated artificial intelligence (AI) system (Deepwise Artificial Intelligence Lab, Deepwise Inc., Beijing, China) and BA was assessed using the TW3-RUS method [22]. Height-for-CA and height-for-BA were converted into standard deviation scores (SDS) according to the standardized growth curve for children and adolescents in China [23]. Changes in height SDS (ΔHtSDS), body mass index (BMI), and the progression of bone maturation [increment of BA over increment of CA (ΔBA/ΔCA)] were calculated. The mid-parental height (MPH) was calculated for each participant, using Tanner's formula [21]. The diagnostic criteria of FAH were defined as a growth velocity of <1 cm/year over the previous year or with a CA or BA ≥15 years.
## Statistical analysis
Normality of the distribution of data was evaluated using the Shapiro-Wilk test. Normally distributed data were described as the mean ± SD, with the median (25th, 75th) reported for data with a non-normal distribution. Continuous variables were compared between groups using either an independent t-test or Mann–Whitney test, as appropriate for the data distribution, with a one-way analysis of variance (ANOVA) or Kruskal-Wallis test used, as appropriate, for comparison between multiple groups. Analyses were performed using SPSS Statistics (version 26.0, IBM Corp., Armonk, NY), with significance set at a P-value <0.05 was considered statistically significant. We removed data points when values were four standard deviations above or below the mean.
Curves of linear growth (height) and the progression of bone maturation were generated using the Lambda-Mu-Sigma (LMS) method (Chartmaker pro version). The LMS method [24] described the generalized data in each age group by three curves, representing the median (M), coefficient of variation (S), and skewness (L), the latter representing the Box-Cox power. The LMS is a well-accepted method for calculating standard growth curves. The goodness-of-fit of each model was tested and optimized using the Q-test for fit, detrended Q-Q plots, and comparisons between fitted and measured values. The 3rd and 97th percentile limits for each curve were calculated and plotted (Graph Pad Prism, version 8.0.2). The height velocity (HV)-for-CA curve and the HV-for-BA curve were generated referenced to the 50th percentile for height, fitted using the LMS method.
## Description of the study group
The flow chart for identifying girls with EP is shown in Figure 1. From the Shenzhen Children's Hospital database, 498 eligible participants and 49 treated girls with EP were identified (Table 1). Of the 498 eligible girls, 436 participants ($88\%$) had a normal BMI and 62 ($12\%$) were overweight. A total of 1,307 BA assessments and height measurements were available for analysis. The average onset age of breast development was 8.1 ± 0.5 (7.1, 9.2) years of age. The average recorded birth weight was 3.2 (3.0, 3.4) kg. The average paternal height was 170.0 (168.0, 174.0) cm and the average maternal height was 159.0 (155.0, 162.0) cm. Of the 498 participants, 71 had reached their FAH at the time of enrollment: median 162.0 (158.0, 165.1) cm, with a median difference to their MPH of 3.0 (1.5, 6.0) cm. The total follow-up time was 1.3 (0.8, 1.9) years, with a median follow-up interval of 0.6 (0.5, 0.8) years. Of the 498 participants, $41\%$ had follow-up data at ≥3 time points.
**Figure 1:** *Flow chart for the selection of participants. EP, early-onset puberty; BA, bone age; SGA, small for gestational age; LGA, large for gestational age; PM, premature infants.* TABLE_PLACEHOLDER:Table 1
## Growth curves
The height-for-CA curves from 7 to 12 years of CA and height-for-BA curves from 7 to 14 years of BA are shown in Figure 2. There was no difference between the fitted and measured values for each CA group and BA group ($P \leq 0.05$). The fit of the curves is presented in Table 2. The fitted height-for-CA curves were compared, graphically, to standardized growth for Chinese adolescents (Figure 2A). From CA 7 to 10 years, the average height was significantly greater for the EP than the reference group (Table 3), with the growth progression for the EP group slowing after a CA of 10 years, with values similar to the standardized reference curves.
**Figure 2:** *Height-for-CA/BA percentile curves and ΔHtSDS-for-CA/BA charts for girls with EP. CA, chronological age; BA, bone age; ΔHtSDS, changes in the height standard deviation score; EP, early-onset puberty.* TABLE_PLACEHOLDER:Table 2 TABLE_PLACEHOLDER:Table 3 For the height-for-BA curves, linear growth slowed down from BA 7–11 years for the EP group compared to the standardized reference (Figure 2B). As BA progressed, the height growth curves for the EP group deviated from the standardized growth curves, shifting downward, with the height gap increasing gradually. A turning point, indicative of catch-up growth, did occur at a BA of 11.2 years, with the height-for-BA curves for the EP group approaching the standardized reference growth curves.
## ΔHtSDS-for-CA and ΔHtSDS-for-BA
The ΔHtSDS-for-CA and ΔHtSDS-for-BA were grouped according to the mean CA and BA over two adjacent follow-up time points (Table 4), with 885 measurements available for each outcome. The ΔHtSDS-for-CA from CA 7–12 years and ΔHtSDS-for-BA from BA 7–14 years are plotted in Figure 2. When grouped by CA, no change in the trend of ΔHtSDS-for-CA values was noted across the CA groups (Figure 2A). However, when grouped by BA, the median ΔHtSDS-for-BA value was <0 (−0.75∼−0.15) from BA 7–11 years, indicative of an attenuation in growth of height-for-BA. As BA increased, an upward trend in the slow progression of ΔHtSDS-for-BA was observed, with the mean ΔHtSDS-for-BA value reaching a value of “0” at a BA of approximately 11.2 years, with values >0 thereafter, indicative of a catch-up growth of height (Figure 2B).
**Table 4**
| Years | ΔHtSDS-for-CA | ΔHtSDS-for-CA.1 | ΔHtSDS-for-BA | ΔHtSDS-for-BA.1 |
| --- | --- | --- | --- | --- |
| Years | N Measures | ΔHtSDS | N Measures | ΔHtSDS |
| 7 (6.50∼) | 20 | 0.10 (−0.04,0.32) | 20 | −0.75 ± 0.79 |
| 8 (7.50∼) | 215 | 0.10 (0.01,0.22) | 86 | −0.71 ± 0.76 |
| 9 (8.50∼) | 380 | 0.17 (0.03,0.30) | 170 | −0.35 ± 0.68ab |
| 10 (9.50∼) | 205 | 0.17 (0.05,0.34)b | 216 | −0.35 ± 0.53ab |
| 11 (10.50∼) | 50 | 0.21 (0.04,0.28) | 237 | −0.15 ± 0.47abcd |
| 12 (11.50∼) | 15 | 0.32 (0.06,0.48) | 143 | 0.39 ± 0.41abcde |
| 13 (12.50∼) | — | — | 14 | 0.28 ± 0.30bcd |
| P-value | — | <0.05 | — | <0.05 |
## Bone maturation curves
The ΔBA/ΔCA value indicates the rate of increment of BA per unit of CA over a 6 month to 1 year interval. Values were grouped by CA and BA. The following percentile values were calculated from the smoothed curves: P3rd, P10th, P25th, P50th, P75th, P90th, and P97th. Percentile ΔBA/ΔCA-for-CA curves for the 7–11 years CA interval and ΔBA/ΔCA-for-BA curves for the 7–12 years BA interval, constructed using the LMS method, are shown in Figure 3, with no significant difference between the fitted and measured values ($P \leq 0.05$; Table 5). The values of ΔBA/ΔCA-for-CA were scattered among all CA groups, with no observable trend compared to BA groups (Figure 3). As for ΔBA/ΔCA-for-BA, values of ΔBA/ΔCA decreased gradually while BA increased over the 7–12 year interval. Nearly half of the participants had significantly progressive BA advancement (median ΔBA/ΔCA, 1.96 in the 8-year-old group) between the BA interval of 7–8 years. As the rate of bone maturation slowed down, the progression in BA also decreased, compared to CA, after a BA of 11.2 years (median ΔBA/ΔCA, 0 at a BA of 11.2 years; Figure 3).
**Figure 3:** *Δba/ΔCA-for-CA/BA percentile curves for girls with EP. CA, chronological age; BA, bone age; EP, early-onset puberty.* TABLE_PLACEHOLDER:Table 5
## Discussion
To our knowledge, this is the first study to describe the pattern of linear growth (height) and progression of bone maturation for girls with EP in China. Our data, thus, can provide a reference to inform clinical assessment and individualized management of this clinical population, preventing over-treatment. The growth curves presented for height and bone maturation are based on a retrospective longitudinal clinical study at single center in China, including the data for 498 participants and 1,307 left hand and wrist radiographs to determine BA. Objectivity and accuracy of BA assessment was ensured by using an AI system and the LMS method, as previously described (25–28).
Our findings underline the importance of BA in identifying a rapid progression in linear growth and puberty development. For EP, BA, therefore, provides a better predictor than CA to evaluate growth and bone maturity. In our study sample, in the early stage of puberty, BA progressed faster than linear growth, with an attenuation in linear growth identified when referenced to BA. Therefore, the predicted FAH might be underestimated, which could lead to over-treatment. Generally, the identification of EFP is based on clinical manifestation, including a shorten interval between pubertal stages and progressive bone maturity. Noteworthy, the accelerated progression in bone maturation, one of the most significant traits of EFP, measured as a ΔBA/ΔCA >1 [18], might not be generalizable to the entire EFP population across different BA ranges. Based on our results, the threshold of BA progression for EFP diagnosis should be set according to the different BA groups.
Multiple studies (29–32) have shown that most girls with EP present growth patterns comparable to those reported in our study, including a younger age for onset of puberty and linear growth, reaching the MPH as their FAH. The height-for-CA curves in our study indicated that girls with EP had a higher average height than the norm reference between the ages of 7–10 years, with a subsequent slowing in linear growth to reach the same FAH as the standardized reference group. A retrospective observational study on the growth of 170 Israeli children and 335 Polish children [33] indicated that, generally, the growth of children and adolescents is a process of reaching the genetic target height (MPH). The greater the difference between the percentile childhood height and the MPH, the earlier the initiation of puberty will be. Comparable to our height-for-CA curves, the FAH in that study approached the normal range of MPH over time.
The earlier age of onset of puberty was not associated with a reduced FAH in our study group. In recent decades, with a global secular trend to earlier onset of puberty [4, 34, 35], there has also been a chronological trend of accelerated and advanced bone maturation, worldwide (16, 17, 36–37). However, the overall FAH has been increasing year-by-year. In China, the mean FAH has increased by 6.1 cm from 1985 to 2019 [15]. An American longitudinal study of 380 girls, followed between birth and a mean age of 15.5 years, indicated a comparable FAH between girls with EP and those with late onset puberty [29]. A meta-analysis conducted by Cheng et al. [ 38] also demonstrated that girls with EP have a greater potential for growth than girls with late-onset puberty (39–41). As shown in our study, girls with EP who had reached their FAH were, on average, 3.0 cm taller than their predicted MPH. Our growth curves indicate that girls with EP are likely to reach their target FAH. This information might help pediatric endocrinologists in more precisely evaluating the growth of girls with EP and prevent over-treatment of this population.
For diagnosis of EFP, clear consensus or standards regarding evaluation of bone maturation have yet to be clearly defined based on sufficient evidence. BA gives a more accurate assessment of growth potential and pace of puberty than CA. In our study, no trend was identified for the ΔBA/ΔCA-for-CA, with no association to the height-for-CA curves. Therefore, this variable does not provide useful information about bone maturation or growth potential. By comparison, the ΔBA/ΔCA-for-BA variable did provide a description of the pattern of linear growth and progression of bone maturation for girls with EP. In our study, a significantly earlier progression in bone maturation (median ΔBA/ΔCA >1.9) was identified in almost half of the participants at a BA of 7–8 years. Thereafter, the progression in BA slowed gradually, with the BA increment lagging CA. At a BA of approximately 11.2 years, the ΔBA/ΔCA leveled off (value of 1) as the ΔHtSDS for BA continues to improve. Therefore, the fact that BA progression leads CA in the early stage of puberty, especially at a BA of 7–8 years, does not always imply an impairment in linear height progression. This is why the predicted FAH is commonly underestimated in the early stage of puberty in girls with EP which, combined with a rather rapid BA progression, can lead to excessive testing and over-treatment. Based on our findings, dynamic observations of BA may be warranted in girls with EP and, thus, identification of the progression in bone maturation should be incorporated in the overall follow-up assessment of this clinical population. With increasing evidence, cut-off values for significant progressing in BA should be set for the different BA intervals.
According to existing guidelines [18], progressive EFP should be suspected when BA progresses at a rate >1 year per year (ΔBA/ΔCA >1). The majority of participants in our study had a ΔBA/ΔCA >1 before a BA of 11 years, with no indication for drug therapy. Of note, more than half of the participants in our study group had a ΔBA/ΔCA >2 at a BA of 7–8 years. This significant progression in bone maturation during this period may be considered as a normal variation and, therefore, it might be more reasonable to judge the progression of bone maturation as a function of BA rather than CA. For example, a ΔBA/ΔCA >1.9 could be considered as normal at a BA of 7 years, while a ΔBA/ΔCA >1 might be of clinical concern after a BA of 11 years. A prospective, multi-center, case-control study in China [42], including 260 girls with EFP, between 2012 and 2014, demonstrated that BA progression in girls with EFP girls could accelerate to a mean ΔBA/ΔCA of 1.9 at a mean BA of 9.2 years, which was between the 50th and 75th percentile for our results. Similarly, a retrospective study [42] of 135 girls in Turkey reported a mean ΔBA/ΔCA of up to 2.0 at a mean BA of 11.1 years for girls with EFP (>75th percentile of our result), while a mean ΔBA/ΔCA of 1.1 at a mean BA of 10.3 years for girls with EP (<50th percentile for our result). Consistent with our findings, girls with EP reached their target MPH. A ΔBA/ΔCA >50th percentile in our results may be indicative of possible EFP, while a ΔBA/ΔCA >75th percentile would be highly suggestive of a fast progression in bone maturation. Further studies with a larger number of participants will be needed to define specific ΔBA/ΔCA cutoff values.
The limitations of our study need to be acknowledged. Foremost is the retrospective design of the study, using cross-sectional data collected over a 5-year period. Therefore, causation between EP and linear growth and progression of BA cannot be defined. Moreover, the data were obtained from a population at a single center. Lastly, the sample size is relatively small and, combined with the single site, may not be representative of girls with EP in the entire Chinese population.
In summary, we provide growth curves for girls with EP in China, showing an initial attenuation in linear growth (height) referenced to BA, with a catch-up period after a BA of 11.2 years. A slowing in the progression of bone maturation was identified at a BA of 7–12 years. Based on our findings, we propose that the progression of bone maturation to predict growth in girls with EP should be referenced to BA and not CA. This might inform an individualized management of EP and prevent over-treatment. Further research, with a larger and more diverse study sample, is required to defined valid cut-off values to assess EP and monitor follow-up.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Research ethics committee of Shenzhen Children's Hospital, China [20211036]. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.
## Author contributions
ZS and SL contributed to conception and design of the study. SL organized the database and performed the statistical analysis. HS contributed to the management of the database. QS helped with statistical analyses. ZS and SL were major contributors in writing the manuscript. ZS substantively revised the manuscript. LP, JC, XZ, LW, and LZ were contributors in acquisition data of girls with EP. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Association between rest-activity rhythm and cognitive function in the elderly:
The U.S. National Health and Nutrition Examination Survey, 2011-2014'
authors:
- Xinyi Sun
- Weiwei Yu
- Mingsi Wang
- Jun Hu
- Yunong Li
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10034093
doi: 10.3389/fendo.2023.1135085
license: CC BY 4.0
---
# Association between rest-activity rhythm and cognitive function in the elderly: The U.S. National Health and Nutrition Examination Survey, 2011-2014
## Abstract
### Background
Circadian rhythm plays an essential role in various physiological and pathological processes related to cognitive function. The rest-activity rhythm (RAR) is one of the most prominent outputs of the circadian system. However, little is known about the relationships between RAR and different domains of cognitive function in older adults. The purpose of this study was to examine the relationships between RAR and various fields of cognitive function in older Americans.
### Methods
This study included a total of 2090 older adults ≥ 60 years old from the National Health and Nutrition Examination Survey (NHANES) in 2011-2014. RAR parameters were derived from accelerometer recordings. Cognitive function was assessed using the word learning subtest developed by the Consortium to *Establish a* Registry for Alzheimer’s disease (CERAD W-L), the Animal Fluency Test (AFT) and the Digital Symbol Substitution Test (DSST). Linear regression was used to determine the relationships between RAR parameters (IS, IV, RA, L5, M10) and cognitive function scores (CERAD W-L, AFT, DSST).
### Results
After adjusting for potential confounders, lower IS and M10 were associated with lower CERAD W-L scores ($$P \leq 0.033$$ and $$P \leq 0.002$$, respectively). Weaker RA and higher L5 were associated with lower AFT scores ($P \leq 0.001$ and $$P \leq 0.001$$, respectively). And lower IS, RA, and higher L5 were associated with lower DSST scores ($$P \leq 0.019$$, $P \leq 0.001$ and $P \leq 0.001$, respectively). In addition, the results of sensitivity analysis were similar to those of our main analyses. The main correlation results between the RAR indicators and cognitive function were robust.
### Conclusions
This study suggested that the weakened and/or disrupted RAR was associated with cognitive decline in different domains in Americans over the age of 60.
## Introduction
With the aging of the world population, the prevalence of cognitive impairment continues to rise. Various forms of dementia, from mild cognitive impairment (MCI) to Alzheimer’s disease (AD), are characterized by cognitive impairment and are rapidly becoming a serious global public health problem [1]. About 40 million people worldwide are suffering from AD, and the number is expected to increase to 50 million by 2030 [2]. To date, there is still no effective treatment to reverse or slow down the progress of cognitive impairment [3].
A large number of studies have shown that different lifestyles may have an impact on the cognitive function of the elderly. The Rancho Bernardo study shows that older adults with regular physical activity have better cognitive function. Physical activity in adolescence may enhance cognitive reserve to prevent age-related decline in executive function [4]. The European Prospective Investigation into Cancer and Nutrition Norfolk (EPIC Norfolk) study shows that higher MedDiet adherence is related to better cognitive function and lower risk of cognitive impairment, especially among the elderly with higher CVD risk [5]. Unhealthy lifestyles like smoking and drinking can also lead to cognitive impairment. Both secondhand and active smokers seemed to perform worse on cognitive tests, work and memory than nonsmoking peers [6, 7]. A cohort study in China shows that poor lifestyle choices, such as drinking, unbalanced diet, low activity participation and air pollution, impair cognitive function in older adults. It is recommended that the elderly should avoid drinking, maintain a balanced diet, exercise more and pay attention to the impact of air quality [8].
In addition, circadian rhythm is an essentially biological process regulated by circadian clock genes and plays an important role in various physiological and pathological processes related to cognitive function [9]. A blunted circadian rhythm increases the speed of biological aging. Dysregulation or disruption of circadian rhythms can also lead to health problems, including hypertension, sleep disorders, diabetes and cardiovascular disease [10]. In the laboratory environment, severe circadian dysregulation will interfere with immune homeostasis, which plays a crucial role in the inflammatory process [11]. RAR is one of the most prominent outputs of the circadian rhythm system [12]. Several measurements calculated from rest-activity cycles are considered to reflect mild long-term disruption of circadian rhythms in real-world settings and can be objectively evaluated by accelerometers. In a recent prospective study, the accelerometer derived RAR measures, including amplitude, acrophase and pseudo-F statistic independently predicted the increased risk of diabetes among older adults [13]. Diabetes is associated with poor cognitive function (executive function and processing speed) [14]. Relevant studies have shown that indicators of RAR irregularity, such as reduced interdaily stability (IS) and increased intradaily variability (IV), are linked with an increased risk of neurodegenerative diseases, emphasizing the critical role of RAR in human health [15].
However, little is known about the associations of RAR with different domains of cognitive function in the U.S. elderly population. Therefore, we aimed to investigate the relationships between the RARs generated by accelerometric measurements and different fields of cognitive function in a large sample of older Americans using the NHANES in 2011-2014.
## Study population
Data were obtained from NHANES, a major program conducted by the National Center for Health Statistics (NCHS) to collect information on U.S. household population health and nutrition. The project includes two parts: family interview and physical examination. Details on sampling methods, survey tools and data collection are described elsewhere. All participants provided informed consent prior to participation [16]. Our sample includes the elderly ≥ 60 years old, who have verified accelerometer records for at least 4 days and cognitive function data in the NHANES 2011-2014 cycle datasets.
## Baseline data collection
During each NHANES cycle, data were collected through physical examinations and participant interviews. Participant interviews collected self-reported data on age (continuous), race (Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black and others), sex (male/female), current smoker (yes/no), current drinker (yes/no), education levels (less than 9th grade/9-11th grade/high school graduate or equivalent/some college or AA degree/college graduate or above), sleep duration (hours), regular exercises habitus (yes/no), daily energy intake (kcal/d), annual household income (<$100,000 or > $100,000), whether to take medication for hypertension (yes/no), whether to take medication for diabetes (yes/no) and whether to take medication for cholesterol (yes/no). Hypertension, diabetes and hyperlipidemia were defined as self-reported hypertension, diabetes and hyperlipidemia, respectively. The NHANES examination data contain height and weight measurements used to calculate body mass index (BMI). All information regarding these methods is publicly available on the NHANES website.
## Measurement of RAR
The R package “nparACT” is used to calculate the following non-parametric variables of RAR, which have been widely described before [17, 18]. Interdaily stability (IS; value range 0 to 1), the stability index of day-to-day rest-activity mode; the larger the value, the more stable and consistent RAR across days (IS ≃ 0 represents Gaussian noise, and IS ≃ 1 represents perfect stability). Intradaily variability (IV; value range: 0 to 2), which reflects the index of 24-hour RAR fragmentation. The higher the value, the more significant the RAR disruption (IV ≃ 0 represents perfect sine wave, and IV ≃ 2 represents Gaussian noise). The relative amplitude (RA) is the relative difference between the most active continuous 10-hour period (M10) and the least active continuous 5-hour period (L5) in the average 24 hours (midnight to midnight). The higher the RA is, the stronger the 24-hour rest-activity oscillation is, reflecting that the activity is higher when awake, and the activity is relatively lower at night. L5 was defined as the average number of minutes of activity during the least active 5 hours of sleep over 24 hours, and M10 was defined as the average number of minutes of activity during the most active 10 hours per 24 hours.
## The assessment of cognitive function
In the NHANES study, cognitive function was assessed using the word learning and recall modules developed by the Consortium to *Establish a* Registry for Alzheimer’s disease (CERAD W-L), the Animal Fluency Test (AFT) and the Digital Symbol Substitution Test (DSST). Although the results of these tests cannot replace the diagnosis based on clinical examination, they have been used to study the relationships between cognitive function and various risk factors [19].
CERAD W-L consists of three consecutive learning experiments and one delayed recall [20]. Participants were asked to read 10 unrelated words aloud in three learning experiments. After the words were presented, participants immediately recalled as many words as possible. The delayed recall occurred about 10 minutes after the beginning of the word-learning experiment. The maximum score of each test is 10, and the highest score for the total word list was 40 (the sum of the three trials plus the delayed recall).
AFT is a language fluency task that checks execution functions [21]. Participants were asked to name as many animals as possible within one minute. Each correctly named animal gets the point. Before the completion of the primary test, an exercise test was conducted in NHANES, asking participants to name three clothes, and then the main test was completed.
DSST is a performance module of the Wechsler Adult Intelligence Scale III (WAIS-III), which requires the integrity of executive function, processing speed, attention, spatial perception and visual scanning [22]. The test was performed using a paper form with a key at the top containing 9 numbers and different symbols. Participants had two minutes to copy the corresponding symbols in 133 boxes adjacent to the number. The total number of correct matches determines the score, ranging from 0 to 133. Higher scores indicate a better cognitive function in each scoring dimension of cognitive function.
## Stratified analysis
Stratified analysis is a subgroup analysis that divides participants in a study into groups based on different levels of relevant covariates and analyzes the strength of the associations between exposure factors and outcomes within each stratum. The study conducted the stratified analysis by age (60-69, ≥70), sex (male, female), BMI (<25.0, 25.0-29.9, ≥30.0) and race (Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black and others).
We performed a stratified subgroup analysis of the relationships between RARs and cognitive function scores by age, sex, BMI and race. The results of stratified analysis were shown in Figure 1 and Supplementary Tables 1 - 3.
**Figure 1:** *Relationships between RAR and cognitive function stratified by risk factors. (A) Relationships between RAR and CERAD W-L scores in stratified analysis. (B) Relationships between RAR and AFT scores in stratified analysis. (C) Relationships between RAR and DSST scores in stratified analysis. Results were adjusted for age (60-69, ≥70), sex (male, female), BMI (<25, 25-29.9, ≥30 kg/m2), race (Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black, others). Dots indicate beta estimates, and black horizontal lines indicate 95% CIs.*
## Sensitivity analysis
We also performed several sensitivity analyses by excluding the elderly who had less than a 9th-grade education level; excluding those who reached peak activity levels between 23:00 and 04:00; and excluding those who had 6 hours or less sleep duration.
In the first two sensitivity analyses, after excluding participants who had less than a 9th-grade education level or those who reached peak activity levels between 23:00 and 04:00, the results of the relationships between RAR and cognitive function were basically the same as those of the main analysis, except that the significant correlations between IS and CERAD W-L scores, and the significant correlations between IS and DSST scores disappeared (Supplementary Tables 4, 5). In the third sensitivity analysis, after excluding participants with 6 hours or less sleep duration, the correlations between RAR and cognitive function remained similar to the main results, except that the correlations between IS and CERAD W-L scores disappeared (Supplementary Table 6). We observed that, except for the unstable correlations of IS on cognitive function, the main correlation results of other RAR indicators on cognitive function were robust. Although the results of sensitivity analyses show that the exclusion of some factor variables has no significant impact on our results, it may reduce the universal representativeness of our research samples.
## Statistical analysis
Continuous variables are expressed as mean ± standard deviation, and categorical variables are expressed as numbers with percentages. Associations of RAR with cognitive function were assessed by general linear regression analysis. We built three models to provide statistical inference. Model 1 was only adjusted for sex (male or female), age (continuous) and race (Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black and others). Model 2 was adjusted for variables in model 1 plus BMI (kg/m2), income, education levels, sleep duration, daily energy intake, regular exercises, current smoker and current drinker. Model 3 was adjusted for variables in model 2 plus self-reported diabetes, self-reported hypertension, self-reported hyperlipidemia, take medication for diabetes, take medication for hypertension and take medication for cholesterol.
For all analyses, $P \leq 0.05$ was considered statistically significant. All data analyses were performed by R Language (version 4.2.1).
## Characteristics of the study population
*The* general characteristic information of participants and RAR indicators are presented in Table 1. Our analysis included a total of 2090 older participants ≥ 60 years old (mean ± SD: 69.0 ± 6.7). Of the total participants, $47.4\%$ were male, and $47.1\%$ were non-Hispanic white, with a mean BMI of 29.11 ± 6.24 kg/m2; $14.2\%$ of participants had an annual household income more than $100,000, and $23.5\%$ had a college degree or above education. Other information, such as lifestyle, disease status, drug intervention information, RAR parameters and cognitive scores, are also summarized in Table 1.
**Table 1**
| Characteristics | All participants(N=2090) |
| --- | --- |
| Age, years, mean (SD) | 69.0(6.7) |
| Male, n (%) | 990(47.4%) |
| Non-Hispanic White, n (%) | 984(47.1%) |
| BMI, kg/m2, mean (SD) | 29.11(6.24) |
| >$100,000 annual household income, n (%) | 297(14.2%) |
| College graduate or above, n (%) | 492(23.5%) |
| Sleep duration, hours, mean (SD) | 7.01(1.39) |
| Daily energy intake, kcal/day, mean (SD) | 1822.44(670.24) |
| Exercise regularly, n (%) | 1168(55.9%) |
| Current smoker, n (%) | 272(13.0%) |
| Current drinker, n (%) | 1431(68.5%) |
| Self-reported diabetes, n (%) | 441(21.1%) |
| Self-reported hypertension, n (%) | 1249(59.8%) |
| Self-reported hyperlipidemia, n (%) | 1134(54.3%) |
| Take medication for diabetes, n (%) | 108(5.2%) |
| Take medication for hypertension, n (%) | 1094(52.3%) |
| Take medication for cholesterol, n (%) | 812(38.9%) |
| Rest-activity parameters, mean (SD) | Rest-activity parameters, mean (SD) |
| IS | 0.52(0.13) |
| IV | 0.68(0.22) |
| RA | 0.83(0.12) |
| L5 | 1.09(0.84) |
| M10 | 11.80(3.70) |
| CERAD W-L, mean (SD) | 25.36(6.48) |
| AFT, mean (SD) | 16.99(5.41) |
| DSST, mean (SD) | 47.45(17.25) |
## Association of RAR with cognitive function
The associations of RAR parameters with cognitive assessment scores are presented in Table 2. In regression models adjusted for covariates, the more stable RAR was significantly associated with better cognitive function. For the CERAD W-L scores, lower IS and M10 are significantly associated with lower CERAD W-L scores (IS: β=2.221, $95\%$CI: 0.175 to 4.246, $$P \leq 0.033$$; M10: β=0.121, $95\%$CI: 0.044 to 0.199, $$P \leq 0.002$$). For the AFT scores, weaker RA level is significantly correlated with lower AFT scores (β=3.406, $95\%$CI: 1.434 to 5.378, $P \leq 0.001$), while higher L5 level is significantly associated with the decrease of the score (β=-0.448, $95\%$CI: -0.723 to -0.174, $$P \leq 0.001$$). For the DSST scores, lower IS and RA levels are significantly correlated with lower DSST scores (IS: β=6.154, $95\%$CI: 0.999 to 11.308, $$P \leq 0.019$$; RA: β= 20.449, $95\%$CI: 14.676 to 26.222, $P \leq 0.001$), while higher L5 level is also significantly correlated with the decrease of the score (β=-2.361, $95\%$CI: -3.167 to -1.554, $P \leq 0.001$). The analysis results showed no significant correlation between IV level and cognitive function scores (CERAD W-L, AFT and DSST). A stable RAR is associated with better cognitive performance, whereas disordered RAR may be associated with cognitive impairment.
**Table 2**
| Unnamed: 0 | Unnamed: 1 | Unadjusted | Unadjusted.1 | Unadjusted.2 | Model 1 | Model 1.1 | Model 1.2 | Model 2 | Model 2.1 | Model 2.2 | Model 3 | Model 3.1 | Model 3.2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | β | 95%CI | P-value | β | 95%CI | P-value | β | 95%CI | P-value | β | 95%CI | P-value |
| CERAD W-L | IS | 1.591 | (-0.537,3.719) | 0.143 | 2.717 | (0.688,4.746) | 0.009 | 2.136 | (0.100,4.172) | 0.040 | 2.211 | (0.175,4.246) | 0.033 |
| CERAD W-L | IV | -2.495 | (-3.783,-1.207) | <0.001 | -0.626 | (-1.884,0.631) | 0.329 | -0.458 | (-1.719,0.804) | 0.477 | -0.407 | (-1.668,0.855) | 0.527 |
| CERAD W-L | RA | 3.939 | (1.568,6.309) | 0.001 | 2.916 | (0.646,5.186) | 0.012 | 2.172 | (-0.141,4.485) | 0.066 | 2.195 | (-0.121,4.512) | 0.063 |
| CERAD W-L | L5 | 0.039 | (-0.299,0.377) | 0.821 | -0.156 | (-0.477,0.166) | 0.343 | -0.090 | (-0.414,0.233) | 0.584 | -0.108 | (-0.432,0.215) | 0.512 |
| CERAD W-L | M10 | 0.311 | (0.237,0.385) | <0.001 | 0.148 | (0.073,0.224) | <0.001 | 0.127 | (0.050,0.205) | 0.001 | 0.121 | (0.044,0.199) | 0.002 |
| AFT | IS | 1.160 | (-0.607,2.926) | 0.198 | 1.314 | (-0.440,3.069) | 0.142 | 0.252 | (-1.498,2.001) | 0.778 | 0.291 | (-1.457,2.038) | 0.744 |
| AFT | IV | -1.705 | (-2.775,-0.635) | 0.002 | -0.418 | (-1.503,0.668) | 0.450 | -0.061 | (-1.144,1.021) | 0.911 | -0.038 | (-1.120,1.043) | 0.944 |
| AFT | RA | 5.544 | (3.595,7.492) | <0.001 | 4.644 | (2.700,6.588) | <0.001 | 3.625 | (1.655,5.595) | <0.001 | 3.406 | (1.434,5.378) | <0.001 |
| AFT | L5 | -0.510 | (-0.787,-0.233) | <0.001 | -0.552 | (-0.827,-0.277) | <0.001 | -0.469 | (-0.743,-0.195) | <0.001 | -0.448 | (-0.723,-0.174) | 0.001 |
| AFT | M10 | 0.142 | (0.079,0.204) | <0.001 | 0.056 | (-0.009,0.121) | 0.092 | 0.010 | (-0.057,0.077) | 0.767 | 0.006 | (-0.060,0.073) | 0.855 |
| DSST | IS | 7.304 | (1.682,12.926) | 0.011 | 11.187 | (5.819,16.555) | <0.001 | 6.227 | (1.049,11.405) | 0.018 | 6.154 | (0.999,11.308) | 0.019 |
| DSST | IV | -5.985 | (-9.392,-2.578) | <0.001 | -1.193 | (-4.525,2.140) | 0.483 | 0.717 | (-2.490,3.925) | 0.661 | 1.060 | (-2.134,4.254) | 0.515 |
| DSST | RA | 27.459 | (21.315,33.602) | <0.001 | 26.489 | (20.598,32.381) | <0.001 | 21.279 | (15.494,27.063) | <0.001 | 20.449 | (14.676,26.222) | <0.001 |
| DSST | L5 | -2.208 | (-3.089,-1.327) | <0.001 | -2.899 | (-3.737,-2.061) | <0.001 | -2.438 | (-3.247,-1.630) | <0.001 | -2.361 | (-3.167,-1.554) | <0.001 |
| DSST | M10 | 0.660 | (0.462,0.858) | <0.001 | 0.234 | (0.034,0.435) | 0.022 | 0.003 | (-0.195,0.201) | 0.974 | -0.014 | (-0.211,0.183) | 0.892 |
## Discussion
For the first time, we used the sample of older American adults to test whether RAR disturbance measured from accelerometer data is associated with cognitive function. We found that impaired circadian rhythm (IS, RA), lower M10 values, and higher L5 values were associated with cognitive decline in different domains. Irregular rest-activity patterns were associated with poor memory (CERAD W-L), executive function (AFT), processing speed and global cognitive function (DSST). In order to avoid the false correlation caused by confounding factors, we further controlled the confounding factors, and the research results remained unchanged; that is, impaired robustness of RAR was related to the decline of cognitive function. In different models, we did not observe significant results in the association of fragmented RAR (estimated by IV) with cognitive decline, suggesting that our bodies may have higher levels of adaptive ability in the rhythm phase shift on daily schedules. These findings suggest that RAR disturbance may be a sign of impaired memory formation and cognitive slowing, which can predict the cognitive function of the elderly, and is also a potential therapeutic target.
In this study, we examined the relationships between different parameters of RAR and cognitive function. RA assesses the overall robustness of the RAR, with larger RA indicating that individuals are more active during the day and less active at night. It has been found to be a sensitive indicator of RAR dysregulation. This parameter reflects the long-term comprehensive influence of environmental and behavioral factors on the circadian rhythm system. Several studies have shown that RA is associated with dementia-related biomarkers such as T-tau and NF-L (23–25). The levels of these biomarkers were reduced in patients with higher amplitudes, implying that higher RA was negatively correlated with cognitive decline, which is consistent with the findings of this study. In addition, previous studies have observed that lower RA is closely related to diseases such as abnormal glucose tolerance and diabetes [13, 26, 27]. Compared with normoglycemic older adults, the elderly with diabetes were more likely to have MCI. This also confirmed our results.
Our study found that a weaker daytime stability (lower IS) was associated with decreased cognitive function. Van Someren et al. suggested maintaining a regular rhythm, adhering to a stricter 24-hour clock and enhancing daytime stability could protect the fragile circadian system in the elderly population and reduce the risk of cognitive impairment [28]. We also observed that lower M10 values were associated with poor cognitive function (CERAD W-L), and higher L5 was associated with more inferior cognitive function (AFT, DSST). Several studies have shown that lower M10 values representing lower daily activity indices and higher L5 values representing higher nocturnal activity indices are associated with increased inflammatory markers (29–31). A recent pre-clinical study reported that neuroinflammation exacerbates cognitive impairment in a rat model of vascular dementia [32].
In this study, we also examined the relationships between RAR and different cognitive domains. DSST is considered as a sensitive indicator of global cognitive function, which depends on processing speed, visual scanning, sustained attention and short-term memory. Compared with older adults with normal RAR, older adults with RAR disorder had lower DSST scores. Previous studies have shown that the negative impact of sleep or circadian rhythm disruption on processing speed has been shown in shift workers and older adults in the general population [33]. The results from the CERAD W-L measurement (word learning and short-term memory test) show that RAR disturbance may affect memory function, which means that memory structures in the brain, including hippocampus, may be damaged [34]. Smagula et al. showed that RAR disruption is associated with hippocampal hyperactivation, which was considered to be one of the pathophysiological changes of early memory decline related to pre-clinical dementia [35]. In animal experiments and epidemiological studies, it has been reported that work and memory function decreased due to insufficient sleep or circadian rhythm disorder [36, 37]. Furthermore, in addition to CERAD and DSST, we included the AFT score representing executive function as an outcome variable in the statistical model, and the results showed that impaired RAR was associated with decreased executive function. RAR disorders may affect different areas of cognitive function by affecting multiple brain functional areas.
Several studies have investigated whether mild intervention with RAR could change the process of cognitive decline and protect brain health. Interventions included timed BLT, consistent sleep-wake times (avoiding shift work, jet lag and naps), behavioral rhythm interventions at mealtime and other activity schedules, increasing or maintaining activity participation, avoiding low daytime light and night light and appropriately timed bright daytime light. These behavioral interventions are low risk, easy to spread, and have no drug interactions or side effects. These interventions may help to strengthen the circadian rhythm to improve rhythm stability and amplitude as well as cognitive function. For example, bright light therapy can reduce the cognitive decline in elderly residents over the course of one year [38], and 4-week doses of bright light therapy could improve MMSE scores in AD dementia patients [39]. However, some studies also showed that bright light changed the RAR in dementia patients, but not the course of dementia. Interventions targeting RAR may need to target specific subgroups or use multiple approaches [40, 41].
This study shows several strengths. First, this study used the sample of older adults from NHANES with high-quality survey methods and quality control. Most of the participants had high compliance with equipment wearing and long equipment wearing time. Second, the cognitive data includes three different cognitive tests, providing more and deeper information than the previous NHANES surveys. In addition, we controlled for multiple important confounders to estimate the correlations between RAR and cognitive function.
Nonetheless, there are some limitations to our study. First, cross-sectional design may lead to a lack of causal relationships between RAR and cognitive function. Second, although we adjusted for a wide range of confounders, unmeasured biomarkers may lead to residual confounders. In addition, the shift work status data was not obtained from the participants in the NHANES 2011-2014, so the sensitivity test to exclude these participants could not be carried out. However, excluding those with peak activity at night (i.e., between 23:00 and 04:00) did not change our results, suggesting that the potential impact of shift work on our study would be minimal. The results still need to be confirmed in the future longitudinal study of randomized intervention.
## Conclusion
This study investigated the relationships between rest-activity rhythm and cognitive function, demonstrating the importance of circadian rhythm on cognitive function in the elderly population. After adjusting for a range of confounders, we found that lower levels of IS, RA and M10, and higher levels of L5 were significantly associated with lower cognitive scores across different domains. Our results provide a new basis for future efforts to improve cognition in older adults by changing daily personal behaviors and lifestyles to influence circadian rhythm.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding authors.
## Ethics statement
The studies involving human participants were reviewed and approved by the study protocol approved by the NCHS Research Ethics Review Board. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
YL, JH, and MW designed the analysis. WY and XS wrote the first draft of the article. XS conducted the statistical analyses. YL and MW revised the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1135085/full#supplementary-material
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|
---
title: Effects of a nurse-led structured home visiting program on quality of life
and adherence to treatment in hemodialysis patients
authors:
- Mina Pooresmaeil
- Sohrab Iranpour
- Masoumeh Aghamohammadi
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10034095
doi: 10.3389/fpubh.2023.1013019
license: CC BY 4.0
---
# Effects of a nurse-led structured home visiting program on quality of life and adherence to treatment in hemodialysis patients
## Abstract
### Purpose
This study aimed to determine the effects of a nurse-led structured home visit program on quality of life and adherence to treatment in patients undergoing hemodialysis.
### Methods
The study was quasi-experimental research in which 62 hemodialysis patients referred to Bu Ali hospital in Ardabil participated in two groups: Intervention ($$n = 31$$) and control ($$n = 31$$). The intervention included a structured and planned home visit program that was performed in five stages over 3 months. Data collection tools were a demographic information form, Kidney Disease Quality of Life Short Form (KDQOL–SF™) and End Stage Renal Disease Adherence Questionnaire (ESRD_AQ) which were completed by patients before, at the end of the first, second, and third month of intervention. SPSS v20 software and descriptive and analytical tests (Chi-square, t-test, ANOVA and repeated measure) were used for data analysis.
### Findings
Examining demographic characteristics showed that there is a negative and significant relationship between age and quality of life scores ($$P \leq 0.004$$), that is, with increasing age, the quality of life score decreases, but other demographic characteristics did not have a significant relationship with quality of life scores and adherence to treatment ($P \leq 0.05$).
Also, the results showed that in the intervention and control groups, during the study, the scores of quality of life and adherence to treatment increased significantly, and this increase was significantly higher in the intervention group than in the control group ($P \leq 0.001$).
The scores of quality of life and adherence to treatment increased significantly both during the study in each group separately and between groups during the study ($P \leq 0.001$).
### Conclusions
According to the significant improvement in quality of life and adherence to treatment in patients following a home-visiting program during 3 months, these interventions can be utilized to improve quality of life and adherence to treatment of patients undergoing hemodialysis.
### Practice implications
Home visiting programs significantly improve the level of knowledge of patients undergoing hemodialysis and their family members, through their involvement in the care process. Having said that, it seems plausible to implement home visits in the standard care plans of hemodialysis patients.
## Introduction
End-Stage Renal Disease (ESRD) is characterized by a glomerular filtration rate of < 15 ml/min. At this stage, various clinical manifestations such as hypertension, anemia, edema, metabolic disorders, and endocrine disorders may occur that require renal replacement therapy such as hemodialysis (HD) [1].
The life of these patients changes due to changes in diet, frequent use of nutritional supplements, fluid restriction and multiple dialysis sessions. Due to the lifestyle changes and treatment, these patients often experience Physical and mental problems [2], all of which can lead to a lower quality of life (QOL) [3]. Quality of life (QOL) is considered an important issue in evaluating the outcomes of patients receiving health care. Although there is no consensus on the definition of quality of life, it has been found that in patients with kidney failure, especially in patients undergoing dialysis, QOL affects more physical aspects and less mental functioning [4]. It is important to pay attention to the quality of life of these patients because, according to some evidence, it is related to medical outcomes, including the reduction of hospitalization and mortality due to hospitalization [5, 6].
Non-adherence to treatment is also one of the main clinical concerns in patients undergoing hemodialysis [7]. Adherence to treatment which is defined as the degree to which individuals' behavior conforms to health or treatment recommendations, is a complex behavioral process and is influenced by several factors, such as patients' personal characteristics, physician-patient interactions, and the quality of the health care system [8].
Poor adherence or non-adherence of patients to treatment is one of the main reasons for failure in a treatment plan, increased complications, prolongation of treatment, and increased healthcare costs [9]. According to reports, ~25–$86\%$ of hemodialysis patients do not follow their treatment regimen [10, 11]. The study conducted by Gerogianni et al. showed that rejection of treatment and treatment limitations were among the most important problems of hemodialysis patients [12]. Furthermore, polypharmacy and the inability to purchase all the required drugs are among the notable problems [13].
Moreover, many patients report feelings of anger, guilt, and fear about their illness, and most of them have no motivation to take care of themselves and adhere strictly to treatment [14]. Therefore, due to the rising trend of chronic renal failure and the prevalence of physical and mental problems in hemodialysis patients and the resulting complications and consequences, the existence of effective interventions as a crucial element in the treatment of these patients is essential [15].
One way to provide care is home visits. During home visit, the patients and their family members are educated on the healthcare needs of their patient in the home environment in order to allow them to meet these needs independently. Home is an intimate environment for the patient and their family members to interact with the nurse, and in some cases a home visit is the only way to access information or to educate, reduce health risks, promote health, and provide services to families [16]. Home visits allow the health professionals to identify the health problems of the patients, and when necessary, set treatment plans in order to improve their quality of life [17]. In addition, the home environment allows for a more realistic assessment, an efficient identification of the risk factors and problems, and the initiation of the interventions in the early stages [18].
There only a small number of studies that have investigated the effect of home visiting programs and their effect on quality of life in particular including the study of Liimatta et al. titled “The effect of home visit on the quality of life of the elderly” [19]. Ahangarzadeh Rezaei et al. [ 20] also investigated the effect of home visiting programs on improving the physical condition of hemodialysis patients and considered it as a basic yet important method in the healthcare [20].
In addition, home visits may provide unique opportunities to identify and address issues that may exacerbate the illness. In the home visiting program, a caregiver may collect vital information about following up on patients' medical visits and how to take medication. Educating patients and their families about medical treatment events, managing acute or chronic conditions, and detecting warning signs of illness are among the other advantages of a home visit program [21].
Despite the rising number of patients requiring hemodialysis and the importance of their education by nurse practitioners, the effect of home visiting programs on quality of life and treatment adherence has not been widely studies in these patients. To that end, the present study aims to determine the effect of a nurse-led structured home visiting program on quality of life and treatment adherence in patients undergoing hemodialysis in Ardabil, Iran.
## Study design
The present study was a quasi-experimental research with a control group.
## Participants
The study population was patients undergoing hemodialysis referred to Bu Ali Hospital and the Red Crescent Center of Ardabil in 2021. Inclusion criteria included patients aged 18–65 years undergoing hemodialysis, with a history of dialysis for more than 6 months and at least twice a week, no history of known mental disorders, no hearing problems, no history of formal education in the last year, and willingness to participate in the study. Change of residence during the study, having a kidney transplant surgery, and cessation of hemodialysis were regarded as exclusion criteria. Sample size was calculated based on the statistical formula n = (z1-α+z1-β)2*(s12+s22)(X¯1-X¯2)2 - α: Probability of first type error; If α = 0.05, Z1-α/2 is equal to 1.96.- β: Probability of second type error; If β = 0.02, Z1-β is equal to 1.96.- σ1: The standard deviation of the trait in the first community- σ2: Standard deviation of the attribute in the second society- X1: Average trait in the first community and the results of [22] and considering α = 0.05 and β = 0.02, the test power of 80, and the possible fall of 72 patients (36 people in the intervention group and 36 people in the control group). The patients were initially sampled randomly (through a lottery system) and were assigned to the intervention and control groups using the permuted block randomization method (Figure 1).
**Figure 1:** *Study flowchart.*
In this study, 6 blocks of 4 were used to randomly assign patients to two intervention and control groups. The intervention group was named A and the control group was named B, and the following 4 conditions were created in each block: Next, having six hypothetical blocks, six numbers (1 to 6) were used for random selection. Seventy two patients were coded after 18 random selections of blocks of 4. Based on the initial estimate of the required number of samples ($$n = 72$$) and the two required groups, 72 codes were prepared, 36 codes of the control group and 36 codes of the intervention group were written.
After receiving the code of ethics from the Ethics Committee of Ardabil University of Medical Sciences, obtaining written consent from the patients, and assuring them that their information was not disclosed, the intervention began. The intervention was the home visit of hemodialysis patients based on home visit model [23]. This intervention consisted of several steps as follows: Initial stage: *In this* stage, after selecting the samples based on the entry criteria and randomization, informed consent was obtained from the samples and then the objectives of the research were explained.
Pre-visit stage: *In this* stage, the duration of hemodialysis, the time of hemodialysis during the day, the drugs received by the patients during dialysis and at home, the amount of ultrafiltration that is reduced on average from the patient during dialysis, the weight of the patients, the settings that are given to the dialysis machine such as sodium, temperature, etc…, the type of vascular access of the patient and how it works, the medical orders in the file, the history of the patient's previous hospitalizations in other medical centers, the problems that arise for the patient during dialysis under the dialysis machine, and finally, the patient's intolerance or non-cooperation during dialysis It was obtained from the clinical records of patients in hemodialysis centers. At this stage, an appointment was also made with the opinion of the clients.
At-home stage: First, the researcher introduced himself to the patient and family members. Again, about the home visit, the objectives of the study were discussed with the patients and their families. Then, in the first visit, all the questionnaires were filled before the start of the intervention. After filling the questionnaires, the training of the patients started. Our training included all aspects of the quality of life and adherence to the treatment Also, about the physiology of the disease, the process of hemodialysis, access to dialysis and related care, education about diet and fluid intake, important points about drugs, activity level, problems of hemodialysis patients such as itching, muscle cramps, depression, disorders Sleep and etc. The educational needs of the patients were discussed for 45–60 min and the questions of the patient and the family were answered.
Quality of life and Adherence to treatment questionnaires are always filled before communicating with patients at this stage.
Final stage: *At this* stage, the educational materials were summarized and the evaluation of the learned items was summarized. Also, an educational booklet was given to the patients. The samples were followed up by phone for 1 month.
Post-visit stage: A report of the activities performed during the visit was made and a planning was made for the next visit. Finally, the questionnaires were scored and entered into the software SPSS.
A monthly home visit was conducted for three consecutive months for the intervention group, and the necessary explanations were provided based on the educational needs of each patient. The patients could also call the researchers with their inquiries before the time of the visit.
In the control group, after obtaining written and informed consent, the purpose of the study was shared with the patient and family members. Afterward, the patients' medical records were studied, and the patient or their main caregiver was contacted every month. Additionally, based on the patients' educational needs, the required explanations were provided. In case of any inquiries, the patients could contact the researchers.
## Data collection
Evaluations were conducted for each patient in the 4 stages including before the intervention, at the end of first, second and third month based on demographic information form (age, sex, marital status, occupation, level of education, medical history, duration of dialysis, and occupational status), Kidney Disease Quality of Life- Short Form (KDQOL–SF™ 1.3), and End Stage Renal Disease- Adherence to Treatment Questionnaire (ESRD_AQ). The demographic information questionnaire was completed only once in the first session, and the next two questionnaires were completed in each of the four sessions. The KDQOL-SF instrument is a standardized self-report instrument that includes 8 health-related quality of life subscales and 11 kidney disease-specific quality of life subscales. The tool of health-related quality of life, which is the general core of KDQOL-SF, is the same 36-question questionnaire (SF-36). This tool has 8 dimensions of physical performance (10 questions), role limitation caused by physical problems (4 questions), role limitation caused by emotional problems (3 questions), social function (2 questions), emotional well-being (5 questions), examines pain (2 questions), fatigue and energy (4 questions), understanding of general health (5 questions) and a general question about personal health. The second part of the KDQOL-SF instrument focuses on health-related items of people with kidney disease and undergoing hemodialysis, which is divided under the title of Kidney Disease Component Summary (KDCS) and includes subscales: symptoms (signs/problems); 12 questions), the effect of kidney disease on life (8 questions), burden of responsibility for kidney disease (4 questions), job status (2 questions), cognitive function (3 questions), quality of social interaction (3 questions), sexual function (2 question), sleep (4 questions), social support (2 questions), encouragement by dialysis department staff (2 questions) and patient satisfaction (1 question). To answer this questionnaire, multiple-choice *Likert is* used, which is assigned a score from zero to 100 for each dimension. Higher scores indicate a better quality of life. The results of the study conducted by Yekaninejad et al. [ 24] indicated high internal consistency on all scales (range of alpha-Cronbach coefficients from 0.73 to 0.93) [24].
A self-report questionnaire of treatment adherence behaviors among patients with end-stage renal disease (ESRD-AQ) was developed by Kim [25]. This 46-item questionnaire is designed for patients needing dialysis treatment and has five sections. The first section examines general information about the patients with end-stage renal disease (5 items), and the remaining four sections, namely attendance at sessions (14 items), medication adherence (9 items), fluid restriction (10 items), and diet (eight items), evaluates treatment adherence in hemodialysis patients. The total score of treatment adherence is the sum of the scores of these five sections. The lowest score of the questionnaire is zero, and the highest score is 1,200. Khalili et al. [ 26] first psychometrically assessed this tool in Iran and the questionnaire was found to be valid. The reliability of the instrument was also confirmed by the Cronbach's alpha coefficient of 0.75 [26].
## Statistical analysis
Data analysis was conducted using SPSS version 25. Descriptive statistics were used to evaluate the samples' demographic characteristics The relationship between demographic characteristics and quality of life scores and adherence to treatment in the intervention and control groups was investigated with linear regression tests, t-test and analysis of variance. The Kolmogorov-Smirnov test was performed to evaluate data normality. To achieve the research objectives, descriptive statistics methods (mean, standard deviation, frequency, and percentage), t-test, Chi-squared test, and repeated measures analysis of variance (ANOVA) were performed. The significance level was considered <0.05.
## Participants' demographic characteristics
Owing to the exclusion of 10 participants from the study (7 patients reluctant to continue cooperation, 1 patient due to change of residence, and 2 patients due to death), this study was conducted on 62 patients undergoing hemodialysis (31 patients in the intervention group and 31 patients in the control group). Table 1 presents the demographic information of the studied patients. As the table shows, the patients' mean age and standard deviation in the intervention and control groups were 48.70 ± 13.98 and 54.38 ± 8.57 respectively. The minimum age of the participant was 22 and the maximum was 64. There was no significant difference between intervention and control group regarding the demographic characteristics ($P \leq 0.05$).
**Table 1**
| Group | Group.1 | Intervention | Intervention.1 | Control | Control.1 | Chi-square test results |
| --- | --- | --- | --- | --- | --- | --- |
| variable | variable | N | % | N | % | |
| Gender | Male | 12 | 38.7 | 17 | 54.8 | 0.15 = P |
| Gender | Female | 19 | 61.3 | 14 | 45.2 | 0.15 = P |
| Marital status | Married | 24 | 77.4 | 24 | 77.4 | 0.29 = P |
| Marital status | Single | 6 | 19.4 | 1 | 3.2 | 0.29 = P |
| Marital status | Widow | 1 | 3.2 | 5 | 16.1 | 0.29 = P |
| Marital status | Divorced | 0 | 0 | 1 | 3.2 | 0.29 = P |
| Job | Unemployed | 10 | 32.3 | 7 | 22.6 | 0.32 = P |
| Job | Worker | 0 | 0 | 1 | 3.2 | 0.32 = P |
| Job | Employee | 1 | 3.2 | 1 | 3.2 | 0.32 = P |
| Job | Housework | 16 | 51.6 | 13 | 41.9 | 0.32 = P |
| Job | Freelance worker | 4 | 12.9 | 9 | 29 | 0.32 = P |
| Level of Education | High school | 17 | 54.8 | 24 | 77.4 | 0.09 = P |
| Level of Education | Diploma | 11 | 35.5 | 6 | 19.4 | 0.09 = P |
| Level of Education | Associate Degree | 1 | 3.2 | 0 | 0 | 0.09 = P |
| Level of Education | Bachelor's degree and higher | 2 | 6.5 | 1 | 3.2 | 0.09 = P |
| Disease background | Yes | 27 | 87.1 | 25 | 80.6 | 0.36 = P |
| Disease background | No | 4 | 12.9 | 6 | 19.4 | 0.36 = P |
| Age (mean ± SD) | Age (mean ± SD) | 48.70 ± 13.98 | 48.70 ± 13.98 | 54.38 ± 8.57 | 54.38 ± 8.57 | P = 0.34* |
## The effect of home visiting program on the subscales and two main dimensions of quality of life
The mean and standard deviation were calculated in all the subscales of the quality of life questionnaire. In most cases, with the progress of the study, a statistically significant difference was observed in the intervention and control groups ($P \leq 0.05$), except for the subscales *Work status* ($$P \leq 0.43$$), Cognitive function ($$P \leq 0.70$$) and Physical functioning ($$P \leq 0.41$$) where the relationship between the intervention and control groups was not significant (Table 2).
**Table 2**
| Dedicated dimension | Dedicated dimension.1 | Before intervention | The first month | The second month | The third month | F | P-value |
| --- | --- | --- | --- | --- | --- | --- | --- |
| (SF-36) | (SF-36) | Mean ±SD | Mean ±SD | Mean ±SD | Mean ±SD | | |
| Symptom/problem list | Intervention group | 65.79 ± 5.55 | 71.23 ± 5.22 | 76.07 ± 5.15 | 76.41 ± 5.33 | 57.25 | <0.001 |
| Symptom/problem list | Control group | 58.53 ± 5.44 | 61.76 ± 5.60 | 64.58 ± 6.01 | 65.67 ± 6.45 | 57.25 | <0.001 |
| Effects of kidney disease | Intervention group | 49.97 ± 10.77 | 56.55 ± 10.02 | 60.28 ± 9.95 | 63.20 ± 10.26 | 17.91 | <0.001 |
| Effects of kidney disease | Control group | 44.55 ± 10.04 | 46.67 ± 9.94 | 48.28 ± 8.41 | 50.30 ± 9.28 | 17.91 | <0.001 |
| Burden of kidney disease | Intervention group | 39.91 ± 8.64 | 64.91 ± 11.02 | 72.58 ± 9.50 | 77.82 ± 10.56 | 185.95 | <0.001 |
| Burden of kidney disease | Control group | 36.29 ± 7.64 | 39.51 ± 7.28 | 42.54 ± 7.29 | 44.75 ± 6.27 | 185.95 | <0.001 |
| Work status | Intervention group | 12.90 ± 28.77 | 32.25 ± 35.46 | 32.25 ± 35.46 | 32.25 ± 35.46 | 0.623 | 0.433 |
| Work status | Control group | 12.90 ± 25.71 | 17.74 ± 30.40 | 24.19 ± 33.84 | 30.64 ± 35.77 | 0.623 | 0.433 |
| Cognitive function | Intervention group | 32.25 ± 9.28 | 29.24 ± 9.05 | 29.24 ± 9.05 | 29.24 ± 9.05 | 0.140 | 0.709 |
| Cognitive function | Control group | 27.52 ± 8.90 | 29.89 ± 10.16 | 32.68 ± 9.94 | 33.33 ± 10.32 | 0.140 | 0.709 |
| Quality of social | Intervention group | 49.24 ± 8.37 | 46.45 ± 7.97 | 46.45 ± 7.97 | 46.45 ± 7.97 | 5.66 | 0.002 |
| Quality of social | Control group | 42.58 ± 9.98 | 46.23 ± 10.31 | 47.95 ± 8.50 | 47.95 ± 8.50 | 5.66 | 0.002 |
| Sexual function | Intervention group | 60.88 ± 23.21 | 68.14 ± 20.37 | 68.54 ± 20.37 | 72.17 ± 18.17 | 3.44 | 0.06 |
| Sexual function | Control group | 54.83 ± 17.28 | 58.46 ± 15.93 | 60.88 ± 15.72 | 62.50 ± 14.43 | 3.44 | 0.06 |
| Sleep | Intervention group | 65.00 ± 6.48 | 70.56 ± 6.44 | 75.08 ± 7.65 | 81.20 ± 7.32 | 44.97 | <0.001 |
| Sleep | Control group | 59.19 ± 8.59 | 60.00 ± 9.21 | 61.45 ± 8.48 | 62.09 ± 8.44 | 44.97 | <0.001 |
| Social support | Intervention group | 81.71 ± 13.16 | 89.24 ± 11.82 | 93.00 ± 9.40 | 95.69 ± 8.57 | 8.210 | 0.006 |
| Social support | Control group | 76.34 ± 17.62 | 79.02 ± 16.65 | 82.25 ± 17.18 | 86.01 ± 14.33 | 8.210 | 0.006 |
| Dialysis staff encouragement | Intervention group | 88.30 ± 30.00 | 91.53 ± 8.15 | 93.54 ± 7.82 | 96.77 ± 5.56 | 7.35 | 0.009 |
| Dialysis staff encouragement | Control group | 85.48 ± 9.18 | 87.09 ± 9.40 | 88.30 ± 8.49 | 89.91 ± 8.79 | 7.35 | 0.009 |
| Patient satisfaction | Intervention group | 94.62 ± 7.92 | 95.69 ± 7.41 | 98.38 ± 5.01 | 98.92 ± 4.16 | 8.412 | <0.001 |
| Patient satisfaction | Control group | 92.47 ± 8.43 | 94.08 ± 8.10 | 95.16 ± 7.69 | 95.69 ± 4.16 | 8.412 | <0.001 |
| General dimension (KDCS) | General dimension (KDCS) | General dimension (KDCS) | General dimension (KDCS) | General dimension (KDCS) | General dimension (KDCS) | General dimension (KDCS) | General dimension (KDCS) |
| Physical functioning | Intervention group | 53.06 ± 10.05 | 56.81 ± 9.03 | 59.03 ± 8.60 | 61.12 ± 7.71 | 0.672 | 0.416 |
| Physical functioning | Control group | 51.29 ± 7.74 | 57.34 ± 7.77 | 59.35 ± 6.79 | 55.64 ± 11.08 | 0.672 | 0.416 |
| Role physical | Intervention group | 4.83 ± 10.04 | 28.22 ± 23.04 | 70.56 ± 23.36 | 48.38 ± 29.53 | 8.103 | 0.006 |
| Role physical | Control group | 12.90 ± 16.92 | 12.90 ± 16.92 | 19.35 ± 21.12 | 21.77 ± 27.78 | 8.103 | 0.006 |
| Pain | Intervention group | 38.30 ± 12.04 | 60.48 ± 17.99 | 70.56 ± 15.99 | 81.04 ± 12.44 | 86.53 | <0.001 |
| Pain | Control group | 30.64 ± 15.75 | 33.06 ± 14.98 | 35.48 ± 13.34 | 37.09 ± 12.70 | 86.53 | <0.001 |
| General health | Intervention group | 31.29 ± 6.32 | 52.74 ± 8.54 | 62.25 ± 8.54 | 70.48 ± 10.25 | 136.947 | <0.001 |
| General health | Control group | 29.51 ± 6.37 | 34.51 ± 6.99 | 39.03 ± 7.89 | 42.90 ± 7.72 | 136.947 | <0.001 |
| Emotional well-being | Intervention group | 50.58 ± 6.97 | 59.87 ± 6.65 | 67.22 ± 6.31 | 74.58 ± 6.24 | 78.89 | <0.001 |
| Emotional well-being | Control group | 43.09 ± 7.56 | 46.19 ± 8.38 | 49.03 ± 10.01 | 51.74 ± 10.11 | 78.89 | <0.001 |
| Role emotional | Intervention group | 12.90 ± 26.77 | 20.10v29.00 | 33.33v28.54 | 36.55 ± 27.69 | 56.83 | <0.001 |
| Role emotional | Control group | 22.58 ± 21.75 | 25.80 ± 23.89 | 29.03 ± 28.20 | 30.10 ± 27.69 | 56.83 | <0.001 |
| Social function | Intervention group | 38.30 ± 9.64 | 54.43 ± 14.98 | 64.51 ± 16.48 | 69.75 ± 16.38 | 33.00 | <0.001 |
| Social function | Control group | 35.08 ± 13.07 | 38.70 ± 13.44 | 40.72 ± 14.05 | 42.33 ± 12.36 | 33.00 | <0.001 |
| Energy/fatigue | Intervention group | 34.83 ± 6.89 | 45.80 ± 8.27 | 55.80 ± 7.75 | 68.06 ± 8.13 | 30.22 | <0.001 |
| Energy/fatigue | Control group | 36.45 ± 9.59 | 38.87 ± 9.37 | 42.74 ± 8.44 | 47.09 ± 9.01 | 30.22 | <0.001 |
Quality of life scores were calculated in two main dimensions (general and specific). To analyze the data, ANOVA was used. The studies showed that the scores of the quality of life in the intervention and control groups in both general and specific dimensions increased significantly during the intervention, and this increase was more in the intervention group than in the control group (Table 3).
**Table 3**
| Unnamed: 0 | Unnamed: 1 | Before intervention | The first month | The second month | The third month | F (P-value*) Intergroup comparison | F (P-value*) Interactive effect (group and time) |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | Mean ±SD | Mean ±SD | Mean ±SD | Mean ±SD | | |
| Quality of life | Intervention group | 35.29 ± 2.18 | 45.47 ± 2.60 | 49.43 ± 2.69 | 52.64 ± 2.77 | 147.761 (< 0.001) | 131.729 (< 0.001) |
| Quality of life | Control group | 37.83 ± 2.73 | 37.85 ± 2.39 | 40.04 ± 2.90 | 41.08 ± 3.60 | 147.761 (< 0.001) | 131.729 (< 0.001) |
| Specific dimension of quality of life | Intervention group | 56.65 ± 4.14 | 63.56 ± 4.18 | 67.56 ± 4.08 | 66.00 ± 4.74 | 94.377 (< 0.001) | 26.150 (< 0.001) |
| Specific dimension of quality of life | Control group | 51.30 ± 2.62 | 54.00 ± 3.23 | 56.42 ± 3.51 | 56.52 ± 4.05 | 94.377 (< 0.001) | 26.150 (< 0.001) |
| General dimension of quality of life | Intervention group | 36.87 ± 4.37 | 50.00 ± 4.25 | 56.92 ± 5.11 | 63.24 ± 5.51 | 83.312 (< 0.001) | 105.210 (< 0.001) |
| General dimension of quality of life | Control group | 36.34 ± 4.471 | 4.25 ± 4.17 | 43.47 ± 5.61 | 44.41 ± 7.43 | 83.312 (< 0.001) | 105.210 (< 0.001) |
| Adherence to treatment | Intervention group | 767.74 ± 155.88 | 900.80 ± 112.45 | 1,026 ± 104.66 | 1,076.61 ± 99.14 | 13.732 (< 0.001) | 3.305 (0.02) |
| Adherence to treatment | Control group | 694.18 ± 118.23 | 897.58 ± 101.03 | 975.00 ± 84.90 | 1,018.54 ± 61.90 | 13.732 (< 0.001) | 3.305 (0.02) |
## The effect of home visiting program on quality of life
The results of the ANOVA indicated a statistically significant difference between the experimental and control groups in terms of changes in the mean score of quality of life in the previous 4 stages until the end of the third month of the intervention ($P \leq 0.05$). The mean scores of quality of life in the intervention group in the pre-intervention stage, the end of the first, second, and third month were 35.29 ± 2.18, 45.47 ± 2.60, 49.43 ± 2.69, and 52.64 ± 2.77, respectively; this upward trend was significant ($p \leq 0.05$). Also, the change in the mean scores in 4 stages, before the intervention (37.83 ± 2.73), the end of the first (37.85 ± 2.39), the second (40.04 ± 2.90), and the third month (41.08 ± 3.60), was significant in the control group (Table 3).
Figure 2A shows the changes in total quality of life scores during the study in both groups.
**Figure 2:** *(A) Changes in the quality of life during the intervention process in both groups. (B) Changes in the adherence to treatment during the intervention process in both groups.*
## The effect of home visiting program on treatment adherence
According to Table 3 and the results of the ANOVA, the average score of adherence to treatment in patients of the intervention group in the time intervals before the intervention (767.74 ± 155.88), in the first month (900.80 ± 112.45) in the end of the second month (1026 ± 104.66) and in the end the third month of the intervention (1,076.61 ± 99.14) had a significant increase ($P \leq 0.05$), in the control group, the score between before and 3 month after the intervention showed a significant change ($P \leq 0.05$). These results were the result of ANOVA showing that the upward trend of treatment adherence scores in the intervention group was significant compared to the control group ($p \leq 0.05$).
Figure 2B shows the changes in total treatment adherence scores during the study in both groups.
Table 3 also shows that the scores of quality of life and adherence to treatment increased significantly both during the study in each group separately and between groups during the study.
Statistical analysis showed that there is a significant relationship between age and quality of life in both the intervention and control groups ($p \leq 0.05$), *In a* way that the quality of life decreases with increasing age. There was no significant relationship between other demographic characteristics in the intervention and control groups with quality of life and adherence to treatment ($p \leq 0.05$).
## Discussion
The findings of this study showed a significant improvement in the quality of life of patients from an unfavorable level in the pre-intervention period to a high level at the end of the third month of the intervention. Therefore, it appears that the use of a well-codified and planned home visiting program can be effective in improving the quality of life of patients undergoing hemodialysis. In this regard, studies conducted on patients with schizophrenia [27], type II diabetes (28–30), burns [31], hypertension [30] and on couples with stress and anxiety [32] reported similar findings. This suggests that home visiting programs can motivate patients to take responsibility for their treatment by actively involving them in the treatment process. Additionally, an effective, one-on-one, and dynamic relationship can be established between the patient and the nurse practitioner which allows for a better understanding of the patients' needs and problems and the nurses' expectations. This improves patient adaptation through the development of self-care and problem-solving skills, thereby playing a crucial role in the individuals' quality of life.
Furthermore, the results of the present study on the effect of home visiting intervention on treatment adherence of hemodialysis patients showed a significant increase in the mean score of treatment adherence of patients in the intervention group at the end of the second and third months of the intervention. This finding is in line with the results of the studies conducted by Lockwood et al. [ 33] on patients with hospital-acquired discharge pelvic fractures, Comulada et al. [ 34] on patients with acquired immunodeficiency infection, Justvig et al. [ 35] on elderly patients with hypertension, and Chow et al. [ 36] on patients with diabetes (33–37). One of the important factors influencing the treatment adherence of patients with chronic diseases is to raise their level of awareness to increase the acceptance of treatment and its continuation [38]. It appears that a home visiting program, such as the one implemented in this study, can successfully improve treatment adherence by raising the level of awareness of the patients. Moreover, considering the relationship between quality of life and treatment adherence, the improved treatment adherence of the patients during the 3 months of home visit intervention can be related to the patients' increased quality of life.
## Limitations
The present study was conducted only on hemodialysis patients in Ardabil, Iran; therefore, its results cannot be generalized to all patients undergoing hemodialysis. Accordingly, it is suggested that the effect of this care model needs to be examined on the abovementioned variables and evaluated with a larger sample size.
Another limitation of our study was the failure to calculate the cost of the intervention and its cost-effectiveness, so it is suggested to calculate the cost of the intervention for future studies.
## Conclusions
The results of this study demonstrate that a nurse-led structured home visiting program significantly improved the quality of life and treatment adherence in hemodialysis patients during the 3 months after the intervention. Although, the cost of the home visit in the intervention group has not been investigated in the study, in several studies conducted on the benefits of home visit plans, the issue of reducing costs has been significantly mentioned (39–41), *It is* therefore recommended to implement this program in the standard care plan of hemodialysis patients by informing them about their disease and reinforcing self-care according to the facilities and conditions of the home environment.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Committee in Biomedical Research at Ardabil University of Medical Sciences (ARUMS) (ethicIR.ARUMS.REC.1400.065). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
MP and MA designed the study and had a role in preparing the manuscript. MP held home visit sessions and collected the data. MP and SI analyzed the data. All authors approved the final manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Lacticaseibacilli attenuated fecal dysbiosis and metabolome changes in Candida-administered
bilateral nephrectomy mice
authors:
- Wiwat Chancharoenthana
- Supitcha Kamolratanakul
- Peerapat Visitchanakun
- Supistha Sontidejkul
- Thanya Cheibchalard
- Naraporn Somboonna
- Sarn Settachaimongkon
- Asada Leelahavanichkul
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10034098
doi: 10.3389/fimmu.2023.1131447
license: CC BY 4.0
---
# Lacticaseibacilli attenuated fecal dysbiosis and metabolome changes in Candida-administered bilateral nephrectomy mice
## Abstract
The impacts of metabolomic changes (reduced short-chain-fatty acids; SCFAs) in uremic condition is not fully understood. Once daily Candida gavage with or without probiotics (different times of administration) for 1 week prior to bilateral nephrectomy (Bil Nep) in 8-week-old C57BL6 mice as the possible models more resemble human conditions were performed. Candida-administered Bil Nep mice demonstrated more severe conditions than Bil Nep alone as indicated by mortality ($$n = 10$$/group) and other 48 h parameters ($$n = 6$$-8/group), including serum cytokines, leaky gut (FITC-dextran assay, endotoxemia, serum beta-glucan, and loss of Zona-occludens-1), and dysbiosis (increased Enterobacteriaceae with decreased diversity in microbiome analysis) ($$n = 3$$/group for fecal microbiome) without the difference in uremia (serum creatinine). With nuclear magnetic resonance metabolome analysis ($$n = 3$$-5/group), Bil Nep reduced fecal butyric (and propionic) acid and blood 3-hydroxy butyrate compared with sham and Candida-Bil Nep altered metabolomic patterns compared with Bil Nep alone. Then, *Lacticaseibacillus rhamnosus* dfa1 (SCFA-producing Lacticaseibacilli) ($$n = 8$$/group) attenuated the model severity (mortality, leaky gut, serum cytokines, and increased fecal butyrate) of Bil Nep mice ($$n = 6$$/group) (regardless of Candida). In enterocytes (Caco-2 cells), butyrate attenuated injury induced by indoxyl sulfate (a gut-derived uremic toxin) as indicated by transepithelial electrical resistance, supernatant IL-8, NFκB expression, and cell energy status (mitochondria and glycolysis activities by extracellular flux analysis). In conclusion, the reduced butyrate by uremia was not enhanced by Candida administration; however, the presence of Candida in the gut induced a leaky gut that was attenuated by SCFA-producing probiotics. Our data support the use of probiotics in uremia.
## Introduction
Uremia is an accumulation of toxins in the blood due to the loss of renal function from both chronic and acute kidney injury that are common health-care problems worldwide [1]. With an inadequate excretion of uremic toxins through the urine, several water-soluble toxins are excreted into the intestine as an alternative route causing an alteration in the intestinal environments that select the growth of some organisms (gut dysbiosis) [2]. As such, the hydrolysis of urea (a major uremic toxin) by urease-producing bacteria and the digestion of other toxins facilitates the survival of some microbes (3–5). Indeed, uremia from both chronic kidney disease (CKD) and acute kidney injury (AKI) induced gut dysbiosis (6–8) and the dysbiosis also worsens uremic complications through several mechanisms [9]. Accordingly, gut bacteria during uremia-induced dysbiosis facilitated the production of gut-derived uremic toxins, such as p-cresol, indoxyl sulfate, and trimethylamine N-oxide, which enhanced damage to endothelium, kidney, heart, and intestine through chronic inflammation, oxidative stress, and atherosclerosis [10, 11]. Additionally, uremic toxins have a direct impact on the enterocytes resulting in enterocytic cell death [2] and also reduce short-chain-fatty-acid (SCFA)-producing bacteria [12]. All of these factors from uremia induce defects of gut permeability (or gut barrier) allowing the translocation of pathogen molecules from the gut into the blood circulation (gut leakage or leaky gut). Indeed, the presence of endotoxin, a major cell wall component of Gram-negative bacteria (the most abundance gut organisms), and (1➔3)-β-D-glucan (BG), the main molecule in the cell wall of fungi (the second most abundance gut microbes), in the blood of patients with uremia are demonstrated as a proof of concept for uremia-induced leaky gut [13, 14]. During uremia, the presence of these pathogen molecules in blood further enhances uremic toxin-induced chronic inflammatory responses, especially with the induction of innate immunity against microbial molecules that foreign to the host [15]. Hence, uremia induces gut dysbiosis and chronic inflammation through impacts of uremia on gut microbes (selection of some bacteria) and host cells (cell injury by oxidative stress from the toxins), then the dysbiosis causes a more severe systemic inflammation with renal function worsening through the leaky gut. However, impacts of uremia on gut dysbiosis in the current literature are mostly focusing on gut bacteria, although gut fungi, especially Candida albicans in human intestines, are the second most abundance microbes in the gut that demonstrate some interaction with gut bacteria and enterocytes [16]. Despite the larger size of fungi (10–12 µm of Candida in a yeast form) than bacteria (0.5–2 µm), the fungal abundance in feces by gene copies using 18S rRNA is 1,000-fold lower than 16S rRNA of bacteria with approximately 267 fungal species compared with more than 3,500 bacterial species in the gut [17]. The bacterial community varies in quantity and composition from the stomach to the colon (102 versus 1011 cells/gram feces in the stomach and colon, respectively), whereas fungi seem to be localized mostly in the colon, with an average of 106 fungal cells per gram of colon content [18]. Interestingly, the presence of gut fungi selectively induces the growth of some gut bacteria (dysbiosis), partly due to i) a digestion ability toward BG of fungal cell wall (glucanase enzymes) as mixing BG into the culture medium enhances the growth of some bacteria [19, 20], and ii) the bacterial tolerance against Candida toxins [21]. Then, fungi in the gut can alter bacterial compositions in the gut and contribute to leaky gut-induced systemic inflammation in uremia through glucanemia and endotoxemia [22] and the abundance of gut organisms might be correlated with the level of pathogen molecules in blood during uremia-induced leaky gut [23]. Due to the difference in the abundance of fungi in rodents versus humans, Candida administration might make the models more resemble humans (19, 24–26). Indeed, the abundance of Candida spp. in mouse feces is not high enough to be detectable by stool culture [27], which is different from cultures of human feces [28]. Unfortunately, impacts of gut fungi, especially C. albicans, in several conditions with leaky gut is not properly considered different from gut bacteria, partly because gut fungi do not seem to cause illness directly. Although we previously demonstrated an impact of oral Candida administration in acute and chronic uremia through bilateral nephrectomy and $\frac{5}{6}$ nephrectomy models [2, 12, 29, 30], respectively, the impact of gut fungi on metabolome analysis has never been described. As such, SCFAs (acetate, propionate, and butyrate) are metabolic products of anaerobic bacterial fermentation, especially on the complex carbohydrates-rich diets, in the intestine that is important for the maintenance of intestinal homeostasis (31–33). Due to the possible depletion of SCFAs by uremic toxin-induced dysbiosis, the administration of probiotics, the health beneficial organisms used in several situations, might attenuate leaky gut and systemic inflammation in several conditions, including uremia [34, 35]. Although the benefits of probiotics in uremic conditions (acute and chronic kidney injury) are mentioned [7], the exploration of SCFAs and metabolome in uremia is still less. Hence, our objective was to explore the impact of gut Candida on the metabolome changes, especially SCFAs, in feces and in blood during acute uremia and also tested the effectiveness of *Lacticaseibacillus rhamnosus* dfa1 which are the recently isolated SCFA-producing Lacticaseibacilli from the Thai healthy volunteers from our previous study [36]. We hypothesized that SCFAs from probiotics themselves or from other probiotic-promoted bacteria might attenuate uremia-induced intestinal damage and tested the hypothesis in vivo and in vitro.
## Animals
Male 8-week-old C57BL/6 mice from Nomura Siam International (Pathumwan, Bangkok, Thailand) were used according to the approval by the Institutional Animal Care and Use Committee of the Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand, following the animal care and use protocol U.S. National Institutes of Health (NIH). Candida-administered bilateral nephrectomy (Bil Nep) or Bil Nep alone, as previously described, were conducted to test the possible impact of gut fungi. Briefly, Candida albicans from the American Type Culture Collection (ATCC 90028) (Fisher Scientific, Waltham, MA, USA) prepared in Sabouraud dextrose broth (SDB) (Oxoid, Hampshire, UK) at 1 × 106 CFU in a 0.3-mL phosphate buffer solution (PBS), or PBS alone, following by *Lacticaseibacillus rhamnosus* dfa1 that isolated from Thai population (Chulalongkorn University, Bangkok, Thailand) [36] at 1 × 108 CFU in 0.3 mL PBS, or PBS alone, were orally administered at 8 and 12 a.m., respectively, for 7 days prior to Bil Nep surgery. Subsequently, Bil Nep was performed 6 h after the last oral gavage (approximately at 6 p.m.) through abdominal incision according to previous publications (37–39). In the sham group, renal vessels and ureters were only identified before closing the abdominal incision, and fentanyl at 0.03 mg/kg of body weight in 0.5 mL normal saline solution (NSS) was subcutaneously injected after the operation for both analgesia and fluid replacement. Notably, the L. rhamnosus were cultured on de Man-Rogosa-Sharpe (MRS) agar (Oxoid) under anaerobic conditions with gas generation sachets (AnaeroPack-Anaero; Mitsubishi Gas Chemical Co., Inc., Japan) at 37°C for 48 h before quantitative preparation by the determination of the optical density at 600 nm (OD600) as previously described [2]. There were 6 mice per group in sham and 8 mice per group for Bil Nep alone and Candida-administered Bil Nep. For probiotics experiments, there were 6 mice per group for sham, Bil Nep, and probiotic-administered Bil Nep, while there were 8 mice per group for Candida-administered Bil Nep with and without probiotics. In the non-survival experiments, mice were sacrificed at 48 h post-surgery under isoflurane anesthesia (with blood and colon collection). Survival analysis was performed using other groups of mice with 10 mice per group and the moribund mice were humanly sacrificed. Colons, 2 cm distal to caecum, were put in tissue frozen in optimal cutting temperature (OCT) compound (Tissue-Tek OCT compound; Sakura Finetek USA, Inc., Torrance, CA, USA) for fluorescent microscopic evaluation. Feces from all parts of the colon were combined and collected for fecal microbiome analysis. Of note, the data at 0 h were collected 3 days before the operation and were used as the baseline values.
## Mouse sample analysis
Serum creatinine (renal injury) and serum cytokines were evaluated by QuantiChrom creatinine colorimetric assay (DICT-500) (Bioassay, Hayward, CA, USA) and enzyme-linked immunosorbent assay (ELISA) (Invitrogen, Carlsbad, CA, USA), respectively. Gut leakage was determined by i) the detection of fluorescein isothiocyanate-dextran (FITC-dextran), an intestinal nonabsorbable molecule in serum, after an oral administration, ii) serum endotoxin (LPS, a major cell wall components of Gram-negative bacteria), iii) bacteremia, and iv) staining of Zona occludens-1 (ZO-1, an enterocyte tight junction molecule) as previously described [2]. For FITC-dextran assays, 0.5 mL of FITC-dextran (molecular weight, 4.4 kDa) (Sigma-Aldrich) at 25 mg/mL was orally administered 3 h prior to blood collection at sacrifice before analysis by fluorescence spectroscopy (Varioskan Flash; Thermo Scientific) at excitation and emission wavelengths of 485 and 528 nm, respectively, with a standard curve of FITC-dextran. Serum LPS was determined by HEK-Blue LPS detection (InvivoGen, San Diego, CA, USA) and bacterial burdens were evaluated by adding 25 µL of blood samples into blood agar for 24 h incubation at 37°C before colony enumeration. For ZO-1 determination, the colons in the 5-µm-thick-frozen sections were stained with a primary antibody against ZO-1 [61-7300] (Thermo Fisher Scientific) (1:200) followed by the secondary antibody Alexa Fluor 546 goat anti-rabbit IgG (A-11035) (Life Technologies, USA) (1:200) and 4’,6-diamidino-2-phenylindole (DAPI; BioLegend, USA) (1:1,000) (nucleus staining color) before visualization and analyzed with a Zeiss LSM 800 confocal microscope (Carl Zeiss, USA) following a previous publication [2].
## Fecal microbiome analysis
Fecal microbiota analysis was performed according to methods reported in previous publications [26, 40] using the total DNA from feces of individual mice. Mouse feces were collected by placing mice in metabolic cages (Hatteras Instruments, Cary, NC, USA) for a few hours before the collection of feces (0.25 g) from each mouse in different cages for microbiome analysis to avoid the influence of allocoprophagy (a habit of mice that ingest feces from other mice). The fecal microbiota analysis was performed in 3 fecal samples from 3 mice per group of sham, Bil Nep alone, and Bil Nep with Candida. Briefly, a power DNA isolation kit (MoBio, Carlsbad, CA, USA), metagenomic DNA quality determination (agarose gel electrophoresis and nanodrop spectrophotometry), universal prokaryotic primers; forward 515F (5’-GTGCCAGCMGCCGCGGTAA-3’) and reverse primer 806R (5’-GGACTACHVGGGTWTCTAAT-3’), and 16S rRNA V4 library (appended 50 Illumina adapter and 30 Golay barcode sequences) were used. Each sample (240 ng) was applied to the MiSeq300 sequencing platform (Illumina, San Diego, CA, USA) with Mothur’s standard quality screening operating procedures in MiSeq platform with aligned and assigned taxa (operational taxonomic units [OTUs]) based on default parameters were used [2].
## Metabolome analysis in plasma and fecal samples
Sample preparation and metabolome analysis by nuclear magnetic resonance (NMR) spectroscopy following previous publications was conducted [41, 42]. Briefly, plasma (400 µL) or feces (0.2 g) at pH of 7.5 in 2.4 mL ultrapure water was vortexed for 10 minutes, centrifuged at 14,000 ×g for 10 minutes at 4°C before transferring the supernatant (500 μL) into an Eppendorf tube for filtering through a Pall Nanosep® (3 kDa molecular weight) (Pall life science, Ann Arbor, MI, USA). Then, the filtrate was mixed 1:1 (vol./vol.) with the buffer, which consisted of 300 mM KH2PO4, $10\%$ (w/w) deuterium oxide (D2O), and 1 mM 3‐(Trimethylsilyl) propionic‐2, 2, 3, 3‐d4 acid sodium salt (TSP) at pH 7.5, as the internal standard [41]. Additionally, NOESY (nuclear overhauser enhancement spectroscopy) 1D‐1H‐NMR measurements were performed in a 500 MHz NMR spectrometer (Bruker, Rheinstetten, Germany); the 1H NMR spectra were aligned and calibrated based on the internal standard (TSP) peak. For each spectrum, chemical shift (δ) across a range of 0.00-10.00 ppm was segmented (binning) with an interval of 0.02 ppm and the signal intensity in each bin was integrated using Topspin (V 4.0.7, Bruker Biospin) to derive a quantity of each spectrum. The identification of each spectrum (metabolite) was assigned according to the ChenomxNMR suite 8.5 library (Chenomx Inc., Alberta, Canada). The MetaboAnalyst 5.0 (http://www.metaboanalyst.ca/) was used for metabolome data normalization (i.e., by sample median and auto-scaled by mean-centering and dividing by the standard deviation of each variable), clustering algorithm by Ward’s method, and statistical analyses [43]. Data visualizations were performed using GraphPad Prism version 8.0 software (GraphPad, La Jolla, CA, USA) and MetaboAnalyst 5.0. There were 5 samples (from 5 mice) in sham and 3 samples (from 3 mice) in Bil Nep and Candida-administered Bil Nep for fecal metabolome analysis. Meanwhile, there were 4 samples (from 4 mice) in sham, 3 samples (from 3 mice) in Bil Nep, and 4 samples (from 4 mice) of Candida-administered Bil Nep for blood metabolome analysis.
## Short chain fatty acid evaluation
Both L. rhamnosus dfa1 and *Enterococcus faecium* dfa1 were prepared in MRS media as mentioned above, while *Bifidobacterium longum* dfa1 was cultured in the brain heart infusion (BHI) broth (Oxoid) according to the previous publications [36, 44]. Then, short chain fatty acids (SCFAs) in the condition media were analyzed by gas chromatography–mass spectrometry (GC-MS) using the headspace solid-phase microextraction method with an Agilent 6890 GC equipped with an Agilent 5973 mass selective detector (Agilent Technologies) according to a previous publication [45]. Briefly, the dimension of the column was 0.25 mm×30 m×0.25 μm with Helium carrier gas at 13.7 ml/min. The temperature program was 10 min isothermal at 50°C, 10 min rising to 240°C with 15°C/min. The injection port temperature is 200°C while the detector port temperature is 250°C. The mass spectrometer was operated in the electron impact mode at 70 eV with a scan range was 40–200 amu. A standard curve was obtained for the calculation of each SCFA concentration. For fecal SCFA, the fecal fatty acids were extracted before the determination by GC-MS according to a previous publication [46]. In brief, feces (20 mg in 500 µL of NSS) were added with $10\%$ H2SO4 before fatty acids separation by anhydrous ether (800 μL) and centrifuged (18,000 g for 15 min). Then, the upper ether phase was mixed with 0.25 g of anhydrous Na2SO4 for 30 min, centrifuged (18,000 g for 5 min), and SCFAs in the upper diethyl ether phase were determined by GC-MS as mentioned above.
## The in vitro experiments on Caco-2 cells
The influence of indoxyl sulfate, a gut-derived uremia toxin, and butyrate, a well-known SCFA, in the enterocytes was examined using the Caco-2 cell line as previously described [12]. As such, the Caco‐2 (ATCCHTB-37) (American Type Culture Collection, Manassas, VA, USA) at 2 × 106 cells/well in Dulbecco’s Modified Eagle Medium (DMEM) were incubated with the different concentrations of indoxyl sulfate (Sigma‐Aldrich, St. Louis, MO, USA) for 24 h before the determination of cell viability with the 2 h incubation (at 37°C in the dark) by 0.5 mg/mL of tetrazolium dye 3‐(4,5‐dimethylthiazol‐2‐yl) ‐2,5‐diphenyltetrazolium (MTT) solution (Thermo Fisher Scientific). The MTT assay is a colorimetric assay for measuring cell metabolic activity based on the ability of nicotinamide adenine dinucleotide phosphate (NADPH)-dependent cellular oxidoreductase enzymes to reduce the MTT tetrazolium dye into the insoluble purple color formazan. After the incubation, the MTT solution was removed and diluted with dimethyl sulfoxide (DMSO) (Thermo Fisher Scientific) before measurement with a Varioskan Flash microplate reader at an absorbance of optical density at 570 nm. On the other hand, Caco‐2 cells at 5 × 104 cells per well were seeded onto the upper compartment of 24‐well Boyden chamber trans wells (Sigma‐Aldrich), using high glucose DMEM supplemented with $20\%$ Fetal Bovine Serum (FBS), $1\%$ HEPES, $1\%$ sodium pyruvate, and $1.3\%$ Penicillin/Streptomycin for 15 days to establish the monolayer of the cells before 24 h incubation with indoxyl sulfate (Sigma‐Aldrich) (0.5 mM) alone or with butyrate (Sigma‐Aldrich) at the indicated concentrations. After that, the transepithelial electrical resistance (TEER) was measured as previously described [47] as demonstrate in ohm (Ω) × cm2 using the epithelial volt‐ohm meter (EVOM2™, World precision instruments, Sarasota, FL, USA) by placing electrodes in the supernatant at the basolateral chamber and in the apical chamber. The TEER values in media culture without Caco‐2 cells were used as a blank and were subtracted from all other measurements. In parallel, the supernatant cytokines were measured by ELISA (Invitrogen).
## Extracellular flux analysis
To explore the impact of uremic toxin and butyrate on cell energy status, the extracellular flux analysis using the Seahorse XFp Analyzers (Agilent, Santa Clara, CA, USA) for the determination of mitochondrial activity and glycolysis through the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR), respectively (25, 48–50). In the Seahorse XFp Analyzers, mitochondrial ATP production rates are determined by the rate of oxygen consumption (OCR) in the oxidative phosphorylation pathway that is needed by mitochondria and the ECAR is a result of lactate production in the glycolysis pathway that is used as a representative for glycolysis activity. Both OCR and ECAR are simultaneously measured in real-time in culture well plates using fluorescent sensors in the machine analyzer. As such, Caco-2 cells (1 × 104 cells/well) were grown in modified DMEM, with 0.5 mM of indoxyl sulfate (a gut-derived uremic toxin) with or without 1 mM butyrate (a representative SCFA) for 24 h in the Seahorse cell culture plate before replacing by Seahorse substrates (glucose, pyruvate, and L-glutamine) (Agilent, 103575–100) in pH 7.4 at 37˚C for 1 h prior to the challenge with different metabolic interference compounds for mitochondrial reactions, including oligomycin, carbonyl cyanide-4-(trifluoromethoxy)-phenylhydrazone (FCCP), and rotenone/antimycin A, and for glycolysis intervention, including glucose, oligomycin, and 2-Deoxy-d-glucose (2-DG), according to the manufacturer’s instructions. Data from Seahorse Wave 2.6 software were also conducted based on the following equations: maximal respiration = (OCR between FCCP and rotenone/antimycin A) – (OCR after rotenone/antimycin A); respiratory reserve = (OCR between FCCP and rotenone/antimycin A) – (OCR before oligomycin); and glycolysis = ECAR between glucose and oligomycin.
## Statistical analysis
Mean ± standard error (SE) was used for data presentation. The differences between groups were examined for statistical significance by one-way analysis of variance (ANOVA) followed by Tukey’s analysis or Student’s t-test for comparisons of multiple groups or 2 groups, respectively. All statistical analyses were performed with SPSS 11.5 software (SPSS, IL, USA) and Graph Pad Prism version 7.0 software (La Jolla, CA, USA). A p-value of < 0.05 was considered statistically significant.
## Candida administration worsened bilateral nephrectomy mice through dysbiosis-induced leaky gut that enhanced systemic inflammation
To resemble human conditions, C. albicans was orally administered before bilateral nephrectomy (Bil Nep) surgery. As such, Candida worsened Bil Nep mice as indicated by mortality, liver injury (alanine transaminase), serum cytokines (TNF-α, IL-6, and IL-10), leaky gut by FITC-dextran assay, endotoxemia, serum (1➔3)-β-D-glucan (BG), and enterocyte tight junction protein (Zona occludens-1; ZO-1), but not renal injury (blood urea nitrogen and serum creatinine), and bacteremia, when compared with the control Bil Nep using only normal saline solution (NSS) gavage (Figures 1A–O). In sham mice, Candida did not alter any parameters when compared with sham control (data not shown). The increased endotoxin (LPS) and BG in serum (Figures 1K, L) along with enterocytes damage (ZO-1) (Figures 1N, O) of Bil Nep mice compared with sham mice indicated uremia-induced leaky gut which was more prominent in Candida-administered Bil Nep compared with Bil Nep alone.
**Figure 1:** *Characteristics of bilateral nephrectomy (Bil Nep) mice orally administered by normal saline solution (NSS) or Candida albicans (Bil Nep+Cand) or control sham surgery (Sham) as determined by survival analysis (A), blood urea nitrogen (B), serum creatinine (C), serum cytokines (TNF-α, IL-6, and IL-10) (D–G), and parameters of gut barrier defect, including FITC-dextran assay (H), endotoxemia (I), (1→3-β-D-glucan) (J), bacteremia (K), and the abundance of tight junction molecule (Zona occluden-1; ZO-1) (L) in the colon (percentage of the green fluorescent color) with the representative fluorescent-stained pictures (original magnification 630x) (M–O) are demonstrated (n = 10/group for A and 6-8/group for others).*
In parallel, Candida also induced gut dysbiosis as indicated by fecal microbiome analysis at 48 h of experiments (Figures 2A–D). With the collection of fecal samples from 3 mice in each experimental group, there was an elevation in Proteobacteria (a major phylum of Gram-negative bacteria including pathogenic microbes), Enterobacteriaceae (a group of pathogenic Gram-negative bacilli), and Muribaculaceae (Gram-negative anaerobes in Bacteroides group) in Candida-administered Bil Nep when compared with Bil Nep alone (Figures 2E–G). Additionally, Candida-induced dysbiosis was also indicated by the reduction in total bacterial abundance and the diversity (Chao-1 and Shannon scores) when compared with Bil Nep alone (Figures 2H–J). Proteobacteria of Bil Nep mice were also more prominent than sham mice despite the non-difference in total bacteria abundance and diversity (Figures 2A–J), supporting uremia-induced gut dysbiosis. Hence, Bil Nep without Candida demonstrated uremia-induced leaky gut through an enhanced pathogenic bacteria (Proteobacteria) resulting in endotoxemia and glucanemia with systemic inflammation-induced liver injury and nearly all of these parameters (fecal Proteobacteria, leaky gut, endotoxemia, glucanemia, serum TNF-a, and liver injury) was worsened by Candida administration in Bil Nep compared with Bil Nep alone.
**Figure 2:** *Fecal microbiota analysis of mice with sham, bilateral nephrectomy (Bil Nep), and Bil Nep with Candida administration at 48 h post-operation as indicated by the relative abundance of bacteria at the phylum and genus levels with the average value (A–D), the abundances of some bacteria in graph presentation (E–G), and the alpha diversity (Chao 1 and Shannon index) with a total abundance of bacteria in operational taxonomic units (OTUs) (H–J) are demonstrated.*
## Altered metabolome characteristics in feces and in the blood of uremic mice with and without Candida administration
The excretion of uremic toxins into the gut was not only inducing fecal dysbiosis (Figures 2A–J) but also altered the fecal metabolome (Figures 3A, B). In comparison with sham mice, Bil Nep mice demonstrated a significant increase in i) nitrogenous bases and derivatives (hypoxanthine, xanthine, and uracil), ii) amino acids (threonine, phenylalanine, lysine, valine, isoleucine, tyrosine, glycine, taurine, alanine, and leucine), iii) energy-related compounds (lactate, glucose, fumarate and ethanol) (Figure 3A). Meanwhile, Bil Nep induced a significant decrease in i) amino acids (lysine, aspartate, methionine, glutamine), ii) SCFAs (propionate and butyrate), and iii) an energy-related metabolite (pyruvate) when compared with sham feces (Figure 3A). The fecal metabolome of Bil *Nep versus* Candida-Bil Nep mice was similar except for the higher formate and acetate in the latter group (Figures 3B–D). Additionally, the separation between sham control versus Bil Nep feces and Bil *Nep versus* Candida-Bil Nep feces was demonstrated by several plot analyses, including principal component analysis (PCA; the data simplification into fewer summary dimensions while retaining trends and patterns) and partial least squares-discriminant analysis (PLS-DA; the data projection into the non-direct observed structure to find the fundamental relations between two matrices) with modified analyses, including orthogonal PLS-DA (OPLS-DA), and sparse PLS-DA (sPLS-DA) (Figures 4A–D). In parallel, blood metabolome in Bil Nep compared with sham demonstrated an increase in i) some amino acids (phenylalanine, lysine, and histidine), ii) uremic toxins (urea and creatinine), and iii) pyruvate, with a decrease in i) some amino acids (taurine, alanine, leucine, isoleucine, and valine), ii) energy-related compounds (glucose, lactate, and ethanol), and iii) SCFAs (3-hydroxybutyrate and acetate) (Figures 5A–D), supported by all plot analyses (Figures 6A–D). Meanwhile, the difference between Bil *Nep versus* Candida-Bil Nep was subtle (Figures 5A, B) as could be demonstrated only by PLS-DA and the modifications (OPLS-DA and sPLS-DA) (Figures 6B–D). Due to the importance of SCFAs in enterocyte homeostasis [18], the reduced butyrate (and propionate) in feces (Figure 3A) and 3-hydroxy butyrate in the blood (Figure 5A) of uremic mice, perhaps due to uremia-induced gut dysbiosis (Figures 2A–J), might partly be responsible for uremia-induced leaky gut (Figures 1J–O). In short, the uremia-induced metabolome alteration in feces and in blood of Bil Nep compared with sham mice from was clearly demonstrated, especially through the reduced butyrate (an important SCFA) in feces and in the blood; however, Candida administration did not worsen the decreased butyrate. These data implied that the worsened conditions of Candida-Bil Nep over Bil Nep alone (Figures 1J–O) were not due to reduced SCFA but possibly because of other adverse effects of the fungi, such as Candida-induced gut dysbiosis (Figures 2A–J) that causing a more severe leaky gut-induced systemic inflammation.
**Figure 3:** *Fecal metabolome analysis of mice with sham, bilateral nephrectomy (Bil Nep), and Bil Nep with Candida administration at 48 h post-operation as indicated by the heat-map of the metabolites (A) with the graphs of some substances (formate, acetate, and butyrate) (B–D) are demonstrated. The color scale bars are demonstrated by log2 fold change. The average values from sham (5 samples) and Bil Nep with or without Candida (3 samples per group) are presented in the column graph for visualization of the comparison.* **Figure 4:** *Fecal metabolome analysis of mice with sham, bilateral nephrectomy (Bil Nep), and Bil Nep with Candida administration at 48 h post-operation as indicated by several score plot analyses, including the relationships among plasma metabolome profiles by principal component analysis (PCA) (A), orthogonal partial least squares-discriminant analysis (OPLS-DA) (B), partial least squares-discriminant analysis (PLS-DA) (C), and sparse partial least squares - discriminant analysis (sPLS-DA) (D) are demonstrated.* **Figure 5:** *Blood metabolome analysis of mice with sham, bilateral nephrectomy (Bil Nep), and Bil Nep with Candida administration at 48 h post-operation as indicated by the heat-map of the metabolites (A) with the graphs of some substances (formate, acetate, and butyrate) (B–D) are demonstrated. The color scale bars are demonstrated by log2 fold change. The average values from sham (4 samples), Bil Nep (3 samples), and Bil Nep with Candida (3 samples) are presented in the column graph for visualization of the comparison.* **Figure 6:** *Blood metabolome analysis of mice with sham, bilateral nephrectomy (Bil Nep), and Bil Nep with Candida administration at 48 h post-operation as indicated by several score plot analyses, including the relationships among plasma metabolome profiles by principal component analysis (PCA) (A), orthogonal partial least squares-discriminant analysis (OPLS-DA) (B), partial least squares-discriminant analysis (PLS-DA) (C), and sparse partial least squares - discriminant analysis (sPLS-DA) (D) are demonstrated.*
## Probiotics attenuated uremia-induced leaky gut, partly through an impact of short-chain fatty acids on enterocytes
Although the increased SCFAs after probiotics administration might be produced from probiotics or the probiotics-promoted bacteria, SCFAs-producing probiotics ensure the enhanced production of SCFAs. As such, several SCFA-related compounds (acetic, butyric, and propionic acid) were detectable in the condition media of several probiotics from our library, including Lacticaseibacilli, Enterococci, and Bifidobacterium, without the significant differences among probiotics (Figures 7A-C). Due to the less difficult preparation processes, Lacticaseibacilli were selected to use in the mice. Although all mice were dead within 64 h post-Bil Nep, Candida-administered Bil Nep (without probiotics) demonstrated the earliest death as all mice died within 48 h (Figure 7D) implying an adverse effect of Candida gavage. In comparison with Bil Nep without Candida, Candida-Bil Nep demonstrated more prominent systemic inflammation (serum cytokines), and leaky gut (FITC-dextran assay and bacteremia but not endotoxemia), despite the similar uremic severity (serum creatinine) and the decreased fecal SCFAs (acetic, butyric, and propionic acid) (Figures 7E–N). In parallel, probiotics attenuated leaky gut severity in Bil Nep mice as indicated by FITC-dextran assay and endotoxemia (but not bacteremia) with enhanced fecal butyric acid without an alteration in other parameters (Figures 7D–N). In Candida-Bil Nep, probiotics improved survival rate, serum IL-6 (not TNF-α and IL-10), endotoxemia, and bacteremia with increased fecal butyric acid (Figures 7D–N).
**Figure 7:** *The short chain fatty acid (SCFA) (acetic, butyric, and propionic acids) in the condition media of several probiotics (Lacticaseibacillus rhamnosus, Enterococcus faecium, and Bifidobacterium longum) compared with the control media (control) (A–C) are demonstrated (independent triplicate experiments were performed). Characteristics of bilateral nephrectomy (Bil Nep) mice with or without Candida administration (Cand) after treatment by Lacticaseibacillus rhamnosus (Lacto) or normal saline solution (NSS) control versus sham mice as indicated by survival analysis (D), serum creatinine (E), serum cytokines (TNF-α, IL-6, and IL-10) (F–H), and parameters of gut barrier defect (FITC-dextran assay, endotoxemia, bacteremia) (I–K), and fecal SCFA (L–N) are demonstrated (n = 10/group for D and 6-8/group for E–N.*
Because i) intestinal secretion of indoxyl sulfate (IS), a gut-derived uremic toxin transforming from indole (tryptophan derivatives of gut-dysbiosis bacteria) at the liver, in uremic conditions is mentioned [51], ii) the enterocyte toxicity of IS [52], and iii) the impact of SCFAs in enterocyte homeostasis [32] and the probiotics-increased fecal butyrate (Figure 7M), IS and butyrate were tested in enterocytes. Indeed, enterocyte toxicity of IS was demonstrated by MTT assay with a $50\%$ reduction in cell viability from 2 mM of IS (Figure 8A). Despite the non-alteration of cell viability by 0.5 mM IS, this concentration induced enterocyte injury, as indicated by epithelial integrity (TEER), supernatant IL-8, and the upregulated inflammatory genes (IL-8 and NFκB), possibly from the reduction of cell energy (mitochondrial and glycolysis activities) (Figures 8B–J). Interestingly, butyrate (at 1 and 4 mM) similarly attenuated enterocyte injury (TEER, supernatant IL-8, IL-8, and NFκB) with an improvement of cell energy status (Figures 8B–J), perhaps through butyrate-related energy supplement [53, 54]. In brief, Lacticaseibacilli were selected from several SCFA-producing probiotics to use in mice due to the simpler laboratory preparation and the probiotic administration enhanced fecal butyrate (Figure 7M) and attenuated the model severity, especially leaky gut, endotoxemia, and bacteremia (Figure 7I–K), similarly between Candida-Bil Nep and Bil Nep alone. The increased fecal butyrate, either from the administered probiotics or other probiotics-induced beneficial bacteria, partly due to the influence of butyrate on uremic toxin-induced enterocyte damage through enhanced cell integrity (TEER), anti-inflammation, and improved cell energy status (mitochondria and glycolysis activities) (Figure 8A–J).
**Figure 8:** *The cell viability (MTT assay) of enterocytes (Caco‐2 cells) after incubation by the different concentrations of indoxyl sulfate (A) is demonstrated. Characteristics of Caco-2 cells after incubation by 0.5 mM indoxyl sulfate with butyrate (1 and 4 mM) or control media (Control) as evaluated by transepithelial electrical resistance (TEER) (B) and supernatant IL-8 (C), expression of IL-8 and NF-κB (D, E) are shown. Additionally, the cell energy status of Caco-2 cells after being activated by 0.5 mM indoxyl sulfate with or without 1 mM butyrate or media control as indicated by indicators for mitochondrial function; oxygen consumption rate (OCR), and glycolysis activity; extracellular acidification rate (ECAR) (F, G) with some energy phenotype profile (maximal respiration, respiratory reserve, and glycolysis) (H–J) are also demonstrated (independent triplicate experiments were performed for all experiments).*
## Discussion
Probiotics attenuated uremia-induced leaky gut in Bil Nep mice with or without Candida partly through the short-chain fatty acids (SCFAs)-improved cell energy of enterocytes.
## The presence of Candida in the gut worsened uremia-induced dysbiosis
The excretion of uremic toxins through the intestine during renal impairment is one of the obvious causes of uremia-induced leaky gut [55], as the direct injury from uremic toxins (such as urea, p-cresol, and indoxyl sulfate) against enterocytes is demonstrated in vitro [52, 56, 57], that is possibly an underlying mechanism of enterocyte apoptosis and leaky gut in uremia [2]. The toxins (water-soluble and protein-bound compounds) contact intestinal epitheliums through the intestinal lumens (luminal side) and from blood delivery as some toxins (such as indoxyl sulfate) are produced in the blood (through livers) before the delivery to other organs, including kidney, brain, bone, and gut [58], while some toxins (such as p-cresol) are produced within the gut [59]. Despite the different production sites, gut-derived uremic toxins [60] are partly responsible for uremia-induced gut dysbiosis, possibly due to the selection of bacteria by the toxins (enhanced bacteria that can utilize the toxins) (61–63) and the direct impacts of uremic toxins on enterocyte damages that also causing dysbiosis (gut inflammation-induced dysbiosis) [64]. Here, the presence of Candida in the gut caused more severe dysbiosis than Bil Nep alone, as indicated by the increased Proteobacteria (especially Enterobacteria) with the possible direct epithelial invasion by Candida spp. [ 65], which might be correlated with the more severe leaky gut in Candida-Bil Nep compared with Bil Nep alone. Indeed, the selection of some bacterial groups by Candida, including Enterobacteria and some Enterobacter spp., is possibly due to the ability of bacteria to digest the Candida cell wall by β-glucanase and cellulolytic enzymes [19, 66]. Additionally, the possibly bactericidal activity of some fungal toxins, such as candidalysin (a cytolytic pore-forming peptide), and fungal by-products, such as acetaldehyde, alcohol, and ammonia, also possibly alter some groups of bacteria more than other groups, leading to Candida-induced gut dysbiosis [67, 68]. In a previous publication, Candida administration in sham control mice does not induce gut dysbiosis and systemic inflammation, while Candida administration in Bil Nep mice worsens gut dysbiosis and systemic inflammation [2] similar to our current results. Not only in acute uremia, Candida administration in chronic kidney disease (CKD) in $\frac{5}{6}$ nephrectomy mice also induces gut dysbiosis and worsens systemic inflammation through leaky gut-induced endotoxemia and glucanemia that are severe enough to induce fibrosis in several internal organs (kidney, liver, and heart) approximately at 20-week post-surgery [12, 29]. In this aspect, the presence of gut fungi enhances the severity of uremia-induced systemic inflammation partly through leaky gut-induced endotoxemia and glucanemia which might depend on the abundance of Gram-negative bacteria and fungi, respectively, in the gut. The monitoring of leaky gut and the abundance of endotoxin and beta-glucan in gut content might be interesting as biomarkers for monitoring of the disease severity in patients with acute or chronic uremia might be beneficial. More studies on these topics are interesting.
## Uremia caused metabolome alteration in feces and in blood with a subtle alteration by Candida gavage
Due to the well-known association between gut bacteria and the alteration of small molecules [69], the metabolome analysis was evaluated. As such, the difference in the pattern of fecal metabolome between uremic mice and sham control was obvious, especially the reduction of amino acids, butyrate, and propionate (but not acetate), as indicated by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) analyses. Perhaps, the enhanced fermentation of proteins in the gut during uremia-induced dysbiosis leads to an increase in amino acids in feces [62, 70] and the reduced normal microbiota from uremia decreases fecal SCFAs [71]. Meanwhile, the reduced fecal creatinine in uremic mice compared with sham supported an increase in bacteria that can metabolite creatinine [62, 72]. Here, the elevation of several amino acids, except for lysine, aspartate, methionine, and glutamine, supported the increased nonessential amino acids and reduced essential amino acids (histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine) in uremia [73]. Indeed, the increase in glutamine and aspartate-associated proteins in uremia are previously mentioned [74, 75]. Nevertheless, Candida gavage subtly altered uremic fecal metabolome with an increase in acetate and formate compared with uremia alone suggesting the promoted acetate- and formate-producing bacteria (perhaps through the digestion of Candida cell wall) [76, 77]. In blood metabolome analysis, fewer compounds, possibly only with the high abundance, were detectable with a similar trend of the fecal metabolome, except for taurine and alanine (higher in feces but lower in the blood of uremia compared with sham), and pyruvate (lower in feces but higher in the blood of uremia compared with sham). This fecal-blood discordance of metabolome results indicates a complex correlation of the individual compounds such as the utilization by enterocytes and the transport mechanisms [78]. In the blood of uremic mice compared with sham, the reduced 3-hydroxy butyrate (a main ketone body transforming from SCFAs by liver) [79] with the non-difference in acetate and isobutyrate (butyrate isomerization) [80] suggested the difference influences of different SCFAs in uremia. Despite the differences in PLS-DA analysis of blood metabolome between Candida-Bil *Nep versus* Bil Nep, the components in blood metabolome were not different between groups, while some alterations in fecal metabolome were detectable suggesting that Candida altered only the metabolites that were possibly too low to be detectable in blood. Despite a similarly low level of fecal SCFAs between Bil *Nep versus* Candida-Bil Nep, Candida-Bil Nep demonstrated more severe dysbiosis (the higher abundance of Proteobacteria with the lower bacterial diversity) suggesting that the presence of Candida in the gut and the selective gut bacteria from Candida had a less impact on fecal SCFAs but demonstrated a higher impact on leaky gut. However, Bil Nep mice with the presence of Candida in the gut might be more resemble to the patients due to the higher abundance of fungi in the human gut compared with mouse intestines [27, 28]. Because of the higher disease severity of Candida-Bil Nep mice over Bil Nep alone, the probiotics that are effective in Bil Nep mice might be ineffective on the Candida-Bil Nep group and the test of probiotics on Candida-Bil nep mice might be more appropriate for further clinical translation.
## Probiotics attenuated uremia-induced leaky gut partly through the improved enterocyte cell energy status by SCFAs
Probiotics attenuate leaky gut from several causes, including acute and chronic uremia, through several mechanisms, including normalized gut dysbiosis, regulation of host immune responses, facilitated mucin production, and induced anti-inflammation by exopolysaccharide and SCFAs (81–83). Among these mechanisms, the production of SCFAs seems to be an interesting mechanism of probiotics as SCFAs, from probiotics themselves or from beneficial bacteria that are promoted by probiotics, are absorbed into enterocytes before using as an energy source [84] with induction of several anti-inflammatory signals (in enterocytes and immune cells) [85]. Although the increased intestinal SCFAs after probiotic administration is well-known [86], data on probiotics-enhanced SCFAs in uremia is still less. In a healthy condition, normal gut microbiota enables the transformation of complex nutrients, including plant cell wall components, into simple sugars that are fermented to form SCFAs, mainly formate, acetate, propionate, and butyrate as contributed to more than $90\%$ of SCFAs in the human gut [87, 88]. With the reduced SCFAs, several probiotic bacteria can produce SCFAs, including *Lacticaseibacillus rhamnosus* GG, *Bifidobacterium longum* SP $\frac{07}{3}$, and *Enterococcus faecalis* AG5 [83, 89]. Although the non-SCFAs-producing probiotics can enhance intestinal SCFAs through the promotion of other beneficial bacteria in the gut, the use of SCFAs-producing bacteria possibly ensures SCFAs production. Despite several isolated probiotics with effectively produced SCFAs in our facility, *Lacticaseibacillus rhamnosus* dfa1 was selected to use due to the easier preparation process of the facultative anaerobes compared with the strictly anaerobic Bifidobacterium preparation. Indeed, the Lacticaseibacilli attenuated leaky gut and systemic inflammation with increased butyrate in uremia mice with or without Candida. Because of the low fecal butyrate both in feces and in blood of uremic mice here, the enhanced butyrate in feces might be a probiotic-activity biomarker as a previous report of low butyrate in uremia patients [90]. However, uremia did not alter acetate (the most abundant SCFA) but reduced propionate only in feces (not in blood). Hence, patients with uremia with low fecal butyrate might be more benefit from the administration of SCFAs or probiotics-induced SCFAs and treatment monitoring of the probiotic effects in these patients through fecal metabolome changes (increased fecal butyrate when compared with the before treatment) might be useful. Notably, the treatment monitoring of probiotics effect by blood metabolome measurement might be less beneficial than fecal metabolome evaluation as a subtle difference in blood metabolome between Bil Nep mice with versus without probiotics.
During uremia, several uremic toxins induced damage to enterocytes as serum from patients with chronic kidney disease, urea, and some gut-derived uremic toxins reduced enterocyte integrity (transepithelial electrical resistance; TEER) [91, 92]. Here, indoxyl sulfate, a gut-derived uremic toxin was used as a representative toxin for the in vitro test in enterocyte cell lines. As such, an impact of butyrate on uremic toxin (indoxyl sulfate)-stimulated enterocytes was demonstrated in vitro (enterocyte integrity and anti-inflammation) along with the increased enterocyte cell energy supporting a previous publication [93]. The administration of butyrate as a chemical drug is interesting due to an easier production process than probiotic preparation; however, the proper dose adjustment will be necessary as butyrate toxicity is possible [94, 95]. Although the more profound enhancement of mitochondrial functions over glycolysis by SCFAs [96, 97] and the increased cell status by butyrate as a source of acetyl coenzyme A (acetyl Co-A) in mitochondrial tricarboxylic acid (TCA) cycle [53] are previously reported, butyrate could not alter cell energy status in the control enterocytes here. On the other hand, indoxyl sulfate (IS) reduced both mitochondrial and glycolysis activity of enterocytes possibly through the enhanced oxidant species (98–100) that were toxic to both mitochondria (oxidant-induced mitochondrial injury) and glycolysis (blockage of glyceraldehyde-3-phosphate dehydrogenase by oxidants) [101]. In enterocytes, butyrate normalized IS-induced mitochondrial defect possibly through the increased acetyl Co-A for mitochondrial TCA [102], while improved IS-reduced glycolysis through the improved cell conditions that facilitate glucose utilization [103]. Notably, the probiotics were not administered in the sham control because there was no dysbiosis (the imbalance of gut microbiota correlating with the unhealthy condition) in sham mice with the well-known safety of L. rhamnosus probiotics [2, 7, 12, 25, 26, 47].
In conclusion, uremia altered metabolome characteristics in both feces, especially reduced butyrate, and the presence of Candida in the gut of Bil Nep mice worsened leaky gut-induced systemic inflammation. Despite the enhanced enterocyte damages by Candida, probiotics attenuated the model severity, as indicated by increased fecal butyrate, strengthened gut barrier, and reduced leaky gut-induced systemic inflammation. Additionally, the improved fecal butyrate levels after probiotic administration might be a monitoring biomarker for a proper probiotic effect. More studies on these topics are interesting.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, PRJNA909950.
## Ethics statement
The animal study was reviewed and approved by The animal care and use protocol (SST $\frac{028}{2564}$) was certified by the Institutional Animal Care and Use Committee of Chulalongkorn University’s Faculty of Medicine in Bangkok, Thailand, in compliance with the US National Institutes of Health criteria.
## Author contributions
The followings are the authors’ contribution: conceptualization: WC and AL. Methodology: WC, SK, PV, SS, TC, NS, SS, and AL. Validation: SS, TC, SS, PV, and AL. Formal analysis: WC, SK, and AL. Investigation: NS, TC, PV, WC, and AL. Resources: WC, SK, and AL. Data curation: AL. Writing-original draft preparation: WC and AL. Writing review and editing: WC and AL. Supervision: AL. And funding acquisition: WC. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'The concordance and discordance of diabetic kidney disease and retinopathy
in patients with type 2 diabetes mellitus: A cross-sectional study of 26,809 patients
from 5 primary hospitals in China'
authors:
- Zhaoxiang Liu
- Xianglan Li
- Yanlei Wang
- Yanxia Song
- Qiang Liu
- Junxia Gong
- Wenshuang Fan
- Chunmei Lv
- Chenxiang Cao
- Wenhui Zhao
- Jianzhong Xiao
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10034101
doi: 10.3389/fendo.2023.1133290
license: CC BY 4.0
---
# The concordance and discordance of diabetic kidney disease and retinopathy in patients with type 2 diabetes mellitus: A cross-sectional study of 26,809 patients from 5 primary hospitals in China
## Abstract
### Introduction
Diabetic kidney disease (DKD) and diabetic retinopathy (DR) share similar pathophysiological mechanisms. However, signs of DKD may be present at diagnosis of diabetes without retinopathy. Risk factors for the development of DKD and DR may not be identical.
### Methods
This study aimed to evaluate the concordance and discordance between DKD and DR by investigating the distribution of DKD and DR in patients with type 2 diabetes mellitus from 5 Chinese cities. A total of 26,809 patients were involved in this study. The clinical characteristics were compared among patients based on the presence of DKD and DR. Logistic regression models were used to analyze the independent risk factors of DKD and DR.
### Results
The prevalence of DKD and DR was $32.3\%$ and $34.6\%$, respectively. Among eligible patients, 1,752 patients without DR had an increased urinary albumin-to-creatinine ratio (ACR) or reduced estimated glomerular filtration rate (eGFR), and 1,483 patients with DR had no DKD. The positive predictive value of DR for DKD was $47.4\%$ and negative predictive value was $67.1\%$. Elder age, male gender, a longer duration of disease, higher values of waist circumference and HbA1c were associated with both DR and DKD. A lower educational level was associated with DR. Higher BP and TG would predict increased prevalence of DKD.
### Conclusions
DKD and DR shared many risk factors, but a significant discordance was present in patients with type 2 diabetes mellitus. DKD was more strongly associated with blood pressure and triglycerides than DR.
## Introduction
Diabetic kidney disease (DKD) affects 20-$40\%$ of patients with diabetes [1, 2]. The prevalence of DKD or chronic kidney disease (CKD) in Chinese patients with diabetes is increasing [3, 4] as type 2 diabetes mellitus becomes an epidemic disease. DKD is diagnosed based on the presence of albuminuria and/or the reduced estimated glomerular filtration rate (eGFR < 60 mL/min/1.73m2) in the absence of signs or symptoms of other kidney diseases. Previous studies suggested that DKD might not solely develop from microalbuminuria to macroalbuminuria to azotemia as Mogensen proposed [5, 6]. The reduced eGFR without albuminuria has been frequently reported in patients with type 1 diabetes mellitus and type 2 diabetes mellitus [7].
Diabetic retinopathy (DR), another microvascular complication, is supposed to share similar pathophysiological mechanisms with DKD, and the two are frequently found simultaneously. El-Asrar et al. reported that type 1 diabetes mellitus patients with DR were 13.39 times more likely to develop DKD than those without DR [8]. Results of a meta-analysis showed that patients with DR were nearly 4 times more likely to be complicated by DKD. Patients with DKD were twice more likely to be diagnosed as DR [9]. DR was typically used as an indicator of DKD in the differential diagnosis [10]. However, discordance of DKD and DR was also discussed. Signs of DKD may be present in the time of diagnosis or in type 2 diabetes mellitus patients without retinopathy [11]. It was reported that risk factors for the development of DKD and DR might not be identical. Japanese scholars demonstrated that systolic blood pressure (SBP) variability was an independent predictor for the development and progression of DKD, rather than DR, in type 2 diabetes mellitus patients [12]. Genetic data revealed that the DR-related single nucleotide polymorphisms did not have an individual or cumulative genetic effect on the risk of DKD, eGFR status or end-stage renal disease (ESRD) outcomes of type 2 diabetes mellitus patients in Taiwan [13]. The most important evidence originates from a series of randomized controlled trials published in recent years (i.e., new classes of antidiabetic drugs have different preventive effects on DKD and DR) [14]. A meta-analysis showed that hypoglycemic medicine glucagon-like peptide 1 receptor agonists (GLP-1RA) reduced the risk of kidney disease progression by $18\%$ (hazard ratio (HR), 0.82, $95\%$ confidence interval (CI): 0.75-0.89, $P \leq 0.001$), while sodium-glucose cotransporter 2 (SGLT2) inhibitors reduced the mentioned risk by $38\%$ (HR, 0.62, $95\%$CI, 0.58-0.67, $P \leq 0.001$) [15]. The preventive effects of GLP-1RA and SGLT2 inhibitors on DR in humans have not yet been reported [14]. Taken together, the concordance and discordance of DKD and DR in patients with type 2 diabetes mellitus exist and need to be further elaborated.
The present study aimed to investigate the concordance and discordance between DKD and DR, as well as the relevant risk factors.
## Study subjects
Patients with type 2 diabetes mellitus who were admitted to Ruijing diabetes hospital chains (China) were enrolled in this study. Five hospitals from Beijing, Lanzhou, Harbin, Chengdu, and Taiyuan were included. The data were collected continuously from March 2016 to December 2021. The inclusion criteria were as follows: diagnosis of type 2 diabetes mellitus was based on the diagnostic criteria presented by the World Health Organization (WHO) in 1999 [16], and patients who aged 18 - 80 years old. Those patients who had severe heart (New York Heart Association III/IV), liver (severe hepatic impairment or liver failure), lung (conditions that may predispose to hypoxemia), or renal diseases (primary nephrotic syndrome, glomerulonephritis, obstructive renovascular disease, nephrectomy, renal transplant, etc.), and those were pregnant, or had been diagnosed with type 1 diabetes mellitus, special type of diabetes or gestational diabetes were excluded. This study was approved by the Ethics Committee of Tsinghua Changgung Hospital (Beijing, China; Approval No. [ 2016] 004). The flowchart of screening patients was shown in Figure 1.
**Figure 1:** *Flowchart of screening patients. ACR, urinary albumin-to-creatinine ratio. EGFR, estimated glomerular filtration rate. DR, diabetic retinopathy. “+” means positive, and “-” means negative.*
## Data collection
Patients’ data were collected at the first visit in each hospital through face-to-face interviews, including demographic data, educational level, smoking status, individual medical history (hypertension, dyslipidemia, and cardiovascular disease), and family history of diabetes mellitus. Blood samples were collected after an overnight (10-14 h) fasting, and the laboratory tests were conducted in the local hospital, including liver function, renal function, fasting plasma glucose (FPG), glycosylated hemoglobin (HbA1c), and lipid profiles (low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglyceride (TG)). HbA1c was measured by high-performance liquid chromatography using the ADAMS A1c, HA-8180T analyzer (Array, Tokyo, Japan) or MQ-2000 PT Balk analyzer (Huazhong, Shanghai, China), which was the second-level reference for glycosylated hemoglobin of International Clinical Chemistry Committee. Blood lipids, liver functions and kidney functions were assessed by automated analysis (AU5800; Beckman Coulter Inc., Brea, CA, USA). Urinary albumin was determined using a DADE BEHRING BN II analyzer (Siemens, Munich, Germany) by nephelometry (N antiserum to Human Albumin Assay, Dade Behring). Urinary creatinine concentration was measured via a Hitachi 7600 analyzer (Hitachi, Tokyo, Japan) using the sarcosine oxidase-PAP method. The urinary albumin-to-creatinine ratio (ACR) was computed and was reported in milligrams per gram (mg/g). Retinopathy status was assessed by fundus photography (TRC-NW100 camera; Nikon, Tokyo, Japan), and all images were graded by an experienced ophthalmologist. Diagnostic criteria of DR were based on the worse eye according to international clinical diabetic retinopathy and diabetic macular edema disease severity scales published in 2002 [17]. All the laboratories participated in the quality control program as requested by the authority. All data were automatically downloaded from hospital information system.
DKD was defined as elevated urinary ACR (≥30 mg/g), or reduced eGFR (<60 mL/min/1.73 m2), or both, for longer than 3 months, excluding clinically significant renal diseases through medical history and laboratory results, in accordance with the current guidelines of Kidney Disease: Improving Global Outcomes (KDIGO) [18, 19]. The eGFR was calculated using the Modification of Diet in Renal Disease (MDRD) study formula [20] as follows: 186 × [serum creatinine (mg/dL)] - 1.154×(age) - 0.203 × (0.742 if female). The diagnosis of albuminuria was divided into three stages according to ACR (ACR < 30 mg/g was defined as non-albuminuria, 30 mg/g ≤ ACR < 300 mg/g as microalbuminuria, and ACR ≥ 300 mg/g as macroalbuminuria).
Diagnosis of metabolic syndrome was made by presence of any three or more of the following [21]: 1. Abdominal obesity (central obesity): waist circumstance ≥90 cm in men or ≥85cm in women. 2. Hyperglycaemia: FPG ≥ 6.1 mmol/L or OGTT 2hPG ≥ 7.8 mmol/L and/or confirmed diabetes that is under treatment. 3. Hypertension: blood pressure ≥$\frac{130}{85}$ mmHg and/or diagnosed and on antihypertensive therapy. 4. Fasting TG ≥ 1.70 mmol/L. 5. Fasting HDL‐C < 1.04 mmol/L.
## Statistical analysis
SPSS 23.0 software (IBM, Armonk, NY, USA) was used for data analysis. Normally distributed data were expressed as the mean ± standard deviation (SD), and abnormally distributed data were expressed as median (interquartile range). The χ2 test was used to compare the clinical categorical variables among different groups. Logistic regression models were established to analyze the independent risk factors of DKD and DR. Risk factors included age (every 10 years), gender (female as 0, male as 1), duration of disease (every 5 years), educational level (junior school or below as 1, high school or above as 2), body mass index (BMI, < 24 kg/m2 as 1, ≥24 kg/m2 as 2), waist circumference (every 10 cm), smoking history (never as 0, with smoking history as 1), HbA1c (< $7\%$ as 1, $7\%$ ~ $9\%$ as 2, ≥ $9\%$ as 3), systolic blood pressure (SBP, <140 mmHg as 1, 140mmHg ~ 160 mmHg as 2, ≥160 mmHg as 3), LDL-C (< 2.6 mmol/L as 1, 2.6 mmol/L ~ 3.3 mmol/L as 2, ≥ 3.3 mol/L as 3), TG (< 1.7 mmol/L as 1, 1.7 mmol/L ~ 5.0 mmol/L as 2, ≥5.0mol/L as 3), and DR (absent as 0, non-proliferative retinopathy (NPDR) as 1, and proliferative retinopathy (PDR) as 2). $P \leq 0.05$ indicated statistical significance.
## Clinical characteristics of patients with type 2 diabetes mellitus
A total of 26,809 patients with type 2 diabetes mellitus were involved in this study. There were 14,813 ($55.3\%$) male patients and 11,996 ($44.7\%$) female patients. The average age, duration of disease, BMI, and HbA1c were 59.2 ± 10.7 years old, 8.6 ± 6.9 years, 25.2 ± 3.4 kg/m2, and 8.6 ± $2.1\%$ (70 mmol/mol), respectively.
Data of ACR and eGFR were available for all patients. There were 18,875 ($70.4\%$) patients with eGFR ≥ 90mL/min/1.73m2, 6,685 ($24.9\%$) patients with eGFR equal to 60-90 mL/min/1.73m2, 1,053 ($3.9\%$) patients with eGFR equal to 30-60 mL/min/1.73m2, and 196 ($0.7\%$) patients with eGFR < 30 mL/min/1.73m2. The majority of patients had normal albuminuria level ($69.1\%$), and $23.5\%$ and $7.4\%$ of them had microalbuminuria or macroalbuminuria, respectively. According to the latest diagnostic criteria for DKD, there were 8,660 ($32.3\%$) patients with eGFR < 60 mL/min/1.73m2 and/or ACR ≥ 30 mg/g, including 384 ($1.4\%$) patients without albuminuria (eGFR < 60 mL/min/1.73m2 and ACR < 30 mg/g), 7,411 ($27.6\%$) patients with eGFR ≥ 60 mL/min/1.73m2 and ACR ≥ 30 mg/g, and 865 ($3.2\%$) patients with eGFR < 60 mL/min/1.73m2 and ACR ≥ 30 mg/g.
Among 8,153 patients who were screened for retinopathy status, there were 2,820 ($34.6\%$) patients who were diagnosed with DR, including 2,592 patients with NPDR and 228 patients with PDR. Comparison between DKD negative ($$n = 5064$$) with DKD positive ($$n = 3089$$) and DR negative ($$n = 5333$$) with DR positive ($$n = 2820$$) were made in those people (Table 1). Patients with DKD were elder, had longer duration of disease, higher values of BMI, waist circumference, HbA1c, BP, LDL-C, TG, and higher proportion of metabolic syndrome than those with DKD-negative. Similar significant clinical indicators were observed in patients with DR compared to those without, except for BMI and TG.
**Table 1**
| Unnamed: 0 | All (n=8153) | DKD negative(n=5064) | DKD positive(n=3089) | P | DR negative(n=5333) | DR positive(n=2820) | P.1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Age (years) | 59.45 ± 10.51 | 58.94 ± 10.30 | 60.28 ± 10.81 | <0.001 | 58.95 ± 10.94 | 60.39 ± 9.60 | <0.001 |
| Duration of disease (years) | 8.80 ± 6.89 | 8.12 ± 6.53 | 9.93 ± 7.30 | <0.001 | 7.97 ± 6.64 | 10.39 ± 7.07 | <0.001 |
| BMI (kg/m2) | 25.24 ± 3.38 | 25.09 ± 3.30 | 25.48 ± 3.50 | <0.001 | 25.18 ± 3.38 | 25.34 ± 3.39 | 0.055 |
| Waist circumference (cm) | 90.14 ± 9.45 | 89.62 ± 9.12 | 90.99 ± 9.82 | <0.001 | 89.86 ± 9.40 | 90.69 ± 9.52 | <0.001 |
| Fasting blood glucose | 10.86 ± 4.11 | 10.38 ± 3.92 | 11.62 ± 4.29 | <0.001 | 10.69 ± 4.07 | 11.21 ± 4.18 | 0.001 |
| HbA1c (%) | 8.82 ± 2.14 | 8.63 ± 2.09 | 9.15 ± 2.18 | <0.001 | 8.74 ± 2.17 | 8.98 ± 2.07 | <0.001 |
| SBP (mmHg) | 134.45 ± 18.42 | 131.78 ± 16.99 | 138.82 ± 19.79 | <0.001 | 133.71 ± 18.29 | 135.85 ± 18.59 | <0.001 |
| DBP (mmHg) | 80.51 ± 11.11 | 79.51 ± 10.63 | 82.16 ± 11.66 | <0.001 | 80.21 ± 11.02 | 81.10 ± 11.25 | 0.001 |
| LDL-c (mmol/L) | 2.73 (2.17,3.33) | 2.72 (2.17,3.30) | 2.76 (2.17,3.38) | <0.001 | 2.72 (2.16,3.30) | 2.77 (2.20,3.37) | 0.003 |
| TG (mmol/L) | 1.70 (1.20,2.49) | 1.62 (1.17,2.34) | 1.81 (1.30,2.70) | <0.001 | 1.70 (1.20,2.50) | 1.70 (1.21,2.47) | 0.249 |
| HDL-c (mmol/L) | 1.34 (1.15,1.56) | 1.34 (1.16,1.56) | 1.34 (1.14,1.57) | 0.683 | 1.34 (1.16,1.57) | 1.33 (1.15,1.56) | 0.342 |
| Smoking history | 10.7% | 10.2% | 11.5% | 0.148 | 10.2% | 11.6% | 0.086 |
| Utilization rate of ACEI/ARB | 25.7% | 21.7% | 32.3% | <0.001 | 22.0% | 32.7% | <0.001 |
| Rate of HbA1c <7% | 21.1% | 24.0% | 16.5% | <0.001 | 23.2% | 17.2% | <0.001 |
| Rate of BP < 130/80 mmHg | 22.2% | 25.4% | 17.1% | <0.001 | 23.1% | 20.6% | 0.008 |
| Rate of LDL-c < 2.6 mmol/l | 43.6% | 44.5% | 42% | 0.029 | 44.2% | 42.4% | 0.130 |
| eGFR (mL/min/1.73m2) | 95.53 ± 19.71 | 98.80 ± 16.93 | 90.18 ± 22.57 | <0.001 | 96.60 ± 20.17 | 93.52 ± 18.65 | <0.001 |
| ACR (mg/g) | 16.90 (5.90,61.92) | 8.00 (3.65,15.00) | 106.71 (48.60,305.40) | <0.001 | 14.10 (5.22,14.10) | 24.48 (7.93,133.18) | <0.001 |
| Proportion of metabolic syndrome | 71.9% | 68.4% | 77.7% | <0.001 | 70.7% | 74.1% | 0.001 |
There were 3,581 patients with DR-negative and DKD-negative, 1,483 patients with DR-positive and DKD-negative, 1,752 patients with DR-negative and DKD-positive, and 1,307 patients with DR-positive and ACR-positive. Patients’ clinical characteristics in the four groups are shown in Table 2. For patients with DR-positive and DKD-positive, they had the longest duration of disease, the highest HbA1c, BP, and TG level, the lowest eGFR, and the highest ACR. DR was more frequent in patients with DKD, while it was not an indicator of DKD. The positive predictive value (PPV) of DR for DKD was $47.4\%$ and negative predictive value (NPV) was $67.1\%$.
**Table 2**
| Unnamed: 0 | DR-DKD-(n=3581) | DR+DKD-(n=1483) | DR-DKD+(n=1752) | DR+DKD+(n=1337) | P |
| --- | --- | --- | --- | --- | --- |
| Age (years) | 58.43±10.63 | 60.17±9.35* | 60.02±11.47* | 60.63±9.86* | <0.001 |
| Duration of disease (years) | 7.53±6.36 | 9.53±6.74* | 8.85±7.11*# | 11.35±7.31*#† | <0.001 |
| BMI (kg/m2) | 25.05±3.31 | 25.17±3.27 | 25.45±3.49*# | 25.52±3.51*# | <0.001 |
| Waist circumference (cm) | 89.40±9.20 | 90.17±9.10* | 90.79±9.72* | 91.25±9.94*# | <0.001 |
| Fasting blood glucose | 10.33±3.93 | 10.50±3.91 | 11.36±4.24*# | 12.00±4.33*#† | <0.001 |
| HbA1c (%) | 8.60±2.14 | 8.69±1.97 | 9.04±2.21*# | 9.30±2.14*#† | <0.001 |
| SBP (mmHg) | 131.51±17.00 | 132.45±16.88 | 138.21±19.89*# | 139.61±19.65*#† | <0.001 |
| DBP (mmHg) | 79.30±10.60 | 80.01±10.69* | 82.05±11.62*# | 82.30±11.72*# | <0.001 |
| LDL-c (mmol/L) | 2.70(2.17,3.28) | 2.75(2.19,3.33) | 2.75(2.14,3.34) * | 2.80(2.21,3.41) *# | <0.001 |
| TG (mmol/L) | 1.63(1.17,2.36) | 1.60(1.17,2.30) | 1.82(1.27,2.77) *# | 1.80(1.30,2.61) *#† | <0.001 |
| HDL-c (mmol/L) | 1.34(1.16,1.57) | 1.33(1.16,1.55) | 1.34(1.15,1.57) | 1.34(1.14,1.57) | 0.803 |
| Smoking history | 10.1% | 10.5% | 10.4% | 12.9%*#† | 0.040 |
| Utilization rate of ACEI/ARB | 19.5% | 27.0%* | 27.2%* | 39.0%*#† | <0.001 |
| Rate of HbA1c <7% | 25.5% | 20.4%* | 18.6%* | 13.8%*#† | <0.001 |
| Rate of BP < 130/80 mmHg | 25.9% | 24.1% | 17.4%*# | 16.7%*# | <0.001 |
| Rate of LDL-c < 2.6 mmol/l | 45.0% | 43.2% | 42.4% | 41.5%* | 0.094 |
| eGFR (mL/min/1.73m2) | 99.27±18.33 | 97.67±12.89* | 91.14±22.54*# | 88.92±22.57*#† | <0.001 |
| ACR (mg/g) | 7.70(3.50,15.00) | 8.50(4.00,15.40) | 85.40(44.86,249.15) *# | 146.70(57.90,374.13) *#† | <0.001 |
| Proportion of metabolic syndrome | 67.7% | 70.0% | 76.9%*# | 78.7%*# | <0.001 |
## Concordance and discordance between DR and DKD
Logistic regression models were established to estimate risk factors for DKD and DR, respectively. Elder age, male gender, a longer duration of disease, higher values of waist circumference and a higher HbA1c level were associated with both DKD and DR. A lower educational level was associated with DR. Higher BP and TG would predict increased prevalence of DKD (Table 3).
**Table 3**
| Unnamed: 0 | DKD (n = 2783 ) | DKD (n = 2783 ).1 | DKD (n = 2783 ).2 | DR (n = 2525) | DR (n = 2525).1 | DR (n = 2525).2 |
| --- | --- | --- | --- | --- | --- | --- |
| | β | OR(95% confidence interval) | P | β | OR(95% confidence interval) | P |
| Age (years) | 0.108 | 1.114(1.06,1.17) | <0.001 | 0.060 | 1.062(1.01,1.117) | 0.018 |
| Gender (M vs F) | 0.107 | 1.113(1.002,1.236) | 0.045 | 0.163 | 1.177(1.058,1.31) | 0.003 |
| Duration (years) | 0.222 | 1.248(1.177,1.324) | <0.001 | 0.373 | 1.452(1.367,1.543) | <0.001 |
| Educational level | -0.043 | 0.958(0.867,1.058) | 0.399 | -0.477 | 0.621(0.562,0.686) | <0.001 |
| BMI (kg/m2) | 0.021 | 1.021(0.911,1.145) | 0.720 | 0.012 | 1.012(0.901,1.136) | 0.845 |
| Waist circumference (cm) | 0.080 | 1.084(1.023,1.148) | 0.006 | 0.061 | 1.063(1.003,1.127) | 0.040 |
| Smoking history | 0.140 | 1.15(0.976,1.355) | 0.096 | 0.102 | 1.108(0.938,1.308) | 0.229 |
| HbA1c (%) | 0.325 | 1.384(1.297,1.476) | <0.001 | 0.211 | 1.235(1.157,1.318) | <0.001 |
| SBP (mmHg) | 0.474 | 1.606(1.483,1.739) | <0.001 | 0.071 | 1.073(0.989,1.165) | 0.090 |
| LDL-c (mmol/L) | 0.045 | 1.046(0.985,1.111) | 0.146 | 0.053 | 1.054(0.992,1.121) | 0.091 |
| TG (mmol/L) | 0.316 | 1.371(1.26,1.491) | <0.001 | -0.048 | 0.953(0.874,1.039) | 0.277 |
Risk factors for diabetes mellitus complicated by an increased ACR versus a reduced eGFR and NPDR versus DR were shown in supplemental materials (Supplementary Tables 1, 2). They shared most risk factors.
## Discussion
It was found that $32.3\%$ and $34.6\%$ of Chinese patients with type 2 diabetes mellitus were complicated by DKD and DR, respectively. Hospital-based investigation and the longer duration of diabetes may be explanation for the difference with that reported previously [22].
As we know, both DR and DKD are microvascular complications of diabetes mellitus. Evidence-based medical research showed that lowering blood glucose and blood pressure reduced the incidence rates of DKD and DR [23]. Because these two complication are tightly correlated, DR is often used in clinical practice to differentiate DKD from other CKDs [24]. However, retinopathy was absent in $56.7\%$ of patients with DKD in this study. In contrast, $52.6\%$ of patients with retinopathy did not have DKD. The discordance was $39.7\%$ (DR-negative and DKD-positive plus DR-positive and DKD-negative) in the present study. A similar finding was reported in an Italian study, the discordance between DR and DKD was $36.6\%$ [25]. Interestingly, data from a real-world study revealed that there was no significant difference in albumin excretion rate between the presence and absence of DR in the whole population [26].
The estimated PPV of DR for DKD was $47.4\%$ in this study, and the NPV of DR for DKD was $67.1\%$. This PPV was lower than the reports from KDIGO, i.e., the PPV of retinopathy for typical diabetic glomerulopathy ranged from $67\%$ to $100\%$ in patients with macroalbuminuria, and the NPV had a broader range of 20-$84\%$. For microalbuminuria, PPVs were lower at around $45\%$, while NPVs were close to $100\%$ [27]. The prevalence of DKD was about $60\%$ in patients with type 2 diabetes mellitus with advanced DR [28]. A meta-analysis demonstrated that the pooled sensitivity and specificity of DR to predict DKD were 0.65 and 0.75, respectively, while PDR had a low sensitivity (0.25) and high specificity (0.98) for predicting DKD, respectively [10]. Taken together, these results suggested that DR was not sensitive enough to predict DKD but it was good indicator to confirm DKD.
Elder age, male gender, a longer duration of disease, a higher value of waist circumference and a higher HbA1c level were correlated with both DKD and DR. These findings were also confirmed in other previous studies [29, 30]. Although DKD and DR share similar mechanisms, numerous studies suggested that DKD and DR may differ in some way. Firstly, a noticeable proportion of patients with type 2 diabetes mellitus had DKD or DR alone [25]. Secondly, a new classification of diabetes had been proposed by a Swedish group according to GAD antibody, BMI, age at onset, HbA1c level, homeostatic model assessment-β (HOMA-β), and HOMA of insulin resistance (HOMA-IR). Among them, cluster 3 (characterized by severe insulin resistance) was related to a higher incidence of kidney disease and cardiovascular disease, while cluster 2 (characterized by severe insulin deficiency) was associated with a higher incidence of DR [31]. More importantly, SGLT2 inhibitors and GLP1-RA, two new classes of hypoglycemic drug, exerted outstanding renal but not retinal protective effects [32]. Therefore, it was reasonable to assume that DKD and DR would be associated with different risk factors and pathogeneses.
Metabolic syndrome was called insulin resistance syndrome. In the present study, the proportion of metabolic syndrome was much higher in patients with DKD than those in DKD negative group. The higher level of SBP and triglyceride were independently associated with DKD but not DR. These were consistent with the new classification according to cluster analysis [31], i.e., patients with severe insulin resistance were more likely to be complicated by kidney disease. A study demonstrated that the visceral adiposity index was found to be strongly associated with the prevalence of DKD, while it was not associated with the prevalence of DR in Chinese subjects [33]. Taken together, these data suggested that improving insulin resistance as well as controlling metabolic syndrome in patients with type 2 diabetes mellitus may be much more important in the prevention of DKD, as compared to in the prevention of DR.
We found that patients with DR were associated with lower educational levels. It was reported that patients with higher educational level may be prone to internalize health information and hence change their life-style, which could explain for lower DR rate in those patients [34].
The present study had several limitations. Firstly, the cross-sectional nature of this study precluded exploration of any cause-effect relationship. Secondly, concomitantly treatment affected the measurement of HbA1c, SBP, triglycerides and other biologic parameters. Thirdly, DKD was diagnosed based only on the clinical characteristics without renal biopsy, so that DKD might be over diagnosed. False positive of increased ACR due to poor blood glucose control may also be a concern. Last but not least, the proportion of screening of DR in patients with proteinuria was $36.2\%$, while it was $27.8\%$ in patients without proteinuria. Patients with albuminuria were more likely to screen their retinopathy status, which might lead to selection bias. More detailed and comprehensive screening of DR are needed for Chinese patients with diabetes. The strength of this study was its large sample size. All participants were from 5 cities in China, and the large sample size might promote the generalization of the findings. The concordance and discordance between DR and DKD were discussed, and the corresponding strategies were put forward for the prevention of DKD.
## Conclusion
The discordance was significant between retinopathy and DKD in type 2 diabetes. DKD was associated with a higher level of components of the metabolic syndrome, DR was more in patients with lower educational level. Further studies are required to discriminate their differences in the development and prevention of DR and DKD.
## Guarantor statement
JX is the guarantor of this work and had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by Ethics Committee of Tsinghua Changgung Hospital. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
ZL conceptualized the study, interpreted the analyses, wrote the initial manuscript, and reviewed and revised the manuscript. XL collected data, contributed intellectually to the research topics, and critically reviewed the scientific content of the manuscript. YW analyzed the data, designed and supervised the statistical analysis, and reviewed and revised the manuscript. YS, QL, JG, WF, and CL collected data, reviewed and revised the manuscript. CC and WZ conceptualized the study, supervised the statistical analysis, and reviewed and revised the manuscript. JX conceptualized the study, coordinated and supervised data collection, acquired funding for the study, and critically reviewed the manuscript for important intellectual content. JX is the guarantor of this research and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1133290/full#supplementary-material
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|
---
title: 'Association between HOMA-IR and ovarian sensitivity index in women with PCOS
undergoing ART: A retrospective cohort study'
authors:
- Yan Li
- Yiwen Wang
- Hai Liu
- Shaodi Zhang
- Cuilian Zhang
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10034104
doi: 10.3389/fendo.2023.1117996
license: CC BY 4.0
---
# Association between HOMA-IR and ovarian sensitivity index in women with PCOS undergoing ART: A retrospective cohort study
## Abstract
### Introduction
Insulin resistance (IR) may play a central role in the pathophysiology of polycystic ovary syndrome (PCOS). Controlled ovarian stimulation (COS) in PCOS women in the setting of assisted reproductive technology (ART) is always a challenge for clinicians. However, it remains unclear whether IR in women with PCOS correlates with reduced ovarian sensitivity to exogenous gonadotropin (Gn). This study aimed to explore the association between homeostasis model assessment of insulin resistance (HOMA-IR) and ovarian sensitivity index (OSI).
### Methods
In this retrospective cohort study, we explored the association between Ln HOMA-IR and Ln OSI based on smoothing splines generated by generalized additive model (GAM). Then the correlation between HOMA-IR and OSI was further tested with a multivariable linear regression model and subgroup analysis.
### Results
1508 women with PCOS aged 20-39 years undergoing their first oocyte retrieval cycle were included consecutively between 2018 until 2022. We observed a negative association between Ln HOMA-IR and Ln OSI by using smoothing splines. In multivariable linear regression analysis, the inverse association between Ln HOMA-IR and Ln OSI was still found in PCOS women after adjustment for potential confounders (β = -0.18, $95\%$ CI -0.25, -0.11). Compared with patients with the lowest tertile of HOMA-IR, those who had the highest tertile of HOMA-IR had lower OSI values (β = -0.25, $95\%$ CI -0.36, -0.15).
### Discussion
Our study provided evidence for the inverse correlation between IR and the ovarian sensitivity during COS in PCOS women. Herein, we proposed new insights for individualized manipulation in PCOS patients with IR undergoing ART.
## Introduction
Polycystic ovary syndrome (PCOS) is a highly prevalent endocrine disorder affecting 6–$21\%$ of women of reproductive age (1–3). Oligomenorrhea, obesity, infertility, hyperandrogenemia and insulin resistance (IR) constitute the common features of PCOS (4–6). In PCOS women, IR may play a central role in the pathophysiology, which occur with a prevalence of $77.5\%$ in overweight and $93.9\%$ in obese subjects [7]. Homeostatic model assessment (HOMA-IR) is broadly used as a surrogate measure of IR in clinical research [7, 8].
In the setting of assisted reproductive technology (ART), controlled ovarian stimulation (COS) in PCOS women is a challenge for clinicians. The ovarian response to COS is reported to vary widely among PCOS patients. While some patients are more likely to show resistance to stimulation, other PCOS women may experience an exaggerated response [9, 10]. Thus, the identification of heterogenous ovarian sensitivity in PCOS populations is the key to striking a balance between ovarian hyperstimulation syndrome (OHSS) and poor ovarian response (POR). Some clinical parameters, including age, body mass index (BMI), anti-mullerian hormone (AMH), and antral follicle count (AFC) have been widely used as predictive markers of ovarian response [11]. However, these markers cannot properly reflect the dynamic process of follicular growth in response to exogenous gonadotropin (Gn) [12]. Recently, ovarian sensitivity index (OSI) has been suggested as an evaluation of ovarian response to Gn stimulation in ART [12]. Higher values of OSI were associated with better ovarian response and greater odds of pregnancy (12–14).
The association between IR and ovarian function in PCOS women has been a debating issue. Previous studies have shown that hyperinsulinemia could promote early folliculogenesis which may result in hyper-response to COS [15, 16]. In contrast, researchers observed increased fasting insulin was associated with decreased numbers of large antral follicles in PCOS patients [17]. Emerging evidence for the interplay between IR and atresia of antral follicles in PCOS has been described [18, 19]. In addition, some researchers reported IR may have an adverse effect on the developmental potential of oocytes when considering the reduced maturation rate [20].
Therefore, it remains unclear whether IR in women with PCOS correlates with reduced ovarian sensitivity to Gn. To our knowledge, few studies have evaluated the association between HOMA-IR and OSI during ART procedures. The aim of this retrospective cohort study was to investigate the association between HOMA-IR and OSI in PCOS women scheduled for in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) cycles, which may offer useful guidance to clinicians for individualized infertility therapies.
## Patients
This study was a retrospective cohort analysis. Women who underwent a standard Gonadotropin releasing hormone (GnRH) agonist or GnRH antagonist protocol in their first IVF/ICSI treatment cycle at reproductive medicine center of Henan Provincial People’s Hospital between June 2018 and May 2022 were consecutively included. Diagnosis of PCOS was based on the *Rotterdam criteria* [21]. Body mass index (BMI) of ≥ 24 kg/m2 was defined as overweight and BMI of ≥ 28 kg/m2 was defined as obesity according to Working Group on Obesity in China [22, 23].
We included individuals (BMI ≥ 18.5 kg/m2) aged between 20 and 39 years with complete data on IR, including fasting glucose (FG), fasting serum insulin (FINS). The exclusion criteria were as follows: FG > 7 mmol/l, untreated thyroid diseases, subjects had received anti-diabetic medications within 3 months prior to evaluation, preimplantation genetic testing (PGT), canceling oocyte retrieval and oocytes freezing.
## FG and FINS measurement
Basal FSH, LH, estradiol, total testosterone, progesterone and prolactin were done during the 2-4 days of the menstrual cycle. Fasted blood samples were collected to measure biochemical markers, including insulin, glucose and thyroid-stimulating hormone (TSH). The inter-assay laboratory coefficient of variation (CV) of FG testing was lower than $3.5\%$, which was detected by ADVIA2400ChemistrySystem (ADVIA 2400, SIEMENS, Germany). FINS concentration was determined by the electro-chemiluminescence immunoassay method (CV < $3.2\%$) on the full-automatic chemiluminescence immunoassay analyzer (Cobas8000 e602; Roche Diagnostics GmbH, Mannheim, Germany) in the laboratory of the Department of Reproductive Endocrinology at Henan Provincial People’s Hospital. Our laboratory is checked for qualification by the External Quality Assessment of Clinical Laboratory Center annually (Ministry of Health of the People’s Republic of China, Beijing, China).
## Indicator calculation
HOMA-IR and OSI were assessed by formula as follows: HOMA-IR = FBG (mmol/L) x FINS (μU/ml)/22.5 [24]; OSI = [(Number of retrieved oocytes/Total gonadotropin dose) × 1,000] [12]; BMI was calculated according to the formula, weight (kg)/height (m)2. Implantation rate was defined as the number of gestational sacs divided by the number of transferred embryos. Clinical pregnancy rate was calculated by the ratio of clinical pregnancy cycle to the total embryo transfer (ET) cycle. Early miscarriage was referred to intrauterine pregnancy loss before 12 weeks of pregnancy, while late miscarriage was defined as a pregnancy loss prior to 28 weeks of gestational age.
## Controlled ovarian stimulation protocol
COS protocols consisted of GnRH agonist down regulation protocol and GnRH antagonist protocol. These dose step-up regimens were individualized according to women’s age, BMI and ovarian reserve. In GnRH agonist down regulation protocol, subcutaneously injected 0.1 mg triptorelin was scheduled for patients from the 6th-8th day after ovulation to the 18th-22th day until sufficient downregulation of the pituitary was achieved. After that, exogenous Gn and 0.05 mg triptorelin was administered simultaneously until the day of human chorionic gonadotropin (HCG) triggering. In the long-acting GnRH agonist down regulation protocol, patients received a single dose of triptorelin acetate (Diphereline; 3.75mg) on day 2-4 of the menstrual cycle. If downregulation of the pituitary was satisfactory after 30-35 days, exogenous Gn was injected to initiate the cycle. In the GnRH antagonist protocol, Gn was administrated on the 2-3 days of the menstrual cycle, and GnRH antagonist (Cetrotide; 0.25 mg) was added daily from day six to seven of stimulation until the day of HCG triggering. The hCG was administered when at least two follicles had reached a mean diameter of 17-18 mm and the serum estradiol (E2) levels were consistent with the ultrasound findings. Ultrasound-guided follicular aspiration was performed at 35-36 hours after the administration of the hCG injection. High-quality embryos meant day 3 embryos that reached 6 to 8 cell stages with cytoplasmic fragmentation less than $10\%$ and equal size blastomeres.
## Statistical analysis
Owing to skewed distribution, HOMA-IR and OSI values were log e transformed to Ln HOMA-IR and Ln OSI. Continuous variables with normal distribution were expressed as mean ± standard deviation (SD). Continuous variables with skewed distribution were presented median with interquartile range (IQR). Categorical variables were expressed as frequency (percentage). The differences between HOMA-IR tertiles were compared using the one-way analysis of variance (normal distribution), Kruskal-Wallis test (skewed distribution) for continuous variables and Pearson’s chi-squared test, or Fisher’s exact test for the categorical variables. Multiple comparison posttest was conducted by using the Bonferroni correction.
The association of Ln HOMA-IR with Ln OSI was fitted and presented as smoothing splines which was generated by a generalized additive model (GAM) after adjustments for age, BMI, AFC, AMH, the initial Gn dose, basal FSH and COS protocol.
To analyze whether Ln HOMA-IR was independently associated with Ln OSI, multivariable linear regression models were used. These models included crude model (not adjusted for covariates), model 1 (adjusted for age, BMI, AMH and AFC), model 2 (adjusted for age, BMI, AMH, basal FSH, initial Gn dose, AFC, and COS protocol). As sensitivity analysis, HOMA-IR was then divided into tertiles and treated as a categorical variable, with the lowest tertile used as the reference. In addition, we performed linear trend tests to obtain P for trend by entering the median value of each HOMA-IR category as a continuous variable in the models.
Subgroup analysis was performed for examination of the association of Ln HOMA-IR with Ln OSI in the strata of age, BMI, AMH, initial Gn dose, and COS protocol. Next, we use log likelihood ratio test to obtain a P-value for interaction for examining the statistical significance of the difference in each subgroup.
Statistical analysis was undertaken by using software packages R (http://www.R-project.org, The R Foundation) and Empower (R) (www.empowerstats.com; X&Y Solutions, Inc., Boston, MA). A two-tailed P value < 0.05 was considered statistically significant.
## Patient disposition
Data from women having PCOS undergoing their first oocyte retrieval cycles were analysed. A total of 2191 medical records between June 2018 and May 2022 were screened and 1508 IVF/ICSI cycles were finally included in the analysis (Figure 1).
**Figure 1:** *Flowchart of data collection process. PCOS, polycystic ovary syndrome; COS, controlled ovarian stimulation; PGT, preimplantation genetic testing.*
## The clinical parameters of patients
Patient characteristics were presented in Table 1. Subjects with higher HOMA-IR tended to be younger and had higher levels of BMI, AFC, FINS, FG, the initial Gn dose, total Gn dose and duration of Gn used. The levels of AMH, basal FSH, basal LH, dominant follicle count on trigger day, number of retrieved oocytes, metaphase II (MII) oocytes, embryos and OSI values were prone to be decreased across the HOMA-IR tertiles. With regards to the clinical outcomes, as shown in Supplementary Figure S1, the early miscarriage rate was significantly higher in T3 group when compared with that of T1 subjects ($P \leq 0.05$). No significant difference was detected for implantation rate, clinical pregnancy rate and late miscarriage rate ($P \leq 0.05$).
**Table 1**
| Variables | Groups of cycles according to the tertiles of HOMA-IR | Groups of cycles according to the tertiles of HOMA-IR.1 | Groups of cycles according to the tertiles of HOMA-IR.2 | P value1 |
| --- | --- | --- | --- | --- |
| Variables | T1 (< 2.32) | T2 (2.32 - 3.87) | T3 (> 3.87) | P value1 |
| Number | 503 | 502 | 503 | |
| Age (y) | 29.44 ± 3.55 | 28.87 ± 3.53 | 28.59 ± 3.82 | <0.001 |
| BMI (kg/m2) | 22.77 ± 2.76 | 25.03 ± 3.38 | 27.95 ± 3.56 | <0.001 |
| AMH (ng/ml) | 7.61 (5.29-10.89) | 7.30 (5.07-10.44) | 6.75 (4.31-9.65) | <0.001 |
| Basal FSH (mIU/ml) | 5.93 ± 1.41 | 5.76 ± 1.50 | 5.48 ± 1.43 | <0.001 |
| Basal LH (mIU/ml) | 8.44 (5.66-13.38) | 7.75 (5.02-11.92) | 7.29 (4.50-10.54) | <0.001 |
| AFC | 22.75 ± 3.72 | 23.17 ± 3.54 | 23.97 ± 3.86 | <0.001 |
| FG (mmol/l) | 4.63 ± 0.47 | 4.84 ± 0.48 | 5.07 ± 0.54 | <0.001 |
| FINS (μU/ml) | 8.05 ± 2.15 | 14.17 ± 2.40 | 27.59 ± 10.67 | <0.001 |
| HOMAIR | 1.72 (1.37-1.99) | 3.00 (2.61-3.41) | 5.38 (4.50-7.00) | <0.001 |
| COS protocol | | | | 0.034 |
| GnRH agonist | 406 (80.72%) | 427 (85.06%) | 435 (86.48%) | |
| GnRH antagonist | 97 (19.28%) | 75 (14.94%) | 68 (13.52%) | |
| Initial Gn dose (IU) | 128.88 ± 31.38 | 135.21 ± 28.54 | 147.69 ± 36.24 | <0.001 |
| Total Gn dose (IU) | 1826.78 ± 875.73 | 2183.34 ± 1168.43 | 2730.55 ± 1218.45 | <0.001 |
| Duration of Gn (d) | 11.22 ± 3.10 | 12.07 ± 3.56 | 13.15 ± 3.51 | <0.001 |
| Dominant follicle count on trigger day | 9.87 ± 4.68 | 9.54 ± 4.84 | 9.09 ± 4.49 | 0.028 |
| Retrieved oocytes | 14.00 (10.00-20.00) | 13.00 (8.00-18.00) | 12.00 (8.00-17.50) | <0.001 |
| MII oocytes | 12.00 (8.00-16.00) | 11.00 (7.00-16.00) | 10.00 (7.00-15.00) | <0.001 |
| Embryos count | 7.00 (4.00-10.00) | 6.00 (3.00-10.00) | 5.00 (3.00-9.00) | 0.003 |
| Endometrium on ET day (mm) | 10.82 ± 2.45 | 11.06 ± 2.42 | 11.00 ± 2.72 | 0.619 |
| OSI | 8.37 (5.14-13.33) | 6.40 (3.88-10.79) | 4.57 (2.85-7.23) | <0.001 |
## Associations between HOMA-IR and OSI
The data distribution of HOMA-IR and OSI was strongly skewed. Thus, we performed log e transformation (Ln HOMA-IR and Ln OSI) before analysis. First, we observed a negative association between Ln HOMA-IR and Ln OSI after adjustment for potential confounders by using smoothing spline fitting curves in GAM (Figure 2). Then, a multivariable linear regression model was performed to analyze the association of Ln HOMA-IR with Ln OSI levels. In addition, we performed sensitivity analysis where HOMA-IR was divided into three groups based on tertiles.
**Figure 2:** *Association between Ln HOMA-IR and Ln OSI. All adjusted for age, BMI, AMH, AFC, initial Gn dose, basal FSH and COS protocol. Red line represents the smooth curve fit between variables and the blue dotted curves represents the 95% of confidence interval.*
As displayed in Table 2, univariable linear regression analysis showed that, the level of Ln HOMA-IR was negatively associated with Ln OSI values (β = -0.39, $95\%$ CI -0.45, -0.32). After adjustment for potential confounders, the negative association between Ln HOMA-IR and Ln OSI was still found, as shown in model 1 (β = -0.14, $95\%$ CI -0.21, -0.07) and model 2 (β = -0.18, $95\%$ CI -0.25, -0.11) by multivariable linear regression analysis. As sensitivity analysis, HOMA-IR was then divided into tertiles and treated as a categorical variable, with the lowest tertile used as the reference. A graded negative association was discovered across the groups (P for trend < 0.001). Compared with participants who had HOMA-IR in T1 (HOMA-IR < 2.32), those with HOMA-IR in T2 (HOMA-IR 2.32 - 3.87) and T3 (HOMA-IR > 3.87) had lower Ln OSI (β = -0.25, $95\%$ CI -0.34, -0.15 and β = -0.57, $95\%$ CI -0.67, -0.48, respectively). After adjusting for the potential confounders, the Ln OSI remained consistently lower in T3 compared with T1 in model 1 (β = -0.21, $95\%$ CI -0.31, -0.10). In model 2, the Ln OSI remained significantly lower in T2 and T3 groups when compared with T1 group (β = -0.10, $95\%$ CI -0.19, -0.00 and β = -0.25, $95\%$ CI -0.36, -0.15, respectively).
**Table 2**
| Variable | Crude Model | Crude Model.1 | Model 1 | Model 1.1 | Model 2 | Model 2.1 |
| --- | --- | --- | --- | --- | --- | --- |
| Variable | β (95%CI) | P value | β (95%CI) | P value | β (95%CI) | P value |
| Ln HOMA-IR | -0.39 (-0.45, -0.32) | <0.0001 | -0.14 (-0.21, -0.07) | <0.0001 | -0.18 (-0.25, -0.11) | <0.0001 |
| HOMA-IR tertile | HOMA-IR tertile | HOMA-IR tertile | HOMA-IR tertile | HOMA-IR tertile | HOMA-IR tertile | HOMA-IR tertile |
| T1 (< 2.32) | 0 (reference) | | 0 (reference) | | 0 (reference) | |
| T 2 (2.32 - 3.87) | -0.25 (-0.34, -0.15) | <0.0001 | -0.07 (-0.17, 0.02) | 0.1164 | -0.10 (-0.19, -0.00) | 0.0391 |
| T 3 (> 3.87) | -0.57 (-0.67, -0.48) | <0.0001 | -0.21 (-0.31, -0.10) | 0.0002 | -0.25 (-0.36, -0.15) | <0.0001 |
| P for trend | | <0.001 | | <0.001 | | <0.001 |
## Subgroup analysis and effect modification
As presented in Table 3, subgroup analysis was performed to explore whether the other variables, including age, BMI, stratification of AMH, initial Gn dose and COH protocol, might influence the association between HOMA-IR and OSI. The subgroups of age, AMH and BMI were stratified according to the clinical cutoff point. The subgroup analysis revealed the inverse association between Ln HOMA-IR and Ln OSI was consistent and significant in the following subgroups: BMI, AMH and initial Gn dose. None of the abovementioned variables significantly modified the association between HOMA-IR and OSI (P for interaction > 0.05 for all covariates). In the subgroups of patients with age ≥ 35 y and using GnRH antagonist protocol, the inverse associations of Ln HOMA-IR with Ln OSI was not statistically significant although the regression coefficient (β) was negative.
**Table 3**
| Subgroups | Subjects n (%) | β (95%CI) | P value | P for interaction |
| --- | --- | --- | --- | --- |
| Age tertile (y): | | | | 0.557 |
| < 35 | 1415 (93.83%) | -0.18 (-0.26, -0.11) | <0.0001 | |
| ≥ 35 | 93 (6.17%) | -0.11 (-0.45, 0.23) | 0.5300 | |
| BMI categories: | | | | 0.393 |
| Normal weight | 628 (41.64%) | -0.19 (-0.29, -0.09) | 0.0002 | |
| Overweight/obese | 880 (58.36%) | -0.25 (-0.35, -0.16) | <0.0001 | |
| AMH (ng/ml): | | | | 0.2802 |
| ≤ 5 | 382 (26.45%) | -0.14 (-0.27, -0.01) | 0.0378 | |
| > 5 | 1062 (73.55%) | -0.21 (-0.30, -0.12) | <0.0001 | |
| Initial Gn dose (IU): | | | | 0.9251 |
| < 150 | 891 (59.08%) | -0.18 (-0.27, -0.08) | 0.0002 | |
| ≥ 150 | 617 (40.92%) | -0.16 (-0.28, -0.04) | 0.0089 | |
| COS procotol | | | | 0.9834 |
| GnRH agonist | 1268 (84.08%) | -0.18 (-0.26, -0.10) | <0.0001 | |
| GnRH antagonist | 240 (15.92%) | -0.16 (-0.35, 0.03) | 0.1031 | |
## Discussion
In this study, we explored the association of HOMA-IR with OSI in a relatively large cohort of women with PCOS. Few studies have been performed on the association between HOMA-IR and OSI. To our knowledge, only one research depicted a decreased OSI in 131 IR-PCOS women compared with 52 non-IR PCOS subjects [11]. However, the sample size was small and the effect size of IR on OSI was unclear. The findings of this study showed that HOMA-IR inversely and consistently correlated with OSI in PCOS patients undergoing ART after adjusting for potential confounders. The present study indicated that IR may be associated with reduced ovarian sensitivity to exogenous Gn during COS.
PCOS has been suggested to possess heterogeneous subpopulations, including lean PCOS, overweight/obese PCOS and PCOS women with serum AMH > 5 ng/ml [25, 26]. A high level of AMH (> 5 ng/ml) has been reported to be correlated with ovarian hyper-response [27, 28]. Interestingly, our results indicated that the negative association of HOMA-IR with OSI remained consistent in BMI and AMH subgroups, suggesting the inverse association between HOMA-IR and OSI was independent. Thus, we assumed that the negative association between HOMA-IR and OSI may be intrinsic to PCOS, which should be managed early on.
Mechanisms underlying decreased ovarian sensitivity in PCOS women with IR have not yet been determined. Johnstone et al. proposed a role for insulin in suppressing growth of 5-10mm follicles in the follicular phase which may contribute to anovulation in PCOS [17]. Women with PCOS exhibited diminished initial E2 responses to FSH compared with controls [29]. Evidence from IR mouse models indicated that maternal IR contributed oxidative stress and defective mitochondrial function in germinal vesicle (GV) and metaphase II (MII) oocytes, which potentially impaired oocyte quality [30]. Although it is thought that genetic variations of FSH receptors influence the degree of ovarian response to stimulation [31]. However, studies performed on this subject showed contradictory results. A difference in response for specific FSH receptor subtypes may be very small, and not likely to be the basis for the wide variation in the number of oocytes retrieved in response to COS [32, 33]. In light of these observations, IR may be linked to ovarian dysfunction in PCOS.
IR plays a key role in the multisystem pathophysiology of PCOS. The reported prevalence of IR in women with PCOS has ranged from about $12\%$ to over $60\%$ due to the use of different cutoffs, different tests, and different populations [34]. HOMA-IR is a measurement frequently used in clinical studies, but no established cutoffs exist [34, 35]. Researchers have used different methods to describe cutoff values such as the 66th percentile, the top quartile, and the 90th or 95th percentile (35–37). Till now, IR have been reported to occur at HOMA-IR levels that range from 2.1 to 3.8 (36, 38–40). Many studies also selected HOMA-IR of 2.5 as an indicator of IR based on the original study by Matthews et al. [ 24]. In our study, similar to the measurements of IR abovementioned, our results indicated that PCOS women who had HOMA-IR values of tertile 2 (2.32 - 3.87) and tertile 3 (> 3.87) had significant decreases in OSI values. Besides, the negative association between HOMA-IR and OSI remained consistent in multivariable linear regression analysis, which suggested IR may be associated with decreased ovarian sensitivity.
ART for PCOS patients is always challenging due to the exaggerated or suboptimal ovarian response to Gn. The current results indicated that PCOS women with high HOMA-IR values entailed an extended stimulation phase and a higher number of Gn ampules, which could lead to a decreased OSI. OSI is a better representation of ovarian response rather than the number of oocytes retrieved and the total Gn dose [41]. Moreover, the subgroup analysis showed the negative association of HOMA-IR with OSI remained consistent in stratification of initial Gn dose. A low-dose Gn stimulation strategy for PCOS patients was recommended [16]. As previously reported, it was suggested that a low starting Gn dose of < 150 IU/day and 25-IU incremental doses every third day should be considered in a COS protocol for PCOS patients with a high HOMA-IR score [42]. It is necessary to decide on both the initial Gn dose and the incremental dose when a low-dose step-up regimen is used. The initial Gn dose was usually calculated depending on age, BMI and ovarian reserve. In this regard, by taking into account these parameters as well as HOMA-IR score, and hence adjusting the starting and incremental Gn dose appropriately, the value of OSI may be increased and the ART outcome may be improved.
In addition, we found the early miscarriage rate was significantly increased in the group of patients with high HOMA-IR values. No significant differences were obtained in implantation rate, clinical pregnancy rate and late miscarriage rate. The findings were in line with the previous studies, which indicated the adverse effect of IR on reproduction [43, 44].
Some limitations existed in our study. First, it was not designed as a prospective study. Chinese ethnicity of our participants may limit generalization of the findings to different ethnic groups. Patient diagnosis, age, and COS protocols may vary from study to study. Moreover, the sample size in PCOS subgroups with age > 35y and using GnRH antagonist protocol was small. As reported, it is advantageous to consider ART outcomes from all cycles in order to draw clinically relevant inferences and to maximize study power [45]. In this study, we restricted analysis to the first cycle because of the concern for potential bias, such as weight loss or anti-hyperinsulinemia medications.
## Conclusion
In conclusion, this study, carried out in a cohort of PCOS women undergoing ART, demonstrated that HOMA-IR value was negatively associated with OSI. By taking into account the insulin resistant status, it may help clinicians for individualized ovarian stimulation in PCOS patients. Future research will be needed to validate our results and investigate the mechanistic links between IR and ovarian sensitivity.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Material, further inquiries can be directed to the corresponding author.
## Ethics statement
The study was approved by the Ethics Committee of the Henan Provincial People’s Hospital (No. 2022139). The study was conducted in accordance with the Helsinki Declaration and patients’ records were anonymized prior to analysis. The need for individual consent was waived by the committee due to the retrospective character of the study.
## Author contributions
YL and YW conceived the study. HL critically reviewed the study and helped to draft the manuscript; YL wrote the manuscript. SZ and CZ participated in its design and coordination. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1117996/full#supplementary-material
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|
---
title: Alterations in white matter integrity and network topological properties are
associated with a decrease in global motion perception in older adults
authors:
- Shizhen Yan
- Yuping Zhang
- Xiaojuan Yin
- Juntao Chen
- Ziliang Zhu
- Hua Jin
- Han Li
- Jianzhong Yin
- Yunpeng Jiang
journal: Frontiers in Aging Neuroscience
year: 2023
pmcid: PMC10034108
doi: 10.3389/fnagi.2023.1045263
license: CC BY 4.0
---
# Alterations in white matter integrity and network topological properties are associated with a decrease in global motion perception in older adults
## Abstract
Previous studies have mainly explored the effects of structural and functional aging of cortical regions on global motion sensitivity in older adults, but none have explored the structural white matter (WM) substrates underlying the age-related decrease in global motion perception (GMP). In this study, random dot kinematogram and diffusion tensor imaging were used to investigate the effects of age-related reductions in WM fiber integrity and connectivity across various regions on GMP. We recruited 106 younger adults and 94 older adults and utilized both tract-based spatial statistics analysis and graph theoretical analysis to comprehensively investigate group differences in WM microstructural and network connections between older and younger adults at the microscopic and macroscopic levels. Moreover, partial correlation analysis was used to explore the relationship between alterations in WM and the age-related decrease in GMP. The results showed that decreased GMP in older adults was related to decreased fractional anisotropy (FA) of the inferior frontal-occipital fasciculus, inferior longitudinal fasciculus, anterior thalamic radiation, superior longitudinal fasciculus, and cingulum cingulate gyrus. Decreased global efficiency of the WM structural network and increased characteristic path length were closely associated with decreased global motion sensitivity. These results suggest that the reduced GMP in older adults may stem from reduced WM integrity in specific regions of WM fiber tracts as well as decreased efficiency of information integration and communication between distant cortical regions, supporting the “disconnection hypothesis” of cognitive aging.
## Introduction
Global motion perception (GMP) is a fundamental visual process that refers to the ability to combine local motion signals within a visual scene into a global percept to obtain information about motion speed and direction (Narasimhan and Giaschi, 2012). For example, in a football scene, the trajectory of each player (a local moving element) constantly changes, but the audience can obtain a global precept of the entire scene by integrating the trajectory of all players (e.g., the players on the field advancing toward a team’s goal). This perceptual process plays an important role in navigation, judgment of motion speed, and avoidance of moving obstacles (Hoffman et al., 2015). One method extensively used to assess GMP is the random dot kinematogram (RDK), which consists of dots moving in various directions: signal dots move in a specific direction, while noise dots move in random directions (Pilz et al., 2017; Ward et al., 2018; Benassi et al., 2021; Joshi et al., 2021). The task is to identify the global direction of the moving dots. GMP is evaluated by measuring the individual motion coherence threshold (MCT): the minimum proportion of signal dots required to correctly identify the direction of global motion. A higher MCT indicates poorer performance and worse global motion sensitivity. Several studies have employed the RDK paradigm to study the effect of age on GMP. These studies found that older adults have reduced global motion sensitivity and significantly higher MCT than younger adults (Snowden and Kavanagh, 2006; Roudaia et al., 2010; Bower and Andersen, 2012). Several studies have shown that aging of the GMP is associated with a decreased ability to perceive hazards while driving (Wilkins et al., 2013; Lacherez et al., 2014). In addition, Yamasaki et al. [ 2016] reported that GMP aging may also be a predictor of cognitive decline in older adults. Thus, age-related declines in GMP are not only detrimental to the quality of life of older people, but also pose a serious threat to their safety as well.
Many studies have attempted to determine the neural mechanism underlying age-related decreases in GMP in older adults. Nevertheless, these studies have mainly focused on the effects of age-related structural and functional alterations in specific cerebral regions (Biehl et al., 2017; Ward et al., 2018; Jin et al., 2020, 2021). To date, little is known about the impact of white matter (WM) degeneration with age on GMP. WM fiber tracts are anatomical substrate underlying information transmission across various cerebral regions and are responsible for enabling information transfer between neurons and coordinating the fundamental functions of brain regions. According to the “disconnected brain” hypothesis of cognitive aging, decreases in structural and functional connectivity contribute to cognitive decline (Damoiseaux, 2017; Fjell et al., 2017; Coelho et al., 2021). More rapid higher-order cognitive functions require efficient communication across brain regions, but age-related alterations in the microstructural architecture of WM fiber tracts disrupt this communication, reducing cognitive function.
Diffusion tensor imaging (DTI) has been widely used to track alterations in WM underlying cognitive aging at a microstructural level. This method provides WM parameters that quantify and characterize the directionality and magnitude of water molecules in brain tissues and address WM integrity (Basser et al., 1994). Four parameters are commonly used to assess WM integrity: fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). FA reflects the anisotropy of water molecule diffusion; MD represents the mean diffusivity of water molecules; AD reflects the diffusivity of water molecules parallel to the axon fibers; and RD indicates the diffusivity of water molecules perpendicular to the axon fibers. Higher FA values and lower MD, AD, and RD values indicate better microstructural integrity of brain tissues (Bennett and Madden, 2014). Numerous DTI studies have found a link between decreased WM integrity and cognitive impairment with age (Bennett and Madden, 2014; de Lange et al., 2016; Merenstein et al., 2021). These studies primarily concentrated on executive function, information processing speed, memory, and general cognitive ability; their findings supported the “disconnected brain” hypothesis (Sullivan and Pfefferbaum, 2007; Davis et al., 2009; Borghesani et al., 2013; Bennett and Madden, 2014; de Lange et al., 2016; Coelho et al., 2021). For example, the longitudinal study by Coelho et al. [ 2021] showed that FA in the corpus callosum (CC) and superior longitudinal fasciculus (SLF) significantly decreased in older adults, and this reduction in WM integrity was significantly associated with decreases in memory, executive function, and general cognitive performance.
Many researchers have investigated the neural underpinnings of GMP in WM (Csete et al., 2014; Braddick et al., 2017; Pamir et al., 2021). Using diffusion magnetic resonance imaging (dMRI), Csete et al. [ 2014] investigated the WM microstructure during motion detection; they observed that the local FA in specific WM regions (e.g., the left optic radiation) of adults was significantly correlated with their motion detection threshold. Advanced probabilistic tractography revealed that the SLF may be the tract that is closely associated with the GMP. Subsequently, Braddick et al. [ 2017] found that global motion sensitivity in children was positively correlated with FA in the right SLF and negatively correlated with FA in the left SLF. Furthermore, Pamir et al. [ 2021] recruited patients with impairment in the visual cortex and observed that the higher the RD in the tracts that connect the right V1 and V5 was, the worse the GMP. These studies demonstrate that GMP has neural correlates, providing indirect evidence for the hypothesis that age-related decreases in GMP may be associated with WM degradation.
In addition, researchers have not only explored changes in WM integrity but have also applied graph theoretical analysis to construct a WM structural network at a macroscopic level to quantitatively assess individual information transmission efficiency, network integration, and functional differentiation by using DTI. Graph theoretical analysis offers a global perspective that overcomes the limitations of previous research, in which each brain region was viewed as a discrete anatomical neural structure. This method provides new insights into the neural activity patterns of the brain and the connectivity mechanisms of various cognitive functions. Moreover, the quantitative topological measures of the WM structural network are sensitive to individual aging. Gong et al. [ 2009] applied graph theoretical analysis and that the local efficiency and overall cortical connectivity of the WM network decreased with increasing age after maturity. Moreover, older adults’ cognitive function is impacted by this reduction in information integration. With 342 healthy older adults, Wen et al. [ 2011] explored the relationship between the WM network and multidimensional cognition and found that discrete neuroanatomical networks were highly associated with cognitive performance in specific domains, such as processing speed and visuospatial and executive function. Thus, exploration of GMP-related WM substrates can enhance understanding of neurological changes associated with age-related decreases in GMP from the perspective of information integration and transmission efficiency.
The aim of the present study was to investigate the effects of age-related reduction in WM integrity and connectivity on GMP at WM microscopic and macroscopic levels using RDK and DTI. Previous studies have revealed that GMP mainly depends on regions of the dorsal visual stream and the parietal lobe (Biehl et al., 2017; Chaplin et al., 2017; Sousa et al., 2018; Ward et al., 2018). Our hypotheses were as follows: [1] Changes in the integrity of WM fiber tracts connecting the visual cortex to other cortical regions would be significantly correlated with changes in GMP in older adults. For example, the SLF provides bidirectional connections among the parietal, frontal, occipital, and temporal lobes and may thus play an important role in transmitting motion information from the occipital lobe to the parietal and prefrontal areas (Kamali et al., 2014); we predicted that its changes in the integrity would be correlated with changes in GMP. [ 2] Older adults would exhibit a reduced ability to integrate information in the WM network, as indicated by significant changes in topological properties (e.g., a significant increase in characteristic path length) that affect GMP.
## Participants
We recruited 118 older and 113 younger adults for this study through an advertisement. The older adults were locals aged 60 years or older from Tianjin, China, and the younger adults were healthy university students. Before the formal experiment, all participants underwent screening for visual function and MRI contraindications. Vision screening was designed to assess the participants’ eye health, i.e., whether there were physiological or pathological abnormalities of the visual system such as myopia, hyperopia, amblyopia, glaucoma, cataracts and age-related macular degeneration. The younger adults completed this assessment by self-reported questionnaires, while the older adults completed it with the help of two clinicians. In addition, the mental health of older adults was assessed using the Mini-Mental State Examination (MMSE), and their brain aging and pathological abnormalities were examined by two imaging clinicians.
Twenty-one older participants were excluded from the study because of the following structural abnormalities: brain tumors, atrophy, leukoaraiosis, infarcts, and cystic lesions. A total of 3 older and 7 younger participants were additionally excluded due to head movement artifacts (ring) in structural images or geometric distortions in DTI. Finally, 106 younger adults aged 18–27 years (23.04 ± 5.17 years old, 66 female) and 94 older adults aged 60–84 years (65.74 ± 4.50 years old, 55 female) were included in this study. The final participants met the following criteria: [1] normal or corrected-to-normal vision; [2] no significant history of neurological or psychiatric disease, serious physical illness, or substance abuse; [3] no contraindications to MRI or structural brain abnormalities; and [4] older adults scored more than 24 points on the MMSE (scores range: 26 ~ 30; M ± SD: 28.80 ± 1.18) (Porter et al., 2017). All participants provided written informed consent and received payment for their participation. The study was approved by the Ethics Committees of Tianjin First Central Hospital and Tianjin Normal University.
## RDK
The stimuli were created in MATLAB 2015a (MathWorks Inc. Natick, MA, United States), generated using a 17-inch HP Zbook17 G3 workstation, and displayed at a resolution of 1920 × 1,080 pixels (refresh rate of 60 Hz) and a mean luminance of 180 cd/m2. A horizontally oriented RDK paradigm was used to assess individual GMP. The stimulus was presented in a circular aperture with a diameter of 11° at the center of the black screen, and it contained 1,00 white dots. The dot diameter was 2.16 arcmin, and the dot density was 0.88 dots/cm2. All dots had a limited lifetime of 500 ms (equivalent to 10 frames). The position of each dot was randomly allocated at the beginning of each trial. Some of these dots (signal dots) moved horizontally to the left or right in a coherent manner. Once the dot moved out of the stimulus region, it was placed at a random position within the aperture, and set to move in the same direction as before. The other parts of dots (noise dots) are plotted in new locations, randomly selected within the display area, on each frame of the sequence. The global motion direction of each trial was randomly assigned, but the numbers of leftward and rightward trials were equal overall. The speed of the dots was set at 1°/s, 1.4°/s, or 1.8°/s to prevent potential anticipation or adaptation effects (Berry et al., 1999; Anton-Erxleben et al., 2013).
We employed a three-down/one-up ($79.37\%$ correct) adaptation staircase procedure to control the coherence level of moving dots. In other words, if three consecutive accurate responses were given, the coherence level was reduced by one step; if one incorrect response was given, it was increased by one step. For each session, there were eight reversals in coherence. The starting coherence level of the dots was $100\%$; the decrements had a step size of $10\%$ (for the first two reversals) and $5\%$ (for the third to eighth reversals). We calculated the average coherence level for the third to eighth reversals and regarded it as the global MCT for each participant. The MCT is the ratio of the minimum number of signal dots to the total number of dots required for an individual to identify the global motion direction of an RDK.
Participants sat in front of the center of the screen with a viewing distance of 60 cm. At the beginning of the sequence, a red fixation point was presented, followed by an RDK. Then, the participants were instructed to complete a two-alternative forced-choice task indicating whether the global direction of the RDK was to the left or to the right as quickly and accurately as possible. After participants responded, a white dot appeared, indicating the beginning of the next trial (Figure 1). The test consisted of six blocks, each block containing 60 trials, and was conducted in two sessions. Before the formal test, participants were given 20 practice trials to familiarize themselves with the procedure. The participants were able to control the rest time between blocks. The entire test took approximately 30 min to complete.
**Figure 1:** *The random dot kinematogram paradigm (RDK). The stimuli contained some white luminance dots moved horizontally to the left or right coherently (signal dots) and other dots moved randomly (noise dots).*
## MRI data acquisition
Both older and younger adults were scanned using a 3.0 T Prisma (Siemens Healthcare, Germany) MRI scanner with a standard 64-channel head coil at the Brain Imaging Research Centers of Tianjin First Central Hospital and Tianjin Normal University, respectively. During the scan, the participants lay flat in the scanner with their heads immobilized by cushions to reduce head movement and wore earplugs to reduce noise and enhance comfort. For each participant, whole-brain anatomical data were collected using a T1-weighted 3-D MPRAGE sequence with the following parameters: echo Time (TE) = 2.98 ms, field of view (FOV) = 256 × 256 mm2, acquisition matrix size = 256 × 256, and voxel size = 3.5 mm, 176 layers, slice thickness = 1 mm, repetition time (TR) = 2,300 ms (older adults) or 2,530 ms (younger adults), and duration of approximately 8 min. DTI was performed using an echo planar imaging (EPI) sequence with the following parameters: TR = 8,500 ms, TE = 63 ms, FOV = 224 mm × 224 mm, acquisition matrix size = 112 × 112, 75 layers, slice thickness = 2 mm, b value = 1,000 s/mm2, 64 diffusion gradient coding directions, and duration of 10 min and 56 s. Older adults underwent two routine scans to exclude individuals with structural brain alterations: tse2d1_15: TR = 3,500 ms, TE = 89 ms, TA = 68 s, FA = 150°, slice thickness = 5 mm, FOV = 195 mm × 240 mm, matrix = 250 × 384, slices = 22. * fl2d1: TR = 250 ms, TE = 2.43 ms, TA = 70 s, FA = 85°, slice thickness = 5 mm, FOV = 195 mm × 240 mm, matrix = 250 × 384, 22 slices.
## DTI data preprocessing and tract-based spatial statistics analysis
DTI data preprocessing was performed using PANDA (Pipeline for Analyzing Brain Diffusion) software,1 which is an automated toolbox for dMRI analysis. In brief, the preprocessing included the following steps: [1] conversion of DICON files to NIfTI images; [2] brain tissue extraction; and [3] correction for head motion artifacts and eddy current distortions. To avoid image distortion, each image was coregistered to the b0 image. Additionally, [4] gradient orientation correction was performed based on the deformation field to estimate the tensor and fiber direction more accurately, and [5] four dMRI measures (FA, MD, AD, and RD) were calculated by fitting a diffusion tensor model.
Tract-based spatial statistics (TBSS) in FSL2 software was performed to enable voxelwise comparison between the groups. In brief, the analysis steps were as follows: [1] individual FA images were aligned to the mean FA standard template (FMRIB58_FA) in Montreal Neurological Institute (MNI) space using the nonlinear registration algorithms of FNIRT; [2] the mean of all aligned FA images was calculated and skeletonized to generate a WM FA skeleton, with analysis limited to major WM tracts using a threshold of FA > 0.2; and [3] individual FA values (obtained by finding the maximum value perpendicular to the local skeletal structure from the nearest skeletal center) were projected onto the mean FA skeleton. These steps were repeated to calculate individual MD, AD, and RD and project these maps onto the mean FA skeleton.
## WM network construction
After preprocessing, a WM structural network, consisting of a collection of nodes connected by edges, was constructed using PANDA. In the present study, the automated anatomical labeling 90 atlas (AAL 90) was used to define the nodes of the WM network, which included a total of 90 cortical and subcortical regions (45 for each hemisphere). To transform the AAL template in MNI space to the DTI space, where the subject data were located, we first coregistered the individual T1 structural images to b0 images with transformations. Then, a nonlinear transformation to register the aligned T1 images in MNI space to the AAL template was applied. Finally, the AAL template in MNI space was transformed into the individual DTI space using the inverse transform to locate the 90 nodes for each participant.
The edges represent the WM connectivity or features of the brain between two nodes. We used the Fiber Assignment by continuous tracking algorithm (FACT) for deterministic tractography to define the connectivity between nodes. Fiber tracking was performed using each voxel with FA greater than 0.2 as a seed point, and tracking was stopped when the turning angle exceeded 35°. Each pair of nodes was considered structurally connected if there was at least one streamline whose end points were located in the pair. The mean FA of the streamline linking the two nodes was defined as the edge and used to construct an FA-weighted matrix. Finally, the FA-weighted structural network was obtained for each participant from their DTI data, which was represented as a 90 × 90 symmetric matrix.
## Graph theoretical analysis
To characterize the topological organization of WM structural connections, topological properties were calculated by the GRETNA toolkit.3 The following four global topological properties were used in this study: the global clustering coefficient (Cp), characteristic path length (Lp), global efficiency (Eg), and local efficiency (Eloc). Cp is mainly used to measure the extent of local clusters or cliquishness of the network. Lp indicates the length of the shortest path of information from one node to another in the network and reflects the extent of the overall routing efficiency of a network. E.g., measures the global information propagation of the network. Eloc represents the local efficiency of the network. We focused on these global network properties to examine the efficiency of global integration and segregation of information flow. We also assessed small-world properties (λ, σ, and γ). Briefly, normalized path length (λ) is a measure that reflects the global integration of the brain, while the normalized clustering coefficient (γ) represents global segregation. Detailed calculations and interpretations of the topological properties are seen in Table 1 and Rubinov and Sporns [2010]. We calculated these two properties at each sparsity threshold (0.05 ~ 0.5, step 0.05) and computed the respective area under the curve (AUC) over the range of sparsity thresholds. The ratio between segregation and integration is the small-worldness (σ) of a network. In a network, λ ≈ 1 and γ ≫ 1 suggest an optimal balance between functional segregation and integration.
**Table 1**
| Network parameters | Definitions | Descriptions |
| --- | --- | --- |
| Global parameters | Characteristic path length Lp=1N(N−1)∑i≠j∈GLij | lij is the shortest absolute path length between nodes i and j. N is the total number of nodes and G is the set of all nodes. Paths are sequences of distinct nodes and links in the network to represent potential routes of information flow between pairs of brain regions. |
| | Clustering coefficient Cp=1N∑i=1EiDnod(i)(Dnod(i)−1)/2 | Dnod(i) is the degree of node i, Ei is the number of edges in the subgraph of node i and N is the number of nodes in the network. |
| | Global efficiency Eg(G)=1N(N−1)∑i≠j∈G1Lij | Eg is computed on disconnected networks. Paths between disconnected nodes are defined to have infinite length and correspondingly zero efficiency. |
| | Local efficiency Eloc(G)=1N∑i∈GEg(Gi) | Gi denotes the subgraph composed of the nearest neighbors of node i. |
| Small-world parameters | Normalized clustering coefficient γ=Cpreal/Cprand | Cpreal is the clustering coefficient of the real network and Cprand is the mean clustering coefficient of 100 matched random network. |
| | Normalized path length λ=Lpreal/Lprand | Lpreal is the characteristic path length of the real network and Lprand is the mean characteristic path length of 100 matched random network. |
| | Small-worldness σ=γ/λ | network is said to be small-world if it satisfies λ ≈ 1 and γ > > 1, or δ = γ/λ > > 1. Small-world organization reflects an optimal balance of functional integration and segregation. |
## Data analysis
The mean MCT at the three dot speeds for each participant was calculated as the individual GMP. Independent-sample t tests were used to analyze group differences in age, body mass index (BMI), and the MCT. The chi-square test was used to analyze sex differences between the two groups.
To investigate age-related alterations in WM microstructure, a permutation-based nonparametric inference was performed in Randomise4 to compare voxelwise differences in the FA skeleton between the younger and older groups. The number of random permutations was set to 5,000, and the threshold-free cluster enhancement (TFCE) method was used to correct for multiple comparisons. Subsequently, to explore the relationship between age-related decreases in GMP and changes in WM integrity, we applied masks to WM regions that differed significantly between groups, and partial correlation analyses between the MCT and FA in these masks were performed separately for the older and younger groups. As sex and BMI may affect individual WM integrity and the present study was not concerned with the effects of these factors, these variables were controlled as covariates (Daoust et al., 2021). The significance threshold of the partial correlation coefficient was set at $p \leq 0.05$, and the same TFCE method was used to correct for multiple comparisons.
We segmented the correlated regions according to the “JHU White-Matter Tractography Atlas” to visualize the location of WM fiber tracts associated with age-related decreases in GMP. We focused on only the tracts that contained at least one cluster with a voxel number greater than 100. The mean FA was extracted from the significant clusters of each tract, and a partial correlation analysis was performed between the mean FA and MCT to determine the strength of their correlation. Finally, we also extracted the AD and RD of older and younger adults in the same significant regions of tracts to investigate whether FA alterations associated with age-related decreases in GMP were impacted by changes in RD or AD. For this analysis, we first used independent-sample t tests to compare RD and AD between the older and younger groups and then performed partial correlation analyses of the MCT with RD and AD in the older and younger groups.
To examine the changes in the topological organization of the WM network with age, we first examined the group differences in small-world properties (λ, σ, and γ) and global topological properties (Cp, Lp, Eg, and Eloc) between older and younger groups using independent-sample t tests. Second, partial correlations between topological properties and the MCT were determined, with sex and BMI as covariates, in the older and younger groups to explore the relationship between alterations in network topology and age-related decreases in GMP. Multiple comparisons were corrected using the Bonferroni method.
## Behavioral results
The demographic information and GMP of the participants are shown in Table 2. The results showed statistically significant differences in age and BMI between the older and younger groups but no significant difference in the sex ratio. Since BMI may affect individual white matter integrity, this variable was controlled as a covariate in subsequent analyses (Daoust et al., 2021). Independent-sample t tests showed that the MCT of the older group was significantly higher than that of the younger group, indicating a decline in GMP and a decrease in global motion sensitivity with age.
**Table 2**
| Term | Older adults (n = 94) | Younger adults (n = 106) | t/χ2 | p | Cohen’s d |
| --- | --- | --- | --- | --- | --- |
| Age | 65.74 ± 4.50 | 23.04 ± 5.17 | 61.94 | <0.001 | 8.78 |
| Sex (female/male) | 55/39 | 66/40 | 0.29 | 0.59 | |
| BMI | 24.08 ± 2.91 | 21.42 ± 3.28 | 6.06 | <0.001 | 0.86 |
| MCT | 34.37 ± 25.06 | 18.66 ± 12.19 | 5.74 | <0.001 | 0.81 |
## TBSS results
The older group exhibited significantly lower FA in most regions of the WM skeleton than the younger group, suggesting that integrity in most WM regions decreases with age. In addition, a small fraction of voxels exhibited a significant increase in FA in older adults (Figure 2).
**Figure 2:** *Results of the tract-based spatial statistics of fractional anisotropy between the older and younger adults. (A) the older adults showed lower FA than the younger adults in a large portion of the WM skeleton, including IFOF, ILF, SLF, ATR, CCG, Fmaj, and Fmin. (B) the older adults showed higher FA than the younger adults in a small fraction of voxels (e.g., brainstem). IFOF, inferior frontal-occipital fasciculus; ILF, inferior longitudinal fasciculus; ATR, anterior thalamic radiation; SLF, superior longitudinal fasciculus; CCG, cingulum cingulate gyrus.*
For older adults, partial correlation analysis showed that the MCT was significantly negatively correlated with FA in four main clusters (Table 3). The effects were spread over large portions of the WM skeleton, including the bilateral CC forceps major (Fmaj) and minor (Fmin), inferior frontal-occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF), anterior thalamic radiation (ATR), SLF, and cingulum cingulate gyrus (CCG). We located the significant regions using the “JHU White-Matter Tractography Atlas,” and the voxel size of each fiber tract is shown in Table 4. We did not find any significant positive correlation between the MCT and FA in the WM regions that differed significantly between groups. We extracted the average FA in the regions of each WM tract that showed significant negative correlations with MCT and plotted the correlation between FA and the MCT to visualize the correlation strength. With sex and BMI included as covariates, the MCT was significantly negatively correlated with FA in the Fmin (r = −0.32, $$p \leq 0.002$$), Fmaj (r = −0.21, $$p \leq 0.044$$), left ATR (r = −0.32, $$p \leq 0.002$$), right ATR (r = −0.34, $$p \leq 0.034$$), left CCG (r = −0.26, $$p \leq 0.011$$), right CCG (r = −0.31, $$p \leq 0.003$$), left IFOF (r = −0.23, $$p \leq 0.027$$), right IFOF (r = −0.22, $$p \leq 0.033$$), and left SLF (r = −0.27, $$p \leq 0.010$$). The FA in the right SLF (r = −0.20, $$p \leq 0.058$$) and left ILF (r = −0.20, $$p \leq 0.055$$) was marginally significantly correlated with the MCT (Figure 3). In the young adults, there was no significant correlation between the MCT and FA in the WM regions that differed significantly between groups.
To explore the underlying causes of FA alterations leading to GMP decline, we additionally extracted the mean AD and RD in the relevant regions of each tract. The results showed that both the AD and RD in each tract in older adults were significantly greater than those in younger adults. The results of partial correlation analysis with sex and BMI as covariates showed that, except for the Fmaj and right SLF, the mean RD in most tracts was positively correlated with the MCT in older adults (Figure 4). However, the MCT was only positively correlated with the AD in the left ATR ($r = 0.30$, $$p \leq 0.003$$).
**Figure 4:** *Results of the partial correlation analysis between RD and MCT in older adults. Except for Fmaj and right SLF, mean RD in most tracts showed a positive correlation with MCT of the older adults.*
## Results of the graph theoretical analysis
The results showed that the WM networks of both the older and younger adults exhibited “small-world” properties (𝜆≈1, γ> > 1) (Figure 5A). Small-world networks have high local and global efficiency, requiring minimal connectivity costs and resulting in a balance between local processing and global integration (Watts and Strogatz, 1998). In addition, both global efficiency and the characteristic path length were found significantly decreased in older adults compared to younger adults, as shown in Table 5 and Figure 5B. Therefore, these results confirm that significant network changes occur in older adults. The results of partial correlation analyses revealed that the MCT was significantly negatively correlated with global efficiency (r = −0.29, $$p \leq 0.005$$) and significantly positively correlated with characteristic path length ($r = 0.32$, $$p \leq 0.002$$) in older adults, and these results survived correction for multiple comparisons. However, we did not find any significant correlations between the MCT and topological properties in younger adults.
**Figure 5:** *Group differences between small-world properties (λ, σ, γ) and global topological properties (Lp, Cp, Eloc, and Eg) of the WM network. 𝜆≈1 and γ> > 1 indicated both the older and younger adults exhibited economic “small-world” properties (A). The older adults showed increased Lp and decreased Eg compared to younger adults. There was no significant difference in Cp and Eloc between the two groups (B). Cp, the global clustering coefficient; Lp, characteristic path length; Eg, global efficiency; Eloc, local efficiency; λ, normalized path length; γ, normalized clustering coefficient; σ, small-worldness, ***$p \leq 0.001.$* TABLE_PLACEHOLDER:Table 5
## Discussion
In the present study, we attempted to explain the age-related decline in GMP according to age-related WM changes in the brain. TBSS analysis and graph theoretical analysis of dMRI data were used to assess WM integrity and construct structural networks that represented the anatomical connectivity of the cerebral cortex at the microscopic and regional levels. The behavioral findings, with the older group exhibiting significantly higher MCT values than the younger group (indicating a decrease in GMP with age), are consistent with previous literature (Biehl et al., 2017; Conlon et al., 2017; Meier et al., 2018; Mikellidou et al., 2018). According to the correlation analyses, the decreased GMP in older adults was associated with decreased WM integrity in specific tracts as well as alterations in characteristic path length and global efficiency in the WM network. The findings support the “disconnection hypothesis” of the aging brain with data on the transmission efficiency of the WM network.
## Microstructural changes in WM associated with age-related decreases in GMP
Older adults showed decreased FA in the majority of WM compared to younger adults, indicating that WM integrity decreased with age; this finding is consistent with the literature (Benitez et al., 2018; Molloy et al., 2021; Dhiman et al., 2022). More importantly, the results revealed correlations between the decrease in global motion sensitivity and decreased WM integrity in the IFOF, ILF, SLF, ATR, CCG, Fmaj, and Fmin. The CC is the largest commissural fiber, and the main WM tract connects the two hemispheres of the brain. This tract is crucial for the transmission of sensory, motor, and cognitive information between the brain hemispheres (Aboitiz et al., 1992; Gazzaniga, 2000). The *Fmaj is* the callosal tract that connects the bilateral occipital lobes through the splenium. In motion processing, the *Fmaj is* associated with visuomotor integration between the two hemispheres (Miller, 1991; Mordkoff and Yantis, 1991; Tamura et al., 2007). In particular, the Fmaj directly connects the right and left V5 (Strong et al., 2019), a crucial area for global motion processing that is responsible for integrating dynamic local visual information into a global percept (Newsome and Pare, 1988; Britten et al., 1993; Rust et al., 2006; Chakraborty et al., 2017). The Fmin refers to the callosal tract that passes through the rostrum and genu of the CC and bends forward to connect the right and left frontal lobes. The dorsolateral prefrontal cortex of the frontal lobe participates in top-down motor control (Kim and Shadlen, 1999), while the medial prefrontal cortex may be associated with spatial working memory in self-navigation (Sherrill et al., 2015). These results suggest that decreased efficiency of information transmission between the right and left frontal and occipital lobes, especially between the right and left V5, may contribute to the reduction in GMP in older adults.
The SLF, ILF, and IFOF are association fibers. The SLF is a bundle that connects the frontal, occipital, parietal, and occipital lobes on the ipsilateral side; the ILF connects the visual areas in the temporal and occipital areas to the amygdala and hippocampus; and the IFOF is the longest association fiber in the brain, linking the frontal and occipital lobes. These tracts connect the occipital lobe to ipsilateral cortical areas and are thought to be associated with spatial information processing (Vaessen et al., 2016). For example, long-term sports training (e.g., table tennis, gymnastics) can increase the structural integrity of these three tracts (Huang et al., 2015; Qi et al., 2021), while brain injuries to these tracts affect visuospatial processing (Chechlacz et al., 2012; McGrath et al., 2013; Hattori et al., 2018). In the present study, we found that decreased WM integrity in the SLF, ILF, and IFOF leads to a decline in GMP (in terms of behavioral performance) in older adults, consistent with previous findings in region of interest (ROI) analyses that GMP is strongly related to the SLF in adults and children (Csete et al., 2014; Braddick et al., 2017). We suggest that the correlation between association fiber integrity and GMP may be due to GMP requirements. The GMP involves processing of local movement across the visual field and integrating local moving elements into a global percept; these two processes depend on the primary visual cortex, located in the occipital lobe, and the middle temporal gyrus, located in the temporal lobe, respectively (Britten et al., 1993; Rust et al., 2006). In the RDK task, participants need to make decisions about the direction of motion; these decisions mainly depend on the intraparietal sulcus, located in the parietal lobe (Kayser et al., 2010). Lack of structural connections between the occipital lobe and other cortices may cause a decrease in motion sensitivity in older adults, explaining why alterations in the SLF, ILF, and IFOF lead to impaired GMP.
Decreased FA in the ATR and CCG was also associated with decreased GMP. The CCG plays a critical role in cognitive control, conflict monitoring in response selection, and spatial attentional control (Morecraft et al., 1993, 2012; Nieuwenhuis et al., 2003). The reduced WM integrity in the CCG of older adults may thus affect spatial attentional control and decision-making processes involving motion direction in GMP. Additionally, thalamic neurons receive sensory and motion information from the external environment and transmit information to the cerebral cortex through the ATR (Perani et al., 2021). The reduced WM integrity of the ATR may have a negative influence on the transmission of visuomotor information to the cerebral cortex, leading to a reduction in GMP.
In addition, we found that most RD values of GMP-related tracts were significantly correlated with the MCT, which further indicated that the decrease in WM integrity may be caused by the increased RD of tracts in older adults. Different patterns of WM changes have been examined to elucidate aging processes in different tracts and their underlying biological profiles. Molloy et al. [ 2021] identified five main patterns of overlap between diffusion measures in WM areas that showed age-related negative correlations with FA. Consistent with the results reported by Molly et al., we also found decreased FA and increased RD in the Fmaj, Fmin, SLF, ILF, IFOF, and CCG, consistent with the “FA and RD only” pattern. This pattern may reflect age-related demyelination of WM tracts (Sullivan and Pfefferbaum, 2007; Molloy et al., 2021).
## Changes in topological properties of WM networks associated with age-related decreases in GMP
In the WM structural network, global efficiency and the characteristic path length are commonly used measures of functional integration, which refers to the ability to rapidly combine specialized information from distributed cortical regions. The shorter the path length and the higher global efficiency of a network the higher the efficiency of information transmission across network nodes (Watts and Strogatz, 1998). Local efficiency and the global clustering coefficient are considered indicators of network segregation in the brain. Network segregation is the ability of densely interconnected groups of brain regions to perform specialized processing and is thought to reflect the local information transmission of the network (Latora and Marchiori, 2001; Rubinov and Sporns, 2010). Thus, our findings showed that the global efficiency of older adults was significantly lower than that of younger adults, while the characteristic path length of older adults was significantly higher; however, we found no significant difference in local efficiency or the global clustering coefficient between the two groups, indicating that the changes in the connectivity of older adults may reduce parallel information processing and the speed and efficiency of information transmission across brain regions (Achard et al., 2012). Moreover, the age-related decrease in global efficiency and the increase in characteristic path length were closely related to the reduction in global motion sensitivity in older adults, suggesting that the reduction in global motion sensitivity in older adults may be affected by the decrease in information integration and communication efficiency between distant cortical regions. Long-range connectivity (e.g., between the frontal and occipital lobes) is thought to play an important role in visuospatial attention, which is a prerequisite for global motion processing (Barceló et al., 2000; Ruff et al., 2006). Therefore, reduced efficiency of information integration and communication between distant cortical regions in older adults may lead to decreased GMP by affecting individual visuospatial attention.
This study has some limitations. First, aging is a lifelong process, but we selected participants with two discrete age ranges; thus, we were unable to explore the relationship of alterations in WM microstructure and network properties with perceptual changes caused by aging along a continuum. Second, this study mainly examined the alterations in WM integrity in 20 main tracts; we did not assess changes in structural connectivity along the dorsal visual stream (such as the connections between V1 and V5). In previous studies, patients with cortical visual impairments exhibited worse GMP, suggesting that structural disconnection along the dorsal visual stream may also cause a decrease in motion sensitivity in older adults (Pamir et al., 2021). In the future, we plan to explore the effects of structural disconnection along the dorsal visual stream on global motion sensitivity in older adults.
## Conclusion
In this study, we used RDK and DTI to study the effects of age-related reductions in WM fiber integrity and connectivity on the decrease in GMP at the microscopic and macroscopic levels. We found that reduced WM integrity in specific fiber tracts, such as the Fmaj, Fmin, ILF, SLF, ATR, IFOF, and CCG, may underlie age-related decreases in GMP in older adults. Moreover, age-related decreases in GMP may also be associated with reduced information integration and communication efficiency between distant cortical areas. This study demonstrated, for the first time, that age-related reductions in WM integrity and connectivity in older adults affect the efficiency of information transfer between brain regions, leading to a decrease in global motion sensitivity. Our results thus support the “disconnection hypothesis” of cognitive aging.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Ethics Committees of Tianjin First Central Hospital and Tianjin Normal University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
SY, YZ, and HJ gave study conceptualization and design. SY, XY, JC, ZZ, HL, JY, and YJ were involved in data collection. SY and HJ helped with data analysis and interpretation. SY, YZ, and HJ contributed to the supervision of the study procedures. SY and HJ contributed to drafting the manuscript. All authors contributed to the article and approved the submitted version.
## Funding
This work was supported by grants from the National Natural Science Foundation of China [31971021] and Tianjin Postgraduate Research Innovation Project (2019YJSB129).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
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|
---
title: 'Global publication trends and research hotspots of the gut-liver axis in NAFLD:
A bibliometric analysis'
authors:
- Shuangjie Yang
- Deshuai Yu
- Junjie Liu
- Yanfang Qiao
- Shuxiao Gu
- Ran Yang
- Xinlou Chai
- Wei Wang
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10034112
doi: 10.3389/fendo.2023.1121540
license: CC BY 4.0
---
# Global publication trends and research hotspots of the gut-liver axis in NAFLD: A bibliometric analysis
## Abstract
### Background
Nonalcoholic Fatty Liver Disease(NAFLD)refers to a spectrum of diseases ranging from simple liver steatosis to nonalcoholic steatohepatitis (NASH) and cirrhosis. Bidirectional cross-talk between the gut-liver axis plays an important role in the pathogenesis of NAFLD. To learn more about the gut-liver axis in NAFLD, this study aims to provide a comprehensive analysis from a bibliometric perspective.
### Method
Literature related to the gut-liver axis in NAFLD from 1989 to 2022 was extracted from the Web of Science Core Collection. Based on Microsoft Excel, CiteSpace and Vosviewer, we conducted to analyze the number of publications, countries/regions, institutions, authors, journals, references, and keywords.
### Results
A total of 1,891 literature since 2004 was included, with the rapid growth of the number of papers on the gut-liver axis in NAFLD annually. These publications were mainly from 66 countries and 442 institutions. Of the 638 authors analyzed, Bernd Schnabl was the one with the most publications, and Patrice D. Cani was the one with the most co-citations. International Journal of Molecular *Sciences is* the journal with the most articles published, and *Hepatology is* the journal with the most citations. The most common keywords are gut microbiota, inflammation, and insulin instance, which are current research hotspots. Short-chain fatty acid, in vitro, randomized controlled trial in clinical, and diabetes mellitus represent the research frontiers in this field and are in a stage of rapid development.
### Conclusion
This is the first study to conduct a comprehensive bibliometric analysis of publications related to the gut-liver axis in NAFLD. This study reveals that gut microbiota, inflammation, insulin resistance, short-chain fatty acids, and randomized controlled trial will be the hotspots and new trends in the gut-liver axis in NAFLD research, which could provide researchers with key research information in this field and is helpful for further exploration of new research directions.
## Introduction
Nonalcoholic Fatty Liver Disease (NAFLD) is a common chronic metabolic disease. According to statistics [1], its prevalence in adults around the world is about $25\%$. As of the end of 2020, a total of 1 billion people in the world are affected by it, causing great impact on social medical care and economy, it has become a worldwide public issue. NAFLD is a multi-systemic metabolic disease [2], its pathogenesis involves multiple factors such as obesity, insulin resistance, inflammation, etc. At present, the mechanism of NAFLD is not clear yet, and there is no effective treatment for this disease currently exists [3]. Therefore, further exploration on the pathogenesis of NAFLD plays an important role in its prevention and treatment.
Gut-liver axis describes the crosstalk relationship among the liver, the gut, and the gut microbiota [4]. It plays a vital role in the onset and progress of NAFLD [5]. Clinical studies in the past ten years have found that the changes in intestinal microbial flora of NAFLD patients are mainly manifested as the decrease of bacterial diversity [6]. At the same time, studies have shown that high fat diet (HFD) causes microbial group disorders in NAFLD mice, leading to damage of intestinal epithelial barrier and gut vascular barrier (GVB) damage, which promotes inflow of pathogen-associated molecular patterns (PAMPs), further exacerbated inflammation responses [7]. Farnesoid X receptor (FXR) is the primary bile acid receptor in the liver and small intestine. Studies have shown that inhibition of the FXR signal conduction in the intestine could reduce the synthesis of liver fatty acids and the ceramide in the intestine, thereby reducing the accumulation of liver lipids and improving HFD induced NAFLD [8]. In addition, many studies suggest that the intestinal flora and its metabolites play an important role in the onset and progress of NAFLD, which are key targets in the NAFLD treatment [5].
Bibliometrics is a discipline that conducts qualitative and quantitative analysis on the literature system and characteristics through mathematics and statistics [9]. It can reflect the development of a certain field in a global context through visual analysis of the countries, institutions, journals, and authors, etc., it could also access and predict the foundation and emerging trend of scientific research through the co-occurrence and emergence analysis on the references and keywords [10]. VOSviewer and CiteSpace are two most commonly used bibliometric analysis software [11]. Through the bibliometric analysis, researchers can quickly learn about the development status of disciplines and use it to guide future researches. This study is based on the visual analysis with CiteSpace and VOSviewer software to clarify the research situation and trend of NAFLD and gut liver axis over the past 20 years, thus to provide new ideas for the development of NAFLD drugs.
## Data extraction
Literature was extracted from the Web of Science Core Collection database and was downloaded on October 24, 2022. The search strategies was as follows: TS= (NASH) OR TS= (non-alcoholic steatohepatitis) OR TS= (nonalcoholic steatohepatitis) OR TS= (NAFLD) OR TS= (non-alcoholic fatty liver disease) OR TS=(nonalcoholic fatty liver disease)OR TS=(MAFLD) OR TS=(metabolic associated fatty liver disease))AND ((TS= Liver (Topic) or TS= (hepatic*)) AND (TS= (gastro* micro*) or TS= (gastro* flora*) or TS=(gut micro*) or TS=(gut flora) or TS=(intestin * micro*) or TS=(intestin * flora))) OR (TS=(gut−liver axis) or TS=(gut liver axis)).We totally retrieved 2037 records. Then we eliminated invalid documents, including meeting abstracts [88], editorial materials [38], corrections [6], and letters [14]. By this filter, 1891 records were included, of which articles [1209] accounted for $63.93\%$ of the total, followed by reviews (682, $36.06\%$). This data extraction and exclusion were independently by two authors (SJY and DSY) and any disagreement was resolved by consulting the corresponding author (JJL). All data was exported and stored in the Data folder in the download_txt format at last.
## Data analysis
This study conducted visual analysis on data with Microsoft Excel 2021, VOSviewer1.6.18 and CiteSpace6.1. R3.
We analyzed the number of annual publications, the H indexes of countries and authors, the number of co-citations, etc. In all retrieved articles, we found one with publication time of 2023, it was incorporated into 2022 analysis to facilitate data process. VOSviewer and CiteSpace was used to realize the visual analysis of distribution of the countries/regions, authors and co-cited authors, institutions, journals and co-cited journals, co-cited references, and keywords. For the keywords and co-cited references, we then performed clustering and burst detection. The clustering labels refer to the three algorithms provided in the Citespace software, including log-likelihood ratio (LLR), latent semantic indexing (LSI), and mutual information (MI) [12]. Figure 1 shows the flow chart of literature screening and data analysis process.
**Figure 1:** *Flow chart for the analysis of the gut-liver axis in NAFLD researches.*
## Annual publication growth and citation analysis
A total of 1,891 articles were included. The total number of non-self-cited citations of retrieved articles was 36,726, and the average times cited per article is 34.22. The H-index of all articles is 115. Figures 2A, B show the annual number of publications (Np) and annual citations (Nc) of the gut-liver axis in NAFLD related research articles. As shown in Figure 2, the first research article in this field was published in 2004, and its development was divided into three stages with 2009 and 2018 as the boundaries. The first stage is the infant stage from 2004 to 2009, the number of articles in this stage was relatively small, and studies on the gut-liver axis in the field of NAFLD just started. The second stage is the development stage from 2010 to 2018, the annual growth rate of the number of publications increased steadily. The third stage is the outbreak stage from 2019 to 2022, the number of publications on the gut-liver axis in the field of NAFLD increased significantly, and the total number of annual publications reached 441 in 2021. It shows that more and more researchers have begun to pay attention to the potential of the gut-liver axis in the field of NAFLD.
**Figure 2:** *Articles related to the study of the gut-liver axis in NAFLD. (A) The Annual and cumulative publication numbers from 2004 to 2022. (B) Annual number of citations (Nc).*
## Distribution of countries/regions
In the past 20 years, a total of 66 countries/regions have published articles on research of the gut-liver axis in NAFLD, as shown in Figures 3A, B and Table 1, *China is* the country with the largest number of publications (674/$35.64\%$), followed by the United States (520/$27.50\%$) and Italy (187/$9.9\%$). In terms of total citations, the United States publications were cited 28,877 times, followed by China [11,522] and Italy [11,287]. Figures 3C, D shows that Europe, the United States, and Asia are the main countries and regions of publications. The color of the nodes and the thickness of lines represent the strength of cooperation between countries, indicating that the United States and China have relatively closer cooperation. The H-index is a new method for evaluating the academic achievements of researchers, so we also conducted statistical analysis on the H-index, as shown in Figures 3E, F, the H-index of the United States ranks first [87], followed by Italy [57] and China [52]. Combining these indicators, we can see that the United *States is* a relatively leading country in this research field.
**Figure 3:** *Contribution of different countries to the study of the gut-liver axis in NAFLD. (A) The network of collaboration map of countries/regions based on VOSviewer. (B) The density visualization of all participating countries. (C) Geographical distribution map of global publications related to the gut-liver axis in NAFLD. (D) The top 30 countries with the most publications. (E) Top 10 countries for H index. (F) Top 10 in terms of co-citation frequency.* TABLE_PLACEHOLDER:Table 1
## Authors
Since the first article published in the field of the gut-liver axis in NAFLD in 2004, 638 authors have conducted studies related to the gut-liver axis in NAFLD. Visual analysis on the authors was conducted via CiteSpace, each node represents an author, and the larger the node, the higher the number of publications. The map (Figure 4A) had 638 nodes and 1,299 edges, and the network density was 0.064. As shown in Figure 4B and Table 2, Bernd Schnabl (29 publications) had the largest number of publications, followed by Jasmohan S. Bajaj (19 publications), Ki Tae Suk (16 publications), and Antonio Gasbarrini (14 publications). The links between nodes represent the collaboration between the authors, indicating that there is collaborative relationship between the authors. The thicker the line, the closer the cooperation. The top 2 collaborative teams are Bernd Schnabl’s team from the University of California San Diego and KI TAE SUK’s team from Hallym University. Most of the authors included in this study are presented as scattered nodes, suggesting that the collaboration among authors needs to be further strengthened.
**Figure 4:** *Authors involved in the study of the gut-liver axis in NAFLD. (A) Visualization of co-occurrence of authors based on CiteSpace. (B) Top 10 authors in terms of number of publications. (C) A visual map of co-cited authors based on CiteSpace. (D) Top 10 authors in terms of co-citations.* TABLE_PLACEHOLDER:Table 2
## Co-cited authors
Co-cited author refers to two or more authors who are cited by at least one article at the same time, indicating that there are similarities in the research of these two authors. Figure 4C shows network of co-cited authors visually through CiteSpace. The top 10 co-cited authors were cited more than 200 times (Figure 4D, Table 3). Patrice D. Cani ranks first in the top 10 co-citations [434], followed by Peter J. Turnbaugh [351] and Herbert Tilg [351]. The highest centrality was achieved by Shi Qi Yang (0.85), Ernst J. Drenick (0.81) and Keary Cope (0.69), indicating that these authors played a role of bridge in the field.
**Table 3**
| Rank | Author | Institutions | Count |
| --- | --- | --- | --- |
| 1 | Patrice D. Cani | Université Catholique de Louvain | 434 |
| 2 | Peter J. Turnbaugh | University of California San Francisco | 351 |
| 3 | Herbert Tilg | Innsbruck Medical University | 351 |
| 4 | Zobair M Younossi | Falls Church | 348 |
| 5 | Lixin Zhu | Tongji University | 346 |
| 6 | Luca Miele | Università Cattolica del Sacro Cuore | 312 |
| 7 | Jérôme Boursier | University of Angers | 297 |
| 8 | Fredrik Bäckhed | University of Gothenburg | 283 |
| 9 | Jorge Henao-Mejia | University of Pennsylvania | 275 |
| 10 | Jasmohan S. Bajaj | Virginia Commonwealth University | 267 |
## Active institutions
Institutional co-occurrence network analysis was conducted by CiteSpace (Figure 5) to find organizations or institutions with relatively mature research. Table 4 lists the top 10 institutions in terms of number of publications and centrality of the gut-liver axis in NAFLD research. The nodes in the figure represent institutions, and larger nodes represent more publications of the institution; the links between nodes represent cooperation among institutions, the color of links indicates the start time of the collaboration, and the thickness of lines indicates the strength of the collaboration. The University of California San Diego (60 publications) takes lead in number of publications, followed by Shanghai Jiao Tong University (50 publications) and Shanghai University of Traditional Chinese Medicine (39 publications).
**Figure 5:** *The network of institutions conducting research related to the gut-liver axis in NAFLD.* TABLE_PLACEHOLDER:Table 4 The purple circle outside the node indicates a high degree of centrality (≥0.10), which may lead to transformative discoveries and may act as a bridge. The top three institutions for centrality are California State University, Los Angeles (0.51), University of California San Diego (0.39) and Catholic University of Louvain (0.38). As in the figure, the cooperation between institutions is relatively close.
## Journals
Source analysis of the included literature showed that International Journal of Molecular Sciences (77 publications) was the journal with the largest number of publications in this field, followed by Nutrients (65 publications) and World Journal of Gastroenterology (47 publications) (Figure 6A). The JCR partitions of the top 10 journals with the largest number of publications are all Q1 or Q2, which indicates that the quality of the publications included in this study is reliable, and it also suggests that researchers can give priority to such journals when publishing articles.
**Figure 6:** *Visualization of journals of the gut-liver axis in NAFLD. (A) Network visualization analysis of source journals based on VOSviewer. (B) Visualization of cited journals based on CiteSpace. (C) The dual-map overlay of journals in the gut-liver axis and NAFLD.*
In Figure 6B, co-citation analysis of journals showed that Hepatology (1,575 total citations) was the most cited journal, followed by Gastroenterology (1,430 total citations) and Journal of Hepatology (1,357 total citations). Among top 10 journals with the most citations, Nature Reviews Gastroenterology & Hepatology has the highest IF of 73.082. $70\%$ of top 10 journals with the most citations were classified as Q1, and the remaining 3 journals were classified as Q2 (Table 5). The journal with the highest centrality is Cell Death & Differentiation (0.97), followed by Archives of Disease in Childhood-Fetal and Neonatal Edition (0.94) and Archives of Biochemistry and Biophysics (0.8) (Table 5), which also shows these journals have higher influence in the field.
**Table 5**
| Rank | Journal | Count | JCR Partitions | Rank.1 | Journal.1 | Centrality | JCR Partitions.1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | Hepatology | 1040 | Q1 | 1 | Cell Death & Differentiation | 0.97 | Q1 |
| 2 | Gastroenterology | 1034 | Q1 | 2 | Archives of Disease in Childhood-Fetal and Neonatal Edition | 0.94 | Q1 |
| 3 | Journal of Hepatology | 1018 | Q1 | 3 | Archives of Biochemistry and Biophysics | 0.8 | Q2 |
| 4 | Plos ONE | 993 | Q2 | 4 | American Journal of Physiology-Cell Physiology | 0.77 | Q1 |
| 5 | Gut | 974 | Q1 | 5 | BMJ-British Medical Journal | 0.73 | Q1 |
| 6 | Nature | 870 | Q1 | 6 | Alimentary Pharmacology & Therapeutics | 0.53 | Q1 |
| 7 | PNAS | 861 | Q1 | 7 | Biochemical and Biophysical Research Communications | 0.4 | Q3 |
| 8 | World Journal of Gastroenterology | 742 | Q2 | 8 | Biostatistics | 0.4 | Q1 |
| 9 | Nature Reviews Gastroenterology & Hepatology | 738 | Q1 | 9 | Cancer Research | 0.39 | Q1 |
| 10 | Scientific Reports | 724 | Q2 | 10 | Gut | 0.37 | Q1 |
The Dual-map of CiteSpace could reflect the development of the research in different disciplines. As shown in Figure 6C, citing articles are shown on the left, cited articles are shown on the right, and the colored curved path in the middle indicates the citation relationship. The four orange or green citation paths indicate that research in Molecular, Biology, Genetics journals and Health, Nursing, Medicine journals are frequently cited by Molecular/Biology/Immunology journals and Medicine/Medical/Clinical journals. At the same time, disciplines such as Veterinary/Animal/Science, Ecology/Earth/Marine, Physics/Materials/Chemistry, Environmental/Toxicology/Nutrition and Psychology/Education/Social displayed in the edge regions of the overlay plots are also involved in the research of the gut-liver axis in NAFLD, which to a certain extent shows that researchers have carried out multidisciplinary cross-cooperative studies in this field.
## Co-cited references
Co-cited references represent the degree of relationship between references. VOSviewer was used to find the top 5 references with the most co-citations (Figures 7A, B; Table 6). Among them, the most cited article was written by Lixin Zhu, pointing out that escherichia, which is related to endogenous ethanol production in the intestinal microbiota of NASH patients, was significantly increased, and the elevation of ethanol concentration was also observed in the blood of NASH patients, suggesting that the escherichia may be a risk factor promoting the progression of diseases from obesity to NASH.
**Figure 7:** *Visualization of co-cited literature on the gut-liver axis in NAFLD. (A) References co-citation network in the gut-liver axis in NAFLD based on VOSviewer. (B) The density visualization of co-cited references based on VOSviewer. (C) References co-citation network in the gut-liver axis in NAFLD based on CiteSpace. (D) Cluster Analysis of Co-cited References based on CiteSpace. (E) Top 25 references with the strongest citation bursts in the gut-liver axis in NAFLD. (F) A timeline of the 15 largest clusters in the gut-liver axis in NAFLD.* TABLE_PLACEHOLDER:Table 6 We used CiteSpace to further analyze the co-cited references, parameters were set as follows: time slicing (2004–2022), years per slice [1], node type (cited reference), selection criteria ($k = 25$), and no pruning. As seen in As seen in Figure 7C, a co-occurrence network with a node number of 1,202, a connection number of 6,145, and a density of 0.0085 was obtained. We then performed a cluster analysis on the cited references based on the log-likelihood ratio (LLR) and 135 clusters were found. Only top 15 clusters are shown in the figure (Figures 7D, F). Among them, the clustering modularity $Q = 0.9477$, and the average silhouette score $S = 0.5816$, indicating this clustering is reasonable and the clustering structure is significant. The value of the cluster number represents the intensity of attention on the cluster topic within the discipline. The smaller the cluster value, the higher the attention. These clusters are mainly in 4 aspects. Firstly, animal models of NAFLD, including (#0 high-fat diet-induced NAFLD, #9 insulin-resistant mice, #13 nonalcoholic fatty liver disease diet, #14 fructose-induced hepatic steatosis). Secondly, the pathogenesis of NAFLD by the gut-liver axis, including (#1 gut-gene interaction, #2 receptor axis, #3 human gut microbiota, #12 microbiota-conjugated bas-fxr). Followed by examinations and tests commonly used in research in this field, including (#4 molecular phenomics, #5 sensing microbe, #7 tissue damage), and finally, the diseases related to NAFLD that have been studied in this field, including (#6 nonalcoholic fatty liver disease, #8 cirrhosis patient, #10 alcohol-associated liver disease, #11 alcoholic liver disease).
Burst detection of cited references represents a shift in research focus in a field. In CiteSpace, we set the parameters minimum Duration=2, γ=1, and screened 25 references with the strongest citation bursts, indicating their importance in the gut-liver axis in NAFLD-related fields (Figure 7E). Among those, the strongest citation burst was for the 2009 article Increased intestinal permeability and tight junction alterations in nonalcoholic fatty liver disease, this article proved for the first time that human NAFLD was associated with increased intestinal permeability, and this abnormality was associated with the increased prevalence of small intestinal bacterial overgrowth (SIBO) of patients, which is of great significance for studies of the gut-liver axis in NAFLD.
## Keyword analysis
Keyword co-occurrence network could help us identify research hotspots and trends in a field. In this study, we used VOSviewer software to perform keyword analysis, the minimum number of occurrences of a keyword was set as 5, and a total of 701 keywords were extracted (Figure 8A, Table 7). 9 clusters were obtained from further cluster analysis of the keywords (Figures 8B, C), representing 9 research directions and study areas. The largest cluster is the cluster 1 (red), with 146 keywords, including the gut-liver axis, bile acid, dysbiosis, probiotics, bacterial translocation, intestinal permeability, LPS, Toll like receptor, TNF-α, etc. Followed by the cluster 2 (green), with 123 keywords, including non-alcoholic fatty liver disease, hepatic steatosis, hepatocellular carcinoma (HCC), liver cancer, diabetes mellitus, metabolic syndrome, cardiovascular disease (CVD), etc. Cluster 3 (blue) has 86 keywords, mainly including expression, NF Kappa b, FXR, bile acid metabolism, nuclear receptor, obeticholic acid, vitamin d receptor. Cluster 4 (yellow) has 83 keywords, mainly including Pathogenesis, inflammation, mechanism, diet, glucose, acid, metabolism, bacterial, gut microbiome, apoptosis, autophagy. Cluster 5 (purple) has 76 keywords, mainly including activation, fatty liver, injury, kupper cell, liver fibrosis, liver failure. Cluster 6 (light blue) has 74 keywords, including gut microbiota, obesity, insulin resistance, lipid metabolism, oxidative stress, chain fatty acid, and adipose tissue. Cluster 1 mainly reflects the definition of the gut-liver axis, and Cluster 3, 4, 5 mainly reflects the pathogenesis of the gut-liver axis in NAFLD and some drug targets under development. The keywords in Cluster 2 and 6 mainly reflect the diseases related to the gut-liver axis in NAFLD and the relationship between gut microbes and metabolic diseases.
**Figure 8:** *The mapping on keywords of the gut-liver axis research in NAFLD. (A) Network map of 701 keywords with frequency more than 5. (B) The cluster of keywords in the studies of the gut-liver axis in NAFLD (divided into 9 clusters by different colors.) (C) Density visualization of keyword clustering. (D) Visualization of keywords based on CiteSpace. (E) Top 25 keywords with the strongest citation bursts.* TABLE_PLACEHOLDER:Table 7 We also use CiteSpace to visualize the keywords co-occurrence network, a map with 661 nodes, 1,383 connections, and 0.063 density was obtained (Figure 8D). Burst detection analysis of the keywords found that studies on short-chain fatty acid, in vitro, randomized controlled trial in clinical, and diabetes mellitus are the latest keywords (Figure 8E), indicating these areas are research hotspots in recent years, and it also suggests future research focuses.
## Discussion
In this study, 1,891 literature related to the NAFLD the gut-liver axis retrieved from the Web of Science core database was analyzed with VOSviewer, CiteSpace and Excel. The number of papers published in this field generally showed an upward trend in the past 20 years, and the average annual co-citations were increasing year by year. Before 2009, there were few studies in this field. After 2009, researchers gradually started to pay attention to the role of the liver gut axis in NAFLD. Especially after 2018, this field has entered a relatively mature stage of development.
China, the United States, and Italy are currently main countries doing research related to the gut-liver axis in NAFLD. In the past 20 years, China has published the most articles, while its average citation was only 17.09, suggesting that Chinese researchers should devote more energy to work on high quality articles. The number of publications of the United *States is* also much higher than that of other countries, and it has advantages in terms of number of papers cited, H-index and TLS, indicating that the United *States is* more prominent in research of the field. Centrality represents the importance of the node in the network. Among the top 10 institutions in the centrality ranking, 7 institutions are from the United States, two are from France and one is belonged to Belgium, which further explains the leading position of the United States in this research field. The connecting links between nodes represent the intensity of cooperation between countries, and close cooperation between countries is conducive to the further development of the research.
Among the top 10 authors with the largest number of publications, Dr. Bernd Schnabl ranked first, followed by Jasmohan S. Bajaj and Ki Tae Suk. Dr. Bernd Schnabl reviewed the connection between the gut-liver axis and liver diseases and pointed out that we could determine the type of liver disease and its possible progression based on microbial changes, provided a new idea for the diagnosis and prognosis of liver-related diseases [18]. His recently published article studied the commensal fungi in the gut-liver axis and suggested that histological disease severity in patients with NAFLD is associated with changes in the fecal mycobiome, indicating us intestinal fungi could be an attractive target to attenuate NASH [19]. Patrice D. Cani is the most co-cited researcher, he is an expert in the field of gut microbiota and his academic interest is mainly focusing on the interaction between gut microbes and the host in obesity, type 2 diabetes, cardiovascular disease, and metabolic diseases. He found that metabolic LPS can trigger obesity and insulin resistance, which are the most common cause of NAFLD. Therefore Prof. Patrice D. Cani begun to pay attention to the research of NAFLD in recent years. Jasmohan S. Bajaj also has a good record in the number of publications and co-citations, who has made important contributions in the field of the gut-liver axis in NAFLD. Jasmohan S. Bajaj [20] studied the changes in the gut microbiome in obese and non-obese NAFLD patients, and found that in non-obese NAFLD patients, the changes of intestinal microbes and metabolites were related to the degree of liver fibrosis, and then confirmed this finding with three animal models, suggesting that changes in gut microbes and metabolites in non-obese NAFLD patients can be used as markers and therapeutic targets of fibrosis. Among the top 10 co-cited authors, Fredrik Bäckhed, Lixin Zhu, and Peter J. Turnbaugh all have centrality value greater than 0.1, indicating that these three authors have great influence in the field.
Keywords reflect the core theme of the article, rank articles according to the frequency of co-occurrence could facilitate the analysis of the research focus in the field [21]. In this study, we can see that the research focus in this field is mainly on the gut microbiota, inflammation, insulin resistance, etc. The interaction between the gut microbiota and the liver is called the gut-liver axis, it is not surprising then that the frequency of gut microbiota is so high in this study. Changes of intestinal flora will cause increase in intestinal permeability, and promote the binding of bacteria and their metabolites to TLRs receptors of the liver, thus induce pro-inflammatory factors such as TNF-α and IL1-β to activate inflammatory responses [22, 23]. Inflammation is an important pathological manifestation in the progression of NASH in NAFLD patients, and it may even exacerbate the deterioration of NASH into HCC. Another clinical study also found that the increase of LPS in NAFLD patients would further aggravate inflammation and lead to insulin resistance [24]. Insulin resistance is also an important pathological manifestation of NASH patients, and it also interacts with the gut-liver axis. Studies [25] have found that in the intestinal tract of mice with insulin resistance, the expression of tight junction proteins decreased, and the content of intestinal bacterial LPS increased.
The burst detection analysis of keywords can represent the research frontiers in the field within a certain period, which could help researchers understand the dynamic changes of the frontiers of disciplines [26]. Burst detection analysis shows that short-chain fatty acid (SCFA) was a research hotspot in the past two years. SCFAs, including acetate, butyrate, and propionate, are metabolic products of intestinal bacteria. They are mainly produced in the distal colon and serve as substrates for gluconeogenesis and lipogenesis, providing nutrients and energy to the host [27]. SCFAs stimulate the secretion of peptide YY (PYY) and GLP-1 by activating G-protein coupled receptors (GPRs) GPR41 and GPR43, thereby suppressing appetite and reducing energy intake [28]. A clinical trial of obese people found that acute supplementation with inulin-propionate ester significantly increased postprandial serum PYY and GLP-1 and reduced energy intake, and long-term supplementation could reduce body weight and increase lipid content in hepatocytes [29]. Similarly, long-term butyrate consumption can prevent HFD-induced hepatic steatosis and insulin resistance in mice, by reducing their food intake. Meanwhile, SCFAs can improve HFD-induced NAFLD by increasing fatty acid oxidation of brown adipose tissue (BAT) via the brain-gut axis [30]. In addition to serving as substrates for energy production, SCFAs play an important role in maintaining intestinal homeostasis. Evidences showed that in both adults and animals, NAFLD is associated with an increased ratio of Firmicutes/Bacteroidetes (31–33) and increasing the intake of SCFAs through a high-fiber diet could promote the growth of the Bacteroidetes and maintain the integrity of the intestinal barrier by upregulating the expression of tight junction proteins [34]. In addition, SCFAs may act as signaling factors to activate AMP-activated kinase (AMPK) to promote TG hydrolysis and β-oxidation of fatty acids, thereby reducing hepatic lipid deposition and liver inflammation in mice [35]. Therefore, supplementing SCFA may be a promising strategy to prevent or treat NAFLD [36, 37]. Clinical trial was another hot topic detected in burst detection analysis. Among the current clinical trials, the most popular intervention is lifestyle intervention, including shifts in dietary patterns (38–40) and exercise [41, 42], while intrahepatic fat loss, changes in serum biomarkers or gut microbiota are the most common primary outcomes. Additional supplemental dietary fiber [43, 44], fecal microbiota transplantation [45], probiotics [46], and some antibiotics [47, 48], have also received considerable attention. Several drugs that have proven effective in animal studies in regulating the liver-gut axis, have also shown clinical potential for the treatment of NAFLD [49]. A phase II a trail [50] showed that Lubiprostone improved AST and hepatic steatosis in patients with NAFLD and constipation by decreasing intestinal permeability. Another randomized controlled trial in NAFLD patients [51] showed that oligonol, a lychee extract that has been shown in mice to alleviate NAFLD by modulating the gut microbiota, could increase the abundance of SCFAs in the gut and reduced hepatic steatosis in patients. Besides, several studies have also focused on gut microbiota-related effects of drugs that may be effective in treating NAFLD, such as Aldafermin (an analog of the gut hormone FGF19) [52, 53] and the FXR agonist PX104 [54].
Cluster analysis of keywords and co-cited references can help researchers better understand the research status in this field [55]. According to the clustering results, the research status of the gut-liver axis in NAFLD could be summarized as follows:
## Animal models of NAFLD
Animal model is an important tool in medical research, and choosing an appropriate animal model is the key to NAFLD drug development. Currently, the commonly used NAFLD animal models include: a. Diet-induced animal models, including nutrient deficient models such as methionine- and choline-deficient (MCD) diet, choline-deficient L-amino acid defined (CDAA) diet, and high-fat diet (HFD)-induced models such as Western-diet mice, fructose-induced mice and HFD induced model; b. Chemical Models, such as Streptozotocin(STZ) -HFD induced NAFLD model, Carbon Tetrachloride(CCL4)-induced model and Diethyl nitrosamine(DEN) model; c. Genetic models, such as combine Type 2 Diabetes Mellitus models(ob/ob mice or db/db mice) or Atherosclerosis models with HFD diets. No matter which modeling it is, the need for dietary induction is impossible to be avoided. However, the type of diet could also affect the changes in the intestinal microbial flora [56]. Therefore, when studying the influence of the gut liver axis in NAFLD, it is also necessary to carefully select an appropriate animal model. According to current researches, the animal model of NAFLD still cannot completely replicate the onset of the disease in clinical patients, and the results of animal experiments need to be further verified by clinical trials such as randomized controlled trials.
## The pathogenesis of the gut-liver axis in NAFLD
The pathogenesis of NAFLD is complex and it is the result of multiple factors including diet, metabolic factors, gut microbial flora, and genetic factors [57]. GVB is an important cause of disease progression in NAFLD patients [58], mainly manifested as the imbalance of intestinal flora, the increase of intestinal permeability, the translocation of bacteria and their products to the liver, thus inducing the activation of Kupper cell to release inflammatory factors [59], which triggers the inflammatory response. Multiple studies have found that Escherichia [14] and *Klebsiella pneumoniae* [60] in the intestines of NAFLD patients were significantly increased, which would produce high-concentrations endogenous alcohol, further accelerating intestinal barrier damage and fat accumulation in the liver. Bile Acids (BAs) are synthesized from hepatic cholesterol and released into the small intestine in the form of bile salts [61], it could prevent bacterial overgrowth while maintain microbial homeostasis in the intestine. At the same time, BAs can act as ligands for FXR and G protein-coupled bile acid receptor 1 (TGR5), regulating lipid, glucose, and energy metabolism [62], it is also involved in many cascade reactions on the gut-liver axis.
Targeting the gut-liver axis has become an emerging strategy for the prevention and treatment of NAFLD. Several potential treatments for NAFLD are currently being developed, including: a. Antibiotics. Antibiotics are controversial drugs for NAFLD. Early-phase clinical studies [47, 63] have shown that short-term use of rifaximin (a rifamycin antibiotic) can improve ALT levels in patients with NAFLD. While several animal studies [64, 65] have shown the efficacy of antibiotics to treat NAFLD, caution should be exercised in the utilization of these antibiotics as deleterious effects on beneficial bacteria species and the appearance of antibiotic-resistant strains [5]. Therefore, further therapeutic strategies for optimizing antibiotic treatment are needed. b. Probiotics, Prebiotics and Symbiotics. Several interventions have addressed the overgrowth of harmful bacteria by promoting the growth of beneficial bacteria. As early as in 2009, Pr. Patrice D. Cani, the author with the most citations in this study, found that administration of oligofructose prebiotics could reduce intestinal permeability and improve liver inflammation and oxidative stress levels in ob/ob mice [66]. However, most of these efficacies are proved in animal experiments, and more clinical trials are required for verification. c. Targeted bile acid metabolism. Currently FXR is the most studied transcriptional factor in bile acid metabolism, activation of FXR could induce metabolic effects and reduce steatosis and inflammation. Obeticholic acid, a first-in-class FXR agonist, has been proven to modify the liver pathological progression (steatosis, inflammation and hepatocyte ballooning, fibrosis) of NASH animal models [7, 67, 68]. However, in the phase 3 randomized placebo-controlled trial [69], only $23\%$ of participants present with the improvement in liver fibrosis after administration for 18 months, and approximately half of those participants treated with 25 mg OCA developed pruritus [70]. d. Others: TLR-4 antagonists, FGF-19 agonists, and GLP-1 agonists, etc.
## NAFLD-related diseases
NAFLD is a multisystem disease that affects a variety of extra-hepatic organs, and causes dysregulation of multiple biological pathways [71]. NAFLD-related diseases mainly include liver complications and metabolic diseases, such as type 2 diabetes mellitus (T2DM), CVD, and chronic kidney disease (CKD). As time progresses, the end-stage of NAFLD may lead to some liver complications, such as cirrhosis and HCC. Clinical studies [72] have shown that the common features of patients of NAFLD with HCC are the lack of protective bacteria in the stool and the aggravation of intestinal inflammation. In addition, the increase of secondary bile acids produced by the microbiota will further aggravate liver inflammation and damage, and accelerate the deterioration from NASH to HCC [73]. Therefore, improving intestinal dysbiosis and regulating BA metabolism through the intervention of the gut liver axis may be an effective method to prevent the progression of HCC.
Evidence [74] shows that NAFLD is an important risk factor for various metabolic-related extrahepatic diseases. NAFLD and T2DM share common pathogenic mechanisms such as lipotoxins, mitochondrial function, cytokines and adipocytokines, bile acid metabolism [75]. Therefore, these two diseases commonly occur together, and drugs for the treatment of T2DM such as thiazolidinediones (glitazones), SGTL2i and GLP-1RA are also commonly used for NAFLD [28, 76]. The leading cause of mortality among patients in NAFLD is CVD, and low-grade inflammation, gut microbial imbalance and oxidative stress may be the main mechanism of NAFLD-caused CVD. Trimethylamine-N-oxide (TMAO) is an intrahepatic metabolite of choline by the gut microbiome, and it is a significant marker of atherosclerosis and increased risk of CVD [77]. Meanwhile, its circulating concentration is also considered related to the severity of NAFLD [78]. Elevated systemic TMAO levels may also have adverse effects on the kidneys [79], causing CKD through the crosstalk of gut-liver-kidney, and damaged kidneys will further aggravate the NAFLD by destroying the intestinal barrier and activating RAAS [80]. In short, gut microbiota, microbiota-derived products, and epithelial barrier integrity represent common pathological mechanisms in NAFLD/NASH and its metabolic comorbidities. Therefore, targeting the gut liver axis is a promising therapeutic strategy for NAFLD-related diseases.
*In* general, although there is a large amount of animal experiment evidence indicating the importance of the gut-liver axis in NAFLD, there still lacks large-scale clinical data to prove the safe dosage and clinical efficacy of drugs targeting the liver-gut axis. This will also be the focus of future researchers.
## Conclusion
According to this bibliometric analysis, research on the gut-liver axis in NAFLD started since 2004. With researchers’ in-depth understanding of gut microbes, new therapeutic targets in the gut-liver axis have a potentially wide application in treating NAFLD. Current research hotspots mainly include animal models of NAFLD, therapeutic targets and mechanism exploration on the gut-liver axis, consisting of gut microbial dysbiosis, impaired intestinal barrier and bile acid metabolism. At present, short-chain fatty acids and clinical research especially randomized controlled trials are new research hotspots in this field.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.
## Author contributions
SY and RY designed the study. SY, DY, YQ, and SG conducted the literature search. DY and JL analyzed the data and wrote the paper. SY and DY approved the final manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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