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title: 'Gender-specific association of blood lipids and reproductive trajectory with
cognitive impairment: A community based cross-sectional study from India'
authors:
- Kevingu Khate
- Vineet Chaudhary
- Imnameren Longkumer
- Kallur Nava Saraswathy
- Naorem Kiranmala Devi
journal: Frontiers in Psychology
year: 2023
pmcid: PMC10008908
doi: 10.3389/fpsyg.2023.1107152
license: CC BY 4.0
---
# Gender-specific association of blood lipids and reproductive trajectory with cognitive impairment: A community based cross-sectional study from India
## Abstract
### Background
Abnormal blood lipid levels in the general population and adverse reproductive events among women have been associated with cognitive impairment (CI). However, their relationship has not been extensively studied in community settings. Hence, this study aims to explore the association of CI with blood lipid levels in both sexes and reproductive events/trajectory among women.
### Methods
A cross-sectional study was conducted among a North Indian rural population. A total of 808 adults were recruited through door-to-door household survey. Data on socio-demographic variables, reproductive profile of women, and cognitive impairment status were collected. Fasting blood sample was collected to estimate serum lipid profile. Multivariate logistic regression was performed to test for association.
### Results
The study demonstrated a lack of association between lipid profile and cognitive impairment among males. Surprisingly, low HDL-C among females was found to be protective against moderate/severe cognitive impairment (value of $$p \leq 0.049$$). Further, menopausal women and those having five or higher live births were found to be at higher risk of CI than pre-menopausal women and those with 1–2 live births, respectively.
### Conclusion
The present study hints toward a gender-specific association of blood lipid levels with CI. Further, higher live births and menopause appear to be important risk factors for CI among women.
## Introduction
Cognitive impairment (CI) can range from mild cognitive impairment (MCI), which is commonly regarded as the transition stage between an expected cognitive decline due to aging and dementia, to more advanced and serious forms of dementia, such as Alzheimer’s disease (Pankratz et al., 2015). The global prevalence of cognitive impairment was estimated to range between 1 and $42\%$ depending on the setting (clinical vs. population-based) and classification used (Ward et al., 2012; Sachdev et al., 2015; Petersen, 2016). In India, various studies have reported wide variation in the prevalence of MCI among different populations ranging from 3.5 to $63.2\%$ (Das et al., 2007; Kumar and Sudhakar, 2013; Sharma et al., 2013; Sengupta et al., 2014; Khullar et al., 2017; Kaur et al., 2018).
Since MCI in itself is a transient phase, not all individuals experiencing MCI progress to dementia (Aretouli et al., 2010; Ganguli et al., 2011). While some MCI patients might develop advanced dementia, others may regain normal cognitive function with time (Aretouli et al., 2010; Ganguli et al., 2011) and intervention (Meng et al., 2021). Though studies have acknowledged the complexity of defining loss of cognitive functions, especially the subclinical expressions of CI, into discrete categories (Ritchie et al., 2001; Richardson et al., 2019), cognitive impairment, for research purposes, has often been classified into three categories, i.e., mild, moderate and severe CI (Murray et al., 2006; Choe et al., 2008).
Cognitive impairment is etiologically complex. Apart from socio-demographic parameters like gender, education level, occupational status, and family medical history, several neurologic, systemic, genetic, biochemical, and psychiatric factors are believed to contribute to the development and progression of CI (Lopez, 2013; Singh et al., 2023). Of these, abnormal lipid levels and hypercholesterolemia are emerging as important modifiable risk factors for CI and other psychiatric conditions (Cicconetti et al., 2004; Khullar et al., 2017; Chaudhary et al., 2022). Further, as estrogen levels are known to play a vital role in neurocognitive function, adverse reproductive history/events and hormonal imbalances are also being investigated for their role in cognitive decline among women (Li et al., 2016; Shimizu et al., 2019; Song et al., 2020).
Despite clinical reports linking blood lipid levels and reproductive trajectory with cognitive health, the relationship between cognitive impairment and lipid levels or women’s reproductive trajectory in community settings remains understudied. Since blood lipid levels, as well as reproductive trajectories, are greatly affected by the ecological and cultural milieu of a community, community-specific studies exploring the relationship between CI and blood lipids or CI and women’s reproductive trajectory are crucial to designing effective interventions. Looking at the paucity of community-based studies, especially from India, the present study aims to explore the association of CI with blood lipid levels in both sexes and reproductive events/trajectory among women of an endogamous North Indian Population.
## Participant selection and data collection
A cross-sectional study design was adopted for the present study. A total of 808 individuals of both sexes (334 male and 474 females), aged 30–70 years (median age 52 years), belonging to Jat community of Palwal district, Haryana, North India, were conveniently recruited. The participants constituted a Mendelian population, sharing a common gene pool where marriages are mostly within the same community. Individuals fulfilling above criteria, with self-reported absence of physical and mental illnesses, were recruited through door-to-door household survey. Individuals suffering from chronic diseases (e.g., cancers and CVDs), those on long-term medications, and pregnant and lactating mothers were excluded from the study.
Data pertaining to socio-demographic variables (age, literacy, occupation, family income, and marital status) were collected using pretested interview schedules. Reproductive profile/trajectory (menopausal status, number of conceptions, number of live births, miscarriage, age at menarche, age at menopause, and years since menopause) among females were also obtained using a standardized questionnaire.
The study was approved by the Institutional Ethics Committee, Department of Anthropology, University of Delhi (Ref No. Anth/$\frac{2018}{2890}$/$\frac{1}{28}$-12-2018). Pre-informed written voluntary consent, transcribed in local language, was obtained from each participant prior to recruitment and data collection.
## Cognitive assessment
The cognitive status of the participants was assessed using Mini-Mental State Examination (MMSE) scale (Folstein et al., 1975). MMSE is a cross-culturally validated and widely used 30-point tool for the assessment of cognitive functions (Folstein et al., 1975). Each item of the MMSE was administered to the participant as per the instruction manual by trained field workers, and the responses were carefully noted. Based on the responses, each participant was scored out of 30. Individuals with scores ≥24 were considered to have normal cognition; those with scores between 19 and 23 were considered to have mild cognitive impairment (CI), while those with 10–18 and 0–9 were considered to have moderate and severe CI, respectively. Due to the extremely low prevalence of severe CI ($$n = 2$$), the severe CI category was merged with the moderate CI category for various statistical analyses.
## Lipid measurements
Lipid profile, which included total cholesterol (TC), triglyceride (TG), and high-density lipoprotein cholesterol (HDL-C), was estimated from serum extracted from overnight fasting blood (~12 h of fasting) using commercial kits (Randox Laboratories Ltd.). Low-density lipoprotein cholesterol (LDL-C) and very low-density lipoprotein cholesterol (VLDL-C) were calculated using Friedewald and Fredrikson’s formula (Friedewald et al., 1972). Normal levels of blood lipids were defined as TC < 200, TG < 150, HDL-C ≥ 40 (for males), and ≥50 mg/dL (for females), LDL-C < 130, and VLDL-C < 30 mg/dL (Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults, 2001).
## Statistical analysis
Statistical analyses were performed using IBM SPSS ver.22 software. The Kolmogorov–Smirnov test was used to determine if a continuous variable is normally distributed. Medians along with respective interquartile ranges (IQR) have been reported for non-normally distributed continuous variables, and numbers with respective percentages have been reported for categorical variables. Mann–Whitney U-test, Kruskal–Wallis, and chi-square test (as appropriate) were used to determine significant differences between various groups. Furthermore, logistic regression analysis was performed to understand the association between the dependent (cognitive impairment) and independent variables (lipid levels and reproductive trajectory). Confounders such as age, education, and employment status were identified and controlled while calculating the odds ratio. All statistical tests were considered significant at value of $p \leq 0.05.$
## General characteristics of participants
The distribution of sociodemographic variables (age, education status, occupation status, family income, and marital status) among individuals with normal cognition and those with mild or moderate/severe CI revealed that the proportions of individuals without any formal education or unemployed individuals among both males and females and older individuals among females were significantly higher in the mild and moderate/severe CI categories when compared to normal cognition category (Table 1). These variables were considered confounders while calculating the odds ratio.
**Table 1**
| Unnamed: 0 | Unnamed: 1 | Males (N = 334) | Males (N = 334).1 | Males (N = 334).2 | Males (N = 334).3 | Females (N = 474) | Females (N = 474).1 | Females (N = 474).2 | Females (N = 474).3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | Normal (N = 217) n (%) | Mild CI (N = 84) n (%) | Mod/Sev CI (N = 33) n (%) | Value of p | Normal (N = 81) n (%) | Mild CI (N = 192) n (%) | Mod/Sev CI (N = 201) n (%) | Value of p |
| Median age (IQR) (in years) | Median age (IQR) (in years) | 54 (46–62) | 57.50 (48.25–63.00) | 59 (50.50–69.00) | 0.079 | 42 (38–48) | 50 (43–58) | 54 (45.50–61.50) | <0.001* |
| Education status | Having formal education | 207 (95.4) | 59 (70.2) | 5 (15.2) | <0.001* | 62 (76.5) | 40 (20.8) | 16 (8.0) | <0.001* |
| Education status | No formal education | 10 (4.6) | 25 (29.8) | 28 (84.8) | <0.001* | 19 (23.5) | 152 (79.2) | 185 (92.0) | <0.001* |
| Employment status | Employed | 73 (33.6) | 12 (14.3) | 1 (3.0) | <0.001* | 4 (4.9) | 2 (1.0) | 1 (0.5) | 0.016* |
| Employment status | Unemployed | 144 (66.4) | 72 (85.7) | 32 (97.0) | <0.001* | 77 (95.1) | 190 (99.0) | 200 (99.5) | 0.016* |
| Annual family income (INR) | <50,000 | 28 (12.9) | 8 (9.5) | 3 (9.1) | 0.635 | 11 (13.6) | 12 (6.3) | 22 (10.9) | 0.110 |
| Annual family income (INR) | ≤50,000 | 189 (87.1) | 76 (90.5) | 30 (90.9) | 0.635 | 70 (86.4) | 180 (93.8) | 179 (89.1) | 0.110 |
| Marital status | Married | 203 (93.5) | 77 (91.7) | 29 (87.9) | 0.097 | 74 (91.4) | 176 (91.7) | 185 (92.0) | 0.934 |
| Marital status | Unmarried | 4 (1.8) | 2 (2.4) | 4 (12.1) | 0.097 | 0 (0.0) | 2 (1.0) | 0 (0.0) | 0.934 |
| Marital status | Widowed | 10 (4.6) | 5 (6.0) | 0 (0.0) | 0.097 | 7 (8.6) | 14 (7.3) | 16 (8.0) | 0.934 |
## Median blood lipid levels and cognition status
The overall distribution of median blood lipid levels with respect to cognitive status revealed that median TG and VLDL-C levels were significantly higher among individuals in the normal category compared to those individuals with mild CI (Table 2). However, after stratification for sex, the trend of higher TG in the normal cognition category than in the mild CI category remained significant only among females (Table 2). On the other hand, in the overall analysis, the median HDL-C level was found to be significantly higher among individuals in the moderate/severe CI group compared to those in the normal cognition group (Table 2). This trend lost statistical significance in the sex-stratified analysis.
**Table 2**
| Unnamed: 0 | Overall (N = 808) | Overall (N = 808).1 | Overall (N = 808).2 | Males (N = 334) | Males (N = 334).1 | Males (N = 334).2 | Females (N = 474) | Females (N = 474).1 | Females (N = 474).2 | Unnamed: 10 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Normal median (IQR) (N = 298) | Mild CI median (IQR) (N = 276) | Mod/Sev CI median (IQR) (N = 234) | Normal median (IQR) (N = 217) | Mild CI median (IQR) (N = 84) | Mod/Sev CI median (IQR) (N = 33) | Normal median (IQR) (N = 81) | Mild CI median (IQR) (N = 192) | Mod/Sev CI median (IQR) (N = 201) | Value of p |
| TC | 175.52 (145.75–209.78) | 173.26 (148.52–204.80) | 175.22 (150.00–204.48) | 176.75 (145.00–211.71) | 173.88 (145.51–206.19) | 180.54 (160.57–213.70) | 172.30 (150.69–202.28) | 172.43 (150.10–202.00) | 174.32 (149.23–203.74) | 0.776α 0.581β 0.978γ |
| TG | 111.21 (75.16–164.24) | 96.08 (72.28–134.05) | 109.95 (74.81–146.21) | 120.90 (83.22–166.25) | 112.78 (84.57–145.44) | 92.12 (68.41–147.66) | 84.16 (66.20–134.75) | 89.91 (67.96–125.10) | 111.87 (76.35–146.44) | 0.017α,* 0.099β 0.008γ,* |
| HDL-C | 46.72 (38.00–55.68) | 48.32 (40.64–55.42) | 51.20 (40.96–61.72) | 46.40 (36.80–56.08) | 43.68 (36.16–53.30) | 52.37 (40.16–65.41) | 47.36 (41.60–53.80) | 49.76 (42.40–56.40) | 50.66 (40.90–61.61) | 0.001α,* 0.060β 0.196γ |
| LDL-C | 106.67 (77.04–133.81) | 102.35 (81.13–127.48) | 99.71 (76.63–128.70) | 108.42 (71.94–135.70) | 104.61 (80.80–127.66) | 110.62 (80.89–132.91) | 105.91 (83.14–128.60) | 100.61 (81.18–128.61) | 98.08 (75.57–126.67) | 0.784α 0.901β 0.597γ |
| VLDL-C | 21.91 (14.99–32.73) | 19.0 (14.45–26.69) | 21.89 (14.91–29.12) | 23.80 (16.61–33.20) | 22.55 (16.91–29.08) | 18.18 (12.65–28.92) | 16.83 (13.24–26.95) | 17.96 (13.59–25.01) | 22.37 (15.27–29.28) | 0.019α,* 0.046β,* 0.006γ,* |
## Odds ratio analyses exploring the association between CI and abnormal blood lipid levels
Sex-stratified adjusted binary logistic regression models suggested a gender-specific relationship between blood lipid parameters and cognition status (Table 3). While none of the studied lipid variables were found to be associated with CI among males, females with low HDL-C were found to be at a reduced risk for moderate/severe cognitive impairment when compared to those with normal HDL-C levels (OR = 0.517; $95\%$ CI = 0.268–0.996; value of $$p \leq 0.049$$; Table 3); however, since the observed value of p (=0.049) was close to the threshold value of p of 0.05, the finding was considered to have suggestive significance, and further analysis was taken up (with respect to HDL-C quartiles) to validate (or rule out) this finding.
**Table 3**
| Variables | Males (N = 334) | Males (N = 334).1 | Males (N = 334).2 | Males (N = 334).3 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7 | Unnamed: 8 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Variables | Mild CI (N = 84) | Mild CI (N = 84) | Mod/Sev CI (N = 33) | Mod/Sev CI (N = 33) | | | | |
| Variables | ORα (95% confidence interval) | Value of p | ORα (95% confidence interval) | Value of p | | | | |
| High TC | 0.871 (0.477–1.589) | 0.652 | 0.892 (0.269–2.962) | 0.852 | | | | |
| High TG | 0.578 (0.306–1.091) | 0.091 | 0.748 (0.204–2.749) | 0.662 | | | | |
| Low HDL-C | 0.773 (0.434–1.374) | 0.380 | 0.487 (0.142–1.673) | 0.253 | | | | |
| High LDL-C | 0.679 (0.355–1.297) | 0.241 | 0.699 (0.192–2.326) | 0.527 | | | | |
| High VLDL-C | 0.586 (0.310–1.106) | 0.099 | 0.657 (0.171–2.519) | 0.540 | | | | |
| | Females (N = 474) | Females (N = 474) | Females (N = 474) | Females (N = 474) | Mild CI (N = 192) | Mild CI (N = 192) | Mod/Sev CI (N = 201) | Mod/Sev CI (N = 201) |
| ORα (95% confidence interval) | Value of p | ORα (95% confidence interval) | Value of p | | | | | |
| High TC | 0.739 (0.356–1.535) | 0.417 | 0.701 (0.301–1.636) | 0.412 | | | | |
| High TG | 0.788 (0.351–1.767) | 0.562 | 0.594 (0.234–1.511) | 0.274 | | | | |
| Low HDL-C | 0.604 (0.319–1.144) | 0.122 | 0.517 (0.268–0.996) | 0.049* | | | | |
| High LDL-C | 0.951 (0.451–2.003) | 0.895 | 0.673 (0.275–1.646) | 0.386 | | | | |
| High VLDL-C | 0.764 (0.338–1.724) | 0.517 | 0.594 (0.234–1.511) | 0.274 | | | | |
## Prevalence of CI in quartiles of HDL-C
Prompted by this rather surprising finding (reduced risk of CI in low HDL-C category among females), the study sample was divided into four categories with respect to HDL-C quartiles to determine the prevalence of CI in various quartiles. This analysis revealed that, among females, the prevalence of moderate/severe CI was highest in the fourth HDL-C quartile, followed by the first, second, and third quartiles (Table 4). This difference in the distribution of individuals with moderate/severe CI in various HDL-C quartiles among females was statistically significant. No such statistically significant trend was observed among males (Table 4).
**Table 4**
| Unnamed: 0 | HDL-C quartiles (in mg/dL) | Normal n (%) | Mild CI n (%) | Mod/Sev CI n (%) | Value of p |
| --- | --- | --- | --- | --- | --- |
| Males, N = 334 | 1st (<37.1) | 55 (26.1) | 23 (27.4) | 5 (15.2) | 0.250 |
| Males, N = 334 | 2nd (37.1–45.3) | 49 (23.2) | 26 (31.0) | 6 (18.2) | 0.250 |
| Males, N = 334 | 3rd (45.3–56.1) | 54 (25.6) | 19 (22.6) | 9 (27.3) | 0.250 |
| Males, N = 334 | 4th (>56.1) | 53 (25.1) | 16 (19.0) | 13 (39.4) | 0.250 |
| Females, N = 474 | 1st (<41.6) | 20 (25.3) | 44 (23.7) | 54 (27.3) | 0.002* |
| Females, N = 474 | 2nd (41.6–49.6) | 26 (32.9) | 49 (26.3) | 40 (20.2) | 0.002* |
| Females, N = 474 | 3rd (49.6–57.8) | 23 (29.1) | 54 (29.0) | 38 (19.2) | 0.002* |
| Females, N = 474 | 4th (>57.8) | 10 (12.7) | 39 (21.0) | 66 (33.3) | 0.002* |
Looking at the significant difference in the distribution of moderate/severe CI in various quartiles of HDL-C among females (lower frequency of moderate/severe CI in middle quartiles and higher in the extreme quartiles), odds ratio analysis (with second and third quartiles as the reference) was performed to understand the risk of CI in the first, fourth and combined first and fourth quartiles (Table 5). This analysis revealed that those in the fourth quartile or in the combined first + fourth quartile were at significantly higher risk of moderate/severe CI than those in the middle quartile (Table 5).
**Table 5**
| HDL-C quartiles | Mild CI (N = 192) | Mild CI (N = 192).1 | Mod/Sev CI (N = 201) | Mod/Sev CI (N = 201).1 |
| --- | --- | --- | --- | --- |
| HDL-C quartiles | OR (95% confidence interval) | Value of p | ORα (95% confidence interval) | Value of p |
| 1st vs. 2nd + 3rd | 0.778 (0.373–1.622) | 0.502 | 1.193 (0.549–2.592) | 0.656 |
| 4th vs. 2nd + 3rd | 2.146 (0.892–5.167) | 0.088 | 4.945 (1.984–12.325) | 0.001* |
| 1st + 4th vs. 2nd + 3rd | 1.168 (0.629–2.170) | 0.623 | 2.194 (1.140–4.224) | 0.019* |
## Reproductive trajectory and cognitive impairment
The distribution of reproductive variables was seen among the groups of women with different cognitive statuses (normal cognition vs. mild CI vs. moderate/severe CI; Table 6). The reproductive events included were menopausal status, number of conceptions, number of live births, miscarriage, age at menarche, age at menopause, and years since menopause. No significant differences in the distribution of the above-mentioned reproductive events were found among the groups of women with different cognitive statuses, except for menopausal status and the number of live births, where the proportions of menopausal women and those having five or more live births were significantly higher in the moderate/severe CI category than the normal cognition category.
**Table 6**
| Unnamed: 0 | Unnamed: 1 | Normal n (%) | Mild CI n (%) | Mod/Sev CI n (%) | Value of p | Mild CI OR (95% confidence interval) | Mod/Sev CI OR (95% confidence interval) |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Menopausal status (N = 340) | Pre-menopause | 34 (55.7) | 44 (30.6) | 23 (17.0) | <0.001* | Reference | Reference |
| Menopausal status (N = 340) | Natural menopause | 17 (27.9) | 78 (54.2) | 93 (68.9) | <0.001* | 3.55 (1.78–7.07)* | 8.09 (3.86–16.95)* |
| Menopausal status (N = 340) | Hysterectomy | 10 (16.4) | 22 (15.3) | 19 (14.1) | <0.001* | 1.70 (0.71–4.06) | 2.80 (1.10–7.12)* |
| Number of conceptions (N = 248) | 0 | 2 (4.3) | 6 (5.8) | 2 (2.0) | 0.201 | 1.87 (0.27–13.20) | 0.83 (0.08–8.24) |
| Number of conceptions (N = 248) | 1–2 | 5 (10.6) | 8 (7.8) | 6 (6.1) | 0.201 | Reference | Reference |
| Number of conceptions (N = 248) | 3–4 | 28 (59.6) | 54 (52.4) | 44 (44.9) | 0.201 | 1.21 (0.36–4.03) | 1.31 (0.36–4.70) |
| Number of conceptions (N = 248) | ≥5 | 12 (25.5) | 35 (34.0) | 46 (46.9) | 0.201 | 1.82 (0.50–6.66) | 3.19 (0.83–12.28) |
| Number of live births (N = 229) | 0 | 1 (2.3) | 0 (0.0) | 0 (0.0) | 0.016* | 0.33 (0.01–9.26) | 0.49 (0.02–13.92) |
| Number of live births (N = 229) | 1–2 | 9 (20.9) | 9 (9.4) | 6 (6.7) | 0.016* | Reference | Reference |
| Number of live births (N = 229) | 3–4 | 27 (62.8) | 64 (66.7) | 53 (58.9) | 0.016* | 2.37 (0.84–6.62) | 2.94 (0.95–9.14) |
| Number of live births (N = 229) | ≥5 | 6 (14.0) | 23 (24.0) | 31 (34.4) | 0.016* | 3.83 (1.06–13.91)* | 7.75 (2.00–29.99)* |
| Miscarriage (N = 208) | No | 33 (89.2) | 66 (79.5) | 77 (87.5) | 0.245 | Reference | Reference |
| Miscarriage (N = 208) | Yes | 4 (10.8) | 17 (20.5) | 11 (12.5) | 0.245 | 2.13 (0.66–6.82) | 1.18 (0.35–3.97) |
| Age at menarche (in years; N = 431) | ≤12 | 48 (65.8) | 127 (70.6) | 108 (60.7) | 0.388 | 1.32 (0.31–5.50) | 0.68 (0.18–2.56) |
| Age at menarche (in years; N = 431) | 13–15 | 3 (4.1) | 6 (3.3) | 10 (5.6) | 0.388 | Reference | Reference |
| Age at menarche (in years; N = 431) | ≥16 | 22 (30.1) | 47 (26.1) | 60 (33.7) | 0.388 | 1.07 (0.24–4.67) | 0.82 (0.21–3.25) |
| Age at menopause (in years; N = 286) | <40 | 19 (57.6) | 62 (53.9) | 71 (52.2) | 0.678 | 1.48 (0.45–4.81) | 0.93 (0.31–2.82) |
| Age at menopause (in years; N = 286) | 40–50 | 5 (15.2) | 11 (9.6) | 20 (14.7) | 0.678 | Reference | Reference |
| Age at menopause (in years; N = 286) | >50 | 9 (27.3) | 42 (36.5) | 45 (33.1) | 0.678 | 2.12 (0.59–7.62) | 1.25 (0.37–4.21) |
| Years since menopause (in years; N = 239) | 0–3 | 2 (7.4) | 9 (9.0) | 7 (6.3) | 0.679 | Reference | Reference |
| Years since menopause (in years; N = 239) | 4–6 | 5 (18.5) | 18 (18.0) | 14 (12.5) | 0.679 | 0.80 (0.13–4.96) | 0.80 (0.12–5.21) |
| Years since menopause (in years; N = 239) | 7–10 | 8 (29.6) | 20 (20.0) | 23 (20.5) | 0.679 | 0.56 (0.10–3.16) | 0.82 (0.14–4.80) |
| Years since menopause (in years; N = 239) | >10 | 12 (44.4) | 53 (53.0) | 68 (60.7) | 0.679 | 0.98 (0.19–5.14) | 1.62 (0.30–8.75) |
In odds ratio analysis, menopausal women (with pre-menopausal category as reference) were found to be at 3.5 and 8-fold significantly higher risk of mild CI and moderate/severe CI, respectively, (value of $p \leq 0.05$). Further, hysterectomized women were also found to be at a 2.8-fold significantly increased risk of moderate/severe CI than pre-menopausal women. Again, women with five or more live births (with 1–2 live birth as the reference category) were at 3.8 and 7.7-fold significantly increased risk of mild CI and moderate/severe CI, respectively, (value of $p \leq 0.05$; Table 6). Other reproductive events were not found to be associated with CI status.
## Discussion
Abnormal lipid levels and adverse reproductive events have been reported to influence cognitive functions and play a role in the development of cognitive impairments (Van Exel et al., 2002; Devore et al., 2004; Power et al., 2018; Lv et al., 2019; Ning et al., 2020; Song et al., 2020; Gong et al., 2022). However, very few studies have methodically investigated their relationship in community settings. Further, even lesser attention has been given to the gender-specific relationship between blood lipids and CI (Ancelin et al., 2013, 2014; Svensson et al., 2019; Choe et al., 2021; Bakeberg et al., 2021a). It is important to highlight that sex is a crucial factor to be considered when conducting cognitive assessment studies (Reekes et al., 2020; Bakeberg et al., 2021a). Literature suggests that women have better verbal and non-verbal reasoning skills, such as language, fluency, memory, and decision-making, when compared to their male counterparts (Li and Singh, 2014). Other studies have also emphasized on sex-specific approach to studying CI because of physiological differences between males and females, which eventually cause cerebrovascular and cognitive decline (Miller et al., 2013; Reekes et al., 2020; Sundermann et al., 2021). Considering these research gaps, the present study attempted to investigate the relationship between blood lipid levels and CI among both males and females and reproductive trajectory and CI among females of an endogamous North Indian Population.
In the present study, median TG and VLDL-C levels were significantly higher among individuals with normal cognition than those with mild CI. This observation is in concordance with some of the previous reports where TG levels have been reported to be significantly higher in control groups (normal cognition group) than in case groups (MCI group; Dimopoulos et al., 2007; Lepara et al., 2009; He et al., 2016). However, contrary findings have also been reported (De Frias et al., 2007; Sims et al., 2008). Since the adverse implications of pathologically high TG can easily outweigh the benefits of high TG in cognitive functions, population-based studies, as well as clinical trials, are warranted to establish population-specific healthy ranges of TG. Again, despite significant differences in the median TG levels between normal cognition and mild CI groups, adjusted logistic regression models failed to find any significant association between TG and CI (in overall as well as sex-stratified analyses). Further research is required to explicate the relationship between TG and CI.
In the present study, the median HDL-C level was found to be higher among individuals with moderate/severe CI than those with normal cognition. In odds ratio analysis, low HDL-C was found to be protective against CI among females but not among males. Also, females in the fourth quartile of HDL-C were at significantly increased risk of CI than those in the second and third quartiles. Overall, these observations hint toward low HDL-C being protective and high HDL-C being a risk for CI among females (suggestive significance). Though these findings are largely in contradiction with previous reports (Reitz et al., 2010; Hottman et al., 2014; Vitali et al., 2014; Bates et al., 2017), Ancelin et al. [ 2014], in their study among elderly individuals, reported an increased risk of decline in cognitive functions among females having high HDL-C than those having low HDL-C. A recent study conducted among females with Parkinson’s disease (PD) has also found elevated HDL-C to significantly contribute to cognitive decline (Bakeberg et al., 2021b). The mechanism behind the higher risk of CI among women having higher HDL-C remains to be clarified, yet, studies have suggested the role of genetic vulnerability related to HDL-C, estrogen receptors, or metabolizing enzymes that could affect HDL-C responsiveness (Ancelin et al., 2014).
Other lipid variables, viz. TC and LDL-C, were not found to be associated with CI in either sex. Previous studies investigating the role of plasma lipid levels in cognitive functions have reported inconsistent findings (Mielke et al., 2005; Reitz et al., 2008; Morley and Banks, 2010; Li et al., 2018). The relationship between plasma lipids and cognitive function, the role of gender in modulating this relationship, as well as the mechanism by which plasma lipids affect cognitive function are still not fully understood.
Coming to the role of reproductive trajectory in cognitive impairment, menopausal women and women with five or more live births were found to be at a higher risk of CI. Previous studies have also reported menopause (Weber et al., 2014; Conde et al., 2021) and higher numbers of children (Beeri et al., 2009; Read and Grundy, 2017) as risk factors for cognitive decline.
Women have been shown to experience cognitive deficiencies during the menopausal transition, notably in areas like working memory, attention, slower processing speeds, and verbal memory (Miller and Cronin-Golomb, 2010; Lin et al., 2018). There is growing evidence that oestrogen has a major neuroprotective and neurotrophic effect on the central nervous system (Conde et al., 2021). The decline in oestrogen levels among post-menopausal women can be one of the factors behind the observed decline in cognitive functions. Menopausal symptoms like vasomotor symptoms, insomnia, and fatigue are also likely to affect the cognitive health of menopausal women (Conde et al., 2021).
Further, the association between cognitive impairment and the higher number of live births may be explained by both biological mechanisms and social/behavioral factors. Pregnancy and childbirth trigger a wide range of alterations in endocrine activities and metabolic processes, including changes in blood lipoprotein level, increased obesity, and central adiposity (Lahmann et al., 2000; Lain and Catalano, 2007; Soma-Pillay et al., 2016). These factors, independently and/or via other metabolic and cardiovascular diseases, may be associated with increased odds for CI. Interestingly, some recent studies have highlighted that the higher number of children affects the cognitive health of both men and women, indicating gender indifference (Ning et al., 2020; Song et al., 2020; Gong et al., 2022). Therefore, social/behavioral explanations appear to be more plausible. Having numerous children and raising them in a society with fewer social supports may result in significant psychological stress throughout the reproductive years and beyond, which may have an adverse effect on brain health (Wilson et al., 2006). A higher number of children is also associated with lower economic status and poor education, which, in turn, may affect brain health (Andel et al., 2006).
There are some important limitations of the study that must be mentioned. First, MMSE has been used for CI screening; while it is a reliable screening tool, it is not the gold standard test for diagnosing CI. Second, this is a single-site study; a multi-site study would have been more suitable because environmental factors can modify the relationship between the lipid variables and CI. Another limitation is the lack of hormonal data among women, which would have further substantiated the study’s findings.
## Conclusion and future directions
The present study hints toward a gender-specific relationship between blood lipid parameters (HDL-C in particular) and CI. While none of the blood lipids were found to be associated with CI among males, low HDL-C was protective against CI among females (suggestive significance). Future studies investigating the mechanisms underlying the interactions between plasma lipids and cognition will stimulate new approaches to the treatment and prevention of cognitive disorders. Furthermore, menopause and the higher number of live births were found to be risk factors for CI among females. Pregnancy, childbirth as well as the menopausal transition are crucial reproductive milestones and are associated with significant hormonal changes and stress that may, in turn, be linked to cognitive deterioration.
## 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 Departmental Ethics Committee, Department of Anthropology, University of Delhi. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
KS and ND designed and executed the research project and conceptualized the manuscript. KK designed and executed the statistical analyses and wrote the first draft of the manuscript. VC and IL reviewed and critiqued the statistical analysis and manuscript preparation. KS, ND, and IL thoroughly reviewed and critiqued the final version of the manuscript. All authors contributed to the article and approved the submitted version.
## Funding
This study was supported by the Research and Development Grant, University of Delhi, New Delhi and Department of Biotechnology, Ministry of Science and Technology, Government of India under grant BT/PRI14378/MED/$\frac{30}{535}$/2010.
## 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: CircRNA_0075723 protects against pneumonia-induced sepsis through inhibiting
macrophage pyroptosis by sponging miR-155-5p and regulating SHIP1 expression
authors:
- Dianyin Yang
- Dongyang Zhao
- Jinlu Ji
- Chunxue Wang
- Na Liu
- Xiaowei Bao
- Xiandong Liu
- Sen Jiang
- Qianqian Zhang
- Lunxian Tang
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10008927
doi: 10.3389/fimmu.2023.1095457
license: CC BY 4.0
---
# CircRNA_0075723 protects against pneumonia-induced sepsis through inhibiting macrophage pyroptosis by sponging miR-155-5p and regulating SHIP1 expression
## Abstract
### Introduction
Circular RNAs (circRNAs) have been linked to regulate macrophage polarization and subsequent inflammation in sepsis. However, the underlying mechanism and the function of circRNAs in macrophage pyroptosis in pneumonia-induced sepsis are still unknown.
### Methods
In this study, we screened the differentially expressed circRNAs among the healthy individuals, pneumonia patients without sepsis and pneumonia-induced sepsis patients in the plasma by RNA sequencing (RNA-seq). Then we evaluated macrophage pyroptosis in sepsis patients and in vitro LPS/nigericin activated THP-1 cells. The lentiviral recombinant vector for circ_0075723 overexpression (OE-circ_0075723) and circ_0075723 silence (sh-circ_0075723) were constructed and transfected into THP-1 cells to explore the potential mechanism of circ_0075723 involved in LPS/nigericin induced macrophage pyroptosis.
### Results
We found circ_0075723, a novel circRNA that was significantly downregulated in pneumonia-induced sepsis patients compared to pneumonia patients without sepsis and healthy individuals. Meanwhile, pneumonia-induced sepsis patients exhibited activation of NLRP3 inflammasome and production of the pyroptosis-associated pro-inflammatory cytokines IL-1β and IL-18. circ_0075723 inhibited macrophage pyroptosis via sponging miR-155-5p which promoted SHIP1 expression directly. Besides, we found that circ_0075723 in macrophages promoted VE-cadherin expression in endothelial cells through inhibiting the release of NLRP3 inflammasome-related cytokines, IL-1β and IL-18, and protects endothelial cell integrity.
### Discussion
Our findings propose a unique approach wherein circ_0075723 suppresses macrophage pyroptosis and inflammation in pneumonia-induced sepsis via sponging with miR-155-5p and promoting SHIP1 expression. These findings indicate that circRNAs could be used as possible potential diagnostic and therapeutic targets for pneumonia-induced sepsis.
## Introduction
Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection [1]. Despite significant advances of sepsis therapy in the past decade, sepsis remains the primary reason for death in the intensive care unit (ICU) [2]. Infection in the respiratory system is the most common of sepsis, which accounts for about $50\%$. Meanwhile pulmonary infections lead to nearly $30\%$ mortality of patients with sepsis, much higher than infections from other sources [3, 4]. However, the mechanisms driving pneumonia-induced sepsis remain poorly understood. Macrophage death is critical to the pathophysiology of pneumonia and related sepsis [5, 6]. Of note, pyroptosis, a sort of programmed cell death driven by NLRP3 inflammasome activation, is a major contributor to sepsis. It is characterized by formation of cell membrane pores and the production of inflammatory factors IL-1β and IL-18 [7]. In reaction to pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs), NLRP3 is activated and oligomerized through NACHT domain, which then recruits apoptosis-associated speck-like protein containing a CARD (ASC) and pro-caspase 1 to form NLRP3 inflammasome. This results in the transformation of pro-caspase1 into active caspase1. The active caspase1 then converts pro-IL-1β and pro-IL-18 to their active forms. With its pore-forming activity, Caspase1 also cleaves gasdermin D (GSDMD) into N-terminal form (N-GSDMD). Inflammatory factors (IL-1β and IL-18) are ultimately released from pores formed by N-GSDMD. Increasing evidence have indicated that NLRP3 inflammasome and pyroptosis in macrophages are essential for the occurrence and development of sepsis [8, 9]. However, whether macrophage pyroptosis is involved in pneumonia-induced sepsis and the precise regulatory mechanisms of macrophage pyroptosis remain not clear.
Circular RNAs (circRNAs) are covalently closed single-strand RNAs generated by mRNA back-splicing, which comprise a widespread subtype of non-coding RNAs [10]. The main function of circRNAs is regulation of transcription and translation of mRNA by sponging miRNAs [11]. Till now, circRNAs have been implicated in numerous areas of biological processes, including cell differentiation, apoptosis, autophagy, and proliferation, which are all closely related to septic pathogenesis [12]. We previously reported global changes of circRNAs and the circRNA-miRNA-mRNA networks in pulmonary macrophages activation from cecal ligation and puncture (CLP)-induced acute respiratory distress syndrome (ARDS) mice model by microarray analysis, suggesting that circRNAs are required for macrophages function and the development of ARDS [13]. Further, we have revealed that circN4bp1 facilitated sepsis-induced ARDS through promoting macrophage polarization by means of miR-138-5p/EZH2 axis in vivo and ex vivo [14]. Recent reports have implicated circular RNAs in the regulation of macrophage pyroptosis. CircACTR2 is identified to promote macrophage pyroptosis and the subsequent fibrosis [15]. Inhibition of circ_0029589 by IFN regulatory Factor-1 (IRF-1) may also promote macrophage pyroptosis and inflammation in patients with acute coronary syndrome (ACS) [16]. However, it remains unknown whether circRNAs regulate macrophage pyroptosis in sepsis, especially pneumonia-induced sepsis. In this investigation, we screened for differential expression circRNAs in plasma of healthy individuals, pneumonia patients without sepsis, and pneumonia-induced sepsis patients using RNA-seq and recognized the significantly downregulated circRNA, circ_0075723, which is generated from the exons of gene NUP153. We also showed that circ_0075723 acted as a negative regulator of macrophage pyroptosis and inflammatory damage in pneumonia-induced sepsis, in addition to the pathways associated with miR-155-5p and SHIP1. Our findings present new insights of circRNAs into the regulation of macrophage pyroptosis and provide possible treatment targets for pneumonia-induced sepsis.
## Clinical samples collection
This study was authorized by the Research Ethics Board of East Hospital, Tongji University (Shanghai, China). All recruited patients or their authorized family members were provided with a consent form. Peripheral blood (4ml) was taken from 7 eligible patients with pneumonia-induced sepsis, 7 pneumonia patients without sepsis and 7 healthy donors. The participants’ clinical parameters are shown in Supplementary Table 1. The pneumonia patients were classified as sepsis according to the Surviving Sepsis Campaign definitions [17] from the emergency and/or general intensive care unit (ICU) of East Hospital. The pneumonia patients without sepsis who came from emergency internal medicine ward of East Hospital and healthy volunteers came to East hospital for routine physical examination. Pneumonia was defined by a new pulmonary infiltrate on chest radiograph accompanied with at least one of the following signs [18]: (a) the presence of cough, sputum production, and dyspnea; (b) core body temperature > 38.0°C; (c) peripheral white blood cell counts > 10 × 109/L or < 4 × 109/L. Among the 21 samples, 3 sepsis samples, 3 pneumonia samples and 3 healthy people samples were used for RNA sequencing analysis, and the remaining samples were used for subsequent tests.
## Cell extraction
Peripheral blood mononuclear cells (PBMCs) were isolated from peripheral blood according to the protocol as previously reported [19] and CD14+ monocytes were sorted from PBMCs with a magnetic cell sorting system (Miltenyi Biotec, Germany). PBMCs were added with CD14 Microbeads(20µl/107cells), and then the CD14+ monocytes were magnetically labeled with CD14 Microbeads. When PBMCs passed through a MACS column, the magnetically labeled CD14+ monocytes were retained within the column, and then CD14+monocytes were extracted as positively selected cell fraction.
## RNA sequencing analysis
Plasma from patients with pneumonia-induced sepsis, pneumonia patients without sepsis, and healthy individuals was isolated using TRIzol reagent (Invitrogen, USA) per the manufacturer’s instructions. NanoDrop ND-1000 was utilized to measure the RNA’s purity and concentration (NanoDrop Thermo). Through denaturing agarose gel electrophoresis, the RNA integrity of the samples was evaluated. The rRNA was removed using the Ribo-Zero rRNA Removal Kit (Illumina, San Diego, CA, USA). Cloud-Seq Biotech (Shanghai, China) performed the high-throughput whole transcriptome sequencing and subsequent bioinformatics analysis as previously reported [20]. The sequencer Illumina HiSeq 6000 was used to obtain paired-end readings. The circular RNA was detected and identified using DCC software (v0.4.4) and the identified circular RNA was annotated using the circBase database and Circ2Tuits. Edger software (v3.16.5) was utilized to identify circRNAs with differential expression.
## RNA extraction and quantitative Real-Time PCR (qRT-PCR)
Total RNA (2µg) was extracted using TRIzol (Invitrogen, USA) followed by reverse transcription of mRNAs and circRNAs using PrimeScript II 1st Strand cDNA Synthesis Kit (Takara, Japan) per the standard manufacturer’s instructions. qRT-PCR assay was performed to measure mRNAs and circRNAs expression with SYBR® Premix Ex Taq™ II (Takara, Japan) using the Roche 480 Real Time PCR System. GAPDH (encoding glyceraldehyde-3-phosphate dehydrogenase) was used as an internal control for circRNAs and mRNAs, and U6 was employed as an endogenous control for the miRNAs. Relative quantification (2−ΔΔCT) was used for result analysis. All the primers used were included in Supplementary Table 2.
## Cell culture and transfection
The human monocytic leukemia cell line THP-1 was purchased from Chinese Academy of Sciences (Shanghai, China) and was grown in RPMI-1640 medium supplemented with $10\%$ fetal bovine serum and $1\%$ penicillin and streptomycin. THP-1 cells were differentiated into macrophages for 3 hr in the presence of 100 nM phorbol myristate (PMA) and replated. For stimulation, cells were primed with different or indicated concentrations (0.1, 0.5 and 1 μg/ml) LPS for 4 hrs in Opti-MEM, then stimulated with 10 μM nigericin for 2 hrs. GenePharma designed and produced the Circ_0075723 overexpression vector, miR-155-5p mimic, SHIP1 overexpression vector and circ_0075723 silence vector (sh-circ_0075723) (Shanghai, China). THP-1 cells were transfected with overexpression vector (2 μg), miRNA mimic (25 nM) or shRNA (25 nM) using Lipofectamine 3000 (Invitrogen) per the manufacturer’s instructions 24h before LPS/nigericin stimulation. After different stimulations, the supernatants of THP-1 cells were collected for IL-1β and IL-18 analyses or applied to culture human lung microvascular endothelial cells (HLMVEC) (Chinese Academy of Sciences, China) for 24 hours.
## Fluorescence in situ hybridization (FISH) assay
The location of circ_0075723 in THP-1 cells is determined by FISH. THP-1 cells are fixed with $4\%$ paraformaldehyde and gradient dehydrated with ethanol. Fluorescent-labeled probe (1 µM) for circ_0075723 is applied during hybridization. We use DAPI (Beyotime, Shanghai, China) to stain the nucleus of macrophages.
## Luciferase reporter assay
The wild-type (WT) sequence and mutant-type (MUT) sequences (binding site mutation with miR-155-5p) of circ_0075723 and SHIP1 were amplified and cloned into PmirGLO reporter plasmid, respectively. The fusion plasmid was cotransfected with either miR-155-5p or miR-NC into HEK293T cells. 48 hours after transfection, the luciferase activity was measured using Picagene Dual SeaPansy luminescence kit (Toyo Inc., Japan) according to the manufacturer’s instructions as reported [21].
## RNase R digestion RNA stability
4 μg total RNA from THP-1 cells was either untreated (control) or treated with 20 units of RNase R (Epicenter; USA, RNR07250) in the presence of 1× reaction buffer and incubated for 30 min at 37°C. RNA was extracted using acid phenol-chloroform after digestion (5: 1). Then, reverse transcription and qRT-PCR were performed, as described in the RNA extraction and qRT-PCR section. THP-1 cells (1 ×105) were placed in 24-well plates and treated with 250 ng/ml actinomycin D (Act D, Sigma) added to the cell culture medium. The levels of circ_0075723 and NUP153 were measured at 0, 8, 12, and 24 hrs.
## RNA pull-down assay
Biotin-labeled circ_0075723 probe and oligo probe were obtained from Ribobio. THP-1 cells transfected with circ_0075723 probe or oligo probe were lysed and used for pull-down assay using the Pierce Magnetic RNA ProteinPull-down Kit (Thermo Fisher Scientific) in accordance with the instructions. qRT-PCR was used to detect the expression of specified miRNAs.
## ELISA analysis
ELISAs were performed to measure the concentrations of IL-18 and IL-1β protein from supernatants according to the manufacturer’s instructions (R&D Systems).
## Immunoblotting analysis
Immunoblotting analysis was performed as described previously [19, 22]. Densitometry analysis of immunoblot results was conducted by using ImageJ software. The results of three replicated experiments are expressed as mean ± standard deviation (SD) (primary antibodies are listed in Supplementary Table 3).
## Statistical analysis
All experiments were done in triplicates and replicated at least three times and all experimental data are presented as the means ± SD. The two-tailed Student t-tests were used for comparisons between two groups, and one-way or two-way analysis of variance (ANOVA) were used for multifactorial comparisons. Statistical analyses were performed with SPSS 20.0 software (SPSS Inc., Chicago, IL, USA) or GraphPad Prism 8.0 (GraphPad Software, La Jolla, CA, United States). A value of $P \leq 0.05$ was considered to indicate a statistically significant difference.
## Specifical expression profiles of circRNAs in pneumonia-induced sepsis
To determine circRNAs expression profiles and to identify those that are differentially expressed in pneumonia-induced sepsis, we selected healthy people and pneumonia patients without sepsis as controls and performed RNA-seq analysis of circRNA in the plasma of these three groups. In total, 32,229 circRNAs were expressed in the plasma samples among the healthy people, pneumonia patients without sepsis and pneumonia-induced sepsis patients (Supplementary Table 4). Using the cutoff values of fold change > 2.0 and $P \leq 0.05$, 382 circRNAs showed significantly differential expression between the pneumonia-induced sepsis patients and healthy people, including 233 circRNAs that were upregulated and 149 circRNAs that were downregulated (Supplementary Tables 5, 6) (Figures 1A, B, Supplementary Figure 1). Meanwhile, 172 differentially expressed circRNAs were detected between the pneumonia-induced sepsis patients and pneumonia patients without sepsis, in which 98 of them were upregulated and 74 were downregulated (Supplementary Tables 7, 8) (Figures 1A, B). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of differentially expressed circRNAs were showed in Supplementary Figure 2. GO enrichment analysis showed that the differentially expressed circRNAs were involved in the biological processes, such as cell energy metabolism and histone modification. On the other hand, KEGG analysis revealed that RNA transport and MAPK signaling pathway, which is associated with inflammatory activation of macrophages, was related to the differential expression of circRNAs. Of all the differential circRNAs, we chose markedly downregulated circ_0075723 for next investigations because one of its most likely targeted gene, SHIP1(Src homology 2 domain–containing inositol-5-phosphatase 1), had been previously documented to be one of the negative regulators of TLR4 signaling which was involved in regulating NLRP3 inflammasome activation and pyroptosis [23, 24]. Furthermore, we validated the expression of circ_0075723 in CD14+ monocytes by qRT-PCR among the three groups and found that circ_0075723 was significantly downregulated in pneumonia-induced sepsis patients comparing to the other two groups (Figure 1C). Taken together, we screened multiple differently expressed circRNAs in pneumonia-induced sepsis compared to healthy people and pneumonia without sepsis by RNA-seq and validated the significant downregulation of circ_0075723 in pneumonia-induced sepsis, the differential expression of circRNAs from sepsis suggest possible functions of circRNAs in pathogenesis of sepsis.
**Figure 1:** *Specifical expression profiles of circRNAs in pneumonia-induced sepsis. (A) Hierarchical cluster analysis of differentially expressed circRNAs between the two compared groups of plasma. (B) Volcano plots showing the differentially expressed circRNAs among the three groups [Plot of circRNA expression log2‐transformed fold‐changes (x‐axis) vs ‐log10 P‐value (y‐axis)]. The red dots represent the circRNAs having fold change > 2.0 and P < 0.05 between the two compared groups of plasma. (C) qRT-PCR analysis of circ_0075723 expression in CD14+ monocytes among the pneumonia-induced sepsis, pneumonia without sepsis and healthy control group. Each group has 4 samples. Data are presented as means ± SD; significant difference was identified with one-way ANOVA. ***p < 0.001 vs. Control or Pneumonia.*
## The characterization of the circ_0075723
Circ_0075723, located at chr6:17648038-17649531, which is derived from the human NUP153 gene and generated by back-splicing mechanism (Figure 2A). The sequence was located at the back-splice junction location of circ_0075723 according to Sanger sequencing (Figure 2B). Further, we treated THP-1 cells with RNase R exonuclease or actinomycin D to confirm circ_0075723 authenticity and found that the expression of circ_0075723 exhibited RNase R (Figure 2C) and actinomycin D resistance (Figure 2D), while that of NUP153 mRNA was significantly decreased. This indicated circ_0075723 was stable in THP-1 cells. We then investigated the sub-cellular location of circ_0075723. By RNA fluorescence in situ hybridization (FISH) assays, we found circ_0075723 was mainly localized in the cytoplasm (Figure 2E). These studies indicated that circ_0075723 as a circRNA, its biological stability may be advantageous to its function.
**Figure 2:** *The characterization of the circ_0075723. (A) The location of circ_0075723 in genome. (B) Sanger sequencing showing the “head-to-tail” splicing of circ_0075723 in THP-1 cell. (C) qRT-PCR analysis of the expression of circ_0075723 and NUP153 in THP-1 cells after treatment with RNase R. Data are presented as means ± SD; significant difference was identified with two-way ANOVA. **p < 0.01; ns: no significant. (D) qRT-PCR analysis of the expression of circ_0075723 and NUP153 in THP-1 cells after treatment with actinomycin (D) Data are presented as means ± SD; significant difference was identified with Student t-tests. **p < 0.01, ***p < 0.001 (E) RNA FISH for circ_0075723. Nuclei were stained with DAPI.*
## Pyroptosis is activated following pneumonia-induced sepsis and Circ_0075723 inhibits pyroptosis of THP-1 in vitro
We then analyzed circ_0075723 function in pneumonia-induced sepsis. Given that pyroptosis, a typical inflammatory cell death, is a major features/characteristics of sepsis [7, 9], we wondered whether circ_0075723 was involved in the regulation of macrophage pyroptosis. Firstly, we found that CD14+ monocytes from pneumonia-induced sepsis patients had considerably greater levels of the proteins TLR4, NLRP3, ASC1, cleaved caspase-1, IL-1, and GSDMD in contrast to pneumonia patients without sepsis and healthy people (Figure 3A, Supplementary Figure 3A). In addition, pneumonia-induced sepsis patients showed a markedly enhanced expression of IL-1β and IL-18 from plasma in comparison to pneumonia patients without sepsis and healthy people (Figure 3B). These results indicated that pyroptosis is activated in pneumonia-induced sepsis. Due to the markedly downregulated expression of circ_0075723 in CD14+ monocytes from pneumonia-induced sepsis patients, we next examined the specific role of circ_0075723 in pyroptosis of pneumonia-induced sepsis. By usage of different doses of LPS together with nigericin to induce pyroptosis of THP1 in vitro, we found that LPS/nigericin treatment increased the production of proteins and cytokines associated with pyroptosis, including TLR4, NLRP3, ASC1, cleaved caspase-1, IL-1β and GSDMD, whereas downregulated the expression of circ_0075723 in a manner dependent on dose (Figures 3C, D). To further examine the direct impacts of circ_0075723 in macrophage pyroptosis, we transfected the circ_0075723-overexpressing vector (OE-circ_0075723) or circ_0075723 silence vector (sh-circ_0075723) to overexpress or knockdown circ_0075723 expression in LPS/nigericin-treated THP-1 cells (Figure 3E). Subsequently, we choose sh2-circ_0075723 for further experiments due to the relatively lower expression of circ_0075723 in transfected THP-1 cells than sh1-circ_0075723 and sh3-circ_0075723 (Supplementary Figure 3B). Overexpression of circ_0075723 in THP-1 cells showed a strong inhibition of pyroptosis-related proteins and cytokines expression, while silencing of circ_0075723 exhibited the opposite effect (Figures 3F, G, Supplementary Figure 3C). *In* general, these studies indicate that macrophages pyroptosis is activated in pneumonia-induced sepsis patients and circ_0075723 essentially prohibits macrophages pyroptosis in vitro.
**Figure 3:** *Pyroptosis is activated following pneumonia-induced sepsis and Circ_0075723 inhibits pyroptosis of THP-1 in vitro. (A) Western blot analysis of TLR4, NLRP3, ASC1, caspase1, cleaved caspase-1, Pro-IL1β, IL-1β, GSDMD and GAPDH in CD14+ monocytes from pneumonia-induced sepsis, pneumonia without sepsis and healthy people. (B) ELISA of IL-18 and IL-1β in the plasma from pneumonia-induced sepsis, pneumonia without sepsis and healthy people. Each group has 4 samples. Data are presented as means ± SD; significant difference was identified with one-way ANOVA. *p < 0.05 vs. Pneumonia; **p < 0.01 vs. Pneumonia; ***p < 0.001 vs. Control. (C) Western blot analysis of TLR4, NLRP3, ASC1, caspase1, cleaved caspase-1, Pro-IL1β, IL-1β, GSDMD and GAPDH in THP-1 cells primed with different doses of LPS (0.1, 0.5 and 1 μg/ml) for 4 h and stimulated with nigericin (10 μM) for 2h. Data are presented as means ± SD; significant difference was identified with two-way ANOVA. ***p < 0.001 vs. PBS; ns: no significant. (D) qRT-PCR analysis of the expression of circ_0075723 primed with different doses of LPS (0.1, 0.5 and 1 μg/ml) for 4 h and stimulated with nigericin (10 μM) for 2h in THP-1 cells. Data are presented as means ± SD; significant difference was identified with Student t-tests. *p < 0.05 vs. PBS; **p < 0.01 vs. PBS. THP-1 cells were transfected with vector or shRNA scrambled control (shRNA NC) or were transfected with circ_0075723-overexpressing lentivirus plasmids (OE-circ_0075723), sh-circ_0075723-expressing lentivirus plasmids (sh-circ_0075723), vector or shRNA scrambled control (shRNA NC) and then were primed with LPS (1 μg/ml) for 4 h and stimulated with nigericin (10 μM) for 2h. (E) qRT-PCR analysis of the expression of circ_0075723 in THP-1 cells. Data are presented as means ± SD; significant difference was identified with Student t-tests. ***p < 0.001 vs. vector or shRNA NC; ###p < 0.001 vs. LPS/nigericin + Vector; &&p < 0.01 vs. LPS/nigericin + shRNA NC. (F) Western blot analysis of TLR4, NLRP3, ASC1, caspase1, cleaved caspase-1, Pro-IL1β, IL-1β, GSDMD and GAPDH in THP-1 cells. (G) ELISA of IL-18 and IL-1β in THP-1 supernatant. Data are presented as means ± SD; significant difference was identified with Student t-tests. **p < 0.01 vs. vector; ***p < 0.001 vs. vector or shRNA NC; #p < 0.05 vs. LPS/nigericin + Vector; ##p < 0.01 vs. LPS/nigericin + Vector; &p < 0.05 vs. LPS/nigericin + shRNA NC; &&p < 0.01 vs. LPS/nigericin + shRNA NC.*
## Circ_0075723 functions as a sponge for miR-155-5p in THP-1
Based on bioinformatic predictions from the miRanda and TargetScan databases, miR-155-5p was predicted to bind with circ_0075723, and circ_0075723–miR-155-5p network was depicted in Figure 4A. Additionally, we found the miR-155-5p level were considerably higher in CD14+ monocytes of pneumonia-induced sepsis patients than that of pneumonia patients without sepsis and healthy people (Figure 4B). Intriguingly, miR-155-5p expression was dramatically increased in LPS/nigericin-treated THP-1 cells in dose-dependent manner (Figure 4C). Further, miR-155-5p expression was modulated via circ_0075723 as miR-155-5p was downregulated by OE-circ_0075723 and upregulated by sh-circ_0075723 (Figure 4D). According to these results, we hypothesized that circ_0075723 may modulate macrophages pyroptosis by sponging miR-155-5p. Therefore, we performed RNA pull-down assay to verify the specific connection between circ_0075723 with miR-155-5p and found that miR-155-5p could directly interact with circ_0075723 (Figure 4E). Besides, dual-luciferase reporter assay revealed that miR-155-5p mimics dramatically decreased the activity of the circ_0075723 wild-type luciferase reporter but had no effect on the Mut luciferase reporter (Figure 4F). As we had observed that circ_0075723 could inhibit macrophages pyroptosis, we further adopted the rescue trials to find that miR-155-5p mimics could partly reduce protective effect of circ_0075723 on macrophages pyroptosis (Figures 4G, H, Supplementary Figure 4). Collectively, our findings indicated that circ_0075723 acts as a “molecular sponge” for miR-155-5p.
**Figure 4:** *Circ_0075723 functions as a sponge for miR-155-5p in THP-1. (A) Schematic showing the predicted miR-155-5p sites in circ_0075723. (B) qRT-PCR analysis of the expression of miR-155-5p in CD14+ monocytes among the pneumonia-induced sepsis, pneumonia without sepsis and healthy control group. Each group has 4 samples. Data are presented as means ± SD; significant difference was identified with one-way ANOVA; ***P < 0.001 vs. Control or Pneumonia. (C) qRT-PCR analysis of the expression of miR-155-5p in THP-1 cells primed with different doses of LPS (0.1, 0.5 and 1 μg/ml) for 4 h and stimulated with nigericin (10 μM) for 2h. Data are presented as means ± SD; significant difference was identified with Student t-tests. **p < 0.01 vs. PBS; ***p < 0.001 vs. PBS. (D) THP-1 cells were transfected with vector or shRNA scrambled control (shRNA NC) or were transfected with circ_0075723-overexpressing lentivirus plasmids (OE-circ_0075723), sh-circ_0075723-expressing lentivirus plasmids (sh-circ_0075723), vector or shRNA scrambled control (shRNA NC) and then were primed with LPS (1 μg/ml) for 4 h and stimulated with nigericin (10 μM) for 2h. qRT-PCR analysis of the expression of miR-155-5p in THP-1 cells. Data are presented as means ± SD; significant difference was identified with Student t-tests. **p < 0.01 vs. vector; ***p < 0.001 vs. shRNA NC; #p < 0.05 vs. LPS/nigericin + Vector; &p < 0.05 vs. LPS/nigericin + shRNA NC. (E) RNA pull-down analysis of the interaction between miR-155-5p and circ_0075723. Data are presented as means ± SD; significant difference was identified with Student t-tests. ***p < 0.001 vs. NC group. (F) Dual-luciferase reporter assay was performed to validate the association between miR-155-5p and circ_0075723. Data are presented as means ± SD; significant difference was identified with one-way ANOVA. **p < 0.01 vs. mimic NC group. THP-1 cells were transfected with vector + mimic scrambled control (mimic NC) or vector + miR-155-5p mimic or were transfected with vector + mimic NC, vector + miR-155-5p mimic, mimic NC + circ_0075723 lentivirus plasmids (circ_0075723 OE), or miR-155-5p mimic + circ_0075723 OE and then were primed with LPS (1 μg/ml) for 4 h and stimulated with nigericin (10 μM) for 2h. (G) Western blot analysis of TLR4, NLRP3, ASC1, caspase1, cleaved caspase-1, Pro-IL1β, IL-1β, GSDMD and GAPDH in THP-1 cells. (H) ELISA of IL-18 and IL-1β in THP-1 supernatant. Data are presented as means ± SD; significant difference was identified with Student t-tests. *p < 0.05 vs. vector + mimic NC; **p < 0.01 vs. vector + mimic NC; ***p < 0.001 vs. vector + mimic NC; ##p < 0.01 vs. LPS/nigericin + vector + mimic NC; ###p < 0.001 vs. LPS/nigericin + vector + mimic NC; &&p < 0.01 vs. LPS/nigericin + circ_0075723-OE + mimic NC.*
## Circ_0075723-miR-155-5p ceRNA modulates macrophage pyroptosis by directly regulating SHIP1
To further study the downstream mRNA targets of circ_0075723-miR-155-5p ceRNA network, bioinformatic analysis of the TargetScan database revealed that miR-155-5p could target the 3′-untranslated region (UTR) of SHIP1 (Figure 5A). By dual-luciferase reporter assay, we showed that miR-155-5p mimics significantly inhibited the wild-type luciferase reporter activity of SHIP1 and validated the connection relationship between SHIP1 and miR-155-5p (Figure 5B). Therefore, SHIP1 might be the gene of interest for miR-155-5p. Additionally, SHIP1 expression was markedly diminished in CD14+ monocytes of pneumonia-induced sepsis patients compared with that of pneumonia patients without sepsis and healthy people (Figure 5C). Corroborating with the clinical findings, vitro tests also confirmed that LPS/nigericin treatment suppressed SHIP1 expression in a dose-dependent way (Figure 5D). As it had reported that SHIP1 was involved in the interplay of miR-155 and TLR4 activation by acting as a key negative regulator of TLR4 signaling [23, 25, 26], while TLR4 signaling might activate NLRP3 inflammasome and promote alveolar macrophage pyroptosis [24]. We hypothesized that circ_0075723 might inhibit macrophages pyroptosis through promoting SHIP1 expression by sponging miR-155-5p. To investigate this assertion, we overexpressed miR-155-5p mimics in the LPS/nigericin activated THP-1 cells and found that SHIP1 expression was increased in the company of the circ_0075723 overexpression, meanwhile miR-155-5p mimics could markedly decrease SHIP1 upregulation induced by the circ_0075723 overexpression (Figure 5E). Besides, we further transfected SHIP1-overexpression lentivirus vector (OE-SHIP1) and (or) miR-155-5p mimics into the LPS/nigericin activated THP-1 cells, and found that the production of pyroptosis-related proteins and cytokines, such as TLR4, NLRP3, ASC1, cleaved caspase-1, GSDMD, IL-1β and IL-18, were decreased in OE-SHIP1 group, meanwhile miR-155-5p mimics could significantly reverse these effects of SHIP1-overexpression (Supplementary Figures 5A, B).
**Figure 5:** *Circ_0075723-miR-155-5p ceRNA modulates macrophage pyroptosis by directly regulating SHIP1. (A) Schematic showing the predicted miR-155-5p sites in SHIP1. (B) Dual-luciferase reporter assay was performed to validate the association between miR-155-5p and SHIP1. Data are presented as means ± SD; significant difference was identified with one-way ANOVA. **p < 0.01 vs. mimic NC group. (C) Western blot analysis of SHIP1 and GAPDH in CD14+ monocytes from pneumonia-induced sepsis, pneumonia without sepsis and healthy control group. Each group has 4 samples. Data are presented as means ± SD; significant difference was identified with one-way ANOVA. **P < 0.01 vs. Pneumonia; ***P < 0.001 vs. Control. (D) Western blot analysis of SHIP1 and GAPDH in THP-1 cells primed with different doses of LPS (0.1, 0.5 and 1 μg/ml) for 4 h and stimulated with nigericin (10 μM) for 2h. Data are presented as means ± SD; significant difference was identified with Student t-tests. *p < 0.05 vs. PBS; **p < 0.01 vs. PBS. (E) THP-1 cells were transfected with vector + mimic scrambled control (mimic NC) or vector + miR-155-5p mimic or were transfected with vector + mimic NC, vector + miR-155-5p mimic, mimic NC + circ_0075723 lentivirus plasmids (circ_0075723 OE) or miR-155-5p mimic + circ_0075723 OE and then were primed with LPS (1 μg/ml) for 4 h and stimulated with nigericin (10 μM) for 2h. Western blot analysis of SHIP1 and GAPDH in THP-1 cells. Data are presented as means ± SD; significant difference was identified with Student t-tests. *p < 0.01 vs. vector + mimic NC; #p < 0.05 vs. LPS/nigericin + vector + mimic; ##p < 0.01 vs. LPS/nigericin + vector + mimic; &&p < 0.01 vs. LPS/nigericin + circ_0075723-OE + mimic NC.*
## Overexpression of Circ_0075723 in macrophages downregulates IL-1β and IL-18 expression and protects endothelial cell integrity
The pathological process of sepsis is complex, and the permeability change caused by vascular endothelial cell damage has a significant role in the pathophysiology of sepsis [27]. We have shown that circ_0075723 in macrophages could inhibit pyroptosis-related pro-inflammatory cytokines IL-1β and IL-18 expression by circ_0075723/miR-155-5p/SHIP1 axis, and previous studies had documented that NLRP3 inflammasome associated cytokines IL-1β and IL-18 could increase endothelial cells permeability through inhibiting VE-cadherin expression in endothelial cells [28, 29]. We further evaluated whether circ_0075723 in macrophages could modulate VE-cadherin expression in endothelial cells through inhibiting NLRP3 inflammasome associated cytokines IL-1β and IL-18 by circ_0075723/miR-155-5p/SHIP1 axis. To examine the proposition, we collected supernatant from aforementioned-LPS/nigericin activated THP-1 cells bearing altered expression of circ_0075723, miR-155-5p and SHIP1, and then used them to culture human lung microvascular endothelial cells (HLMVEC) in vitro. Firstly, Western blotting outcomes demonstrated that VE-cadherin expression in HLMVEC cells was downregulated culturing in supernatant harvested from LPS/nigericin-treated THP-1 cells in contrast to control and further dramatically downregulated in supernatant from sh-circ_0075723-transfected THP-1 cells, whereas transfected OE-circ_0075723 THP-1 cells supernatant exhibit the opposite (Figure 6A). Besides, the expression trend of VE-cadherin in HLMVEC cells was contrary to the expression of IL-1β and IL-18 in above supernatant (Figure 3G). Furthermore, rescue tests showed that the effect of circ_0075723 and SHIP1 on VE-cadherin expression in HLMVEC cells and IL-1β and IL-18 expression in the supernatant could be inhibited by miR-155-5p mimics (Figures 4H, 6B, C, Supplementary Figure 5B). Collectively, these results may indicate that Overexpression circ_0075723 downregulates IL-1β and IL-18 expression, promotes VE-cadherin expression in endothelial cells and further protects endothelial cell integrity.
**Figure 6:** *Circ_0075723 in macrophages regulate endothelial permeability through the inhibition expression of IL-1β and IL-18 by circ_0075723/miR-155-5p/SHIP1 axis. (A) Western blot analysis of VE-cadherin in HLMVEC cultured with the supernatant for 24 h. The supernatant was collected from aforementioned-LPS-primed THP-1 stimulated with nigericin bearing altered expression of circ_0075723. Data are presented as means ± SD; significant difference was identified with Student t-tests. *p < 0.05 vs. Vector or shRNA NC; ##p < 0.01 vs. LPS/nigericin + Vector; &p < 0.05 vs. LPS/nigericin + shRNA NC. (B) Western blot analysis of VE-cadherin in HLMVEC cultured with the supernatant for 24 h. The supernatant was collected from aforementioned-LPS-primed THP-1 stimulated with nigericin bearing altered expression of circ_007572 and miR-155-5p. Data are presented as means ± SD; significant difference was identified with Student t-tests. *p < 0.05 vs. vector + mimic NC; #p < 0.05 vs. LPS/nigericin + vector + mimic NC; ##p < 0.01 vs. LPS/nigericin + vector + mimic NC; &p < 0.05 vs. LPS/nigericin + mimic NC + circ_0075723-OE. (C) Western blot analysis of VE-cadherin in HLMVEC cultured with the supernatant for 24 h. The supernatant was collected from aforementioned-LPS-primed THP-1 stimulated with nigericin bearing altered expression of miR-155-5 and SHIP1. Data are presented as means ± SD; significant difference was identified with Student t-tests. *p < 0.05 vs. vector + mimic NC; #p < 0.05 vs. LPS/nigericin + vector + mimic NC; &p < 0.05 vs. LPS/nigericin +mimic NC + SHIP1-OE.*
## Discussion
Sepsis, especially caused by pneumonia, affects a great many patients all over the world, with high morbidity, mortality and economic expenses [30]. Although comprehension of the pathogenesis has grown, and modern therapeutic technologies, such as the use of proper antibiotics, vigorous resuscitation and organ support have also made great progress, the high mortality rate caused by sepsis remains a significant issue [17, 31]. Therefore, it is necessary to clarify the potential mechanism to find effective targets for the treatment of pneumonia-induced sepsis. In this investigation, RNA-seq was used to determine the expression profile of circRNAs in the plasma of pneumonia-induced sepsis patients. Through screening the differentially expressed cicRNAs, we identified that circ_0075723 as a significantly downregulated circRNA in the serum and monocytes of pneumonia-induced sepsis patients as compared to healthy people and pneumonia patients without sepsis. Moreover, we found that circ_0075723 protected against macrophage pyroptosis through targeting circ_0075723-miR-155-5p-SHIP1 axis. In addition, we found that circ_0075723 suppressed macrophage pyroptosis-induced endothelial permeability by up-regulating VE-cadherin expression. In sum, we firstly determined the influence of circ_0075723/miR-155-5p/SHIP1 axis on macrophage pyroptosis, which represents a new mechanism for pneumonia associated sepsis progression. The newly identified circ_0075723 may be a possible therapeutic target for pneumonia-induced sepsis.
It is generally recognized that sepsis pathophysiology is extremely complex, hence understanding the underlying molecular mechanisms in the occurrence and development of the disease is still a prerequisite to find effective biomarkers and specific treatments to improve survival rate [32]. Through regulating the patients’ immune system against different pathogens, circRNAs were revealed to be essential for the pathogenesis of sepsis and sepsis-induced organ dysfunction [33]. However, to date, very little clinical research documented specifically expressed circRNAs in the peripheral blood of sepsis patients. Recent research showed the differential expression of circRNAs in lung tissues of patients with sepsis-induced ARDS [34]. We also reported the expression of circN4bp1 in the PBMCs being a diagnostic and predictive marker in ARDS post sepsis [14]. In the clinical setting, pneumonia-induced sepsis is one of the most prevalent causes of sepsis and is associated with the greatest fatality rate [3, 4]. Therefore, we set out to establish the expression profile of circRNAs using RNA-seq in the plasma of sepsis originated from pneumonia. We discovered that there were variations in plasma circRNA expressions from pneumonia-induced sepsis patients relative to pneumonia patients without sepsis and healthy people, which could contribute to the development and course of the disease. Furthermore, we identified that circ_0075723 was significantly decreased in the plasma and CD14+ monocytes of sepsis patients secondary to pneumonia.
Uncontrolled or excessive inflammation is a hallmark of sepsis. A increasing body of research has shown that pyroptosis, a distinct instance of proinflammatory programmed death, contributes to the excessive inflammatory responses of sepsis and sepsis-related organ damage [35]. Therefore, targeting NLRP3 inflammasome activation and the subsequent pyroptosis would be a critical for the therapy of sepsis. Macrophages, as one of the most important cells of the innate immune system, play an important role in inflammatory and immune processes [6]. CD14+ monocytes are the major subpopulation of monocytes [36], and several clinical studies have shown that changes in the number and function of circulating CD14+ monocytes in patients with sepsis (37–39). However, there have been no reports of CD14+ monocytes pyroptosis in clinical patients with sepsis. Recently, LPS-triggered TLR4 signaling is involved in promoting pulmonary macrophage pyroptosis with activation of NLRP3 inflammasome and elevated expression of the pyroptosis-related proinflammatory cytokines IL-1β and IL-18 [24]. Platelet endothelial cell adhesion molecule-1 has been shown to safeguard from sepsis-associated diffuse intravascular coagulation (DIC) through inhibiting macrophage pyroptosis [40]. In additional, caspase-11-mediated inflammasome activation and macrophage pyroptosis were controlled by the cAMP metabolism, which attenuated excessive inflammatory responses in sepsis [41]. Consistently, we did reveal the upregulation of TLR4, activation of NLRP3 inflammasome, enhanced cleavage of GSDMD, IL-1β and IL-18, and increased release of IL-1β and IL-18 in serum and CD14+ monocytes of pneumonia-induced sepsis patients comparing to pneumonia individuals without sepsis and healthy controls, shedding light on the importance of macrophage pyroptosis in the clinical pathogenesis of sepsis.
Previous studies indicated that circRNAs potentially regulated the macrophage pyroptosis in other clinical conditions, such as ACS [16] and renal fibrosis [15]. In our study, we also showed this function in pneumonia-induced sepsis. Since a main way by which circular RNAs exert their effects is by sponging miRNAs via ceRNA crosstalk [11], we identified possible miRNAs that interact with circ_0075723 using bioinformatics. Our study discovered potential regulatory connections between miR-155-5p and SHIP1, as well as between circ_0075723 and miR-155-5p. The dysregulation of all three genes in pneumonia-induced sepsis patients was confirmed in monocyte/macrophage THP-1 cells. It has been reported that serum exosome-derived miR-155 promoted macrophage proliferation and inflammation involved in sepsis-related acute lung injury [42] and SHIP1 regulated Phagocytosis and M2 Polarization in *Pseudomonas aeruginosa* Infection as a negative regulator of inflammatory responses [43]. Previous studies also documented that SHIP1 is the major target of miR-155 in a wide range of inflammatory diseases [25, 26] and negatively regulates LPS-triggered TLR signaling [22]. We discovered that miR-155-5p may bind to the 3′UTR of SHIP1 and suppress its expression, further upregulating the levels of TLR4, thereby activating macrophage pyroptosis as evidenced by the enhanced expression of NLRP3, caspase-1, ASC1, GSDMD and related cytokines as IL-1β and IL-18. In addition, we found that circ_0075723 could increase SHIP1 expression in macrophage in vitro, whereas miR-155-5p mimics could partially counteract this impact. We also confirmed that circ_0075723 could attenuate the macrophage inflammation and pyroptosis involved in blocking the formation and activation of NLRP3 inflammasome in vitro. Therefore, we have revealed a new mechanism of the NLRP3 inflammasome activation through the circ_0075723/miR-155-5/SHIP1 axis, which has vital clinical transformation prospects.
One hallmark of acute sepsis is microvessel dysfunction, in which increased endothelial permeability especially lung vascular permeability plays pivotal roles in pneumonia origin sepsis [27, 44]. Endothelial permeability is controlled by VE-cadherin, a central component of endothelial adherens junctions (AJs) that modulate the integrity of endothelial junctions and lung fluid balance [45, 46]. Previous research demonstrated that pyroptotic immune cells, such as macrophages, release IL-1β and IL-18, which alter vascular integrity and cause organ damage [24, 28]. For example, over-released IL-18 caused diabetic retinopathy by increasing retinal vascular permeability [28], and IL-1β could destroy vascular integrity during sepsis-induced lung injury through suppressing VE-cadherin expression in lung endothelial cell [24]. Consistent with these findings, we further discovered that circ_0075723-miR-155-5p-SHIP1 signaling suppressed IL-1β and IL-18, release from macrophages, which maintained endothelial barrier stability of vascular endothelial cells by repressing the VE-cadherin expression. Future investigations are required to delineate the consequences and the underlying mechanisms of macrophage pyroptosis in mediating endothelial cells permeability of sepsis in vivo and further experiments are needed to verify that circ_0075723 suppressed macrophage pyroptosis in the sepsis mouse model. In summary, our research uncovers a new mechanism by which circ_0075723 inhibits macrophage pyroptosis and inflammation via sponging miR-155-5p, thus enhancing SHIP1 expression. These findings imply that circ_0075723 may represent a novel therapeutic target for treating pneumonia-induced sepsis.
## Data availability statement
The data presented in the study are deposited in the GEO repository, accession number GSE218494.
## Ethics statement
The studies involving human participants were reviewed and approved by the Research Ethics Board of East Hospital, Tongji University (Shanghai, China). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
DY, DZ, JJ, CW, NL, XB, XL, SJ, and QZ designed, carried out experiments, and performed the genetic analyses. DY, DZ, and JJ wrote the manuscript. LT guided and coordinated the work. 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.2023.1095457/full#supplementary-material
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|
---
title: Identification and investigation of depression-related molecular subtypes in
inflammatory bowel disease and the anti-inflammatory mechanisms of paroxetine
authors:
- Lijun Ning
- Xinyuan Wang
- Baoqin Xuan
- Yanru Ma
- Yuqing Yan
- Ziyun Gao
- Tianying Tong
- Zhe Cui
- Haoyan Chen
- Xiaobo Li
- Jie Hong
- Zhenhua Wang
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10008943
doi: 10.3389/fimmu.2023.1145070
license: CC BY 4.0
---
# Identification and investigation of depression-related molecular subtypes in inflammatory bowel disease and the anti-inflammatory mechanisms of paroxetine
## Abstract
### Background
Up to 40 per cent of people with active inflammatory bowel disease (IBD) also suffer from mood disorders such as anxiety and depression. Notwithstanding, the fundamental biological pathways driving depression in IBD remain unknown.
### Methods
We identified 33 core genes that drive depression in IBD patients and performed consensus molecular subtyping with the NMF algorithm in IBD. The CIBERSORT were employed to quantify the immune cells. Metabolic signature was characterized using the “IOBR” R package. The scoring system (D. score) based on PCA. Pre-clinical models are constructed using DSS.
### Results
Using transcriptome data from the GEO database of 630 IBD patients, we performed a thorough analysis of the correlation between IBD and depression in this research. Firstly, the samples were separated into two different molecular subtypes (D. cluster1 and D. cluster2) based on their biological signatures. Moreover, the immunological and metabolic differences between them were evaluated, and we discovered that D. cluster2 most closely resembled IBD patients concomitant with depression. We also developed a scoring system to assess the IBD-related depression and predict clinical response to anti-TNF- therapy, with a higher D. score suggesting more inflammation and worse reaction to biological therapies. Ultimately, we also identified through animal experiments an antidepressant, paroxetine, has the added benefit of lowering intestinal inflammation by controlling microorganisms in the digestive tract.
### Conclusions
This study highlights that IBD patients with or without depression show significant variations and antidepressant paroxetine may help reduce intestinal inflammation.
## Introduction
Inflammatory bowel disease (IBD), which includes Crohn’s disease (CD) and ulcerative colitis (UC), is considered to be caused by an abnormal immune response to enteric microbiota and environmental triggers in a genetically vulnerable host, and its incidence and prevalence are growing globally [1, 2]. IBD is a severe gastrointestinal illness that causes symptoms like abdominal pain and feculent blood, as well as a lower quality of life and deficiencies in social roles, which may put people with IBD at a higher risk of depression and anxiety than the general population (3–6). Notably, active IBD is connected with the occurrence of anxiety and sadness in up to $40\%$ of patients [7]. Nevertheless, the molecular biological mechanisms underlying such IBD patients combined with depression are unclear thus far.
Given the strong genetic and hereditary correlates between IBD and depression (8–10), recognizing the mechanism that links IBD to depression and anxiousness is essential for designing therapeutic and prevention measures, as well as predicting prognosis in IBD patients properly. Over the last two decades, there has been a wealth of research linking depression and anxiety to systemic immunological engagement, which includes abnormalities in inflammatory mediators, immune cell populations, and antibody titers [11, 12]. A study on neuroendocrine-immune interactions in patients with major depressive disorder(MDD) found that the pro-inflammatory cytokine TNF-α was significantly higher in the serum of MDD patients than in normal controls, confirming increased inflammation and dysregulation of the immune system in MDD patients [13]. Similarly, immunopathological processes like macrophage polarization and neutrophil infiltration are also present in IBD. As IBD involves aberrant activation of innate and adaptive immune responses [14], immune dysfunction may be a common pathway between it and depression. Moreover, recent studies have found that the metabolic disorder is another mechanism that shadows the onset of IBD and the state of depression. Researchers discovered that elevated levels of tryptophan metabolites suggest a high activity of tryptophan degradation in persons with active IBD after investigating over 500 IBD patients [15]., which means tryptophan deficiency could contribute to the development of IBD or aggravate disease activity. Another study showed that disturbances of the kynurenine metabolic pathway, one of the metabolic pathways of tryptophan, collectively contribute to the development of depression-like behaviour [16]. Therefore, the relationship between IBD and depression may be examined from the perspective of immune activation and metabolic dysfunction.
In addition, as a common gut-brain therapy, antidepressants tend to be beneficial not only in treating mood disorders but also in relieving discomfort and reducing the risk of relapse in patients with IBD [17], although data remains limited. Antidepressants have been demonstrated in certain studies to aid with comorbid functional problems, regulate chronic diarrhea during IBD remission, and maybe decrease inflammation and improve disease activity [17]. Furthermore, given the immunomodulatory effects of serotonin and its reuptake inhibitors [18, 19], we hypothesize that SSRI-type antidepressants may play an essential role in modulating the immune-inflammation. Hence, it is essential to explore how antidepressants alleviate intestinal inflammation.
As sequencing technology advances, a growing amount of transcriptomic data become available in public databases, offering a handy tool for a thorough investigation into the mechanisms underlying both IBD and depression. In this study, we integrated the transcriptomic data of 630 IBD samples from Gene Expression Omnibus (GEO) databases for subsequent analysis. Firstly, we utilized consensus clustering analysis of the Non-negative Matrix Factorization (NMF) algorithm to stratify samples with qualitatively different molecular subtypes. We then explored the differences between the two subtypes through a series of bioinformatic approaches from an immunological and metabolic perspective, respectively, and one of the above subtypes was considered to be IBD combined with depression. Moreover, a scoring scheme was constructed to quantify the IBD-related depression gene signature and anticipate the therapeutic outcome of patients to anti-TNF-alpha medication. We eventually discovered an antidepressant, paroxetine, that can alleviate intestinal inflammation, and its anti-inflammatory mechanism works by regulating gut microorganisms.
## Obtaining and preprocessing IBD datasets
The Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) is a collection of publicly available gene expression data and associated clinical information. This study recruited three IBD cohorts (GSE87473, GSE92415, and GSE112366) on the same platform for future investigation (Table S1). To adjust for batch effects in the various cohort datasets, the “ComBat” method was implemented in the “sva” R package. Using the “limma” R package using the criterion p. adjust 0.05 and | Log2FC | > 0.2, we were able to find the DEGs that are significantly different in expression between IBD patients and healthy controls.
## Consensus molecular clustering of 33 core genes that drive depression in IBD patients by NMF
Our NMF consensus clustering was based on the expression levels of the 33 central genes, allowing us to isolate distinct molecular subtypes. Concretely, the expression levels of 33 core genes that drive depression in IBD patients (Matrix A) were decomposed into two nonnegative matrices W and H (i.e., A≈WH). The outputs of Matrix A were aggregated after repeated factorization to generate consensus clustering of IBD samples. The consensus clustering was carried out using the R package ‘NMF’ (version 0.30.4) and the brunet method. The best number of clusters was optimized using cophenetic, dispersion, and silhouette parameters.
## Gene set variation analysis (GSVA) and functional annotation
Using the “h.all.v7.5.1.symbols.gmt” gene set obtained from the “MSigDB” database, we did a GSVA enrichment analysis (“GSVA” R packages) to describe the biological processes different across depression-related IBD molecular subtypes. Statistical significance was assumed when the p-value was less than 0.0001. Functional annotation was carried out with the help of the “clusterProfiler” R package (version 4.2.0).
## Evaluation of immune infiltration levels and metabolism gene signatures
The proportion of infiltration of the 22 immune cells was assessed by the “CIBERSORT” R package [20]. The activation of metabolic gene signature was analyzed using the “IOBR” R package [21]. ( https://github.com/IOBR/IOBR).
## Construction of the IBD-related depression gene signature scores
We developed an IBD-related depression gene signature scoring scheme (D. score) using principal component analysis (PCA). Principal components 1 and 2 of the PCA analysis based on the DEGs were retrieved and served as the signature score. We then adopted the mathematical formula [22, 23] to calculate the D. score: D. score = ∑(PC1i+PC2i).
## Mice, models and treatments
The 6-8-week-old male C57BL/6 mice were housed and reared in specific pathogen-free conditions, at the animal center of Renji Hospital affiliated with Shanghai Jiao Tong University School of Medicine. For the DSS-induced colitis model, the mice were administered $3\%$ DSS (molecular weight, 36,000–50,000 Da; MP Biomedicals) in drinking water for seven days, followed by three days of DSS-free water. In addition, the Disease Activity Index (DAI), a score that reflects the severity of the disease, were recorded daily, including diarrhea, bloody stools and body weight [24]. After $10\%$ formalin and paraffin embedding, mice colonic tissues were collected and stained with H&E. The pathology scores were calculated from two parameters for a maximum score of 8; one parameter is cell infiltrate (normal, 0; mild 5 mucosa, 1; moderate in mucosa, 2; marked in mucosa, 3; moderate/severe in mucosa and submucosa, 4; transmural, 5), and another architecture (no erosion, 0; focal erosion, 1; focal ulceration, 2; extended ulcerations, 3) [25]. Paroxetine (Med Chem Express, USA) and Fluoxetine (Med Chem Express, USA) was suspended in phosphate buffer saline (20 mg/kg/d), and mice were given that suspension intragastrically for ten days until the end of the experiment (Figure 1A).
**Figure 1:** *Antidepressants can resolve intestinal inflammation in a preclinical model of (D) cluster2 patients. (A) Induction of experimental ulcerative colitis. Mice received 3% (wt/vol) DSS dissolved in drinking water for 7 days followed by normal drinking water for 3 days. (B) Body weight change. (C, D) Representative pictures of colon gross appearance and colon length. (E) DAI score. (F, G) Representative microscopic pictures of H&E staining (40× and 100× magnification) and histology scores. Data are pooled from each independent experiment with n = 5 mice per group; The asterisks represented the statistical P-value (ns P > 0.05; *P < 0.05; **P < 0.01; ***P < 0.001).*
## RNA sequencing analysis
After the mice were killed, their colons were removed and frozen at -80 degrees Celsius. Trizol reagent was used to extract and purify the total RNA from each sample (Invitrogen, USA). NanoDrop 2000 was used for analyzing the concentration and purity of RNA (NanoDrop, USA). We utilized 1 ng of RNA in each sample as the starting point. Following the manufacturer’s instructions, sequencing libraries were prepared by performing steps such as mRNA purification, fragmentation, cDNA synthesis, adaptor ligation, library purification, and library amplification using an Illumina TruseqTM RNA sample prep Kit. Following that, an Agilent Bioanalyzer 2100 was used to assess the library’s quality, and a Qubit 2.0 Fluorometer was used to quantify the results (Invitrogen, Carlsbad, USA). Following the manufacturer’s recommendation, we carried out the paired-end sequencing using an Illumina HiSeq 2000.
## DNA extraction and 16S-rRNA sequencing
E.Z.N.A.Soil DNA Kit (Omega, M5635-02, USA) was used to extract genomic DNA from mouse feces following the manufacturer’s instructions. Each PCR product’s DNA concentration was measured using a Qubit® 4.0 Green double-stranded DNA test (ThermoFisher, USA). Illumina HiSeq sequencing technology was utilized to sequence the library. Valid data were obtained after first stitching and filtering raw data. By measuring the abundance of OTUs, we were able to calculate -diversity indices. Principal coordinate analysis was used to illustrate variety (PCoA). LefSe’s difference comparison was used to find characteristics whose abundances varied greatly between categories. Analysis of functional predictions made by the program “Tax4Fun” was used to infer the metabolic roles of bacteria.
## Reverse-transcription and real-time PCR
The intestinal tissues of mice were processed with the Trizol reagent to get total RNA (Invitrogen, USA). In each of the samples, 1 ng of total RNA was used for the process of reverse transcription, and the PrimeScriptTM RT Master Mix was used (Takara). After that, quantitative RT-PCR was carried out in triplicates by using the StepOnePlus real-time PCR machine in conjunction with the TB Green PCR Master Mix reagent from Takara as the detector (Applied Biosystems). Gapdh was considered an internal control for mRNA expression. The relative expression levels of Grk2 and Slc6a4 were quantified using the 2−ΔΔCt method.
The following primers were used for real-time PCR analysis:
## Statistical analyses
All of the data was analyzed and visualized using R-4.1.1 and GraphPad Prism 8.4, using Student’s t-tests for comparisons between two groups and one-way analysis of variance (ANOVA) for comparisons across multiple groups. The findings are presented in the form of the mean together with the standard deviation (SD). Not significant (NS) indicates that no statistically significant difference was found between the groups. (* p 0.05, **p 0.01, ***p 0.001, ****p 0.0001)
## Identification of two molecular subtypes by 33 genes that drive depression in IBD patients
According to compiled data from published reports [26], up to forty per cent of individuals with active IBD also suffer from anxiety and despair (Figure 2A). We firstly identified potential target genes that drive depression in IBD patients. Three GEO datasets (IBD) with available clinical information (GSE87473, GSE92415, GSE112366) were enrolled into one meta-cohort. And then, we intersected DEGs with previously reported 153 depression-associated genes [27] and eventually obtained 33 genes (termed “33 core genes” hereafter) that might drive depression in IBD patients (Figures 2B, S1A). The above 33 core genes were significantly enriched in immune and metabolic-related pathways by GO enrichment analyses (Figures 2C, S1B). To further investigate transcriptome features, we used the NMF algorithm’s consensus clustering analysis to classify samples into qualitatively distinct molecular subtypes relying on the expression of 33 core genes. ( Figures S1C, S1D). Ultimately, we identified two distinct clusters, including 270 cases in depression cluster 1 and 360 cases in depression cluster 2, termed these clusters D. cluster1 and D. cluster2. Further analysis demonstrated that the expression of 33 genes was remarkably different in the above two molecular subtypes (Figures 2D). We then carried out GSVA enrichment analysis based on the “H: hallmark gene sets” to investigate the underlying molecular biological alterations that are associated with the two unique depression-related IBD molecular subtypes. GSVA results showed that D. cluster2 was considerably enriched in processes associated with the stimulation of inflammation, including interferon-gamma/alpha response, TNF-alpha signalling via NF-κB, IL-6 JAK-STAT3 signalling and inflammatory response. Nevertheless, D. cluster1 presented enrichment pathways prominently associated with metabolic-related pathways, such as the bile acid metabolism and fatty acid metabolism pathways (Figure 2E). In addition, the expression of pro-inflammatory-related signature factors (S100A8, S100A9, TNF-α, IL-1B, IL-6, IL-17, INF-γ) was upregulated in D. cluster2 at the mRNA level, while intestinal barrier function-related marker protein (LGR5, MUC2) was down-regulated. In addition, IL-22, an inflammation-associated cytokine is also upregulated in D. cluster2, but its role in inflammation remains controversial (Figures 2F, G). We consequently speculate that the patients of D. cluster2 might have a worse prognosis due to inflammatory activation and intestinal barrier dysfunction.
**Figure 2:** *Identification of two molecular subtypes by 33 genes that drive depression in IBD patients. (A) The schematic representation of the proportion of IBD patients with or without depression. (B) The 33 core genes that might drive depression in IBD patients were shown in the Venn diagram. (C) Functional annotation for 33 core genes using GO enrichment analysis. (D) The difference in mRNA expression levels of 33 core genes between two molecular subtypes. (E) Heatmap shows the GSVA score of representative Hallmark pathways curated from MSigDB in distinct molecular subtypes. The GEO cohort composition (GSE87473, GSE92415, GSE112366) were used as sample annotations. (F, G) The mRNA expression levels of intestinal inflammatory markers (S100A8, S100A9, TNF-α, IL-1B, IL-6, IL-17, IL-22, INF-γ) and barrier function markers (LGR5, MUC2) between two molecular subtypes. The asterisks represented the statistical P-value (**P < 0.01; ***P < 0.001; ****P < 0.0001).*
## Depression-related molecular subtypes characterized by distinct immune and metabolism landscapes
Given that immune manipulation plays an indispensable role in IBD, we constructed a heatmap and boxplot with CIBERSORT [20], a deconvolution approach based on support vector regression for classifying subsets of immune cells, to visualize and compare the relative abundances of 22 immune infiltrating cell subpopulations among distinct Depression-related molecular subtypes in IBD patients (Figures 3A, B). Pro-inflammatory immune cells, such as Neutrophils and macrophage M1, were mainly enriched in the D. cluster2, while macrophages M2 and regulatory T (Treg) cells, which embrace anti-inflammatory effects, were markedly elevated in the D. cluster1 subtype. We further investigated the specific association between every 33 genes and immune cell infiltration by using Spearman’s correlation analyses (Figure S2A). High expression of BTN3A1, OLFM4, FADS2, LRFN5 and PAX5 was significantly associated with pro-inflammatory immune cells (Neutrophils or macrophages M1), whereas RERE, CDSN, FH, FHIT, HIST1H3H, PCLO, SEMA6D, SYNE2 and ZSCAN16 expression exhibited a positive correlation with the anti-inflammatory, immune response (macrophages M2 or regulatory T cells). The above results indicated that the variation in immune infiltration between subtypes resulted from differences in the expression of 33 core genes.
**Figure 3:** *Depression-related molecular subtypes characterized by distinct immune and metabolism landscapes. (A, B) Heatmap and boxplot show the abundance of 22 immune infiltrating cells in two depression-related molecular subtypes. The upper and lower ends of the boxes represented the interquartile range of values. The lines in the boxes represented the median value, and the black dots showed outliers. (C, D) Heatmap and boxplot show the metabolic mapping in two depression-related molecular subtypes. The upper and lower ends of the boxes represented the interquartile range of values. The lines in the boxes represented the median value, and the black dots showed outliers. The asterisks represented the statistical p-value (ns P > 0.05; *P < 0.05; **P < 0.01; ***P < 0.001).*
Moreover, we also explored the metabolic mapping differences between the two subtypes. We discovered that D. cluster2 downregulates a variety of metabolic pathways compared to D. cluster1, including glucose metabolism (Glycolysis, Gluconeogenesis, Citric Acid Cycle and Oxidative Phosphorylation), lipid metabolism (Shingo-lipid Metabolism, Glycerolipid Metabolism and Fatty Acid Elongation), amino acid metabolism (Alanine Aspartate and Glutamate Metabolism, Tryptophan Metabolism, Arginine and Proline Metabolism, Glycine Serine and Threonine Metabolism) and so on (Figures 3C, D). In particular, pathways related to metabolic homeostasis [Retinol Metabolism [28], Steroid Hormone Metabolism [29] and Nicotinate and Nicotinamide Metabolism [30]] were also downregulated. Therefore, D. cluster2 can be regarded as a hypometabolic subtype. Notably, kynurenine metabolism, which has been reported to contribute to the development of depression-like behavior [15], is significantly activated in D. cluster2. Given the above characteristics of the patients in D. cluster2, it is hypothesized that those patients may suffer from depression and inflammatory bowel disease simultaneously. To validate those hypotheses, we compared the expression levels of previously published depression-related factors [31] between the two subtypes, and we observed those genes that were upregulated in depression were also upregulated in D. cluster2, compared to D. cluster1 (Figure S2B).
## Construction of the Depression score (D. score) of IBD and investigation of its clinical significance
Even though IBD patients were split into two molecular categories using a consensus clustering technique based on the expression of 33 core genes, the underlying genetic alterations and expression perturbations within these subtypes were unclear. In light of these questions, we further aim to investigate the possibility of the difference in the transcriptional expression of 33 key genes between the two molecular subtypes of inflammatory bowel disease. Two hundred sixty-three differentially expressed genes (DEGs) were identified as hallmark genes associated with depression in individuals with IBD. Analysis of these signature genes using GO enrichment revealed that immune-related biological processes, regulation of response to stimulus, and inflammatory response were significantly over-represented (Figure 3SA). These findings added to the evidence that DEGs were inflammatory and immune-related, making them a potential “gene signature” for IBD-related depression. Based on the 263 most representative depression phenotype-related signature genes in IBD patients, we performed an unsupervised consensus clustering analysis and obtained two stable transcriptomic phenotypes (Figures S3B, C). These stratifications separated patients into two distinct depression gene signature groupings with unique biological characteristics, denoted as the gene. cluster1 and gene. cluster2. ( Figure 4A).
**Figure 4:** *Construction of the Depression score (D. score) of IBD and exploration of its clinical relevance. (A) Unsupervised clustering of DEGs among the two molecular subtypes to classify patients into different genomic subtypes termed as the gene. cluster 1 and gene. cluster 2 respectively. The gene signature subtypes, (D) clusters, and disease subtypes were used as patient annotations. (B) Alluvial diagram showing the changes of D.clusters, disease subtypes, gene clusters and D.score. (C) Correlations between D.score and the known gene signatures in meta-cohort using Spearman analysis. A negative correlation was marked with blue and a positive correlation with orange. (D) Differences in D.score between two (D) clusters in GEO meta-cohort. (E) Differences in D.score between two gene clusters in GEO meta-cohort. (F) Differences in D.score between two disease subtypes in GEO meta-cohort. The asterisks represented the statistical p-value (**P < 0.01; ****P < 0.0001).*
Although our results categorized IBD patients into two subgroups based on the 33 core genes, our analyses were limited to the patient population and were unable to accurately predict the depressive state of individual IBD patients. Therefore, we developed a scoring scheme termed the Depression score (D. score), which is based on the depression-related signature genes, to quantify the states of individual IBD patients. To better illustrate the characteristics of IBD-related depression gene signature, we also used the *Spearman analysis* to examine the correlation between known biological characteristics and the D. score (Figure 4C). The correlation matrix’s heatmap revealed that the D. score was significantly positively linked with kynurenine metabolism., immune activation process and inflammatory response but negatively correlated with primary bile acid biosynthesis and Immunosuppressive-relevant signatures. The alluvial diagram was used to visualize the attribute changes of individual patients (Figure 4B). These results indicated that D. cluster2 and gene. cluster2 was linked to a higher D. score, whereas D. cluster1 and gene. cluster1 exhibited a lower D. score (Figures 4D, E). In addition, we found that UC patients had a higher D. score than CD patients (Figure 4F), which means UC patients were more likely to experience depression.
## The role of the D. score in assessing intestinal inflammation and predicting anti-TNF-alpha benefits
Monoclonal antibody biotherapy (anti-TNF) is a breakthrough therapy for IBD, yet $30\%$ of patients do not react, and some even develop drug resistance (32–34). We used the D. score to assess IBD patients’ intestinal inflammation by comparing inflammatory factors and gut barrier integrity-associated proteins at the mRNA level. As expected, proinflammatory-related signature factors (S100A8, S100A9, TNF-α, IL-1B, IL-6, IL-17, IL-22) expression were upregulated in the high D. score cluster, while intestinal barrier function-related signature protein (LGR5, MUC2) was down-regulated. In other words, a lower D. score in IBD patients was significantly associated with better gut barrier function and lower intestinal inflammatory load (Figure 5A). Following the discovery of the D. score’s strong correlation with the immune-inflammatory response, we tested the hypothesis that the genetic signature of depression may be used to predict patients’ responses to anti-TNF-alpha therapies. OSM and OSMR are over-expressed in the great majority of active IBD lesions, especially in individuals whose condition is resistant to treatment with anti-TNF-alpha antibodies [35]. Our research shows that patients with low D. scores have an obviously low expression of OSM and OSMR, which indicates a potential response to anti-TNF-alpha immunotherapy (Figures 5B, C). In addition, another possible biomarker and therapeutic target for IBD is CMKLR1 (also known as ChemR23), which is of significant relevance for anti-TNF-alpha-resistant patients [36]. We also found that CMKLR1 was enriched in the high D. score cluster (). Furthermore, a consistent result has also been observed in our meta-cohort, lower D. scores in IBD patients were significantly associated with better clinical outcomes that the majority of patients responded to golimumab treatment and higher anti-TNF-alpha immunotherapies benefits (Figure 5E). In conclusion, our data provide compelling evidence that the D. score is related to the efficacy of anti-TNF-alpha immunotherapies and may be used to predict the outcome of IBD case scenarios. ( Figure 5F).
**Figure 5:** *The role of the D. score in assessing intestinal inflammation and predicting anti-TNF-alpha benefits. (A) The mRNA expression levels of intestinal inflammatory markers (S100A8, S100A9, TNF-α, IL-1B, IL-6, IL-17, IL-22, INF-γ) and barrier function markers (LGR5, MUC2) in high D. score versus low D. score subgroups. (B), (C) and (D) The mRNA expression levels of anti-TNF-alpha-resistant biomarkers (CMKLR1, OSM and OSMR) in high D. score versus low D. score subgroups. The asterisks represented the statistical P-value (*P < 0.01; ***P < 0.001; ****P < 0.0001). (E) The fraction of patients with clinical response to anti-TNF-alpha immunotherapy (golimumab) in low or high D. score groups (Chi-square test; p<0.0001). (F) Summary diagram of the biological characteristics in low or high D. score groups.*
## Antidepressants can resolve intestinal inflammation in a preclinical model of D. cluster2 patients
In Western nations, antidepressants are used by $10\%$ to $30\%$ of persons with IBD (37–39), and the systematic reviews (40–43) and a narrative review [44] that has been carried out to this point have shown that antidepressants have a positive influence on the well-being of patients who have IBD, however, the evidence supporting their involvement in the treatment of the condition is scant. We furthermore wanted to investigate whether antidepressants could alleviate intestinal inflammation while relieving mental health in IBD patients and explore the molecular mechanisms involved. Previous research established that mice given DSS had physiological alterations similar to IBD and depressive-like behaviours [45, 46]. Thus, we consider DSS-treated mice to be a preclinical model for D. cluster2 patients who have both IBD and depression. Given that Selective Serotonin Reuptake Inhibitors (SSRIs) are the most widely used antidepressants nowadays, we selected two of them for the following experiment.
We then treated C57BL/6 mice with $3\%$ DSS and gave them either paroxetine (PX) orally or fluoxetine (FX), two frequently used SSRIs, to see whether the drugs would have a therapeutic impact on colitis. ( Figure 1A). Surprisingly, PX administration alleviated body weight loss (Figure 1B) and increased colon length (Figures 1C, D). The DAI score used to assess the severity of colitis symptoms was significantly lower in the DSS+PX group compared to the DSS group. ( Figure 1E). Relieved disruption of glandular structures and crypt foci and reduced inflammatory cell infiltration in the colonic epithelium were observed in the DSS+PX group compared to the DSS group by H&E-stained (Figures 1F, G). Although FX also had a modest protective effect, disease indexes such as body weight, bowel length, and DAI scores showed no statistical significance. Collectively, PX can alleviate intestinal inflammation in a preclinical model of D. cluster2 patients and could be a potential medicine for the treatment of IBD.
## Paroxetine down-regulates multiple inflammatory pathways and alters host metabolism
To illuminate the underlying mechanisms of how PX remitted the colitis severity, we performed mRNA sequencing (RNA-seq) with mice colon (Table S2). Our results demonstrated that PX treatment in the DSS mice model down-regulated several factors of inflammation and chemokines (Figures 6A, B), especially several core pro-inflammatory factors (S100a8, S100a9, Tnf-α, Il-1β, Il-6). Besides, we found that paroxetine decreased the expression of OSM, implying that it might improve the effectiveness of anti-TNF-α therapy. Through GO and KEGG enrichment analysis, we discovered that paroxetine could mediate various biological processes to alleviate intestinal inflammation. On the one hand, PX down-regulates several inflammatory processes, such as response to cytokine, response to stress, inflammatory response, cytokine−cytokine receptor interaction, IL−17, TNF and NF−κB signalling pathway (Figures 6D, E). On the other hand, PX also upregulates several metabolic pathways that are missing in D. cluster2, which indirectly suggests that it can reverse specific metabolic pathways, for instance, steroid metabolic process and fatty acid metabolic process, and thereby alleviate intestinal inflammation (Figure 6C).
**Figure 6:** *Paroxetine down-regulates multiple inflammatory pathways and alters host metabolism. (A) Volcano plot showing the differentially expressed genes between DSS+PX and DSS groups by RNA-seq. (B) Boxplot showing the mRNA expression levels of intestinal inflammatory markers (S100a8, S100a9, Tnf-α, Il-1b, Il-6, Il-17a, Il-22, Il23a, Ptger4), barrier function markers (Lgr5, Muc2), and anti-TNF-alpha-resistant biomarkers (Osm and Osmr) in DSS+PX versus DSS groups. The asterisks represented the statistical P-value. (C) GO enrichment analysis of top 20 upregulated genes in DSS+PX groups. (D) GO enrichment analysis of top 20 downregulated genes in DSS+PX groups. (E) KEGG analysis of downregulated DEGs between DSS+PX and DSS groups. (F) Quantitative PCR analysis of Slc6a4 expression in mice colon tissues of DSS+PX, DSS, and control groups. (G) Quantitative PCR analysis of Grk2 expression in mice colon tissues of DSS+PX, DSS, and control groups. Data are pooled from each independent experiment with n = 5 mice per group; The asterisks represented the statistical P-value (ns P > 0.05; *P < 0.05; **P < 0.01; ****P < 0.001).*
## Paroxetine alleviates intestinal inflammation by modulating the structure and function of gut microbiota
Even though the above results have validated the anti-inflammatory phenotype of PX, its molecular mechanism remains to be further investigated. From the literature survey, we learned that PX has two targets (Slc6a4 and Grk2) in the intestine. However, the expression of the above targets decreased after DSS treatment, and their expression levels were not altered after both DSS and DSS +PX treatment, suggesting that the anti-inflammatory effect of PX is not exerted through the previous mechanism (Figures 6F, G). In our earlier conclusions, we found that PX significantly downregulated the S100a8 and S100a9 (Figure 6B), which are essential for gut microbiota development [47], so we hypothesized that paroxetine might exert its anti-inflammatory effects through gut microbes. We, therefore, performed a 16S ribosomal RNA analysis of mice feces. The alpha diversity found no statistical difference between DSS+PX and DSS groups (Figure 7A). Disparities in the organization of the gut microbiota were found between the DSS+PX and DSS groups, as shown by principal coordinate analysis (PCoA), which was based on Bray–Curtis metric distances. ( Figure 7B).
**Figure 7:** *Paroxetine alleviates intestinal inflammation by modulating the structure and function of gut microbiota. (A) Alpha diversity includes Chao1, Ace, and Shannon indices. (B) Genus-level principal coordinate analysis (PCoA) plot based on Bray–Curtis distances. (C) Heatmap of the selected most differentially abundant features at the genus level. (D) LDA score plot of differentially abundant taxonomic features (LDA score for discriminative features>3). (E) Taxonomic cladogram produced from LEfSe analysis. (F) LDA score plot of differentially abundant of function prediction features (LDA score for discriminative features >3). Orange bars indicate taxa enrichment in DSS+FX groups, purple bars indicate taxa enrichment in DSS+PX groups and green bars indicate taxa enrichment in DSS groups.*
Moreover, we analyzed the geography of the gut microbiota to provide light on the variations in the makeup of the gut flora. Firmicutes represented the richest phyla, followed by Proteobacteria in the DSS+PX group, while Bacteroidota enriched in the DSS group (Figures 7C, D). Compared with the DSS group, the DSS+PX group displayed differential biological compositions at the genus level (Figure 7C). LEfSe showed that treatment with paroxetine was dominated by members of Firmicutes (Turicibacter) and Proteobacteria members. In contrast, increased levels of Bacteroidota (Bacteroides) were instead observed in the DSS group (Figures 7D, E) (Table S3). The results of the microbial function prediction indicated that paroxetine could shift the microbial composition (Figure 7F) from Carbohydrate metabolism (glycan biosynthesis and metabolism, starch and sucrose metabolism, amino and nucleotide sugar metabolism, fructose and mannose metabolism, galactose metabolism) toward amino acid metabolism (membrane transport and ABC transporters).
## Discussion
In this study, we describe the transcriptional heterogeneity of 33 core genes that drive depression in IBD patients and use these key factors to molecularly classify patients into two subtypes with distinct immune and metabolic profiles, and identify D. cluster2 with features of IBD combined with depression.
Our research demonstrates that D. cluster1 can be regarded as a low-inflammatory subtype, characterized by an enrichment of anti-inflammatory immune cells, such as macrophages M2 and regulatory T cells (Treg), and metabolic pathways (Retinol Metabolism, Steroid Hormone Metabolism and Nicotinate and Nicotinamide Metabolism) and a reduction in pro-inflammatory cytokines. In contrast, D. cluster2 contains a bunch of pro-inflammatory immune cells (Neutrophils and macrophages M1), cytokines and metabolic pathways, such as kynurenine metabolism associated with both intestinal inflammation and depression, corresponding to the high-inflammatory subtype. The above results indicate that as depression-related symptoms occur in people with IBD, their intestinal inflammation becomes more aggressive, which undoubtedly exacerbates an existing condition. Thus, it explains why IBD patients with symptoms of depression were more likely to have a greater risk of flare-ups, need a higher dose of medication, be admitted to the hospital, visit the emergency room, or have surgery [48].
Moreover, we found that intestinal barrier function-related marker protein (LGR5, MUC2) was down-regulated in D. cluster2 compared to D. cluster1. On the one hand, these results could prove that depression combined with IBD accelerates the disruption of intestinal barrier function and aggravates gut inflammation. On the other hand, disruption of the intestinal barrier will lead to disruption of the gut vascular barrier (GVB), which is similar to the blood-brain barrier (BBB) and connects the bowel to the liver. Non-closure of the GVB allows inflammation to spread to distant organs, such as our brain, which can lead to mental deficiencies [26]. This resounding evidence explains how intestinal inflammation contributes to the development of depression.
Further, to shed light on the association between depression and molecular subtypes of IBD, a scoring system was established to comprehensively assess enteric inflammation in individuals with IBD. Our research demonstrates that the higher D. score in IBD patients was significantly associated with worse gut barrier function and higher intestinal inflammatory load, which suggests that the D. score provides a reliable indicator of the inflammatory condition of the gut in IBD. Our data also revealed a markedly negative correlation between D. score and anti-TNF-alpha biotherapeutic benefits. Consistent with previous studies, IBD patients with depression were at increased risk of nonresponse to biological therapy [49]. This recommended D. score served as a viable and stable instrument for the thorough evaluation of individual responses to anti-TNF-alpha biotherapeutics.
Considering that many anti-inflammatory medicines also have anti-depressive and anti-anxiety properties, the use of antidepressants as adjuvant treatment in IBD has been highlighted [17]. Although antidepressants have been used in the treatment of colitis in previous studies [50, 51], the anti-inflammatory mechanisms have not been thoroughly investigated. Subsequently, in clinical practice, we selected the two most widely used antidepressants for animal experiments and uncovered that paroxetine could resolve intestinal inflammation in a preclinical model of UC with depressive symptoms, whereas fluoxetine does not. This is consistent with the results of a single randomized, double-blind study involving 26 people with IBD, in which fluoxetine was shown to be no more effective than a placebo [52]. Through high-throughput sequencing technology, we also show that paroxetine can inhibit the expression of multiple inflammatory factors at the mRNA level and down-regulate multiple inflammation-related pathways, thus explaining the mechanism of paroxetine anti-inflammation at the molecular level.
Gut bacteria protect the intestinal epithelium under normal physiological circumstances [53]. When intestinal flora is disturbed, a wide range of bacteria and inflammatory chemicals may compromise the integrity and permeability of tight junctions, hence compromising the gut mucosa’s capacity to operate as a barrier [54]. Environmental disturbances and gut barrier dysfunction frequently enhance vulnerability to inflammatory-related conditions. Considering that the gut microbiome plays a crucial role in the etiology of IBD [55], the investigation into whether or if the gut flora contributes to the protective effect of paroxetine on colitis is warranted. In addition, the last decade has witnessed an exponentially growing interest in gut microbiota and the gut-brain axis in health and disease. Accumulating evidence from preclinical and clinical research indicates that gut microbiota, and their associated microbiomes, may influence pathogenic processes and thus the onset and progression of various diseases, including neurological and psychiatric disorders. The hormones and neurotransmitters produced by the nervous system may have a modulating effect on immune function as well as metabolic function in the gut through the regulation of gut microorganisms [56, 57]. Therefore, as a disorder of the brain-gut axis, intestinal flora may play an important role in the pathogenesis of IBD patients with depression.
Here, the PCoA results showed that there were differences in biological community structure between the DSS and DSS+PX groups, even though our alpha diversity results revealed no significant difference. Multiple studies have shown that the phylum *Firmicutes is* often decreased in proportionate abundance in the feces of IBD patients compared to healthy persons, but the phylum Proteobacteria, including Enterobacteriaceae and Escherichia coli, is frequently increased [58]. Our study shows that paroxetine treatment of mice with colitis can modulate gut dysbiosis and reverse the alters described above according to LEfSe analysis. In other words, paroxetine elicited gastroprotective effects in mice maybe by increasing the growth of Turicibacter [59], a beneficial gut bacterium, and by decreasing populations of pathogenic Bacteroides. The results of the microbial function prediction indicated that paroxetine could shift the microbial composition from Carbohydrate metabolism toward Amino acid metabolism. It is demonstrated that paroxetine not only alters the composition of intestinal microorganisms but also systematically transforms their functional mapping, thereby alleviating intestinal inflammation by rescuing intestinal microecology.
There may be some possible limitations in this study [1]. The use of paroxetine to treat DSS-treated mice is prophylactic rather than therapeutic, and exploring its therapeutic value in subsequent experiments will further increase its clinical transformational implications [2]. We did not investigate in an in-depth manner the long-term therapeutic implications of paroxetine, improving this scientific question in subsequent studies will contribute to better understanding of the role of antidepressants in the treatment of IBD.
## Conclusions
Our study revealed for the first time to our knowledge a group of IBD patients with depression by molecular typing and illustrated their immunological and metabolic profile in detail. Quantitatively evaluating the IBD-related depression gene signature of individual patients will promote more effective immunotherapy strategies and antidepressant paroxetine may help reduce intestinal inflammation.
## Data availability statement
The data presented in the study are deposited in the NIH National Center for Biotechnology Information Sequence Read Archive (SRA) repository and accession numbers can be found below: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA904496; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA903959.
## Ethics statement
The animal study was reviewed and approved by Ethics Committee of Shanghai Renji Hospital.
## Author contributions
ZW, JH, and XL conceptualized and supervised the study; ZW, XL, and JH acquired funding; LN, XW, YM, TT, and BX acquired and analyzed the data; LN, XW, YY, and ZG performed the investigation and experiments; LN, XW, ZC, and BX drafted the manuscript; ZW, HC, XL, and JH reviewed and edited 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.2023.1145070/full#supplementary-material
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|
---
title: 'Thyroid autoimmunity and adverse pregnancy outcomes: A multiple center retrospective
study'
authors:
- Yun Xu
- Hui Chen
- Meng Ren
- Yu Gao
- Kan Sun
- Hongshi Wu
- Rui Ding
- Junhui Wang
- Zheqing Li
- Dan Liu
- Zilian Wang
- Li Yan
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10008944
doi: 10.3389/fendo.2023.1081851
license: CC BY 4.0
---
# Thyroid autoimmunity and adverse pregnancy outcomes: A multiple center retrospective study
## Abstract
### Background
The relationship between thyroid autoimmunity (TAI) and adverse pregnancy outcomes is disputable, and their dose-dependent association have not been fully clarified.
### Objective
To investigate the association and dose-dependent effect of TAI with multiple maternal and fetal-neonatal complications.
### Methods
This study is a multi-center retrospective cohort study based on singleton pregnancies of three medical college hospitals from July 2013 to October 2021. The evolution of thyroid function parameters in TAI and not TAI women were described, throughout pregnancy. The prevalences of maternal and fetal-neonatal complications were compared between the TAI and control group. Logistic regression was performed to study the risk effects and dose-dependent effects of thyroid autoantibodies on pregnancy complications, with adjustment of maternal age, BMI, gravidity, TSH concentrations, FT4 concentrations and history of infertility.
### Results
A total of 27408 participants were included in final analysis, with 5342 ($19.49\%$) in the TAI group and 22066 ($80.51\%$) in control group. TSH concentrations was higher in TAI women in baseline and remain higher before the third trimester. Positive thyroid autoantibodies were independently associated with higher risk of pregnancy-induced hypertension (OR: 1.215, $95\%$CI: 1.026-1.439), gestational diabetes mellitus (OR: 1.088, $95\%$CI: 1.001-1.183), and neonatal admission to NICU (OR: 1.084, $95\%$CI: 1.004-1.171). Quantitative analysis showed that increasing TPOAb concentration was correlated with higher probability of pregnancy-induced hypertension, and increasing TGAb concentration was positively correlated with pregnancy-induced hypertension, small for gestational age and NICU admission. Both TPOAb and TGAb concentration were negatively associated with neonatal birthweight.
### Conclusion
Thyroid autoimmunity is independently associated with pregnancy-induced hypertension, gestational diabetes mellitus, neonatal lower birthweight and admission to NICU. Dose-dependent association were found between TPOAb and pregnancy-induced hypertension, and between TGAb and pregnancy-induced hypertension, small for gestational age and NICU admission.
## Introduction
Thyroid autoimmunity (TAI) is defined as the presence of antibodies to thyroperoxidase (TPOAb) or thyroglobulin (TGAb) [1]. The prevalence of TAI is 5-$15\%$ in reproductive aged women [2] and even higher in pregnant women (5-$20\%$) (3–6).
Compared to thyroid dysfunction, the impact of TAI on pregnancy might be underestimated. Available evidence predominantly links the adverse pregnancy outcomes in TAI women to hypothyroidism [7], and clinical guidelines recommended levothyroxine supplement as the only treatment method [8]. However, euthyroidism was found in the majority of ($75\%$) pregnant women with TAI [9]. Increasing studies showed that the association of TAI with miscarriage and preterm birth remained significantly after adjustment for thyroid dysfunction [10], and the pregnancy outcomes has not found to be improved by thyroid hormone replacement. Dhillon-Smith reported that adverse neonatal outcomes were not different after levothyroxine supplement in TPOAb positive women [11]. Similarly, a recent multicenter RCT (T4LIFE trial) showed that, supplement of levothyroxine did not improve pregnancy outcomes in euthyroid TAI women [12]. These results indicated that TAI in itself may induce adverse pregnant outcomes besides via mediating thyroid destruction.
Although with different results, the association between TAI and recurrent miscarriage and preterm birth were identified by prospective cohort studies and meta-analysis [10, 13, 14]. Recently, increasing studies focus on the impact of TAI on other pregnancy complications, including pregnancy-induced hypertension [15, 16], gestational diabetes [17, 18], and adverse fetal-neonatal outcomes [16, 19, 20], but the relationship had not been fully clarified to draw any conclusions. The possible reason of this is that most of the studies included small sample and only focused on single outcome and without adjustment of confounders. In addition, as the reflection of thyroid autoimmunity process, there is a dose-dependent association of thyroid autoantibodies with TSH and free thyroxine level in pregnant women [21], however, study assessing dose-dependent association of thyroid autoantibodies with adverse pregnancy outcomes were seldom to date. To accessing their dose-dependent associations should provide insights toward distinguishing low-risk from high-risk individuals and optimizing clinical decision-making strategies.
Therefore, based on our large multicenter cohort, the purpose of the present study is to verified the association, as well as the dose-dependent effect of TAI with various maternal and fetal-neonatal complications.
## Methods
The study was registered in Chinese Clinical Trial Registry (ChiCTR2200064466) and was approved by the ethical committees of Sun Yat-sen Memorial Hospital of Sun Yat-sen University with a waiver of informed consent (SYSEC-KY-KS-2020-200).
## Study design and participants
This is an observational cohort study based on the electronic medical record in three college hospitals, the Sun Yat-sen Memorial Hospital, the First Affiliated Hospital, and the Sixth Affiliated Hospital of Sun Yat-sen University. Data of pregnant women delivered in the Department of Obstetrics in these three hospitals from July 2013 to October 2021 were included for primary screening base on the following criteria: [1] with thyroid autoantibodies (TPOAb or TGAb) results obtained in the first and second trimester or within one year before pregnancy; [2] 18-55 years old; [3] with complete records of pregnant outcomes. Participants with the following criteria were excluded: [1] with medical history of thyroid diseases before pregnancy (i.e. hyperthyroidism, thyroid cancer, surgical history on thyroid, and pituitary diseases); [2] termination due to fetal abnormality, chromosomal abnormality, maternal chronic diseases or personal reasons; [3] with multiple pregnancy.
## Collection of information
From the medical records, we collected basial and gestational characteristics (age, height, weight at admission for delivery, gestational age, gravidity, parity, delivery mode, past medical history and family medical history), gestational complications and adverse outcomes (preterm birth, gestational diabetes mellitus, pregnancy-induced hypertension, postpartum hemorrhage and premature rupture of membrane), neonates (birthweight, Apgar score, need for intensive neonatal care, neonatal death). For each participant, the serum concentrations of TPOAb, TGAb, thyroid stimulating hormone (TSH), free triiodothyronine (FT3), free thyroxine (FT4) during pregnancy were all obtained and the mean gestational weeks of the first blood sample detection were 15.4 ± 6.4 gestational weeks. These thyroid parameters were examined by chemiluminescent immunoassay, and the detail of laboratory measurements from each hospital was summarized in Supplementary Table S1.
## Definition of TAI and pregnancy complications
TPOAb and TGAb positivity was defined according to the cutoffs provided by the manufacturers. Participants with positive results of either TPOAb or TGAb in the first or second trimester during pregnancy, or within one year before pregnancy were included in the TAI group, while those with both TPOAb and TGAb in normal range were in control group. The definition of thyroid function parameters was according to the reference range for each trimester of each hospital.
Adverse pregnancy outcomes comprised maternal and fetal-neonatal outcomes and the definition were according to the practical guidelines of each disease. Preterm birth (PTB) was defined as termination of pregnancy between 28 and <37 gestational weeks. Gestational diabetes mellitus (GDM) diagnosed via 75g glucose OGTT test when one or more plasma glucose (PG) meet or exceed the thresholds: fasting PG 5.1 mmol/L, 1h-PG 10.0 mmol/L, and 2h-PG 8.5 mmol/L, according to the International Association of Diabetes and Pregnancy Study Groups-2010 guidelines [22], and women diagnosed with overt diabetes before pregnancy were excluded. Pregnancy-induced hypertension (PIH) was defined as maternal blood pressure exceeding $\frac{140}{90}$ mmHg induced by pregnancy after 20 weeks’ gestation with or without proteinuria and edema [23]. Premature rupture of membrane (PROM) was defined as abruption of the amniotic sac before labor onset. Postpartum hemorrhage was defined as a cumulative blood loss of greater than or equal to 500mL for vaginal delivery or 1,000 mL for cesarean. Large for gestational age (LGA) and small for gestational age (SGA) which determined according to the National Institute of Child Health and Human Development (NICHD) standard for Asian population [24]. Macrosomia was defined as neonatal birthweight heavier than 4000 grams and Apgar Score ≤7 at 1 to 10 minute after born were defined as Low Apgar Score.
## Study outcomes and data analysis
The primary outcome was the association between TAI and various pregnancy complications. Maternal outcomes including PTB, PIH, GDM, PROM and postpartum hemorrhage. Fetal-neonatal outcomes including need for intensive neonatal care, Low Apgar Score, macrosomia, LGA and SGA.
Research data was analyzed by Python 3.8. Continuous variables were reported with mean and standard deviation, and categorical variables were reported with number and percentage. The difference between group were compared by Chi-squared test for categorical variables. Independent Student’s t test and Mann-Whitney test were applied to compared continuous variables with normally and normally distributions, respectively. Two-sided P values less than 0.05 were considered statistically significant.
The association between TAI and pregnancy complications were firstly assessed by chi-squared test. Then, multiple logistic regression analysis was applied to adjusted confounders and build multiple models: [1] Model1: adjusted for maternal age and BMI; [2] Model2: Model 1+gravidity; [3] Model3: Model 2+TSH and FT4 concentration; [4] Model4: Model3 + history of infertility. For the risk of GDM and PIH, family medical history of diabetes and hypertension were considered, respectively. For the risk of preterm birth, history of recurrent miscarriage was considered. The concentrations of TPOAb and TGAb were first compared in participants with and without each maternal and fetal-neonatal complications, and those complications with TPOAb or TGAb difference were further studied for their dose-dependent effect. The associations between pregnancy complications with TPOAb or TGAb concentrations were analyzed via logistic regression with the same adjustment above respectively. The association between neonatal birthweight and TPOAb or TGAb concentrations were analyzed by ANCOVA with adjustment of confounders in the Model 4.
## Basal characteristics of participants
A total of 33589 pregnancies records met the inclusion criteria. Of these, a total of 6181 were excluded due to maternal thyroid diseases before pregnancy ($$n = 4303$$), pituitary diseases or ectopic endocrine tumor ($$n = 26$$), terminated due to fetal abnormality ($$n = 179$$), chromosomal abnormality ($$n = 104$$), fetal tumor ($$n = 8$$), maternal chronic diseases or personal reason ($$n = 30$$), and multiple pregnancy ($$n = 1531$$). The final study cohort comprised 27408 pregnancies. Figure 1 demonstrated the flow chart of data selection.
**Figure 1:** *Flow chart of research population selection.*
Of all 27408 participants, 5342 ($19.5\%$) pregnancies which have at least one record showed positive TPOAb or TGAb (TAI group) while 22066 ($80.5\%$) pregnant women with negative TPOAb and TGAb results in all detected records (control group). In the TAI group, 2641($49.4\%$) were only TPOAb positive, 917 ($17.2\%$) were only TGAb positive, and 1784 ($33.4\%$) were positive for both antibodies. In TAI groups, the mean concentration of TSH first tested in pregnant period was higher (1.94 ± 2.42μIU/ml vs. 1.43 ± 1.01μIU/ml, $P \leq 0.001$), and ratio of pregnancies with TSH concentration between 2.5 and 4μIU/ml ($17.8\%$ vs. $10.3\%$, $P \leq 0.001$) or exceeded 4.0μIU/ml ($7.0\%$ vs. $2.0\%$, $P \leq 0.001$) were increased significantly (Table 1). That means pregnant women in TAI group have lower ratio of meeting treatment target and higher probability of subclinical hypothyroidism.
**Table 1**
| Characteristics | TAI Positive | TAI Negative | P value |
| --- | --- | --- | --- |
| Number | 5342 (19.5%) | 22066 (80.5%) | |
| Maternal Age (years) | 32.1 ± 4.6 | 31.7 ± 4.6 | < 0.001 |
| BMI (kg/m2) | 26.1 ± 3.0 | 26.1 ± 3.0 | 0.331 |
| Gravidity | Gravidity | Gravidity | Gravidity |
| Primigravida | 1682 (31.5%) | 7913 (35.9%) | < 0.001 |
| Multigravida | 3653 (68.5%) | 14125 (64.1%) | |
| Parity | Parity | Parity | Parity |
| Nullipara | 3185 (60.4%) | 12277 (56.1%) | < 0.001 |
| Multipara | 2090 (39.6%) | 9616 (43.9%) | |
| History of Recurrent Miscarriage | History of Recurrent Miscarriage | History of Recurrent Miscarriage | History of Recurrent Miscarriage |
| Yes | 850 (16.0%) | 1224 (5.6%) | < 0.001 |
| No | 4446 (84.0%) | 20633 (94.4%) | |
| History of Infertility | History of Infertility | History of Infertility | History of Infertility |
| Yes | 792 (14.8%) | 2575 (11.7%) | < 0.001 |
| No | 4550 (85.2%) | 19491 (88.3%) | |
| Family History of Diabetes | Family History of Diabetes | Family History of Diabetes | Family History of Diabetes |
| Yes | 395 (7.4%) | 1680 (7.6%) | 0.607 |
| No | 4947 (92.6%) | 20386 (92.4%) | |
| Family History of Hypertension | Family History of Hypertension | Family History of Hypertension | Family History of Hypertension |
| Yes | 726 (13.6%) | 3197 (14.5%) | 0.097 |
| No | 4616 (86.4%) | 18869 (85.5%) | |
| Free T4 Concentrations (pmol/L) | 12.58 ± 3.80 | 11.88 ± 3.30 | < 0.001 |
| TSH Concentrations (μIU/ml) | 1.94 ± 2.42 | 1.43 ± 1.01 | < 0.001 |
| TSH Concentrations Classification | | | < 0.001 |
| ≤ 2.5 μIU/ml | 3973 (75.2%) | 19143 (87.7%) | |
| 2.5-4.0 μIU/ml | 941 (17.8%) | 2244 (10.3%) | |
| > 4.0 μIU/ml | 371 (7.0 % ) | 451 (2.0 % ) | |
The basal characteristics of the participants were summarized in Table 1. Pregnant women in the TAI group were older (32.1 ± 4.6 years vs. 31.7 ± 4.6 years, $P \leq 0.001$), with more gravidity (multigravida: $68.5\%$ vs. $64.1\%$, $P \leq 0.001$), but less parity (multipara: $39.6\%$ vs. $43.9\%$, $P \leq 0.001$). Participants in the TAI group were with higher proportion of recurrent miscarriage history ($16.0\%$ vs. $5.6\%$, $P \leq 0.001$) and infertility history ($14.8\%$ vs. $11.7\%$, $P \leq 0.001$). The gestational age at terminate was mild lower in TAI group (267 ± 25 days vs. 270 ± 21 days, $P \leq 0.001$) and the neonates born to mother with TAI were with lower birthweight (3089.1 ± 457.3 gram vs. 3139.4 ± 453.0 gram, $P \leq 0.001$). The differences of BMI, family histories of diabetes and hypertension were not statistically significant between groups.
## Evolution of maternal thyroid parameters during pregnancy
To describe the evolution of thyroid function parameters in TAI and not TAI women, throughout pregnancy. We obtained all results of TSH, FT4, and FT3 during pregnant period. In both TAI and control group, the concentration of TSH decreased in the first eight to twelve gestational weeks and gradually increase from then on (Figure 2A), however, FT4 and FT3 both continuously decreased during the pregnant period (Figure 2B, C). Compared to control group, the concentrations of TSH in the TAI group were significantly higher in both first and second trimester (first trimester: 1.91 ± 1.05 μIU/ml vs. 1.31 ± 1.05 μIU/ml, $P \leq 0.001$; second trimester: 1.86 ± 1.54 μIU/ml vs. 1.56 ± 1.04 μIU/ml, $P \leq 0.001$; third trimester: 1.92 ± 1.71 μIU/ml vs. 1.85 ± 1.29 μIU/ml, $$P \leq 0.155$$) (Figure 2A), and the concentrations of FT3 were lower during period (first trimester: 4.81 ± 0.70 μIU/ml vs. 4.88 ± 0.69 μIU/ml, $P \leq 0.001$; second trimester: 4.41 ± 0.78 μIU/ml vs. 4.50 ± 0.63 μIU/ml, $P \leq 0.001$; third trimester: 4.12 ± 0.62 μIU/ml vs. 4.23 ± 1.08 μIU/ml, $$P \leq 0.001$$) (Figure 2C) and FT4 were higher (first trimester: 14.37 ± 4.00 μIU/ml vs. 12.76 ± 3.56 μIU/ml, $P \leq 0.001$; second trimester: 11.67 ± 3.36 μIU/ml vs. 11.06 ± 2.94 μIU/ml, $P \leq 0.001$; third trimester: 11.01 ± 3.01 μIU/ml vs. 10.36 ± 2.96 μIU/ml, $P \leq 0.001$) (Figure 2B).
**Figure 2:** *Evolution of maternal thyroid parameters during pregnancy. (A) Variation of TSH concentrations (lines) and sample size (bars) in each gestational month (the first and second bars represent 6-12 months and 0-6 months before pregnancy). (B) Variation of Free T4 concentrations (lines) and sample size (bars) in each gestational month (the first and second bars represent 6-12 months and 0-6 months before pregnancy). (C) Variation of Free T3 concentrations (lines) and sample size (bars) in each gestational month (the first and second bars represent 6-12 months and 0-6 months before pregnancy). (D) Evolution of TSH, Free T4 and Free T3 level by TPOAb concentrations (related to the concentrations of TSH, Free T4 and Free T3 in cases with TPOAb concentration lower or equal to the 85 percentile).*
Then, we described the variation of thyroid function indicators with TPOAb concentration increasing. Because the reference ranges were different among kits, we used population-based percentiles of TPOAb concentrations for each kit to investigate the quantitative effect on thyroid function, and the 85 percentiles represent positive results of all kits. As seen in Figure 2D, we found TSH increased in the cases with TPOAb concentrations higher than 85 percentiles, and FT4 decrease with TPOAb concentrations in the cases with TPOAb concentrations higher than 90 percentiles, but FT3 did not have dose–response effect of TPOAb.
## Thyroid autoimmunity and pregnancy complications
The prevalence of pregnancy complications was compared between TAI and control group (Table 2). Pregnant women in the TAI group were with higher proportion of PIH ($4.06\%$ vs. $3.45\%$, $$P \leq 0.037$$), GDM ($20.39\%$ vs. $18.45\%$, $$P \leq 0.001$$) and PTB ($8.95\%$ vs. $7.56\%$, $$P \leq 0.001$$). Neonates born to mothers in the TAI group were with less macrosomia ($1.55\%$ vs. $2.20\%$, $$P \leq 0.003$$) and LGA ($4.04\%$ vs. $4.77\%$, $$P \leq 0.045$$), but more incidence of NICU admission ($24.11\%$ vs. $21.77\%$, $P \leq 0.001$). In addition, no difference was found in other outcomes.
**Table 2**
| Unnamed: 0 | TAI Positive | TAI Negative | P value |
| --- | --- | --- | --- |
| Maternal Outcomes | Maternal Outcomes | Maternal Outcomes | Maternal Outcomes |
| Pregnancy Induce Hypertension | 212 (4.06%) | 748 (3.45%) | 0.037 |
| Gestational Diabetes Mellitus | 1059 (20.39%) | 3974 (18.45%) | 0.001 |
| Preterm Birth | 469 (8.95%) | 1645 (7.56%) | 0.001 |
| Premature Rupture of Membrane | 1060 (19.84%) | 4454 (20.19%) | 0.589 |
| Postpartum Hemorrhage | 377 (7.06%) | 1711 (7.75%) | 0.090 |
| Fetal-neonatal Outcomes | Fetal-neonatal Outcomes | Fetal-neonatal Outcomes | Fetal-neonatal Outcomes |
| Neonatal Outcome | | | < 0.001 |
| Normal | 3962 (75.74%) | 16965 (78.08%) | |
| NICU Admission | 1261 (24.11%) | 4730 (21.77%) | |
| Neonatal Death | 8 (0.15%) | 33 (0.15%) | |
| Apgar Score | | | 0.546 |
| 0-3 | 25 (0.48%) | 94 (0.43%) | |
| 4-7 | 103 (1.97%) | 477 (2.20%) | |
| 8-10 | 5096 (97.55%) | 21131 (97.37%) | |
| Macrosomia | 83 (1.55%) | 486 (2.20%) | 0.003 |
| Large for Gestational Age | 174 (4.04%) | 844 (4.77%) | 0.045 |
| Small for Gestational Age | 545 (11.64%) | 2036 (10.77) | 0.094 |
Then, we investigated the association between TAI and complications in logistic regression (Table 3). After adjusted for maternal age, BMI, gravidity, TSH and F4 concentrations, and history of infertility, TAI were positively associated with PIH (OR: 1.206, $95\%$CI: 1.019-1.428, $$P \leq 0.030$$), GDM (OR: 1.088, $95\%$CI: 1.001-1.183, $$P \leq 0.046$$), PTB (OR: 1.129, $95\%$CI: 1.001-1.273, $$P \leq 0.048$$) and admission of NICU (OR: 1.084, $95\%$CI: 1.004-1.171, $$P \leq 0.040$$), and negatively associated with macrosomia (OR: 0.768, $95\%$CI: 0.599-0.985, $$P \leq 0.038$$) and large for gestational age(OR: 0.833, $95\%$CI: 0.695-0.999, $$P \leq 0.049$$). The results of PIH and GDM were remain statistically significant after additional adjustment by family history of hypertension and diabetes respectively (PIH: OR: 1.215, $95\%$ CI 1.026-1.439, $$P \leq 0.024$$; GDM: OR: 1.088, $95\%$ CI 1.001-1.183, $$P \leq 0.048$$). The result of preterm birth was not statistically significant after additional adjustment by history of recurrent miscarriage (OR=1.082, $95\%$ CI 0.958-1.222, $$P \leq 0.205$$).
**Table 3**
| Unnamed: 0 | Model 1 | Model 1.1 | Model 2 | Model 2.1 | Model 3 | Model 3.1 | Model 4 | Model 4.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | OR (95% CI) | P | OR (95% CI) | P | OR (95% CI) | P | OR (95% CI) | P |
| Maternal Outcomes | Maternal Outcomes | Maternal Outcomes | Maternal Outcomes | Maternal Outcomes | Maternal Outcomes | Maternal Outcomes | Maternal Outcomes | Maternal Outcomes |
| Pregnancy Induce Hypertension | 1.186 (1.005-1.400) | 0.043 | 1.195 (1.012-1.410) | 0.036 | 1.215 (1.026-1.438) | 0.024 | 1.206 (1.019-1.428) | 0.030 a |
| Gestational Diabetes Mellitus | 1.090 (1.004-1.183) | 0.039 | 1.089 (1.003-1.181) | 0.042 | 1.093 (1.006-1.188) | 0.036 | 1.088 (1.001-1.183) | 0.046 b |
| Preterm Birth | 1.144 (1.017-1.286) | 0.025 | 1.138 (1.012-1.280) | 0.031 | 1.140 (1.011-1.285) | 0.032 | 1.129 (1.001-1.273) | 0.048 c |
| Premature Rupture of Membrane | 1.022 (0.944-1.105) | 0.596 | 1.032 (0.954-1.117) | 0.431 | 1.055 (0.974-1.144) | 0.188 | 1.056 (0.975-1.144) | 0.183 |
| Postpartum Hemorrhage | 0.920 (0.815-1.038) | 0.176 | 0.923 (0.818-1.042) | 0.195 | 0.926 (0.819-1.046) | 0.217 | 0.920 (0.814-1.041) | 0.186 |
| Fetal-neonatal Outcomes | Fetal-neonatal Outcomes | Fetal-neonatal Outcomes | Fetal-neonatal Outcomes | Fetal-neonatal Outcomes | Fetal-neonatal Outcomes | Fetal-neonatal Outcomes | Fetal-neonatal Outcomes | Fetal-neonatal Outcomes |
| NICU Admission | 1.134 (1.052-1.223) | 0.001 | 1.140 (1.057-1.230) | 0.001 | 1.091 (1.010-1.179) | 0.027 | 1.084 (1.004-1.171) | 0.040 |
| Low Apgar Score | 0.902 (0.731-1.113) | 0.336 | 0.904 (0.733-1.116) | 0.347 | 0.925 (0.747-1.144) | 0.471 | 0.920 (0.744-1.139) | 0.446 |
| Macrosomia | 0.735 (0.575-0.940) | 0.014 | 0.732 (0.572-0.936) | 0.013 | 0.769 (0.599-0.986) | 0.038 | 0.768 (0.599-0.985) | 0.038 |
| Large for Gestational Age | 0.813 (0.680-0.972) | 0.023 | 0.804 (0.672-0.961) | 0.016 | 0.834 (0.696-1.000) | 0.050 | 0.833 (0.695-0.999) | 0.049 |
| Small for Gestational Age | 1.096 (0.984-1.219) | 0.095 | 1.111 (0.998-1.237) | 0.054 | 1.098 (0.985-1.225) | 0.092 | 1.097 (0.984-1.223) | 0.096 |
## Quantitative association between thyroid autoantibodies and complications
To investigate the quantitative association with complications, we first analyze the concentrations (as percentiles) of TPOAb and TGAb in participants with and without each maternal and fetal-neonatal complications. As shown in Supplementary Table S2, TPOAb concentrations were higher in pregnant women with pregnancy induced hypertension, gestational diabetes mellitus and preterm birth, and lower in women who given birth to macrosomia. TGAb concentrations were higher in pregnant women with pregnancy induced hypertension, preterm birth, and in mother who given birth to babies with small for gestational age or needed therapy in NICU (Supplementary Table S2).
Then, the associations between TPOAb concentrations and pregnancy induced hypertension, gestational diabetes mellitus, preterm birth, and macrosomia, as well as the associations between TGAb concentrations and pregnancy induced hypertension, preterm birth, SGA and NICU admission were analyzed via logistic regression (Figure 3). After adjusted for maternal age, BMI, gravidity, TSH and FT4 concentrations, and history of infertility, the probability of PIH rise in paralleled with increasing TPOAb (OR: 1.284, $95\%$CI: 1.008-1.635, $$P \leq 0.043$$) and TGAb concentration (OR: 1.450, $95\%$CI: 1.104-1.905, $$P \leq 0.008$$) respectively. The effect remains significant even after adjustment with family history of hypertension (TPOAb: OR: 1.290, $95\%$CI: 1.012-1.644, $$P \leq 0.040$$; TGAb: OR: 1.479, $95\%$CI: 1.125-1.945, $$P \leq 0.005$$). In addition, increasing TGAb concentration were associated with rising positivity of SGA (OR: 1.272, $95\%$CI: 1.067-1.516, $$P \leq 0.007$$) and NICU admission (OR: 1.140, $95\%$CI: 1.004-1.294, $$P \leq 0.043$$).
**Figure 3:** *Quantitative association of thyroid autoimmunity and pregnant complications. aAdjusted for Age, BMI, Gravidity, TSH level, Free T4 level, and history of infertility. bAdjusted for Age, BMI, Gravidity, TSH level, Free T4 level, history of infertility, and family history of hypertension. cAdjusted for Age, BMI, Gravidity, TSH level, Free T4 level, history of infertility, and family history of diabetes. d Adjusted for Age, BMI, Gravidity, TSH level, Free T4 level, history of infertility, and history of recurrent miscarriage.*
We also performed an ANCOVA analysis on the association between thyroid autoantibodies and birthweight (Figure 4A, B), and found that neonatal birthweight was decreased with TPOAb and TGAb level elevating (TPOAb: $$P \leq 0.002$$; TGAb, $$P \leq 0.005$$).
**Figure 4:** *Quantitative Association of Birthweight and Thyroid Autoantibody. (A) Birthweight decreased with increasing TPOAb concentrations (percentiles). (B) Birthweight decreased with increasing TGAb concentrations (percentiles).*
## Discussion
In this large multi-center cohort study comprised 27408 pregnancies, we investigated the impact of TAI on multiple types of gestational complications and adverse pregnant outcomes. We verified that pregnant women with positive thyroid autoantibodies had increased risks of pregnancy-induced hypertension, gestational diabetes mellitus, and neonatal admission of NICU. Besides, we found the dose-dependent effect of TPOAb concentrations on pregnancy-induced hypertension, and TGAb concentration on pregnancy-induced hypertension, small for gestational age, neonatal birthweight and NICU admission. In addition, we describe the evolution of maternal serum TSH, FT4 and FT3 and determined their dose-dependent association with thyroid autoantibody. This result may provide insights toward better understanding the difference between TAI women and their control individuals.
Our present study showed that TAI in pregnancy was associated with increasing risk of PIH by approximate $20\%$, independent from thyroid function. Although hypertensive disorder is increased in patients with hypothyroidism (25–27), the association of thyroid autoimmunity with pregnancy-induced hypertension is disputable. Our result supported the finding of the recent Ma’anshan cohort (the MABC study) in Chinese pregnant women that TAI was positively associated with gestational hypertension [15]. Saki’s study also observed higher systolic blood pressure and a higher incidence of preeclampsia in pregnant women with either TPOAb or TGAb positive pregnant women [28]. Still, there were some other studies shown no association between TAI and PIH, potentially because a relatively low incidence of individuals with both TAI and PIH in general pregnancies and small sample size did not have the power to find the association. Additionally, some studies in which thyroid autoantibodies were tested in the third trimester did not show association with PIH [20, 29], potentially because that, thyroid autoantibody levels fall and reaching its nadir in the third trimester [6] and the impact of thyroid autoantibodies on blood pressure may exist before this period. Family history contribute a lot to PIH, but previous study did not consider it as confounder. After adjusted for parameters including hypertension family history, we determined the association between thyroid autoantibodies and PIH, more importantly, with a dose-dependent manner. The quantitative association of pregnancy outcomes with thyroid antibodies, especially TGAb were seldom reported, and our result provide evidence that the probability of PIH rise by increasing TPOAb and TGAb level respectively. Taken together, the present results indicated the importance of monitoring blood pressure during pregnancy period in women with TAI, both TPOAb and TGAb positive in early pregnant period should be concerned, especially who with high concentration.
Although increasing studies show the relationship between TAI and GDM [17, 18, 30], it remains not wildly concerned in clinical practice. Thyroid dysfunction may impact the regulation glucose metabolism and increase risk of GDM [17] and current evidence predominantly links it to hypothyroidism. However, Huang’s study found that TAI in itself has higher risk of GDM [31]. In the present study, we also identified TAI as independent risk factor of GDM in pregnant women, the result was not impacted by thyroid function even after adjustment with family history of diabetes. That means blood glucose monitoring is important for TAI pregnant women no matter thyroid dysfunction or not. The attention to sugar metabolism even should last for postpartum by reason of Tang’s study also reported that TAI may increase the risk of diabetes mellitus after pregnancy [17]. Similar to PIH, studies on GDM in which thyroid autoantibodies positive in the third trimester did not observed a significantly association. Notably, the majority of studies shown positive association between TAI and GDM were perform in Asian population. The underlining mechanism remain uncleared and it needs further investigation.
The association between TAI and preterm birth has been found in previous studies (14, 32–35). In the present study, we found a higher proportion of preterm birth in pregnant women with TAI, and the association between TAI and preterm birth was statistically significant after adjusted for maternal age, BMI, gravidity, TSH and F4 concentrations, and history of infertility. However, after adding history of recurrent miscarriage as confounder, the association between TAI and preterm birth was not found. This result indicated that the impact of TAI on preterm birth needed further investigation.
The impact of thyroid autoantibodies on other outcomes of the developing fetus and neonates is far from elucidated. Although the risk of SGA in TAI group did not meet statistical significance, its probability rises by increasing TGAb level. In addition, neonates born to TAI women tend to be with lower birthweight and higher risk of NICU admission. This result support the point in previous studies that thyroid autoantibodies affected fetal growth [29, 31]. The mechanism of lower birthweight and higher risk of fetal adverse outcomes is not fully clarified, but we learn from one report that placenta weight is lower in TAI group [19]. Thus, parameters of fetal growth and development, as well as maternal nutrition supplement should be aware in pregnant women with TAI.
To better understanding the difference between TAI women and their control individuals, we describe the evolution of maternal TSH, FT4 and FT3 throughout pregnancy base on this large multi-center cohort study. Our result shown that TSH concentrations were higher in TAI women in baseline and remain higher before the third trimester. TAI pregnancies also have higher probability of subclinical hypothyroidism, which is consistent with previous studies [21]. Higher FT4 concentration in the TAI group seems to be controversial with elevated TSH concentrations. However, the lower concentration of FT3 in TAI group may explain this phenomenon, since the negative feedback efficiency of FT3 was stronger than FT4 [36]. Increase level of both FT4 and TSH was also reported in previous study [37]. The potential reason of high FT4 in TAI group might because of the supplementation of levothyroxine in TAI women, but the detail information was not able to obtained in the present retrospective study. As biologically active hormone, FT3 was not elevated sync with FT4 maybe the potential reasons that levothyroxine supplement did not improve pregnancy outcomes in TAI women in previous study [11, 12]. To determine the reason of higher FT4 and lower FT3 concentration in TAI pregnant women, further study is needed to study the impact of thyroid antibodies on deiodinase.
There were several strengths of the present study. First, we used a large cohort of pregnant women from three centers in our study to obtain robust results. To the best of our knowledge, the number of participants in this original cohort study was largest on the topic of TAI and pregnancy complications. And based on this cohort, we were able to analyze a number of complications and adverse outcomes both maternal and fetal-neonates by adjusting multiple cofounders. Second, the quantitative association of thyroid autoantibody with pregnancy outcomes was seldom reported. Our result of their dose-dependent association adds to the limited knowledge on the complicated and multifactorial mechanisms underlying pregnancy outcomes. Third, we described the evolution of maternal TSH, FT4 and FT3 in TAI pregnancies and their variation by increasing thyroid autoantibody level. This result may provide insights toward better understanding the difference between TAI women and their control individuals.
Our study also had some limitation. First, the medicine history of levothyroxine (LT-4) supplementation was not obtained due to the retrospective design, and thus the impact of LT-4 supplementation on pregnancy complications were not able to analyze in the present study. Second, although consist of a large number of pregnant women from three centers, the present study is performed in South China where is iodine rich area, and may not represent pregnant women in general population. Third, the underlining mechanism of thyroid autoantibodies and complications were not analyzed in this study. Considering the high prevalence and clinical significance of TAI in pregnancy, further study is needed to determine how thyroid autoantibodies affect related complications.
## Conclusion
We illustrated the independent association between TAI and adverse pregnancy outcomes, including PIH and GDM. We also found neonates born to women with TAI were with lower birthweight and at higher risk for NICU admission. The quantitative association found in the present study between TPOAb and PIH, and between TGAb and PIH, SGA and NICU admission indicates that the dose-dependent effect of thyroid autoimmunity on pregnancy complications should be taken into account in future research and clinical practice.
## 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 Medical Ethics Committee, Sun Yat-sen Memorial Hospital, Sun Yat-sen University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
YX designed the study, analyzed the data and drafted the manuscript. HC and YG provided the clinical data. MR, KS and DL critical review of the study design. HW and RD provided laboratory data. JW and ZL provided clinical data. ZW and LY contributed to the design and critically reviewed 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.2023.1081851/full#supplementary-material
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---
title: An insight into gut microbiota and metabolites in the mice with adenomyosis
authors:
- Peipei Chen
- Kun Wang
- Mingyan Zhuang
- Xianyun Fu
- Shidan Liu
- Minmin Chen
- Ya Lei
journal: Frontiers in Cellular and Infection Microbiology
year: 2023
pmcid: PMC10008959
doi: 10.3389/fcimb.2023.1075387
license: CC BY 4.0
---
# An insight into gut microbiota and metabolites in the mice with adenomyosis
## Abstract
### Background
Adenomyosis (AM) is a benign uterine disease characterized pathologically by the invasion of endometrial tissue into the myometrium. The pathogenesis of AM is still far from clear. Although the gut microbiome and metabolomics are thought to contribute to a variety of diseases, the role of them in AM has not been revealed.
### Objective
To investigate changes in the gut microbiota and derived metabolites in AM mice.
### Method
Female ICR mice were randomly assigned to AM and control groups, and pituitary transplantation was employed to perform AM modeling. Then, the fecal samples were obtained for microbial (16S rRNA gene sequencing) and metabolomic (liquid chromatography mass spectrometry, LC-MS) analysis.
### Result
The results of gut microbiota analysis showed that the intestinal microbiota composition of AM mice was altered. The ratio of Firmicutes/Bacteroidetes and the relative abundance of Lactobacillus in AM group increased compared with the control group. Sixty differential expressed metabolites were identified in intestinal metabolites, mainly involved in steroid hormone biosynthesis, cysteine and methionine metabolism, and alanine, aspartate, and glutamate metabolism. Further, correlation analysis verified that L-methionine and L-cystine were negatively correlated with Bacteroides and positively correlated with Desulfovibrio. The Pregnenolone, Androsterone glucuronide, and Testosterone glucuronide were negatively correlated with Unidentified_Ruminococcaceae and Alistipes, whereas they positively correlated with Bacteroides.
### Conclusion
AM mice have a unique gut microbiome and intestinal metabolites.
## Introduction
Adenomyosis (AM), pathologically characterized by the migration of the myometrium into endometrial tissue, is a significant threat to the woman’s health due to its high incidence (Bourdon et al., 2021). The typical manifestations of AM, including abnormal uterine bleeding, pelvic pain, dysmenorrhea, and infertility, seriously reduce the quality of women’s life and work (Stratopoulou et al., 2021; Moawad et al., 2022). Although the pathogenesis of AM remains controversial, sex steroid hormone aberrations, including estrogen and progesterone, as well as immune disorders, are widely recognized to be responsible for the increased cell proliferation in AM (Vannuccini et al., 2017).
The gut microbiome is the collection of all the microorganisms in the human gastrointestinal tract (Ni et al., 2020). Recent evidence suggested that gut microbes act as an extra organ by actively participating in shaping and maintaining human physiology (Motiani et al., 2020). Alterations in gut microbial composition and function can regulate gut permeability, digestive metabolism, and immune responses. AM is an inflammatory-associated and estrogen-dependent disease (Barcena et al., 2013). The altered balance of gut microbes was confirmed, resulting in a pro-inflammatory state (Gomaa, 2020). Besides, studies have shown that the gut microbiota can also affect estrogen levels (Motiani et al., 2020; Song et al., 2020; Qi et al., 2021). Although differential microbiota has been identified in endometriosis (Ata et al., 2019; Huang et al., 2021; Svensson et al., 2021), the relationship between gut microbes and AM has not been revealed.
As a non-neoplastic disease, the metabolites with altered levels are particularly involved in immune activation, cell proliferation, and cell migration in AM (Bourdon et al., 2021). Therefore, information-riched metabolic profiles are increasingly attracting the attention of researchers. With the development of high sensitivity and specificity of mass spectrometry techniques, exploring the potential mechanisms by metabolomic analysis becomes feasible (Yang et al., 2017a). It has been found that serum differential metabolites contribute to immune activation in AM patients, resulting in the upregulation of cell proliferation and migration (Bourdon et al., 2021). Other researchers confirmed that the differential metabolites in the myometrium of AM are associated with AM inflammatory response, oxidative stress, cell proliferation, apoptosis, and energy metabolism processes (Song et al., 2022). The gut microbiota, involved in a variety of metabolic processes including glucose, amino acid, bile acid, and choline, may be responsible partly for the formation of the pathogenic microenvironment in AM.
In this study, the altered intestinal microbiota composition of AM mice was demonstrated by 16s rRNA sequencing, and sixty differential expressed metabolites were also identified in intestinal metabolites. Besides, exploring the possible correlation between the differential intestinal metabolites and alteration of the gut microbiota further reveal their underlying mechanisms in the progress of AM.
## Experimental animals
ICR mice (7 weeks old) were obtained from Beijing Weitong Lihua Laboratory Animal Technology Co., Ltd (NO. 2016-0006). Animal welfare and experimental procedures conformed to the Guidelines for the Care and Use of Laboratory Animals of China Three Gorges University. All animals had a normal diet and circadian rhythm during the experiment. The experimental protocol was approved by the Ethical Committee in Research Medical College of China Three Gorges University of Medical Sciences (NO. 20190801).
## Construction of the AM mouse model
16 female mice aged seven weeks were randomly assigned to the control group ($$n = 8$$) and the AM group ($$n = 8$$). In this experiment, the pituitary transplantation method was used for the modeling of AM (Marquardt et al., 2020). The female mice were injected intraperitoneally with propofol for anesthesia (Xian Nippon Pharmaceutical Co. Ltd. Xian, China, No. H19990282, 100 mg•kg-1). Then, a 2 cm longitudinal incision was made to the right of the lower abdomen. The pituitary acquired from the age matched male mice were injected into the right uterus of mice by trocar. Before closing the incision, gentamicin solution (0.25 ml, 20000 units/ 20 g) was dropped into the enterocoelia.
## Sample collection
After six weeks, all the mice were sacrificed by cervical dislocation. The right uterine of the mice were obtained, and feces from the colons were collected. Both uterine and fecal samples were stored at -80 °C.
## HE staining
After fixed by $4\%$ paraformaldehyde, the uterine tissues were embedded in paraffin and sectioned at 5 μm. Histopathological alterations were observed by hematoxylin and eosin (HE) staining.
According to the results of the HE staining, the boundary between the endometrium and the myometrium in the control group was distinct. On the contrary, in the AM group, the glands and stromal cells of the endometrial layer invaded into the myometrium (black arrow), as shown in Figure 1A. The result indicated that the modeling of AM was successful.
**Figure 1:** *HE staining and body weight changes of mice in control and AM groups. (A) HE staining of transverse uterine sections in control group (left) and AM group (right). (B) Changes in body weight of mice after modeling (n=8 per group). CO, control group, AM, AM group. *P<0.05.*
## 16s rRNA sequencing and data processing
The CTAB/SDS method was used to extract total genome DNA from fecal samples. The V3-V4 region of the bacteria 16S rRNA gene was targeted and PCR amplified with primer 341F (5′-CCTAYGGGRBGCASCAG-3′) and 806R (5′-GGACTACNNGGGTATCTAAT-3′). All PCR reactions were carried out with Phusion® High-Fidelity PCR Master Mix (New England Biolabs). GeneJETTM Gel Extraction Kit (Thermo Scientific) was used to purify mixture of PCR products. Then, they were analyzed by Illumina NovaSeq6000 for machine sequencing.
Raw tags were merged by the reads of each sample with FLASH (V1.2.7) software. The raw tags were quality filtered using the QIIME (V1.9.1) quality controlled procedure to produce the clean tags. Uparse software (V7.0.100) was used for sequences analysis. Sequences with ≥$97\%$ similarity were clustered to the same OTUs. The OTUs sequences were classified by species annotation according to the silva SSUrRNA database (Quast et al., 2013). Alpha and beta diversities were calculated by QIIME (V1.9.1). Linear discriminant analysis (LDA) coupled with effect size (LEfSe) was applied to evaluate the differentially abundant taxon.
## Fecal metabolome analysis
Fecal samples (100 mg) were ground with liquid nitrogen in the Eppendorf tubes, and added with $80\%$ methanol and $0.1\%$ formic acid. The mixtures were resuspended by well vortex, kept on the ice for 5 min, and subsequently centrifuged at 15000 g, 4 °C for 10 min. The supernatant (300 μL) was diluted with 150 μL of LC-MS grade water to $53\%$ methanol conten and then was centrifuged at 15000 g, 4°C for 10 min. Finally, the supernatant was collected and injected into the LC-MS system for metabolomics analysis. The quality control (QC) samples mixed with all test samples in this study were used to evaluate the stability of the analytical system during operation to ensure the reliability of the results.
A Vanquish UHPLC system (Thermo Fisher, Germany) and a Qrbitrap Q Exactive™ HF mass spectrometer (Thermo Fisher, Germany) were used to perform LC-MS analyses. The data matrix, obtained by LC-MS analyses, was imported into a SIMCA version 14.0, Umetrics, Umea, Sweden). Firstly, principal component analysis (PCA) was performed to visualize the distribution of all samples. Then, orthogonal partial least squares discriminant analysis (OPLS-DA) was used to discriminate the metabolites of the two groups. By variable importance plot (VIP>1.0) and P-values ($P \leq 0.05$), the differential metabolites responsible for discriminating between the two groups were identified. MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/) was conducted to perform pathway analysis, and Impact > 0.1 was utilized as a screening threshold to identify probable metabolic pathways (Song et al., 2022).
## Statistical analysis
All data were expressed as mean ± standard deviation. GraphPad Prism 8.0.1 was used to draw a comparison chart of gut microbial relative abundance at different levels, and unpaired t-test (SPSS 17.0) was used for comparison between groups. $P \leq 0.05$ was considered statistically significant. Spearman correlation coefficients between differential metabolites and bacterial genera were calculated using R language (Pheatmap package), and the data matrix was displayed as a heatmap.
## Body weight changes in mice
The body weight of the mice changed after feeding for 6 weeks. However, significant weight gain occurred in the mice from the AM group in contrast with the control group ($$P \leq 0.044$$) (Figure 1B).
## Composition of the gut microbiota in the mice with AM
The results of 16S rRNA sequencing technology showed that the structure of the gut microbiota of AM mice has varied. After taxonomic assignment, 988 OTUs were obtained (Supplementary Table S1). The rank-abundance plot (Figure 2A) indicated that the gut microbiota of AM mice has changed. The results of Alpha diversity indiced that there was no significant difference between the two groups in Observed species, Chao1, Simpson, and Shannon. ( Observed species index, $$P \leq 0.32$$; Chao1 index, $$P \leq 0.68$$; Simpson index, $$P \leq 0.38$$; Shannon index, $$P \leq 0.74$$; Figure 2B). The beta diversity of gut microbiota, based on the weighted and unweighted PCoA, revealed a separation of the control and AM groups (Figures 2C, D).
**Figure 2:** *Diversity analysis of gut microbiota in control and AM groups. (A) Rank-abundance curves. The abscissa position of the extension endpoint of the sample curve was the number of OTU. (B) Boxplot of alpha diversity (Observed species, Chao1, Shannon, and Simpson index) for gut microbiota. Weighted uniFrac PCoA plots (C) and unweighted uniFrac PCoA plots (D). PC1 and PC2 represented the two suspected influencing factors of microbial composition migration. The percentage represented the contribution of principal coordinate components to sample composition differences. The closer the two sample points were, the more similar the species composition of the two samples was. ns, not significantly different.*
At the phylum level (Figure 3A), the ratio of Firmicutes/Bacteroidetes in the AM group was increased compared to that in the control group (0.36 vs. 0.13, $$P \leq 0.109$$). Firmicutes ($$P \leq 0.055$$, Figure 3B) was enriched in the AM group compared to the control group, whereas Bacteroidetes ($$P \leq 0.015$$, Figure 3C) was significantly more abundant in the control group. Among the top 20 most abundant genera (Figure 3D), Bacteroides ($$P \leq 0.001$$, Figure 3E) was found to be significantly more abundant in the control group, while Lactobacillus exhibited increased relative abundance in the AM group ($$P \leq 0.263$$, Figure 3F).
**Figure 3:** *Composition of gut microbiota in control and AM groups. (A) Composition of species at phylum level. (B) The relative abundance of Firmicutes (n=8 per group). (C) The relative abundance of Bacteroidetes (n=8 per group). (D) Composition of species at genus level. (E) The relative abundance of Bacteroides (n=8 per group). (F) The relative abundance of Lactobacillus (n=8 per group). *, P<0.05, **, P<0.01.*
## Characteristic bacterial analysis of gut microbiota in the mice with adenomyosis
To distinguish the crucially different flora in the two groups, we performed LEfSe analysis. At every level of classification, from phylum to genus, significant species that greatly influenced the differences between the two groups were discovered by LDA score (Figure 4A). At the genus level, we found seven kinds of significantly characteristic bacteria (LDA>3.0), including Granulicatella, Porphyromonas, Parvibacter, Anaerotruncus, Halomonas, Subdoligranulum, and Enterorhabdus, which were more abundant in the AM group, compared with the control group (Figure 4B).
**Figure 4:** *Linear discriminant analysis (LDA) integrated with effect size (LEfSe). (A) Cladogram indicating the phylogenetic distribution of microbiota. (B) The differences in relative abundance between the control and AM groups.*
## Fecal metabolomic characteristics of AMs mice
The results of PCA analysis showed that the degree of dispersion in the AM group was greater than that in the control group. The cluster of QC samples in the PCA score plot demonstrated satisfactory stability and repeatability of the metabolic profiling method (Figure 5A). The OPLS-DA analysis found significant differences in the metabolites between the two groups (Figure 5B), and the predictive ability of the OPLS-DA model was verified reliable and no overfitting (R2X=0.458, Q2=0.518) (Figure 5C).
**Figure 5:** *Fecal metabolomic analysis. (A) Principal component analysis (PCA). The distance of each coordinate point represented the degree of aggregation and dispersion between samples. A close distance indicated high similarity between the samples. PC1 and PC2 represented the contribution values of the first and second principal components, respectively. (B) OPLS-DA analysis displayed the grouped discrimination of the control and AM groups by the first two PCs. (C) OPLS-DA model validation. The abscissa representsed the replacement reservation degree of the replacement test. The ordinate represented the values of R2
(green dot) and Q2
(blue square), and the two dashed lines represented the regression lines of R2
and Q2
. QC, quality control.*
We identified 60 metabolites that were differentially expressed between the control and AM groups via OPLS-DA analysis and t-test (VIP > 1.0, $P \leq 0.05$) (Supplementary Table S2). Compared to the information in the Human Metabolome Database (HMDB, https://hmdb.ca/metabolites), the four types with the highest abundance are carboxylic acids and their derivatives carboxylic acids and derivatives ($23\%$), fatty acyls ($22\%$), steroids and steroid derivatives ($17\%$), benzene and substituted derivatives. ( $7\%$) (Figure 6A). 36 metabolites were up-regulated in the AM group in comparison to the control group, while the rest metabolites represented the trend of down-regulation (Supplementary Table S2). The clustering heatmap of the differential metabolites was shown in Supplementary Figure S1. To systematically evaluate the perturbed metabolism in AM, the pathway analyses were performed. As a result, we found there were striking differences in steroid hormone biosynthesis, cysteine and methionine metabolism, and alanine, aspartate, and glutamate metabolism between the control and AM groups (Supplementary Table S3, Figure 6B).
**Figure 6:** *Differential metabolite analysis between the two groups. (A) Pie chart of HMDB subclass compounds. The different colors in each pie represented different HMDB classifications, and the area represented the relative proportion of metabolites in the classification. The number in the brackets represented the amount of corresponding metabolite class. (B) Pathway analysis of significantly altered metabolites.*
## Correlation between differential metabolites and gut microbiota in mice with adenomyosis
The results of the correlation analysis was shown in Figure 7. The top 20 species at the genus level and the 9 differential metabolites were included for analysis. The metabolites increased in the AM group, including L-methionine and L-cystine, were significantly negatively correlated with Bacteroides and positively correlated with Desulfovibrio. The metabolites decreased in the AM group, including Pregnenolone, Androsterone glucuronide, and Testosterone glucuronide, were negatively correlated with unidentified_Ruminococcaceae and Alistipes, whereas positively correlated with Bacteroides.
**Figure 7:** *Heatmap of correlation between metabolites and top 20 relative abundance species at the genus level. The correlation (R) value is displayed in different colors in the figure. The legend on the right shows the range of colors for different R values, with red representing positive correlations and blue representing negative correlations. *P<0.05, **P < 0.01.*
## Discussion
As a complex and important part of the human body, the gut microbiota plays a role in the immune regulation and pathogens resistance of their host (Jandhyala et al., 2015; Sidhu and van der Poorten, 2017). One of the major ways the gut microbiota interacts with the host is through intestinal metabolites, small molecules produced as intermediate or end products of microbial metabolism (Zhang et al., 2022). The combined analysis of gut microbiota and intestinal metabolomics helps to understand the interaction between the intestinal flora and the host (Zhou et al., 2020). In this study, 16S rRNA sequencing and non-targeted metabonomic analysis methods were adopted to reveal the variation of the intestinal metabolomics and gut microbiota in mice with AM, and the relationship between them was further investigated by correlation analysis.
In terms of gut microbiota, we found that despite the similarity in alpha diversity and beta diversity, bacterial relative abundance varied between the two groups at phylum and genus levels. Bacteroidetes are the major members of the gut microbiome in AM mice, followed by Firmicutes, considered as the two most dominant bacteria in the gut microbiome at the phylum level (Yang et al., 2022). The appropriate ratio of the Firmicutes/Bacteroidetes has been supposed to benefit the homeostasis of the host, while the disturbance of the ratio may link to complications such as diabetes and inflammatory bowel disease (Stojanov et al., 2020; Yanez et al., 2021). Firmicutes/Bacteroidetes ratio is elevated in type 2 diabetes patients, and Firmicutes were positively correlated with proinflammatory gene expressions (Bahar-Tokman et al., 2022). Studies have also shown that the ratio of the two intestinal flora may change the body weight. Researchers demonstrated that obese mice have lower levels of Bacteroidetes and a higher proportion of Firmicutes than lean mice (Yang et al., 2017b). It has been proved that the proportion of Firmicutes to Bacteroidetes increased in both the genital and intestinal tracts of endometriosis (Shan et al., 2021), and similar trends were verified in our research. Besides, our study on AM model mice found that the weight gain of AM model mice was significantly higher than control mice, companies with the increased ratio of Firmicutes/Bacteroidetes. It is reported that the risk of clinically suspected endometriosis was higher among women who were overweight compared to normal weight (Rowlands et al., 2022), and the positive associations between endometriosis and body size in adulthood are evident (Rossi et al., 2021). Researchers believe there is pathophysiological interaction between endometriosis and obesity, especially in angiogenesis and inflammation (Pantelis et al., 2021). This may explain the significant increase in body weight of AM mice. We speculate that controlling metabolism through gut microbiota may be a potential therapeutic target for AM in the future.
In addition, Lactobacillus, a species of Firmicutes, was found upregulated at the genus in the AM group. The increased level of the Lactobacillus has been demonstrated to associate with disorder of the sex hormone, resulting in the high CA125 levels, severe pain, and infertility in endometriosis (Chang et al., 2022). The increased estrogen and low progesterone levels have been recognized to be responsible for the endometriotic disease (Seifert, 2020). In the liver, the estrogen binded with glucuronic acid or sulfate via UDP-glucuronyltransferase and sulfotransferase is eventually discharged from bile into the intestine. However, binding estrogens may be uncoupled by intestinal bacteria possessed β-glucuronase and β-glucosidase, reabsorbed into the bloodstream, from the intestine, forming the enterohepatic circulation of estrogens. Increased enterohepatic circulation can lead to the excess of estrogen, which is closely related to the occurrence and development of ectopic endometrium (Tian, 2022). Lactobacilli have been figured out to contain genes encoding β-glucuronase, which might explain why increased Lactobacilli contribute to endometriosis (Cao et al., 2020). We also found that Lactobacillus were significantly increased in AM group compared with the control group, and supposed that the gut microbiota may contribute to the imbalance of estrogen/progesterone in AM.
Although serum differential metabolites, including 3-hydroxybutyrate, glutamic acid, proline, and choline, have been confirmed in AM patients (Bourdon et al., 2021), the metabolic changes in the gut of AM have not been thoroughly studied. Our research focused on the changes of metabolites in the intestine, and the results revealed that the metabolic profiles of the AM group were significantly different from those of the control group.
Firstly, it was found in our research that the progesterone in AM was significantly reduced, along with the decrease of intermediate products including progesterone and prognenolone. Estrogen is a key promoter for endometriotic lesion growth and progression, whereas progesterone is a master regulator tightly controlling estrogen actions. The researchers claimed that the established inflammatory environment of endometriosis disrupted the balance of hormonal regulation and reduced coordinated progesterone responses (MacLean and Hayashi, 2022). Oral contraceptives containing progesterone have been tried as a treatment for endometriosis and proved effective in two-thirds of patients (Donnez and Dolmans, 2021). For example, Norethindrone acetate, typical progesterone, has been widely used in the treatment of pelvic pain and irregular bleeding in endometriosis by inhibiting ovulation and reducing the level of prostaglandinn (MacLean and Hayashi, 2022). It is also confirmed that pregnenolone sulfate, the intermediate product of progesterone, has been associated with a reduced risk of post-surgical pelvic pain in young patients with endometriosis (Sasamoto et al., 2022). So, we supposed that the decreased progesterone in the intestinal metabolism may be responsible for the access of AM.
In addition, a hypoandrogen state was found in AM mice, reflecting in the reduction of androsterone glucosidate and androsterone glucosidate, which are two major testosterone metabolites. There is growing evidence that androgens are key regulators of body fat distribution in both men and women (Tchernof et al., 2018). Studies have confirmed that BMI at the age of 18 is negatively correlated with androgens (dehydroepiandrosterone, dehydroepiandrosterone sulfate, androstenedione, testosterone) and 5α -glucuronic acid metabolites (Oh et al., 2021), the mechanism of which may attribute to the highly positive correlation between insulin resistance and low testosterone (Bianchi and Locatelli, 2018). Our study found that the down-regulated androsterone glucosidate was significantly positively correlated with Bacteroidetes. Therefore, we speculated that the weight gain of AM mice caused by the imbalance of the Firmicutes/Bacteroidetes ratio may be partly related to the down-regulation of androgen levels.
Besides, the upregulation of sulfur-containing amino acids, including phosphoserine, methionine, and cystine, was observed in AM group. Phosphoserine is a crucial intermediary in serine production (Mattaini et al., 2016). Serine can acquire sulfur delivered by methionine catabolism to form cysteine, which exists mainly in cystine (Cys-S-S-Cys) outside the cell. After being transported to the cell by cysteine transporters, cysteine participates in the synthesis of glutathione (GSH) due to its highly reduced state. As an antioxidant, GSH maintains cellular redox homeostasis, which is crucial in the development of tumors (Guo et al., 2021). Appropriately increased redox levels can support survival and proliferation by activating signaling pathways that can contribute to tumor growth. Therefore cancer cells need to maintain an intricate balance of antioxidant levels to survive, which helps to explain the increased biosynthesis of GSH in tumor cells and its positive correlation with high metastasis (Bansal and Simon, 2018). AM is characterized by a tumor-like malignant proliferation. Although there are few studies on the metabolic changes of GSH in AM, some researchers confirmed that in the myometrium of AM, glutamate glutathione and oxidized glutathione were increased, (Zhang et al., 2017; Song et al., 2022). Consistently, our research showed that methionine, cystine, and phosphoserine, significantly increased in the intestinal metabolites of AM mice, highlighting that increased synthesis of sulfur-containing amino acids may be a potential metabolic marker for abnormal AM proliferation.
Sulfate-reducing bacteria (SRB) are the main driving force of the sulfur biological cycle. Firmicute Lactobacillus, a kind of SRBS, has been upregulated in the AM mice in contrast to the control group. Researchers have demonstrated a positive correlation between cysteine concentration and Firmicute Lactobacillus abundance in the reproductive tract (Bloom et al., 2022). It may relate to the cysteine β -synthase and cysteine γ -lyase contained in the Lactobacillus, which contribute to the synthesis of cysteine from serine and methionine by a reverse super sulfur pathway (Matoba et al., 2020). Meanwhile, Enterobacteriaceae, another kind of SRB, was found to be upregulated in AM mice. Studies have verified that the Enterobacteriaceae can activate sulfate transport by sulfate osmosis enzyme and participate in sulfite reduction reaction via NADPH-sulfite reductase under aerobic conditions, contributing to the production of cysteine (Wang, 2022). Increased Desulfovibrio of SRB has also been presented. Desulfomycin contained in Desulfovibrio, reducing sulfate to H2S, has been demonstrated to be a crucial sulfite reductase in anaerobic conditions (Kushkevych et al., 2020). At the same time, sulfide may have a reverse-regulating effect on the flora. It was found that adding sulfide elevates the abundance of Firmicutes and Desulphurvibrio (Zhao et al., 2020),while cysteine uptake inhibition selectively reduces the growth of Lactobacillus in vitro (Bloom et al., 2022). Consistent with the existing studies, we found that Enterobacteriaceae and Desulfurvibrio were positively correlated with up-regulated sulfur-containing amino acids in AM, suggesting the potential relations between the SRB and increased metabolism of sulfur-containing amino acids. However, its specific mechanism needs further verified.
In conclusion, this study performed a comprehensive analysis of gut microbes and metabolites in AM mice by 16S rRNA sequencing and fecal metabolomics. AM mice have unique gut microbiota and metabolites, and the alteration in gut microbes may contribute to the regulation in metabolites. It provides different insights into the pathogenesis of AM from new approaches and perspectives.
The present study has some limitations. First, the relatively small sample size might lead to low statistical power. Second, there are certain restrictions on the research of the illness process because only a mouse model was employed. Thirdly, follow-up verification has yet to be conducted in this experiment. Further research will be done to confirm in the future.
## 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: NCBI, PRJNA895179.
## Ethics statement
The animal study was reviewed and approved by Ethical Committee in Research Medical College of China Three Gorges University of Medical Sciences.
## Author contributions
Conception and design: MZ and XF. Perform the experiments: SL, MC and YL. Performed the microbiome and metabolomic analysis: PC and KW. Drafted the manuscript: PC. Final approval of the completed manuscript: MZ and XF. 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/fcimb.2023.1075387/full#supplementary-material
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|
---
title: 'Rethinking Restriction in Residential Aged Care: Dis/Abling Movement and Relations
in the Time of COVID-19'
journal: Space and Culture
year: 2023
pmcid: PMC10008998
doi: 10.1177/12063312231159227
license: CC BY 4.0
---
# Rethinking Restriction in Residential Aged Care: Dis/Abling Movement and Relations in the Time of COVID-19
## Abstract
Restricting movement is a major focus in policy directives to reduce the spread of COVID-19 in aged care homes. In this article, we rethink dominant framing of restriction through a critical examination of the politics of good care and ethnographic attention to spatial extensions and interdependencies between residents, care workers, and assistive technologies. Drawing on ethnographic observations in two South Australian care facilities, analysis of aged care policies and national inquiries into aged care, and relevant media reporting, we examine how restriction to movement, misconceptualized as a good form of care, has suppressed residents’ physical and social needs and ruptured abling assemblages of resident mobility. We propose that walking alongside aged and frail residents offers new ways for thinking about care and re-abling relational approaches to care in times of crisis.
## Introduction
Movement is a central theme in these pandemic times. Even the motility of a virus is dependent on the movement of its hosts. Without bodies carrying infection, the transmission of COVID-19 would stop. However, moving is a core component of our social life. Bodies move in order to care for others and to be cared for, to reach out and assist, and to make one’s way in the world. Decisions about movement become particularly complicated and contested in settings such as residential aged care facilities, where residents’ bodies often depend on the movements of others who must assist them to move. In these spaces, the bodies of residents and care workers must conjoin to enable movements that neither aged and frail bodies nor viruses could make on their own.
Movement is thus a persistent concern in the lives of aged care residents and those who care for them. Having falls, or losing the ability to walk or move safely, is what brings most people into residential aged care; however, ethnographic observation of residents’ everyday lives revealed a more nuanced picture of movement (and its restriction) in care settings. Residents’ repeated attempts to stand up from sitting could be seen as demonstrative of a desire to walk and a need for assistance to do so. Yet these same movements, when observed by nurses in the context of clinical assessment, could be interpreted as “impaired balance” compounded by “confusion,” indicating a “loss of mobility” and heightened “risk of falls.” While residents exhibit a wide range of physical abilities, aged care discourse often describes their movements using terms like “exercise of the physically impaired” or the “risky behavior” of residents living with dementia. Corresponding to these multiple interpretations of movement, daily care activities were organized at times as “mobility care” and, at other times, as “fall prevention”: Such movement could be either encouraged and enabled or, conversely, forcibly restrained. The basis of COVID-19 health policy, primarily informed by epidemiological discourse, was that movement generated virus transmission, and thus, the most effective infection-control measures in aged care facilities would be ones that minimized movement. Under this policy directive, restrictive practices were strictly implemented across Australia, which resulted in the confinement of residents in their rooms.
In this article, we examine the complex lived realities of aged care homes before and during the COVID-19 pandemic. We draw on the work of Mol [2008] to consider what constitutes good care, exploring the disjunctures between residents’ needs to move and the politicization of care. Central to this analysis is our positioning of movement as vital to the social worlds of residents. The issue of mobility is not just about the individual and their ability or inability to move. In line with Schillmeier [2007], we also attend to the ordinary acts and materialities of everyday life that make up the multiple lived scenarios of older persons with disabilities. Schillmeier suggests that notions of disability and ability cannot be separated; thus, dis/ability should be seen as “complex sets of heterogeneous practices that (re-)associate bodies, material objects, and technologies with sensory practices” (Schillmeier, 2007, p. 195). Foregrounding these ideas, we examine how bodies, especially as they age, are always dependent upon multiple persons and entities to live and to get around in the world. We use the Deleuzian concept of assemblage to analyze how residents’ bodies entwine with this multiplicity—of persons, diseases, policies, viruses—involved in the delivery of everyday care. In this way, joining Schillmeier’s [2007] notion of dis/ability with the Deleuzian concept of assemblage (Deleuze & Guattari, 1988) we consider resident im/mobility in the context of the complex and shifting interplay between bodily impairments, material surroundings, aged care discourses, and policies to create what we term dis/abling assemblages.
Some assemblages operate to assist residents, while others constrain bodies, preventing movement and limiting the sensorial and spatial dimensions of residents’ worlds. Restrictive practices are entrenched in aged care homes, and their use has been amplified by COVID-19. From restraint technology such as sensor mats and “princess chairs” to workplace policies that prioritize fall prevention over mobility care, residents’ surroundings are specifically designed to limit movement. Examining the multiple and shifting dis/abling assemblages of resident movement, we think about care with science and technology studies (STS) scholars (Martin et al., 2015; Mol, 2002, 2008; Puig de la Bellacasa, 2011) and ask what is or is not assembled and why? Attending to the complex entanglements of policies, practices, needs, desires, resources, and interests, we show that the political dimensions of care come to define the movement policy for residents. The COVID-19 emergency policy has now extended restriction to movement in many ways, meaning that assemblages that previously enabled residents to move have been severely dismantled.
This article critically examines the notion of good care (Mol et al., 2010; Schillmeier, 2014) by tracing the movements—and restrictions—of aging bodies and care workers before and during the pandemic. First, it draws on a pre-COVID-19 ethnographic study of two South Australian nursing homes to illustrate some residents’ experiences of aging, how it affected their bodies and movements, and the generative capacity of certain relational interdependencies. When residents struggled to make their bodies do the things they wanted to do, enabling assemblages were made possible in everyday spatial and sensory extensions of residents’ bodies and their engagements with other people and things (Dennis, 2007; Latimer & Munro, 2009; Petty, 2021). From the touch of carers to well-placed handrails and walking aids, residents’ bodies were able to move and to walk. Good care became apparent as residents related to others who responded and enabled them to move safely by providing an assemblage of attention, aids, and assistance. This form of good care, advocated in quality standards and policies, was certainly perceived by staff as ideal but sadly was provided only occasionally.
The emergence of COVID-19 in Australian residential aged care facilities in 2020 led to a constellation of restrictive practices that totally severed residents’ sensory and spatial interconnections with the world: With the banning of visitors and confining residents to their rooms, there was an extreme absence of touch. A “back-to-basics” policy was applied that short-circuited the normal distribution of care, rupturing residents’ connections with the world outside the facility. This could be seen as a rhetorical veneer used by administrators to legitimize reductions in resources and staff. With fewer hands on deck, there were fewer resources available with which to assemble good care for residents.
This article is based on 12 months of ethnographic fieldwork in two South Australian care facilities (2015–2016) and a discourse analysis of aged care policies, reports, and national inquiries into aged care before and during the pandemic, supplemented with COVID-19-related reporting in news and social media from 2020 to 2022. Carrying out participant observation in the nursing homes, Zhang [2023] regularly walked with residents and observed their day-to-day interactions with care staff, family members, and assistive devices. In walking with residents, Zhang came to witness how residents lived in tension between moving and not moving and how they relied on multifaceted interdependencies involving people and technologies to move and to walk (Zhang, 2023). Insights gained from fieldwork on resident movement guided the discourse analysis and led to a rethinking of restriction to movement both in relation to and beyond COVID.
## Dis/Abling Assemblages
Difficulty with moving is strongly related to admission into residential aged care, and such were the cases of residents we met in the care homes. In this section, we turn to these residents and to their experiences of moving and aging. Residents spoke to the ethnographer and to staff and visitors about their “numb feet” and “bad legs” in a shared vernacular common to those with aged and frail bodies who are experiencing the cumulative chronicity of diseases (Warren et al., 2013). From arthritis to diabetes, dementia, and Parkinson’s, most residents experienced limited movement. Mrs. Casey, aged in her late 70s, had diabetes, and this condition led to sensory neuropathy; she could not feel her feet, and her legs would wobble and give way under her heavy body weight. Diabetes in its late stages can inhibit movement and, for Mrs. Casey, that meant she experienced pain in her legs when walking and was at risk of falling. Like other residents in the facility, difficulty with moving and a perceived risk of falls changed how she could live her everyday life.
Mrs. Casey was effectively confined in her room. She rarely saw or talked to anyone other than staff who delivered meals and pills. Increasingly bored, sedated, and isolated, she put on more weight and had frequent mood swings. The longer she was alone in her room, the stronger became her desire to walk. Unlike able-bodied young people who move with ease, aged residents struggle to move on their own. “ Bent backs” and “sore legs” produced discomfort and risks to walking and standing, yet Mrs. Casey still felt a continual urge to move and pushed herself to do a daily walk. With support from the facility’s integrated care management team, Mrs. Casey was allowed access to certain resources that came under the funding category of mobility care: additional assessments by a physiotherapist, a prescribed walking aid, and training sessions with a physio assistant, as well as the allocation of staff time to assist her to relearn how to walk. Before and after lunch, personal care workers were scheduled to help her stand and supervise her daily walk.
How residents can be assisted to move freely and safely plays a key role in them living well and feeling at home in the nursing homes (Zhang, 2023). Mr. Harris was admitted into the aged care facility due to the progression of his Parkinson’s disease. He felt safer in the nursing home environment, where aid devices and technologies were available and staff readily came to his assistance. Whenever his bodily condition allowed, he wanted to spend time on his feet. Meandering along the halls of the nursing home, he connected with other people and things in many ways: by gripping the handlebar of his walker, by feeling the texture of the carpeted floor, by blinking under the ceiling lights, or by feeling the occasional breeze wafting through the front door. His walking merged in a flow of movements with that of other residents, staff, and visitors, and in this way, Mr. Harris wove himself into the lived fabric of the nursing home.
When walking becomes a routine activity, particular material things, places, and people are regularly and consistently incorporated into the self through sensory extension (Dennis, 2007; Petty, 2021). Sometimes Mr. Harris observed people coming and going, and at other times, he would reach out and engage with them. Walking activates connective and associative processes that can enable residents to be in touch with the world. This notion of sensory extension is central to Latimer and Munro’s [2009] notion of relational extension. It involves establishing person-world connectedness through routine practices and creating a sense of belonging. As a productive and relational activity, walking could create embodied space “where human experience and consciousness take on material and spatial form” (Low, 2003, p. 9): Not only residents’ bodily space is extended through lifting the foot off the ground and reaching toward the floor, their perceptual field is also broadened by looking ahead and afar. Residents’ bodies and everyday movements are entwined and enabled in the sociomaterialities of the nursing home.
Aid equipment, technologies, and facilities such as corridors, lounges, and dining areas are incorporated into the living environment to support residents’ movements and maintain their safety. Shared handrails support residents’ efforts to move. Thick carpets on the floors protect them from fall-related injuries. A four-wheel walker, like the one that Mr. Harris used, is designed to help balance and make it easier for residents to get around. In the delivery of good care, as Schillmeier and Domènech [2010] have described, technologies such as call-bell and sensor mat alarm systems are used to facilitate connection between residents and staff. Here, the pressure-sensitive mat (a rubber device embedded with electronic wires) is a fall-prevention device that can alert staff to resident movement and the need for help.
Residents often require a helping hand to move and to walk, making staff assistance a key human resource. It entails reaching out to residents through physical proximity, close contact, and intimate touch. Whether it be the helping hand that helps residents up out of the deep recesses of a “comfort chair” or that grasps the handle of a walker to help guide it, staff labor in the kind of “body work” (Twigg et al., 2011) that requires focus and physical exertion. It calls on their own bodies to move in response to residents’ movements. It necessitates becoming attuned to residents’ bodies—a wobble in the legs, a few accelerated jerky steps, a grimace on the face, or any other signal that might suggest a resident is about to lose their footing.
The complexities of resident movement and their multifaceted interdependencies with other people and things point to the processual nature of walking. Taking a Deleuzian approach, walking is always a becoming-walking in which the aligned steps and interlaced hands of care staff and residents come together as one, co-functioning in an assemblage (Deleuze & Guattari, 1988). Assemblage is a key concept for Deleuze and Guattari [1988]. It refers to the becomings that occur when objects, beings, events, processes, policies, and discourses coexist. Responding to residents’ wish to move, the assemblage of resources—staff assistance, aid devices, and technologies—and residents’ own efforts come together in abling assemblages of mobility.
However, residents were not always supported to move, to stand, or to walk as they wished. Providing resources to enable residents to move and protect them from falls and fall-related injuries is an important aspect of service delivery in care facilities. Yet, the ethnographer observed situations where residents were restrained from moving under the “duty of care” relating to fall prevention. An example is that of Mrs. Wilson, a resident with later-stage Alzheimer’s disease. She frequently walked around the facility, sometimes at pace. As her dementia progressed, her gait became erratic, and she would often lose her balance. After successive falls and bruises, she was assigned a “princess chair” in the lounge. This is a pressure-cushioned chair that is designed with a curved backrest to provide extra postural support for residents sitting down for long hours. While it is not ostensibly a method of restraint, the backward tilt of the chair inhibits movement and limits engagement with other residents and staff.
From a policy perspective, restrictive practices are not the right way of doing care. In Australian aged care policy, restraint is defined as any practice, device, or action that interferes with a person’s ability to make a decision or which restricts their free movement (Department of Health and Ageing, 2012, p. 1). Although providers are required to operate with a “no restraints” policy, identifying and preventing restrictive practices in aged care can be challenging. In practice, physical and pharmacological restrictive practices are often used on people with cognitive impairment due to dementia, who exhibit “behavioral symptoms,” such as persistent attempts to stand and to walk, despite impaired balance and frequent falls. Restrictive practices are often justified on the basis that they are safety measures to avoid harm to themselves and others. As a care worker explained, “It’s safer for Mrs. Wilson to remain seated.” Instead of enabling Mrs. Wilson to stay active and to walk, safety was prioritized, and this entailed extended long periods of sitting, only relieved by a repositioning routine in which Mrs. Wilson’s position in the princess chair was moved to prevent pressure sores.
This was also the case with Mr. Harris when his gait deteriorated rapidly due to the progression of his Parkinson’s disease. After being assessed as at “high risk of falls,” Mr. Harris was prescribed a new form of mobility care, the “two-staff-assisted daily walking routine,” and a fall-prevention plan that specifically prohibited him from walking on his own. His walking aid was removed, and all attempts to walk, viewed as potentialities for falls, were monitored via a sensor mat in front of his armchair. Just as Mrs. Wilson was trapped in the princess chair, Mr. Harris was prevented from walking by an assemblage of fall prevention, which became restrictive, disabling, and even inhumane, especially when he could not get to the toilet independently.
Residents’ risk of falling could be mitigated by non-restrictive means, that is, by ongoing supervision and prompt assistance; however, that required provision of much more staff support, which was not available. Hence, fall prevention was prioritized due to an ongoing shortage of staff and lack of funding to recruit more. Recognizing his need for mobility, management introduced defined practices, such as the 10- to 15-minute staff-assisted walking routine, as a gesture to “best possible care” within resource constraints.
## Good Care: For Whom and in What Ways?
The provision of “hands on” care to the level required to support frail aging bodies to move, and to move safely, is increasingly being squeezed out of the Australian aged care sector (Zhang, 2023). As recent critical studies reveal, the notion of “care” is being wielded for political ends in times of crisis (Chatzidakis et al., 2020; Hobart & Kneese, 2020). Those studies describe exponential growth in the neoliberal and corporatized versions of care enacted by institutions as they seek to “increase their legitimacy by presenting themselves as socially responsible” (Chatzidakis et al., 2020, p. 11). These forms of “carewashing” present market-driven “care fixes” (Dowling, 2022) that ignore the systemic failures that undermine care, especially for the most vulnerable. Let us examine the regulatory frameworks that shape the aged care sector in Australia. Long before COVID-19 made the headlines or infiltrated aged care homes, the hands-on care provided by workers was being subsumed by the ever-increasing administrative burden due to accreditation processes, through which care home operators must regularly show that they meet prescribed standards of care. 1 In the audit culture (Strathern, 2000) of the contemporary Australian nursing home, care routines become tasks that must be itemized and meticulously documented by staff. Care must be accounted for because the accounting of care is critical to remain an accredited provider. Facilities that fail to meet the accreditation standards (e.g., the 1997 Aged Care Act and the Aged Care Quality and Safety Commission Act 2018) lose government funding. Meeting accreditation requirements is thus vital.
Adequate staff-supported resident walking that seeks to enable mobility and reduce the risk of falls remains largely a pipe dream in the existing system because low staffing levels and limited time for each resident keep staff fully occupied with essential care activities like washing and feeding, rather than assisting residents to walk. In her influential work, Mol [2002, 2008] teases out the concept of care, with its multiple meanings and enactments, offering a critical framework to show how conflicting perspectives of care coexist and intersect in practice. For STS scholars, “what care looks and feels like is both context-specific and perspective-dependent” (Martin et al., 2015, p. 625) and often asks for attentiveness to emerging requirements of care in particular situations (Schillmeier, 2017).
In nursing homes, an active policy to prevent falls and resulting harm is critical to meeting quality standards and compliance processes. Falls must be documented, risk assessments undertaken, and prevention strategies implemented by increasingly casualized workers who are “hugely overstretched, vulnerable, and less able to care” (Chatzidakis et al., 2020, p. 2). Consequently, restrictive practices such as those outlined above are used in the interests of fall prevention, while residents’ personal desires are largely ignored. Prolonged sitting was encouraged in the case of Mrs. Wilson by providing the princess chair, while Mr. Harris’s walking was severely restricted by removing his four-wheel walker and installing a sensor mat.
As the accreditation process focuses on monitoring compliance rather than quality improvement (Productivity Commission, 2011, p. 101), service providers choose to foreground falls risk rather than the more hidden risks of isolation and inactivity, in order to reduce their own risk of liability. This obligation to keep residents safe, described as “duty of care,” is enacted at all levels: in staff training programs and in internal communications, such as handovers or memos, resulting in care practices that reduce risk by limiting resident movement.
Movement itself is thus curtailed in the name of care. Certain outcomes, such as low incidents of falls and injuries, might look good on paper and appear to provide residents with safety and comfort but certainly cannot make residents feel good when they are restrained from moving and unable to do the things they want to do. In care homes, staff assistance is itemized as service provision through hourly-paid labor. Staff are rostered and remunerated for shift work that is recorded as hours worked. Thus, the availability of staff assistance is always timed and limited by funding levels. Unlike the “abling” assemblages of mobility care described above, which rely on staff time and labor, the “disabling” assemblages of fall prevention can be implemented with minimal resources: a 10- to 15-minute staff-assisted walk or the repositioning of someone sitting in a princess chair. But this kind of care failed to meet the often-unspoken needs of residents who wanted to keep moving and maintain connection with others (Zhang, 2023).
Even before the emergence of COVID, the aged care sector in Australia was in crisis. In its final report, the Royal Commission into Aged Care Quality and Safety 2 noted that care, as provided in the existing system, cannot assist older people to live a good quality of life (Pagone & Briggs, 2021). The report identified that the fundamental systemic flaw with the Australian aged care system is its focus on the funding requirements of care providers rather than the care needs of older people. In response to this institutional failing, and the proliferation of cases of abuse and neglect, there are calls for fundamental reform of the aged care system to place aged people themselves at the center of aged care provision.
Aged Care Quality Standards were introduced in July 2019 and designed to shift the rhetoric from “ticking boxes” to giving older people control over their care by tailoring care to their individual needs (Aged Care Quality and Safety Commission, 2019). Unlike the old Accreditation Standards that focus on the outcomes for providers, the new Quality Standards center residents and their families as “consumers of aged care services” (Aged Care Quality and Safety Commission, 2019) in a deliberate shift toward a consumer choice framework.
Reframing the aged care sector into a market-led for-profit service industry occurred in tandem with the co-contribution funding model, launched as part of aged care reform in 2012. These changes transformed what was once a heavily government-subsidized sector. Reflecting a neoliberal policy agenda, older people and their families became “customers,” seen as responsible for making choices about what they wanted and how much they would pay for it. Thus, the government implicitly transferred its responsibility for residential aged care to senior Australians themselves, their families, and service providers.
In her book, The logic of care: Health and the problem of patient choice, Mol [2008] argues that the mantra of individual choice undermines ways of thinking and acting crucial to health care. Using examples from diabetes clinics and diabetes self-care, she argues that good care is not a matter of individuals making well-argued choices but is something that emerges when all parties negotiate and collaborate to attune medical knowledge and technology to the diseased bodies and complex lives of patients. Unlike the diabetic patients in Mol’s study, who were mostly able to self-care, frail aged care residents rely on staff physical assistance to support all their daily activities, from washing to walking. In this context, good care does come in the form of knowledge and technologies but also, quite critically, from bodily presence and attendance. Care workers align their own minds and bodies with those of residents in order to respond to needs and wishes (Driessen, 2018). If care is seen in practical terms—in walking, with or without staff standby; in sitting, in a chair that is set back or upright; in lending a hand or supplying an assistive device to enable residents to walk safely—the rhetoric of individual responsibility embedded in the phrase “consumer choice” is irrelevant, if not incongruous. Daily walking routines are less a matter of individual choice but a complex set of care relations that are implicated in the accomplishment of feeling at home (Schillmeier, 2014) through walking. To paraphrase Puig de la Bellacasa [2011], they are a matter of good care. Good care, as voiced by residents and staff and supported by fieldwork observations, is more about enabling older people to move and to do the things they would like to, but are unable to do on their own, and assembling human and material resources to do so.
After years of neoliberal approaches to aged care provision, providers are increasingly understaffed and underfunded. With limited access to training and resources in the current system, underpaid staff are often overwhelmed and out of their depth (Pagone & Briggs, 2021, p. 78). The next section extends the discussion on the politics of good care to considering what happened in Australian residential facilities during the COVID-19 pandemic. We will reveal how staff shortages, increased workloads, and insufficient funding during COVID further dismantled any potential for abling assemblages.
## “Back to Basics”: Short-circuiting Care During COVID-19
We know that care that enables residents to move is often not available to them when they need it (Kontos et al., 2011), and even before the pandemic, good care emerged not because of the system but in spite of it. In the nursing homes, staff employed tactics of la perruque—not to benefit themselves but to assist the residents whom they were employed to care for (De Certeau, 1984). This meant borrowing time from one resident, whenever possible, to respond to another resident’s immediate needs. During the pandemic, however, residents and staff found themselves in ever more challenging situations where their already limited access to resources was further cut back. In this section, we turn to the emergence of COVID-19 in Australian aged care homes to examine its impact on policies, daily practices, and material surroundings, and we analyze how, when residents’ access to many resources was strictly limited, the abling assemblages that supported them to move were severely ruptured.
Transmission of COVID both affects and results from the multifaceted interdependencies between residents, care workers, and assistive devices already described in this article. Assisting residents with daily living activities means that shaky hands, supportive touch, and a stable walking aid come together in physical proximity, creating intimate circuits of care through which the COVID-19 virus may move easily from one body to another, spreading infection widely. First identified in December 2019, then pronounced a global pandemic in March 2020, the COVID illness can lead to severe acute respiratory syndrome coronavirus 2. Prior to effective treatments or vaccines, public health interventions were vital to combat the spread of infection and mortality. Stopping the transmission of COVID entails monitoring and restricting the interactions of people and their bodily fluids. When exhaled by an infected person, aerosols or droplets containing the virus are inhaled through contact with the eyes, nose, or mouth of another person. People may also become infected by touching contaminated surfaces. It was early understood that minimizing movement decreases the risk of infected people passing the virus to others.
The first COVID outbreak in an Australian residential aged care facility occurred on March 3, 2020, at Dorothy Henderson Lodge in Sydney, and the then prime minister, Scott Morrison, announced restrictions to residential aged care on March 18, 2020. These measures included restricting visitor numbers, physical distancing, and limiting residents’ movements and activities in communal areas (Alderslade, 2020b). In South Australia, the Residential Aged Care Facilities COVID-19 Direction (South Australia Government, 2020) was enacted in aged care facilities on August 13, 2020. Under this protocol, providers were required to implement precautionary restrictive measures including density management and physical distancing in all communal areas, that is, the spaces where people (residents, staff, or visitors) and things (handrails, call-bells, furniture) come into contact with each other. As the risk of transmission increases once the “density,” or “maximum number” of people in a single area, is reached, access to communal areas was reduced. Residents’ bodies, thus, became quantified, with their movements documented and monitored. Providers were also required to implement other infection-control measures such as screening staff and visitors for COVID-like symptoms and regular cleaning of shared surfaces. Communal areas were now regarded as labor-intensive sites of infection control to which residents’ access must be restricted.
As described earlier, prior to COVID-19, residents in this study often reduced their sense of spatial and social isolation by venturing beyond the confines of their individual rooms. Stepping outside their doors, they came into contact with shared handrails in the corridors and carpeted pathways that led to tables and chairs in the lounge and dining areas where they could commune with other persons. Movements they experienced in those outings consisted of helpful assemblages of grab rails, walking aids, and staff assistance, all supporting their attempts to move. However, communal spaces, where residents could overcome their bodily restrictions through spatial extension, were now, for the most part, out of bounds. The previously productive elements that enabled resident movement, such as a hand supporting a resident’s back when they walked, were now classed as hazardous routes of transmission.
Policy directives emphasizing infection control served to amplify the climate of risk aversion among aged care providers, who responded to legal and policy requirements with excessive restrictive practices—often exceeding recommendations. Nationwide, many aged care facilities decided to ramp up and enforce a total lockdown, closing their doors to family and friends, and confining residents to their rooms. This happened even among facilities that had not suffered outbreaks (Alderslade, 2020c). Thousands of residents in homes without COVID-positive cases endured months of isolation in their individual rooms. According to the national Communicable Diseases Network Australia [2021], this was not justified. Residents were in effect “grounded” in a disabling “environment that [was] unresponsive to the needs and aspirations of people with disability” (Matereke, 2020, p. 88), and residents were doubly, or even triply, burdened and excluded: imprisoned in a “comfort chair” and shut in their individual room, which became their lived experience in care.
In a special report 3 on COVID (Pagone & Briggs, 2020), the Australian Royal Commission into Aged Care Quality and Safety tells the story of a daughter (who they name UY) and her father, who was a nursing home resident with motor neurone disease. He was non-verbal and relied on physical touch to communicate. His facility in New South Wales went into lockdown in March 2020 due to COVID. This meant that UY could no longer hug or touch her father or hold his hand while going for a walk in the gardens of the facility. UY told the commission that her father could not understand why he could no longer touch and hug his family: His health deteriorated rapidly, and he died. UY believed her dad had an innate need for connection and that he deteriorated because it was denied to him. As UY stated,A nursing home can never be what a family is to someone. It will never fill the gap, but it is a tool to help families with their loved ones. It will never replace the love and connection a family can give to loved ones, and it should not assume that it has the right and authority to do that. ( Pagone & Briggs, 2020, p. 7) The impact of COVID-19 on aged care policies ruptured the abling assemblages that formerly allowed residents to move and severed their vital connections with the outside world.
Restraint on resident movement and restriction of visits from family or friends has had tragic, irreparable, and lasting ill effects. So why did nursing homes enforce restraint and restriction, even in the absence of confirmed COVID-19 cases? Known critical shortages of care staff and personal protective equipment (PPE) in residential facilities during the pandemic might offer a clue in an answer to this question. We contend that it was not the risk of infection but the lack of human and material resources that underpinned the draconian policy of restraint and restriction that was enforced in aged care homes in 2020 to 2021.
Implementing infection control in addition to daily care delivery requires higher staffing levels. To allow movements of residents and visitors within facilities in ways that are COVID-safe, a set of transmission-based precautions need to be integrated into daily care routines. Having care workers dressed in appropriate PPE, such as a mask, apron, and gloves, can reduce the risk of close-contact transmission while assisting residents in physical proximity. Allocating sufficient staff for regular surface cleaning of the tables, chairs, and handrails could mitigate the risk of transmission while still allowing access to these areas. In addition to measures of density management and symptom screening, the implementation of infection control and the provision of daily care may achieve a balance to create COVID-safe assemblages that enable residents to move more freely despite the restrictions on movement. In short, additional staff, skills, and resources are required to provide care safely during an outbreak of COVID.
However, the 2022 Senate Select Committee on COVID-19 inquiry into how the government responded to the outbreak found that residential aged care providers were forced to operate with inadequate resources. Already in crisis in the lead up to the pandemic, the aged care system depends on an increasingly casualized workforce. Women and those from minority ethnic and migrant backgrounds (Department of Health, 2020) are overrepresented in an understaffed, de-professionalized, and demoralized workforce (Hodgkin et al., 2017). Since COVID, the Australian Government announced the allocation of A$500 million to the sector, yet this assistance has not materialized into more hands delivering care. A national survey conducted by the Australian Nursing and Midwifery Federation found that $80\%$ of survey participants reported no increase in care staff to prepare for a potential COVID-19 outbreak (Alderslade, 2020a). Without sufficient government support, the impact of inadequate staffing, poor infection-control measures, and PPE shortages combined to produce an extreme vulnerability to COVID-19 within the aged care sector (Buhler et al., 2021).
Aged care workers were required to carry on with “business as usual” despite insufficient supplies of PPE to protect themselves or the residents (Pagone & Briggs, 2020). On one independent aged care website, a staff member reported, “I understand there are limited supplies of PPE, but I still believe that we aren’t being given a fair opportunity to protect ourselves and our other residents that we have to care for” (Alderslade, 2020a). The secretary of the Victorian Health Workers Union expressed their view to the commission that union members “right now feel like they’re [down] at the bottom of the Titanic” (Pagone & Briggs, 2020, p. 25). In another blunder, aged care residents and staff were not prioritized in Australia’s vaccination effort; limited supplies exacerbated delays in the vaccine rollout (The Senate Select Committee on COVID-19, 2022, p. 62) and made aged care residents and those who care for them more vulnerable to the virus and to increased restrictions to movement.
Two years into the pandemic, the ensuing Omicron wave has further deepened the crisis in Australia’s aged care sector, with staffing fallen to an all-time low. Care workers are pushed to their limit to make sure that even “basic care” gets done (Tooby, 2022). “ Basic” refers to daily activities under the “personal care” funding category, including eating, drinking, toileting, and washing. These practices were deemed fundamental to the maintenance of life, but activities that fell under other funding categories, such as staff-assisted walking (under mobility care) or social lifestyle activities (e.g., coffee and chat), were classed as “non-essential” and “high risk” and curtailed. Thus, fundamental elements of care were scrapped, reducing the potential for residents to reconfigure their declining bodies and capacities through sensorial extensions and amalgamations. In the words of an assistant nurse working in an aged care facility in New South Wales, staff cannot sometimes even provide “the basics” to its residents, such as a shower (Davey, 2022). Good care requires “tinkering” (Mol et al., 2010) with a multifaceted assemblage of people and things. It requires resources, especially in the form of human assistance. And it takes significantly more time and energy for staff to conduct infection-control measures on top of their regular care routines.
Going “back to basics” was a market-driven “care fix” (Dowling, 2022) to aged care staff shortages, increased workloads, and insufficient funding, exacerbated by the impact of the COVID-19 pandemic. Ongoing monitoring of resident and visitor movement, as well as the constant screening, cleaning, and management of bodies, surfaces, and buildings to prevent virus transmission, increased staff workloads significantly. In this context, the prioritization of restrictive measures served to decrease the workloads of already overburdened staff.
## Conclusion
There has always been disproportionate “risk and liability” associated with nursing home residents’ right to move. COVID-19 amplified the risk discourse and exacerbated neoliberal care agendas. Infection-control measures locked residents out of communal areas, instructed them to “stay in their personal rooms,” and restricted visits from families and friends. Care facilities appeared to prioritize residents’ safety but cast them into a “triple jeopardy” (Shakespeare et al., 2021). This increased their risk of poor mental health outcomes, reduced their access to care, and amplified the adverse impacts of social and physical isolation. Meanwhile, undervalued and underpaid aged care workers, required to work at the front lines of the pandemic, experienced increased exhaustion, depression, anxiety, and stress (Adelson et al., 2021). Their precarious position of being unable to afford to work at only one site (as mandated), or stay at home while sick, only rendered them and their residents more vulnerable to contracting coronavirus.
This article reveals how resident mobility is contingent on relational interdependencies between residents and those who care for them and how restricting movement can disable resident mobility and rupture their social worlds. Zhang’s experience of walking and moving with residents, particularly for those with physical and cognitive decline, offers grounds for rethinking the issue of restriction, both in relation to the spread of the virus and to movement in general as it applies to residents and staff in aged care facilities. In this article, we propose the need for alternative interventions that prioritize valuing the sensory and social worlds in which residents experience their lives. This involves a shift from previous policy emphasis on eliminating risks of infectious disease toward a relational approach that emphasizes the embodied interdependencies of residents and staff and addresses the structural inequities of contemporary aged care provision.
To support residents to exercise their right to move, we call for new interventions that enable relational approaches to care (Kontos et al., 2017), especially in times of crisis when access to resources is constrained. Governments and care providers all have a responsibility to ensure that in a pandemic, there are (adequately renumerated) staff available to develop strategies that are ethically attuned with residents’ needs for freedom of movement. Facilities do not need to apply severe restraint if they can offer sufficient individual hands-on care. We also call for policies that address the structural inequalities of the aged care workforce. Aged care work is physically, mentally, and emotionally demanding, but care staff are underpaid and overworked. In times of crisis, aged care facilities require more care, not less. It is only by taking a multifaceted approach with heterogenous elements and practices, with supportive infrastructure and increased numbers of well-trained and paid staff, that good care can be assembled.
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|
---
title: A qualitative study of the sources of chronic obstructive pulmonary disease-related
emotional distress
authors:
- Diana Zanolari
- Daniela Händler-Schuster
- Christian Clarenbach
- Gabriela Schmid-Mohler
journal: Chronic Respiratory Disease
year: 2023
pmcid: PMC10009049
doi: 10.1177/14799731231163873
license: CC BY 4.0
---
# A qualitative study of the sources of chronic obstructive pulmonary disease-related emotional distress
## Abstract
### Objective
The aim of this study is to identify the sources of illness-related emotional distress from the perspective of individuals living with mild to severe chronic obstructive pulmonary disease (COPD).
### Methods
A qualitative study design with purposive sampling was applied at a Swiss University Hospital. Eleven interviews were conducted with individuals who suffered from COPD. To analyze data, framework analysis was used, guided by the recently presented model of illness-related emotional distress.
### Results
Six main sources for COPD-related emotional distress were identified: physical symptoms, treatment, restricted mobility, restricted social participation, unpredictability of disease course and COPD as stigmatizing disease. Additionally, life events, multimorbidity and living situation were found to be sources of non-COPD-related distress. Negative emotions ranged from anger, sadness, and frustration to desperation giving rise to the desire to die. Although most patients experience emotional distress regardless of the severity of COPD, the sources of distress appear to have an individual manifestation.
### Discussion
There is a need for a careful assessment of emotional distress among patients with COPD at all stages of the disease to provide patient-tailored interventions.
## Introduction
Chronic obstructive pulmonary disease (COPD) is an incurable disease, and its illness trajectory is characterized by an inexorable decline of lung function frequently accompanied by acute exacerbations.1 COPD brings with it high emotional distress,2 and increased prevalence of anxiety and depression compared to the general population.3 While patients living with COPD report various sources of emotional distress, high symptom burden due to breathlessness, coughing, fatigue, insomnia or weight is a major cause.4 Other sources of distress are hospitalization or fear of the future.5 Additionally, dependence on family members and role changes may engender guilt and frustration.6 Illness-related emotional distress has been found to be an important and independent factor for insufficient self-management leading to worse outcomes in other chronic conditions: In patients with diabetes illness-related emotional distress is known to negatively affect self-management resulting in higher glycated hemoglobin levels.7 Cancer-related emotional distress appears to be associated with significantly poorer adherence to chemotherapy8 Furthermore, several studies indicate that such illness-related emotional distress might also have a negative impact on self-management, reporting a decreased adherence to treatment for COPD.9,10 Several studies show illness-related emotional distress having a similarly negative impact on patients with COPD. For persons with COPD, illness-related emotional distress can result in poorer health outcomes such as self-reported functional limitations, poorer exercise tolerance, higher frequency of acute exacerbations and an increased length of hospital stay.3,4,6,11 Given the high prevalence of emotional distress in COPD and the negative impact of illness-related emotional distress, a careful assessment of emotional distress is needed in order to improve care of COPD patients.12 However, despite the importance of illness-related emotional distress in chronic conditions and self-management, to date there is no instrument to assess illness-related emotional distress in COPD nor is there any systematic knowledge of the causes of illness-related emotional distress in COPD.
The aim of this study was to identify the sources of illness-related distress from the perspective of individuals with COPD at all stages of the disease. It provides the basis for the development of a patient-reported experience measurement to assess the level and the sources of COPD-related emotional distress and provide patient-tailored interventions.
## Study design
A qualitative research design using framework analysis was applied, which was developed by social researchers in the UK as an approach to analyzing qualitative data for policy research.13 The rule-based analysis was oriented towards mapping the lifeworld of the participants. This was done by transcribing the interviews and, in addition, by constantly comparing the material. For this purpose, a coding system was developed to make it possible to link the codes to overarching categories. The aim of this process was to achieve a maximum of objectivity in the evaluation.14 Ethics approval was granted by the Ethics Committee of Zurich, Switzerland (reference number: 2018-01455). Informed consent forms were signed by all participants.
As the theoretical framework to guide the data collection and analysis, the recently presented model for illness-related emotional distress for chronic respiratory disease was used, which is based on a systematic literature review and synthesis of five symptom management models.15 The model defines illness-related emotional distress as interaction between bodily symptom distress, treatment distress, distress due to restrictions in daily life roles and distress due to unpredictability. In addition to the level of emotional distress, the model describes individual’s goals and self-efficacy convictions as key drivers for self-management decisions in chronic respiratory diseases.
## Sampling and setting
The study was conducted between September 2018 and May 2019 at the Department of Pulmonology at University Hospital Zurich (USZ). There, individuals suffering from mild to severe COPD are treated both as outpatients and inpatients.
Inclusion criteria were a medical diagnosis of COPD and in- or outpatient treatment at USZ in the prior 12 months. Exclusion criteria were an inability to read or speak German or cognitive impairments.
Purposive sampling was applied to achieve maximum variation. A selection of clinical features that can lead to increased mortality were used as sampling criteria, i.e., global initiative for chronic obstructive lung disease (GOLD) stage (COPD 1–4), sex, age, BMI, use of oxygen at rest and the number of exacerbations in the last 12 months.16,17
## Recruitment and participants
As shown in Table 1, patient data relevant for the purposive sampling were extracted by the first author from USZ`s electronic medical history database in September 2018. Based on experience, a sample size of 10–12 patients was targeted and a decline rate of $50\%$ (half) to $33\%$ (one third) was assumed. Thus, a sample of 18 patients were chosen in which the purposive sampling criteria were equally distributed ($25\%$ for GOLD criteria and $50\%$ for all other criteria). Those patients were informed about the study via a letter sent by post. A stamped, pre-addressed envelope was enclosed to return the signed informed consent form. One week later the first author called the patient to answer open questions. With patients who were willing to provide written informed consent, a date for the interview was fixed. Of the 18 eligible patients, five were unreachable and two patients refused to participate due to time constraints. Table 1.Criteria for purposive sampling. Criteria for purposive samplingAge<65≥65SexFemaleMaleBMI<18.5≥18.5FEV1 in stable phase (GOLD stage)≤3031–$50\%$51–$80\%$≥$81\%$Use of oxygen at rest0 –1.75 L/min>1.75 L/minExacerbations in the last year0 severeAND/OR≤1 moderatea≥1severeAND/OR>1 moderateasevere: lead to hospitalization, moderate: not lead to hospitalization.
## Data collection
Semi-structured narrative in-depth interviews were conducted by the first author. The interview guide is shown in Table 2. To describe the participants in detail, demographic data, the COPD Assessment Tool (CAT) as well as the Modified Medical Research Council (mMRC) were used at the end of each interview. Table 2.Interview guide. Questions1. What does it mean for you to live with COPD?2. Can you tell me what unpleasant emotions you experience sometimes because of COPD in everyday life?3. What do you mean: why are these emotions triggered?4. You mentioned strains like XY. What feelings does this trigger in you?5. What other aspects of COPD cause emotional distress?6. Can you describe a situation in which you felt particularly distressed?
All interviews were audiotaped and transcribed verbatim. All interviews were conducted and transcribed by the first author who was working as a nurse at USZ. With the exception of one patient who knew the first author from prior hospitalizations, none of the participants was previously known to the first author. After 10 interviews, no substantial information was added, so data collection was stopped after the eleventh interview.
## Data analysis
The iterative data analysis followed the five stages of framework analysis according to Ritchie and Lewis.13,14 A mixed inductive and deductive approach was used to analyze the data. To manage and analyze the data, NVivo 10® software was used. The five stages are described in Table 3.Table 3.Stages of the analysis. Stage 1 Familiarization. To get familiar with the respective data sets, the transcripts were read multiple times by the first author and, when necessary, the audio tapes were listened to againStage 2 Constructing an initial thematic framework. Inductive category development. First, data from all interviews was analyzed inductively by line-to-line coding by the first author. Codes were summarized inductively into subcategoriesDeductive category development. Based on the model of emotional distress in respiratory disease,15 the four sources of emotional distress were used as an initial framework for the main categories. Namely: ‘bodily symptoms’, ‘treatment’, ‘predictability’ and ‘restriction in daily life roles’Integration of deductive and inductive category development. The final theoretical framework was developed by constantly comparing and contrasting data with the deductively derived main categories and the inductively evolved subcategories. This was done by the first author, in regular discussions and consensus with the second and last author. As a result, ‘restriction in daily life roles’ was subdivided into two main categories: ‘restricted physical mobility’ and ‘restricted social participation’. COPD as stigmatizing disease was added as an additional main category because it evolved inductively from the data. This resulted in the final thematic framework as depicted in Table 5Stage 3 Indexing and sorting. The data was labelled according to the thematic framework by the first author and also partly by the last authorStage 4 Data summary and display. The framework matrix was built with the categories (main and subcategories) in columns and with the individuals in rows. Data was summarized per cell, accompanied by defining quotes from participants by the first authorStage 5 Abstraction and interpretation. Data was compared and contrasted using purposive sampling criteria, e.g. comparing data between patients with different GOLD stages by the first author and in regular discussions with the last author
## Trustworthiness
To ensure confirmability and dependability, members of the research team met at least monthly to discuss the quality of the interviews as well as temporary results. Additionally, the first and the fourth authors coded two transcripts independently. With the exception of one code, all codes matched. The different coding was discussed and a consensus found.
## Sample characteristics
Eleven patients participated (7 females, aged 51–81 years, FEV1 ($17\%$–$96\%$) (Table 4). Of the interviews, seven took place at patients’ homes, three at a hospital and one in a rehabilitation clinic. The interviews lasted between 30 and 78 min. Table 4.Participant characteristics. Participant characteristicsN = 11Age in years (median, range)67 (51–81)Gender female/male (number)$\frac{7}{4}$BMI kg/m2 (median, range)21.5 (15.8–42)FEV1 (median, range)50 (17–96)Use of oxygen (number) None6 Up to 1.75 L/min2 More than 1.75 L/min3Stage of COPD (number) COPD 1 A2 COPD 1 B1 COPD 2 A1 COPD 2 D1 COPD 3 D3 COPD 4 B1 COPD 4 D2MMRC score (median, range)18 (2–27)CAT score (median, range)2 (0–4)Exacerbations in the last year (median, range)1 (0–6)*Marital status* (number) Married5 Divorced3 Widowed3Living situation (number) Living alone5 Living with spouse5 Living with a child1Nationality (number) Swiss9 Other2Highest level of education (number) Compulsory education3 Apprenticeship4 Higher apprenticeship/college4Working situation (number) Retired6 Unemployed1 Employed full-time1 Employed part-time1 Invalidity insurance pension2
## Qualitative results
Patients reported six main sources of COPD-related emotional distress, namely, physical symptoms, treatment, restricted physical mobility, restricted social participation, unpredictability of disease course and stigma. In addition, three influencing factors emerged: life events, living situation and multi-morbidity. The main and subcategories are displayed in Table 5.Table 5.Categories. Main categorySubcategoryPhysical symptomsBreathlessnessCough/mucusFatiguePainErectile dysfunctionIncontinenceTreatmentOxygen therapyRecommendation for smoking cessationRelationship with the treatment teamLack of benefit from medicationsRestricted physical mobilityEnormous physical and mental effortUncontrollable circumstances outside the homeRevealing of illnessRestricted social participationLoss of daily contactsDifficulties participating in social activitiesDifficulties participating in conversationsUnpredictability of disease courseInability to plan the future lifeUncertainty in relationshipsCOPD as stigmatizing diseaseFailure in the pastLabel of “Brought about their own illness”Non-COPD-related distressMultimorbidityLive eventsLiving situation Emotional distress arose due to an interaction of all of these sources. The basis of emotional distress is formed by physical symptoms and treatment. The two in combination, especially breathlessness and oxygen therapy, lead directly to restricted physical mobility and are contributing factors for restricted social participation. Distress due to the unpredictability of disease course and COPD as a stigmatizing disease result from the overall experience of COPD (Figure 1).Figure 1.Sources of emotional distress and their interaction.
## Physical symptoms
Physical symptoms led to emotional distress when they are uncontrollable and not able to be explained. Breathlessness was the leading symptom, followed by coughing and mucus, fatigue, pain, erectile dysfunction and incontinence.
For the majority, breathlessness was a permanent and very stressful physical symptom that dominates everyday life and thoughts. As a consequence, participants experienced despair, powerlessness and unwillingness to live.“(…) The first thought is about breathing and the last thought is about breathing (…) that's already really stressful (…) I have to tell you honestly, sometimes I think: "I’ve had enough". No, no, I can’t go on anymore (...) It's too much for me (…) Sometimes it makes me feel desperate. To the extent that I don't want to live anymore (…)”Emma, COPD GOLD 2 D Coughing was experienced as disruptive for all participants. Patients with COPD three and four experienced coughing combined with feelings of despair and helplessness, because episodes can last for hours. For patients with COPD GOLD Stage 1 and 2, coughing may have been the only sign of COPD. Coughing was described as distressing because it interfered with everyday activities such as having a conversation or sleeping.
Breathlessness and coughing caused fatigue. Furthermore, fatigue was reported as a source of distress by one patient with COPD 2 because the source of the symptom was not clear to her: It was present in the morning despite of having had enough sleep.
Pain was mentioned by nine participants, but only three participants spoke about pain related to COPD. For one patient with mild COPD, who underwent an operation due to pneumothorax, the pain was not so distressing. She was told that it might last for 6 months which helped her cope with it. In contrast, another patient who always felt chest pain when he experienced breathlessness described the pain as more distressing than the breathlessness itself.
One patient with COPD GOLD 3 experienced erectile dysfunction as burdensome because he assumed that the reason lies in his masculinity. He experienced relief after his family doctor had explained the connection with COPD and suggested the treatment with medication.
Three patients, one of them male, spoke of incontinence due to COPD and how highly distressing it was because of its unpredictability and negative impact on daily activities. They either did not reach the toilet in time or they lost urine during coughing. These situations were accompanied by feelings of shame and anger.
## Treatment
Each of the participants described at least one therapeutic element as a source of emotional distress. Distressing elements were oxygen therapy (CIPAP, BIPAP included), the recommendation for smoking cessation, relationship with the treatment team and lack of benefit from medications. Participants with COPD 1 and 2 described increased emotional distress in relation to smoking cessation and treatment teams. Participants with COPD 3 and 4 named oxygen therapy as the strongest trigger for various negative emotions.
Two male patients in need of oxygen therapy in the form of BIPAP or CIPAP emphasized that despite having to adjust to the noise and uncomfortable mask initially, they did not feel emotionally distressed because it had no impact on their daily activities. This contrasts strongly with patients who needed continuous oxygen therapy COT via nasal cannula. They felt restricted in mobility due to the weight of the device, the shortness of the tube and the device’s lack of practicability.
For all patients who had not yet quit smoking, the recommendation for smoking cessation triggered feelings of anger. On the one hand, they had not stopped yet because they did not see the point in it, had no motivation or had already made several frustrating attempts with distressing adverse effects such as stomach pain. On the other hand, smoking cessation was experienced as a continually recurring topic mentioned by doctors.
The following were mentioned as a source of distress impeding the relationship with the treatment team and leading to considering a change of physician: a lack of time invested by the team and a lack of continuity, insufficient information about treatment options and COPD in general and not being taken seriously, particularly by pneumologists and general practitioners.
Two of the eleven participants, both with moderate COPD, did not have to take medication for COPD. Patients with severe and very severe COPD who had to take pills and inhale regularly all described this as part of their daily routine and not as distressing. Medication triggered negative feelings only when it did not bring relief from symptoms.
## Restricted physical mobility
Participants with COPD 3, 4 and one patient with COPD 2 emphasized that their radius of movement got smaller as their disease progressed. For patients with a high symptom burden and oxygen therapy, there were three hindrances contributing to restricted physical mobility: enormous physical and mental effort, uncontrollable circumstances outside the home and looking obviously ill in front of others. All three barriers again led to negative emotions as they contributed to the fact that patients eventually became housebound. In contrast, participants with COPD 1 and 2 who did not suffer from any symptoms and did not need oxygen therapy pointed out that COPD did not cause them distress because they experienced no limitations in their daily activities.
Patients with COPD 3 and 4 stated that COPD affected all areas of life because everything entailed an enormous physical and mental effort. Even normal things such as taking a shower or going downstairs to the cellar took physical effort that they are sometimes incapable of making. Due to breathlessness and dependence on oxygen therapy in particular, the mobility of patients with severe and very severe COPD (and for one patient with moderate COPD), was restricted. As soon as the patients moved they were confronted with their physical limitations. This resulted in reduced scope of movement which eventually led to an inability to take care of themselves or to carry out daily activities like vacuum cleaning, lifting things, cleaning the house and carrying out daily morning routines like personal hygiene. These physical restrictions triggered intense negative emotions such as anger, sadness and frustration. This enormous physical effort also entailed a mental effort as patients were obliged to constantly assess whether the (physical) effort was in fact worth it. This continuously distressing evaluation process intensified as the disease progresses. The incessant uncertainty of what their health condition will be like from one moment to the next and the unpredictability of symptom severity may lead to continual anxiety during daily activities or when patients leave the house.
In addition, uncontrollable circumstances when outside the home, including strong smells, crowded places, or narrow spaces are described as very distressing because they can cause breathlessness and general discomfort. Several patients stated that they never knew whether they might unexpectedly have to return back home because of sudden breathlessness. This anxiety persists in every kind of daily activity whether it happens in public or at home. One woman points out:“(...) somehow everything scares you a little. Sometimes, if you`re not feeling well enough to go outside and you still leave your apartment (...) you think: (…) Will I be able to return back home on my own?’”Maria, COPD GOLD 3 D When being physically active, patients had to stand still, use an oxygen device/nasal cannula, or to breathe heavily and visibly. Physical activity revealed the illness in front of others, which made it into another source of distress, triggering feelings of shame and anger. Participants explained that they did not want others to see them being ill or suffering.
## Restricted social participation
As a result, their participation in social life with activities such as pursuing a hobby, meeting other people or going to work grew increasingly more difficult. At this point in the interview, some participants started to cry and expressed strong negative emotions ranging from anger, disappointment and frustration to depression. Eventually, the lives of some participants, especially the older ones, were completely restricted to their own homes. Three sources of emotional distress were found regarding restricted social participation: losing daily contacts, difficulty participating in social activities and difficulty participating in conversations.
Due to the increasing inability to leave their apartment, it grew more difficult to maintain social contacts on a day-to-day basis, leading to feelings of being home bound and being forgotten by the outside world.
Not being able to participate in social activities such as hiking, cycling, skiing, dancing, attending a play or concert or going to a restaurant with others triggered feelings of loneliness, worthlessness and frustration.“(…) when you can’t go out and do anything, you get depressed. If they call and ask you to come to the cinema or the theatre and you have to say: I can’t, you’re bound to get depressed (…) and no one visits you. ”Ruth, COPD GOLD 4 D Some patients realized at a certain point that they were no longer able to keep up with others and that meeting people spontaneously or keeping long-term appointments was challenging. Consequently, they only left the house with friends and family members who supported them and were willing to adapt activities to their state of health. Being dependent on other people’s consideration resulted in feelings of being a burden or of experiencing themselves as “a disabled person” when, for example, they had to ask others to reduce their pace. Patients in a relationship pointed out that their partners were also subject to all of these restrictions. They emphasized that they did not want to be a burden and therefore let their partners pursue their hobbies on their own, even if they feared drifting apart.
Another source of distress was the declining ability to have longer conversations, be it on the telephone or face-to-face. For some patients, as the disease progressed it became more and more difficult to speak while walking or having a meal with friends due to breathlessness.
## Unpredictability of disease course
Unpredictability of disease course was for most patients a source of strong emotional distress, because knowing that the disease and the limitations will get worse made it difficult to plan the future. This triggered feelings of anxiety, sadness and uncertainty:“It turns your existence upside down (...) You had a plan for the time after your retirement. And that plan won’t work anymore. You have to turn everything around and you have to change it (the life plan) completely (...).”Kevin, COPD GOLD 4 B Uncertainty over the course of the disease also entailed uncertainty regarding how existing partnerships would develop in future or whether life made any sense at all. Four female participants talked about how they sometimes thought they would be better off dead because they no longer wanted to live like this or endure the disease progression. Two participants revealed that they were planning to rely on euthanasia.
## COPD as a stigmatizing disease
All of the patients who had previously been smokers reported the failure of not having quit earlier, connected with feelings of guilt and shame. Moreover, some participants, regardless of severity of COPD, reported feeling that they have been labelled as having “brought about their own illness”, namely by health professionals, relatives, friends, but also by society in general. Most participants acknowledged smoking as a major cause of COPD. At the same time, they all asserted that reasons other than smoking have also contributed to their acquiring COPD, for example, exposure at workplaces and air pollution, but that nobody had taken that seriously. This was a potential source of anger. Both patients suffering from antitrypsine deficiency strongly emphasized that they had never smoked. In addition, they stressed that they did not want to “give a false impression” or of “having screwed up by themselves”
## Non-COPD-related distress
Participants reported some sources of non-COPD-related distress that can interact negatively with COPD-related distress. Namely, multimorbidity, life events and living situation.
For older patients with COPD 3 and 4 who suffered from multimorbidity such as heart failure or osteoporosis, COPD was accompanied by the most limiting symptoms. However, they related that the situation, which involved other health problems, was also linked to emotional distress. In contrast, two patients with COPD 1 lung cancer who had had myocardial infarctions regarded COPD as a “side issue” and therefore less of a burden than other health problems.
Stressful life situations such as disputes at work, in the neighbourhood, within the family or even the death of a relative were described as challenging. Such events intensified breathlessness. Patients reported that, due to COPD, they were no longer as resilient as before when confronted with critical life events.
The living situation and the general environmental situation of patients could be distressing. Living alone, on an upper floor without a lift, or problems accessing shopping facilities could also reinforce the COPD - related distress.
## Discussion
This study aimed to identify sources of emotional distress arising from living with COPD from patients’ personal perspectives. To our knowledge, this is the first study with the aim of finding sources of distress. Physical symptoms, treatment, restricted physical mobility, restricted social participation, COPD as a stigmatizing disease and unpredictability of the disease course were identified as major sources of COPD-related emotional distress. Additionally, three significant triggers of non-COPD-related emotional distress, namely multimorbidity, life events and living situation were found to have the potential to interact negatively with COPD-related emotional distress.
In line with the existing body of research,5 shortness of breath, cough and phlegm, as well as pain were identified as common and distressing symptoms in patients with COPD. Although several quantitative studies indicate that sexual dysfunction and urinary incontinence are very common in both women and men with COPD,18,19 our study underlines their distressing aspect, particularly in younger patients. It highlights the need to systematically screen and discuss these intimate symptoms with patients.
A restricted social life has been identified as distressing in other studies,20 with the reaction of our participants indicating that it may be the strongest contributing source of COPD-related emotional distress This highlights the need for strategies to be developed regarding COPD patients that might increase their feelings of security when leaving the house. This can include calling in a companion service, calling a taxi, creating an action plan and involving relatives.21 In addition, peer support and interaction with nurses was perceived as helpful to diminish the feeling of isolation.22 Patients who smoked reported experiencing stigmatization by health professionals, friends and family, as well as society in general. This is in line with other findings that report stigma as being an additional contributor to social isolation, feelings of loneliness and guilt and an influencing factor on medication-adherence and help-seeking.23,24 Notably, in the present study the relationship to health professionals was described as an important source of distress. Unmet needs and a lack of trust in the treatment team have been reported as significantly impeding disease management in patients with COPD.25 Our study highlights the necessity of a coordinated care model for patients with COPD, with a focus on continuity in care as a perquisite to building a trustful relationship, which in turn has the potential to impact outcomes.22,26 This would facilitate the approach when addressing sensitive topics such as smoking cessation, urinary incontinence, sexual dysfunction, stigmatization, and social isolation as well as the consideration of euthanasia.
By contrasting our study to the conceptual model of emotional distress,15 our study confirmed the theoretical framework that COPD-related emotional distress arose from an interaction of all six sources. New was the identification of stigma as an additional source, which in turn influences social interaction and self-image. Interestingly, this is the main difference to the model, where stigmatizing disease was not identified as a source of distress. One explanation may be that the model development also included studies with Cystic Fibrosis (CF). As CF is an inherited disease, the aspect of a “self-inflicted disease” might be omitted and thus perceived stigmatization might not be an important and distressing issue.
This study highlights the need for the current practice to be supplemented by the assessment and treatment of COPD-related emotional distress. Furthermore, all patients currently experiencing emotional distress as well as those without any signs should be regularly assessed to recognize the presence of sources of distress at an early stage of the disease. An assessment should be developed to assess sources of distress in these patients. The present study serves as the basis for the development of such a questionnaire and the results should be evaluated and confirmed by a quantitative study. In addition, the interaction of the different sources of COPD-related emotional distress and their impact on adherence and decision-making regarding self-management needs further investigation.
This study was conducted pre-COVID, prompting the question of whether the pandemic would have influenced the results. COPD patients were more vulnerable to COVID-19 and at risk for complications and poorer outcomes.27 Patients who were aware of being at high-risk, redrew socially in order to avoid infection, especially in the early stages of the pandemic. This led to increased levels of anxiety and feelings of loneliness.28 This indicates that patients with COPD may have experienced higher levels of illness-related emotional distress during the pandemic, but that the sources were the same as found in this research, namely unpredictability and restricted social participation.
This study has some strengths and limitations: Comparing and contrasting the patients’ stories using Framework Analysis allowed the depiction of a common story. The purposive sampling resulted in a heterogeneous group that allowed a broad description of patients’ experiences. However, there are also limitations that must be mentioned. First, participants were recruited from a single hospital and included only two patients with a migration background. Possibly patients from other geographical and cultural backgrounds may have described other sources of distress.
No additional sources of distress were detected after the tenth interview. In addition to redundancy in the reported sources of distress, a conceptual depth of how the sources were reported by patients in different stages of the disease was observed. This is at a rather early stage, but not surprising because the research question had a clear focus, was discovery-oriented, and the research was embedded in a pre-existing theoretical framework.29
## Conclusion
Living with COPD is associated with emotional distress and the sources are manifold, ranging from burdensome symptoms or treatment to restrictions in daily life and social participation, as well as stigma and unpredictability of the disease. Further, emotional distress seems to affect self-management in COPD, highlighting the importance of the concept of illness-related emotional distress in COPD. The next steps would be the development of a patient-reported experience measure for assessing illness-related emotional distress to clarify its role in self-management and patient outcomes.
## ORCID iDs
Christian Clarenbach https://orcid.org/0000-0003-2158-2321 Gabriela Schmid-Mohler https://orcid.org/0000-0002-2610-636X
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|
---
title: TRIM37 interacts with PTEN to promote the growth of human T-cell acute lymphocytic
leukemia cells through regulating PI3K/AKT pathway
authors:
- Honglan Qu
- HASEN Gao-wa
- Yanyan Hou
- Mengwei Ren
- Jun Li
- Baoshong Jing
- YanDan Du
journal: Frontiers in Oncology
year: 2023
pmcid: PMC10009101
doi: 10.3389/fonc.2022.1016725
license: CC BY 4.0
---
# TRIM37 interacts with PTEN to promote the growth of human T-cell acute lymphocytic leukemia cells through regulating PI3K/AKT pathway
## Abstract
### Background
TRIM37 has been reported to be associated with the tumorigenesis of cancers. However, the role of TRIM37 in T-cell acute lymphoblastic leukemia (T-ALL) remains unclear. This study aimed to characterize the effect of TRIM37 on T-ALL.
### Methods
TRIM37 expression in T-ALL patients and T-ALL cell lines was determined by qRT-PCR and Western blot. Knockdown or overexpression of TRIM37 was conducted by transferring small-interfering TRIM37 or lentivirus-mediated transducing into T-ALL cells. CCK-8 assay and flow cytometry assay were conducted to analyze the proliferation and apoptosis of T-ALL cells. Co-immunoprecipitation experiments were conducted to investigate the relationship between TRIM37 and PTEN and the ubiquitination of PTEN.
### Results
Our results suggested that TRIM37 expression was upregulated in the blood of T-ALL patients and T-ALL cell lines. Knockdown of TRIM37 noticeably inhibited the proliferation and promoted apoptosis of T-ALL cells. Ectopic expression of TRIM37 promoted the proliferation and suppressed the apoptosis rate of MOLT-4 cells and enhanced the phosphorylation of AKT. Moreover, TRIM37 interacted with PTEN and accelerated the degradation of PTEN via TRIM37-mediated ubiquitination in T-ALL cells. Moreover, TRIM37 reduced the sensitivity of T-ALL cells to bortezomib treatment. Additionally, PI3K/AKT signaling pathway was involved in the function of TRIM37 in T-ALL. TRIM37 contributed to the proliferation of T-ALL cells and reduced the susceptibility of T-ALL cells to bortezomib treatment through ubiquitination of PTEN and activating PI3K/AKT signaling pathway.
### Conclusions
Our study suggested that TRIM37 could be considered as a therapeutic target for T-ALL.
## Introduction
T-cell acute lymphoblastic leukemia (T-ALL) was derived from the malignant transformation of progenitor T cell infiltrating the bone marrow and peripheral blood. Clinically, T-ALL patients have exhibited typical symptoms including increased white blood cells count, hematopoietic failure, neutropenia, anemia, and thrombocytopenia [1]. The occurrence of T-ALL is a multi-step process, resulting from abnormal genes that regulate cell growth, proliferation, survival, and differentiation. Among children and adult patients with ALL, T-ALL accounts for $15\%$ and $25\%$ of the cases, respectively [2, 3]. Although intensive chemotherapy had improved the prognosis of T-ALL, the therapy strategy could not completely relieve the hematological tumor. Drug resistance and recurrence are the main factors affecting survival rates of T-ALL patients, but effective treatment for patients with relapse and poor prognosis still remains deficient [4, 5]. Therefore, research efforts focusing on exploring the molecular mechanism of the development of T-ALL are urgently needed to search therapeutic targets and develop efficient and anti-leukemic drug with low toxic.
Tripartite motif (TRIM) protein family comprises more than 80 members in human and is found in all multicellular eukaryotes. Numerous evidences supported that TRIM family proteins participate in the occurrence and development of various diseases, namely, cancer, inflammation, pathogen infection, neuropsychiatric disorders, chromosomal abnormalities, and developmental diseases (1–3). Like the typical form of TRIM protein family members, TRIM37 consists of a RING finger domain at N-terminal, B-box domains, and a C-terminal coiled-coil (CO) domain [6, 7]. The E3 ubiquitin ligase activity of RING domain can mediate the ubiquitination process of the target proteins by cooperating with the E2 ubiquitin binding enzyme [4]. TRIM37 has been reported to be associated with the tumorigenesis and poor prognosis in multiple types of solid tumors. TRIM37 promotes transformation in breast cancer through acting as an H2A ubiquitin ligase by binding with PRC1 and PRC2 [8]. High expressional level of TRIM37 resulted in enhanced proliferation, migration, and invasion of HCC cells [9], gastric cancer cells [10], and colorectal cancer (CRC) cells by epithelial-mesenchymal transition (EMT) stimulated by Wnt-catenin [11]. TRIM37 contributes to the aggressiveness by activating NF-κB signaling pathway in non–small-cell lung cancer cells [12]. Some TRIM family members such as TRIM31 [5], TRIM14 [13], TRIM65 [14], and TRIM22 [15] have been reported to participate in the progression of acute or chronic myeloid leukemia. We hypothesized that TRIM37 may play a role in the blood disease. After searching online, we found few studies about the TRIM37 in the blood disease. Therefore, we focused on the specific role and the regulatory mechanism of TRIM37 in T-ALL.
The phosphatidylinositol-3-kinase (PI3K)/Akt signaling pathway exerts critical role in a variety of steps of tumorigenesis. Activation of PI3K/AKT signaling pathway is commonly accompanied by AKT phosphorylation, which further stimulate AKT downstream target factors [16]. Constitutively active PI3K/Akt signaling pathway is a common reason for abnormal cell proliferation and drug resistance of T-ALL and predicts a poorer prognosis of T-ALL patients [17]. Recent studies demonstrated that TRIM37 markedly promoted metastasis of glioma cells and lung cancer cells through activating PI3K/Akt signaling pathway, implying a positive regulatory network between TRIM37 and PI3K/AKT in tumor [18, 19]. Therefore, the proposed study aimed to investigate the role of TRIM37 in T-ALL and to explore the potential mechanism.
In the present study, we found TRIM37 was upregulated in T-ALL patients and T-ALL cell lines. Knockdown or overexpression of TRIM37 revealed the oncogenic role and bortezomib resistance effect of TRIM37 in T-ALL. Moreover, immunoprecipitation experiments proved that TRIM37 mediated the ubiquitination of PTEN, which led to PI3K/AKT signaling pathway activation in T-ALL cells.
## Clinical samples
Human peripheral blood mononuclear cells were obtained from patients ($$n = 25$$) with T-ALL and normal individuals ($$n = 15$$) from The First Affiliated Hospital of Soochow University. All the participants signed the informed consent forms, and the project was approved by the Ethical and Scientific Committee of the Inner Mongolia Forestry General Hospital [20220816] and written informed consent was acquired from each participant.
## Cell culture and transfection
Human T-ALL cell lines, namely, Jurkat, MOLT-4, and RPMI8402 were brought from American Type Culture Collection (ATCC; Manassas, VA, USA) and cultured at 37°C in $5\%$ CO2 in RPMI 1640 medium with $10\%$ fetal bovine serum (FBS; Invitrogen, Carlsbad, CA, USA), 10 mM Hepes, and 100 U of penicillin/streptomycin. Human HEK 293T cells used for lentivirus package were maintained in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with $10\%$ FBS, 100 U of penicillin/streptomycin, 10 mM Hepes, and 2 mM L-glutamine (Invitrogen; Carlsbad, CA, USA) and were grown in humidified atmosphere with $5\%$ CO2 atmosphere at 37°C.
Lipofectamine™ 2000 Transfection Reagent was used for plasmids transfection in HEK293T cells. Delivery of siRNA targeting TRIM37 and the negative control (si-NC) was conducted using Invitrogen™ DMRIE-C Transfection Reagent.
The plasmids of pLVX-TRIM37-Puro (Clontech, Mountain Vie, CA, USA) or empty vector control, together with psPAX2 and pMD2.G (Addgen, Watertown, MA, USA), were delivered into HKE293T cells by Lipofectamine™ 2000 (Invitrogen, Carlsbad, CA, USA) to generate lentivirus. Supernatants containing virus were harvested at 48h post-transfection. MOLT-4 cells were incubated with viral supernatants for 24h and then refreshed the supernatants with fresh medium supplemented with 0.25 μg/ml of puromycin for TRIM37-positive cell selection.
## Quantitative real-time PCR
Total RNA was isolated by TRIzol (Tiangen, Beijing, China), and the first strand of cDNA was synthesized with the PrimeScript RT reagent kit (Dalian, China) in accordance with manufacturer’s instruction. qPCR SYBR green real-time PCR master mix (TaKaRa, Dalian, China) was applied for PCR reactions run on ABI 7900HT sequence detection system. The relative mRNA expression was normalized to inner control GAPDH and was calculated by 2−ΔΔCt method. The sequence of primers used for detecting TRIM37 is synthesized as follows: F: 5’-TGGACTTACTCGCAAATG-3’, R: 5’-ATCTGGTGGTGACAAATC-3’.
## Co-immunoprecipitation assay
MOLT-4 cells were lysed in RIPA buffer containing 50 mM pH 7.5 tris-HCl, 150 mM NaCl, $1\%$ Nonidet P-40, 1 mM EDTA, $0.25\%$ sodium deoxycholate, 1 mM Na3VO4, 1 mM NaF, $2\%$ phenylmethylsulfonyl fluoride (Millipore, MA, USA) and protease inhibitor (Millipore, Bedford, MA, USA). Cell debris was eliminated after centrifuging at 10,000g at 4°C for 10 min. The obtained supernatants was transferred into fresh tubes for immunoprecipitation with 1 μg of indicated antibodies overnight at 4°C, followed by reaction with 12 μl of protein A/G Plus-agarose (Santa Cruz Biotechnology, Dallas, TX, USA) for 1h at 4°C. The immunocomplexes were then collected by centrifugation, washed by ice-cold RIPA buffer for five times and analyzed by Western blot experiments.
## Western blot assay
Proteins from the immunoprecipitate or cell lysates were separated by SDS-PAGE and then transferred onto polyvinylidene difluoride membranes (Millipore, Bedford, MA, USA) to incubate with primary antibodies incubation overnight at 4°C. After washing by 1× TBST buffer, the membrane was reacted with horseradish peroxidase (HRP) labeled-secondary antibodies for 1h at room temperature and protein bands were visually recorded.
## Cell proliferation assay
CCK-8 reagent (Beyotime, Shanghai, China) was adopted for detection following user’s manual. CCK-8 solution of 10 μl was added into cells seeded in 96-well plate at density of 6 × 103 cells/well. Cells were harvested at indicated time points, and the cell proliferation was quantified after 2h incubation and was determined by the reading of OD450 on a microplate reader (Bio-Rad, Hercules, VT, USA).
## Cell apoptosis assay
Apoptosis of human T-ALL cell lines was determined using Annexin V-FITC/propidium iodide (AV/PI) Apoptosis Detection Kit (YEASEN, Shanghai, China). The experimental Jurkat and RPMI8402 cells were incubated with medium containing Annexin V-FITC and PI staining solution for 10 min without light. After adding 500 μl of 1× binding buffer, all the samples were gently mixed and placed on ice until flow cytometry detection performed by a FACS Calibur (BD Biosciences, San Jose, NJ, USA).
## Statistical analysis
All the experiments were performed at least three times. Data in the current study were exhibited as mean ± standard deviation (SD), represented three independent experiments with similar results. One-way ANOVA or a two-tailed Student’s t-test was used to calculate significant differences. The gray value of protein was determined by ImageJ software (National Institutes of Health, MD, USA). P value less than 0.05 indicated statistical difference.
## TRIM37 was upregulated in patients with T-ALL and T-ALL cells
To investigate the function of TRIM37 in T-ALL, we firstly quantified the mRNA expression of TRIM37 in T-ALL patients and healthy individuals. Peripheral blood mononuclear cells from the patients with T-ALL and healthy donors were isolated, and qRT-PCR was conducted to analyze the mRNA level of TRIM37. Compared with control group, the expression of TRIM37 was significantly higher in patients with T-ALL (***$P \leq 0.001$, Figure 1A). Furthermore, the mRNA and protein expression levels of TRIM37 were observed in human T-ALL cells, namely, Jurkat, MOLT-4, MOLT13, and RPMI8402. Both the mRNA level and protein amount of TRIM37 were greatly upregulated in T-ALL cell lines compared with normal cells (***$P \leq 0.001$, Figures 1B, C). Based on the expression level of TRIM37 in different T-ALL cell lines, Jurkat and RPMI8402 cell lines were selected for knockdown studies and MOLT-4 cell line was selected for overexpression studies.
**Figure 1:** *TRIM37 expression was elevated in patients with T-ALL and T-ALL cells. (A) The mRNA abundance of TRIM37 in T-ALL patients (n = 25) and normal individuals (n = 15) was examined by qRT-PCR. ***p < 0.001, compared with normal group. (B, C) The levels of TRIM37 in Jurkat, MOLT-4, MOLT13, RPMI8402, and two normal cell lines (N1 and N2) were quantified by qRT-PCR and Western blot assays. ***p < 0.001, compared with N1 group.*
## Deficient expression of TRIM37 suppressed the proliferation of human T-ALL cells
To deeply explore the effect of TRIM37 on the progression of T-ALL, three siRNAs targeting TRIM37 were chemically synthesized and delivered into Jurkat and RPMI8402 cells. The knockdown efficiency was evaluated by qRT-PCR and Western blot experiments. As presented in Figures 2A, B, the three siRNAs targeting TRIM37 effectively suppressed the mRNA and protein levels of TRIM37 (***$P \leq 0.001$). Cell proliferation assays revealed that knockdown of TRIM37 markedly repressed cell growth and promoted the apoptosis of Jurkat and RPMI8402 cells (*$P \leq 0.05$, Figures 2C–E). In addition, Western blot analysis showed that knockdown of TRIM37 improved the expression of apoptotic inducer Bax and apoptotic marker cleaved caspase 3 and decreased the abundance of anti-apoptotic molecule Bcl2 (Figure 2F). Interestingly, knockdown of TRIM37 had no effect on protein level of AKT, while obviously reduced the phosphorylation of AKT (Figures 2F, G).
**Figure 2:** *TRIM37 silencing suppressed the growth of human T-ALL cells. (A, B) The knockdown efficiency of siRNAs targeting TRIM37 was quantified by qRT-PCR and Western blotting. ***p < 0.001, compared with siNC group. (C, D) The CCK-8 assays for cell growth detection of Jurkat and RPMI8402 cells. *p < 0.05 compared with siNC group, **p < 0.01 compared with siNC group, ***p < 0.001 compared with siNC group. (E) Flow cytometry assays were performed to evaluate the apoptosis of Jurkat and RPMI8402 cells. ***p < 0.001, compared with siNC. (F, G) Western blotting was employed to examine the protein contents of TRIM37, Bax, Bcl2, cleaved caspase 3, p-AKT, and AKT in Jurkat and RPMI8402 cells transfected with/or without siTRIM37-1 or siTRIM37-2. ***p < 0.001, compared with siNC.*
## PI3K/AKT inhibitor LY294002 abolished the function of TRIM37 on cell proliferation in human MOLT-4 cells
To further study whether TRIM37 regulate the proliferation of human T-ALL cells through AKT signaling pathway, MOLT-4 cell line stably expressing TRIM37 was established by lentivirus transfection. As shown in Figures 3A, B, the mRNA and protein expression of TRIM37 was significantly induced in oeTRIM37 group (***$P \leq 0.001$). CCK-8 assays revealed that overexpression of TRIM37 promoted cell growth of MOLT-4 cells (*$P \leq 0.05$). However, PI3K/AKT inhibitor LY294002 suppressed the cell growth of MOLT-4 cells at 24h post-treatment (Figure 3C). In addition, LY294002 treatment also significantly abolished the effect of TRIM37 on cell growth of MOLT-4 cells. As expected, overexpression of TRIM37 repressed the apoptotic rate of MOLT-4 cells, whereas inhibition of PI3K/AKT signaling pathway greatly promoted apoptosis of MOLT-4 cells transduced with TRIM37 (Figure 3D), which was further reflected by decreased amount of Bax and cleaved caspase 3 and upregulated Bcl2 (Figure 3E). In addition, ectopic expression of TRIM37 also increased the activation of PI3K/AKT signaling pathway, and the inhibitory effect of LY294002 on PI3K/AKT signaling were verified by Western blotting (Figure 3E).
**Figure 3:** *The PI3/AKT inhibitor LY294002 impaired the function of TRIM37 in human MOLT-4 cells. (A, B) The stable expression of TRIM37 in MOLT-4 cells was verified by qRT-PCR and Western blot assays. ***p < 0.001 compared with oeNC group. (C) The cell proliferation of MOLT-4 cells transduced with/or without trim37 was detected by CCK-8 assays. *p < 0.05, compared with oeNC + DMSO; ***p < 0.001 compared with oeNC + DMSO; !!!p < 0.001 compared with oeTRIM37 + DMSO. (D) The apoptosis of MOLT-4 cells transduced with/or without TRIM37 was detected by Flow cytometry. *p < 0.05 compared with oeNC + DMSO; ***p < 0.001 compared with oeNC + DMSO; !!!p < 0.001 compared with oeTRIM37 + DMSO. (E) Western blot experiments were exploited to examine the protein contents of Bax, Bcl2, cleaved caspase-3, p-AKT, and AKT in MOLT-4 cells transduced with/or without trim37. ***p < 0.001 compared with oeNC + DMSO, !!!p < 0.001 compared with oeTRIM37 + DMSO.*
## TRIM37 downregulated PTEN expression through ubiquitination
PTEN is located upstream of the PI3K/AKT signaling pathway and can negatively regulate the PI3K/AKT signaling pathway. In the current study, we found that the expressional level of PTEN was negatively correlated with the abundance of TRIM37 in both T-ALL patients and normal individuals (Supplementary Material Figure S1). Because of the positive correlation between TRIM37 and PI3K/AKT signaling, we speculated that PTEN was involved in the TRIM37-PI3K/AKT regulatory axis. TRIM37 overexpression led to a rapid degradation of PTEN under CHX treatment (Figure 4A). However, addition of MG132, one proteasome inhibitor could prevent the loss of PTEN, implying the inhibition of PTEN depended on proteasome (Figure 4B). Furthermore, the interaction between TRIM37 and PTEN was determined by CO-IP. Endogenous TRIM37 and PTEN were pulled down by anti-PTEN and anti-TRIM37 antibodies, respectively. The interacting complex was detected by Western blotting. As shown in Figure 4C, TRIM37 specifically interacted with PTEN in MOLT-4 cells (IgG was used as negative control). Moreover, we immunoprecipitated the endogenous PTEN protein and conducted immunoblotting with anti-ubiquitin antibody. The results displayed that PTEN was ubiquitinated in the cells transfected with si-NC, whereas silence of TRIM37 significantly decreased the ubiquitination of PTEN, indicating TRIM37 could mediate ubiquitination of PTEN to decrease the expression of PTEN (Figure 4D).
**Figure 4:** *TRIM37 mediated the ubiquitination of PTEN in human MOLT-4 cells. (A) PTEN protein level was measured under 10 μM of CHX. (B) The effect of proteasome on PTEN expression was examined by Western blot assays. (C) Co-immunoprecipitation (Co-IP) experiments to investigate the interaction between TRIM37 and PTEN. Immunoprecipitation (Co-IP) experiments to detect the ubiquitination of PTEN. (D) Ubiquitination assay was used to examine the ubiquitination of PTEN in siNC and siTRIM37 transfecting cells.*
## TRIM37 affected the therapeutic effect of Bortezomib on human T-ALL cells
Given the fact that proteasome inhibitor bortezomib could sustain PTEN expression (Fujita et al., 2006), prompting us to hypothesize that TRIM37 may reduce the sensitivity of cells to bortezomib treatment through inhibiting PTEN. To verify the speculation, we performed FACS assays to investigate the co-effect of bortezomib and TRIM37 on apoptosis of human T-ALL cells. As shown in Figure 5, overexpression of TRIM37 significantly decreased cell apoptosis and impaired the effect of bortezomib on MOLI-4 cells. Inversely, knockdown of TRIM37 noticeably promoted cell apoptosis, and co-treatment of bortezomib greatly enhanced the effect of TRIM37 silence on mediating pro-apoptotic effect of Jurkat cells, which indicated that downregulated TRIM37 could improve the sensitivity of cells to bortezomib therapy.
**Figure 5:** *TRIM37 affected the therapeutic effect of Bortezomib on human T-ALL cells. *p < 0.05 compared with oeNC + DMSO; ***p < 0.001 compared with oeNC + DMSO; !!!p < 0.001 compared with oeTRIM37 + DMSO.*
## Discussion
The alteration of TRIM37 level contributed to the metastasis of several types of solid tumors and indicated the correlation between TRIM37 and poor prognosis of patients [8, 9, 11]. However, the effect of TRIM37 in the progress of T-ALL has not been fully elucidated. In the current study, we firstly found that TRIM37 was increased in T-ALL patients and T-ALL cell lines. TRIM37 could promote the proliferation and repressed the apoptosis of T-ALL cells, indicating the oncogenic effect of TRIM37 on the development of T-ALL, and TRIM37 might be a potential therapeutic target for T-ALL clinical treatment.
Activation of PI3K leads to the production of phosphatidylinositol-3-phosphate (PIP3), which interacts with the PH domain of AKT to induce PDK1-mediated phosphorylation of AKT. The activated AKT further stimulates downstream target molecules to participate in regulation of various physiological processes [16]. Excessive activation of the PI3K/AKT pathway is closely related to malignization of tumor (20–22) and is one of the most common reasons for the malignant development of T-ALL [23]. In the present study, we proved that TRIM37 might positively regulated PI3K/AKT signaling pathway and our findings were consistent with the previous findings [18, 19]. Moreover, aberrant PI3K/AKT signaling pathway counteracted the effect of TRIM37 on proliferation and metastasis of T-ALL cells (Figure 3), implying TRIM37 might promote the development of T-ALL through positively regulating of PI3K/AKT signaling pathway.
PTEN has been identified to negatively regulate the P13K/AKT signaling pathway by phosphorylating PIP3 to PIP2 [24]. As one of the most vital tumor suppressors, the change of PTEN directly affected the occurrence, development, treatment, and prognosis of leukemia [25, 26]. In T-ALL, high frequency of PTEN abnormalities, namely, mutation and inactive form can be commonly detected [27]. Interestingly, Western blot experiments in clinical samples displayed that TRIM37 negatively correlated with the protein level of PTEN, suggesting that TRIM37 was a negative regulator for PTEN in T-ALL (Supplementary Material Figure S1). Post-translational modification is the major manner for modulating the intracellular PTEN [28]. Previous study showed that K13 and K289 lysine sites on PTEN can be ubiquitinated to affect the stability and localization of PTEN [29], which is crucial for PI3K/AKT signaling pathway activation. TRIM37 decreased PTEN expression in a protease-dependent manner. Furthermore, TRIM37 directly interacted with PTEN to facilitate the ubiquitination modification on PTEN, which subsequently restored PI3K/AKT pathway activation. Our findings preliminarily defined a mechanism for PTEN regulation at the post-translational level in T-ALL cells, indicating that protease inhibitor can be considered as potential reagent in the combination therapy for T-ALL treatment. We could not eliminate RING domain-independent manner in the modulation of PTEN expression. The interaction domain between TRIM37 and PTEN remains to be studied to deeply understand the precise regulatory mode between TRIM37 and PTEN.
Ubiquitination plays an important role in cancer development and tumor metastasis through modulating the stability and functionality of oncoprotein and tumor suppressors [30]. Recently, proteasome inhibitors have already been applied in clinical treatment of cancer [31]. Bortezomib has been approved for clinical treatment of myeloma (MM), R/R MM, and mantle cell lymphoma [32] and can be combined with chemotherapy in acute lymphoblastic leukemia and lymphoma [33]. The mechanism for bortezomib is that the boron atom in bortezomib has high affinity to the catalytic site of the 26S proteasome [34], so that bortezomib can possessed anti-tumor function as proteasome inhibitors (PIs) medication. Bortezomib could markedly restore PTEN level and elevate the drug sensitivity in trastuzumab-resistant cells (35–37). Combined with the results that TRIM37 mediated the ubiquitination of PTEN, we examined whether TRIM37 affected the effect of bortezomib on T-ALL cells growth. As expected, the effect of bortezomib on T-ALL cells apoptosis verified that TRIM37 promoted T-ALL cells proliferation depended on ubiquitination. In addition, knockdown of TRIM37 greatly boost the sensitivity of T-ALL cells to bortezomib (Figure 5). These results suggested decreased level of TRIM37 assist in elevating the efficacy of anti-tumor therapy, indicating that TRIM37 was one potential therapeutic target for clinical treatment of T-ALL. However, the insufficiency of the study is that we only conducted experiments in vitro. Both the survival analysis and the in vivo study of TRIM37 were required to further investigate the bio-function of TRIM37 in the malignization of T-ALL and the association between TRIM37 and the clinical prognosis of T-ALL patients.
## Conclusions
The proposed study revealed that TRIM37 was upregulated in T-ALL. TRIM37 promoted the proliferation of T-ALL cells and reduced the sensitivity to bortezomib of T-ALL cells at least in part through the excessive activation of PI3K/Akt signaling pathway and by ubiquitination of PTEN, suggesting TRIM37 might be used as therapeutic targets for T-ALL treatment.
## 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
In this study, the experiment involving human was permitted by the ethics committee of the Inner Mongolia Forestry General Hospital, Yakeshi city 022150, Inner Mongolia, China and written informed consent was acquired from each participates. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
YD contributions to conception and design the project; HQ performed the experiment and wrote the draft; HG-w, YH, MR, JL, and BJ help to analyses the data and edit graph. 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.2022.1016725/full#supplementary-material
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|
---
title: 'CD40LG and GZMB were correlated with adipose tissue macrophage infiltration
and involved in obstructive sleep apnea related metabolic dysregulation: Evidence
from bioinformatics analysis'
authors:
- Xiaoping Ming
- Weisong Cai
- Zhen Li
- Xiuping Yang
- Minlan Yang
- Dingyu Pan
- Xiong Chen
journal: Frontiers in Genetics
year: 2023
pmcid: PMC10009156
doi: 10.3389/fgene.2023.1128139
license: CC BY 4.0
---
# CD40LG and GZMB were correlated with adipose tissue macrophage infiltration and involved in obstructive sleep apnea related metabolic dysregulation: Evidence from bioinformatics analysis
## Abstract
Both obesity and obstructive sleep apnea (OSA) can lead to metabolic dysregulation and systemic inflammation. Similar to obesity, increasing evidence has revealed that immune infiltration in the visceral adipose tissue (VAT) is associated with obstructive sleep apnea-related morbidity. However, the pathological changes and potential molecular mechanisms in visceral adipose tissue of obstructive sleep apnea patients need to be further studied. Herein, by bioinformatics analysis and clinical validation methods, including the immune-related differentially expressed genes (IRDEGs) analysis, protein-protein interaction network (PPI), functional enrichment analysis, a devolution algorithm (CIBERSORT), spearman’s correlation analysis, polymerase chain reaction (PCR), Enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry (IHC), we identified and validated 10 hub IRDEGs, the relative mRNA expression of four hub genes (CRP, CD40LG, CCL20, and GZMB), and the protein expression level of two hub genes (CD40LG and GZMB) were consistent with the bioinformatics analysis results. Immune infiltration results further revealed that obstructive sleep apnea patients contained a higher proportion of pro-inflammatory M1 macrophages and a lower proportion of M2 macrophages. Spearman’s correlation analysis showed that CD40LG was positively correlated with M1 macrophages and GZMB was negatively correlated with M2 macrophages. CD40LG and GZMB might play a vital role in the visceral adipose tissue homeostasis of obstructive sleep apnea patients. Their interaction with macrophages and involved pathways not only provides new insights for understanding molecular mechanisms but also be of great significance in discovering novel small molecules or other promising candidates as immunotherapies of OSA-associated metabolic complications.
## 1 Introduction
Obstructive sleep apnea (OSA), accounting for one-seventh of the world’s adult population, is a condition of apnea and hypopnea caused by the collapse of the upper airway during sleep, accompanied by snoring, disturbance of sleep structure, frequent oxygen desaturation, and daytime sleepiness (Benjafield et al., 2019). OSA is particularly common in obese people, and its incidence is increasing in parallel with the obesity pandemic (Bonsignore 2022). Long-term suffering from the disease can lead to systemic inflammation and metabolic dysregulation, such as cardiovascular disease, type 2 diabetes, non-alcoholic fatty liver disease, and hyperlipidemia (Punjabi 2008; Priou et al., 2012; Tan et al., 2014; Parikh et al., 2019). The interaction between these conditions has a momentous effect on patient care and mortality (Lyons et al., 2020). In addition, Continuous positive airway pressure is the primary treatment for adult OSA, but its efficacy in improving cardiovascular and metabolic outcomes is lacking (Light et al., 2018). Taking all those aspects together, it is of great significance in identifying the molecular mechanisms involved in OSA development and in subsequently discovering therapies for OSA-associated metabolic complications.
Although OSA patients have a 2-3 fold increased risk of developing multiple end-organ morbidities, none of them emerge any specific end-organ dysfunction (Kheirandish-Gozal & Gozal 2019). An intense investigation of systemic inflammation as a contributing factor to OSA-related morbidity has been conducted because of the heterogeneity of the clinical phenotype (Drager et al., 2010; Bhattacharjee et al., 2011; Gozal et al., 2012; Kheirandish-Gozal & Gozal 2013; Drager et al., 2015). Chronic intermittent hypoxia, one hallmark features of OSA, can preferentially activate NF-κB mediated proinflammatory signaling pathway, leading to a systemic inflammatory state in OSA patients, but little is known about the tissues that produce pro-inflammatory mediators in reaction to OSA (Murphy et al., 2017). As both obesity and OSA can lead to similar metabolic complications, it is reasonable to speculate that adipose tissue may be one of the target tissues in response to proinflammatory mediators. Adipose tissue is not only an organ of storing energy but also a highly active endocrine organ regulating metabolism (Ryan et al., 2019). Evidence from rodent experiments showed that intermittent hypoxia in OSA induced insulin resistance and atherogenesis through fat inflammation (Poulain et al., 2014; Murphy et al., 2017). Similar to obesity, adipose tissue macrophages play a vital role in intermittent hypoxia-induced fat inflammation (Poulain et al., 2014; Murphy et al., 2017). However, the largest and by far best-studied parts of fat are located in the gonadal region of rodents, there is no similarity to these pads in humans (Ryan et al., 2019). Hence, we cannot be easily translated the findings obtained from rodent studies into human conditions.
To date, most of the research on human adipose tissue has been focused on the field of obesity, while research on the OSA subject is relatively rare due to the lack of attention to OSA condition, multi-disciplinary limitations, and ethical factors in a routine sampling of adipose tissue in humans. Aron-Wisnewsky J et al. found no relationship between OSA and HAM56-labeled total adipose tissue macrophage infiltration by sampling omental adipose tissue during bariatric surgery (Aron-Wisnewsky et al., 2012), but they did not analyze the detailed phenotyping of the macrophages. Grab et al. showed that OSA can alter fat gene expression particularly in metabolic dysregulation by analyzing the transcriptomic profile of subcutaneous and visceral fat in humans (Gharib et al., 2013; Gharib et al., 2020). They also found that “Immunity and Inflammation” was one of the most upregulated modules in OSA, but detailed information about inflammatory immune-related gene expression profiles and their interaction with immune cell infiltration needs to be further studied.
In the current study, we assessed the microarray dataset GSE38792 that contains visceral adipose tissue (VAT) of OSA patients from Gene Expression Omnibus (GEO) and carried out an integrated bioinformatics analysis. The components of immune infiltration in VAT of OSA patients were also analyzed using the CIBERSORT algorithm method (Newman et al., 2015; Kawada et al., 2021). More importantly, VAT samples from obese individuals with complete overnight polysomnography (PSG) examination (the gold standard for the diagnosis of OSA) were obtained for external validation through multidisciplinary approach. To our knowledge, this is the first study that using clinical samples to validate the findings of dataset analysis, which provides direct evidence for adipose tissue as a proinflammatory target organ for OSA-associated metabolic complications. The aim of this study was to identify and validate the immune-related differentially expressed genes (IRDEGs) and the characteristics of immune infiltration in VAT of OSA patients and to provide new knowledge for understanding molecular mechanisms of OSA-induced metabolic complications.
## 2.1 Ethics statement
The study protocol was ethically authorized by the Ethics Committees of Zhongnan Hospital of Wuhan University (approval number: 2019021), and the signatures of informed consent were obtained from all patients. In order to protect patient confidentiality, we have settled strict procedures to ensure the privacy and anonymity of the participants and excluded their identification before data analysis. We only collected the data we need to meet the research objectives and ensure that data is kept securely.
## 2.2 Data sources
Microarray data of GSE38792 was obtained from the GEO database [GPL6244 platform, Affymetrix Human Gene 1.0 ST Array (transcript gene version), last accessed on 22 January 2023, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE38792]. The datasets contain eighteen VAT samples, including ten samples from OSA patients and eight from control patients. The immune-related gene sets (IRGs) were obtained from the IMMPORT database (https://www.immport.org/resources,last accessed on 22 January 2023).
## 2.3 Identification of immune-related differentially expressed genes
We analysis the differentially expressed genes (DEGs) through a “limma” package in R software (Ritchie et al., 2015). A p-value <0.05 was the threshold for identifying DEGs. The Venn online tool (http://bioinformatics.psb.ugent.be/webtools/Venn/) was used to identify IRDEGs, namely the intersection part between DEGs and IRGs.
## 2.4 Construction protein-protein interaction network
After determining the IRDEGs between OSA and control patients, we used the STRING database (https://string-db.org/, version 11.0) to construct the PPI network, the default confidence threshold was 0.4 (Szklarczyk et al., 2017). Then the network was exported to the Cytoscape software (version 3.8.0) for visualization (Shannon et al., 2003). Next, the MCODE plugin (version 2.0.0) in Cytoscape was adopted to find functional gene clusters in the PPI network (Bader and Hogue 2003). The cluster finding parameters were system default. The modules with the highest established score were screened out, and all the genes in this module were identified as the hub genes.
## 2.5 Functional enrichment analyses of hub genes
To further analyze the biological processes and pathways of the hub genes, gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were performed. A free online platform was applied for the functional enrichment analysis and visualization (https://www.bioinformatics.com.cn; last accessed on 22 January 2023). The powerful enrichment criteria were a p-value <0.05.
## 2.6 Analysis of immune cell infiltration
CIBERSORT is a deconvolution algorithm to evaluate the proportions of immune cell subtypes that has been tested for RNA (Ribonucleic Acid) sequencing measurements in Gene expression profiling (Newman et al., 2015). We used the CIBERSORT package to analysis the immune cell infiltration based on the formatted gene expression matrix data in VAT samples. Principal component analysis (PCA) was conducted to determine the difference between OSA and control samples. The different immune infiltration levels of each immune cell between the two groups were analyzed by the “vioplot” package in R version 4.1.1 (https://github.com/TomKellyGenetics/vioplot), a p-value <0.05 was thought to be statistically difference.
## 2.7 Correlation analysis between hub genes and immune infiltration cells
Spearman’s correlation analysis was adopted to evaluate the relationship between hub genes and immune cells. The analysis process and result visualization were conducted by online platform (https://www.xiantao.love; http://www.bioinformatics.com.cn; last accessed on 22 January 2023).
## 2.8 Construction transcription factors regulated network and target drugs analysis
To reveal the potential mechanism of the fat immune dysfunction in OSA patients, we predicted transcription factors (TFs)-target gene pairs among the hub genes using the iRegulon plugin in Cytoscape (Janky et al., 2014). The TF-target pairs from Track rankings database with normalized enrichment score (NES) > 5 was selected. With the correlation results between immune-related hub genes and immune cells, we constructed a TF-mRNA-immune cells regulated network. Besides, we predicted target drugs through the Drug-Gene Interaction Database (DGIdb) (Cotto et al., 2018). The account of hub genes and selected predicted TFs were uploaded to DGIdb to find potential drugs as therapeutic targets of OSA. The results were visualized by https://www.bioinformatics.com.cn (last accessed on 22 January 2023).
## 2.9 Study population and clinical sample collection
To external validate the findings from dataset analysis, a total of 20 obese patients were recruited from our Bariatric and Metabolic Disease Surgery Center, Zhongnan Hospital of Wuhan University, Wuhan, China, from June 2020 to February 2021. VAT samples (omental adipose tissues) were harvested during bariatric surgery. All patients underwent preoperative overnight PSG to screen OSA. The presence of OSA was diagnosed by apnea-hypopnea index (AHI). Briefly, the diagnose criteria was AHI ≥5/h. Of the 20 obese patients, ten patients with AHI ≥5/h were divided into the OSA group, and the rest with AHI <5/h were regard as the control group. Gender, age, and body mass index were matched between the two groups. The clinical characteristics of 20 obese patients can be seen in Table 1.
**TABLE 1**
| Variables | Control (n = 10) | OSA (n = 10) | p-value |
| --- | --- | --- | --- |
| Gender(female) | 10 | 10 | - |
| Ages (years) | 25 ± 9 | 30 ± 7 | 0.229 |
| BMI (kg/m2) | 31.6 ± 3.7 | 32.9 ± 2.7 | 0.381 |
| AHI (events/h) | 3.1 ± 1.1 | 33.8 ± 22.1 | <0.001 |
| Comorbidity | | | |
| NAFLD | 8 | 8 | |
| Hypertension | 0 | 1 | |
| T2D | 0 | 3 | |
| Hyperlipidemia | 2 | 5 | |
| Hyperuricemia | 8 | 5 | |
## 2.10 RNA extraction
Total RNA from the VAT samples was extracted with TRIzol reagent (15596-026, Invitrogen, America) following a modified isolation protocol (Roy et al., 2020). Briefly, weight a maximum of 500 mg of pure VAT sample and add 1 ml TRIzol accordingly. Homogenise them and keep the tubes on ice. After centrifuge the homogenate, carefully remove the fat layer by pipetting. Add 200 µL of chloroform for phase separation, transfer the upper aqueous layer to another new tube, add 500 µL of isopropyl alcohol for RNA precipitation. Discard the supernatant and add 1 ml of $75\%$ ethanol twice for RNA wash. At last, air-dry the RNA pallet and dissolve it with 30 µL DEPC-treated (Diethypyrocarbonate) water. Quantify RNA concentration and purity was detected by an OD (Optical density) at 260 nm and 280 nm using a Spectrophotometer (NanoDrop One, Thermo Fisher Scientific, Inc.). RNA integrity was examined by electrophoresis in a $1\%$ agarose gel.
## 2.11 Quantitative real-time polymerase chain reaction analysis
cDNA (complementary Deoxyribo Nucleic Acid) was reverse transcribed using All-in-one RT Supermix Kit (R333-01, Vazyme, Nanjing, China) following the manufacturer’s instructions, and RT-qPCR (Real-time quantitative polymerase chain reaction) was performed in triplicate using ChamQ SYBR qPCR Master Mix Kit (Q311-02, Vazyme, Nanjing, China), including the analysis of identified hub gene and macrophage marker gene expression. The thermal cycle profile was as follows: an initial activation was 30 s at 95°C, followed by 40 cycles of denaturation (10 s at 95 °C), annealing (30 s at 60°C) and extension (15 s at 95°C). PCR products were evaluated by melting curve analysis for their specificity and identity. The sequences of primers are available in Supplementary Table S1. The relative expression levels of mRNA (messenger Ribonucleic Acid) were calculated using the 2−ΔΔCt method with the normalization to the reference gene of ACTB (Actin Beta).
## 2.12 Enzyme-linked immunosorbent assay analysis
Total protein from the VAT samples was extracted using RIPA buffer (P0013B, Beyotime, Beijing, China), and the concentrations of protein were detected using a BCA assay (P0009, Beyotime, Beijing, China). A total of 45 µg protein was used and performed with sample diluent to make up the volume to 100 µL for each ELISA kit. The protein expression levels of three genes (CRP, sCD40L, and GZMB) were analyzed in triplicate using QuantiCyto Human ELISA kits (Cat#: EHC011 for CRP, EHC118 for sCD40L, EHC117 for GZMB, Neobioscience, Shenzhen, China) following the manufacturer’s instructions. Briefly, Standard and sample general dilution was added to blank wells, and samples or different concentrations of protein standard (100 µL/well) were added to the other corresponding wells. The reaction wells then incubated at 37°C in the dark for 90 min. After washing the plate 5 times, biotinylated antibody diluent was added to blank Wells, and biotinylated antibody working solution (1:30 dilution, 100 µL/well) was added to the remaining Wells. The reaction wells then incubated at 37 °C in the dark for 60 min. After washing the plate 5 times again, dilution of enzyme conjugate was added to blank wells and working solution of enzyme conjugate (1:30 dilution, 100 µL/well) was added to the remaining wells. The reaction wells then incubated at 37 °C in the dark for 30 min. After washing the plate another 5 times, chromogenic substrate (TMB) 100 µL/well was added and incubated at 37°C for 15 min in the dark. The reaction termination solution 100 µL/well was added, and the OD 450 nm value was measured immediately after mixing (within 3 min). Save and record the readings on the instrument.
## 2.13 Immunohistochemistry analysis
VAT samples were fixed in formalin, embedded in paraffin, and cut into 5-µm-thick sections, and then stained for immune-histochemical detection of macrophage polarization markers and interested protein. Briefly, the paraffin sections were deparaffinized and rehydrated with graded alcohols after being incubated at 65°C for 20 min. Antigen retrieval was performed by microwave heating in pH 6.0 Sodium citrate solution. The sections were incubated in $3\%$ H2O2 for 10 min and blocked in $5\%$ bovine serum albumin (BSA) for 30 min at room temperature. Then followed by incubation with primary antibodies overnight at 4°C and secondary antibody for 30 min at room temperature. Negative controls were performed by replacing the primary antibody with phosphate buffer solution (PBS). The use of Diaminobenzidine (DAB) as a chromogen was to visualize positive cells. Primary antibodies CD11c (1:400 dilution, 17342-1-AP, Proteintech Group, China) and CD206 (1:500 dilution, ab64693, Abcam,United States) were used to identify M1 and M2 macrophages, respectively (Julla et al., 2019; Zhou et al., 2020; Galarraga-Vinueza et al., 2021). Primary antibodies CCL20/MIP3α (1:100 dilution, DF2238, Affinity, China) was also used for immunohistochemistry (IHC) analysis. The target protein expression was evaluated by integrated optical density (IOD)/area assay through ImageJ software.
## 2.14 Statistical analysis
Public dataset analysis was completed in R software (version 4.0.1) and online platform, experimental data were analyzed with GraphPad Prism 8 (Graph-Pad Software, CA, United States). Values were presented as mean ± SD (Standard Deviation). The student’s t-test was adopted to analyze the two independent groups regarding gene and protein expression levels, and the statistically significant criteria was a p-value <0.05.
## 3.1 Identification of immune-related differentially expressed genes
The raw microarray data from GSE38792 was normalized by the RMA (Robust Multi-Array Average) method to eliminate batch expression difference (Figures 1A, B). Then we identified a total of 2016 unique DEGs in the VAT of OSA patients compared with normal controls by the screening threshold of $p \leq 0.05.$ Figures 1C, D showed the volcano and heatmap plots of DEGs. Figure 1E displayed the Venn diagram of 122 IRDEGs. The heatmap of 122 IRDEGs was shown in Figure 1F, including 44 down-regulated genes and 78 up-regulated genes.
**FIGURE 1:** *Identification of immune-related differentially expressed genes (IRDEGs). Box plots show the distribution of the relative gene expression before (A) and after (B) normalization of GSE38792. Each box corresponds to one sample. The middle line corresponds to the median. (C) Volcano plot of differentially expressed genes (DEGs). DEGs were screened with the criteria of p-value <0.05. (D) The cluster circular heat map showing the top 10 upregulated and downregulated DEGs. (E) Venn diagram showing the intersection of DEGs and immune-related genes (IRGs). (F) The heatmap of 122 IRDEGs.*
## 3.2 Protein-protein interaction network
PPI network comprising 103 nodes and 354 edges was constructed using the STRING database to investigate the underlying biological functions of IRDEGs (Figure 2A). Through the MCODE plugin in Cytoscape, we found the most significant module (Score = 8.222) in the PPI network of 122 IRDEGs, comprising 10 immune-related hub genes (TLR3, IL33, GZMB, IL1R1, CRP, CXCL8, CCL5, TSLP, CCL20, and CD40LG) (Figure 2B). The functional annotation of these genes was provided by GeneCards (https://www.genecards.org/) in Supplementary Table S2.
**FIGURE 2:** *Protein-protein interaction (PPI) network construction, hub gene identification and functional enrichment analysis. (A) The PPI network based on STRING database and Cytoscape software, red color represents upregulated genes and blue color represent downregulated genes. (B) Hub genes identified by Cytoscape MCODE plug-in, red color represents upregulated genes and blue color represent downregulated genes. (C) Sankey dot of GO enrichment analyses of hub genes. (D) Sankey dot of KEGG pathway enrichment analyses of hub genes. The dot plot showed the hub genes specific to GO terms or KEGG pathways and the total number of genes in each enriched pathway. External validation of the hub genes at mRNA level (E) and protein level (F).*
## 3.3 Functional enrichment analysis of hub genes
GO and KEGG analysis were performed to explore the biological processes and pathways of the immune-related hub genes. They mainly involved in cytokine activity, chemokine activity, CCR chemokine receptor blinding, G protein-coupled receptor binding, and phospholipase activator activity from the results of GO analysis (Figure 2C; Supplementary Table S3). The significantly enriched pathways were cytokine-cytokine receptor interaction, rheumatoid arthritis, toll-like receptor signaling pathway, chemokine signaling pathway, and nod-like receptor signaling pathway as revealed by KEGG analysis results (Figure 2D; Supplementary Table S4).
## 3.4 External validation for the immune-related hub genes in collected clinical samples
Through seeking multidisciplinary collaboration, we successfully collected VAT samples to validate the immune-related hub genes from OSA and control patients. The relative mRNA expression level of the 10 immune-related hub genes were shown in Figure 2E. The levels of CRP, CD40LG, CCL20, GZMB, IL1R1, IL33, and CCL5 in VAT of OSA group were significantly up-regulated, while only the relative mRNA expression of CRP, CD40LG, CCL20 and GZMB were consistent with the bioinformatics analysis results. As proteins are the executors of gene function, we subsequently analyzed the protein expression level of CRP, CD40LG, CCL20 and GZMB, as shown in Figure 2F. The sCD40L and GZMB levels were higher in the VAT of OSA patients (270.71 ± 85.37 vs. 136.99 ± 28.27 pg/ml, $$p \leq 0.011$$; 1,337.68 ± 394.55 vs. 814.95 ± 278.33 pg/ml, $$p \leq 0.017$$), which were consistent with the relative mRNA expression levels. The CRP levels in VAT showed the same tendency in OSA patients (2.41 ± 0.55 vs. 2.13 ± 0.46 ng/ml), but no statistically significant ($$p \leq 0.369$$) was found. While the protein expression levels of CCL20 in IHC showed no difference (Supplementary Figure S1).
## 3.5 Immune infiltration analysis
Immune infiltration analysis was performed between OSA and control patients. Figure 3A displayed the relative proportion of immune cell subtypes after filtering out the samples with $p \leq 0.05.$ In VAT, monocytes and macrophages accounted for the highest proportion of immune cell types, followed by CD8 T cells. The PCA results as displayed in Figure 3B showed the group-bias clustering among the groups. Compared with control patients, OSA patients were characterized by macrophage infiltration and contained a higher proportion of M1 and M2 macrophages in VAT ($p \leq 0.05$, respectively) (Figure 3C).
**FIGURE 3:** *The landscape of immune infiltration in VAT between OSA and controls. (A) The relative percentage of 22 subpopulations of immune cells in 11 samples from GSE38792 datasets. (B) Principal components analysis performed on all samples. (C) Violin plot of differences in 22 infiltrating immune cells between OSA and normal controls. The normal group was marked as blue color and OSA group was marked as red color. p values <0.05 were considered as statistical significance.*
## 3.6 Analysis of the characteristics of macrophage infiltration in collected clinical samples
External validation of human VAT macrophage infiltration characteristics was also performed. The relative mRNA expression level of M1 marker CD11c was increased by nearly 0.5-fold change while M2 markers CD206 was decreased by 0.4-fold change in the OSA group when compared to the control group (Figure 4A). The protein expression level of human VAT macrophage markers further revealed that obese OSA patients were characterized by significantly higher protein expression levels of M1 macrophages (CD11c,1.43 ± 0.50 vs. 0.61 ± 0.21 IOD/AREA, $p \leq 0.001$), while a lower M2 macrophages (CD206, 0.51 ± 0.15 vs. 1.10 ± 0.38 IOD/AREA, $p \leq 0.001$) in VAT (Figures 4B, C). Compared to control patients, the presence of OSA accelerated the conversion of VAT macrophages to pro-inflammatory phenotype in obese patients.
**FIGURE 4:** *Analysis of the characteristics of macrophage infiltration in collected clinical samples. (A) The relative mRNA expression level of macrophage markers by RT-qPCR methods. (B) The protein expression level of macrophage markers by immunohistochemistry methods. The target protein expression was evaluated by integrated optical density (IOD)/area assay through ImageJ. (C) Representative immunohistochemistry images from the same plane. Magnification, ×200, scale bar = 100 μm. Data are presented as the mean ± SD (n = 10), **p < 0.01.*
## 3.7 Correlation analysis between hub genes and immune infiltration cells
Significant correlation between four identified hub genes and immune infiltration cells were shown in Figure 5. CRP was positively correlated with neutrophils ($r = 0.71$, $$p \leq 0.014$$) and negatively correlated with M2 macrophages (r = -0.68, $$p \leq 0.021$$), and mast cells resting (r = -0.66, $$p \leq 0.027$$); CD40LG was positively correlated with CD8 T cells ($r = 0.79$, $$p \leq 0.004$$) and negatively correlated with CD4 memory resting T cells (r = -0.69, $$p \leq 0.018$$) and M0 macrophages (r = -0.72, $$p \leq 0.013$$); CCL20 was positively correlated with monocytes ($r = 0.77$, $$p \leq 0.005$$) and negatively correlated with M1 macrophages (r = -0.72, $$p \leq 0.012$$); GZMB was positively correlated with monocytes ($r = 0.91$, p ≤ 0.001), CD8 T cells ($r = 0.61$, $$p \leq 0.049$$) and negatively correlated with M2 macrophages (r = -0.80, $$p \leq 0.003$$), and mast cells resting (r = -0.61, $$p \leq 0.048$$).
**FIGURE 5:** *Correlation between hub genes and Immune infiltration cells. Spearman’s correlation analysis between CRP
(A), CD40LG
(B), CCL20
(C), GZMB
(D) and infiltrating immune cells, respectively.The four hub genes were validated by RT-qPCR. The size of the dots represents the strength of the correlation between genes and immune cells; the larger the dots, the stronger the correlation. The color of the dots represents the p-value, the redder the color, the lower the p-value. p < 0.05 was considered statistically significant. CRP, C-Reactive Protein; CCL20, C-C Motif Chemokine Ligand 20; CD40LG, CD40 Ligand; GZMB, Granzyme B.*
## 3.8 The transcription factors regulated network and target drugs in OSA patients
We obtained 11 TFs (NR2F2, FOXA1, NFIC, HDAC2, EP300, TEAD4, CEBPB, GATA2, RCOR1, FOXA2, and RXRA) and 27 TF-target pairs (Supplementary Table S5). The statistically significant correlation immune cells (r > 0.5 and $p \leq 0.05$) were monocytes, M0 macrophages, M1macrophages, M2 macrophages, neutrophils, resting mast cells, CD4 memory resting T cells and CD8 T cells. Interestingly, RXRA and NR2F2 were also among 122 IRDEGs, indicating that the two TFs play a key role in the potential mechanism of VAT homeostasis of OSA patients. The 27 TF-target pairs and the relationship between target genes and immune cells were demonstrated in Figure 6A. Through the DGIdb database, we also found several potential drugs that target hub genes and TFs, Figure 6B showed a visualization plot of drug-gene network.
**FIGURE 6:** *The transcription factors (TFs) regulated network and target drugs in OSA patients. (A) The alluvial plot showing the regulatory network of TFs-genes-immune cells. The left column represents predicted TFs, the middle column represents immune-related hub genes, the right column represents immune cells, and the edge represents the relationship between them. A larger edge width indicates the number of TFs and immune cells (B) Drug-gene network using drug-centric fashions. Yellow circles indicate predictive drug, and blue squares indicate immune-related hub genes.*
## 4 Discussion
Given the strong link between OSA, obesity and their related comorbidity, adipose tissue may act as a key player in the pathogenesis and progress of OSA (Ryan et al., 2019). Compared to subcutaneous adipose tissue, VAT secretes more hormones and proinflammatory cytokines that induce metabolic dysfunction, due to VAT hormones preferential enter the portal circulation and directly alter glucolipid metabolism by the liver (Liu and O'Byrne 2020). Therefore, we initially focus on the visceral adipose tissue (VAT) homeostasis of OSA patients through a multidisciplinary approach. In addition, Gharib et al. [ 2013] found that “Immunity and Inflammation” were the most upregulated modules in the VAT of OSA patients, but little is known about the key genes and their relationship with immune cell infiltration in this module. Herein, we performed the dataset analysis to explore the effect of immune-related genes and immune infiltration characteristics on OSA-related metabolic dysregulation. To our knowledge, this is the first study that collects clinical VAT samples to validate the findings from bioinformatics analysis, which provides direct evidence for adipose tissue as a proinflammatory target organ for OSA-associated metabolic complications. Furthermore, our study found the biological function of CD40LG and GZMB might be important for the VAT homeostasis of OSA patients, those two immune-related genes were first reported in the VAT of OSA patients, and their interaction with macrophages and involved pathways might provide new insights for understanding molecular mechanisms of OSA-related metabolic dysregulation.
In the present study, we reanalyzed the only visceral fat transcriptome dataset GSE38792 including OSA patients and identified a total of 122 IRDEGs. Then we used the 122 IRDEGs to construct a PPI network and found 10 hub immune-related genes, including IL1R1, CRP, IL33, CD40LG, CCL5, CCL20, CXCL8, TLR3, TSLP, and GZMB. After an initial validation by RT-qPCR, the relative mRNA expression of four hub genes (CRP, CD40LG, CCL20, and GZMB) was consistent with the bioinformatics results, while three genes (IL1R1, IL33, and CCL5) showed the opposite results, and the rest three genes (CXCL8, TLR3, and TSLP) showed no significant difference. Interestingly, the seven validated hub genes played a pro-inflammatory role when the relative mRNA expression level increased. We further validated the protein expression level of the four hub genes (CRP, CD40LG, CCL20, and GZMB) by ELISA and IHC methods. Finally, CD40LG and GZMB were verified to be consistent with the bioinformatics results regardless of the mRNA and protein expression levels. Hence, the results of the bioinformatic analysis are not always reliable, especially in small sample datasets, experimental validation is necessary to increase confidence. In addition, we did not further establish an immune-related diagnostic model for OSA when compared to previous studies (Li et al., 2017; Gu et al., 2019; Cao et al., 2021; Peng et al., 2021; Liu et al., 2022), because of the invasive procedures for harvesting VAT.
CD40LG, which binds CD40 and triggers pro-inflammatory mediators on the surface of various cell types, was also found to increase in children with OSA and decreased after adenotonsillectomy (Gozal et al., 2007). Soluble CD40 ligand (sCD40L) can be a marker for endothelium-related activation and a variety of cardiovascular disorders (Mach et al., 1998; Lutgens & Daemen 2002; Lobbes et al., 2006). Adult male patients with moderate to severe OSA also had significantly higher serum sCD40L levels than obese control subjects and nasal continuous positive airway pressure significantly decreased serum levels of sCD40L (Minoguchi et al., 2007). Previous studies have only examined serum sCD40L levels in OSA patients, and we detected the protein expression of sCD40L in the VAT for the first time. Our GO analysis results revealed that CD40LG was mainly involved in receptor-ligand activity, chemokine activity, cytokine activity, signaling receptor activator activity and regulation of production of molecular mediator of immune response. With regard to KEGG pathway analysis, CD40LG was mainly involved in Cytokine-cytokine receptor interaction pathway. Notably, CD40LG in adipose tissue was mainly involved in the progression of OSA by regulating cytokine interaction. GZMB belongs to the granzyme subfamily of proteins and is involved in the signaling pathways of apoptosis, necrosis, and inflammation. Mahzad Akbarpour found that the low GZMB levels in intratumoral CD8+ T cells under tumor microenvironment contributes to the maintenance of self-renew ability of cancer stem cells, which might explain the poorer outcomes of the presence of OSA in cancer patients (Akbarpour et al., 2017). To date, rare studies have reported the expression of GZMB in VAT, and our study showed that GZMB was upregulated in VAT of OSA patients, but the role of GZMB in the occurrence and development of OSA-related morbidity needs further studied. In addition, the correlation between OSA and elevated CRP levels has been reported (Gozal et al., 2012; Tie et al., 2016). Although our results showed no statistical difference, the relative expression of CRP in VAT was slightly elevated. White fat inflammation was a major contributor to increased CRP in obesity, and OSA should be taken into consideration to explain the high CRP levels in obese patients (Paepegaey et al., 2015). In summary, the biological functions and involved signaling pathways of CD40LG and GZMB indicated their important roles in immunity and inflammation modules in the VAT of OSA patients, which may broaden the knowledge of previous findings.
The biological functions of CD40LG and GZMB were associated with immune cells, so we performed adipose tissue immune infiltration analysis between OSA and control patients. Our data demonstrated the immune cell changes of VAT in the OSA group. Consistent with previous findings (Peng et al., 2021), monocyte-macrophages accounted for the highest proportion of immune cell types, followed by CD8 T cells. The changes in M1 and M2 macrophage proportion showed a significant difference between OSA and the control group. Macrophages are mainly involved in inflammatory responses and microbial killing and their role in the adipose tissue immune microenvironment that induces the pro-inflammatory M1 phenotype and subsequent insulin resistance has been reported in rodent experiments (Murphy et al., 2017; Ryan 2017). Similarly, chronic intermittent hypoxia in OSA-induced adipose tissue macrophage inflammation contributes to dyslipidemia and atherogenesis (Poulain et al., 2014). Then we validated the results using RT-PCR and IHC methods and found that macrophage infiltration, especially pro-inflammatory M1 phenotype in VAT, was a hallmark feature in OSA patients independently of obesity. The presence of OSA exacerbates macrophage infiltration in adipose tissue and is metabolically dysfunctional in obese patients. Our results favor macrophages and inflammation are involved in OSA-related metabolic dysfunction, CD11c-labeled proinflammatory macrophage may be the predominant macrophage subset in VAT of OSA patients, which provides direct evidence for adipose tissue as a proinflammatory target organ for OSA-associated metabolic complications.
By analyzing the correlation between the validated hub genes and immune cells, we found that the expression of CD40LG was positively correlated with CD8 T cells and negatively correlated with M0 macrophages and memory resting CD4 T cells. CD40LG is expressed on the surface of T cells, and CD8 T cells are crucial members of adaptive immune response (Stelzer et al., 2016). The interaction between T cells and macrophages may be induced by CD40LG. GZMB was positively correlated with monocytes and CD8 T cells and negatively correlated with M2 macrophages and resting mast cells. GZMB is generally secreted by cytotoxic T lymphocytes and induces target cell apoptosis (Stelzer et al., 2016). The negatively correlation with M2 macrophages hinted that GZMB might occur in the early stage of adipose tissue inflammation. CRP was positively correlated with neutrophils and negatively correlated with M2 macrophages and resting mast cells. CCL20 was significantly positively correlated with monocytes, and negatively correlated with M1 macrophages. Studies have shown that adipose-resident macrophage numbers are positively related to circulating inflammatory markers such as CRP and TNFα (CD40LG belongs to TNF family members), and adipose inflammation is thought to be the main source of systemic inflammation and metabolic disorder associated with obesity (Paepegaey et al., 2015; Kunz et al., 2021). Therefore, better understanding of the relationship between immune-related genes and immune infiltration cells may contributes to discovering novel small molecules or other promising candidates as immunotherapies of OSA-associated metabolic complications.
In order to find novel immunotherapies of OSA-associated metabolic complications, we predicted candidate TFs and target drugs for the hub genes by cytoscape software and online database. We discovered two TFs, namely RXRA and NR2F2, were also belonging to the 122 IRDEGs. RXRA, Retinoid X Receptor Alpha, is a common binding partner to many other nuclear receptors such as PPARs, vitamin D receptors and liver X receptors. It also promotes myelin debris phagocytosis and remyelination by macrophages (Natrajan et al., 2015). Bexarotene, one of the small molecules predicted for target RXRA gene, can improve cholesterol homeostasis and inhibit atherosclerosis progression in a mouse model of mixed dyslipidemia (Lalloyer et al., 2006). NR2F2, Nuclear Receptor Subfamily two Group F Member 2, is an important regulator of differentiation, which has been linked to tissue homeostasis and its abnormal expression may lead to infertility, aberrant development of the vascular system, and metabolic diseases (Stelzer et al., 2016). Although no drugs were predicted to target NR2F2 gene in the DGIdb database, its role in inflammation and immunity cannot be ignored. Imatinib, a protein kinase inhibitor predicted for target CD40LG gene, has been reported to ameliorate COVID-19-induced metabolic complications (Li et al., 2022). In summary, the candidate TFs and target drugs for the immune-related hub genes contribute to finding novel immunotherapies of OSA-associated metabolic complications, but more animal and clinical trials are essential for drug efficacy validation and achieving clinical translation.
Nevertheless, our research also has some limitations. Firstly, CIBERSORT is an analytical tool based on limited existing gene expression data that may underestimate the potential heterotypic interactions of cells. We believe that the immune cell characteristics of adipose tissue in OSA patients will be better illumination with the widespread of single-cell sequencing (Acosta et al., 2017). Secondly, the sample size is small both in the training dataset and in external validation, owing to the difficulty of collecting VAT samples that meet the inclusion criterion (eg. overnight PSG completed; gender, age, and body mass index must be matched). Thirdly, we only chose CD11c and CD206 primary antibodies to label M1 and M2 macrophages, respectively. Since there are so many markers for macrophages (Shapouri-Moghaddam et al., 2018), we might have overlooked the role of other macrophage subsets in fat homeostasis of OSA patients. Finally, we only validate the hub genes and macrophage infiltration in the VAT of OSA, further research is needed to comprehensively identify the potential mechanism of each IRDEG (immune-related differentially expressed gene) and their interaction with immune cells in OSA-related cell and mouse models.
## 5 Conclusion
In conclusion, our research found that CD40LG and GZMB played important roles in immunity and inflammation modules in the VAT of OSA patients, and pro-inflammatory M1 macrophage in VAT was a hallmark feature in OSA patients independently of obesity. The interaction between CD40LG, GZMB and adipose tissue macrophages not only provides new insights for understanding molecular mechanisms but also be of great significance in discovering novel small molecules or other promising candidates as immunotherapies of OSA-associated metabolic complications.
## 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 the Ethics Committees of Zhongnan Hospital of Wuhan University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
XM, WC, and XC conceived and designed the study and wrote the manuscript. XM, WC, ZL, XY, MY, DP, and XC collected clinical samples and performed experiments. XM, WC, ZL, DP, and XC analyzed the data. XM, WC, ZL, DP, and XC revised the manuscript. All authors have read 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
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.2023.1128139/full#supplementary-material
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|
---
title: 'Niaoduqing alleviates podocyte injury in high glucose model via regulating
multiple targets and AGE/RAGE pathway: Network pharmacology and experimental validation'
authors:
- Yipeng Fang
- Yunfei Zhang
- Chenxi Jia
- Chunhong Ren
- Xutao Zhao
- Xin Zhang
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10009170
doi: 10.3389/fphar.2023.1047184
license: CC BY 4.0
---
# Niaoduqing alleviates podocyte injury in high glucose model via regulating multiple targets and AGE/RAGE pathway: Network pharmacology and experimental validation
## Abstract
Purpose: The aim of present study was to explore the pharmacological mechanisms of Niaoduqing granules on the treatment of podocyte injury in diabetic nephropathy (DN) via network pharmacology and experimental validation.
Methods: Active ingredients and related targets of Niaoduqing, as well as related genes of podocyte injury, proteinuria and DN, were obtained from public databases. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) network analysis were performed to investigate the potential mechanisms. High glucose (HG) -induced MPC5 cell injury model was treated with the major core active ingredients of Niaoduqing and used to validate the predicted targets and signaling pathways.
Results: Totally, 16 potential therapeutic targets were identified by intersecting the targets of Niaoduqing and disease, in which 7 of them were considered as the core targets via PPI network analysis. KEGG enrichment analysis showed that AGE-RAGE signaling pathway was identified as the most crucial signaling pathway. The results of in vitro experiments revealed that the treatment of Niaoduqing active ingredients significantly protected MPC5 cells from HG-induced apoptosis. Moreover, Niaoduqing could significantly attenuate the HG-induced activation of AGE-RAGE signaling pathway, whereas inhibited the over-expression of VEGF-A, ICAM-1, PTGS-2 and ACE in HG-induced MPC5 cells.
Conclusion: Niaoduqing might protect against podocyte injury in DN through regulating the activity of AGE/RAGE pathway and expression of multiple genes. Further clinical and animal experimental studies are necessary to confirm present findings.
## 1 Introduction
Diabetic nephropathy (DN) is one of the most common complications of diabetes mellitus, which involves the entire kidneys (Anders et al., 2018). In China, the prevalence of total diabetes in adults was $11.2\%$ (Li et al., 2020a). DN develops in approximately $20\%$–$40\%$ of patients with diabetes and consequently has become the leading cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) (Zhang et al., 2016; American Diabetes Association, 2020). Over the years, lifestyle change, risk factor control, proteinuria inhibition and interstitial fibrosis prevention are the primary modes of treatment for DN. However, current management approaches cannot stop the progression of renal failure (Yang et al., 2019a).
The pathogenesis of DN is complex and multifactorial, among which podocyte injury plays the key role. In diabetes, declining insulin sensitivity, oxidative stress and inflammatory reaction cause permanent functional and/or structural change of podocytes, that is regarded as one of the major causes of proteinuria. Podocyte damage, including dysfunction, shedding and apoptosis, is considered as the early pathological change underlying various glomerular diseases, including DN (Cao et al., 2014; Ni et al., 2018; Podgórski et al., 2019). Proteinuria is one of the early clinical manifestations of diabetic kidney disease, and persistent proteinuria accelerates the progression of renal disease (Brinkkoetter et al., 2019; Yang et al., 2019b). Thus, podocyte might be a potential therapeutic target for DN (Ni et al., 2018), and controlling proteinuria represents an effective method delaying the progression of diabetic kidney damage. Angiotensin converting enzyme inhibitors (ACEI) and angiotensin receptor blocker (ARB) are widely used in patients with proteinuria, in order to reduce albuminuria and decrease the risk of cardiovascular diseases through inhibiting the activity of renin-angiotensin system (RAS); however, whether ACEI or ARB can prevent the progression towards ESRD are still uncertain (Marre et al., 2004). According to the theory of traditional Chinese medicine (TCM), Chinese compound medicines treat diverse diseases through “multi-component, multi-targets and multi-pathways” method (Zhang et al., 2020). The complex mechanisms of DN suggest that a combination of medicines may play better therapeutic activities in DN. Niaoduqing granules, consisted of 9 herbal medicines, are commonly used in ESRD. As Li et al. ( 2022a) reported, Niaoduqing granules can effectively improve renal function, inhibit renal fibrosis and decrease the level of inflammatory responses through regulating MAPK/NF-κB signaling pathway in the ESRD model induced by $\frac{5}{6}$ nephrectomy. TGF-β is considered as one of the crucial targets for the anti-fibrosis of Niaoduqing (Miao et al., 2010; Lu et al., 2013; Huang et al., 2014; Wu et al., 2016). As Huang YR et al. reported, Niaoduqing granules ameliorate tubule-interstitial fibrosis and renal dysfunction in the renal failure model induced by adenine and unilateral ureteral obstruction though promoting extracellular matrix degradation and maintaining MMP-2/TIMP-1 balance or regulating TGF-beta1/Smad signaling pathway in kidney tissue (Huang et al., 2014). In addition, Niaoduqing can also treat tubule-interstitial fibrosis via inhibiting tubular epithelial-to-mesenchymal transition (EMT) and regulating TGF-beta1/Smad pathway (Lu et al., 2013). The interaction between Niaoduqing and TGF-β1 may be related to the methylation/demethylation regulation of TGF-β1 promoter (Miao et al., 2010). What’s more, Niaoduqing granules can ameliorate CKD-related anemia though erythropoietin (EPO) receptor signaling pathway (Wang et al., 2017). Except for ameliorating renal function and fibrosis, Niaoduqing also has good effects on managing uremic pruritus (Lu et al., 2021). Niaoduqing also regulate the amino acid, lipid and energy metabolisms in the chronic renal failure rat model (Zhu et al., 2018). The therapeutic effects of Niaoduqing on DN have been evaluated in some Chinese articles. As Wu et al. [ 2009] found in an intervention research including 76 DN patients without dialysis or kidney transplant, Niaoduqing exposure can effectively improve the clinical symptoms ($92.10\%$ vs. $65.78\%$, $p \leq 0.05$), and reduce the level of blood urea nitrogen (BUN, 15.9 ± 1.75 mmol/L vs. 16.9 ± 1.34 mmol/L, $p \leq 0.05$), blood creatinine (Scr, 383.2 ± 74.58 μmol/L vs. 425.74 ± 86.32 μmol/L, $p \leq 0.05$) and urine protein (0.81 ± 0.67 g/24 h vs. 1.38 ± 0.45 g/24 h, $p \leq 0.05$). Compared with using ACEI/ARB alone, the better renal function is observed in patients received combination therapy with Niaoduqing (Li et al., 2016; Wei and Ruan, 2018). What’s more, a recent network meta-analysis reported that Niaoduqing has a better effect on controlling proteinuria in patients with early stage DN, compared with other six kinds of TCM (Zhao et al., 2022). Although some studies have proved the positive role of Niaoduqing in the treatment of diabetes nephropathy and proteinuria, the underline mechanisms of Niaoduqing for early stage DN are still unclear and need to be further explored.
Network pharmacology is an analytic tool for systematic pharmacology based on the “network target, multi-component” strategy, which has been widely applied to analyze the active ingredients and core potential therapeutic targets of drugs to disease, especially in Chinese compound medicines (Hopkins, 2007; Kibble et al., 2015). In the present study, we explored the core ingredients and potential mechanisms of Niaoduqing granules on the treatment of podocyte damage and proteinuria in DN through network pharmacology followed by experimental validation, so as to search for novel and effective therapeutic strategies for podocyte protection and proteinuria reduction in DN. The flow chart of our study was shown in Figure 1.
**FIGURE 1:** *Flow chart of present study.*
## 2.1 Screen the active ingredients and targets of Niaoduqing granules
Niaoduqing granules consist of nine components, including Bai Shao, Bai Shu, Che Qian Cao, Da Huang, Dan Shen, Fu Ling, Huang Qi, Ku Shen and Sang Bai Ye. The active ingredients of the above night components were screened through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, https://tcmsp-e.com/tcmsp.php) according to the condition of oral bioavailability (OB) ≥ $30\%$ and drug-like properties (DL) ≥ 0.18 (Ru et al., 2014). Related targets of the active ingredients, defined as Niaoduqing-related targets, were selected through TCMSP database and Swiss Target Prediction website (http://www.swisstargetprediction.ch/) (Ru et al., 2014; Daina et al., 2019). In Swiss Target Prediction website, only the top 100 predicted targets with probability greater than 0 were included. SMILE strings, which should be used in the Swiss Target Prediction website, were obtained through Pubchem website (https://pubchem.ncbi.nlm.nih.gov/). The conversions from protein names to the unique entry gene IDs were performed through the uniprot database (https://www.uniprot.org/).
## 2.2 Screen differentially expressed genes related to DN from GEO database
DN related databases (GSE1009, GSE96804 and GSE111154) were obtained from Gene Expression Omnibus (GEO) database. After removing duplicate and missing data, we screened the differentially expressed genes (DEGs) according to the following criteria: │log FC│≥1 and p-value < 0.05. Volcano plots and heat maps were used to represent the DEGs.
## 2.3 Collect related targets and potential therapeutic targets
Disease targets of DN, proteinuria and podocyte injury were attained by searching GeneCards database (https://www.genecards.org/) (Stelzer et al., 2016), the Online Mendelian Inheritance in Man database (OMIM, https://omim.org/) (Amberger and Hamosh, 2017), Therapeutic target database (TTD, http://db.idrblab.net/ttd/) (Li et al., 2018), DisGeNET database (https://www.disgenet.org/home/) (Piñero et al., 2015), NCBI (https://www.ncbi.nlm.nih.gov/) and PharmgKB database (https://www.pharmgkb.org/) (Whirl-Carrillo et al., 2021) with “diabetes nephropathy,” “proteinuria” and “podocyte injury” as keywords and “Homo sapiens” as the organism.
All disease targets obtained from the above databases and the DEGs of DN obtained from GSE1009, GSE96804 and GSE111154 datasets were pooled together, and those targets appearing in at least two databases and datasets were defined as DN-related targets in present study. Similar protocol was applied to screen the proteinuria-related genes: only the overlapping targets appearing in at least two databases were identified as proteinuria-related genes.
Niaoduqing-related targets were intersected with the DN-related targets, the proteinuria-related targets and the podocyte injury-related targets to identify the potential therapeutic targets of Niaoduqing on the treatment of podocyte injury and proteinuria in DN. “ venn” and “VennDiagram” packages from R language were used to create the Venn diagram to depict the intersections between different databases and datasets. Cytoscape 3.6.1 software was used to construct the relationship network among night components, active ingredients and the potential therapeutic targets.
## 2.4 The analysis of PPI network, GO and KEGG
The protein-protein interactions (PPI) results among potential therapeutic targets were obtained from the SRTING database (https://cn.string-db.org/, Version: 11.5), with the minimum required interaction score set at “median confidence (0.400)” level. Cytoscape 3.6.1 and its CytoNCA plugin were used to further analyze the original PPI network. Three topological parameters, including betweenness centrality, closeness centrality and degree value, were calculated and considered as the evidence for the screen of core targets. The higher the values were, the more important the targets were (Azuaje et al., 2011). Nodes with all three parameters higher than the median were used to build the sub-network and considered as the core targets.
Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using “Cluster profiler” package from R language to investigate the probable molecular mechanisms of Niaoduqing on the treatment of podocyte injury and proteinuria in DN. The top 10 enriched enters of molecular function (MF), biological process (BP) and cellular components (CC) in GO analysis were presented in bar chart. The top 20 enriched pathways were shown in bubble chart.
## 2.5 Molecular docking
The core ingredients and core targets were used in the molecular docking analysis. Firstly, the three-dimensional structure of core active ingredients was obtained from Pubchem website and translated into PDB format files using PyMOL software. Secondly, the 3D structure of the core target was obtained through the protein docking bank database (PDB, https://www.rcsb.org/). The water molecules, metal ions and small molecule ligands were removed, and the active pockets were identified by PyMOL software. Thirdly, AutoDock Vina 1.1.2 (Trott and Olson, 2010) was used to convert the ingredients and targets into PDBQT format files and perform molecular docking simulation. This binding energy estimated the stability of the target and the ingredient complexes. The first representative binding pose, with the lowest binding energy in our docking result, was visualized by PyMOL software.
## 2.6.1 Drugs and reagents
Niaoduqing Granules were obtained from KangCheng Pharmaceutical Industry, China (No. Z20073256). The lyophilized powder of quercetin (Que, Q4951) was acquired from Sigma (St. Louis, United States). The lyophilized powders of kaempferol (Kae, S2314) and luteolin (Lut, S2320) were obtained from Selleck (Shanghai, China). Anti-PI3K antibody (110kD, AF5112) was purchased from Affinity Biosciences (Ohio, United States). Anti-phospho-PI3KCA antibody (p-PI3KTyr317, 110kD, bs-5570R) was obtained from Bioss Biotech (Beijing, China). Anti-AKT antibody (60 kD, 4691S), anti-phospho-AKT antibody (p-AKTSer473, 60kD, 4060S), anti-caspase-3 antibody (17kD, 9662) and anti-Bax antibody (20kD, 2772S) was acquired from Cell Signaling Technology (CST, Danvers, MA, United States). Anti-NF-κB antibody (65kD, A2547) and anti-phosphor-NF-κB p65/RelA antibody (p-NF-κBSer536, 65kD, AP0124) was purchased from ABclonal Biotech (Wuhan, China). Goat anti-mouse IgG second antibody (C1308), goat anti-rabbit IgG second antibody (C1309) and anti-actin antibody (42kD, C1313) was acquired from Pulilai Biotech (Beijing, China). Annexin V—Alexa Flour 488/PI Apoptosis Kit (FXP022) was purchased from 4A Biotech (Suzhou, China). CCK-8 (CK04) was obtained from Dojindo Laboratorise (Shanghai, China). Fetal bovine serum (FBS) was purchased from Zeta Life (California, United States).
## 2.6.2 Cell culture
The conditionally immortalized mouse podocyte cell line Mouse Podocyte Clone 5 (MPC5) cells were purchased from Jennio Biotech (Guangzhou, China). MPC5 cells were maintained in RPMI 1640 medium supplemented with $15\%$ FBS, 2 mM L-Glutamin, 100 IU/mL penicillin-streptomycin, and 5 U/mL recombinant mouse interferon-γ (IFN-γ, Yeasen Biotech, Shanghai, China, 91212ES60) at 33°C in a humidified atmosphere with $5\%$ CO2. To induce differentiation, MPC5 cells were shifted from 33°C to 37°C and cultured without IFN-γ for 14 days. To establish high glucose (HG) model, extra glucose (Sigma, St. Louis, United States, G7021) was added to growth medium and differentiated MPC5 cells were cultured under high glucose condition (44 mM). Since the solution of Niaoduqing granules showed toxic effect on the survival and proliferation of MPC5 cells, the mixture of three major active ingredients of Niaoduqing (Que + Lut + Kae) were used as an alternative for the in vitro experiments. MPC5 cells were randomly divided into four groups: the control group, Que + Lut + Kae (1 μg/mL) group, HG group and HG + Que + Lut + Kae group.
## 2.6.3 CCK-8 assay for cell viability
MPC5 cells were re-plated in 96-well plates (5,000 cells per well) and cultured at 37°C overnight. Then, growth medium was removed and 100 μL of culture medium with different concentrations of Niaoduqing granules (0.0625, 0.25, 1, 4, 16 μg/mL) and Que + Lut + Kae mixture (0.0625, 0.25, 1, 4, 16, 64 μg/mL) was added. Twenty four and 48 h after treatment, 100 μL of basic medium and 10 μL CCK-8 solution were added to each well and incubated for another 2 h. The optical density (OD) value was measured at 490 nm. The cell viability was calculated using the following formula: Cell viability% = [(ODvalue of experimental group) − (ODvalue of cell-free group)]/[(ODvalue of control group)-(ODvalue of cell-free group)] × $100\%$.
## 2.6.4 Real time quantitative PCR (RT-qPCR) analysis
Total RNA was extracted from each group of MPC5 cells 48 h after treatment using Trizol method (Accurate Biotechnology, Changsha, China). The cDNA was synthesized using Evo M-MLV RT kit (Accurate Biotechnology, AG11734). The mRNA was quantified using the 2× SYBR Green Pro Taq HS Premix II (Accurate Biotechnology, AG11736), with β-actin gene as the internal control. The differences of the gene expression were analyzed using the delta-delta Ct method (2−△△CT). The primer sequences for RT-qPCR are shown in Table 1.
**TABLE 1**
| Gene name | Forward primer sequences (5′-3′) | Reverse primer sequences (5′-3′) |
| --- | --- | --- |
| Ace | CCAACAAGATTGCCAAGCTCA | AGTGGCTGCAGCTCCTGGTA |
| β-actin | ACCAACTGGGACGACATGGAGAAG | TACGACCAGAGGCATACAGGGACA |
| Col1a1 | TGGCCTTGGAGGAAACTTTG | CTTGGAAACCTTGTGGACCAG |
| Icam-1 | GCCTTGGTAGAGGTGACTGAG | GACCGGAGCTGAAAAGTTGTA |
| Il-6 | TTATATCCAGTTTGGTAGCATCCAT | AGGCTTAATTACACATGTTCTCTGG |
| Nos-3 | ATTTCCTGTCCCCTGCCTTCCGC | GGTTGCCTTCACACGCTTCGCC |
| Ptgs-2 | TTCAACACACTCTATCACTGGC | AGAAGCGTTTGCGGTACTCAT |
| Rage | CAGGGTCACAGAAACCGG | ATTCAGCTCTGCACGTTCCT |
| Ren | GAGGCCTTCCTTGACCAATC | TGTGAATCCCACAAGCAAGG |
| Spp-1 | TGGGCTCTTAGCTTAGTCTGTTG | CAGAAGCAAAGTGCAGAAGC |
| Tgfβ1 | CCACCTGCAAGACCATCGAC | CTGGCGAGCCTTAGTTTGGAC |
| Tnf-α | CCCTCACACTCAGATCATCTTCT | GCTACGACGTGGGCTACAG |
| Vegf-α | CTTTTCGTCCAACTTCTGGGCTCTT | CCTTCTCTTCCTCCCCTCTCTTCTC |
| Wt-1 | TACAGATGCATAGCCGGAAGCACA | TCACACCTGTGTGTCTCCTTTGGT |
## 2.6.5 Western blot
Protein was extracted from different groups of MPC5 cells using RIPA lysis buffer (Beyotime Biotech, Beijing, China, P0013K) containing 1:100 protease inhibitors and 1:100 phosphatase inhibitors. The protein was quantified using bicinchoninic acid kit (BCA, Beyotime Biotech, Beijing, China, P0012) according to the manufacturer’s instruction. After adding 5 × protein loading buffer, all samples were denatured by boiling at 100°C for 10 min and separated by Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). The electrophoresis was performed at a constant voltage of 60 V for 60 min initially and then switched to 120 V. The gel was further blotted to PVDF membrane for 120 min at a constant current of 200 mA. After blocking with $5\%$ skimmed milk, membranes were incubated with primary antibody at a concentration of 1:1000 at 4°C overnight and further incubated with secondary antibody at a concentration of 1:5000 for 1 h at room temperature. The protein blots were visualized using enhanced chemiluminescence reagent (NCM Biotech, Suzhou, China, P10100). Quantitative analysis was completed using ImageJ.
## 2.6.6 Flow cytometric analysis
Apoptosis was determined using an Annexin V/Alexa Fluor 488/propidium iodide Apoptosis Detection Kit (FXP022-100, 4A Biotech Co., Ltd.) according to the manufacturers’ instructions. Briefly, MPCs cells were washed twice with cold phosphate-buffered saline (PBS) and then re-suspended in 100 µL of 1 × binding buffer. The cell suspension was incubated with AnnexinV-Alexa Flour 488 (5 µL) for 5 min in dark at room temperature, then 10 µL of PI solution and 400 µL of PBS was added. Samples were measured on Accuri C6 flow cytometer (BD Biosciences) and data were analyzed by FlowJo 8.0 software (Tree Star, Ashland, OR).
## 2.7 Statistical analysis
Data shown in present study repeated at least three times. All data were showed as mean and standard deviation of the mean (SD) and analyzed by SPSS23.0 (SPSS, Armonk, New York, United States). Student’s t-test and Bonferroni test in ANOVA were used to make comparisons between two and multiple groups. $p \leq 0.05$ was considered as a significant difference (*$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$, NS = non-statistically significant). GraphPad Prism 8 (GraphPad Software, United States) was used to visualize the results.
## 3.1 Collection of active ingredients and predicted targets
A total of 206 active ingredients were identified, including 32 from Sang Bai Ye, 13 from Bai Shao, 8 from Bai Shu, 10 from Che Qian Cao, 16 from Da Huang, 67 from Dan Shen, 16 from Fu Ling, 20 from Huang Qi and 45 from Ku Shen (Supplementary Table S1). There were 11 common ingredients discovered in at least two herb compounds, in which 6 ingredients (hederagenin, sitosterol, formononetin, baicalin, gallic acid-3-O-(6′-O-galloyl)-glucoside, (24S)-24-Propylcholesta-5-Ene-3beta-Ol) were common to two herbs and 5 ingredients (quercetin, luteolin, kaempferol, beta-sitosterol, mairin) were common to three herbs (Supplementary Table S1). Among the active ingredients, 142 got predicted targets from TCMSP database and 133 obtained targets information from Swiss Targets Prediction website. 28 active ingredients had no target information (Supplementary Table S2). Finally, after eliminating the duplicates, we identified 1,022 predicted targets of Niaoduqing (Supplementary Table S3), and the relationship between active ingredients and predicted targets was shown in Supplementary Table S4.
## 3.2 Potential therapeutic targets of Niaoduqing on the treatment of podocyte injury and proteinuria in DN
1325, 574 and 115 DEGs were detected in the GSE1009 (Baelde et al., 2004), GSE96804 (Pan et al., 2018) and GSE111154 (Sircar et al., 2018), and the DEGs were further presented by volcano map and heat map, shown in Supplementary Figures S1–S3. The green and red nodes indicated downregulated and upregulated DEGs in the volcano map. In the heat map, red color nodes represented the high expression, while blue color nodes represented the down expression. We further obtained 3448, 95, 559 and 6 targets in “GeneCards,” “OMIM,” “NCBI” and “TTD” database using “diabetes nephropathy” as the keyword and “homo sapiens” as organism. After taking the intersection of targets appearing in at least two databases and datasets using Venn diagram, 986 overlapping targets was identified as DN-related targets (shown in Figure 2A). 3760, 239, 4 and 2 targets associated with proteinuria were found in “GeneCards,” “DisGeNET,” “OMIM” and “PharmgKB”. 213 overlapping targets, which appeared in at least two databases, were considered as proteinuria related-targets shown in Figure 2B. 2072 and 3 targets were found using “podocyte injury” as the keyword and “homo sapiens” as organism in “GeneCards” and “OMIM” database. After removing 1 duplicating gene, 2074 unique targets were defined as the related targets of podocyte injury.
**FIGURE 2:** *Screening Potential Therapeutic Targets of Niaoduqing on the treatment of podocyte injury and proteinuria in DN. (A) The Venn diagram of disease targets in 4 databases and 3 GEO datasets. Overlapping targets in at least two databases and dataset were considered as DN-related targets (marked in red color). (B) The Venn diagram of disease targets in 4 databases. Overlapping targets in at least two databases and dataset were considered as proteinuria-related targets (marked in red color). (C) The Venn diagram of potential therapeutic targets of Niaoduqing on the treatment of podocyte injury and proteinuria in DN. 16 overlapping targets was identified as the potential therapeutic targets (marked in red color with star).*
Further taking the intersection of targets of Niaoduqing, DN-related targets, proteinuria-related targets and podocyte injury-related targets, we obtained 16 potential therapeutic targets of Niaoduqing on the treatment of podocyte injury and proteinuria in DN (shown in Figure 2C). The detail information about targets of DN, proteinuria, podocyte injury, and the overlapping potential therapeutic targets was shown in Supplementary Tables S5–S8.
## 3.3 Construction of the compound-ingredients-therapeutic targets network
Cytoscape 3.6.1 software was used to construct the compound-ingredients-therapeutic targets network. The network was constructed by 133 active ingredients and 16 potential therapeutic targets, with 149 nodes and 234 edges. Different color was used to represent different compounds of active ingredients (Shown in Figure 3). The higher the topological parameters were, the more nodes connected to it. The data about topological parameters of the nodes was shown in Supplementary Table S9. In all of the active ingredients, quercetin had the highest topological parameters, following by luteolin, kaempferol, 7-O-methylisomucronulatol and danshexinkun D, which were considered as the core ingredients of Niaoduqing on the treatment of podocyte injury and proteinuria in DN.
**FIGURE 3:** *The compound-ingredients-therapeutic targets network of Niaoduqing on the treatment of podocyte injury and proteinuria in DN. The purple circle represented the potential therapeutic targets, while the colorful square represented the active ingredients from different compounds.*
## 3.4 PPI network and core targets screening
Importing 16 potential therapeutic targets into STRING database, we established an active ingredients-disease co-expression targets PPI network, which contained 16 nodes and 160 edges. We further screened targets with all three parameters higher than the median to construct the sub-network. We found that VEGFA, NOS3, ICAM1, PTGS2, ACE, SPP1 and REN were the core targets with the highest topological parameters (shown in Figure 4). The data about topological parameters of present network was shown in Supplementary Table S10.
**FIGURE 4:** *The protein-protein interaction network among 16 potential therapeutic targets. 7 core targets with the highest topological parameters were determined. Yellow nodes represented the selected targets (core targets), and green ones represented the remaining targets.*
## 3.5 GO and KEGG pathway analyses of potential therapeutic targets
GO analysis and KEGG enrichment analyses were performed based on the above 16 potential therapeutic targets of Niaoduqing on the treatment of podocyte injury and proteinuria in DN. The top 10 GO enrichment terms of MF, BP and CC were shown in Figure 5A. In addition, KEGG analysis was carried out to determine the key pathways of the overlapping genes (Supplementary Table S11), and the top 20 enriched signaling pathways were shown in Figure 5B. In all of them, AGE-RAGE signaling pathway in diabetic complications (hsa04933) exhibited the most significant enrichment, following by fluid shear stress and atherosclerosis (hsa05418), renin-angiotensin system (hsa04614), rheumatoid arthritis (hsa05323), diabetic cardiomyopathy (hsa05415) and HIF-1 signaling pathway (hsa04066). In addition to this, several inflammation signaling pathways and vascular barrier associated pathways were enriched, including VEGF signaling pathway (hsa04370), TNF signaling pathway (hsa04668), PI3K-Akt signaling pathway (hsa04151) and Focal adhesion (has 04510).
**FIGURE 5:** *GO and KEGG enrichment of the 16 potential therapeutic targets. (A) The bar plot of top 10 GO enriched terms of BP, CC and MF function. (B) The bubble chart of top 20 KEGG enrichment pathways. The redder the bar, the smaller the p-value; the larger the bar, the greater the number of genes enriched in this processes and pathways.*
## 3.6 Molecular docking analysis
The potential therapeutic targets of Niaoduqing were further docked with the top five ingredients through molecule docking analysis. We acquired their docking methods and binding energies, and found that the binding energies of all molecular docking were less than −5.5 (Shown in Table 2). PTSG2 and DPP4 interacted with all five core ingredients, while NOS3 interacted with four of them. The most binding results with the lowest binding energy of each core ingredients were shown in Figure 6.
## 3.7 CCK-8 assay for cytotoxicity analysis
To determine the cytotoxicity and appropriate concentration of the crude Niaoduqing granules solution and its purified core active ingredients, differentiated MPC5 cells were exposed to drugs at different concentrations and CCK-8 assays were performed to detect the cell viability. As shown in Figure 7, the cell viability significantly decreased in Niaoduqing solution exposure group from the lowest to the highest concentration ($\frac{1}{16}$ to 64 μg/mL); therefore, the crude extract of Niaoduqing was not applicable for the in vitro experiment. In contrast, the mixture of three major purified Niaoduqing active ingredients (Que + Lut + Kae) did not show significant cytotoxicity up to 4 μg/mL ($p \leq 0.05$). Therefore, 1 μg/mL of Que + Lut + Kae was considered as a safe concentration and further used in our study.
**FIGURE 7:** *The cytotoxicity of the crude Niaoduqing granules solution and its purified core ingredients was detected by CCK-8 assay. The crude Niaoduqing solution showed obvious cytotoxic effect on MPC5 cells at all concentrations. No cytotoxicity of Que + Lut + Kae mixture was observed at the concentrations ranged from 1/16 μg/mL to 4 μg/mL. The data were represented visually with bar graphs. Data were presented as mean ± SD (n = 3 per group) of the representative data from three independent experiments. *p < 0.05, NS, non-statistically significant, compared with control group; analyzed by S-N-K test in ANOVA.*
## 3.8 In vitro validation of the predicted core targets
To validate the potential therapeutic targets of Niaoduqing in HG-induced podocytes injury model, RT-qPCR was performed to detect the relative expression levels of the above predicted targets. As shown in Figure 8, HG exposure significantly upregulated the expression levels of Vegf-α (***$p \leq 0.001$), Ptgs-2 (***$p \leq 0.001$), Icam-1 (*$$p \leq 0.026$$) and Ace (***$p \leq 0.001$) expression, but had no effect on Ren ($$p \leq 1.000$$), Spp-1 ($$p \leq 0.052$$) and Nos-3 ($$p \leq 0.524$$) expression. Compared with HG group, the upregulation of mRNA expression levels of Vegf-α (***$p \leq 0.001$), Ptgs-2 (***$p \leq 0.001$), Icam-1 (*$$p \leq 0.034$$) and Ace (***$p \leq 0.001$) were significantly attenuated in HG + Que + Lut + Kae group. There was no statistical difference of expression levels for all target genes between control and Que + Lut + Kae group (all $p \leq 0.05$).
**FIGURE 8:** *The effects of three major ingredients of Niaoduqing (Que + Lut + Kae) on predicted target genes. The treatment of Que + Lut + Kae mixture significantly attenuated the upregulation of the mRNA expression levels of Vegf-α, Ptgs-2, Icam-1 and Ace in HG-induced MPC5 cells. The data were represented visually with bar graphs. Data were presented as mean ± SD (n = 3 per group) of the representative data from three independent experiments; *p < 0.05, **p < 0.01, ***p < 0.001, NS, non-statistically significant.*
## 3.9 Niaoduqing ingredients attenuate high glucose induced activation of AGE/RAGE signaling pathway
To further investigate the potential mechanisms and evaluate the results of our network pharmacology analysis, the activity of AGE/RAGE signaling pathway in diabetic complications (hsa04933) was detected, which was the most enriched signaling pathway in KEGG analysis. As shown in Figures 9A, B, compared with the control group, the phosphorylation of PI3KCA (Tyr317, *$$p \leq 0.028$$) and NF-κB (Ser536, **$$p \leq 0.005$$), as well as the mRNA levels of target genes including Rage (***$p \leq 0.001$), Tnf-α (**$$p \leq 0.002$$), Tgf-β1 (**$$p \leq 0.004$$), Col1a1 (***$p \leq 0.001$) significantly increased in HG group. Strikingly, Niaoduqing ingredients treatment significantly reduced HG-induced the increase of phosphorylation of NF-κB (Ser536, *$$p \leq 0.024$$) and upregulation of the expression of Rage (***$p \leq 0.001$), Tnf-α (**$$p \leq 0.001$$), Tgf-β1 (**$$p \leq 0.004$$), and Col1a1 (***$p \leq 0.001$). Although Que + Lut + Kae treatment also inhibited HG-induced phosphorylation of PI3KCA (Tyr317, $$p \leq 0.244$$) and AKT (Ser473, $$p \leq 1.000$$), but with no statistical significance. The details of AGE/RAGE signaling pathway were shown in Figure 9C. Our results indicated that the therapeutic effect of Niaoduqing might be through the regulation of ARG/RAGE signaling pathway.
**FIGURE 9:** *Three major ingredients of Niaoduqing (Que + Lut + Kae) participated in the regulation of AGE/RAGE signaling pathway in diabetic complications (hsa04933). (A) The protein expression and phosphorylation levels of PI3K, AKT and NF-κB in MPC5 cells were detected by Western blot. (B) The mRNA expression levels of Rage, Tnf-α, Il-6, Tgf-β1 and Col1a1 in MPC5 cells were determined by RT-qPCR. (C) The detail of AGE/RAGE signaling pathway in diabetic complications (hsa04933). Boxes represented the detected sites. Red boxes indicated the positive results, while green boxes indicated the inconclusive results. Data represented the mean ± SD of triplicate independent experiments; *p < 0.05, **p < 0.01, ***p < 0.001, NS, non-statistically significant.*
## 3.10 Niaoduqing ingredients protect against HG-induced MPC5 cell apoptosis
Previous study showed that Niaoduqing had better effect on controlling proteinuria in patients with early stage DN (Zhao et al., 2022). Since podocyte damage is the major cause of proteinuria, Niaoduqing might be able to protect podocyte during early stage DN. Western blot and flow cytometry analysis were performed to further determine the protective effect of Niaoduqing against HG-induced MPC5 cell apoptosis. Flow cytometry data also showed that Que + Lut + Kae treatment significantly lowered the increase of HG-induced apoptosis rate of MPC5 cells (Figure 10A). As shown in Figure 10B, compared with the control group, Bax (**$$p \leq 0.001$$) and cleaved-Caspase-3 (**$$p \leq 0.012$$) protein levels significantly increased in HG-induced group, while Que + Lut + Kae treatment reduced HG-induced increase of protein levels of Bax (**$$p \leq 0.006$$) and cleaved-Caspase-3 (**$$p \leq 0.006$$). Our data suggested that Niaoduqing might reduce proteinuria through the protection of podocyte from high glucose induced apoptosis.
**FIGURE 10:** *Three major ingredients of Niaoduqing (Que + Lut + Kae) reduced HG-induced MPC5 cells apoptosis rate. (A) Annexin V/propidium iodide staining and Flow cytometry were performed to determine the apoptosis rate of MPC5 cells. Q2 and Q3 indicated the early and late apoptosis, respectively. (B) The protein levels of Bax and cleaved-Caspase-3 in MPC5 cells were detected by Western blot. Data represented the mean ± SD of triplicate independent experiments; *p < 0.05, **p < 0.01, ***p < 0.001, NS, non-statistically significant.*
## 4 Discussions
Diabetic nephropathy poses a significant threat to the global public health and places enormous economic burden due to high morbidity, high mortality but poor control rate worldwide. To date, we still do not have effective treatment approach to stop or delay the progression of DN (Waanders et al., 2013). Podocyte is the major component of glomerular filtration barrier, and its injury would lead to the leakage of protein (proteinuria). Podocyte injury is considered as the major contributor to DN development, especially in the early stage. Several pathological processes, including persistent proteinuria inflammatory reaction, oxidative stress, vascular endothelial barrier injury and tissue fibrosis are all involved in the development of DN. Due to the complex mechanisms of DN development, treatment simply focusing on single target or pathway might be difficult to achieve satisfactory therapeutic results. Niaoduqing granule, a common clinically used TCM in CKD and ESKD, could treat diseases through a “multi-component, multi-targets and multi-pathways” way. However, the therapeutic effect and the underlying mechanisms of Niaoduqing on the treatment of DN and podocytes injury are still uncertain, especially of the early-stage DN.
In our network pharmacology analysis, 138 active components were considered as potential effective materials of Niaoduqing in podocytes protection and proteinuria reduction. Among the active components, various flavonoids were obtained, including quercetin, luteolin, kaempferol and so on. Flavonoids consisted of a large group of polyphenolic compounds of plant secondary metabolites that can be found widely in vegetables and fruits, and have numerous biological functions in the treatment of various diseases (Seo et al., 2019). In this study, quercetin, luteolin and kaempferol were determined as the most three core ingredient due to its highest topological parameters and the most related overlapped targets. Additionally, with the good docking score, all of them could be considered for the subsequent analysis of Niaoduqing. Due to the cytotoxic injury of the crude extract of Niaoduqing to MPC5 cells, the mixture produced by mixing the purified quercetin, luteolin and kaempferol on a 1:1:1 scale was used in the in vitro experiment. Although only three core ingredients could not fully represent Niaoduqing compound, they might be considered as one of the best alternative methods for clarifying the therapeutic effect of Niaoduqing in vitro experiment. Quercetin is an effectively ingredient in alleviating diabetes and related complications (Yan et al., 2022). It inhibits inflammation, oxidative stress, fibrosis, hyperglycemia and dyslipidemia to stop the progression of DN in a time-dependent and dose-dependent manner (Li et al., 2022b). Luteolin is considered as a potential medicine for kidney intervention in DN, which has anti-inflammatory, anti-oxidative stress and anti-fibrosis properties (Zhang et al., 2021). It also delays apoptosis, deletion, fusion and mitochondrial membrane potential collapse of podocytes, and maintains the normal filtration function of basement membrane through regulating the Nphs2 and NLRP3 inflammasome (Yu et al., 2019; Xiong et al., 2020). Kaempferol also has various biological functions. Except for anti-inflammation and anti-oxidative stress, it can enhance the release of insulin and GLP-1 to inhibit fibrosis of kidney in DN model (Sharma et al., 2020; Luo et al., 2021). Except for them, some other ingredients have been reported to correlate with podocyte protection. Wang et al. [ 2022] found that paeoniflorin can restore autophagy and inhibit apoptosis to protect podocyte from injury via inhibiting VEGFR2-PI3K-AKT activity. As Ertürküner et al. [ 2014] reported, mesangial matrix and podocyte has less damage and micro-albuminuria level decreased in the catechin-treated group compared with the untreated diabetic group, and catechin exposure even has the better protective effect on podocyte structure compared with ACEI. Xu et al. [ 2016] reported that matrine inhibits podocyte damage caused by adriamycin and improves renal function by maintaining the Th17/Treg balance. In addition, rhein and pachymic acid can ameliorate podocyte damage via regulating Wnt/β-catenin signaling pathway in DN mice (Duan et al., 2016; Chen et al., 2017). Multiple compounds and ingredients of Niaoduqing involved in the treatment of podocyte injury and proteinuria in DN, and flavonoids were considered as the most predominant effective constituents.
With the help of network pharmacology and experimental verification, we firstly identify some therapeutic targets of Niaoduqing in improving DN. Seven core potential targets with the higher topological parameters were screened out through PPI network construction and four of which were confirmed by in vitro cell experiment, including VEGF-A, ICAM1, PTGS2 and ACE. Our therapeutic targets mainly concentrate on the molecular process of vascular endothelial barrier, inflammatory reaction and RAS. All of those processes are involved in the pathogenesis of DN. Podocytes can produce VEGF-A, which is an important angiogenic factor and can induce vascular hyper-permeability and inflammation through interaction with endothelial VEGF receptor-2 (Tufro and Veron, 2012). The maintenance of normal VEGF-A levels is crucial for normal kidney structure and function, and either overexpression or insufficient of VEGF-A leads to kidney injury (Sivaskandarajah et al., 2012; Locatelli et al., 2022). ICAM-1 is one of the trans-membrane glycoprotein of the immunoglobulin supergene family, which is widely expressed on endothelium, epithelium, macrophage, and so on. ICAM-1 expression would be upregulated under high glucose condition, which mediates the infiltration of inflammatory cells into renal glomeruli and results in kidney damage (Miyatake et al., 1998; Galkina and Ley, 2006). Inhibition of ICAM-1 expression effectively blocks inflammatory cell infiltration into the glomeruli and alleviates kidney injury (Miyatake et al., 1998; Chen et al., 2016). PTGS-2, also known as COX-2, is one of the key enzymes in catalyzing the conversion of arachidonic acid into prostaglandin and leukotriene, which exacerbates local inflammatory reaction. ACE is the core compounds of RAS. The activation of RAS has been recognized as one of the key potential mechanisms of kidney injury, including DN. The most commonly used antihypertensive drugs, ACEI and ARB, are recommended and widely used in DN patients to inhibit the RAS and improve outcomes (Liu et al., 2020). In the present study, the increased mRNA expression levels of VEGF-A, ICAM-1, PTGS-2 and ACE in HG-induced group indicated that these genes may contribute to the development of DN and podocytes injury. The significant decrease of the expression levels after drug treatment suggested that the four hub targets may be the potential therapeutic targets of Niaoduqing in the management of DN and podocytes damage.
In KEGG pathway enrichment analysis, the potential molecular mechanism of Niaoduqing’s treatment of podocytes injury and proteinuria in DN was most enriched in AGE-RAGE signaling pathways (has04933). Two of the four hub targets (VEGF-A and ICAM1) were involved in this pathway. AGE/RAGE pathway has been demonstrated to be involved in the development of DN (Pathomthongtaweechai and Chutipongtanate, 2020). In the present study, some representative indicators of AGE/RAGE pathway were detected to evaluate its activity, including Rage, PI3K/AKT, NF-κB, VEGF-A, ICAM-1, IL-6, TNF-A, Caspase-3, TGF-B1, COL-1A1. Binding to their receptors RAGE, AGEs can activate downstream signaling pathways a, including TGF-β, p21-RAS and MAPK, and lead to indirect kidney injury (Wautier et al., 2001; Yeh et al., 2001). The upregulation of RAGE expression could be considered as the evidence of pathway activation. The downstream signal molecules of AGE/RAGE pathway, including PI3K/AKT and NF-κB, were detected. Although we observed obvious differences in the phosphorylation level of PI3KTyr317 and AKTSer473, no statistical differences were obtained due to some fluctuating individual values and low number of replicates. NF-κB is a crucial transcription factor involved in the regulation of inflammation, immune response and stress responses. The upregulation and activation of NF-κB is observed in preclinical DN models and kidney tissues of patients with DN (Opazo-Ríos et al., 2020). Targeting NF-κB is confirmed to be an effective method for DN (Opazo-Ríos et al., 2020). In present study, Niaoduqing effectively inhibited the activation of NF-κB in HG-induced injury model. What’s more, some phenotypes mediated by AGE/RAGE pathway were detected in our study. The increase of VEGF-A, ICAM-1, TNF-A and cleaved-Caspase-3 indicated the vascular barrier dysfunction, imbalance of inflammatory reaction and podocytes apoptosis in the HG-induced group. Niaoduqing alleviating those abnormal changes revealed its protective effect on podocytes in DN development. In sum, the administration of Niaoduqing effectively ameliorated podocytes damage caused by HG through partially regulating AGE/RAGE pathway.
Fibrosis of renal tissues is another crucial pathological feature of DN, especially in the end stage. The activation of myofibroblastic and inflammatory cells, extracellular matrix (ECM) expansion and collagens accumulation are identified as the key links of fibrosis development, in which EMT and endothelial to mesenchymal transition (EndMT) are the main sources of matrix-producing myofibroblasts (Srivastava et al., 2021). Inflammatory cytokines are the key profibrotic factors, including tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) (Zheng et al., 2016). Many classical pathways have been reported to be closely related to kidney fibrosis, including Wnt signaling pathway and transforming growth factor β (TGF-β) signaling pathway. The loss of glucocorticoid receptor can promote fibrogenesis in kidney tissues via activating Wnt signaling pathway and interfering with metabolism of fatty acids (Srivastava et al., 2021). Fibroblast Growth Factor Receptor 1 (FGFR1), the endothelial receptor of fibroblast growth factor (FGF), is essential for combating EndMT, and the activation of FGFR1 signaling pathway has been reported to inhibit TGFβ signaling and TGFβ-induced EndMT (Woo et al., 2021). The deficiency of FGFR1 in endothelium can lead to serious fibrosis associated with EndMT (Li et al., 2020b), Compared with the control mice. Sirtuin-3 (SIRT3), one of the NAD-dependent mitochondrial deacetylases, also plays a crucial role in blocking tissues fibrosis via regulating TGF-β/Smad signaling pathway (Srivastava et al., 2018). The loss of SIRT3 can leads to induction of abnormal glycolysis and defective metabolism of kidney tissues, which is responsible for the progression of kidneys fibrosis in diabetes (Srivastava et al., 2018). In present study, we found that the expression of profibrotic factors (TNF-A) and fibrotic markers (TGF-β1 and COL-1A1) significantly increased after high glucose exposure, but the increase was inhibited by Niaoduqing ingredients treatment. These data suggested a therapeutic potential of Niaoduqing in alleviating podocytes fibrosis and inhibiting EndMT.
Recently, there are many potential drugs that have been proven to be effective against DN. As the commonly used anti-hypertension drugs, both ACEI and ARB show good effect on inhibiting kidney fibrosis, but their therapeutic effects are not completely consistent (Srivastava et al., 2020). The author found that DPP-4 and TGF-β signaling are identified as the downstream signals of ACEI in the treatment of kidney fibrosis, but both of them are not regulated by ARB. The anti-fibrotic effects of ACEI but no ARB, partly depend on N-acetyl-seryl-aspartyl-lysyl-proline (AcSDKP), which controls the metabolic switch between glucose and fatty acid metabolism. Another commonly used drug, sodium-dependent glucose transporters 2 inhibitor (SGLT-2i) is considered as a protector of kidney tissues in many kinds of kidney diseases. The application of SGLT-2i can reduce the progression of DN through promoting ketone body induced mechanistic target of rapamycin complex 1 (mTORC1) signaling inhibition (Tomita et al., 2020) The protective effect of SGLT-2i is also related to the inhibition of EMT and aberrant glycolysis (Li et al., 2020c). Compared with the individual application, the combination of SGLT-2i, ACEI and endothelin receptor antagonism can enhance their cardiac and renal protective effects in Type 2 diabetic model (Vergara et al., 2022). Tsuprykov O showed that dipeptidyl peptidase-4 (DPP-4) inhibitor, Linagliptin, has the comparable efficacy to ARB in preventing CKD progression in the $\frac{5}{6}$ nephrectomy rats models. However, there may be differences in the underline mechanism of them (Tsuprykov et al., 2016). In addition, due to Warburg effect, which represents the abnormal shift of energy metabolism from mitochondrial oxidative phosphorylation to aerobic glycolysis, promotes fibrogenesis in kidney tissues, inhibiting glycolysis is considered as a potential anti-fibrotic method (Wei et al., 2019). As Wei et al. [ 2019] reported, both dichloroacetate and shikonin, two glycolysis inhibitors, effectively inhibited the process of renal interstitial fibrosis, and dichloroacetate was recommended because of its higher anti-fibrosis efficiency and lower toxicity. Those drugs are of concern and warrant further research, especially the comparison between Niaoduqing and those drugs.
There were some limitations in this current study. Firstly, whether the mixture of the purified quercetin, luteolin and kaempferol could fully substitutes for Niaoduqing granules is still unclear. Collecting animal serum containing *Niaoduqing via* serologic pharmacology method as previously described is an optional method to solve this problem (Lu et al., 2013). Secondly, missing data of animal experiment is another notable limitation. Further animal experimental validation using *Niaoduqing is* warranted. Thirdly, some of the disease therapeutic targets and pathways may be missed, because we only pay attention to the top predicted targets and pathways. Other predicted targets and pathways need to be further confirmed by both in vitro and in vivo experiments in the future. Due to the above limitations of present study, our results should be interpreted with caution.
## 5 Conclusion
In present study, we found that the active ingredients of Niaoduqing, including quercetin, luteolin and kaempferol, could ameliorate the podocyte injury in DN through multi-ingredients, multi-targets and multi-pathways method using network pharmacology method and experimental verification. VEGFA, ICAM1, PTGS2, ACE may be the major targets, and AGE/RAGE signaling pathway in diabetic complications (hsa04933) might be one of the core signaling pathways. Further evidence of in vivo experiment and clinical data are necessary to confirm our findings.
## 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
YF designed the study, conducted experiments, interpreted the results and wrote the draft of the manuscript. YZ extracted the raw data from public databases, and arranged all figures and tables shown in the final manuscript. CJ contributed to article review. YF and XZ performed experimental validation. CR, XZ, and XZ interpreted the results, revised the manuscript and approved the final version. All of the authors gave final approval of the version to be published and agreed to be accountable for all aspects of the work.
## 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.2023.1047184/full#supplementary-material
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|
---
title: The gene regulatory molecule GLIS3 in gastric cancer as a prognostic marker
and be involved in the immune infiltration mechanism
authors:
- Yi Ding
- Zehua Wang
- Chen Chen
- Chenxu Wang
- Dongyu Li
- Yanru Qin
journal: Frontiers in Oncology
year: 2023
pmcid: PMC10009178
doi: 10.3389/fonc.2023.1091733
license: CC BY 4.0
---
# The gene regulatory molecule GLIS3 in gastric cancer as a prognostic marker and be involved in the immune infiltration mechanism
## Abstract
### Background
Gastric cancer is the most prevalent solid tumor form. Even after standard treatment, recurrence and malignant progression are nearly unavoidable in some cases of stomach cancer. GLIS Family Zinc Finger 3 (GLIS3) has received scant attention in gastric cancer research. Therefore, we sought to examine the prognostic significance of GLIS3 and its association with immune infiltration in gastric cancer.
### Method
Using public data from The Cancer Genome Atlas (TCGA), we investigated whether GLIS3 gene expression was linked with prognosis in patients with stomach cancer (STAD). The following analyses were performed: functional enrichment analysis (GSEA), quantitative real-time PCR, immune infiltration analysis, immunological checkpoint analysis, and clinicopathological analysis. We performed functional validation of GLIS3 in vitro by plate cloning and CCK8 assay. Using univariate and multivariate Cox regression analyses, independent prognostic variables were identified. Additionally, a nomogram model was built. The link between OS and subgroup with GLIS3 expression was estimated using Kaplan-Meier survival analysis. Gene set enrichment analysis utilized the TCGA dataset.
### Result
GLIS3 was significantly upregulated in STAD. An examination of functional enrichment revealed that GLIS3 is related to immunological responses. The majority of immune cells and immunological checkpoints had a positive correlation with GLIS3 expression. According to a Kaplan-Meier analysis, greater GLIS3 expression was related to adverse outcomes in STAD. GLIS3 was an independent predictive factor in STAD patients, as determined by Cox regression (HR = 1.478, $95\%$CI = 1.478 (1.062-2.055), $$P \leq 0.02$$)
### Conclusion
GLIS3 is considered a novel STAD patient predictive biomarker. In addition, our research identifies possible genetic regulatory loci in the therapy of STAD.
## Introduction
Globally, gastric cancer (GC) is the fourth highest cause of cancer-related mortality [1]. Most GC patients have a dismal prognosis due to late diagnosis and inadequate response to existing therapy. Despite continued advances in treatment, GC mortality remains high. Approximately $50\%$ of patients with advanced GC experience recurrence after the first curative resection, *The prognosis* for patients with progressive GC that is recurrent or unresectable remains dismal, with a median survival time of fewer than 12 months with current standard treatment [2]. Therefore, we explored prognostic genetic biomarkers to predict patient survival and response to individualized therapy.
Biomarkers are specific indicators of normal biological, pathogenic, or pharmacological responses to therapeutic interventions. They are features that are objectively measured and evaluated. Effective biomarker screening, it is possible to detect GC earlier and reduce GC mortality. Biomarkers can be produced directly by cancer cells or non-cancerous cells responding to the tumor. The biomarkers found in gastric cancer today are broadly classified into three categories, immune, molecular, and genetic related. Carcinoembryonic antigen (CEA) is gastric cancer most common tumor marker in gastric cancer [3]. CA125, CA19-9, CA72-4, and alpha-fetoprotein have also been reported to contribute to the prognosis of gastric cancer (4–6). Furthermore, tumor markers associated with invasion and metastasis and extracellular matrix (ECM) adhesion and degradation continue to play a role in cancer prognosis. Upregulation or alteration of ECM molecules usually indicates the malignant progression of cancer cells. These include proteases, calmodulin, mucin, and CD44 splice variants (7–9). *While* genetic changes include genetic instability represented by microsatellite instability [10], reactivation of telomerase activity, inactivation of tumor suppressor genes, and activation of oncogenes [11]. Current biomarkers commonly used for clinical testing are CA19-9, CEA, CA72-,4, and PG I/II. The sensitivity and specificity of present biomarker tests still need to be improved and not the best choice for screening GC. Studies based on multiple biomarker assays only help to monitor prognostic indicators in gastric cancer patients after treatment [12]. Therefore, further studies on biomarkers are necessary.
GLI-Similar 3 (GLIS3) is a member of the GLIS subfamily of Krüppel-like zinc-finger transcription factors that regulate gene expression [13, 14]. GLIS3 is essential for controlling numerous physiological processes and has been linked multiple diseases, including neonatal diabetes, glaucoma, polycystic kidney disease, neurological disorders, congenital hypothyroidism, and cancer (15–18). The expression pattern of GLIS3 varies significantly in different types of cancers. GLIS3 is detected in the highly proliferative group of central neurological tumors such as ventricular meningioma and cerebral glioblastoma [19, 20]. In contrast, reduced GLIS3 expression was observed in chromophobe renal cell carcinoma [21]. However, any association of GLIS3 with gastric cancer has hardly been carefully studied.
These data were obtained from TCGA. We analyzed the pattern of GLIS3 expression in gastric cancer and its predictive value. A high GLIS3 level predicted a poor prognosis for people with GC. In addition, GLIS3 is related to immunological response, which offers a novel perspective for tailored therapy. According to this article, high GLIS3 expression is related to poor outcomes in GC patients, and that GLIS3 helps to predict the prognosis of GC patients.
## Patient data sets
We universally processed RNAseq data in TPM format for TCGA, GEO database GSE62254 using UCSC XENA (https://xenabrowser.net/datapages/) via Toil [22]. STAD (gastric cancer) data from TCGA. In addition, the mRNA expression data (407 samples, process type: HTSeq-FPKM) and clinical information were extracted from the TCGA database (https://cancergenome.nih.gov). This work follow TCGA publication criteria to the letter.
## Quantitative reverse transcription PCR
Quantitative real-time polymerase chain reaction (qRT-PCR) RNA samples were obtained from 10 pairs of primary adenocarcinoma tissues and paraneoplastic tissues provided by Linzhou Cancer Hospital (Henan, China). All participants provided written informed permission for this study, which was authorized by the First Affiliated Hospital of Zhengzhou University’s institutional ethics. Following the manufacturer’s directions, total RNA was extracted using a TRIzol reagent (Servicebio, Wuhan, China). A cDNA synthesis kit (Servicebio, Wuhan, China) was used to reverse-transcribe identical quantities of RNA (1 μg). Complementary DNAs (cDNAs) were analyzed by qPCR using SYBR Green Supermix reagent (Thermo Fisher, America) at a final dilution of 1:5. Using GAPDH as a reference gene. The following primers were used in this study: GLIS3 F, TTACAGAGGGCAATGAATGCAG; R, AGACTCACGCGAAATAAGGGA; GAPDH F, CAGGAGGCATTGCTGATGAT; R, GAA GGCTGGGCTCATTT.
## Western blot
Total protein was extracted from grown cells using RIPA buffer (epizme, Shanghai, China) containing protease and phosphatase inhibitors, and total protein was determined using a BCA protein assay kit (Thermo Fisher, USA). Protein samples were separated using $10\%$ SDS-PAGE, transferred to polyvinylidene difluoride (PVDF) membranes (Millipore, Billerica, MA), and incubated with primary and secondary antibodies. Protein bands were identified by a protein imaging system (Amersham Imager 600).
## Cell counting kit-8 assays
CCK-8 assays were performed in 96-well plates at a cell density of 1*103 cells/well, providing 200 µl of medium ($10\%$FBS and RPMI-1640 culture medium) per well. After the prescribed time (every 24H), CCK-8 reagent and 100 μl of media were added to each well, and cells were incubated at 37°C for 2 hours. The absorbance at 490 nm was measured using an enzyme marker to compute the cell growth rate.
## Colony formation assay
After inoculation of 1000 cells per well in a 6-well plate, cell culture was performed for one week. $4\%$ paraformaldehyde was used to fix the cells for 30 minutes, and $1\%$ crystalline violet staining solution was used to stain them for 30 minutes at room temperature. These plates were air-dried and thoroughly washed before being photographed.
## Wound healing assay
Gastric cancer cells were seeded in 6-well plates. After the cells grew to $100\%$ fusion, the cell layer was scratched with the tip of a 200 µl pipette, and the medium containing $10\%$ fetal bovine serum was replaced with a serum-free medium. Images of the cells were captured at 0 and 48, respectively.
## Cell migration assay
In migration assays, 5x104 gastric cancer cells were inoculated into Transwell chambers in serum-free medium; the chambers were inserted above a 24-well plate containing $20\%$ FBS medium. After incubation at 37˚C with $5\%$ CO2 for 24 hours, the Transwell chamber was removed, and the medium in the smaller chamber was discarded and washed with PBS. The cells were then fixed with $4\%$ paraformaldehyde for 30 min and stained with $0.1\%$ crystal violet for 30 min. The top unmigrated cells were gently swabbed off with a cotton swab and observed and photographed under a microscope.
## Differential expression gene analysis
The median GLIS3 expression was used as the cut-off value (HTseq-Count) to distinguish between low and high GLIS3 expression in STAD samples. And Differential expression gene (DEG) analysis was performed using the DESeq2 R package (1.26.0) [23, 24].
## Functional enrichment analysis
The DEGS threshold for functional enrichment analysis was defined as logFC greater than two and adjusted P value less than 0.05 for upregulated gene sets. Gene ontology (GO), including biological process (BP), cellular component (CC) and molecular function (MF), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using the clusterProfiler package (version 3.14.3 version) (for enrichment analysis); org.Hs.eg.db package (version 3.10.0) (for ID conversion).
## Gene set enrichment analysis
GSEA is a computational tool for determining if a previously defined set of genes demonstrates statistically and persistently significant differences between two biological states [25]. We utilized the ClusteProfile R Package (3.14.3) to investigate functional and route differences between the two groups with distinct GLIS3 expressions. The number of permutations for each analysis was set to 1000. Significant enrichment was determined to exist when the False discovery rate (FDR) was less than 0.25, and p.adjust was less than 0.05 [26].
## Immunoassay
Using the GSVA R package (1.34.0), we performed a single sample gene set enrichment analysis (ssGSEA) for the immune infiltration study of GLIS3 [27]. Twenty-four distinct types of invading immune cells were analyzed [28]. The link between GLIS3 and immunological checkpoints, including PD1, PD-L1, CTLA4, LAG3, TIGIT, and CD48, was then investigated further (ggplot2 3.3.3).
## Statistical analyses
Using the R programming language, all statistical analyses and visualizations were generated (version 3.6.3). Wilcoxon rank-sum test was used to evaluate the expression of GLIS3 in samples that were not paired. The diagnostic value of GLIS3 gene expression was determined using ROC curves, with the area under the ROC curve serving as the diagnostic value. Univariate COX analysis was performed to screen for potential prognostic markers, and multivariate COX analysis was used to confirm the influence of GLIS3 expression on survival in conjunction with other clinical variables. Combining GLIS3 expression with clinical factors, a nomogram was developed to predict STAD patients’ overall survival at 1, 3, and 5 years. Utilizing Kaplan-Meier survival analysis, the survival distribution was estimated. P-values below 0.05 were considered statistically significant.
## High expression of GLIS3 in gastric cancer
Comparing GLIS3 expression in normal tissues and tumor samples from the TCGA and GTEx databases, we discovered that GLIS3 expression differed significantly in the majority of cancer types (Figure 1A). We verified the expression of GLIS3 mRNA in gastric cancer tissues by quantitative qRT-PCR. We found that GLIS3 mRNA expression was upregulated in gastric cancer tissues ($$n = 10$$) compared with normal gastric tissues ($P \leq 0.001$, Figure 1B). In addition, GLIS3-related protein expression data are available in the HPA database. Immunohistochemical results showed that GLIS3 expression was higher in gastric cancer compared to normal tissues. ( Figure 1C).
**Figure 1:** *GLIS3 expression and functional analysis. (A) Elevated or decreased GLIS3 in cancer and paracancerous tissues in different tumor types from The Cancer Genome Atlas (TCGA) database. (B) qRT-PCR for the detection of GLIS3 gene expression in 10 cases of paracancerous tissues and gastric cancer tissues. (C) GLIS3 immunohistochemical staining of gastric cancer and normal gastric tissues in the HPA database. (Scale Bar=100μm) (D) A total of 354 up-regulated genes and 252 down-regulated genes were identified as statistically significant in the GLIS3 high and low expression groups. Where red dots indicate upregulated genes, blue dots imply downregulated genes, and gray is not statistically significant. (E) GO enrichment analysis and connection diagram with a visual network; BP, biological process; CC, cellular composition; MF, molecular function. (***P <0.001, ns, No sense).*
## Identification of DEGs with GLIS3 and functional enrichment analysis
DEG identification with GLIS3 was performed using |logFC| >2 and PADJ <0.05. A total of 606 DEGs comprised 354 up-regulated genes, and 252 down-regulated genes were discovered between the two groups of low and high GLIS3 expression (Figure 1D). The following are the outcomes of GO functional analysis and KEGG enrichment analysis. BP includes a humoral immune response, protein activation cascade, and digestion. CC consisted of an immunoglobulin complex, blood microparticle, and cornified envelope. MF has antigen binding, serine-type endopeptidase inhibitor activity, and peptidase inhibitor activity. KEGG covered fat digestion and absorption, neuroactive ligand-receptor interaction, and the interaction between cytokine and Pancreatic secretion (Figure 1E).
Using the MSigDB library, we performed GSEA analysis to identify better the biological processes associated with GLIS3. Reactome gpcr ligand binding, G alpha-I signaling events, class A 1 rhodopsin-like receptors, leishmania infection, and platelet activation signaling and aggregation exhibited significant differential enrichment among the significantly enriched gene collections (Figures 2A, B).
**Figure 2:** *Enrichment analyses and PPI network (A) Enrichment analyses from GSEA. GLIS3 participates in five related pathways in gastric cancer: Reactome gpcr ligand binding, G alpha-I signaling events, class A 1 rhodopsin-like receptors, leishmania infection, and platelet activation signaling and aggregation. MSigDB was used for the gene set database. 1000 random sample permutations were performed. NES, normalized enrichment score; FDR, false discovery rate. (B) Mountain range map for GSEA enrichment analysis. (C) A PPI network consisting of 10 HUB genes. (D) Heat map showing the top 50 genes associated with GLIS3 co-expression in gastric cancer. (***P <0.001).*
To find out the potential relationship between GLIS3 and other genes in gastric cancer, PPI network analysis was performed with the help of an online string (https://string-db.org/) database (Figure 2C). The 50 genes associated with GLIS3 with $P \leq 0.05$ and the highest correlation were also represented using a single gene co-expression heat map (Figure 2D). In this case, CLU3 and SUFU are strongly associated with cancer progression, and recent studies suggest that they appear to be associated with iron death sensitivity (29–31)
## GLIS3 implies the proliferation and metastasis of gastric cancer cells
To further test our hypothesis. The silencing of GLIS3 was achieved in gastric cancer cells AGS and MKN28 by transient transfection with Lipofectamine 3000 containing 1μg siRNA, and the transfection efficiency was detected by WB and qPCR after 48H collection of protein and RNA (Figures 3A, B). To verify whether GLIS3 affects the proliferative ability of gastric cancer, CCK8 and plate cloning experiments were performed using siRNA groups and a blank control (CTL) group. ( Figures 3A, D) The results of the experiments showed that the proliferation ability of the cells was inhibited. Following that, transwells and cell scratch assay were used to detect the ability of gastric cancer cells to spread. The metastatic ability of gastric cancer cells was similarly inhibited after GLIS3 silencing, as indicated by the results (Figures 3E, F).
**Figure 3:** *GLIS3 in vitro functional validation (A) Validation of GLIS3 knockdown efficiency in gastric cells by qRT-PCR assay. (B) Western Blot was used to analyze the silencing efficiency of gastric cancer cells. (C) The effect of GLIS3 knockdown on CCK-8 cell proliferation in MKN28 and AGS cells. (D) Plate cloning assay of the effect of GLIS3 knockdown on cell proliferation capacity in MKN28 and AGS cells. (Scale Bar=5mm) (E) GLIS3 was silenced in gastric cancer cells, and cell wound healing and microscopic observations were photographed at 0 and 48 h after scratching the AGS and MKN28cells surface. (F) Transwell assay detects the effect of GLIS3 silencing on the migratory ability of MKN28 and AGS cells. (***P <0.001, **P < 0.01, *P< 0.05).*
## Correlation between immune infiltration of GC and GLIS3 expression
Tumor immune infiltration plays an important role in predicting OS incidence. the proportion of 24 immune cell subtypes in different GLIS3 expression groups showed that Mast cells ($P \leq 0.001$), NK CD56 bright cells ($$P \leq 0.069$$), TFH (T follicular helper, $$P \leq 0.133$$), Th1 cells ($$P \leq 0.288$$), pDCs (plasmacytoid dendritic cells, $P \leq 0.05$), Eosinophils ($P \leq 0.005$), iDCs (immature DCs, $P \leq 0.005$), Macrophages ($P \leq 0.005$), Neutrophils ($$P \leq 0.005$$), NK cells ($P \leq 0.005$), Tcm (Central Memory T cell, $$P \leq 0.224$$), CD8 T cells($$P \leq 0.343$$), Tem (Effective Memory T Cell, $P \leq 0.005$), B cells ($$P \leq 0.208$$), and DC (dendritic cell, $P \leq 0.$ 05), were significantly increased in high GLIS3 group, while aDCs (activated DCs, $$P \leq 0.288$$), Treg (regulatory T cells) ($$P \leq 0.221$$), T cells ($$P \leq 0.924$$), NK CD56 dim cells ($$P \leq 0.090$$), Cytotoxic cells ($$P \leq 0.936$$), Tgd (T gamma delta, $$P \leq 0.936$$), T helper cells ($$P \leq 0.804$$), Th17 cells ($$P \leq 0.662$$), and Th2 cells ($$P \leq 0.005$$) were significantly decreased (Figures 4A, B).
**Figure 4:** *GLIS3 expression in STAD in relation to immune infiltration. (A) Expression of GLIS3 in gastric cancer is closely associated with immune cell infiltration. (B) Correlation of GLIS3 expression with 24 immune cells in gastric cancer. (C) Correlation of GLIS3 expression with the degree of infiltration of 24 immune cells and specific p-values in gastric cancer. (**P < 0.01, *P< 0.05, ns, No sense).*
Furthermore, the expression of GLIS3 was associated with Treg (regulatory T cells, r=− 0.018, $$P \leq 0.724$$), NK CD56 dim cells (r=−0.045, $$P \leq 0.380$$), Tgd (T gamma delta, r =− 0.009, $$P \leq 0.863$$), T helper cells (r=− 0.028, $$P \leq 0.590$$), Th17 cells (r= − 0.041, $$P \leq 0.427$$), and Th2 cells (r=− 0.167, $$P \leq 0.001$$) shown negative correlation. A positive correlation was found between GLIS3 expression and infiltration levels of aDCs (activated DCs, $r = 0.006$, $$P \leq 0.901$$), B cells ($r = 0.111$, $P \leq 0.05$), CD8 T cells ($r = 0.103$, $P \leq 0.05$), Cytotoxic cells ($r = 0.035$, $$P \leq 0.494$$), DC (dendritic cell, $r = 0.187$, $P \leq 0.001$), Eosinophils ($r = 0.208$, $P \leq 0.001$), iDCs (immature DCs, $r = 0.207$, $P \leq 0.001$), Macrophages ($r = 0.233$, $P \leq 0.001$), Mast cells ($r = 0.374$, $P \leq 0.001$), Neutrophils ($r = 0.162$, $P \leq 0.005$), NK CD56 bright cells ($r = 0.120$, $P \leq 0.05$), NK cells ($r = 0.203$, $P \leq 0.001$), pDCs (plasmacytoid dendritic cells, $r = 0.142$, $P \leq 0.05$), T cells ($r = 0.026$, $$P \leq 0.621$$), Tem (Effective Memory T Cell, $r = 0.189$, $P \leq 0.001$), Tcm (Central Memory T cell, $r = 0.046$, $$P \leq 0.370$$), TFH (T follicular helper, $r = 0.113$, $$P \leq 0.029$$), and Th1 cells ($r = 0.126$, $P \leq 0.05$) (Figure 4C).
## Expression of GLIS3 is associated with immune checkpoints
Immune checkpoints are a series of molecules expressed on immune cells and regulate the degree of immune activation. Tumor cells express substances that activate immune checkpoints, blocking the antigen presentation process in tumor immunity, suppressing immune function and causing immune escape. In relation, the expression of GLIS3 in connection to immunological checkpoints such as PD1, PD-L1, CTLA4, CD200, CD276, CD28, CD44, CD80, CD86, HAVCR2, NRP1, and VSIR was studied. GLIS3 is positively correlated with many immune checkpoints. Among them, CD200, CD28, CD44, NRP1, and VSIR had a robust correlation ($P \leq 0.001$).CD276, CD80, CD86 and HAVCR2 expression levels were favorably linked with GLIS3 expression ($P \leq 0.05$, Figures 5A, B). These results suggest that GLIS3 is intimately involved in regulating immune interactions and may regulate tumor immune escape.
**Figure 5:** *Correlation of GLIS3 expression with immune checkpoints. (A) Correlation between GLIS3 expression in GC and immune checkpoints (PD1, PD-L1, CTLA4, CD200, CD276, CD28, CD44, CD80, CD86, HAVCR2, NRP1, and VSIR) (B) Heat map depicting immune checkpoints correlated with GLIS3 expression in TCGA-STAD. (*** P <0.001, **P < 0.01, * P< 0.05).*
## Clinical characteristics and prognosis analysis related to GLIS3 expression
From the TCGA data portal in October 2022, 375 patients with the required clinical features were extracted. Table 1 lists the detailed clinical features. Among 375 subjects, 188 demonstrated high GLIS3 expression, and 187 demonstrated low expression. There were 134 men and 241 women present. The average age of the participants was 65. The mean age of all participants was 65 years. Stage STAD: 53 patients in stage I, 111 in stage II, 150 in stage III, and 38 in stage IV. GLIS3 expression was connected with regional lymph node condition, PFI event, DSS event, and Anatomic neoplasm subdivision (Table 1)
**Table 1**
| Characteristic | Low expression of GLIS3 | High expression of GLIS3 | p |
| --- | --- | --- | --- |
| n | 187 | 188 | |
| N stage, n (%) | | | 0.008 |
| N0 | 63 (17.6%) | 48 (13.4%) | |
| N1 | 52 (14.6%) | 45 (12.6%) | |
| N2 | 39 (10.9%) | 36 (10.1%) | |
| N3 | 24 (6.7%) | 50 (14%) | |
| PFI event, n (%) | | | 0.007 |
| Alive | 138 (36.8%) | 113 (30.1%) | |
| Dead | 49 (13.1%) | 75 (20%) | |
| DSS event, n (%) | | | 0.045 |
| Alive | 141 (39.8%) | 122 (34.5%) | |
| Dead | 37 (10.5%) | 54 (15.3%) | |
| Anatomic neoplasm subdivision, n (%) | | | 0.049 |
| Antrum/Distal | 69 (19.1%) | 69 (19.1%) | |
| Cardia/Proximal | 22 (6.1%) | 26 (7.2%) | |
| Fundus/Body | 71 (19.7%) | 59 (16.3%) | |
| Gastroesophageal Junction | 14 (3.9%) | 27 (7.5%) | |
| Other | 4 (1.1%) | 0 (0%) | |
Analyzing the primary clinical characteristics of the low and high GLIS3 expression groups, GLIS3 expression was higher in the PFI event death group, and expression increased with increasing staging in the N stage. ( Figures 6A, B). Based on GLIS3 gene expression data, ROC curve analysis was done to determine the diagnostic utility of this gene. With a measurement of 0.781, the area has a high diagnostic value. ( Figure 6C).
**Figure 6:** *Diagnostic value of GLIS3 expression for STAD. Relationship between GLIS3 expression and clinicopathological features of STAD and diagnostic value. (A) PFI Event. (B) N Stage. (C) ROC analysis of GLIS3 showed that GLIS3 has the ability to differentiate between tumor and normal tissue. (D) Expression, risk score and survival time distribution of GLIS3. (E) Nomogram for predicting 1-year, 3-year and 5-year OS probabilities in patients with gastric cancer. (F) Calibration curve model to validate the predictive value of OS prediction for 1- year, 3-year and 5-year survival (**P < 0.01, *P< 0.05).*
Univariate analysis revealed that age, TMN classification, pathologic stage, Primary therapeutic outcome, and Residual tumor are linked with GLIS3 expression level and OS ($P \leq 0.05$). In addition, these risk factors were included in multivariate COX regression models (Table 2). The association between risk score, survival time, and the GLIS3 expression profile was then investigated. ( Figure 6D) Clinical characteristics were incorporated into the nomogram model, and the anticipated probabilities of the calibration curve were congruent with the observed data. ( Figures 6E, F).
**Table 2**
| Characteristics | Total(N) | Univariate analysis | Univariate analysis.1 | Multivariate analysis | Multivariate analysis.1 |
| --- | --- | --- | --- | --- | --- |
| Characteristics | Total(N) | Hazard ratio (95% CI) | P value | Hazard ratio (95% CI) | P value |
| T stage | 362 | | | | |
| T1 | 18 | Reference | | | |
| T2 | 78 | 6.725 (0.913-49.524) | 0.061 | 27439473.697 (0.000-Inf) | 0.996 |
| T3 | 167 | 9.548 (1.326-68.748) | 0.025 | 29401820.042 (0.000-Inf) | 0.996 |
| T4 | 99 | 9.634 (1.323-70.151) | 0.025 | 32118936.113 (0.000-Inf) | 0.995 |
| N stage | 352 | | | | |
| N0 | 107 | Reference | | | |
| N1 | 97 | 1.629 (1.001-2.649) | 0.049 | 1.792 (0.718-4.471) | 0.211 |
| N2 | 74 | 1.655 (0.979-2.797) | 0.060 | 2.332 (0.761-7.145) | 0.138 |
| N3 | 74 | 2.709 (1.669-4.396) | <0.001 | 2.892 (0.946-8.844) | 0.063 |
| M stage | 352 | | | | |
| M0 | 327 | Reference | | | |
| M1 | 25 | 2.254 (1.295-3.924) | 0.004 | 0.515 (0.137-1.935) | 0.326 |
| GLIS3 | 370 | | | | |
| Low | 185 | Reference | | | |
| High | 185 | 1.478 (1.062-2.055) | 0.020 | 1.070 (0.687-1.666) | 0.766 |
| Pathologic stage | 347 | | | | |
| Stage I | 50 | Reference | | | |
| Stage II | 110 | 1.551 (0.782-3.078) | 0.209 | 1.058 (0.286-3.916) | 0.932 |
| Stage III | 149 | 2.381 (1.256-4.515) | 0.008 | 0.901 (0.158-5.141) | 0.907 |
| Stage IV | 38 | 3.991 (1.944-8.192) | <0.001 | 1.789 (0.256-12.490) | 0.557 |
| Age | 367 | | | | |
| <=65 | 163 | Reference | | | |
| >65 | 204 | 1.620 (1.154-2.276) | 0.005 | 1.656 (1.052-2.606) | 0.029 |
| Primary therapy outcome | 313 | | | | |
| PD | 64 | Reference | | | |
| SD | 16 | 0.590 (0.267-1.305) | 0.193 | 1.016 (0.396-2.605) | 0.974 |
| PR | 4 | 0.750 (0.233-2.412) | 0.629 | 0.654 (0.150-2.861) | 0.573 |
| CR | 229 | 0.215 (0.145-0.319) | <0.001 | 0.279 (0.169-0.461) | <0.001 |
| Residual tumor | 325 | | | | |
| R0 | 294 | Reference | | | |
| R1 | 15 | 1.910 (0.961-3.797) | 0.065 | 1.214 (0.508-2.901) | 0.662 |
| R2 | 16 | 7.866 (4.325-14.304) | <0.001 | 2.111 (0.645-6.906) | 0.217 |
| Histological type | 369 | | | | |
| Mucinous Type | 19 | Reference | | | |
| Diffuse Type | 63 | 3.474 (1.048-11.515) | 0.042 | 2.657 (0.579-12.203) | 0.209 |
| Signet Ring Type | 11 | 8.442 (2.234-31.893) | 0.002 | 2.949 (0.513-16.944) | 0.225 |
| Not Otherwise Specified | 202 | 4.095 (1.291-12.987) | 0.017 | 3.297 (0.764-14.228) | 0.110 |
| Papillary Type | 5 | 5.925 (1.193-29.429) | 0.030 | 9.728 (1.431-66.124) | 0.020 |
| Tubular Type | 69 | 3.310 (1.000-10.956) | 0.050 | 1.886 (0.415-8.571) | 0.411 |
Kaplan-Meier survival analysis was performed using the TCGA database and GEO database GSE62254. Poor prognosis in patients with high GLIS3 expression. The results after performing subgroup analysis showed that poor prognosis in patients with high GLIS3 expression was associated with T stage ($P \leq 0.05$), M stage ($P \leq 0.05$), Age ($P \leq 0.05$), male ($P \leq 0.05$), race-white ($P \leq 0.05$) and Histologic grade ($P \leq 0.05$), respectively (Figure 7).
**Figure 7:** *Kaplan-Meier curve for overall survival in gastric cancer. (A) High levels of GLIS3 expression in TCGA database and GEO database often correlate with poor prognosis (OS) in GC patients. (B) Kaplan-Meier prognostic analysis of Age≦65, Stage T1&T3, Stage M0&M1, Male, Histological Grade, White Ethnicity Regarding GLIS3 high and low expression scores in GC.*
## Discussion
Gastric cancer is the fourth highest cause of cancer-related death [1]. Gastric cancer begins in the innermost layer of the stomach, infiltrates more profound into the stomach wall, and spreads to nearby lymph nodes, the liver, the lung, and the peritoneum. Since early stomach cancer is typically asymptomatic, many individuals are discovered with the disease at an advanced stage. Surgical resection may be able to cure early-stage, locally-confined stomach cancer. Advanced tumors can only be treated with palliative care and have a poor prognosis. Exploring the genetic processes of gastric carcinogenesis and prognostic markers may lead to developing more effective treatments for people with gastric cancer.
The mouse GLIS3 gene with five C2H2-type zinc finger motif highly similar to the Gli and *Zic* gene families was found for the first time in 2003 [14]. GLIS3 possesses DNA-binding transcription factor and DNA-binding transcription activator activity, RNA polymerase II specificity, and is implicated in the formation of pancreatic -cells and the thyroid [18]. The possible prognostic impact of GLIS3 in gastric cancer has not been reported. Our data indicate that the expression of GLIS3 is substantially linked with immune infiltration and OS in patients with GC. We examined the relationship between GLIS3 and immune cells, which suggests that GLIS3 may be associated with immune infiltration. As the tumor microenvironment has been explored, Immune cells play a complex and crucial role in tumor growth [32].
We found that GLIS3 expression positively correlated with most immune cells. In tumors with high GLIS3 expression, immune cells were highly infiltrating. And GLIS3 tended to show increased expression in gastric cancer. The tumor microenvironment (TME) is conducive to the growth and expansion of cancer cells. Many cell types are involved in the TME and host anti-tumor immune responses, and normal tissue destruction also occur in the TME (33–35). This may be why increased GLIS3 expression promotes gastric carcinogenesis and a bad prognosis: disruption of the TME in concert with immunosuppressive cells results in immune evasion. Among immune cells, macrophages demonstrated a stronger connection with GLIS3 expression ($P \leq 0.001$) M1-type macrophages can destroy tumor cells and protect against pathogen invasion, but M2-type macrophages primarily promote tumor growth, invasion, and metastasis. The majority of macrophages in tumor tissues have the phenotype and function of M2 macrophages, and their degree of infiltration is adverse effect (36–38). Due to the phenotypic alteration of tumor-associated macrophages, the immune milieu is shifted from an anti-tumor state to an immunosuppressive state, indicating an increased risk of tumor invasion. Mast cells are immune cells seen in human cancers present in all vertebrates and were named by Paul Ehrlich (39–41). Mast cell density is correlated with angiogenesis, the number of metastatic lymph nodes, and patient survival in gastric cancer. Mast cells promote the development of gastric cancer by releasing angiogenic (VEGF-A, CXCL8, MMP-9) and lymphangiogenic components (VEGF-C, VEGF-F) (41–45). And in our immune infiltration analysis, mast cells were the immune cells with the most significant positive correlation with GLIS3, suggesting a higher infiltration rate of mast cells in tumors, leading to dysregulation of antitumor effects and correlating with poor patient prognosis [46]. Also, NK cells, which are highly associated with GLIS3 expression, impact on immunotherapy, and targeting NK cells may improve anti-tumor immune responses [47].
Furthermore, we discovered a clear correlation between GLIS3 expression and immunological checkpoints such as NRP1, CD200, and CD276. Research by Dario A.A. Vignali’s team suggests that blocking NRP1, a potential immune checkpoint in T cells, could improve immunotherapy and help prevent cancer recurrence [48]. And CD200 (OX-2), on the other hand, is a cell surface glycoprotein that confers immune escape by suppressing the alloimmune and autoimmune responses through its receptor CD200R [49].B7-H3 (CD276) is overexpressed in a variety of tumor types. It is a promising target for anticancer immunotherapy. In addition to its immunomodulatory effects, B7-H3 has intrinsic tumorigenic activities that enhance cell proliferation, migration, invasion, angiogenesis, metastasis, and anti-tumor drug resistance [50]. From this, we can prove that GLIS3 may alter tumor immunology and may be a potential immunotherapy treatment target, instead of a simple prognostic biomarker. In terms of prognosis, in the group with high GLIS3 expression, the chance of survival was lower for T stage, M stage, age, male, white race, and histologic grade, indicating that GLIS3 has some predictive effect on prognosis.
To predict 1- years, 3- years, and 5-years OS in GC, we built a prognostic nomogram model of GLIS3 expression levels based on COX regression analysis. Time-dependent ROC curves demonstrate the nomogram’s dependable prediction capabilities. Our model may give a new starting point for prognostic prediction and individualized assessment of patients with GC. Nonetheless, this study still has certain drawbacks. The regulatory mechanisms and signaling pathways linked with GLIS3 require additional analysis. Future multicenter research should be conducted to validate the predictive model.
## Conclusion
GLIS3 is significantly expressed in GC, and high expression is related to a bad prognosis. Our research indicates that GLIS3 is a potential prognostic factor and genetic therapeutic target. Future research will concentrate on the mechanism of action of GLIS3 in GC so that GLIS3 can become a therapeutic and prognostic factor for the benefit of patients.
## Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://xenabrowser.net/datapages/ https://cancergenome.nih.gov https://www.ncbi.nlm.nih.gov/geo.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
YQ, YD, and ZW conceived and designed the experiments. CW and ZW performed functional enrichment analyses. DL and ZW completed the supplemental experiment. YD and CC analyzed the results and wrote the manuscript. The manuscript was written through the contributions of all authors. 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: Dry eye disease severity and impact on quality of life in type II diabetes
mellitus
authors:
- Tetiana Zhmud
- Natalia Malachkova
- Robert Rejdak
- Ciro Costagliola
- Marina Concilio
- Galyna Drozhzhyna
- Damiano Toro Mario
- Svitlana Veretelnyk
journal: Frontiers in Medicine
year: 2023
pmcid: PMC10009182
doi: 10.3389/fmed.2023.1103400
license: CC BY 4.0
---
# Dry eye disease severity and impact on quality of life in type II diabetes mellitus
## Abstract
### Aim
To assess the severity of dry eye disease (DED) in humans, its impact on quality of life (QoL) and to grade the damage incurred by the anterior ocular surface in patients diagnosed with type 2 diabetes mellitus (T2DM).
### Patients and methods
Forty-six patients (mean age ± SD = 63.8 ± 6.7 years) diagnosed with T2DM were enrolled in the experimental group and 26 healthy individuals constituted the control group (67.9 ± 8.9 years). The diagnosis and gradation of DED were conducted in accordance with the International Task Force severity grading scheme. Disease-specific questionnaires were used to obtain the Ocular Surface Disease Index (OSDI) and assess the negative effects of the disease on the patient’s QoL. The severity of conjunctival redness and corneal/conjunctival staining was assessed by Efron and Oxford scales, respectively.
### Results
According to OSDI scores, the entire experimental group presented symptoms of DED: $54.4\%$ were diagnosed with mild DED and $46.6\%$ with moderately severe DED. No cases of severe DED were diagnosed in either the experimental or control group. In the control group, $57.7\%$ of individuals did not have the disease. A significant difference between the experimental and control groups was recorded for both OSDI scores ($p \leq 0.01$) and health-related QoL ($p \leq 0.01$). It was observed that keratopathy influenced the mean OSDI values of patients. The mean OSDI value was 25.14 ± 3 in the experimental group diagnosed with keratopathy, 19.3 ± 3.5 in the subgroup with no indications of corneal injury ($$p \leq 0.000002$$), and 13.0 ± 3.0 in the control group ($p \leq 0.000002$). Based on the DEWS scheme, a grade I severity level was observed in $46\%$ of control subjects and $33\%$ of patients diagnosed with T2DM ($$p \leq 0.4915$$); grades II and III were detected in the bulk of the experimental group ($$p \leq 0.0051$$; $$p \leq 0.1707$$). None of the subjects in the control or experimental groups manifested grade IV severity of DED.
### Conclusion
In comparison to healthy adults, DED adversely impacts the QoL of type 2 DM patients, regardless of the disease’s association with keratopathy.
## Introduction
The 10th edition of the International Diabetes Atlas [2021] predicts an increase in the global prevalence of diabetes mellitus (DM) from 537 million in 2021 to 786 million in 2045 [1]. Indeed, a steady surge has been observed in the number of patients diagnosed with type 2 diabetes mellitus (T2DM) [2] with about 541 million adults reporting impaired glucose tolerance, a powerful predisposing factor for T2DM [1].
DM can affect all ocular tissues. Patients with DM manifest symptoms in both anterior and posterior segments of the eye, causing ailments such as blepharitis, cataract, diabetic retinopathy, and macular edema. Dry eye has been recognized as a ubiquitous ocular symptom in diabetic patients. Nevertheless, this condition is often overlooked by healthcare providers [3].
Hom et al. reported that $53\%$ of patients with either diabetes or glucose intolerance were diagnosed with clinically relevant dry eye disease (DED) [4]. Yu et al. Have also been able to demonstrate a positive correlation between proliferative diabetic retinopathy and tear film dysfunction [5].
The Dry Eye Workshop II of the Tear Film and Ocular Surface Society (TFOS DEWS II) defined DED as “A multifactorial disease of the ocular surface characterized by a loss of homeostasis in the tear film and accompanied by ocular symptoms, in which tear film stability and hyperosmolarity, ocular surface inflammation and damage, and neurosensory abnormalities play etiological roles” [6]. About 344 million people worldwide have either been directly or indirectly afflicted by DED, marking it as a growing global medical concern. DED is markedly more prevalent in diabetics than in healthy subjects and also more common in people with T2DM than in type 1 diabetes mellitus (T1DM) [7, 8]. Recent publications have reported that the prevalence of DED ranges from 36 to $72\%$ in T2DM patients (9–11). Pathophysiologically, DED is a multifactorial disorder; several intrinsic and extrinsic factors may further worsen this condition, such as diabetes, immunological and metabolic disturbances. Chronic hyperglycemia results in abnormal tear dynamics and osmolarity, activation of the inflammatory cascade, and innate immune responses which, in turn, induce oxidative stress [12]. Persistent symptoms of DED, such as visual disturbance, blurred vision, ocular discomfort, burning, foreign body sensation, and photophobia affect the physical as well as the mental quality of life (QoL), reducing both to below-healthy standards [13, 14]. Patient responses in questionnaires on QoL are important tools to evaluate and document psychometric characteristics of well-being and levels of independence [15] of such a drastically expanding population. Several psychometric tests have been developed and validated for the assessment of health and QoL in patients with DED. Specifically, the Ocular Surface Disease Index (OSDI) score has been designed to expeditiously assess ocular symptoms consistent with DED, their impact on vision, and eventually on QoL. To our knowledge, this is the first study assessing the inclemency of DED, its prevalence, and its impact on QoL. We have also aimed to grade the damage of the anterior ocular surface in patients with T2DM, based on DEWS, compared to a healthy individual.
## Study design
This study was designed to be prospective and observational and performed at Vinnytsia Regional Clinical Hospital, named after “N.I. Pirogov,” from June to December 2021. The study protocol was approved by the local Committee of Bioethics of the National Pirogov Memorial University. Procedures were performed following the Declaration of Helsinki. Written and informed consent was obtained from all patients included in the study.
All consecutive patients addressed to the eye clinic with a diagnosis of T2DM were enrolled. Healthy individuals, sex- and age-matched, were constituted as the control group.
Exclusion criteria comprised of prior history in ocular surgery, use of ocular topical treatment within seven days of the study, and affliction with systemic diseases considered as independent risk factors for DED (Sjogren syndrome, rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis). The majority of T2DM patients included in the study had standard glycemic control (mean HbA1c = 7.0 ± $0.7\%$, range from 5.6 to $9.0\%$).
All the patients underwent a thorough ophthalmologic examination and specific tests to evaluate tear film quantity (Schirmer test) and quality (fluorescein tear break-up time (TBUT)).
The International Task Force severity grading scheme (dry eye severity grading scheme), recommended by DEWS, was used to diagnose and grade DED [2007] [16, 17]. This scheme is based on nine parameters and classifies DED into four severity levels. The nine parameters include self-reported discomfort, palpable signs, and symptoms noted in ocular surface examination (conjunctival redness and staining, corneal staining, corneal/tear signs, meibomian gland dysfunction (MGD), TBUT, Schirmer test). The Efron grading scale was used to evaluate the degree of conjunctival redness [18], while the Oxford Scheme was used for corneal/conjunctival staining [19].
The Oxford *Scheme is* a grading scale consisting of a panel series marked A to E, representing patterns, used as standard images to grade the degree of staining observed in patients with DED. Severity is assessed by the number of punctate dots recorded on slip-lamp examination [19].
Quality-of-life (QoL), appertaining to health, was gauged by the OSDI (Allergan, Irvine, CA, United States) as per the recommendation of the TFOS DEWS II [6]. The responses of patients to custom questionnaires were reliable and valid for both DED and QoL assessment owing to satisfactory psychometric elements [20]. The questionnaire rating consisted of twelve modules distributed into three subscales, viz., ocular symptoms, vision functionalities, and environmental triggers. For each module, patients asserted a particular frequency and/or severity of the symptom on the five-point Likert scale. The all-inclusive score had a range of 0 to 100, with a cut-off value of 12 (positive for DED if the score is ≥13). DED could then be classified as mild (from 13 to 22), moderate (23–32), and severe (≥ 33) based on the scores obtained [21].
## Statistics
The software suite STATISTICA v.10.0 (StatSoft, Europe) was used for data analysis in this study. Continuous variables were analyzed as mean value ± standard deviation (SD), while absolute variables were measured in terms of proportions. An independent 2-tailed t-test was conducted for comparing quantitative variables with the bell curve (OSDI, presence of keratopathy), whereas Fisher’s exact test was used for the comparison of qualitative variables. The relationship between OSDI and the severity of DED was evaluated using Spearman’s correlation analysis. p-values <0.05 were considered statistically significant.
## Results
Forty-six patients diagnosed with T2DM and DED were included in the experimental group (mean age ± SD = 63.8 ± 6.7 years) and twenty-six age- and sex-matched healthy participants were enrolled in the control group (mean age ± SD =67.9 ± 8.9 years). Table 1 displays the demographic as well as the clinical data of participants from both experimental and control groups. The acuteness of DED was assessed through the OSDI questionnaire (Table 2) and the severity scheme issued by DEWS. The OSDI score ascribed DED symptoms to all the patients of the experimental group. Twenty-five patients, representing $54.4\%$ of the experimental group, and 11 patients comprising $42.3\%$ of the control group were diagnosed with mild DED and moderate DED was reported only by 21 patients ($45.6\%$) in the experimental group. No patients with severe DED were identified in either the experimental or the control group. QoL apropos health differed significantly in the experimental and control groups ($p \leq 0.01$). Patients in the experimental group were further divided into two subgroups based on the presence and absence of corneal involvement. By comparing the mean values of the OSDI scores, we were able to record significant variations between the three groups, as can be inferred from Table 3. The mean OSDI score was 25.14 ± 3 for the experimental subgroup diagnosed with keratopathy and 19.3 ± 3.5for the subgroup with no corneal injury ($$p \leq 0.000002$$). Additionally, 32 of the 46 patients ($69.6\%$) in the experimental group had a substantially distinct mean OSDI score as compared to controls, suggesting lower QoL in T2DM patients. Retired patients diagnosed with DED experienced eye soreness (rs = 0.345, $$p \leq 0.0188$$; rs = 0. 631, $p \leq 0.01$) and limited routine activities, mostly watching television and reading ($\frac{26}{58}$, $44.8\%$ and $\frac{24}{58}$, $41.4\%$, respectively).
Following regulations in the DEWS scheme, eleven patients of the control group tested negative for DED. Grade I severity level was recorded in $46\%$ of control individuals and $33\%$ of patients with T2DM. Grades II and III were predominantly detected in patients of the experimental group. None of the participants in either control or experimental groups were diagnosed with grade IV DED severity (Table 4). Furthermore, we have been able to accomplish a positive correlation between the mean OSDI score and DEWS grade ($r = 0.705$; $p \leq 0.01$) in the experimental group and a negative correlation with the Schirmer test and TBUT (Table 5).
The corneal and conjunctival staining, graded using the Oxford scale (Figure 1) ascertained grade I punctate staining in the majority of diabetic patients included in the study ($63\%$). While $21.7\%$ of participants were diagnosed with grade II punctate staining, only one patient ($2.2\%$) displayed grade III fluorescein-stained corneal erosions. In comparison, $7.7\%$ of individuals from the control group were diagnosed with grade I abnormal corneal/conjunctival staining (Figure 1). Pathological patterns associated with conjunctival redness were absent in $88.5\%$ of the control group and $13\%$ of the experimental group ($$p \leq 0.0001$$). Twenty-eight T2DM patients ($61\%$) and three control individuals ($11.5\%$) were diagnosed with grade I severity (mild redness of bulbar conjunctiva, slightly engorged major vessels) according to the Efron grading scale ($$p \leq 0.0078$$). A majority of subjects from both the experimental and control groups complained of mild discomfort on exposure to environmental triggers. Thorough scrutiny revealed conjunctival and limbal redness with a mild ciliary flush in diabetic patients diagnosed with grade II severity on the Efron scale. One patient from the study group was diagnosed with grade III severity of conjunctival redness associated with keratopathy and a decreased meniscus (Figure 2).
**Figure 1:** *Distribution of severity grades of corneal/conjunctival staining (Oxford scheme) in the study and control groups.* **Figure 2:** *Efron grades distribution in the study and control groups.*
## Discussion
Recently, an extensive demographic study employed the Short Form Health Survey (SF36) to demonstrate a significant reduction in QoL, particularly mental health, of patients diagnosed with DED. Co-morbidities may be minimized, but complete eradication of negative effects has not been demonstrated to date. [ 14].
All patients recruited in the study group were afflicted with DED with the severity of the disease varying from mild to moderate (54.4 and 45.6, respectively) according to the OSDI scores (mean score ± SD = 20.1 ± 4.03 points). Only $42\%$ of healthy individuals from the control group reported mild DED symptoms (mean score = 13.3). Our data and results differ from those reported by Fuerst et al. in that they noted DED in only $52\%$ of diabetic persons in their study ($$n = 26$$) with severity ranging from mild to severe (mild $16\%$, moderate $18\%$, severe $18\%$) [22], and from the results of a prospective observational study conducted on 58 diabetic patients by Ribeiro et al. who reported a diagnosis of moderate to severe DED in only $26.2\%$ of the patients [23]. Yazdani-Ibn-Taz et al. evaluated QoL based on OSDI in both T2DM-diagnosed patients and T1DM-diagnosed patients (mean score = 33.23 and 26.16, respectively). They noted symptoms indicative of DED in $46.7\%$ [14] of T2DM-diagnosed patients not afflicted with diabetic retinopathy [8]. As reported in a recent study by Naik et al. [ 24], there is a significant positive correlation between poor glycemic control (with abnormal levels of Hb1Ac) and a higher degree of dry eyes. Since all the patients enrolled in our experimental group possessed relatively good glycemic control (HbA1c value ± SD = 7.0 ± 0 $0.7\%$), we can presume a minimal impact on the risk of DED in our patients. Hence, we are inclined to presume that the differences in our findings and previous studies might be caused by poor glycemic control of the patients in the previous studies. Furthermore, we propose diabetic neuropathy as a potential factor that negatively impacts corneal sensitivity and might lead to lower OSDI scores in patients diagnosed with chronic T2DM (median = 9 years, range 1–27 years).
The worldwide prevalence of DED is in the expected range of 5 to $50\%$, and it is notably higher among subjects over 60 years of age with increasing incidences among the elderly [25, 26]. Garcia-Alfaro et al. reported an $80.5\%$ prevalence of DED (mean OSDI =29.20 ± 19.4) among postmenopausal women (54.18 ± 6.84 years) with $37.7\%$ of participants severely diseased, resulting in lower QoL [26]. All female participants in our study in both experimental and control groups experienced only mild or moderate DED and no symptoms of severe affliction.
The most common risk factors of diabetic kerato-epitheliopathy include abnormalities of tear dynamics, decreased corneal sensitivity, and impaired regeneration of corneal epithelial cells [27]. Fourteen patients in our experimental group ($30.4\%$) were diagnosed with superficial keratopathy. We found that the OSDI score was significantly different between diabetic patients without keratopathy (mean = 19.3 ± 3.5) and healthy controls (mean = 13.3 ± 3.0). We propose that this difference is potentially determined through other QoL-decreasing factors in T2DM patients rather than through damage to the corneal epithelial cells alone. However, we cannot disregard possible bias in the responses of patients to the questionnaires.
Evaluation of the severity of DED through the dry eye severity scheme yielded a positive association between the value scale and OSDI score in the experimental group. The OSDI questionnaire contains both symptoms and signs to assess and compare the patient’s complaints with objective findings. As suggested by Naik et al., the OSDI questionnaire should be an integral part of the ophthalmological examination of diabetic patients to screen all patients for ocular surface changes, especially for patients with a long history of DM and poor glycemic control [24].
No patients presented signs correlated to grade IV severity of DED; neither disabling discomfort nor corneal ulcerations were reported, the Schirmer test ranged from 2 to 15 mm, and minimal TBUT was 3 s. Though $56\%$ of participants from the control group also exhibited DED, based on DEWS, they only had mild disease.
This study has several limitations. For one, the bias in DED severity owing to good glycemic control of patients included in the experimental group. Secondly, the relatively small sample of patients and the unbalanced distribution of patients’ sex in favor of women ($59\%$ of the study group population, as DED commonly affects women more than men). The third limitation was the sporadic cases of grade III corneal damage based on the Oxford scheme and on the Efron scale, which impeded statistical analysis of the association between QoL and this subgroup. Therefore, further prospective studies with larger sample sizes might be designed to evaluate how OSDI may vary in patients with T2DB according to the severity of DM. Also, novel diagnostic tests to analyze the complex ocular surface system should be considered in topical therapy. To support our findings, we have nevertheless conducted a thorough analysis of all the subjects in the experimental as well as control groups based on clinical tests and standardized grading scales, as well as an efficient statistical evaluation of all the data collected. To conclude, we demonstrated that DED is associated with lower QoL among patients with type 2 DM, both with and without keratopathy, in comparison to healthy controls.
## Conclusion
Diabetic retinopathy is a commonly occurring, well-documented ocular morbidity. Developing comorbidities, poor glycemic control, advanced age, and even the female sex factor in the advancement of DED in patients diagnosed with DM. This study, in particular, was conducted to assess the effects of DED on the QoL of patients pre-diagnosed with DM. This study has been able to ascertain that mean OSDI values are commensurate with the DEWS grades used to determine the severity of DED in patients of the experimental group. Through expeditious data collection and methodical statistical analysis, we have been able to determine a substantial positive correlation between lower OSDI scores and corneal sensitivity caused by diabetic neuropathy. This study has also determined that DED negatively impacts T2DM patients, irrespective of their association with keratopathy. The OSDI scores employed to evaluate the ocular surfaces in subjects of both the control and the experimental groups were also salutary in determining the adverse effects of DED on patients’ QoL. A comparison of health-associated QoL of healthy individuals with DED-diagnosed patients offers insights into the challenges faced by patients in performing daily routine tasks. Early detection and subsequent intervention will be pragmatic in dealing with DED. Hence, as has already been suggested, OSDI questionnaires must be essentially integrated into the ophthalmic examination of diabetic patients.
## 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 study was reviewed and approved by local Committee of Bioethics of National Pirogov Memorial University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
TZ: conceptualization and writing. TZ and SV: formal, statistical analysis, and investigation. NM: data curation. GD: methodology. CM: review and editing. TM: review. TM, RR, and CC: validation. TZ, NM, and GD: original 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
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|
---
title: Association between social support and depressive symptoms among Chinese nurses
with formal employment versus contract-based employment
authors:
- Chang Fu
- Xuedan Cui
- Lei Geng
- Fenglin Cao
journal: Frontiers in Psychiatry
year: 2023
pmcid: PMC10009186
doi: 10.3389/fpsyt.2023.1037499
license: CC BY 4.0
---
# Association between social support and depressive symptoms among Chinese nurses with formal employment versus contract-based employment
## Abstract
### Background
Inequalities may exist in social and health status among nurses with different employment types. Few studies have investigated the relationship between social support and depressive symptoms among formally employed nurses compared with those in contract-based employment. This study aimed to examine the associations between social support and depressive symptoms among Chinese nurses with different forms of employment.
### Methods
The present cross-sectional study was performed with 1,892 nurses from 12 tertiary hospitals in Shandong Province, China. The Social Support Rating Scale and the 10-item Center for Epidemiologic Studies Depression Scale were used to measure social support and depressive symptoms, respectively. The association between social support and depressive symptoms among participants was explored using multiple linear regression analysis.
### Results
The prevalence of depressive symptoms was $45.7\%$. The mean score for total social support was 40.16 (SD = 7.47), which was lower than the norms in the general Chinese population. Formally employed participants’ total social support scores were statistically significantly higher than those of contract-based employees (p ≤ 0.05). After controlling for confounding factors, the multiple linear regression analysis showed that subjective support and support utilization scores were inversely associated with depressive symptoms. Objective support scores were negatively associated with depressive symptoms only among contract-employment nurses.
### Conclusion
Chinese nurses have a higher prevalence of depressive symptoms and lower social support than the *Chinese* general population. Compared with contract-employment nurses, formally employed nurses had higher social support. Inverse associations exist between social support and depressive symptoms among nurses with different types of employment. It is suggested that improving Chinese nurses’ social support levels and reducing their depressive symptoms, especially for nurses employed through contracts, are critical.
## Introduction
Nurses who experience a high intensity of work and work-related pressure are generally prone to suffer from heavy work stress and burnout, which can lead to depression [1]. Depression is a multidimensional disorder and has several negative effects on an individual’s health outcomes [2]. Depression can harm an individual’s work performance, interpersonal and social communication, and quality of life [3]. Depressive symptoms not only affect nurses’ health status but may also impact patients’ quality of care [2]. Worldwide, there is a high occurrence of depressive symptoms among nurses. Previous studies found that $32.4\%$ of Australian nurses, $18\%$ of American nurses, and $43.83\%$ of Chinese nurses experienced depressive symptoms (3–5). Therefore, hospital administrators and scholars worldwide should pay attention to nurses’ mental health.
In recent years, investigations on the relationship between social support and mental health have shown that social support has a protective effect on mental health. Social support is significantly associated with recovery from post-traumatic stress disorder [6], and older adults with good social support have a lower incidence of depression [7]. Social support refers to the existence or availability of people one can rely on and from whom one can experience love, care, and value [8]. In developed countries, medical professionals are well-respected and often have a high level of social support. The intensive relationship between medical professionals and patients needs to improve in China [9]. Currently, the level of social support available to Chinese nurses is unknown because few surveys have investigated the relationship between social support and depressive symptoms for this group. Social support, as a multidimensional concept, consists of three dimensions: objective support, subjective support, and support utilization [6, 10]. Different types of social support may have varied effects on individuals’ health [9], and it is unclear which types of social support are protective against depressive symptoms among nurses.
Although the nature of nursing employment varies by country, it is usually divided into permanent employment and fixed-term contract-based employment. For example, in Europe, permanent nurses are part of the primary labor market, and they work on an indefinite basis with good working conditions and development opportunities [11]. By contrast, nurses employed through contracts are part of the secondary labor market and often experience poor working conditions, including job insecurity, low wages, and few benefits [11]. Similarly, in China, nursing employment can be divided into “bianzhi” (permanent/formal employment) and contract-based jobs [12]. The Chinese public often considers “bianzhi” jobs as formal employment guaranteed by the government, from which an individual employer cannot dismiss the incumbent. “ Bianzhi” nurses have a steady income and certain benefits, including housing, health insurance, pension, etc. By contrast, nurses in contract-based positions are hired by the hospital; they do not have lifetime employment and might experience lower incomes and limited benefits [12]. Previous studies have found that contract-employment nurses experience higher work stress and lower levels of organizational justice [13], which indicates that nurses with different employment types may have different levels of social support and health outcomes. However, few studies have compared the different associations between social support and depressive symptoms among formally employed (bianzhi) nurses and contract-employment nurses in China.
Therefore, this study aimed to: [1] examine the level of social support among Chinese nurses; [2] investigate the prevalence of depressive symptoms among Chinese nurses; and [3] investigate the associations between social support and depressive symptoms among formally employed nurses and contract-employment Chinese nurses.
## Study design and participants
From 30 July to 30 September 2020, a cross-sectional questionnaire survey was conducted among nurses in Shandong Province, China. Shandong Province, located in eastern China, which has 16 prefecture-level cities with a population of 100.7 million. It is a typical province in China in terms of population demographics, society, and culture [14]. The survey adopted a multistage random sampling method. First, the prefecture-level cities were divided into high, medium, and low groups based on per capita GDP in 2019. Second, two prefecture-level cities were randomly chosen from each group and two tertiary hospitals were randomly selected from each of the two cities. Third, two-thirds of the departments were selected from internal medicine, surgery, obstetrics and gynecology, pediatrics, emergency, and others in each sampled hospital. Administrative and logistics departments were excluded. A questionnaire survey was administered to nurses in the selected departments. All information was collected based on nurses’ self-report. The inclusion criteria included voluntary participation, registration as a nurse, and being employed by the hospital. The exclusion criteria were as follows: nurses who were on vacation or who were participating in a continued education study course in another hospital, and persons with severe mental or physical impairments that would prevent them from participating [15]. A total of 1,933 nurses participated in this study. After excluding those with missing data, 1,892 questionnaires were included in the analysis, showing an effective rate of $97.9\%$.
## Depressive symptoms
A 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) was used to measure depressive symptoms. The CESD-10 is a simplified version of the Center for Epidemiological Studies Depression Scale (CESD) revised by Andresen in 1994 [16]. It has high reliability and validity within the Chinese population [17]. The CESD-10 measures the extent to which an individual has experienced depressive symptoms in the past 7 days. Answers for each item include rarely (<1 days), some (1–2 days), occasionally (3–4 days), and most of the time (5–7 days). The total score ranges from 0 to 30, with higher scores suggesting higher levels of depressive symptoms. The cutoff score to identify individuals with depressive symptoms was 10 [16]. In this study, Cronbach’s alphas for the CESD-10 were 0.696 and 0.674 in the formal employee and contract-based employee groups, respectively.
## Social support
The Social Support Rating Scale (SSRS) was used to measure social support. The Chinese version of the SSRS, which was developed by Professor Xiao Shuiyuan, has been widely used in China [6, 18]. The scale comprises 10 items and includes three dimensions: subjective support, objective support, and support utilization. Subjective support refers to the emotional support that an individual experiences and is closely related to their subjective feelings. An example of a question covering subjective support is: “How many friends do you have and how much support and help can they provide you?” Objective support refers to the actual support received by an individual. An example of a question covering objective support is: “What are your sources of financial support, and what helps you solve practical problems when you are in an emergency?” The utilization of support refers to an individual’s active utilization of various types of social support. An example of a question covering the utilization of support is: “How do you seek help when you have trouble?” The total score of the SSRS is the sum of the scores of subjective support, objective support, and support utilization, with higher scores indicating better levels of social support [9]. In this survey, the Cronbach’s alpha for the SSRS was 0.79 in the formal employee group and 0.76 in the contract-based employee group.
## Other variables
Demographic characteristics included age, sex, marital status, and educational background. Marital status was divided into married and single. Educational background was categorized as junior college or lower, bachelor’s degree, and master’s degree or higher. Professional characteristics included department, professional title, employment types, and working hours per week. The department was divided into internal medicine, surgery, obstetrics and gynecology, pediatrics, emergency, and others. Professional title was categorized as primary, intermediate, and senior. Employment type was divided into formal or contract-based employees. Working hours per week was divided into ≤40, 41–50, 51–60, and >60 h.
## Statistical analyses
All statistical analyses were conducted using SPSS 20.0. The distribution of depressive symptoms was analyzed using both the Kolmogorov–Smirnov test and histogram plot, which showed a normal distribution. The variance inflation factor (VIF) was used to test for multicollinearity in independent variables. The VIF was <10, indicating no multicollinearity [19]. For descriptive statistics, continuous variables were described using means and standard deviations, and categorical variables were described using percentages. Variables were compared between formal employee and contract-based employee groups using independent t -tests for continuous variables and chi-square tests for categorical variables. Multiple linear regression analysis was used to examine the association between employment type and social support scores. Multivariate logistic regression analysis was used to examine the association between employment type and depressive symptoms. Multiple linear regression analysis was used to examine the association between social support and depressive symptoms among nurses. To describe the sensitivity analysis, the association between social support and depressive symptoms of participants was examined using multivariate logistic regression analysis. Statistical significance was set at <0.05.
## Ethical considerations
The present study was approved by the Ethical Review Committee of the School of Nursing and Rehabilitation, Shandong University (approval number: 2020-R-50). All participants provided informed consent for inclusion before participating in the survey.
## Sample characteristics
Table 1 shows the participants’ sociodemographic characteristics, work characteristics, status of depressive symptoms, and social support scores. The participants’ mean age was 33.9 years (SD = 7.3 years). Moreover, $93.9\%$ were women, $80.1\%$ were married, $88.1\%$ had a bachelor’s degree, $39.3\%$ worked in the surgical department, and nearly half had a primary professional title ($47.4\%$) or an intermediate professional title ($48.3\%$). More than half of the participants worked between 41 and 50 h per week ($68.0\%$). The majority ($78.3\%$) were contract-based employees. The mean CES-D 10 score was 9.17 (SD = 5.39), and $45.7\%$ of the participants had depressive symptoms. The prevalence of depressive symptoms among contract-based employees was significantly higher than among formal employees ($47.3\%$ vs. $40.1\%$). The mean scores for total social support, subjective support, objective support, and support utilization were 40.16 (SD = 7.47), 24.04 (SD = 4.91), 8.28 (SD = 2.38), and 7.84 (SD = 1.98), respectively. Differences in age, sex, marital status, educational background, department, professional title, work hours per week, scores of social support, and depressive symptoms between the formal and contract-based employees were also statistically significant ($p \leq 0.05$). After controlling the possible confounding factors, the multiple regression analysis showed that there was a statistically significant association between the employment type and social support scores (formal employees have a higher level of social support than contract employees; Supplementary Table 1), while the association between employment type and depressive symptoms was not statistically significant (Supplementary Table 2).
**Table 1**
| Characteristics | Sample (n = 1892) | Formal employee (n = 411) | Contract employee (n = 1,481) | t/χ 2 | Value of p |
| --- | --- | --- | --- | --- | --- |
| Age, years, mean ± SD | 33.91 ± 7.27 | 42.78 ± 7.50 | 31.45 ± 4.91 | 28.975 | <0.001 |
| Sex, (%) | | | | 19.617 | <0.001 |
| Male | 6.1 | 1.5 | 7.4 | | |
| Female | 93.9 | 98.5 | 92.6 | | |
| Marital status, (%) | | | | 56.245 | <0.001 |
| Married | 80.1 | 93.2 | 76.5 | | |
| Single | 19.9 | 6.8 | 23.5 | | |
| Education background, (%) | | | | 196.141 | <0.001 |
| Junior college or less | 7.2 | 3.2 | 8.3 | | |
| Bachelor | 88.1 | 79.3 | 90.5 | | |
| Master or above | 4.8 | 17.5 | 1.2 | | |
| Department, (%) | | | | 27.591 | <0.001 |
| Internal medicine | 28.2 | 30.7 | 27.5 | | |
| Surgery | 39.3 | 43.8 | 38.1 | | |
| Obstetrics and gynecology | 5.0 | 4.6 | 5.1 | | |
| Pediatrics | 7.1 | 7.3 | 7.1 | | |
| Emergency | 8.7 | 2.4 | 10.4 | | |
| Other | 11.6 | 11.2 | 11.7 | | |
| Professional title, (%) | | | | 522.137 | <0.001 |
| Primary | 47.4 | 6.3 | 58.7 | | |
| Intermediate | 48.3 | 74.7 | 41.0 | | |
| Senior | 4.3 | 19.0 | 0.3 | | |
| Work hours per week, (%) | | | | 9.139 | 0.028 |
| ≤40 | 8.8 | 11.2 | 8.1 | | |
| 41–50 | 68.0 | 69.8 | 67.5 | | |
| 51–60 | 13.8 | 12.4 | 14.2 | | |
| >60 | 9.5 | 6.6 | 10.3 | | |
| Total social support scores, mean ± SD | 40.16 ± 7.47 | 41.99 ± 7.61 | 39.65 ± 7.35 | 5.684 | <0.001 |
| Subjective support scores, mean ± SD | 24.04 ± 4.91 | 24.91 ± 4.93 | 23.80 ± 4.89 | 4.069 | <0.001 |
| Objective support scores, mean ± SD | 8.28 ± 2.38 | 9.01 ± 2.36 | 8.07 ± 2.35 | 7.149 | <0.001 |
| Support utilization score, mean ± SD | 7.84 ± 1.98 | 8.07 ± 2.12 | 7.77 ± 1.93 | 2.572 | 0.010 |
| Depressive symptoms, (%) | | | | 6.571 | 0.010 |
| Yes | 45.7 | 40.1 | 47.3 | | |
| No | 54.3 | 59.9 | 52.7 | | |
| Depressive scores, mean ± SD | 9.17 ± 5.39 | 8.27 ± 5.91 | 9.41 ± 5.22 | −3.555 | <0.001 |
## The differences between social support scores among nurses and norms in the general Chinese population
Tables 2, 3 show the differences in social support between nurses in our sample and the norms in the general Chinese population. The formal employees’ scores for total social support, objective support, and support utilization were statistically significantly lower than the norms in the general population [20] ($p \leq 0.05$). Formal employees’ subjective support scores were statistically significantly higher than those of the general Chinese population ($p \leq 0.05$). Contract-based employees’ scores for total social support, objective support, and support utilization were statistically significantly lower than the norms in the *Chinese* general population [20] ($p \leq 0.05$).
## Multiple linear regression analysis
Table 4 reveals the associations between the three dimensions of social support and depressive symptoms among formal and contract-based participants. After adjusting for all covariates, the findings showed that among formal employees, subjective support and support utilization scores were inversely associated with depressive symptoms (subjective support: β = −0.237, SE = 0.064, $p \leq 0.001$; support utilization: β = −0.824, SE = 0.137, $p \leq 0.001$). Among contract-based employees, subjective support, objective support, and support utilization scores were also inversely associated with depressive symptoms (subjective support: β = −0.284, SE = 0.031, $p \leq 0.001$; objective support: β = −0.291, SE = 0.063, $$p \leq 0.035$$; support utilization: β = −0.559, SE = 0.070, $p \leq 0.001$). However, there was no statistically significant correlation between objective support scores and depressive symptoms among those with formal employment ($p \leq 0.05$). In the sensitivity analyses, the results of the multivariate logistic regression model showed the same associations between the three dimensions of social support and depressive symptoms among both formal and contract-based employees (Supplementary Table 3).
**Table 4**
| Variables | Formal employee | Formal employee.1 | Formal employee.2 | Contract employee | Contract employee.1 | Contract employee.2 |
| --- | --- | --- | --- | --- | --- | --- |
| Variables | β (SE) | 95% CI of β | Value of p | β (SE) | 95% CI of β | Value of p |
| Subjective supports score | −0.237(0.064) | −0.362 to −0.111 | <0.001 | −0.284 (0.031) | −0.345 to −0.223 | <0.001 |
| Objective supports score | −0.132(0.129) | −0.386 to 0.122 | 0.308 | −0.291 (0.063) | −0.414 to −0.168 | <0.001 |
| Support utilization score | −0.824(0.137) | −1.093 to −0.554 | <0.001 | −0.559 (0.070) | −0.695 to −0.422 | <0.001 |
| Age | −0.062(0.044) | −0.150 to 0.025 | 0.163 | 0.076 (0.035) | 0.008 to 0.143 | 0.029 |
| Sex (ref. Male) | | | | | | |
| Female | 1.203(2.106) | −2.938 to 5.344 | 0.568 | 0.650 (0.481) | −0.293 to 1.593 | 0.176 |
| Marital status (ref. Married) | | | | | | |
| Single | −0.141(1.072) | −2.249 to 1.968 | 0.896 | 1.871 (0.380) | 1.124 to 2.617 | <0.001 |
| Education level (ref. Junior college or less) | | | | | | |
| Bachelor | 1.167(1.464) | −1.711 to 4.044 | 0.426 | −0.688 (0.444) | −1.559 to 0.183 | 0.122 |
| Master or above | 0.614(1.604) | −2.540 to 3.768 | 0.702 | −1.574 (1.204) | −3.935 to −0.787 | 0.191 |
| Department (ref. Other) | | | | | | |
| Internal medicine | 1.482(0.878) | −0.244 to 3.207 | 0.092 | 1.244 (0.436) | 0.389 to 2.099 | 0.004 |
| Surgery | 1.691(0.837) | 0.046 to 3.336 | 0.044 | 1.284 (0.415) | 0.471 to 2.098 | 0.002 |
| Obstetrics and gynecology | 0.882(1.367) | −1.806 to 3.569 | 0.519 | 1.325 (0.648) | 0.055 to 2.596 | 0.041 |
| Pediatrics | 3.549(1.193) | 1.203 to 5.896 | 0.003 | 1.440 (0.589) | 0.285 to 2.596 | 0.015 |
| Emergency | 1.539(1.779) | −1.959 to 5.037 | 0.388 | 0.898 (0.529) | −0.139 to 1.935 | 0.090 |
| Professional title (ref. Primary) | | | | | | |
| Intermediate | 0.145(1.110) | −2.037 to 2.327 | 0.896 | 0.054 (0.295) | −0.525 to 0.632 | 0.855 |
| Senior | −1.749(1.374) | −4.450 to 0.953 | 0.204 | −3.888 (2.393) | −8.582 to 0.806 | 0.104 |
| Weekly working hours (ref. ≤40) | | | | | | |
| 41–50 | 2.769(0.802) | 1.192 to 4.347 | 0.001 | −1.137 (0.449) | −2.017 to −0.256 | 0.011 |
| 51–60 | 2.972(1.038) | 0.931 to 5.012 | 0.004 | −0.155 (0.539) | −1.212 to 0.903 | 0.774 |
| >60 | 6.004(1.259) | 3.529 to 8.478 | <0.001 | −0.214 (0.576) | −1.344 to 0.917 | 0.711 |
| ∆F | 10.885 | | | 23.903 | | |
| R 2 | 0.333 | | | 0.227 | | |
| Adjusted R2 | 0.303 | | | 0.218 | | |
## Discussion
This is the first study to investigate the association between different types of social support and depressive symptoms among Chinese nurses engaged in different forms of employment. As such, it provides useful information for health policymakers, hospital administrators, and nurses to consider when contemplating effective measures to prevent and reduce depressive symptoms.
In this study, the prevalence of depressive symptoms was $45.7\%$, which was higher than nurses in Guangdong province of China ($37.59\%$) [3], and much higher than that in *Chinese* general population ($12.6\%$) [21]. Nurses have unique working conditions, as they often become overloaded with their clinical work; therefore, they experience long-term occupational pressure, which leads them to have poor mental health [3]. Our results also showed that the prevalence of depressive symptoms among contract-based nurses was higher than that of formally employed nurses ($47.3\%$ vs. $40.1\%$). Therefore, the prevalence of depressive symptoms among nurses (especially contract-based nurses) requires the attention of hospital managers.
Our results showed that the level of social support among contract-based nurses was lower than that of formally employed nurses. The social inequalities between formally employed and contract-based nurses may explain this phenomenon [12]. Individuals with higher social status may have more social resources and are easier to get social support. Our data also showed that the social support scores of both formally employed and contract-based nurses were significantly lower than those in the general Chinese population. There are three possible explanations for this phenomenon. First, in China, nurses often have a lower professional status than doctors; they are not valued by hospital administrators [22]. Furthermore, nurses need to cope with tense nurse–patient relationships, and they seldom receive sufficient respect from patients [22]. Second, nurses may face difficulty in balancing family roles with work [23]; if nurses do not handle work–family conflicts, they may find it difficult to gain empathy from their work colleagues or other family members, leading to decrease social support. Third, because nurses work long hours, they may not have sufficient time to participate in social organizations; therefore, they have less social interaction [15]. These explanations suggest that, to improve nurses’ social support, effective measures across society need improvement.
Subjective social support is a psychological perception of reality [24] that reflects an individual’s satisfaction with how they are supported, understood, and respected by others [9]. Subjective support was a strong predictor of mental health improvement [25]. Our results found that subjective support was negatively associated with depressive symptoms, indicating that nurses with more subjective support had fewer depressive symptoms than those who lacked subjective support. Individuals with more subjective support often have greater levels of satisfaction with their social support. A previous study reported that individuals who were more satisfied with their support were less likely to suffer from depression [26]. In addition, subjective support can help individuals build a positive self-image and self-efficacy, which are protective factors for depressive symptoms [27]. Thus, improving nurses’ subjective support can help to reduce their depressive symptoms.
A previous study demonstrated that nurses with higher objective support scores have genuinely received more support from their family members, government organizations, and social organizations [9]. In this study, there was no significant association between the objective support scores and depressive symptoms of formally employed nurses. Objective assessment of received social support is less meaningful than subjective measures of social support, and it may have less effect on individuals’ mental health than subjective support [24]. However, in this study, we found that objective support scores were inversely associated with depressive symptoms among contract-based nurses. There are two possible explanations for this observation. First, formally employed nurses have a higher occupational status in the hospital than contract-based nurses, as their position is guaranteed by the government, and they have extensive benefits [28]. In addition, formally employed nurses often have higher educational levels, income levels, and social status [12]. Therefore, formally employed nurses may take support from family members or social organizations for granted, whereas contract-based nurses may find themselves feeling grateful for this support owing to their relatively low status. A second possible reason is the treatment inequity between contract-based and formally employed nurses. A previous study has found that while both groups play an equal role in job responsibility, differences in treatment do exist [12]. Such feelings of inequality can affect contract-based nurses’ work satisfaction, which may eventually lead to depression [29]. Contract-based nurses who receive objective support may disregard their feelings of inequality, which can reduce the prevalence of depressive symptoms.
Our findings showed that social support utilization scores were inversely associated with depressive symptoms in both formally employed and contract-based nurses. Social support utilization reflects the degree to which individuals utilize available social support [6]. According to the SSRS, higher support utilization scores indicate that a person may actively participate in social organizations (such as party, religious, or community organizations) and have many ways to seek help from others (such as family members, friends, or social organizations) when they experience trouble [9]. Help from a varied use of social resources can help nurses overcome their troubles and relieve stress [30], which may, in turn, help them reduce their depressive symptoms [31, 32]. Furthermore, participation in social organizations may promote social interactions among nurses and free them from stressful work, which would help them experience fewer depressive symptoms [33].
## Limitations
This study had some limitations. First, this was a cross-sectional study; therefore, causal relationships between social support and depressive symptoms among Chinese nurses could not be investigated. Second, the responses in this study were self-reported, which may have caused recall bias. Third, the participants in this study were all selected from tertiary hospitals; thus, it may not be possible to generalize the results to primary and secondary hospitals.
## Conclusion
Our study found that Chinese nurses have a higher prevalence of depressive symptoms than the *Chinese* general population. Formally employed nurses had a higher level of social support than contract-employment nurses. The level of social support for both formally employed and contract-employment nurses was lower than that in the general population. Both subjective support and support utilization scores were negatively associated with depressive symptoms among both formally employed and contract-based nurses. Objective support scores were negatively associated with depressive symptoms only for contract-based nurses. The findings of this study can be used to develop strategies to improve nurses’ social support and reduce depressive symptoms among them.
## Policy implications
To improve nurses’ mental health, our findings suggest that hospital administrators should pay attention to the role of nurses and arrange their working hours reasonably to reduce work pressure. They should also improve contract-based nurses’ benefits (e.g., housing, health insurance, and pensions). We also suggest that nurses’ family members should understand their unique working conditions, share housework, and take care of them. Policymakers should promote nurses’ contributions and foster a nurse-friendly social environment. Finally, we recommend that nurses should actively participate in social organizations to enhance their social interaction.
## Data availability statement
The data analyzed in this study is subject to the following licenses/restrictions: The datasets generated and/or analyzed during the current study are not publicly available due to agreements with participants who restricted data sharing but are available from the corresponding author on reasonable request. Requests to access these datasets should be directed to CF, [email protected].
## Ethics statement
The studies involving human participants were reviewed and approved by Ethical Review Committee of the School of Nursing and Rehabilitation, Shandong University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
CF contributed to the study design. CF, XC, and LG contributed to the data collection. CF contributed to the data analysis. CF, LG, and FC wrote the main manuscript text and revised the manuscript. All authors contributed to the article and approved the submitted version.
## Funding
This study was funded by the Surface Project of National Natural Science Foundation of China (grant number: 32071084). The funding source played no role in the design of this study; collection, analysis, and interpretation of data; writing of the report; or decision to submit the article 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/fpsyt.2023.1037499/full#supplementary-material
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|
---
title: 'The role of brain structure in the association between pubertal timing and
depression risk in an early adolescent sample (the ABCD Study®): A registered report'
authors:
- Niamh MacSweeney
- Judith Allardyce
- Amelia Edmondson-Stait
- Xueyi Shen
- Hannah Casey
- Stella W.Y. Chan
- Breda Cullen
- Rebecca M. Reynolds
- Sophia Frangou
- Alex S.F. Kwong
- Stephen M. Lawrie
- Liana Romaniuk
- Heather C. Whalley
journal: Developmental Cognitive Neuroscience
year: 2023
pmcid: PMC10009199
doi: 10.1016/j.dcn.2023.101223
license: CC BY 4.0
---
# The role of brain structure in the association between pubertal timing and depression risk in an early adolescent sample (the ABCD Study®): A registered report
## Abstract
### Background
Earlier pubertal timing is associated with higher rates of depressive disorders in adolescence. Neuroimaging studies report brain structural associations with both pubertal timing and depression. However, whether brain structure mediates the relationship between pubertal timing and depression remains unclear.
### Methods
The current registered report examined associations between pubertal timing (indexed via perceived pubertal development), brain structure (cortical and subcortical metrics, and white matter microstructure) and depressive symptoms in a large sample (N = ∼5000) of adolescents (aged 9–13 years) from the Adolescent Brain Cognitive Development (ABCD) Study. We used three waves of follow-up data when the youth were aged 10–11 years, 11–12 years, and 12–13 years, respectively. We used generalised linear-mixed models (H1) and structural equation modelling (H2 & H3) to test our hypotheses.
### Hypotheses
We hypothesised that earlier pubertal timing at Year 1 would be associated with increased depressive symptoms at Year 3 (H1), and that this relationship would be mediated by global (H2a-b) and regional (H3a-g) brain structural measures at Year 2. Global measures included reduced cortical volume, thickness, surface area and sulcal depth. Regional measures included reduced cortical thickness and volume in temporal and fronto-parietal areas, increased cortical volume in the ventral diencephalon, increased sulcal depth in the pars orbitalis, and reduced fractional anisotropy in the cortico-striatal tract and corpus callosum. These regions of interest were informed by our pilot analyses using baseline ABCD data when the youth were aged 9–10 years.
### Results
Earlier pubertal timing was associated with increased depressive symptoms two years later. The magnitude of effect was stronger in female youth and the association remained significant when controlling for parental depression, family income, and BMI in females but not in male youth. Our hypothesised brain structural measures did not however mediate the association between earlier pubertal timing and later depressive symptoms.
### Conclusion
The present results demonstrate that youth, particularly females, who begin puberty ahead of their peers are at an increased risk for adolescent-onset depression. Future work should explore additional biological and socio-environmental factors that may affect this association so that we can identify targets for intervention to help these at-risk youth.
## Introduction
Adolescence is a period of increased vulnerability to mental health conditions, particularly internalising difficulties such as depression (Malhi and Mann, 2018, Thapar et al., 2012). Earlier-onset of depression is associated with a more severe illness course (Thapar et al., 2012) and with a range of psychosocial and physical difficulties which perpetuate across the lifespan (Clayborne et al., 2019, Fergusson and Woodward, 2002, Naicker et al., 2013). Given the emergence of depression during the adolescent period, the role that pubertal development may play in this heightened vulnerability has garnered increasing attention (Graber, 2013, Hamlat et al., 2019, Pfeifer and Allen, 2021, Ullsperger and Nikolas, 2017). Earlier pubertal timing has been associated with increased risk for depression in both males and females (Graber, 2013, Hamlat et al., 2020, Mendle et al., 2010, Ullsperger and Nikolas, 2017). Further, genetic studies have found that earlier age of menarche is implicated in depression (Howard et al., 2019). Adolescence is also a time of immense neurobiological change (Mills et al., 2016, Tamnes et al., 2017, Vijayakumar et al., 2016) and brain structural differences have been found in both adults (Schmaal et al., 2017, Schmaal et al., 2020, Shen et al., 2017, Shen et al., 2019) and adolescents (Schmaal et al., 2017, Shen et al., 2021) with depression. However, the role that neural mechanisms may play in the relationship between pubertal timing and depression risk is not well understood. Here, we therefore examine whether brain structure mediates the association between pubertal timing and depressive symptoms in a large sample of adolescents (aged 9–13 years) from the Adolescent Brain Cognitive Development (ABCD) Study®.
## Defining and measuring pubertal timing
Pubertal timing measures pubertal development relative to same-age, same-sex peers, such that an individual can be categorised as developing ahead (early), in-line (on-time) or after (late) their peers. Measures of pubertal timing are most often derived from methods used to assess pubertal status, such as the Pubertal Development Scale (PDS; Petersen et al., 1988) and Tanner Stage Line Drawings (TS; Marshall and Tanner, 1969, Marshall and Tanner, 1970). However, other measures used include age of menarche and sex hormone measures (Goddings et al., 2019, Ullsperger and Nikolas, 2017). Pubertal maturation as assessed by the PDS and TS focuses on the development of secondary sex characteristics (e.g., testicular, breast, and pubic hair development), which stem directly from changes in sex hormones. These measures are completed by a clinician (TS), or via self- (or parent-) report (PDS/TS). Most often, a pubertal timing score is derived by regressing a pubertal status score on chronological age to calculate a sex-specific residual for each person (Barendse et al., 2021, Dorn and Biro, 2011, Mendle et al., 2010, Ullsperger and Nikolas, 2017). The residual score represents how much an individual’s pubertal development deviates from what is expected for their age with positive and negative scores indicating earlier and later timing, respectively. It is worth noting that pubertal development consists of two phases: adrenarche, usually occurring between the ages 6–9 years (Biro et al., 2014), and gonadarche, which typically takes place between the ages 9–14 years for females and 10–15 years for males.
## Pubertal timing and psychopathology
Historically, research on pubertal timing effects on psychopathology has highlighted that youth, particularly females (Graber, 2013, Hamlat et al., 2019, Hamilton et al., 2014), who undergo puberty earlier than their peers are at an increased risk for psychopathology (Conley et al., 2012, Ge and Natsuaki, 2009, Hamilton et al., 2014). However, a recent meta-analysis suggests that earlier pubertal timing is detrimental to both sexes and that later pubertal timing is not significantly associated with psychopathology (Ullsperger and Nikolas, 2017). Although a number of conceptual models (Brooks-Gunn et al., 1985, Brooks-Gunn et al., 1994, Petersen et al., 1988) have been proposed to explain the association between earlier pubertal timing and increased risk for psychopathology, the “maturation disparity hypothesis” (Brooks-Gunn et al., 1985, Ge et al., 2001, Ge and Natsuaki, 2009), has received the most empirical support (Graber, 2013, Ullsperger and Nikolas, 2017). The maturation disparity hypothesis posits that early developing youth experience psychological distress due to an incongruity between their accelerated physical development and asynchronous maturation of cognitive and emotional brain regions.
Importantly, the psychological and social changes that occur during adolescence such as heightened self-awareness and social sensitivity (Blakemore and Mills, 2014, Pfeifer and Peake, 2012), increased risk-taking behaviour and impulsivity (Bjork and Pardini, 2015, Defoe et al., 2015, Romer, 2010) as well as greater peer influence on behaviour (Albert et al., 2013, Blakemore, 2018, Knoll et al., 2015) are likely underpinned by the distinct developmental trajectories of temporal and limbic areas (involved in emotion and reward processing) and prefrontal regions (involved in cognitive control) (Albert et al., 2013, Casey et al., 2008, Mills et al., 2014, Steinberg, 2008). It has been postulated that earlier developing youth therefore experience a greater discordance in the mismatch between the earlier developing affective regions and the more protracted development of cognitive regions (Ge and Natsuaki, 2009, Ullsperger and Nikolas, 2017), which may place them at an increased risk for mental health difficulties. Given that the onset of puberty is about 18 months earlier for females than males, this maturation disparity hypothesis may also explain the preponderance of depression (2:1) in females compared to males from adolescence onwards (Conley et al., 2012, Hankin, 2006, Hankin, 2015, Hankin and Abramson, 1999). Although the maturation disparity hypothesis best accounts for the extant findings, a more nuanced model that considers the role of biological and psychosocial factors as potential mediators or moderators in the association between earlier pubertal timing and increased risk for psychopathology is needed.
## Pubertal timing and brain structure
Research on typical neurodevelopment demonstrates a reduction in grey matter volume and cortical thickness during adolescence, while cortical surface area increases throughout childhood before plateauing by mid-adolescence, and slightly decreasing thereafter (Bethlehem et al., 2022, Ducharme et al., 2016, Mills et al., 2016, Vijayakumar et al., 2016, Wierenga et al., 2014). These patterns of human brain development, were recently evidenced in a collaborative paper involving > 100 studies and > 123,000 MRI scans (Bethlehem et al., 2022), which is the largest aggregated sample to date. However, research has also shown that pubertal development impacts neurodevelopment beyond age-related changes (Vijayakumar et al., 2018). For example, a number of studies demonstrate extensive negative associations between pubertal timing (indexed via physical and hormonal measures) and cortical volume and thickness, mainly in regions implicated in cognitive control, decision making, and emotion regulation, such as the prefrontal cortex, anterior cingulate cortex, and the temporal lobe (Koolschijn et al., 2014, Pfefferbaum et al., 2016). Notably, few studies have examined surface area changes during puberty as surface area is often obscured when investigating volumetric estimates — a product of cortical surface area and thickness (Vijayakumar et al., 2016). Given that surface area and cortical thickness reflect distinct neurobiological processes (Wierenga et al., 2014) and are genetically independent of each other (Winkler et al., 2010), examining pubertal timing in relation to surface area maturation may reveal novel associations.
Regarding associations between subcortical measures and pubertal development, research has focused on the amygdala, hippocampus, and striatal regions given their role in emotion regulation, reward processing, and decision making (Bhanji and Delgado, 2014, Dalgleish, 2004). A number of cross-sectional and some longitudinal studies have reported that more advanced pubertal maturation is associated with an increase in volume of the amygdala and hippocampus and a decrease in volume in striatal areas (Blanton et al., 2012, Hu et al., 2013, Goddings et al., 2014, Goddings et al., 2019). Although these findings provide insight into the role of puberty in subcortical brain development, there is a dearth of research that specifically examines pubertal timing (i.e., pubertal development relative to same-age, same-sex peers) (Goddings et al., 2019) and its association with brain morphological changes (Koolschijn et al., 2014, Neufang et al., 2009, Peper et al., 2009). Further, longitudinal data has shown that there are unique but co-existing age effects that complicate examining the relationship between puberty and structural brain development (Goddings et al., 2019). For example, a recent longitudinal study demonstrated a positive linear association between perceived pubertal maturation (indexed via TS) and the hippocampus, amygdala, caudate and pallidum. However, these associations did not remain significant when age was controlled for (Vijayakumar et al., 2021). Further inconsistencies have emerged in the literature when utilising different measures of pubertal development (Koolschijn et al., 2014, Vijayakumar et al., 2018), and also in studies using large age ranges (Satterthwaite et al., 2014, Urošević et al., 2014). There is also some research suggesting that pituitary gland volume mediates the association between earlier pubertal timing and increased risk for depression in adolescence but more research is needed on this topic (Whittle et al., 2012). Additionally, the current literature does not allow for the identification of clear sex differences in the association between cortical and subcortical structure and pubertal timing, likely owing to the paucity of longitudinal, large-scale research in this area (Herting and Sowell, 2017, Vijayakumar et al., 2018).
There is less research examining the association between pubertal development and white matter microstructure (Goddings et al., 2019) and findings are mixed (Vijayakumar et al., 2018). There is some degree of support for a positive association between pubertal timing and fractional anisotropy (FA; Herting et al., 2012; Peper et al., 2015). However, findings have been inconsistent when considering the relation between pubertal hormones and FA, and between all indices of pubertal development (physical maturation and hormonal measures) and mean diffusivity (MD; Herting et al., 2012; Peper et al., 2009). These discrepancies may be attributed to the various diffusion tensor imaging (DTI) techniques employed and the relatively small sample sizes. Future large-scale neuroimaging research that leverages harmonised protocols and considers the unique and contemporaneous effect of age is needed to disentangle the associations between pubertal timing and white matter microstructure.
Large, population-based research projects, such as the ABCD Study®, directly address limitations of earlier research (e.g., small sample sizes, inconsistent protocols) and will allow us to investigate how brain changes across adolescence are related to developmental outcomes, especially the emergence of mental health difficulties (Casey et al., 2018). The ABCD Study includes magnetic resonance imaging (MRI) data, assessments of psychiatric disorders, and hormonal and physical puberty measures in 9–10-year-old US children at baseline (N = ∼11,800). Our previous work with the ABCD Study® explored the temporal origins of depression-related imaging features and demonstrated that depression ratings in early adolescence were associated with similar cortical and white matter microstructural differences to those seen in adult samples (Shen et al., 2021). These findings suggest that neuroanatomical abnormalities may be present early in the disease course. However, the cascade of neurobiological changes associated with the onset of puberty may have an important role in risk for depression and may allow further mechanistic insight into the origins of these depression-related imaging features (Dahl et al., 2018, Pfeifer and Allen, 2021).
## Current study
While research has shown that earlier pubertal timing is associated with an increased risk for depression, the underlying neurobiological mechanisms remain unclear. The goal of the present study was to investigate whether brain structure (cortical and subcortical metrics, and white matter microstructural measures) mediates the association between earlier pubertal timing (indexed via perceived physical development) and increased depressive symptoms in a young adolescent sample. We first tested if earlier pubertal timing when youth were aged 10–11 years (Year 1) was associated with higher depressive symptoms two years later when they were aged 12–13 years (Year 3). We then examined whether specific brain structural metrics at Year 2 (identified via our pilot analyses, see Supplementary Information), mediated the association between earlier pubertal timing and later depressive symptom severity. Given the differences in the average age of puberty-onset for males and females, we ran our models separately for males and females.
Specifically, our key hypotheses were that earlier pubertal timing at Year 1 would be associated with increased depressive symptoms at Year 3 (H1), and that this relationship would be mediated by global (H2 a-b) and regional measures (H3a-g) outlined in Table 1. These regions of interest were consistent with existing literature on puberty- and depression-related imaging features in adolescence (Schmaal et al., 2017, Shen et al., 2021, Vijayakumar et al., 2018). We did not make formal hypotheses about sex differences in the current study due to inconsistent findings in the literature. Table 1Hypotheses tested in this registered report. Table 1Research QHypothesisAnalysis TestEffect of InterestThreshold for evidenceIs earlier pubertal timing associated with later depression?H1: Earlier pubertal timing will be associated with later higher depressive symptomsGeneralised linear mixed effects modelBeta value and p valueß ≥ 0.01 and p ≤ 0.05Does brain structure mediate the association between earlier pubertal timing and later depression?Informed by our pilot analyses, the association between earlier pubertal timing and increased depressive symptoms will be mediated by:Global measuresH2a: Reduced global cortical volume, surface area, thickness and sulcal depthH2b: Reduced global FARegional measuresH3a: Reduced cortical thickness in temporal regions, namely, the middle temporal gyrus and insulaH3b: Reduced cortical thickness in frontal regions namely, the lateral orbito-frontal cortex and middle frontal gyriH3c: Reduced cortical volume in temporal regions, namely, middle temporal gyrus and bank of the superior temporal sulcusH3d: Reduced cortical volume in fronto-parietal regions, namely, the middle frontal and postcentral gyriH3e Reduced FA in the cortico-striatal tract and corpus collosumH3f: Increased sulcal depth in the pars orbitalisH3g: Increased volume in the ventral diencephalonMulti-level structural equation modelIndirect effect in mediation modelß ≥ 0.01 and p ≤ 0.05 The results of this multi-modal study will inform our understanding of how pubertal timing and brain structure may be associated with depression during adolescence. Undertaking this project as a registered report with shared analytic code applied to an openly available dataset will further increase the replicability and reproducibility of this work.
## Participants
The data used in the current study were drawn from the ABCD curated annual data release 4.0. and used Year 1, Year 2, and Year 3 follow up data. ABCD participants were recruited from 21 sites across the United States (Garavan et al., 2018). Approximately N = ∼11,800 9–10-year-olds participated in the baseline assessment. We included individuals with quality-controlled pubertal development measures (physical) at Year 1 and quality-controlled brain imaging measures (cortical and subcortical size/metrics, and white matter measures) at Year 2. Missing depression outcome and covariate data were handled using appropriate methods (see Missing Data section). This resulted in a final sample of approximately N = ∼5000 individuals, which represents about $50\%$ of the full sample. This smaller sample size can be attributed to the partial follow-up data available at the time of the 4.0 data release (Fall 2021).
To inform hypotheses for the current registered report, specifically the brain regions of interest (ROIs), we conducted pilot analyses using data from the baseline timepoint, when youth were aged 9–10 years ($$n = 9339$$, males = 4802, females = 4537) from data release 4.0. Participants were included in the pilot analyses if they had quality controlled pubertal development, depression, and brain imaging measures. Given that the main analyses did not use any baseline data, we did not anticipate that this decision would significantly impact our findings. Findings from the pilot analyses are reported in the Supplementary Information.
## Measures
All variables (excluding imaging variables) as per the NDA ABCD data dictionary/Data Exploration and Analysis Portal (DEAP) portal field names can be found in Table 2 below. Table 2Name and description of study variables used in this registered report. Table 2Field name (s)Descriptionpds_1_p; pds_2_p; pds_3_p; pds_f4_p; pds_f5b_pCaregiver: PDS female items, which were summed to generate PDS total score.pds_1_p; pds_2_p; pds_3_p; pds_m4_p; pds_m5_p. Caregiver: PDS male items, which were summed to generate PDS total scorecbcl_scr_syn_withdep_rCBCL withdrawn-depressed syndrome subscale raw scoreage_yearsAge of child in yearsanthro_bmi_calcBody mass index (BMI) (DEAP field name, non-NDA)race.6level6-level derived race variable (white, black, Asian, AIAN/NHPI, other, mixed)demo_comb_income_pHousehold incomeasr_scr_depress_rParental moodacs_raked_propensity_scoreImputed raked propensity weight. The raked propensity weight merges the ACS and ABCD data (with missing data imputed), estimates the propensity model, computes and scales/trims the propensity weights and finally rakes the scaled weights to final ACS control totals by age, sex and race/ethnicity.site_id_lABCD study sitemri_info_deviceserialnumberScanner IDdmri_dti_meanmotionDTI average framewise displacement in mmrel_family_idFamily IDField names are the column names in the original curated ABCD data release or in the DEAP portal.
## Independent variable — pubertal timing measure
Protocols previously outlined by Cheng et al. [ 2021] and Herting et al. [ 2021] were consulted in the preparation of the pubertal timing measures.
The Pubertal Development Scale (PDS) was used to examine the perceived development of secondary sex characteristics such as growth spurts, body hair growth, skin changes, breast development and menarche in girls, and voice changes and growth of facial hair in boys. In line with existing work on puberty measures in the ABCD Study, and given previous research showing that youth tend to over-report their perceived physical development at younger ages (Schlossberger et al., 1992), caregiver PDS report was utilised instead of child self-report in the current analysis. The PDS includes five-items, and each characteristic is rated on a 4-point scale (1 = no development; 2 = development has barely begun; 3 = development is definitely underway; and 4 = development is complete; except menstruation, which is coded 1 = has not begun, 4 = has begun). Thus, higher scores reflect more advanced pubertal maturation. We did not examine age of menarche in the current analysis due to the small number of females ($3\%$ and $13\%$) in the ABCD sample that have reached this developmental milestone at baseline and Year 1 data collection, respectively (Herting et al., 2021).
In line with existing research on pubertal timing, the PDS total score was regressed on age for girls and boys separately and the standardised residual obtained constituted the continuous measure of pubertal timing (Dorn et al., 2006, Hamilton et al., 2014). Only participants with complete data for $\frac{5}{5}$ PDS items were included in the analysis.
Changes to the sample size at each stage of the quality control process can be found in Fig. 1.Fig. 1Pubertal Development Scale quality checking decision tree at Year 1. ( curated annual release 4.0). Notes: Master sex = Q: Biological sex of subject, Answer: Male, Female, Other, Not Reported. Fig. 1
## Dependent variable — depressive symptoms
Depressive symptoms (DS) for children were assessed using the Child Behaviour Check List (CBCL) parent report. The CBCL is one of the most widely used measures to examine internalising and externalising difficulties in youth (Achenbach and Rescorla, 2004). It comprises raw scores as well as standardised (T-scores) based on national norms in young people aged 6–18 years. We quantified current depressive symptoms using the CBCL “withdrawn-depressed” syndrome subscale raw scores, which examine depressive symptoms within the past two weeks. Raw scores were chosen over T-scores for our analyses because they reflect all the variation in symptoms that occur in the sample. Due to the substantial percentage of individuals in a normative sample who obtain low scores on the CBCL syndrome subscales, the T-score assignments begin at 50 which means that all individuals in the lowest $50\%$ are assigned a T-score of 50.
## Hypothesised mediator — brain structural measures
Brain imaging data were acquired and processed by the ABCD team. A 3 T Siemens Prisma, General Electric 750 or Phillips scanner was used for data acquisition. A unified protocol for the scanning was used to harmonise between sites and scanners (Casey et al., 2018). Protocols used for data acquisition and processing are described elsewhere (Casey et al., 2018, Hagler et al., 2019). In brief, T1-weighted data was acquired by magnetisation-prepared rapid acquisition gradient echo scans with a resolution of 1 × 1 × 1 mm3, which was used for generating cortical and subcortical structural measures, and diffusion-weighted data was obtained by high angular resolution diffusion tensor imaging (DTI) scans, used for generating white matter microstructural measures.
Imaging data was quality controlled according to recommended QC criteria outlined by ABCD in the 4.0 release notes: “MRI Quality Control (QC) and Recommended Image Inclusion Criteria”. ABCD have created a data structure abcd_imgincl01 that provides modality-specific summary imaging inclusion flags that indicate whether an individual meets all QC criteria for the modality, and are scored as 1 = include, 0 = exclude. These summary variables account for factors such as imaging QC and post-processing (see public release notes for full description). Public release notes are available here. We included individuals that met all the recommended inclusion criteria (i.e., score = 1) on the “imgincl_t1w_include” variable for the T1w data and the “imgincl_dmri_include” variable for the DTI data. To account for additional motion artefact in the DTI data, we included a measure of mean framewise displacement (variable name: dmri_dti_meanmotion) in our DTI models.
Three types of brain structural measures were used in the present study: grey matter cortical and subcortical metrics, and white matter microstructural measures. The derivation of brain structural measures followed a hierarchical order from global measures at the whole-brain level to individual structures.
Cortical measures were generated using Freesurfer 5.3.0 (https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferWiki). Four types of cortical measures were used: surface area, thickness, volume and sulcal depth. First, global measures were generated for each cortical measure over the whole brain. The Desikan-Killiany atlas was used for parcellation of 34 bilateral cortical structures and 16 bilateral subcortical structures. For bilateral brain structures, we generated an average measure across the left and right hemisphere to use in our analyses.
White matter microstructural measures included fractional anisotropy (FA) and mean diffusivity (MD). Global measures of FA and MD were generated over the whole brain. The AtlasTrack was used to map boundaries of the 14 bilateral and 3 unilateral major tracts (Hagler et al., 2009). FA/MD values were then derived for each tract.
## Covariates
Research examining how puberty is related to developmental processes and outcomes indicates that several factors may shape these associations. Examining variability in these constructs is crucial to understanding their unique contributions to developmental and psychological outcomes (Saragosa-Harris et al., 2022). For example, differences in race/ethnicity and body mass index (BMI) have been associated with early pubertal timing (e.g., earlier age of menarche and onset of breast development) (Biro et al., 2013, Chumlea et al., 2003) and an increased risk for depression (Anderson and Mayes, 2010, Quek et al., 2017). Further, youth raised in families with low socioeconomic status, especially those with significant financial hardship, are at an increased risk for psychopathology (Bradley and Corwyn, 2002, Herting et al., 2021, Peverill et al., 2021). Research has also demonstrated that parental mood can influence the reporting of their child’s psychopathology (Maoz et al., 2014).
Although the imaging QC protocol outlined by ABCD excluded participants with excessive head motion across all imaging modalities (i.e., structural and DTI), motion-related confounds have been found to systematically impact structural connectivity measures derived from DTI data (Baum et al., 2018). Children of the same age may exhibit developmental differences in cranial or brain size, which need be considered to determine whether regional differences are independent of global effects (Mills et al., 2016, O’Brien et al., 2011). While there is currently no consensus on whether to use intracranial volume (ICV) or whole brain volume (WBV), some research suggests that WBV may be a more viable measure to use as it has been found to be more stable across developmental samples than ICV (Mills et al., 2016). The importance of controlling for site effects to account for inter-site variability has been well documented (Feaster et al., 2011). Although ABCD data collection took place at 21 sites, 30 different MRI scanners were used during data collection as some sites had more than one scanner. Therefore, the potential for variability across scanners is also important to consider (Saragosa-Harris et al., 2022). As ABCD has been oversampled for twins and siblings, it is important to account for relatedness between individuals when using the related sample (Gelman and Hill, 2006, Iacono et al., 2018).
Taking these findings together, when modelling the relationships between pubertal timing, brain structure and depressive symptoms, we included the following variables as fixed effects in our models: age, race/ethnicity, BMI, household income; parental current mood, and a DTI average framewise displacement measure (for DTI models only). We included family ID and site (for non-imaging models) and scanner ID (for all other models) as random effects.
## Consideration of outcome neutral conditions
Effect sizes (β values) and FDR (false discovery rate) corrected p values (where appropriate) were the main parameters of interest in the main analyses of the current study. The minimum effect size of interest, β ≥ 0.01 and a p ≤ 0.05, was considered statistically significant. This was informed by our previous work using the ABCD sample, which examined baseline cross-sectional brain structural associations with depression ratings in adolescence (Shen et al., 2021).
## Main analyses
All analyses were conducted in R Version 4.1. and Mplus Version 8.8. Scripts for all analyses can be found at https://github.com/niamhmacsweeney/ABCD_puberty_depression. Supplementary Data (including model outputs in R and Mplus) and the approved Stage 1 protocol for this registered report can be found on the OSF repository for this project here.
The analytic approach comprised two steps: H1) examine the associations between pubertal timing and depressive symptoms in adolescents (see Fig. 2) and H2 and H3) determine whether brain structural measures (identified via pilot analyses) mediate this association (see Fig. 3). All hypotheses are outlined in Table 1.Fig. 2Effect of pubertal timing on depressive symptoms without considering mediation (c: total effect).Fig. 2Fig. 3Effect of pubertal timing on depressive symptoms including mediation of brain structure. Fig. 3 We note that regardless of the effect sizes for our first association test (i.e., the total effect), we still conducted the mediation analysis in an attempt to accurately quantify any indirect effects (Agler and De Boeck, 2017). Further, although the models in our pilot analyses were run separately for males and females, and thus generated some varying ROIs, our main analyses used all the ROIs identified from both male and female models due to non-hypothesised sex differences in the current study.
When testing our hypotheses, we first ran our base model and if results met our specified threshold for evidence (see Table 1), we ran our full model structure. This allowed us to explore whether our main associations were attenuated by the presence of additional covariates. Hypothesis 1(H1): Pubertal timing → depressive symptoms.
Independent variable: Pubertal timing at Year 1 (youth aged 10–11 years). This was indexed by the PDS, which is a continuous measure.
Dependent variable: Youth current depressive symptoms at Year 3 (aged 12–13 years), as reported by caregiver. Depressive symptoms were indexed using the CBCL “withdrawn-depressed” syndrome subscale, which is in count data format.
Generalised linear mixed effects models (GLM) were conducted to test the associations, using the ‘lmerTest’ function in R (Kuznetsova et al., 2017). Models to test H1 are listed in Table 3.Table 3Model specifications for pubertal timing and depressive symptoms association. Table 3Sex specific modelsCovariates(Base model)Covariates(Full model)Depressive symptoms ∼ pubertal timingFixed: age, race/ethnicityRandom: family, siteFixed: age, race/ethnicity, BMI, site, household income, parental current moodRandom: family, siteModels were conducted for males and females separately.
Total number of tests across all levels of model adjustment: 2 (females) and 2 (males).Hypothesis 2(H2): Pubertal timing → brain structural measures -> depressive symptoms.
We tested whether brain structural ROIs measured at Year 2 partially and significantly mediated associations between pubertal timing at Year 1 and depressive symptoms at Year 3 (see Fig. 3). ROIs were determined based on our pilot analyses, which are detailed in the Supplementary Information.
From our pilot analyses, several brain structural measures were found to be significantly associated with pubertal timing and depressive symptoms, and thus informed the following hypotheses: The association between earlier pubertal timing and increased depressive symptoms would be mediated by:
## Global measures
•H2a: Reduced global cortical volume, surface area, thickness and sulcal depth.•H2b: Reduced global FA
## Regional measures
•H3a: Reduced cortical thickness in temporal regions, namely, the middle temporal gyrus and insula.•H3b: Reduced cortical thickness in frontal regions namely, the lateral orbito-frontal cortex and middle frontal gyrus.•H3c: Reduced cortical volume in temporal regions, namely, middle temporal gyrus and bank of the superior temporal sulcus.•H3d: Reduced cortical volume in fronto-parietal regions, namely, the middle frontal and postcentral gyri.•H3e Reduced FA in the cortico-striatal tract and corpus collosum.•H3f: Increased sulcal depth in the pars orbitalis•H3g: Increased cortical volume in the ventral diencephalon.
We ran mediation analysis using multi-level structural equation modelling (MLSEM) with Mplus software (Preacher et al., 2010) and via the “MplusAutomation” package in R (Hallquist and Wiley, 2018). MLSEM enables the stratification of within individual, between family and site/scanner variance, therefore allowing us to capture these random effects.
This model characterised associations between pubertal timing, brain structural ROIs and depressive symptoms. We undertook single and multiple mediator models, depending on the brain ROI. Multiple mediator models allowed us to examine the proportion of variance in the pubertal timing-depression associations uniquely explained by all brain structural ROIs and allowed for comparisons between different ROIs. For this analysis, we simultaneously entered individual brain structural ROIs as covarying mediators. A combined cluster variable was used to model random effects in Mplus due to model parameter requirements.
The primary outcomes of interest were the direct effect between the pubertal timing measure and depressive symptoms, and the indirect paths between these two variables that are mediated by brain structural ROIs. Statistical significance of this indirect effect was used to indicate that a significant mediation of the total effect is present. An effect was considered statistically significant when p ≤ 0.05 and there was a minimum effect size (β value ≥ 0.01). As outlined in the “consideration of outcome neutral conditions” section, this minimum effect size was based on our previous work where effect sizes for brain structural associations with depression ratings in the ABCD sample were found to be in the region of 0.01–0.03 (Shen et al., 2021). Bootstrapping with 1000 repetitions was used to calculate robust standard errors.
The Base model included age, race/ethnicity, and DTI motion as fixed effects, and family ID and scanner ID as random effects. The full model included the same random effects as the base model but with the additional fixed effects: WBV, BMI, household income, and parental current mood.
## Sensitivity analyses
In addition to the main models, we conducted sensitivity analysis to examine the association between earlier pubertal timing and the potential change (or rather worsening) of depressive symptoms between timepoints (i.e., Year 1 and Year 3), by including Year 1 depressive symptoms as an additional covariate in our full model as a sensitivity analysis. Further, to examine potential demographic and socio-economic bias in the selection of ABCD participants, we included a population-weighting variable in our full model that calibrates ABCD weighted distributions to nationally representative controls from the American Community Survey (ACS).
In our analysis of change in depressive symptoms over time and pubertal timing, we report a significant relationship in females only (Base model: ß = 0.17 [IRR = 1.17]; $p \leq 0.001$; fully adjusted model: ß = 0.16 [IRR = 1.73]; $p \leq 0.001$). Base and fully adjusted models for females and males are reported in the Supplementary Information. In our sensitivity analyses, we also found similar results to our main analyses when adding a population stratification weight to our models. See Supplementary Information for further details.
## Missing data
Missing outcome and covariate data were handled using appropriate methods (Matta et al., 2018). Multiple imputation by chained equations (MICE) was used to treat missing data for H1 using the “mice” package in R (Buuren and Groothuis-Oudshoorn, 2011). For H2 and H3, full information maximum likelihood (FIML) estimation in Mplus was used to handle missing data in our mediation analyses. As sensitivity analyses, we compared our imputed analysis approach to complete case analysis for H1.
Compared to complete case analysis for H1, similar effect sizes were observed when missing data was imputed using MICE. Details of our imputation methods and results can be found in the Supplementary Information.
## Exploratory analyses
To identify any additional relevant brain structural measures that may not have been identified in the pilot analyses due to the use of baseline data only, we also undertook exploratory whole brain analysis to examine whether any other brain structural measures (at Year 2) mediated the association between pubertal timing (at Year 1) and depressive symptoms (at Year 3). Multiple comparison correction (FDR method) was applied using the “p.adjust” function in R and applied to each brain measure category separately. These analyses were considered post-hoc and thus reported as exploratory findings.
## Project timeline
Our Stage One registered report obtained an in-principle acceptance in August 2022, and we submitted our Stage 2 manuscript for review in December 2022.
## Results
Sample characteristics are presented in Table 4 with further information on the pilot analyses given in the Supplementary Information. Table 4Descriptive statistics for sample. Table 4CharacteristicF, $$n = 2726$$aM, $$n = 3001$$ap-valuebAge (Y1)10.98 (0.63)11.00 (0.64)0.082Age (Y2)11.96 (0.64)11.98 (0.65)0.2 Missing (N)3435Age (Y3)12.87 (0.64)12.91 (0.65)0.021PDS total score10.37 (3.09)7.72 (2.12)< 0.001Youth depressive symptoms1.49 (2.19)1.30 (1.90)0.012 Missing (N)4646Race/ethnicity0.072 White1876 / 2696 ($69.58\%$)2175 / 2975 ($73.11\%$) Black296 / 2696 ($10.98\%$)278 / 2975 ($9.34\%$) Asian70 / 2696 ($2.60\%$)73 / 2975 ($2.45\%$) AIAN/NHPI23 / 2696 ($0.85\%$)17 / 2975 ($0.57\%$) Other105 / 2696 ($3.89\%$)111 / 2975 ($3.73\%$) Mixed326 / 2696 ($12.09\%$)321 / 2975 ($10.79\%$) Missing (N)3026BMI (Y1)19.43 (4.24)19.43 (4.10)0.8 Missing (N)5143Household income0.6< $500047 / 2558 ($1.84\%$)66 / 2813 ($2.35\%$) $5,000-$11,99963 / 2558 ($2.46\%$)69 / 2813 ($2.45\%$) $12,000-$15,99955 / 2558 ($2.15\%$)43 / 2813 ($1.53\%$) $16,000-$24,99989 / 2558 ($3.48\%$)111 / 2813 ($3.95\%$) $25,000-$34,999142 / 2558 ($5.55\%$)133 / 2813 ($4.73\%$) $35,000-$49,999212 / 2558 ($8.29\%$)233 / 2813 ($8.28\%$) $50,000-$74,999364 / 2558 ($14.23\%$)388 / 2813 ($13.79\%$) $75,000-$99,999404 / 2558 ($15.79\%$)438 / 2813 ($15.57\%$) $100,000-$199,999848 / 2558 ($33.15\%$)964 / 2813 ($34.27\%$)> $200,000334 / 2558 ($13.06\%$)368 / 2813 ($13.08\%$) Missing (N)168188DTI mean FD1.20 (0.43)1.24 (0.53)0.14 Missing (N)566476Parent depressive symptoms3.96 (3.59)3.95 (3.54)> 0.9 Missing (N)3538Y1 = year 1; Y2 = year 2; Y3 = year 3. Youth depressive symptoms = CBCL withdrawn depressed total raw score; AIAN/NHPI = American Indian/Alaska Native/Native Hawaiian and other Pacific Islander; Household income = yearly gross household income; DTI mean FD = mean framewise displacement from year 2 DTI data; Parent depressive symptoms = Depressive Problems ASR DSM-5-Oriented Scale.aMean (SD); n / N (%)bWilcoxon rank sum test; Pearson's Chi-squared test Frequencies for perceived pubertal development and youth depressive symptoms, based on parent report, are shown in Fig. 4.Fig. 4Frequencies (N) for parent summary scores from the Pubertal Development Scale (PDS). ( A) Total pubertal development score; (B) PDS Category score counts ranging from pre- to post- pubertal. Note that PDS category score (variables: “pds_p_ss_female_category” and “pds_p_ss_male_category” data were not available for 60 participants in ABCD release 4.0 so $$n = 5667$$ (full sample $$n = 5727$$) for Figure 4.6b. ( C) Gonadal score averaging gonadal PDS items and ranging from 1 = not begun to 4 = complete; (D) Adrenal score averaging adrenal PDS items ranging from 1 = not begun to 4 = complete. Fig. 4
## A note on the interpretation of effect sizes
All variables (except youth depressive symptoms [a count variable with a Poisson distribution] and the population weight propensity score) were converted to z-scores before entering our analyses to ensure they could be consistently interpreted across our hypotheses. The estimated ß coefficients should be interpreted as follows: for a unit change in the predictor variable, the difference in the logs of the expected counts is predicted to change by the respective ß value, while holding the other predictor variables in the model constant. To further aid the interpretation of our main results, we report incidence rate ratios (IRR) alongside the ß values and associated p-values. The incidence rate ratio is the exponentiated ß coefficient and can be interpreted as a relative risk.
## Hypothesis 1
Our first hypothesis tested whether earlier pubertal timing at year 1 (10–11 years) was associated with higher depressive symptoms at year 3 (12–13 years). In our base model, both females and males who started puberty earlier than their peers were more likely to report higher depressive symptoms two years later. Basic model: Females: ß = 0.27; [IRR = 1.31]; $p \leq 2$ × 10-16; males: ß = 0.08 [IRR = 1.09]; $$p \leq 0.005.$$ In our fully adjusted model (with covariates BMI, household income, and parental depressive symptoms), our main effect size was attenuated for females but remained significant (ß = 0.20 [IRR = 1.22], $p \leq 2$ ×10-16). However, the observed effect size for males (ß = 0.04; [IRR = 1.045], $$p \leq 0.15$$) no longer met our threshold for evidence (see definition, Table 1). Thus, we find partial support for our first hypothesis. The results (reported using IRRs) for Hypothesis 1 are illustrated in Fig. 5. All statistics (e.g., ß values, IRRs, standard errors, and p values) for Hypothesis 1 are reported in Tables S1 & S2 in the Supplementary Information. Fig. 5Hypothesis 1 results: Incidence Rate Ratios (IRRs) for the association between pubertal timing and youth depressive symptoms. Results for females are shown in (A) and males are shown in (B). Base models are shown in top panel and fully adjusted models are presented in the bottom panel. The neutral line or vertical intercept line is shown in bold and indicates no effect. Blue IRRs indicate a greater depression risk while red IRRs represent a decreased depression risk. Error bars represent $95\%$ confidence interval. Fig. 5
## Exploratory analyses related to Hypothesis 1
To explore whether there were specific aspects of pubertal development that were driving the association between earlier pubertal timing and increased depression risk, we ran two independent models (using H1 model set up) with adrenarcheal timing (AT) and gonadarcheal timing (GT) scores as predictors. For females, both AT and GT were significantly associated with later youth depression (Base model: AT: ß = 0.23 [IRR = 1.26]; $p \leq 0.001$; GT: ß = 0.24 [IRR = 1.28]; $p \leq 0.001$), and these effects remained significant in the fully adjusted model (AT: ß = 0.17 [IRR = 1.18]; $p \leq 0.001$; GT: ß = 0.17 [IRR = 1.19]; $p \leq 0.001$). For males, only AT was significantly associated with later youth depression (Base model: AT: ß = 0.10 [IRR = 1.11]; $$p \leq 0.001$$; GT: ß = 0.05 [IRR = 1.04]; $$p \leq 0.154$$). Neither association was significant in the fully adjusted model for males. The methods and results of this exploratory analysis are reported in full in the Supplementary Information (Tables S3 – S8).
## Hypothesis 2 — global brain measures
Our second hypothesis tested whether global brain structural measures at Year 2 (specifically, lower global volume, cortical thickness, surface area, and sulcal depth (H2a) and FA (H2b)) mediated the association between earlier pubertal timing at Year 1 and higher depressive symptoms at Year 3. As reported in Table 5, for both females and males, we did not find support for these hypotheses in the current analyses due to the absence of an indirect effect (Females: H2a: ß = −0.001 [IRR = 0.99], $$p \leq 0.89$$; H2b: ß = 0.001 [IRR = 1.00], $$p \leq 0.57$$; Males: H2a: ß = 0.003 [IRR = 1.00], $$p \leq 0.43$$; H2b: ß = −0.001 [IRR = 0.99], $$p \leq 0.39$$) from our predictor (pubertal timing) to our outcome (youth depressive symptoms) through our hypothesised mediator (brain structure).Table 5Multiple mediator model results for Hypothesis 2 & 3. ROI = region of interest; PT = pubertal timing; DS = depressive symptoms. Table 5FemalesMalesHypothesisMediator (ROI)TypeEffectEstimateSEp-valueEstimateSEp-valueH2aGlobal cortical volume, thickness, surface area, sulcal depthIndirectDirectTotalPT → ROI → DSPT → DSPT → DS-0.0010.2230.220.0070.0320.0310.898< 0.001< 0.0010.0030.0800.0830.0040.030.0290.4250.0080.004H2bGlobal fractional anisotropyIndirectDirectTotalPT →ROI → DSPT → DSPT → DS0.0010.2260.2270.0010.0320.0320.565< 0.001<0.001-0.0010.0780.0770.0010.0310.0310.3950.0100.011H3aCortical thickness of the middle temporal gyrus and insulaIndirectDirectTotalPT →ROI → DSPT → DSPT → DS-0.0080.2310.2230.0050.0320.0310.059< 0.001< 0.001-0.0010.0850.0830.0020.0290.0290.6110.0040.004H3bCortical thickness of the lateral orbito-frontal cortex and middle frontal gyrusIndirectDirectTotalPT → ROI → DSPT → DSPT → DS-0.0070.2280.2220.0050.0310.0310.140< 0.001< 0.0010.0000.0830.0830.0030.0290.0290.850.0050.004H3cCortical volume of the middle temporal gyrus and bank of the superior temporal sulcusIndirectDirectTotalPT → ROI → DSPT → DSPT → DS0.0020.2200.2220.0050.0320.0310.709< 0.001< 0.001-0.0040.0870.0830.0040.0290.0290.3620.0030.004H3dCortical volume of the middle frontal gyrus and postcentral gyrusIndirectDirectTotalPT → ROI → DSPT → DSPT → DS0.0040.2170.2210.0050.0320.0310.433< 0.001< 0.0010.0030.080.0830.0040.030.0290.3740.0070.004H3eFractional anisotropy of the cortico-striatal tract and corpus collosumIndirectDirectTotalPT → ROI → DSPT → DSPT → DS0.0010.2660.2270.0030.0330.0320.826< 0.001< 0.001-0.0030.0760.0730.0030.0310.030.2780.0130.016H3fSulcal depth of the pars orbitalisIndirectDirectTotalPT → ROI → DSPT → DSPT → DS0.0000.2220.2220.0020.0310.0310.925< 0.001< 0.001-0.0020.0850.0840.0020.0290.0020.3710.0040.371H3gCortical volume of the ventral diencephalonIndirectDirectTotalPT → ROI → DSPT → DSPT → DS0.0000.2220.2220.0010.0310.0310.888< 0.001< 0.001-0.0010.0840.0830.0010.0290.0290.6680.0040.004
## Hypothesis 3 — regional brain measures
Our third hypothesis investigated whether regional brain structural measures at Year 2 (identified via our pilot analyses and listed in Table 1) mediated the association between earlier pubertal timing at Year 1 and later youth depressive symptoms at Year 3. As shown in Table 5, for both females and males, our results did not find support for our hypotheses due to an absence of an indirect effect (Females: ß range: −0.008 to 0.004 [IRR range = 0.99–1.00], p range: 0.05–0.89; Males: ß range: −0.004 to 0.003 [IRR range = 0.99–1.00], p range: 0.27–0.85).
All model statistics and Mplus outputs for these analyses, including single mediator model results (not reported in Table 5), can be found in the https://osf.io/rw3s6/?view_only=39ce180796ad4688bdbcf80563a726b5. Supplementary Data.
## Exploratory analyses related to Hypothesis 2 & 3
We conducted exploratory analyses to identify whether any other brain structural measures (beyond those identified in the pilot analyses) mediated the association between earlier pubertal timing and depressive symptoms. Full details of these exploratory analyses can be found in the Supplementary Information. In brief, we first examined associations between pubertal timing (Year 1) and brain structure (Year 2), and between brain structure (Year 2) and youth depressive symptoms (Year 3). Lower volume of the accumbens area was the only brain measure found to be associated with both pubertal timing and depressive symptoms. Thus, we tested for mediation using the same methods employed in our confirmatory analyses.
## Brain structural associations with pubertal timing
For females, earlier pubertal timing was associated with reduced global cortical thickness (ß = −0.10; pFDR = 4.4 ×10-5) and global cortical volume (ß = −0.09; pFDR = 1.3 ×10-5). Regionally, earlier pubertal timing was associated with reduced cortical thickness and volume in temporal, frontal and parietal regions (ß range: −0.12 to −0.08; pFDR range: 2.2 ×10-6 to 0.0008). See Fig. 6.Fig. 6Exploratory analyses results: Significant cortical associations with earlier pubertal timing in female youth. pFDR ≤ 0.001.Fig. 6 For males, earlier pubertal timing was not significantly associated with global brain measures. The only regional brain measure that demonstrated a significant association with earlier pubertal timing was increased volume of the ventral diencephalon (ß = −0.07; pFDR = 0.001).
## Brain structural associations with depressive symptoms
For females, no significant associations were found between global brain measures and depressive symptoms. However, we report several significant regional associations with depressive symptoms, namely, reduced volume in the accumbens area (ß = −0.105 [IRR = 0.90], pFDR = 0.024, increased sulcal depth in the bank of the superior temporal sulcus (ß = 0.133 [IRR = 1.14], pFDR = 0.003) and precuneus (ß = 0.12 [IRR = 1.12], pFDR = 0.020), as well as increased MD in the inferior fronto-occipital fasciculus (ß = 0.11 [IRR = 1.12], pFDR = 0.050).
For males, we did not find any significant associations between global brain measures and depression symptoms. Regionally, depressive symptoms were associated with reduced volume in the accumbens area (ß = −0.10 [IRR = 0.90], pFDR = 0.012), pallidum (ß = −0.08 [IRR = 0.92], pFDR = 0.052), and thalamus (ß = −0.08 [IRR = 0.92], pFDR = 0.056), as well as reduced surface area in the medial orbitofrontal gyrus (ß = −0.11 [IRR = 0.89], pFDR = 0.029).
## Testing accumbens area volume as a mediator
For both females and males, we did not find any evidence of a mediating effect of accumbens area volume on the association between earlier pubertal timing and increased depressive symptoms (Females indirect effect: ß = 0.005, $$p \leq 0.14$$; Fig. 7a; Males indirect effect: ß = 0.004, $$p \leq 0.09$$; Fig. 7b).Fig. 7Exploratory analyses results: Mediation paths and statistics for main effect of pubertal timing and depressive symptoms, mediated through accumbens area volume. Results for females are shown in (A) and males are shown in (B).Fig. 7
## Discussion
In the present study, we investigated whether earlier pubertal timing was associated with an increased risk for later depressive symptoms in adolescence, and whether certain a priori brain structural measures mediated this association, in a large, demographically diverse sample of youth. We found that earlier pubertal timing when youth were aged 10–11 years was significantly associated with increased depressive symptoms two years later, when youth were aged 12–13 years. The observed association was stronger for female adolescents compared to males and importantly, this association did not remain significant in males when controlling for other factors associated with depression risk. In females, earlier pubertal timing was also related to worsening depressive symptoms over time. Thus, the first hypothesis of this registered report was partially supported. Regarding Hypothesis 2 & 3, the hypothesised brain structural measures were not found to mediate the association between earlier pubertal timing and later depressive symptoms. Although our exploratory analyses demonstrated significant brain structural associations with earlier pubertal timing and youth depressive symptoms, we also did not find evidence of brain structural mediation when examining regions beyond those specified a priori.
The current results advance our understanding of how pubertal timing relates to brain structural maturation and depression risk beyond age-related changes using one of the largest available samples to date. Our findings suggest that while a robust association exists between earlier pubertal timing and increased depressive symptoms, particularly for females, brain structure did not mediate this association in our analyses over these time points. These results highlight the need to consider additional biological factors (e.g., genetics), other brain metrics (e.g., brain function, brain age gap estimates (AGE)) and socio-environmental risk factors when examining the association between earlier pubertal timing and increased depression risk, and the differential impact they may have across sexes and time. Given the significant burden of depression in adolescence and beyond, further longitudinal research is urgently needed so that we can better understand how to support young people as they navigate this formative developmental transition.
## Earlier pubertal timing is associated with later youth depressive symptoms
A substantive body of evidence has demonstrated that youth that begin puberty ahead of their peers are at an increased risk of psychopathology, including depression. Using one of the largest sample sizes to date (N = ∼ 5300), our findings extend prior work and emphasise the detrimental effects that earlier pubertal maturation can have on depression risk in adolescence. Our results are broadly consistent with the maturation disparity hypothesis which posits that both early maturing males and females are at an increased risk for mental health difficulties during adolescence, due to an incongruency in their physical, cognitive, social, and emotional development (Brooks-Gunn et al., 1985, Ge et al., 2001, Ge and Natsuaki, 2009). Our findings are similar to earlier meta-analytic work (Ullsperger and Nikolas, 2017) and recent findings from the ABCD Study (McNeilly et al., 2022) in that we also found a significant small effect size for the association between earlier pubertal timing and internalising difficulties. However, we additionally report a greater magnitude of effect for early maturing females compared to males, suggesting that female youth that hit puberty ahead of their peers are particularly at risk for mood difficulties.
Ullsperger and Nikolas [2017] did not find evidence of a female-specific vulnerability for internalising difficulties in early maturing youth. A direct comparison of results is difficult here given that we used a depression-specific outcome measure, whereas Ullsperger and Nikolas [2017] used a broader index of internalising difficulties that comprised distress, fear, and eating pathology. Of note, when the authors examined male and female samples separately, they found a significant association between pubertal timing and distress for females but not males, although in the whole group analysis, a sex moderation effect was not found. Using baseline ABCD data, McNeilly et al. [ 2022] report similar effect sizes for male and female youth aged 9–10 years. However, as the authors note, the female-specific vulnerability for internalising difficulties does not emerge until the age of 12 years or older, which may explain why we observed a greater magnitude of effect for females in our study which used later follow up ABCD data (youth aged 12–13 years when depression was measured).
Importantly, at the time pubertal development was assessed in our study (youth aged 10–11 years), most males were pre-pubertal or in the early stages of puberty, while the females exhibited a much broader spread of pubertal maturation. Thus, the greater magnitude of effect we observe for females could reflect a temporal effect. That is, the distress associated with the experience of maturing ahead of your peers may not manifest straight away and could become apparent in male youth at a later timepoint when they are more pubertally mature. Further, our sensitivity analyses revealed that when controlling for earlier depressive symptoms, earlier pubertal timing was associated with the worsening of symptoms (between ages 10–11 years and 12–13 years) in females but not males. This may reflect the significantly higher incidence of depressive symptoms in females compared to males in the current sample.
It may also be that the inconsistent findings in the current literature reflect a general direct effect between earlier pubertal timing and depressive symptoms that is comprised of sex-specific vulnerabilities. For example, previous research has shown that ethnicity, life stress, and cognitive processes (e.g., rumination) moderate the risk of earlier pubertal timing for later psychopathology according to sex (Alloy et al., 2016, Hamilton et al., 2014). Indeed, the findings of the current study underscore the importance of considering this nuance: the main effect between earlier pubertal timing and increased depressive symptoms was no longer significant for males only when additional socio-demographic factors (e.g., BMI) were controlled for. This provides further evidence that while early maturing youth are at an increased risk for depression in adolescence, there may be sex-specific biological and social/environmental mechanisms that influence this risk. Future research that embraces a biopsychosocial conceptual framework (e.g., Affective, Biological and Cognitive (ABC) model of depression; Hyde et al., 2008) is needed to refine existing theories so that they better reflect the complex interplay of risk (and resilience promoting) factors that underpin the association between pubertal timing and psychopathology in adolescence and how this may vary across sexes (Ullsperger and Nikolas, 2017).
Our exploratory analyses found that both adrenarcheal and gonadarcheal timing were associated with depressive symptoms in females, consistent with prior findings (Barendse et al., 2021). However, for males, we report that the observed association between early pubertal timing and later depressive symptoms was driven by adrenarcheal aspects of pubertal maturation. As mentioned, while the males and females in the ABCD sample are the same chronological age, there are marked differences in their pubertal progression. Thus, multiple timepoints of data, where there is similar variation in pubertal maturation for both sexes, are needed to properly investigate sex differences in pubertal timing, as well as tempo, and their relation to depression risk. These data will soon be available from the ABCD Study and the results from the current study have laid a strong foundation for this future work.
## Brain structure does not mediate the association between earlier pubertal timing and later depressive symptoms
We did not find that cortical, subcortical, or white matter microstructural measures mediated the association between earlier pubertal timing and increased depressive symptoms in adolescents, in both our confirmatory and exploratory mediation analyses. These results suggest that although pubertal timing was associated with alterations in brain morphology above and beyond age-related changes, and there were associations between brain features and depression, these brain structural features did not mediate increased risk for later depressive symptoms in this sample (9–13 years). The present findings highlight that much work remains in our effort to better understand what it is about the experience of developing ahead of your peers that confers vulnerability to depression in adolescence.
This vulnerability is likely comprised of multiple and interacting biological and socio-environmental factors that exert varying degrees of risk across time and individuals. How other neuroimaging features, such as brain function and multi-modal brain metrics (e.g., brainAGE), relate to depression risk in early maturing youth remain underexplored (Pfeifer and Allen, 2021). It may be that certain brain features (e.g., structural and/or functional) mediate the association between earlier pubertal timing and depression in some but not all youth, and such associations may only exist at certain developmental windows (e.g., in mid adolescence). Shifting our focus from group-level analyses to a person-centred longitudinal approach, which will soon be possible in ABCD with the release of multiple timepoints (>3) of imaging data, will allow us to better assess null findings, such as those in the current study.
Access to large-scale, longitudinal neuroimaging data in developmental samples will undoubtedly create many exciting avenues for future research. However, there has been much debate about how meaningful the small effect sizes consistently reported in large-scale neuroimaging studies are and crucially, what they really add to our ability to predict developmental outcomes when compared to clinical and psychosocial data (Dick et al., 2021). Given the complex web of interactions that shape development, it is perhaps unsurprising that an individual measure (e.g., global cortical volume or the volume of the nucleus accumbens) only explains a small amount of variance in our outcome of interest (e.g., depression). If we are ever to use an “ecological neuroscience” approach that combines neuroimaging with other biological and psycho-social factors to reliably predict developmental outcomes, we need to be able to do so at the level of the individual (Ferschmann et al., 2022). The availability of longitudinal multimodal data paired with the advent of advanced analytic methods holds great promise in answering such research questions and should be explored in subsequent work.
Our exploratory work extends existing research by examining brain structural associations with pubertal timing in one of the largest samples to date (N = ∼5000). Crucially, our analyses pertain to brain structural associations with pubertal timing specifically which is distinct from examining pubertal development controlling for age, a distinction that is often overlooked in the extant literature. We found that earlier pubertal timing was associated with lower cortical volume and thickness both globally and regionally in frontal, temporal, and parietal regions. Our results also demonstrated that a decrease in the volume of the nucleus accumbens was related to earlier pubertal timing. The current findings are consistent with prior work that has used both physical and hormonal puberty measures (Goddings et al., 2014, Goddings et al., 2019, Vijayakumar et al., 2018). Although a positive association between pubertal timing and FA has been reported previously (Herting et al., 2012, Peper et al., 2015), we did not find that white matter microstructure was associated with pubertal timing in our sample. Importantly, the above findings pertain to female youth only and the only brain structural association with pubertal timing observed in males was increased volume of the ventral diencephalon. Due to differences in the age of puberty onset between sexes, and the age range (9–13 years) of the current sample, we may not yet see puberty-related brain structural effects in males. Longitudinal work is needed to further understand how pubertal timing affects brain structural development over time, which will help elucidate whether the sex differences observed in the current study attenuate as the pubertal stages of females and males align.
Our earlier work (Shen et al., 2021), and that of others (Schmaal et al., 2017), has demonstrated that global and regional alterations in cortical and white matter microstructural measures are associated with depression in adolescence. The results of the current study are broadly aligned with these earlier findings such that we see differences in some temporal, parietal and frontal regions, as well as in fronto-occipital white matter tracts, with a consistent directionality of effects. We also report alterations in subcortical areas such as reduced volume of the nucleus accumbens (in both sexes), pallidum, and thalamus (males only). Together with previous work, the current findings can inform future longitudinal research that models individual differences in brain development. This work is needed to determine how cross-sectional depression-related imaging features relate to patterns of brain maturation over time (i.e., do they reflect accelerated or delayed development) and how they vary across individuals (Becht and Mills, 2020).
## Limitations and future directions
Although our study benefits from a large sample size and a mediation analysis that used temporally distinct timepoints, there are important limitations to consider. Firstly, our measure of pubertal timing is cross-sectional and based on parent-report. Using a cross-sectional measure of pubertal timing precludes investigating whether the rate of change in pubertal maturation (i.e., tempo) matters for developmental outcomes. For example, if a young person is an “early developer” at the start of puberty but “on-time” by mid-puberty, how does this relate to depression risk. Longitudinal data with repeated measures of pubertal development will be essential in answering such research questions. Additionally, we prioritised parent-report of youth pubertal development (over youth self-report) because youth have been found to over-report their pubertal development in the early stages of adolescence (Schlossberger et al., 1992). There was also a large number of “I don’t know” responses in the early waves of ABCD puberty data collection (Cheng et al., 2021). Nonetheless, adolescent report may better capture the more intimate body changes associated with puberty, especially in the later pubertal stages (Dorn et al., 1990). Importantly, self- (and parent-) report measures of pubertal development assess the outcome of prolonged systemic hormonal effects and thus are limited in their ability to make inferences about the biological mechanisms relating pubertal timing to neurodevelopment (Goddings et al., 2019). Due to the availability of multi-modal puberty and imaging data, ABCD is well-positioned to advance this line of research and could build upon emerging multi-verse analysis methods (Barendse et al., 2021).
There are a number of measurement error considerations related to the PDS that warrant attention. For example, PDS responses mix rate of change and stage, such that someone experiencing rapid pubertal changes (i.e., tempo) might be more likely to select the response “definitely underway” compared to someone with a more protracted pubertal development. Similarly, the yearly interval between assessments in ABCD may mean that aspects of pubertal development may be described as “complete” even though further changes could occur later. As longitudinal data become available in ABCD, such considerations should be explored so that we can map and interpret patterns of pubertal maturation as accurately as possible, while acknowledging the limitations of the measures available. Akin to the limitations associated with parent-report of pubertal development, examining adolescents’ self-report of depressive symptoms, and how this relates to parent report, is an important consideration for future work (Shen et al., 2021). Additionally, our use of parent report for both the predictor and outcome measures could have resulted in shared methods variance. Adopting a multi-informant approach will help address this limitation in subsequent research.
Although the temporal distance between pubertal timing, brain structure, and youth depression measures was a strength of the analyses undertaken in the current study, due to the varying availability of follow-up data, we did not examine any changes in our variables of interest between timepoints. Moreover, a brain structure previously found to mediate the association between earlier pubertal timing and depression in adolescence, namely, the pituitary gland (Whittle et al., 2012), was not available in the brain parcellations provided in the ABCD curated data release and was thus not tested as a potential mediator. We recommend using the raw ABCD imaging data to test the pituitary gland specifically as a mediator in further research. Further, subsequent work should also reflect the temporality inherent to development by examining domains such as pubertal tempo (the rate at which pubertal development occurs), and how this relates to both brain structural and functional changes across adolescence, as well as depressive symptom trajectories.
Charting individual differences in development has gained increasing attention in recent times but longitudinal studies with multiple timepoints are necessary to generate developmental pathways (Bethlehem et al., 2022, Mills et al., 2021). For example, do differences in pubertal timing represent a stable risk factor that predicts the emergence of depression or do other factors (e.g., early life stress, loneliness) exert varying degrees of influence during adolescence (Colich and McLaughlin, 2022). Given that knowledge gaps still exist in our understanding of normative brain development during adolescence (particularly in terms of brain function), global neuroimaging metrics, such as “BrainAGE” may better capture deviations (e.g., acceleration) from typical neuromaturation, and on what scale this occurs (globally, or in particular brain networks) (Colich and McLaughlin, 2022, Popescu et al., 2021). Emerging research has already begun to explore such questions by examining brain maturation and puberty in early adolescence using deep learning brain age prediction models (Holm et al., 2023).
## Conclusion
The current study makes a significant contribution to our understanding of how pubertal maturation relates to youth depression by directly testing whether brain structural features mediate this association, which had not been previously examined in a sample of this size and with multiple timepoints of data (Pfeifer and Allen, 2021). We found that while early maturing youth, particularly females, were at an increased risk for depression, brain structure was not found to mediate the observed association in either sex. Central to the new conceptual model proposed by Pfeifer and Allen [2021] is the consideration of neural, social, and pubertal processes simultaneously and how they co-evolve and interact over time. Therefore, the work undertaken in this registered report can be used a framework for future studies, whose design should reflect the complex interplay of these processes as much as possible. Given the complexities of development, “team-science” and “open-science” practices will allow us to better understand the aspects of pubertal and brain development that contribute to adolescent mental health vulnerabilities in a reproducible and collaborative manner (Zanolie et al., 2022). Such efforts will be crucial to reaching our collective goal of creating an environment that gives young people every chance to flourish in their development.
## Data access
N.M and all co-authors self-certify that they did not observe any of the statistical models outlined in the confirmatory analysis until after the in-principle acceptance was issued. See Supplementary Information for full information on data access. All scripts (R and Mplus) used in this registered report are available on the GitHub repository for this project: https://github.com/niamhmacsweeney/ABCD_puberty_depression.
## Supplementary material
Supplementary material.
## Data availability
The code used for the analyses in this registered report can be found at https://github.com/niamhmacsweeney/ABCD_puberty_depression.git.
## CRediT authorship contribution statement
Niamh MacSweeney: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing, Project administration. Judith Allardyce: Formal analysis, Methodology, Writing – review & editing. Amelia Edmonston-Stait: Methodology, Writing – review & editing. Xueyi Shen: Data curation, Methodology, Writing – review & editing. Hannah Casey: Methodology, Writing – review & editing. Stella W. Y. Chan: Writing – review & editing, Funding acquisition. Breda Cullen: Methodology, Writing – review & editing. Rebecca M. Reynolds: Methodology, Writing – review & editing. Sophia Frangou: Writing – review & editing. Alex S. F. Kwong: Methodology, Writing – review & editing, Supervision. Stephen M. Lawrie: Methodology, Writing – review & editing, Supervision, Funding acquisition. Liana Romaniuk: Methodology, Writing – review & editing, Supervision. Heather C. Whalley: Conceptualisation, Methodology, Writing – review & editing, Supervision, Funding acquisition.
## Data statement
All the data used in this registered report is from the Adolescent Brain and Cognitive Development (ABCD) Study. Direct access to ABCD data requires approval from the NDA Data Access Committee (see nda.nih.gov/abcd/request-access for instructions).
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---
title: Identification of biomarkers for the diagnosis of chronic kidney disease (CKD)
with non-alcoholic fatty liver disease (NAFLD) by bioinformatics analysis and machine
learning
authors:
- Yang Cao
- Yiwei Du
- Weili Jia
- Jian Ding
- Juzheng Yuan
- Hong Zhang
- Xuan Zhang
- Kaishan Tao
- Zhaoxu Yang
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10009268
doi: 10.3389/fendo.2023.1125829
license: CC BY 4.0
---
# Identification of biomarkers for the diagnosis of chronic kidney disease (CKD) with non-alcoholic fatty liver disease (NAFLD) by bioinformatics analysis and machine learning
## Abstract
### Background
Chronic kidney disease (CKD) and non-alcoholic fatty liver disease (NAFLD) are closely related to immune and inflammatory pathways. This study aimed to explore the diagnostic markers for CKD patients with NAFLD.
### Methods
CKD and NAFLD microarray data sets were screened from the GEO database and analyzed the differentially expressed genes (DEGs) in GSE10495 of CKD date set. Weighted Gene Co-Expression Network Analysis (WGCNA) method was used to construct gene coexpression networks and identify functional modules of NAFLD in GSE89632 date set. Then obtaining NAFLD-related share genes by intersecting DEGs of CKD and modular genes of NAFLD. Then functional enrichment analysis of NAFLD-related share genes was performed. The NAFLD-related hub genes come from intersection of cytoscape software and machine learning. ROC curves were used to examine the diagnostic value of NAFLD related hub genes in the CKD data sets and GSE89632 date set of NAFLD. CIBERSORTx was also used to explore the immune landscape in GSE104954, and the correlation between immune infiltration and hub genes expression was investigated.
### Results
A total of 45 NAFLD-related share genes were obtained, and 4 were NAFLD-related hub genes. Enrichment analysis showed that the NAFLD-related share genes were significantly enriched in immune-related pathways, programmed cell death, and inflammatory response. ROC curve confirmed 4 NAFLD-related hub genes in CKD training set GSE104954 and other validation sets. Then they were used as diagnostic markers for CKD. Interestingly, these 4 diagnostic markers of CKD also showed good diagnostic value in the NAFLD date set GSE89632, so these genes may be important targets of NAFLD in the development of CKD. The expression levels of the 4 diagnostic markers for CKD were significantly correlated with the infiltration of immune cells.
### Conclusion
4 NAFLD-related genes (DUSP1, NR4A1, FOSB, ZFP36) were identified as diagnostic markers in CKD patients with NAFLD. Our study may provide diagnostic markers and therapeutic targets for CKD patients with NAFLD.
## Introduction
Chronic kidney disease (CKD) is defined as structural or functional abnormalities of the kidney caused by various causes for ≥ 3 months [1]. Epidemiological studies show that there are approximately 434.3 million people with CKD in Asia, most of whom come from developing countries like China and India [2]. The effective control of chronic kidney disease is a huge public health challenge worldwide [3].
Previous studies have suggested that acute kidney injury, hypertension, and diabetes are risk factors for CKD [4]. Recently, accumulating evidence indicates that Non-alcoholic fatty liver disease (NAFLD) may be associated with the development of CKD (5–8).
NAFLD is a heterogeneous disease in which the vast majority are non-alcoholic fatty liver (NAFL) and less than $20\%$ are non-alcoholic steatohepatitis (NASH). Nash has typical characteristics which include inflammation, hepatocyte ballooning, and hepatic injury with or without fibrosis [9, 10]. NAFLD is often associated with a variety of metabolic diseases, including hypertension, diabetes, insulin resistance, etc, which are risk factors for CKD. However, the degree of fibrosis in NAFLD was independently associated with CKD progression even when confounding factors such as metabolic diseases were excluded (11–15). Excess fat may association with CKD progression in NAFLD patients by inducing lipotoxicity, inflammation, oxidative stress and fibrosis through pro-inflammatory adipokines and lipocalin [16, 17].
Despite growing evidence of the strong association between NAFLD and CKD, the key molecules and potential mechanisms involved remain unclear. Here, bioinformatics and machine learning were attempted to discover the diagnostic markers and related signaling pathways of CKD in the context of NAFLD, which were hoped to provide a basis for the clinical treatment of CKD patients with NAFLD.
## Data acquisition and preliminary processing
Four data sets [GSE104954, GSE104948 [18], GSE32591 [19],GSE66494 [20]] containing gene expression profiles for Chronic kidney disease (CKD) samples and one date set [GSE89632 [21]] of non-alcoholic fatty liver disease (NAFLD) were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/).Details for the data sets were provided in Table 1. The principal search flow of the article was illustrated in Figure 1.
## Weighted gene co-expression network analysis and module gene selection in NAFLD patients
The WGCNA method was used to construct gene coexpression networks and identify functional modules. First, the median absolute deviation (MAD) of each gene was determined, and genes with MAD values in the bottom $50\%$ were removed. Second, ineligible genes and samples were removed with the goodSamplesGenes function, and a scale-free coexpression network was built. Third, an appropriate “soft” threshold power (β) was determined to calculate intergenic adjacency; then, the adjacency values were converted into a topological overlap matrix (TOM), and gene proportions and phase dissimilarities are determined. Fourth, modules were detected using hierarchical clustering and dynamic tree cutting functions. Finally, gene significance (GS) and module membership (MM) correlations were calculated, and the corresponding module gene information was extracted for further analysis.
## Identification of differentially expressed genes between CKD samples and controls
The DEGs were found in GSE104954 data set using the “limma” R package with inclusion criteria of |log2 FC| ≥ 0.5 and p-adjust < 0.05. DEGs were shown by volcano and expression levels of the 50 most significantly expressed genes were displayed by heatmaps, respectively.
## Acquisition of NAFLD-related shared genes
Intersection of DEGs in GSE104954 and WGCNA module genes in GSE89632 were defined as NAFLD-related shared genes, represented by a *Venn schema* by the online website jvenn (http://jvenn.toulouse.inra.fr/app/example.html).
## Enrichment analysis
For enrichment analysis of NAFLD-related shared genes, Gene Ontology (GO) annotations of genes from the R package org.Hs.eg.db based on the R package “clusterProfiler”, and the minimum number of genes per gene set was 5 and the maximum was 5000. Gene annotations for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were operated through KOBAS-i online tool (http://kobas.cbi.pku.edu.cn/) [22], and a false discovery rate <0.05 was considered statistically significant.
## Establishment of protein-protein interaction network and identification of NAFLD-related hub genes by cytoscape software and machine learning
The NAFLD-related shared genes were uploaded to the STRING database (http://string-db.org/) to construct the PPI network with a PPI score threshold (medium confidence≥0.700). *Hub* genes were screened by the Cytoscape (Version 3.9.1) plug-in APP MCODE (Version 2.0.0). At the same time, the machine learning methods random forest (RF) were used to screen for hub genes. The MeanDecreaseGini (MDG) was used to measure the importance of genes by the RF algorithm with “randomForest” package, and hub genes were defined as MDG greater than 1.5. The final NAFLD-related hub genes were obtained by the intersection of the results of cytoscape software and machine learning.
## Verification of NAFLD-related hub genes expression in the CKD data sets
Data set GSE32591 and GSE66494 were used to identify the expression level of the hub genes. GSE 32591 is composed of 29 control samples and 64 samples with lupus nephritis. GSE66494 contains 53 biopsy samples, including 8 control samples and 45 CKD samples.
## Construction of receiver-operating characteristic curves to assess diagnostic efficacy
ROC curves were constructed by “pROC” package in Xiantao Academic (https://www.xiantao.love/products) to evaluate the diagnostic value of NAFLD-related hub genes in the CKD training set GSE104954 and other CKD validation date sets (GSE32591,GSE66494 and GSE104948). Further the diagnostic value of hub gene in the NAFLD data set GSE89632 was also evaluated.
## Immune infiltration analysis and correlation analysis
The composition and abundance of 22 types immune cells can be estimated from the CKD and control samples transcriptome in GSE104954 date set by CIBERSORTx (https://cibersortx.stanford.edu/). The correlations of NAFLD-related hub genes expression with immune cell infiltrations were investigated in Xiantao Academic, as were their respective correlations.
According to the results of enrichment analysis, NAFLD-related shared genes may be involved in the immune-related mechanisms of CKD progression. Therefore, the correlation between the 4 diagnostic markers genes with immune cell infiltration in CKD is noteworthy for further exploration. First, CIBERSORTx was used to evaluate the proportions of 22 immune cell in GSE104954 data set (Figure 8A). B cells memory, Macrophages M1, Mast cells resting, T cells gamma delta were significantly upregulated in CKD samples; however, the levels of B cells naive, Treg cells, Mast cells activated were significantly decreased. Next, the correlation of the four diagnostic markers genes with CKD immune cells was explored (Figure 8B). The FOSB expression was positively correlated with the ratios of B cells naive, Treg cells and Mast cells activated, and negatively correlated with Mast cells resting, T cells gamma delta, M0 Macrophages and M1 Macrophages. ZFP36 and DUSP1 were negatively correlated with the ratios of Treg cells and NK cells resting. NR4A1 was positively correlated with the ratios of B cells naive, Dendritic cells resting and Mast cells activated, and negatively correlated with Mast cells resting, M1 Macrophages and B cells memory. When exploring the interrelationships between the expression of the four diagnostic markers genes, it is interesting to note that they were all positively correlated with each other (Figure 8C), suggesting that they may participate in a common mechanism to promote CKD development. Additionally, the interplay of immune cells was explored (Figure 8D). Treg cells and Mast cells activated had the strongest positive correlation with one another ($r = 0.53$). In contrast, resting mast cells showed the strongest negative correlation with activated mast cells (r = -0.83).
**Figure 8:** *The immune landscape of CKD samples in GSE104954 and correlation analysis. (A) The violin theme of immune cell proportions. (B) The correlation between four diagnostic markers genes and immune cells. (C) The correlation matrix of four diagnostic markers genes. (D) Correlation matrix of ratios of immune cells.*
In summary, CKD samples showed significant changes in immune cell infiltration compared with controls, and the four diagnostic markers genes expression was significantly correlated with immune cell infiltration.
## Statistical analysis
All statistical analyses of bioinformatics studies in this study were conducted using R software. The differences between the groups were tested using a nonparametric Wilcoxon signed-rank test. Correlation analysis was performed using Spearman’s correlation. In comparison, $p \leq 0.05$ was considered statistically significant (*$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$, ****$p \leq 0.0001$).
## Key module genes in NAFLD samples were identified by WGCNA
GSE89632 is a representative dataset for investigation of NAFLD and we used it to obtain the most relevant modular genes for NAFLD (23–25).First, WGCNA was used for the identification of the most relevant modular genes for NAFLD.β = 26 (scale-free R2 = 0.85) was selected as the “soft” threshold based on the scale independence and average connectivity (Figure 2A). Then, different colors are chosen to represent 9 gene co-expression modules (GCMs), which are presented in Figure 2B. The correlation between NAFLD samples and GCMs is shown in Figure 2C, and the darkturquoise module (412 genes) which was regarded as critical modules demonstrated the highest correlation with NAFLD samples (correlation coefficient = -0.85, $$p \leq 5.3$$e-19). In addition, a significant positive correlation was observed between module membership and gene significance in darkturquoise modules for NAFLD samples ($r = 0.68$, $$p \leq 4.2$$e-57), as shown in Figure 2D.
**Figure 2:** *Identification of module genes via WGCNA in NAFLD date set GSE89632. (A) β = 26 is selected as the “soft” threshold with the combined analysis of scale independence and average connectivity. (B) Gene coexpression modules represented by different colors under the gene tree. (C) Correlation plot between module membership and gene significance of genes included in the darkturquoise module. (D) Heatmap of the association between the darkturquoise modules and NAFLD samples. NAFLD, Non-alcoholic fatty liver disease; WGCNA, weighted gene co-expression network analysis.*
## Identification of NAFLD-related shared genes between CKD and NAFLD
Next, 386 DEGs were found in GSE104954 data set, of which 227 were up-regulated, and 159 of these genes were down-regulated. Figure 3A shows the DEGs by the volcano diagram. The heatmap of the top 50 most significant DEGs in the data set is plotted in Figure 3B. Then, 386 DEGs and 412 module genes were intersected, and 45 NAFLD-related shared genes were subsequently obtained, as presented in the Venn diagram in Figure 3C (Detailed results were provided in Supplementary Materials S1).
**Figure 3:** *Screening of differentially expressed genes (DEGs) and NAFLD-related share genes. (A) The volcano plot of DEGs in CKD date set GSE104954.(B) Heat map of the 50 most significantly differentially expressed genes, where red and green indicate the most significantly up-regulated and down-regulated differentially expressed genes in the CKD samples, respectively. (C) NAFLD related shared genes were obtained by intersection of NAFLD module genes obtained by WGCNA in date set GSE89632 and DEGs from CKD in date set GSE104954. NAFLD, Non-alcoholic fatty liver disease; CKD, chronic kidney diseases.*
## Enrichment analyses of 45 NAFLD-related shared genes
In order to explore the biological functions and pathways of NAFLD-related shared genes in the development of CKD, GO and KEGG enrichment analyses were performed for 45 shared genes. A total of 563 significantly related biological processes and 23 KEGG signaling pathways were obtained (Detailed results were provided in Supplementary Materials S2). GO analysis of shared genes was performed to reveal their biological functions (Figures 4A–C). As we have seen, in the GO category, most of the share genes mostly involved in the “programmed cell death”, “inflammatory response”, “positive regulation of metabolic process”, and “immune system process”(BP); “Extracellular matrix”, “collagen-containing extracellular matrix” (CC); “DNA-binding transcription activator activity, RNA polymerase II-specific”, “DNA-binding transcription factor activity”, etc (MF). The results of KEGG pathway enrichment showed that the most involved pathways were the IL-17 signaling pathway, TNF signaling pathway, MAPK signaling pathway, Apoptosis, Toll-like receptor signaling pathway, and so on, which are closely related to the immune response and inflammation (Figure 4D).
**Figure 4:** *Enriched items in GO and KEGG analyses of 45 NAFLD share genes. (A) Enriched items in the GO BP analysis. (B) Enriched items in the GO CC analysis. (C) Enriched items in the GO MF analysis. (D) Enriched items in the KEGG pathway analysis. NAFLD, Non-alcoholic fatty liver disease; GO, gene ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes; NAFLD, Non-alcoholic fatty liver disease.*
## Identification of NAFLD-related hub genes via cytoscape software and machine learning and their differential expression was validated
To reveal the interaction of each protein, the PPI network of the shared genes were built according to the STRING database. There were 38 edges and 45 nodes in Figure 5A, followed by analysis using Cytoscape software. MCODE plugin was used to discover the important modules in the PPI network and the results showed that 8 hub genes in two clusters were tightly connected as the important modules in Figure 5B. On the other hand,7 hub genes with MeanDecreaseGini> 1.5 were determined by the random forest algorithm in Figure 5C. A Venn diagram in Figure 5D showed the intersection of 4 hub genes (DUSP1, FOSB, NR4A1, ZFP36), which were used as NAFLD-related hub genes. Moreover, in the other two CKD datasets (GSE32591, GSE66494), the 4 NAFLD-related genes were significantly down-regulated (Figures 6A, B), which was consistent with the change in GSE104954 (Supplementary Materials S3).
**Figure 5:** *Identification of NAFLD-related hub genes by the intersection of cytoscape software and machine learning. (A) Protein-protein interaction (PPI) network of 45 NAFLD-related share genes. (B) The 8 genes in two clusters with the most significant associations were accessed using the MCODE plug-in. (C) Random Forest analysis for NAFLD-related hub DEGs. (D) Venn diagram demonstrates the final NAFLD-related hub genes obtained by the intersection of cytoscape software and machine learning. DEGs, differentially expressed genes; NAFLD, Non-alcoholic fatty liver disease; MCODE, molecular complex detection.* **Figure 6:** *The downregulation of 4 NAFLD-related hub genes was verified by two CKD data set. (A) Expression of NAFLD-related hub genes in the GSE32591 date set. (B) Expression of NAFLD-related hub genes in the GSE66494 date set. NAFLD, Non-alcoholic fatty liver disease; CKD, chronic kidney diseases.*
## The ROC curve was used to evaluate diagnostic efficacy in CKD and NAFLD
The ROC curves of 4 NAFLD-related genes (DUSP1, FOSB, NR4A1, ZFP36) with AUCs of 0.961, 0.954, 0. 866, and 0.960 in the training set GSE104954, respectively (Figure 7A). Meanwhile, in the validation set GSE32591, the AUCs of these hub genes were 0.828,0.796,0.927 and 0.689, respectively, which did not distinguish whether the source of the sample was glomerular or tubulointerstitial (Figure 7B). At the same time, in the other validation set GSE66494, the AUCs of hub genes were 0.958,1.000,0.889, and 0.932, respectively (Figure 7C). Comprehensive analysis of the results of the validation and training sets showed that the 4 NAFLD-related genes can serve as effective markers for the diagnosis of CKD.
**Figure 7:** *The diagnostic efficacy of 4 NAFLD-related hub genes was verified by ROC curve in CKD and NAFLD data sets. (A–D) The ROC curve of 4 NAFLD-related hub genes in date set GSE104954, GSE32951, GSE66494 and GSE89632.*
GSE104948 and GSE104954, as sister datasets, represent glomerular and tubulointerstitial transcript level information of the same cohort of samples, respectively. 4 NAFLD-associated hub genes have the same good diagnostic efficacy for CKD in GSE104948 (Supplementary Materials S4). Similarly, in the data set GSE32591, both tubulointerstitial and glomerular samples were sampled, and further exploration revealed that the 4 diagnostic markers showed good efficacy in different anatomical structures (Supplementary Materials S5). What should be noted is that these 4 CKD diagnostic markers also have good diagnostic value for NAFLD in GSE89632 date set, and the ROC curves with AUCs of 0.951,0.968,0.974, and 0.915, respectively (Figure 7D). This finding may suggest that four genes may play a significant role in the development of CKD patients with NAFLD.
## Discussion
NAFLD and CKD are both significant global public health burden, and there is evidence that NAFLD is independently associated with a high risk of CKD despite the exclusion of other metabolic diseases, while the underlying mechanisms are not clear [26]. In our study, by obtaining DEGs and important module genes by WGCNA, 45 NAFLD-related share genes were obtained, and their enrichment analysis revealed that immune, inflammatory and programmed cell death pathways were significantly enriched. Further, 4 CKD diagnostic markers genes were obtained by cytoscape software and machine learning, which demonstrated good diagnostic value in both the training and validation sets of CKD. Interestingly, 4 CKD biomarkers also had good diagnostic performance for NAFLD in dataset GSE89632, indicating that they may be important targets for the development of CKD in NAFLD patient.
DUSP1, dual-specificity protein phosphatase 1, is a member of the dual-specific phosphatase (DUSPs) family. Mitogen-activated protein kinases (MAPKs) was closely related to inflammation and immune, and DUSP1 improves microvascular fibrosis and inflammation through dephosphorylation of MAPKs [27, 28]. Overexpression of DUSP1 alleviates renal tubular injury by regulating mitophagy and interrupte Mff-related excessive mitochondrial fission. At the same time, lncRNA NR_038323 reduced the degree of renal fibrosis by targeting DUSP1, suggesting that DUSP1 is a potential therapeutic target for CKD with NAFLD (29–31). However, the main evidence come from diabetic renal disease, and whether it is applicable to other types of CKD requires further study.
FOSB is a member of the FOS family, which is part of activator protein-1 (AP-1). AP-1 is associated with immune and cancer progression. Previous studies have shown that FOSB can be used as a diagnostic marker for lgA kidney disease [32]. It has also been shown that MicroRNA-27a-3p targets FOSB to regulate the level of inflammation and fibrosis in lgA nephropathy [33]. Zinc finger protein 36 (ZFP36) participates in posttranscriptional regulation by targeting different mRNAs, which was closely related to inflammatory diseases and autoimmune disease [34]. The dysregulated expression of ZFP36 may play an important role in the pathogenesis of inflammatory diseases including CKD. While it has been suggested that it could be used as a diagnostic marker of CKD [35, 36]. Further studies are needed to identify the underlying mechanism for FOSB and ZFP36 in CKD with NAFLD.
The orphan nuclear receptor 4A1 (NR4A1), which is also known as Nur77, belongs to the nuclear receptor superfamily, and is involved in inflammation and energy metabolism pathways [37]. Previous studies have shown that it can be used as a therapeutic target for chronic kidney disease, which is consistent with our study [38]. There are also studies show that Yiqi Huoxue Tongluo recipe, a traditional Chinese medicine, can alleviate renal inflammation and fibrosis by increasing the expression level of NR4A1. In contrast, the loss of NA4A1 results in increased kidney ingury associated with macrophage. Interestingly, in our results, NR4A1 expression was significantly negatively correlated with proinflammatory M1 macrophage infiltration. A recent study showed that the induction of anti-inflammatory macrophages expressing NR4A1/EAR2 could suppress M1 proinflammatory responses, thereby inhibiting immune-mediated crescent glomerulonephritis (39–41). Therefore, increasing the expression level of NR4A1 may be one of the potential strategies for the treatment of CKD with NAFLD.
Dysfunction of immune cells promotes inflammation and kidney fibrosis in CKD, so the immune infiltration status of CKD samples was explored [42]. Previous studies have demonstrated that macrophage polarization plays an important role in CKD development [43]. In our results, M1 macrophages were significantly upregulated. However, M2 macrophages are believed to be associated with fibrosis, but not M1 macrophages. While the role of M2 macrophages in CKD is contradictory [44, 45].
Tregs cells are down-regulated in CKD, which is consistent with our results [46]. There is some evidence to suggest that Tregs cells can inhibit inflammation and fibrosis in CKD [47]. In turn, the CKD microenvironment changes the energetic metabolism of Tregs cells, thus inhibiting the protective effect of Tregs [48]. Therefore, immune cells interact with the inflammatory microenvironment. B cells are thought to be involved in the progression of Membranous nephropathy (MN) by releasing antibodies against podocytes, so depletion therapy targeting B cells may be a potential treatment [49]. However, existing data suggest that depletion of B cells does not achieve the expected effect in lgA nephropathy [50]. Because of the significant heterogeneity of CKD, the potential significance of B-cell depleting therapy requires specific analysis [51]. The MC-specific protease tryptase is released by mast cells, thereby activating significant fibrosis and inflammation [52]. In our results, the expression of four CKD diagnostic markers was closely related to the infiltration of multiple immune cells, which also confirmed the important role of immune mechanisms in the development of inflammatory and fibrosis in CKD patients with NAFLD.
There are limitations to our study. First, CKD is an umbrella term with significant heterogeneity, and we were not able to analyze specific types of CKD; Second, we focus on the pathogenesis and diagnostic markers of CKD in the context of NAFLD, and it is important to note that CKD has the opposite effect on NAFLD, which is beyond the scope of our discussion; Third, our findings were required to validate in vivo and in vitro to better guide clinical practice, although the decreased expression of DUSPI and ZFP36 in CKD has been confirmed by related studies [36, 53].
## Conclusion
In this study, 4 NAFLD-related genes (DUSP1, NR4A1, FOSB, ZFP36) were identified as diagnostic markers in CKD patients, and NAFLD may accelerate the development of CKD through immune and inflammatory pathways. The changes in immune cell infiltration in CKD and the significant correlation with diagnostic markers were also elucidated. Our study may provide diagnostic markers and therapeutic targets for CKD patients with NAFLD.
## 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
YC, YD, and WJ contributed equally to this work. YC and YD wrote the manuscript. WJ performed the data processing. JD, JY and HZ Participated in chart making. XZ edited the article. KT and ZY conceived and designed the scientific question. 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.2023.1125829/full#supplementary-material
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|
---
title: Knowledge and attitudes of health care workers about monkeypox virus infection
in Southern Italy
authors:
- Grazia Miraglia del Giudice
- Giorgia Della Polla
- Lucio Folcarelli
- Annalisa Napoli
- Italo Francesco Angelillo
- Walter Longanella
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10009274
doi: 10.3389/fpubh.2023.1091267
license: CC BY 4.0
---
# Knowledge and attitudes of health care workers about monkeypox virus infection in Southern Italy
## Abstract
### Background
This present survey sought to investigate the level of knowledge and the attitudes pertaining the monkeypox (mpox) virus infection among a sample of health care workers (HCWs) in Italy, as well as the possible role of different factors on these outcomes.
### Methods
The cross-sectional survey was performed from July through October, 2022 at four randomly selected hospitals located in Southern Italy.
### Results
The questionnaire was completed by 421 HCWs, for an overall $59\%$ response rate. Less than two-thirds were able to define the disease and the correct answer of the transmission mechanisms ranged from $22.8\%$ for contact with contaminated objects to $75.8\%$ through close contact with body fluids. Only $4\%$ and $12.8\%$ indicated HCWs and elderly/frail/people with underlying immune deficiencies as risk groups. The mean overall score of the knowledge assessment on mpox was 3.4 (0–9). The multivariate logistic regression analysis showed that HCWs with a lower number of years of working experience and those who had acquired information about mpox from scientific journals were more likely to have a higher level of knowledge. The average score of the perception of the severity of the disease was 6.3. A similar score with a value of 6.1 has been observed for the statement that mpox is a serious problem for the population. Regarding the level of concern about contracting mpox, the mean score was 5.1. Only $10.5\%$ reported that they feel that this disease can be prevented, with an overall mean score of 6.5. Almost all HCWs reported that they are still living as usual, with no modification of their behavior for fear of contracting the mpox. The results of the multivariate logistic regression model showed that women, HCWs with a higher level of knowledge about mpox, and those who needed additional information about mpox were more likely to have a higher level of perception of the severity of the disease.
### Conclusion
This survey has demonstrated that HCWs had an unsatisfactory level of knowledge toward mpox and only nearly half showed positive attitudes. Strategic health training programs should be made so that knowledge can be acquired.
## 1. Introduction
Monkeypox (mpox) is a viral zoonosis caused by an enveloped double-stranded DNA virus that belongs to the *Orthopoxvirus genus* and the Poxviridae family (1–4) and intimate contact with an infected person, infectious rashes or lesions, body fluids, respiratory droplets, and sexual contact are mechanisms of transmission among humans [5, 6]. The first identified known human case was recorded in 1970 in the Democratic Republic of the Congo and the cases primarily occur in the tropical rainforest regions of Central and West African areas [7]. However, since early May 2022, an emerging broad outbreak of mpox infection is spreading in different geographical areas where the disease is not endemic, including Europe, Americas, Australia, and Middle East. The primary cause of the development and spread of this kind of epidemic disease is the interaction between humans and animals [8]. As of January 9, 2023, more than 84.000 confirmed cases have been reported in more than 110 countries worldwide [9]. In Italy, the first case of mpox was reported on May 20, 2022, and since the start of the outbreak and so far, as of January 10, 2023, a total of 951 confirmed cases have been reported [10]. Moreover, on July 23, 2022, the World Health Organization declared the escalating current global mpox outbreak a Public Health Emergency of International Concern [11].
Taking into consideration the current mpox scenario, health care workers (HCWs) in their practice may play an active role in making effective and targeted strategies for the prevention of the disease by educating and influencing the population. Indeed, previous epidemiologic studies have clearly established that HCWs' level of knowledge and their communication toward the prevention of several infectious diseases are key strategies in motivations different groups of individuals (12–18).
Few studies have focused the attention on the knowledge and attitudes about the mpox (19–25) notably among HCWs (26–29). Identifying and understanding the knowledge and attitudes in this group is essential for the development of effective and strategic health communication. Therefore, this present survey sought to investigate the level of knowledge and the attitudes pertaining the mpox virus infection among a sample of HCWs in Italy as well as to understand the possible role of different factors on these outcomes.
## 2. Materials and methods
This survey is part of a larger research project also examining attitudes and practices about COVID-19 among HCWs. The methodology is described in greater detail in a previous manuscript [30] and briefly summarized below.
## 2.1. Setting and sampling
The cross-sectional survey was performed between July 28 and October 14, 2022 at four randomly selected hospitals located in the Campania region, Southern Italy. A total of 421 HCWs were randomly selected. The required sample size of 384 HCWs was determined assuming a frequency of $50\%$ of respondents who had a high perception of the severity of mpox, with a two-sided $95\%$ confidence interval, and a margin error of $5\%$.
## 2.2. Data collection
The health director of each hospital received an invitation letter for asking the permission to conduct the study in their institution that included detailed information about the study regarding the background, objectives, and methodology. After their permission, the research team identified an HCW in each ward who distributed the questionnaire to the randomly selected study participants, then collected the filled questionnaires within an envelope to maintain anonymity, and then returned directly to the research team. At the beginning of the questionnaire, it was specified the study objectives and procedure, the voluntary participation, that the respondents' identities remain anonymous to the research team, that they had the right to quit their participation at any stage without any restriction, and that filling and returning the questionnaire were considered as their consent to participate and agreement to the terms of the study. Participants did not receive any gift or financial incentive in appreciation for completing the survey.
## 2.3. Survey development
The average time needed to complete the self-administered survey was ~5 min and it was structured into four subsets of questions, each with a specific focus: 1. socio-demographic, general, and professional characteristics (14 questions), including gender, age, relationship status, degree of education, duration of employment in the health care profession, and area of working activity; 2. source(s) for searching their information about the mpox and the need for additional information (2 questions); 3. knowledge about mpox which contained six questions with topics such as the definition, cause, modes of transmission, risk groups, number of observed cases in Italy and in the geographic area. Five questions were open-ended, and one was multiple-choice; 4. mpox attitudes (4 questions) and behavior (1 question), measuring perception of seriousness and danger of the disease and importance of its prevention, perception of risk of getting the disease for themselves, for familiars, and colleagues, and whether they made any modification of their behavior for fear of contracting the mpox. The questions on the attitudes were rated on a Likert-type scale ranging from 1 to 10, where the maximum score of “10” was assigned for the most acceptable/desired attitude and “1” for the least desirable, while the question on the behavior had “yes” or “no” as response options. The survey was pilot tested for clarity and understandability on a convenience sample of 10 HCWs and none of them have been included in the study. Internal consistency was satisfactory, with a Cronbach's alpha coefficient of 0.77.
## 2.4. Statistical analysis
The statistical software STATA 15.1 was used to analyze the data. Frequency, mean, and standard deviation were used to describe the principal characteristics of the participants, as well as behavior and attitude toward mpox. Univariate analysis, by using chi-square test and Student t-test, was performed to evaluate predictors of the different outcomes of interest. Any independent variable with a p-value <0.25 in the univariate analysis was further included in the multivariate logistic regression models, where odds ratios (OR) and their corresponding $95\%$ confidence intervals (CI) were calculated. It has been investigated whether several independent variables predicted the following primary research questions: level of knowledge about mpox (Model 1) and perception of the severity of the mpox (Model 2). A knowledge-based score was created for each participant by assigning 1 point for each correct answer regarding definition, cause, modes of transmission, and risk groups and 0 for each incorrect or no answer. The total score was calculated for everyone by adding the points of each of the 13 questions (maximum score 13). For the purpose of analysis, the outcome of Model 1 has been dichotomized according to the total knowledge score calculated for each individual, with the study sample that has been divided into two categories with cut-off point the median value of the score of 3 (< 3 = 0 and >3 = 1). The perception of the severity of the disease as dichotomized outcome of Model 2, with cut-off point the median value of 6 (score < 6 = 0 and >6 = 1). The following independent variables of interest were tested in the univariate analysis because they are potentially related to all outcomes: gender (male = 0; female = 1), age, in years (continuous), marital status (unmarried/separated/divorced/widowed = 0; married/cohabitant = 1), physician/dentist (no = 0; yes = 1), currently working in medical wards (no = 0; yes = 1), length of practice, in years (continuous), having underlying at least one chronic medical condition (no = 0; yes = 1), scientific journals as source of information about mpox (no = 0; yes = 1), and need for additional information on mpox (no = 0; yes = 1). The variable level of knowledge about mpox (< 3 = 0; ≥3 = 1) was also included in Model 2. Statistical significance was assessed by two-tailed tests with p-value equal or < 0.05.
## 3. Results
A total of 714 HCWs were randomly selected and invited to participate in the present survey, and 421 returned the questionnaire, for an overall response rate of $59\%$. The distribution of the main socio-demographic, general, and professional characteristics of the sample is summarized in Table 1. Most respondents were female, the average age was 41.7 years, less than half were married/cohabitant, more than half were nurses/midwives, more than two-thirds worked in medical wards, almost one-third had worked in a COVID-19 area, the mean length of working experience was 13.5 years, and only $15.9\%$ reported at least one chronic medical condition.
**Table 1**
| Characteristics | N | % |
| --- | --- | --- |
| Age, years | 41.7 ± 12.5 (22–77)* | |
| Gender | Gender | Gender |
| Female | 273 | 65.5 |
| Male | 144 | 34.5 |
| Marital status | Marital status | Marital status |
| Unmarried/separated/divorced/widowed | 194 | 53.3 |
| Married/cohabited with a partner | 221 | 46.7 |
| Professional role | Professional role | Professional role |
| Nurse/Midwife | 225 | 53.4 |
| Physician/Dentist | 133 | 31.6 |
| Other | 63 | 15 |
| Length of practice, years | 13.5 ± 12 (1–44)* | |
| Current working area | Current working area | Current working area |
| Medical | 295 | 70.4 |
| Other | 124 | 29.6 |
| Having worked in a COVID-19 area | Having worked in a COVID-19 area | Having worked in a COVID-19 area |
| No | 285 | 67.7 |
| Yes | 136 | 32.3 |
| At least one chronic medical condition | At least one chronic medical condition | At least one chronic medical condition |
| No | 354 | 84.1 |
| Yes | 67 | 15.9 |
Table 2 showed the frequency of correct responses to each of the questions assessing mpox knowledge in the questionnaire. The overall level of knowledge was limited. No HCW gave the correct answer regarding all questions and $3.5\%$ acknowledged that they do not know any of the answer. Regarding each question, $61.5\%$ was able to define the disease and the correct answer of the transmission mechanisms ranged from $22.8\%$ for contact with contaminated objects to $75.8\%$ through close contact with body fluids. The knowledge of the participants referring to the risk groups, ranged from only $4\%$ for those who indicated HCWs/laboratory personnel to $12.8\%$ for elderly, frail, and people with underlying immune deficiencies. The mean overall score of the knowledge assessment on mpox was 3.4, with a minimum score of 0 and the maximum score of 9. The median value of the total score was found to be 3 and almost two-thirds ($63.2\%$) had a score higher or equal than this value. The different factors associated with the two outcomes of interest on multivariate logistic regression analysis are reported in Figure 1. The results showed that the number of years of working activity and the sources of information about mpox yielded a statistically significant association with the knowledge level. HCWs with a lower number of years of working experience (OR = 0.96, $95\%$ CI: 0.93–0.99) and those who had acquired information about mpox from scientific journals (OR = 3.5, $95\%$ CI: 2.02–6.08) were more likely to have a higher level of knowledge than HCWs with a higher number of years and those who did not have used this source of information (Model 1).
Participants' response to questions concerning their attitudes toward mpox, measured on a Likert-type scale ranging from 1 to 10, showed that the average score of their perception of the severity of the disease was 6.3 with $7.7\%$ and $2.4\%$ of participants selecting the “10” or “1” response, respectively. A similar score with a value of 6.1 has been observed for the statement that mpox is a serious problem for the population. When asked about their level of concern about contracting mpox, the mean score was 5.1 and $4.3\%$ and $8.1\%$ of all respondents said that they were very afraid of getting the mpox and not afraid at all, respectively. Additionally, only $10.5\%$ respondents reported that they feel that this disease can be prevented and the overall mean score toward this attitude was 6.5. Finally, almost all HCWs reported that they are still living as usual, with no modification of their behavior for fear of contracting the mpox. The results of the multivariate logistic regression model showed that three variables reached statistically significant association with HCWs' perception of the severity of the disease. Women (OR = 1.66; $95\%$ CI: 1.07–2.55), HCWs who had a higher level of knowledge about mpox (OR = 1.68; $95\%$ CI: 1.08–2.6), and those who needed additional information about mpox (OR = 1.74; $95\%$ CI: 1.13–2.66) were more likely to have a higher level of perception of the severity of the disease (Model 2 in Figure 1).
Lastly, the vast majority of the interviewed HCWs ($89.6\%$) reported that they had search different sources to get information about mpox. Participants declared that their most trusted sources for obtaining information about this topic were the mass-media, Internet, and scientific journals, with values of $59.7\%$, $56.5\%$, and $28.6\%$, respectively. Almost two-thirds ($64.6\%$) of all HCWs reported that that they would be interested in opportunities to learn more regarding mpox.
## 4. Discussion
This survey contributes to the limited scientific literature with new information regarding the level of knowledge and the attitudes pertaining the mpox virus infection as well as the contribution of factors that are associated among a sample of HCWs in the hospital settings in Italy. Numerous interesting themes emerged from the responses. Firstly, HCWs had a low level of knowledge toward mpox. Secondly, respondents had positive attitudes toward mpox. Thirdly, the vast majority reported getting information about mpox although only less than one-third from scientific journals. Fourthly, several factors have been observed to be associated with the two outcomes of interest.
This study revealed important knowledge gaps pertaining to mpox across the sample with a very low mean overall score, 3.4 out of 13, and this is also evident from the low number of HCWs who gave correct answers to the different questions, even about some basic aspects, related to definition, transmission mechanisms, and categories of people that are at higher risk. One possible explanation is that the frequency of the disease had not already achieved tremendous prominence at the time of this survey, and no activities had been made to raise the knowledge levels among HCWs, so they may not have reached an adequate level of knowledge. It is worth mentioning that the rate of correct response that a virus was the cause of the mpox ($61.5\%$) in this study was consistent with the $61.9\%$ observed in the Kingdom of Saudi Arabia among HCWs [31], whereas it was much lower than what has been reported elsewhere in previous studies, although some of them conducted among groups of individuals in different settings. Indeed, in two studies conducted in Jordan based on medical students and HCWs revealed values of $77.2\%$ [22] and $92.6\%$ [22], in China in the general population of $83.1\%$ [32], in Italy in occupational physicians, public health professionals, and general practitioners of $95.1\%$ [29], and in Kuwait in physicians of $99\%$ [27]. Moreover, the value was higher than that observed in Turkey among physicians with only $0.4\%$ that were aware that this was a bacterial infection [33]. Regarding the transmission mechanisms, the present study showed that the correct answers of the responding HCWs ranged from $22.8\%$ for contact with contaminated objects to $75.8\%$ through close contact with body fluids and $42.3\%$ indicated the droplets. Some of the already mentioned studies have shown that the knowledge on the transmission through droplets were very similar to the present value. Indeed, $41.3\%$ of HCWs in the Czech Republic [34], $45.3\%$ in China [32], and $47.3\%$ in Turkey [33] were aware of this mode of transmission, while $47.5\%$ has been observed among Chinese men who have sex with men [35]. Lastly, almost all ($98.8\%$) Italian physicians acknowledged the potential transmission by means of respiratory droplets [29]. In the present study, of concern was the knowledge gap about the groups at risk which ranged from $4\%$ for those who indicated HCWs/laboratory personnel to $12.8\%$ for elderly, frail, and people with underlying immune deficiencies. A considerably higher value has been observed in the previously cited survey among men who have sex with men with $40.2\%$ of the respondents that correctly indicated them as high-risk group [35]. The lack of knowledge of the disease, how it is transmitted as well as regarding risk groups is troubling because knowing is a prerequisite to facilitate the HCWs for an effective implementation of a successful control and educational activities and, therefore, this may have negative effects upon control and prevention efforts. Indeed, a body of literature showed that HCWs' knowledge is one of the most influential predictive factors regarding discuss and recommend primary preventive interventions to their patients [15, 36, 37]. Therefore, based on the knowledge gaps pertaining to the different aspects of mpox, it is necessary and crucial for health authorities to encourage HCWs to obtain information from trustworthy sources and proper education and training are also needed to address misconceptions and to improve the level of knowledge. Additionally, it is interesting to underline that HCWs' knowledge toward mpox significantly affected their attitude. Indeed, HCWs who had a higher level of knowledge were more likely to have a higher level of perception of the severity of mpox.
Apart from identifying HCWs' knowledge, another purpose of this survey was to find their beliefs and attitudes toward mpox. An interesting result was that $4.3\%$ felt that they were at high risk of getting mpox with an overall mean score of 5.1, measured on a Likert-type scale ranging from 1 to 10. The present finding does not allow inferring the basis on which this belief emerged, but we may speculate that it might have arisen from the fact that only a small number of respondents correctly indicated themselves as a group at risk. A previous study showed that $49.6\%$ of HCWs in Saudi Arabia was afraid of contracting mpox [28] and in the already mentioned survey in Turkey $20.1\%$ of participants were more concerned about mpox than COVID-19 [33], whereas $75\%$ of men who have sex with men showed concerns about their susceptibility to mpox infection [35].
It should be noted that the most frequently cited sources of information about mpox were mass-media, Internet sites, and scientific journals. The observation of the high frequency of internet users for seeking information on this topic among respondents raises concern and this could perhaps partially explain the low level of knowledge since these sources have long been acknowledged in prior international literature to disseminate misleading health information (38–43). The multivariate logistic regression analysis found a positive association between HCWs' knowledge score and source of information. Indeed, participants who were exposed to scientific journals had greater odds of having a higher level of knowledge compared to those who did not have used it. This finding is not unexpected in view of the fact that HCWs can benefit from updated access to accurate and correct information and this is also supported by evidence which identified these sources for enhancing public health education on a variety of topics. Indeed, previously published literature have shown that individuals gathering information from scientific journals or from institutional sources had a higher level of knowledge, a more positive attitude, and were more likely to adopt appropriate public health behavior and to accept the vaccination (44–48). The finding here clearly illustrates how important the information is when it comes from scientific journals that, therefore, should be used more prominently as a regular source of information for HCWs as an important strategy for improving their level of knowledge. Lastly, another feature observed in the current survey that should be noted was the significant association between attitudes and information with those who reported that that they would be interested in acquiring more information about mpox were more likely to have a higher perceived level of the severity of the disease. Therefore, HCWs should be the target group for educational programs.
The multivariate logistic regression analysis in the current survey identified additional determinants as having a significant influence on the different outcomes of interest. Number of years of working activity in healthcare profession and gender were predictive of knowledge and attitude among the sampled HCWs. HCWs in activity with a lower number of years were more knowledgeable than those with more experience. One of the possible justifications might be that HCWs who have less experience are more active and might have more interest in acquiring information, read scientific journals, and participate in recent proper training and education than those with more years of activity. In addition, female HCWs were more likely to have a higher level of perception that the mpox is characterized as a serious health risk, which is consistent with previous studies, in which female is an important factor for concern toward infectious diseases [44, 45, 49, 50].
There are inherent potential methodological limitations that should be considered when interpreting the results of the present survey. First, the cross-sectional design was designed to measure association between the explanatory variables and the different outcomes of interest, and a causal relationship cannot be determined. Second, the participants were selected from hospitals located only in one region of the country. Therefore, there is a possibility that the study findings may not be entirely generalizable to the true level of knowledge and attitudes of other HCWs across the country. Third, a self-administered questionnaire had been used to collect data and participants may answer questions in a socially desirable manner so conclusions may contain social desirability bias. However, the questionnaire was anonymous with no identifying data collected and this may have reduced the risk of such bias. Despite the limitations, this data provides relevant and valuable information on the level of knowledge and attitudes and the associated factors of Italian HCWs toward mpox.
In conclusion, this unique survey has demonstrated that HCWs had an unsatisfactory level of knowledge toward mpox and nearly half showed positive attitudes. Gender, years of working activity, and sources of information were the significant determinants of knowledge and attitude levels, and these factors should be taken into account when tailoring effective and strategic health training programs should be made. Therefore, the recommendation considering the results is that such programs for HCWs should be made so that knowledge about the risks posed by the mpox, as well as by other zoonotic infectious diseases, and the preventive measures can be acquired.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
Ethical review and approval were not required for the study on human participants in accordance with the national legislation. Informed consent from the participants was obtained by filling and returning the questionnaire.
## The collaborative working group
Walter Longanella (Health Direction, San Giovanni di Dio Ruggi D'Aragona Hospital, Largo Città Ippocrate, 84131 Salerno, Italy), Mario Massimo Mensorio (Health Direction, Sant'Anna e San Sebastiano Hospital, Via Ferdinando Palasciano, 81100 Caserta, Italy), Federica Cantore (Health Direction, San Giuseppe Moscati Hospital, Contrada Amoretta, 03100 Avellino, Italy).
## Author contributions
GMdG, GDP, LF, and AN participated in the conception and design of the study and contributed to the data collection. GMdG and GDP contributed to the data analysis and interpretation. IFA the principal investigator, designed the study, was responsible for the statistical analysis and interpretation, and wrote the article. All authors have read and approved the final version of the article and agree to be accountable for all aspects of the work.
## 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: 'New onset type 2 diabetes mellitus risks with integrase strand transfer inhibitors-based
regimens: A systematic review and meta-analysis'
authors:
- Violet Dismas Kajogoo
- Wondwossen Amogne
- Girmay Medhin
journal: Metabolism Open
year: 2023
pmcid: PMC10009287
doi: 10.1016/j.metop.2023.100235
license: CC BY 4.0
---
# New onset type 2 diabetes mellitus risks with integrase strand transfer inhibitors-based regimens: A systematic review and meta-analysis
## Abstract
### Objectives
The development of diabetes mellitus (DM) in patients taking integrase strand transfer inhibitors (INSTIs) has raised concerns. It's critical because, in most guidelines, INSTIs are the preferred third agent at first-line regimens. This study investigates the excess risk of developing DM among people living with HIV (PWH) on INSTIs-based regimens compared to those with other combination antiretroviral therapies (cART).
### Methods
A search from PubMed, clinicaltrials.gov, Latin America and Caribbean health sciences literature, Cochrane, and google scholar to retrieve case-control and cohort studies were done. The literature search was performed for studies from January 2007 to January 2021. Data were extracted from studies and pooled as risk ratios (RR) with a $95\%$ confidence interval (CI) using Stata 14 software. The protocol was registered in PROSPERO, ID: CRD42021230282.
### Results
This review included ten studies, resulting in 62 400 participants. There was no significant difference in the incidence of DM between participants receiving INSTIs-based regimens versus other cARTs (RR 0.97, $95\%$ CI: 0.92–1.03; participants = 50 958; studies = 4; I2 = $86.8\%$, chi-square = 22.67). There is no statistically significant difference in DM among people treated with INSTIs-based regimens compared to those treated with boosted protease inhibitors (PIs)-based regimens (RR 0.97, $95\%$ CI 0.92–1.03; participants = 49 840; studies = 3; I2 = $89.3\%$, chi-square = 18.65). DM incidence was lower in INSTIs-based regimens than in those using non-nucleoside reverse transcriptase inhibitors (NNRTIs)-based regimens (RR 0.80, $95\%$ CI 0.69–0.91; participants = 42 346; studies = 2; I2 = $0\%$, chi-square = 0.18).
### Conclusion
The present review shows a nonsignificant difference in the incidence of DM in patients receiving INSTIs-based regimens compared to other regimens. However, there was a lower incidence of DM in the INSTIs group compared to the NNRTIs-based and PIs compared to the NNRTIs-based. When the INSTIs drugs dolutegravir, raltegravir, and elvitegravir were compared, there was a lower incidence of DM in raltegravir compared with elvitegravir.
## Background
HIV/AIDS has a devastating global impact on health. It has caused approximately 39 million deaths, and more than 36 million live with the virus globally [[1], [2], [3]]. The 20.7 million people living with HIV (PWH) in sub-Saharan Africa account for $67\%$ of the global HIV prevalence. Yearly in this region alone, there are 730 000 new HIV infections and 300 000 AIDS-related deaths. Worldwide, $73\%$ of adults have access to combination antiretroviral therapy (cART) [4].
There have been advances in cART and progress globally toward implementing treatment-as-prevention programs. Despite the above efforts, approximately 2 million people are newly infected with HIV every year globally [1,3,5]. Access to cART has increased survival for PWH [[6], [7], [8]]. With the longer life expectancy made possible with cART, many people living with HIV face an increased burden of noncommunicable diseases (NCDs) [6].
People with HIV are more likely to develop diabetes mellitus (DM) than the general population because of multiple factors, including HIV-1, lipodystrophy, heightened inflammation, increasing prevalence of obesity, hepatitis C co-infection, and racial/ethnic preference [9]. In the Multicenter AIDS Cohort Study, insulin resistance markers were higher in all groups of HIV-infected men than HIV-uninfected control subjects, even those not receiving cART, suggesting an effect of the viral infection [10]. In the ten-year diabetes incidence study, lipohypertrophy, lipoatrophy, and elevated BP were associated with DM [11]. A longitudinal observational cohort in Latin America found a high incidence of the following outcomes: impaired fasting glucose, DM, overweight, and obesity following cART initiation [12].
Insulin is a hormone that regulates blood sugar/glucose, resulting in raised blood sugar [13]. Globally, the total number of people living with DM has risen from 108 million in 1990 to 422 million in 2014, with the prevalence rising more in low- and middle-income counties than in high-income counties [14]. Furthermore, traditional metabolic disease risk factors intersect with HIV-specific risk factors in PWH, including metabolic perturbations related to cART [15,16]. Certain protease inhibitors, such as indinavir (IDV), lopinavir (LPV), and ritonavir (RTV), have been shown to reversibly induce insulin resistance, probably by inhibiting glucose translocation through GLUT4 [17]. Nucleoside reverse transcriptase inhibitors (NRTIs) such as zidovudine and stavudine directly and/or indirectly affect glucose metabolism [17]. Disruptions of glucose and body fat metabolism in PWH have been observed since the advent of cART. Older cART regimens contributed substantially to insulin resistance and body composition changes, and the current regimens have more subtle effects on glucose and fat metabolism [18].
INSTIs are now included in most cART combinations in both naive and experienced PWH. It is because of relatively high tolerability, a higher genetic barrier to resistance, and a greater likelihood of sustained treatment success than other classes [19,20].
The development of DM in INSTIs use is not well understood. The interaction between dolutegravir (DTG) and melanocortin four receptor (MC4R) in vitro and binding of radiolabeled α melanocyte-stimulating hormone (MSH) to MC4R may explain weight gain, which is, in turn, a risk factor for the development of DM [21]. On October 16, 2007, the US Food and Drug Administration (FDA) approved raltegravir (RAL) to treat HIV infection with other cART agents [22]. Since 2016, there has been a concerted effort to implement dolutegravir-based first-line cART regimens in low and middle-income countries following the WHO guidelines released in 2016 and 2018 [23]. South Africa and Uganda have amended their cART guidelines to transition to dolutegravir-based first-line regimens and away from efavirenz-containing regimens [23]. In the review, we aimed to synthesize the literature on the effects of INSTIs on insulin sensitivity and the onset and incidence of DM in patients with HIV. We compared these results with other cART drug classes.
## Patient and public involvement
None.
Researchers followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA) Guideline [24,25]. The protocol for this systematic review and meta-analysis has been registered with the International Prospective Register of Systematic Reviews (PROSPERO) database, ID: CRD42021230282.
## Eligibility criteria
Researchers included longitudinal cohort studies (prospective and retrospective) and case controls conducted globally and in all settings. Clinical trials were not included because there were no completed published articles. The studies recruited adult individuals who live with HIV on cART, with no restrictions on doses or regimens, and evaluated the incidence of DM in these individuals.•Participants: All studies recruited PWH and cART. No restrictions on doses or regimens. The researchers excluded studies that recruited patients with comorbidities such as TB and other opportunistic infections, pregnancy, or breastfeeding patients. Those diagnosed with diabetes mellitus at baseline, who had been diagnosed with type 1 or juvenile diabetes mellitus, gestational diabetes, and pre-clinical studies.•Setting: Global studies were included regardless of the continent and region. All studies were written in the English language.•Study design: Cohorts and case-control studies were reviewed.•Publication dates from January 2007 to January 2021.•Intervention/exposure: The use of cART with standard doses of the drugs and regimens is inclusive. All cART contains two nucleoside reverse transcriptase inhibitors (NRTIs). So, Drug classes PI, NNRTI, and INSTIs were singled out as the third drug in the cART combination.•Outcome: The following definitions and outcomes were used.
Diabetes mellitus (DM) – DM in the primary manuscripts was defined by evidence of glycosylated hemoglobin (HbA1c) ≥$6.5\%$, initiation of diabetes-specific medication, or new DM diagnosis along with diabetes-related medication (to exclude prediabetes from the outcome) [26]. We also used fasting plasma glucose (FPG) 126 mg/dL or oral glucose tolerance test (OGTT) 200 mg/dL or random plasma glucose (RPG) ≥200 mg/dL in diagnosis. DM diagnoses were established using physician diagnosis mapped to the International Classification of Diseases, the 10th Revision codes [27], modified from the standard criteria for DM diagnosis from the American Diabetes Association [28] and expert Committee on the Diagnosis and Classification of *Diabetes mellitus* [29].
Hyperglycemia – To confirm new-onset hyperglycemia in the primary manuscripts, clinical charts of all patients with incident hyperglycemia were verified by two clinicians [30]. The diagnosis was defined as specific guidelines [31].
## Primary and secondary objectives
The primary objectives were to evaluate the incidence of DM in HIV individuals on INSTIS and compare it with the other drug classes. The primary comparison was between INSTIs -based regimens and non-INSTIs-based regimens.
The secondary objectives were to evaluate the incidence of DM across the other cART classes. The comparison was specifically INSTIs versus PI and INSTIs versus NNRTI- based regimens. The other secondary objective was to evaluate the onset of hyperglycemia across cARTs.
## Search strategy
A search from PubMed, clinical trials.gov, Latin America and Caribbean health sciences literature, and google scholar was done.
The search strategies in PubMed for the MeSH terms and text words were “diabetes [Text Word]' OR “diabetes mellitus” [MeSH Terms] OR (“Diabetes Mellitus, Type 2" [Mesh]) AND “integrase inhibitors” [MeSH Terms] OR HIV integrase inhibitors [Text Word] AND antiretroviral [Text Word] OR “Antiretroviral Agents” [Mesh] OR “Antiretroviral Therapy, Highly Active” [Mesh] AND “incidence” [Text Word] OR “Incidence” [Mesh] AND “Epidemiology” [Mesh] AND “epidemiology” [Subheading] AND “Cohort Studies” [Mesh] AND rate [All Fields] OR “Incidence” [Mesh].
## Study selection, data collection, and data analysis
Data management and analysis were done with The Cochrane Handbook for Systematic Reviews of Interventions [32], Stata 14 and Mendeley. Two authors independently reviewed the results, and disagreements were resolved through discussion. When clarification was necessary, the corresponding authors of the manuscripts were contacted [33].
## Data extraction and management
Data extraction and rating for the certainty of the evidence were performed by two authors independently by screening titles and abstracts of cohorts and case-control studies about HIV and diabetes to minimize the likelihood of error. Data extracted included participants, interventions, methods and outcomes, author and year of publication, country, study design, data collection, participants, follow-up duration, interventions, drugs, and treatment outcomes. Information was extracted using a structured data extraction format adapted from Cochrane. Disagreement between authors was resolved through discussion and consensus. For dichotomous outcomes, the number of occurrences of diabetes (event) and the total number of participants in the particular treatment group were documented.
## Treatment of missing data
When the information sought from available reports about the study design and relevant data elements was missing, mail contacts with the investigators were made to request the data.
## Assessment of risk of bias in included studies
Based on critical domains, the two authors independently judged these risks as low, unclear, or high [34]. The Cochrane Collaboration's tool for assessing the risk of bias in longitudinal studies was used. These included the following questions: was the selection of exposed and non‐exposed cohorts drawn from the same population? Can we be confident in the assessment of exposure? Can we be confident that the outcome of interest was not present at the start of the study? Did the study match exposed and unexposed for all variables associated with the outcome of interest, or did the statistical analysis adjust for these prognostic variables? Can we be confident in assessing the presence or absence of prognostic factors? Can we be confident in the assessment of the outcome? Was the follow-up of cohorts adequate? Were co‐Interventions similar between groups? [ 35] In all cases, an answer of 'Yes' will indicate a low risk of bias, and an answer of ‘No’ will indicate a high risk of bias. Studies were checked for evidence from multiple publications.
## Measures of treatment effect
Hyperglycemia and diagnosis of diabetes were the primary outcomes of the review. RR was used to summarize the dichotomous outcomes. Results of the outcomes are presented as forest plots with summary statistical estimates and $95\%$ confidence intervals.
## Assessment of heterogeneity
Heterogeneity was assessed by calculating Chi2 (threshold $p \leq 0.1$) and I2 statistics (threshold I2 > $40\%$), with values greater than $50\%$ considered as substantial heterogeneity (I2 > $50\%$), it was identified and reported.
## Data synthesis
A systematic narrative synthesis was provided in which summary results were presented using texts, tables, figures, and forest plots. Studies were identified with the first author and the year of publication. The Mantel– Haenszel statistical method and effect measure risk ratio were employed for data analysis, synthesis, and creation of forest plot).
Risk of bias summary: review authors' judgments about each risk of bias for the included studies.1.Was the selection of exposed and non‐exposed cohorts drawn from the same population?2.Can we be confident in the assessment of exposure?3.Can we be confident that the outcome of interest was not present at the study start4.Did the study match exposed and unexposed for all variables associated with the outcome of interest, or did the statistical analysis adjust for these prognostic variables?5.Can we be confident in assessing the presence or absence of prognostic factors?6.Can we be confident in the assessment of the outcome?7.Was the follow-up of cohorts adequate?8.Were co‐Interventions similar between groups?
## Search results, study characteristics, and risk of bias
A total of 3907 studies were identified from different databases, and 38 studies were full-text reviewed and assessed for eligibility. Ten studies that fulfilled the inclusion and exclusion criteria were part of the analysis (Fig. 1). The ten studies included had a total of 62 400 participants who were HIV positive on cART (Table 1). Table 2 summarizes our risks of bias. The quality of evidence was described as high, moderate, or low, depending on the heterogeneity. Fig. 1PRISMA flow diagram for article selection process. Fig. 1Table 1Summary characteristics of selected studies [30,31,[36], [37], [38], [39], [40], [41], [42], [43]].Table 1Author and year of publicationCountryStudy designYear of data collectionParticipantsFollow-up durationIntervention drugs in the particular studyTreatment outcomes of DM or hyperglycemia (event of total)Mohammed Lamorde et al., 2020 [30]UgandaCase-controlMarch 2018–March 20196647One yearDolutegravir non-dolutegravir first-line16 of 34171 of 3230Hyperglycemia total = 17Ursenbach et al., 2020 [36]France and overseasCohort2009–201719 462Eight years (median follow-up was 572days)INSTIs31 of 3403NNRTI77 of 5601PI157 of 10 458DM = 265 casesHsu et al., 2020 [37]USARetrospective CohortAugust 1, 2013–March 31, 20187494Five years (median follow-up was 1.5 years)INSTIs98 of 6527DTG49 of 2816EVG/c46 of 3504RAL3 of 207bDRV (PI)10 of 967DM = 108 casesRebeiro et al., 2020 [38]USA and CanadaCohortJanuary 2007–December 201722 884Ten years (median follow-up was 1.6–3 years)INSTIs129 of 5184NNRTI359 of 10 846PI234 of 6855DM = 722 casescohorts with >50 recordsDTG25 of 1210(INSTIS)EVG/c48 of 2315RAL53 of 1081DM = 126 casesSummers et al., 2020 [39]USARetrospective cohort2006–2017111811 years (median follow-up of two years)INSTIs8 of 177non-INSTIs group15 of 693DM = 23Almeida SEM et al., 2009 [31]BrazilRetrospective cohortJanuary 2003 and March 200411014 months (interquartileNNRTI17 of 84range 2–16 months)PI5 of 24Hyperglycemia total = 22Tien et al., 2007 [40]USACohort$\frac{1994}{5}$–$\frac{2001}{21524}$Seven yearsNNRTI41 of 1420 (person time)PI41 of 1641 (person time)NRTI9No therapy25DM = 116Samad F et al., 2017 [41]CanadaRetrospective cohort1997–201570318 years (median follow-up of 13 years)NNRTI30 of 202PI86 of 460Others16 of 41DM = 132Bam NE et al., 2020 [42]South AfricaCase-controlApril 2015–March 201653111 monthsD4T/3TC/EFV21 of 58AZT/3TC/EFV40 of 58AZT/3TC/LPV27 of 31AZT/3TC/RTV19 of 23AZT/3TC/NVP1 of 269 of 359DM = 177Han WM et al., 2019 [43]Asia and Pacific regionCohort2003–2017192714 years (follow-up of at least six months)NRTI + NNRTI102 of 1497NRTI + PI22 of 364Others3 of 66DM = 1273 TC – lamivudine, AZT – zidovudine, bDRV – boosted darunavir, D4T – stavudine, DTG – dolutegravir, EFV – efavirenz, EVG – elvitegravir, LPV – lopinavir, NRTI – nucleotide reverse transcriptase inhibitors, NVP – nevirapine, RAL – raltegravir, RTV – ritonavir. The definitions of the outcomes (DM and hyperglycemia) were according to the primary studies. Table 2Risk of bias of the included studies [30,31,[36], [37], [38], [39], [40], [41], [42], [43]].Table 2
## Comparison of DM in INSTIs versus non-INSTIs cART
There was no overall significant difference between the two treatment groups in the four studies comparing incidences of DM in INSTIs and non-INSTIs cART [[36], [37], [38], [39]]. Studies by Ursenbach [36] and Rebeiro [38] show that DM cases were lower for participants treated with INSTIs, while Hsu [37] and Summers [39] were in favor of the other cART treatment group having a protective effect (reduced risk of DM). ( RR 0.97, $95\%$ CI 0.92–1.03; pcARTicipants = 50 958; studies = 4; I2 = $86.8\%$, chi-squared = 22.67). The heterogeneity with both chi2 ($p \leq 0.1$) and I2 (81–$100\%$) was high.
## Comparison of DM in INSTIS versus PI
In three studies comparing incidences of DM in INSTIs and PI [[[36], [37], [38]], there were no overall significant differences in participants treated with INSTIs or those treated with PI. Two studies [36,38] were in favor of INSTIs reducing the risk of DM, and one study [37] was for PIs reducing DM risk. ( RR 0.97, $95\%$ CI 0.92–1.03; participants = 49 840; studies = 3; I2 = $89.3\%$, chi-square = 18.65). The heterogeneity with both chi2 ($p \leq 0.1$) and I2 (81–$100\%$) was high.
## Comparison of DM in INSTIS versus NNRTI
In the two studies comparing incidences of DM in INSTIs and NNRTI [36,38], results showed that there is a lower incidence of DM in the INSTIs group as opposed to the NNRTI group. Ursenbach [36] favors INSTIs to have the protective effect, and Rebeiro [38] favors NNRTI to have the protective effect. ( RR 0.80, $95\%$ CI 0.69–0.91; participants = 42 346; studies = 2; I2 = $0.00\%$, chi2 = 0.18). The heterogeneity was nonsignificant.
## Comparison of DM in PI versus NNRTI
In the six studies comparing incidences of DM in PI and NNRTI [36,38,[40], [41], [42], [43]], incidences were generally lower for participants treated with PI than for those treated with NNRTI. Five studies favored PIs having a reduced risk of DM, and one study [42] favored NNRTIs to have a protective effect. ( RR 1.03, $95\%$ CI (0.97–1.09); studies = 6; I2 = $15.0\%$, chi-square = 5.88). Heterogeneity was nonsignificant (low) (Fig. 2).Fig. 2Forest plots showing comparison of DM in INSTI vs. non-INSTI ART [[36], [37], [38], [39]], INSTI vs. PI [[36], [37], [38]], INSTI vs. NNRTI [36,38], and PI vs. NNRTI [36,38,[40], [41], [42], [43]].Fig. 2
## Comparison of DM in DTG versus EVG
In the two studies comparing incidences of DM in DTG and EVG [37,38], there was no significant difference between the two treatment groups. Hsu [37] favors EVG and Rebeiro [38] favors DTG. ( RR 1.11, $95\%$ CI 0.92–1.29; studies = 2; I2 = $0.00\%$, chi-squared = 0.64). The heterogeneity was nonsignificant. ( low).
## Comparison of DM in DTG vs. RAL
The two studies [37,38] showed no significant difference between the two treatment groups. The study by Hsu [37] favors raltegravir, while Rebeiro [38] favors DTG. ( RR 0.97, $95\%$ CI 0.90–1.03; studies = 2; I2 = $93.3\%$, chi2 = 14.89). The heterogeneity was high.
## Comparison of DM in RAL versus EVG
In the two studies comparing incidences of DM in RAL and EVG [37,38], there were lower incidences for participants treated with RAL than for those treated with EVG. ( RR 1.65, $95\%$ CI (1.34–1.97); studies = 2; I2 = $0.00\%$, chi-square = 0.67). The heterogeneity was nonsignificant. ( low) (Fig. 3).Fig. 3Forest plots showing Comparison of DM in INSTI group [37,38].Fig. 3
## Hyperglycemia
Two studies, Mohammed Lamorde et al., 2020 [30] and Almeida SEM et al., 2009 [31], compared outcomes of hyperglycemia. Study [30] compared dolutegravir and non-dolutegravir-based regimens. The percentage of hyperglycemia in DTG was $0.46\%$, and non-DTG was $0.03\%$. In Almeida [31], the percentage of hyperglycemia in NNRTI was $20.2\%$, and PI was $20\%$.
## Discussion
The review's focus was to determine the incidences of developing DM in PWH on the different cART available and compare the new class of INSTIs and the non-INSTIs group. The main findings of this meta-analysis are that the incidences of DM were without significant difference between the two treatment groups (INSTIs versus other cARTs). ( moderate quality of evidence).
When comparing INSTIs and other groups, there was no statistical significance in incidences of DM in the participants (moderate quality of evidence). There was also no statistical significance when comparing INSTIs and PI. In contrast, incidences of DM were significantly lower in INSTIs compared to NNRTI (high-quality evidence). When comparing PIs with NNRTIs, the DM incidence was generally significantly lower for participants treated with PIs than those treated with NNRTIs (high-quality evidence). When comparing the individual drugs in the INSTIs group, dolutegravir vs. elvitegravir, incidences of DM were without significant difference (high quality of evidence). In dolutegravir vs. raltegravir, incidences of DM were without significant difference (moderate quality of evidence). In raltegravir vs. elvitegravir, incidences of DM were generally significantly lower for participants treated with raltegravir (high quality of evidence).
Heterogeneity in the four studies comparing DM in INSTIs vs. other drugs could be because, in two studies, there was no prior exposure to other medications (Ursenbach [36] and Rebeiro [38]). While in Hsu [37] and Summers [39], participants were previously exposed to other cARTs before the switch to INSTIs. In Summers [39], the study participants were only female.
Authors came across similar meta-analyses on incidences of DM, metabolic syndromes, and hyperglycemia that compared their findings among HIV and non-HIV patients. A study showed that the overall prevalence of metabolic syndromes among people living with HIV was $21.5\%$ ($95\%$ CI 15.09–26.86) versus uninfected $12.0\%$ ($95\%$ CI 5.00–$21.00\%$), with substantial heterogeneity [44]. Two studies compared PIs and DM. One showed that PIs are associated with an increased risk of metabolic syndrome (MS), but no evidence of risk of DM increase was found. We know that metabolic syndromes usually happen before the development of DM and are a risk factor; studies with a longer follow-up duration may be needed to detect an association between PI use and the onset of DM [45]. Another was conducted in pregnant mothers, which revealed increased gestational diabetes (GDM) in studies using first-generation protease inhibitors (risk ratio 2.29, $95\%$ CI: 1.46–3.58) and studies using the strictest diagnosis criteria, the National Diabetes Data *Group criteria* for 3-h oral glucose tolerance test (risk ratio 3.81, $95\%$ CI: 2.18–6.67) [46]. O'Halloran et al. showed that INSTIs use was associated with an increased risk of new-onset diabetes mellitus/hyperglycemia in the six months following cART initiation [47].
This study focused on assessing any greater risks of developing DM in INSTIs. A recent case study [48] revealed a patient developed hyperglycemia three weeks after switching from efavirenz. Mohammed's findings [30] indicate more significant percentages of hyperglycemia in DTG, $0.46\%$, compared to non-DTG, $0.03\%$. Weight gain is one of the risk factors for the development of DM, and obesity is a health problem worldwide. INSTIs have been associated with weight gain. Supporting this is a study [15] showing that at 18 months, PWH on dolutegravir gained 6.0 Kg, compared to 2.6 Kgs for NNRTIs ($P \leq 0.05$) and 0.5 kg for elvitegravir ($P \leq 0.05$).
We also found similar case series on bictegravir [49]; The first case showed that four months after the switch, the patient presented to the emergency department (ED) with abdominal pain, blood glucose concentration was greater than 400 mg/dL, elevated blood ketone level was 4.5 mmoL/l. The second case showed that three weeks after the transition, he developed polyuria, polydipsia, and unintentional 15 Kg weight loss. Evaluations from the laboratory in the ED revealed hyperglycemia (>500 mg/dL) and elevated blood ketones (4.4 mmol/L). In the last case, two months after the transition, he presented to the ED for nausea, vomiting, polyuria, and polydipsia. His blood glucose concentration was >600 mg/dL, and the blood ketones were 4.2 mmol/L.
## Study limitations
Most of the studies are longitudinal cohorts, retrospective cohorts, and case-control studies. There remains a need for randomized controlled trials, as some findings are heterogeneous. We also acknowledge that studies with INSTIs are limited, and there is a need to conduct more studies. When a subgroup analysis on the different drugs of INSTIs was done, the authors managed to get two studies for comparison. No study in this meta-analysis investigated the association between bictegravir and type 2 DM.
## Conclusions
The present systematic review and meta-analysis show no significant difference in the incidences of DM in patients receiving INSTIs-containing cART regimens compared to other cART regimens. Among the INSTIs, there was also no significant difference between DTG and RAL or DTG and EVG. But there were lower incidences in RAL when compared to EVG.
## Credit author statement
Violet Dismas Kajogoo: Conceptualization, Investigation, Methodology, Software, Writing – original draft preparation. Wondwossen Amogne: Data curation, Supervision, Validation, Writing – review & editing. Girmay Medhin: Formal analysis, Supervision, Visualization, Writing – review & editing.
## Funding
This study was funded by the Center for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), $\frac{10.13039}{501100007941}$Addis Ababa University, as a masters scholarship.
## Availability of data and material
Relevant data will be found in the manuscript and its supporting information files.
## 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.
## Supplementary data
The following is the *Supplementary data* to this article. Multimedia component 1Multimedia component 1
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|
---
title: 'Association between adherence to the Mediterranean Diet and the
Eatwell Guide and changes in weight and waist circumference in post-menopausal
women in the UK Women’s Cohort Study'
authors:
- Nicola Best
- Orla Flannery
journal: Post Reproductive Health
year: 2023
pmcid: PMC10009324
doi: 10.1177/20533691231156643
license: CC BY 4.0
---
# Association between adherence to the Mediterranean Diet and the
Eatwell Guide and changes in weight and waist circumference in post-menopausal
women in the UK Women’s Cohort Study
## Abstract
### Objective
This study investigated the associations between adherence to the Mediterranean Diet and the Eatwell Guide (EWG) and changes in weight and waist circumference in post-menopausal women.
### Study Design
Post-hoc analysis of post-menopausal women from the UK Women’s Cohort Study.
### Main outcome measures
Changes in weight, waist circumference and the risk of abdominal and general obesity.
### Results
4162 post-menopausal women were selected. Higher adherence to both the EWG and the Mediterranean Diet was associated with smaller increases in waist circumference over 4 years (EWG: β −0.47, CI −0.75, −0.20 per 1 tertile increase in score), (Mediterranean Diet: β −0.29, CI −0.58, −0.01 per 1 tertile increase in score); and lower risk of abdominal obesity (EWG: OR 0.55, CI 0.43, 0.70 third versus the first tertile), (Mediterranean Diet: OR 0.60, CI 0.46, 0.76 third versus the first tertile), but was not associated with weight changes (EWG: β 0.14, CI −0.07, 0.36 per 1 tertile increase in score), (Mediterranean Diet: β 0.03, CI −0.19, 0.25 per 1 tertile increase in score) or risk of becoming overweight or obese (EWG: OR 1.09, CI 0.77, 1.52 third versus the first tertile), (Mediterranean Diet: OR 0.91, CI 0.65, 1.27 third versus the first tertile).
### Conclusions
The results suggest that adherence to either the Mediterranean Diet or the EWG can help to prevent abdominal obesity in post-menopausal women.
## Introduction
Weight gain, particularly abdominal obesity, is prevalent among women in menopause,1,2 and 66–$69\%$ of women over 45 in the UK are overweight or obese.3 Weight gain is considered age- and lifestyle-related; however, the drop in estrogen during menopause influences the fat distribution, particularly in the abdominal area.1,4 Abdominal obesity is associated with adverse metabolic events, including cardiovascular disease, the leading cause of death in post-menopausal women.1 Poor dietary quality is an important modifiable factor in the prevention of obesity, and improved dietary quality has been associated with a lower risk of overweight or obesity in both men and women.5,6 There are few studies on dietary patterns in post-menopausal women, but the limited evidence suggests that improvements in diet quality are associated with smaller increases in weight and waist circumference (WC); however, the optimum dietary pattern is undecided.7–9 This study examines how adherence to the Mediterranean Diet and the Eatwell Guide (EWG) influences weight and WC in post-menopausal women in a UK cohort.
## Study population
The UK Women’s Cohort Study (UKWCS) was initially established to investigate the relationships between diet and chronic disease, particularly cancer, and this cohort’s complete details have been published.10 A total of 7859 post-menopausal participants were identified from their answers on the baseline questionnaire, and 4162 were selected after the following exclusions: 1760 had missing ($$n = 1756$$) or implausible ($$n = 4$$) anthropometric data; 375 had implausible daily energy intake of less than 500 kcals or more than 3500 kcals per day,11 and a further 1562 had missing confounding variables ($$n = 1536$$) or discordant waist measurements ($$n = 26$$).
## Dietary assessment
Dietary information was obtained from a self-administered 217-item validated Food Frequency Questionnaire (FFQ). These values were then used to generate a score for each participant for the EWG and the Mediterranean diet. The Mediterranean Diet score was based on the original score described by Trichopoulou, Kouris-Blazos,12 which was adapted for use with the UKWCS dataset.13 The median value used to derive the Mediterranean Diet score is shown in Table 1. Adherence to the EWG was assessed the same way as previously used in a study by Scheelbeek, Green.14 The dietary intake of each participant was compared to the recommended intake in the EWG except for the values for total fat, which were compared to the Public Health England (PHE) government dietary recommendations.15,16Table 1.Median values used for derivation of the Mediterranean Diet score. Indicator valueMDS component10Vegetables (g/day)≥294.4<294.4Legumes (g/day)≥29.4<29.4Fruit and nuts (g/day)≥302.7<302.7Cereals (g/day)≥222.0<222.0Fish (g/day)≥26.0<26.0MUFA + PUFA: SFA≥1.53<1.53Meat (g/day)<39.9≥39.9Poultry (g/day)<12.9≥12.9Dairy (g/day)<102.8≥102.8Alcohol (g/day)5–25<5 or >25
## Anthropometric measurements
Anthropometric measurements were recorded from the baseline and Phase 2 questionnaire and were self-reported measurements on WC, height and weight. Participants were categorised into abdominal obesity categories based on their WC, where abdominal obesity was classified as having a WC of ≥88 cm. Participants were also categorised into weight categories based on their BMI, where a BMI over 25 kg/m2 was classified as overweight or obese.
## Covariate measurements
Demographic and socioeconomic information was self-reported in the baseline questionnaire. The variables controlled for were age, physical activity, education, smoking and use of Hormone Replacement Therapy (HRT). These were thought to have links between dietary patterns and obesity and have been controlled for in previous studies.17,18 Although ethnicity was identified as a potential confounder, it was not included in this analysis as the majority ($99.3\%$) of the participants selected for this study, who supplied their ethnicity, were white.
## Statistical analysis
All statistical analysis was conducted using the Statistical Package for the Social Sciences (SPSS),19 and statistical significance was reported as <0.05.
Hierarchical multiple linear regression models were used to evaluate the association between the Mediterranean Diet and EWG scores (by tertile of adherence and as a continuous scale) and changes in WC (cm; continuous) from baseline to Phase 2. The first model was minimally adjusted for age (years; continuous) and baseline WC (cm; continuous); the second model included adjustments for total energy intake (kcal; continuous), time from baseline to Phase 2 (year; continuous), physical activity (met the physical activity recommendations Yes/No; dichotomous), smoking (never/current/former; nominal), education (No qualifications, O Levels, A levels, Degree; nominal) and HRT (never/current/former; nominal). Finally, the model was adjusted for changes in BMI (kg/m2 continuous) to understand how weight changes explained any differences. Hierarchical linear regression was then repeated to look at the association between adherence to dietary patterns and changes in weight. All the same adjustments were made, except baseline WC was replaced with baseline BMI in the first model.
For those with a normal (<88 cm) WC at baseline, the relationship between dietary scores and risk of abdominal obesity was assessed using binary logistic regression for each one-point increase in score (continuous) and tertile increase in score (categorical). The first model was minimally adjusted for age, and the second model included adjustments for total energy intake (kcal; continuous), time from baseline to Phase 2 (years; continuous), physical activity (Yes/No; nominal), smoking (never/current/former; nominal), education (none/O level/A level/Degree; nominal) and HRT (never/current/former; nominal). The binary logistic regression was then repeated for those with a BMI of less than 25 kg/m2 to investigate the relationship between adherence to dietary patterns and the risk of becoming overweight or obese.
## Results
After a mean of 4.1 (SD 0.7) years, the mean weight increase across all participants was 1.2 (SD 4.8) kg, and the mean increase in WC was 6.7 (SD 6.8) cm. At baseline, the prevalence of abdominal obesity was $7.7\%$, and at Phase 2, $24.4\%$. The prevalence of overweight or obese participants was $32.3\%$ at baseline, and at Phase 2, $37.5\%$. Weight, WC, BMI, time from baseline to Phase 2 and the percentage of participants with general and abdominal obesity decreased along the tertiles for the Mediterranean Diet and the EWG. The percentage meeting the requirements for physical activity and having higher qualifications also increased along the tertiles. In addition, those in the highest tertile of adherence for the Mediterranean diet were younger and less likely to smoke, and those in the highest tertile for the EWG had a smaller increase in WC (Tables 2 and 3).Table 2.Characteristics of participants according to tertiles of adherence to the Mediterranean Diet. Continuous variables are presented as the median and interquartile range (IQR) and categorical variables as percentages p-values obtained from the Kruskal–Wallis H test for continuous variables and Chi-squared test for categorical variables. Mediterranean Diet score tertiles (0–10)1st ($$n = 1008$$)2nd ($$n = 2223$$)3rd ($$n = 931$$)(0–3)(4–6)(7–10)VariablesMedianIQRMedianIQRMedianIQRpAge (years)58.110.157.610.057.210.40.03Time from baseline (years)4.00.43.90.53.90.70.002Weight baseline (kg)64.411.464.012.762.012.2<0.001Weight Phase 2 (kg)65.314.164.012.763.512.8<0.001Weight change (kg)0.94.50.94.10.94.10.40WC baseline (cm)76.112.773.710.271.17.6<0.001WC Phase 2 (cm)81.514.680.013.378.714.0<0.001WC difference (cm)5.78.35.77.65.77.60.31BMI baseline (kg/m2)24.04.523.44.223.04.0<0.001BMI Phase 2 (kg/m2)24.54.623.94.523.44.5<0.001BMI change (kg/m2)$0.41.70.31.60.31.50.42\%$%%Physical activitya42.952.359.8<0.001HRT Never52.554.356.00.44 Current33.232.129.5 Past14.313.614.5Education No formal19.317.916.2<0.001 O level35.730.524.9 A level25.327.129.3 Degree or above19.624.529.5Smoking Never65.561.851.5<0.001 Current8.86.95.5 Former25.731.339.4Abdominal obesity baselineb10.87.25.5<0.001Abdominal obesity Phase 2b30.423.420.2<0.001Overweight or obese baselinec38.332.425.7<0.001Overweight or obese Phase 2c43.036.833.2<0.001IQR: interquartile range; WC: waist circumference; BMI: body mass index; HRT: hormone replacement therapy.aPhysical activity, percentage meeting recommendations.bAbdominal obesity WC >88 cm.cOverweight or obese ≥25 kg/m2.Table 3.Characteristics of participants according to tertials of adherence to the Eatwell Guide. Continuous variables are presented as the median and interquartile range, categorical variables as percentages-p values obtained from the Kruskal–Wallis H test for continuous variables and the Chi-squared test for categorical variables. EWG tertiles (0–9)1st ($$n = 1205$$)2nd ($$n = 1988$$)3rd ($$n = 969$$)(0–2)(3–4)(5–9)VariablesMedianIQRMedianIQRMedianIQRpAge (years)57.810.157.510.257.710.00.79Time from baseline (years)4.00.43.90.53.90.70.02Weight baseline (kg)63.512.263.512.762.113.6<0.001Weight Phase 2 (kg)65.314.563.512.763.513.6<0.001Weight change (kg)0.94.00.94.10.94.50.20WC baseline (cm)76.210.271.110.271.17.6<0.001WC Phase 2 (cm)81.514.480.013.878.713.6<0.001WC difference (cm)6.38.35.77.65.17.60.01BMI baseline (kg/m2)23.94.623.44.023.24.0<0.001BMI Phase 2 (kg/m2)24.14.624.04.723.74.40.001BMI change (kg/m2)$0.31.50.31.60.41.60.20\%$%%Physical activitya46.353.454.9<0.001HRT Never54.155.152.70.62 Current32.730.932.4 Past13.214.014.9Education No formal17.217.319.90.40 O level31.531.128.1 A level27.626.827.2 Degree or above23.724.824.8Smoking Never61.561.560.30.05 Current8.07.45.3 Former30.531.134.5Abdominal obesity baselineb10.36.86.3<0.001Abdominal obesity Phase 2b30.123.518.9<0.001Overweight or obese baselinec37.231.328.4<0.001Overweight or obese Phase 2c40.337.234.60.02EWG: Eatwell Guide; IQR: interquartile range; WC: waist circumference; BMI: body mass index; HRT: hormone replacement therapy.aPhysical activity, percentage meeting recommendations.bAbdominal obesity WC >88 cm.cOverweight or obese ≥25 kg/m2.
Linear regression analysis identified a significant negative association between an increase in Mediterranean Diet and EWG score and changes in WC in all fully adjusted models (Table 4). The association between the EWG score and WC was stronger than that seen with the Mediterranean Diet. No significant associations were seen between adherence to the Mediterranean Diet or the EWG and changes in weight (Table 5).Table 4.Multiple linear regression models describing the association between an increase in Mediterranean Diet Score or EWG score (continuous variable, per tertile increase) and change in waist circumference between baseline and Phase 2 (β coefficients and $95\%$ confidence intervals).β$95\%$ CIpMediterranean Diet (tertiles) Model 1a−0.27−0.57, 0.040.08 Model 2b−0.25−0.57, 0.060.12 Model 3c−0.29−0.58, −0.010.05Mediterranean (continuous) Model 1a−0.11−0.22, −0.0040.04 Model 2b−0.11−0.22, 0.010.07 Model 3c−0.12−0.23, −0.020.02EWG (tertiles) Model 1a−0.38−0.67, −0.090.01 Model 2b−0.38−0.68, −0.080.01 Model 3c−0.47−0.75, −0.200.001EWG (continuous) Model 1a−0.19−0.33, −0.060.01 Model 2b−0.20−0.35, −0.050.01 Model 3c−0.24−0.38, −0.12<0.001CI: confidence intervals; EWG: Eatwell Guide.aModel 1 includes age and baseline waist circumference.bModel 2 additionally includes the time from baseline to Phase 2, total energy intake, smoking, education, physical activity, smoking and HRT.cModel 3 additionally includes BMI changes from baseline to Phase 2.Table 5.Multiple linear regression models describing the association between an increase in Mediterranean Diet Score or Eatwell Guide score (continuous variable, per tertile increase) and change in weight between baseline and Phase 2 (β coefficients and $95\%$ confidence intervals).β$95\%$ CIpMediterranean Diet (tertiles) Model 1a−0.05−0.27, 0.160.64 Model 2b0.03−0.19, 0.250.80Mediterranean Diet (continuous) Model 1a−0.14−0.09, 0.060.71 Model 2b0.16−0.06, 0.100.70EWG (tertiles) Model 1a0.20−0.001, 0.410.05 Model 2b0.14−0.07, 0.360.20EWG (continuous) Model 1a0.09−0.004, 0.190.06 Model 2b0.06−0.04, 0.170.23CI: confidence intervals; EWG: Eatwell Guide.aModel 1 includes age and baseline BMI.bModel 2 additionally includes the time from baseline to Phase 2, total energy intake, smoking, education, physical activity, smoking and HRT.
Binomial regression models identified that a higher index score for both the Mediterranean Diet and the EWG was associated with a reduced risk of becoming abdominally obese in all models (Table 6). However, a higher index score was not significantly associated with the risk of becoming overweight or obese (Table 7).Table 6.Binomial logistic regression models describing the relationship between adherence to the Mediterranean Diet and Eatwell Guide and becoming abdominally obese in participants with a waist circumference of less than 88 cm at baseline. ( Odds ratio and $95\%$ confidence intervals).ORa$95\%$ CIORb$95\%$ CIMediterranean Diet Tertile 1 (0–3)1Reference1Reference Tertile 2 (4–6)0.760.63, 0,920.740.61, 0.90 Tertile 3 (7–10)0.650.52, 0.830.600.46, 0.76 Continuous (0–10)0.910.88, 0.950.900.86, 0.94EWG Tertile 1 (0–2)1Reference1Reference Tertile 2 (3–4)0.740.62, 0.890.750.62, 0.90 Tertile 3 (5–9)0.540.42, 0.680.550.43, 0.70 Continuous (0–9)0.840.80, 0.900.840.80, 0.90OR: odds ratio; CI: confidence intervals; EWG: Eatwell Guide.aAdjusted for age.bFurther adjusted for the time from baseline to Phase 2, total energy intake, smoking, education, physical activity and HRT.Table 7.Binomial logistic regression models describing the relationship between adherence to the Mediterranean Diet and Eatwell Guide and becoming overweight or obese in participants with a weight less than 25 kg/m2 at baseline. ( Odds ratio and $95\%$ confidence intervals).ORa$95\%$ CIORb$95\%$ CIMediterranean Diet Tertile 1 (0–3)1Reference1Reference Tertile 2 (4–6)0.820.63, 1.080.860.65, 1.13 Tertile 3 (7–10)0.860.63, 1.180.910.65, 1.27 Continuous (0–10)0.970.92, 1.030.980.92, 1.04EWG Tertile 1 (0–2)1Reference1Reference Tertile 2 (3–4)1.200.92, 1.561.190.91, 1.57 Tertile 3 (5–9)1.170.85, 1.601.090.77, 1.52 Continuous (0–9)1.030.95, 1.111.010.93, 1.10OR: odds ratio; CI: confidence intervals; EWG: Eatwell Guide.aAdjusted for age.bFurther adjusted for the time from baseline to Phase 2, total energy intake, smoking, education, physical and HRT.
## Discussion
This study has found that higher adherence to the EWG and the Mediterranean *Diet is* associated with lower gains in WC and a reduced risk of abdominal obesity in post-menopausal women. Cespedes Feliciano, Tinker7 found similar results in their prospective cohort study of post-menopausal women. They examined four different dietary indices, including those based on the American Healthy Eating Guidelines adapted to incorporate more foods predictive of preventing disease (AHEI-2010) and the Alternate Mediterranean Diet Score (AMDS). They found that each $10\%$ increase in dietary quality score was associated with between 0.10 cm (AMDS) and 0.20 cm (AHEI-2010) smaller increases in WC. A prospective cohort study of 32,119 men and women in Italy also observed that increased adherence to the Italian Mediterranean Diet was significantly associated with negative changes in WC and a reduced risk of becoming abdominally obese.18 Similarly, in Spain, increased adherence to the Mediterranean Diet was associated with smaller WC increases after 10 years. In addition, they also saw a decreased incidence of abdominal obesity, but this did not reach significance.20 Cross-sectional studies have also observed an association with adherence to the Mediterranean Diet and lower WCs17,21 and a reduced risk of abdominal obesity with higher adherence to the Healthy Eating Index in America22; however, in a study of Mexican Americans, the improvements in diet quality were associated with a lower risk of abdominal obesity in men but not in women.23 No significant associations were seen between adherence to the Mediterranean Diet or the EWG and weight changes or the risk of becoming overweight or obese in those with a BMI of less than 25 kg/m2 at baseline. Similar results for weight gain have been seen previously in post-menopausal women where adherence to the Mediterranean Diet was not significantly associated with weight gain in fully adjusted models, and adherence to AHEI-2010 was associated with a higher risk of gaining weight.8 An increase in adherence to the Mediterranean Diet was also not significantly associated with changes in weight over 5 years in an extensive study of both men and women in Italy. However, when the results were stratified by BMI, a significant weight reduction was seen in those with a BMI less than 25 kg/m2 18. Cross-sectional studies have also not found a significant association between healthy eating patterns and BMI.17,21 In contrast to this study, some other studies have shown that adherence to the Mediterranean *Diet is* associated with reduced weight gain 24 and a reduced likelihood of becoming overweight or obese.18,24 However, in the multicentre, prospective study, significant heterogeneity was seen between countries, and one study in the UK saw a non-significant increase in weight gain.24 These conflicting results are a possible indication that there may be variations in the diet in the UK compared to Mediterranean regions, and a similar lack of association between adherence to the Mediterranean Diet and weight was seen in a younger population in Sweden.25 Differences in results with the Mediterranean Diet may also be linked to differences in the scores. This study’s score was based on median values specific to the population, so results are not directly comparable between studies.26
The strength of this study is the availability of baseline and follow-up data from a large prospective cohort, the use of validated questionnaires for the dietary intake alongside the collection of additional data on potential confounders used in the regression models. This study, however, does have several limitations. The anthropometric measurements were self-reported, and the FFQ was administered only on a single occasion at baseline. In addition, the cohort’s population is generally healthier,27 and the study is limited to those who returned the Phase 2 questionnaire and those who had complete and plausible data.
The results of this study add to the paucity of evidence in this area and suggest that adhering to dietary guidelines can help prevent abdominal adiposity in post-menopausal women. Adherence to guidelines in the UK is currently very low. For higher adherence, women need to consume more fibre, fruit, vegetables and oily fish and less free sugars and saturated fats.28 Current recommendations are that public health interventions should routinely include diet and lifestyle advice alongside appropriate HRT prescribing at perimenopause. Doing this could limit the adverse health implications seen in post-menopausal women and reduce the levels of avoidable health issues in the female population.29,30
## ORCID iDs
Nicola Best https://orcid.org/0000-0003-2716-039X Orla Flannery https://orcid.org/0000-0002-4669-2781
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|
---
title: Cross-reactivity influences changes in human influenza A virus and Epstein
Barr virus specific CD8 memory T cell receptor alpha and beta repertoires between
young and old
authors:
- Fransenio Clark
- Anna Gil
- Ishwor Thapa
- Nuray Aslan
- Dario Ghersi
- Liisa K. Selin
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10009332
doi: 10.3389/fimmu.2022.1011935
license: CC BY 4.0
---
# Cross-reactivity influences changes in human influenza A virus and Epstein Barr virus specific CD8 memory T cell receptor alpha and beta repertoires between young and old
## Abstract
Older people have difficulty controlling infection with common viruses such as influenza A virus (IAV), RNA virus which causes recurrent infections due to a high rate of genetic mutation, and Epstein *Barr virus* (EBV), DNA virus which persists in B cells for life in the $95\%$ of people that become acutely infected. We questioned whether changes in epitope-specific memory CD8 T cell receptor (TCR) repertoires to these two common viruses could occur with increasing age and contribute to waning immunity. We compared CD8 memory TCR alpha and beta repertoires in two HLA-A2+ EBV- and IAV-immune donors, young (Y) and older (O) donors to three immunodominant epitopes known to be cross-reactive, IAV-M158-66 (IAV-M1), EBV-BMLF1280-288 (EBV-BM), and EBV-BRLF1109-117 (EBV-BR). We, therefore, also designed these studies to examine if TCR cross-reactivity could contribute to changes in repertoire with increasing age. TCR high throughput sequencing showed a significant difference in the pattern of TRBV usage between Y and O. However, there were many more differences in AV and AJ usage, between the age groups suggesting that changes in TCRα usage may play a greater role in evolution of the TCR repertoire emphasizing the importance of studying TRAV repertoires. With increasing age there was a preferential retention of TCR for all three epitopes with features in their complementarity-determining region (CDR3) that increased their ease of generation, and their cross-reactive potential. Young and older donors differed in the patterns of AV/AJ and BV/BJ pairings and usage of dominant CDR3 motifs specific to all three epitopes. Both young and older donors had cross-reactive responses between these 3 epitopes, which were unique and differed from the cognate responses having features that suggested they could interact with either ligand. There was an increased tendency for the classic IAV-M1 specific clone BV19-IRSS-JB2.7/AV27-CAGGGSQGNLIF-AJ42 to appear among the cross-reactive clones, suggesting that the dominance of this clone may relate to its cross-reactivity with EBV. These results suggest that although young and older donors retain classic TCR features for each epitope their repertoires are gradually changing with age, maintaining TCRs that are cross-reactive between these two common human viruses, one with recurrent infections and the other a persistent virus which frequently reactivates.
## Introduction
CD8 T cell recognition of virus-infected cells requires a specific interaction between short peptides presented by HLA Class I molecules on infected cell surfaces and TCRαβ heterodimers on CD8 T cells. These virus epitope-specific memory CD8 T cells develop complex TCR repertoires that are specific for that epitope. State-of-the-art high throughput and single cell sequencing provide a more unbiased understanding of antigen-specific TCR repertoires. CD8 T cell TCR repertoires to common viruses, IAV, cytomegalovirus (CMV) are highly diverse and individualized i.e. “private” [1]. However, despite this diversity there are clonotypes with “public” features, i.e. preferential usage of particular variable (V) region or conserved or identical amino acid motifs within the complementarity-determining regions (CDR3α/β) for each epitope that appear to be favored for expansion, likely due to selection for optimal structural interactions [2, 3].
We have been studying TCR repertoires to both IAV and EBV immunodominant epitopes in HLA-A2+ donors, focusing on IAV-M1, EBV-BM and EBV-BR, in order to identify their public characteristics to better understand antigen-specific TCR selection. Our recent results in IAV-immune healthy donors would suggest that the number of contacts between TCR and peptide major histocompatibility complex (pMHC) is a controlling factor in determining TCR selection [3] and that antigen-specific TCR repertoires have evolved to permit “focused diversity”. It is likely that public dominant TCR, if selected for best fit, can rapidly recognize their antigen, while the highly private diverse side of the repertoire could be useful if the antigen mutates. The structure of both the TCR alpha and beta chain appear to play a role in interaction with the peptide/MHC complex to differing extents depending on the epitope. For instance, for many epitopes, including IAV-M1, the CDR3β plays the dominant role while for others, like EBV-BM, both chains contribute equally (3–6). The TCRα interaction often occurs with CDR1 or CDR2 rather than the CDR3. However, in two recent publications we have shown that CDR3α can play a critical role in selection of the TCR repertoire to the EBV-BR epitope due to structural constraints [7, 8]. We have also shown that EBV-BM and EBV-BR repertoires are even more diverse and highly dynamic during an inflammatory response, acute infectious mononucleosis (AIM) (10,000 unique clonotypes/epitope/donor), than in healthy seropositive donors (1,000 unique clonotypes/epitope/donor). However, only $10\%$ of the unique clonotypes present during AIM persist into memory, while the other $90\%$ are replaced in 6 months with a completely new repertoire [7]. It is important that we study and better understand epitope-specific TCR repertoire organization and how it evolves particularly with increasing age. As individuals age virus-specific immunity appears to wane.
Generally, TCRβ repertoire has been more extensively studied than TCRα, largely because techniques to study it, both antibody and sequencing, were easier to develop than for TCRα or alpha chain. However, it has become clear that TRAV gene segments can play an equally important role as TRBV in selection of antigen-specific repertoires as seen in EBV-BR specific TCR repertoires in patients with AIM [7, 8]. As IAV-M1, EBV-BM, and EBV-BR TRBV repertoires are relatively well-documented and well-studied, many public TRBVs have been identified. BV19 has been identified as the most dominant BV family used in response to IAV-M1 [3]; BV20, BV2, BV14, and BV29 in response to EBV-BM (7, 9–12) with anyone individual donor usually using one or two of these dominantly. Despite, the immunodominance of EBV-BR, it’s TCR repertoire is under-studied until recently. EBV-BR is unique in its ability to use multiple different TRBV families with an average of 4-5 different ones dominating in any one donor and often pairing with the public TRAV8.1 (7–9).
In addition, our lab has worked extensively to describe the concept of TCR cross-reactivity and explore changes to TCR repertoire in mouse models (1, 13–15) using viruses such as vaccinia virus (VV) [16, 17], lymphocytic choriomeningitis (LCMV) [18], IAV [19], CMV [20] and *Pichinde virus* (PV) [21, 22] that model chronic/persistent and acute viral infections in humans. As the results of these studies revealed an intricate network of TCR cross-reactivity between these viruses that cause acute and persistent viruses, our lab naturally pursued an examination of TCR cross-reactivity in humans. Two of the most common viruses that result in acute and persistent infections are IAV and EBV, respectively.
Our research is among the first to directly demonstrate that TCR repertoire determines severity of disease in humans [23, 24]. In our studies using our well characterized AIM cohort we have documented expansions of EBV-specific and cross-reactive CD8 T cells in primary EBV infection and mapped a network of cross-reactive CD8 T cell responses between EBV and another common human virus, IAV [25, 26]. AIM varies in severity from a mild transient flu-like illness to a prolonged and severe syndrome. In 32 young adults with AIM, we found that disease severity directly correlated with the frequencies of IAV-M1+ and IAV-M1+EBV-BM+ tetramer+ CD8 T cells (and weakly with EBV-BM) [23]. Moreover, memory IAV-M1-specific CD8 T cell frequencies > $0.36\%$ (direct ex vivo tetramer staining) were associated with a 5-fold greater risk of severe AIM. IAV-M1 tetramer+ cells were functionally cross-reactive, proliferating to and producing cytokines to EBV-BM. Cross-reactive IAV-M1-specific CD8 T cells associated with severe AIM had a distinct TRBV usage that correlated with disease severity [23].
However, this cross-reactivity between IAV-M1 and EBV-BM may also protect against EBV infection depending on the TCR repertoire. By early adulthood, $95\%$ of the population has been infected with EBV, but $5\%$ of individuals remain seronegative even when they should have been exposed and yet appear to resist infection [27]. We have identified 5 rare individuals, who were EBV seronegative, who had elevated IAV-M1 tetramer+ CD8 T cell frequencies ex vivo [24]. EBV-BM or BR-stimulated cultures from these donors exhibited high frequencies of cross-reactive IAV-M1 tetramer+ cells. These cultures produced IFNγ to EBV epitopes and lysed EBV-infected targets, suggesting that these individuals may indeed be protected from infection. They had highly unique oligoclonal IAV-M1-specific TCR repertoires that differed from young EBV seronegative donors [24]. Altogether, these two studies link heterologous immunity via cross-reactive CD8 T cells to CD8 TCR repertoire selection, function, and disease outcome in a common and important human infection.
To help us better understand how TCR repertoire may influence disease outcome recent studies have shown that there is now enough data available from MHC/peptide structures and antigen-specific TCR sequencing databases to develop novel algorithms that could assist in using the TCRa and TCRb repertoire sequences to track epitope-specific repertoires [6, 28]. Paul Thomas and colleagues [6] developed an algorithm examining single cell TCR sequences, TCR distance measure, TCRdist, that enabled visualization of the epitope-specific repertoires through clustering and dimensionality reduction. To calculate TCRdist scores between 2 TCRs, each TCR is first mapped to the amino acid sequences using a similarity-weighted Hamming distance, with a gap penalty introduced to capture variation in length and a higher weight given to the CDR3 loop. This algorithm can help identify for any antigen-specific response the preferential usage of TCR BV/BJ/AV/AJ and their preferential pairings. This algorithm also could define the preferential usage of particular amino acids in certain positions of the CDR3 as compared to other TCR in the antigen-specific population (motif 1) and as compared to a naïve TCR repertoire (motif 2). This information can be used to identify which features of the TCR are public and important for interaction with that ligand. Once one is able to identify the distance between TCRs one can potentially predict how they cluster based on similar traits and potentially which antigen they might recognize and their potential to recognize two antigens and be cross-reactive. Mark Davis and colleagues [29] used a similar approach called GLIPH to identify public features of TCR that were activated by M. tuberculosis stimulation in infected patients. They constructed the TCR and inserted them into Jurkat cells and screened a plasmid library of M. tuberculosis peptides to identify their ligands. These technologies would be particularly useful for defining TCRs that recognize potentially cross-reactive low affinity and hard to identify ligands such as in autoimmune diseases, or cancer.
Despite the development of robust EBV-specific humoral [30] and cell-mediated immunity (31–34), EBV establishes persistence via latent infection of memory B cells [35]. In healthy people, EBV is known to continuously go into lytic cycle and the immunosuppression of an acute IAV infection may further increase the rate of reactivation. Thus, we would predict that being infected with two viruses at the same time would greatly enhance selection of CD8 T cells that are cross-reactive during acute IAV infection. We have evidence that not only IAV-M1, but also EBV-BM and EBV-BR tetramer frequencies increase during acute asymptomatic IAV with changes in their TCR repertoire [36]. Here, we dissected IAV-M1, EBV-BM and EBV-BR TCRαβ repertoires in the two age groups, young and older donors, all persistently infected with EBV and previously exposed to IAV. We show with the assistance of TCR dist analyses of not only TRBV, but the under studied TRAV high throughput sequence and single cell data, that there are definable changes in epitope-specific TCR repertoires to these two ubiquitous viruses with increasing age influenced by TCR cross-reactivity.
## Study population
Our studies include young adults and older adults that are healthy, HLA-A2.01+, IAV-immune and EBV seropositive. EBV serology was confirmed by the presence of viral capsid antigen (VCA) IgG specific antibodies in addition to staining with EBV-specific tetramers. IAV immunity was confirmed by staining with IAV-specific tetramers. The young adults (Y) (18-21 years old) in this study were a part of an EBV Sero-surveillance cohort developed by Drs. Liisa Selin and Katherine Luzuriaga at University of Massachusetts Amherst (UMA). These donors were followed from freshman year to senior year, during which they donated blood once a semester. Older donors (O) (>60 years old) were volunteers acquired at University of Massachusetts Medical School (UMMS). Volunteers were allowed to donate up to 150ml blood in 3 months, in accordance with our IRB. All participants in this study were required to sign a consent form. This study was approved by the Institutional Review Board (IRB) committee at University of Massachusetts Medical School, Worcester, Massachusetts.
## HLA-typing
Monoclonal antibodies specific to HLA-A2.01(clone BB7.2, Biolegend, San Diego, CA, HLA-B8.01 (clone BB7.1, Santa Cruz Biotechnology, Dallas, TX), and HLA-B7.01 (clone 8.L.215 Biotin, Abcam, Cambridge, MA) were added to 100ul of whole blood and stained for 30 minutes in the dark at room temperature. Cells were washed with 1ml of Hank’s Balanced Salt Solution (HBSS) (Gibco, Grand Island, NY) and spun at 1330rpm for 4mins, 25°C. The cells were incubated in the dark for 30 minutes, then washed with 1ml of HBSS. PE Streptavidin (Biolegend, San Diego, CA) was added to the cells and incubated for 30 minutes and washed. To lyse red blood cells, 2ml of 1X BD FACS Lysing Solution (Becton Dickinson, Waltham, MA) were added for 10 minutes. Cells were washed once with HBSS and spun. Cells were resuspended in 300ul FACS buffer (500ml HBSS, $2\%$ Fetal Calf Serum) and analyzed on the LSRII (Becton Dickinson, Waltham, MA).
## PBMC isolation
Fresh whole blood was mixed with Hank’s Balanced Salt Solution (Gibco, Grand Island, NY) at 1:2 ration and half of this mixture was layered over 15mls of Ficoll-Paque Plus (GE Healthcare Bio-Sciences, Pittsburgh, PA) [23]. Layered cells were placed in a centrifuge and spun at 1800rpm for 40 minutes with no brake at 25°C. PBMC from the buffy coat were collected and washed twice with 20ml HBSS.
## CD8 T cell isolation
Counted and re-suspended PBMC in 20μl of anti-CD8 micro-beads (Miltenyi Biotech, Auburn, CA) and 80μl of MACS buffer [4°C Phosphate-buffered saline, 2.5g of Bovine Serum Albumin (Sigma-Aldrich, St.Louis, MO)], 2ml 0.5M EDTA [pH 8.0 (Invitrogen, Grand Island, NY)] degassed with sterile mesh filter] per 107 cells based on Miltenyi MACS system protocol [23]. PBMC and anti-CD8+T micro-beads mixture were incubated in the dark at 4°C for 15 minutes. Mixture was washed with 20ml of MACS buffer. Miltenyi Biotech MACS system was used to isolate CD8+T cells.
## CD8+ T cell short-term culture
HLA A*0201 specific transporter associated with antigen transport (TAP)-deficient T2A2 cells, which express low amounts of MHC Class-I protein on their surface, were used as antigen presenting cells [3, 23, 24, 26]. Cells were plated at 4 x 106 T2A2 (ATCC #CRL-1992) cells per 3ml of T2A2 media (500ml RPMI, $10\%$ Fetal Calf Serum, $1\%$ HEPES, $1\%$ Penicillin-Streptomycin, $1\%$ L-Glutamine) for 3 hours at 37°C with 1μM of peptide (final concentration= 1mM). T2A2 cells were irradiated with 3000 RAD and washed to remove unbound peptide. T2A2 cells re-suspended in T cell media [AIM-V (Gibco, Grand Island, NY) supplemented with $14\%$ human AB serum [(Interstate Blood Bank INC, Memphis, TN), $16\%$ MLA-144 supernatant (Rabin et al, 1981), 10 U/ml human rIL-2 (Becton Dickinson, Waltham, MA), $1\%$ L-Glutamine (Gibco, Grand Island, NY), $0.5\%$ β-mercaptoethanol (Sigma-Aldrich, St.Louis, MO), $1\%$ HEPES (Hyclone, Logan, UT)]. Plated 1 x 106 of CD8+ T cells with 2 x105 T2A2 cells loaded with a single peptide in a 4ml total volume of T cell media into a 12 well plate were cultured for 3 weeks.
## Method to study crossreactivity
In these studies we assessed both types of cross-reactive CD8 T cells, single tetramer+ and double tetramer+ from IAV-M1 peptide stimulated short term cultures [23, 26]. In order to examine single tetramer+ cross-reactive CD8 T cells we sorted EBV-BM (M1BM) or EBV-BR (M1BR) tetramer+ cells from IAV-M1 stimulated short term cultures for TCR high throughput sequencing. We also sorted M1+BR+ double tetramer+ cells from the IAV-M1 stimulated short-term cultures of two young donors who had this population. We used the same methodologies as previously [23, 26], where we did all of the same controls in our culture system, stimulating short term IAV-M1, EBV-BM, EBV-BR, tyrosinase and CMV-pp65 cell lines on each magnet sorted CD8 T cell population of each donor. This is a useful technique to study lower affinity functional cross-reactivity as, we observe crossreactive cells, for instance EBV-BR tetramer binding cells growing in IAV-M1 stimulated cultures only and not growing in any of the other cultures which act as controls. With this method we study both functional single tetramer binding crossreactivity and double tetramer staining crossreactivity. This culturing technique allows us to circumvent issues with tetramers blocking the binding of the other tetramer during crossreactive responses due to differing affinities, as we have previously described can be a significant problem particularly ex vivo [23, 26].
## Peptides
CD8+ T cells were stimulated with IAV-specific and EBV-specific peptides that were synthesized to >$90\%$ purity (21st Century Biochemical, Marlborough, MA). The following lytic EBV peptides were used: EBV-BMLF1 280-288 (GLCTLVAML) and EBV-BRLF1 109-117 (YVLDHLIVV). For IAV-M1 specific responses, CD8+ T cells were stimulated with IAV-M1 58-66 (GILGFVFTL). T2-A2 cells were pulsed with peptides and used at concentration of 0.1 mg/ml. Peptides used for intracellular assays were used at a 1mg/ml concentration in Dimethyl sulfoxide (Sigma-Aldrich, St. Louis, MO).
## Tetramers and dextramers
IAV-M1 tetramer, EBV-BMLF1 tetramer, EBV-BRLF1 tetramer were provided by in-house tetramer core facility and NIH Tetramer Core Facility (Atlanta, GA). These tetramers including IAV-M1 dextramer (Immudex, Copenhagen, Denmark) were assembled and conjugated to either allophycocyanin (APC) or phycoerythrin (PE) or brilliant violet (BV) 421. Tyrosinase (in-house tetramer core facility and NIH Tetramer Core Facility, Atlanta, GA) and CMVpp65 (in-house tetramer core facility and NIH Tetramer Core Facility, Atlanta, GA) were used as negative controls for all experiments.
## Extracellular staining
3 x 105 freshly isolated or cultured CD8+ T cells were placed into a 96 well plate. Cells were stained with tetramers and dextramers for 30 minutes at room temperature (RT). Cells were washed twice with FACS buffer (500ml Hank’s Balanced Salt Solution with $2\%$ Fetal Calf Serum). Cells were fixed using 100μl of Cytofix (Becton Dickinson Biosciences, San Jose, CA) for 5 mins in the dark at RT. The cells were washed, spun at 1330rpm for 4mins, 25°C and re-suspended in FACS buffer and prepared for flow cytometry.
## Intracellular staining
Cell were prepared at 1 x 106 cells in 200μl of T cell media with Golgi-Stop, Golgi-Plug (Becton Dickinson Biosciences, San Jose, CA), which allowed the accumulation of cytokines in the Golgi-complex, and anti-CD107a/b antibodies (mAb eBioH4A3, eBiosciences, San Diego, CA). Cultured cells were incubated for 5 hours at 37°C in the presence of 5μM of the same peptide used for 3-week stimulation. Cells were washed twice in FACS Buffer and spun down to remove unbound peptides and antibodies, the cells were washed twice using FACS buffer. Cells used in the cell surface stain with dextramers and tetramers were incubated for 30 minutes at RT. Cells were washed twice and fixed. Cells were then permeabilized with Cytofix/Cytoperm (Becton Dickinson Biosciences, San Jose, CA) for 20 minutes at RT. Cells were washed twice using FACS buffer. The following antibodies were used to detect production of cytokines: anti-IFN-γ (0.2μg clone B27, Becton Dickinson, San Jose, CA), anti-MIP-1β (0.2μg clone D21-1351, Becton Dickinson, San Jose, CA), and anti-TNF-α (mAb11, eBiosciences, San Diego, CA) for 30 minutes at RT. Cells were washed twice and fixed using Cytofix (Becton Dickinson Biosciences, San Jose, CA) for 20 minutes. Cells were washed and spun at 1330rpm for 4mins, 25°C then re-suspended in FACS buffer for flow cytometry.
## CD8 T cell sorting
Freshly isolated or cultured CD8+ T cells stained with tetramers and dextramers were collected into a 1.5ml FACS tubes with 400μl of FACS buffer. In order to examine whether and how cross-reactivity might influence or change TCR repertoire with increasing age we also assessed both types of cross-reactive CD8 T cells, single tetramer positive and double tetramer from IAV-M1 peptide short term cultures [23, 26]. In order to examine single tetramer+ cross-reactive CD8 T cells we sorted EBV-BM (M1BM) or EBV-BR (M1BR) tetramer+ cells from IAV-M1 stimulated short term cultures for TCR high throughput sequencing. These were present in both young and older donors. We also sorted M1+BR+ double tetramer+ cells from the IAV-M1 stimulated short-term cultures of two young donors who had this population. Cells were sorted at the University of Massachusetts Medical School FACS Core Facility in the Biosafety Level 3 (BSL-3) suite (UMASS Medical School, Worcester, MA), using a BSL-3 BD FACS Aria Cell Sorter.
## TcR V beta repertoire staining
The TcR V beta repertoire kit contained antibodies to 24 V beta families (Beckman Coulter, Fullerton, CA). Cells were stained with these antibodies and tetramers or dextramers for 20 minutes at RT to determine the V beta repertoire of antigen specific cells. The cells were washed twice in FACS Buffer and spun. Cell were resuspended in FACS buffer and analyzed using flow cytometry.
## TCR repertoire high throughput sequencing
Tetramer-positive cells were sorted and then RNA isolated. Following preparation of a cDNA library, samples were sent to Adaptive Biotechnologies, Seattle, WA). TCRα and TCRβ repertoires data were analyzed using ImmunoSEQ Analyzer version 2.0, available online through Adaptive Biotechnologies. Supplemental Table S4 summarizes the TCR sequencing characteristics of each sorted population sequenced. The detailed TCR sequencing data can be accessed via in the Adaptive Biotechnologies database at Email: [email protected]; Password: gil2022review.
## Single cell PCR
Tetramer+ CD8 T cells were single cell sorted on FACS Aria (Becton Dickinson, San Jose, CA) into 96-well plates and prepared for total RNA isolation (Qiagen, Hilden, Germany). After reverse transcription into cDNA [SuperScript VILO cDNA synthesis kit (Invitrogen)] the PCR was performed following the protocol previously described [8]. CDR3 amplicons were purified (ExoSAP-IT) and sequenced with primers that recognized constant regions of TRAC and TRBC. Sanger DNA sequencing was performed by Genewiz (Cambridge, MA).
Statistics: Pearson correlation and 2 way-ANOVA multi-variant analysis with correction for multiple comparisons was used to analyze data. TCRdist was used to analyze the paired single cell data [analysis method from Dash et al. [ 6]; Kamga et al. [ 8]]. A modified version of TCRdist was used to analyze the high throughput TRAV or TRBV repertoire data, which is available on the following website: https://github.com/thecodingdoc/tcrdistScripts.
## Characteristics of patient populations and CD8 T cell populations
For these TCR repertoires studies, we recruited and enrolled healthy, IAV-and EBV-immune, HLA-A2.01+ donors. We used 2 age groups defined as young, 18-22 years old, and older, >60 years old (Supplemental Tables S1A, B). For the TCR high throughput sequencing studies, the average age of the 4 young EBV sero-positive donors was 19±1 years old, and for the 5 older EBV sero-positive donors was 71±4. We studied the two extremes of age as our earlier studies [36] indicated significant changes in these particular virus-specific TCR repertoires. Also, our understanding of epitope-specific TCR repertoires in both young and older donors in comparison to middle-aged donors is still limited.
We have previously determined the CD8 memory T cell frequencies and TRBV repertoires by mAb staining to IAV-M1, EBV-BM and EBV-BR epitope-specific responses ex vivo in these same individuals in a cross-sectional study [36]. Here, we will examine in more detail the differences in both TRBV and TRAV usage in these two age groups by high throughput sequencing and single cell sequencing of tetramer positive cells. For these rather extensive studies we need to use large numbers of cells so we did short term culture with peptide stimulation using a technique that we have published on extensively [9, 23, 24] (Supplemental Figures S1A-D). In previous studies [3, 9] and this manuscript, we showed that the same BV families are used before and after stimulation with peptide and there is a high degree of correlation but there are some shifts in the relative proportions that do not rule out differential expansion altogether (i.e. TRBV$\frac{4}{5}$/6 for IAV-M1, TRBV-3 for EBV-BRLF1 in the current data). In particular, we will focus on studying not only virus-specific differences, but also cross-reactive CD8 TCR repertoires to assess if cross-reactivity may play a role in the co-evolution of virus-specific TCR repertoires with increasing age (Supplemental Figures S1A, B).
## TRBV family usage and diversity as measured by monoclonal antibody staining for EBV epitope-specific responses differs between young and older donors
Initially, using tetramer and TRBV monoclonal antibody mAb co-staining of epitope-specific cells from short term culture on a larger number of donors (Supplemental Table S1) we observed that there were significant differences in the pattern of TRBV usage of EBV-BM and EBV-BR specific responses between older and young donors (EBV-BM older BV14 3-fold > young; EBV-BR older BV28 3.5-fold > young) (Figure 1A). There also was a significant change in preferential hierarchy of TRBV usage for the two EBV-specific epitopes within each donor group (Figure 1A ii-iii). In EBV-BM responses young preferentially used BV29.1, while older donors used BV29.1 and BV14.1. In EBV-BR responses, the young preferentially used BV6.5, while older used BV28. Interestingly, for IAV-M1 responses, TRBV19 was highly dominant in both groups as has been previously reported for older and middle aged donors [6, 37, 38]. These changes in TRBV usage, particularly in the EBV-specific responses are consistent with our ex vivo findings [36] and are highly suggestive that TRBV repertoire does evolve and change with increasing age.
**Figure 1:** *TRBV usage (A) as measured by mAb staining for EBV epitope-specific responses differs between young (Y) and older (O) donors. Following short-term culture with either IAV-M1, EBV-BM or EBV-BR peptide, cognate (same specificity as the stimulating peptide) tetramer+ cells in each culture were stained with TRBV8 mAbs (Y n=12-13, O n=7-9). (A) Heatmap analysis shows that TRBV usage differed between Y and O donors in EBV-specific responses either when frequency was directly compared between the groups or if the hierarchy of TRBV usage within the group was examined. A single TRBV family, BV19, dominated IAV-M1-specific responses in both Y and O donors. The dominant BV usage for each specificity is shown below each heatmap. (B) Strong correlations in TRBV usage between short-term cultured and ex vivo antigen-specific responses. TRBV repertoire as assessed by TRBV monoclonal antibody (mAbs) staining of ex vivo tetramer+ CD8 T cells were compared to those in short-term culture in the same donors as assessed by BV mAb staining (i-iii) or TCR high throughput sequencing (iv-vi) (Y n=4, O n=4-5). Multi-variant 2-way ANOVA with adjusted p-value, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 (A, B). Pearson’s correlation coefficient (r), r and p values indicated on graph.*
## Strong correlations in TRBV usage between short-term cultured and ex vivo antigen-specific responses
In order to determine if short-term culture would alter the ex vivo antigen-specific TCR repertoire we compared tetramer+ CD8 T cell repertoires of the short-term cultured cells either by mAb staining or high throughput sequencing to ex vivo mAb staining. The TRBV repertoire frequencies in short term culture as measured by mAb staining or TCR high throughput sequencing when using the mean value for the same young and older donors (Supplemental Table S1A), where data was available using both methods, directly correlated with the ex vivo mAb staining results for IAV-M1, EBV-BM and EBV-BR epitope-specific responses (Figure 1B i-iii). It should be noted that we did observed some global functional differences in the young and older cultured CD8 T cells (Supplemental Figures S1C, D), but this did not affect their TCR repertoires (Figure 1B). These results suggest that our short-term culture method does not significantly alter epitope-specific TCR repertoires.
## Specific features of the CDR3 dominate in IAV-M1 and EBV-cognate and cross-reactive TCR repertoires with increasing age
There are certain general CDR3 features that have been reported to dominate in antigen-specific CD8 T cell responses, which include increased usage of amino acids with convergent recombination (RAA) (increased usage of amino acids that have multiple ways of being derived) (39–41), increased N nucleotide additions to the VDJ joining region (NNA), and increased usage of multiple glycines or glycine runs (GGG). Multiple glycines, in particular, have been associated with increased flexibility and cross-reactivity [42, 43]. There is some evidence, that there is a greater ease of generation of TCRs that use CDR3 with convergent recombination and shorter CDR3 (less N nucleotide additions) [44, 45]. We were interested in determining if with increasing age there was a greater selection of TCR that have these features in both the virus-specific (cognate) and cross-reactive repertoires. In order to obtain more detailed information about TCR repertoire changes in TRBV, but also in TRAV required TCR high-throughput sequencing of tetramer-sorted epitope-specific and cross-reactive populations.
The IAV-M1, EBV-BM and EBV-BR cognate and cross-reactive TRBV and TRAV repertoires differed significantly between the older and young donors in use of RAA, NNA and GGG (Figures 2A–C) as summarized in Supplemental Table S2. As there were many significant differences between older and young, we will highlight some of the most important ones. The most consistent change in CDR3 features between the groups, was an increased retention in older of GGG in the TRBV of all five epitope-specific repertoires, cognate IAV-M1, EBV-BM, EBV-BR, and cross-reactive M1BR and M1BM (Figure 2A i-v), as well as, three of the TRAV epitope-specific repertoires, EBV-BM, EBV-BR and M1BR (Figure 2A ii,iii,v). This suggests a greater retention of potentially more flexible TRAV and TRBV chains or TCRs that could have double usage as cross-reactive TCR with increasing age.
**Figure 2:** *Specific features of the CDR3 dominate in IAV-M1 and EBV-cognate and cross-reactive TCR repertoires with increasing age. IAV-M1- and EBV-BM and EBV-BR- specific (cognate) and cross-reactive short-term cultured cells from younger and older donors were tetramer-sorted for high throughput sequencing (Y n=4, O n=4-5). M1BM or M1BR are cross-reactive EBV-BM or EBV-BR single tetramer+ cells sorted from IAV-M1 stimulated short term cultures. Significant differences were found in the number of glycines (Ai-v, number of nucleotide additions (Bi-v), and number of nucleotides per amino acids (Ci-v) between Y and O and between the epitope specific and cross-reactive responses (
Figure S1
) for TRAV and TRBV. Multi-variant 2-way ANOVA with adjusted p-value, **p < 0.01, ***p < 0.001, ****p < 0.0001.*
Older donors showed significantly less NNA in TRBV cognate IAV-M1, EBV-BM and EBV-BR specific responses than young (Figure 2B i-v), which suggests a retention of TCR that are potentially easier to make. However, the older cross-reactive responses, M1BM and M1BR, had more NNA than young suggesting that they are retaining longer CDR3 that may enhance their cross-reactivity. Older donors also showed a significantly increased usage of RAA in both TRAV and TRBV for EBV-BM and EBV-BR-specific responses (Figure 2C i-v) suggesting that TCRs easier to generate are retained with increasing age.
We also noted some differences in the overall pattern of TRAV (Figure 3A) and TRBV (Figure 3B) CDR3 lengths in the IAV-M1 and EBV epitope-specific responses between young and older donors. In the TRAV repertoires, the older used relatively similar CDR3 lengths to the young donors, except they used a shorter CDR3 (older, 10-mer vs younger, 11-mer) in the IAV-M1 response and a longer CDR3 (older: 12-mer vs young: 9-mer) in the EBV-BR. Overall, the cross-reactive TRAV and TRBV of the cross-reactive M1BR and M1BM responses used longer CDR3 than their corresponding cognate response and the older had even longer CDR3 than young in the TRBV (M1BR: older, 13-mer vs young, 11-mer; M1BM: older, 14-mer vs young, 11-mer).
**Figure 3:** *TRAV and TRBV CDR3 lengths of IAV-M1 and EBV epitope specific responses differ between young and older donors. TRAV and TRBV CDR3 lengths (amino acids) were determined for IAV-M1, EBV-BM, and EBV-BR cognate and cross-reactive short-term cultured CD8 T cells that were tetramer-sorted and sequenced (Y n=4, O n=4-5). M1BM or M1BR are cross-reactive EBV-BM or EBV-BR single tetramer+ cells sorted from IAV-M1 stimulated short term cultures. CMVpp65 epitope specific responses were used as a control, which included young and middle-aged donors (n=3). Heatmap analyses of preferential TRAV CDR3 length usage shows different preferential hierarchies between different epitope-specific responses and for the same epitope between Y and O in TRAV (A) and TRBV (B). Below heatmap is the hierarchy of the dominant CDR3 lengths used by the indicated response. Multi-variant 2-way ANOVA with adjusted p-value, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.*
Overall, these results would suggest that with increasing age there is a preferential selection or retention of TCR that have CDR3 features that increase their ease of generation and cross-reactive potential.
## TRAV, TRAJ, TRBV and TRBJ family usage in IAV and EBV-specific and cross-reactive responses differ between young and older
With the use of TCR high-throughput sequencing and heatmap display we were able to show that there were changes not only in TRBV but also TRAV family usage as well as J family usage with increasing age for all three epitope specific responses. For both age groups, all of the cognate responses predominantly used the classic public TRAV that have been previously reported [3, 7, 9], (IAV-M1: AV27, AV38; EBV-BM: AV5, AV8, AV12; EBV-BR: AV8, AV12) (Figure 4A i-iii). The cross-reactive M1BR response used both AV8, AV12 but also AV5 (public for EBV-BM), AV16, AV14 and AV21 (Figure 4A iv). The cross-reactive M1BM response used AV5, AV8, AV12 but also, AV1, AV25, AV29, AV38 (public for IAV-M1) and AV41 (Figure 4A iv). There were, however, significant differences in AV family usage between older and young in both cognate (IAV-M1: AV8, older>young; AV38, older<young; EBV-BM: AV5, older>young, AV29, older>young; EBV-BR: AV12, older<young; AV21, older>young) and cross-reactive responses (M1BR; AV21, older<young; M1BM: AV5, older>young; AV12, older>young) (Supplemental Table S2; statistical analyses shown in Supplemental Table S3).
**Figure 4:** *Significant differences in TRAV and AJ family usage in IAV and EBV-specific and cross-reactive responses between young (Y) and older (O) donors. TRAV and AJ families determined for IAV-M1, EBV-BM, and EBV-BR cognate and cross-reactive responses in short-term cultured CD8 T cells that were tetramer-sorted and sequenced (Y n=4, O n=4-5). M1BM or M1BR are cross-reactive EBV-BM or EBV-BR single tetramer+ cells sorted from IAV-M1 stimulated short term cultures. (A) Heatmap analyses of TRAV (A) and AJ (B) family usage, showed significant differences in epitope-specific responses between Y and O. Multi-variant 2-way ANOVA with adjusted p-value, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. (Also see
Supplemental Table S3
for statistical analyses).*
For both age groups, all of the cognate responses predominantly used the classic public TRAJ families that have been previously reported [3, 7, 9], (IAV-M1: AJ42, AJ52; EBV-BM: AJ31, AJ12; EBV-BR: AJ34, A21) (Figure 4B i-ii). The cross-reactive M1BR response used both AJ34, AJ21 but also AJ31 (public for EBV-BM), AJ27, AJ26, AJ49 and DJ01.1 family (Figure 4B iv). The cross-reactive M1BM response did not use AJ31 but instead used AJ34 (public for EBV-BR), AJ26, AJ33, AJ49, and AJ52 (public for IAV-M1) (Figure 4B v). There were, however, significant differences in AJ family usage between older and young in both cognate (IAV-M1: AJ51, older<young; AJ58, older<young; EBV-BM: AJ45, older>young, AJ29, older<young) and cross-reactive responses (M1BR; AJ31, older<young; M1BM: AJ26, older<young; AJ34, older>young; AJ49, older>young) (summarized Supplemental Table S2, statistical analyses shown in Supplemental Table S3).
For both age groups, all of the cognate responses predominantly used the classic public TRBV families that have been previously reported [3, 7, 9], (IAV-M1: BV19; EBV-BM: 10 different BV including BV14, BV29, BV20, BV2, BV9, BV10; EBV-BR: 14 different BV including BV6, BV3, BV4, BV5, BV19, BV, 27, BV28) (Figure 5A i-iii). The cross-reactive M1BR response also used 14 different BV with a greater usage of BV3 than in cognate EBV-BR (Figure 5A iv). The cross-reactive M1BM response used 11 different BV including a greater usage of BV19 (public for IAV-M1) than in cognate EBV-BM responses. ( Figure 5A iv). Although the overall hierarchy and pattern of BV family usage appeared to differ between older and young for each epitope, there were few significant differences in BV family usage between older and young in both cognate (EBV-BR: BV6, older<young; BV10, older>young) and cross-reactive responses. There are only 13 different TRBJ families and there was dominant usage of BJ2.1, and BJ2.7 by all cognate and cross-reactive responses with no major differences between older and young donors. ( Supplemental Table S2, statistical analyses shown in Supplemental Table S3).
**Figure 5:** *Significant differences in TRBV and BJ family usage in IAV and EBV-specific and cross-reactive responses between young (Y) and older (O) donors. TRBV and BJ families determined for IAV-M1, EBV-BM, and EBV-BR cognate and cross-reactive responses in short-term cultured CD8 T cells that were tetramer-sorted and sequenced (Y n=4, O n=4-5). M1BM or M1BR are cross-reactive EBV-BM or EBV-BR single tetramer+ cells sorted from IAV-M1 stimulated short term cultures. (A) Heatmap analyses of TRBV (A) and BJ (B) family usage, showed significant differences in epitope-specific responses between Y and O. Multi-variant 2-way ANOVA with adjusted p-value, *p < 0.05, **p < 0.01. (Also see
Supplemental Table S3
for statistical analyses).*
Since we are interested in determining whether TCR cross-reactivity could play a role in the changes in TCR repertoire with increasing age to these three epitopes it is noteworthy that AV8 and AV12 family are dominantly used by all three epitope-specific responses, as well as, both cross-reactive responses. Young donors used TRAV21 family in their M1BR response, while older used TRAV21 in their cognate BR response. Older had a dominant usage of TRAV5 in both cognate and cross-reactive BM responses, which may suggest cross-reactivity is playing a role in the dominant selection of this TRAV in EBV-BM responses. This public EBV-BR TRAV8 family usage was significantly increased in the older IAV-M1 response as compared to young. TRAV12 was common in EBV-BM, EBV-BR and significantly used more by older cross-reactive M1BM than in young donors. The dominant TRAJ family for M1BM and M1BR responses differed from their cognate counterparts in young and older suggesting that features of TRAJ may play a role in the specificity of TCR cross-reactivity. There is also a great deal of overlap between the dominant TRBV usage of the 3 cognate and 2 cross-reactive responses, including BV19, BV3, BV7, BV27, BV6 and BV29. These types of overlaps in AV, BV, BJ usage between epitope-specific responses greatly increases the chance that these TCR repertoires could contain cross-reactive TCR.
Overall, the TCRb high throughput sequencing data was consistent with the mAb staining data showed in unpublished manuscript before, in that there were fewer significant direct differences in TCRb usage than TCRa between young and older, although there were hierarchy differences. If cross-reactivity is driving the change in TCR repertoire with increasing age this may arise from the fact that there is a great deal of overlap in BV usage between these epitope-specific responses. These data could be interpreted to suggest that perhaps TRAV usage may play a greater role in evolution of the TCR repertoire and in determining specificity of TCR cross-reactivity.
## TRAV and TRAJ gene usage, pairing and CDR3 motifs of IAV-M1, EBV-BM and EBV-BR differ between young and older donors
To examine changes in TRAV usage between older and young donors in more detail we performed TCRdist quantitative analyses using the top 400 clonotypes by frequency in IAV-M1, EBV-BM and EBV-BR-specific TCR repertoires. TCRdist analysis quantifies clusters of TCRs with similar features, enabling the visualization and dimensionality of these clusters on a 2D projection of the TCRdist landscape [6]. The distance between 2 or more TCRs is calculated using a similarity-weighted Hamming distance, based on amino acids in the CDR loops that contact pMHC. A gap penalty is based on variations in CDR length and the CDR3 loop is given a higher weight as it is primarily responsible for antigen-specific recognition [6]. In the original TCRdist analysis program, epitope-specific single cell TCR sequencing data can be presented as ribbon plots which show patterns of TCR AV/AJ/BV/BJ pairings (num_clones, indicates the number of clones analyzed). Genes are colored by frequency within the repertoire with red>green>blue>cyan>magenta>black [6]. The arrows indicate fold increase usage of those V or J regions compared to naïve random repertoire suggesting antigen-driven expansion (no. of arrow heads are log2) [6]. The CDR3 motif analysis in this program, enables the determination of which amino acids are commonly used in certain positions of the CDR3, indicating that they may be important for antigen recognition based on the enrichment of certain amino acids when compared to a naïve background. The CDR3 motif analysis generates two motifs, motif 1 shows the amino acids which are enriched in comparison to the total tetramer+ population of that specificity; motif 2 shows the amino acids which are enriched compared to a naïve random CD8 T cell repertoire [6]. Here, we have adapted the TCRdist program to analyze high throughput TRAV or TRBV sequences (Figures 6 – 10; Supplemental Table S2).
**Figure 6:** *The TRAV and TRAJ gene pairing and CDR3 motifs for IAV-M1, EBV-BM and EBV-BR differ between young (Y) and older (O) donors. This was determined after TCR high throughput sequencing of tetramer-sorted CD8 T cells in Y and O donors (Y, n=4; O, n=4-5) using ribbon-plot analyses. Ribbon plots show patterns of TCR V-J pairings in TRAV in young (A, B) older donors (num_clones, indicates the number of clones analyzed). Genes are colored by frequency within the repertoire with red>green>blue>cyan>magenta>black. The arrows indicate significant fold increase usage of those V or J regions compared to naïve random repertoire suggesting antigen-driven expansion (no. of arrow heads are log2). Underneath each ribbon plot are the unique clearly defined CDR3 motifs of the TRAV repertoire of the indicated antigen-specificity. There can be multiple different CDR3 motifs for any one specificity. For each CDR3 motif, the upper motif 1 (labeled Mf1 in Y IAV-M1 as a representative) shows the amino acids which are enriched in comparison to the total tetramer+ population of that specificity; the lower motif 2 (labeled Mf2 in young IAV-M1 as a representative) shows the amino acids which are enriched compared to a naïve random CD8 T cell repertoire. Both indicate that the identified amino acids are important for an antigen peptide/MHC contact. Naive repertoires do not generate motifs as this requires the presence of clonal expansions. (analysis method from Dash et al. (6); Kamga et al. (8). The text within the bars joining particular AV and AJ regions indicate the fold increased usage of that pairing (and statistical significance) compared to a naïve random TCR repertoire. Bar in between the Mf1 and Mf2 depicts which part of the CDR3 is derived from the V (light grey), N (red), D (black) and J (dark gray) regions.*
As seen in Figure 6; Supplemental Figure S2, S4 and summarized Table S2 there were differences in the pattern and specific TRAV and TRAJ gene usage in IAV-M1, EBV-BM and EBV-BR responses of O and Y donors consistent with the family usage data (Figure 4).
In the IAV-M1 repertoire, the AV/AJ gene pairing analyses showed older like the young, retained enriched usage of certain significant AV/AJ gene pairings such as the public TRAV27/J42 (2x greater than naïve repertoire), AV38/AJ52 (8x), plus the less commonly described TRAV25/AJ42 (3x) (Figures 6A, B). However, the young had some atypical AV/AJ gene pairings not observed in older donors including V2/AJ42 (3.4x), AV27/AJ37(4.8x), AV1.2/AJ33(7.8x) and AV1.2/AJ12(13x). The older had enhanced usage of TRAV12.2, 8.6, and 24 which was not observed in young. Both older and young used the public AV27-GGGSQ-JA42 CDR3a motif, but the older did not maintain the public CDR3a motif AV38-FMxNAGGT-J52, that was observed in young. Instead, older retained TCR with atypical AV families paired with AJ42 containing variations of the public motif like, AV12.1/AV12.2/AV8.1- NxGGGSQ-TRAJ42 and AV$\frac{12.2}{8.1}$/$\frac{2}{5}$-NGGGSQ-AJ42. Interestingly, in all 3 epitope-specific responses, AJ42 gene usage was increased above random naïve repertoire. These data suggest AJ usage could enhance or contribute to the cross-reactivity that exists between these 3 epitopes.
In EBV-BM repertoire, older donors used only 2 dominant AV retaining the public AV5 and AV12.2 family and 3 dominant AJ, including AJ24,11, and 12, while young used 4 dominant AV including AV5, 12.2, but also AV2, 1.2 and 2 dominant AJ in common with older, including AJ11, 12 but also used AJ42, 20, 30 (Figures 6A, B). The AV/AJ gene pairing analyses showed older donors, retained enriched usage of the public AV5/AJ31 (5.4x), as well as unique AV14/DV4/J24 (2x) and AV2/AJ42(6.6x) (also present in IAV-M1), and was the only one in common with young donors who used it at 8x above the naïve random TCR repertoire. The young had some atypical AV/AJ gene pairings not observed in older including, AV5/AJ37(5x), AV1.2/A31(6.5x), AV1.2/AJ12(5.5x), AV12.1/AJ12(6.88x), and AV12.3/AJ52(19x). Both young and older used the public CDR3a motif, AV5-CA(E/D)DxNARLM-AJ31. The older also used a new CDR3a motif AV14-CAMRGGGMT-J42.
In EBV-BR repertoire older and young donors retained increased usage of the public AV8.1 and AV12.2 paired with multiple different AJ families (Figures 6A, B), further supporting our earlier observations that TRAV8.1 plays a major role in EBV-BR TCR repertoire selection [7]. However, older had lost the public AV$\frac{8.1}{8.3}$/$\frac{16}{12.2}$-VKDTDKL-J34 and in fact had no identifiable CDR3a motif. It should be noted that this motif in young can associate with multiple different AV besides AV8.1. This lack of a public motif is highly suggestive of more variable repertoires or private repertoires between older donors. The AV/AJ gene pairing analyses showed older and young had enriched usage of AV2/AJ42 (7-8x), which was also used by the IAV-M1 and EBV-BM responses of both groups. The older also had increased usage of TRAV27/AJ42(5x), which is usually associated with being a public repertoire feature in IAV-M1 responses. The AJ gene usage was unique for older and young, but they both did have a dominant AJ42(2x) usage, as they did in IAV-M1 and EBV-BM responses. As mentioned earlier, AJ42 is one of the public features used by IAV-M1 responses [43].
Overall, these data suggest that while the classical public TRAV and TRAJ genes were being used for all 3 epitope-specific repertoires, there are significant differences in both AV and AJ usage and pairing in young and older donors. The overlap in certain gene usages between epitopes would increase the potential for TCR cross-reactivity.
## TRBV and TRBJ gene usage, pairing and CDR3 motifs of IAV-M1, EBV-BM and EBV-BR differ between young and older donors
As seen in Figures 7A, B; Supplemental Figures S3, S5 and summarized Supplemental Table S2 there were differences in the pattern and specific TRBV and TRBJ gene usage in IAV-M1, EBV-BM and EBV-BR responses of older and young donors. Perhaps not surprisingly, in the IAV-M1 TRBV repertoire, both older and young donors maintained a significantly greater usage of the public BV19(4x) the public BV19/BJ2.7(1.5x) in comparison to the naïve random TCR repertoire (Figures 7A, B). However, the older had increased usage of the atypical BV21.1(4x), while young increased usage of the atypical BV6.4(2x). Older donors also showed a significant enrichment of BJ2.6(2x) usage. Both older and young donors used the public CDR3b motif BV19-CASSIRSSYEGY-J$\frac{2.7}{2.3}$/2.1. Interestingly, this same motif but restricted to BV19/J2.7 pairing begins to dominate in the EBV-BM and EBV-BR TCR repertoires of the older but not the young. There appears to be enriched usage of ‘IRSS” in the EBV-BM and BR repertoires of the older as compared to ‘xRSx” in the IAV-M1 response. Older donors had another dominant CDR3b motif, V$\frac{7.7}{7.3}$/$\frac{6.6}{5.6}$/$\frac{10.3}{10.2}$/21.1-QSRANVLTF-J2.6, accounting for the enrichment of the unusual BJ2.6 usage in the older donors. This same motif although not present in IAV-M1 repertoires in young was the most dominant motif in the EBV-BM TRBV repertoire of the young (although associated with different BV) and the older. This is highly suggestive that this particular TRBV motif may be selected into the IAV-M1 TCR repertoire because of cross-reactivity with EBV-BM. There was a second novel dominant CDR3b motif in the older BV$\frac{4.1}{17}$/$\frac{9}{7.2}$/$\frac{5.8}{5.4}$/$\frac{4.3}{4.2}$-SSQDWTGNTDT-J2.3, which was largely selected on BJ2.3 paired with multiple different BV. This same motif was also present in the EBV-BM repertoire of older but not young donors.
**Figure 7:** *TRBV and TRBJ gene pairing and CDR3 motifs for IAV-M1, EBV-BM and EBV-BR CD8 T cell populations differ between young and older. This was determined after TCR high throughput sequencing of tetramer-sorted CD8 T cells in Y and O donors (Y, n=4; O, n=4-5) using ribbon-plot analyses. Ribbon plots show patterns of TCR V-J pairings in TRBV in young (A, B) older donors (num_clones, indicates the number of clones analyzed). Genes are colored by frequency within the repertoire with red>green>blue>cyan>magenta>black. The arrows indicate fold increase usage of those V or J regions compared to naïve random repertoire suggesting antigen-driven expansion (no. of arrow heads are log2). Underneath each ribbon plot are the unique clearly defined CDR3 motifs of TRBV repertoire of the indicated antigen-specificity. There can be multiple different CDR3 motifs for any one specificity. For each CDR3 motif, the upper motif 1 (labeled Mf1 in young IAV-M1 as a representative) shows the amino acids which are enriched in comparison to the total tetramer+ population of that specificity; the lower motif 2 (labeled Mf2 in young IAV-M1 as a representative) shows the amino acids which are enriched compared to a naïve random CD8 T cell repertoire. Both indicate that the identified amino acids are important for an antigen peptide/MHC contact. Naive repertoires do not generate motifs as this requires the presence of clonal expansions. (analysis method from Dash et al. (6); Kamga et al. (8). The text within the bars joining particular BV and BJ gene regions indicate the fold increased usage of that pairing (and statistical significance) compared to a naïve random TCR repertoire. Bar in between the Mf1 and Mf2 depicts which part of the CDR3 is derived from the V (light grey), N (red), D (black) and J (dark gray) regions.*
In the EBV-BM TRBV repertoire, there has been a complete shift in BV/BJ dominance hierarchy in older as compared to young donors(Figures 7A, B). The older showed an increased usage of BV19/BJ2.7(2.2x) pairing (public for IAV-M1 responses), which is a non-canonical pairing for EBV-BM responses in young, middle-aged donors or in AIM [7, 9]. In comparison, young preferentially used the public gene pairings, BV29.1/BJ1.4(5x) and BV20.1/BJ1.3(5x). The older retained usage of the public BV20.1/BJ1.3(7x). However, they also had increased usage of BV2/BJ2.2(4.5x), and BV$\frac{3.2}{1.4}$(7.9x) pairings, with increased usage of BV14(2x), and less typical BV4.1(2x), BV10.2(4x) and BV21.1(8x). Young donors also had increased usage of BV 21.1(2x), 10.2(2x), as well as, BV6.4(2x) (not increased in older). For EBV-BM TRBV repertoire, older had 8 unique CDR3b motifs never previously described, that were generated using several different BV, while young donors had one predominant CDR3b motif. The CDR3 motif “QRANLVLT,” which was generated with BJ2.6 associated with multiple BV was the dominant motif for young, but the public motif, “QSPGG” associated with BV14 was also present co-mingled within the other motif. As noted above the older CDR3b motifs contained strong overlaps with IAV-M1 motifs, including multiple(x)BV-QRANLVLT-JB2.6 BV19-CASSIRSSYEQY-27, multiple(x)BV-SSQDWTGNTDT-BJ2.3 suggesting these may be selected to dominate because of TCR cross-reactivity.
The EBV-BR TRBV repertoire had also completely shifted in TRBV/BJ dominance hierarchy in older as compared to young (Figures 7A, B). Like EBV-BM, older had a dominant usage of the TRBV19/BJ2.7 pairing. In contrast, young used multiple different BV relatively equally but TRBV6.4, 7.8, 13 and TRBV14 were significantly above the naïve repertoire. In contrast, older donors showed an increase usage of BV19, 10.2, and 21.1. Only older showed an increased usage of the BJ1.6 gene. Older donors also had an increased usage of BV28/BJ1.5(4.6x) and BV10.2/BJ1.1(5x). Young donors used 8 different CDR3b motifs, where the BJ portion appeared to be important in selection, while older had two major CDR3b motifs. The CDR3b motifs do not have a dominant BV, but instead the BJ dominated including BJ2.1 and BJ2.7 usage. In the older the most dominant motif was the IAV-M1 public BV19-CASSIRSSYEQY-27. The second older motif was a unique, V$\frac{10.2}{4.3}$/10.1-CASSxDGMNTEA-J1.1.
Overall, these results strongly suggest that as the TCR repertoire narrows in older they are retaining TCR that are cross-reactive between two very common human viruses IAV and EBV, that we are exposed to frequently, one with recurrent infections and the other a persistent virus, which frequently reactivates.
## The hierarchy of TRAV and TRAJ gene usage, pairing and CDR3 motifs of cross-reactive M1BR, M1BM and M1+BR+ CD8 T cell populations are unique and differ between young and older donors
In order to examine whether and how cross-reactivity might influence or change TCR repertoire with increasing age we assessed both types of cross-reactive CD8 T cells, single tetramer+ and double tetramer+ from IAV-M1 peptide stimulated short term cultures [23, 26]. In order to examine single tetramer+ cross-reactive CD8 T cells we sorted EBV-BM (M1BM) or EBV-BR (M1BR) tetramer+ cells from IAV-M1 stimulated short term cultures for TCR high throughput sequencing. We also sorted M1+BR+ double tetramer+ cells from the IAV-M1 stimulated short-term cultures of two young donors who had this population. As seen in Figures 8A, B and summarized Supplemental Supplemental Table S2 there were differences in the pattern and specific TRAV and TRAJ gene usage in the cross-reactive vs their cognate counterpart in each donor group suggesting they are unique populations with their own characteristics that make them capable of responding to two different antigens. However, like the cognate repertoires the M1BR and M1BM repertoires of older vs young donors differ suggesting that the older are retaining or developing a particular subset of cross-reactive T cells.
**Figure 8:** *The hierarchy of AV and AJ gene pairing and CDR3 motifs of cross-reactive M1BM, M1BR, and M1+BR+ CD8 T cell populations differ from cognate and between young (A) and older donors (B). This was determined after TCR high throughput sequencing of tetramer-sorted CD8 T cells in Y and O donors (Y, n=4; O, n=4-5) using ribbon-plot analyses (6, 8). The figure legends of
Figures 6
,
7
, provide a detailed description of the ribbon-plot analyses that is applicable to this figure. M1BM or M1BR are cross-reactive EBV-BM or EBV-BR tetramer+ cells sorted from IAV-M1 stimulated short term cultures. M1+BR+ are double tetramer+ co-staining CD8 T cells sorted from the IAV-M1 short-term culture.*
The M1BR TRAV repertoire in older dramatically differed from the cognate EBV-BR. Older had increased usage of AV8.1(8x) (public AV for EBV-BR) and this could pair with many different AJ that also showed increased usage including AJ34(2x), AJ21(4x) or AJ37(2x). This contrasted with the EBV-BR repertoire, VA 12.2(2x) and AV8.1(2x) usage were co-dominant and they paired with so many different AJ that none was dominant. In older all of the other features of TCRAV usage were unique to the cross-reactive M1BR as compared to the cognate. These included increased usage of AV8.6-AJx(18.9x), AV16-AJ49(10.9x), AV14-DV4-AJ21(6.9x), AV38.1-AJx(24.8x) (associated with IAV-M1), and V17-AJx(20.6x). Curiously, the cross-reactive M1BR, unlike the cognate EBV-BR, had a dominant CDR3a motif, AV8.1-CAxKxTDKLIF-AJ$\frac{34}{37}$, which although not identical is reminiscent of the public EBV-BR motif seen in young and described in AIM donors [7]. This might suggest that IAV-M1 cross-reactivity even in AIM leads to the selection of these dominant clonotypes. These results suggest that there are unique AV/AJ pairings that lead to cross-reactive responses that may be more stringent than cognate EBV-BR.
In contrast, the young cross-reactive M1BR response maintains many of the same AV usage as the young cognate EBV-BR including AV2(2x), AV12.2(2x), AV1.2(2x). Young had increased usage of the atypical AV2-AJ42(9x) for M1BR, which appeared previously in all 3 epitope-specific (cognate) responses of the young, but is not present in the older. Young donors showed unique pairings as compared to EBV-BR, such as, AV17-AJ21(10x), AV1.2-AJ33(7.3x), AV1.2-AJ12(7.3x), AV14-DV4-AJ21(6.9x) (also present in older). Curiously, in contrast to the older the young M1BR population did not yield a CDR3 motif while their cognate EBV-BR had the public AV8.1-VKDTDKL-J34. These results might suggest that the older overtime have selected more skewed, and narrow cross-reactive M1BR responses with public features compared to the young donors.
However, in the young donors we also had the unique cross-reactive double tetramer M1+BR+ population that was only isolated in two of four young donors. In the M1+BR+ repertoire there was increased usage of AV27 (2x)(like IAV-M1), AV8.1(4x)(like EBV-BR), AV25(2x)(unique) and AV3(2x)(unique), as well as AJ42(8x)(like IAV-M1), AJ37(2x)(unique), AJ21(2x)(unique) and AJ27(2x)(unique). The most dominant pairings were AV27/AJ42(1.5x) (public for IAV-M1), AV8.1/AJ37(9.6x) (public for EBV-BR), AV27/AJ37(3.2x)(unique), AV25/AJ42(2x)(unique), AV3/AJx(15x)(unique), AV38/AJ52(17.3x)(public for IAV-M1), and AV14/DV4/AJ21(15x)(unique). Two of the CDR3 motifs, exhibited glycine runs, “xGGGx,” (AV$\frac{27}{13.1}$/8.1CAGx(G/S)GGGSQGNJ42) and (AV$\frac{27}{8.1}$/$\frac{13.1}{17}$-(A/S)GGGSQ/J42) and (AV38.1-FMxTNAGGTS/52) and were variants of public motifs in IAV-M1 repertoires. This double tetramer+ population appears to combine features of both cognate responses, as well as having unique features.
In the M1BM repertoire the AV and AJ usage differed from cognate EBV-BM and IAV-M1 in the older, except in the increased usage of AV5(4x) (public for EBV-BM) paired with many different AJ, AV8.1(2x) (public for EBV-BR) and AJ12.2(2x)(used by EBV-BM and EBV-BR). There was increased usage of unique pairings AV8.3-AJ49(9.3x), AV8.6-AJ4(9.8x) and AV12.1/AJ12(8.8x). One of the most dominant pairings once again was AV2-AJ42(9.3x) (also seen in young). The 3 CDR3 motifs that were generated contained AV5-AJ31 and AV8-AJ34 pairings and differed from those observed in the cognate EBV-BM response. The AV5-CAED-AJ31 motif which was identified is perhaps a variant of the public EBV-BM motif AV5-xEDNNAx-AJ3. A second motif was unique AV5-CAESxGxLxF-AJ$\frac{35}{29}$/37. The AV$\frac{8.1}{16}$/1.1-CAVKDTDKLI-AJ34/J23 motif is a variant of the public EBV-BR motif.
Overall, these data suggest that young donors had most likely such private diverse cross-reactive TCR repertoires that no motifs were identified for either M1BR or M1BM. In contrast, it would appear that older donors are most likely retaining selected cross-reactive TCR that have been stimulated by both antigens at some point leading to clonal expansions and identifiable public features. The results also suggest rather logically, that a TCR that has some features of either cognate response may be more likely to be cross-reactive. However, these cross-reactive responses can also have totally unique public features, while displaying minor features if any of the cognate responses. We will use single cell sequencing to determine whether the TCR AV/AJ/BV/BJ gene pairings in cross-reactive responses will demonstrate a combination of public repertoire features of IAV and EBV (i.e. M1BR, AV8.1/AJ34 and BV19/BJ2.7). The single cell data will allow the determination of factors/features that may provide a mechanism by which TCR cross-reactivity can occur.
## The hierarchy of TRBV and TRBJ gene usage, pairing and CDR3 motifs of cross-reactive M1BR, M1BM and M1+BR+ CD8 T cell populations are unique and differ between young and older donors
In the M1BR TRBV repertoire, older had a significant increase in unique BV/BJ pairings as compared to IAV-M1- or EBV-BR-specific responses, such as, BV6.6-BJ2.5(2.7x), which had a unique CDR3 motif BV6.6(x24BV)-CASSPLTGAETQF-BJ$\frac{2.5}{2.3}$/1.1, BV11.2-BJ2.5(4.8x), BV3.2(2x) which had a unique CDR3 motif BV3.2(x9BV)-KTYGY-J1.2. Also, there was an increased selection of the atypical BV21.1 with an 8-16-fold increase in all responses of the older including all 3 cognate epitope responses and the M1BM and M1BR cross-reactive responses. Unlike the cognate EBV-BR in either older or young there were 11 different distinct CDR3b motifs that were predominantly unique in older. This would suggest that there are more stringent requirements for cross-reactive M1BR TCRb than cognate EBV-BR, which is largely selected on TRAV. These CDR3 motifs were largely derived from the N region. They appeared to have highly variable TRBV usage, which was associated with particular TRBJ suggesting that the BJ region may play a significant role in specificity and selection of these cross-reactive TCR. They did have the CDR3b motif BVx-QSRANVLTF-BJ2.6, which was common to IAV-M1, EBV-BM, and M1BM repertoires in older and dominant in young EBV-BM. Older in M1BR and young in M1+BR+ responses had the CDR3 motif BVx-KTYGY-BJ1-2 which was not seen in cognate responses.
In young donors, the M1BR repertoire had increased usage of BV29.1(2x)(public for EBV-BM). There also was a significant 3.7-fold increase in the unique BV6.4/BJ2.3. Once again indicating the importance of TRBV in selection of EBV-BR cross-reactive TCR, the young donors had increased usage of several BV including, BV13(2x), BV14(2x), and BV10.2(4x). Young donors did not have a CDR3 motif. The lack of CDR3 motifs as compared to older donors might as with TRAV/AJ relate to the higher diversity and private nature of cross-reactive responses in young.
The M1+BR+ repertoire in the two young donors, had increased usage of BV19(4x), (public for IAV-M1). As seen in the pairing for IAV-M1, in M1+BR+, BV19 is most commonly paired with BJ2.7. There were increases in unique BV3.2(2x), BVx-BJ2.1(7.7x) and BJ2.6(2x) usage. The most dominant CDR3 motif was BV19-CASSIRS(S/T)YEQYF/-$\frac{2.7}{2.3}$/2.2, which is most commonly used in IAV-M1 responses consistent with this TCRBV motif playing a role in TCR cross-reactivity (see also Single cell sequence Table 1). There was also another unique CDR3 motif BV$\frac{3.2}{5.4}$/$\frac{5.5}{4.2}$/$\frac{19}{11.3}$-F9E/V)N(E/D)E-J$\frac{2.5}{2.7}$ most likely specific for cross-reactive responses (see Single cell sequence Table 1).
**Table 1**
| Epitopespecificity | CLONE ID | AV | CDR3a | AJ | BV | CDR3b | JB | No. |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| A. IAV-M1 | ES179M1-04 | 27*01 | CAAGGSQGNLIF | 42*01 | 19*01 | CASSIRSSYEQYF | 2-7*01 | 1.0 |
| | ES556M1-01 | 5*01 | CAETGGGSQGNLIF | 42*01 | 19*01 | CASSIRSSYEQYF | 2-7*01 | 1.0 |
| | ES556M1-02 | 27*01 | CAGGGSSNTGKLIF | 37*02 | 19*01 | CASSIRSSYEQYF | 2-7*01 | 1.0 |
| | ES556M1-14 | 27*01 | CAGASGNTGKLIF | 37*01 | 19*01 | CASSIRSSYEQYF | 2-7*01 | 1.0 |
| | ES556M1-16 | 27*01 | CAGGGSQGNLIF | 42*01 | 19*01 | CASSIRSSYEQYF | 2-7*01 | 1.0 |
| | ES556M1-17 | 27*01 | CAGGGSSNTGKLIF | 37*02 | 19*01 | CASSIRSSYEQYF | 2-7*01 | 1.0 |
| | D044M1-04 | 27*01 | CAGGGSQGNLIF | 42*01 | 19*02 | CASSIRSSYEQFF | 2-7*01 | 4.0 |
| | D044M1-18 | 27*01 | CAGGGSQGNLIF | 42*01 | 19*02 | CASSIRSSYEQYF | 2-7*01 | 11.0 |
| | D044M1-22 | 13-1*02 | CAPSGGGSQGNLIF | 42*01 | 19*02 | CASSIRSSYNEQFF | 2-7*01 | 3.0 |
| | ES556M1-07 | 27*01 | CAGVDGGSQGNLIF | 42*01 | 19*01 | CASSIRSSYEQYF | 2-7*01 | 1.0 |
| | ES556M1-08 | 16*01 | CARKSYGQNFVF | 26*01 | 19*01 | CASSIRSSYEQYF | 2-7*01 | 1.0 |
| M1BM | ES556M1BM-03 | 27*01 | CAGGGSQGNLIF | 42*01 | 19*01 | CASSIRSSYEQYF | 2-7*01 | 1.0 |
| | ES556M1BM-09 | 27*01 | CAGGGSQGNLIF | 42*01 | 19*01 | CASSIRSSYEQYF | 2-7*01 | 1.0 |
| | D044M1BM-08 | 27*01 | CAGGGSQGNLIF | 42*01 | 19*01 | CASSIRSSYEQFF | 2-7*01 | 1.0 |
| BRM1 | ES179BRM1-05 | 27*01 | CAGGGSQGNLIF | 42*01 | 19*02 | CASSIRSSYEQYF | 2-7*01 | 9.0 |
| | ES179BRM1-09 | 27*01 | CAGGGSQGNLIF | 42*01 | 19*02 | CASSIRSSYEQYF | 2-7*01 | 1.0 |
| M1+BR+ | ES179M1+BR+10 | 27*01 | CAGGGSQGNLIF | 42*01 | 19*02 | CASSIRSSYEQYF | 2-7*01 | 1.0 |
| | ES179M1+BR+12 | 27*01 | CAGGGSQGNLIF | 42*01 | 19*02 | CASSIRSSYEQYF | 2-7*01 | 2.0 |
| | D044M1+BR+02 | 27*01 | CAGGGSQGNLIF | 42*01 | 19*02 | CASSIRSSYEQYF | 2-7*01 | 2.0 |
| B. IAV-M1 | ES556M1-04 | 38-2/DV8*01 | CAYSSSAGGTSYGKLTF | 52*01 | 19*01 | CASSIGLYGYTF | 1-2*01 | 1.0 |
| | D044M1-10 | 38-2/DV8*01 | CAYMINAGGTSYGKLTF | 52*01 | 19*02 | CASSIGVYGYTF | 1-2*01 | 1.0 |
| | D044M1-01 | 38-2/DV8*01 | CAYSPNAGGTSYGKLTF | 52*01 | 19*02 | CASSMGLYGYTF | 1-2*01 | 2.0 |
| EBV-BM | D044BM-09 | 5*01 | CAEPRDSNYQLIW | 33*01 | 27*01 | CASIGSGYPYNEQFF | 2-1*01 | 13.0 |
| | D044BM-12 | 29/DV5*01 | CVYRNSNARLMW | 31*01 | 27*01 | CASIGSGYPYNEQFF | 2-1*01 | 1.0 |
| M1BM | D044M1BM-07 | 12-2*01 | CAVNNQAGTALIF | 15*01 | 27*01 | CASIGSGYPYNEQFF | 2-1*01 | 1.0 |
| | D044M1BM-14 | 12-2*01 | CAVNSQAGNALIF | 15*01 | 27*01 | CASIGSGYPYNEQFF | 2-1*01 | 1.0 |
| | D044M1BM-20 | 38-2/DV8*01 | CAYSPNAGGTSYGKLTF | 52*01 | 27*01 | CASIGSGYPYNEQFF | 2-1*01 | 1.0 |
| | D044M1BM-05 | 5*01 | CAEPRDSNYQLIW | 33*01 | 27*01 | CASIGSGYPYNEQFF | 2-1*01 | 1.0 |
| | D044M1BM-22 | 5*01 | CAEPRDSNYQLIW | 33*01 | 27*01 | CASIGSGYPYNEQFF | 2-1*01 | 5.0 |
| | D044M1BM-19 | 5*01 | CAEPRDSKYQLIW | 33*01 | 27*01 | CASIGSGYPYNEQFF | 2-1*01 | 1.0 |
| BRM1 | D044BRM1-05 | 5*01 | CAEPRDSNYQLIW | 33*01 | 27*01 | CASIGSGYPYNEQFF | 2-1*01 | 5.0 |
| | ES179BRM1-08 | 38-1*01 | CAFMTNAGGTSYGKLTF | 52*01 | 19*02 | CASSQGSHGYTF | 1-2*01 | 1.0 |
| M1+BR+ | D044M1+BR+03 | 24*01 | CAPNSGYSTLTF | 11*01 | 27*01 | CASIGSGYPYNEQFF | 2-1*01 | 1.0 |
| C. M1BM | ES556M1BM-08 | 27*01 | CAGGGSQGNLIF | 42*01 | 14*01 | CASSQSPGGTGTF | 2-7*01 | 1.0 |
The M1BM TRBV repertoires of both older and young are very different in hierarchy and usage from each other and from the cognate EBV-BM. In older there was increased usage of BV3.2(2x), BV11.2(2x) and BV29(2x)(public for EBV-BM), 29.1/BJ1.4(6.7x), BV2/J2.2(4.9x), BV20.1/J1.3(8.6x) and BV$\frac{5.1}{2.7}$(2.9x). The most widely used BV amongst most epitope responses for older, BV21.1, was increased 16-fold. The most common CDR3 motif was BV14(x9BV)-ASSQSPGG-J$\frac{2.5}{2.1}$/$\frac{1.1}{2.2}$/$\frac{2.6}{2.4}$, which is a variant of the public EBV-BM motif. The CDR3 motif “QSRANVTL” was associated with BJ2.6 usage and was present in the cognate EBV-BM responses as well M1BM responses for older. The BJ usage appeared to be the most dominant specificity and selection factor of the CDR3 motifs. In contrast, young had increase usage of BV14(2x) (public for EBV-BM). There was increased usage of unique BV including BV11.3(2x) and BV10.2(4x). BV21.1 found in several other responses in older usage was increased 16-fold. There were 4 unique CDR3 motifs which largely differed from older and young cognate EBV-BM and Y M1-BM except for the BVx-QSRANVLTF-BJ2.6. There appeared, as with M1BR, to be stringent requirements in BV and BJ usage as well as CDR3 motifs in the cross-reactive M1BM TCR in both young and older donors. This only makes sense as the cross-reactive TCR has to recognize two different epitopes, while cognate-specific TCR are only have to recognize one epitope.
## Single cell TCR sequencing and TCR cross-reactivity
Since we postulate that TCR cross-reactivity is playing a role in repertoire evolution with increasing age we more closely examined TCR repertoire of cognate and cross-reactive tetramer-sorted CD8 T cells at the single cell level. For these studies as we were addressing features of cognate and cross-reactive TCR we pooled the data of young and older donors. We were interested in addressing two particular questions. First, we wanted to determine if there was evidence that the TRBV ‘IRSS” motif expressing clones, which are public for IAV-M1 repertoires were actually preferentially selected by the cross-reactivity with the EBV epitopes with increasing age as is suggested by our high throughput sequencing data. Second, we wanted to determine if there was any evidence that the cross-reactive clones had TCR features that would increase their ability to interact with two antigens. For this type of comparison the Kernel Principal Components Analysis (kPCA) 2D projection plots which show the AV/AJ/BV/BJ pairing of the single cell analyses [6] was highly useful. Each point on the plot represents a single TCR clone, and the location of the clones is based on TCR dist measurements placing similar TCR clones closer together on the 2D plot. Each clone can be tracked to determine the gene usage and pairing by the color and location.
At a glance it is clear that the characteristics including CDR3 motifs and distributions of the TCR clones are unique for each epitope and for each of the cross-reactive populations (Figure 9, Supplemental Table S4). The cognate IAV-M1, EBV-BM and EBV-BR had many characteristics that have been previously identified and shown in the high throughput sequencing data. It should be noted that these single cell studies identified a new EBV-BM specific TRAV motif AV-12.1-CVVNGxDS-AJ12.1. It appeared to pair with TRBV motif TCRBV2-CASS.GtVap-BJ2.2. The pKCA analyses (Figure 9) and Table 1 showing the single cell sequences of cross-reactive clones that are M1BR, M1+BR+ and M1BM, as well as BRM1 specific, and contrast them to clones with some similar features, if there are any in IAV-M1, EBV-BM, EBV-BR-specific, demonstrate findings compatible with our hypothesis that certain TCR clones are preferentially retained in the older due to TCR cross-reactivity. For instance, there is a clear selection for clones specifically expressing AV27-CAGGGSQGNLIF-AJ42 paired with BV19-CASSIRSSYEQY-JB$\frac{2.7}{2.1}$ in the M1+BR+, BRM1 and M1BM cross-reactive populations suggesting that this unique clone which dominates the EBV-BM and EBV-BR TCR repertoires of older donors has some ability to interact with all 3 epitopes (Table 1A). It may be at differing affinities to the different epitopes which might make it difficult to derive a crystal structure to determine exactly how it interacts with EBV-BM and EBV-BR, although it does appear to bind EBV-BM and EBV-BR tetramers. If this type of clone which is most likely not optimum for EBV control begins to dominate the EBV-BM and EBV-BR TCR repertoires in older donors they may have difficulty controlling this persistent virus, perhaps enhancing chances of developing EBV-associated cancers [27].
**Figure 9:** *The hierarchy of BV and BJ gene pairing and CDR3 motifs of cross-reactive M1BM, M1BR, and M1+BR+ CD8 T cell populations differ from cognate and between young (A) and older donors (B). This was determined after TCR high throughput sequencing of tetramer-sorted CD8 T cells in Y and O donors (Y, n=4; O, n=4-5) using ribbon-plot analyses (6, 8). The figure legends of
Figures 6
,
7
, provide a detailed description of the ribbon-plot analyses that is applicable to this figure. M1BM or M1BR are cross-reactive EBV-BM or EBV-BR tetramer+ cells sorted from IAV-M1 stimulated short term cultures. M1+BR+ are double tetramer+ co-staining CD8 T cells sorted from the IAV-M1 short-term culture.*
There is also a new unique cross-reactive TCR that predominates in the M1BM repertoire AV5-CAEPRDSNYQLIW-J33.1 paired with BV27-CASIGSGYPYNEQFF-2.1, where the AV5 usage is public for EBV-BM and the motif could be a variant that could recognize EBV-BM, while the BV27 has been shown in our studies to be used by all three epitope-specific responses, but this clone contains a CDR3b motif reminiscent of the public IAV-M1 BV19-xGxY-J2.1 [3] as shown in the Table 1B, which obligately pairs with AV38/AJ52 in the cognate IAV-M1 response. A second cross-reactive M1BM clone has a public IAV-M1-specific TCRa, AV27-CAGGGSQGNLIF-42 paired with a public EBV-BM-specific TCRb, BV14-CASSQSPGGTGTF-2.7. This clone is EBV-BM tetramer+ but able to proliferate in response to IAV-M1 peptide. Without an appropriate TCRb, it is most likely low affinity to IAV-M1 and unlikely to bind IAV-M1 tetramer, but could easily proliferate during an acute IAV infection like it did in the IAV-M1 stimulated short term culture, yet not being an optimum response to protect against IAV infection.
The cross-reactive M1BR repertoire, had increased usage of clones expressing the public EBV-BR-specific AV8.1-CAVKDTDLIF-AJ34 (or variants of it) paired with multiple different TCRb chains some of which did express the IAV-M1 public BV19 family (Table 1D). Once again, as these M1BR clones are high affinity to EBV-BR, but low affinity to IAV-M1 it would not be ideal for them to start to proliferating during acute IAV infection. There was one M1+BR+ clone which did stain with both tetramers, AV16-CALKDTDKLIF-AJ34 paired with BV25-CASSEWFSYNEQFF-BJ2.1 which might be interesting for future crystal structure studies to determine exactly how this TCR can interact with both ligands (Table 1D). There are also other completely unique clones without public features that are able to bind both tetramers that could be used for crystal structure studies (Table S6). There was also a relatively unique public M1BR cross-reactive TCR which expressed the public EBV-BR AV8.1 with a relatively unknown motif AV8.1-CAxGNNNARLMF-J31.1 paired with a unique cross-reactive motif BV3.2-CASSQALTDYGYTF-1.2. Once again this clone is most likely low affinity to IAV-M1 (i.e. BR tetramer+ in an IAV-M1 culture), but capable of proliferating during IAV infection resulting in a less than optimum functional response which is suggested by the fact that epitope-specific responses in older proliferated better than young, but had decreased ability to produce cytokines (Supplemental Figure S1).
## Discussion
Our study shows that IAV and EBV epitope-specific TCR repertoires change with increasing age and that TCR cross-reactivity likely plays a role in the repertoire changes between young and older donors. TCR high-throughput sequencing of tetramer-sorted epitope-specific and cross-reactive populations, and accessing TCR algorithms, such as, TCRdist [6], allowed us to obtain detailed information about TCR repertoire changes in not only TRBV/BJ, but also in TRAV/AJ usage. TCRa and TCRb repertoires directed at the HLA-A2-restricted immunodominant epitopes IAV-M1, EBV-BM and EBV-BR cognate and cross-reactive responses differed significantly between the older and young donors at every level we examined including CDR3 features, V and J usage and V/J pairing. Overall, these results strongly suggest that as the TCR repertoire narrows in older they are retaining TCR that are cross-reactive between these two very common human viruses IAV and EBV, that we are exposed to frequently, one with recurrent infections and the other a persistent virus, which frequently reactivates. For example, both high throughput sequencing and single cell sequencing suggest that a cross-reactive TCR clone AV27-CAGGGSQGNLIF-AJ-42 paired with BV19-CASSIRSSYEQY-JB$\frac{2.7}{2.1}$ previously considered to be a public clone in the IAV-M1 TCR repertoire [3] begins to dominate the EBV-BM and EBV-BR specific TCR repertoires in the older donors. This result suggests that the cross-reactivity with EBV-specific epitopes, leads to it being tweaked whenever EBV reactivates over a lifetime, making this clone so public that we have found it in the IAV-M1 repertoire of all the 40+ HLA-A2+ donors we have examined. However, these cross-reactive responses may not be optimal for control of one of these viruses. Cross-reactivity, with dual use of TCR may be the only alternative for an aging immune response [46], where the thymus has involuted and TCR repertoire keeps narrowing to help control a multitude of pathogens. This increased use of cross-reactive TCR may at some level save lives, but it may also contribute to the waning of virus-specific immunity with increasing age.
Our results suggest that with increasing age there is a preferential retention of TCR that have CDR3 features that increase their ease of generation (39–41) [44, 45], like the use of convergent recombinant amino acids and fewer N nucleotide additions, and cross-reactive potential by the use of glycine runs that are thought to be more flexible [42, 43] [47] (Figures 2A–C; summarized in Supplemental Table S2). Also, we were able to show that there were changes not only in TRBV, but also TRAV family usage, as well as, J family usage with increasing age for all three epitope specific responses. The TCRb high throughput sequencing data was consistent with the mAb staining data, in that there were fewer significant direct differences in TCRb usage than TCRa usage between young and older. If cross-reactivity is driving the change in TCR repertoire with increasing age this may arise from the fact that there is a great deal of overlap in BV usage between these epitope-specific responses. These data could be interpreted to suggest that perhaps TRAV usage may play a greater role in evolution of the TCR repertoire and in determining the specificity of TCR cross-reactivity further emphasizing the importance of studying TCRAV repertoire.
Here, we adapted the TCRdist program to analyze high throughput TRAV/AJ or TRBV/BJ sequences (Figures 6 – 10; Supplemental Table S2) were able to show there were differences in the pattern and specific TRAV/AJ and TRBV/BJ gene usage, pairing and CDR3 motifs in IAV-M1, EBV-BM and EBV-BR and cross-reactive responses of older and young donors. The cognate responses used public TCRa and TCRb features for all 3 epitope-specific repertoires, however, there were unique public features defined for the cross-reactive responses that differed from their cognate counterparts suggesting they are unique populations with their own characteristics, that make them capable of responding to two different antigens. The overlap in certain AV gene usages between epitopes, such as AV8 and AV12, would increase the potential for TCR cross-reactivity. Interestingly, AV12 has been found to be a public response in HLA-A2-restricted SARS-CoV2 YLQ epitope responses [48]. As mentioned above, surprisingly, in the older donors the most dominant motif in the EBV-BM and EBV-BR TRBV repertoires was BV19-CASSIRSSYEQY-27, which known for being a public motif for IAV-M1. Overall, these results strongly suggest that as the TCR repertoire narrows in older donors they are retaining TCR that are cross-reactive between two very common human viruses IAV and EBV, that we are exposed to frequently, one with recurrent infections and the other a persistent virus, which frequently reactivates.
**Figure 10:** *Kernel Principle Components Analysis of single cell TCRab sequencing shows that the cross-reactive populations differ from cognate, at times using combinations of TCR features specific for the two different ligands (A–F). Tetramer-sorted single cell CD8 T cells from representative Y and O donors were transcribed into cDNA, then amplified AV/AJ and BV/BJ gene combinations using primers from a published multiplex PCR technique (6, 8). TCR single cell sequencing data was combined from Y and O donors (Y, n=2; O, n=2). Kernel Principal Components Analysis (kPCA) 2D projection plots were used to show the AV/AJ/BV/BJ pairing of the single analyses (6). Each point on the plot represents a single TCR clone, the location of the clone is based on TCRdist measurements which place similar TCR clones closer together on the 2D plot. Each clone can be tracked to determine the gene usage and pairing by the color and location. Each of the four gene segments, TRAV, TRAJ, TRBV, and TRBJ (left to right) has a separate plot. The last two plots, represent the CDR3 motif generated for TRAV/AJ and TRBV/BJ genes. (for details on sequences see
Tables 1
and
S4
).*
However, like the cognate repertoires, the cross-reactive M1BR and M1BM repertoires of older vs young donors differ suggesting that the older donors are retaining or developing a particular subset of cross-reactive T cells. Overall, these data suggest that young donors had most likely such private cross-reactive TCR repertoires that no motifs were identified for either M1BR or M1BM. In contrast, it would appear that older donors are most likely retaining selected cross-reactive TCR that have been stimulated by both antigens at some point leading to clonal expansions and identifiable public features. The results also suggest that a TCR that has some features of cognate responses may be more likely to be cross-reactive. However, these cross-reactive responses can also have totally unique public features, while displaying minor features if any of the cognate responses.
By using single cell sequencing we were able show some of the factors or features that may help a TCR to recognize two different epitopes. The single cell clones, further confirmed at a glance that the characteristics, including CDR3 motifs and distributions of the TCR clones are unique for each epitope and for each of the cross-reactive populations (Figure 9; Supplemental Table S4). They also further confirmed that the TRBV BV19/IRSS/J2.7 motif expressing clones, which are public for IAV-M1 repertoires were actually preferentially selected by cross-reactivity with the EBV epitopes. They also provided evidence that the cross-reactive clones had TCR features that would increase their ability to interact with two antigens. For instance, there is a clear selection for clones specifically expressing AV27-CAGGGSQGNLIF-AJ42 paired with BV19-CASSIRSSYEQY-JB$\frac{2.7}{2.1}$ in the M1+BR+, BRM1 and M1BM cross-reactive populations suggesting that this unique clone which dominates the EBV-BM and EBV-BR TCR repertoire of older donors has some ability to interact with all 3 epitopes (Table 1A). It may be at differing affinities to the different epitopes which might make it difficult to derive a crystal structure to determine exactly how it interacts with EBV-BM and EBV-BR (although it does appear to bind all three tetramers). If this type of clone which is most likely not optimum for EBV control begins to dominate the EBV-BM and EBV-BR TCR repertoires in older donors they may have difficulty controlling this persistent virus, enhancing chances of developing cancers. There is also a new unique cross-reactive TCR that predominates in the M1BM repertoire AV5-CAEPRDSNYQLIW-J33.1 paired with BV27-CASIGSGYPYNEQFF-2.1, where the AV5 family is public for EBV-BM and the motif could be a variant that could recognize EBV-BM, while the BV27 family has been shown in our studies to be used by all three epitope-specific responses, but this clone contains a CDR3b motif reminiscent of the public IAV-M1 BV19-xGxY-J2.1 [3] as shown in the Table 1B, which obligately pairs with AV38/AJ52 in the cognate IAV-M1 response. A second cross-reactive M1BM clone has a public IAV-M1-specific TCRa, AV27-CAGGGSQGNLIF-J42 paired with a public EBV-BM-specific TCRb, BV14-CASSQSPGGTGTF-J2.7. This clone is EBV-BM tetramer+ but able to proliferate in response to IAV-M1 peptide. Possibly without an appropriate TCRb, it is likely low affinity to IAV-M1 and unlikely to bind IAV-M1 tetramer, but could easily proliferate during an acute IAV infection, yet not be an optimum response to IAV.
The cross-reactive M1BR repertoire, had increased usage of clones expressing the public EBV-BR-specific AV8.1-CAVKDTDLIF-AJ34 (or variants of it) paired with multiple different TCRb chains some of which did express the IAV-M1 public BV19 family (Table 1D). Once again, as these M1BR clones are likely high affinity to EBV-BR, but low affinity to IAV-M1 it would not be ideal for them to start to proliferating during acute IAV infection. There was also a relatively unique public M1BR cross-reactive TCR which expressed the public EBV-BR AV8.1 with a relatively unknown motif AV8.1-CAxGNNNARLMF-J31.1 paired with a unique cross-reactive motif TCRb BV3.2-CASSQALTDYGYTF-1.2. This clone is most likely low affinity to IAV-M1 but capable of proliferating during IAV infection resulting in a less than optimum responses.
These studies highlight how important the develop of new tools and algorithms to study TCR repertoires, such as TCRdist and GLIPH [6, 29], can lead to our better understanding the evolution of antigen-specific repertoires. Other investigators are developing models that may assist us in predicting TCR specificity and cross-reactivity (28, 47–49). Developing more advanced computational methods for designing highly specific and potent TCR for use in engineering T cell therapies requires large amounts of accurate data on antigen-specific TCR repertoires and MHC-peptide complexes. Taken together all of these findings suggest that we have finally reached a paradigm shifting moment in our understanding of TCR structure and repertoire that could lead to a much better understanding of T cell mediated diseases and/or the development of T cell specific treatments.
Disease etiology and diagnosis by TCR repertoire analysis is beginning to gain more attention as technology improves [50]. Several lines of evidence suggest that EBV-specific CD8 T cells are important for the control of EBV long term [51], including successful treatment of EBV-associated lymphoproliferative disorders and post-transplant associated EBV infections by adoptive transfer of EBV-specific CD8 T cells [52, 53]. Recently, using high-throughput sequencing in multiple sclerosis (MS) patients, a disease associated with EBV-induced acute infectious mononucleosis, the TCR repertoire from the cerebrospinal fluid was found to be enriched in EBV-reactive CD8 T cells that were distinct from the blood37. TCR repertoires are increasingly being linked to disease [50] like recovery from cancer [54, 55], and our work which suggests that TCR repertoire differences contribute to protection against infection or impact disease severity [23, 24]. Recent work [56] suggests that defective CD8 T cell control of EBV reactivation in multiple sclerosis (MS) patients leads to an expanded population of EBV-infected, autoreactive B cells; this is supported by preliminary results of a Phase I clinical trial that demonstrated improvement of MS symptoms following infusion of autologous EBV-specific CD8 T cells, which are thought to bring the virus under control [57]. These types of T cell therapies make it imperative that more-advanced methods integrating computational biology and structural modeling become available for designing highly specific and potent TCRs. Methods to predict optimum TCR features to be recognized and activated by a particular antigen and for identifying TCR antigen-specificity groups without the need to isolate antigen-specific T cells would be highly valuable and are beginning to be developed [6, 29]. Recently, progress has been made in developing algorithms that identify crossreactive epitopes, between strains of similar viruses like IAV and coronaviruses [58].
These results also impact our understanding of the current COVID19 pandemic, where a disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), can present in many forms. *It* generally causes a mild and sometimes asymptomatic disease in children but is more pathogenic in adults and can be quite severe in aged populations, especially in individuals with pre-existing conditions. Yet, individuals of similar age and health status may experience widely different disease processes and severity. In severe cases lungs may encounter a highly inflammatory cytokine storm and be full of CD8 T cells experiencing various degrees of clonal exhaustion [59]. Reasons for the variation in pathogenesis are unknown and could be influenced by viral dose and genetics of the host, but it is likely that T cell-dependent heterologous immunity and cross-reactivity play a role [60]. In mouse models, virally induced pathologies have been linked to cross-reactive epitopes and can vary widely among individuals, even those with similar genetics and infection histories. In several models with syngeneic hosts, variability in the pathogenesis has been linked to the private specificity of the T cell repertoire responding to the cross-reactive epitope [17, 18, 25]. *Even* genetically identical hosts have different naïve TCR repertoires as well as different TCR repertoires to the same epitope of a pathogen. These results suggest that how an individual would respond to SARS-CoV-2 would be dependent in part on that person’s TCR repertoire and history of previous infections.
Heterologous immunity is likely common in human virus infections, and, like COVID-19 pathogenesis, there are several common human viruses that cause more severe disease in adults than in children. Here, the disease in adults is usually associated with more immunopathological lesions. These include such viruses as measles, mumps, chicken pox, and EBV. Like SARS-CoV2, EBV causes mild to subclinical infections in children, but it can cause acute infectious mononucleosis (AIM) in young adults. AIM, in HLA-A2+ individuals, is associated with a high frequency of CD8 T cells producing high levels of interferon gamma and being cross-reactive between EBV and IAV [23, 26, 61]. In fact, the severity of AIM directly correlated with frequency of reactivated IAV-M158 tetramer+ cells and its’ TCRBV usage. The main pathognomonic feature of AIM is the potent CD8 T cell response, much like that occurring in lungs of severe COVID-19 cases [62]. Interestingly, a recent report examining TCR repertoires suggest that in HLA-A2+ patients certain SARS-CoV-2 epitope-specific responses are also cross-reactive with IAV epitopes and in they have been found to use the TRBV sequence ‘CASS(I/x)RS(T/A/S)EQYF” [63]. This suggests that prior immunity to IAV can predispose hosts to severe EBV infection, but may also effect SARS-CoV-2 infection outcome. In an attempt to model this in the absence of EBV infection, mice immune to IAV were challenged intranasally with lymphocytic choriomeningitis virus (LCMV). These mice developed a severe pneumonia that was dependent on cross-reactive CD8 T cell responses to either of two epitope pairs, depending on the private specificity of the response. Interestingly, the development of this pneumonia was blocked by injecting IAV-immune mice with antibody to IFNg prior to the LCMV challenge [19] These similarities between COVID-19 pathogenesis and heterologous immunity would suggest that there may be cross-reactive epitopes between SARS-CoV-2 and previously encountered infections, though it is usually hard to predict where cross-reactivities would occur. However, humans get infected with a number of other coronaviruses that cause common colds and serologically cross-react with SARS-CoV-2, providing a challenge for the development of antibody screening tests. Further, studies using an algorithm for T cell epitopes have predicted many potential cross-reactivities across a variety of class 1 MHC molecules (64–66). Other recent reports document some CD4 and CD8 T cell cross-reactivity between SARS-CoV-2 and other coronaviruses (67–70). One of these studies suggests that if there is a broader cross-reactive epitope usage the patients may be more likely to have milder disease [68]. Another study actually correlates severity of acute COVID to specific cross-reactive TCR repertoires between coronaviruses in HLA-A-2+ patients [70]. If such cross-reactivity exists, it may be an issue in the development of the much-needed vaccines for SARS-CoV-2. However, the presence of cross-reactive T cell epitopes in complex vaccines may lead to high variability in the outcomes [21]. Further, when epitopes cross-react only partially with a memory T cell pool specific to another epitope, the T cell response that develops may be very narrow and oligoclonal, with a potential to allow for T cell-escape mutants [61]. The presence of a narrow oligoclonal repertoire like we see in the older donors to a T cell epitope during an acute infection is likely a product of this cross-reactivity process. Interestingly, a recent study showed that exposure order determined the distribution between spike-specific and non-spike-specific responses in COVID19 CD8 T cell response [71]. Vaccination after infection lead to expansion of spike-specific T cells and differentiation to CCR7(neg)CD45RA(pos) effectors. In contrast, individuals having a breakthrough infection after vaccination, developed vigorous non-spike-specific responses. Their extensive epitope-specific T cell antigen receptor (TCR) sequence analyses showed that all exposures elicited diverse repertoires characterized by shared public TCR motifs, with no evidence for repertoire narrowing from repeated exposure [71]. Given our present results and all of these issues we suggest that the examination of T cell cross-reactivity and TCR repertoire should be given high priority in COVID-19 research.
## Data availability statement
The original contributions presented in the study are publicly available. This data can be found here: https://www.ncbi.nlm.nih.gov/sra/PRJNA928775.
## Ethics statement
The studies involving human participants were reviewed and approved by The Institutional Review Board (IRB) at University of Massachusetts Medical School, Worcester, MA, USA. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
FC, AG, IT, NA, DG and LSK contributed to literature review. FC, AG, LS contributed to writing. FC, AG, IT, NA, DG and LKS were responsible for editing. AG and LS oversaw the writing process and provided mentorship and guidance. 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.2022.1011935/full#supplementary-material
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|
---
title: Evaluation of delay discounting as a transdiagnostic research domain criteria
indicator in 1388 general community adults
authors:
- E. E. Levitt
- A. Oshri
- M. Amlung
- L. A. Ray
- S. Sanchez-Roige
- A. A. Palmer
- J. MacKillop
journal: Psychological Medicine
year: 2023
pmcid: PMC10009385
doi: 10.1017/S0033291721005110
license: CC BY 4.0
---
# Evaluation of delay discounting as a transdiagnostic research domain criteria indicator in 1388 general community adults
## Abstract
### Background
The Research Domain Criteria (RDoC) approach proposes a novel psychiatric nosology using transdiagnostic dimensional mechanistic constructs. One candidate RDoC indicator is delay discounting (DD), a behavioral economic measure of impulsivity, based predominantly on studies examining DD and individual conditions. The current study sought to evaluate the transdiagnostic significance of DD in relation to several psychiatric conditions concurrently.
### Methods
Participants were 1388 community adults (18–65) who completed an in-person assessment, including measures of DD, substance use, depression, anxiety, posttraumatic stress disorder, and attention-deficit hyperactivity disorder (ADHD). Relations between DD and psychopathology were examined with three strategies: first, examining differences by individual condition using clinical cut-offs; second, examining DD in relation to latent psychopathology variables via principal components analysis (PCA); and third, examining DD and all psychopathology simultaneously via structural equation modeling (SEM).
### Results
Individual analyses revealed elevations in DD were present in participants screening positive for multiple substance use disorders (tobacco, cannabis, and drug use disorder), ADHD, major depressive disorder (MDD), and an anxiety disorder (ps < 0.05–0.001). The PCA produced two latent components (substance involvement v. the other mental health indicators) and DD was significantly associated with both (ps < 0.001). In the SEM, unique significant positive associations were observed between the DD latent variable and tobacco, cannabis, and MDD (ps < 0.05–0.001).
### Conclusions
These results provide some support for DD as a transdiagnostic indicator, but also suggest that studies of individual syndromes may include confounding via comorbidities. Further systematic investigation of DD as an RDoC indicator is warranted.
## Introduction
Understanding psychiatric illness relies on classifying mental illness into discrete and independent categories using systems such as the Diagnostic and Statistical Manual of Mental Disorders and International Classification of Diseases. However, a fundamental concern with diagnostic categories is that they define disorders exclusively by signs and symptoms associated with the individual's subjective experiences and overt presentations, rather than underlying psychological and neurobiological substrates (Lilienfeld, 2014; Lilienfeld & Treadway, 2016). Furthermore, there is a substantive overlap of symptomology across mental illnesses and significant heterogeneity within disorders. Collectively, these issues adversely impact progress in understanding and diagnosing mental disorders (Etkin & Cuthbert, 2014).
Novel approaches have been developed that challenge the notion of categorical classification systems and one prominent framework is the National Institute of Mental Health's (NIMH) Research Domain Criteria (RDoC) (Insel et al., 2010). Specifically, RDoC seeks to develop a new mental health nosology that focuses on transdiagnostic dimensional constructs that reflect the mechanisms that cause and maintain psychiatric disorders (Kozak & Cuthbert, 2016). These transdiagnostic constructs are anticipated to provide a deeper understanding of psychopathology and to promote the use of higher-resolution dimensional measurements, holding promise of enhancing prevention, detection, and treatment of mental illness (Sharp, Miller, & Heller, 2015). In addition, RDoC seeks to contribute to a shift toward precision medicine, which focuses on characterizing the individual pathophysiological features of a disease in an individual to optimize treatment (Cuthbert & Insel, 2013). In psychiatry, there is typically no single pathognomonic feature of a disorder that yields a diagnosis. As such, RDoC aims to reconceptualize the diagnostic classification system to identify specific psychological and biological indicators that allow for a more objective, accurate, and reliable diagnostic system, one that is more amenable to research (Kelly, Clarke, Cryan, & Dinan, 2018). Importantly, RDoC is not the only novel framework for nosology in psychiatry (e.g. the Hierarchical Taxonomy of Psychopathology; Kotov, Krueger, & Watson, 2018).
One candidate RDoC indicator is delay discounting (DD), a person's orientation toward smaller immediate rewards over larger delayed rewards that are considered a behavioral economic index of impulsivity (Madden & Bickel, 2010). Typically, DD is measured using decision-making tasks, where the value of the reward and the delay in time are systematically varied, or pre-configured choices reflecting time-reward trade-offs of differing discounting rates. The higher the discounting rate of delayed rewards, the more impulsive the individual is considered. DD is highly relevant to RDoC as a candidate transdiagnostic indicator because it has been found to be elevated in numerous psychiatric conditions. Studies have found significantly increased levels of DD in individuals with alcohol use disorder, tobacco use disorder, opioid use disorder, other substance use disorders, and gambling disorder (e.g. Bickel, Odum, & Madden, 1999; MacKillop, Anderson, Castelda, Mattson, & Donovick, 2006; Madden, Petry, Badger, & Bickel, 1997; Petry, 2001). Elevated levels of DD have also been observed in a number of other conditions, including attention-deficit hyperactivity disorder (ADHD) (Barkley, Edwards, Laneri, Fletcher, & Metevia, 2001; Demurie, Roeyers, Baeyens, & Sonuga-Barke, 2012; Hurst, Kepley, McCalla, & Livermore, 2011), obesity and eating disorders (Amlung, Petker, Jackson, Balodis, & MacKillop, 2016; Manwaring, Green, Myerson, Strube, & Wilfley, 2011; Stojek & MacKillop, 2017); and major depressive disorder and anxiety (Pulcu et al., 2014; Steinglass et al., 2017).
Meta-analyses of individual studies likewise implicate DD with multiple forms of psychopathology. Syntheses of investigations of addiction using both case-control (MacKillop et al., 2011) and dimensional designs (Amlung, Vedelago, Acker, Balodis, & MacKillop, 2017) have reported significant associations across studies. Recently, a meta-analysis of DD and psychiatric conditions other than addictive disorders also found consistent evidence of elevated DD across disorders (Amlung et al., 2019). Of particular interest, extremes on the spectrum of DD are differentially associated with disorders characterized by self-regulatory under control v. over control. For example, there is consistent evidence of significantly higher DD in disorders characterized by under control, such as substance use disorders (Amlung et al., 2017) and ADHD (Jackson & MacKillop, 2016), and consistent evidence of significantly lower DD in anorexia (Amlung et al., 2019). Although the etiological role of DD remains actively under investigation (e.g. Oshri et al., 2019; Owens et al., 2017), there is evidence that it is a heritable phenotype (Anokhin, Grant, Mulligan, & Heath, 2015; Sanchez-Roige et al., 2018) and that it is associated with greater addiction liability in preclinical and human models (e.g. Dougherty et al., 2014; Oberlin & Grahame, 2009; Perry, Larson, German, Madden, & Carroll, 2005; VanderBroek, Acker, Palmer, de Wit, & MacKillop, 2016), suggesting that it plays a role in the development of addictive disorders. Collectively, these results suggest that DD is a promising transdiagnostic psychological mechanism (Bickel et al., 2019) and may therefore be compatible within the RDoC framework. Within the five RDoC domains, DD is a sub-construct nested in Positive Valence Systems.
However, there are limitations in the current literature regarding empirical studies testing DD as a transdiagnostic indicator. In particular, DD is typically examined in studies exclusively examining individual forms of psychopathology (e.g. individuals with alcohol use disorder compared to a control group), adjusting for pertinent covariates, such as income, but without incorporating concurrent psychopathology. Psychiatric comorbidity among conditions is well known to be high (Hasin & Grant, 2015), but there are few studies that have addressed the relationship between DD and multiple forms of psychopathology simultaneously, meaning that across this diverse literature, the potential for confounding is also high. Notably, there is evidence of shared genetic underpinnings of DD and both substance use and psychiatric conditions. Sanchez-Roige et al. [ 2018] found DD had a significant genetic correlation with ever smoking, daily smoking level, and successful quitting (inversely), as well as with the presence of major depressive disorder and severity of depressive symptoms, implying transdiagnostic relevance. Nonetheless, few behavioral studies have explicitly examined whether DD is transdiagnostically informative in relation to multiple conditions concurrently.
To address these issues, and to test the hypothesis that DD is a transdiagnostic construct more explicitly, the current study sought to examine these questions in a large non-treatment-seeking sample of community adults. Specifically, three complementary strategies were used to offer different vantage points. First, the study individually examined DD in relation to clinical cut-off scores for a number of common psychiatric syndromes [i.e. substance use disorders, major depressive disorder, anxiety disorder, posttraumatic stress disorder (PTSD), and ADHD], paralleling early case-control studies. Second, the study used principal components analysis (PCA) to generate aggregate indicators of psychopathology and examined DD in relation to these measures of latent overlap. Third, the study used structural equation modeling (SEM) to examine the unique relations between DD and psychopathology when simultaneously modeling multiple syndromes concurrently.
## Participants
The sample consisted of a cohort of community adults ($$n = 1388$$) who completed a one-time in-person assessment as part of enrollment in a health research registry at St. Joseph's Healthcare Hamilton. To be eligible, participants were required to be between the ages of 18–65 and agree to complete a one-time in-person assessment of health-related information, psychological variables, and other related information. Participants were also required to have no major or terminal medical conditions that would preclude voluntary participation in any subsequent studies.
## Delay discounting
DD was assessed using the Monetary Choice Questionnaire (MCQ; Kirby, Petry, & Bickel, 1999), which comprises 27 dichotomous choices between receiving a smaller monetary reward sooner, or a larger monetary reward later (e.g. ‘Would you rather have $30 today or $80 in 30 days’). All choices were for hypothetical monetary rewards. Hyperbolic temporal discounting functions, Mazur's [1987] k parameter, are inferred from participant choices at three levels of reward magnitude: small ($25–$35), medium ($55–$65), and large ($75–$85). Three control items that offered larger and smaller rewards with no delay (e.g. Would you rather $85 today, or $55 dollars today) were admixed among the items. These provide a quality control metric to measure low effort or attention. Participants choosing the lower monetary reward for any of these items were excluded. In addition, consistency was calculated for each of the reward magnitudes to assess the degree of correspondence between each of the responses and their inferred k value. Individuals who had <$90\%$ consistency were excluded.
## Psychiatric indicators
Alcohol Use Disorder Identification Test (AUDIT; Saunders, Aasland, Babor, *De la* Fuente, & Grant, 1993). The AUDIT is a screening tool for alcohol severity. It is comprised of 10 items, with three categories, alcohol intake (items 1–3), alcohol dependence (items 4–6), and adverse consequences (items 7–10), and a total score range from 0 to 40. Participants are asked about their alcohol use within the past year. A score of 8 and above is considered the standard cut-off for hazardous drinking. The AUDIT demonstrated good internal consistency (α = 0.80).
Drug Use Disorder Identification Test (DUDIT; Berman, Bergman, Palmstierna, & Schlyter, 2005). The DUDIT is a screening tool for problematic drug use, with questions pertaining to frequency, dependency, physical and psychological issues associated with drug use excluding alcohol, cannabis, and cigarette use. It contains 11 items, with scores ranging from 0 to 44. A score of 8 and above is considered the standard cut-off for problematic drug use. The measure demonstrated excellent internal consistency (α = 0.91).
Cannabis Use Disorder Identification Test Revised (CUDIT-R; Adamson et al., 2010). The CUDIT is a measure of cannabis use frequency and severity, adapted from the AUDIT. It contains 10 items, with scores ranging from 0 to 40. A score of 6 and above for males and 2 and above for females is considered the standard cut-off for hazardous cannabis use. The measure was found to have good internal consistency (α = 0.78).
Fagerstrom Test for Nicotine Dependence (FTND; Heatherton, Kozlowski, Frecker, & Fagerstrom, 1991). The FTND is a six-item measure of the intensity of nicotine dependence, with scores ranging from 0 to 10. Scores from 1 to 2 are associated with low dependence, 3–4 associated with low to moderate dependence, 5–7 associated with moderate dependence, and 8 and above associated with high dependence. A score of 5 and above is considered the standard cut-off for problematic nicotine use. The measure demonstrated acceptable internal consistency (α = 0.74).
Adult ADHD Self Report Scale (ASRS; Kessler et al. 2005a, 2005b). The ASRS is an 18-item self-report measure of ADHD symptoms, with scores ranging from 0 to 72. A cut-off of 14 and above is used as a screen for ADHD. The measure demonstrated excellent internal consistency (α = 0.90).
Patient Health Questionnaire (PHQ-9; Spitzer, Kroenke, & Williams, 1999): The PHQ-9 is a self-report measure of depressive symptoms. It contains nine items, and each symptom is evaluated as to whether or not they occurred over the past 2 weeks. A cut-off of 10 and above is used as a screen for depression. The measure was found to have good internal consistency (α = 0.89).
Patient Health Questionnaire Anxiety Subscale (PHQ-Anx; Kroenke, Spitzer, Williams, & Löwe, 2010). The PHQ-*Anx is* a seven-item scale assessing anxiety symptoms. Scores range from 0 to 3 with a total score ranging from 0 to 21. Each symptom is evaluated as whether or not they occurred over the past 2 weeks. A cut-off of 10 and above is used as a screen for anxiety. The measure demonstrated good internal consistency (α = 0.76).
Posttraumatic Stress Disorder Checklist-5 (PCL-5; Weathers, Litz, Herman, Huska, & Keane, 1993). The PCL-5 is a 20-item self-report measure assessing PTSD symptom severity. Symptoms are evaluated based on their occurrence over the past 2 weeks. A cut-off of 32 and above is used as a screen for PTSD. The PCL-5 as a whole demonstrated good internal consistency (α = 0.95).
## Data analysis
The data were assessed for missing values, normality, and outliers. In total, four individuals were excluded based on the MCQ control items and 40 participants were excluded based on low consistency. Excluded participants constituted $3.07\%$ of the sample. As preliminary analyses, zero-order correlations were conducted for each of the variables to examine the relationship between the MCQ, each psychiatric condition, and candidate covariates including sex, age, and income. This was to characterize unadjusted associations for health behaviors and potential nuisance variables. A three-step data analytic strategy was implemented. First, to examine the hypothesis that DD will be elevated in conditions independently, ANCOVAs of DD were conducted based on clinical cut-offs for each of the clinical indicators, adjusting for age and income and comparing individuals who screened positive to those who screened negative for a given measure. In these analyses, a single consolidated index of DD was generated via PCA using each of the three reward magnitudes. In contrast to a mean or omnibus scoring, PCA was employed as it includes the differential loading of the three magnitudes on the aggregate DD variable, providing somewhat greater resolution, and it is more similar to the latent variable approach in the SEM analyses. Conceptually, these analyses addressed the extent to which DD was elevated when multiple forms of psychopathology were examined concurrently, albeit independently. Second, a PCA of the psychopathology variables was conducted (direct oblimin rotation) to identify latent aggregations of the psychiatric syndromes and partial correlations (adjusting for income and age), and subsequently the components were examined in relation to the PCA-derived DD variable. Conceptually, this analysis was to examine the extent to which DD was associated with observed forms of overlapping psychopathology. Third, a structural equation model was used to explore the association between DD and each psychiatric condition when modeled together simultaneously using Mplus (Muthén and Muthén, 1998–2012). A single latent variable was created from each of the reward magnitudes on the MCQ (i.e. $30, $55, and $80), in accordance with previous literature that suggests an inverse association between the rates of discounting and the delayed reward magnitude (Kirby, 1997), as well as studies reporting significant differences across all three reward magnitudes on the MCQ (Kirby & Maraković, 1996; Kirby et al., 1999). As such, modeling DD using three reward magnitudes instead of simply using a mean value provides the opportunity to characterize the loadings of the specific magnitudes for the latent indicator. In particular, SEM was selected as it can explore the simultaneous unique associations between each of the disorders and the latent construct of DD, while controlling for the correlations among the dependent variables. In addition, SEM also has the ability to explicitly assess measurement error, estimate a latent construct that is not observable in the data, and can generate a structure and assess the fit of the data to that structure. Conceptually, this analysis was intended to examine the specificity of associations among DD and other health behaviors. The model was examined using the established criteria for the four model fit indices: Comparative Fit Index (CFI) >0.90 (Ullman, 2001), Tucker–Lewis Index (TLI) >0.95 (Hu & Bentler, 1999), the Root Mean Square Error of Approximation (RMSEA) <0.08 (MacCallum, Browne, & Sugawara, 1996), the root mean square residual (SRMR) <0.10 (Hu & Bentler, 1999), and chi-square test of model fit (χ2) ($p \leq 0.05$ Hu & Bentler, 1999).
## Preliminary findings
Missing data were unsystematic and rare (maximum/variable = $0.28\%$) and were therefore estimated using the full information maximization likelihood approach. To account for skewness, all k values were log10 transformed, which provided adequate correction. Participant characteristics are in Table 1. Frequencies of participants scoring at or above the clinical cut-off score for each disorder are also presented in Table 1. A heat map of zero-order (Pearson) correlations (r) is presented in Table 2; exact associations are in supplemental materials. Significant correlations were found between each reward magnitude on the MCQ and most mental health variables, with the exception of the AUDIT (all magnitudes) and the ASRS (large magnitude only). Of the candidate covariates, only age and income were significantly associated with each of the reward magnitudes, and therefore sex was dropped from the subsequent analysis. Effect sizes for significant associations between DD and symptom domains were generally small in magnitude. Table 1.Participant characteristics ($$n = 1388$$)Variable%/M (s.d.)/medianSex (% female)57.9Age38.99 (13.70)Race (% white)81.9Education (years)15.34 (3.22)Income⩾$75 000 to <$90 000 CAD% above cut-offPHQ-Anx3.75 (3.57)7.6PHQ-95.07 (5.14)15.2PCL-512.13 (13.57)9.7ASRS8.52 (4.40)$22.9\%$ past-month useM (s.d.) past-month usersAUDIT4.24 (4.16)$16.189.9\%$4.57(4.14)CUDIT2.18 (4.73)$11.329.2\%$3.71(5.69)DUDIT1.27 (4.04)$12.612.9\%$7.72(7.86)FTND0.52 (1.53)$7.522.0\%$1.01(2.02) Table 2.Zero-order correlations among variablesFTND, Fagerstrom Test for Nicotine Dependence; PHQ, Patient Health Questionnaire-9; PHQ-Anx, Patient Heath Questionnaire Anxiety Scale; PCL, Posttraumatic Stress Disorder Checklist-5; AUDIT, Alcohol Use Identification Test; CUDIT, Cannabis Use Identification Test; DUDIT, Drug Use Identification Test; ASRS, Adult ADHD Self Report Scale; DD, delay discounting.
## Individual syndrome comparisons
Significant results from the ANCOVAs are presented in Fig. 1. Significant elevations in DD were present for tobacco use disorder ($F = 19.35$, $p \leq 0.001$), cannabis misuse ($F = 10.07$ $$p \leq 0.002$$), drug use disorder ($F = 7.88$, $$p \leq 0.005$$), depression ($F = 19.22$, $p \leq 0.001$), PTSD ($F = 12.65$, $p \leq 0.001$), anxiety ($F = 4.04$, $$p \leq 0.045$$), and ADHD ($F = 6.36$, $$p \leq 0.012$$). The effect sizes were all small, ranging from 0.003 for ADHD and anxiety to 0.014 for tobacco use disorder and depression. Of note, DD was not significantly elevated in individuals screening positive for alcohol misuse. Fig. 1.Estimated marginal means (±SEM) of PCA-derived levels of delay discounting by clinical cut-off for each domain. Numbers reflect the ns screening positive or negative. Note: FTND, Fagerstrom Test for Nicotine Dependence; PHQ, Patient Health Questionnaire-9; PHQ-Anx, Patient Heath Questionnaire Anxiety Scale; PCL, Posttraumatic Stress Disorder Checklist-5; AUDIT, Alcohol Use Identification Test; CUDIT, Cannabis Use Identification Test; DUDIT, Drug Use Identification Test; ASRS, Adult ADHD Self Report Scale.
## Principal components analysis of psychiatric indicators
Two components were identified using the PCA oblimin rotation analysis, accounting for $58.58\%$ of the total variance. The first component significantly contributed to explaining the relationship between depression, anxiety, PTSD, and ADHD; and the second component significantly contributed to explaining the relationship between substance use disorders (see pattern matrix in Table 3). Partial correlations adjusting for income and age revealed significant positive relationships between the first component and PCA-derived DD variable ($r = 0.150$, $p \leq 0.001$), and the second component and DD ($r = 0.140$, $p \leq 0.001$). Table 3.Pattern matrix from principal component analysis of each psychiatric indictorVariableComponent12PHQ-Anx0.892−0.051PHQ-90.8960.014PCL-50.8510.056ASRS0.7300.002AUDIT−0.0530.557CUDIT0.0800.691DUDIT0.0910.730FTND−$0.0450.657\%$ of variance42.2216.36FTND, Fagerstrom Test of Nicotine Dependence; PHQ-9, Patient Health Questionnaire-9 (depressive symptoms); PHQ-Anx, Patient Heath Questionnaire Anxiety Scale; PCL-5, Posttraumatic Stress Disorder Checklist-5; AUDIT, Alcohol Use Identification Test; CUDIT, Cannabis Use Identification Test; DUDIT, Drug Use Identification Test; ASRS, Adult ADHD Self Report Scale
## Structural equation modeling evaluation
The model revealed an excellent model fit for each of the fit indices, CFI = 0.996, TLI = 0.993, RMSEA = 0.029, and SRMR = 0.005. Of note, χ2 = 42.711 ($$p \leq 0.002$$, df = 20), however this value is highly sensitive to sample sizes above 400 and may not be as interpretable as other fit indices (Saris, Satorra, & Van der Veld, 2009). Standardized model results are in Fig. 2; coefficients with $95\%$ confidence intervals are in online Supplementary materials. As expected, all three magnitudes for the MCQ loaded well on the single latent variable (standardized coefficients >0.90). Significant positive associations were observed between the latent variable of DD and severity of tobacco dependence (FTND), cannabis misuse (CUDIT), and depression (PHQ-9). Alcohol misuse (AUDIT), illicit drug use (DUDIT), anxiety (PHQ-Anx), PTSD (PCL-5), and ADHD (ASRS) were not significantly associated with DD in the concurrent model. With regard to covariates, age, but not income, was positively associated with impulsive DD. Fig. 2.Model of a latent variable of delay discounting at three reward magnitudes in relation to dimensional indicators of substance use, attention-deficit hyperactivity disorder, depression, anxiety, and posttraumatic stress disorder. Notes: Solid lines and bolded values indicate significant loadings, and dotted lines indicate non-significant loadings. FTND, Fagerstrom Test for Nicotine Dependence; PHQ-9, Patient Health Questionnaire-9 (depressive symptoms); PHQ-Anx, Patient Heath Questionnaire Anxiety Scale; PCL-5, Posttraumatic Stress Disorder Checklist-5; AUDIT, Alcohol Use Identification Test; CUDIT, Cannabis Use Identification Test; DUDIT, Drug Use Identification Test; ASRS, Adult ADHD Self Report Scale; DD, delay discounting.
## Discussion
The current study sought to evaluate DD as a transdiagnostic RDoC indicator by examining its relationship to a number of psychiatric domains independently and concurrently. Elevated DD was observed for individuals scoring above the clinical cut-off for tobacco use disorder, cannabis misuse, drug use disorder, depression, PTSD, anxiety, and ADHD. In concurrent examinations, using PCA, DD was significantly associated with a two-component structure reflecting substance use disorders and all other mental disorders, which may suggest some transdiagnostic potential for DD across disorders. However, using SEM, a more precise measurement approach, the results implicate DD in tobacco use, cannabis use, and depression, but not other substance use, ADHD, anxiety, or PTSD, thus not supporting DD as a transdiagnostic process across the majority of conditions. Notably, there were associations across forms of psychopathology, in some cases of large magnitude. This is not surprising given epidemiological studies documenting high rates of comorbidity (Conway, Compton, Stinson, & Grant, 2006; Kessler et al., 2005a, 2005b), but as these behaviors and symptoms are ‘fellow travelers’, the possibility of confounding is present. Further, in our subsequent analysis exploring the relationships between DD and each psychiatric condition separately, the results suggest that exclusively examining DD in terms of individual relationships with specific forms of psychopathology may not be accounting for key variables, such as smoking status or level of depression, and thus included a third variable confound in which the observed link was in fact attributable to an unobserved variable (e.g. smoking). Repeated instances of inadvertent confounding would spuriously imply transdiagnostic relevance at the higher level of the literature. This is of course conjecture, but the facts of the current results fundamentally indicate some specificity in the links between DD and psychiatric domains, not a fully domain-general transdiagnostic relationship.
Among the significant associations in the combined analysis, the most robust association was with smoking, which may be because the very nature of cigarette smoking recapitulates DD itself (i.e. many small episodes of using cigarettes at the cost of future outcomes). For example, a pack-a-day smoker is engaging in 20 decisions a day for the smaller-sooner reward. In addition, studies have suggested when combining tobacco use with other substances, smoking accounts for a significant portion of the variance in DD. For example, studies have suggested that individuals with an SUD such as alcohol use disorder or cocaine use disorder discount substantially more when combined with heavy smoking, as compared to drinking or cocaine use alone (García-Rodríguez, Secades-Villa, Weidberg, & Yoon, 2013; Moody, Franck, Hatz, & Bickel, 2016). While many studies have explored the relationship between smoking alone and DD, most of the literature on DD does not explore smoking simultaneously with other SUDs. As such, it may be that once smoking is included, the association between other substances naturally attenuates. Interestingly, DD was also significantly associated with the severity of cannabis use. This association has been less frequently observed in the literature compared to other substances and studies that have examined DD and cannabis use have found inconsistent results, with some finding increased DD is associated with cannabis use (e.g. Sofis, Budney, Stanger, Knapp, & Borodovsky, 2020), and other studies finding no such relationship (e.g. Johnson et al., 2010). A recent meta-analysis, however, observed a small omnibus effect size between increased DD and cannabis use frequency and severity (Strickland, Lee, Vandrey, & Johnson, 2020), suggesting an aggregate association. Here, it was notable that cannabis use was most highly correlated with illicit drug use, which is common, and previous studies on DD and illicit drugs have not (to our knowledge) adjusted for cannabis use, thus potentially introducing a confound.
A surprising aspect of the current results was that the substance use associations were specific to smoking and cannabis in the SEM analyses, and non-significant for alcohol and illicit drugs, although the absence of associations is not without precedent. Individual studies have reported null findings for alcohol (e.g. MacKillop, Mattson, Anderson MacKillop, Castelda, & Donovick 2007; Moody et al., 2016), for example. In addition, most substance use studies have focused on a single drug at a time and have also not systematically cataloged and adjusted for other substances that were not the focus of the study. Thus, the relationship between DD and substance use may be more nuanced than typically thought. In support of this, a recent study using the Addiction Neuroclinical Assessment (ANA) approach to deep-phenotyping heavy drinking found that DD loaded into the executive dysfunction domain (Nieto et al., in press) and that domain was associated with a family history of alcoholism but not drinking itself.
Amongst the other mental health conditions, DD was specifically associated with depression in the SEM analyses. One explanation for this finding is higher DD may be related to a specific feature of depression, either a symptom or a cluster of symptoms or behaviors. For example, Pulcu et al. [ 2014] found increased hopelessness in individuals with depression was significantly associated with elevated DD. In other words, individuals who have a negative, bleak outlook on the future may experience an increased tendency to discount the value of a larger reward later. However, it is important to note that not all studies confirmed these findings. For example, Lempert and Pizzagalli [2010] observed decreased DD in individuals who displayed greater anhedonia. In addition, it is also important to note that depression was highly correlated with other psychopathology in the sample, such as anxiety and PTSD. Screening tools for these conditions tend to commonly measure negative affectivity and are thus not orthogonal from one another. Nonetheless, among these three domains, the results clearly implicate depressive symptoms as being uniquely associated with DD.
Another notable finding is the positive association between DD and age. There have been conflicting accounts in the literature regarding the relationship of DD and age (Green, Fry, & Myerson, 1994; Read & Read, 2004); however, these studies typically do not explore conditions simultaneously or use discrete age bands rather than a continuous variable. The results from this study suggest that age may be a significant factor when accounting for multiple conditions, but further research is needed to confirm this association.
This study's findings should be considered in the context of its strengths and limitations. First, this study utilized validated dimensional screening instruments, but not formal diagnostic tools. As a result, performance on these measures does not reflect a definitive clinical diagnosis. Moreover, while these tools are widely used, it may be the case that other measures may be more sensitive. Second, this was a cross-sectional study and therefore no conclusions can be made about the temporal ordering of the relationship between DD and psychopathology in these findings. Third, this study did not examine the full breadth of psychopathology, such as personality disorders, eating disorders, or psychotic disorders. However, the study did utilize a large sample size, providing high statistical power and increasing the generalizability of the findings. In addition, the study examined participants who were non-clinical community adults, again increasing generalizability. However, as this is a non-clinical sample, the level of severity of symptoms is substantively lower than clinical samples. As such, it could be the case that differences in DD become more pronounced at higher levels of severity. For these reasons, the current findings should not be considered definitive, but illustrative of the need for more investigation of DD in the context of multiple domains of psychopathology.
At a broader level, another consideration is that DD as an indicator is a form of revealed preference that may in fact reflect multiple underlying mechanisms. That is, multiple conditions may be associated with the phenotype of elevated DD, but for different reasons. For example, it may be that high DD is variably a result of lower executive control, higher reward processing, present-focused ruminative cognition, or hopelessness about the future. Triangulating potentially different behavioral or neurobiological mechanisms is an important future direction. Taken together, there is a high need for future DD studies that are carefully designed to test RDoC hypotheses.
In sum, the findings from this study suggest that the prospects for DD as a transdiagnostic indicator may be more complex than conjectured. Exploring DD independently suggests associations with a large number of psychiatric domains, but concurrently examining DD in relation to multiple psychiatric conditions reveals more limited linkages. The findings reveal some transdiagnostic significance in concurrent models (for smoking, cannabis use, and depression), but no evidence in relation to a broad swath of psychiatric domains. More broadly, this study emphasizes the need for additional methodologically rigorous and careful study designs investigating DD and other transdiagnostic RDoC indicators.
## Financial support
The authors’ contributions were partially supported by NIH grant R01 AA025849, NIH grant K01 DA045219-02, NIH K24 AA025704, the Tobacco-Related Disease Research Program grant #28IR-0070, the Michael G. DeGroote Centre for Medicinal Cannabis Research, and the Peter Boris Chair in Addictions Research.
## Conflict of interest
JM is a senior scientist and principal in BEAM Diagnostics, Inc. No other conflicts of interest to declare.
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|
---
title: Dietary intake, physical activity and sedentary behaviour patterns in a sample
with established psychosis and associations with mental health symptomatology
authors:
- Rebecca Martland
- Scott Teasdale
- Robin M. Murray
- Poonam Gardner-Sood
- Shubulade Smith
- Khalida Ismail
- Zerrin Atakan
- Kathryn Greenwood
- Brendon Stubbs
- Fiona Gaughran
journal: Psychological Medicine
year: 2023
pmcid: PMC10009388
doi: 10.1017/S0033291721003147
license: CC BY 4.0
---
# Dietary intake, physical activity and sedentary behaviour patterns in a sample with established psychosis and associations with mental health symptomatology
## Abstract
### Background
People with psychosis experience cardiometabolic comorbidities, including metabolic syndrome, coronary heart disease and diabetes. These physical comorbidities have been linked to diet, inactivity and the effects of the illness itself, including disorganisation, impairments in global function and amotivation associated with negative symptoms of schizophrenia or co-morbid depression.
### Methods
We aimed to describe the dietary intake, physical activity (PA) and sedentary behaviour patterns of a sample of patients with established psychosis participating in the Improving Physical Health and Reducing Substance Use in Severe Mental Illness (IMPaCT) randomised controlled trial, and to explore the relationship between these lifestyle factors and mental health symptomatology.
### Results
A majority of participants had poor dietary quality, low in fruit and vegetables and high in discretionary foods. Only $29.3\%$ completed ⩾150 min of moderate and/or vigorous activity per week and $72.2\%$ spent ⩾6 h per day sitting. Cross-sectional associations between negative symptoms, global function, and PA and sedentary behaviour were observed. Additionally, those with more negative symptoms receiving IMPaCT therapy had fewer positive changes in PA from baseline to 12-month follow-up than those with fewer negative symptoms at baseline.
### Conclusion
These results highlight the need for the development of multidisciplinary lifestyle and exercise interventions to target eating habits, PA and sedentary behaviour, and the need for further research on how to adapt lifestyle interventions to baseline mental status. Negative symptoms in particular may reduce patient's responses to lifestyle interventions.
## Introduction
The rates of cardiovascular disease (CVD), type 2 diabetes mellitus (T2DM) and metabolic syndrome are two-fold higher in people with psychotic disorders compared to healthy controls (Module et al., 2011; Vancampfort et al., 2015, 2016), contributing to a 10–20 year reduced life expectancy (Jayatilleke et al., 2017; John et al., 2018). This physical health disparity has been linked to side effects of antipsychotic medication, including weight gain and excessive hunger, wider determinants of health (e.g. poverty, smoking, inactivity and diet) and to the illness itself, including disorganisation, impairments in global function and amotivation associated with the negative symptoms of schizophrenia or co-morbid depression (De Hert et al., 2011; Firth et al., 2019).
A poor-quality diet, characterised by a low intake of fruit and vegetables, wholegrains, fibre and fish, and high in sodium, sugar and ultra-processed foods is associated with an increased incidence of cardiometabolic disease (Mullee et al., 2019; Srour et al., 2019), and an increased incidence of disability-adjusted life-years (DALYs) (Afshin et al., 2019; Rico-Campà et al., 2019). In 2017, 255 million DALYs were attributed to poor dietary intake globally, with the leading dietary risk factors being low intake of whole grains (82 million DALYs), high sodium intake (70 million DALYs) and low fruit intake (65 million DALYs) (Afshin et al., 2019). Additionally, low levels of physical activity (PA) and high levels of sedentary behaviour are associated with increased cardiometabolic disease risk, and related premature mortality (Garber et al., 2011; Lear et al., 2017). In a sample of 130 000 people from 17 countries, meeting PA guidelines was associated with hazard ratios of 0.72 (0.67–0.77) for mortality, and 0.80 (0.74–0.86) for major CVD (Lear et al., 2017).
People with psychosis are less likely to meet the World Health Organisation International Food Consumption Recommendations (WHO-IFCR) (⩾5 portions of fruit/vegetables, 30 g fibre and <5 g salt per day; <$10\%$ of daily calorie intake in saturated fat) (WHO, 2015) than are the general population, with poorer diet quality linked to the chronicity of psychosis (Dipasquale et al., 2013; Firth et al., 2018a; Scoriels et al., 2019; Teasdale et al., 2019). Poorer diet quality may be associated with increased appetite due to antipsychotic medication, and with amotivation and disorganisation which may impede the ability to plan and prepare meals (Teasdale et al., 2019). Moreover, people with psychosis spend an average of 12.6 h per day sedentary (Stubbs, Williams, Gaughran, & Craig, 2016b; Vancampfort et al., 2017), and only $56.6\%$ meet the World Health Organisation's (WHO) (WHO, 2010) recommendations of 150 min of PA per week (Stubbs et al., 2016a).
Despite the increased focus on physical health in people with mental illness, there is a paucity of evidence exploring the relationship between dietary intake and symptomatology or global function in people with psychosis. This includes a paucity of well-designed prospective studies, further limited by over reliance on unvalidated tools to measure dietary intake, thereby reducing the generalisability of findings (Teasdale et al., 2019). Moreover, there is minimal research exploring the intersectionality of risks between mental health and PA and sedentary behaviour, and lack of longitudinal data in those with severe mental illnesses (SMI).
The Improving Physical Health and Reducing Substance Use in Severe Mental Illness (IMPaCT) Programme was set out to determine the extent of physical health and lifestyle risks in people with psychosis and to evaluate a health promotion intervention (Gaughran et al., 2020). IMPaCT highlighted the variation in PA in people with established psychosis, with $44\%$ engaging in low-intensity, $44\%$ in moderate and $12\%$ in high intensity PA, with the level of PA associated with cardiometabolic risk markers including obesity and dyslipidaemia. Similar variations were evident in relation to dietary fat and fibre intake.
In this paper we aimed to expand this by: Describing in greater detail the reported dietary intake, PA and sedentary behaviour patterns over time of patients with established psychosis participating in the IMPaCT randomised controlled trial (RCT) (baseline, and 12- and 15-month follow-ups) using data generated from tools validated for use in primary care and/or schizophrenia. Testing the hypothesis that there would be a relationship between negative symptoms, depression severity and global function with PA, sedentary behaviour and diet, in particular fruit and vegetable intake, at study baseline and again at 12-month follow-up (at completion of the supervised intervention).Testing the hypothesis that there would be a relationship between negative symptoms, depression severity and global function at study baseline with PA, sedentary behaviour and fruit and vegetable intake 12-months later, and with change in these lifestyle factors from study baseline to 12-month follow-up.
## Study design
This is a secondary analysis of data from the IMPaCT RCT (Gaughran et al., 2017) which took place between 2010 and 2016 and was part of a National Institute for Health Research (NIHR) funded Programme (RP-PG-0606-1049). Participants were randomised to receive a module-based, health promotion intervention (IMPaCT therapy), through their care coordinator in addition to usual mental health care delivered by care coordinators (TAU) or to TAU alone. All care coordinators were offered a 1-h training session in best practice for physical health awareness to ensure standardized TAU. IMPaCT therapy used motivational interviewing (MI) and cognitive behavioural therapy (CBT) to address lifestyle choices and support behaviour change in key areas including exercise, diet, tobacco smoking, alcohol use, cannabis use and T2DM. Care coordinators received 4 days training in delivering the modular intervention and fortnightly supervision. IMPaCT therapy showed no significant effect on the physical or mental health subscales of the short form-36 (SF-36) questionnaire v. TAU at 12 or 15 months. No effect was observed for cardiovascular risk indicators, except for high-density lipoprotein cholesterol, which improved more with IMPACT therapy compared to TAU, demonstrating the challenges of reducing cardiovascular risk among those with established psychosis (Gaughran et al., 2017). Full details of the study, setting, recruitment and outcome measures have been reported elsewhere (Gaughran et al., 2013, 2017, 2020). Ethical approval was obtained from the South London and Maudsley and the Institute of Psychiatry NHS Ethics Committee (REC Ref. no. 09/HO$\frac{80}{41}$). Dietary intake and behaviour patterns of the IMPaCT sample have been previously reported (Gardner-Sood et al., 2015; Gaughran et al., 2017) although longitudinal results have not been reported, and no correlational analyses between these factors and mental health symptomology, and global function, have been conducted.
## Setting
Participants were recruited from community mental health teams in five mental health NHS trusts across England (Gaughran et al., 2013, 2017).
## Subjects
The inclusion criteria were as follows: aged 18–65 years; a primary diagnosis of psychotic illness [meeting International Classification of Diseases (ICD)-10 diagnosis criteria and encompassing: F20–29, including schizophrenia, schizoaffective disorder, bipolar affective disorder and delusional disorder, F31.2, F32.3 and F33.3], and capacity to provide informed consent. The exclusion criteria included: a primary diagnosis of intellectual disability; a first-episode psychosis (FEP); a physical health condition (such as cancer), that could independently influence metabolic measures; pregnant or less than 6 months postpartum; or a life-threatening/terminal medical condition (Gaughran et al., 2013).
## Data collection
Demographic data including age, gender, ethnicity and educational attainment were extracted via patient records and interview with the participant. Mental health was measured using the Positive and Negative Syndrome Scale (PANSS; Kay, Opler, and Lindenmayer, 1989), the Montgomery–Asberg Depression Rating Scale (MADRS; Montgomery and Asberg, 1979) and the Global Assessment of Functioning (GAF; American Psychiatric Association, 2012) through face-to-face interview.
## Co-primary outcomes
The co-primary outcomes of interest in this paper were dietary intake and PA at baseline, 12-months and 15-months. Dietary intake was assessed using a modified version of the self-report Dietary Instrument for Nutrition Education (DINE; Roe, Strong, Whiteside, Neil, and Mant, 1994). The DINE has been validated for use in primary care (Roe et al., 1994) and has been used in populations with SMI without documented issues with feasibility (Attux et al., 2013; Brown & Chan, 2006; Osborn et al., 2018). The DINE is a semi-quantitative food frequency questionnaire which includes 19 food groups. A modified version of the DINE was utilised to overcome limitations relating to the original DINE including inaccuracy of absolute nutrient values and lack of details regarding specific foods. The modified version utilised in this study included all questions from the original DINE, with an additional seven questions across 17 food categories, commonly consumed by the population under investigation, including combined fresh fruit and vegetable intake, salt use, soft drinks and hot beverages (online Supplementary Appendix One). The primary dietary outcome was a combined intake of fresh fruit and vegetables; this outcome was added to the DINE questionnaire as a supplementary question and categorised into three groups (⩽3 per month, 1–4 weekly, ⩾5 weekly) for feasibility of analysis.
PA was assessed via the self-report International Physical Activity Questionnaire (IPAQ; Craig Marshall et al., 2003). The IPAQ has been validated for the assessment of activity patterns in schizophrenia (Faulkner, Cohn, & Remington, 2006). For each respondent, the number of minutes spent over the past 7 days participating in moderate and/or vigorous activity was collected and categorised into two groups (<150 min of moderate and/or vigorous activity per week; ⩾150 min of moderate and/or vigorous activity per week). Sedentary behaviour was quantified as minutes spent sitting per day.
## Statistical analysis
Data analysis was conducted using Statistical Package for the Social Sciences (SPSS) version 25 (Chicago, USA).
First, descriptive measures were presented, by treatment arm, for demographic and clinical variables as well as dietary intake, PA and sedentary behaviour at baseline and 12- and 15-month follow-ups, and the distribution of data observed. Missing data were treated as missing.
Next, we assessed the association between mental health (PANSS negative symptoms score, MADRS score and GAF score) and [1] fresh fruit and vegetable intake (⩽3 per month, 1–4 weekly, ⩾5 weekly), [2] PA (below or above the recommended 150 min of moderate and/or vigorous activity per week) and [3] minutes of sedentary behaviour per day, at baseline and 12 months. A logistic regression model was employed to assess the association between mental health and fruit and vegetable intake, and PA. A linear regression model was used to assess the association between mental health and sedentary behaviour. A regression model was performed adjusting for age, ethnicity, gender and educational attainment. For the 12-month follow-up separate analyses were conducted for the two treatment arms.
Following this, we assessed the association between baseline mental health and [1] fresh fruit and vegetable intake, [2] PA and [3] sedentary behaviour at 12 months, and changes in 1–3 from baseline to 12 months using logistic and linear regression models adjusting for confounders as above and separated by treatment arm.
To account for multiple testing an adjusted p value ⩽0.006 was used for significance (Bland & Altman, 1995). To obtain this value the standard p value of 0.05 was divided by 9 to account for analysis of three mental health measures and three lifestyle variables.
## Basic characteristics
This secondary analysis used baseline data from 406 randomly selected patients with established psychosis, with follow-up data from 318 participants and 301 participants who completed 12-month and 15-month follow-ups, respectively.
Overall, 406 participants were randomised as part of the IMPaCT RCT, 213 to the treatment arm (IMPaCT therapy) (mean age: 43.8 ± 10.1, $55\%$ male) and 193 to TAU (mean age: 44.7 ± 10.2, $61\%$ male). The most frequent ethnicity was Caucasian ($55.1\%$), followed by Black African or Black Caribbean ($34\%$). Eighty-eight percent resided in urban locations and $73.2\%$ were educated to at least General Certificate of Secondary Education (GCSE) level (or equivalent). Mental health symptom severity was similar between groups at baseline. Additional details regarding main clinical characteristics are presented elsewhere (Gaughran et al., 2017).
## Descriptive analysis: dietary intake, PA and sedentary behaviour
Three-hundred ninety-seven participants completed the DINE questionnaire at baseline, and 310 and 293 participants completed the DINE questionnaire at 12- and 15-month follow-ups, respectively (data were missing from several participants due to the length of the questionnaire/time required for completion). At baseline, $58\%$ reported consuming ⩾5 servings of fresh fruit and/or vegetables a week, with $53\%$ consuming ⩾3 servings of fruit per week and $58\%$ consuming ⩾3 servings of vegetables per week. In reference to discretionary foods: $41\%$ reported consuming ⩾3 servings of biscuits, chocolate or crisps per week, $31\%$ drank ⩾500 ml of fizzy drinks per day and $18\%$ reported consuming ⩾3 servings of fried food each week. Similar patterns in dietary intake were observed at 12- and 15-month follow-ups.
All 406 participants provided data at baseline regarding their PA in the previous week, and all 318 and 301 participants provided PA data at 12- and 15-month follow-ups, respectively. At baseline, $82\%$ reported taking no vigorous PA and $42\%$ reported no moderate PA. Additionally, $22\%$ reported walking for ⩾10 min on one or less days. Only $29\%$ reported completing ⩾150 min of moderate and/or vigorous activity per week. The average amount of time spent sitting was 495 ± 235 min per day or 8.3 h, and $72\%$ spent ⩾6 h per day sitting. Similar patterns in PA were observed at 12- and 15-month follow-ups.
A detailed description of dietary intake and PA at baseline and 12-month and 15-month follow-up is provided in Table 1. Figure 1 demonstrates the relationship between dietary quality, PA and sedentary behaviour at baseline. Fig. 1.Illustration of the relationship between dietary quality, PA and sedentary behaviour. Table 1.Diet and PA characteristics of the study populationLifestyle componentBaseline12 months15 monthsAllIMPaCTTAUAllIMPaCTTAUAllIMPaCTTAUFresh fruit and vegetables⩽3 servings per month, %11.111.510.610.310.110.511.912.811.01–4 servings per week, %30.733.028.224.526.622.428.731.825.6⩾5 servings per week, %58.255.561.265.263.367.159.455.463.5Fruita⩽2 servings per week, %46.650.742.039.844.335.148.152.044.23–5 servings per week, %15.616.314.922.322.523.217.714.920.7⩾6 servings per week, %37.833.043.137.935.841.734.233.135.1Vegetablesb⩽2 servings per week, %42.142.142.042.541.144.045.147.342.83–5 servings per week, %26.427.825.024.427.221.322.920.924.8⩾6 servings per week, %31.530.133.033.131.634.632.031.832.4Fried foodc<1 serving per week, %41.142.639.438.236.739.749.148.050.31–2 servings per week, %41.141.640.441.442.440.435.240.529.7⩾3 servings per week, %17.915.820.220.420.919.915.711.520.0Cakes, pies, puddings<1 serving per week, %39.041.636.244.044.943.044.446.042.81–2 servings per week, %37.836.439.430.432.927.831.732.431.0⩾3 servings per week, %23.222.024.525.622.229.123.921.626.2Biscuits, chocolate, crisps<1 serving per week, %30.029.730.328.228.527.833.135.930.31–2 servings per week, %29.028.229.828.825.332.522.918.227.6⩾3 servings per week, %41.142.139.943.046.239.744.045.942.1Fish (not fried)<1 serving per week, %47.651.243.648.953.244.445.246.943.4⩾1 serving per week, %52.448.856.451.146.855.654.853.156.6Fizzy drinks<500 ml per day, %68.868.469.173.270.376.374.671.677.8⩾500 ml per day, %31.231.630.926.829.723.725.428.422.2Takeaways⩽3 per month, %51.453.149.553.549.158.353.156.250.01–2 per week, %33.532.534.633.236.529.833.432.934.0⩾3 per week, %15.114.416.013.214.511.913.411.016.0Vigorous PA0 days in the last week, %81.582.680.387.186.887.484.186.282.01–2 days in the last week, %10.38.911.96.35.07.58.65.911.4⩾3 days in the last week, %8.18.57.86.68.25.07.37.96.6Moderate PA0 days in the last week, %41.944.139.441.647.535.847.053.340.71–2 days in the last week, %32.730.535.232.227.237.131.828.335.3⩾3 days in the last week, %25.325.425.426.225.327.021.218.424.0Walking for ⩾10 min or more⩽1 day in the last week, %21.522.120.825.527.023.924.523.026.02–6 days in the last week, %34.333.834.931.833.330.229.125.732.77 days in the last week, %44.244.144.342.839.645.946.451.341.3Percentage meeting 150 min recommended activity per week, %29.329.129.527.422.832.122.519.126.0Mean time spent sitting per day (min) [s.d.]495.4 [234.5]511.5 [236.4]477.8 [231.8]486.2 [229.7]492.1 [240.1]480.4 [219.5]487.1 [257.2]501.6 [276.1]472.5 [236.7]Percentage sitting for ⩾6 h per day, %72.276.767.2d71.270.771.765.965.566.2aIncluding fresh, frozen and canned fruit.bIncluding fresh, frozen and canned, and excluding peas, beans and lentils.cIncluding fish, chips, cooked breakfasts and samosas.dSignificant difference between IMPaCT and TAU groups [χ2(1, $$n = 395$$) = 4.430, $$p \leq 0.043$$].
## Associations between mental health symptoms and diet, PA and sedentary behaviour (Table 2)
At baseline, those with higher PANSS negative symptom scores and greater depression severity were more likely to consume ⩽3 servings of fresh fruit and/or vegetables each month than to consume ⩾5 servings of fresh fruit and/or vegetables a week [odds ratio (OR) 1.10, $95\%$ confidence interval (CI) 1.03–1.17; and OR 1.05, $95\%$ CI 1.02–1.09 respectively], although this did not withstand correction for multiple testing. These associations were not observed at 12 months and no association between global function and fresh fruit and vegetable intake was found (Fig. 1). Table 2.Cross-sectional associations between mental health symptoms and dietary intake, PA and sedentary behaviour at baseline and 12-month follow-upBase PANSS negativeBase MADRSBase GAFBaselineFruit and vegetablesR2 = 0.095, χ2 = 7.080, $$p \leq 0.029$$*R2 = 0.095, χ2 = 8.702, $$p \leq 0.013$$*R2 = 0.073, χ2 = 1.263, $$p \leq 0.532$$PAR2 = 0.076, χ2 = 3.336, $$p \leq 0.068$$R2 = 0.072, χ2 = 3.197, $$p \leq 0.074$$R2 = 0.077, χ2 = 4.718, $$p \leq 0.030$$*Sedentary behaviourβ = −0.015, B = −0.743, $$p \leq 0.774$$β = 0.118, $B = 2.939$, $$p \leq 0.024$$*β = −0.171, B = −3.010, $$p \leq 0.001$$FU PANSS negativeFU MADRSFU GAF12 monthsFruit and vegetablesIMPaCTR2 = 0.092, χ2 = 2.173, $$p \leq 0.337$$R2 = 0.088, χ2 = 1.547, $$p \leq 0.461$$R2 = 0.078, χ2 = 0.254, $$p \leq 0.881$$TAUR2 = 0.193, χ2 = 2.293, $$p \leq 0.242$$R2 = 0.087, χ2 = 0.956, $$p \leq 0.318$$R2 = 0.194, χ2 = 2.348, $$p \leq 0.309$$PAIMPaCTR2 = 0.208, χ2 = 8.502, $$p \leq 0.004$$R2 = 0.140, χ2 = 0.740, $$p \leq 0.390$$R2 = 0.135, χ2 = 0.285, $$p \leq 0.594$$TAUR2 = 0.136, χ2 = 4.698, $$p \leq 0.030$$*R2 = 0.106, χ2 = 0.680, $$p \leq 0.410$$R2 = 0.139, χ2 = 3.554, $$p \leq 0.059$$Sedentary behaviourIMPaCTβ = 0.036, B = −1.859, $$p \leq 0.661$$β = 0.212, $B = 5.462$, $$p \leq 0.009$$*β = −0.149, B = −3.120, $$p \leq 0.072$$TAUβ = 0.046, $B = 1.855$, $$p \leq 0.576$$β = 0.050, $B = 1.191$, $$p \leq 0.539$$β = −0.199, B = −3.461, $$p \leq 0.016$$*FU, 12-month follow-up; PANSS, The Positive and Negative Syndrome Scale; MADRS, Montgomery–Asberg Depression Rating Scale; GAF, global assessment of functioning; TAU, treatment as usual. Associations that meet the standard p value for significance (p ≤ 0.05) are given in bold.*Does not withstand Bonferroni-adjusted p value of ⩽0.006.
At baseline, those with higher global function were more likely to meet the recommended PA guideline than to not meet this guideline (OR 1.02, $95\%$ CI 1.00–1.04), although this association did not withstand correction for multiple testing. This cross-sectional association persisted in the TAU group at 12 months (OR 1.03, $95\%$ CI 1.00–1.06) but in the IMPaCT therapy group, the relationship between global function and meeting PA guidelines was no longer apparent, this association did not withstand correction for multiple testing. At 12 months, those with greater PANSS negative symptoms were less likely to meet the recommended PA guideline than to not meet this guideline although that relationship had not been evident at baseline (baseline: OR 0.95, $95\%$ CI 0.91–1.01; 12 months: IMPaCT therapy: OR 0.86, $95\%$ CI 0.76–0.96, TAU: 12 months: OR 0.92, $95\%$ CI 0.85–1.00) and this association withstood correction for multiple testing in the IMPaCT group only. No association between depression severity and PA was found.
There was a negative association between global function and sedentary behaviour at baseline (baseline: β = −0.17, $95\%$ CI −4.78 to −1.24), which withstood correction for multiple testing. This cross-sectional association persisted in the TAU group at 12 months (TAU: β = −0.20, $95\%$ CI −6.27 to −0.65) along with a positive association between depressive symptoms and sedentary behaviour at baseline and, in the IMPACT therapy group only, at 12 months (baseline: β = 0.12, $95\%$ CI 0.39–5.49; 12 months: IMPaCT: β = 0.21, $95\%$ CI 1.40–9.53), although these associations did not withstand correction for multiple testing. There was no association between negative symptoms and sedentary behaviour.
## Associations between mental health symptoms at baseline and diet, PA and sedentary behaviour at 12 months (Table 3)
No significant associations between baseline mental health symptoms and either diet or sedentary behaviour at 12 months were observed in either treatment arm. Table 3.Association between mental health symptoms at baseline and dietary intake, PA and sedentary behaviour at 12-month follow-up, and change in dietary intake, PA and sedentary behaviour from baseline to 12-month follow-upBase PANSS negativeBase MADRSBase GAF12 month fruit and vegetablesIMPaCTR2 = 0.114, χ2 = 5.164, $$p \leq 0.076$$R2 = 0.090, χ2 = 1.520, $$p \leq 0.468$$R2 = 0.079, χ2 = 0.253, $$p \leq 0.881$$TAUR2 = 0.183, χ2 = 0.482, $$p \leq 0.786$$R2 = 0.185, χ2 = 1.021, $$p \leq 0.600$$R2 = 0.184, χ2 = 0.967, $$p \leq 0.61712$$ month PAIMPaCTR2 = 0.198, χ2 = 7.648, $$p \leq 0.006$$R2 = 0.140, χ2 = 1.130, $$p \leq 0.288$$R2 = 0.141, χ2 = 1.337, $$p \leq 0.248$$TAUR2 = 0.134, χ2 = 3.699, $$p \leq 0.054$$R2 = 0.103, χ2 = 0.003, $$p \leq 0.959$$R2 = 0.184, χ2 = 9.880, $$p \leq 0.00212$$ month sedentary behaviourIMPaCTβ = −0.053, B = −2.566, $$p \leq 0.533$$β = 0.130, $B = 3.282$, $$p \leq 0.114$$β = −0.034, B = −0.670, $$p \leq 0.681$$TAUβ = 0.092, $B = 4.556$, $$p \leq 0.285$$β = 0.061, $B = 1.543$, $$p \leq 0.455$$β = −0.134, B = −2.174, $$p \leq 0.098$$Change in fruit and vegetablesIMPaCTR2 = 0.207, χ2 = 4.892, $$p \leq 0.180$$R2 = 0.211, χ2 = 5.583, $$p \leq 0.134$$R2 = 0.238, χ2 = 10.329, $$p \leq 0.016$$*TAUR2 = 0.187, χ2 = 5.530, $$p \leq 0.137$$R2 = 0.151, χ2 = 2.145, $$p \leq 0.543$$R2 = 0.138, χ2 = 0.128, $$p \leq 0.988$$Change in PAIMPaCTR2 = 0.300, χ2 = 13.153, $$p \leq 0.004$$R2 = 0.241, χ2 = 2.723, $$p \leq 0.436$$R2 = 0.248, χ2 = 4.087, $$p \leq 0.252$$TAUR2 = 0.177, χ2 = 4.115, $$p \leq 0.249$$R2 = 0.149, χ2 = 4.21, $$p \leq 0.936$$R2 = 0.218, χ2 = 12.076, $$p \leq 0.007$$*Change in sedentary behaviourIMPaCTβ = 0.028, $B = 1.586$, $$p \leq 0.751$$Β = −0.065, B = −1.936, $$p \leq 0.443$$β = 0.092, B = −2.130, $$p \leq 0.282$$TAUβ = −0.116, B = −6.869, $$p \leq 0.191$$β = 0.140, $B = 4.176$, $$p \leq 0.092$$β = −0.053, B = −1.037, $$p \leq 0.519$$PANSS, The Positive and Negative Syndrome Scale; MADRS, Montgomery–Asberg Depression Rating Scale; GAF, global assessment of functioning; TAU, treatment as usual. Associations that meet the standard p value for significance (p ≤ 0.05) are given in bold.*Does not withstand Bonferroni-adjusted p value of ⩽0.006.
In the IMPaCT group, those with greater baseline PANSS negative symptoms were more likely to not meet the recommended PA guideline at 12 months than to meet this guideline (OR 1.16, $95\%$ CI 1.03–1.30). Moreover, in the TAU group, those with higher global function at baseline were more likely to meet than not meet the recommended PA guideline at 12 months (OR 1.05, $95\%$ CI 1.02–1.08), while those with lower global function at baseline were less likely to meet the recommended PA guidelines at 12 months. This relationship was not found in the IMPaCT therapy group. All these associations withstood correction for multiple testing.
## Associations between mental health symptoms at baseline and change in diet, PA and sedentary behaviour over 12 months (Table 3)
Those in the IMPaCT group with greater baseline global function were less likely to increase their fresh fruit and vegetable intake over the following year than to continue their baseline level of fresh fruit and vegetables intake, whether that be ⩾5 (OR 0.94; $95\%$ CI 0.91–0.98) or <5 servings a week (OR 0.94; $95\%$ CI 0.90–0.99). Those with lower global function at baseline were more likely to increase their fresh fruit and vegetable intake over the following year than to continue with their baseline level of fresh fruit and vegetables intake, but this association did not withstand correction for multiple testing. Baseline function had no effect on likely change in fresh fruit and vegetable intake in the TAU group. No significant associations between baseline depression severity or negative symptoms and subsequent change in fruit and vegetable intake were found.
Those in the IMPaCT group with higher baseline PANSS negative symptoms were more likely to remain exercising below clinical guidelines over the year than to increase their PA over 12 months from below to meeting recommendations (OR 1.29, $95\%$ CI 1.07–1.55, $$p \leq 0.008$$). Those with lower baseline PANSS negative symptoms were more likely to increase their PA over 12 months from below to meeting recommendations than to remain exercising below guidelines over the year, and this association withstood correction for multiple testing. Baseline negative symptoms did not affect the likelihood of changing activity category in the TAU group. Those in the TAU group with higher global function at baseline were more likely to increase their activity level from below to meeting recommendations over 12 months (OR 1.05, $95\%$ CI 1.01–1.09) or to meet clinical guidelines at both baseline and 12 months (OR 1.06; $95\%$ CI 1.02–1.11) than to continue exercising below clinical guidelines over the year. Those with lower global function were less likely to experience positive changes in PA over the following year, nor to meet clinical guidelines at both baseline and 12 months than to continue exercising below clinical guidelines over the year, but this association did not withstand multiple testing. No differential effect of baseline function on change in PA levels was observed in the IMPaCT therapy group.
No significant associations between baseline depression severity and change in PA were found. Baseline mental health symptoms were not associated with changes in sedentary behaviour.
## Discussion
This study set out to describe the dietary intake, PA and sedentary behaviour patterns of a sample of patients with established psychosis. Moreover, we sought to explore the relationship between negative symptoms, depression severity and global function with these lifestyle factors through cross-sectional associations, and to explore associations between mental health symptomology and these lifestyle factors at future time points, and changes in these lifestyle factors over 12 months. This study found that a majority of the sample had poor dietary quality, low in fruit and vegetables and high in discretionary foods. Moreover, only $29.3\%$ met PA guidelines of ⩾150 min of activity per week while $72.2\%$ spent ⩾6 h per day sitting.
Those receiving IMPaCT therapy with more negative symptoms at baseline were less likely to meet PA recommendations at 12 months, at which time point, there was a significant cross-sectional relationship between PA and negative symptoms. Those with more negative symptoms receiving IMPaCT therapy also had fewer positive changes in PA from baseline to 12-month follow-up than those with fewer negative symptoms at baseline. These associations were not found in the TAU group.
Those with lower global function at baseline had higher levels of sedentary behaviour. Those with lower baseline global function receiving TAU were less likely to meet PA recommendations at 12-months.
Associations concerning depression severity and dietary intake did not withstand multiple testing. Moreover, the 12-month analyses were more likely to fail to withstand multiple testing due to the smaller sample size.
Poor dietary intake and limited activity in psychosis has been postulated as being linked to the lethargy, avolition and disturbances in planning and organisational capability associated with psychosis, which may contribute to reduced motivation and ability to plan and prepare meals (Albaugh, Singareddy, Mauger, & Lynch, 2011; Elman, Borsook, & Lukas, 2006; Firth et al., 2018a; Mangurian, Sreshta, & Seligman, 2013). This hypothesis is strengthened by the associations we found between negative symptoms and global function with activity, although findings between negative symptoms and global function with diet did not withstand correction for multiple testing which may be a feature of the complexity of this measure. Dietary choices may also be influenced by financial constraints and the side effects of psychotropic medication, particularly second-generation antipsychotic medication (including excessive hunger and cravings for high-calorie food) (Teasdale et al., 2019). The majority of this group ($94\%$) was receiving psychotropic medication (Gardner-Sood et al., 2015).
Poor dietary intake, low levels of PA and high levels of sedentary behaviour are lifestyle factors that can cause or aggravate cardiometabolic diseases, contribute to years lived with disability and premature mortality (Gakidou et al., 2017). We have shown high levels of these lifestyle risk factors in a large sample of patients with established psychosis. These findings have important clinical implications. Psychiatric practitioners should make diet and exercise part of the overall treatment regime to offset the almost inevitable poorer physical health outcomes associated with having a severe and enduring psychosis. These results highlight the need for the development and implementation of effective and practical multidisciplinary lifestyle and exercise interventions to target eating habits, PA and sedentary behaviour in established psychosis in the hope of reversing cardiometabolic risk.
IMPaCT therapy, which used MI and CBT techniques, did not alter lifestyle risk factors in the overall analysis (Gaughran et al., 2017). In this study, when examining for the effect of baseline mental status, those with lower baseline global function were less likely to be in the 150 min+ category for PA at 12 months and were less likely to show change in activity levels when receiving TAU, but this impediment did not apply in the group receiving IMPACT therapy, suggesting that lower function at baseline did not impede any effectiveness of IMPaCT therapy. Negative symptoms however, appeared to pose more of a challenge. In IMPaCT, there was an association between more negative symptoms at baseline and persistent sub-threshold PA levels, with those who increased their exercise to target having fewer negative symptoms at the outset, although this association was not found in the TAU group. There was little differential effect of baseline depressive symptoms.
These varying effects of baseline mental health status on change in lifestyle factors in response to an intervention highlight the diversity of needs in people with psychosis and the consequent challenges of evaluating and comparing lifestyle interventions aimed towards this group. It also raises the possibility that those with negative symptoms in particular may require more tailored approaches to effect lifestyle change.
In other settings, multiple multidisciplinary lifestyle interventions to improve diet and PA have been trialled including cooking programmes (Lovell et al., 2014), dietetic consultations (Teasdale et al., 2014, 2016), exercise interventions (Romain et al., 2018; Rosenbaum et al., 2016) and peer-support platforms, smartphone apps and fitness trackers (Aschbrenner, Naslund, Shevenell, Kinney, & Bartels, 2016; Macias et al., 2015; Muralidharan et al., 2018). Future research will be needed to determine which forms of intervention, modes of delivery and levels of support are needed for optimal adherence and effectiveness in a given person with psychosis to combat poor lifestyle factors and cardiometabolic disease risk (Firth et al., 2019).
Our data suggested that those with more severe negative symptoms partake in low levels of PA. These results may suggest that future trials of interventions aimed at improving the lifestyle of those with established psychosis may need to be tailored to the symptom profile of individual patients, paying particular attention to negative symptoms. Additionally, future research evaluating treatments for negative symptoms should evaluate the effect on lifestyle metrics alongside. It has been proposed that PA should be assessed as a vital sign in every contact with a health professional (Sallis, 2011). Moreover, smartphones, wearable devices (Firth et al., 2018b; Naslund & Aschbrenner, 2019) and prospective record keeping could provide accurate methods of lifestyle assessment, although there is the need for exploration for the validity of these methods in people with SMI and the need to look for innovative methods that explore eating behaviours as well as dietary intake (Teasdale et al., 2019).
Finally, bidirectional relationships have been postulated between mental health symptomatology and both PA and nutrition (Da Silva et al., 2012; Raudsepp, 2016). Exercise can stimulate the serotonin system, dopaminergic system, hypothalamic–pituitary–adrenal (HPA) axis and increase brain-derived neurotrophic factor (BDNF) expression and potentially influence hippocampal volume and connectivity (Adlard, Perreau, & Cotman, 2005; Droste et al., 2003; Kandola, Ashdown-Franks, Hendrikse, Sabiston, & Stubbs, 2019; Weicker & Strüder, 2001). Moreover, the high antioxidant content in fruit and vegetables may reduce oxidative stress and decrease the risk of depression (Rahe, Unrath, & Berger, 2014). High intake of fruit and vegetables, and low intake of saturated fat, has also been postulated to affect mental health via modulation of inflammatory pathways, epigenetics, the HPA axis, BDNF expression, tryptophan–kynurenine metabolism and gut microbiota (Marx et al., 2021), although further research is needed into this relationship in people with psychosis (Van Der Pols, 2018).
## Limitations
Limitations of this study include the self-report nature of dietary and PA measures. Both the DINE and IPAQ required participants to detail their behaviour patterns from the previous week, thus relying on memory and cognitive function which may be impaired in established psychosis (Bora, Yücel, & Pantelis, 2010; Zanello, Curtis, Badan Bâ, & Merlo, 2009). Self-report may also be subject to social desirability biases and one's own interpretation (Podsakoff & Organ, 1986). To overcome these potential barriers, measures were completed in the presence of a mental health researcher and additional clarification was given regarding serving sizes and examples of foods when completing the DINE. Nonetheless, in future research digital technologies and food and exercise diaries could supplement questionnaire findings. Second, only fruit and vegetables were included in the correlational analysis for dietary factors due to limitations with the DINE in accurately quantifying food sources and mineral/nutrient intake. Furthermore, although other food groups have been associated with mental health, the WHO has set international recommendations for fruit and vegetable intake whereas recommendations for other food groups lack consensus. Future analyses should address overall dietary patterns and overall diet quality (Firth et al., 2020). Additionally, assessment of fruit and vegetables intake was based on weekly consumption at low amounts (i.e. <5 servings a week; ⩾5 weekly) due to the categorical nature of the DINE questionnaire. Future research should analyse fruit and vegetables intake in line with daily intake recommendations. Third, correlational analyses may have been mediated by the impact of cardiometabolic risk. In an analysis of IMPaCT baseline data (Gardner-Sood et al., 2015), hypertension and obesity were independently associated with GAF score and obesity was associated with depressive symptomatology. That said, no associations between cardiometabolic risk factors and PANSS score were found, and the baseline distribution of cardiometabolic risk factors was similar between treatment groups. Fourth, depression and negative symptoms include low energy and/or reduced activity, thus people with less PA or more sedentary behaviour may be more likely to meet these criteria. For example, the MADRS scale includes the item lassitude or difficulty initiating everyday tasks, and the PANSS negative scale includes the item passive/apathetic social withdrawal. Correlations between PA, negative symptoms and depression severity must be interpreted with caution.
Fifth, $22\%$ of patients in IMPaCT RCT did not have 12-month data (Gaughran et al., 2013, 2017), raising the possibility of selective retention. However, there were similar mean scores for clinical characteristics, physical health measures and dietary intake between the total sample at baseline and those with 12-month follow-up data (Gaughran et al., 2017). Finally, the IMPaCT sample was limited to individuals with established psychosis, who had experienced multiple psychotic episodes, and who were receiving secondary care; they therefore required greater clinical support than those receiving primary care in London. The findings, therefore, may not be generalisable to those with early psychosis or less severe psychotic symptoms (Gardner-Sood et al., 2015). That said, a recent prospective study with 293 UK adults with FEP found that $57\%$ consumed a carbonated drink daily, $66.5\%$ added salt to food during cooking and $78.5\%$ consumed take-away meals, three-quarters of whom did so more than once weekly. Additionally, $77\%$ did not meet PA guidelines of 150 min per week and $57.3\%$ sat for more than 6 h per day (Gaughran et al., 2019). Thus, it appears that similar eating behaviours and patterns of PA and sedentary behaviour are found during early psychosis, although further research is needed.
## Conclusion
This research has highlighted high levels of lifestyle risk factors, namely low fruit and vegetable consumption and PA and high levels of sedentary behaviour, in a sample of individuals with established psychosis, and has shown that various mental health symptoms may be associated with these poor lifestyle factors and negative symptoms in particular may reduce patient's responses to lifestyle interventions. Future research should evaluate the effect of treating mental health symptoms on lifestyle metrics. It also highlights a potential need for further research on how to adapt lifestyle interventions to baseline mental status and how to take such factors into account when planning evaluation strategies.
## Financial support
RM is supported by a Ph.D. studentship from the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. BS is supported by a Clinical Lectureship (ICA-CL-2017-03-001) jointly funded by Health Education England (HEE) and the National Institute for Health Research (NIHR). FG is supported by the National Institute for Health Research's (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London and by the Maudsley Charity and the National Institute for Health Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King's College Hospital NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.
## Conflict of interest
Robin Murray has received honoraria for non-promotional talks for Janssen, Sunovian, Lundbeck, Otsuka and Recordati. FG has received support or honoraria from Lundbeck, Otsuka and Sunovion, and has a family member with previous professional links to Lilly and GSK. Brendon Stubbs has received honoraria for unrelated work from ASICS and Parachute. ZA has received support or honoraria from Janssen, Lundbeck, Sunovion and Lilly.
## Ethical standards
Ethical approval was obtained from the joint South London and Maudsley and the Institute of Psychiatry NHS Ethics Committee (REC Ref. no. 09/HO$\frac{80}{41}$). All participants provided written informed consent. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.
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|
---
title: 'Labour market marginalisation in young adults diagnosed with attention-deficit
hyperactivity disorder (ADHD): a population-based longitudinal cohort study in Sweden'
authors:
- Magnus Helgesson
- Emma Björkenstam
- Syed Rahman
- Klas Gustafsson
- Heidi Taipale
- Antti Tanskanen
- Lisa Ekselius
- Ellenor Mittendorfer-Rutz
journal: Psychological Medicine
year: 2023
pmcid: PMC10009402
doi: 10.1017/S0033291721002701
license: CC BY 4.0
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# Labour market marginalisation in young adults diagnosed with attention-deficit hyperactivity disorder (ADHD): a population-based longitudinal cohort study in Sweden
## Abstract
### Background
The objective of this population-based register study was [1] to investigate the association between young adults diagnosed with attention-deficit/hyperactivity disorder (ADHD) and subsequent labour market marginalisation (LMM) in two comparison groups, i.e. matched young adults from the general population without ADHD and unaffected siblings to persons with ADHD and [2] to assess the role of comorbid disorders.
### Methods
This study included all young adults in Sweden, aged 19–29 years, with an incident diagnosis of ADHD 2006–2011 ($$n = 9718$$). Crude and multivariate sex-stratified hazard ratios (HRs) with $95\%$ confidence intervals (CIs) were measured 5 years after the diagnosis of ADHD for the risk of disability pension, long-term sickness absence (SA) (>90 days), long-term unemployment (>180 days) and a combined measure of all three in young adults with ADHD compared to their siblings without ADHD and a matched comparison group.
### Results
In the adjusted analyses young adults with ADHD had a 10-fold higher risk of disability pension (HR = 10.2; CI 9.3–11.2), a nearly three-fold higher risk of long-term SA (HR = 2.7; CI 2.5–2.8) and a $70\%$ higher risk of long-term unemployment (HR = 1.7; CI 1.6–1.8) compared to the matched comparison group. The risk estimates were lower compared to siblings for disability pension (HR = 9.0; CI 6.6–12.3) and long-term SA (HR = 2.5; CI 2.1–3.1) but higher in the long-term unemployed (HR = 1.9; CI 1.6–2.1). Comorbid disorders explained about one-third of the association between ADHD and disability pension, but less regarding SA and long-term unemployment.
### Conclusions
Young adults with ADHD have a high risk for different measures of LMM and comorbidities explain only a small proportion of this relationship.
## Introduction
During the past decades, there has been an increased awareness that attention-deficit hyperactivity disorder (ADHD) is not only affecting children as previously believed, but that symptoms of inattention might also persist into adulthood. In a study including 10 countries on three continents, about $3.5\%$ of individuals in working-age were found to have symptoms of ADHD (de Graaf et al., 2008). Because ADHD is characterised by attention deficiency, lack of impulse control and problems controlling activity level, the disorder may lead to significant negative consequences affecting the individual's ability to work (Helgesson, Tinghog, Niederkrotenthaler, Saboonchi, & Mittendorfer-Rutz, 2017; Hirvikoski, Lindström, Nordin, Jonsson, & Bölte, 2017; Wiklund, Patzelt, & Dimov, 2016). Furthermore, the number of young adults who have been diagnosed with ADHD in young adult age has increased markedly since 2000 (Edvinsson, 2017; Giacobini, Medin, Ahnemark, Russo, & Carlqvist, 2018; Rydell, Lundström, Gillberg, Lichtenstein, & Larsson, 2018; Thomas, Sanders, Doust, Beller, & Glasziou, 2015). Consequently, labour market marginalisation (LMM), i.e. severe problems in obtaining and keeping a job, could be a serious problem for young adults with ADHD. Because young adults have most of their working life ahead of them, early LMM may lead to long-term productivity loss. However, the most significant impact is on the individual who will likely risk economic hardships and potentially further deteriorating health (Janlert & Hammarstrom, 2009; McKee-Ryan, Song, Wanberg, & Kinicki, 2005). Still, to date information on the magnitude of LMM in young adults is lacking. Hence, it seems warranted to conduct a tailor-made design intervention for this patient group.
The scope of LMM can only be assessed by including information on unemployment, which most existing studies have done (de Graaf et al., 2008; Kupper et al., 2012) and on work disability, i.e. sickness absence (SA) and disability pension. Many of the individuals diagnosed with ADHD might never enter the labour market. Thus, there is a risk of underestimating the actual rate of marginalisation in young adults with ADHD (Helgesson et al., 2017). Because Sweden´s welfare system has several measures of LMM, which often act as communicating vessels, a combined measure of LMM which allows comparison to other countries is desirable. Moreover, few studies have assessed the consequences of ADHD in young adults and hence the working-age population. Most of these studies are conducted on self-reported data using a cross-sectional design, which might result in underestimation of both the prevalence of ADHD and the consequences (i.e. LMM) (de Graaf et al., 2008; Kupper et al., 2012). It is, therefore, vital to measure the scope of marginalisation in this patient group using population-based studies with longitudinal data of high quality.
Commonly, young adults with ADHD suffer from comorbidities (e.g. depression, anxiety, substance use, asthma, diabetes mellitus and stress-related and autism-spectrum disorders) (Aduen et al., 2018; Bjorkenstam, Pierce, Bjorkenstam, Dalman, & Kosidou, 2020; Chen, Lee, Yeh, & Lin, 2013; Cortese et al., 2018; Edvinsson, Lindstrom, Bingefors, Lewander, & Ekselius, 2013; Kupper et al., 2012). These comorbidities may further negatively affect occupational functioning and increase the difficulties of obtaining and retaining a job. Still, detailed studies of the role of a full range of different comorbid diagnoses regarding the association between ADHD and subsequent LMM are not available. Moreover, studies have reported large sex differences in the prevalence of ADHD (Edvinsson et al., 2013). However, there are also differences in the symptomatic profile, where women more often have internalising problems, whereas men tend to have more externalising problems (Edvinsson et al., 2013; Gershon & Gershon, 2002). Given that the risk of LMM also differs by sex, analyses stratifying for sex are needed in related studies.
Other socio-demographic factors also influence the association between ADHD and LMM. For instance, educational attainment seems to be lower in young adults with ADHD compared to the general population (Kupper et al., 2012). In Sweden and other Scandinavian countries, education level is an important determinant of LMM, as most jobs demand at least upper secondary education (Nilsson, 2017). Moreover, other socio-demographic factors (e.g. civil status, number of children at home, type of living area and work-related factors, such as previous labour market integration) may be relevant in the association between ADHD and labour market integration (Giacobini et al., 2018). Finally, concerning ethnicity, migrants seem to have a higher prevalence of ADHD compared to the host population (Lehti, Chudal, Suominen, Gissler, & Sourander, 2016). It is therefore essential to consider all these socio-demographic factors in the data analyses regarding LMM. In addition to the characteristics mentioned above, psychosocial determinants during childhood and adolescence, as well as family-related and genetic factors, may influence the future risk of LMM in young adults with ADHD (Svedberg et al., 2011). For this reason, the present study compared the risk of LMM in young adult patients with ADHD to the unaffected general population and the patients' siblings without ADHD, who share familial and genetic factors, including predisposition for certain disorders.
This population-based longitudinal register study aimed [1] to assess the risk of LMM − measured as long-term unemployment, long-term SA and disability pension − and a combined measure of these three outcome measures, in young adult men and women with incident ADHD and compare it to comparison groups consisting of persons in the general population without ADHD and unaffected siblings to persons diagnosed with ADHD and [2] to investigate the role of comorbid disorders in these relationships.
## Registers
Data from five registers were merged individually based on the de-identified unique personal identification number given to all permanent residents in Sweden. Information was available for each individual retrospectively from 1 January 2005 and prospectively until 31 December 2016 from the following five Swedish nationwide registers: [1] Longitudinal Integration Database for Health Insurance and Labour Market Studies (LISA), hosted by Statistics Sweden: all socio-demographic variables, year of emigration and unemployment; [2] Microdata for Analysis of Social Security (MIDAS) hosted by the Swedish Social Insurance Agency: date, duration and grade of SA and disability pension; [3] the Multi-Generation Register (MGR), hosted by Statistics Sweden, with information on parents and siblings to persons with ADHD; [4] the National Patient Register (NPR): primary and secondary diagnoses for ADHD and all comorbid disorders during the year of the cohort entry date (CED, 2006–2011); and [5] Cause of Death Register: date of mortality (2005–2016). Databases 4 and 5 are hosted by the Swedish National Board of Health and Welfare.
## Study population
The study base was made up of 16 647 young male and female adults between 19 and 29 years of age (mean age = 23.4 years) who had the first primary or secondary diagnosis of ADHD from either inpatient or specialised outpatient health care. This information was derived from the NPR between 2006 and 2011. The year of the incident diagnosis served as the CED. Individuals who had an ongoing disability pension ($$n = 4727$$) at the CED or a record of ADHD medication before the CED ($$n = 2202$$) were excluded. The final study population comprised of 9718 young adults with ADHD.
We defined two comparison groups from the five registers. For one of the comparison groups, we selected five individuals from the general population without ADHD matched for sex, age and educational level) with no ongoing disability pension ($$n = 48$$ 590). The second comparison group consisted of siblings not affected by ADHD in the same age range (19–29 years) as their siblings with ADHD and who were not on disability pension ($$n = 5582$$). Siblings were matched on both mother and father, i.e. only full siblings were considered in the analysis. In the case of two (or more) siblings with ADHD who were close in age, the person with the ADHD diagnosis and the earliest cohort entry date was chosen as the exposed individual.
## Exposure variable
ADHD was defined based on the diagnostic code (F90) of the International Classification of Diseases, 10th Revision (ICD-10).
## Outcome measures
The cohort was followed for 5 years from the CED (2006–2016) for [1] disability pension, [2] long-term SA (>90 annual net days of SA registered at the Swedish Social Insurance Agency), [3] long-term unemployment (>180 annual days registered as full-time unemployed at the Swedish Public Employment Service) and [4] total LMM, defined as granting either the disability pension, long-term SA or long-term unemployment.
## Covariates
Covariates in the analyses included [1] socio-demographic factors (sex, age, educational level, family composition, type of living area and country of birth), all measured on 31 December the year before CED, [2] work-related factors (unemployment and SA, both measured during the year before CED) and [3] comorbid disorders (information about the primary and secondary diagnoses of inpatient and specialised outpatient health care in 2005–2011 due to depression and bipolar disorders (ICD-10: F30-F34), anxiety- and stress-related disorders (ICD-10: F40-F48), autism-spectrum disorders (ICD-10: F84), substance use (ICD-10: F10-F19), behavioural and emotional disorders (ICD-10: F91-F98), schizophrenia/non-affective psychoses (ICD-10: F20-F29), mental retardation/disorders of psychological development (ICD-10: F70-F83, F85-F89), other mental disorders (ICD-10: Other F codes), musculoskeletal disorders (ICD-10: M01-M99), asthma (ICD-10: J45), diabetes mellitus (ICD-10: E10-E14), neoplasms (ICD-10: C00-D48), cardiovascular disorders (ICD-10: I00-I99), accidents (ICD-10: S00-S99) and other somatic disorders (ICD-10: remaining codes for somatic disorders except O.80 and Z00–99). The categorisation of all covariates is presented in Table 1. Table 1.Characteristics of patients with ADHD, diagnosed in specialised health care in 2006–2011 ($$n = 9718$$) and individuals without ADHD (general population $$n = 48$$ 590, matched for sex, age and educational level and the patients' siblings $$n = 5582$$) (number (n) and per cent (%) distribution)General populationSiblingsIndividuals with ADHDGeneral populationIndividuals with ADHDSiblingsTotal, n (%)9718 (16.7) 48 590 (83.3)4382 (44.0)5582 (56.0)SexFemale4166 (42.9) 20 830 (42.9)1866 (42.6)2813 (50.4)Male5552 (57.1) 27 760 (57.1)2516 (57.4)2769 (49.6)Age19–24 years6037 (62.1) 30 185 (62.1)2818 (64.3)3364 (60.3)25–29 years3681 (37.9) 18 405 (37.9)1564 (35.7)2218 (39.7)Educational level (years)Elementary school (<10)4665 (48.0) 23 325 (48.0)1985 (45.3)1203 (21.6)Upper secondary school (10–12)4221 (43.3) 21 055 (43.3)1965 (44.8)3174 (56.9)University (>12)842 (8.7)4210 (8.7)432 (9.9)1205 (21.6)Region of birthSweden8954 (92.1) 38 560 (79.4)4180 (95.4)5244 (93.9)Other Nordic countries73 (0.8)501 (0.8)18 (0.4)24 (0.4)EU27100 (1.0)1014 (2.1)10 (0.2)18 (0.3)Rest of world591 (6.1)8615 (17.7)174 (5.0)296 (5.3)Family compositionaMarried/cohabiting without children116 (1.2)1520 (3.1)46 (1.0)131 (2.3)Married/cohabiting with children744 (7.7)6808 (14.0)328 (7.5)741 (13.3)Single without children8318 (85.6) 38 699 (79.6)3793 (86.6)4492 (80.5)Single with children 540 (5.6)1565 (3.2)215 (4.9)218 (3.9)Type of living areaBig cities3329 (33.4) 18 661 (38.4)1465 (33.4)2009 (36.0)Medium-sized cities3567 (36.7) 17 682 (36.4)1614 (36.8)2031 (36.4)Small towns2822 (29.0) 12 247 (25.2)1303 (29.7)1542 (27.6)Unemployment at baseline0 days5728 (58.9) 35 763 (73.6)2577 (58.8)4064 (72.8)1–180 days3419 (35.2) 11 041 (22.7)1551 (35.4)1314 (23.5)>180 days571 (5.9)1786 (3.7)254 (5.8)204 (3.7)SA at baseline0 days8142 (83.8) 46 096 (94.9)3653 (83.4)5210 (93.3)1–90 days660 (6.8)1809 (3.7)312 (7.1)261 (4.7)>90 days916 (9.4)685 (1.4)417 (9.5)111 [2]Comorbidity (ICD-10-codeb in parentheses)Mental disorders Anxiety- and stress-related disorders (F40-48)2154 (22.2)927 (1.9)991 (22.6)165 (3.0) Autism-spectrum disorders (F84)144 (1.5)35 (0.1)73 (1.7)< 10 Behavioural and emotional disorders (F91-98)224 (2.3)29 (0.1)106 (2.4)< 10 Depression and bipolar disorders (F30-34)1688 (17.4)696 (1.4)802 (18.3)126 (2.3) Eating disorder (F50)145 (1.5)71 (0.2)66 (1.5)19 (0.3) Mental retardationc (F70-83, F85-89)59 (0.6)18 (0.1)30 (0.7)< 10 Schizophrenia/psychoses (F20-F29)129 (1.3)75 (0.2)69 (1.6)< 10 Substance use (F10-19)1706 (17.6)761 (1.6)780 (17.8)120 (2.2) Other mental disorders (all remaining F-codes)909 (9.4)295 (0.6)405 (9.2)51 (0.9)Somatic disorders Accidents (S00-S99)1263 (13.0)3181 (6.6)570 (13.0)395 (7.1) Asthma (J45)100 (1.0)187 (0.4)48 (1.1)33 (0.6) Cardiovascular disorders (I00-I99)112 (1.2)300 (0.6)46 (1.0)37 (0.7) *Diabetes mellitus* (E10-E14)96 (1.0)362 (0.8)47 (1.1)59 (1.1) Musculoskeletal disorders (M01-M99)490 (5.0)1492 (3.1)235 (5.4)187 (3.4) Neoplasms (C00-D48)122 (1.3)551 (1.1)52 (1.2)79 (1.4) Other somatic disorders (all remaining codesd)3475 (35.8) 10 246 (21.1)1544 (35.2)1304 (23.4)aWith children living at home.bInternational Classification of Diseases, Version 10.cIncluding disorders of psychological development.dExcept for ICD-10 codes O80 (single delivery) and Z00-Z99 (factors influencing health status and contact with health services).
## The Swedish social insurance regulations
Individuals ⩾16 years of age can receive sickness benefits if they have an income from an established business or employment. During the first 14 days, except for the first day, which is a qualifying day, the employer covers these sickness benefits. From day 15, the Swedish Social Insurance Agency then takes over to continue to pay the benefits, and from that day, data are available in registers. Individuals between 19 and 29 years of age can receive a time-restricted disability pension when their work capacity is reduced or if compulsory education is not completed at the age of 19. Persons from 30 years of age can only be granted permanent disability pension. Individuals >16 years of age can be enrolled at the Swedish Public Employment Service where they can receive unemployment benefits. From age 20, basic levels of unemployment benefits can also be received without earlier income from work.
## Statistical analyses
Cox proportional hazard regression models with competing risks (cause-specific hazards) were applied to calculate hazard ratios (HRs) with $95\%$ confidence intervals (CIs) in order to determine the association between ADHD and subsequent outcomes of LMM (Andersen, Geskus, de Witte, & Putter, 2012; Koller, Raatz, Steyerberg, & Wolbers, 2012). Emigration and mortality were regarded as competing events in all the models. In the analyses regarding both long-term SA and long-term unemployment, also disability pension was seen as a competing event. The follow-up period was 5 years, starting from 1 January in the year following the incident diagnosis of ADHD (CED). All analyses were conducted in three steps: [1] A crude model, [2] Model 1, adjusted for sex, age, educational level (by matching with the general population), family composition, type of living area and country of birth, [3] Model 2, like model 1 and additionally adjusted for unemployment and SA in the year before the CED and [4] Model 3, like model 2 and additionally adjusted for all comorbid disorders.
For the sibling analyses, conditional Cox proportional hazard regression models with competing risks (cause-specific hazards) were performed to adjust for shared familial confounders, i.e. genetic factors and unmeasured shared confounders such as socioeconomic status, neighbourhood or stable parental factors. To assess the contribution of specific comorbid conditions a set of analyses was conducted. Here, individuals with specific comorbid disorders were excluded one at a time, with the remaining individuals compared to the matched comparison group. All analyses were carried out using SAS, version 9.4 (SAS Institute Inc.).
## Results
Periods of unemployment, as well as SA in the year before the CED, were much more prevalent in young adults diagnosed with ADHD than in both comparison groups (Table 1). Moreover, young adults with ADHD were less often married and more likely to live in small towns compared to young adults without ADHD. The most striking finding, however, was that the prevalence of common mental comorbidity was nearly 10 times higher, and the prevalence of most comorbid somatic disorders was twice as common in young adults with ADHD than in the comparison group without ADHD. The most commonly occurring mental comorbid disorders were depressive, anxiety and substance use disorders.
In total, $21\%$ of the young adults with ADHD were granted disability pension within 5 years after the incident diagnosis, which can be compared to just $2\%$ in both the matched comparison group (Table 2) and the patients' siblings without ADHD (Table 3). In the crude model young adults diagnosed with ADHD had more than a 15-fold higher risk of disability pension (HR = 15.59, Table 2) compared to the matched comparison group. The risk estimates were slightly decreased when adjusting for socio-demographic factors ($6\%$ lower risk estimate), and they did not decrease when adjusting for work-related factors, however, they decreased substantially when adjusting for comorbid disorders (an additional $29\%$ lower risk estimate). In the final model young adults with ADHD still had over 10 times higher risk of receiving disability pension (HR = 10.2) compared to the matched comparison group (Table 2). The adjusted risk estimates were comparable between adult women and men and somewhat lower when compared to the comparison group of siblings (HR = 8.9, Table 3). Table 2.HRs with $95\%$ CIs for LMM, measured as disability pension, long-term SA (>90 days) and long-term unemployment (>180 days) in persons with diagnosed attention-deficit hyperactivity disorder (ADHD) ($$n = 9718$$) compared to a matched cohort of individuals without ADHD ($$n = 48$$ 590)Crude modelModel 1aModel 2bModel 3cn (%)HR ($95\%$ CI)HR ($95\%$ CI)HR ($95\%$ CI)HR ($95\%$ CI)Disability pensionAllGeneral population749 [2]1111ADHD2075 [21]15.59 (14.34–16.95)14.66 (13.46–15.96)14.53 (13.34–15.84)10.18 (9.27–11.19)WomenGeneral population349 [2]1111ADHD1038 [25]17.01 (15.07–19.20)15.75 (13.91–17.83)15.54 (13.71–17.60)11.06 (9.64–12.68)MenGeneral population400 [1]1111ADHD1037 [19]14.42 (12.85–16.18)13.84 (12.31–15.56)13.71 (12.17–15.43)9.65 (8.47–10.98)Long-term SAAllGeneral population3498 [7]1111ADHD2336 [24]4.27 (4.05–4.50)4.32 (4.09–4.55)3.24 (3.07–3.43)2.67 (2.51–2.84)WomenGeneral population2044 [10]1111ADHD1174 [28]3.82 (3.56–4.11)3.78 (3.51–4.07)2.94 (2.72–3.18)2.45 (2.25–2.68)MenGeneral population1454 [5]1111ADHD1162 [21]4.96 (4.59–5.36)5.11 (4.72–5.53)3.68 (3.38–4.00)3.03 (2.76–3.32)Long-term unemploymentAllGeneral population6423 [13]1111ADHD1840 [19]1.71 (1.62–1.80)2.03 (1.92–2.14)1.76 (1.67–1.86)1.65 (1.55–1.75)WomenGeneral population2471 [12]1111ADHD595 [14]1.43 (1.31–1.57)1.79 (1.63–1.97)1.65 (1.50–1.82)1.55 (1.40–1.73)MenGeneral population3952 [14]1111ADHD1245 [22]1.88 (1.76–2.00)2.18 (2.04–2.32)1.83 (1.71–1.96)1.68 (1.56–1.81)Total LMMAllGeneral population9948 [20]1111ADHD5243 [54]3.57 (3.46–3.70)3.94 (3.81–4.08)3.27 (3.15–3.38)2.69 (2.59–2.80)WomenGeneral population4516 [22]1111ADHD2336 [56]3.58 (3.41–3.77)3.96 (3.76–4.17)3.37 (3.20–3.55)2.78 (2.62–2.96)MenGeneral population5432 [20]1111ADHD2907 [52]3.57 (3.41–3.73)3.95 (3.77–4.13)3.21 (3.06–3.36)2.63 (2.49–2.77)aAdjusted for sex, age and educational level by matching, as well as for family composition, type of living area and region of birth.bAs Model 1 and additionally adjusted for baseline unemployment and baseline SA.cAs Model 2 and additionally adjusted for comorbidities for mental and somatic disorders. Table 3.HRs with $95\%$ CIs for disability pension, long-term SA, long-term unemployment and total LMM in patients with diagnosed ADHD ($$n = 4382$$) compared to their siblings without ADHD ($$n = 5582$$)Patients with ADHDCrude modelModel 1aModel 2bModel 3cn (%)HR ($95\%$ CI)HR ($95\%$ CI)HR ($95\%$ CI)HR ($95\%$ CI)Disability pensionSiblings99 [2]1111ADHD902 [21]13.92 (11.06–17.50)11.84 (9.22–15.21)11.86 (9.22–15.25)9.00 (6.61–12.27)Long-term SASiblings517 [9]1111ADHD1042 [24]3.42 (3.03–3.86)4.20 (3.63–4.86)3.06 (2.61–3.59)2.54 (2.11–3.05)Long-term unemploymentSiblings611 [11]1111ADHD861 [20]2.27 (2.02–2.55)2.03 (1.79–2.30)1.92 (1.68–2.19)1.85 (1.60–2.14)Total LMMSiblings1128 [20]1111ADHD2340 [53]3.79 (3.48–4.12)3.77 (3.43–4.14)3.23 (2.93–3.57)2.77 (2.48–3.10)aAdjusted for sex, age, educational level, family composition, urban area and region of birth.bAs Model 1 and additionally adjusted for baseline unemployment and baseline SA.cAs Model 2 and additionally adjusted for comorbidities for mental and somatic disorders.
Some $24\%$ of individuals with ADHD experienced long-term SA (>90 days) during the follow-up compared to $7\%$ in the general population. This difference translates to an HR of 4.3 for individuals diagnosed with ADHD in the crude model (Table 2). The risk estimates for long-term SA did not decrease after adjusting for socio-demographic factors but substantially decreased after adjusting for work-related factors ($24\%$ lower risk estimate) and comorbid disorders (an additional $13\%$ lower risk estimate). In the final model the risk of long-term SA was thus nearly three times higher in young adults with ADHD than in the matched comparison group (HR = 2.7, Table 2). Sex-stratified analyses showed that young adult men with ADHD had higher risk estimates of long-term SA (HR = 3.0) than young adult women with ADHD (HR = 2.5). The risk estimates of long-term SA were slightly lower compared to the siblings (HR = 2.5, Table 3).
Some $19\%$ of young adults diagnosed with ADHD were long-term unemployed during the follow-up compared to $13\%$ in the general population. In the crude model this equals a $70\%$ higher risk of long-term unemployment (HR = 1.7) compared to the matched comparison group (Table 2). The risk estimates were slightly altered when adjusting for the covariates and the risk of long-term unemployment was still about $70\%$ higher in young adults with ADHD compared to the comparison group in the final model (HR = 1.7, Table 2). In contrast, the risk estimates for long-term unemployment were slightly higher compared to the siblings (HR = 1.9, Table 3). The differences between men and women were small and insignificant.
Concerning the combined measure (i.e. total LMM), young adults diagnosed with ADHD had almost a four times higher risk in the crude model (HR = 3.6) compared to the matched comparison group (Table 2). The risk estimates for the combined LMM decreased when adjusting for work-related factors ($8\%$ lower risk estimate) and comorbid disorders (an additional $16\%$ lower risk estimate). In the final model young adults diagnosed with ADHD had nearly three times the risk compared to those in the matched comparison group (HR = 2.7, Table 2). The risk estimates were slightly higher for the siblings (HR = 2.8, Table 3) and the difference in risk estimates between men and women was trivial and not significant.
When excluding specific mental comorbidities, other mental disorders (HR = 14.5, Table 4), autism spectrum disorders (HR = 14.6), behavioural/emotional disorders (HR = 14.6) and mental retardation (HR = 14.6) showed lowest risk estimates for disability pension. For long-term SA, anxiety- and stress-related disorders (HR = 3.1) and depression/bipolar disorders (HR = 3.1) had the lowest risk estimates. Comorbid mental disorders were of less importance for long-term unemployment. The somatic comorbidities, which had the lowest risk estimates for granting disability pension, were asthma (HR = 14.6, Table 4) and neoplasms (HR = 14.6). Somatic comorbidities were of little importance for long-term SA and long-term unemployment. Table 4.HRs with $95\%$ CIs for disability pension, long-term SA (>90 days), long-term unemployment (>180 days) and total LMM (the reference group was the general population) when persons with different comorbid disorders were excluded and compared to persons with diagnosed ADHD ($$n = 9718$$)Disability pensionLong-term SALong-term unemploymentTotal LMMaHR ($95\%$ CI)HR ($95\%$ CI)HR ($95\%$ CI)HR ($95\%$ CI)Without excluding any disorder14.53 (13.34–15.84)3.24 (3.07–3.43)1.76 (1.67–1.86)3.27 (3.15–3.38)Mental disordersAnxiety- and stress-related disorders15.16 (13.76–16.71)3.07 (2.88–3.28)1.79 (1.69–1.90)3.10 (2.99–3.23)Autism-spectrum disorder14.60 (13.38–15.92)3.24 (3.06–3.43)1.77 (1.67–1.87)3.26 (3.15–3.38)Behavioural/ Emotional disorders14.56 (13.35–15.87)3.26 (3.08–3.45)1.77 (1.67–1.87)3.28 (3.16–3.40)Depression/Bipolar disorders15.21 (13.84–16.70)3.14 (2.95–3.34)1.82 (1.72–1.93)3.19 (3.07–3.32)Eating disorder14.51 (13.31–15.82)3.23 (3.05–3.42)1.77 (1.67–1.87)3.26 (3.15–3.38)Mental retardation14.56 (13.36–15.87)3.25 (3.07–3.44)1.77 (1.67–1.86)3.27 (3.15–3.39)Schizophrenia/Psychoses14.82 (13.58–16.18)3.25 (3.07–3.44)1.76 (1.67–1.86)3.26 (3.14–3.38)Substance use14.85 (13.55–16.26)3.30 (3.11–3.50)1.74 (1.64–1.85)3.26 (3.13–3.38)Other mental disorders14.57 (13.31–15.95)3.16 (2.98–3.35)1.78 (1.69–1.89)3.20 (3.08–3.32)Somatic disordersMusculoskeletal disorders14.95 (13.68–16.33)3.27 (3.08–3.47)1.78 (1.68–1.88)3.28 (3.17–3.41)Asthma14.61 (13.41–15.93)3.25 (3.07–3.44)1.77 (1.68–1.87)3.27 (3.16–3.39)Diabetes mellitus14.76 (13.55–16.10)3.26 (3.08–3.45)1.77 (1.67–1.86)3.28 (3.16–3.39)Neoplasm14.57 (13.36–15.88)3.25 (3.07–3.44)1.76 (1.67–1.86)3.26 (3.15–3.38)Cardiovascular disorders14.67 (13.46–16.00)3.26 (3.08–3.45)1.76 (1.67–1.86)3.27 (3.15–3.38)Accidents15.15 (13.83–16.60)3.24 (3.05–3.45)1.80 (1.70–1.90)3.30 (3.18–3.43)Other somatic disorders16.86 (15.11–18.80)3.44 (3.20–3.70)1.84 (1.72–1.96)3.39 (3.24–3.54)aAdjusted for sex, age and educational level by matching and family composition, type of living area and region of birth, baseline unemployment and baseline SA.
## Discussion
Young adults who were diagnosed with ADHD had a nearly three-fold higher risk for the combined measure of LMM, in which there was an almost 10-fold higher risk of being granted disability pension, about a three-fold higher risk of long-term SA and a $70\%$ higher risk of long-term unemployment compared to the matched comparison group of the same age without ADHD. Comorbid disorders were attributed to about one-third of the association between ADHD and disability pension, but much less for SA and long-term unemployment. Of these comorbidities, autism-spectrum disorders, behavioural/emotional disorders, and mental retardation were key determinants for subsequent disability pension. Depression/bipolar disorders and anxiety- and stress-related disorders were leading risk factors for subsequent long-term SA. Comorbid disorders were less likely to affect long-term unemployment.
The results of this study show that the total burden of ADHD (measured as LMM) was exceedingly high. Two other Swedish longitudinal studies also reported high levels of both disability pension and work absence in adults diagnosed with ADHD (Edvinsson & Ekselius, 2018; Virtanen et al., 2020). In addition, several international cross-sectional studies reported that, for various reasons, work absence seems to be high in patients with ADHD (de Graaf et al., 2008; Fredriksen et al., 2014; Halmoy, Fasmer, Gillberg, & Haavik, 2009; Kupper et al., 2012). As compared to earlier research on ADHD and work-related outcomes, our study was the first that had a broad focus on work-related outcomes. The population-based design and the use of administrative data make our findings robust compared to earlier studies, which were mostly based on small samples and self-reported data. In this study measures based on medical decisions and unemployment were included. The reason for including all the measures is that Sweden and other Nordic countries have a generous social insurance scheme. This scheme is a positive phenomenon that prevents many individuals with functional disabilities facing a financial crisis. Of note, this study revealed a pattern in which the increased risk of long-term unemployment in persons diagnosed with ADHD was just slightly higher compared to the general population. Instead, the risk of work disability (in particular, disability pension) was remarkably high. This outcome was further reinforced as approximately one-fourth of all individuals diagnosed with ADHD during 2006–2011 were awarded a disability pension already before the diagnosis of ADHD and were thus excluded from this study. In countries without welfare contingencies these individuals are at risk of both poverty and declining health. Many young adults in Sweden with mainly mental disabilities have been granted a time-restricted disability pension in young adulthood (Westerholm et al., 2015). For 9 of 10 individuals, this temporary disability pension was transformed into a permanent disability pension at the age of 30 years (Social Insurance Agency, 2012). Authorisation of a disability pension at the age of 19 may thus be the start of lifelong welfare dependence and marginalisation, which might be even more detrimental to the health of an individual as confirmed in several studies (McKee-Ryan et al., 2005; Paul & Moser, 2009). This has hence the potential of a major deterioration in public health. Therefore, the recommendations and policies for work rehabilitation among individuals with ADHD might need revision.
Comorbid mental and somatic disorders are reported to be common in individuals diagnosed with ADHD (Edvinsson et al., 2013; Halmoy et al., 2009). Our results were no exception, showing a 10 times higher prevalence of comorbid mental disorders in individuals diagnosed with ADHD than in the matched comparison group. However, comorbid disorders only contributed to a small fraction of the association between ADHD and the measures of LMM. A study on adults diagnosed with ADHD during childhood reported that those with comorbid disorders had a $60\%$ higher risk of work disability, i.e. disability pension and SA compared to a reference group without such disorders (Virtanen et al., 2020). Thus, comorbid disorders explained to a lesser extent the higher rate of days of disability pension and SA in the current study. One explanation for this discrepancy could be that persons in our study were diagnosed during adulthood v. during childhood in the other study and thus our participants may have had less severe symptoms during childhood. Another explanation could be that the measures are not comparable, as that study measured the total amount of days on work disability, whereas our study employed a dichotomised variable. One might conclude that both mental and somatic comorbidities play some role in explaining the high risk of LMM in persons diagnosed with ADHD. However, after considering comorbidities in our study, the risk of LMM was still extremely high. Accordingly, the symptomatic picture of ADHD is responsible for the problematic situation regarding work in young adults diagnosed with ADHD. Of the comorbidities, depression/bipolar disorders, and anxiety- and stress-related disorders had the highest impact on long-term SA. The most important comorbid disorders authorising disability pensions were comorbid autism-spectrum disorders, behavioural/emotional disorders, and mental retardation. Other studies confirm that individuals with these disorders have a high risk of work disability (Helgesson et al., 2017, 2018; McEvilly, Wicks, & Dalman, 2015; Virtanen et al., 2020).
Still, we found a substantially higher risk of LMM in individuals with ADHD compared to their siblings; however, these estimates were lower than when compared to the matched controls from the general population. This finding indicates that a part of the high risk of LMM could be attributed to familial factors. The same pattern was also seen in a study on persons diagnosed with obsessive−compulsive disorders (Pérez-Vigil, Mittendorfer-Rutz, Helgesson, Fernández de la Cruz, & Mataix-Cols, 2018). *In* general, the role of familial factors has also been studied in twins for SA and disability pension, regardless of diagnosis. Here, heredity did explain parts of the relationship with later work disability (Svedberg et al., 2011). Thus, one additional contribution of the current study is that familial factors were of some importance, but the risk of LMM in young adults with ADHD was still high compared to their siblings.
A few studies have reported sex differences in the symptomatic expression of ADHD (Gershon & Gershon, 2002). In our study we found only small sex differences in LMM, in which only long-term sickness absence had a more substantial difference in the risk estimates. This result may be an indication of that the more internalising problems seen in women might only affect their propensity to be on long-term SA (Edvinsson et al., 2013; Gershon & Gershon, 2002). Even if the symptoms seem different between women and men (Edvinsson et al., 2013), we conclude that men and women diagnosed with ADHD have equal risk estimates for LMM.
## Strengths and limitations
The major strengths of this study include the high quality and completeness of register data that allow individual information on many covariates over a long period. This strength includes the advantage of little loss to follow-up. The inclusion of several measures of LMM is another strength. The large population-based study also provides the possibility of investigating many comorbid disorders. Depending on welfare regime, the scope of LMM might vary between countries. Therefore, a combined measure of LMM, that captures both unemployment and work disability, was introduced. This combined measure may be comparable with the findings from other countries. Limitations of the study also warrant discussion. Both ADHD and all comorbid disorders have been measured by the information available in specialised health care, which most likely represents young adults with medical conditions of greater severity. This fact means that information on less severe conditions has not been covered. Still, estimates on ADHD might not have been strongly affected, as such patients are predominantly treated in specialised health care settings. In our study only persons diagnosed in young adult age were included. Because ADHD cannot be acquired during the life course, the population in this study may have less severe symptoms of ADHD than persons diagnosed during childhood. Moreover, the measure of SA does not include information during the first 14 days of sick leave for employees. Furthermore, information on individuals who are unemployed but who are not registered at The Swedish Employment *Agency is* not covered in the available dataset. Still, because our outcome variables comprise long-term measures, we are confident that this lack of information does not have a significant effect on the estimates. Finally, despite the availability of a large number of covariates, some information on e.g. body mass index was not available in the registers.
## Conclusions
Young adults with ADHD have a high risk of LMM. Of clinical importance is that comorbidity with other disorders does not play a major role in the association between ADHD and LMM. Regardless of comorbidities, establishment of early interventions targeting work capacity in young individuals with ADHD might be a clinical important intervention to prevent long-term marginalisation of these individuals.
## Financial support
Funding Statement: This study was supported by AFA Insurance (grant number: 180295).
## Conflict of interest
None.
## Ethical standards
The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.
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|
---
title: 'Social engagement and allostatic load mediate between adverse childhood experiences
and multimorbidity in mid to late adulthood: the Canadian Longitudinal Study on
Aging'
authors:
- Leslie Atkinson
- Divya Joshi
- Parminder Raina
- Lauren E. Griffith
- Harriet MacMillan
- Andrea Gonzalez
journal: Psychological Medicine
year: 2023
pmcid: PMC10009404
doi: 10.1017/S0033291721003019
license: CC BY 4.0
---
# Social engagement and allostatic load mediate between adverse childhood experiences and multimorbidity in mid to late adulthood: the Canadian Longitudinal Study on Aging
## Body
Multimorbidity involves the co-occurrence of at least two chronic health conditions, with comorbidities unrelated to an index diagnosis (Nguyen et al., 2019; Wang, Si, Cocker, Palmer, & Sanderson, 2018; Yurkovich, Avina-Zubieta, Thomas, Gorenchtein, & Lacaille, 2015). It is the latter clause that impedes understanding of this condition. What common dynamics underlie the seemingly distinct disease processes involved? This nescience compounds intervention complexity, foments piecemeal service provision, exacerbates treatment burden, and undermines patient compliance (Gallacher et al., 2014). Rates of multimorbidity are high worldwide, particularly in the 65+ age group (Nguyen et al., 2019), although absolute numbers are greater amongst those under age 65, due to population age distribution (Sheikh et al., 2016). Furthermore, rates are escalating (Steffler et al., 2021; Uijen & van de Lisdonk, 2008; Ward & Schiller, 2013), and climate change may further complicate multimorbidity patterns (Thienemann, Ntusi, Battegay, Mueller, & Cheetham, 2020). Multimorbidity is burdensome (Sheikh et al., 2016) and costly (Wang et al., 2018). Management of multimorbidity itself carries risks (Sheikh et al., 2016), and the condition is strongly related to age at mortality (Dugravot et al., 2020). Not surprisingly, multimorbidity is considered a major challenge of the 21st century [Institute of Medicine, 2012; World Health Organization (WHO), 2008].
There is evidence that multimorbidity is influenced by adverse childhood experiences (ACEs; Felitti et al., 1998; Tomasdottir et al., 2015), defined as potentially traumatic childhood events or environmental aspects that undermine the child's sense of safety (Centers for Disease Control and Prevention, 2019). ACEs, individually and in combination, are linked to the delay of early developmental milestones (Jensen, Berens, & Nelson, 2017), early- and late-life psychiatric outcomes (Gershon, Sudheimer, Tirouvanziam, Williams, & O'Hara, 2013; Taylor & Rogers, 2005), chronic physical disease, and premature mortality (Berens, Jensen, & Nelson, 2017). Moreover, the more numerous the ACEs, the greater the number of adverse outcomes experienced by the individual (Anda et al., 2006; Atkinson et al., 2015). Thus, the number of adversities is linked to the accumulation of simultaneous cognitive, mood, lifestyle (smoking, school dropout, and arrest), and physical disease outcomes in early adulthood (Atkinson et al., 2015), and to the comorbidity of outcomes, including physical health, mental health, substance abuse, memory, and interpersonal difficulties in mid to late adulthood (Anda et al., 2006). The power and latitude of empirical findings suggest that cumulated ACEs may offer deep insights into coordinated developmental trajectories that encompass diverse outcomes (Atkinson et al., 2015). In particular, they may help explain the recalcitrant complexities of multimorbidity.
Investigators propose two mediation models to explain the long-term consequences of early adversity. One involves chains of psychosocial risk: early environmental adversity activates a chain reaction in which one negative factor activates another (Rutter, 1989; Turner, Thomas, & Brown, 2016). Other investigators propose that biological embedding mediates the link between early adversity and subsequent outcomes (Anda et al., 2006; Belsky et al., 2017; Danese & McEwen, 2012; Evans, Li, & Whipple, 2013). ACEs activate physiological stress in a roughly dose-response manner, thereby undermining the development of diverse biological functions. This embedded risk, with its pervasive influence, magnifies the probability of multiple, seemingly unrelated comorbidities across psychosocial, functional, and physical health domains, and carries this risk across the lifespan. Neither model has been empirically validated (Atkinson et al., 2015; Evans et al., 2013; Nelson et al., 2020; Turner et al., 2016).
Although ‘sharply contrasting’ (Turner et al., 2016), the psychosocial chain and biological embedding models are not mutually exclusive; both dynamics may link ACEs to cumulative outcome (Atkinson et al., 2015). Presumably, many features serve to mediate the relation between ACEs and outcomes, but Atkinson et al. [ 2015] emphasize the criterion of parsimony. They proposed that putative mediators be associated with diverse risk factors and with diverse outcome factors, and with the sum of each. Further, the mediators must be programmed relatively early in life and show some stability across time.
By way of potential environmental mediators, Atkinson et al. [ 2015] posited psychosocial variables such as attachment security as an early form of social engagement. Attachment is related to numerous outcomes spanning diverse psychological and physical health domains (Carter, 2017; Maunder & Hunter, 2015) and meets early programming and stability criteria (Fraley, 2002). Attachment security is also related to the accumulation of early adversities (Belsky & Fearon, 2002). Of import here, attachment was posited as an exemplar of the broader construct of relationality because life course models of human development universally incorporate the relational dynamic (Atkinson, 2019). This is so for psychosocial (Ainsworth, Blehar, Waters, & Wall, 1978), sociocultural/cognitive (Fogel, 1993; Vygotsky, 1978), neurophysiological (Gunnar, Hostinar, Sanchez, Tottenham, & Sullivan, 2015; McEwen & Wingfield, 2003), psycho-evolutionary (Slavin & Kriegman, 1992), and biological-evolutionary (Trivers, 1974) theories. The relational imperative conforms to the biology of an altricial species wherein the young are utterly dependent on adult caregivers (Atkinson, 2019). The impact of relationality is profound: It shapes and constrains the interpretation of context. Indeed, the meaning is ‘not entirely ‘in’ the self or ‘in’ the other’; it is intersubjective and ‘becomes available’ via active engagement (Fogel, 1993) as ‘the property of a dyad, not an individual’ (Hilburn-Cobb, 2004). Significant in the current context, ‘health happens between people’ (Maunder & Hunter, 2015). Thus, more engaging maternal communication regarding diet is linked to better dietary adherence and medical outcomes (Chisholm et al., 2014a), and fewer behavioral difficulties (Chisholm, Gonzalez, & Atkinson, 2014b) in young children with type I diabetes. Similarly, insecure attachment is associated with poorer type 2 diabetes management and more negative disease outcomes (Ciechanowski et al., 2004) in adulthood. Adult attachment is also associated with poorer self-management among patients with multimorbidity (Brenk-Franz et al., 2015), and this association is mediated by the patient–provider relationship (Brenk-Franz et al., 2017). The centrality of social engagement to human development, healthy and otherwise, suggests that it is likely an important mediator between ACEs and multimorbidity.
In terms of a biological mechanism, and in the context of multiple, simultaneous risks and coinciding adverse outcomes, the mediating role of allostatic load has been hypothesized (Anda et al., 2006; Atkinson et al., 2015; Belsky et al., 2017). Allostasis involves the active adjustment of physiological function in adaptive anticipation of environmental change. There is a cumulative physiological cost to allostasis, however, termed ‘allostatic load’ (Schulkin & Sterling, 2019). If environmental challenges are repeated and/or chronic, cumulative physiological dysregulation, or ‘allostatic overload’ develops across multiple systems that support allostasis (McLoughlin, Kenny, & McCrory, 2020). Allostatic overload undermines physiological flexibility, thereby potentiating varied maladaptive outcomes across cognitive, psychiatric, and physical domains (Evans, 2003; McEwen, 2000). Consistent with the criteria for the parsimonious selection of mediators (Atkinson et al., 2015), physiological stress responses are programmed early in life with enduring effects (O'Connor, 2015; Teicher et al., 2003). Moreover, allostatic load increases with levels of cumulative adversity exposure. Thus, Evans and coworkers (Belsky et al., 2017; Evans, 2003; Evans, Kim, Ting, Tesher, & Shannis, 2007) showed that cumulative physical and psychosocial risk exposure in 8- to 10-year-old children was positively related to cumulative allostatic load [consisting of summed cortisol, epinephrine and norepinephrine, resting diastolic and systolic blood pressure, and body mass index (BMI)] assessed 3–4 years later. Belsky et al. [ 2017] showed that the cumulation of childhood risk factors prospectively predicts cumulative allostatic load (as assessed with an index consisting of 18 biomarkers related to cardiovascular, metabolic, endocrine, pulmonary, hepatic, renal, immune, and periodontal systems) at ages 26–38. Furthermore, Belsky et al. [ 2017] replicated these findings with participants' retrospective personal history reports in adulthood. A recent review (Guidi, Lucente, Sonino, & Fava, 2021) showed that allostatic load is linked to numerous chronic disorders, including cardiovascular disease, cancer, diabetes, periodontal disease, mood and anxiety disorders, post-traumatic stress disorders, psychoses, and alcohol dependence.
There are theoretical reasons to believe that ACEs are linked to multimorbidity via social engagement and allostatic load, and such data as exist are consistent with this model. However, to our knowledge, these relations have not been empirically validated in a coherent model. We aim to validate the full model here.
## Abstract
### Background
Adverse childhood experiences (ACEs) are associated with multimorbidity in adulthood. This link may be mediated by psychosocial and biological factors, but evidence is lacking. The current study evaluates this mediation model.
### Method
We analyzed data from the Canadian Longitudinal Study of Aging ($$n = 27$$ 170 community participants). Participants were 45–85 years at recruitment, when allostatic load and social engagement data were collected, and 3 years older at follow-up, when ACEs and multimorbidity data were collected. Structural equation modeling was used to test for mediation in the overall sample, and in sex- and age-stratified subsamples, all analyses adjusted for concurrent lifestyle confounds.
### Results
In the overall sample, ACEs were associated with multimorbidity, directly, β = 0.12 ($95\%$ confidence interval 0.11–0.13) and indirectly. Regarding indirect associations, ACEs were related to social engagement, β = −0.14 (−0.16 to −0.12) and social engagement was related to multimorbidity, β = −0.10 (−0.12 to −0.08). ACEs were related to allostatic load, β = 0.04 (0.03–0.05) and allostatic load was related to multimorbidity, β = 0.16 (0.15–0.17). The model was significant for males and females and across age cohorts, with qualifications in the oldest stratum (age 75–85).
### Conclusions
ACEs are related to multimorbidity, directly and via social engagement and allostatic load. This is the first study to show mediated pathways between early adversity and multimorbidity in adulthood. It provides a platform for understanding multimorbidity as a lifespan dynamic informing the co-occurrence of the varied disease processes represented in multimorbidity.
## Study design and population
The Canadian Longitudinal Study on Aging (CLSA) is a national, population-based, longitudinal study involving a stratified random sample of 51 338 community-dwelling participants aged 45–85 years at recruitment (Raina et al., 2009, 2019). Residents in the Canadian territories, on First Nation reserves, or in nursing homes, full-time members of the armed forces, or individuals with significant cognitive impairment were excluded from study participation. Physiological measures were collected on a subsample, the Comprehensive Cohort, which included 30 097 participants at recruitment, randomly selected within sex and age strata among individuals residing within 25–50 km of a CLSA data collection site in 11 locations across Canada. Of these participants, 27 170 ($90.3\%$) and 28 783 ($95.6\%$) provided blood and urine samples, respectively (see Fig. 1 for flow chart of study participant selection process). This sample included 14 133 ($52.3\%$) females and 13 632 ($49.7\%$) males. Information was collected through in-home interviews and participants visited research sites for collection of physical and biological measures. For the present study, we analyzed baseline data (collected September 2011 to May 2015) pertaining to allostatic load and social engagement, and first follow-up data (July 2015 to July 2018) pertaining to ACEs and multimorbidity. Fig. 1.Process of selection of study participants.
## Adverse childhood experiences (ACEs)
A questionnaire consisting of 14 items adapted from the Childhood Experiences of Violence Questionnaire (CEVQ) (Tanaka et al., 2012; Walsh, MacMillan, Trocmé, Jamieson, & Boyle, 2008) and the National Longitudinal Study of Adolescent to Adult Health Wave III questionnaire (Harris & Udry, 2018) were used to measure exposure to ACEs before the age of 16. Frequency and severity of childhood exposure to physical, emotional, and sexual abuse, neglect, and intimate partner violence were assessed on an ordinal scale (never, 1–2 times, 3–5 times, 6–10 times, or more than 10 times) and responses were dichotomized in accordance with the CEVQ guidelines to identify the presence or absence of the exposure. Physical abuse was considered present if the participant endorsed being: [1] slapped on the face, head or ears, or hit or spanked with something hard three or more times; [2] pushed, grabbed, or shoved, or having something thrown to hurt three or more times; or [3] kicked, bit, punched, choked, burned, or physically attacked in some other way one or more times (Tanaka et al., 2012). Sexual abuse was considered present if the participant reported being sexually touched against their will or being threatened or forced into unwanted sexual activity one or more times (Tanaka et al., 2012). Emotional abuse was present if the participant reported parents or guardians swearing, saying hurtful or insulting things that made them feel unloved or unwanted three or more times. Neglect was present if the participant reported parents or guardians not having taken care of their basic needs one or more times. Exposure to intimate partner violence was present if the participant saw or heard parents or guardians say hurtful things to each other six or more times or hit each other three or more times (Tanaka et al., 2012). Other forms of household adversity, such as parental divorce or separation, parental death, and living with a family member with mental health problems, were assessed dichotomously as ‘yes’ or ‘no’. An ACEs index was created by summing the individual ACEs that participants experienced (Cronbach's α = 0.73).
## Allostatic load index
An allostatic load index was constructed to assess cumulative physiological dysregulation. Hematological, cardiometabolic, and biomarkers comprising white blood cells, HbA1c, albumin, alanine aminotransferase, creatinine, hemoglobin, ferritin, C-reactive protein, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, systolic and diastolic blood pressure, heart rate, BMI, waist circumference, and waist to hip ratio comprised the allostatic load index. High risk was defined using the upper or lower 25th percentile of the sample distribution of the specific biomarker, consistent with recommendations (Seeman, Singer, Rowe, Horwitz, & McEwen, 1997). An allostatic load index was calculated by summing the number of biomarkers falling within the high-risk categories. This count-based formula is used most often in the literature (Gallo et al., 2012; Juster, McEwen, & Lupien, 2010) and is recommended to harmonize international work (McLoughlin et al., 2020).
## Social engagement
Social engagement was assessed by combining social support and social participation measures. Social support was assessed with the 19-item Medical Outcomes Study and Social Support Survey, measuring perception of emotional support, instrumental assistance, information, guidance and feedback, personal appraisal support, and companionship (Sherbourne & Stewart, 1991). Scores were calculated by averaging responses and transforming them to range from 0 to 100 (Sherbourne & Stewart, 1991). Social participation was assessed as the frequency of participation (at least daily, weekly, monthly, or yearly) in community-related activity. Again, social support and social participation were combined into a single social engagement measure.
## Multimorbidity
Based on the stability of prevalence estimates, Fortin, Stewart, Poitras, Almirall, and Maddocks [2012] recommended the inclusion of at least 12 chronic diseases in the construction of a multimorbidity index, each with high prevalence and impact or burden in the population. We constructed an index of 21 conditions meeting these conditions in North America. These involve skeletal, nervous, endocrine, cardiovascular, lymphatic, and respiratory organ systems, viz. heart disease, myocardial infarction, angina, stroke, transient ischemic attack, peripheral vascular disease, hypertension, diabetes, chronic obstructive pulmonary disease, Parkinson's disease, epilepsy, multiple sclerosis, migraines, osteoarthritis, osteoporosis, kidney disease, cataracts, glaucoma, cancer, mood disorders, and anxiety disorders. Participants reported chronic conditions diagnosed by a health professional with past and/or expected minimum duration of 6 months. The number of conditions endorsed was summed for each participant.
## Covariates
To limit the possibility that relations between ACEs, mediators, and outcomes were confounded by adult life choices and circumstances (Turner et al., 2016), several covariates were incorporated. Cigarette smoking was assessed as never, occasional, or current smoker with 0–15 cigarettes/day, and current smoker with >15 cigarettes/day. Nutritional intake quality was assessed using the Seniors in the Community Questionnaire (Keller, Goy, & Kane, 2005). Meeting recommendations for fruit/vegetable intake is often used as an indicator of diet quality. Less than four daily servings of fruits/vegetables was considered unhealthy. Physical activity was assessed using the Physical Activity Scale for the Elderly scale (Washburn, Smith, Jette, & Janney, 1993). Participants were considered physically active if they engaged in moderate or vigorous activity three or more times/week. Participants also reported the frequency of alcohol consumption during the last 12 months on a scale of 0 (‘never’) to 7 (‘almost every day’). Annual household income was categorized as less than $20 000, $20 000–49 999, $50 000–99 999, $100 000–149 999, and $150 000 and above. Data on all covariates were collected at baseline visit. As a final check on the model, to ensure that results could not be solely attributed to the effect of baseline multimorbidity and allostatic load, rather than vice versa, baseline multimorbidity was also entered into the model as a covariate.
## Statistical analysis
Descriptive analysis was adjusted for the sampling design and performed using inflation weights provided by the CLSA (CLSA Methodology Working Group, 2017). Structural equation modeling with Full Information Maximum Likelihood (FIML) was used to test for mediation and manage missing data. Under the missing at random assumption, FIML estimates a likelihood function for each individual based on the observed variables, such that all available data are used in deriving unbiased parameter estimates and standard errors where data are missing (Enders & Bandalos, 2001; Wothke, 2000).
A latent variable for social engagement was created from social support and participation scales. The path models tested the direct effect of ACEs on allostatic load, social engagement, and multimorbidity, the direct effect of allostatic load and social engagement on multimorbidity, and the indirect effect of ACEs on multimorbidity via allostatic load index and social engagement. We included a covariance between the two mediators – social engagement and allostatic load – and tested the path from social engagement to allostatic load. Overall model and age- and sex-stratified models were tested. Age- and sex-stratified models were adjusted for annual income, physical activity, nutritional intake, and smoking, and the overall model was further adjusted for age and sex. Model fit was assessed using the Comparative Fit Index (CFI) value of >0.95, Standardized Root Mean Square Residual (SRMR) of >0.05, and the Root Mean Square Error of Approximation (RMSEA) of <0.05 indicate good fit. Analyses were conducted in SAS v.9.4.
## Results
Table 1 presents descriptive sample features. Figure 2 shows a path diagram of the structural model of factors associated with multimorbidity in the overall CLSA sample. Observed variables are denoted by rectangles, the latent construct by an ellipse. The RMSEA for the overall model was 0.06, SRMR was 0.02, and CFI was 0.97, indicating good fit of data with the hypothesized structural model. Fit indices for sex- and age-stratified models also indicate good data fit. In the overall sample, the R2 for multimorbidity indicates that ACEs, allostatic load, and social function together account for $26.5\%$ of the variation in multimorbidity. Table 1.Descriptive statistics by age and sex: adverse childhood experiences, multimorbidity, mediators, and covariatesOverall sampleAges 45–54Ages 55–64Ages 65–74Ages 75–85MaleFemaleMaleFemaleMaleFemaleMaleFemaleN27 7652141 (52.5)2252 (47.5)4379 (50.5)4757 (49.5)4114 (47.7)4122 (52.3)2998 (45.7)3002 (54.3)Total ACEs score, mean (s.e.)1.4 (0.01)1.6 (0.04)1.7 (0.04)1.4 (0.03)1.7 (0.03)1.2 (0.03)1.4 (0.03)1.0 (0.03)1.0 (0.03)ACEs index09845 (35.9)688 (32.5)673 (31.5)1491 (35.0)1462 (32.1)1506 (38.0)1454 (37.3)1261 (44.8)1310 (48.4)17250 (26.9)545 (26.1)548 (24.7)1185 (27.9)1126 (24.5)1152 (29.7)1018 (26.0)854 (30.8)822 (29.1)23950 (15.5)331 (16.2)326 (16.0)669 (16.5)745 (16.1)580 (15.1)579 (15.0)389 (13.9)331 (11.5)3 + 5226 (21.8)493 (25.2)601 (27.8)847 (20.6)1202 (27.3)644 (17.2)814 (21.7)290 (10.5)335 (11.0)Multimobidity, mean (s.e.)2.4 (0.02)1.2 (0.04)1.6 (0.04)1.8 (0.03)2.3 (0.04)2.8 (0.04)3.4 (0.05)3.8 (0.05)4.5 (0.06)Multimorbidity, n (%)03733 (19.3)744 (36.9)582 (27.9)967 (25.6)722 (18.5)373 (10.8)218 (5.9)88 (3.4)39 (1.8)14952 (22.8)591 (30.7)576 (28.4)1084 (27.9)1013 (24.0)721 (19.9)525 (15.3)269 (10.6)173 (7.0)24686 (19.2)355 (18.0)422 (21.1)797 (20.0)895 (20.6)765 (20.6)697 (18.8)459 (18.3)296 (11.8)3+11 157 (38.7)289 (14.5)480 (22.6)1130 (26.4)1682 (36.9)1768 (48.7)2205 (60.0)1693 (67.8)1910 (79.3)Allostatic load index, mean (s.e.)4.2 (0.02)5.2 (0.08)2.4 (0.06)5.5 (0.06)3.0 (0.05)5.3 (0.06)3.5 (0.05)4.8 (0.06)3.4 (0.05)Social participation, mean (s.e.)3.0 (0.01)2.9 (0.02)3.0 (0.01)2.9 (0.01)3.0 (0.01)3.0 (0.01)3.1 (0.01)3.0 (0.01)3.1 (0.01)Social support, mean (s.e.)82.6 (0.10)83.0 (0.40)84.9 (0.33)82.5 (0.30)83.1 (0.27)83.1 (0.30)81.8 (0.29)81.4 (0.36)77.7 (0.37)Total annual household income, n (%)<$ 20 0001303 (4.2)55 (2.6)80 (3.0)150 (3.2)211 (4.1)148 (3.2)284 (6.8)92 (3.1)283 (11.2)$20 000–<50 0005617 (17.8)166 (8.3)228 (9.3)475 (10.3)735 (14.5)804 (20.2)1257 (32.1)765 (29.0)1187 (44.3)$50 000–<100 0009286 (33.5)529 (25.3)665 (29.9)1242 (28.4)1536 (34.2)1666 (42.5)1511 (40.9)1276 (44.0)861 (32.6)$100 000–<150 0005218 (22.6)593 (29.5)569 (27.7)1132 (27.5)1033 (24.1)772 (19.7)475 (13.3)439 (15.5)205 (8.7)$150 000 or more4617 (21.8)731 (34.2)617 (30.1)1219 (30.6)975 (23.1)520 (14.4)254 (6.9)227 (8.4)74 (3.2)Low physical activity, n (%) 20 585 (72.9)1415 (66.5)1586 (70.3)2949 (67.6)3535 (74.4)2929 (70.8)3259 (79.7)2381 (79.4)2531 (85.0)Low nutritional intake, n (%) 11 302 (39.6)1049 (48.8)632 (26.8)2290 (52.1)1341 (27.2)2146 (51.9)1218 (28.2)1667 (54.5)959 (30.7)Smoking, n (%)Never smoker 16 568 (87.1)1094 (83.1)1121 (81.1)2589 (84.9)2700 (86.0)2801 (90.5)2400 (89.8)2146 (95.4)1717 (93.8)Occasional smoker447 (2.6)44 (3.5)60 (3.7)110 (3.8)94 (2.8)47 (1.5)52 (1.7)20 (1.0)20 (0.9)Current smoker 0–15cigarettes/day1089 (6.4)117 (8.7)166 (10.9)176 (5.9)231 (7.4)129 (4.3)141 (5.0)62 (2.6)67 (3.5)Current smoker 15+cigarettes/day690 (3.9)63 (4.7)61 (4.3)176 (5.4)124 (3.9)130 (3.7)84 (3.4)23 (1.0)29 (1.7)Alcohol consumption, mean (s.e.)3.9 (0.01)4.1 (0.04)3.6 (0.04)4.3 (0.03)3.7 (0.03)4.5 (0.04)3.6 (0.04)4.4 (0.05)3.4 (0.05) Fig. 2.Structural model of factors influencing multimorbidity. Model adjusted for age, sex, income, smoking, nutrition, and alcohol consumption. Covariance between social engagement and allostatic load was included. All paths are statistically significant, $p \leq .0001.$ ACEs = Adverse childhood experiences We estimated the direct and indirect effects of ACEs on multimorbidity. ACEs, allostatic load, and social engagement significantly predicted multimorbidity (Fig. 2). The number of ACEs was positively associated with the number of chronic conditions, β = 0.12 ($95\%$ confidence interval 0.11–0.13). Higher social engagement was associated with a decrease in chronic conditions, β = −0.10 (−0.12 to −0.08), whereas increased allostatic load was positively associated with the number of chronic conditions, β = 0.16 (0.15 to 0.17). In addition to direct relationships, allostatic load and social engagement mediated between ACEs and multimorbidity. Increase in the number of ACEs was associated with lower social engagement, β = −0.14 (−0.16 to −0.12) and higher allostatic load, β = 0.04 (0.03–0.05), which in turn significantly predicted multimorbidity. The total effect of ACEs (β = 0.14; 0.13–0.15) on multimorbidity equals the sum of the direct pathway (β = 0.12) and the indirect pathways (β = 0.02; Fig. 2).
The relation between ACEs and multimorbidity stratified by sex and age (Table 2) was examined. The results were consistent with those of the overall model; however, the direct effect of ACEs and the indirect effect of ACEs through social engagement and allostatic load on multimorbidity were stronger in females than males. ACEs, allostatic load, and social engagement together accounted for $28\%$ of the variation in multimorbidity among females and $23\%$ among males. Regarding age, the direct and indirect effects of ACEs on multimorbidity were stronger in the younger age group (45–54 years) and weakened with increasing age. Among 75–85-year-old individuals, ACEs had a direct effect on multimorbidity, but the indirect effects via allostatic load and social engagement were not significant. Within this age group, social engagement had a direct effect on multimorbidity, but allostatic load did not. ACEs, allostatic load, and social engagement together accounted for $15\%$ of the variation in multimorbidity in 45–54- and 55–64-year-old age groups, $9\%$ in the 65–74-year-old group, and % in the 75–85-year-old group. Table 2.Pathway estimates from structural equation model for the overall sample and by sex and age groupsPathwayMalesFemales45–54 years55–64 years65–74 years75–85 yearsACEs→multimorbidity0.10 (0.08, 0.12)0.13 (0.11, 0.15)0.15 (0.13, 0.17)0.13 (0.11, 0.15)0.11 (0.08, 0.14)0.11 (0.08, 0.14)Indirect0.01 (0.01, 0.01)0.03 (0.02, 0.04)0.03 (0.02, 0.04)0.03 (0.02, 0.04)0.02 (0.01, 0.03)0.006 (n.s.) ( −0.002, 0.01)Total0.11 (0.09, 0.13)0.15 (0.13, 0.17)0.18 (0.16, 0.20)0.16 (0.14, 0.18)0.13 (0.10, 0.16)0.10 (0.07, 0.13)ACEs→allostatic load0.04 (0.02, 0.06)0.05 (0.03, 0.07)0.06 (0.04, 0.08)0.03 (0.01, 0.05)0.03 (0.00, 0.06)0.004 (n.s.) ( −0.03, 0.04)ACEs→social engagement−0.11 (−0.13, −0.09)−0.17 (−0.20, −0.14)−0.14 (−0.17, −0.11)−0.17 (−0.20, −0.14)−0.14 (−0.18, −0.10)−0.09 (−0.14, −0.04)Allostatic load→multimorbidity0.15 (0.13, 0.17)0.16 (0.14, 0.18)0.24 (0.21, 0.27)0.21 (0.19, 0.23)0.14 (0.11, 0.17)0.10 (0.06, 0.14)Social engagement→multimorbidity−0.07 (−0.10, −0.04)−0.12 (−0.15, −0.09)−0.12 (−0.17, −0.07)−0.13 (−0.17, −0.09)−0.10 (−0.13, −0.07)−0.06 (−0.12, 0.001)All estimates are direct effects unless indicated. Values in parentheses are $95\%$ confidence intervals. n.s., non-significant. Model adjusted for covariates, viz., income, smoking, nutrition, activity level, and alcohol consumption. Covariance between social engagement and allostatic load was included.
As a final check on the robustness of the model, we analyzed the full-sample data again, using all aforementioned variables, but with the addition of baseline multimorbidity as a covariate. The model is significant (Table 3), explaining $73\%$ of the variance (a $47\%$ increase over the model without baseline multimorbidity). All path coefficients are smaller, as compared to the model without multimorbidity, but they are significant. These changes in model parameter estimates are likely due to the stability of multimorbidity, which is increasingly difficult to reverse (Wang et al., 2018); the current data show a bivariate correlation of $r = 0.82$ between baseline and follow-up multimorbidity scores. The addition of the baseline multimorbidity covariate shows that the model's significance is not simply due to illness that preceded the measures of social engagement and allostatic load. Despite the fact that this more inclusive model explains greater variance than does the model without baseline multimorbidity, we use the model without multimorbidity for explanatory purposes below. We do so because we assume that baseline multimorbidity levels are themselves related to ACEs, and to prior social engagement, and allostatic load, consistent with the life course model we propose here. So, although the model that includes baseline multimorbidity accounts for substantially more variance than the one that does not, the additional variance does not reflect the power of the model presented here, but the stability of multimorbidity. Table 3.Pathway estimates from structural equation model for the overall sample, baseline multimorbidity covariedPathwayβ (s.e.)ACEs→multimorbidity0.02 (0.004)Indirect0.003 (0.0009)Total0.02 (0.004)ACEs→allostatic load0.02 (0.0006)ACEs→social engage−0.13 (0.009)Allostatic load→multimorbidity0.05 (0.004)Social engage→multimorbidity−0.02 (0.006)Social engage→allostatic load–R2 for multimorbidity$73\%$All estimates represent direct effects unless indicated.s.e., standard error. Model adjusted for income, smoking, nutrition, activity level, alcohol consumption, and baseline multimorbidity. Covariance between social engagement and allostatic load was included.
## Discussion
As hypothesized, ACEs were related to multimorbidity and this relation was mediated by social engagement and allostatic load (Fig. 2). To our knowledge, this is the first study to integrate three prominent factors (ACEs, social engagement, allostatic load) into a mechanistic, lifespan model predicting multimorbidity. Both mediators – social engagement and allostatic load – were selected a priori because they are sensitive to early adversity and have a central role in physical and mental health across the lifespan (Atkinson et al., 2015). The findings support the theory that each ACE increases stress, undermining social engagement and contributing to allostatic load, thereby increasing the risk of multimorbidity (Anda et al., 2006; Atkinson et al., 2015; Belsky et al., 2017; Berens et al., 2017; Felitti et al., 1998; Nelson et al., 2020; Shonkoff & Garner, 2012).
The model was validated in the overall sample, and in the sex- and age-stratified subsamples (Table 2). Regarding sex, the model pathways from ACEs to both mediators and to multimorbidity, and from mediators to multimorbidity, were consistently stronger in females, compared to males. This may be because females experience more ACEs than do males, as shown here (Table 1, Total ACEs Score 3+) and elsewhere (Nguyen et al., 2019; Tomasdottir et al., 2015), females experience qualitatively different ACEs [females more frequently report sexual abuse, neglect, and household dysfunction (Haahr-Pedersen et al., 2020); males more often report interpersonal violence (McAnee, Shevlin, Murphy, & Houston, 2019)], and females experience greater complexity of ACE co-occurrence patterns (Haahr-Pedersen et al., 2020; McAnee et al., 2019). Therefore, it is likely that females are more vulnerable to adverse outcomes and their precursors than are males (Haahr-Pedersen et al., 2020).
The full model, including pathways between ACEs and multimorbidity, ACEs and both mediators, and both mediators and multimorbidity, was established by age span 45–54. The model remained significant across subsequent age groups (see Table 2 for pathway estimates) but there was a general diminution of all pathways with increasing age. At 75–85, the model remained significant and largely intact, but some pathways were not significant. The direct pathway between ACEs and multimorbidity remained significant, as did the pathways between ACEs and social engagement, social engagement and multimorbidity, and allostatic load and multimorbidity. However, the total indirect pathway between ACEs and multimorbidity, through social engagement and allostatic load, was non-significant in this age group. This may be related to the facts that ACEs and mediators are temporally separated and increasingly moderated by intervening events, and multimorbidity inexorably increases with advancing age, regardless of prior influences (Epel, 2020).
However, social engagement remained significantly linked to ACEs and multimorbidity in the 75–85-year age group. The finding confirmed prior observations that the speed of allostatic load and morbidity accumulation was reduced by richer late-life social networks (Dekhtyar et al., 2019), potentially via gene expression (Brown et al., 2020; Rentscher, Carroll, Cole, Repetti, & Robles, 2020) and greater adherence to positive health behaviors (Epel, 2020). Indeed, developmental models, including geroscience models (Epel, 2020), unanimously acknowledge the importance of social engagement across the lifespan (Atkinson, 2019).
It might be argued that the association between ACEs and multimorbidity is simply due to the possibility that ACEs predict common variance shared by chronic disease outcomes. This explanation is unlikely, however. The morbidities comprising the multimorbidity index presented here were largely independent, with a median φ coefficient of 0.04, interquartile range = 0.01–0.07. These statistics are consonant with the definition of multimorbidity as the co-occurrence of chronic health conditions, with comorbidities unrelated to an index diagnosis (Nguyen et al., 2019; Wang et al., 2018; Yurkovich et al., 2015). Rather, the relations between ACEs and multimorbidity appeared related to the broad impact of ACEs, social engagement, and allostatic load on diverse and statistically independent morbidities.
The model presented here supports the theory that multimorbidity is a developmental process beginning with early experience and extending across the lifespan. This conceptualization is further supported by the fact that relations amongst ACEs, mediators, and multimorbidity held even after six risk factors, all pertaining to current or relatively late-life conditions (economic challenge, cigarette smoking, alcohol consumption, nutrition, physical activity, and baseline multimorbidity) were covaried in the model. These risks are themselves related to ACEs and chronic disease (Christakis, 2016; Dietz, Douglas, & Brownson, 2016; Ford, Butler, Hughes, Quigg, & Bellis, 2016; Vander Weg, 2011); as such, they comprise particularly stringent covariates.
These data further support recommendations regarding the need for prevention and early intervention in early life (Nelson et al., 2020), when healthy aging apparently begins. It is important to note that social engagement and biological embedding are potently influenced by childhood experience and are increasingly difficult to reverse (Fraley, Gillath, & Deboeck, 2021; Wang et al., 2018). As such, early interventions could forestall the onset of disease across multiple conditions simultaneously (Belsky et al., 2017).
At the same time, interventions at any age are potentially useful (Shonkoff, Boyce, & McEwen, 2009). Social engagement may be particularly important in this regard, as a potentially modifiable factor (Yu, Steptoe, Chen, & Jia, 2021) associated with ACEs and multimorbidity across the full age range assessed here, and may potentially augment interventions aimed at extending the years of healthy function (healthspan; Epel, 2020). Frequently, contemporary interventions involve a ‘whack a mole approach’ (Epel, 2020), comprising ad hoc, piecemeal, and in some cases, iatrogenic polypharmacy (Sheikh et al., 2016), with medications prescribed separately by diagnosis (Sathanapally et al., 2020; Sehgal et al., 2013). Supplementation with social interventions (Turner et al., 2016) may exert a more generalized influence, in accord with the World Health Organization's call for simplified multimorbidity management (Sheikh et al., 2016). Moreover, patients with multimorbidity themselves prioritize continued social function, although treating clinicians often do not (Sathanapally et al., 2020).
The model validated here represents a basic platform that can be broadened to encompass additional risk indices and outcomes, mediators, and moderators. Respecting additional mediators, and by way of example, child variables such as self-regulation are central to development and are programmed early (Evans, Fuller-Rowell, & Doan, 2012; Nigg, 2017). Later in life, self-management skills and behaviors, related to adult attachment, quality of relationships with care providers, and chronic disease, may become important mediators (Brenk-Franz et al., 2015, 2017; Ciechanowski et al., 2004). The most effective moderators may involve those which, like the mediators, exert their effects early and broadly. For example, pleiotropic genes like the dopamine receptor D4 (HGNC:3025) influence diverse neurobiological processes and psychiatric/neurological phenotypes, including reaction to stress (Ptácek, Kuzelová, & Stefano, 2011), central to the allostatic load construct.
The platform might also be expanded via alternative analytic strategies. For example, the current analysis assessed a parallel mediation model, wherein social engagement and allostatic load contributed simultaneously to chronic disease onset. However, an expanded, serial mediation model might better represent the complexity of development (Nelson et al., 2020); or again, the analytic approach used here assessed for mediation and disregarded moderation. But it is possible that social engagement and allostatic load both mediate between ACEs and multimorbidity, and are, at the same time, moderated by ACEs (Hayes, 2017). Indeed, in a post hoc analysis, we found that ACEs moderate the impact of allostatic load (but not social engagement) on multimorbidity. Thus, future research might adopt a counterfactual approach to examine natural, pure, and controlled direct and indirect effects (Hayes, 2017). Considerations presented in the last two paragraphs may augment the platform presented here.
This study has the following limitations. The statistical modeling was correlation-based, precluding unequivocal causality claims. This would require random assignment of participants, experimental manipulation of early life experiences, and very long-term follow-up, impossible with humans. Nevertheless, model results were hypothesized a priori based on logic and theory, replicated multiple times across sex and age, and were based on a longitudinal design, all criteria that buttress causal inferences of correlation-based mediation models (Hayes, 2017).
Exposure to ACEs, social engagement, current behaviors, and diagnoses were based on self-report, with potential response bias. Furthermore, ACEs were reported retrospectively. However, the ACEs-outcome link does not appear dependent on the time of reporting, the association between ACEs count and adverse outcomes emerges consistently in both prospective and retrospective studies, and comparisons of data across these studies show no evidence of report bias (Karatekin & Hill, 2019). Importantly, Belsky et al. [ 2017] found that early life risk factors, assessed prospectively, predicted allostatic load, and they replicated these findings using retrospective self-reports.
Regarding multimorbidity estimates, the use of self-reported clinical diagnoses is common in the epidemiological literature. Within-diagnosis reliability estimates are high (Dal Grande, Fullerton, & Taylor, 2012). Reporting errors typically involve underreporting, and show consistent specificity, albeit variable sensitivity. In any event, the approach provides useful estimates of population prevalence (Chun, Kim, & Min, 2016; Martin, Leff, Calonge, Garrett, & Nelson, 2000). For example, Cho, Chang, Ahn, Shin, and Ryu [2018] found that women underreported smoking but sensitivity and specificity rates of self-reported diagnosis (thyroid cancer) were $98.1\%$ and $99.8\%$, respectively, when compared to a national registry. Wang and Sungsuk [2019] also showed that smoking was underreported, particularly among women, but the bias did not affect the prediction of cardiovascular disease. Such findings suggest that insofar as bias exists in the current data, it is not sufficient to invalidate findings. This is not to say, by way of proviso, that ACE scores are accurately associated with health difficulties at the individual level; rather, they are related to mean group differences (Baldwin et al., 2021). Nevertheless, clinical trials and interventions for multimorbidity will require quick, inexpensive assessments, such that validation of self-report utility is crucial (Belsky et al., 2017).
In the present study, allostatic load and multimorbidity are assessed 3 years apart. It might be argued that this temporal spacing is insufficient to assess the link between them. However, it is not yet clear what constitutes ideal timing and, in any case, optimal timing may be age-dependent. In earlier adulthood, when allostasis and multimorbidity are less confounded, longer time spans may be more sensitive to cumulating change and gradual disease onset. However, as Pace of Aging (Belsky et al., 2017) accelerates, and allostatic overload and multimorbidity come to coexist as the former progresses into the latter, shorter time spans may better capture the more rapid shifts. This much is consistent with the present data. Allostatic load played a statistically significant mediating role between ACEs and multimorbidity in all age groups except the oldest. The 3-year temporal spacing between measurement of allostatic load and multimorbidity is sensitive to the distinction between these constructs in the younger age groups; it is conceivable that a shorter time span between allostatic load and multimorbidity would have been useful in the assessment of the 75- to 85-year age group.
A related confound involves the fact that social engagement and allostatic load were assessed concurrently, at baseline visit. It is possible that individuals were already experiencing chronic conditions, such that social engagement deficits did not influence multimorbidity, but were influenced by it. We would not deny the likelihood that relations between these processes are transactional (Hostinar, Sullivan, & Gunnar, 2014). However, the mediation model tested here was prespecified (Atkinson et al., 2015), relationality serves a primary function in health and disease across the lifespan, and the measures of engagement used here are highly reliable [The Medical Outcomes Study and Social Support Survey has 1-year retest reliability of $r = 0.78$ in a sample with chronic disease (Sherbourne & Stewart, 1991)]. Moreover, we assessed the impact of multimorbidity at baseline, and the model and all its paths remained significant, suggesting that the history of multimorbidity is not the driving factor in the mediation model proposed here. It is true, however, that a prospective longitudinal study with repeated measures of all constructs and cross-lagged analyses would help resolve ambiguities.
Another limitation of the present study involves the lack of some important covariates and moderators. These include early moderators such as family of origin of medical history, itself linked to multimorbidity (Singh et al., 2019). In addition, features that emerge in the 3 years between the assessment of mediators and multimorbidity [e.g. self-management, relationship between participant and care-providers (Brenk-Franz et al., 2015, 2017)] may mediate or moderate relations between predictors and outcome. Exploration of such variables is recommended in future studies. On the other hand, the present study does control for income, tobacco and alcohol use, diet, and physical activity level, all importantly related to both early adversity and multimorbidity. Furthermore, we conducted an analysis controlling for multimorbidity at baseline assessment. Despite the influence of this extremely potent covariate on multimorbidity at follow-up (baseline and follow-up multimorbidity correlate at $r = 0.82$), the model, and all its paths, remained significant, attesting to its robust nature.
The integrative model presented here is consonant with predictions and consistent across sex and age in a national, population-based longitudinal study. To our knowledge, this is the first empirical demonstration of mediation in the context of ACEs and outcome generally, and ACEs and multimorbidity, in particular. From a clinical perspective, the findings indicate that the knowledge of childhood adversity may serve to identify those at risk for early multimorbidity. The data also suggest that intervention in childhood may reduce the probability of multimorbidity. Furthermore, social engagement facilitated at any age for those at high risk of multimorbidity may reduce or delay negative health conditions. Moreover, the model presented here provides a basic platform that, with expansion, may help identify the life course events and processes that will further explain multimorbidity and provide intervention targets.
## Data
Data are available from the Canadian Longitudinal Study on Aging (www.clsa-elcv.ca) for researchers who meet the criteria for access to de-identified CLSA data.
## Financial support
The Canadian Longitudinal Study on *Aging is* funded by the Canadian Institutes of Health Research (CIHR) and the Canada Foundation for Innovation. This analysis was supported by a CIHR CLSA catalyst grant (#ACD – 170300). Parminder Raina holds a Tier 1 Canada Research Chair in Geroscience and the Raymond and Margaret Labarge Chair in Research and Knowledge Application for Optimal Aging. Lauren Griffith holds the McLaughlin Foundation Professorship in Population and Public Health. Andrea Gonzalez holds a Tier 2 Canada Research Chair in Family Health and Preventive Interventions. Harriet MacMillan is supported by the Chedoke Health Chair in Child Psychiatry.
## Ethical standards
The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.
## Disclaimer
The opinions expressed in this manuscript are the authors' own and do not reflect the views of the Canadian Longitudinal Study on Aging.
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title: A population-wide analysis of the familial risk of suicide in Utah, USA
authors:
- Amanda V. Bakian
- Danli Chen
- Chong Zhang
- Heidi A. Hanson
- Anna R. Docherty
- Brooks Keeshin
- Douglas Gray
- Ken R. Smith
- James A. VanDerslice
- David Z. Yu
- Yue Zhang
- Hilary Coon
journal: Psychological Medicine
year: 2023
pmcid: PMC10009406
doi: 10.1017/S0033291721003020
license: CC BY 4.0
---
# A population-wide analysis of the familial risk of suicide in Utah, USA
## Abstract
### Background
The degree to which suicide risk aggregates in US families is unknown. The authors aimed to determine the familial risk of suicide in Utah, and tested whether familial risk varies based on the characteristics of the suicides and their relatives.
### Methods
A population-based sample of 12 160 suicides from 1904 to 2014 were identified from the Utah Population Database and matched 1:5 to controls based on sex and age using at-risk sampling. All first through third- and fifth-degree relatives of suicide probands and controls were identified ($$n = 13$$ 480 122). The familial risk of suicide was estimated based on hazard ratios (HR) from an unsupervised Cox regression model in a unified framework. Moderation by sex of the proband or relative and age of the proband at time of suicide (<25 v. ⩾25 years) was examined.
### Results
Significantly elevated HRs were observed in first- (HR 3.45; $95\%$ CI 3.12–3.82) through fifth-degree relatives (HR 1.07; $95\%$ CI 1.02–1.12) of suicide probands. Among first-degree relatives of female suicide probands, the HR of suicide was 6.99 ($95\%$ CI 3.99–12.25) in mothers, 6.39 in sisters ($95\%$ CI 3.78–10.82), and 5.65 ($95\%$ CI 3.38–9.44) in daughters. The HR in first-degree relatives of suicide probands under 25 years at death was 4.29 ($95\%$ CI 3.49–5.26).
### Conclusions
Elevated familial suicide risk in relatives of female and younger suicide probands suggests that there are unique risk groups to which prevention efforts should be directed – namely suicidal young adults and women with a strong family history of suicide.
## Introduction
Suicide is the 10th leading cause of death in the USA (Hedegaard, Curtin, & Warner, 2018). While the risk of other top causes of mortality has declined recently in the USA, suicide rates increased by $30\%$ between 1999 and 2016 (Stone et al., 2018). Suicide's etiology is complex with predisposing, mediating, and short-term risk factors from genetic and environmental sources implicated in its causal pathway (Turecki, 2014). Low predictive performance of existing prediction tools (Belsher, Smolenski, & Pruitt, 2019) and a lack of effective evidence-based interventions for suicide mortality (Nelson et al., 2017; Riblet, Shiner, Young-Xu, & Watts, 2017) indicate the ongoing need for improved understanding of suicide's risk factors.
The aggregation of suicide mortality (herein referred to as ‘suicide’) in families is a strong risk factor for suicide with evidence originating largely from twin (Juel-Nielsen & Videbech, 1970; Pedersen & Fiske, 2010; Roy, Segal, Centerwall, & Robinette, 1991), adoption (Kendler, Ohlsson, Sundguist, Sundguist, & Edwards, 2020; Petersen, Sørensen, Andersen, Mortensen, & Hawton, 2013; Schulsinger, Kety, Rosenthal, & Wender, 1979; von Borczyskowski, Lindblad, Vinnerljung, Reintjes, & Hjern, 2011), and population-based family studies (Agerbo, 2005; Agerbo, Nordentoft, & Mortensen, 2002; Cheng et al., 2014; Egeland & Sussex, 1985; Garssen, Deerenberg, Mackenbach, Kerkhof, & Kunst, 2011; Kim et al., 2005; Qin & Mortensen, 2003; Qin, Agerbo, & Mortensen, 2002, 2003; Runeson & Åsberg, 2003; Tidemalm, Runeson, & Waern, 2011). While twin and adoption studies offer robust designs for the investigation of familial suicide risk among close relatives, population-based family studies often include large sample sizes, which allow for the calculation of familial suicide risks in specific kinships across a broad range of relatives. The preferred family study is prospective and uses random sampling thereby minimizing the risk of selection bias (Hopper, Bishop, & Easton, 2005). To date, research into the familial risk of suicide using population-based family study designs has largely been limited to work done in Northwestern European countries using data linked across health and multi-generational registries (but see Cheng et al., 2014; Kim et al., 2005). These studies have primarily measured the familial aggregation of suicide in first-degree relatives (Agerbo, 2005; Agerbo et al., 2002; Garssen et al., 2011; Qin & Mortensen, 2003; Qin et al., 2002, 2003; Runeson & Åsberg, 2003) with a single study (Tidemalm et al., 2011) examining familial risk in second- and third-degree relatives (e.g. cousins). In combination, population-based family studies report a two (Agerbo, 2005; Agerbo et al., 2002; Garssen et al., 2011; Qin & Mortensen, 2003; Qin et al., 2002, 2003; Runeson & Åsberg, 2003; Tidemalm et al., 2011) to 15-fold [in monozygotic twins (Tidemalm et al., 2011)] increase in the risk of suicide among first-degree relatives of suicide probands. Some of this work suggests that suicide's familial liability may be higher in female relatives compared to males (Cheng et al., 2014; Qin et al., 2003; Qin & Mortensen, 2003) especially if the suicide proband is also female or young at time-of-death (Cheng et al., 2014; Garssen et al., 2011; Qin & Mortensen, 2003). Little has been reported on the familial liability of suicide across specific kinships in more distantly related relatives.
In the USA, data resources comparable to the health registries in Northwestern Europe are not widely available for examining the patterns of familial suicide transmission using a population-based family study design. The exception being the pioneering work done in the Old Order Amish in which all suicides in a 100-year period were identified ($$n = 26$$ deaths) and linked to their extended pedigrees (Egeland & Sussex, 1985). This study found that $73\%$ of suicides clustered in four multigenerational families, which encompassed only $16\%$ of the total population. The study's focus on the isolated Old Order Amish community, however, greatly limits the generalizability of its findings.
The current urgency to reduce suicide rates and develop well-performing predictive models of suicide in US populations (Gordon, Avenevoli, & Pearson, 2020) necessitates the estimation of specific measures of how suicide aggregates in American families. The US state of *Utah is* unique in the availability of data resources that make feasible a population-based analysis of the familial aggregation of suicide. The Utah Population Database (UPDB) is a repository for multiple population-wide sources of biomedical-related information and includes information on multi-generational pedigrees (Smith & Fraser, 2018). Using multigenerational data from the UPDB linked to mortality information, the current study aimed to determine the familial aggregation of suicide in Utah in first- through fifth-degree relatives. To expand upon prior work, we also examined if and how the familial risk of suicide varies in first- through fifth-degree relatives based on sex and age of the suicide proband and sex of the proband's relative. Analyses were conducted using a unified modeling approach (Lee, Rebora, Valsecchi, Czene, & Reilly, 2013), which minimizes the total number of models needed to test kinship and interaction effects while simultaneously maximizing statistical efficiency. Finally, the attributable risk and the population attributable fraction of the familial risk of suicide were calculated to measure its contribution on individual and population levels to suicide death in Utah.
## Study population and sampling design
The current study used a prospective cohort design to determine the familial aggregation of suicide in relatives of suicide probands v. relatives of non-suicide controls. The study population was identified in the UPDB, a unique, multi-source comprehensive data resource containing genealogical, demographic, and vital records on over 11 million current and previous Utah residents (Smith & Fraser, 2018). The suicide group included all Utah suicides from 1904 to 2014 who were 10 years or older at time of death (six suicides were excluded for age <10 at time of death) based on a suicide manner of death indication on a death certificate. For deaths from 1957 to 2014, International Classification of Diseases (ICD) cause-of-death coding was used to identify additional suicides including ICD-6 codes E970-E979, ICD-7 codes E963 and E970-E979, ICD-8 and 9 codes E950-D959, and ICD-10 codes X60-X84 and U03. For deaths from 1904 to 1956, identification of suicide was based on UPDB translation of death certificate causes-of-death text into corresponding ICD-10 codes using the 2000 Mortality Medical Data System developed by the US Department of Health and Human Services (Lu, 2003; MMDS, 2015). Covariate information obtained for suicide probands included sex, birth year, and death year. Each proband was matched to five non-suicide controls based on sex and birth year using at-risk sampling whereby a potential control had to be alive at the time of the suicide proband's death but could have died by suicide at a later time.
The UPDB maintains extensive, multi-generational genealogies with founding family members belonging to Utah's original European settlers who migrated to present-day Utah beginning in the mid-19th century. Data from Utah state vital records including birth, death, and divorce certificates are used to construct de novo genealogies, and follow-up information is acquired through regular linkage to state driver license information, records from the Centers of Medicaid and Medicare, and to the Social Security Death Index. The largest families in UPDB include up to 18 generations. First (i.e. siblings, parents, children), second (i.e. grandparents, grandchildren, aunts, uncles, nieces, nephews), third (i.e. great-grandparents, great-grandchild, first cousins, great-uncles, great-aunts, great-nieces, great-nephews), and fifth (i.e. second cousins) degree relatives of suicide probands and matched controls were identified. Additional information obtained for relatives included their sex, birth year, last known year residing in Utah or death year, and manner of death.
Suicide probands were excluded from the analysis if they did not link to known relatives or all relatives were missing critical information (e.g. sex, no follow-up or death information, $$n = 3335$$), all of their identified relatives were missing birth year information ($$n = 898$$), or if appropriate controls were not available ($$n = 347$$) (Fig. 1). Family clusters were identified to take into account the non-independent, correlated structure of the data. A single family cluster included the combined set of all first through fifth-degree relatives of each proband and their matched controls (online Supplementary Fig. S1). Fig. 1.Flow diagram describing the Utah suicide proband, control, and relative cohorts used in the study.
## Familial risk of suicide
The analysis was conducted in the relatives of the suicide probands and controls whereby the exposure was the suicide proband or the matched control. The familial aggregation of suicide was quantified in a unified modeling framework (Lee et al., 2013) by hazard ratios (HR) from an unsupervised Cox regression model, which measured the incidence of suicide in relatives of suicide probands v. controls. Age was used as the underlying time-scale. To account for left censoring due to missing manner of death information, relatives of suicide cases and controls born before 1904 entered the model starting with their age in 1904. All relatives were followed until suicide, death by non-suicide, loss to UPDB follow-up (e.g. migration out of Utah) or 31 December 2014, whichever came first. Individuals populating the suicide proband/control and relative (analysis) cohorts could have contributed to the analysis in multiple ways. First, a small proportion of the control group died of suicide at a later date thereby serving as both a suicide proband and control ($$n = 245$$). Second, individual relatives may have belonged to more than one family cluster. A robust sandwich variance estimator (Wei, Lin, & Weissfeld, 1989) was used to account for dependence among individual family members. Additional covariates included in the model were characteristics of the proband such as sex and characteristics of the relatives such as birth year, sex, and type of relationship to the proband (e.g. sibling, aunt, first cousin).
The hazard of suicide in specific kinships (e.g. children–parents, grandchildren–grandparents) were estimated using two-way interactions between exposure (i.e. relative was exposed to a suicide or control proband) and relative's relationship to the proband. Similarly, four-way interactions that included exposure, proband's sex, relative's sex, and relative's relationship to the proband were used to test for moderation of the familial suicide HR based on sex of the suicide proband or relative. To investigate age effects, the unified Cox regression model was stratified by suicide probands <25 years or ⩾25 years at time of death. This age was selected as prior Utah-based work examining clinical correlates of youth suicide examined suicide through age 25 (Keeshin, Gray, Zhang, Presson, & Coon, 2018). Sensitivity analyses were conducted to examine the robustness of study findings. First, to investigate the potential influence of families with extremely high aggregation of suicide among first-degree relatives, families with [1] three or [2] four or more siblings who died by suicide were removed from the analysis. Next, to investigate the influence of alternative age cut-offs, additional Cox regression models were stratified by suicide probands [1] ⩽18 years or >18 years, and [2] <41 years or ⩾41 years, which is mean age of suicide in Utah (Table 1). Finally, to test the potential influence of using at-risk sampling whereby a matched control could have died by suicide at a later date, additional age-stratified (<25 v. ⩾25 years) Cox regression models were formulated with the 245 matched controls who later died by suicide removed from the analysis. Table 1.Demographic characteristics of Utah suicides, matched controls and relatives of suicides and matched controlsSuicide probandsNon-suicide probandsProbands ($$n = 12$$ 160)Relatives ($$n = 2$$ 112 462)Controls ($$n = 53$$ 787)Relatives ($$n = 11$$ 367 660)CharacteristicN%N%N%N%SexFemale249620.51 030 09048.8 10 92020.35 538 82848.7Male966479.51 082 37251.2 42 86779.75 828 83251.3Age at time of suicide, mean (s.d.), years41.74 (17.3)52.41 (26.1)41.53 (17.5)52.40 (26.2)Age range, years<34451337.1589 47327.920 47938.13 207 28128.234–64625151.4700 05033.127 08450.43 717 95932.7>64139611.5822 93939.0622411.64 442 42039.1Birth range, years<1850270.233 3971.61120.2179 4901.61850–18752241.879 0423.810181.9418 4893.71876–19008016.6231 06010.937326.91 244 31911.01901–1925191615.8493 87423.4877616.32 653 23323.31926–1950277822.9520 17924.611 99722.32 759 54724.31951–1975452437.2464 99822.019 31935.92 491 50221.91976–2000188715.5252 19211.9881816.41 391 46612.2>20003<0.137 7201.815<0.1 229 6142.0N, number; s.d., standard deviation.
## Attributable risk and population attributable fraction
The attributable risk of suicide is a calculation of the percent of suicides among relatives of suicide probands that is attributable to familial risk. The population attributable fraction is a measure of the percent of Utah suicides that is attributed to familial risk. Age-adjusted attributable and population attributable risk fractions were estimated using the indirect standardization approach according to the STDRATE procedure in SAS (Yuan, 2013). The calculation of attributable risk and the population attributable fraction included all 12 160 suicide deaths used in the main analysis.
Analyses were conducted in SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) and a Type I error was set at 0.05. Institutional Review Board approval was obtained from the University of Utah and the Utah Resource for Genetic and Epidemiologic Research. STROBE reporting guidelines were followed in preparing this study.
## Sample characteristics
The final sample included 12 160 Utah suicide probands identified in UPDB born before 2005 with death dates from 1904 to 2014; suicide probands were matched to 53 787 non-suicide probands (Fig. 1). Relatives of suicide and non-suicide probands included 2 112 462 and 11 367 660 first through fifth-degree relatives (includes duplicate individuals that belong to more than one suicide or non-suicide family cluster), respectively. Table 1 summarizes the demographic characteristics of the suicide, matched control, and relatives groups.
## Familial risk of suicide in first through fifth-degree relatives
A significantly heightened familial risk of suicide was measured in all first through fifth-degree relatives (Table 2). The overall hazard of suicide was 1.26 [$95\%$ confidence intervals (CI) 1.22–1.30] among all first through fifth-degree relatives. First-degree relatives of suicide probands were at more than three times the risk of suicide compared with first-degree relatives of non-suicide probands (HR 3.45; $95\%$ CI 3.12–3.82). The HR of suicide was 3.20 ($95\%$ CI 2.71–3.77) in parents, 3.46 ($95\%$ CI 2.98–4.03) in children, and 3.67 ($95\%$ CI 3.18–4.24) in siblings of suicide probands v. non-suicide probands. The familial risk of suicide in second- through fifth-degree relatives displayed a decreasing dose–response relationship with the HRs ranging from 1.85 ($95\%$ CI 1.67–2.06) in nieces/nephews to 1.07 ($95\%$ CI 1.02–1.12) in second cousins of suicide probands. A closer examination of parent–child dyads in which both a parent and a child died by suicide showed that in $75\%$ of these cases, the parent died first. However, the mean number of years between parent–child suicides is considerably shorter when the child dies first (9.9 v. 19.2 years; online Supplementary Table S1). Table 2.Familial risk of suicide in first-, second-, third-, and fifth-degree relatives of suicides and matched controls in UtahaSuicide probandsNon-suicide probandsRelation to probandN suicidesN relativesN suicidesN relativesHazard ratio$95\%$ CIFirst-degree relativesOverall113473 4501760390 8223.453.12–3.82Parent27520 97739995 2463.202.71–3.77Child27218 234510121 4353.462.98–4.03Sibling58734 239851174 1413.673.18–4.24Second-degree relativesOverall1483198 37044731 071 8591.791.65–1.94Grandparent16230 482453148 8691.751.45–2.10Grandchild16223 844639159 5231.701.43–2.02Uncle or aunt57473 3811645373 3341.781.60–1.99Niece or nephew58570 6631736390 1331.851.67–2.06Third-degree relativesOverall2768563 93211 6683 025 0421.271.20–1.35Great grandparent10747 135436237 1241.200.95–1.53Great grandchild11125 565561175 5201.351.09–1.67Great uncle or aunt628147 9992403737 2511.291.17–1.43Great niece or nephew646129 6512714721 3521.301.18–1.44First cousin1276213 58255541 153 7951.251.15–1.36Fifth-degree relativesOverallb67671 276 71034 1796 879 9371.071.02–1.12N, number.aModel was adjusted for relative's birth year and sex and proband's sex.bAll fifth-degree relatives included in this study were second cousins.
## Differences by sex
The familial risks of suicide based on sex of the proband and relative in specific kinships are shown in Fig. 2. Among first-degree relatives, the HR of suicide was 6.99 ($95\%$ CI 3.99–12.25) in mothers of female suicide probands compared with mothers of non-suicide female probands. Female probands were also associated with high suicide risks in sisters (HR 6.39; $95\%$ CI 3.78–10.82), daughters (HR 5.65; $95\%$ CI 3.38–9.44), sons (HR 4.48; $95\%$ CI 3.26–6.14), grandmothers (HR 3.84; $95\%$ CI 1.95–7.58), and granddaughters (HR 3.27; $95\%$ CI 1.68–6.35). Fig. 2.Suicide hazard ratios (HR) ± $95\%$ confidence intervals (y-axis) in relatives of suicide probands v. controls in first- through fifth-degree relatives stratified by suicide proband's sex. Relative of suicide proband is listed on the x-axis. The models were adjusted for relative's sex and birth year and proband's sex.
Differences in the familial risk of suicide according to the proband or relative's sex persisted out to third-degree relatives in some specific kinships. Among great-aunts, the HR associated with the suicide of a great-niece was 2.06 ($95\%$ CI 1.37–3.09) compared with an HR of 1.35 ($95\%$ CI 1.06–1.73) associated with the suicide of a great-nephew. In contrast, in many kinships where the proband was male, the familial risk of suicide did not differ between male and female relatives. For example, the HR of suicide was similar for brothers (HR 3.40; $95\%$ CI 2.85–4.07) and sisters (HR 3.83; $95\%$ CI 2.92–5.03) of male suicide probands.
## Differences by age and age sensitivity analysis
Figure 3 displays the hazard ratios in first-degree relatives of suicide probands v. matched controls stratified by age (<25 v. ⩾25 years). The suicide HR was 4.29 ($95\%$ CI 3.49–5.26) for all first-degree relatives of suicide probands who were <25 years at death compared with 3.33 (CI 2.98–3.72) for all first-degree relatives of suicide probands who were ⩾25 years at death. Higher HR estimates for relatives of younger suicide probands v. relatives of older suicide probands persisted across all specific first-degree kinships as well as for most second through fifth-degree kinships, although the risk differences for relatives of younger v. older suicide probands narrows as the relationship becomes more distant (online Supplementary Fig. S2). The highest familial risk of suicide reported in this study was measured among daughters of a parent who died by suicide before age 25 (HR 16.36; $95\%$ CI 4.36–61.44). Using 18 years as the age cut-off for youth suicide did not impact study findings concerning age effects (online Supplementary Table S2). However, data sparseness when using age 18 years as the cut-off prevented HR estimation in some first-degree kinships. The effects of age on suicide risk appeared to disappear when stratifying suicide deaths by the mean age of suicide (41 years) in Utah (online Supplementary Table S3). Study results for the <25 v. ⩾25 years age-stratified models were robust to the removal of matched controls who later died by suicide (online Supplementary Table S4). Fig. 3.Suicide hazard ratios (HR) ± $95\%$ confidence intervals (y-axis) in first-degree relatives of suicide probands v. controls stratified by <25 v. ⩾25 years of age at time of death. Relative of suicide proband is listed on the x-axis. The models were adjusted for relative's sex and birth year and proband's sex.
## Sensitivity analysis, attributable risk, and population attributable risk fraction
Forty-three families with three or more siblings who died by suicide were removed from the analysis and four families with four or more siblings who died by suicide were removed from the analysis. The removal of families with high aggregation of suicide among siblings did not substantively alter the general study findings (online Supplementary Table S5). Finally, the attributable risk of familial suicide was 0.20 ($95\%$ CI 0.19–0.22) and the population attributable risk fraction of familial suicide was 0.04 ($95\%$ CI 0.035–0.042).
## Discussion
The current study represents the first total population-wide investigation of the familial risk of suicide in the USA. Our study included all suicides occurring in a 110-year period in Utah as well as 13.5 million relatives of suicide probands and control. The identification of first- through fifth-degree relatives of suicide probands and controls, born as far back as the 1800s, allowed us to thoroughly examine the familial risk of suicide by degree of genetic relationship.
We found that significant familial liabilities of suicide extend out to fifth-degree relatives and that the sex and age of a suicide proband impacts the suicide risk in his/her relatives. Study findings highlight the importance of acquiring information on an individual's family history of suicide, including the age and sex of the suicide decedent, to prevent suicide in potentially high-risk individuals.
Overall, the risk of suicide was $26\%$ higher among all combined first- through fifth-degree relatives of suicide cases compared with controls. While prior studies have not considered up to fifth-degree relationships, our Utah-based estimates for some first through third-degree kinships are consistent with familial liability estimates reported in similarly designed studies conducted in Northwestern European populations. The current study and a Danish-based study (Qin et al., 2003) both report an overall 3.5-fold increase in the familial risk of suicide in first-degree relatives of suicide probands. A Swedish-based study (Tidemalm et al., 2011) identified relative risks of 3.1 in siblings (v. 3.7 in Utah), 1.6 in nieces/nephews (v. 1.9 in Utah), and 1.5 in first cousins (v. 1.3 in Utah) of suicide probands. In contrast, two population-based studies conducted in Denmark (Qin et al., 2002) and Sweden (Runeson & Åsberg, 2003) identified a more modest two-fold increase in the familial risk of suicide in first-degree relatives.
The opportunity to compare Utah-specific estimates with Northwestern European-based estimates may be particularly relevant given the ancestry of Utah's suicides. While the majority of Utah suicide cases self-identify as White, non-Hispanic (see Limitations section), a recent molecular analysis, allowing for a more refined look at ancestry, determined that $80\%$ of a population-based sample of Utah suicide decedents from 1996 to 2017 ($$n = 4379$$) were of majority Northwestern European ancestry (Docherty et al., 2020). Interestingly, at 21.2 per 100 000 persons (AFSP, 2019), Utah's age-adjusted suicide rate is considerably higher than those observed in Northwestern Europe [e.g. the age-adjusted suicide rate is 11.7 per 100 000 persons in Sweden (World Health Organization, 2019)]. While prior work demonstrates consistency in relative suicide rates among European countries over time (Hansen & Pritchard, 2008), stark differences in age-adjusted suicide rates between Utah and Northwestern Europe suggest that a unique set of factors, beyond ancestral similarities, influence suicide risk differences between locations.
Suicide aggregates in families due to a combination of genetic, shared environmental or gene–environment interactions and the current study was not designed to tease apart the unique contribution of each of these factors. In consideration of genetic contributors, our discovery of significantly elevated familial suicide risks in second- through fifth-degree kinships is an important and novel contribution. The detection of significant familial clustering of suicide well beyond the nuclear family may be evidence of genetic sharing within Utah's extended families, as more distantly related relatives are less likely to share environmental exposures. A considerable genetic contribution to suicide supports findings from prior adoption (Kendler et al., 2020; Petersen et al., 2013; Schulsinger et al., 1979; von Borczyskowski et al., 2011) and twin (Juel-Nielsen & Videbech, 1970; Pedersen & Fiske, 2010; Roy et al., 1991) studies. The specific genes implicated in suicide, however, remain unclear. While over 200 genes are associated with suicidal behaviors (Lutz, Mechawar, & Turecki, 2017), many of these gene findings require replication. Further, the majority of gene-focused suicide studies have been conducted in individuals exhibiting suicidal behavior and not in suicide decedents (Coon et al., 2020) (but see Coon et al., 2020; Darlington et al., 2014; Docherty et al., 2020; Otsuka et al., 2019; Tombáca et al., 2017). To better understand suicide's genetic architecture, additional work is needed in suicide death cohorts.
Several environmental contributors to suicide are known to be transmitted through families and may impact study findings, especially among close kinships. Increased suicide risk during youth and adolescence has been linked to exposure to early-life adversity from parental neglect, substance abuse, criminality (Björkenstam, Hjern, Björkenstam, & Kosidou, 2018; Björkenstam, Kosidou, & Björkenstam, 2017; Brent et al., 1994), and interpersonal violence (Rajalin, Hirvikoski, & Jokinen, 2013). Epigenetic modification following exposure to early-life adversity of genes involved in pathways underlying suicide including neural plasticity, neuroprotection, stress, and cognition provides evidence of potential biological effects from such exposures (Turecki & Brent, 2016). Beyond youth and adolescent suicide, a family history of psychiatric conditions and hospitalization for psychiatric conditions among first-degree relatives has been shown to be linked to an increased risk of suicide across the lifespan (Agerbo et al., 2002; Qin et al., 2002, 2003). In contrast, little is currently known about how a family history of early-life adversity and psychopathology influences the risk of suicide in more distantly related relatives (e.g. beyond first-degree relationships).
Our findings of heightened familial risks of suicide in extended family members may be evidence of gene–environment interactions. While distant relatives are less likely than close relatives to share environmental exposures, the environmental factors they do share are more likely to be those that are widespread across a population (Amundadottir et al., 2004). Suicide population attributable fraction estimates attest to the important contribution of widespread, population-level environmental factors to risk. For example, the attributable risks of suicide associated with being single range from $10.3\%$ (Qin et al., 2003) to $25.6\%$ (Mortensen, Agerbo, Erikson, Qin, & Westergaard-Nielsen, 2000).
We found that sex and age influence the familial risk of suicide. Although suicide is more common in males, our work supports prior research demonstrating especially heightened liabilities of suicide in first-degree relatives of female (Cheng et al., 2014; Garssen et al., 2011; Petersen et al., 2013; Qin et al., 2003; Qin & Mortensen, 2003) and younger (Garssen et al., 2011; Qin & Mortensen, 2003) suicide decedents. Our findings also indicate that female and youth transmission of risk extends beyond the nuclear family. The heritability of suicide mortality has been hypothesized to be higher in women than men (Pedersen & Fiske, 2010), which might in-part explain the high risks of suicide in female-specific kinships (e.g. mother-daughters, sisters). Alternatively, the loss of a mother may represent the disappearance of a critical source of support and caregiving. Such loss may be especially heightened among daughters as parental attachment and the transmission of learned behaviors to children appears to be more strongly linked to their same-sex parent (Diener, Isabella, Behunin, & Wong, 2008). In addition, psychopathology is a strong risk factor for suicide (Arsenault-Lapierre, Kim, & Turecki, 2004; Qin et al., 2002) with some forms of psychopathology (i.e. major depressive disorder) occurring more frequently in women (Salk, Hyde, & Abramson, 2017; Oquendo et al., 2001). Although suicide and psychopathology risk both cluster in families, their transmission, despite some overlap, has been shown to be distinct (Brent, Bridge, Johnson, & Connolly, 1996; Egeland & Sussex, 1985; Qin et al., 2003). It is not known, however, if the familial transmission of suicide and psychopathology differs by sex and is more likely to occur among women. In contrast, it is important to note that the low absolute suicide rate in females in Utah could result in relatively high hazard ratios. In terms of age effects, an inverse relationship has been observed between early age-of-onset and familial risk for a variety of medical and psychiatric conditions (Gillespie, Gale, & Bingley, 2002; Kendler, Gatz, Gardner, & Pedersen, 2005; Kharazmi, Fallah, Sundquist, & Memminki, 2012; Nestadt et al., 2000).
Limitations of the current study are important to recognize. First, although the models were adjusted for relative's sex and birth year and proband's sex, they were not adjusted for additional confounders including socioeconomic status, urbanicity, and history of mental illness and trauma. Prior work indicates that familial suicide risk estimates are robust to adjustment by additional covariates, especially among first-degree relatives who likely share potential confounders, however their inclusion has been shown to slightly attenuate effect sizes (Qin et al., 2002, 2003). *The* generalizability of study findings to the broader USA is unknown and may, in particular, be influenced by the racial and ethnic composition of Utah's suicide population. Information in the UPDB indicates that $95.2\%$ of the suicide deaths since 1904 self-identified as White, non-Hispanic. Of note, the 3335 suicide deaths excluded from the study due to a lack of relatives in UPDB, who might be expected to be more recent in-migrants to Utah and therefore more racially and ethnically diverse, were of similar race and ethnicity ($92.3\%$ White, non-Hispanic). Although racially and ethnically homogeneous, Utah's age-adjusted suicide rates are among the highest in the USA (AFSP, 2019) suggesting that Utah-specific discoveries may apply to other high suicide risk populations in the USA with similar demographic compositions. Finally, the current study relied on death certificate determination of suicide, which is susceptible to possible misclassification of suicide deaths as undetermined or accidental (Mohler & Earls, 2001; Ohberg & Lonnqvist, 1998; Rosenberg et al., 1988). In particular, the majority of Utah's population are members of the Church of Jesus Christ of Latter Day Saints (LDS), and evidence suggests that suicide misclassification may be more likely in areas with strong single religion identities (Prichard & Hansen, 2015). Utah-based work, however, reports that highly active male members of the LDS church are at a reduced risk of suicide relative to their less active and non-LDS peers (Hilton, Fellingham, & Lyon, 2002). Consistency in the method of death determination in *Utah is* further assisted by the use of a single, centralized Office of the Medical Examiner. Any death misclassification is likely to result in a conservative determination of suicide with the associated bias attenuating effect estimates toward the null.
Despite these limitations, our study has a number of strengths. The UPDB provides access to unprecedented resources for conducting population-wide analyses; a similar study of this magnitude is not currently feasible elsewhere in the USA. The depth of genealogical and death certificate data maintained in UPDB is more extensive than what is available in similar, primarily European health registries. We used a unified modeling approach, which minimized the formulation of multiple models thereby improving effect estimate precision. Further, our modeling approach accounted for participant clustering within multiple families reducing the risk of selection bias (Bai, Sherman, Khoury, & Flanders, 2000).
In conclusion, we found that the familial liability of suicide extends from first- through fifth-degree relatives in Utah with the magnitude of risk in relatives varying based on their sex and the sex and age of the suicide proband. The attributable risk of familial suicide was estimated to be $20\%$ suggesting that our findings have important implications for suicide prevention on an individual-level and within a family. In particular, our study indicates an increased intensity of interventions for suicidal young adults and women with a strong family history of suicide. Further attention to this approach to suicide prevention may be especially warranted in the USA given recent increases in suicide rates among young adults (Miron, Yu, Wilf-Miron, & Kohane, 2019), especially females (Ruch et al., 2019).
## Financial support
This work was supported by the National Institutes of Health (HC, grant number R01MH099134; KS, grant number R01AG022095; ARD, grant number K01MH093731; AVB, grant number R01ES032028), the American Foundation for Suicide Prevention (AVB & ARD), the Brain & Behavior Research Foundation (ARD), the Clark Tanner Foundation (HC & AVB), and the University of Utah EDGE Scholar Program (ARD). We also acknowledge partial support for the Utah Population Database from the National Cancer Institute (grant P30CA2014), the University of Utah, the University of Utah's Program in Personalized Health, and the Utah Clinical and Translational Science Institute.
## Conflict of interest
None.
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|
---
title: Quality of life, patient satisfaction, and cardiovascular outcomes of the randomised
2 x 3 factorial Copenhagen insulin and Metformin therapy (CIMT) trial – A detailed
statistical analysis plan
authors:
- Markus Harboe Olsen
- Thomas P. Almdal
- Sten Madsbad
- Christian Ovesen
- Christian Gluud
- Simone B. Sneppen
- Leif Breum
- Christoffer Hedetoft
- Thure Krarup
- Louise Lundby-Christensen
- Elisabeth R. Mathiesen
- Michael E. Røder
- Henrik Vestergaard
- Niels Wiinberg
- Janus C. Jakobsen
journal: Contemporary Clinical Trials Communications
year: 2023
pmcid: PMC10009439
doi: 10.1016/j.conctc.2023.101095
license: CC BY 4.0
---
# Quality of life, patient satisfaction, and cardiovascular outcomes of the randomised 2 x 3 factorial Copenhagen insulin and Metformin therapy (CIMT) trial – A detailed statistical analysis plan
## Abstract
An updated literature search 28th of January 2022 did not identify any additional randomised clinical trials evaluating the effects of adding metformin to insulin in people with type 2 diabetes. Therefore, the CIMT trial is the hitherto largest and longest randomised clinical trial assessing the effects of metformin in addition to insulin in people with type 2 diabetes on quality of life, well-being, and development of cardiovascular complications.
### Background
The evidence on the effects of metformin and insulin in type 2 diabetes patients on quality of life, patient satisfaction, and cardiovascular outcomes is unclear.
### Methods
The Copenhagen Insulin and Metformin Therapy (CIMT) trial is an investigator-initiated multicentre, randomised, placebo-controlled trial with a 2 × 3 factorial design conducted at eight hospitals in Denmark. Participants with type 2 diabetes were randomised to metformin ($$n = 206$$) versus placebo ($$n = 206$$); in combination with open-label biphasic insulin aspart one to three times daily ($$n = 137$$) versus insulin aspart three times daily in combination with insulin detemir once daily ($$n = 138$$) versus insulin detemir once daily ($$n = 137$$).
We present a detailed description of the methodology and statistical analysis of the clinical CIMT outcomes including a detailed description of tests of the assumptions behind the statistical analyses. The outcomes are quality of life (Short Form Health Survey (SF-36)), Diabetes Medication Satisfaction Questionnaire, and Insulin Treatment Satisfaction Questionnaire (assessed at entry and 18 months after randomisation) and cardiovascular outcomes including time to a composite of either myocardial infarction, stroke, peripheral amputation, coronary revascularisation, peripheral revascularisation, or death.
### Discussions
This statistical analysis plan ensure the highest possible quality of the subsequent post-hoc analyses.
### Trial registration
The protocol was approved by the Regional Committee on Biomedical Research Ethics (H-D-2007-112), the Danish Medicines Agency (EudraCT: 2007-006665-33 CIMT), and registered within ClinicalTrials.gov (NCT00657943, 8th of April 2008).
## Background
The Copenhagen Insulin and Metformin Therapy (CIMT) trial is an investigator-initiated multicentre, randomised, placebo-controlled superiority trial with a 2 × 3 factorial design [1]. The CIMT trial evaluated the effect of an 18-month treatment with metformin versus placebo and simultaneously the effect of three insulin analogue regimens on the progression of mean carotid intima media thickness in patients with type 2 diabetes [1]. The CIMT trial was conducted from May 2008 to December 2012 and the trial results in relation to changes in carotid intima media thickness have been reported [2,3]. Metformin in combination with insulin did not reduce the carotid intima media thickness [2]; nor did the three insulin regimens [3]. Multiple exploratory analyses have since been carried out, but the prespecified patient-centred clinical outcomes quality of life, patient satisfaction, and time to a number of clinical and cardiovascular outcomes have not yet been reported [1,[4], [5], [6]].
One previous systematic review assessed the effects of adding metformin to insulin in people with type 2 diabetes [7]. We identified only three trials assessing the influence of metformin versus placebo on health-related quality of life or well-being [[7], [8], [9], [10]]. All three included trials were at high risk of bias, and all three trials had a shorter duration and included fewer participants than the CIMT trial [7]. None of the included trials reported significant differences regarding the patient-reported outcomes [[7], [8], [9], [10]]. Only Douek et al. [ 8] reported results of quality of life assessments in a way suitable for a meta-analysis, and it was therefore not possible to meta-analyse quality of life as an outcome [7]. The reporting of cardiovascular complications for the comparison of combining metformin with insulin compared with insulin monotherapy in people with type 2 diabetes was infrequent, and when meta-analyses could be performed, the data were sparse and effect estimates were non-significant [7].
Here, we describe a detailed statistical analysis plan of post-hoc analyses assessing the effects of metformin in addition to insulin and the effect of three different insulin regimens on patient-reported outcomes and cardiovascular complications in the CIMT trial.
## Methods
The methodology of the CIMT trial have previously been published [1]. The CIMT trial participant inclusion criteria were: type 2 diabetes; age >30 years; body-mass index >25 kg/m2; HbA1c ≥ $7.5\%$ (58 mmol/mol); and treatment with oral glucose-lowering drugs for at least one year and/or insulin treatment for at least three months [1]. After a screening visit, participants were centrally randomised at the Copenhagen Trial Unit according to the 2 × 3 factorial design. Participants were randomised 1:1:1 to treatment with one of three insulin analogue regimens and in a factorial way randomised 1:1 to treatment with metformin versus placebo (Fig. 1). Randomisation was stratified by age >65 years (yes/no), insulin treatment at trial entry (yes/no), and treatment at Steno Diabetes Center (yes/no) [1]. The insulin treatment was open-labelled, whereas participants, investigators, and medical staff were blinded to the metformin and placebo intervention (numbered identical containers) [1].Fig. 1– A study scheme of the factorial designed trial, where participants are ranodmised into both factors. Thus, each participant is allocated in both Factor 1 and Factor 2, and receives a combination of the two treatment schemes. Fig. 1 The protocol was approved by the Regional Committee on Biomedical Research Ethics (H-D-2007-112) and the Danish Medicines Agency (EudraCT: 2007-006665-33 CIMT), registered within ClinicalTrials.gov (NCT00657943), and conducted in accordance with The Declaration of Helsinki and guidelines for Good Clinical Practice. Informed consent was obtained from each participant enrolled in the trial.
The design of the CIMT trial and the influence of metformin and insulin on the primary outcomes have previously been reported [2,3]. Here we report the detailed statistical analysis plans for evaluating the effects of these intervention on the patient-centred outcomes quality of life, patient satisfaction, and cardiovascular outcomes.
We plan on reporting the results of these outcomes in two separate papers: one comparing metformin versus placebo comparison and one comparing the three insulin analogue regiments.
## Quality of life and patient satisfaction
•Short Form Health Survey (SF-36) [11]. SF-36 is questionnaire and a general assessment of quality of life measures. Both a physical component score and a mental health component score are reported [11].•Insulin Treatment Satisfaction Questionnaire (ITSQ) [12].•Diabetes Medication Satisfaction Questionnaire (Diab-Medsat) [13].
These continuous outcomes were assessed at 18 months after randomisation [1].
## Cardiovascular outcomes
•Time to myocardial infarction, stroke, peripheral amputation, coronary revascularisation, peripheral revascularisation, or death [1].•Time to myocardial infarction, stroke, peripheral amputation, or death [1].•Time to cardiovascular mortality [1].
The cardiovascular outcomes were assessed during the full observation period from randomisation of each patient into the trial and until June 11th, 2013 [1].
The outcome assessors and the participants were blinded to metformin versus placebo but not to the three insulin regimens [1]. We used the Danish Central Civil Register and the National Patient Register to obtain data on the cardiovascular outcomes [1].
## Sample size and power considerations
Based on the sample size calculation in relation to the primary outcomes of the trial (change in mean intima media thickness of the common carotid arteries), we planned to include a total of 950 patients in the trial [1]. However, the trial was stopped due the end of the financing grant after the inclusion of a total of 412 participants [2,3]. For this factorial trial, this means that there is a comparison on 206 participants on metformin versus 206 participants on placebo and another comparison of three groups on different insulin regimens each including 137, 137, and 138 participants.
To increase the interpretability of the trial results and decrease the risks of random errors [14], we present power estimations for all planned outcomes [15]. We originally adjusted our threshold for statistical significance (the alpha value used in the sample size estimations) to 0.01 because of the multiple comparisons [1]. To take into account that the CIMT trial was stopped early (approximately halfway), we will now use an alpha value of 0.001 for the power estimations [14]. The following power estimations are based on the total 412 randomised participants for the metformin versus placebo comparison and a total of 274 randomised participants for each of the three pairwise insulin comparisons.
## Short Form Health Survey (SF-36)
We consider 4 points as the minimal clinically relevant difference for SF-36 [14,[16], [17], [18], [19], [20], [21]], an alpha of 0.001, and a standard deviation (SD) of 9.5 points [12]. For the metformin versus placebo comparison, we will have a power of $83.0\%$ to confirm or reject a 4-point increase on the SF-36. For each of the three pairwise insulin comparisons, we will have a power of $56.3\%$ to confirm or reject a 4-point increase on the SF-36.
## Insulin Treatment Satisfaction Questionnaire (ITSQ)
We consider 4 points as the minimal clinically relevant difference for ITSQ [14,20], an alpha of 0.001, and a standard deviation (SD) of 9 points [22]. For the metformin versus placebo comparison, we will have a power of $88.3\%$ to confirm or reject a 4-point increase on the ITSQ. For each of the three pairwise insulin comparisons, we will have a power of $63.7\%$ to confirm or reject a 4-point increase on the ITSQ.
## Diabetes Medication Satisfaction Questionnaire (Diab-Medsat)
We consider 4 points as the minimal clinically relevant difference for Diab-Medsat [14,20,23], an alpha of 0.001, and a standard deviation (SD) of 9 points [24]. For the metformin versus placebo comparison, we will have a power of $88.3\%$ to confirm or reject a 4-point increase on the Diab-Medsat. For each of the three pairwise insulin comparisons, we will have a power of $63.7\%$ to confirm or reject a 4-point increase on the Diab-Medsat.
## Time to myocardial infarction, stroke, peripheral amputation, coronary revascularisation, peripheral revascularisation, or death
We consider a $29\%$ risk of experiencing one or more of the following outcomes: non-fatal myocardial infarction, stroke, peripheral amputation, coronary revascularisation, peripheral revascularisation, or death in the placebo group [25], an alpha of 0.001, and a hazard ratio reduction of $30\%$. For the metformin versus placebo comparison, we will only have $9.0\%$ power to confirm or reject a hazard ratio reduction of $30\%$. For each of the three pairwise insulin comparisons, we estimate that we will only have $4.5\%$ power to confirm or reject a hazard ratio reduction of $30\%$.
## Time to the first of the following clinical outcomes myocardial infarction, stroke, peripheral amputation, or death
We consider a $12.5\%$ risk of experiencing one or more of the following outcomes: non-fatal myocardial infarction, stroke, peripheral amputation, or death in the placebo group [25], an alpha of 0.001, and a hazard ratio reduction of $30\%$. For the metformin versus placebo comparison, we will only have $2\%$ power to confirm or reject a hazard ratio reduction of $30\%$. For each of the three pairwise insulin comparisons, we estimate that we will only have $1.2\%$ power to confirm or reject a hazard ratio reduction of $30\%$.
## Time to cardiovascular death
We consider a $3.8\%$ risk of experiencing death from cardiovascular disease in the placebo group [25], an alpha of 0.001, and a hazard ratio reduction of $30\%$. For the metformin versus placebo comparison, we will have almost $0\%$ power to confirm or reject a hazard ratio reduction of $30\%$. For each of the three pairwise insulin comparisons, we estimate that we will have $0\%$ power to confirm or reject a hazard ratio reduction of $30\%$.
We plan to present the results of these three cardiovascular outcomes in the Supplemental material because of the low statistical power.
## Stratification and design variables
We used three stratification variables in the randomisation: age >65 years (yes/no), insulin treatment at trial entry (yes/no), and treatment at Steno Diabetes Center (yes/no) [1].
Other design variables were sex (male/female), prior cardiovascular disease (yes/no), statin treatment at entry (yes/no), and glutamic acid decarboxylase antibodies at entry (positive/negative) [1].
## Definition of populations the following populations will be analysed
•A modified intention-to-treat population. This population includes all randomised participants except randomised participants who did not fulfil the inclusion criteria; randomised participants fulfilling one or more exclusion criteria; or randomised participants who did not receive any of the planned interventions [1].•A per-protocol population. This population includes all those included in the modified intention-to-treat population, except participants who did not attend more than four out of the planned six visits following randomisation [1].
## Handling of missing data
We will handle missing data according to the recommendation by Jakobsen and colleagues [26]. In short, we will consider using multiple imputation if it is not valid to ignore missing data [26]. If multiple imputation is used, then the primary result of the trial will be based on these data [26]. To take account of the possibility that data may be ‘missing not at random’, we will use a best-worst and worst-best case scenario as sensitivity analyses, which will assess the potential impact of the missing data on the trial results [26]. In the ’best-worst case’ scenario it is assumed that all patients lost to follow-up in the experimental group have had a beneficial outcome; and all those with missing outcomes in the control group have had a harmful outcome [26]. Conversely, in the ’worst-best case’ scenario, it is assumed that all patients who were lost to follow-up in the experimental group have had a harmful outcome; and that all those lost to follow-up in the control group have had a beneficial outcome [26]. When continuous outcomes are analysed, a ‘beneficial outcome’ will be defined as the group mean plus two SDs of the group mean, and a ‘harmful outcome’ will be defined as the group mean minus two SDs of the group mean for ‘best-worst case’ imputation [26].
When assessing quality of life and patient satisfaction, we will in secondary analyses assign the value ‘0’ for participants who died during the 18-months trial period.
We do not expect any missing values for the time to a cardiovascular outcome as all deaths and admissions to Danish hospitals are registered in the Civil Registry and the National Patient Registry and were adjudicated for clinical events by the blinded adjudication committee [2].
We will use the three stratification variables used in the randomisation and the four predefined design variables during the multiple imputation procedure to estimate the missing values, i.e. age >65 years (yes/no), insulin treatment at trial entry (yes/no), and treatment at Steno Diabetes Center (yes/no) as well as sex (male/female), prior cardiovascular disease (yes/no), statin treatment at baseline (yes/no), and glutamic acid decarboxylase antibodies (positive/negative) [26].
## Thresholds for statistical and clinical significance
We will assess if the thresholds for statistical significance and clinical significance are crossed using the five-step procedure as suggested by Jakobsen and colleagues [14]. This procedure will include adjustments of thresholds for significance according to the number of outcome comparisons and the number of randomised participants in relation to the planned sample size [14]. Accordingly, we will analyse all continuous outcomes using Trial Sequential Analysis to take into account the early stopping of the trial [14,27]. When analysing the results using Trial Sequential Analysis, we will use the minimal important differences and the SDs defined in the paragraph above (see ‘Sample size and power considerations’), an acceptable risk of type I error of $1\%$, and an acceptable risk of type II error of $20\%$ [1,14]. We will use a Bayes factor threshold for significance of 0.1 based on the a priory anticipated intervention effect [14].
## Analysis of quality of life and patient satisfaction
Primary analysis: linear regression adjusted for the baseline value and the three stratification variables age >65 years (yes/no), insulin treatment at trial entry (yes/no), and treatment at Steno Diabetes Center (yes/no) [1].
Secondary analysis: linear regression adjusted for the baseline value and the stratification variables (see above) and the design variables (sex (male/female), prior cardiovascular disease (yes/no), statin treatment at entry (yes/no), and glutamic acid decarboxylase antibodies (positive/negative)) [1]. We will in a supplementary analysis adjust for metformin administration before trial entry (yes/no).
All analyses will primarily include the modified intention-to-treat population. We will also perform the analyses adjusted for the stratification variables including the per-protocol population [1].
## Analysis of the cardiovascular outcomes
Primary analysis: Cox regression analysis adjusted for the three stratification variables (age >65 years (yes/no), insulin treatment at trial entry (yes/no), and treatment at Steno Diabetes Center (yes/no)) [1].
Secondary analysis: Cox regression analysis adjusted for stratification variables (see paragraph above) and other design variables (sex (male/female), prior cardiovascular disease (yes/no), statin treatment at entry (yes/no), and glutamic acid decarboxylase antibodies (positive/negative)) [1]. We will in a supplementary analysis adjust for metformin administration before trial entry (yes/no).
## Assessments of underlying statistical assumptions
We plan to systematically assess if the assumptions underlying each of the used statistical methods are fulfilled [28].
For all analyses, we will test for major interactions between each covariate and the intervention variable (test of interaction). In turn, we will include each possible first order interaction between included covariates and the intervention variable. For each combination, we will test if the interaction term is significant and assess the effect size. We will only consider that there is evidence of an interaction if the interaction is statistically significant after Bonferroni adjusted thresholds (0.05 divided by number of possible interactions) and if the interaction shows a clinically significant effect. If it is concluded that the interaction is significant, we will consider both presenting an analysis separately for each (e.g. for each site if there is significant interaction between the trial intervention and ‘site’) and an overall analysis including the interaction term in the model.
## Assessments of underlying statistical assumptions for linear regression
We will visually inspect quantile-quantile plots of the residuals [28,29] to assess if the residuals are normally distributed and use residuals plotted against covariates and fitted values [28,29] to assess for homogeneity of variances. If the plots do not show a straight line, we will consider transforming the outcome, e.g. using log transformation or square root and/or use robust standard errors [28,29].
## Assessments of underlying statistical assumptions for cox regression
We will visually inspect log-log plots stratified by treatment and adjusted for the effects of all covariates (continuous and categorical) to asses if the assumption of proportional hazards between the compared intervention groups is fulfilled [[28], [29], [30]]. If the assumption of proportional hazards seems violated, we will consider stratifying the regression analysis for the variable not fulfilling the assumption or using a non-parametric test (e.g. log rank test) as the primary analysis method or split the observation period into two (or more) separate observation periods.
## Statistical reports
Blinded data on all outcomes will be analysed by two independent investigators [28]. Two independent statistical reports will be sent to the CIMT Steering Committee, and if there are discrepancies between the two primary statistical reports, then possible reasons for that will be identified and it will be considered which is the most correct result [28]. A final statistical report will be prepared, and all three statistical reports will be published as supplementary material.
## Characteristics of patients at baseline
We will present a description of baseline characteristics by intervention group. Discrete variables will be summarised by frequencies and percentages. Percentages will be calculated according to the number of patients with available data. Where values are missing, the actual denominator will be stated. Continuous variables will be summarised using standard measures of central tendency and dispersion using either mean ± SD for data with normal distribution or median and interquartile range for non-normally distributed data.
## Deviations from the initial design and methods of the CIMT trial
Based on our sample size estimation for the primary outcome of the CIMT trial allowing for five comparisons of the involved intervention groups we planned to include 950 patients [1]. However, eventually only 412 participants were included, as the trial had to be stopped at the scheduled duration of the trial due to lack of financial support. In the present analysis plan we schedule to analyse these clinical outcomes based on a strict factorial 2 × 3 design comparing the effects of metformin versus placebo and comparing the three different insulin analogue regimens. Given the low power in relation to the clinical outcomes, we consider all outcomes to be exploratory and that most emphasis should be given to the quality of life and patient satisfaction results according to our result of the power calculations.
In the original protocol it was not specified how the patient-important quality of life data and clinical outcomes would be analysed and presented [1]. Given the fact that the trial is a 2 × 3 factorial design and two sets of data are presented, we anticipate publishing the data in two separate papers.
## Discussion
To avoid risks of outcome reporting bias and data-driven biased results, this paper presents the detailed statistical analysis plan for the CIMT trial analysing patient-relevant outcomes at a time when the data have been gathered but not yet inspected or analysed. This statistical analysis plan follows the guidelines provided by Gamble et al. [ 31]. Clinical trials ought to be analysed according to a pre-specified analysis plan in order to prevent reporting bias and data driven analysis results [[32], [33], [34], [35]]. We have an obligation to report these patient-centred outcomes of quality of life, patient satisfaction, and cardiovascular outcomes and make the results available for future systematic reviews.
## Strengths
This statistical analysis plan has a number of strengths. The overall trial methodology has been pre-defined, we take problems with multiplicity and other risks of random errors into account, use validated statistical methods, and we systematically test for the underlying statistical assumptions [1,14,28]. Furthermore, we will analyse data in accordance to the modified intention-to-treat principle and, if necessary, use multiple imputations, and a best-worst/worst-best case scenario to assess the potential impact of the missing data on the results [26]. We will use the five-step procedure proposed by Jakobsen and colleagues to assess whether the thresholds for statistical and clinical significance are crossed [14].
## Limitations
The statistical analysis plan also has limitations. First, the primary outcome of the CIMT trial was an non-validated surrogate outcome [1], and the analyses of the patient-centred outcomes described in the present statistical analysis plan should therefore be considered exploratory. Second, the power estimations of the cardiovascular outcomes showed that we do not have sufficient data to confirm or reject realistic intervention effects [15], so the results of the cardiovascular outcome will only be reported in the supplementary material. Third, this statistical analysis plan has been developed long after data accrual was stopped. However, the plan was developed and agreed upon without knowledge of the intervention effects or specific data.
We plan on a subsequent 10-year follow-up study of the cardiovascular outcomes, assessed using similar methodology as described in this statistical analysis plan. This would compensate for the low power regarding the cardiovascular outcomes because of only 18 months follow-up in 412 patients, we plan to prolong the follow-up for the participants after end of the trial. This would raise the power of our analyses of cardiovascular outcomes, but risk confounding by changes to anti-diabetic treatment after stop of the CIMT interventions.
## Conclusions
To avoid risks of outcome reporting bias and data-driven biased results, this article presents the detailed statistical analysis plan for the CIMT trial analysing patient-relevant outcomes at a time when the data have been gathered but not yet inspected or analysed.
## Funding
The investigators received an unrestricted grant from $\frac{10.13039}{501100004191}$Novo Nordisk to enable conduct of the trial. The trial and its main 2 × 3 factorial design was $100\%$ initiated and conducted by the investigators. Novo Nordisk was allowed to comment on the protocol, on protocol changes during the trial, and on the manuscript prior to submission.
## Author contributions and consent for publication
MHO, JCJ, CG, CO, and TA: Drafted the present manuscript and revised and approved the final version. All remaining authors contributed with: 1) substantial contribution to the conception and design of the work; 2) acquisition and interpretation of data; 3) critically revising the work for intellectual content; and 4) agreement to be accountable for all aspects of the work.
## Ethics approval and consent to participate
Central ethical approval has been confirmed from the Regional Committee on Biomedical Research Ethics (H-D-2007-112) and the Danish Medicines Agency (EudraCT: 2007-006665-33 CIMT), registered within ClinicalTrials.gov (NCT00657943), and the trial was conducted in accordance with The Declaration of Helsinki and guidelines for Good Clinical Practice. Informed consent was obtained from each participant enrolled in the trial.
## Consent for publication
Not applicable.
## Availability of data and material
All relevant data are available.
## Declaration of competing interest
Sten Madsbad: Advisory boards: AstraZeneca; Boehringer Ingelheim; Eli Lilly; Intarcia Therapeutics; Merck Sharp & Dohme; Novartis; Novo Nordisk; Sanofi. Lecture fees: AstraZeneca; Boehringer Ingelheim; Merck Sharp & Dohme; Novo Nordisk; Sanofi. Research Grant Recipient: Novo Nordisk; Boehringer Ingelheim. Leif Breum: Advisory boards: AstraZeneca; Boehringer Ingelheim; Merck Sharp & Dohme; Novo Nordisk; Sanofi. Lecture fees: AstraZeneca; Lundbeck, Otsuka. Louise Lundby-Christensen, Christian Ovesen, Thomas Almdal: own shares in Novo Nordisk A/S. Elisabeth R Mathiesen: Advisory board: Novo Nordisk. Markus Harboe Olsen. Janus C Jakobsen, Christian Gluud, Simone B Sneppen, Christoffer Hedetoft, Michael E. Røder, Henrik Vestergaard, Niels Wiinberg, Thure Krarup: none declared.
## Data availability
No data was used for the research described in the article.
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|
---
title: 'Distinct metabolic features of genetic liability to type 2 diabetes and coronary
artery disease: a reverse Mendelian randomization study'
authors:
- Madeleine L. Smith
- Caroline J. Bull
- Michael V. Holmes
- George Davey Smith
- Eleanor Sanderson
- Emma L. Anderson
- Joshua A. Bell
journal: eBioMedicine
year: 2023
pmcid: PMC10009453
doi: 10.1016/j.ebiom.2023.104503
license: CC BY 4.0
---
# Distinct metabolic features of genetic liability to type 2 diabetes and coronary artery disease: a reverse Mendelian randomization study
## Body
Research in contextEvidence before this studyWe searched Medline for epidemiological and Mendelian randomization (MR) studies containing the terms ‘type 2 diabetes’ or ‘coronary artery disease’, and ‘metabolomic’ or ‘metabolites’, among any human population, without date restrictions. Previous cross-sectional and MR studies have identified metabolites, in particular lipids, that may play roles in the pathogenesis of type 2 diabetes (T2D) and coronary artery disease (CAD), yet direct comparisons of the influence of genetic liability to T2D and CAD had not been previously conducted. Added value of this studyWe present results supporting largely distinct metabolic profiles of genetic liability to T2D and to CAD using pleiotropy-robust MR models and the largest metabolite GWAS data to date. Implications of all the available evidenceThis added knowledge of the differing molecular phenotypes of T2D and CAD has implications for the prediction and prevention of these commonly co-occurring diseases. Future research utilising untargeted metabolomics platforms across other populations will help to further characterise T2D and CAD liabilities at a molecular level. Metabolites associated with genetic liability to disease are candidates for functional follow-up studies and prediction modelling.
## Summary
### Background
Type 2 diabetes (T2D) and coronary artery disease (CAD) both have known genetic determinants, but the mechanisms through which their associated genetic variants lead to disease onset remain poorly understood.
### Methods
We used large-scale metabolomics data in a two-sample reverse Mendelian randomization (MR) framework to estimate effects of genetic liability to T2D and CAD on 249 circulating metabolites in the UK Biobank ($$n = 118$$,466). We examined the potential for medication use to distort effect estimates by conducting age-stratified metabolite analyses.
### Findings
Using inverse variance weighted (IVW) models, higher genetic liability to T2D was estimated to decrease high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) (e.g., HDL-C: −0.05 SD; $95\%$ CI −0.07 to −0.03, per doubling of liability), whilst increasing all triglyceride groups and branched chain amino acids (BCAAs). IVW estimates for CAD liability suggested an effect on reducing HDL-C as well as raising very-low density lipoprotein cholesterol (VLDL-C) and LDL-C. In pleiotropy-robust models, T2D liability was still estimated to increase BCAAs, but several estimates for higher CAD liability reversed and supported decreased LDL-C and apolipoprotein-B. Estimated effects of CAD liability differed substantially by age for non-HDL-C traits, with higher CAD liability lowering LDL-C only at older ages when statin use was common.
### Interpretation
Overall, our results support largely distinct metabolic features of genetic liability to T2D and CAD, illustrating both challenges and opportunities for preventing these commonly co-occurring diseases.
### Funding
$\frac{10.13039}{100010269}$Wellcome Trust [218495/Z/19/Z], UK MRC [MC_UU_$\frac{00011}{1}$; MC_UU_$\frac{00011}{4}$], the $\frac{10.13039}{501100000883}$University of Bristol, Diabetes UK [$\frac{17}{0005587}$], $\frac{10.13039}{501100000321}$World Cancer Research Fund [IIG_2019_2009].
## Evidence before this study
We searched Medline for epidemiological and Mendelian randomization (MR) studies containing the terms ‘type 2 diabetes’ or ‘coronary artery disease’, and ‘metabolomic’ or ‘metabolites’, among any human population, without date restrictions. Previous cross-sectional and MR studies have identified metabolites, in particular lipids, that may play roles in the pathogenesis of type 2 diabetes (T2D) and coronary artery disease (CAD), yet direct comparisons of the influence of genetic liability to T2D and CAD had not been previously conducted.
## Added value of this study
We present results supporting largely distinct metabolic profiles of genetic liability to T2D and to CAD using pleiotropy-robust MR models and the largest metabolite GWAS data to date.
## Implications of all the available evidence
This added knowledge of the differing molecular phenotypes of T2D and CAD has implications for the prediction and prevention of these commonly co-occurring diseases. Future research utilising untargeted metabolomics platforms across other populations will help to further characterise T2D and CAD liabilities at a molecular level. Metabolites associated with genetic liability to disease are candidates for functional follow-up studies and prediction modelling.
## Introduction
Globally, overall incidence rates of type 2 diabetes (T2D) and coronary artery disease (CAD) are increasing in parallel, together affecting over 500 million adults.1,2 Both diseases have roots in disordered metabolism, but they differ in their life course development and clinical presentation. Genome-wide association studies (GWAS) have provided robust evidence that both T2D and CAD have genetic influences,3,4 but the mechanisms through which the associated genetic variants increase disease risk remain poorly understood.
Recent advances in metabolomics technologies have enabled detailed insight into the metabolic features of cardiometabolic diseases and their potential underlying mechanisms.5 The application of metabolomics in observational epidemiological studies has helped to identify metabolites involved in the pathogenesis of T2D, or that might contribute to T2D incidence.6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 For example, a recent meta-analysis identified associations of incident T2D with differences in several lipid and non-lipid metabolites, including higher levels of branched chain amino acids (BCAAs), some fatty acids and acylcarnitines, amongst others.17 For CAD, metabolomics in observational studies has helped to identify key metabolic characteristics of the disease,18, 19, 20, 21, 22, 23, 24, 25 to predict and diagnose CAD,26, 27, 28, 29 and to predict outcomes and mortality in CAD patients.30, 31, 32 However, observational studies are limited in their ability to infer whether metabolic traits are causal, rather than just predictive for disease, as these studies are liable to bias due to residual confounding by lifestyle factors and other diseases.
Mendelian randomization (MR), a genetic epidemiological approach that uses genetic variants associated with modifiable exposures to estimate the unbiased effects of such exposures on outcomes,33 has been applied to investigate associations between lipid levels and T2D and CAD.34, 35, 36, 37 The two diseases appear to have differing lipid aetiology: higher low-density lipoprotein cholesterol (LDL-C) and triglycerides increase CAD risk, whereas higher LDL and high-density lipoprotein cholesterol (HDL-C) reduce T2D risk.36 More recently, MR has been used in ‘reverse’ to estimate effects of genetic liability for disease and potential biomarkers, to reveal features of the developing disease process, which include both causal and non-causal factors for disease incidence.38 In reverse-MR, genetic liability to disease is analysed as the exposure, and exposure-outcome/trait associations could represent either causes or consequences of the disease, or associations with other/unmeasured traits which cause the disease—with any non-causal associations of potential value for revealing predictive biomarkers. As the burden of disease in a population increases (often with increased age), then the effects of genetic liability would be expected to be larger, and the potential for results to reflect consequences of disease to be larger. Previous reverse-MR studies have highlighted that liability to T2D and CAD can effect levels of some metabolites in early life.39,40 These MR studies in children have the advantage of low disease prevalence (and therefore little reverse causality), and low medication use, so are useful for identifying causal factors. Studies in older adults are better for identifying predictors of disease, as there is greater disease prevalence, however increased medication use with age can distort results. Previous reverse-MR studies of the impact of genetic liability to T2D or CAD on metabolic traits are limited by their small sample sizes. Moreover, the metabolic profiles of genetic liability to T2D and CAD have not been directly compared within the same analytical setting.
Here, we compare the metabolic features of genetic liability to T2D and to CAD using a reverse-MR framework, with the aim of revealing the metabolic traits which characterise the development of each disease. We used the largest, most recent summary-level GWAS data on circulating metabolites measured using targeted metabolomics (N∼120,000) from the population-based UK Biobank cohort study, representing a 5-fold larger sample size than previous studies. To examine any distortions of effect estimates by medication use, we examined the effects of T2D and CAD liability on the use of metformin and statins, and repeated analyses using data from age-stratified GWAS of metabolites, given that medication use differs markedly by age. We also estimated the effects of disease liability on lifestyle-related risk factors including adiposity, smoking, and alcohol consumption to see if our reverse-MR framework would detect these expectedly more distal associations.
## T2D liability
We identified single nucleotide polymorphisms (SNPs) that were independently associated with T2D at $P \leq 5$ × 10−8 from a large-scale GWAS meta-analysis combining data from 32 GWAS, including 74,124 T2D cases and 824,006 controls of European ancestry.3 Across included studies, female sex ranged from $37\%$ to $64\%$ and mean (SD) age range was 23.9 (2.1) to 61.3 (2.9) years. T2D was diagnosed across included studies using a range of criteria, including use of oral diabetes medication, diagnostic fasting glucose or HbA1c levels, or self-report. The T2D GWAS was conducted with and without adjustment for BMI, and we used the 231 SNPs identified in the non-BMI-adjusted GWAS to reduce potential for collider bias.41 For variants that were in linkage disequilibrium (LD) based on R2 > 0.001, the SNP with the lowest P-value in each set of variants in LD was retained. This left 167 independent SNPs associated with T2D (Supplementary Table S1) for inclusion in our reverse-MR analysis.
## CAD liability
SNPs for CAD were identified in a large-scale GWAS meta-analysis of 10 GWAS conducted among middle-aged adults, with study-specific sample and variant filters applied and adjustment for study-specific covariates.42 The study had a total sample of 181,522 cases among 1,165,690 study participants, who were $46\%$ female and largely of European ancestry (>$95\%$). Potential ancestry effects were accounted for by transethnic comparison with a GWAS from Biobank Japan. Case status was defined based on relevant hospital codes for prevalent or incident CAD. The GWAS detected 241 genetic variants independently associated with CAD at $P \leq 5$ × 10−8. For variants that were in LD based on R2 > 0.001, the SNP with the lowest P-value in each set of variants in LD was retained, leaving 145 SNPs (Supplementary Table S1).
## Outcome data on circulating metabolites and medication use
Summary statistics from GWAS of circulating metabolites conducted among European participants of the UK Biobank study were used. Details of the UK Biobank design, participants, genomic quality control (QC) and its strengths and limitations have been reported previously.43, 44, 45 Briefly, 502,549 adults aged 40–69 years were recruited between 2006 and 2010 via 22 assessment centres across England, Wales, and Scotland. Non-fasting EDTA plasma samples from a random subset of participants ($$n = 118$$,466) were analysed for levels of 249 metabolic traits (165 concentrations and 84 derived ratios) using targeted high-throughput proton nuclear magnetic resonance (1H NMR) spectroscopy (Nightingale Health Ltd; biomarker quantification version 2020).46 These metabolic traits comprised routine lipids, lipoprotein subclass profiling, fatty acid composition and various low-molecular-weight metabolites including BCAAs, ketone bodies, glycolysis-related traits and inflammatory glycoprotein acetyls. All metabolic trait measurements were normalized and standardised prior to analyses using rank-based inverse normal transformation. Genetic association data were obtained from the IEU-OpenGWAS platform and have been generated using the MRC IEU UK Biobank GWAS pipeline.47 The BOLT-LMM software option was used to conduct a full GWAS, which accounts for participant relatedness, and the model was adjusted for genotype array, sex, and fasting time. We also included C-reactive protein (CRP) for comparison with glycoprotein acetyls, measured in UK *Biobank via* the same blood samples, analysed by immunoturbidimetric-high sensitivity analysis on a Beckman Coulter AU5800 ($$n = 469$$,772).
Statins and metformin are commonly prescribed medications for the prevention/treatment of CAD and T2D, respectively, particularly among adults identified as having higher risk.48 These medications can alter the levels of circulating metabolites49,50 and thus may distort associations of genetic liability to T2D and CAD with metabolites (via mediation). We therefore additionally estimated the effects of T2D and CAD liability on the self-reported use of statins (atorvastatin and simvastatin) and metformin obtained from a sample of 462,933 UK Biobank participants using interviews, self-reporting and medical records (UK Biobank Data-Field 20003), to inform the interpretation of metabolite results. Genetic association data were previously generated for these medication traits using the MRC IEU UK Biobank GWAS pipeline,47 adjusting for age at baseline, sex and genotyping array. These medication outcomes also served as positive controls, with liability to CAD expected to most strongly raise statin use, and liability to T2D expected to most strongly raise metformin use.
For the purposes of further examining distortions by medication use, we used summary statistics from metabolite GWASs that were performed on the same UK Biobank sample but divided into age tertiles (three equal-sized groups, containing ages: 39–53 years, 53–61 years, and 61–73 years), and which used the same standardisation and covariate adjustments. Each tertile created a set of summary statistics to be analysed. Based on previous work in UK Biobank,51 the estimated prevalence of statin medication use within these same tertiles (youngest to oldest) is $5\%$, $17\%$, and $29\%$, respectively.
The samples used to generate genetic instruments for T2D and CAD included participants from UK Biobank, meaning that they overlap with the outcome sample (∼$26\%$ and ∼$48\%$ overlap, respectively), which may bias estimates. However, strong genetic instruments for both exposures (F-stat >10) mean that bias from sample overlap is likely to be small.52 Data used for additional outcomes (adiposity, smoking and alcohol consumption) is described in Supplementary File.
## Ethics
UK Biobank participants provided written informed consent. Ethical approval was obtained from the North West Multi-centre Research Ethics Committee (NRES Committee North West - Haydock, 21/NW/0157), as a Research Tissue Bank approval.
## Statistics
*To* generate MR estimates of the effect of genetic liability to T2D and CAD on metabolites (with ages combined and within age tertiles), and on medication use and additional outcomes noted above, we integrated estimates of the association of SNPs with exposures (T2D and CAD separately), with estimates of the association of those same SNPs with each metabolic trait measured in UK Biobank. Summary statistics were harmonised using the ‘harmonise_data’ function within the TwoSampleMR package.53 Using the same package, we used inverse variance weighted (IVW) regression in the main analysis, which assumes that none of the SNPs are pleiotropic (exclusion restriction), as well as the two other core IV assumptions: relevance and independence.33 To examine and correct for potential horizontal pleiotropy, we used 3 additional sensitivity methods for each exposure-outcome pair: MR-Egger (which allows for all SNPs to be pleiotropic),54 weighted median (which allows for up to half of the weighted SNPs to be pleiotropic and is less influenced by outliers than other models),55 and weighted mode (which assumes that the most common effect is consistent with the true causal effect, i.e. not biased by horizontal pleiotropy).56 To aid interpretation, all estimates and standard errors were multiplied by 0.693 (loge2) and represent the normalised SD unit difference in each outcome trait (metabolites, medications, adiposity, smoking, and alcohol) per doubling of genetic liability to T2D or CAD, based on previous recommendations.57
## Additional and sensitivity analyses
To estimate the direct effects of liability to either disease and avoid any biases present due to shared effect of genetic variants on liability to both diseases, we performed multivariable MR with liability to T2D and CAD as exposures, and metabolites, adiposity, medication, and lifestyle factors as outcomes. This was performed using the MVMR R package,58 using an IVW model with all SNPs associated with either exposure included as instruments. We did not conduct any sensitivity analyses for our MVMR model as this was not our primary estimation of interest and pleiotropy-robust sensitivity models are not yet well-developed for multivariable MR.
Sensitivity analyses were performed to examine the influence of outlying SNPs on effect estimates. Radial-MR methods were utilised to re-estimate the model excluding all outlying SNPs according to Cochrane's Q statistic, for both T2D liability and CAD liability. This analysis was conducted using the RadialMR R package.59 The TCF7L2 gene is known to increase risk of T2D, but is paradoxically associated with reduced BMI,60 and thus expected to be a uniquely strong outlier among the instruments used for T2D. We therefore additionally performed the T2D liability MR with the TCF7L2 SNP (rs7903146) excluded for metabolite, adiposity, medication, and lifestyle outcomes.
To correct for multiple testing in a way that accounts for the correlated nature of our metabolic outcome traits, the P-value threshold (usually 0.05) for guiding statistical interpretation can be divided by 33 (the number of principal components explaining $95\%$ of the variance in the metabolic outcomes studied here in previous multi-cohort analyses61) to give 0.002. Because our study involves effect estimation, we consider effect size and precision most informative, with exact P-values included as measures of the strength of evidence against the null hypothesis.62,63 Statistical analyses were performed in R version 4.0.2.
Multivariable MR results, representing the direct effects of liability to either disease, were similar to univariable MR results across all metabolites, adiposity, medication and lifestyle outcomes (Supplementary Fig. S9; Supplementary Table S8).
Radial-MR sensitivity analyses, which were performed to identify and exclude SNPs with outlying effects, showed little difference in effect estimates after outlier exclusion for T2D liability, although the estimates with the outliers excluded were more precise (Supplementary Fig. S10; Supplementary Table S9). However, for CAD liability, effect estimates for some metabolites were attenuated or even reversed (Supplementary Fig. S11). For example, with outlying SNPs excluded, higher liability to CAD was estimated to decrease LDL-C (−0.02 SD; $95\%$ CI −0.03 to 0.00). Estimates from weighted median and weighted mode models appeared not to be influenced by these outliers and were largely in line with the Radial-MR results when outlying SNPs were excluded.
The analyses for liability to T2D on metabolites, adiposity traits and lifestyle outcomes but with the SNP representing the TCF7L2 gene excluded were similar to effect estimates generated using the full SNP set for T2D liability (Supplementary Figs. S12 and S13; Supplementary Table S10).
Lastly, estimates of the effects of T2D and CAD liability on each metabolite (ages combined), adiposity, and medication use were visualized in an XY plot to compare the overall pattern of effects of liability to either disease across outcomes (Fig. 5). To better visualize the comparison across different feature classes, only a subset of metabolites is included on the XY plot (the same as included across Fig. 1, Fig. 2, Fig. 3, Fig. 4); the slope of this regression line was −0.16 with an R2 of 0.02. When including all 266 features (all metabolites, adiposity, smoking, alcohol, CRP), the slope of the regression line was −0.49 with an R2 of 0.11. Together, this indicates a weak association between profiles, giving further evidence that genetic liability to T2D and to CAD have distinct metabolic features. Fig. 5Comparison of the effects of T2D and CAD liability on adiposity, metabolic traits (those included in Fig. 1, Fig. 2, Fig. 3, Fig. 4), medication use, smoking and alcohol use. Effect estimates are SD unit differences in adiposity or metabolic trait, or log odds for medication use and smoking/alcohol status, per doubling of odds of T2D or CAD, based on IVW models. T2D, type 2 diabetes; CAD, coronary artery disease; SD, standard deviation; IVW, inverse variance weighted.
## Role of the funding source
The funding sources had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.
## Results
Instrument count and strength are reported in Supplementary File.
## Lipids and lipoproteins
IVW estimates for the effect of higher liability to T2D and to CAD on lipids tended to differ between diseases (Fig. 1). Higher T2D liability was estimated to decrease total, HDL-C and LDL-C (e.g., HDL-C −0.05 SD; $95\%$ CI −0.07 to −0.03), with no strong evidence of an effect on VLDL-C. IVW estimates for CAD liability on cholesterol were less precise but suggested an effect on lowering HDL-C and raising total-C, VLDL-C and LDL-C. T2D liability appeared to increase all triglyceride groups (e.g., triglycerides in VLDL 0.04 SD; $95\%$ CI 0.01–0.07), whilst CAD liability had little effect on triglyceride groups except for LDL triglycerides which were increased. T2D liability was estimated to increase VLDL particle size (0.04 SD; $95\%$ CI 0.02–0.07), and decrease LDL and HDL particle size (−0.04 SD; $95\%$ CI −0.06 to −0.02 for both), whilst CAD liability was estimated to increase LDL particle size (0.06 SD; $95\%$ CI 0.02–0.10), and decrease HDL particle size (−0.06 SD; $95\%$ CI −0.11 to −0.01). T2D liability appeared to decrease, whilst CAD liability appeared to increase, apolipoprotein B, although estimates were imprecise for CAD; whilst liability to either disease appeared to decrease apolipoprotein A1 (T2D −0.03 SD; $95\%$ CI −0.05 to −0.01; CAD −0.07 SD; $95\%$ CI −0.11 to −0.03). Effect estimates across sensitivity models were imprecise for T2D and CAD with each lipid trait (e.g., MR Egger estimate for effect liability to T2D on total-C 0.00 SD; $95\%$ CI −0.05 to 0.04; Egger P-values largely >0.05; Supplementary Tables S2–S4; Supplementary Fig. S1). Of note, the effect of higher CAD liability appeared inverse for VLDL-C, LDL-C, and apolipoprotein B in weighted median and weighted mode models (Fig. 2a; Supplementary Tables S2–S4). Evidence of heterogeneity was strong for all lipid and lipoprotein traits, e.g., Cochran's Q P-value = 4.22e−188 for the IVW estimate for T2D liability on total-C (Supplementary Table S2).Fig. 1Effect of T2D and CAD liability on lipids and lipoproteins. Effect estimates are normalised SD unit differences in metabolite per doubling of liability to T2D or CAD based on IVW models. T2D, type 2 diabetes; CAD, coronary artery disease; SD, standard deviation; IVW, inverse variance weighted. Fig. 2Effect of CAD liability on lipids and lipoproteins (a) using different MR methods and (b) age tertiles (weighted median method). Effect estimates are normalised SD unit differences in metabolite per doubling of liability to disease. MR, Mendelian randomization; SD, standard deviation.
The magnitudes of the effect of liability to T2D or CAD on statin or metformin use were small. As expected, higher T2D liability most strongly increased odds of taking metformin (Supplementary Fig. S2; OR 1.02; $95\%$ CI 1.01–1.02), whilst higher CAD liability most strongly increased odds of taking simvastatin (OR 1.03; $95\%$ CI 1.03–1.04). The effect of higher T2D liability on metformin use was robust across weighted median and weighted mode models but attenuated in the MR-Egger model (Egger intercept P-value = 6.09e−16 [Supplementary Tables S3 and S4]). Estimates of CAD liability for atorvastatin and simvastatin use were consistent across sensitivity models (Supplementary Table S2).
The estimated effects of T2D and CAD liability on several lipids, particularly non-HDL cholesterol, differed markedly by age (Supplementary Fig. S3; Supplementary Table S5). Among these age-stratified results, some estimated effects of T2D liability on lipids were inconsistent across sensitivity models, including for apolipoprotein B and LDL-C, however, there was little evidence that Egger intercepts for these estimates differed from zero (Supplementary Table S6). Based on outlier-robust weighted median and mode models, higher CAD liability was estimated to decrease LDL-C and apolipoprotein B but only within the oldest age tertile (where statin use was $29\%$); these effects diminished to the null within the intermediate age tertile (where statin use was $17\%$) and the youngest age tertile (where statin use was $5\%$) (Fig. 2b).
## Fatty acids and amino acids
Effects on fatty acid traits were largely opposite for T2D liability and CAD liability (Fig. 3). For example, higher T2D liability was estimated to decrease the ratio of docosahexaenoic acid to total fatty acids (−0.02 SD; $95\%$ CI −0.03 to −0.01) whereas CAD liability was estimated to increase the same ratio (0.03 SD; $95\%$ CI 0.00–0.05). CAD liability was estimated to decrease the ratio of saturated to total fatty acids and increase the ratio of docosahexaenoic acid to total fatty acids, and both of these were consistent across sensitivity models. Higher T2D liability was estimated to increase all amino acids (except for glycine), including total BCAAs (IVW 0.05 SD; $95\%$ CI 0.04–0.07), which was robustly positive across sensitivity models (Supplementary Fig. S1). There was consistently little evidence of an effect of CAD liability on any amino acid, except for total BCAAs and valine for which MR-Egger estimates suggested a decrease (Supplementary Tables S2–S4; Supplementary Fig. S4). Evidence of heterogeneity was strong for most fatty acid and amino acid traits (Supplementary Table S2).Fig. 3Effect of T2D and CAD liability on fatty acids and amino acids. Effect estimates are normalised SD unit differences in metabolite per doubling of liability to T2D or CAD based on IVW models. T2D, type 2 diabetes; CAD, coronary artery disease; SD, standard deviation; IVW, inverse variance weighted.
The effects of T2D liability on fatty acids and amino acids as estimated by IVW models were fairly consistent with age, exceptions including total fatty acids, ratio of saturated to total fatty acids, and histidine (Supplementary Fig. S5). For histidine, estimates were consistently null in the intermediate tertile, and consistently positive in the oldest and youngest tertiles (Supplementary Tables S5–S7). The effect of higher CAD liability on total fatty acids decreased with age, with the youngest tertile exhibiting an increase, the intermediate tertile a null effect, and the oldest tertile a decrease (Supplementary Table S5). The effect of higher CAD liability on both omega-3 to total fatty acids and docosahexaenoic acid to total fatty acids ratios was null in the youngest tertile but positive in both the intermediate and oldest tertiles (consistent across sensitivity models [Supplementary Tables S5–S7]).
## Glycolysis traits and other metabolites
As expected, T2D liability consistently increased glucose (0.08 SD; $95\%$ CI 0.06–0.10) across sensitivity models (Supplementary Tables S2–S4; Supplementary Fig. S1); yet there was no effect of CAD liability on glucose (Fig. 4; Supplementary Fig. S4). There was little evidence of effect of T2D liability or CAD liability on other glycolysis related metabolites, ketone bodies or fluid balance metabolites. Only T2D liability was estimated to slightly increase glycoprotein acetyls (IVW 0.02 SD; $95\%$ CI 0.00–0.04), although there were inconsistencies across models (Supplementary Fig. S1). Effect estimates for CRP were similar to those for glycoprotein acetyls. Evidence of heterogeneity was strong for most pre-glycaemic and other metabolic traits (Supplementary Table S2).Fig. 4Effect of T2D and CAD liability on glycolysis related metabolites, ketone bodies, fluid balance metabolites and inflammation metabolites. Effect estimates are normalised SD unit differences in metabolite per doubling of liability to T2D or CAD based on IVW models. T2D, type 2 diabetes; CAD, coronary artery disease; SD, standard deviation; IVW, inverse variance weighted.
Estimates of the effects of T2D and CAD liability were largely consistent across age tertiles for glycolysis traits and ketone bodies, except for CAD liability on lactate and pyruvate which was positive in the youngest and null in the intermediate and oldest tertiles (Supplementary Fig. S6). The effect of higher T2D liability on glycoprotein acetyls was positive in the youngest (IVW 0.04 SD, $95\%$ CI 0.02–0.06) and null in the oldest tertile. This was not robust across sensitivity models (Supplementary Tables S6 and S7).
## Adiposity, smoking, and alcohol consumption
*Higher* genetic liability to T2D was estimated to increase all adiposity outcomes according to IVW estimates (Supplementary Fig. S7; e.g., BMI: 0.03 SD; $95\%$ CI 0.01–0.05). However, there was evidence to suggest those estimates may be biased by horizontal pleiotropy; MR-Egger P-values were <0.05 for all adiposity traits (Supplementary Table S3). There was little evidence of an effect of liability to CAD on any of the adiposity outcomes according to IVW, weighted median and weighted mode estimates, however MR-Egger estimates suggested a small effect on decreased adiposity (e.g., trunk fat mass: −0.03 SD; $95\%$ CI −0.07 to 0.00). Evidence of heterogeneity was strong for all adiposity traits (Supplementary Table S2). The effects of T2D and CAD liability on both smoking status and alcohol drinking status were null (Supplementary Fig. S8). Higher T2D liability was estimated to increase alcohol intake frequency in IVW models (0.02 SD, $95\%$ CI 0.01–0.04); this was null in sensitivity models (Supplementary Tables S3 and S4). Higher CAD liability had no effect on alcohol intake frequency. There was no effect of liability to either disease on pack years of smoking.
## Discussion
In this study, we directly compared the metabolic profiles of genetic liability to T2D and CAD, two diseases which commonly co-occur but which involve different pathophysiology and clinical presentation. We applied a reverse-MR framework38 using new summary-level GWAS data on metabolomics from UK Biobank, which enabled a 5-times larger sample size over previous studies.39,40 Our most robust results suggest that genetic liability to T2D and to CAD have largely distinct metabolic features, including increased BCAAs in T2D across adulthood and decreased LDL-C and apolipoprotein B in CAD, illustrating both challenges and opportunities for preventing these commonly co-occurring diseases. Our results for some metabolites that greatly differ across age tertiles (across which statin use greatly differs) also suggest that medication use can severely distort the atherogenic effects of CAD liability, resulting in paradoxically favourable effects of disease liability in older adults. The use of age-stratified MR may limit such biases.
Statins and metformin are commonly used for the prevention/treatment of CAD and T2D, respectively. The overall prevalence of statin use in UK *Biobank is* $16\%$, with men almost twice as likely to be taking statins as women, and use increasing markedly across age tertiles.51 Statins and metformin are known to lower LDL-C48,64 and will likely have direct and indirect effects on other metabolic traits.49 As expected, our results suggest that increased T2D liability most strongly increases the odds of metformin use whilst increased CAD liability most strongly increases the odds of statin use, providing positive controls and suggesting specificity of genetic instruments used for each disease. Medication use may therefore modify the effect of T2D or CAD liability on metabolic traits and distort results in the form of underestimated, or even reversed, effects of genetic liability. These distortions are evidenced by our differing results across age tertiles, where level of medication use varies markedly. For example, our results based on (non-pleiotropy-robust) IVW models suggest that higher CAD liability increases LDL-C and apolipoprotein B which is consistent with a previous reverse-MR conducted across younger samples,40 suggesting that perturbations in LDL-C in early life due to CAD liability persist into adulthood. We did, however, see attenuation of these effects in the oldest age tertile which could be due to the increased statin use in the older sample; and the direction of effect was inconsistent across pleiotropy-robust sensitivity models, suggesting that these particular results were vulnerable to outlier SNP effects. Results based on pleiotropy/outlier-robust weighted median and mode models suggested that CAD liability has an inverse effect on LDL-C, VLDL-C, and apolipoprotein B; this again differed substantially by age, with inverse effects only at older ages, and with attenuated or null effects at younger ages. Medication use may explain inconsistencies between results for T2D or CAD liability and LDL-C from this study and previous studies, particularly studies of younger people who are less likely to be taking medication.40,65 By stratifying the cohort by age as a proxy for medication use before performing metabolite GWAS, we were likely better positioned to overcome these distortions and isolate the effects of disease liability. Stratifying by age (as a proxy) instead of medication use itself, which is affected by disease liability, also reduced the potential for collider bias.
Increasing HDL-C has been hypothesized as a mechanism to reduce CAD risk, based largely on conventional non-genetic epidemiology studies.66 However, evidence from MR studies has shown that HDL-C-raising genetic variants do not reduce CAD risk,37,67 and a meta-analysis showed that HDL-C modifying treatments did not reduce cardiovascular mortality.68,69 Our current study detected an effect of CAD liability on decreasing HDL-C levels, suggesting that reduced HDL-C is an early (likely non-causal) feature of CAD development, potentially explaining why lower HDL-C appears to be associated with higher CAD risk in observational settings. We also found some evidence to suggest that both LDL-C and HDL-C are reduced in response to increased T2D liability, which is consistent with evidence from a previous reverse-MR study among young people for HDL-C but not LDL-C (which was raised),39 and a conventional ‘forward’-MR study that found increased HDL-C and LDL-C decreased risk of T2D.36 *This is* also in accordance with evidence that statin therapy (which lowers LDL-C) increases risk of T2D.70 However, there were inconsistencies in estimates for effect on these traits across pleiotropy-robust sensitivity models.
Our results suggest that T2D and CAD liability confer opposite effects on fatty acid metabolism, which were largely consistent across pleiotropy/outlier-robust sensitivity models. A study that used a nontargeted mass spectrometry-based approach found that palmitic acid and linoleic acid levels were elevated in severe coronary heart disease.18 However, we did not detect these to be affected by liability to CAD in our study, suggesting that these metabolic changes may succeed rather than precede disease. We saw evidence of substantial age differences in these effects, where higher CAD liability increased these ratios in the older tertiles but not the youngest, which could be an effect of medication or improved lifestyle in response to a CAD diagnosis.
Consistent with previous studies, we found consistent evidence across ages and sensitivity models that T2D liability robustly increased total BCAAs17,39 whereas there was no evidence of an effect of CAD liability on BCAAs, highlighting that increased total BCAAs are exclusively a feature of T2D liability. One study, using a mass spectrometry-based metabolomic platform, found that fasting concentrations of BCAAs were elevated up to 12 years prior to the onset of T2D.71 *This is* supported by our data, highlighting BCAAs as biomarkers of T2D development.
Although both T2D and CAD are associated with inflammation,72 evidence of an effect on levels of glycoprotein acetyls from this study was not robust for liability to either disease when viewing ages collectively. However, higher T2D liability did appear to modestly raise glycoprotein acetyls within the youngest age group, which is consistent with a similar study conducted in a younger cohort.39 A previous MR study found that genetically higher insulin resistance was positively associated with glycoprotein acetyls, so it would be expected that higher liability to T2D raises the same biomarker.73 However, it is difficult to draw conclusions about the role of inflammation in either disease process from our study given that we only considered two inflammatory markers, omitting other potentially important ones such as interleukin-6 and interleukin-1β that may play a role in disease pathogenesis.74,75 *It is* well established that excess adiposity is a causal risk factor for T2D,76 and there is strong causal evidence that adiposity increases CAD risk in conventional (forward-direction) MR studies.77 Presently, we did not find consistent evidence that either T2D or CAD liability raises adiposity. This likely reflects the more distal nature of adiposity as compared with circulating metabolites, and possibly highly pleiotropic variants among the outcome SNPs.60 We also did not find consistent evidence that either T2D or CAD liability increases smoking or alcohol behaviours. Although increased smoking and drinking are risk factors for both T2D and CAD,78, 79 the lack of signal seen in our study suggests that they are more distal features of disease liability. This provides further evidence that the reverse-MR approach is useful for revealing more proximal factors such as metabolites that are directly involved in the disease process and highlights the importance of combining evidence from MR study designs in different directions to reveal the full scope of factors contributing to disease development.
## Study limitations
The samples used to generate genetic instruments for T2D and CAD included participants from UK Biobank, the same sample from which we obtained genetic association estimates for metabolic traits, medication use, and lifestyle-related outcomes (∼$26\%$ and ∼$48\%$ overlap, respectively). This may have led to bias in the results in the direction of the observational association, however, given the strong genetic instruments for T2D and CAD liability, bias from sample overlap is likely small.52 Another limitation is the unrepresentative nature of UK Biobank (initial response rate ∼$5\%$) and therefore is vulnerable to various forms of selection bias. Replication of this study in other large cohort studies and application of different approaches will allow more robust metabolic characterisation of T2D and CAD liability, although UK *Biobank is* currently the largest such data in existence. The metabolite GWAS with all ages collectively was not age-adjusted, leaving the potential for distortions by age within those metabolite effect estimates; although we were able to additionally examine these effects within age tertiles. Another limitation is using age as a proxy for medication use, where we are assuming that differences by age primarily reflect differences by medication use, whereas other factors which vary by age may also help explain these age differences. The frequency of statin use did vary greatly by age, however, and the pattern of effects seen here are specific to the target traits of statins and mirror the pattern of effects seen in MR studies and clinical trials of statin use,48,49 which together support our assumption. Use of only summary-level data limits the capacity to fully explore effects of other factors such as medication use, sex, and ethnicity, which may influence metabolites. A further limitation is the reliance on targeted NMR metabolomics, rather than mass spectrometry which is not yet available at large scale but offers a broader representation of metabolites beyond lipid subclasses; targeted NMR is often considered more clinically relevant, however. The smoking pack-years GWAS was restricted to ever-smokers, which may induce sampling bias and invalidate the MR assumptions for that analysis.
## Conclusions
Our most robust findings based on pleiotropy-robust models and the largest metabolite GWAS data to date suggest that T2D liability increases BCAAs across mid to late adulthood, whilst CAD liability decreases LDL-C and apolipoprotein B in older adults only. Such paradoxically favourable effects of CAD liability in older adults likely reflect mediation by statin use in adulthood. Overall, our results support largely distinct metabolic profiles of genetic liability to T2D and to CAD, illustrating both challenges and opportunities for preventing these commonly co-occurring diseases.
## Contributors
JAB, ELA and CJB conceived and planned the study and supervised analyses. MLS conducted the analyses and wrote the first draft. MLS and JAB verified the underlying data. JAB, ELA, CJB, ES, GDS and MVH critically reviewed the intellectual content of manuscript drafts and with MLS approved the final version for submission.
## Data sharing statement
All summary level GWAS results are publicly available through the IEU-OpenGWAS platform, accessible at https://gwas.mrcieu.ac.uk/.
## Declaration of interests
MVH has collaborated with Boehringer Ingelheim in research, and in adherence to the University of Oxford's Clinical Trial Service Unit & Epidemiological Studies Unit (CSTU) staff policy, did not accept personal honoraria or other payments from pharmaceutical companies. MVH became a full-time employee of 23andMe during the study and owns stock in 23andMe. GDS reports Scientific Advisory Board Membership for Relation Therapeutics and Insitro. No other conflicts of interest to declare.
## Supplementary data
Supplementary Tables Supplementary File Supplementary Figures
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|
---
title: Heart rate variability behavior in young men after short-term carotenoid-containing
supplementation
authors:
- Hsien-Tsai Wu
- Jian-Jung Chen
journal: Heliyon
year: 2023
pmcid: PMC10009683
doi: 10.1016/j.heliyon.2023.e14102
license: CC BY 4.0
---
# Heart rate variability behavior in young men after short-term carotenoid-containing supplementation
## Abstract
### Background
Heart rate variability can reflect the risk of developing cardiovascular disease (CVD), while carotenoids are good for CVD prevention. However, the acute effect of short-term carotenoid-containing supplementation on heart rate variability in young men is unclear.
### Methods
Thirty young men between 20 and 29 years of age without personal or family history of cardiovascular diseases were randomly divided into control and experimental groups. The anthropometric data, physiological parameters, and serum biochemical data were acquired, which were without significant difference between the two groups, at the beginning of trial. The participants in the experimental group consumed one pack of compound nutritional supplements in the morning (e.g., 10 AM) and another pack at night (e.g., 7 PM) each day. Heart rate variability was measured again once a month. Repeated measures analysis of variance with Roy’s largest root test and Bonferroni post hoc test were applied for primary outcomes.
### Results
Repeated measures analysis of variance indicated a significant time interaction effect for the estimated marginal means of percussion entropy index scale (T1 versus T3, T1 versus T4, and T2 versus T4 with $$p \leq 0.009$$, 0.005, and 0.032, respectively). Roy’s largest root test indicates there were significant differences between the means of the index after the intervention between two groups only on T3 and T4 ($$p \leq 0.007$$, η2 = 0.232 and $$p \leq 0.028$$, η2 = 0.162, respectively).
### Conclusion
Short-term carotenoid-containing supplementation could help young men by increasing heart rate variability capacity compared to controls over three months.
## Introduction
Cardiovascular disease (CVD) is a growing global health threat that affects not only high-income countries but also those with low income [1]. More importantly, significantly younger subjects who are at risk of developing CVD have been focused on in recent years [2], similarly to how elder subjects and their CVD risk was a subject of focus a decade ago. A previous study addressed young adults with type 2 diabetes and arterial stiffness with a poor cardiovascular risk profile, specifically risk factors related to the lower HRV and metabolic syndrome [3]. Accordingly, the findings in our earlier study [4] suggested that the non-linear index of HRV (e.g., percussion entropy index, PEI) was effective in the early identification of young men at high atherosclerotic risk of CVD and thus an important protective factor. Moreover, a previous study found increased sympathetic activity (HRV dysfunction) associated with a higher risk of cardiovascular disease [5]. A number of similar studies [[3], [4], [5], [6]] reported that greater HRV was modestly associated with lower risk of CVD during lifetime. Therefore, in this study, we attempted to adopt a non-linear HRV index [4,7] to reflect the health status of the cardiovascular systems of young subjects with and without carotenoid-containing supplementation.
In addition to improving the different presently available treatment strategies, increasing attention has been paid to the development of preventive measures. That includes the identification of risk factors for CVD, the early detection of diseases as well as the use of prophylactic medications and nutritional supplements in clinical practice [8]. In contrast to prophylactic treatments that act directly on blood vessels, the effects of carotenoid-containing supplements are less well-defined [9]. The results of previous studies investigating the benefits of nutritional supplements in subjects at risk of CVD are varied [10]. While some studies demonstrated a positive impact of nutritional supplements on the cardiovascular system [[11], [12], [13]], no evidence of any impact was found in other reports [14]. On the other hand, although carotenoids (any of a class of mainly yellow, orange, or red-fat-soluble pigments) were known of much earlier, details of their synthesis and metabolic products were not elucidated until the 1960s. Many receptors and hormones may also be involved in the metabolism of carotenoids and need to be explored at molecular level [15]. In a previous review study [16], the authors discussed the current research being undertaken to increase carotenoid contents in plants and the benefits to human health. The pathophysiology of many chronic and acute conditions, especially of CVD, is explained by inflammation and oxidative stress. Numerous studies have independently confirmed that carotenoids possess antioxidant biological properties and are well known to be important for human health and CVD prevention [[17], [18], [19]].
Although the majority of young men are free from cardiovascular disease (CVD), this group of subjects is gradually acquiring more CVD risk factors, especially due to unhealthy lifestyle factors, such as poor dietary habits, low exercise, no nutritional supplementation, and lack of sleep [20]. The study in [21] concluded that alterations in autonomic balance are already present in young adults, and metabolic syndrome was associated with lower HRV based on the frequency domain linear HRV index in 1889 subjects aged 24–39 years. On the other hand, the aim of a recent study [22] was to determine the effectiveness of heart rate biofeedback training on HRV and blood pressure in individuals with a family history of CVD by investigating the relationship between HRV and blood pressure in young adults at risk for CVD. Recently, Pearson correlation analyses [23] showed that the increased levels of carotenoids and vitamins were positively correlated with higher HRV (based on frequency domain linear HRV index) in 1074 (aged 34–84) individuals. From the viewpoint of data analysis, that study focused on creating a first step towards a comprehensive approach to the impacts of short-term carotenoid-containing supplementation on HRV in young men. Specifically, one novel application of a non-linear HRV index as well as the repeated measures ANOVA method was performed to assess the effects. We hypothesized that short-term carotenoid-containing supplementation (good for reducing the risk of CVD) of young men, i.e., over a period of three months, could improve HRV, thereby reducing the risk of CVD. As such, the aim of the study was to investigate the effects of providing the carotenoid-containing supplementation during the trials on changes in the young men’s HRV over a three-month intervention program.
## Study Protocol
This study adopted a modified air pressure sensing system (APSS) to obtain pressure signals from wrist for medical index computation (e.g., PEI for heart rate variability (HRV)) [7,24] over 16 minutes. Our aim was to investigate PEI with and without short-term carotenoid-containing supplementation in young men with regard to the different clinical effects as determined under repeated measures ANOVA.
## Inclusion criteria
Among July 14, 2017 to June 13, 2018 and November 2011 and May 2012, a total of 35 young-aged males aged between 20 and 29 were recruited for investigation (5 subjects quit before the end of the experiment, i.e., control group, $$n = 20$$, and experimental group, $$n = 10$$, all HbA1c ＜ $6.5\%$) in a randomized controlled trial (i.e., the complete randomization was typically performed by a computer program). All of the age-controlled healthy young subjects had no personal or family history of cardiovascular disease.
## Exclusion criteria
Vegetarians and those who had previously consumed daily nutritional supplements with in the last 6 months were excluded in this study. More importantly, subjects without successful measurements of heart rate variability were also excluded from the study.
The participants would receive 60 packs of compound nutritional supplements each month. For the current study, the participants in the experimental group consumed one pack of compound nutritional supplements in the morning (e.g., 10 AM) and another pack at night (e.g., 7 PM) each day (double-check by telephone). The reference compounds are commercially available (LifePak, PHARMANEX ®, Utah, USA). Each pack contains one vitamin tablet (667 mg), one phytonutrient tablet, and two mineral tablets. Each commercially available pack of compound carotenoid-containing supplements contained the following: β-carotene (3 mg), folate (300 μg), lutein (20 mg), lycopene (15 mg), vitamin K1 (40 μg), vitamin E (134 mg), vitamin D3 (5 μg), vitamin C (500 mg), vitamin B12 (30 μg), vitamin B6 (10 mg), vitamin B1 (7.5 mg), vitamin B2 (4.25 mg), and vitamin A (1500 mg). On the other hand, the participants in the control group did not take the above carotenoid-containing supplements. Moreover, all participants were asked not to change their lifestyle in the trials. The dose of micronutrients in the study could supply a comprehensive blend of nutrients to support a healthy cardiovascular system [25]. In addition, the reference compounds for LifePak are not all the same for the young males, young females, and elder people in the study. To simplify research, the current study is a better way to focus on young males.
## Experimental procedure
On the day of first examination (T1), the participants were required to fast for at least 8 hours and were taken to the outpatient clinic department in Hualien Hospital for medical assessment and blood samples, including determination of high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglyceride, total cholesterol, fasting blood sugar concentration, and glycosylated hemoglobin (HbA1c) concentrations. Then, participants waited outside the clinic until the doctor’s assessment. When the doctor’s assessment was done, the subjects were taken to a health clinic for physiological data measurements (e.g., age, body weight, height, waist circumference, and blood pressure), completion of the questionnaire, and computation of HRV. All the physiological data measurements were taken in a temperature controlled room (26 ± 1oC). Subsequently, the subjects were asked to take a five minute rest, then two sets of refined APSS pressure cuffs (i.e., a wrist cuff and an upper arm cuff) were attached to the right arm for measurement of PEI over 16 minutes. In addition, PEI measures were conducted again after one month (T2), two months (T3), and three months (T4). The Institutional Review Board (IRB) of Hualien Hospital and Taichung Tzu Chi Hospital approved, and the data was used this study.
## Percussion entropy index for HRV assessment
For two time series (i.e., peak-to-peak intervals (PPI) and waveform amplitudes (WA) series of 9 min WPP in reactive hyperemia phase) of length $$n = 700$$, the modified percussion entropy index is computed with the following three steps algorithm:1.A binary transformation of PPI and WA is used to obtain a = {a1, a2,..., aN} and b = {b1, b2,..., bN}, respectively in the following equations [1], [2]:[1]ai={0,PPI(i+1)≤PPI(i)1,PPI(i+1)>PPI(i);[2]bi={0,WA(i+1)≤WA(i)1,WA(i+1)>WA(i);2.The percussion rate for each scale factor s is obtained as:[3]PRsm=1(n−m−s+1)∑$i = 1$n−m−s+1count(i),where m is the embedded dimension vectors and count(i) represents the match number between a(i) = {ai, ai+1,..., ai+m-1} and b(i+s) = {bi+s, bi+s+1,..., bi+s+m-1};3.PEI is calculated as:[4]PEI(m,S)=ln[∑$s = 1$SiPRsm∑$s = 1$SiPRsm+1],lnisnaturallogarithmicoperator,[5]wherePRsm+1=1(n−m−s+2)∑$i = 1$n−m−s+2count(i),where equations [3], [4], [5] show $m = 2$, and $s = 1$ for healthy young men in this study. Thepercussionrates(PRs=12 and PRs=13) were with different lengths of fluctuation vectors 2 and 3, respectively. The percussion count in equation [3] increased one when the two compared patterns of fluctuation were identical. A high percussion rate represents high similarity in the pattern of fluctuation and indicates the subject is healthier. The percussion count number increased 1 when the two compared patterns of fluctuation were identical. The high PEI values in equation [4] represented the test subject with more healthy, as addressed in [7,24].
## Statistical Analysis
The statistical software package, Version 14.0 (SPSS Inc., Chicago, IL, USA) was used to analyze the data. Descriptive statistics (means, standard deviations, and frequencies) were used to describe the young male subjects. The values in Table 1 are represented as the means ± standard deviation (SD), and a single-sample Kolmogorov–Smirnov test was adopted for testing the normality of the distribution followed by non-parametric tests (i.e., Mann-Whitney-Wilcoxon test). Changes in dependent variable (i.e., PEIs for four measurements) and between-subject factors (i.e., control group vs. experimental group) from baseline to post-measures were assessed using repeated measures analysis of variance (ANOVA) to determine “time” and “time-by-group” differences using Roy’s largest root test after parametric tests checking [26]. To conduct multiple comparisons, Bonferroni post hoc test for unequal sample sizes was adopted. Results were considered significant at $p \leq 0.05.$ Finally, two illustrated plots for estimated marginal means of PEI versus group and time were shown to allow an easy understanding of the different effects for the experimental group compared to the control group. Table 1General characteristics (mean, standard deviation, and frequency) of the participants. Table 1ParameterControl GroupExperimental Groupp ValuesMean ± SD or N (%)Mean ± SD or N (%)Number of young men20 ($66.7\%$)10 ($33.3\%$)N/AAge, year23.96 ± 2.4623.28 ± 3.300.210Body height, cm173.08 ± 5.47174.89 ± 6.280.355Body weight, kg68.73 ± 9.6270.75 ± 13.180.328WC, cm78.81 ± 8.1779.67 ± 10.230.510BMI, kg/m224.82 ± 3.8826.91 ± 4.210.277SBP, mmHg119.08 ± 13.74118.83 ± 12.640.689DBP, mmHg77.65 ± 7.8476.95 ± 9.140.798HDL, mg/dL47.52 ± 7.5142.83 ± 6.910.182LDL, mg/dL89.68 ± 17.5697.82 ± 23.460.179TC, mg/dL154.55 ± 19.39160.84 ± 21.670.713TG, mg/dL91.48 ± 24.9194.17 ± 31.320.312FBS, mg/dL92.48 ± 4.9594.56 ± 8.380.457HbA1c, %5.34± 0.275.41 ± 0.380.513The final number of test subjects was 30. WC, waist circumference; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HDL, high-density lipoprotein; LDL, low-density lipoprotein cholesterol; FBS, fasting blood sugar; HbA1c, glycosylated hemoglobin. The p values of parameters larger than 0.05 are regarded as not statistically significant between the two groups for the nonparametric Mann-Whitney-Wilcoxon test. The p-values of control group versus experimental group for the normality test were listed: Age(0.021, 0.008), Body height(0.138, 0.018), Body weight(0.354, 0.020), WC(0.316, 0.019), BMI(0.409, 0.027), SBP(0.390, 0.031), DBP(0.198, 0.058), HDL(0.154, 0.037), LDL(0.179, 0.018), TC(0.127, 0.025), TG(0.843, 0.042), FBS(0.252, 0.047), and HbA1c(0.047, 0.008).
## Results
It is an important issue for young males (randomly grouped but with a small size in the study) to take care of their health, and it also depends on them understanding how to assess their cardiovascular system and knowing what steps are needed to improve their health and substantially delay CVD. Repeated measures analysis of variance (ANOVA) was adopted to assess the health status of the cardiovascular systems of young males after short-term carotenoid-containing supplementation.
## Baseline General Characteristics of Randomly Grouped Subjects
There were no statistically significant differences in many of the investigated variables (e.g., the demographic data (age), anthropometric data (body height, body weight, waist circumference), physiological parameters (systolic blood pressure and diastolic blood pressure), and serum biochemical data (high-density lipoprotein, low-density lipoprotein, cholesterol, triglyceride, fasting blood sugar, glycosylated hemoglobin)) between control and experimental groups at the baseline of the intervention (before taking carotenoid-containing supplementation every day) (all $p \leq 0.05$) (Table 1).
## Repeated Measures ANOVA
The Levene's test of equality of error variance was performed for the null hypothesis that the error variance of the dependent variable is equal across groups in the study.
## Repeated measures ANOVA and Bonferroni post hoc test
All of the p-values of PEIs for four measurements for control group and experimental group for the normality test were larger than 0.05 in Table 2. Therefore, all the PEIs for four measurements are normally distributed set of data. The repeated measures ANOVA indicates a significant TIME interaction effect for the estimated marginal means of PEI scale (T1 versus T3, T1 versus T4, T2 versus T4 with $$p \leq 0.009$$, 0.005, and 0.032, respectively) (Table 2). Base on the fact that PEIs for four measurements are normally distributed, the appropriate steps for interpreting the SPSS output with a repeated-measures ANOVA and Bonferroni post hoc tests were adopted in Table 3, Table 4. Table 3 also addressed tests of between-subjects effects ($F = 5.467$, $$p \leq 0.027$$, η2 = 0.163). Roy’s largest root test indicated a significant difference between means of PEI after the intervention between two groups only for T3 and T4 ($$p \leq 0.007$$, η2 = 0.232; $$p \leq 0.028$$, η2 = 0.162, respectively). None of the other scales showed any TIME or TIME × GROUP interaction effects (Table 4).Table 2Pairwise comparisons based on estimated marginal means of PEI between two different measurements with Bonferroni adjustment for multiple comparisons. Table 2(I) time(J) timeMean Diff. ( I-J)Std. Errorp Values$95\%$ CI for DifferenceaLower BoundUpper BoundT1T2-2.4580.8720.052-4.9320.017T3-3.195∗0.9080.009-5.773-0.617T4-5.613∗1.5050.005-9.884-1.341T2T12.4580.8720.052-0.0174.932T3-0.7380.8291.000-3.0921.617T4-3.155∗1.0450.032-6.122-0.188T3T13.195∗0.9080.0090.6175.773T20.7380.8291.000-1.6173.092T4-2.4171.0490.173-5.3960.561T4T15.613∗1.5050.0051.3419.884T23.155∗1.0450.0320.1886.122T32.4171.0490.173-0.5615.396All of the p-values of PEIs for four measurements for control group and experimental group for the normality test were larger than 0.05 using Kolmogorov–Smirnov test. Adjustment for multiple comparisons: Bonferroni. The mean difference is significant at the 0.05 level. CI: confidence interval. Table 3Tests of between-subject effects. Table 3SourceType III Sum of SquaresdfMean SquareFp ValuesPartial Eta Squared (η2)385970.5221385970.52216311.5220.0000.998Group129.3601129.3605.4670.0270.163Error662.5482823.662The factor “Group” means experimental group versus control group (reference).Table 4Parameter estimates of different dependent variables (T1, T2, T3, and T4) for the experimental group ($$n = 10$$) versus the control group ($$n = 20$$).Table 4Dependent VariableParameterBStd. Errorp ValuesT1Intercept56.8001.6130.000Group = 2-1.0751.9760.591T2Intercept60.7501.0070.000Group = 21.9101.2340.133T3Intercept62.5001.1040.000Group = 23.9351.3520.007T4Intercept64.9701.4200.000Group = 24.0401.7400.028B: regression coefficient; Group = 2; i.e., experimental group, control group (Group = 1) is regarded as reference.
## The changes in estimated marginal means of PEI with “time” and “group” views
The changes in estimated marginal means of PEI with “time” and “group” views. For the control group, all scales with the exception of one decreased in their mean values from T1 to T4. As illustrated in Figure 1, although the estimated marginal means of PEI of the experimental were smaller than the control group at T1 measurement, the values of the experimental group increased significantly more than the control group from T2 to T4, resulting in the experimental group having substantially higher scores than the control group after intervention (i.e., T2, T3, and T4). From another perspective, the estimated marginal means of PEI for the experimental and control groups for the four measurement periods ((T1, T2, T3, and T4) are shown in Figure 2. At the beginning of the trial, the estimated marginal means of PEI of the control group were larger than the experimental group. However, the estimated marginal means of PEI of the experimental group were larger than the control group after intervention (i.e., T2, T3, and T4).Figure 1The changes in estimated marginal means of PEI within the four time periods for the experimental group ($$n = 10$$) versus the control group ($$n = 20$$). The estimated marginal means of PEI for the experimental group obviously increased under longer intervention times (e.g., PEI: $56\%$∼$65\%$). On the other hand, the estimated marginal means of PEI for the control group did not change much (e.g., PEI: $58\%$∼$61\%$).Figure 1Figure 2Distribution of the estimated marginal means of PEI scale scores for the experimental and control groups during the four measurement periods ((T1, T2, T3, and T4). It is obvious that the experimental group presented smaller estimated marginal means of PEI than the control group at the beginning of the trial. Subsequently, the experimental group presented larger estimated marginal means of PEI than the control group in the T2, T3, and T4 measurement periods. Figure 2
## Discussion
As a pilot study that may be enlarged to more people evaluating the effect on a long term, the primary objective of this study was to investigate the effects of providing short-term carotenoid-containing supplementation on PEI in equation [4] changes in young men over a three-month intervention program. The effectiveness of an intervention is usually evaluated through the changes in the mean values of the experimental group, representing progress or regress of the HRV variable. In the present study, the hypothesis was confirmed only for one variable. The daily carotenoid-containing supplementation improved test subjects’ estimated marginal means of PEI in the experimental group ($F = 5.467$, $$p \leq 0.027$$, η2 = 0.163) (Table 3). Similar to the current study, a previous study showed that increased levels of carotenoids and vitamins were positively correlated with higher HRV [27]. While the control group (the group without taking carotenoid-containing supplementation every day) had larger estimated marginal means of PEI than those of the experimental group in the first month (T1 period), these results were not statistically significant (Figure 1, Figure 2). More importantly, a significant time interaction effect for estimated marginal means of PEI scale shown was by repeated measures ANOVA (T1 versus T3, T1 versus T4, T2 versus T4 with $$p \leq 0.009$$, 0.005, and, respectively) (Table 2). In addition, a significant difference between the means of estimated marginal means of PEI after the intervention between two groups only on T3 and T4 was also indicated by Roy’s largest root test ($$p \leq 0.007$$, η2 = 0.232 and $$p \leq 0.028$$, η2 = 0.162, respectively) (Table 4). In a recent study [23], the authors found that blood concentrations of antioxidant micronutrients, carotenoids, and vitamins were associated with beneficial changes according to the values of frequency domain linear HRV index in a cross-sectional analysis. In addition, study in [28] was designed to evaluate the consumer-perceived efficacy of an oral supplement containing a mix of tomato carotenoids and oil-soluble vitamins in improving skin appearance after 12 weeks of supplement use in 60 females, aged 35 to 55 years. Therefore, in this study, we attempted to adopt a non-linear HRV index to reflect the health status of the cardiovascular systems of young males (not female) with and without carotenoid-containing supplementation. With all considered, it is not likely that normal hormone cycles/fluctuations in a cross-sectional analysis are going to have a profound effect on young females. In addition, the reference compounds (LifePak, PHARMANEX®, Utah, USA) in the study are not all the same for young male, young female and elder person in the study. For simplify research, the current study is a better way to focus on young male. Finally, a randomized, controlled pilot trial was implemented in this study, and short-term carotenoid-containing supplementation helped young men to increase their HRV capacity compared to the control group over three months.
A recent study [29] addressed the effects of body composition on the cardiovascular system, especially focusing on HRV and vascular endothelial function. In the study by Weggen et al., after acute antioxidant supplementation, lower vascular or autonomic function was found in the recipients undergoing the supplementation regime (vitamins C and E; α-lipoic acid) compared to in the healthy controls, potentially implicating oxidative stress as a contributor to this blunted vascular or autonomic function [30]. A number of integrative studies have demonstrated decreases in blood pressure, cholesterol, blood glucose, and measures of stress, which all impact cardiovascular morbidity and mortality [31], in response to short-term carotenoid-containing supplementation; however, its application in young men is novel, and the impacts on HRV have not been previously reported. The aim of this study was to demonstrate that short-term carotenoid-containing supplementation improves HRV in young men, thus serving as further encouragement to young adults to take up healthier lifestyles. Nutritional supplementation was identified in a recent study as one of the important factors determining success in attempts to optimize public health [7,32]. Therefore, the promotion of healthy lifestyles and the prevention of ill health are fundamental to public health. Hence, young men need to understand that their cardiovascular system can benefit from carotenoid-containing supplementation and should be encouraged to live healthier lifestyles [33].
Some strengths and limitations of this study are worth noting. The most important strength is the experimental nature of the study, including the use of a new non-linear cross-entropy index of HRV. The study demonstrated that short-term carotenoid-containing supplementation could help young men by increasing heart rate variability capacity compared to controls over three months. However, this work is still preliminary and has some limitations, like the small sample size, the long term effect of diet supplementation, and the restriction only to male. Furthermore, to our best knowledge, this is the first time this type of research on short-term carotenoid-containing supplementation effects on HRV has been conducted on young men in Taiwan. Second, although it is well accepted that dietary intake and physical activity are significant factors affecting cardiovascular heath for young males, details on the dietary intake and/or participants’ exercise habits were not available. Third, the effects of the carotenoid-containing supplement on females were not studied due to the design of the present study intentionally avoiding confounding factors of this gender. Finally, the methods adopted in the present study for the assessment of HRV are not commonly used and have not been standardized in the international literature. More data are needed to strengthen their uses in this clinical setting.
## Conclusions
The results of this study show that a short-term carotenoid-containing supplementation program had an impact on HRV regulation in young men. The findings suggest that young males who have not only high daily intakes of vegetables and fruit but also carotenoid-containing supplementation could benefit from good impacts on HRV regulation and the cardiovascular system.
## Author contribution statement
Jian-Jung Chen: Conceived and designed the experiments; Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.
Hsien-Tsai Wu: Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.
## Funding statement
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
## Data availability statement
Data will be made available on request.
## Declaration of interest’s statement
The authors declare no competing interests.
## Institutional Review Board Statement
All subjects were asked to submit written consent during the experimental procedure. This nonrandomized experimental study (protocol no.: TTCRD106-18) was conducted from July 14, 2017 to June 13, 2018 in Taichung Tzu Chi Hospital, Taiwan, according to the protocol approved by the Research Ethics Committee of Taichung Tzu Chi Hospital. The study protocol was also approved by the Institutional Review Board of Hualien Hospital, Taiwan (protocol no. 98-06-02 and date of approval (16 July 2011)) and procedures were in accordance with the ethical standards of the responsible committee on human experimentation and with the Helsinki declaration of 1975, as revised in 1983.
## Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
## Code availability
The PEI computation programs are accessible upon demand from the authors
## Supplementary data
The following is the *Supplementary data* to this article:
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|
---
title: Sleep apnea in patients with exacerbated heart failure and overweight
authors:
- Petar Kalaydzhiev
- Nikolay Poroyliev
- Desislava Somleva
- Radostina Ilieva
- Dimitar Markov
- Elena Kinova
- Asen Goudev
journal: 'Sleep Medicine: X'
year: 2023
pmcid: PMC10009711
doi: 10.1016/j.sleepx.2023.100065
license: CC BY 4.0
---
# Sleep apnea in patients with exacerbated heart failure and overweight
## Abstract
Sleep disorders are a common concomitant comorbidity in patients with heart failure. The aims of our study are to determine the incidence and phenotypic characteristics of sleep apnea in overweight patients with exacerbated heart failure and to assess the degree of involvement of systolic and diastolic function impairment in the individual group. From 100 screened patients with heart failure in our department from 2015 to 2017, 61 met the inclusion criteria and participated in the study. $82\%$ ($$n = 50$$) of the patients had obstructive sleep apnea (OSA), and $18\%$ ($$n = 11$$) had central sleep apnea (CSA). The CSA group had a significantly lower left ventricular ejection fraction (LVEF) than the OSA group (EF% 49.6 ± 8.5 vs 41.8 ± 11.4; $$p \leq 0.013$$). A negative correlation was found between LVEF and the number of central apnea events (r = −0.52; $p \leq 0.001$). More frequent hospitalizations for heart failure (HF) and higher mortality rate were found in the CSA group. Screening for sleep apnea in patients with exacerbated heart failure and obesity is necessary for the complex treatment of these patients.
## Highlights
•Obstructive sleep apnea is more common.•There was no association between AHI and NTproBNP.•Reduced systolic function is associated with more central sleep apnea events.•Patients with central sleep apnea and HF have more frequent rehospitalizations.
## Introduction
Sleep apnea is more common in patients with heart failure (HF) than in the general population. There are two main types of sleep apnea. Obstructive sleep apnea is caused by an obstruction of the upper airway, while central sleep apnea is caused by a lack of signal from the central nervous system to the respiratory muscles. The comorbidity rate in patients with OSA and heart failure is high. The frequency varies from 11 to $38\%$ in different studies. In patients with CSA, this percentage is even higher 28–$82\%$ [1]. This statistic raises the question of the diagnosis and treatment of these comorbidities. In these patients, hospitalization due to exacerbation of HF is a more frequent phenomenon compared to patients without sleep apnea [2]. Treatment options are being explored, and currently positive results are reported only in the obstructive type, while in the case of central apnea, the studies have not reported definite benefits [3]. The high comorbidity rate may be due to the fact that the two disorders, sleep apnea and heart failure, share common risk factors [4,5]. There is a lack of sufficient data on the type of sleep apnea that prevails during hospitalization for exacerbated heart failure. It is not established whether there is an association between high NTproBNP (N-Terminal Fragment of the Prohormone Brain-Type Natriuretic Peptide - NT-proBNP) and Apnea-Hypopnea Index (AHI) and whether it can be used as a predictive value for the severity of sleep apnea in these patients, which we will seek an answer in the present study. Our study aims to differentiate the frequency and type of sleep apnea in patients with overweight and exacerbated heart failure, evaluate the systolic and diastolic function in the individual groups, and determine whether there is a correlation between the severity of heart failure and the type of sleep apnea.
## Materials and methods
We conducted a single-centre, prospective cohort study in which patients hospitalized in the Cardiology Clinic of UMHAT “Tsaritsa Joanna - ISUL” took part from 2015 to 2017. In 100 consecutive patients with clinical and laboratory evidence of exacerbated heart failure - New York Heart Association (NYHA) class II/III and Body mass index (BMI) > 25 kg/m2, additional laboratory methods were used to assess the degree of heart failure using NTproBNP. Measurements were conducted on a Point of Care - Roche Cobas h 232 System. Patients with NTproBNP values > 300 pg/ml were included. The screening for sleep apnea was conducted using the Epworth Sleepiness Scale (ESS) and an ApneaLink™ somnographic screening system, which was attached to patients on the first night of their stay in the unit. The number and type of apneas and hypopneas per hour were measured - Apnea-Hypopnea Index (AHI). The somnographic recordings were analysed with ApneaLink™ Reporting Software to determine the sleep apnea phenotype. All patients with ESS >6 pts and AHI >5 were included. Two-dimensional (2D) echocardiography was used to assess systolic and diastolic function. To assess systolic function, measurement of the left ventricular ejection fraction using the Simpson method was used, and to measure diastolic function, the ratio E/e'm (the ration of the wave of early diastolic filling of the mitral inflow- E and the tissue Doppler velocity of the medial mitral annulus-e'm) was used. Exclusion criteria were: acute respiratory failure, acute coronary syndrome, severe renal or hepatic failure, and chronic lung diseases (COPD). Sixty-one patients met the inclusion criteria. They were divided into two groups according to the type of sleep apnea - with CSA and with OSA. Patients were followed up for HF hospitalizations and mortality rate over a two-year period. The statistical analysis was performed by SPSS 22.0 (Chicago, Illinois). Statistical methods for comparison using Pearson's chi-squared test and Student's t-test as appropriate. Correlation analysis for linear dependence were used. Simple linear regression was performed to test significantly predicted value. The Kaplan-Meier method was used to analyze the survival rate and first HF hospitalization. The comparison between the two groups was performed using the Log Rank (Mantel-Cox) test. Data with a p-value <0.05 were considered significant.
## Results
From 100 screened consecutive patients, sleep apnea was found in $61\%$ of them ($$n = 61$$). Of these, $82\%$ ($$n = 50$$) had obstructive sleep apnea, and $18\%$ ($$n = 11$$) had central sleep apnea. Regarding the demographic indicators of age and gender, no significant differences were found in the individual groups.
When comparing the ejection fraction (EF%) of the left ventricle between the two groups, significantly lower values of EF were recorded in the group with central sleep apnea compared to the group with OSA (LVEF % 49.6 ± 8.5 vs 41.8 ± 11.4; $$p \leq 0.013$$).
There is also a difference in the diastolic dysfunction indicators, with the E/e'm ratio in the CSA group being significantly higher (E/e'm-17.1 ± 3.7 vs 20.9 ± 2.5; $$p \leq 0.002$$). The NTproBNP values also support this data, patients with CSA have significantly higher values compared to patients with OSA (2857.36 ± 1090.90 pg/ml vs 1359.12 ± 740.64 pg/ml, $$p \leq 0.001$$).
Regarding BMI, significantly higher values were found in the OSA group compared to the patients with CSA (BMI 38.5 ± 7.1 vs 31.9 ± 4.5; $$p \leq 0.005$$).
No significant difference was registered in the degree of severity regarding sleep disorder. Comparison of AHI between the two groups showed no difference (OSA 41.8 ± 23.2 vs CSA 37.7 ± 12.6; $$p \leq 0.575$$). The data summary is presented in Table 1.Table 1Summary of demographic, echocardiographic and sleep parameters. Table 1OSA group ($$n = 50$$)CSA group ($$n = 11$$)P ValueAge, yr66.2 ± 9,166.1 ± 11.90.991Sex, m%m $52\%$m $54\%$0.878ESS12.1 ± 2.910,6 ± 3.20.144LVEF, %49.6 ± 8.541.8 ± 11.40.013AHI41.8 ± 23.237.7 ± 12.60.575BMI38.5 ± 7.131.9 ± 4.50.005E/e'm17.1 ± 3.720.9 ± 2.50.002NTproBNP, pg/ml1359.12 ± 740.642857.36 ± 1090.90.001Av. Saturation, %83.9 ± 6.886.6 ± 6.60.257Low Saturation, %65.3 ± 12.767.6 ± 12.80.590Av. Pulse, bpm75.5 ± 11.176.4 ± 11.70.825Max. Pulse, bpm131.1 ± 42.5121.4 ± 51.20.511N of Desaturation381.1 ± 212.6397.5 ± 184.40.813Obstructive sleep apnea(OSA); Central sleep apnea(CSA); male %(m%); Epworth Sleepiness Scale (ESS); Apnea-Hypopnea Index (AHI); The ration of the wave of early diastolic filling of the mitral inflow- E and the tissue Doppler velocity of the medial mitral annulus-e'm(E/e'm); Left ventricular ejection fraction in %(LVEF%); N-Terminal Fragment of the Prohormone Brain-Type Natriuretic Peptide – (NT-proBNP); beats per minute (bpm).
Standard heart failure therapy during the study period included angiotensin converting enzyme inhibitors or angiotensin receptor blockers (ACEi or ARB), Sacubitril/Valsartan, beta blockers and diuretics. Patients in both groups were on standard heart failure therapy. The percentage distribution is shown in Table 2. There was no significant difference in therapy between the two groups. Table 2Distribution of used medications. Table 2MedicationOSA group ($$n = 50$$)CSA group ($$n = 11$$)P ValueBeta blockers (%)$92\%$$82\%$0.294ACEi or ARB or Sacubitril/Valsartan (%)$84\%$$64\%$0.133Mineralocorticoid blockers (%)$70\%$$82\%$0.350Loop and thiazide diuretics (%)$62\%$$82\%$0.185Obstructive sleep apnea(OSA); Central sleep apnea(CSA); angiotensin converting enzyme inhibitors (ACEi); angiotensin receptor blockers (ARB).
After conducting a correlation analysis, a strong negative correlation was found between the number of central apnea events (apneas and hypopneas) and the left ventricular ejection fraction r = −0.52, $p \leq 0.001.$ The distribution and data are presented in Fig. 1.Fig. 1Correlation analysis of the relationship between ejection fraction and number of central sleep apnea events. EF% - ejection fraction %.Fig. 1 Simple linear regression was used to test if the left ventricular ejection fraction significantly predicted the number of central apnea events. The overall regression was statistically significant (R2 = 0.32, F[1, 59] = 28.26, $p \leq 0.000$). It was found that the left ventricular ejection fraction significantly predicted the number of central apnea events (β = -1.829, $p \leq 0.000$).
A strong correlation was also found between BMI and the degree of daytime sleepiness based on the ESS ($r = 0.649$; $p \leq 0.001$). Simple linear regression was used. It was found that the BMI significantly predicted the ESS (β = 0.27, $p \leq 0.000$).
No correlation was found between AHI and NTproBNP ($r = 0.038$; $$p \leq 0.770$$).
Patients from both groups were followed up regarding first hospitalization for heart failure and mortality over a period of 24 months. Mortality in the OSA group for 2 years was $38\%$ ($$n = 19$$), and $63.6\%$ ($$n = 7$$) in the CSA group.
First hospitalization in patients with CSA occurs significantly sooner than in patients with OSA. The average number of months without hospitalization for HF in patients with CSA was 5.3 months versus 12.8 months in patients with OSA (Log Rank (Mental-Cox) $$p \leq 0.009$$). Refer to Fig. 2.Fig. 2Survival Functions Kaplan-Meier method for time to first hospitalization for Heart Failure (HF) in months Obstructive sleep disease (OSD); Central sleep disease (CSD).Fig. 2 The OSA group had a median survival of 18.7 months versus 13.09 months in the CSA group. The data approached but did not exceed the limit of significance ($$p \leq 0.063$$). The survival curve is presented in Fig. 3.Fig. 3Survival Functions Kaplan-Meier method survival in months. Obstructive sleep disease (OSD); Central sleep disease (CSD).Fig. 3
## Discussion
For the first time in Bulgaria, a prospective cohort study is being conducted on sleep apnea and exacerbated heart failure and overweight. In our study we found that $61\%$ of tested patients have sleep apnea. $82\%$ ($$n = 50$$) of patients had OSA, and $18\%$ ($$n = 11$$) had CSA. On a global scale, a study on this topic was conducted by Schulz R et al. [ 6]. Two hundred and three patients with exacerbated heart failure were studied, of whom 145 ($71\%$) had sleep apnea. The distribution of the OSA and CSA percentage is also at the expense of OSA, without obesity being an inclusion criterion. The percentage of patients with central sleep apnea in the study by Sin and coworkers was significantly higher which may be due to the larger number of patients studied [7]. In 2017 Arzt M et al. performed an extensive analysis of patients in the German registry SchlaHF for patients with reduced systolic function. A high rate of CSA was also found in their study [8]. The high repeatability of the data confirms the need to screen heart failure patients for sleep apnea.
We found significant differences between patients with obstructive sleep apnea and those with central sleep apnea in terms of ejection fraction and diastolic function. Decreased systolic function, elevated NTproBNP values, and increased left ventricular filling pressures, increase the risk of central apnea events. A possible pathophysiological explanation for this phenomenon is that patients with reduced systolic function also have a lower left ventricular filling pressure, as well as increased pulmonary pressure, which increases the risk of hyperventilation and Cheyne-Stokes breathing during sleep [8,9]. We used linear regression analysis to demonstrate the strong correlation between the occurrence of central apneas and decreased systolic function. Left ventricular ejection fraction has a significant predictive value for occurrence of Cheyne-Stokes breathing (R2 = 0.32, F[1, 59] = 28.26, $p \leq 0.000$).
BMI is a definite risk factor for both SA and heart failure [10,11]. Sin DD et al. studied 450 patients with congestive heart failure, with obesity being the leading risk factor for concomitant sleep disorder. Similar results were published by Lee SJ et al., who found a strong correlation between BMI and daytime sleepiness (ESS) [12]. In our research, we also confirm this dependence.
To support the diagnosis of exacerbated heart failure, we additionally used NTproBNP tests. As expected, they were significantly higher in the CSA group, corresponding to the lower systolic function in these patients. We found no correlation between AHI and NTproBNP. A similar comparison was also conducted by Hübner RH et al. in the study of 60 patients with obstructive sleep apnea and heart failure [13]. This shows us that NTproBNP can't help us with the degree of severity of sleep apnea as well as its type.
In our study, we found that patients with central sleep apnea had significantly more frequent hospitalization for heart failure than patients with obstructive sleep apnea (Log Rank (Mental-Cox) $$p \leq 0.009$$) Fig. 2. This conclusion was also reached by Khayat R et al. in their study [14]. Similar to us, they followed up patients with exacerbated heart failure and sleep disorders, and rehospitalization for heart failure in the CSA group was significantly higher than that in the OSA group. The lower systolic function and higher percentage of hospitalizations in the CSA group determines the worse quality of life in these patients. Although both groups were on standard heart failure therapy, lower systolic function in the CSA group was an independent risk factor for higher rehospitalization and higher mortality [15].
Mortality in the CSA group was higher, approaching but not reaching significance. In another of their publications, Khayat and coworkers discuss the increased mortality rate in patients with CSA and acute heart failure [16].
Timely diagnosis could help the addition of adjunctive therapy to patients with sleep disorders and exacerbated heart failure, which would improve the prognosis especially in patients with low systolic function [17].
One of the major limiting factors in our study is the low number of patients in the CSA group. Analyzing the cohort in more recruited patients would allow the conclusions drawn to be confirmed or rejected. Studies in this field are highly limited due to the multistage conduct of the studies, the hospitalization of the patients, and the severe general condition of exacerbated symptoms of heart failure. Globally, the main studies also have low numbers of patients.
Due to economic constraints, continued positive airway pressure (CPAP) therapy was not added to the patients with OSA, which allowed the comparison between the two groups. Adding CPAP therapy to the management of patients with OSA would undoubtedly improve the prognosis of these patients [18].
Since patient recruitment and follow-up, there have been significant advances in heart failure therapy and the use of new classes of medications such as SGLT2 inhibitors, which in our patients were not included [19]. Which confirms the need for new studies in this area.
## Conclusion
Sleep apnea is a common comorbidity in patients with exacerbated heart failure and obesity. OSA occurs to a greater extent than CSA. Patients with reduced systolic function are at higher risk of central sleep apneas events. Low LVEF% can be used as a prognostic factor regarding the occurrence of central sleep apnea events. Controlling sleep apnea can reduce patient readmissions and mortality. Large-scale, long-term randomized trials will be needed to test the possibility of finding an effective therapy for central sleep apnea would improve the prognosis of patients with exacerbated heart failure and overweight.
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|
---
title: Method development for simultaneous estimation of Amlodipine Besylate and Perindopril
Tertbutyl amine in fixed-dose
authors:
- Muhammad Farooq Saleem Khan
- Lutafullah Tahir
- Xu Zhou
- Ghulam Bary
- Muhammad Sajid
- Ahmad Khawar Shahzad
- Ilyas Khan
- Abdullah Mohamed
- Riaz Ahmad
journal: Heliyon
year: 2023
pmcid: PMC10009730
doi: 10.1016/j.heliyon.2023.e14209
license: CC BY 4.0
---
# Method development for simultaneous estimation of Amlodipine Besylate and Perindopril Tertbutyl amine in fixed-dose
## Abstract
The fixed-dose combination of Amlodipine Besylate (ADB) with Perindopril Tertbutylamine (PTBA) drug is used to treat patients with mild-to-moderate hypertension. In recent times researchers are interested to find the efficient analytical method development and validation for the simultaneous determination of ADB and PTBA in a fixed-dose, film-coated tablet. Therefore, the current study was performed with a reverse-phase liquid chromatography method developed to simultaneously analyze ADB and PTBA in film-coated tablets as fixed-dose combinations. The linearity of the proposed method was calculated by preparing six different mixtures of both ADB and PTBA in the mobile phase. The concentration of both the analytes was analyzed at 56mg/100 mL to 84mg/100 mL and 32mg/100 mL to 48mg/100 mL, respectively. The ratio of acetonitrile and phosphate buffer was 35:65. The flow rate was adjusted to 1.5 ml per minute to reduce the retention time. The validation study was performed for the parameters specificity, linearity, precision, range, limit of detection, limit of quantification, accuracy/biasness, and robustness. The relative percentage standard deviation for Perindopril Tertbutyl amine was $0.148\%$, and for *Amlodipine is* $0.312\%$. These results show that the advanced analysis method for simultaneous analysis of fixed-dose is precise. The theoretical IR spectra were also calculated by Gaussian 9.2 by employing the B3LYP functional at density functional theory (DFT) level study. All these parameters studied in this work authenticate the effectiveness of the developed validation method and ensure its repeatability/reproducibility accordingly. To the best of our knowledge, this is the first time to develop a new fast, and easy method for simultaneous identification and quantification of ADB and PTBA by high-performance liquid chromatography (HPLC) with a time-efficient and cost-effective approach.
## Introduction
During the last few decades, due to high blood pressure and hypertension, the risk of death due to heart disease has increased. This risk is more prominent in older patients and younger people; this risk has not been established lucidly [1]. The cross-sectional survey data of India exhibited that the average age of the younger patient was 49 years for stage 1 and stage 2 hypertension. So it is necessary to treat younger patients with drugs that proved efficacious in dropping blood pressure to diminish the death risk, caused due to heart diseases [2,3].
Both Amlodipine Besylate (ADB) and Perindopril Tertbutyl amine (PTBA) are used to treat patients suffering from high blood pressure. Perindopril is an inhibitor that inhibits the Angiotensin-Converting Enzyme. It is employed alone or in combination with other drugs to treat patients with high blood pressure [4].
ADB (Fig. 1) belongs to a group of drugs that are called calcium channel blockers. It is also used exercised to treat patients suffering from high blood pressure and a heart disease called angina. It relegates the risk of a heart attack. By using Amlodipine, the blood pressure gets lower, the heart muscles are relaxed, and cardiac arteries are dilated to avoid the blockage of blood vessels; hence, preventing heart attack. Chemically ABD is “3-ethyl 5-methyl 4RS-2-[(2-aminoethoxy) methyl]-4-(2-chlorophenyl)-6-methyl-1,4- dihydropyridine-3,5- dicarboxylate benzene sulphonate" (Figure-1), and PTA is “2- Methyl Propane-2-amine (2S,3As,7As)-1-[(2S)-2- 2[[(1S)-1-(ethoxycarbonyl) butyl]amine] propanoyl]octahydro-1H-indol-2-carboxylate” (Figure-2) [2,5].Fig. 1Molecular structure of Amlodipine. Fig. 1Fig. 2Molecular structure of Perindopril tert-butylamine. Fig. 2 *Previous data* divulged that $51\%$ of deaths are caused by high blood pressure, $45\%$ by heart diseases and coronary heart diseases are due to hypertension. Calcium channel blockers like ADB and angiotensin II receptor blockers like valsartan are used to treat high blood pressure [6]. Combining these two drugs as a single dosage is becoming popular day by day to achieve better patient adherence to treatment and reduce the pill load for patients [7]. During a fixed-dose combination continuation study, the continuation of ramipril treatment was 271 days, and on lisinopril, the continuation of therapy was 211 days out of 360 days [8]. There was a significant difference in adherence of patients to their treatment for hypertension when we changed the combination of drugs to be used for hypertension [9,10]. The adherence of patients with a fixed-dose combination of ramipril and *Amlodipine is* more favorable than a fixed-dose combination of lisinopril and Amlodipine [5,11].
In recent years, combination therapy has been proven effective in controlling hypertension compared to single-pill drugs. It has also widely improved the patient's compliance with relief. The researchers suggested that the combination drug or polypill be prescribed instead of multiple medications [12]. Some studies suggested liquid chromatography (LC) with mass spectrometry (MS) method by using electrospray ionization mode (EIM) as a good tool for development and validation at a simultaneous determination of, Amlodipine, valsartan (VAL) [12,13].
There are more useful methods reported to monitor drug delivery to targeted places. These methods employed the dose in combining the medicine with biologically suitable magnetic nanoparticles [14]. During the cartilage repair process blood flow into the space in the Synovial joints and can cause severe damage to the joint. The dilution of blood with Synovial fluid can modify the biophysical properties of the blood [15]. To decrease blood viscosity, the technique of laser radiation showed a significant role in the treatment of various viral and auto-immune diseases [16].
The demand for analysis of compounds has increased in low concentrations these days. This has gained importance, particularly in different fields of research and development of new drugs, including synthesis of new medicines, proposed therapy of drugs, differentiation of structure of other compounds, and analysis of these compounds [17,18]. Therefore, it is necessary to prepare a substance with the highest purity, separates the required compounds from impurities, or eliminate unwanted components. So currently, “instrumental analysis” including simple measurements and the preparation of the sample, performing of analysis, and evaluation of the report of samples has also attained significance. This process has become part of “instrumental analysis”. The market claims based on quality issues have increased which makes the products free of complaints. The requirement for the analysis of more samples in a short time is in demand. To meet the market demand, progress has been made in reducing the sample volumes, automation, and thorough screening. The knowledge of techniques used and information obtained from the analysis is necessary to have correct and reliable results [2].
Only four reported methods describe the simultaneous analysis of ADB in pharmaceutical preparations in the literature. LC-MS/MS, HPLC/PDA, HPLC, and HPLC/UV methods. These methods have some limitations in consuming more time and complex analysis. Therefore, more methods are needed to be developed with more efficiency, sensitivity, and swift analytical methods for validation [19,20,21].
It is used as an angiotensin-converting enzyme (ACE) inhibitor, which is long-acting in treating high blood pressure, and heart failure [22]. In the human body, Perindopril tertbutyl amine is metabolized to perindoprilat in the liver, perindoprilat is an active metabolite, and its route of extraction is the urine [23].
The literature survey shows that some analytical methods have been developed for spectrophotometric analysis of PTBA. A few analytical reports are available for the study of Perindopril using different analytical techniques employing Liquid Chromatography (HPLC), Liquid chromatography with a combination of mass spectrometry and gas chromatography both bulk analysis and blood plasma samples [10]. During the review of stability studies of Perindopril, an analytical method has been found which is based on reversed-phase HPLC. This analytical procedure was dependent upon pH. Another analytical approach was used during the dissolution of a fixed-dose combination containing PTBA and indapamide [24]. The previously available analytical methods are more complicated, difficult to prepare reagents, and take a longer time for analysis. These methods generate more waste, and efficiency was also not so good.
Different methods are available for the estimation of ADB. These methods have been developed on various instruments (HPLC, HPTLC). The other techniques include spectrophotometric analysis with mass spectrum [25,26].
To the best of our knowledge, this is the first time to develop a new, fast and easy method for simultaneous identification and quantification of ADB and PTBA by high-performance liquid chromatography (HPLC) with a time-efficient and cost-effective approach. A simple, accurate, precise, and stability-indicating HPLC method was developed to simultaneously estimate ADB and PTBA in a fixed-dose combination of pharmaceutical products and validated by ICH guidelines. The developed methods have a great potential to detect the impurities in the targeted pharmaceutical product. The developed method can be used as a routine analysis for the accurate and precise, assay of ADB and BTBA in the finished dosage form of pharmaceutical products.
## Chemicals
The chemicals enlisted in Table 1 were used to develop and validate the analytical method for simultaneous determination of Amlodipine and Perindopril tert-butyl amine in fixed-dose combination in tablets. Table 1List of chemicals. Table 1Sr.#Name of ChemicalBach NumberManufacturerExpiry Date1.Potassium Di-hydrogen PhosphateAM0973673Merck/GermanyFebruary 28, 20212.Acetonitrile1840391 626Merck/GermanyJune 30, 20193.Orthophosphoric acid $85\%$E2950Honeywell Germany4.Methanol5.Amlodipine Besylate working standard18AD023Cadila Pharmaceutical, IndiaJan 20236.Amlodipine Besylate Raw Material19AD023Cadila Pharmaceutical, IndiaJan 20247.Perindopril Tert-Butyl Amin Raw MaterialPEA-18002Aarti Drugs IndiaMarch 20238.Perindopril Tert-Butyl Amin working standard565-317-Zhejian Huahai china06-10-20209.Amper $\frac{10}{4}$ mg TabletsBQ-0012Next Pharmaceutical Products, Pakistan12–2010.Amper $\frac{5}{4}$ mg TabletsBS-0003Next Pharmaceutical Products, Pakistan09–2011.Coversam $\frac{10}{4}$ mg tabletsB[10]20052Sevier11–202012.Coversam $\frac{5}{4}$ mg TabletsB[10]1914Servier Research and Pharmaceuticals, Pakistan03–2021
## Equipment and apparatus
The equipment listed in Table 2 was used during the research work for the development of an analytical method for the analysis of Amlodipine and Perindopril. Table 2List of equipment. Table 2Sr.#Equipment NameManufacturer and ModelSpecifications1Precise Weighing BalanceRADWAG/PS510-R1S/N: 491373Max: Capacity: 510 gmReadability: 0.001 gm2Analytical BalanceRARADWAG Wagi ElektroniczneModel: AS 220.R1, Made in PolandS/N432966 Max:220 g $d = 0$,1 mg 12–16V DC/250 mA + 10′C/+40′C3pH MeterAdwa AD1020,pH/mV/ISE & Temperature Meter4FTIRAgilent Technologies Cary 630FTIRDiamond ATR, Power 110–240 VAC, $\frac{60}{50}$ Hz Spectral range KBr 6300–350 cm-1 ZnSe 5100–600 cm -15Filtration AssemblyGS Model NO.: AS$\frac{201}{6}$ HP 220–240V 50HZ 3.4 kG N.W., 23L/MIN, 600mmHG Vacuum degree, 25.5*13.5*17 cm(H*W*L), ROHS6Sonication BathElma E 30 H ElmasonicHeater plus Sonicator7HPLCAgilent Technologies1260 Infinity IIPressure range of up to 600 bar with a flow rate of up to 5 mL/min allows the use of almost any column – conventional, sub-2μm-particle, or superficially porous columns.8Hot plate with a magnetic stirrerBarnstead/ThermolyneCIMARECHotplate plus StirrerApparatus, including glassware used for method development and validation, is enlisted in S1.
## Development of method
Various trials were tried while developing analytical methods for the quantitative determination of Amlodipine and Perindopril Tertbutyl amine. Method development was carried out using the mobile phase of multiple chemicals and various concentrations to optimize the mobile phase proposed for the analysis of Amlodipine and Perindopril tertbutyl amine as active bulk in the tablet dosage form. Different samples were prepared and run on HPLC under other chromatographic conditions. These chromatographic conditions include a change in the flow rate and composition of the mobile phase. However, some parameters like the temperature of the column oven and wavelength were kept constant during the method development process. The design of the mobile phase and other chromatographic systems tried during the study to develop methods for simultaneous analysis of Amlodipine and Perindopril Tertbutyl amine are given in S2.
After many trials, a combination of phosphate buffer pH 2.6, and acetonitrile was selected as the mobile phase for further analysis of Amlodipine and Perindopril Tertbutyl amine and during the execution of the validation study. A different process was performed while developing an analytical method for the simultaneous determination of Amlodipine and Perindopril Tertbutyl amine in pharmaceutical film-coated tablet dosage forms. Preparation of solutions and mobile phase, validation methods, and verification of developed analytical method.
## FTIR instrumentation
FTIR analyses were carried out on IR Affinity-1 [00722] FTIR spectrophotometer (Shimadzu, Japan) with IR Solution 1.50 version of the software for data analysis DLATGS detector with a ceramic light source of high luminance Casian, [27]. The experiment was performed with a recording of KBr disc spectra in mid-IR regions 4000cm-1 and 400cm-1, with 45 scans having a resolution of 4cm-1.
## Methods of quantum chemical studies
Quantum chemistry (quantum mechanical calculations) has been done with GaussView 05 software and Gaussian 09 from Gaussian, Inc. USA, to produce the optimized molecular structures [28]. The IR spectra have been calculated by optimizing the frequency of each compound by employing Density Functional Theory (DFT). Then the 6–31 G (d,p) basis set with Becke 3-parameter-Lee-Yang-Parr (B3LYP) functional was also used for simulation [29,30].
## Results and discussions
Amlodipine besylate is a member of the medicines known as a calcium channel blocker. It relaxes the blood vessels, and as a result of relaxing blood vessels, blood can flow easily and hence help lower the blood pressure. It is also used as preventive medicine for some symptoms of chest pain known as angina. Perindopril is an angiotensin-converting enzyme inhibitor used to treat hypertension, heart failure, and other related symptoms. It has two different salts, perindopril tertbutyl amine, and perindopril arginine. Both have the same therapeutic effect; however, there is some difference in the bioequivalence of both salts.
Both Amlodipine and perindopril are used in fixed-dose combinations to treat high blood pressure. Fixed-dose combination can be used to treat high blood pressure and/or stable coronary artery disease in those patients whose conditions are controlled by using these two medicines separately. This combination has reduced the pill load on patients by using one tablet instead of two tablets.
This research work developed a reverse-phase liquid chromatography method to simultaneously analyze Amlodipine and perindopril tertbutyl amine in film-coated tablets as fixed-dose combinations. Validation of this developed method was performed under the guidelines provided by the International Conference of Harmonization (ICH), British Pharmacopeia (B.P.), and the United States Pharmacopeia (USP). The developed method was verified by qualitative and quantitative determination of Amlodipine and perindopril tertbutyl amine in film-coated tablets. The Amper $\frac{5}{4}$ mg, Amper $\frac{10}{8}$ mg, Coversam $\frac{5}{4}$ mg, and Coversam $\frac{10}{8}$ mg were analyzed to verify this developed method.
## Method development
The analytical method was developed by using different buffers and organic solvents like acetonitrile. Different flow rates were also tried for the simultaneous quantification of both analytes. The tailing factor, additional peaks, and peak symmetry were the major problems. The detail of different chromatographic conditions results is given below in Table 3.Table 3Results obtained at different chromatographic conditions. Table 3Sr.#Chromatographic ConditionsResults Obtained from AnalysisColumnMobile phaseFlow rateInjection VolumeWavelength1Agilent Zorbax Eclipse C18, (Length: 10 cm, dia:4.6 mm particle size 5 μm)Water $100\%$1 ml/min20 μl210 mmVery long retention time and tailing factor2Agilent Zorbax Eclipse C18, (Length: 10 cm, dia:4.6 mm particle size 5 μm)Water: Methanol 50:$50\%$ V/V1 ml/min20 μl210 mmPoor Peak symmetry with long retention time and tailing factor3Agilent Zorbax Eclipse C18, (Length: 10 cm, dia:4.6 mm particle size 3.5 μm)Water: Acetonitrile 50:$50\%$ V/V1.5 ml/min20 μl210 mmPoor peak symmetry and tailing factor4Agilent Zorbax Eclipse C18, (Length: 10 cm, dia:4.6 mm particle size 3.5 μm)Buffer: Acetonitrile85:$15\%$ V/v1.5 ml/min20 μl210 mmPoor peak Symmetry5Agilent Zorbax Eclipse C18, (Length: 10 cm, dia:4.6 mm particle size 3.5 μm)Buffer: Acetonitrile70:$30\%$ V/v1.5 ml/min20 μl210 mmTailing Factor6Agilent Zorbax Eclipse C18, (Length: 10 cm, dia:4.6 mm particle size 3.5 μm)Buffer: Acetonitrile65:$35\%$ V/v1.5 ml/min20 μl210 mmFine peak symmetry and tailing factor <2 Water was used in more significant concentrations in the initial trials, and separation was poor. The peaks had low symmetry and a long tailing factor, and the retention time was very long about 30 min. Amlodipine is slightly soluble in water, so separating Amlodipine from the sample was difficult and took a long time with poor peak symmetry.
The combination of organic solvents like methanol with water makes the separation of perindopril easy, but with this combination, the separation of Amlodipine was still not good. Amlodipine is freely soluble in acidic pH, so a buffer at lower pH with organic solvent was tried. Methanol was replaced with acetonitrile, and better symmetry of peak was observed during analysis. In later trials, water was replaced with some buffers at lower pH, and better separation was observed.
The short retention time was achieved by using different combinations of buffer and acetonitrile. The injection volume of 20Ul was kept constant throughout the trials. The flow rate was tried between 1 ml/min to 1.5 ml/min.
The best results were observed by using the combination of phosphate buffer at pH 2.6 and acetonitrile at wavelength 210 nm. The ratio of acetonitrile and phosphate buffer was 35:65. The flow rate of 1.5 ml per minute was adjusted to reduce the retention time. The typical chromatogram is shown in S3.
The retention time for Perindopril tertbutyl amine was about 1.3 min, and for Amlodipine was about 2.3 min. The developed method is time-saving as the retention time for both the analytes is minor than 4 min and also cost-effective as a combination of phosphate buffer and acetonitrile was used as the mobile phase. A comparison with previous published results show that current method is more efficient (Table 4). From the composition of the mobile phase, it is obvious to know that the preparation of the mobile phase is effortless and is in a ratio of 35:65 (Acetonitrile and Phosphate buffer).Table 4A comparison of retention time of current study with previous studies. Table 4AnalyteRetention time (Min)ReferenecePerindopril Erbumine3.172[31]Amlodipine Besylate5.504[31]Amlodipine7.69[32]perindopril7.31[33]Perindopril tertbutyl amine1.3Current studyAmlodipine2.3Current study The results obtained from replica injections of amlodipine and perindopril tertbutyl amine are presented in Table 5. The concentration of perindopril tertbutyl amine was 0.4 mg/ml, and that of Amlodipine was 0.7 mg/ml. Table 5Results obtained from Replicate Injections of Standard Solutions. Table 5Perindopril Tertbutyl AmineAmlodipineConcentrationReplicatePeak AreaConcentrationReplicatePeak Area0.4 mg/mlReplicate-15421.3710.7 mg/mlReplicate-117548.05Replicate-25422.762Replicate-217432.01Replicate-35423.347Replicate-317406.862Replicate-45432.419Replicate-417454.905Replicate-55440.034Replicate-517435.893Replicate-65438.538Replicate-617453.315Mean5429.745Mean17455.173Standard Deviation8.022Standard Deviation$54.470\%$ Standard Deviation$0.148\%$ Standard Deviation$0.312\%$The table shows that the relative percentage standard deviation for Perindopril Tertbutyl amine is $0.148\%$, and for *Amlodipine is* $0.312\%$. This shows that the advanced analysis method for simultaneous analysis is precise and can be used to analyze tablets containing a fixed-dose combination of Amlodipine and Perindopril Tertbutyl amine in different compositions and strengths.
## Analytical method validation
The development of analytical methods and their validations are continuous and interdependent procedures used to analyze newly developed pharmaceutical products and other chemical entities. Validation provides the confidence that an analytical approach will produce reproducible results without bias errors under a given set of pre-defined conditions. The validated method produces reproducible results using the same or different laboratories and persons on other reagents and analytical equipment.
Here, an analytical method for the simultaneous determination of amlodipine and perindopril tertbutyl amine was developed and validated per ICH, BP, and USP guidelines. The validation study was performed for the parameters.•Specificity•Linearity•Range•Precision•Limit of detection (LOD)•Limit of Quantification (LOQ)•Accuracy/Biasness•Robustness
## Specificity
The specificity/selectivity of this advanced method was confirmed by using the sample containing both the analytes and the placebo. The analyte sample gives the specific peak at a particular retention time. This retention time is the qualitative indication and identification of a particular analyte. When a placebo was run under the same chromatographic conditions, no peak was observed. However a sample containing the particular analytes, distinct peaks at a specific retention time were observed for ADB (Fig. 3a) and PTBA (Fig. 3b). The obtained peaks for Standard preparations and sample preparations corresponded to each other, depicting that the developed method was specific for these two particular analytes. Fig. 3Specificity chromatogram for (a) Amlodipine Besylate and (b)Perindopril tertbutyl amine. Fig. 3
## Linearity
Linearity is a relationship between two variables that are directly dependent upon each other. A straight line was obtained when we plot a graph between these two variables. The linearity of an analytical procedure is its ability to show the results directly proportional to the analyte concentration in the given sample. The linearity also limits analytical methods for accurate measurement of the compound under analysis. Linearity can be measured by preparing different concentrations of solutions from standard stock solutions or adding the different weights of analyte into the diluent directly. Therefore, a graph was plotted between the concentration of analyte and peak areas obtained during analysis. The calibration curve was drawn between the analyte concentration and the peak area's mean. The correlation coefficient was calculated, and it was found to be greater than 0.990. The linearity results for ADB (Fig. 4a) and PTBA (Fig. 4b) showed value of R2 as 0.999.Fig. 4Linearity for (a) Amlodipine Besylate (as a single Analyte) and (b) Perindopril Tertbutyl Amine (as a single Analyte).Fig. 4 During the validation of this method, the linearity for individual analytes was measured by making solutions of different concentrations. The combined linearity is measured by dissolving different weights of both analytes directly into the mobile phase.
## Linearity for Perindopril Tertbutyl Amine and Amlodipine Besylate (mixture of both analytes)
The linearity of the developed method for Perindopril Tertbutyl Amine (from the mixture) was measured at various concentrations. The results show a correlation coefficient of 0.99 for experimental data of ADB (Fig. 5a) and PTBA (Fig. 5b). Similarly, the linearity of the developed method for Amlodipine Besylate (mixture) was measured at various concentrations. The results show a correlation coefficient of 0.99 for experimental data. Fig. 5Linearity for (a) Perindopril Tertbutyl Amine and (b) Amlodipine Besylate (Mixture of both analytes).Fig. 5
## Range for amlodipine Besylate
The developed method has shown that the range of determination of Amlodipine Besylate, as a single analyte, is from 0.05 mg/ml to 0.09 mg/ml, and a linear curve was obtained. The determination range of amlodipine besylate in the mixture with perindopril is from 0.56 mg/ml to 0.84 mg/ml, and a linear curve was obtained within these ranges.
## Range for Perindopril tertbutyl amine
The developed method has shown that the range of determination of Perindopril Tertbutyl Amine, as a single analyte, was from 0.02 mg/ml to 0.06 mg/ml, and a linear curve was obtained. The range of determination of Perindopril Tertbutyl Amine in the mixture with amlodipine besylate was from 0.32 mg/ml to 0.48 mg/ml, and a linear curve was obtained within these ranges.
## Precision
The precision of an analytical method is the closeness of results obtained from the different sampling of the same homogenous sample. The precision of an analytical method in current study was determined in terms of repeatability, intermediate precision, and reproducibility.
## Repeatability
The repeatability of the developed method was calculated by preparing a solution containing a 0.4 mg/ml concentration of perindopril tertbutyl amine and a 0.7 mg/ml concentration of amlodipine besylate. Six replicates of prepared samples were analyzed, results were calculated (S4), repeatability was calculated, and effects were observed within acceptable limits. The % relative standard deviation of the peak areas of six replicas should be less than 2. The % relative standard deviation of the peak areas for Amlodipine besylate and Perindopril Tertbutyl amine are 0.322 and 0.148, respectively, which are well within the given limits (S5).
## Intermediate precision
Intermediate precision is the calculation of the closeness of results by using a particular analytical method, under the given set of conditions, but on different days or by various analysts, or by using another instrument. Intermediate precision is measured in terms of the relative standard deviation of the peak areas obtained during the analysis using HPLC. An acceptance limit for intermediate precision is not more than $3\%$ relative standard deviation per ICH guidelines.
Six replicate injections were used to calculate the intermediate precision on different days. On each day, fresh samples were prepared to obtain the results. The solutions contain 0.7 mg/ml concentration of amlodipine besylate and 0.4 mg/ml concentration of perindopril tertbutyl amine. The results are shown in S6 and S7.
## Reproducibility
Reproducibility is used for the standardization of any developed analytical method. The variation in the results obtained from different laboratories and locations were calculated to standardize the analytical process. According to the ICH guidelines, reproducibility is required only when the developed analytical method is to be standardized. So, the precision of this developed method was only expressed in terms of repeatability and intermediate precision.
## Limit of detection
The limit of detection (LOD) is measured from the calibration curve obtained during the linearity study. Five different concentrations of analytes were used to get the calibration curve. From the curve, the value of the Y-intercept and LOD was calculated. The LOD for PTBA and ADB was observed as 0.0323 mg/ml (Fig. 6a) and 0.0495 mg/ml (Fig. 6b) respectively. Fig. 6The measurement of the calibration curve of PTBA and ADB at different concentrations (a) PTBA, and (b) ADB.Fig. 6
## Limit of Quantitation
The limit of Quantitation (LOD) is measured from the calibration curve obtained during the linearity study. Five different concentrations of analytes are used to get the calibration curve. The value of the Y-intercept is calculated from the curve, and LOQ is calculated. The Perindopril Tertbutyl Amine quantification limit was observed as 0.0979 mg/ml, and the correlation coefficient was 0.9838. Whereas the limit of quantification of Amlodipine Besylate was observed as 0.1499 mg/ml, and the correlation coefficient was 0.9876.
## Accuracy
The accuracy of an analytical method is measured to calculate the closeness of results obtained, from a developed method, with the accepted reference targets. Different samples are prepared by adding the known different amounts of analytes. Then these samples are analyzed by using the developed analytical method. The percentage recovery of the added known amount is the degree of accuracy of a developed analytical method. Nine measurements over at least three different concentrations were used for accuracy. These prepared concentrations should cover the given specified range. It means three replicate injections of three concentrations. ( ICH Guideline 2005) S8. The accuracy of the developed method for both amlodipine besylate and perindopril tertbutyl amine. The mean area of working standard of amlodipine besylate was 17448.036, and for perindopril, tertbutyl amine was 5389.389.
## Robustness
The robustness of the developed method was confirmed by making a small but deliberate change in different components of the analytical method, as described in the methodology section. The robustness of the developed method was confirmed analyte ADB (Table 6a) and analyte PTBA (Table 6b).Table 6 (a)Robustness of Amlodipine Besylate. Table 6 (a)ReplicatesCondition 1Condition 2Buffer Increase $5\%$Buffer Decrease $5\%$Area at Standard Temperature 35 °CArea at -5 °C (30 °C)Area at +5 °C (40 °C)Replicate117166.41917073.32617294.50617326.72817337.044Replicate217035.39717022.53917297.79817346.65217316.671Replicate317014.96217091.36517293.11217315.48617315.326Mean17071.96217062.41017295.13917329.62217323.014SD$82.49435.6882.40615.78312.169\%$age RSD0.480.210.010.090.07ReplicatesCondition 3Condition 4Flowrate Increase $15\%$Flowrate Decrease $15\%$Buffer at pH 2.62Buffer at pH 2.51Buffer at pH 2.70Replicate114882.02420284.63717392.84817323.78817350.421Replicate214949.32920292.76217394.72617342.72617396.573Replicate315028.53320325.63017452.22217337.12517380.728Mean14953.29520301.01017413.19217334.54617375.907SD$73.33521.70533.8119.72923.451\%$age RSD0.490.110.190.060.13Table 6 (b)Robustness for perindopril tertbutyl amine. Table 6 (b)ReplicatesCondition 1Condition 2Buffer Increase $5\%$Buffer Decrease $5\%$Area at Standard Temperature 35 °CArea at -5 °C (30 °C)Area at +5 °C (40 °C)Replicate15459.7515334.5155359.2135368.4065370.406Replicate25319.2305341.3265360.8555372.8195374.390Replicate35218.3275344.1765356.7055369.7395371.782Mean5365.7695340.0055358.9255370.3225372.192SD$81.3924.9642.0902.2632.024\%$age RSD1.520.090.040.040.04ReplicatesCondition 3Condition 4Flowrate Increase $15\%$Flowrate Decrease $15\%$Buffer at pH 2.62Buffer at pH 2.51Buffer at pH 2.70Replicate14662.0126304.7115426.5435394.5805398.562Replicate24676.0336302.3885425.0665395.6065402.662Replicate34678.7946304.9605430.6545391.8865406.731Mean4672.2806304.0195427.4215394.0245402.652SD$8.9981.4192.8961.9224.085\%$age RSD0.190.020.050.040.08
## Summary of validation parameters
The results of all the parameters used to validate this developed method were within the acceptable range and are shown in S9.
## Verification of developed method
The method was developed to simultaneously determine Amlodipine and perindopril tertbutyl amine with a fixed-dose combination in film-coated tablets. This developed method was applied by analyzing different brands and the different strengths of these two analytes. Two additional brands were selected for verification of this developed method. Two strengths of these two brands (four products) were analyzed using this developed method.
## Quantitative analysis of Coversam 10/4 mg and Coversam 5/4 mg tablets
The coversam $\frac{10}{4}$ mg batch film-coated tablets, Batch Number B[10] 20052, and Coversam $\frac{5}{4}$ mg, Batch Number B[10]19114, manufactured by Servier research and pharmaceuticals Pakistan Pvt. Ltd., were analyzed by this developed method. Typical chromatograms of the analysis of these two products are shown in S-10.
## Quantitative analysis of AMPER 10/4 mg and AMPER 5/4 mg tablets
AMPER $\frac{5}{4}$ mg film-coated tablets, Batch Number BQ0012, and AMPER $\frac{10}{4}$ mg film-coated tablets, Batch Number BS0003, manufactured by NEXT Pharmaceutical Products Pvt. Ltd., were analyzed by this developed method. Typical chromatograms of the analysis of these two products are shown in S-11.
## System suitability
System suitability tests are performed to verify the performance of analytical equipment, HPLC, as a whole. This verification helps conclude that equipment, operations, electronic system, and samples were analyzed to constitute a system as a whole and work as an integral system. When system suitability parameters are within specified limits, the confidence and reliability of the system enhance.
The following parameters were considered during the verification of this developed method.•Resolution•Column efficiency•Tailing factor
## Amlodipine
The FTIR spectrum of amlodipine besylate shows absorbance bands in the range of (750, 1715, 1650, 1697, 1735, 1375, 3500-3100, 1100, 3500-3100, 1600-1450, & 1300-1000 cm−1). The data shows absorbance band related to the carbonyl group (C <svg xmlns="http://www.w3.org/2000/svg" version="1.0" width="20.666667pt" height="16.000000pt" viewBox="0 0 20.666667 16.000000" preserveAspectRatio="xMidYMid meet"><metadata> Created by potrace 1.16, written by Peter Selinger 2001-2019 </metadata><g transform="translate(1.000000,15.000000) scale(0.019444,-0.019444)" fill="currentColor" stroke="none"><path d="M0 440 l0 -40 480 0 480 0 0 40 0 40 -480 0 -480 0 0 -40z M0 280 l0 -40 480 0 480 0 0 40 0 40 -480 0 -480 0 0 -40z"/></g></svg> O) as ranging from 1731 to 1687 cm−1 for the samples of the Amlodipine with potassium bromide (KBr) and was found to be within 2.0 units of absorbance. The height of peak intensity for the peak range of 1731-1687 cm−1 for ADB (Fig. 7a) and PTBA (Fig. 7c) was used to calibrate the curve. It is to be mentioned that the calibration curve for the data results was calculated by equation y = c + bx, where (.) characterizes peak area and (x) characterizes concentration of Amlodipine. The FTIR spectrum specifies that there was negligible interference of excipients used to design a tablet dosage system Casian [18]. In addition, the theoretical data was also calculated by Gaussian 9.2 by employing the B3LYP functional at density functional theory (DFT) level [34]. The theoretical IR spectra are in line with experimental results for ADB (Fig. 7b) and PTBA (Fig. 7b) [35]. The ΔEgap was calculated by low energy unoccupied molecular orbital (LUMO) and high energy molecular orbital (HOMO). The energy gap corresponds to the absorption; therefore, experimental amlodipine data were compared with theoretical calculations in line with experimental data. Fig. 7Experimental and theoretical IR spectra (a) experimental spectra of amlodipine (b) theoretical spectra of amlodipine and (c) experimental spectra of perindopril tert-butylamine, and (d) theoretical spectra of perindopril tert-butylamine. Fig. 7
## Energy calculations of amlodipine
The experimental, analytical technique Fourier Transform Infrared (FTIR) spectroscopy was used to measure Perindopril Tertbutylamine (drug) spectra. The data were analyzed to find out any interactions between Perindopril and Amlodipine. The theoretical IR spectra were also calculated by Gaussian 9.2 by employing the B3LYP functional at density functional theory (DFT) level study (Fig. 8a). The theoretical results are in line with the experimental data. It can be concluded that there was no significant change in the characteristic peaks of the drug in combination. Fig. 8HOMO and LUMO energy gap of (a) ADB and, (b) PTBA.Fig. 8 The experimental, analytical technique Fourier Transform Infrared (FTIR) spectroscopy was used to measure Perindopril Tertbutylamine (drug) spectra. The data were analyzed to find out any interactions between Perindopril and Amlodipine. The theoretical IR spectra were also calculated by Gaussian 9.2 by employing the B3LYP functional at density functional theory (DFT) level study (Fig. 8b). The theoretical results are in line with the experimental data. It can be concluded that there was no significant change in the characteristic peaks of the drug in combination.
## Conclusion
The developed method has been validated per international conference of harmonization (ICH) guidelines. The validation study was performed for the parameters specificity, linearity, range, precision, limit of detection (LOD), Limit of Quantitation (LOQ), accuracy, and robustness for both analytes. The developed method gives the linear response, and a calibration curve was observed linear in the range of 0.56 mg/ml to 0.84 mg/ml for Amlodipine and in the range of 0.32 mg/ml to 0.48 mg/ml for perindopril tertbutyl amine. The precision of this developed method was calculated in terms of repeatability, and intermediate precision and value were observed precisely, having a relative standard deviation < 2 for both the analytes. LOD and LOQ were 0.0495 mg/ml and 0.1499 mg/ml for Amlodipine besylate whereas 0.0323 mg/ml and 0.0979 mg/ml for perindopril tertbutyl amine. The percentage of recovery was measured by spiking $80\%$, $100\%$, and $120\%$ of both the analytes in placebo, where the accuracy was calculated based on the recovery of these analytes. The percentage recovery of Amlodipine was $99.03\%$ to $100.71\%$, and for perindopril, tertbutyl amine was $97.62\%$ to $102.1\%$. After validation, this developed method was verified by analyzing two registered products with two different strengths, COVERSAM AM and AMPER. Very accurate results were obtained with a similar peak and retention time pattern without any interference from excipients. These results showed that a new fast and easy method was employed for simultaneous identification and quantification of ADB and PTBA by HPLC with a time-efficient and cost-effective approach. However, the currently proposed method shows some limitations such as the use of sophisticated laboratory instruments (HPLC) and conducting experiments at a standard temperature of 35 °C. So, this developed method can be used to quantitatively determine amlodipine and perindopril tertbutyl amine in finished dosage form film-coated tablets.
## Author contribution statement
Muhammad Farooq Saleem Khan: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.
Lutafullah Tahir: Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.
Xu Zhou: Conceived and designed the experiments; Analyzed and interpreted the data.
Ghulam Bary: Analyzed and interpreted the data.
Muhammad Sajid, Ahmad Khawar Shahzad, Riaz Ahmad: Analyzed and interpreted the data; Wrote the paper.
Abdullah Mohamed: Conceived and designed the experiments.
Ilyas Khan: Conceived and designed the experiments; Wrote the paper.
## Funding statement
This work was supported by the Natural Science Foundation of Sichuan Province China [2018JY0327].
## Data availability statement
No data was used for the research described in the article.
## Declaration of interest’s statement
The authors declare no conflict of interest.
## Supplementary data
The following is the *Supplementary data* to this article:Multimedia component 1Multimedia component 1
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|
---
title: 'Preeclampsia: Narrative review for clinical use'
authors:
- Paulino Vigil-De Gracia
- Carlos Vargas
- Joanne Sánchez
- Jorge Collantes-Cubas
journal: Heliyon
year: 2023
pmcid: PMC10009735
doi: 10.1016/j.heliyon.2023.e14187
license: CC BY 4.0
---
# Preeclampsia: Narrative review for clinical use
## Abstract
### Aim
Preeclampsia is a very complex multisystem disorder characterized by mild to severe hypertension.
### Methods
PubMed and the Cochrane Library were searched from January 1, 2002 to March 31, 2022, with the search terms “pre-eclampsia” and “hypertensive disorders in pregnancy”. We also look for guidelines from international societies and clinical specialty colleges and we focused on publications made after 2015.
### Results
The primary issue associated with this physiopathology is a reduction in utero-placental perfusion and ischemia. Preeclampsia has a multifactorial genesis, its focus in prevention consists of the identification of high and moderate-risk clinical factors. The clinical manifestations of preeclampsia vary from asymptomatic to fatal complications for both the fetus and the mother. In severe cases, the mother may present renal, neurological, hepatic, or vascular disease. The main prevention strategy is the use of aspirin at low doses, started from the beginning to the end of the second trimester and maintained until the end of pregnancy.
### Conclusion
Preeclampsia is a multisystem disorder; we do not know how to predict it accurately. Acetylsalicylic acid at low doses to prevent a low percentage, especially in patients with far from term preeclampsia. There is evidence that exercising for at least 140 min per week reduces gestational hypertension and preeclampsia. Currently, the safest approach is the termination of pregnancy. It is necessary to improve the prediction and prevention of preeclampsia, in addition, better research is needed in the long-term postpartum follow-up.
## Introduction
Preeclampsia is a multisystemic disorder characterized by mild to severe hypertension during the second half of pregnancy or postpartum, leading to adverse pregnancy outcomes. It is estimated that approximately 76,000 women and half a million fetuses and neonates die each year from this disease worldwide [1]. The exact process that leads to the development of this pathology is unknown, and we can agree on the existence of three stages: the first is an anomalous invasion of the trophoblast into the spiral arteries, which generates an inadequate remodeling of the spiral arteries; the second stage then occurs, which is an alteration in the production of angiogenic and antiangiogenic factors. The time of onset is unknown, nor is it known with which alteration of these factors or at what gestational age this second stage begins and ends. Then, the third stage follows the clinical manifestation, which is very typical and widely known in this pathology [[2], [3], [4]]. Clinical manifestations can range from asymptomatic conditions to seizures and death [4]. Preeclampsia can be subdivided into early onset or late onset using 34 weeks as a cutoff or preterm or term using 37 weeks of gestation as a cutoff [1]. Approximately 80–$90\%$ are late onset or term preeclampsia and, in addition, of better evolution with fewer complications.
Despite much research aimed at predicting preeclampsia, it is not yet known with good or adequate precision how to predict this pathology, and we continue to rely on clinical risk factors [5]. In the last three decades, there has been much research on the prevention of preeclampsia, and we will analyze the main research and recommendations of specialized organizations. Acetylsalicylic acid (aspirin) at low doses remains the only drug measure with a moderate effect in preventing preeclampsia according to clinical risk factors [1,6,7].
The management of preeclampsia remains focused on a timely diagnosis and termination of pregnancy, of course that perinatal results are influenced by gestational age [4,5]. The use of antihypertensives is still elementary, especially in severe hypertension and the use of magnesium sulfate to prevent seizures [4,5,8]. Complications with preeclampsia vary greatly according to the study, ranging from no maternal or fetal complications to death of the mother and fetus or neonate [4,9].
The objective of this review is to update on preeclampsia, including its pathophysiology, diagnosis, clinical manifestations, prediction, prevention, maternal and perinatal complications, management, future scenarios and conclusions.
## Search strategy and article selection criteria
PubMed and the Cochrane Library were searched from January 1, 2002 to March 31, 2022, with the search terms “pre-eclampsia” and “hypertensive disorders in pregnancy”. We cross-referenced these terms with the following: “pathophysiology”, “definition”, “prediction”, “prevention”, “management”, “clinical trials”, “aspirin”. We also look for guidelines from international societies and clinical specialty colleges and we focused on publications made after 2015.
## Definition and classification of preeclampsia
Preeclampsia is a very complex multisystemic hypertensive syndrome typical of pregnancy. Over the years, the definition of this pathology has changed. Formerly, it was defined as the appearance of hypertension accompanied by proteinuria after 20 weeks of gestation. Today, the International Society for the Study of Hypertension in Pregnancy (ISSHP) proposes a broader definition, which is the most accepted internationally [1,10]. Society defines preeclampsia as the presence of systolic blood pressure greater than or equal to 140 mmHg or diastolic blood pressure greater than or equal to 90 mmHg in a pregnant woman at 20 or more weeks of gestation, with normal blood pressures before pregnancy. It must be verified in a minimum of 4 h later. In addition to hypertension, one or more of the following manifestations must have appeared recently [10]: Proteinuria: greater than or equal to 300 mg of proteinuria in 24-h urine sample or protein/creatinine ratio in a random urine sample greater than or equal to 0.3 mg/mg, or urine dipstick result of 2 or more for protein. Organ dysfunction: Acute kidney injury: creatinine greater than or equal to 1 mg/dL. Hepatic compromise: transaminases greater than 40 IU/L with or without pain in the right upper quadrant or epigastric pain. Neurological complications: persistent scotoma, severe headache, blindness, altered mental status, eclampsia, or clonus. Hematological disorders: platelet count lower than 150,000/μL, disseminated intravascular coagulation or hemolysis. Uterus-placental dysfunction: fetal growth restriction, altered umbilical artery Doppler or death.
There are several classifications of preeclampsia in clinical practice. According to the time of onset, it is divided into early and late, using 34 weeks of pregnancy as a cutoff point [1,11]. On the other hand, it is based on the presence or absence of signs and symptoms of severity. The American College of Gynecologists and Obstetricians (ACOG) [5] considers a diagnosis of preeclampsia with severity data as the presence of any of the following: Systolic blood pressure greater than or equal to 160 mmHg or a diastolic blood pressure greater than or equal to 110 mmHg, verified on two occasions at least 4 h apart, unless antihypertensive therapy is initiated before this; platelet count less than 100 × 109/L; elevation of liver enzymes to twice the upper limit of normal concentration, severe and persistent pain in the right upper quadrant or epigastric pain that does not resolve with medications; renal insufficiency with a serum creatinine concentration greater than or equal to 1.1 mg/dL or double the serum creatinine concentration in the absence of kidney disease; pulmonary edema; new onset headache that does not respond to medications; visual disturbances.
It should be noted that pregnant women with a previous diagnosis of hypertension with onset of proteinuria or maternal organ dysfunction after 20 weeks are classified as chronic hypertensive with added preeclampsia [4].
## Pathophysiology of preeclampsia
When addressing the pathophysiology of preeclampsia, one must start from the fact that it is not completely known and that everything indicates a multifactorial cause, which is why it is known as the disease of “theories” because it is an enigmatic and elusive disorder.
It is known that the development of preeclampsia has maternal genetic, immunological, and inflammatory factors, leading to failure of placentation and intolerance between maternal, paternal (placental) and fetal tissues. These two major causes lead us to the focus of the pathophysiology, which is the reduction of utero-placental perfusion and therefore to utero-placental tissue ischemia, which in the end will result in generalized endothelial damage.
The theories that support this concept can be evidenced in the model by Odgen et al. [ 12], in which the clamping of the aorta below the renal arteries in nonpregnant dogs did not cause hypertension; in contrast, performing the same procedure in pregnant animals caused hypertension. Finally, this hypertension disappeared when performing hysterectomy, a concept that has been supported in other studies when clamping the uterine arteries in pregnant dogs, obtaining the same results [13].
This model of uteroplacental ischemia is supported by in vivo studies, numerous studies with injection of radioactive sodium and other radioactive tracers, in which it is observed that pregnant women without preeclampsia presented a utero-placental flow in term pregnancies of approximately 600 mL/min, but not in pregnant women with preeclampsia, where the flow decreased significantly and could fall up to $50\%$ if it was severe preeclampsia, compared to mild preeclampsia [14,15]. Similar to a model in nonhuman primates, baboons, when performing uterine artery ligation, we observed findings similar to preeclampsia, such as hypertension, proteinuria, increased circulating concentrations of soluble vascular endothelial growth factor receptor 1 (sVEGFR-1; also known as soluble fms-like tyrosine kinase-1 [sFlt-1]) and endoglin. In addition, there is an increase in the concentrations of proinflammatory cytokines, such as tumor necrosis factor α (TNF-α) and interleukin (IL)-6 [16]. Another notable finding is that the administration of short interfering RNAs, which silence three of the sFlt-1 messenger RNA (mRNA) isoforms, was observed to suppress the overexpression of sFlt-1, consequently resulting in a reduction of hypertension and proteinuria. This leads to the hypothesis that the “toxin” that can be seen as responsible for hypertension may be sFlT-1 [13,17].
To date, it is impressive that the etiology of preeclampsia has defects in the placenta that lead to ischemia as its main component. However, the dysfunctional maternal cardiovascular system has recently been implicated as a significant factor in preeclampsia [11]. Both pathologies share common symptoms, hypertension, cerebral edema, and cardiac dysfunction.
The lack of physiological transformation of the spiral arteries, in which they maintain smooth muscle and narrow diameter, is believed to make these vessels prone to the effect of circulating vasoconstrictor agents; additionally, these vessels are more likely to develop atherosclerosis, causing the vessel lumen to be narrower, and therefore, making placental perfusion more compromised [18].
We can summarize that there is a link between preeclampsia and placental ischemia, as we have observed in studies in animal models [12], leading to hypertension and proteinuria; there is lower placental flow in pregnant women with preeclampsia [14]; failure of the physiological transformation of the spiral arteries; and an increase in the relationship between maternal placental growth factor (PlGF) and sFlt-1 [17,18].
## Risk factors of preeclampsia
It is well known that preeclampsia has a multifactorial genesis. High-level, well-developed studies seem to converge on certain risk factors, behaving as a common denominator.
However, there are additional factors and complications that predispose women to have subsequent pregnancies with an increased probability of developing preeclampsia, such as spontaneous premature birth (1.1 to $1.8\%$ if it is > 32 weeks and $3.2\%$ if it is earlier than 28 weeks of gestation) and fetal growth of 2–3 standard deviations below the mean [19].
Previously, infectious diseases were associated with a risk factor for preeclampsia; however, they could not be confirmed. Currently, there are studies that indicate an association of preeclampsia and urinary tract infection [20] and certain specific reports in which it has been associated with malaria [21,22], cytomegalovirus [23], human immunodeficiency virus [24] and, more recently, infection by SARS-CoV-2 [14,25].
There are multiple studies on SARS-CoV-2 infection, being a recent pathology and a global problem, studies which we can cover through a meta-analysis, through which the association of SARS-CoV-2 and pregnancy with a significant increase in preeclampsia (OR, 1.58; $95\%$ CI, 1.39–1.8), preeclampsia with severe characteristics (OR, 1.76; $95\%$ CI, 1.18–2.63), eclampsia (OR, 1.97; $95\%$ CI, 1.01–3.84) and HELLP syndrome (OR, 2.01; $95\%$ CI, 1.48–2.97) was confirmed [25].
Diabetes mellitus has also been associated with preeclampsia in multiple studies, from systematic reviews (aRR, 3.56; $95\%$ CI, 2.54–4.99) [26] to retrospective studies of 647,392 pregnancies (aOR, 1.29; $95\%$ CI, 1.19–1.41) [27]. These findings have been supported by comparing them with studies showing a decreased risk of preeclampsia when treated with diet, metformin and insulin, including two systematic reviews [28,29].
Maternal age is an important risk factor. It is suggested that the risk of preeclampsia increases up to $30\%$ for each year after 34 years of age. In a systematic review, it was established that maternal age ≥35: RR 1.2, $95\%$ CI 1.1–1.3; maternal age ≥40: RR 1.5, $95\%$ CI 1.2–2.0 [30]. It should be considered that older patients may present other additional risk factors, such as hypertension, diabetes mellitus, metabolic syndrome, and cardiac disease, which can induce the development of preeclampsia [30].
Body mass index with BMI> 25 kg/m2 [RR, 2.1, $95\%$ CI 2.0–2.2], considered as overweight, and a BMI> 30 kg/m2 [RR, 2.8, $95\%$ CI 2.6–3.1), considered as a range of obesity, act as a risk factor. By increasing body weight from 5 to 7 kg/m2, the risk of developing preeclampsia doubles [31]. We can corroborate this in a meta-analysis of 29 retrospective studies, with 1,980,761 participants and 67,075 cases of preeclampsia, where it was shown that pregnant women with a BMI> 30 kg/m2 (aOR, 2.93; $95\%$ CI, 2.58–3.33) had a significantly increased risk, and if the pregnant patient fell into the range of severe obesity, the risk increased even more (BMI≥35 kg/m2; aOR, 4.14; $95\%$ CI, 3.61–4.75) [32].
By conducting more studies and with a higher level of evidence, the different etiological factors of preeclampsia are understood, whose list increases, which in turn marks a common denominator of “endothelial cell dysfunction, intravascular inflammation and syncytiotrophoblast stress,” bringing with it the possible impact of maternal cardiovascular dysfunction, inadequate placentation, and possible involvement of infectious components.
## Clinical manifestations
The clinical manifestations of preeclampsia vary from asymptomatic pictures to fatal complications for both the fetus and the mother. In severe cases, there may be renal, neurological, hepatic, or vascular system involvement [33].
Next, we will detail the main signs and symptoms associated with this pathology divided by organs and systems [4]: Neurological: headache, visual disturbances, hyperreflexia, clonus, or seizures. Hepatic: pain in the epigastrium or in the right upper quadrant. Hematologic: petechiae or dark urine color. Cardiorespiratory: dyspnea, tachypnea, chest pain or confusion. Uterus-placental and fetal: transvaginal bleeding, decreased fetal movements, uterus with increased tone.
ACOG [5], in its most recent practice bulletin, emphasizes that relying on maternal signs or symptoms for the diagnosis of preeclampsia is conflicting. Epigastric pain or severe pain in the right upper quadrant should not be attributed to alternative diagnoses. Likewise, headache should not respond to treatment with acetaminophen or be secondary to other etiologies [6]. On the other hand, the ISSHP [10] indicates that, in the presence of hypertension, the appearance of headache should be considered part of preeclampsia until proven otherwise.
A prospective and multicenter international study evaluated different variables to predict adverse pregnancy outcomes in 2023 patients with preeclampsia. Among their results, $52\%$ of the patients reported at least one symptom, and of these, $5.2\%$ had maternal or perinatal adverse effects compared to $5.3\%$ of those who were asymptomatic. The authors found no correlation between clinical symptoms and unfavorable outcomes in pregnant women with preeclampsia [34].
In the case of HELLP syndrome in approximately $90\%$ of cases, the main symptoms were pain in the right upper quadrant and general malaise, while in $50\%$ of cases, they were nausea and vomiting. In the case of eclampsia, the main symptoms that precede seizures are neurological (severe, frontal, or persistent headache), blurred vision, photophobia or altered mental status [5].
## Prediction of preeclampsia
The justification for predicting a pathology is to use more effective prevention strategies. There is no test or combinations of tests in the first or second trimester of pregnancy that can predict all cases of preeclampsia far from term or at term [5,7,8]. Two strategies have been studied, analyzed, and suggested to predict preeclampsia. One is based on clinical risk factors obtained with the questionnaire and the other on a screening with multiple factors (algorithm): clinical findings, mean arterial pressure, uterine artery pulsatility index determined by Doppler and blood serum placental growth factor (FCP).
Regarding the strategy based on risk factors, they are divided into high- and moderate-risk [6]. High risk: diabetes, chronic arterial hypertension, kidney disease, autoimmune diseases, abnormal uterine artery Doppler (positive), previous history of preeclampsia, or history of fetal or neonatal death associated with preeclampsia. Moderate risk: first pregnancy, family history of preeclampsia, multiple pregnancy, age greater than 40 years, Table 1. Unfortunately, only approximately $10\%$ of women who develop preeclampsia far from term have clinical risk factors [10]. If we use the risk factors as recommended by ACOG [5] and that are similar to those of the World Health Organization (WHO) [6], it was shown in a study [35] that this detects $94\%$ of preeclampsia in pregnancies at less than 32 weeks of gestation, $90\%$ of preeclamptic women have less than 37 weeks of gestation and $89\%$ of preeclamptic women have ≥37 weeks of gestation, but with $64\%$ of false-positives. That is, a high percentage of preeclampsia is detected, but 6 out of 10 of them with these factors do not have preeclampsia. Table 1Clinical risk factors. Table 1Maternal age> 35 yearsRR = 1.5 (1.2–3.0)NulliparityRR = 2.71 (1.96–3.74)Previous history of PE$14.7\%$ = 1 and $31.9\%$ = 2Pregnancy intervalIdeal 1 to 5 years. Assisted reproductionDouble the riskFamily History of PE3–4 times more in sisters and daughtersObesityBMI> 30 kg/m2 = 2–4 times more riskRaceMore risk: Afro-Caribbean and South AsianComorbiditiesHigher risk: Pregestational diabetes, chronic hypertension, kidney disease, SLE, APS The strategy based on the algorithm or with multiple factors should be performed between 11 and 14 weeks of gestation [1,35].
Prospective screening data with a prevalence of $2.9\%$ of preeclampsia show that using this algorithm, $75\%$ of preeclampsia is detected far from term and 43–$47\%$ of preeclampsia at term with a false-positive of $10\%$ [36,37].
Adding to this algorithm the pregnancy-associated plasma protein A (PAPP-A) does not improve the results, so it is not recommended to add it [1]. The International Federation of Gynecology and Obstetrics (FIGO) recommends screening the first trimester (11–14 weeks) [1] to predict preeclampsia based on the algorithm (multiple factors) mentioned above. The disadvantages of screening based on the algorithm of several factors are as follows: a computerized program is required to calculate the risk and is not possible in many countries of the world, and especially is not at hand for the doctor in the office, it requires economic expenses to make the biochemical markers, requires experts to perform uterine artery Doppler and ultrasound equipment of adequate quality. In addition, it is necessary to do this between 11 and 14 weeks because after this period, its effectiveness is unknown. At that gestational age, many of the patients in low- and middle-income countries have not started prenatal care.
Organizations such as ACOG [7] and WHO [6] recommend screening for the risk of preeclampsia based only on the presence of risk factors obtained from the clinical history.
## Preeclampsia prevention
Many drugs and strategies have been studied to reduce the possibility of hypertension during pregnancy. Diets have been studied, including low salt, vitamin, mineral and food intake, exercise, bed rest, use of calcium, aspirin, and low molecular weight heparin, among others, and some are still being investigated, such as metformin. No strategy or drug can prevent hypertension during pregnancy with high probability; however, there are approaches that have shown benefit.
We present strategies that can help under some conditions.
Calcium: Supplementation of calcium between 1 and 2.5 g per day after 20 weeks of pregnancy to women with a low or high risk of preeclampsia whose calcium intake is less than 900-600 mg/day (unlikely in many countries of the world, possibly in some populations or areas) is associated with a decrease in preeclampsia [8,38]. There is not enough evidence to suggest the ideal gestational age to start the use of calcium when it is justified [39]. In addition, using calcium before pregnancy or in the first half of pregnancy in patients with a history of hypertension during previous pregnancy shows no benefits [40].
When there is an indication to use calcium to prevent preeclampsia and there is also another beneficial strategy, such as aspirin, it can be used together.
## Exercises
The adverse effects of exercise in pregnancy are unknown, except for the patient presenting with some disease that contraindicates it.
A systematic review of controlled trials shows that exercising reduces gestational hypertension ($$n = 5316$$; OR 0.61, $95\%$ CI 0.43 to 0.85) and preeclampsia ($$n = 3322$$; OR 0.59, $95\%$ CI 0.37 to 0.9). compared to not exercising [41]. To achieve these benefits, risk reduction, pregnant women should perform this activity for at least 140 min per week, including brisk walking, water aerobics, stationary cycling, or resistance training [41].
## Aspirin
The concept of the imbalance between prostacyclin and thromboxane A2 as part of the pathogenesis of preeclampsia led researchers to use low-dose aspirin to prevent preeclampsia [42]. Multiple randomized trials and systematic reviews have been conducted to answer the question of the benefit of aspirin in preventing preeclampsia. A recent systematic review/meta-analysis [43] evaluated 45 randomized trials including 20,909 pregnant women, using aspirin with doses of 50–150 mg per day and using aspirin as a cutoff point before or after 16 weeks. They found benefits in the prevention of preeclampsia (RR, 0.57; $95\%$ CI 0.43–0.75; $P \leq 0.001$; R2, $44\%$; $$P \leq 0.036$$) and severe preeclampsia (RR, 0.47; $95\%$ CI, 0.26–0.83; $$P \leq 0.009$$; R2, $100\%$; $$P \leq 0.008$$) and for fetal growth restriction (RR, 0.56; $95\%$ CI, 0.44–0.70; $P \leq 0.001$; R2, $100\%$; $$P \leq 0.044$$).; all when aspirin intake was started before 16 weeks. The preventive effect, according to this meta-analysis [43], was modest if it started after 16 weeks and had no effect on growth restriction. However, a meta-analysis of individual patients who involved 31 randomized studies and 32,217 pregnant women (published the same year as the previous one) found that the effect of prevention of preeclampsia and complications using aspirin is not affected if it starts before or after 16 weeks [44]. Many patients in the world and more in countries with fewer resources initiate prenatal control after the first trimester, so since there are benefits of aspirin initiated after 16 weeks, they would not be excluded.
The other question that arises is the dose to be used. A long multicenter, double-blind, placebo-controlled study [45] in women at risk using multiple screening (algorithm) [36,37] showed that taking aspirin at a dose of 150 mg per day at bedtime decreased the incidence of preterm preeclampsia (OR, 0.38; $95\%$ CI, 0.20–0.74; $$P \leq 0.004$$). However, there were no differences between neonatal complications, nor did it decrease preeclampsia at term (>37 weeks) or preeclampsia in patients with chronic hypertension. For all the above, organizations such as ACOG [7], WHO [6] and US preventive services Task force [46] (based on the best evidence) suggest the use of aspirin at low doses (75–81 mg/day), starting as soon as the patient is taken in or it can be initiated at up to 28 weeks of pregnancy and the clinical risk factors already described in the prediction section can be used as criteria to offer it, and that dose is maintained until preeclampsia appears or until term. FIGO [1] and ISSHP [10] recommend 150 mg/day of aspirin, based on the findings of the ASPRE study [45]; that is, they recommend using these doses after performing the multiple screening (algorithm) between 11 and 14 weeks and if it is positive (at risk) to start prevention with that dose of aspirin. More studies using 150 mg of aspirin are needed, also studies comparing 150 mg versus 81 mg per day. On the other hand, there are findings of more obstetric bleeding using 150 mg/Day [4,6,47].
## Low molecular weight heparin
Low molecular weight heparins have been associated with the prevention of preeclampsia; however, the results of the studies are very conflicting [48,49]. The most recent systematic review and meta-analysis [49] analyzed 15 randomized studies (2795 patients) and found a reduction in the development of preeclampsia (OR, 0.62; $95\%$ CI, 0.43–0.90; $$P \leq 0.01$$), small for gestational age (OR, 0.61; $95\%$ CI, 0.44–0.85; $$P \leq 0.003$$), and perinatal death (OR, 0.49; $95\%$ CI, 0.25–0.94; $$P \leq 0.03$$), mainly if it was started before 16 weeks. Unfortunately, the quality of this evidence varies greatly and ranges from very low to moderate due to lack of randomization, imprecision, and great heterogeneity of the studies. Therefore, it is necessary to conduct randomized studies of adequate quality and quantity of patients before suggesting the use of low molecular weight heparins to prevent preeclampsia.
## Management of preeclampsia
The international guidelines for the management of hypertensive disorders of pregnancy and preeclampsia are similar in many aspects, but there are important differences in terms of the definition of the severity of preeclampsia [5,50,51]. In addition, two major elements that aid in the definition and management of biomarkers are being introduced [52] and on the other hand, maternal morbidity prediction models (fullPIERS) [53], this affects the decisions of use of corticosteroids, antihypertensives, magnesium sulfate time and time of pregnancy termination. It should be noted that preeclampsia is progressive and tends to become more severe over time [4].
In the management of preeclampsia, we must consider the following: Possibility of predicting the second and third trimesters; it is preeclampsia; severity data present; fullPIERS or PREP, PREP-L, sFlt-1/PlGF with> $20\%$ risk of maternal morbidity; what type of health facility are you in?; what is the gestational age and when should the delivery be?; medication to be used.
Prediction in the second and third trimesters: It was already presented and discussed in the previous session.
Definition of preeclampsia: As seen above, this must be clear, and a good definition is necessary. ISSHP [8] and FIGO [1,51] do not consider pulmonary involvement, and ACOG [5] does not consider utero-placental involvement in the definition. In addition, in the definition of preeclampsia, there are groups that include sFlt-1/PlGF> 38 pg/mL as a criterion [52]. All this has already been the subject of a comparison, and it has been determined that the inclusion of fetal compromise and angiogenic and antiangiogenic factors can be beneficial for the mother and fetus.
Severity data: These include symptoms of involvement of each organ, such as headache, seizures, dyspnea, epigastric pain, shock due to subcapsular hematoma rupture, hematuria, oliguria, poor increase in uterine height, eclampsia, pulmonary edema, HELLP syndrome type 1, acute renal injury, rupture of subhepatic hematoma, IUGR, fetal death and laboratory parameters that require stabilization and immediate termination of pregnancy [4], Fig. 1.Fig. 1Symptoms and complications of severe preeclampsia. Fig. 1 Prediction of maternal and perinatal morbidity: Using fullPIERS, PREP, PREP-L, sFlt-1/PlGF calculators [52,53]. Early-onset preeclampsia is associated with severe maternal and perinatal complications. The fullPIERS (Preeclampsia Integrated Estimate of Risk) model has shown internal and external validity to predict maternal complications within 48 h for women admitted with preeclampsia at any gestational age. This ability to recognize women at higher risk of complications could help their management.
Health system and level of care: management is preferable where there is an intensive care unit or special care unit, availability of other specialties, complete laboratory and imaging, blood bank. If these services are not available, after stabilizing the patient, the patient should be referred. If there are criteria of severity, with the initial management of antihypertensives, magnesium sulfate, fetal maturation, double venous cannulation, and bladder catheter [51].
Ideal gestational age for delivery. Numerous investigations and systematic reviews have been published to define the best moment: <24 weeks [54]. In an investigation with 55 women with severe preeclampsia younger than 24 $\frac{6}{7}$ weeks with expectant management, 52 died intrauterus, 1 perinatal death and 2 lived with some psychomotor or motor deficits and mothers with HELLP syndrome, eclampsia, transfusions.
24–34 weeks: In the MEXPRE study [55], 267 pregnant women with severe preeclampsia were studied: 133 were randomized to immediate post-corticosteroid interruption, and 134 were randomized to expectant management. Perinatal mortality was similar, and maternal mortality was similar, but in the expectant group, there was more premature detachment of the placenta and small for gestational age. In the TOTEM [56] study, 56 pregnant women were evaluated with expectant management and delivery after fetal maturation with corticosteroids, showing no neonatal benefits.
34–37 weeks: The HYPITAT 2 study [57], in this study with 703 pregnant women with preeclampsia without data of severity between 34 and 37 weeks, did not significantly decrease maternal morbidity but tripled the risk of fetal distress. The subsequent study of neonates at 2 and 5 years showed no differences in development. The PHOENIX [58] study studied 901 women with mild preeclampsia, demonstrating that maternal morbidity decreased in active management and increased neonatal morbidity.
> 37 weeks: HYPITAT 1 [59]. In this study, 756 women with preeclampsia without data of severity of more than 36 weeks for active or expectant management were studied, and it was demonstrated that active management reduces the risk of maternal morbidity.
Medication to be used: Antihypertensives: can be used to maintain a stable pressure and avoid the hypertensive crisis of systolic blood pressure >160 mm Hg, diastolic blood pressure> 110 mm Hg, or both: Oral nifedipine: 10 mg every 20 min for a maximum of 50 mg. Intravenous Labetalol: 20 mg initially and if necessary to repeat in 15–20 min, it is doubled to 40 mg, and if 15–20 min later, it is maintained in hypertensive crisis, it is now doubled to 80 mg, and this last dose can be repeated twice more every 15–20 min if necessary. Intravenous hydralazine should be used 5–10 mg intravenously every 20 min for 3 to 5 doses if necessary. Magnesium sulfate: for prevention of eclampsia and fetal neuroprotection in children younger than 32 weeks. In addition, adding that with impregnation and receiving at least 8 h with magnesium sulfate prior to delivery, there would be no benefits of continuing with magnesium sulfate postpartum. The protection is similar if it is removed [60], if it is left for 6 h [61], for 12 h or if it is left for 24 h postpartum [62].
Corticosteroids: for fetal lung maturation preferably with betamethasone 12 mg intramuscularly and repeated in 24 h or dexamethasone 12 mg intramuscularly and repeated in 24 h [63].
## Maternal and perinatal complications of preeclampsia
The main complications occur in 6 target organs, including pulmonary edema and utero-placental involvement [51], Fig. 1. We will mention the 6 most important: *Eclampsia is* defined as the appearance of one or more generalized tonic-clonic seizures not related to other medical conditions in women with hypertensive disorder of pregnancy. There is loss of autoregulation of cerebral blood flow, blood–brain barrier damage, edema and if it is associated with HELLP syndrome, possible cerebral hemorrhage that is the main cause of death [64] and that can be at the subarachnoid, intraparenchymal or intraventricular level. In management, several schemes of magnesium sulfate are presented, and it is the best anticonvulsant for these cases.
Pulmonary edema is the final accumulation of fluid in the pulmonary alveoli. There are two types: cardiogenic (also called hydrostatic) $64.3\%$ in preeclampsia and noncardiogenic (due to increased permeability) $14.3\%$ in preeclampsia [65]. It is essential to distinguish them for management: in cardiogenic cases, diuretics are used to reduce afterload, and in noncardiogenic cases, they require mechanical ventilation with low tidal volume. It arises as a result of 3 conditions, increased pressure, overhydration and endothelial damage, Fig. 2.Fig. 2Pulmonary edema is the final accumulation of fluid in the pulmonary alveoli. There are two types: cardiogenic (also called hydrostatic) the main in preeclampsia and noncardiogenic (due to increased permeability).Fig. 2 Hepatic rupture is the spontaneous rupture or distention of the Glisson capsule in women with severe preeclampsia and HELLP syndrome and is due to the distension and tension produced by a hematoma or hepatic subcapsular edema. This rupture is caused by periportal hemorrhage and intravascular fibrin deposition in the hepatic sinusoid, obstruction and massive intravascular congestion that contributes to increased hepatic pressure and necrosis that leads to subcapsular and intraparenchymal hemorrhage. After endothelial damage, there is extravasation of red blood cells, thrombosis, hemorrhage, hematoma formation and rupture [66]. The right lobe is involved in $75\%$ of cases and the accuracy of the degree of liver damage should be determined with the AAST liver trauma classification (there are 6° from grade I: subcapsular hematoma that occupies less than $10\%$ of the surface of the liver. lobe up to grade VI with hepatic avulsion) or World Society of Emergency Surgery (WSES) grade I-IV and its multiple management based on liver packaging until liver transplantation [67].
## HELLP syndrome
HELLP syndrome is one of the most severe complications of preeclampsia, causing great maternal and perinatal morbidity and mortality. After having defined that a patient has preeclampsia, she must have the triad: (H) Hemolysis, elevated liver enzymes (EL) and thrombocytopenia (TC). In management, the use of dexamethasone has been shown to improve the number of platelets and decrease hospital time [68]. Fig. 3.Fig. 3HELLP syndrome is one of the most severe complications of preeclampsia, Hepatic dysfunction with platelet consumption is the most characteristic in HELLP syndrome. Fig. 3
## Acute kidney injury (AKI)
The International Organization for Kidney Disease: Improving Global Outcomes (KDIGO) defines AKI in 2020 as a sudden decrease in the glomerular filtration rate (GFR) manifested by an increase in serum creatinine or oliguria between 48 h and 7 days.), with the severity (stage) of AKI determined by the severity of the increase in creatine or oliguria [69]. The anatomopathological findings include glomeruloendotheliosis, podocyturia and microangiopathic thrombosis [69], in addition to the presence of hematuria as a sign of severity. Classification in AKI stages 1, 2 and 3 of the last classification and its management may require replacement therapy and hemodialysis [69].
## Uterine-placental involvement
The definition of IUGR is based on the calculation of the weighted fetal ultrasound and the Doppler velocimetry of the uterine, umbilical, and middle cerebral arteries [70]. To see the degree of involvement (I-IV), ductus venosus [71] was added. IUGR is caused by uteroplacental insufficiency that can increase perinatal morbidity and mortality. Preeclampsia of early onset, that is, before 34 weeks [71], is mostly associated with IUGR. Preeclampsia and IUGR are associated with small placentas in childbirth and show combinations of villus hypermaturity, infarcts, and decidual vasculopathy.
## Conclusions
Preeclampsia is a multisystem disorder originating at the level of the uterus and placenta, and we know much about the damage it produces at the maternal-fetus-neonatal level. We do not know how to predict it accurately, and therefore, we do not know biochemical or biophysical markers that allow us to act before clinical manifestations or damage to the mother and her child. Despite decades of researching how to prevent this disease, we are far from having a nonpharmacological or pharmacological measure that allows us to avoid a high percentage of this pathology. We use acetylsalicylic acid at low doses to prevent a low percentage, especially in patients with far from term preeclampsia that, as we know, are the ones that present the least, although they are the ones that present more complications. Management consists basically of treating hypertension, avoiding seizures, interrupting pregnancy, and treating complications. Maternal complications of preeclampsia are not limited to pregnancy; today, we know that they have more possibilities of cardiovascular and metabolic disorders for the rest of their lives. Evidence shows that women with preeclampsia are twice as likely to develop chronic hypertension, type 2 diabetes mellitus and hypercholesterolemia compared to those without hypertension. It is necessary to continue research on preeclampsia to learn more about this pathology, to have better maternal and perinatal outcomes and to make the difference between preterm and term preeclampsia.
It is necessary to continue doing research focused on the use of biomarkers (fetus, placenta, and mother). It is necessary to improve the prediction and prevention of preeclampsia, in addition, better research is needed in the long-term postpartum follow-up. Currently, the safest approach is the termination of pregnancy.
## Author contribution statement
Paulino E. Vigil-De Gracia, Jorge Collantes-Cubas: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.
Carlos Vargas, Joanne Sánchez: Conceived and designed the experiments; Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.
## Funding statement
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
## Data availability statement
No data was used for the research described in the article.
## Declaration of interest’s statement
The authors declare no competing interests.
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|
---
title: 'Exploring the constituent mechanisms of hepatitis: a dynamical systems approach'
authors:
- Joanne L Dunster
- Jonathan M Gibbins
- Martin R Nelson
journal: Mathematical Medicine and Biology
year: 2022
pmcid: PMC10009886
doi: 10.1093/imammb/dqac013
license: CC BY 4.0
---
# Exploring the constituent mechanisms of hepatitis: a dynamical systems approach
## Abstract
Hepatitis is the term used to describe inflammation in the liver. It is associated with a high rate of mortality, but the underlying disease mechanisms are not completely understood and treatment options are limited. We present a mathematical model of hepatitis that captures the complex interactions between hepatocytes (liver cells), hepatic stellate cells (cells in the liver that produce hepatitis-associated fibrosis) and the immune components that mediate inflammation. The model is in the form of a system of ordinary differential equations. We use numerical techniques and bifurcation analysis to characterize and elucidate the physiological mechanisms that dominate liver injury and its outcome to a healthy or unhealthy, chronic state. This study reveals the complex interactions between the multiple cell types and mediators involved in this complex disease and highlights potential problems in targeting inflammation in the liver therapeutically.
## 1. Introduction
Hepatitis is a general term used to describe ongoing, damaging inflammation in the liver that can lead to the development of fibrosis and ultimately liver failure. There are over 100 types of liver disease that have inflammation as a core component. Common causes of an inflamed liver are hepatitis C, a blood borne virus; medications, such as paracetamol; alcohol, which can result in alcohol-related liver disease (ARLD); and obesity and diabetes, which can result in nonalcoholic steatohepatitis (NASH) (Sato et al., 2016). The liver is remarkable in the wide range of functions it performs: it helps convert food into energy; detoxifies chemicals, drugs and toxins; stores vitamins and produces hormones and proteins. Many of these functions require inflammation, the liver being constantly bombarded by a stream of dietary and bacterial products that have inflammatory potential. However, while the liver is generally thought to be a non-immunological organ, it is known that in a healthy liver constant exposure to inflammatory stimuli results in regulation of inflammation (Rius et al., 2012; Koyama & Brenner, 2017). Failure in regulating inflammation can result in a self-perpetuating process where scar tissue is laid down (liver fibrosis) that can progress to cirrhosis and ultimately liver failure Solovyev et al. [ 2013]. Given the importance of the liver, it is perhaps surprising that so few treatments for liver disease exist (Koyama & Brenner, 2017), with most existing treatments just focusing on the removal of the stimulus of inflammation so that the liver can heal itself. For example, while treatments (anti-virals) are used to target the virus hepatitis C, which causes liver failure, for liver diseases such as ARLD or NASH the patient would be advised to take preventative measure such as cessation of drinking or a change of diet to limit failure (Sato et al., 2016).
Several authors have proposed models that capture damage to the liver. Remien and coworkers constructed a model that captures the effects of acetaminophen (paracetamol) on the liver’s key cell type, hepatocytes and the downstream consequences on the synthesis of clotting factors (Ramachandran et al., 2012). This model, which has two key enzymes produced by damaged hepatocytes and clotting factor synthesis as outputs, was able to predict laboratory levels of these outputs for patients admitted to hospital following acetaminophen overdose. To study the effects of alcohol on acetaminophen-induced liver damage, this model was later updated to include the mechanisms of alcohol metabolism. The analysis suggested that hepatocyte damage depends on a trade-off between induction and inhibition of key enzymes involved in alcohol metabolism, with the risk of liver damage being increased if acetaminophen is ingested shortly after alcohol, but with simultaneous ingestion resulting in less damage (Ghosh et al., 2021a). A further study by these authors extended this work to investigate ischemic hepatitis and the ability to predict commonly used biomarkers (Ghosh et al., 2021b). Webb and coworkers also focus on the damage to liver by acetaminophen; having constructed a model that captures the main dynamics of acetaminophen metabolism, they used singular perturbation analysis to identify which reactions dominate during the successive stages of metabolism, identifying the critical cutoff between safe and overdose cases (Webb et al., 2015). Friedman and Hao augment their extensive prior work on fibrosis in tissues other than the liver (Hao et al., 2014, 2015) to produce a spatial model of liver fibrosis that captures the interactions between a range of resident cells (such as macrophages, T cells and hepatic stellate cells) and mediators known to regulate fibrosis (Friedman & Hao, 2017). With parameters from their previous work, they provide numerical simulations demonstrating the dependence of scar density in liver fibrosis in terms of the concentrations of the regulators TIMP and HA.
Beyond the context of studying liver tissue specifically, there is a growing repertoire of mathematical models of inflammation in published literature, both in the generic sense (with many mechanisms spanning multiple health scenarios) and in relation to specific tissues or ailments. Generally, many of these models take the approach of simplifying the vast and complex interactions of numerous cell types and inflammatory mediators by constructing models that include only the most important cell types (e.g. neutrophils and macrophages) and generic descriptions of pro- and anti-inflammatory mediators that combine the effects of numerous individual mechanisms. For example, the early work of Lauffenburger and coworkers has examined interactions between motile bacteria, a single type of immune cell and a generic inflammatory mediator, with one focus being upon how the spatial migration of immune cells impacts upon the inflammatory outcome (Ladero et al., 2009; Lauffenburger & Keller, 1979). More recently, mathematical models of inflammation have been published that focus on a wide range of tissues/conditions, including applications in wound healing (Waugh & Sherratt, 2006), spinal chord injury (Smith et al., 2019), sepsis (Cockrell & An, 2018) and many others (although, to date, we are not aware of any such models in the context of liver disease). For a more detailed review of previous inflammation models, see Dunster & Nelson [2022]. Of particular relevance to this study are the three previous models of Dunster et al. [ 2014], which describe the interactions of neutrophils (both active and apoptotic), macrophages and pro- and anti-inflammatory mediators in response to generic tissue damage. These ordinary differential equation (ODE) models incorporate a thorough catalogue of interactions and, through dynamical systems analysis and numerical simulations, these models revealed the interactions (and associated parameters) that play the most important role in determining the switch between resolution of damage and chronic injury. In particular, these models identified the phagocytic ability of macrophages and the strength of pro-inflammatory feedback from apoptotic neutrophils as the two most dominant mechanisms in controlling this switch, both of which can be considered as targets for therapeutic interventions. As the models of Dunster et al. [ 2014] form the starting point for the construction of our hepatitis model below, we review the relevant detail of the governing ODEs in the following section. We note that the work of Dunster et al. [ 2014] was also later extended to a spatial setting via partial differential equation (PDE) or agent-based approaches (Bayani et al., 2020a,b) and, while we do not pursue this direction here, the model below is a useful precursor to more complex spatial models of hepatitis progression.
Here, we construct, to our knowledge, the first mathematical model of inflammation as it occurs within, and interacts with the key cell types of, the liver. We start by developing the model (Section 2) before demonstrating its range of outcomes (Section 3). The model is bistable with the healthy response (resolution of damage) being represented by a steady state in which all pro-inflammatory components reach zero, and unhealthy responses (in which liver disease perpetuates) manifesting as either steady states that have positive levels of pro-inflammatory components (with tissue damage also resulting in liver cells being replaced by extracellular matrix (ECM)), or by solutions that oscillate temporally. In Section 4, we use bifurcation analysis to investigate the manner in which variation of key model parameters drives switching between these outcomes, before finally discussing these findings and noting therapeutic implications (in Section 5). Our analysis makes use of the softwares R and Matlab for numerical simulations, and XPP-AUTO for computation of bifurcation diagrams; code to reproduce our numerical simulations and bifurcation analyses can be downloaded from http://github.com/cardiomaths/hepatitisModelling or is available as an archive at time of publication from https://figshare.com/articles/software/hepatitisModelling/$\frac{19740364}{1.}$
## 2. The mathematical model
Our model of hepatitis (depicted in Fig. 1) is intentionally simple: it neglects spatial effects and focuses on the better-known interactions between the liver’s key cell types and those of the acute immune response, neglecting disease-specific details. The inflammatory components of our model are based on earlier work of generic inflammation in a sterile environment (Dunster et al., 2014), these interactions being replicated under damage to the liver (Tanaka & Miyajima, 2016; Torres et al., 2019; Woolbright & Jaeschke, 2017). In this model, populations of active neutrophils (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$n$\end{document}), apoptotic neutrophils (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$a$\end{document}) and macrophages (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$m$\end{document}) interact with pro- and anti-inflammatory mediators (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$c$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$g$\end{document}, respectively). Active neutrophils arrive (at rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\chi _n$\end{document}) in response to pro-inflammatory mediators and die (by apoptosis) at rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu $\end{document}, both of which are influenced by pro- and anti-inflammatory mediator concentrations. Active neutrophils are a source of pro-inflammatory mediators (at rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_n$\end{document}) but this ceases once neutrophils become apoptotic, until they break down (at rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _a$\end{document}), losing their cell membrane via a process called necrosis that spills their contents into the tissue, increasing pro-inflammatory mediator concentrations (at magnitude \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_a$\end{document}).
**Fig. 1.:** *Interactions between the cells and mediators captured in our model of hepatitis. Damage causes a rise in pro-inflammatory mediators, increasing influx of neutrophils and macrophages. Neutrophils (in active or apoptotic form) cause a rise in pro-inflammatory mediators through the release of their toxic content. Macrophages exist in pro- or anti-inflammatory phenotypes which release the associated mediators; both phenotypes remove damaged or dead cells. Pro-inflammatory stimuli damage hepatocytes and activate stellate cells, the later stimulating the production of extracellular matrix (ECM). Dead hepatoctytes are removed by macrophages, being replaced by healthy hepatocytes or ECM, depending on the activation state of stellate cells. Dimensional parameters associated with particular processes are placed next to the relevant arrows and summarized in Table 2.*
The original model of inflammation in a generic context (Dunster et al., 2014) included just a single population of macrophages; however, it is known that hepatic macrophages form highly heterogeneous populations and, while macrophages consist of a mix of recruited and resident cells, we simplify this and instead focus on just two populations of macrophages that are well known to increase in numbers under hepatitis (Isogawa et al., 2005; Pellicoro et al., 2014; Triantafyllou et al., 2018; Torres et al., 2019; Colino et al., 2020; Wang et al., 2021). Our model incorporates a predominantly pro-inflammatory macrophage phenotype, denoted \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$m_i$\end{document}, and an entirely restorative phenotype, denoted \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$m_r$\end{document}. The pro-inflammatory macrophages, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$m_i$\end{document}, secrete pro-inflammatory mediators (at rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m$\end{document}) but also remove dead cells—an anti-inflammatory effect. The restorative macrophages, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$m_r$\end{document}, release anti-inflammatory mediators (at rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_g$\end{document}) and remove dead cells (Pellicoro et al., 2014). The removal of dead cells by the first pro-inflammatory macrophage population is known to promote a switch in macrophage phenotype (modelled with rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_{m1}$\end{document}) to the later restorative population which have a greater phagocytic capacity (Pellicoro et al., 2014; Tang et al., 2021). Following the original model, we include a parameter \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document} to represent the rate of phagocytosis of dead cells and introduce a further dimensionless scaling parameter \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi _2$\end{document} to capture the relative phagocyctic ability of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$m_r$\end{document} macrophages over \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$m_i$\end{document}. The reversal of macrophage phenotypes is controversial; some authors believing that opposing phenotypes derive from populations of monocytes. We follow Pellicoro et al. [ 2014] and include the regression from restorative to pro-inflammatory phenotype, at rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_{m2}$\end{document}. Macrophages are slow to die and we follow our original model and include this at rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _{m}$\end{document} but augment this by including the influence of restorative macrophages on the rate that pro-inflammatory macrophages die (with associated rate parameter \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _{m2}$\end{document}) (Robinson et al., 2016). These modelling assumptions result in the following equations for the inflammatory cells and mediators: (1a)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{align*} \frac{dn}{dt} &= \chi_n\frac{c}{1+\frac{g}{\beta_{gc}}} - \nu\frac{1+\frac{g}{\beta_g}}{1+\frac{c}{\beta_c}} n,\\\frac{da}{dt} &= \nu\frac{1+\frac{g}{\beta_g}}{1+\frac{c}{\beta_c}} n - \gamma_a \,a - \phi\, a\, (m_i + \phi_2\, m_r),\tag{(1b)}\\\frac{dm_i}{dt} &= \chi_m\, c - k_{m1}\, \phi\, a\, m_i + k_{m2}\, m_r - \gamma_m\, m_i\left(1+\gamma_{m2}\,m_r\right),\tag{(1c)}\\ \frac{dm_r}{dt} &= k_{m1}\, \phi\, a\, m_i - k_{m2}\, m_r - \gamma_m\, m_r,\tag{(1d)}\\\frac{dc}{dt} &= k_a\,\gamma_a \frac{a^2}{\beta_a^2 + a^2} + k_n\frac{n^2}{\beta_n^2+n^2} + k_{m}\, m_i - \gamma_c\, c,\tag{(1e)}\\\frac{dg}{dt} &= k_g\, m_r - \gamma_g\, g.\tag{(1f)} \end{align*}\end{document}The liver is comprised predominantly of hepatocytes, approximately $80\%$ by weight, and they are responsible for performing the majority of the liver’s functions (Duarte et al., 2015). Under inflammatory conditions hepatocytes are vulnerable to damage and the liver accumulates excessive ECM, leading to a deterioration in liver function and, if not resolved, ongoing inflammation can lead to cirrhosis and ultimately liver failure (Bataller & Brenner, 2005; Gressner & Weiskirchen, 2006; Dirchwolf & Ruf, 2015; Khailaie et al., 2020). We assume that the structure of the liver tissue itself is fundamentally composed of hepatocytes (both active and damaged, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$h$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$h_a$\end{document}, respectively) and ECM (represented by variable \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$e$\end{document}). We prescribe the following equations for these, which are configured such that the quantity \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$h_T=h+h_a+e$\end{document} is conserved: (1g)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{align*} \frac{dh}{dt} &= \chi_h \,\phi \frac{s}{s_T} h_a (m_i + \phi_2 m_r) + \gamma_e\, e - \nu_2\, h\, c + \gamma_h\, h_a \frac{s}{s_T},\\\frac{dh_a}{dt} &= \nu_2\, h\, c - \chi_h\,\phi\, h_a (m_i + \phi_2\, m_r) - \gamma_h h_a,\tag{(1h)}\\\frac{de}{dt} &= \chi_h\, \phi\, \frac{s_a}{s_T} h_a (m_i + \phi_2\, m_r)- \gamma_e\, e + \gamma_h\, h_a \frac{s_a}{s_T}.\tag{(1i)} \end{align*}\end{document}Above, pro-inflammatory mediators increase hepatocyte damage (at rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2$\end{document}), damaged hepatocytes are removed by macrophages (with rate parameters \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\chi _h$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi _2$\end{document}) and hepatocytes lyse at rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _h$\end{document}. As hepatocytes are lost, they are replaced with either new hepatocytes or ECM, which itself lyses at rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _e$\end{document} being replaced by hepatocytes. The replacement of hepatocytes by ECM is promoted by active hepatic stellate cells. These exist in liver capillaries in a quiescent state (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$s$\end{document}) but, under the influence of pro-inflammatory mediators can transform (with rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$r_1$\end{document}) to a myofibroblast-like phenotype (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$s_a$\end{document}), a process termed activation (Liu et al., 2021). They can also return to their original state in the presence of anti-inflammatory mediators (with rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$r_2$\end{document}) (Liu et al., 2021; Wynn & Barron, 2010). The equations for stellate cells, once again configured such that the total population of stellate cells (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$s+s_a=s_T$\end{document}) are conserved, are as follows: (1j)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{align*} \frac{ds}{dt} &= r_2 s_a \left(1 + \frac{g}{\beta_{gc}}\right) - r_1 s c, \end{align*}\end{document}(1k)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{align*} \frac{ds_a}{dt} &= r_1 s\, c - r_2 s_a \left(1 + \frac{g}{\beta_{gc}}\right). \end{align*}\end{document}Finally, we modify the equations for inflammatory mediators (1e,1f) to reflect positive and negative feedbacks from hepatocytes. When the fraction of active hepatocytes is high, we expect strong production of anti-inflammatory mediators, the rate of production of which we denote as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_h$\end{document} (per cell). Similarly, if the proportion of damaged hepatocytes is high, we expect production of pro-inflammatory mediators at rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_{ha}$\end{document} per cell (Lauffenburger & Kennedy, 1983). Accordingly, we modify (1e,1f) as follows: (1l)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{align*} \frac{dc}{dt} &= k_a\,\gamma_a \frac{a^2}{\beta_a^2 + a^2} + k_n\frac{n^2}{\beta_n^2+n^2}+ k_{ha}\, \frac{h_a}{h_T} + k_{m}\, m_i - \gamma_c\, c,\tag{(1l)}\\\frac{dg}{dt} &= k_g\, m_r + k_{h}\, \frac{h}{h_T} - \gamma_g\, g.\tag{(1m)} \end{align*}\end{document}We close the system [1] by imposing initial conditions \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$n[0]=a[0]=m_i[0]=m_r[0]=g[0]=h_a[0]=e[0]=s_a[0]=0$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$h[0]=h_T$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$s[0]=s_T$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$c[0]=c_0$\end{document}, i.e. we assume that at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$$t = 0$$\end{document} liver tissue is healthy, being composed of a population of active hepatocytes (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$h_T$\end{document}) and quiescent stellate cells (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$s_T$\end{document}) and damage is stimulated by a burst of pro-inflammatory mediators of concentration \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$c_0$\end{document}. A network diagram representing the events incorporated into this system of equations is shown in Fig. 1 while a summary of the model’s variables and parameters are given in Tables 1 and 2, respectively.
This system [1] is nondimensionalized, using tildes to represent dimensionless quantities. In a similar manner to previous work (Dunster et al., 2014) time is scaled so that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\tilde {t} = \gamma _c t$\end{document} and the variables representing immune cells and mediators are scaled as follows: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$$ \begin{align*} c=k_{a}\tilde{c}\text{,}\qquad n=\frac{\chi_n\,k_{a}}{\gamma_{c}}\tilde{n}\text{,}\qquad a=\frac{\chi_n\,k_{a}}{\gamma_{c}}\tilde{a}\text{,}\qquad m_i=\frac{\chi_m k_{a}}{\gamma_{c}}\tilde{m_i}\text{,} \qquad m_r=\frac{\chi_m k_{a}}{\gamma_{c}}\tilde{m_r}\text{,}\qquad g=\beta_{gc}\tilde{g}\text{.} \end{align*}$$\end{document} The remaining variables, representing hepatocytes, stellate cells and ECM, are scaled such that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$$ \begin{align*} h=\frac{h_Tk_a \gamma_c}{k_{ha}}\tilde{h}\text{,}\qquad h_a=\frac{h_Tk_a \gamma_c}{k_{ha}}\tilde{h_a}\text{,}\qquad e=\frac{h_Tk_a \gamma_c}{k_{ha}}\tilde{e}\text{,}\qquad s_=s_T\tilde{s}\text{,}\qquad s_a=s_T\tilde{s_a}\text{.} \end{align*}$$\end{document} Under the above rescalings, [1] transforms to give the following system of eleven dimensionless equations, where tildes are dropped for clarity: (2a)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{align*} \frac{dn}{dt} &= \frac{c}{1+g} - \nu\frac{1+\frac{g}{\beta_g}}{1+\frac{c}{\beta_c}} n,\\\frac{da}{dt} &= \nu\frac{1+\frac{g}{\beta_g}}{1+\frac{c}{\beta_c}} n - \gamma_a a - \phi\, a\, (m_i + \phi_2\, m_r),\tag{(2b)}\\\frac{dm_i}{dt} &= c - k_{m1}\, \phi\, a\, m_i + k_{m2}\, m_r - \gamma_m\, m_i\left(1+\gamma_{m2}\,m_r\right),\tag{(2c)}\\\frac{dm_r}{dt} &= k_{m1}\, \phi\, a\, m_i - k_{m2}\, m_r - \gamma_m\, m_r,\tag{(2d)}\\\frac{dc}{dt} &= \gamma_a \frac{a^2}{\beta_a^2 + a^2} + k_n\frac{n^2}{\beta_n^2+n^2}+ h_a + k_{m}\, m_i - c,\tag{(2e)}\\\frac{dg}{dt} &= k_g\, m_r + k_{h}\, h - \gamma_g\, g,\tag{(2f)}\\\frac{dh}{dt} &= \chi_h\phi \, s \, h_a (m_i + \phi_2 \, m_r) + \gamma_e \, e - \nu_2 \, h \, c + \gamma_h \, h_a \, s,\tag{(2g)}\\\frac{dh_a}{dt} &= \nu_2 \, h \, c - \chi_h\phi \, h_a (m_i + \phi_2 m_r) - \gamma_h \, h_a,\tag{(2h)}\\\frac{de}{dt} &= \chi_h\phi \,s_a \,h_a (m_i + \phi_2 m_r)- \gamma_e \,e + \gamma_h\, h_a\, s_a,\tag{(2i)}\\\frac{ds}{dt} &= r_2 \,s_a (1 + g) - r_1\, s c,\tag{(2j)}\\\frac{ds_a}{dt} &= r_1\, s\, c - r_2\, s_a (1 + g),\tag{(2k)} \end{align*}\end{document}which depends upon the following new dimensionless parameters: (3a)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{gather*} \tilde{\phi}=\frac{\phi\chi_m k_a}{\gamma_c^2}\text{,}\quad \tilde{\nu}=\frac{\nu}{\gamma_c},\quad \tilde{\beta_g}=\frac{\beta_g}{\beta_{gc}},\quad \tilde{\beta_c}=\frac{\beta_c}{k_a},\quad \tilde{\beta_a}=\frac{\beta_a \gamma_c}{\chi_n k_a}\text{,}\quad \tilde{\beta_n}=\frac{\beta_n \gamma_c}{\chi_n k_a}\text{,}\quad \tilde{\gamma}_{a}=\frac{\gamma_{a}}{\gamma_c},\quad \tilde{\gamma}_{e}=\frac{\gamma_{e}}{\gamma_c}, \end{gather*}\end{document}(3b)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{gather*} \tilde{\gamma}_{h}=\frac{\gamma_{h}}{\gamma_c},\quad \tilde{\gamma}_{g}=\frac{\gamma_{g}}{\gamma_c}\text{,}\quad \tilde{\gamma}_{m}=\frac{\gamma_{m}}{\gamma_{c}},\quad \tilde{\gamma}_{m2}=\frac{\gamma_{m2}\chi_m k_a}{\gamma_c},\quad \tilde{k}_{m1}=\frac{k_{m1}\chi_n}{\chi_m},\quad \tilde{k}_{m2}=\frac{k_{m2}}{\gamma_c},\quad \tilde{k}_{m}=\frac{k_{m}\chi_m}{\gamma_c^2}, \end{gather*}\end{document}(3c)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{gather*} \tilde{k}_n=\frac{k_n}{k_a\gamma_c},\quad \tilde{k}_g=\frac{k_g \chi_m k_a}{\beta_{gc}\gamma_c^2},\quad \tilde{k}_{h}=\frac{k_{h}k_{a}}{k_{ha}\beta_{gc}},\quad \tilde{r}_1=\frac{r_1 k_a}{\gamma_c},\quad \tilde{r}_2=\frac{r_2}{\gamma_c},\quad \tilde{\nu}_2=\frac{\nu_2k_a}{\gamma_c}. \end{gather*}\end{document}The initial conditions transform to give \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$n[0]=a[0]=m_i[0]=m_r[0]=g[0]=h_a[0]=e[0]=s_a[0]=0$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$h[0]=1$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$s[0]=1$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$c[0]=c_0$\end{document}, with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$c_0$\end{document} being varied to reflect the severity of the stimulus.
The nondimensional parameter groupings that appear in [2] are summarized in Table 3. We note that obtaining precise values for many of the dimensional parameters is difficult, there being a lack of liver-specific reaction rates in the literature. We therefore carry default baseline values for those parameters appearing in the left-hand side of Table 3 from the previous works of Dunster et al. [ 2014] and Bayani et al. ( 2020a). The rate of transition of macrophages from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$m_i$\end{document} to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$m_r$\end{document} phenotypes is thought to be an order of magnitude greater than the reverse process (Tang et al., 2021). We therefore set \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_{m1}=30$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_{m2}=0.3$\end{document} as our default values for these parameters. The remaining parameters are difficult to infer accurately from the current literature. We expect that the pro-inflammatory feedbacks that appear on the right-hand side of (2e) are of similar magnitudes, in general. While the feedbacks from active/apoptotic neutrophils are known to saturate as these populations increase in size (see Dunster et al., 2014, and the references therein), this is not the case with the macrophage feedback parameterized by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m$\end{document}. Since the population of inflammatory macrophages is, in general, much larger than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mathcal {O}[1]$\end{document} in our model, we expect \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m$\end{document} to be orders of magnitude smaller than other pro-inflammatory rate parameters. We hence set \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m=0.0001$\end{document} in order to preserve a balance between the scales of neutrophil, hepatocyte and macrophage feedbacks in (2e). ( Numerical investigations indicate that such small choices of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m$\end{document} are also necessary to preserve the essential biological bistability in our model, as we will see below.) Intuitively, we expect \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _{m2}\ll 1$\end{document}, and hence set \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _{m2}=0.01$\end{document}, and on the basis that we expect anti-inflammatory mediator production by macrophages and hepatocytes to occur at similar rates, we set \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _h=0.1$\end{document}. Since we expect turnover of hepatocytes (and ECM) to be slower than the rate of apoptotic neutrophil lysis (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _a$\end{document}), but faster than the rate of loss of macrophages (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _m$\end{document}), we set \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _h=\gamma _e=0.1$\end{document}. The parameter \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\chi _h$\end{document} effectively represents a ratio between how quickly macrophages remove apoptotic neutrophils (which occurs at rate proportional to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document}) and how quickly macrophages remove active hepatocytes (which occurs at rate proportional to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\chi _h\phi $\end{document}). On the assumption that these processes are in fact of similar rates, we set \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\chi _h=1$\end{document} by default. We assume that switching between stellate cell phenotypes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$s$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$s_a$\end{document} occurs at similar rates in both directions, and hence set \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$r_1=r_2=1$\end{document} by default. We examine the model’s sensitivity to our choices of these parameter values below.
**Table 3**
| Parameter | Baseline values | Parameter.1 | Baseline values.1 |
| --- | --- | --- | --- |
| \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu $\end{document} | 0.1 | \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2$\end{document} | 0.01 |
| \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document} | 0.1 | \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi _2$\end{document} | 10.0 |
| \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _a$\end{document} | 1.0 | \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\chi _h$\end{document} | 1.0 |
| \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _m$\end{document} | 0.01 | \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _e$\end{document} | 0.1 |
| \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _g$\end{document} | 1.0 | \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _h$\end{document} | 0.1 |
| \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_n$\end{document} | 0.01 | \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _{m2}$\end{document} | 0.01 |
| \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_g$\end{document} | 0.1 | \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_{h}$\end{document} | 0.1 |
| \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\beta _a$\end{document} | 0.1 | \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m$\end{document} | 0.0001 |
| \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\beta _n$\end{document} | 0.1 | \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_{m1}$\end{document} | 30.0 |
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## 3. Permissible outcomes
The model of [2] has the potential to exhibit a range of distinct outcomes, with the switch between these outcomes being driven by the tandem effect of varying initial conditions (i.e. the strength of the damage stimulus) and/or variations in the parameter values that govern the strength of the individual interactions. Some of these outcomes are illustrated in Fig. 2. In Fig. 2(a), we illustrate a healthy (fully resolved) outcome in which (after an initial peak in the concentrations of some inflammatory components) the levels of all pro-inflammatory components ultimately settle at zero. This outcome corresponds to a steady state solution of [2] with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$h=$s = 1$$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$g=k_h/\gamma _g$\end{document} and all other variables equal to zero. ( We investigate the stability of this steady state below.) Alternatively, for some parameter choices, the model can exhibit various types of chronic response: either chronic steady states, with pro-inflammatory variables taking positive values (Fig. 2(b and c)); or sustained oscillations as shown in Figure 2(d). Depending on our choice of parameters, the model may be bistable, with both healthy and chronic outcomes permissible and determined by our choice of initial conditions, or monostable, with either a healthy or chronic outcome being guaranteed for all choices of initial condition, the other being unstable.
**Fig. 2.:** *Typical outcomes produced by the model of (2). Solid/dashed lines respectively show: active/apoptotic neutrophils; \documentclass[12pt]{minimal}
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}{}$c_0=0.3$\end{document}); unspecified parameter values are as given in Table 3.*
Computing the stability of chronic states is intractable from an analytic perspective in general; we hence perform this task numerically via bifurcation analysis using the software XPP-AUTO below. However, we are able to compute the stability of the trivial (healthy) steady state analytically, by constructing the Jacobian matrix corresponding to the system of [2], evaluating it at these steady state values and requiring that all the eigenvalues of the resulting matrix have negative real part for stability. For ease of calculation, we reduce the system to nine equations, noting that the equations for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$e$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$s_a$\end{document} can be removed and these variables replaced by the algebraic expressions \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$$e = 1$-h-h_a$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$s_a=1-s$\end{document} due to conservation. The reduced system provides nine eigenvalues, six of which are negative for all choices of parameters. The remaining three eigenvalues (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\lambda $\end{document}) are given by solutions to the cubic equation [4]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{align*}& \lambda^3+\underbrace{\left(1+\gamma_h+\gamma_m\right)}_{a_2}\lambda^2+\underbrace{\left(\gamma_h+\gamma_m-k_m-\nu_2+\gamma_h\gamma_m\right)}_{a_1}\lambda+\underbrace{\left(\gamma_h\gamma_m-\gamma_h k_m -\gamma_m\nu_2\right)}_{a_0}=0. \end{align*}\end{document} Applying the Routh–Hurwitz stability condition, we ascertain that [4] has three roots of negative real part provided that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$a_0,a_1,a_2>0$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$a_1a_2>a_0$\end{document}. Expanding these conditions provides the following restrictions on parameters, which must all be satisfied in order for a healthy (resolved) outcome to be permissible: (5a)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{gather*} \left(\gamma_h+\gamma_m\right)^2+\left(\gamma_h+\gamma_m\right)\left(\gamma_h\gamma_m+1\right)>k_m\left(\gamma_m+1\right)+\nu_2\left(\gamma_h+1\right), \end{gather*}\end{document}(5b)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{gather*} \gamma_h+\gamma_m+\gamma_h\gamma_m>k_m+\nu_2, \end{gather*}\end{document}(5c)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{gather*} \gamma_m\left(1-\frac{\nu_2}{\gamma_h}\right)>k_m. \end{gather*}\end{document}For the parameters used here, at least, satisfying the final of these inequalities is sufficient to ensure that the other two are satisfied also. Thus, the potential for a healthy (resolved) outcome is determined by a delicate balance between the pro-inflammatory contributions of macrophages (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m$\end{document}), the susceptibility to damage of hepatocytes (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2$\end{document}) and the rates of removal of these cells (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _m$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _h$\end{document}, respectively). For sufficiently large choices of the parameter capturing susceptibility of hepatocytes to damage (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2$\end{document}), in particular, the model is guaranteed to yield a chronic outcome.
Below, we investigate the broader role of each of the key parameters in our model in determining the existence and stability of the various solutions discussed above, focusing in particular upon mechanisms that are (or could be) potential therapeutic targets.
## 4. Bifurcation analysis
Here, we examine the behaviour and outcomes of the model as we vary parameters. Since there are 23 nondimensional parameters in this model, we focus on those parameters that have previously been shown to be key to controlling the switch between healthy and chronic outcomes or represent mechanisms that are under investigation as therapeutic targets for hepatitis, such as the inhibition or promotion of mediators and the prevention of damage to hepatocytes (Koyama & Brenner, 2017). Throughout the following sections, we compute bifurcation diagrams via the numerical continuation software XPP-AUTO. ( For an accessible introduction to this software, see Gandy & Nelson, 2022.)
## 4.1 Varying rates of neutrophil apoptosis and macrophage phagocytosis
The previous works of Dunster et al. [ 2014] and Bayani et al. ( 2020a) identified the rates of neutrophil apoptosis (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu $\end{document}) and the removal of apoptotic neutrophils due to phagocytic action by macrophages (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document}) as key drivers in determining the boundary between bistability and guaranteed resolution of inflammatory damage. We, here, examine the role of these two parameters in our model.
In Fig. 3, we construct bifurcation diagrams that summarize the existence and stability of our various solutions for varying choices of either \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document} or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu $\end{document} (holding all other parameters fixed at the values given in Table 3). In Fig. 3 (and all subsequent bifurcation diagrams), solid/dashed lines demark stable/unstable solutions respectively, with black lines representing steady states and red lines showing the amplitudes of periodic solutions. Unstable solutions are interesting only from a theoretical (dynamical systems) perspective and will not be observed in simulations; it is only the stable solutions that correspond to realistic, observable outcomes. Figure 3(a) illustrates how the stability of our solutions evolves as the parameter for macrophage phagocytosis (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document}) is varied. Here, we see that the healthy steady state (with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$$c = 0$$\end{document}) is stable for all choices of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document} (since the parameter values of Table 3 satisfy the stability condition of (5c) regardless of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document}). The healthy (resolved) outcome is therefore always permissible for these parameter values. For low values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document}, a chronic steady state (with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$c>0$\end{document}) exists and is also stable; the model is therefore bistable for these choices of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document}, with the observed outcome being determined by the magnitude of the damage stimulus imposed by the initial conditions. As \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document} is increased through \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi =\phi _{HB}\simeq 0.04$\end{document}, the chronic steady state becomes unstable via a subcritical Hopf bifurcation (which also gives rise to an unstable periodic solution). For \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi>\phi _{HB}$\end{document}, the model is monostable and damage is guaranteed to resolve since the only stable configuration is the healthy steady state. In Fig. 3(b), we also show the corresponding levels of ECM for the solutions shown in panel (a), since increased levels of ECM are commonly associated with chronic outcomes. Here, lower levels of macrophage phagocytosis (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document}) result in greater numbers of apoptotic neutrophils, and therefore higher levels of pro-inflammatory, more hepatocyte damage and stellate cell activation, and thus greater levels of ECM production via (2i). ( Indeed, we can make similar observations as a function of any other parameter that stimulates (or reduces) pro-inflammatory mediator concentrations, and we revisit this with a particular focus on the effect of hepatocyte damage upon ECM production later in this manuscript.) In Fig. 3(c), we hold fixed \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi =0.1$\end{document} and instead vary the rate of neutrophil apoptosis, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu $\end{document}. We observe similar behaviour to Fig. 3(a), with the model being bistable for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu <\nu _{HB}\simeq 0.006$\end{document}, and monostable (guaranteeing resolution) for larger values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu $\end{document}. In this case, the Hopf bifurcation is supercritical, giving rise to a branch of stable periodic solutions that exist in a narrow window of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu $\end{document}-values, until these are eliminated via a saddle node of periodic orbits (SNPO). In Fig. 3(d), we track the position of this Hopf bifurcation in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$(\phi,\nu)$\end{document}-space, determining regions of bistability (B) and monostability with guaranteed resolution (M:Res). Intuitively, Fig. 3(d) reveals that for very low rates of neutrophil apoptosis, raising the rate of macrophage phagocytosis is insufficient in eliminating the possibility for chronic outcomes (since, in this case, neutrophils remain in their active state for much longer and hence are not removed by macrophages, which only target apoptotic cells).
**Fig. 3.:** *Bifurcation diagrams for the system (2) and the parameters values given in Table 3. In (a–c), solid (resp. dashed) black lines represent stable (resp. unstable) fixed points; solid (resp. dashed) red lines represent stable (resp. unstable) periodic orbits. (a,b) Bifurcation diagrams for values in Table 3, showing \documentclass[12pt]{minimal}
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## 4.2 Responses to reducing pro-inflammatory mediator production
Here, we investigate the model’s response to reductions in the rates of production of pro-inflammatory mediators, focusing in particular upon production by apoptotic neutrophils (via \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _a$\end{document} and dimensional parameter \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_a$\end{document}), active neutrophils (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_n$\end{document}) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$m_i$\end{document} macrophages (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m$\end{document}). In Fig. 4, we show bifurcation diagrams that illustrate the effect of varying each of these parameters in turn. The green arrows in Fig. 4(a–c) illustrate how the curves that bound regions of bistability (or equivalently regions of guaranteed resolution of damage) move in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$(\phi,\nu)$\end{document}-space as each neutrophil-based feedback is reduced, e.g. due to therapeutic intervention.
**Fig. 4.:** *Bifurcation diagrams for the system (2) showing the effect of variations in the strength of pro-inflammatory mediator feedbacks by apoptotic neutrophils (\documentclass[12pt]{minimal}
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}{}$k_a$\end{document}), active neutrophils (\documentclass[12pt]{minimal}
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}{}$k_n$\end{document}) and \documentclass[12pt]{minimal}
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}{}$k_m$\end{document}), for the parameters values given in Table 3. In (a–c), green arrows indicate decreases in the parameter of interest. (a) Positions of Hopf bifurcations for \documentclass[12pt]{minimal}
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}{}$\gamma _a=10$\end{document} (dotted). (b) Positions of Hopf (black) and saddle-node (red) bifurcations for \documentclass[12pt]{minimal}
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}{}$k_a$\end{document} halved/doubled (dash-dotted/dashed respectively) compared with baseline values (solid line). (c) Positions of Hopf bifurcations for \documentclass[12pt]{minimal}
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}{}$k_n=0.1$\end{document} (dotted). (d) The effect of \documentclass[12pt]{minimal}
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}{}$(\phi ,k_m)$\end{document}–space, showing the positions of Hopf (solid black) and saddle-node (solid red) bifurcations affecting chronic steady states, and the transcritical bifurcation affecting the healthy steady state as per (2) (dashed). (Abbreviations: B indicates bistable; M:Res, monostable (resolution); M:Chr, monostable (chronic); FH, Fold–Hopf bifurcation.)*
In Fig. 4(a and b), we examine how reducing the strength of the pro-inflammatory feedback from apoptotic neutrophils shifts the boundary between bistability and resolution. There are two parameters that govern this: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _a$\end{document}, which is the rate of neutrophil necrosis; and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_a$\end{document}, which is a measure of the amount of pro-inflammatory mediator released on this event. ( We note here that the parameter \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_a$\end{document} is dimensional and was scaled out under our nondimensionalisation. Hence, it does not appear in [2] directly; investigating the effect of variations in this parameter therefore requires revisiting some of the dimensionless parameter groups given in [3].) In Fig. 4(a), we observe that gradually decreasing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _a$\end{document} has only a relatively marginal effect upon the system’s outcomes, moving the boundary between bistability and resolution slowly to the left in the figure and hence slightly enlarging the region of parameterspace in which resolution is guaranteed. In Fig. 4(b), we instead investigate the effect of manipulating \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_a$\end{document}. To do this, we start with the baseline set of parameters given in Table 3, represented by the solid line in the figure. To obtain the dash-dotted line in the figure, we then halve \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_a$\end{document}, which corresponds to reducing the parameters \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_g$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_h$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$r_1$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2$\end{document} by a corresponding amount and also doubling \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\beta _c$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\beta _a$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\beta _n$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _{m2}$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_n$\end{document} (due to [3]). Likewise, to obtain the dashed curve, we double \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_a$\end{document} from its original value in Table 3 and make the converse adjustments to the remaining parameters. We then plot the boundary of bistability in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$(\phi,\nu)$\end{document}-space as before. Note that we here plot \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document} on the horizontal axis for clarity of results; however, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document} itself also scales with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_a$\end{document} as per (3a). We observe, here, that reducing the strength of pro-inflammatory mediator production by apoptotic neutrophils via \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_a$\end{document} results in an enlargement of the region of guaranteed resolution, as the boundary is shifted left in the figure. We note, however, that the position of this boundary scales roughly linearly with the extent to which \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_a$\end{document} is varied. Plotting \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi /k_a$\end{document} on the horizontal axis would yield a figure qualitatively similar to that of panel (a). It is interesting, from a dynamical systems perspective, to note that for the larger choice of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_a$\end{document} (dashed line) and some small values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu $\end{document} there exist two Hopf bifurcations that bound a narrow window of oscillations. Additionally, for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _a=10$\end{document} in Fig. 4(a) or for the larger choice of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_a$\end{document} in Fig. 4(b), we observe the tangential collision of a branch of Hopf bifurcations with a branch of saddle-nodes (shown in red). This collision point is a Fold–Hopf (FH) bifurcation and results in the Hopf bifurcation itself being eliminated. The associated dynamics are not particularly significant here, since they involve changes in the number of additional unstable chronic branches. In Fig. 4, we have only plotted the sections of the saddle-node branches that bound our bistability region, for clarity.
In Fig. 4(c), we show the effect of reducing the pro-inflammatory feedback of active neutrophils (with parameter \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_n$\end{document}). This has a more significant impact upon the observed monostable and bistable regions, with a reduction of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_n$\end{document} driving the boundary between these in the direction of the green arrow shown, with guaranteed resolution of damage in a growing region of parameterspace. In particular, as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_n$\end{document} is reduced, the system is able to guarantee recovery from damage for successively weaker levels of macrophage phagocytic ability (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document}).
In Fig. 4(d), we show how the production of pro-inflammatory mediators by macrophages (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m$\end{document}) affects the outcomes of the model. We have already seen, in (5c) above, that large choices of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m$\end{document} result in the healthy (fully resolved) steady state being unstable. This therefore results in an area of parameterspace (in this case, the region \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m>0.009$\end{document}) in which chronic damage is the only permissible outcome. Regardless of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document}, reducing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m$\end{document} through this threshold results in the healthy steady state becoming stable. For \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi \lesssim 0.48$\end{document}, the chronic steady state also remains stable initially, resulting in a region of bistability in which the outcome depends upon the magnitude of the damage stimulus. For \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi \gtrsim 0.48$\end{document}, the chronic steady state is destabilized immediately as the healthy configuration becomes stable. For all choices of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m<0.009$\end{document} here, the production of pro-inflammatory mediators via \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m$\end{document} is sufficiently weak that it can be overcome by sufficiently strong macrophage phagocytic ability, yielding a region of guaranteed resolution (M:Res). This region is bounded above by branches of either Hopf or saddle-node bifurcations, which meet tangentially at a FH bifurcation as described above. ( Again, we only plot the portions of the saddle-node branches that bound our region of bistability, here, for clarity).
## 4.3 Stimulating production of anti-inflammatory mediators
In Fig. 5, we illustrate the effect of increasing the levels of anti-inflammatory mediators by macrophages (via \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_g$\end{document}) and hepatocytes (via \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_h$\end{document}). Again, we illustrate how the boundary between regions of bistability (B) and guaranteed resolution (M:Res) moves in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$(\phi,\nu)$\end{document}-space as we vary each of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_g$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_h$\end{document} individually. In Fig. 5, the green arrow indicates the effect of increasing each of these parameters (which is akin to increasing the rates of production of anti-inflammatory mediators). Essentially, the effect of increasing either \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_g$\end{document} or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_h$\end{document} is very similar to that observed on decreasing the production of pro-inflammatory mediators above (in Fig. 4), with the boundary of the bistable region begin shifted left (and down) in both panels of Fig. 5. That is, if the levels of anti-inflammatory mediators present in the system are increased, the system can withstand reductions in the phagocytic ability of macrophages (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document}), with resolution of damage being guaranteed for smaller values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document}. ( Once again, the boundaries of the bistable region are generally branches of Hopf bifurcations, although it is possible in some cases that these branches collide tangentially with branches of saddle-nodes at FH points, as marked by FH in Fig. 5.)
**Fig. 5.:** *Bifurcation diagrams for the system (2) showing the effect of variations in the strength of anti-inflammatory mediator feedbacks by \documentclass[12pt]{minimal}
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}{}$k_h$\end{document}), for the parameters values given in Table 3. Green arrows indicate increases in the parameter of interest. (a) Positions of Hopf bifurcations for \documentclass[12pt]{minimal}
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}{}$k_h=1$\end{document} (dash-dotted). (Abbreviations: B, bistable; M:Res, monostable (resolution); FH, Fold–Hopf bifurcation.)*
It is perhaps evident, here, that up-regulating anti-inflammatory mediator production by macrophages or hepatocytes has a greater effect on the system than does down-regulating pro-inflammatory mediator production via neutrophils (cf. Fig. 4(a and c)); this is evident in that the shift in the boundary of the bistable region is more substantial in Fig. 5 (for the parameters studied here, at least). It is perhaps pertinent to note that the effects of manipulating pro-inflammatory feedbacks from neutrophils or anti-inflammatory feedbacks from macrophages or hepatocytes are all manifest in changes to the stability of chronic steady state and therefore present switches from monostable (resolved) to bistable only. Manipulation of the pro-inflammatory feedback from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$m_i$\end{document} macrophages (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m$\end{document}) plays a much stronger role in determining outcomes, however, since this parameter also has an effect upon the stability of the healthy steady state, disruption of which can result in guaranteed chronicity (M:Chr in Fig. 4(d)).
## 4.4 Hepatocyte damage
In this section, we examine the impact that hepatocyte damage (i.e. stimulated hepatocyte apoptosis) has upon the outcomes of our model. The primary parameter of relevance here is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2$\end{document}, which captures the susceptibility of hepatocytes to damage. Increasing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2$\end{document} results in an increase in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$h_a$\end{document} (the number of hepatocytes) which provides a stimulated pro-inflammatory feedback via the third term in the right-hand side of equation (2e). In parallel to this, the corresponding reduction in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$h$\end{document} (the number of healthy hepatocytes) reduces the strength of the anti-inflammatory feedback given by the second term in the right-hand side of (2f). Intuitively, we thus expect stimulated hepatocyte damage to worsen the inflammatory outcome.
We have already established, in (5c), that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2$\end{document} plays a strong role in determining the permissible long-term outcomes of our model, since variations in this parameter directly impact upon the stability of the healthy steady state. Equation (5c) reflects that the key players in determining, in tandem, whether a resolved outcome is permissible are macrophages and hepatocytes, with associated feedback parameters \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2$\end{document} and decay parameters \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _m$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _h$\end{document}, respectively. It is the combined contribution of these cell groups that determines whether a healthy (resolved) outcome can present; if the pro-inflammatory contributions of hepatocytes are sufficiently upscaled (via \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2$\end{document}), then, unless there is a compensating reduction in the macrophage feedback (via \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m$\end{document}), the healthy steady state will become unstable and chronic outcomes become certain. ( We may also make a similar (converse) argument regarding manipulation of the decay parameters \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _m$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _h$\end{document}.) In Fig. 6, we illustrate bifurcation diagrams that show how variations in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2$\end{document} effect not only the stability of the healthy steady state but also the nature of chronic outcomes. In Fig. 6(a), we show a bifurcation diagram that plots the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$c$\end{document}-coordinate of steady states and periodic orbits as a function of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2$\end{document} for the parameter values given in Table 3. As we know from (5c), the healthy steady state loses stability at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2=0.099$\end{document}: for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2<0.099$\end{document}, the healthy steady state is stable and healthy outcomes are guaranteed; for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2>0.099$\end{document}, the healthy steady state is a saddle and only chronic outcomes (either steady state or periodic) are permissible. The branch of chronic steady states undergoes a supercritical Hopf bifurcation at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2\simeq 1.5$\end{document}. For \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2\gtrsim 1.5$\end{document}, a stable chronic steady state exists and is the guaranteed outcome. For \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2\lesssim 1.5$\end{document}, the chronic steady state is unstable and surrounded by a stable periodic orbit, whose amplitude grows as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2$\end{document} is further decreased, until the periodic orbit eventually collides with a stable manifold of the saddle that represents the healthy steady state; the periodic orbit is there eliminated via a homoclinic bifurcation. Figure 6(b) shows the levels of ECM corresponding to the branches of Fig. 6(a), with large values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2$\end{document} resulting in chronic steady states with ECM levels elevated by approximately $10\%$–$20\%$. ( These values are broadly consistent with the experimental observations reported in Bedossa & Paradis, 2003, which suggest that fibrotic liver tissue can be comprised of approximately $15\%$ ECM.) In Fig. 6(c), we show a similar bifurcation diagram, but for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi =0.25$\end{document} (and all other parameters as in Table 3). We once again observe switching between healthy steady states, chronic steady states and oscillatory solutions; however, for these parameters, the oscillations lie between two Hopf bifurcations, rather than being eliminated via collision with the healthy steady state as in Fig. 6(a). Figure 6(d) illustrates corresponding ECM levels; as we have already observed in Fig. 3(c), larger values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document} seem to correlate with lower levels of ECM deposition. As shown in Fig. 6(e), the branch of Hopf bifurcations can be traced in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$(\nu _2,\phi)$\end{document}-space and, together with the transcritical bifurcation of (5c), bounds a self-contained region of parameterspace in which oscillatory solutions exist. The size of this region of oscillations scales inversely with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m$\end{document}, as shown in Fig. 6(f); as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m$\end{document} is increased, the transcritical bifurcation of (5c) moves left in the figure, while the region of oscillations decreases in size. For sufficiently large increases in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m$\end{document}, beyond the point marked FH in Fig. 4(d), oscillations are eliminated entirely as the corresponding Hopf bifurcation collapses onto a branch of saddle nodes, as per Fig. 4(d). The amplitude and wavelength of these oscillatory solutions can be controlled by varying the remaining model parameters (as shown for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2=0.5$\end{document} in Fig. 7). For the parameters studied here, it seems that the amplitude of these oscillations is predominantly controlled by a balance between the strength of pro-inflammatory feedbacks from neutrophils (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\beta _a$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _a$\end{document}), the negative feedbacks of macrophages via anti-inflammatory mediators (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_g$\end{document}) and the ability of macrophages to remove apoptotic neutrophils via phagocytosis (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi _2$\end{document}). Conversely, the wavelength of these solutions is most strongly affected by the necrosis (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _a$\end{document}) and macrophage decay (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma _h$\end{document}) parameters, as well as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\chi _h$\end{document}, which is related to the relative ability of macrophages to remove damaged hepatocytes, compared with apoptotic neutrophils. It appears, therefore, that hepatocyte damage plays a strong role in the existence of oscillatory solutions via \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2$\end{document}, but the nature of these oscillations themselves are predominantly controlled by mechanisms related to macrophage–neutrophil interactions.
**Fig. 6.:** *Bifurcation diagrams for the system (2), showing the role of the hepatocyte apoptosis rate parameter \documentclass[12pt]{minimal}
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}{}$\nu _2$\end{document}. In (a–d), solid (resp. dashed) black lines represent stable (resp. unstable) fixed points; solid (resp. dashed) red lines represent stable (resp. unstable) periodic orbits. (a,b) Bifurcation diagrams for the parameters given in Table 3. For \documentclass[12pt]{minimal}
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## 5. Conclusion
Hepatitis is associated with ongoing inflammation, but the inflammatory infiltrate is normally short lived and resolves without impacting liver tissue. If inflammation is allowed to persist, then scar tissue is formed that can progress to cirrhosis characterized by a loss of liver function and can ultimately lead to liver failure.
We have developed a model of hepatitis that captures the interactions between the liver’s key cell types and the acute immune response. The model has two fundamental steady states, representing disparate outcomes. Firstly, there exists a trivial solution, where after a stimulus the inflammatory components settle to zero and the cells of the liver return to their original positive values. Secondly, for some parameters, there may be a second steady state where the inflammatory components settle to a positive value and the liver’s key cell types fail to return to healthy values. We equate these steady states to healthy resolution and ongoing, self-perpetuating liver damage, respectively. Depending on our choice of parameters values, our model may exhibit guaranteed resolution of damage, with only the resolved state stable; guaranteed chronicity, with the resolved state being unstable; or the model may be bistable with the outcome attained being determined by the severity of the damage stimulus. We have explored the effects that perturbation of parameters has on model outcomes via numerical simulation and bifurcation analysis and used the model to identify how individual parameters govern the behaviour of the system.
We found that bistability and hysteresis arise in large regions of parameter space. In Fig. 3, we investigated the role of two key mechanisms known for their variability under the influence of inflammatory conditions on model outcomes; these are the rate that macrophages remove apoptotic neutrophils (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document}) and the rate that neutrophils die via apoptosis (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu $\end{document}). As in previous work on generic inflammation, we found that when macrophages are inefficient at removing dead cells the model is bistable but increasing macrophage efficiency ensures resolution to a healthy outcome. This supports recent work that investigates the use of resolvins and protectins, a class of autacoids that can increase rates of macrophage phagocytosis, as therapeutics for non-alcoholic fatty liver disease (Remien et al., 2012). Neutrophils release toxic contents both when active and apoptotic, the latter occurring under necrosis. This dichotomy makes it unclear intuitively if neutrophil apoptosis has a pro- or anti-inflammatory effect. Here, under low levels of neutrophil apoptosis the system is bistable but again shifts to a monostable outcome, that ensures resolution, at higher values. The dependence between neutrophil apoptosis and macrophage phagocytosis was shown and highlights that the anti-inflammatory effect of macrophages is neutralized under low levels of neutrophil apoptosis, a situation known to occur in inflammatory conditions.
In Fig. 4, we investigated the influence of the many different sources of pro-inflammatory mediators on regions of bistability and resolution. We found that the ability to down-regulate any one mechanism increases regions of resolution. But, we found that controlling the production of pro-inflammatory mediators from macrophages was particularly important. Excess production of these mediators from macrophages shifts the model into regions of parameterspace where self-perpetuating liver disease is guaranteed, highlighting the therapeutic benefit of targeting this mechanism, which is under investigation for the treatment of acute liver failure (Wang et al., 2021). The therapeutic use of macrophages modified in vitro to generate proresolution features has been postulated for fibrotic liver disease (Pellicoro et al., 2014). Interestingly, our model indicates that stimulation of anti-inflammatory mediator production via restorative macrophages (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_g$\end{document}) constitutes one effective switch to promote guaranteed resolution of damage (see Fig. 5(a)); this is in direct agreement with the conclusions of Pellicoro et al. [ 2014].
Damage to hepatocytes, which comprise $80\%$ of a healthy liver, occurs not only under rising levels inflammatory mediators but as a direct result of the toxicity of some chemicals and medications, such as acetaminophen (paracetamol), a common cause of liver failure. As such, methods to protect hepatocytes from damage is under multiple investigations (Koyama & Brenner, 2017; Boland et al., 2020). While our current model simplifies hepatocyte damage to a single inflammatory mechanism we showed, in Fig. 6, the effect of variation in this mechanisms finding that it, alongside macrophage function, is key to determining if resolution of inflammation, and hence liver damage, is possible.
The emergence of oscillatory dynamics is of interest, not only from a mathematical perspective but also biologically in the ongoing search for interventions. For example, oscillations in serum ferritin (a marker of a chronic inflammatory state) are associated with antiviral therapy in chronic hepatitis C (Kronborg et al., 2021); oscillations in interleukin IL-2 (a pro-inflammatory mediator) are reported in adaptive strategies for IL-2 therapies (which are of relevance to hepatitis treatment) (Ju & Tacke, 2016); and (although not explicitly modelled here), oscillatory dynamics in T cell functions have been associated with hepatitis B virus (Hilscher & Shah, 2020). Our analyses have illustrated (in Section 4.4 in particular) that this model emits oscillations for a wider range of parameter values than does the previous model of generic inflammation of Dunster et al. [ 2014], with the rate of hepatocyte damage (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2$\end{document}) being the parameter that (in tandem with the macrophage phagocytosis parameter, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\phi $\end{document}) exhibits the strongest influence over the existence of oscillations (as illustrated in Fig. 6). Furthermore, in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$(\nu _2,\phi)$\end{document}-space, the size of the region of oscillatory solutions scales inversely with the rate that macrophages produce pro-inflammatory mediators, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k_m$\end{document}. It is notable that, while the existence of oscillatory solutions is strongly linked to the scale of hepatocyte damage (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\nu _2$\end{document}), the scale (amplitude) and timing (wavelength) of these oscillations seems to be predominantly governed by mechanisms related to macrophage–neutrophil interactions (as shown in Fig. 7).
There are many ways in which our mathematical model could be extended and improved going forward. In the model presented here, we adopt a relatively simplistic description of the very diverse range of cell types and mediators that play a role in maintaining liver health. For example, while our model includes two opposing macrophage phenotypes (which may broadly be regarded as representative of entirely pro- or anti-inflammatory populations), in reality the range of macrophage phenotypes is known to be much broader than this (Dunster, 2016). While the model could be enhanced to account for more macrophage phenotypes (or perhaps a continuous spectrum of behaviours), we note that this would present numerous challenges with regard to accurate parameterization of the model—a task which is already relatively complex under our current two-phenotype description. Our model also neglects other cell types that undoubtedly play a role in disease progression, e.g. T cells and platelets. Platelets, in particular, are known to effect multiple (and often contradicting) mechanisms that underlie hepatitis (Chauhan et al., 2016; van der Heide et al., 2019). They can increase migration of cells, such as neutrophils, into the liver thus amplifying liver damage, and modify the hepatic cellular and cytokine milieu driving both hepatoprotective and hepatotoxic processes (Chauhan et al., 2016). Platelets from different donors can be stratified into a range of functional phenotypes that could well play a role in determining disease progression (Dunster et al., 2021). Investigation of how these divergent platelet effects impact upon the dynamics of our model remains one of our primary targets for future work. Our model also neglects explicit descriptions of individual cytokines such as interleukins IL–2, IL–10 and IL–12 and IFN–\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\gamma $\end{document}, instead grouping these into generic mediators. Our analysis here has focussed on the outcomes of hepatitis (i.e. the switch between resolution, chronic damage and oscillatory outcomes); however, it is pertinent to note that dynamical models can also offer insight into the timing of events that are often hard to observe experimentally. Experimental data to date have shown that a typical inflammatory timecourse comprises an early peak in neutrophil numbers, followed by a peak in inflammatory macrophages, and later followed by peaks in restorative macrophages (Tang et al., 2021). While our simulations show neutrophil peaks preceding those of macrophages, as expected, some of our simulations predicted restorative macrophage phenotypes reaching peak levels slightly before pro-inflammatory phenotypes, which contradicts current evidence. Investigation of this particular facet of the model indicate that the timing of these macrophage peaks can be manipulated by modification of parameter values, with saturation constants, in particular, seeming to play a key role. However, since performing this type of model tuning in a meaningful manner would require a greater quality of parameterization data than is currently available, this has not been a focus of our current analysis and remains one target for future consideration. While the simplicity of our model allowed us to reduce the model’s parameters and focus on what are thought to be the dominant mechanisms, in order to facilitate analytical progress, the model could be easily extended to incorporate more detailed descriptions of these aspects as corresponding clinical or research data become available. Finally, our model neglects a spatial description of the liver’s structure and hence does not account for positions of key cell types and geometric aspects of cell recruitment and mediator spreading. A natural extension to our model would be to incorporate these spatial descriptions. This could involve moving to either a PDE or agent-based modelling approach, both of which have been previously considered in a more generic inflammatory setting by e.g. Bayani et al. ( 2020a,b). The agent-based approach is particularly favourable here, as it has previously been shown to provide more realistic descriptions of chemotactic migration by immune cells, and the role this plays in resolving inflammatory damage Bayani et al. ( 2020b). Again, this remains a target for future study.
Given that there are currently no disease-modifying treatments for hepatitis and that there is a significant lack of sufficient liver biopsy samples, mouse models and biomarkers to provide data on the progression of the disease and the state of inflammation in the liver (Lauffenburger & Kennedy, 1983), we conjecture that mathematical and computational models of hepatitis can play a pivotal role in understanding the complex mechanisms that govern liver disease. Going forward, mathematical models such as that presented here offer great scope in addressing questions around which mechanisms are the optimal targets for therapeutic interventions, and also how best to strategize dosing frequencies and scales, for instance. While we have begun to address some of these questions here, there is a great deal of opportunity to further develop this model and others in the future, with scope for model development to guide the collection of experimental and clinical data, with these data influencing model design, parameterization and validation in tandem.
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|
---
title: Rural and socioeconomic differences in the effectiveness of the HEART Pathway
accelerated diagnostic protocol
authors:
- James C. O'Neill
- Nicklaus P. Ashburn
- Brennan E. Paradee
- Anna C. Snavely
- Jason P. Stopyra
- Greg Noe
- Simon A. Mahler
journal: Academic Emergency Medicine
year: 2023
pmcid: PMC10009897
doi: 10.1111/acem.14643
license: CC BY 4.0
---
# Rural and socioeconomic differences in the effectiveness of the HEART Pathway accelerated diagnostic protocol
## Abstract
### Background
The HEART *Pathway is* a validated accelerated diagnostic protocol (ADP) for patients with possible acute coronary syndrome (ACS). This study aimed to compare the safety and effectiveness of the HEART Pathway based on patient rurality (rural vs. urban) or socioeconomic status (SES).
### Methods
We performed a preplanned subgroup analysis of the HEART Pathway Implementation Study. The primary outcomes were death or myocardial infarction (MI) and hospitalization at 30 days. Proportions were compared by SES and rurality with Fisher's exact tests. Logistic regression evaluated for interactions of ADP implementation with SES or rurality and changes in outcomes within subgroups.
### Results
Among 7245 patients with rurality and SES data, $39.9\%$ ($\frac{2887}{7245}$) were rural and $22.2\%$ were low SES ($\frac{1607}{7245}$). The HEART Pathway identified patients as low risk in $32.2\%$ ($\frac{818}{2540}$) of urban versus $28.1\%$ ($\frac{425}{1512}$) of rural patients ($$p \leq 0.007$$) and $34.0\%$ ($\frac{311}{915}$) of low SES versus $29.7\%$ ($\frac{932}{3137}$) high SES patients ($$p \leq 0.02$$). Among low‐risk patients, 30‐day death or MI occurred in $0.6\%$ ($\frac{5}{818}$) of urban versus $0.2\%$ ($\frac{1}{425}$) rural ($$p \leq 0.67$$) and $0.6\%$ ($\frac{2}{311}$) with low SES versus $0.4\%$ ($\frac{4}{932}$) high SES ($$p \leq 0.64$$). Following implementation, 30‐day hospitalization was reduced by $7.7\%$ in urban patients (adjusted odds ratio [aOR] 0.76, $95\%$ confidence interval [CI] 0.66–0.87), $10.6\%$ in low SES patients (aOR 0.68, $95\%$ CI 0.54–0.86), and $4.5\%$ in high SES patients (aOR 0.83, $95\%$ CI 0.73–0.94). However, rural patients had a nonsignificant $3.3\%$ reduction in hospitalizations.
### Conclusions
HEART Pathway implementation decreased 30‐day hospitalizations regardless of SES and for urban patients but not rural patients. The 30‐day death or MI rate was similar among low‐risk patients.
## INTRODUCTION
Emergency departments (ED) in the United States see 7–9 million patients with acute chest pain annually. 1, 2 Care of these patients is focused on accurate diagnosis of acute coronary syndrome (ACS) and costs an estimated 10 billion dollars annually. 3, 4, 5, 6, 7 However, these costly and lengthy evaluations ultimately diagnose fewer than $10\%$ with ACS. To address these inefficiencies, accelerated diagnostic protocols (ADPs), such as the history, electrocardiogram (ECG), age, risk factors, and troponin pathway (HEART Pathway), have been developed to improve ACS risk stratification and lower costs. 8, 9, 10, 11 The HEART *Pathway is* a validated and widely used ADP for the risk stratification of ED patients with chest pain. 12, 13, 14 In prior studies, the HEART Pathway safely increased early discharges from the ED, decreased cardiac testing, hospital length of stay, and costs. 14, 15, 16 Thus, its safety and effectiveness have been well established, including in important subgroups such as women and African Americans. However, its performance has yet to be tested among rural and low socioeconomic status (SES) patients.
Patients living in rural areas and those living with poverty have significant cardiovascular health disparities compared to their urban and high SES counterparts. 17, 18, 19, 20 Since the 1980s, patients in rural areas have suffered what has been called the “rural mortality penalty,” with increased morbidity and mortality from the same illnesses as their urban counterparts. 21, 22, 23 The American Heart Association published a call to action to systematically reduce rural health care disparities. 24 Lower SES is also associated with decreased life expectancy and higher rates of ACS. 25, 26 Patients with low SES are more likely to experience a cardiovascular event and have worse outcomes than their high SES counterparts. 27, 28 Given these well‐known disparities, we seek to determine whether the safety and effectiveness of the HEART Pathway differs among rural versus urban patients and for patients with low versus high SES.
## Study design and oversight
This study was a preplanned secondary analysis of the HEART Pathway Implementation Study. Participants were recruited from November 2013 to January 2016 after approval by the Wake Forest University Health Sciences Institutional Review Board. The trial was registered with clincaltrials.gov (NCT02056964). The HEART Pathway Implementation Study's methods have been previously described. 29 This article follows the Strengthening and Reporting of Observational Studies in Epidemiology (STROBE) guidelines. 30
## Study setting and population
The study was conducted at three hospitals in North Carolina: one large urban academic medical center with approximately 114,000 ED visits annually; a freestanding ED in a rural county with approximately 12,000 annual ED visits; and a small, rural county community hospital with approximately 37,000 annual ED visits. The study population included patients ≥21 years of age being investigated for possible ACS. Patients with ST‐segment elevation myocardial infarction (STEMI) were excluded. Patients without rurality and SES data were excluded from this analysis. A patient flow diagram is included as Figure S1.
At the academic medical center and freestanding ED, participants were accrued into the preimplementation (November 2013–October 2014) or postimplementation (February 2015–January 2016) cohort. The community ED accrued patients in the preimplementation cohort from January 2015–July 2015 and the postimplementation cohort from August 2015–January 2016. Patients were accrued into their cohort based on their first ED visit for possible ACS. To prevent repeat ED users/high ED utilizers from being overly represented in the preimplementation cohorts, patients with an ED visit for possible ACS at each site the year before the study were excluded ($$n = 523$$). For transfers, patients' care at the receiving hospital was considered part of their index encounter.
## Data collection
Index encounter data were extracted from the electronic health record (EHR; Clarity‐Epic Systems Corporation). EHR variables or diagnoses and procedure codes (CPT, ICD‐9, and ICD‐10) were used to obtain patient demographics, comorbidities, troponin results, HEART Pathway assessments, dispositions, diagnoses, and vital status. For 30‐day outcomes, data were collected from the EHR, insurers' claims data, and state death index data. Patients using Blue Cross Blue Shield (the dominant insurer in North Carolina), MedCost, and North Carolina Medicaid had claims information available for study use. Death index data from the North Carolina State Center for Health Statistics was used.
## HEART Pathway implementation
The HEART Pathway ADP was fully integrated into EPIC as an interactive clinical decision support (CDS) tool. All adult patients presenting with chest pain who had at least one troponin ordered during the postimplementation period had an interruptive pop‐up alert for the HEART Pathway CDS in the EHR. In addition, the HEART Pathway tool was integrated into an EHR flowsheet, which provided manual access to the HEART Pathway for patients presenting with other symptoms concerning for ACS, such as dyspnea or arm pain.
The HEART Pathway CDS tool prompted providers to answer a series of questions to prospectively risk stratify patients in real‐time. Patients with known coronary artery disease (CAD; prior MI, prior coronary revascularization, or coronary stenosis ≥ $70\%$) or acute ischemic ECG changes (e.g., new T‐wave inversions or ST‐segment depression in contiguous leads) were immediately classified as non–low risk.
Among patients without STEMI, known CAD, or acute ischemic ECG changes, providers answered additional flowsheet questions to determine a history, ECG, age, and risk factor score (HEAR score), calculated based on the HEART Pathway trial algorithm (Impathiq Inc.). 14 The HEART Pathway risk assessment was automatically calculated based on the HEAR score and 0‐ and 3‐h troponin measures. Patients with HEAR scores ≤ 3 and without elevated troponin measures were classified as low risk and recommended for discharge without stress testing or coronary imaging. During the postimplementation period, discharged low‐risk patients were asked to follow up with their primary care physician within 30 days. Patients with a HEAR score ≥ 4, an elevated troponin, known CAD, or ischemic ECG changes were classified as non–low risk and designated for further testing (Figure 1). During the preimplementation period, the HEART Pathway CDS tool was not available to providers and HEAR scores were not recorded. Serum troponin was measured throughout the study period using the ADVIA Centaur platform TnI‐Ultra assay (Siemens) or the Access AccuTnI+3 assay (Beckman Coulter). The package insert 99th percentile upper reference limit for each assay was used to determine a positive result at 0 and 3 h.
**FIGURE 1:** *The HEART Pathway algorithm. CAD, coronary artery disease; STEMI, ST‐segment elevation myocardial infarction*
## Rurality and SES
Consistent with prior studies, rurality (urban vs. rural location) and high versus low SES were determined using patient ZIP codes and geographic identifiers (GEOIDs). Uniform Data System (UDS) crosswalk files from 2010 were used to link a patient's ZIP code to the corresponding ZIP Code Tabulation Area 5‐Digit (ZCTA5). Each ZCTA5 was classified as either urban or rural based on census defined urbanized areas (UAs) and urban clusters (UCs). If at least $50\%$ of the population in a ZCTA5 lived in a UA or UC then the ZCTA5 was classified as urban; otherwise it was classified as rural. 31, 32, 33, 34, 35, 36 SES was determined using national area deprivation index (ADI) percentiles from the University of Wisconsin School of Medicine and Public Health. 37 The ADI is a validated, factor‐based deprivation index that uses 17 poverty, education, housing, and employment indicators drawn from U.S. Census data to measure the SES of a census tract. 19, 20, 38, 39, 40 GEOIDs from the U.S Census Bureau were linked to ADI percentiles when available. For patients who were missing a GEOID, ZIP+4 codes were linked to ADI percentile. Consistent with previous research, an ADI percentile at or above 85 was classified as low SES and below 85 was high SES. 39, 41, 42
## Outcomes
The primary safety outcome was the composite of all‐cause death or MI at 30 days (inclusive of index visit). Coronary revascularization, a secondary endpoint, was defined as coronary artery bypass grafting or percutaneous coronary intervention. MI and coronary revascularization were determined using diagnosis and procedure codes validated by prior cardiovascular trials. 14, 16, 43, 44, 45, 46 Major adverse cardiac events, the composite of all‐cause death, MI, or coronary revascularization, was also evaluated as a secondary endpoint.
The primary effectiveness outcome was hospitalization at 30 days (from index visit through 30‐day follow‐up). Hospitalization was defined as an inpatient admission, transfer, or observation stay (including index observation unit care). Secondary outcomes included objective cardiac testing (OCT; stress testing, coronary computed tomography angiography, or invasive coronary angiography) at index and through 30 days of follow‐up as well as early discharge rates, defined as the proportion of patients discharged from the ED without OCT. In the postimplementation period, nonadherence to the HEART Pathway was also determined, where nonadherence was defined as low‐risk patients receiving OCT or hospitalization or non–low‐risk patients receiving early discharge from the ED.
## Statistical analysis
Statistical design for the HEART Pathway Implementation *Study is* described previously. 13, 14 Patient characteristics were described by pre‐ and postimplementation cohorts within each subgroup. Rurality, SES, and 30‐day death or MI proportions were compared between pre‐ and postimplementation cohorts using chi‐square tests. Postimplementation, the percentage of patients identified as low risk and non–low risk were calculated to determine the sensitivity, specificity, and positive and negative predictive of the HEART Pathway for death and MI. Corresponding $95\%$ exact binomial confidence intervals (CI) were computed. For positive and negative likelihood ratios, $95\%$ CIs were calculated using the method of Simel et al. 47 The proportion of patients classified as low risk was compared between subgroups using Fisher's exact test. Nonadherence to the protocol was compared between subgroups using a chi‐square test.
Unadjusted logistic regression and absolute percentage differences were used to evaluate the relationship between pre‐ and postimplementation periods and study outcomes within each subgroup. Multivariable logistic regression models were then used to adjust for potential confounders, which were selected a priori: age, sex, race, insurance status, enrollment site, smoking, body mass index (BMI), rurality (for SES subgroups only), SES (for rurality subgroups only), and presence of chest pain versus other symptoms concerning for ACS (EHR flowsheet use). To test for significant differences in implementation between rurality and SES, logistic regression models were fit using the overall population including rurality by implementation cohort (pre vs. post) or SES by implementation cohort interaction terms. The same potential confounders were included in these models. Absolute percentage differences with $95\%$ CIs were computed between subgroups for hospitalization, OCT, and early discharge in the pre‐ and postimplementation cohorts separately.
Patients were excluded from all analyses if they were missing GEOID or ZIP code as this was required to classify patients based on rurality and SES. Under the assumption that these data were missing at random, the complete case analysis (with adjustment) should provide unbiased estimates. This assumption is reasonable given that missing data were due to addresses being incomplete or erroneous or there being a mismatch between address, city, or state. Patients with missing HEAR scores were not excluded from any pre‐ versus postimplementation analyses.
## RESULTS
The HEART Pathway Implementation Study accrued 8474 patients over 24 months, of which 7245 had complete rurality and SES data (Figure 1). The cohort was $53.7\%$ ($\frac{3889}{7245}$) female and $33.9\%$ ($\frac{2454}{7245}$) non‐White with a median (IQR) age of 55 (45–66) years. There was a higher proportion of rural patients in the preimplementation cohort compared to the postimplementation cohort ($43.1\%$ [$\frac{1375}{3193}$] vs. $37.3\%$ [$\frac{1512}{4052}$]; $p \leq 0.001$). The pre‐ and postimplementation cohorts had similar proportions of patients with low SES ($21.7\%$ [$\frac{692}{3193}$] vs. $22.6\%$ [$\frac{915}{4052}$]; $$p \leq 0.36$$). The rate of 30‐day death or MI was similar pre‐ versus postimplementation ($7.0\%$ [$\frac{222}{3193}$] vs. $7.6\%$ [$\frac{309}{4052}$]; $$p \leq 0.28$$). Cohort characteristics by rurality and SES are summarized in Table 1. Test characteristics of the HEART Pathway by subgroup are summarized in Tables 2 and 3. Safety and effectiveness endpoints by subgroup are summarized in Tables 4 and 5. Absolute percentage reductions for each subgroup are shown in Table 6. Safety events among low‐risk patients are summarized in Table S1.
## Rurality
In the postimplementation period, the HEART Pathway identified $28.1\%$ ($\frac{425}{1512}$) of rural patients as low risk and $53.6\%$ ($\frac{811}{1512}$) as non–low risk, $6.8\%$ ($\frac{102}{1512}$) had low‐risk HEAR scores but lacked serial troponin measurements, and $11.5\%$ ($\frac{174}{1512}$) had an incomplete HEAR score. Among urban patients, $32.2\%$ ($\frac{818}{2540}$) were low risk and $53.2\%$ ($\frac{1350}{2540}$) were non–low risk. An additional $7.1\%$ ($\frac{180}{2540}$) of urban patients were missing serial troponin measurements but had low‐risk HEAR scores, and $7.6\%$ ($\frac{192}{2540}$) had an incomplete HEAR score. The proportion of rural patients classified as low risk was $4.9\%$ ($95\%$ CI $1.1\%$–$7.1\%$) lower than urban patients ($$p \leq 0.007$$).
At 30 days, death or MI occurred in $9.3\%$ ($\frac{141}{1512}$) of rural patients in the postimplementation cohort compared to $8.0\%$ ($\frac{110}{1375}$) in the preimplementation cohort (aOR 1.30, $95\%$ CI 0.99–1.77). Among urban patients, death or MI occurred in $6.6\%$ ($\frac{168}{2540}$) postimplementation versus $6.2\%$ ($\frac{112}{1818}$) preimplementation (aOR 1.23, $95\%$ CI 0.95–1.59). Among low‐risk patients, 30‐day death or MI occurred in $0.2\%$ ($95\%$ CI $0.0\%$–$1.3\%$) of rural versus $0.6\%$ ($95\%$ CI $0.3\%$–$1.4\%$) urban ($$p \leq 0.67$$). The interaction between the HEART Pathway implementation and rural versus urban community was not significant for 30‐day death or MI ($$p \leq 0.79$$).
Among rural patients, $56.0\%$ ($\frac{846}{1512}$) were hospitalized within 30 days in the postimplementation cohort compared to $59.3\%$ ($\frac{815}{1375}$) preimplementation, a reduction of $3.3\%$ (aOR 0.86, $95\%$ CI 0.72–1.02). In urban patients, $55.6\%$ ($\frac{1413}{2540}$) were hospitalized postimplementation versus $63.4\%$ ($\frac{1152}{1818}$) preimplementation, a reduction of $7.8\%$ (aOR 0.76, $95\%$ CI 0.66–0.87). In the postimplementation cohort, nonadherence to the HEART Pathway occurred in $14.4\%$ ($\frac{178}{1236}$) of rural patients and $15.6\%$ ($\frac{339}{2168}$) of urban patients ($$p \leq 0.33$$).
## SES
In the postimplementation period, use of the HEART Pathway identified $34.0\%$ ($\frac{311}{915}$) of low SES patients as low risk and $48.9\%$ ($\frac{447}{915}$) as non–low risk, $7.8\%$ ($\frac{71}{915}$) had low‐risk HEAR scores but lacked serial troponin measurements, and $9.4\%$ ($\frac{86}{915}$) had an incomplete HEAR score. Among high SES patients, $29.7\%$ ($\frac{932}{3137}$) were low risk, $54.6\%$ ($\frac{1714}{3137}$) non–low risk, $6.7\%$ ($\frac{211}{3137}$) had low‐risk HEAR scores without serial troponin measurements, and $8.9\%$ ($\frac{280}{3137}$) had an incomplete HEAR score. The proportion of low SES patients classified as low risk was $4.3\%$ ($95\%$ CI $0.7\%$–$7.8\%$) greater than high SES patients ($$p \leq 0.02$$).
Among low SES patients, 30‐day death or MI occurred in $6.4\%$ ($\frac{59}{915}$) postimplementation versus $7.7\%$ ($\frac{53}{629}$) preimplementation (aOR 0.98, $95\%$ CI 0.66–1.47). In high SES patients, 30‐day death or MI occurred in $8.0\%$ ($\frac{250}{3137}$) versus $6.8\%$ ($\frac{169}{2501}$) in post‐ and preimplementation cohorts, respectively (aOR 1.35, $95\%$ CI 1.09–1.67). However, this was driven by increased detection of index MI events (aOR 1.44, $95\%$ CI 1.15–1.81) and not by death or MI during the 30‐day follow‐up period (aOR 0.97, $95\%$ CI 0.58–1.63). Among low‐risk patients, 30‐day death or MI occurred in $0.6\%$ ($95\%$ CI $0.2\%$–$2.3\%$) of low SES versus $0.4\%$ ($95\%$ CI $0.2\%$–$1.1\%$) of high SES patients ($$p \leq 0.64$$). The interaction between the HEART Pathway implementation and low versus high SES was not significant for 30‐day death or MI ($$p \leq 0.11$$).
Among patients with low SES, $51.1\%$ ($\frac{468}{915}$) were hospitalized within 30 days compared to $61.7\%$ ($\frac{427}{692}$) preimplementation, a reduction of $10.6\%$ (aOR 0.68, $95\%$ CI 0.54–0.86). In high SES patients, $57.1\%$ ($\frac{1791}{3137}$) were hospitalized postimplementation versus $61.6\%$ ($\frac{1540}{2501}$) preimplementation, a reduction of $4.5\%$ (aOR 0.83, $95\%$ CI 0.73–0.94). Nonadherence rates were similar in low SES and high SES patients, $14.9\%$ ($\frac{113}{758}$) versus $15.3\%$ ($\frac{404}{2646}$; $$p \leq 0.81$$), respectively.
## DISCUSSION
This analysis, which is the first to evaluate the performance of the HEART Pathway among rural, urban, low SES, and high SES patients with acute chest pain, demonstrates that clinicians can safely use the HEART Pathway to risk stratify patients in each of these key subgroups. Low‐risk patients, in each subgroup, had 30‐day death or MI rates below the $1\%$ threshold that most providers consider acceptable. 48 Effectiveness (defined by a 30‐day reduction in hospitalizations) was shown among urban, low SES, and high SES patients, but not in rural patients. Similarly, rates of early discharge were increased by HEART Pathway implementation in all subgroups except rural patients. When adjusting for potential confounders, the HEART Pathway reduced OCT rates only in urban patients.
Given a large body of evidence demonstrating a significant gap in cardiovascular outcomes among rural versus urban patients, the safety of HEART Pathway in rural patients is notable. A recent study by Cross et al. 49 found that age‐adjusted cardiovascular mortality rates were significantly higher among rural versus urban patients and the mortality gap is widening. In addition, a recent study examining mortality rates among urban and rural hospitals found that death rates for acute MI, acute ischemic stroke, and heart failure were all higher in rural patients. 50 While safety event rates were similar among rural and urban patients, we did observe a rural disparity in the HEART Pathway effectiveness outcomes. The underlying cause of this difference is unclear and may be multifactorial. We initially hypothesized that providers may have been more likely to admit rural patients, even those at low risk, because of their reduced access to local outpatient resources, such as stress testing, and their distance from tertiary care.
However, adherence to the HEART Pathway disposition recommendations were similar across all subgroups. Thus, physician nonadherence does not appear to explain the effectiveness differences in rural patients. An additional hypothesis is that HEART Pathway effectiveness was reduced, because rural patients have an increased prevalence of cardiovascular risk factors, such as known CAD, hypertension, diabetes, or obesity, in epidemiologic studies. 51, 52, 53, 54, 55 This difference may reduce the proportion of patients classified as low risk and therefore eligible for early discharge from the ED without OCT. In our cohort, rural patients did have a higher prevalence of known CAD. However, rates of other cardiovascular risk factors were similar. Thus, future studies may be needed to explore the cause of HEART Pathway performance differences and how to mitigate this disparity in the rural population.
The HEART Pathway was safe and effective in reducing hospitalizations regardless of SES. There were no significant interactions between HEART Pathway implementation and SES, meaning the effect of implementation on all safety and effectiveness outcomes was not different based on SES. This is important, because prior studies have demonstrated that patients with low SES often suffer meaningful cardiovascular health outcome disparities. A recent study demonstrated that large improvements in cardiovascular mortality have occurred in high SES patients, but not patients with low SES. 56 Schultz et al. 27 state that the increased burden of cardiovascular disease in low SES patients is a result of a complicated “constellation of biological, behavioral, and psychosocial risk factors.” However, our study shows that HEART Pathway implementation may standardize chest pain care and achieve similar outcomes for patients regardless of SES.
Prior studies of the HEART Pathway have demonstrated its ability to reduce OCT rates. 13, 14 However, in this analysis, after adjustment for potential confounders, such as age, sex, race, insurance status, enrollment site, smoking, and BMI, the HEART Pathway significantly reduced OCT only in urban patients. Thus, testing rates were similar before and after HEART Pathway implementation in patients regardless of SES and among those from rural areas. These results suggest that providers' heavily weigh other patient factors, beyond their HEART Pathway risk assessments, to determine who receives OCT.
The HEART Pathway has been previously shown to be safe and effective across subgroups of age, sex and race. 14, 15, 57, 58, 59 This study meaningfully adds to the existing HEART Pathway literature by reporting its performance based on rurality and SES. Although studies show that chest pain accounts for $3.5\%$ of rural ED visits, no prior analyses have evaluated the performance of a chest pain ADP specifically in rural patient populations. 60 Furthermore, while recent data suggest that patients with a low SES have high incidence of chest pain and experience disparities in chest pain care and outcomes, no prior studies have focused on ADP performance in patients with low SES. 28 Thus, this study is an important step in the investigation of chest pain related health care disparities. With this new evidence in hand, emergency clinicians can confidently use the HEART Pathway in their everyday practice. Clinicians can safely risk stratify patients, regardless of SES. However, clinicians must also be aware that the HEART *Pathway is* associated with decreased hospitalization and increased early discharges among urban, but not rural, patients. Future studies should focus on equalizing care for patients living in rural areas and for those with low SES. Based on our results, broad implementation of the HEART Pathway may reduce disparities in chest pain care for patients with low SES, but is unlikely to reduce rural disparities.
## LIMITATIONS
This study has limitations. Although our three sites were diverse in size and location, the results may not be generalizable to all health systems. Secular trends and provider maturation effects are possible threats to the validity of the results. However, event rates in the study were consistent over time. The HEART Pathway Implementation Study was completed 6 years ago, before high‐sensitivity troponin assays were available in the United States. However, despite the age of the data, our results provide new insights about the performance of the HEART Pathway, which remains relevant as it is still widely used for chest pain risk stratification in U.S. EDs. Using the EHR to collect events may have decreased event detection compared to traditional methods of follow‐up. However, supplementing the EHR with death index and claims data identified only 16 additional 30‐day safety events. Finally, SES was determined based on patients' neighborhood census tract instead of their income. However, the use of ADI‐derived SES is consistent with prior studies and is strongly correlated with other individual SES measures. 37, 39 *In this* study, high versus low SES was determined using the 85th ADI percentile. This is consistent with the original ADI methodology literature and supported by the Centers for Disease Control and Prevention. 39, 41, 42 However, other methods involving quintiles, quartiles, and the 50th ADI percentile exist. 61, 62 Some patients were missing zip code and/or GEOID data. These missing data were due to addresses being incomplete or erroneous or there being a mismatch between address, city, or state. Patients experiencing homelessness were not systematically excluded.
## CONCLUSIONS
Clinicians can safety use the HEART Pathway to risk stratify patients with acute chest pain regardless of their rurality or socioeconomic status. Effectiveness of the HEART Pathway in decreasing hospitalizations and increasing early discharges was demonstrated in urban, low socioeconomic status, and high socioeconomic status groups, but not rural patients. Reductions in OCT rates were only seen in urban patients. Further study is needed to determine why the effectiveness of the HEART Pathway differed in the rural population.
## FUNDING INFORMATION
This project was funded by the Donaghue Foundation and the Association of American Medical Colleges (AAMC). Dr. O'Neill receives funding from Cytovale. Dr. Ashburn receives funding from NHLBI (T32HL076132). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Dr. Snavely receives funding from Abbott Laboratories and HRSA (1H2ARH399760100). Dr. Stopyra receives research funding from NCATS/NIH (KL2TR001421), HRSA (H2ARH39976‐01‐00), Roche Diagnostics, Abbott Laboratories, Pathfast, Genetesis, Cytovale, Forest Devices, Vifor Pharma, and Chiesi Farmaceutici. Dr. Mahler receives funding/support from Roche Diagnostics, Abbott Laboratories, Ortho Clinical Diagnostics, Siemens, Grifols, Pathfast, Quidel, Genetesis, Cytovale, and HRSA (1H2ARH399760100). He is a consultant for Roche, Quidel, Genetesis, Inflammatix, Radiometer, and Amgen and the chief medical officer for Impathiq Inc.
## CONFLICT OF INTEREST
The authors declare no potential conflict of interest.
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|
---
title: Correlation of asprosin and Nrg-4 with type 2 diabetes Mellitus Complicated
with Coronary Heart Disease and the Diagnostic Value
authors:
- Min Zhong
- Xue Tian
- Qitian Sun
- Lihui Li
- Yanan Lu
- Zengbin Feng
- Yu Gao
- Shuying Li
journal: BMC Endocrine Disorders
year: 2023
pmcid: PMC10009920
doi: 10.1186/s12902-023-01311-8
license: CC BY 4.0
---
# Correlation of asprosin and Nrg-4 with type 2 diabetes Mellitus Complicated with Coronary Heart Disease and the Diagnostic Value
## Abstract
### Purpose
Asprosin is a newly discovered adipose factor secreted by white fat, which is involved in glucose metabolism and inflammation. Neuregulin-4 (Nrg-4) is a new adipose factor released from brown adipose tissue and is considered to play an important role in metabolism. This study aims to explore the association between serum Asprosin, Nrg-4 level and coronary heart disease(CHD) in patients with type 2 diabetes mellitus(T2DM) and the diagnostic value.
### Patients and methods
157 patients with T2DM were enrolled from Affiliated Hospital of Chengde Medical University between December 2020 to July 2021. These patients were divided into T2DM without CHD group (T2DM-0, $$n = 80$$) and T2DM with CHD (T2DM-CHD, $$n = 77$$). Serum Asprosin and Nrg-4 expression was detected by enzyme-linked immunosorbent assay, and the correlations between Asprosin or Nrg-4 and clinical and biochemical indicators were analyzed. A receiver operating characteristics curve analysis and area under the curve (AUC) were used to evaluate diagnostic accuracy.
### Results
Serum Asprosin level of the T2DM-CHD group were significantly higher and Nrg-4 level significantly lower than those of the T2DM-0 group. Spearman correlation analysis showed that serum Asprosin levels were significantly positively correlated with diabetes course,history of hypertension, fasting plasma glucose(FPG), glycosylated hemoglobin A1c(HbA1C), triglycerides(TG),triglyceride glucose index(TyG index) and urea, and negatively correlated with ALT (all $p \leq 0.05$). Nrg-4 was negatively correlated with history of hypertension, body mass index(BMI), FPG, HbA1C, TG, and TyG indexes (all $p \leq 0.05$), and positively correlated with high-density lipoprotein cholesterol(HDL-C)($p \leq 0.05$).Logistic regression analysis showed that after adjusting potential confounders, Asprosin was a risk factor for diabetes mellitus, Nrg-4 was a protective factor. The AUC of Asprosin for diagnosing T2DM-CHD was 0.671 ($95\%$ confidence interval [CI] 0.584–0.759), and the AUC of the Nrg4 index for diagnosing T2DM-CHD was 0.772 ($95\%$ CI 0.700-0.844). The AUC of Asprosin and Nrg-4 for the combined diagnosis of T2DM-CHD was 0.796 ($95\%$ CI 0.726–0.864).
### Conclusion
Asprosin and Nrg-4 may be novel diagnostic biomarkers for T2DM with CHD, as they effectively improved the diagnostic accuracy for T2DM-CHD.
## Introduction
Diabetes mellitus(DM) is a chronic non-communicable disease that threatens human health all over the world. About 1.164 billion Chinese suffer from DM, a number that is projected to increase to 1.405 billion in 2030. Over $90\%$ of these patients suffer from type 2 diabetes mellitus(T2DM). A number of diabetic complications, particularly those related to the cardiovascular system, have captured the attention of researchers due to the rising incidence rate of T2DM[1]. A major risk factor for T2DM morbidity and mortality is coronary heart disease(CHD), which is the most common cardiovascular complication of T2DM [2]. As a consequence, identifying T2DM-CHD novel risk markers may prove beneficial for the effective treatment and prevention of cardiovascular complications associated with T2DM.
Asprosin is a 140-amino-acid C-terminal profibrillin, which is predominantly secreted and expressed by white adipose tissue. It has been shown that Asprosin could be induced by fasting and recruited to the liver, resulting in rapid liver glucose release into the circulation [3]. Researchers found that elevated asprosin levels were observed in people and mice with insulin resistance, T2DM, or obesity [4–6]. Furthermore, asprosin levels are pathologically elevated in cardiovascular disease (CAD) patients, correlated with adiposity, dyslipidemia, and insulin resistance, suggesting a possible link between asprosin and CAD pathophysiology [7]. Asprosin appears to be a promising therapeutic target for metabolic disorders based on the current research. The level of aspartate aminotransferase in patients with T2DM-CHD, however, has not been studied.
Neuregulin 4 (Nrg-4), an adipokine secreted by the brown adipose tissue (BAT) [8]. It belongs to the group of extracellular ligands known as epidermal growth factors (EGFs), which regulate cell-cell interactions within the nervous system, heart, chest, and other organ systems [9]. The biological functions of Nrg-4 include the inhibition of apoptosis, the promotion of neurite outgrowth, and the inhibition of inflammation [10, 11].
Researchers have reported an association between decreased Nrg-4 levels and T2DM mellitus, obesity, insulin resistance (IR) and hyperglycemia [12, 13]. Nrg-4 has been demonstrated to have anti-atherogenic and anti-inflammatory properties in recent studies [14]. As a result, it may provide evidence that diabetes and cardiovascular risk are related.
However, to date, there is no data on the role of Asprosin and Nrg-4 in diabetes and CHD. In this study, we explored the changes in Asprosin and Nrg-4 levels in patients who have either T2DM alone or CHD secondary to T2DM in order to develop effective diagnostic and predictive strategies. Furthermore, the correlation between Asprosin and Nrg-4 was evaluated in relation to other routine biochemical parameters. The results of this study may provide new ideas and methods for the prevention of T2DM complicated with CHD.
## Study population
In this case-control study, 157 T2DM patients who were hospitalized in the Department of Endocrinology and Department of Cardiovascular Medicine in Affiliated Hospital of Chengde Medical University from December 2020 to July 2021 were included, including 80 T2DM patients without CHD(T2DM-0 group); 77 T2DM patients with CHD (T2DM-CHD group) were included. Inclusion criteria: [1] *The diagnosis* of T2DM met the 1999 WHO diabetes diagnostic criteria [15]. [ 2] *The diagnosis* of CHD complies with the diagnostic criteria for CHD formulated by the Cardiovascular Branch of the Chinese Medical Association in 2018 [16]. [ 3] Aged ≥ 18 years. Exclusion criteria: [1] Those with acute infection and stress; those with severe heart and liver failure; those with all degrees of renal insufficiency; those with pregnancy, breastfeeding, and malignant tumors; those who regularly took estrogen, glucocorticoids and other drugs for the past month. [ 2] T1DM and special types of diabetes. [ 3] Exclude acute and chronic complications of diabetes.
## Ethical approval and consent to participate
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study. The ethical committee of the Affiliated Hospital of Chengde Medical College confrmed the study (the committee’s reference number: CYFYLL2020147).The methods were carried out in accordance with the approved guidelines.
## Information and data Collection
A standardized questionnaire survey was conducted on the research subjects by investigators who received unified training. The data collected included age, sex, course of diabetes, history of hypertension and history of drug. The height, weight and blood pressure were measured by a trained physician with a unified measurement tool and the body mass index (BMI) was calculated, BMI was calculated as the body weight (kg) divided by the square of the height (m2).
Biochemical parameters such as blood urea nitrogen (BUN), serum creatinine (SCr), fasting plasma glucose (FPG), glycosylated hemoglobin A1c (HbA1c), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were measured using standard procedures in the hospital clinical laboratory. Triglyceride glucose index (TyG index) = ln [fasting TG(mg/dL) * FBG (mg/dL)/2], the unit of TG and FPG in the TyG index is mg/dl. All participants had been fasting for 10 h before blood collection. A volume of 5 mL of cubital venous blood was drawn from each patient and transferred to a vacuum blood collection tube.
Determination of serum Asprosin and Nrg-4 levels: The kit for detecting *Asprosin serum* levels by enzyme-linked immunosorbent assay was purchased from abcam company. The kit for detecting Nrg-4 serum level by ELISA was purchased from Phoenix Company of the United States.
## Statistical methods
SPSS 26.0 (IBM, USA) was performed to analyze data. Continuous variables were presented as mean ± standard deviation (SD) or median (25th and 75th percentiles: P25, P75) in the case of normal or non-normal distribution, and differences between the two groups were examined by independent-sample t-test or Mann–Whitney U test correspondingly. Categorical variables were expressed as counts and percentages, and the comparison between groups was analysed by chi-square test. The correlation between Asprosin or Nrg-4 and other clinical variables was analyzed by Spearman correlation analysis. Binary logistic regression analysis was used to analyze the influencing factors of CHD in hospitalized T2DM patients according to 3 Models (Model 1 was adjusted for gender, age, duration of diabete and history of hypertension. Model 2 was adjusted for gender, age, duration of diabete, history of hypertension, BMI, FPG, HbA1C and TyG index. Model 3 was adjusted for gender, age, duration of diabete, history of hypertension, BMI, FPG, HbA1C, TyG index, TC, TG, HDL-C, LDL-C, ALT, AST, SCr, BUN). The receiver operating characteristic (ROC) curve was used to analyze the predictive value and optimal cut point value of Asprosin and Nrg-4. $p \leq 0.05$ means the difference is statistically significant.
## Basic characteristics of the two groups of people
The clinical characteristics and biochemical indicators of the 80 diagnosed T2DM patients and 77 diagnosed T2DM-CHD were showed in Table 1. Patients in T2DM with CHD tended to be older, had diabetes for a longer duration, higher BMI, FPG, TG, TyG index, SCr, BUN levels. These patients were more likely to have a history of hypertension and oral blood pressure, lipid-lowering, and glucose-lowering drugs. Compared with T2DM-0 group, patients with T2DM-CHD had significantly lower in TC and LDL-C. There were no significant differences between the two groups in gender, HbA1C,HDL-C, and insulin application ($p \leq 0.05$).
Table 1Clinical Characteristics of All Patients with T2DM with and without CHDVariableT2DM-0 group($$n = 80$$)T2DM-CHD group($$n = 77$$)p valueMale(%)40[50]42(54.5)0.569Age(year)55.51±6.3659.59±7.10<0.001Diabetes duration (year)6(1,11.75)12[6,16]<0.001HA (%)37[46]54[70]0.003BMI(kg/m2)24.47(23.05,27.24)26.89(24.58,29.38)<0.001FPG (mmol/L)8.50(7.00,10.65)10(7.55,12.40)0.039HbA1C(%)9.50(7.95,10.80)8.80(7.30,10.25)0.065TyG index9.52±0.679.80±0.770.022TC (mmol/L)4.76(3.94,5.96)4.11(3.12,4.95)<0.001TG (mmol/L)2.11(1.37,2.81)2.46(1.64,3.29)0.032HDL-C (mmol/L)1.02(0.84,1.20)0.95(0.80,1.16)0.12LDL-C (mmol/L)2.77(1.96,3.49)2.04(1.54,2.93)0.001ALT (U/L)21.10(15.06,32.28)24.52(15,35.50)0.382AST (U/L)21.51(15.80,28.69)24.67(20.27,32.95)0.076SCr (umol/L)57.40(45.90,65.88)64.50(51.75,79.55)0.005BUN (mmol/L)5.55(4.57,6.51)5.79(5,7.66)0.026Antihypertensive drugs(%)28[35]50(64.90)<0.001Statins(%)7(8.70)46(57.90)<0.001Oral hypoglycemic drugs(%)64[80]73(94.81)0.005Insulin application(%)48[60]43(55.84)0.598Data presented as means ± SD, median (P25, P75), or n (%); t-tests for continuous data, Mann–Whitney U tests for abnormally distributed variables, and chi-square test for categorical data. T2DM-0 group: type 2 diabetes mellitus patients without coronary heart disease; T2DM-CHD group: type 2 diabetes mellitus patients with coronary heart disease; HA: Hypertension; BMI: body-mass index; FPG: fasting plasma glucose; HbA1c: glycated hemoglobin A1c; TC: total cholesterol; TG: triglyceride; HDL-C: high-density lipoprotein cholesterol; LDL-C: low–density lipoprotein cholesterol; ALT: alanine aminotransferase; AST: aspartate aminotransferase; BUN: blood urea nitrogen; SCr: serum creatinine. $p \leq 0.05$ was considered statistically significant
## Serum asprosin and Nrg-4 levels in two groups of people
Compared with the T2DM-0 group, the serum Asprosin level in the T2DM-CHD group was significantly increased with 15.86 (13.24, 20.05)ng/ml in T2DM-0 vs. 22.63 (13.94, 27.97) ng/mL in T2DM-CHD, and the difference was statistically significant ($p \leq 0.01$).(Fig. 1A) The level of Nrg-4 was significantly decreased in the T2DM-CHD group with 13.38 (11.00, 17.18)ng/ml in T2DM-0 vs. 9.12 (7.55, 12.80) ng/mL in T2DM-CHD, and the difference was statistically significant ($p \leq 0.01$). ( Fig. 1B) Fig. 1Levels of Asprosin (A) and Nrg-4 (B) in plasma of T2DM-0 group and T2DM-CHD group. p value < 0.05 was considered significant
## Correlations between asprosin or Nrg-4 levels and the clinical basic indicators
According to Spearman correlation analysis, Asprosin levels were positively correlated with the diabetes duration($r = 0.301$, $p \leq 0.001$), history of hypertension($r = 0.204$, $$p \leq 0.01$$), FPG ($r = 0.463$, $p \leq 0.001$), HbA1C ($r = 0.24$, $$p \leq 0.03$$), TG ($r = 0.182$, $$p \leq 0.023$$), TyG index ($r = 0.327$, $p \leq 0.001$) and BUN ($r = 0.161$, $$p \leq 0.045$$), and negatively correlated with ALT (r = -0.224, $$p \leq 0.005$$). Asprosin levels were not associated with BMI, TC, LDL-C, AST and other variables (Table 2).
Table 2Correlation between Asprosin, Nrg-4, and other variablesvariableAsprosinNrg-4rp valuerp valueMale(%)0.1120.163-0.0010.992Age(year)0.1410.077-0.0740.358Diabetes duration(year)0.301<0.001-0.150.06BMI(kg/m2)0.1420.077-0.180.024HA (%)0.2040.01-0.1790.025FPG(mmol/l)0.463<0.001-0.652<0.001HbA1C (%)0.240.03-0.2550.001TC (mmol/l)0.0080.9250.0380.64TG (mmol/L)0.1820.023-0.355<0.001HDL-C (mmol/L)-0.0450.5780.1770.026LDL-C (mmol/L)-0.0030.9710.0370.644TyG index0.327<0.001-0.534<0.001ALT (U/L)-0.2240.005-0.0630.433AST (U/L)-0.0420.605-0.130.106SCr (umol/L)0.1440.0710.0530.512BUN (mmol/L)0.1610.0450.0030.975Oral hypoglycemic drugs(%)0.0510.529-0.0120.883Insulin application(%)0.1560.051-0.0290.722A Spearman correlation was performed and a value of $p \leq 0.05$ was considered as statistically significant. HA: Hypertension; BMI: body-mass index; FPG: fasting plasma glucose; HbA1c: glycated hemoglobin A1c; TC: total cholesterol; TG: triglyceride; HDL-C: high-density lipoprotein cholesterol; LDL-C: low–density lipoprotein cholesterol; TyG index: triglyceride glucose index; ALT: alanine aminotransferase; AST: aspartate aminotransferase;BUN: blood urea nitrogen; SCr: serum creatinine.
Spearman correlation analysis showed that Nrg-4 levels were negatively correlated with the history of hypertension(r = -0.179, $$p \leq 0.025$$), BMI (r = -0.18, $$p \leq 0.024$$), FPG (r = -0.652, $p \leq 0.001$), HbA1C (r = -0.255, $$p \leq 0.001$$), TG (r = -0.355, $p \leq 0.001$), TyG index (r = -0.534, $p \leq 0.001$), and positively correlated with HDL-C ($r = 0.177$, $$p \leq 0.026$$). Nrg-4 levels were not associated with TC, LDL-C, ALT, AST and other variables (Table 2). In addition, Asprosin was negatively correlated with Nrg-4 (r=-0.329, $p \leq 0.001$).
## Logistic regression analysis of the effects of asprosin and Nrg-4 on T2DM-CHD
The association between the levels of Asprosin and Nrg-4 and T2DM-CHD was analyzed in 3 Models. After adjustment for Model 1, a 1 unit (0.1ng/ml) increase in Asprosin significantly increased the rate of existence of T2DM-CHD by 1.072-fold (OR 1.072, $95\%$ CI: 1.023–1.125; $$p \leq 0.004$$). Plasma Asprosin concentrations were significantly associated with the development of T2DM-CHD even after controlling for gender, age, duration of diabete, history of hypertension, BMI, FPG, HbA1C, TyG index, TG, HDL-C, LDL-C, ALT, AST, Scr, BUN (OR 1.087, $95\%$ CI: 1.018–1.161, $$p \leq 0.013$$), indicating that there was a 1.087-fold increase in the odds of having T2DM-CHD for each 1 ng/ml increase in Asprosin levels (Table 3).
Table 3Logistic regression analysis of associations between T2DM-CHD and Asprosin, Nrg-4ModelAsprosinNrg-4OR($95\%$CI) p OR($95\%$CI) p 11.072(1.023,1.125)0.0040.74(0.658,0.834)<0.00121.074(1.013,1.139)0.0170.665(0.557,0.795)<0.00131.087(1.018,1.161)0.0130.67(0.557,0.806)<0.001Model 1,adjusted for gender, age, duration of diabete and history of hypertension;Model 2,adjusted for gender, age, duration of diabete, history of hypertension, BMI, FPG, HbA1C and TyG index; Model 3,adjusted for gender, age, duration of diabete, history of hypertension, BMI, FPG, HbA1C, TyG index, TC, TG, HDL-C, LDL-C, ALT, AST, SCr, BUN $p \leq 0.05$ was considered statistically significant After adjustment for Model 1, the odds of T2DM-CHD decreased by $26\%$ per 1 unit (0.1ng/ml) increase in serum Nrg-4 level (OR 0.74, $95\%$ CI 0.658–0.834; $p \leq 0.001$). The OR for T2DM-CHD remained statistically significant even after adjusting for all potential confounders (OR 0.67, $95\%$ CI 0.557–0.806; $p \leq 0.001$) (Table 3).
## Area under the ROC curve and predictive value of asprosin and Nrg-4
To explore the predictive value of circulating Asprosin and Nrg-4 for T2DM-CHD, we analyzed the ROC curves of circulating Asprosin and Nrg-4. The results revealed that the best cutoff value for circulating Asprosin to predict CHD was 19.008ng/ml (sensitivity: $66.2\%$, specificity: $71.2\%$, and AUC 0.671), and the best cutoff value for circulating Nrg-4 to predict CHD was 11.175ng/ml (sensitivity: $67.5\%$, specificity: $75\%$, and AUC 0.772) in patients with T2DM. The combined prediction was analyzed using a ROC curve. The results revealed that sensitivity, specificity, and AUC of the combination of Asprosin and Nrg-4 for diagnosing T2DM-CHD were $64.9\%$, $81.2\%$, and 0.796; such a combination demonstrated significantly improved diagnostic efficacy (Fig. 2).
Fig. 2ROC curve analysis of Asprosin, Nrg-4 and their combination in the diagnosis of T2DM complicated with CHD. p value < 0.05 was considered significant
## Discussion
Adipose tissue secretes a variety of biologically active substances such as adipokines. Previous studies have found that Asprosin, a new type of adipokines secreted by white adipose tissue, is associated with the severity of unstable angina and acute coronary syndrome [17]. This study found that the level of serum Asprosin in the T2DM-CHD group was significantly higher than that in the T2DM-0 group. Although there are few studies at home and abroad on the relationship between Asprosin and diabetes complicated with CHD. However, an Iranian clinical case-control study found that the serum asprosin concentration in the CHD group was significantly higher than that in the healthy group [7]. In addition, compared with the healthy group, a study found that Asprosin was significantly increased in the T2DM-0 group, and it was associated with the ratio of IR and TC/HDL-C (a risk factor for atherosclerosis in CHD) [18]. Therefore, this study provides further evidence of the association between Asprosin and related indicators.
It was found that Asprosin was significantly positively correlated with known cardiovascular risk factors such as diabetes duration, FPG, HbA1C, TG, and TyG index, which was used as a reliable and landmark clinical indicator of IR [19]. Consistent with the results of the Iranian study, serum Asprosin levels in CHD patients were positively correlated with FPG, TG, and HOMA-IR [7]. Basic studies have shown that Asprosin induces β-cell apoptosis by inhibiting β-cell autophagy through adenosine monophosphate-activated protein kinase (AMPK) and mammalian target of rapamycin (mTOR) signaling pathways [20]. Wang et al5 found that Asprosin may lead to β-cell dysfunction and impaired glucose tolerance in patients with T2DM. In this study, it was found that the level of Asprosin was positively correlated with the TyG index, and was significantly increased in the T2DM-CHD group, indicating that IR is not only an important pathological basis of diabetes, but also an important cause of CHD, suggesting that Asprosin may be related to IR, and may play a role in the occurrence of CHD. have important meaning. According to the study by Romere et al., [ 3] it was found that Asprosin increases the level of circulating glucose by promoting hepatic gluconeogenesis, which provides a basis for the finding that *Asprosin is* related to FPG and HbA1C in this study. Hypertriglyceridemia is a risk factor for atherosclerosis. This study also found that serum Asprosin levels were significantly positively correlated with TG. However, in the basic data of this population, it was found that the levels of TC and LDL-C in CHD patients were significantly lower, which was contrary to the results of previous studies. It may be related to the significantly increased application of lipid-lowering drugs in this group of patients compared with the T2DM-0 group. A recent cross-sectional study found that there was no correlation between the use of oral hypoglycemic agents, insulin and Asprosin in T2DM patients after multiple linear regression analysis [21], which is consistent with the results of this study. Although another study found that sodium-glucose cotransporter 2 inhibitors could reduce serum asprosin levels in newly diagnosed T2DM patients [22], due to the small number of patients in this study who used sodium-glucose cotransporter 2 inhibitors, no correlation was found, and future studies should increase the sample size.
After adjusting for related confounding factors, *Asprosin is* still an independent risk factor for T2DM combined with CHD, which indicates that it is closely related to the occurrence of diabetes combined with CAD, which is similar to the results of a domestic study [23]. The study also found the best cut-point value of Asprosin for the diagnosis of diabetes combined with CHD in clinical practice, which has certain value in the prediction and diagnosis of T2DM combined with CHD, but it needs further verification with large samples.
Nrg-4, another novel adipokines, is mainly secreted by brown adipose tissue and is associated with dyslipidemia, IR, inflammation, and oxidative stress, which are involved in the pathogenesis of obesity, diabetes, and metabolic syndrome [24, 25]. The results of the study showed that compared with the T2DM-0 group, the Nrg-4 level in the T2DM-CHD group was significantly lower. Consistent with the results of Tian et al., [ 26] it was found for the first time that the serum Nrg-4 concentration in CHD patients was significantly reduced, and it was significantly negatively correlated with the SYNTAX score, which reflects the severity of coronary artery disease. In the correlation analysis, this study not only found that Nrg-4 was significantly positively correlated with HDL-C, just as a cross-sectional study found that serum Nrg-4 levels in T2DM patients were positively correlated with HDL-C, [24] but also found that Nrg-4 was positively correlated with HDL-C was significantly negatively correlated with FPG, HbA1C, TyG index, BMI and TG. Previous studies have shown that Nrg-4 has a very significant effect on insulin secretion [27]. Previous studies have also found that high-fat-fed Nrg-4 knockout mice have higher plasma TG concentrations, higher FPG and plasma insulin levels, suggesting that Nrg-4 deficiency can lead to glucose tolerance after diet-induced obesity. Sexual reduction and IR [28]. Conversely, high-fat-fed mice inhibited adipogenesis due to overexpression of Nrg-4, thereby preventing high-fat diet-induced obesity and fatty liver, and improving insulin sensitivity [29]. These studies confirmed that Nrg-4 was closely related to FPG, TG, and TyG index. In addition to animal studies, studies in obese patients have shown that serum Nrg-4 levels are inversely associated with the risk of metabolic syndrome, suggesting that Nrg-4 concentrations may be a protective factor for the development of metabolic syndrome [30]. The above studies further suggest that Nrg-4 may be involved in glucose, lipid metabolism and IR. This study also did not find a correlation between Nrg-4 and oral hypoglycemic drugs and insulin therapy, which is consistent with the previous study not finding that Nrg-4 is related to oral hypoglycemic drugs [31].
Similarly, after adjusting for various confounding factors, Nrg-4 was still an independent protective factor for T2DM complicated with CHD. Tian et al., [ 26] also found that even if the logistic regression model was adjusted for age, sex, BMI, and HbA1C, the severity of coronary artery lesions in CHD patients was negatively correlated with serum Nrg-4 levels. It is suggested that Nrg-4 may be a protective factor for CAD. This study is consistent with the above findings. Therefore, we speculate that Nrg-4 may be related to the pathogenesis of diabetes and CHD. In addition, the study also found the best cut-point value of Nrg-4 in clinical diagnosis of T2DM complicated with CHD, which has certain value in predicting whether diabetes is complicated with CHD. Studies have shown that Nrg-4 may improve IR and glucose and lipid metabolism through other factors. mechanism to prevent CHD [30]. Correlation analysis showed that Asprosin and Nrg-4 were significantly negatively correlated, and Asprosin combined with Nrg-4 had higher predictive value and higher specificity ($81.2\%$). Although no research has found whether there is a common pathway between the two, whether they are related to the pathogenesis of glucose and lipid metabolism needs further research.
In addition, we found that patients in the T2DM-CHD group were older and had a longer diabetes duration. A previous study found that older age or age at diagnosis and longer diabetes duration proportionally increased the risk of macrovascular events and death, with the greatest risks observed in the oldest age groups with the longest duration of diabetes [32]. However, Logistic regression analysis showed that Asprosin and Nrg4 are still associated with T2DM-CHD after adjusting for factors such as age and diabetes duration. Thus, the level of Asprosin and Nrg4 could be the promising clinical biomarker for predicting T2DM with CHD.
The limitation of this study is a small number of study cases, which limits the conclusions of this study. Furthermore, this was a cross-sectional study, and the effects of the Asprosin and Nrg-4 on the prognosis of T2DM-CHD patients were not observed further. In the future, multicenter, large sample and prospective studies are needed to further confirm the conclusions of this study. In-depth study of its specific molecular mechanism contributes to the early detection of diseases and provides new targets for treatment.
## Conclusion
In conclusion, serum Asprosin level increased and Nrg-4 level decreased in patients with CHD, which is closely related to glucose and lipid metabolism disorder and insulin resistance, and has certain significance for the prediction and diagnosis of T2DM with CHD. Serum Asprosin and Nrg-4 are expected to be potential markers for the diagnosis of T2DM combined with CHD.
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|
---
title: Occupational quartz and particle exposure affect systemic levels of inflammatory
markers related to inflammasome activation and cardiovascular disease
authors:
- Alexander Hedbrant
- Christopher Engström
- Lena Andersson
- Daniel Eklund
- Håkan Westberg
- Alexander Persson
- Eva Särndahl
journal: Environmental Health
year: 2023
pmcid: PMC10009934
doi: 10.1186/s12940-023-00980-1
license: CC BY 4.0
---
# Occupational quartz and particle exposure affect systemic levels of inflammatory markers related to inflammasome activation and cardiovascular disease
## Abstract
### Background
The inflammatory responses are central components of diseases associated with particulate matter (PM) exposure, including systemic diseases such as cardiovascular diseases (CVDs). The aim of this study was to determine if exposure to PM, including respirable dust or quartz in the iron foundry environment mediates systemic inflammatory responses, focusing on the NLRP3 inflammasome and novel or established inflammatory markers of CVDs.
### Methods
The exposure to PM, including respirable dust, metals and quartz were determined in 40 foundry workers at two separate occasions per worker. In addition, blood samples were collected both pre-shift and post-shift and quantified for inflammatory markers. The respirable dust and quartz exposures were correlated to levels of inflammatory markers in blood using Pearson, Kendall τ and mixed model statistics. Analyzed inflammatory markers included: 1) general markers of inflammation, including interleukins, chemokines, acute phase proteins, and white blood cell counts, 2) novel or established inflammatory markers of CVD, such as growth/differentiation factor-15 (GDF-15), CD40 ligand, soluble suppressor of tumorigenesis 2 (sST2), intercellular/vascular adhesion molecule-1 (ICAM-1, VCAM-1), and myeloperoxidase (MPO), and 3) NLRP3 inflammasome-related markers, including interleukin (IL)-1β, IL-18, IL-1 receptor antagonist (IL-1Ra), and caspase-1 activity.
### Results
The average respirator adjusted exposure level to respirable dust and quartz for the 40 foundry workers included in the study was 0.65 and 0.020 mg/m3, respectively. Respirable quartz exposure correlated with several NLRP3 inflammasome-related markers, including plasma levels of IL-1β and IL-18, and several caspase-1 activity measures in monocytes, demonstrating a reverse relationship. Respirable dust exposure mainly correlated with non-inflammasome related markers like CXCL8 and sST2.
### Conclusions
The finding that NLRP3 inflammasome-related markers correlated with PM and quartz exposure suggest that this potent inflammatory cellular mechanism indeed is affected even at current exposure levels in Swedish iron foundries. The results highlight concerns regarding the safety of current exposure limits to respirable dust and quartz, and encourage continuous efforts to reduce exposure in dust and quartz exposed industries.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12940-023-00980-1.
## Introduction
The aim of this study was to determine effects on the inflammatory response mediated by particulate matter (PM) exposure in the iron foundry environment. The potential of PM to induce inflammatory reactions is well established, and the inflammatory potential of different PM is one key factor to determine their toxicological effects in humans. Exposure to airborne PM is a major concern for human health, and exposures to ambient and household PM with an aerodynamic diameter of 2.5 µm or less (PM2.5) is estimated to cause 7 million deaths annually [1]. The mechanisms regarding PM exposure and their harmful effects in the lungs include direct toxicity of the particles per se, as well as damage due to the inflammatory responses elicited in response to PM exposure. The effects of PMs are not restricted to the lung tissues, as particles in the nano-sized range as well as the inflammatory mediators generated at the site of exposure may enter the systemic circulation and could thereby exert both toxic and inflammatory effects by direct interaction with distal tissues and organs [2].
Chronic, sub-clinical low-grade inflammation is considered to play a central role in the etiology of several non-communicable diseases, including cardiovascular diseases (CVD), obesity- [3] and age-related diseases [4]. PM exposure contributes to increased risk of CVDs and diseases of the respiratory system [5, 6], and increasing evidence indicate that the systemic low-grade inflammation induced by PMs exposure is a main mechanism of these diseases [2, 7–9]. Importantly, not only chronic exposure to PM, but also acute short-term increases in PM exposure, has been associated with health risks; mainly linked to cardiovascular events, including hospitalization and deaths [10–12]. Short-term increases in ambient PM exposure have furthermore been found to induce acute biological effects, including increases in inflammatory mediators, changes in small metabolites, and effects on heart rate and blood pressure; processes central for the intricate etiology of CVD [9, 10, 13, 14].
The associations between PM exposure and effects on systemic inflammation and inflammatory diseases, including CVDs, are best characterized for ambient PM. However, similar evidence is also emerging for occupational PM exposures. For example, a recent study demonstrated increased risk of rheumatoid arthritis due to various occupational inhalable agents, including e.g., quartz dust and welding fumes [15].
The iron foundry environment is a milieu with high levels of PM, generally far above the levels found in outdoor environments; however, the chemical composition of the PM and their size distribution found in foundries differ to ambient PM and could therefore differ also in toxicological aspects. Iron foundry dust contains high levels of quartz (crystalline silica), which is a well-known hazardous particle that can cause inflammation in the respiratory tract and lung diseases, such as silicosis. Additionally, long-term quartz exposure has also been linked to CVD for workers in foundries [16, 17], metal mines, and potteries [18, 19].
Quartz particles are potent at inducing inflammatory mediators in vitro, and are particles known to activate the NLRP3 inflammasome complex that upon assembly activates the protease caspase-1, which in turn cleaves the pro-forms of interleukin (IL)-1β and IL-18 to their active forms. However, whether PM and quartz exposures at current exposure levels in the iron foundry environment cause inflammatory effect in exposed workers has not been described.
In this study, PM presented in the foundries was characterized by metal and quartz content, and the respirable PM and quartz exposures present at two Swedish iron foundries were analyzed with the aim to correlate exposures to the biological host response. Accordingly, the impact of quartz and PM exposure in iron foundry workers on inflammatory markers were broadly classified into three categories: 1) general markers of inflammation, 2) novel inflammatory biomarkers of CVD, and 3) markers of NLRP3 inflammasome activation.
## Exposures to PM and quartz in the iron foundries
The exposures to airborne PM, respirable quartz, and inhalable metal particles are shown in Table 1. For all measurement days, that were part of this study, the foundries had a normal production without major breaks. The personal average respirable dust and quartz exposures, when adjusted for respirator use, were 0.65 and 0.020 mg/m3, respectively. The highest respirator adjusted values for respirable dust and quartz were 2.5 and 0.1 mg/m3, respectively, which are levels equivalent to the current Swedish occupational exposure limits (OELs). The PM exposures separated for different workstations is not presented in the table; however, the PM levels were highest in the blasting/fettling areas (mean respirable dust 4.9 mg/m3, $$n = 8$$). In these areas, the workers used protective equipment for the vast majority of the time, thus the respirator adjusted exposures in the blasting/fettling areas were similar to those levels found for individuals performing work in the casting/mold making areas (i.e. adjusted average exposures around 0.8 mg/m3 for both these groups). As a result, the highest average respirable dust exposure, when adjusted for respirator use, was found at the melting departments (1.0 mg/m3). Regarding respirable quartz, the highest average exposure was observed in the blasting/fettling areas also when adjusting for respirator use (0.045 mg/m3). The within/between worker variation in exposure levels comparing the two measurements, are shown in Fig. 1. The intra-individual exposures had a strong correlation for the two separate measures regarding respirable dust, with a Pearson’s r value of 0.95 ($p \leq 0.0001$) or 0.74 ($p \leq 0.0001$) for non-adjusted and respirator adjusted exposures, respectively. For respirable quartz, the Pearson’s r correlation was weaker (0.79, $p \leq 0.0001$) or 0.48 (p 0.005) for non-adjusted and respirator adjusted exposures, respectively. Table 1Particle exposure measuresPersonal measurements (mg/m3)NAMSDGMGSDMinMaxRespirable dust721.32.050.573.50.05811Respirable dust, resp. adj720.650.570.432.80.0582.5Respirable quartz720.0660.180.0164.60.00121.0Respirable quartz, resp. adj720.0200.0220.0113.40.00110.10Stationary measurements (mg/m3) Inhalable dust244.76.623.12.80.7735 Respirable dust241.140.950.832.20.223.7 Respirable quartz240.0260.0320.01430.00180.14 PM10242.742.472.12.10.5411.7 PM2.5241.522.390.812.80.1611.7 PM1 (ultrafine)240.400.410.262.60.0401.84Total particle area (A-trak) µm2/cm3245605763113.3322600Particle number (P-trak)/cm324134 774111 10990 2452.615 800467 600Different PM exposure measures (8-h time weight average levels) measured at two Swedish iron foundries. The personal measurements include 72 measurements from 40 individuals (32 individuals sampled twice). The stationary measurements were positioned at the casting/mold making site ($$n = 10$$), core making ($$n = 4$$), sand preparation ($$n = 3$$), melting ($$n = 3$$), shake out ($$n = 1$$), fettling ($$n = 2$$), and feedstock area ($$n = 1$$)GM Geometric mean, GSD Geometric standard deviation, AM Arithmetic mean, SD Arithmetic standard deviation, resp. adj. Respirator adjusted exposure valuesFig. 1Within/between worker variation for the 32 individuals that were sampled during two separate measurements. Non-respirator adjusted exposure to respirable dust (A), respirable quartz (B), respirator adjusted exposure for respirable dust (C), and respirable quartz (D). Three workers, indicated by red dots, changed work tasks between the two measurements; two changed work tasks in the production line and one changed from production line to product controller. Two extreme high values were not visually shown in the figure, as noted in the figure, for readability of the lower exposures
## Dust characterization
To get better understanding on the characteristics of the PM exposures at the iron foundries, the inhalable dust, collected from the stationary measurements, were analyzed for metal content, whereas a particle impactor was used to analyze the size distribution of PM2.5 and to determine the quartz content for the different fractions. As shown in Table 2, the metal content of the inhalable dust varied considerably, constituting 5–$45\%$ of the total mass. Iron was the main metal found in the inhalable dust, followed by magnesium and aluminum when comparing the geometric means. All of the metals analyzed had mean or median exposure levels well below the Swedish OELs. One measurement in the fettling/blasting area was most likely above the Swedish OEL for iron, copper, and chromium, when considering that the OEL is set for other dust fractions than the analyzed inhalable fraction. However, the workers in our study did always use protective equipment while performing these work tasks. Table 2Metal analysis of the inhalable dust fractionN = 22 dust filtersAMSDGMGSDMinMaxSwedish OELOEL dust fractionInhalable dust (µg/m3)4 5296 8792 9472.280534 1005 000InhalableMetals, % of total mass2112181.9545Metals (µg/m3) Iron (Fe)9762 83126443413 5003 500Respirable Magnesium (Mg)157164704.65.1480N/A Zink (Zn)691807.99.90.41840N/A Aluminum (Al)6475442.2133455 000Total dust Calcium (Ca)3661192.86.0295N/A Chromium (Cr)321350.6313 < 0.061635500Total dust Manganese (Mn)14273.94.90.50101200Inhalable Barium (Ba)7.9182.83.80.4189500Total dust Copper (Cu)5.2131.160.0646210Respirable Lead (Pb)3.76.50.767.10.04622100Inhalable Nickel (Ni)2.14.10.476.3 < 0.1219500Total dust Antimony (Sb)0.481.10.163.8 < 0.0955.3250Inhalable Vanadium (V)0.421.30.094.2 < 0.0596.2200Total dust Molybdenum (Mo)0.330.760.0944.2 < 0.0593.510 000Total dust Cobalt (Co)0.210.540.0733.2 < 0.0592.620Inhalable Arsenic (As)0.110.14 < 0.141.9 < 0.120.7010Inhalable Cadmium (Cd)0.0730.10 < 0.0582.3 < 0.0560.364Inhalable Tallium (Tl)0.00510.0052 < 0.00661.8 < 0.00560.024N/AConsidering the OEL dust fractions, the total dust fraction is comparable to the inhalable fraction (approx. 100 µm in aerodynamic diameter and smaller), whereas the respirable fraction represents particles approximately 4 µm and smaller. The dust filters were placed accordingly: casting/mold making site ($$n = 10$$), core making ($$n = 4$$), sand preparation ($$n = 3$$), melting ($$n = 2$$), fettling ($$n = 2$$), and feedstock area ($$n = 1$$). N/A indicate that no Swedish OELs is set for the given elementAM Arithmetic mean, SD Arithmetic standard deviation, GM Geometric mean, GSD Geometric standard deviation, OEL Occupational exposure limit, N/A Not applicable The particle size distribution and quartz content of the collected dust was measured with a particle impactor (Table 3). The results demonstrate that $20\%$ of the total mass on average is in the < 0.25 µm range, i.e., nano-sized particles. However, the quartz content is highest at the largest particle fraction (> 2.5 µm), and steadily decreases with smaller particle fractions, constituting $1\%$ of the mass at the smallest fraction (< 0.25 µm). The color of the particles is distinctly different in the < 0.25 µm fraction compared to the other size fractions (Fig. 2), indicating different particle composition in this fraction, with a higher content of e.g., iron oxides. Table 3Size distribution of the respirable fraction, and quartz content of the different size ranges% Of total mass ($$n = 8$$)Quartz content (%) ($$n = 8$$)PM size (µm)MedianMeanSDMinMaxMedianMeanSDMinMax > 2.543.444.99.634.758.64.74.62.01.77.82.5–1.018.018.55.511.427.63.73.81.42.15.81.0–0.59.09.43.24.213.51.82.31.80.55.70.5–0.256.06.53.12.012.81.01.51.40.23.9 < 0.2519.520.77.08.632.11.21.10.70.12.4PM Particulate matter, SD Standard deviationFig. 2Impactor filters with particles from different size fractions (< 0.25—> 2.5 µm; from three separate measurements, where each row represent filters from a separate measurement occasion) collected in two Swedish iron foundries. A distinct brown/red color is observed in the smallest fraction (< 0.25 µm)
## Pre-shift and post-shift levels of inflammatory markers
The studied inflammatory markers included plasma concentration of inflammatory proteins, WBC counts, and ex vivo inflammasome activation in monocytes from study participants´ whole blood. Differences in the pre- and post-shift levels were observed for most of the measured inflammatory markers (Fig. 3 A-C), indicating a diurnal variation and/or work-related effect, such as particle exposure. Significant differences were observed for $\frac{8}{14}$ detected plasma proteins (Fig. 3 A), for all reported WBC counts except for lymphocytes (Fig. 3 B), and for all measures of caspase-1 activity (Fig. 3 C). The total WBC count as well as the neutrophil and monocyte counts were found to be increased in the afternoon, while the number of eosinophils were slightly reduced at this time point. A significantly lower plasma concentration was found in the post-shift samples for four of the measured proteins (CRP, SAA, CXCL8, and CCL2), whereas four proteins were significantly higher in concentration in the post-shift samples when compared to pre-shift levels (IL-1Ra, IL-18, sST2, and MPO). Regarding ex vivo inflammasome activation measured as caspase-1 enzymatic activity in monocytes, a higher percentage of caspase-1 activated cells was observed in the post-shift samples for all experimental conditions, including non-treated cells as well as in cells treated with the inflammasome stimuli LPS and/or ATP. The pre- and post-shift inflammatory marker levels, the over-shift differences, and their p-values are shown in Additional file 1: Table S1.Fig. 3Over-shift difference in inflammatory markers. The data represent average log2 fold change difference in inflammatory markers in blood, when comparing post-shift with pre-shift levels for the 40 foundry workers included in the study. A Inflammatory mediators measured in plasma, B Percent caspase-1 (inflammasome) activated monocytes following indicated ex vivo treatment, C WBC counts and cell ratios. Whiskers of the Box plots indicate min and max values. The asterisks indicate significant over-shift difference in inflammatory marker levels, calculated using the Wilcoxon signed-rank test. * P-value < 0.05, ** < 0.01, *** < 0.001. LPS: lipopolysaccharide, ATP: adenosine triphosphate, NLR: neutrophil–lymphocyte ratio, LMR: lymphocyte-monocyte ratio
## Correlation network of studied inflammatory markers
A correlation heatmap using Pearson correlation of: 1) the average morning levels of the studied inflammatory markers for each study participant, and 2) selected individual properties (age, BMI, smoking) is shown in Fig. 4. From the heatmap, two distinct clustered blocks appear. The first block includes general markers of inflammation that are correlated with each other; the acute phase proteins CRP and SAA, the cytokine IL1-Ra, and the cell ratio of NLR. In addition, BMI was correlated with most of the above-mentioned markers. The other cluster with correlated markers included MPO, IL-18, CCL2, GDF-15, ICAM-1, and VCAM-1. Furthermore, many of the NLRP3 inflammasome related markers did correlate, including plasma levels of the inflammasome-released cytokines IL-1β and IL-18 with most of the caspase-1 enzymatic activity measures in monocytes from ex vivo inflammasome stimulated whole blood. These correlations were most pronounced for IL-1β with LPS activation and IL-18 with ATP activation of caspase-1. sST2, belonging to the IL-1 receptor family, also correlated with the caspase-1 measures, and in addition, with IL-1β as well as IL-18.Fig. 4Pearson correlation heatmap of the inflammatory markers of the study using the average morning sample values ($$n = 40$$) and selected individual properties (BMI, age, smoking). Correlations coefficients < 0.3 are not visualized. All but six of the correlations with a coefficient > 0.3 had a p-value < 0.05. The six features > 0.05 all had p-values between 0.05 and 0.06. Box Cox transformation was applied to normalize the distribution of the data. White blood cell counts were excluded to improve readability of the figure. NLR: neutrophil–lymphocyte ratio, LMR: lymphocyte-monocyte ratio, casp1 act.: caspase-1 activation of monocytes ex vivo following indicated activators; LPS: lipopolysaccharide, ATP: adenosine triphosphate
## Correlation of inflammatory markers to particle exposure
The inflammatory markers significantly correlated with respirable dust and respirable quartz exposure, respectively, using Pearson’s r and Kendall \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\uptau$$\end{document}τ, are shown in Table 4. The over-shift difference in inflammatory marker levels was used as a measure of acute effects of exposure from that workday. In these analyses, the inflammatory marker and exposure data for each sampling occasion were used ($$n = 69$$). In addition, the morning pre-shift samples were used to evaluate effects of exposure from recent days/weeks, with the assumption that the averaged exposure for the two sampling occasions for each worker reflects the average daily exposure levels for the time-period between the first and second measurement. The average exposure levels for each worker were analyzed with the morning pre-shift inflammatory marker levels of the follow-up measurement ($$n = 32$$), or the average morning inflammatory marker levels of the two separate measures ($$n = 40$$).Table 4Correlation of inflammatory markers with exposure using Pearson’s r or Kendall \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\uptau$$\end{document}τ statisticsInflammatory markersSample dataExposurePearson's rKendall \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\uptau$$\end{document}τCaspase-1 ex vivo activation in monocytesrP-value\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\uptau$$\end{document}τP-valueCaspase-1, LPS activatedMorning \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{\times }$$\end{document}ׯ 1st, 2ndResp. dust-0.552.65 × 10–4-0.340.0021Morning 2ndResp. dust-0.602.56 × 10–4-0.436.20 × 10–4Morning 2ndResp. quartz-0.400.0016Caspase-1, ATP activatedMorning \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{\times }$$\end{document}ׯ 1st, 2ndResp. quartz-0.410.0089Morning 2ndResp. quartz-0.594.35 × 10–4Shift differenceResp. dust0.407.69 × 10–40.280.0011Shift differenceResp. quartz0.390.00120.260.0027Caspase-1, LPS + ATP activatedMorning \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{\times }$$\end{document}ׯ 1st, 2ndResp. quartz-0.480.0019-0.300.0075Morning 2ndResp. quartz-0.663.31 × 10–5-0.452.98 × 10–4Inflammatory mediators CXCL8Morning 2ndResp. dust0.360.0045 SAAShift differenceResp. dust-0.220.0087Shift differenceResp. quartz-0.270.0014 sST2Morning 2ndResp. quartz-0.550.0010-0.380.0022The inflammatory markers with a significant correlation to respirable dust and/or quartz are shown ($p \leq 0.01$). The full list of studied inflammatory markers is found in Fig. 3. Over-shift differences in inflammatory markers are used as a measure of effects from current days exposure, whereas the morning samples are used as a measure of effects from recent days/weeks of exposure, assuming similar exposures as measured for the time period between the first and second measurement. Morning \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{\times }$$\end{document}ׯ 1st, 2nd: the average inflammatory marker and exposure level from the two sampling occasions for each participant. Morning 2nd: The morning inflammatory marker levels of the second measurement (follow-up measurement) correlated to the averaged exposure level for the two sampling measuresLPS Lipopolysaccharide, ATP Adenosine triphosphate, resp. Respirable Regarding acute over-shift effects, caspase-1 activity in monocytes following ex vivo ATP treatment correlated with both respirable dust and respirable quartz levels. In addition, there was a negative correlation between SAA and both exposure measures.
For the morning samples indicating effects from recent days/weeks exposure, the ex vivo inflammasome activation assays had a significant negative correlation to exposure. The respirable dust and respirable quartz exposure was negatively correlated with caspase-1 activation following LPS-treatment ex vivo, whereas the respirable quartz exposure had a negative correlation with caspase-1 activation following ATP or LPS + ATP ex vivo treatment. sST2 and CXCL8 were the only non- inflammasome related inflammatory markers that significantly correlated with exposure for the morning samples; sST2 showing a negative correlation with quartz and CXCL8 a positive correlation with respirable dust exposure.
To evaluate the impact of covariates, such as age, smoking and BMI, a mixed model analysis was performed on the inflammasome-related markers as well as on other markers that were found to have significant correlation to quartz or dust exposure using Pearson or Kendall \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\uptau$$\end{document}τ correlation, i.e., CXCL8, SAA and sST2. In the mixed models, shown in Table 5, the over-shift difference and morning sample analyses gave similar results regarding what markers significantly correlated to exposures, resembling the results for the morning values using Pearson correlation. For both the over-shift and morning measures, ex vivo caspase-1 activation following ATP treatments alone or in combination with LPS were found to significantly negative correlate to quartz exposure. LPS treatment alone also correlated negatively to respirable dust exposure; however, only significantly for the over-shift difference. Compared to the Pearson analysis, there was a negative correlation to quartz exposure for some additional inflammasome markers including IL-1β (over-shift difference) and IL-18 (morning levels and over-shift difference). In addition, respirable dust exposure positively correlated to the inflammatory markers CXCL8 (morning samples), and sST2 (over-shift difference), and sST2 additionally correlated negatively with quartz exposure. Additional statistics for the mixed models are shown in Additional file 1: Table S2 and Table S3. The mixed models of caspase-1 activation by LPS + ATP demonstrated the best fit to the observed data, determined by the adjusted R2 value and by visual inspection (Fig. 5).Table 5Mixed model analysis on the effect of respiratory dust or quartz exposure on selected inflammatory markersInflammatory markerSample dataExposureP valueR2 adjDirectionOther variables includedInflammasome-related cytokines IL-1βMorningQuartz0.150.047DownInfectionShift differenceQuartz0.00390.25DownAge, workplace, smoking, stress IL-18Shift differenceQuartz0.0140.29DownAgeMorningQuartz0.0490.32DownAge, BMI, stress, infection IL-1raN/S by AICCaspase-1 ex vivo activation in monocytes Caspase-1 LPS actMorningResp. dust0.0520.54DownWorkplace, stress infectionShift differenceResp. dust0.00360.24DownBMI, workplace, stress Caspase-1 ATP actMorningQuartz2.76 × 10–50.47DownAge, BMIShift differenceQuartz,Resp. dust0.00280.160.44DownUpPre-shift value, age, BMI, workplace Caspase-1 LPS + ATP actMorningQuartz1.90 × 10–60.61DownAge, BMI, workplace, smokingShift differenceQuartz9.63 × 10–50.45DownAge, BMI, smokingOther inflammatory mediators significantly correlated with exposure using Pearson or Kendall \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varvec{\uptau}}$$\end{document}τ CXCL8Shift differenceResp. dust0.0600.15UpBMIMorningResp. dust0.0280.20UpBMI SAAN/S by AIC sST2MorningQuartz0.00470.27DownWorkplaceShift differenceQuartz0.0200.13DownBMI, workplaceResp. dust0.029UpMixed model analysis performed on inflammasome-related markers and inflammatory markers which significantly correlated with exposure using Pearson or Kendall \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\uptau$$\end{document}τ. The Akaike information criteria was used to select models, by testing the variables respiratory quartz exposure, respiratory dust exposure, BMI, age, smoking status, symptoms of infection last two weeks, mental stress, and workplace (foundry A or B). For the over-shift difference data, the pre-shift inflammatory marker levels were also included as a variable. For the morning samples, the inflammatory marker data from the second sampling and the average exposure for each individual was included in the analyses. For the over-shift differences, the biomarker data and exposure data from both sampling occasions were included in the analyses. The R2 adj. value was adjusted for the number of parameters included in the modelN/S Not selected, act. Activated, resp. RespirableFig. 5Visualizing the fit of the mixed model correlating quartz exposure with over-shift differences (A) and morning levels (B) of caspase-1 ex vivo activation with LPS + ATP in monocytes from participating foundry workers ($$n = 69$$ measurements from 40 individuals for the over-shift difference (A), and $$n = 32$$ for the morning, 2nd measurement (B)). The parameters quartz exposure, age, BMI, and smoking were included in model (A), and in addition workplace (B) determined by the Akaike information criterion. Caspase-1 enzymatic activity was stimulated by treatment of the blood samples with 10 pg/mL LPS for 3 h, followed by 1 h treatment together with 250 µM ATP and the FAM-YVAD-FMK caspase-1 enzymatic activity probe, before determination of the percent caspase-1 activated monocytes. The figure displays the Box Cox transformed data. LPS: lipopolysaccharide, ATP: adenosine triphosphate
## Discussion
Respirable dust and quartz exposure in the iron foundry environment is a potential health hazard that, in accordance with other particle exposure, e.g., in the ambient environment, could mediate inflammatory reactions contributing to the development of e.g., CVD. Therefore, in this study, the dust and quartz exposure, respectively, were characterized in two Swedish iron foundries and their effects on markers of inflammation were studied, including: 1) general markers of inflammation that included the acute phase proteins CRP and SAA, leukocyte cell counts, such as total WBC and the cell ratios NLR and LMR, and the cytokines/chemokines IL-6, CXCL8 and CCL2, 2) established and novel potential CVD biomarkers, including ICAM-1 [20], VCAM-1 [21], GDF-15 [22], MPO [23], sST2 [24], and CD40L [25], and 3) NLRP3 inflammasome associated markers, including IL-1β, IL-18, IL-1Ra, and inflammasome activation to ex vivo activating stimuli, measured by caspase-1 enzymatic activity in monocytes.
Previous studies, using lag days of exposure, have found that PM exposures can affect different inflammatory markers either very fast, with lag 0–24 h [26, 27], or more slowly, with the largest effects after 24–95 h post exposure [26], or from the average 5-day lag measure [26, 28]. In the present study, the over-shift variation in inflammatory marker levels were analyzed in regard to exposure to find effects on fast inflammatory responses to exposure. Slower, retained inflammatory responses from recent days/weeks of exposure were studied by correlating the average exposure for the two exposure measures with either i) the morning pre-shift inflammatory marker levels for the follow-up measurement or ii) the average morning inflammatory marker levels from the two measures. Although large temporal variability in exposure is possible for single days, the correlation across the repeated exposure measurements was significant, indicating similar daily exposures for this time-period for most workers. During the time between the two campaigns, three workers reported a change in work tasks and three workers reported days of absence due to childcare or sick leave. Thus, the majority of workers were fixed to their work tasks, data that strengthen the study design.
The average respirable dust and quartz exposures (1.3 and 0.065 µg/m3, respectively) were similar to our previous measures in a Swedish iron foundry cohort (0.85 and 0.052 mg/m3, respectively) [29]. When adjusted for respirator use, no measurement exceeded the current Swedish OEL of 2.5 and 0.1 mg/m3, for respirable dust (average 0.65 mg/m3) and quartz (average 0.020 mg/m3), respectively. However, in six out of 72 measurements, the levels of respirable quartz were above 0.5 mg/m3, demonstrating exposure levels close to OELs in some parts of the production line. To further characterize the foundry dust, we measured the size distribution of the PM2.5, composed of particles roughly comparable in size to the respirable fraction that collects PM < 4 µm. As shown by the stationary measurements of respirable dust and PM2.5 in Table 1, the average exposure to these two size fractions in the foundries were at similar levels. Interestingly, ultrafine/nanoparticles (< 0.25 µm) constituted a large mass portion ($37\%$) of the PM2.5. Such small nano-particles may have the ability to cross the lung-epithelial barrier, thereby gaining access to the circulatory system where they may induce direct effects on other parts of the body, including cardiovascular tissues [2]. On the other hand, our results show that the quartz content is reduced in the fractions of smaller particles, going from $3.7\%$ in the > 2.5 µm fraction down to $1.1\%$ in the < 0.25 µm fraction. Thus, the majority of quartz particles reaching the lower parts of the lungs are likely in the µm range and retained in that part of the lung. By visual observation of the collected particles, the smallest particles (< 0.25 µm) appear, however, to be constituted by a larger proportion of metal content, such as iron oxides, as indicated by the red/brown color observed in the majority of those dust samples. Effects of exposure to iron oxides of low µm-sized particles from occupational exposure or in controlled human exposure chamber studies generally show low toxicity [30]; however, the toxicity of iron oxide particles in the nano-sized range needs further investigation.
Further, the metal content of collected inhalable dust filters was analyzed, revealing large variations in the metal content in regard to different sampling occasions and locations in the foundries, ranging from 5–$45\%$ of the total mass. The most abundantly identified metal was iron, followed by magnesium and aluminum. The fettling area was the only location that had particle levels for metals (i.e., iron, copper, chromium) most likely above the Swedish OELs, given that the OELs was set for a different size fraction. However, the workers participating in the current study always wore protective equipment when working at this particular workplace. The large variation in the composition of the dust may complicate the use of general classification of dust fractions, such as respirable dust, when correlating inflammatory markers with exposure. In particular acute effects are difficult to evaluate given the fact that a certain fraction of the dust particles within the mix may exert the majority of the biological responses.
Effects of short-term exposure have been shown for ambient particles, including effects on inflammation [13, 14], likely contributing to increased mortality and morbidity in CVDs associated with acute exposure [31]. In the current study, effects of exposure to respirable quartz and respirable dust were examined by using the over-shift differences or morning pre-shift levels in inflammatory markers. Some of the inflammatory markers investigated in the current study had significantly different levels in the morning and afternoon samples without displaying significant correlation to exposure; data indicating diurnal variations, with examples of both significantly higher levels in the afternoon (e.g., neutrophils and MPO) and lower levels (e.g., CCL2 and CRP). Other markers did not display any significant diurnal variation, including the adhesion molecules ICAM-1 and VCAM-1 as well as the cytokines IL-1β and GDF-15.
There was a significant correlation between some of the over-shift differences in inflammatory marker levels and exposure, including respirable dust and respirable quartz exposure with SAA and ex vivo ATP-induced caspase-1 activation. SAA is an acute phase protein and a sensitive marker of inflammation induced by e.g., infection, injury or stress, and elevated levels have been associated with adverse cardiovascular outcomes [32, 33]. In our previous study, SAA was found to correlate with dust exposure in foundry workers [29]. Increased levels of SAA have also been found in the biological response to acute exposure to ambient ultrafine particulate matter [13]. However, regarding SAA in the current study, when adjusting for covariates in the mixed models, no exposure measures were selected as variables by the Akaike information criterion, suggesting a minor role of exposure on SAA levels compared to variables like BMI and symptoms of infection. Instead, when adjusting for covariates, several inflammasome-related markers were found to correlate to respirable quartz, including IL-1β, IL-18, and ex vivo caspase-1 activity. In addition, the inflammatory markers sST2 correlated with quartz and respirable dust exposure. Most markers had a negative correlation to exposure, indicating either a smaller increase or a larger decrease in the post-shift levels compared to the basal morning levels.sST2, a receptor of the IL-1 receptor family binding the ligand IL-33, is suggested to have prognostic and diagnostic value in CVDs, especially for heart failure [34, 35]. In addition, elevated sST2 levels has been reported in different pulmonary diseases and to give prognostic value of mortality in these diseases [36, 37]. There are only a few studies that have examined sST2 in the context of PM exposure; one demonstrating elevated sST2 serum levels in non-farmers exposed to organic dust in a pig barn [38], and one study demonstrating elevated sST2 mRNA levels in lung homogenates in diesel exhaust particles plus house mite dust exposed mice [39].
Effects of dust or quartz exposure on inflammation were also assessed by the morning pre-shift inflammatory marker levels, indicating a more sustained low-grade inflammatory effect of recent days/weeks of exposures. The morning levels were mainly correlated with exposure for inflammasome-related markers, i.e., all of the ex vivo caspase-1 activity measures, and when adjusting for covariates, also IL-18, showing a negative correlation to exposure. In addition, the morning levels correlated with respirable dust and quartz exposure for the inflammatory markers CXCL8 and sST2, respectively, in line with results for the over-shift difference. CXCL8 (interleukin-8) is a chemoattractant for neutrophils that has been found to be produced in response to PM exposure, e.g., in bronchial wash fluid following diesel exhaust particles (DEP) [40], and to be produced by lung epithelial cells following DEP and silica exposure [41].
When adjusting for covariates, both the morning levels and the over-shift difference had very similar results, indicating correlation to quartz exposure for the inflammasome-related markers IL-18 and ex vivo caspase-1 activation. In a previous study of 85 foundry workers, similar results were found, showing correlation between respirable dust and quartz and effects on inflammasome/caspase-1 activation, and in addition correlation of IL-18 and IL-1Ra with dust exposure [42]. Taken together, these results indicate that the dust and quartz exposure in the iron foundry environment affect the NLRP3 inflammasome/caspase-1 axis. The inflammasome generally require two signals to be assembled, leading to activation of pro-caspase-1 into active caspase-1 that in turn cleaves IL-1β and IL-18 into their active forms. In our ex vivo inflammasome activation experiments, LPS was used as the first “priming” signal that induces transcription of NLRP3 inflammasome components, including NLRP3 and the pro-forms of the cytokines IL-1β and IL-18. Further, ATP was used as the secondary “triggering” signal that govern the assembly of the components caspase-1, NLRP3, and ASC into an active inflammasome. Interestingly, respirable dust exposure correlated with caspase-1 effects by LPS treatment, indicating an effect on inflammasome priming, whereas the respirable quartz exposure correlated to caspase-1 effects by ATP treatments, indicating effects on the secondary inflammasome assembly signal and that the monocytes extracted from foundry workers were not naïve but predisposed (primed) for inflammasome activation. Quartz is a well-known activator of the inflammasome [43], and these results suggest that quartz exposure has a biological effect that was induced also at the current exposure levels detected in the foundries of the study, i.e., below the OEL. The inflammasome is suggested to play an important role in particle/quartz mediated diseases, including CVD [44] and lung diseases, such as silicosis and chronic obstructive pulmonary disease [43, 45]. Therefore, finding exposure levels that do not affect inflammasome signaling would be desirable in order to prevent biological impact before turning into disease.
There are some limitations of the study, mainly the relatively low number of participants. This limitation was dealt with using repeated measures to give high quality of the exposure and biomarker data. The diurnal variation can be viewed as a problematic issue when comparing biomarker levels at different time points. However, observing a reduced or increased change in the diurnal variation could also be an important aspect of the inflammatory response, rather than considering merely increased levels of inflammatory markers post-shift as signs of inflammation. Finally, acute effects on systemic inflammation may take longer than one day to wash-out. For example, a study on ambient air pollution and the effects on inflammatory/coagulation markers in susceptible individuals demonstrated the strongest correlations for PM2.5 with CRP for a lag time of 24–95 h post exposure, or the 5-day lag average exposure, while other markers, including sCD40L and PAI-1 demonstrated significant correlation with PM2.5 only for the 0–23 h lag time [26]. Thus, it seems that inflammatory markers vary in their response times to exposure, and that there is likely no way to get around this, e.g., by sampling on a Monday. Therefore, we chose a workday in the middle of the workweek rather than a Monday for blood sampling and exposure measurement.
## Conclusions
The results demonstrate that exposure to respirable dust and quartz in the iron foundry environment correlate with systemic inflammatory markers, including effects on inflammasome activation and with the inflammatory markers sST2, and CXCL8. The exposure levels were, when corrected for respirator use, below the Swedish OELs. Still, as correlation of exposure to biological effects were detected, concerns must be raised about the safety of the current exposure levels, and iron foundries and industries of similar exposures are therefore encouraged to continuously work towards reduced dust and quartz exposures in the workplace.
## Study group
The study was performed on 40 individuals working at two Swedish iron foundries, employing in total ca 90 and 25 individuals, respectively. One foundry manufactures parts for wind turbines and the other manufacture mainly custom orders. The produced castings at both sites are mainly comprised of iron and grey iron alloys. Descriptive statistics of the study population and their employment can be seen in Table 6. The participants were employed for work tasks throughout the production chain, including mainly work in generating the casted goods (mold making, core making, melting, casting, shake out, and fettling). Additional work included product controller, truck driver, feedstock work, maintenance, and administrative/leadership work. Exclusion criteria included pregnancy and diabetes. Table 6Descriptive statistics of the 40 iron foundry workers included in the studyGenderMaleFemale382AgeMeanSDMinMax42112063BMIMeanSDMinMax2952041Employment time (years)MeanSDMinMax97027SmokingCurrent smokerEx-smokerNever-smoker101119WorkplaceFactory AFactory B2020Main worktask($$n = 72$$ measurements)Casting/moldingCore MakingMeltingFettling1991210Shake outMaintenance/ quality controlOther work in productionAdministrative work3937
## Study design
The inclusion of the study was performed between January 2019 and February 2020 at two different Swedish iron foundries during eight different sampling occasions, sampling ca 10 individuals per campaign. At the smaller foundry, all participants, who met the inclusion criteria were offered to participate, and at the larger foundry, all foundry workers working the dayshift at the sampling dates were offered to participate. A schematic illustration of the sampling procedure is shown in Fig. 6. Sampling was done during the months October – March to avoid the pollen season. Air sampling of dust levels was performed during the third day following a work-free weekend. On the same day, venous blood was collected in the morning before work (pre-shift), and in the afternoon after an eight-hour work-shift (post-shift). The morning samples were overnight fasting samples. Repeated measures were conducted on the same individuals at least 3 weeks after the first measurements. On the repeated measurement, it was not possible to sample 8 of the individuals as they were absent from work on the sampling occasion, due to sick leave, childcare, or work travel. *To* generate blood plasma, the tubes with collected venous blood were immediately put on ice and were within 30 min centrifuged at 2 000 × g for 15 min. Li-heparin plasma was refrigerated until analysis of CRP, whereas the EDTA-plasma was frozen on site and then stored in the biobank until subjected to biomarker analysis. In addition, on-site measurement of white blood cell (WBC) counts and ex vivo stimulation of whole blood for assessment of inflammasome activation was performed. A questionnaire was completed by all participants, gathering information about working conditions, age, sex, body mass index (BMI), and smoking habits. Fig. 6Schematic illustration of the sampling procedure
## Aerosol measurements and characterization of quartz and metal content
Measurements of respirable dust and quartz were carried out as personal sampling for all study participants, using a SKC aluminium cyclone (SKC 225–01-01, SKC, Eighty Four, PA) equipped with a nitrocellulose membrane filter and an air pump (SKC AirCheck 5 000) operating at a constant airflow rate of 2.5 L/min. The workers received the sampling equipment before work started and they carried the equipment throughout their eight-hour work-shift. In addition, stationary measurements were performed for additional particle measures, including inhalable dust, respirable dust, PM10, PM2.5, PM1, particle number concentration, and particle surface area concentration. Also, a Sioutas cascade impactor (SKC) was used to measure dust and quartz levels at five cut-points (2.5 µm, 1 µm, 0.5 µm, 0.25 µm, and < 0.25 µm aerodynamic diameter). The dust concentrations on the filters were analyzed gravimetrically and the quartz content of collected respirable dust and impactor filters were analyzed by X-ray diffraction on a X’Pert PRO instrument (Malvern Panalytical, Malvern, Worcestershire, United Kingdom) with the angles 20°, 26° and 50°. Prior to analysis, the collected dust filters were ashed using a EMITECH K1050X radio frequency plasma asher (Emitech, Montigny-le-Bretonneux, France) at 60 °C for 14 h, and the remaining inorganic material was wet filtered onto a silver membrane filter (0.8 µm pore size) to be used in the X-ray diffraction analysis. For the inhalable particle fraction, the metal content of the collected dust was analyzed using an iCAP Q ICP-MS instrument (Thermo Fisher Scientific, Waltham, MA) performed after the filters had been dissolved in acid (concentrated nitric acid with $10\%$ hydrogen peroxide).
## Cytokine measurements
The plasma concentration was determined for 16 different proteins, both in the morning (pre-shift) and afternoon (post-shift) samples for the 40 study participants. The proteins measured, and details of the analyses, are shown in Table 7. High sensitivity CRP was analyzed at the Department of Clinical Chemistry, Örebro University Hospital, and IL-1β was analyzed by Quanterix (Billerica, MA) sample analysis service at the company’s facility. All other protein biomarkers were analyzed by Mesoscale Diagnostics (Rockville, MD) technology at Örebro University according to the manufacturer’s instructions, with technical duplicates, and signals were detected using the QuickPlex SQ120 instrument (Mesoscale diagnostics).Table 7Inflammatory proteins measured in blood plasmaAnalyteAbbreviationSample typeAnalysis methodGeneral inflammatory proteins C-reactive proteinCRPLi-heparin plasmaHigh sensitivity C-X-C Motif Chemokine Ligand 8CXCL8, (IL-8)EDTA plasmaMSD—U-plex Interleukin 6IL-6EDTA plasmaMSD—U-plex Interleukin 17AIL-17AEDTA plasmaMSD—U-plex Interleukin 33IL-33EDTA plasmaMSD—U-plex C–C motif ligand 2CCL2 (MCP-1)EDTA plasmaMSD—U-plex Serum amyloid ASAAEDTA plasmaMSD—V-plexNovel inflammatory CVD markersICAM-1EDTA plasmaMSD—V-plex Intercellular adhesion molecule 1GDF15EDTA plasmaMSD—R-plex Vascular cell adhesion molecule 1VCAM-1EDTA plasmaMSD—V-plex CD40 ligandCD40LEDTA plasmaMSD—R-plex Growth/differentiation factor 15GDF-15EDTA plasma MyeloperoxidaseMPOEDTA plasmaMSD—R-plex Soluble suppression of tumorigenesis 2sST2EDTA plasmaMSD—R-plexNLRP3 inflammasome related proteins Interleukin 1 betaIL-1βEDTA plasmaSimoa bead tech Interleukin 1 receptor antagonistIL-1RaEDTA plasmaMSD—U-plex Interleukin 18IL-18EDTA plasmaMSD—U-plexEDTA Ethylenediaminetetraacetic acid, MSD Mesoscale Diagnostics
## White blood cell counts
Cell counts were performed in freshly isolated blood for total WBCs, neutrophils, lymphocytes, monocytes, eosinophils, and basophils using the Hemocue WBC Diff instrument (Hemocue, Ängelholm, Sweden). From these data, the neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR) was calculated.
## Ex vivo NLRP3 inflammasome activation
For analysis of caspase-1 activity in circulating monocytes, 100 µL freshly isolated whole blood (EDTA treated, used within 3 h after sampling) from the study participants was mixed with an equal volume of RPMI 1640 cell culture media (Thermo Fisher Scientific, Waltham, MA), either with or without lipopolysaccharide (LPS (ultrapure LPS-B5, Invivogen, San Diego, CA), resulting in 10 pg/mL final LPS concentration. The samples were incubated at 37 °C for three hours before addition of 4.5 µL FAM-FLICA-YVAD-Caspase-1 probe (Immunochemistry Technologies, Bloomington, MN), and 1 µL CD14-APC antibody (IM2580, Beckman Coulter, Brea, CA) or isotype control. In addition, some samples received 250 µM adenosine triphosphate (ATP), resulting in 6 different sample treatments per individual: 1) CD14 isotype control, 2) autofluorescence control (no caspase-1 probe), and the following (samples 3-6) with CD14-APC antibody and caspase-1 probe: 3) non-treated sample, 4) LPS treated sample, 5) ATP treated sample, and 6) LPS + ATP treated sample. The samples were incubated at 37 °C for 1 h prior to fixation and lysis of red blood cells with 1.7 mL eBioscience 1-Step Fix/Lyse Solution (Thermo Fisher Scientific). Following lysis for 15 min, the samples were washed twice with FLICA wash buffer prior to analysis of caspase-1 enzymatic activity by flow cytometry using an Accuri C6 instrument (BD, Franklin Lakes, NJ). In the flow cytometer analysis, monocytes were gated from a CD14 and side scatter plot, and 2 000 gated events were collected for data analysis of caspase-1 activity. Representative plots are shown in Fig. 7.Fig. 7Representative flow cytometry plots used to determine the percent of monocytes in the caspase-1 positive (inflammasome activated) peak. A Gating of CD14+ monocytes. B-E Caspase-1 enzymatic activity in gated monocytes measured by the FAM-YVAD-FMK probe in B untreated cells, C LPS treated cells, D ATP treated cells, E LPS + ATP treated cells. V2-R indicates caspase-1 (inflammasome) activated cells
## Exposure measures
For all participants, 8-h time-weighted average (TWA) exposures were calculated for the personal respirable dust and quartz measurements. Out of the 40 individuals, sampled up to two times (in total 72 personal measurements), respirators were used by 10 individuals (15 measurements) for some part of the day. Therefore, both the respirator-adjusted exposure levels and unadjusted exposure levels were calculated. For 9 of the 15 measurements where respirators were used, a full facepiece with external air supply was used, and for the remaining 6 measurements a half facepiece equipment was used. For the measures where a half facepiece was used, the time spent with respirators were less than half of the workday. In contrast, workers using a full facepiece reported using the respirators for a majority of the time. To calculate respirator-adjusted exposures, zero exposure was assumed during the time a respirator was worn, and during the time without respirator, the exposure was calculated from the background exposure measured by a stationary measurement performed in the proximity of the worker. Since respirators were most likely used during operations with peak exposures, we find the use of background exposures for the time-points without respirator use most accurate. However, if no stationary measurement was considered relevant for an individual, the average personal exposure levels were instead used for the time-points without respirator use.
## Statistical analysis
For analyzing the short-term acute effects of exposure, the post-shift, pre-shift biomarker difference was correlated to exposure. In these analyses, the up to two over-shift biomarker differences and PM exposure levels for each individual were used in the analyses ($$n = 69$$, for 3 out of the 72 measurements, blood was not retrieved both pre- and post-shift). For analyzing effects of recent days/weeks exposure to respirable dust and quartz with biomarker levels, the average exposure levels for the two repeated measurements were used, with the exception of one individual who permanently changed work tasks from production line to product controller after the first measurement. The exposures were correlated with the morning pre-shift levels, either using the average morning pre-shift levels from the two repeated measures (morning \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{\times }$$\end{document}ׯ 1st, 2nd, $$n = 40$$), or using the morning samples of the follow-up measurement (morning 2nd, $$n = 32$$). For the morning \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{\times }$$\end{document}ׯ measure, the eight individuals that did not come for the follow-up measurement were included. Data were normalized using the Box Cox transformation to approach Gaussian distributions. Log-transformation was selected for all plasma proteins and caspase-1 activity measures. The power transform \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{{x}^{\uplambda }-1}{\uplambda }$$\end{document}xλ-1λ was selected for exposure measures (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\uplambda$$\end{document}λ= 0.2235 and 0.1408 for respirable dust and respirable quartz, respectively), age (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\uplambda$$\end{document}λ= 1.3822 and BMI (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\uplambda$$\end{document}λ= -1.8007). For WBC measures and smoking, no transformation was performed due to these parameters either containing zero or negative values or being categorical. Correlation of exposure with normalized biomarker levels were calculated using both ordinary Pearson correlation and Kendall \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\uptau$$\end{document}τ rank correlation, with a p-value threshold of 0.01 for significance. The low p-value was chosen to decrease the chance of type I errors (false positives) due to the large number of comparisons. To compare if the over-shift biomarker levels differed significantly, p-values were calculated using the Wilcoxon signed rank test. To visualize correlation of biomarker levels, exposures, and individual features (age, BMI, smoking (never/Ex/current smoker)), a heatmap was created from the Pearson correlation of the average morning biomarker levels.
For the mixed model used to model exposure with the over-shift difference in biomarker levels, the following model was considered:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Y={C}_{0}+{C}_{1}{X}_{1}+{C}_{2}{X}_{2}+{C}_{3}{X}_{3}+{C}_{4}{X}_{4}+{C}_{5}{X}_{5}+{C}_{6}{X}_{6}+{C}_{7}{X}_{7},$$\end{document}Y=C0+C1X1+C2X2+C3X3+C4X4+C5X5+C6X6+C7X7, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Y$$\end{document}Y is the over-shift difference in biomarker level (post-shift – pre-shift value), \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${C}_{0},\dots {C}_{7}$$\end{document}C0,⋯C7 the estimated parameters, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{1},\dots {X}_{7}$$\end{document}X1,⋯X7 the predictors considered (morning value of estimated parameter, respirable dust exposure, respirable quartz exposure, age, BMI, symptoms of infection last two weeks [0,1], mental stress (0–5), workplace [0,1] or smoking (never/ex/current)).
In order to only select the relevant predictors and to avoid overfitting, all possible subsets of these predictors was tested and the best one according to the Akaike information criterion was selected (effectively setting \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${C}_{i}=0$$\end{document}Ci=0 for any unselected predictors).
The statistics were calculated, and Fig. 5 generated, using MATLAB v. R2019 (MathWorks, Natick, MA). Graphs in Fig. 1 and 3 were generated using GraphPad Prism v. 5.03 (GraphPad Software, San Diego, CA), Fig. 4 was generated using the MetaboAnalyst software v.5.0 (www.metaboanalyst.ca). Figure 7 was generated using the BD Accuri C6 software v. 1.0.264.21 (BD).
## Supplementary Information
Additional file 1: Table S1. Biomarkers included in the study, with the morning (pre-shift) and afternoon (post-shift) levels shown ($$n = 40$$ foundry workers). If a participant was sampled more than once, the average level for each time-point is shown. In addition, the over-shift difference (post-pre shift levels) of each biomarker is shown. P-value for the over-shift difference was calculated with the Wilcoxon signed-rank test. * Below detection limit for most of the measurements. Table S2. Mixed model data for the over-shift difference of selected inflammatory markers. Table S3. Mixed model data of selected inflammatory markers for the morning 2nd measurement biomarker levels.
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|
---
title: 'Prevalence and risk factors of cardiovascular disease among people living
with HIV in the Asia-Pacific region: a systematic review'
authors:
- Witchakorn Ruamtawee
- Mathuros Tipayamongkholgul
- Natnaree Aimyong
- Weerawat Manosuthi
journal: BMC Public Health
year: 2023
pmcid: PMC10009940
doi: 10.1186/s12889-023-15321-7
license: CC BY 4.0
---
# Prevalence and risk factors of cardiovascular disease among people living with HIV in the Asia-Pacific region: a systematic review
## Abstract
### Background
Cardiovascular diseases (CVD) due to atherosclerosis have become one of the major causes of death among people living with HIV (PLHIV) since effective antiretroviral therapy (ART) has been available throughout the world. However, the epidemiologic evidence of this problem from the Asia-Pacific region remains unclear. We conducted a systematic review of the situation and risk factors for CVD among PLHIV in countries with the greatest impact of CVD attributable to HIV in the Asia-Pacific region.
### Methods
A systematic search in PubMed/MEDLINE, EMBASE, and the Cochrane Database of Systematic Reviews databases for articles published before 2019 was conducted. Publications reported situations and risk factors both traditional and HIV-specific for CVD among PLHIV in the region were included. Two reviewers working on duplicate and quality assessments, independently extracted data, and thematically analyzed the data.
### Results
Among PLHIV, the prevalence of subclinical CVD ranged from 10 to $28\%$ and the incidence rate of clinical CVD ranged from 0.37 to 1.17 /100 person-years. Clinical CVD was frequently observed in the early era of the highly active antiretroviral therapy. A higher prevalence of subclinical CVD such as abnormal cIMT and carotid plaques was frequently observed in the PLHIV rather than in the nonHIV population and a high proportion of early onset of CVD was found among young PLHIV adults. The traditional risk factors for CVD such as hypertension, diabetes and smoking behavior were prevalent in both PLHIV and nonHIV populations ranging from 5 to $45\%$. HIV-specific risk factor, and lower CD4 presented almost twice the significantly increased risks for CVD while the synergistic interaction among traditional risk factors, i.e., diabetes mellitus, dyslipidemia and family history steeply increased the risk for CVD among PLHIV by almost 20 times.
### Conclusion
The limited existing data suggested the risk of early CVD among PLHIV. We identified the crucial gaps in HIV/CVD work from the Asia-Pacific region and recommended longer prospective studies with larger sample sizes or meta-analyses to better capture CVD risk and interactions of crucial risk factors of this vulnerable population in this region.
### Registration number
INPLASY202290108 (https://inplasy.com/inplasy-2022-9-0108/).
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-023-15321-7.
## Background
The beneficial outcome of the highly active antiretroviral therapy (HAART) on people living with human immunodeficiency virus (PLHIV) is to increase quality of life and life expectancy [1]. The number of PLHIVs, aged over 50 years, has increased worldwide and is linked to an increased burden of noncommunicable diseases (NCDs). The double disease burden, HIV and NCDs in particular cardiovascular diseases (CVD), may cause a hectic burden on the economy of families and countries [2]. This situation requires global public health attention, particularly in low to middle income countries. From 1990 to 2015, the global burden of CVD attributable to HIV increased greater than three times from 0.74 million to 2.57 million disability-adjusted life-years [3]. A modeling study estimated that by the end of 2030 about $70\%$ of PLHIV will be those aged older than 50 years, and $78\%$ will have CVD [4].
A higher risk was observed of CVD among PLHIVs than that among HIV-negative individuals approximately 1.5 to 3 folds although the distribution of traditional risk factors for CVD among both groups did not differ [5–9]. Several recent studies suggested that underlying mechanisms of HIV-specific risk factors likely contributed to accelerated CVD in PLHIV, i.e., side effects of antiretroviral therapy (ART) and systemic chronic inflammation due to immune activation against HIV [5–10].
The Asia-Pacific region is the second region with a greater burden of HIV after sub-Saharan Africa, (5.8 and 25.7 million, respectively) [11]. The Asia-Pacific region has currently confronted the emerging challenge of CVD among PLHIV. A recent global burden of disease study revealed that the CVD population attributable to HIV was comparable with traditional risk factors [3]. Similarly, related studies suggested an increased incidence of CVD among PLHIV; however, most studies were conducted in high-income countries where epidemiologic evidence was unsuited for the Asia-Pacific region due to different socio-economic contexts [3, 7, 12–13]. To substantiate the situation of CVD and its risk factors among PLHIV remains indispensable for evidence-based public health in this low to middle-income region [3, 7, 9]. In this review, we addressed the knowable epidemiologic evidence of CVD among PLHIV in Asia-Pacific countries to provide existing scientific evidence to alert public health professionals in the region confronting the syndemic of HIV and CVD.
## Search strategy
This systematic review of clinical and subclinical CVD among PLHIV in countries with the greatest impact of CVD attributable to HIV in the Asia-Pacific region, i.e., Thailand, Papua New Guinea, Bhutan, Cambodia, Myanmar, the Solomon Islands, Malaysia and Indonesia [3] was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [14] and reported following the Meta-analysis of Observational Research in Epidemiology guidelines [15]. This study has been registered in the INPLASY website with registration number INPLASY202290108 (https://inplasy.com/inplasy-2022-9-0108/).
The first author identified articles published in English through searching PubMed/MEDLINE, EMBASE, and the Cochrane Database of Systematic Reviews databases from any date to 31 December 2019 including Medical Subject Headings (MeSH) “Human immunodeficiency virus”, “People living with HIV”, “Cardiovascular”, “Cerebrovascular”, “Thailand”, “Cambodia”, “Myanmar”, “Bhutan”, “Papua New Guinea”, “the Solomon Islands”, “Malaysia”, or “Indonesia”. EndNote X8 (Clarivate Analytics, PA, USA) was used to collect, deduplicate, manage and review the searched articles. The detailed search strategy was provided in Additional file 1.
## Study eligibility
To identify the eligibility of each study, the procedure was performed in a stepwise manner. First, the titles and abstracts of the identified articles were screened for appropriateness by the first author (WR) consulting with the senior author (MT). All identified articles through database searching under the search strategy were included. We excluded non-English articles, before HAART implementation articles, conference abstracts, case reports/case series and randomized controlled trials. We also excluded studies not using cardiovascular disease as the outcome, not involving PLHIV and conducted among nonadult populations (ages < 18 years). Next, the selected article full texts were independently reviewed by two authors (WR and MT) to collect pertinent data in greater detail. Study selection disagreements were resolved by authors’ discussion. Finally, we extracted data for qualitative synthesis.
## Data extraction and quality assessment
Data were extracted on publication date, study area (country), study design, study period, sample size, age, CVD outcomes and measurements, incidence or prevalence of CVD outcomes among PLHIV and risk factors for CVD.
We used the Newcastle-Ottawa scale (N-O scale) to critically assess the quality of non-randomized studies [16]based on the study design, i.e., cohort, case-control and cross-sectional studies. The tool comprises three domains, i.e., participant selection, comparability and exposure/outcome assessment in the selected studies [17]. The N-O scale was then categorized in three levels following the standards of the Agency for Healthcare Research and Quality (AHRQ); good quality (> 3 stars in the selection domain, 1–2 stars in the comparability domain, 2–3 stars in the outcome/exposure domain); fair quality (2 stars in the selection domain and 1–2 stars in the comparability domain or 2–3 stars in the outcome/exposure domain); poor quality (< 1 star in selection domain or zero star in the comparability domain or < 1 star in the outcome/exposure domain) [17].
The data extraction and critical appraisal were conducted by the first author (WR). The second author (MT) independently checked and discussed all the results. In case of disagreement, the third author (NA) arbitrated. A detail of the quality assessment result is described in Additional File 2. We thematically analyzed and synthesized data from selected articles concerning the situation of CVD and risk factors which were classified as traditional risk factors and HIV-specific risk factors following Nou E et al. [ 18].
## Study selection and study characteristics
A total of 1,641 articles were identified through the literature search. After removing all duplicates, titles and abstracts of 1,467 records were screened, and 1,407 articles were excluded. Of the 1,407 excluded articles, 185 were published in the pre-HAART era, 75 constituted case reports and case series, 796 revealed irrelevant study objectives, 310 lacked cardiovascular outcomes and 41 lacked PLHIV. We further searched for 60 full texts and then excluded 14 abstract conferences, 6 systematic reviews, 5 studies among child populations, 19 studies in other regions and 5 studies not measuring CVD outcomes. Finally, 11 eligible studies were included in the summary and qualitative synthesis (Fig. 1). Among 11 eligible studies, the study quality of 9 studies was good, and 2 studies were poor (Additional File 2).
Fig. 1PRISMA Flow Diagram for Selection Eligible Study The 11 studies, comprised 7 cross-sectional studies, 2 retrospective cohort studies, 1 prospective cohort study and 1 case-control study. Among 8 counties with the greatest impact of CVD attributable to HIV in the Asia-Pacific region, we found eligible publications from only 3, i.e., Indonesia, Malaysia and Thailand. The sample size of each study ranged from 50 to 1,813 subjects (Table 1).
Table 1Published studies on CVD outcomes among PLHIV in Asia-Pacific counties with the greatest impact of CVD attributable to HIV statusStudy(Country, year)Study designN =Age (years)Outcome measurementsFindingsKarim et al. 2017 [19](Indonesia, 2013–2014)Prospective cohort1-year follow-up:PLHIV: 67Median:31 years (range 19–48 years)1. Carotid Intima-Media thickness (cIMT)2. Diameter of carotid artery1. Right cIMT among PLHIV (0.70 mm) was higher than uninfected (0.58 mm) after 12 months of ART initiation, $p \leq 0.05.2.$ Left cIMT among PLHIV (0.65 mm) was higher than that of uninfected (0.58 mm) after 12 months of ART initiation, $p \leq 0.05.3.$ No difference in the diameter of the right and left carotid artery after 12 months of ART initiation.4. Traditional risk factors associated with cIMT and carotid artery diameter after 6 months of ART initiation, $p \leq 0.05.5.$ HIV-related factors associated with carotid artery diameter after 3 months of ART initiation, $p \leq 0.05.$Subsai et al. 2006 [20](Thailand, 2002–2004)*Poor qualityRetrospectiveCohort2 years follow-up:PLHIV: 506Mean: 36.8 ± 7.9Hemorrhagic stroke or ischemic strokeThe incidence rate of hemorrhagic stroke:$\frac{1.17}{100}$ pys The incidence rate of ischemic stroke: $\frac{2.35}{100}$ pysThe incidence rate of stroke in the HAART era is higher than in the pre-HAART era. Sitticharoenchai et al. 2019 [21](Thailand, 2010–2015)*Good qualityRetrospectivecohort5 years follow-up: 2010 to 2015PLHIV: 1,813Median: 44(range 38–50)1. Coronary artery diseases2. Ischemic stroke1. Incidence rate of CVD: $\frac{3.75}{1000}$ pys2. Previous ischemic stroke and family history of CVD associated with CVD event, Adj OR 34.69 ($95\%$CI 5.15-233.45) and Adj OR 6.89 ($95\%$CI 2.57–18.48).3. Interaction effect between diabetes mellitus and dyslipidemia on CVD: Adj OR 17.2 ($95\%$CI 7.8–38.3)4. Interaction effect between family history of CVD, diabetes mellitus and dyslipidemia on CVD: Adj OR 22.2 ($95\%$CI 4.1-118.5).5. High proportion of CVD among young HIV adult age < 55 years, $52.95\%$Aurpibul et al. 2019 [22](Thailand, 2015)*Good qualityCross-sectionalPLHIV: 107Non-HIV: 48PLHIVMean: 58.7 ± 6.5Non-HIVMean:59.7 ± 6.51. Subclinical atherosclerosis measured by Cardio-ankle vascular index (CAVI)2. Peripheral artery disease (PAD) measured by Ankle- brachial index (ABI)1. Prevalence of subclinical atherosclerosis ($23\%$ and $29\%$) and prevalence of PAD ($6\%$ and $8\%$) between PLHIV and NonHIV2. DM associated with CAVI among PLHIV, Adj OR 1.54 ($95\%$CI 1.01–2.35).Putcharoen et al. 2019 [23](Thailand, 2016–2017)*Good qualityCross-sectionalPLHIV: 60Non-HIV: 30PLHIVMedian: 54.9 (range 52–60)Non-HIVMedian: 53 (range 50–60)Carotid Intima-Media thickness (cIMT)1. No difference in median overall cIMT between PLHIV (0.665 mm) and NonHIV (0.649 mm).2. Of PLHIV, $10\%$ was observed plague.3. Male and hypertension associated with thicker cIMT among PLHIV, β = 0.041 ($95\%$CI 0.001–0.081) and β = 0.047 ($95\%$CI 0.003–0.092).Siwamogsatham et al. 2019 [24](Thailand, 2016–2017)*Good qualityCross-sectionalPLHIV: 316Median age: 54.4 (IQR 51.7–59.4)Carotid Intima-Media thickness (cIMT)1. Subclinical CVD $28.2\%$2. Age positively associated with subclinical atherosclerosis, Adj OR 1.06 ($95\%$CI 1.003–1.12).3. CD4 count (< 200 cells/mm3) associated with subclinical atherosclerosis, Adj OR 1.80 ($95\%$CI 1.02–3.18).Utama et al. 2019 [25](Indonesia, 2017)*Good qualityCross-sectionalPLHIV: 50Mean: 30.60 ± 5.58Carotid Intima-Media thickness (cIMT)1. Older age increases the diameter of cIMT at β 0.012 ($95\%$CI 0.002–0.022).2. CD4/CD8 ratio increases the diameter of cIMT at β= -0.791 ($95\%$CI -0.99 to -0.592).Rajasuriar et al. 2015 [26](Malaysia, NA)*Good qualityCross-sectionalPLHIV: 84Median:41 (IQR 36–46)Carotid Intima-Media thickness (cIMT)Prevalence of subclinical atherosclerosis: $27.4\%$Aurpibul et al. 2019 [27](Thailand, 2015)*Good qualityCross-sectionalPLHIV: 362Non-HIV−: 362PLHIV: Mean 57.8 ± 5.6Non-HIV: Mean 58.1 ± 5.7Peripheral artery disease (PAD) measured by ABI1. Prevalence of PAD among PLHIV ($5\%$) and nonHIV ($7\%$) did not differ.2. Prevalence of abnormal ABI among PLHIV ($20\%$) were lower than that of uninfected($27\%$), p0.03.3. Female sex and underweight associated with abnormal ABI among PLHIV, Adj OR 2.09 ($95\%$CI 1.20–3.67) and Adj OR 1.73 ($95\%$CI 1.02–2.95).Nakaranurack, Manosuthi. 2018 [28](Thailand, 2011)*Good qualityCross-sectionalPLHIV: 87445.5 ± 8.3CVDPrevalence of CVD among PLHIV: $1.3\%$Lee et al. 2012 [29](Thailand, 2009–2010)*Good qualityCase-ControlPLHIV with stroke: 37PLHIV without stroke: 74PLHIV with stroke: 50.5 ± 11.1PLHIV without stroke: 50.4 ± 13.4Stroke (cerebral infarction and intracerebral hemorrhage)*Tuberculous meningitis* (Adj OR 11.9; $95\%$CI 1.2-117.2) and smoking (Adj OR 6.9; $95\%$CI 2.3–21.2) are associated with stroke among PLHIV.
## CVD among PLHIV in the Asia-Pacific Region
Early detection of CVD was reported in 11 studies, 7 studies identified subclinical CVD diagnosed by CAVI [22], cIMT [19, 23–26], and ABI [19, 22, 27] and another 4 studies identified clinical CVD, i.e., stroke [20, 21, 28, 29] and atherosclerosis [21, 28].
Subclinical CVD, measured using ABI and cIMT reported in seven studies, ranged from 10 to $28\%$ [19, 23–27]. A lower prevalence of subclinical CVD was reported in a study in a younger PLHIV population [25] than that in other studies [19, 20-24, 26, 27]. The prevalence of subclinical CVD such as atherosclerosis was higher in PLHIV than in nonHIV populations [19][23].
Clinical CVD, stroke and coronary artery diseases were reported in two cohort studies ranging from 0.37 to 1.17 /100 person-years [20–21] and the prevalence of $1.3\%$ from a cross-sectional study [28] among PLHIV in Thailand. However, the incidence of ischemic stroke ($\frac{2.35}{100}$ person-years) was higher than hemorrhagic stroke ($\frac{1.17}{100}$ person-years) [20]. Clinical CVD was frequently observed in the early highly active ART era, and a higher incidence was observed in longer follow-up time.
## Risk factors for CVD among PLHIV
The characteristics of CVD risk factor data among PLHIV collected by selected studies were classified in two groups: traditional risk factors and HIV-specific risk factors for CVD including adverse effects of antiretroviral therapy and factors related to systemic immune activation and HIV status [22].
## Traditional risk factors for CVD among PLHIV
The traditional risk factors for CVD among PLHIV in the selected studies were frequently assessed. Moreover, these risk factors were prevalent in both PLHIV and nonHIV populations. Hypertension presented at 13 to $45\%$, diabetes mellitus was 5 to $24\%$ [21, 24, 26, 27–28] and smoking was 13 to $45\%$ [25–29]. Although the prevalence of traditional risk factors between the PLHIV and the nonHIV populations did not conclusively differ [21-23, 26], DM, dyslipidemia and family history presented synergistic effects on CVD risks reported in the PLHIV cohort.
## HIV-specific risk factors for CVD
Although several selected studies intended to identify associations between HIV-specific risk factors and CVD among PLHIV [21–26, 28, 30], only 3 of 11 studies demonstrated the association of HIV-specific risk factors and subclinical or clinical CVD status among PLHIV in this region [24]. Poor immune system (CD4 cell count < 200 or CD8/CD4 ratio < 1) increased the risk of subclinical CVD [24, 25]. A cross-sectional study in Thailand reported the association between low nadir CD4 counts (< 200 cells/mm3) and carotid artery stenosis (abnormal cIMT > 0.9 mm) and/or presence of carotid plaques (adj OR 1.80; $95\%$CI 1.02–3.18) [24] similar to a study in Indonesia (β= -0.791) [25]. One cohort study reported a relationship between the duration of ART exposure with abnormal cIMT [19]. However, other HIV-specific risk factors such as antiretroviral therapies, duration of antiretroviral therapies exposure, statin use, fibrosis-4 index and high sensitivity c-reactive protein did not present any association [24]. Although clinical CVD prevalence between nonHIV and PLHIV did not significantly differ in both populations aged above 55 years, the onset of subclinical CVD such as abnormal cIMT and carotid plaques was earlier among the PLHIV than among nonHIV [19, 22-24].
## Discussion
This systematic review revealed a higher prevalence of subclinical and clinical CVD among PLHIV than that in the nonHIV population, a higher risk of subclinical CVD among poorer immune PLHIV and synergistic interaction between diabetes mellitus, dyslipidemia and family history on CVD risk among PLHIV. Although this systematic review underscored the significant risk of CVD among PLHIV in the Asia-Pacific region, the limitation in existing data remains. First, published data included small sample sizes which were less likely to detect the statistical association. Additionally, cohort studies did not have a longer follow-up time and were less likely to capture clinical CVD risks. NonHIV populations in three cross-sectional studies were not tested for HIV so the nonHIV populations likely mixed with populations with and without HIV. Those limitations can lead to underestimating the CVD risk; therefore, the CVD risk among PLHIV in this study can be used to raise attention from public health professionals in this region. Additionally, most studies used a cross-sectional design which did not capture the risk of CVD over time, and our study includes only English publications to prevent the challenges of accurate translation. This study did not meta-analyze the pooled effect of CVD risk factors because HIV/CVD studies are very few and likely inadequate for statistical analysis.
Obviously, the prevalence of subclinical atherosclerosis among PLHIV are the same as that in nonHIV populations in those study populations aged above 50 years [22–23, 26]. This situation reflected the double burdens of diseases in this region. The PLHIV regularly visit hospitals and attend health education sessions to improve healthy lifestyles to reduce the risk of NCDs [22, 33]. However, a prospective cohort study reported a higher prevalence of subclinical CVD compared with nonHIV populations which was consistent with data from other parts of the world [3, 5, 8–9, 35]. The higher CVD risk among PLHIV is related to multifactorial factors, both traditional and HIV-related risk factors for CVD [5–9].
The associations between the major traditional risk factors and CVD among PLHIV found in our review were consistent with results from other related reviews [6–7, 36]. These factors including diabetes, hypertension, dyslipidemia and smoking served major roles to increase oxidative stress in the cardiovascular system and led to chronic systemic inflammation, endothelial dysfunction, atherosclerosis progression and direct effects on cardiac performance through abnormal hormones or cytokines secretions. [ 36–42] Identifying extremely high risks of CVD among PLHIVs with DM, dyslipidemia and family members with CVD remains crucial.
The existing data were only from cross-sectional studies which cannot assess all potential exposures and CVD events over time. the prospective ascertainment for CVD among PLHIV and potential exposures will provide crucial information to identify future optimal interventions in this region. Although the association between HIV-specific risk factors and CVD has been reported in other studies [7, 42], HIV management may differ across regions especially between high and low to middle income countries. The study of HIV-specific risk factors for CVD and synergistic effects between traditional and HIV-specific risk factors should be conducted in this region.
## Conclusion
The limited existing data suggested the risk of early CVD among PLHIV. Extreme CVD risk among PLHIV with DM, dyslipidemia, and family history should highlight the need of NCDs intensive prevention program. We identified the crucial gaps in HIV/CVD work from the Asia-Pacific Region and recommended a further prospective study with large sample size and longer follow-up time or conducting a meta-analysis to better capture CVD risk, and interaction between HIV-related and traditional risk factors in this vulnerable population in this region.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1 Supplementary Material 2
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|
---
title: Supplemental Clostridium butyricum modulates lipid metabolism by reshaping
the gut microbiota composition and bile acid profile in IUGR suckling piglets
authors:
- Xin Zhang
- Yang Yun
- Zheng Lai
- Shuli Ji
- Ge Yu
- Zechen Xie
- Hao Zhang
- Xiang Zhong
- Tian Wang
- Lili Zhang
journal: Journal of Animal Science and Biotechnology
year: 2023
pmcid: PMC10009951
doi: 10.1186/s40104-023-00828-1
license: CC BY 4.0
---
# Supplemental Clostridium butyricum modulates lipid metabolism by reshaping the gut microbiota composition and bile acid profile in IUGR suckling piglets
## Abstract
### Background
Intrauterine growth restriction (IUGR) can cause lipid disorders in infants and have long-term adverse effects on their growth and development. Clostridium butyricum (C. butyricum), a kind of emerging probiotics, has been reported to effectively attenuate lipid metabolism dysfunctions. Therefore, the objective of this study was to investigate the effects of C. butyricum supplementation on hepatic lipid disorders in IUGR suckling piglets.
### Methods
Sixteen IUGR and eight normal birth weight (NBW) neonatal male piglets were used in this study. From d 3 to d 24, in addition to drinking milk, the eight NBW piglets (NBW-CON group, $$n = 8$$) and eight IUGR piglets (IUGR-CON group, $$n = 8$$) were given 10 mL sterile saline once a day, while the remaining IUGR piglets (IUGR-CB group, $$n = 8$$) were orally administered C. butyricum at a dose of 2 × 108 colony-forming units (CFU)/kg body weight (suspended in 10 mL sterile saline) at the same frequency.
### Results
The IUGR-CON piglets exhibited restricted growth, impaired hepatic morphology, disordered lipid metabolism, increased abundance of opportunistic pathogens and altered ileum and liver bile acid (BA) profiles. However, C. butyricum supplementation reshaped the gut microbiota of the IUGR-CB piglets, characterized by a decreased abundance of opportunistic pathogens in the ileum, including *Streptococcus and* Enterococcus. The decrease in these bile salt hydrolase (BSH)-producing microbes increased the content of conjugated BAs, which could be transported to the liver and function as signaling molecules to activate liver X receptor α (LXRα) and farnesoid X receptor (FXR). This activation effectively accelerated the synthesis and oxidation of fatty acids and down-regulated the total cholesterol level by decreasing the synthesis and promoting the efflux of cholesterol. As a result, the growth performance and morphological structure of the liver improved in the IUGR piglets.
### Conclusion
These results indicate that C. butyricum supplementation in IUGR suckling piglets could decrease the abundance of BSH-producing microbes (*Streptococcus and* Enterococcus). This decrease altered the ileum and liver BA profiles and consequently activated the expression of hepatic LXRα and FXR. The activation of these two signaling molecules could effectively normalize the lipid metabolism and improve the growth performance of IUGR suckling piglets.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s40104-023-00828-1.
## Background
From the moment of birth, a newborn must begin adapting to a different nutritional environment and obtaining energy from milk [1]. From d 3 to weaning, fat predominates in porcine milk. Due to its high energy value, this fat provides approximately $60\%$ of the energy required for the growth of newborn piglets [2, 3]. Thus, a functional and efficient lipid metabolic system is crucial for the growth and development of newborns during the suckling period. However, piglets with intrauterine growth restriction (IUGR) show abnormal lipid metabolism and impaired growth performance, which severely impacts their health and results in considerable losses in animal production [4, 5]. Therefore, it is necessary to identify a method to regulate lipid metabolism in IUGR suckling piglets.
Emerging as a kind of probiotic, C. butyricum is a Gram-positive anaerobe that produces butyric acid. It is one of the earliest microbial colonizers in infants and primarily exists in the distal small intestine and colon of animals [6–8]. C. butyricum also exhibits resistance to acidic pH levels, high temperature and bile salts. Therefore, C. butyricum is regarded as a useful and safe additive [9], and previous studies have shown that it can improve growth performance, protect against pathogenic bacteria and strengthen immunity in weaned piglets [10–12].
Additionally, C. butyricum plays a role in the regulation of lipid metabolism, and this feature has been demonstrated in models of aged laying hens, high fat diet (HFD) mice and corticosterone-challenged ducks [13–15]. These previous studies showed that C. butyricum could regulate fatty acid (FA) metabolism by modifying the expression of lipogenesis-related genes, such as acetyl-CoA carboxylase (ACC), and lipolysis-related genes, such as peroxisome proliferator activated receptor alpha (PPARα) [14, 16]. C. butyricum has also been shown to modulate cholesterol metabolism by elevating the mRNA expression of CYP7A1 and CYP8B1 to increase cholesterol efflux [15]. However, it remains unclear whether C. butyricum supplementation could relieve the disordered lipid metabolism of IUGR suckling piglets.
The gut microbiota, as a crucial regulator of host metabolism, has the capacity to produce or modulate metabolites that function as metabolic substrates and signaling molecules in the host [17]. Disruptions to the gut microbiota may lead to various metabolic disorders including obesity, type 2 diabetes and malnutrition [18]. Previous studies revealed that IUGR can disturb the micro-ecological equilibrium of the gut and, as a result, negatively impact normal metabolic pathways [19, 20].
An increasing body of evidence indicates that the metabolic regulation of the gut microbiota is realized through the gut-liver axis [21], and as an important metabolite of the gut microbiota, bile acid (BA) can function as a signaling molecule and exert an impact on host metabolism [22]. During BA metabolism, the bile acid-activated receptors farnesoid X receptor (FXR) and liver X receptor (LXR) are highly expressed in the enterohepatic tissues, and both of these intracellular sensors can be activated to maintain lipid homeostasis through the gut-liver axis [23, 24]. In addition, others have shown that C. butyricum treatment can alter the BA profile of the liver and ileum and simultaneously affect the intestinal microbiota composition of the host [14, 15]. These substantial findings suggest that the addition of C. butyricum may exert an effect on BA metabolism via the gut microbiota. Nevertheless, how C. butyricum supplementation influences lipid metabolism and the gut microbiota–BA metabolism relationship still requires further exploration.
Therefore, in the present study, the aims were to determine whether C. butyricum supplementation could be an effective means of regulating lipid metabolism in IUGR piglets during the suckling period and to explore the underlying mechanism from the perspective of the gut-liver axis.
## Animals and experimental design
All experiments were conducted in accordance with the guidelines of the Institutional Animal Care and Use Committee of Nanjing Agricultural University. Forty healthy sows (Landrace × Yorkshire) in their third parity and with similar expected dates of confinement (≤ 3 d) were initially selected. After screening, eight sows that had similar litter sizes (12.13 ± 0.60) and met the selection criteria for IUGR were selected. The newborn piglets (Duroc × [Landrace × Yorkshire]) that weighed within 0.5 standard deviation (SD) of the mean birth weight (BW) of the littermates were defined as normal birth weight (NBW), whereas those with 2 SD lower BW were defined as IUGR [25, 26]. According to this criterion, two IUGR (0.90 ± 0.08 kg) and one NBW (1.62 ± 0.10 kg) male piglets were chosen from each sow. The sixteen IUGR and eight NBW piglets were then randomly allocated to three groups: the NBW-CON group (NBW piglets that received 10 mL sterile saline per day, $$n = 8$$), the IUGR-CON group (IUGR piglets that received 10 mL sterile saline per day, $$n = 8$$) and the IUGR-CB group (IUGR piglets that received 10 mL bacterial fluid per day, $$n = 8$$). After colostrum feeding, all piglets were randomly assigned to four sows (6 piglets/sow; NBW-CON = 2, IUGR-CON = 2, IUGR-CB = 2) with similar physiological condition for lactation. When 2 d of adaptation finished, the gavage trial was conducted from d 3 to d 24. The dose of C. butyricum in bacterial fluid was 2 × 108 CFU/kg BW, and the BW of the piglets was measured every 3 d. All piglets were kept in lactation crates and nursed by sows, and sow milk was the only available dietary sustenance for the piglets during the study.
The C. butyricum used in the study was provided by Qingdao Vland Biological Technology Co., Ltd. (Qingdao, Shandong, China). The spore count was 5 × 109 CFU/g. The strain was C. butyricum wl-53, which was initially isolated from the feces of healthy chickens and was conserved in the China Center for Type Culture Collection (CCTCC No. M2019252, Wuhan, Hubei, China).
## Sample collection
Early in the morning of d 24, the piglets were weighed and the measurements were recorded as the final body weight (FBW) before blood collection. Then, blood sample was collected from the precaval vein of each piglet before sacrifice. Plasma was obtained by centrifugation at 3000 × g for 15 min at 4 °C, and stored at −80 °C for subsequent analysis. All piglets were killed by exsanguination after electrical stunning, after which fresh samples of liver and ileum chyme were immediately collected. After flushing the liver with saline, liver samples about 1 cm3 in size were collected from the left lobe and fixed in $4\%$ paraformaldehyde solution for histological analysis. The remaining parts of the liver and the samples of chyme collected from the ileum were snap-frozen in liquid nitrogen and then stored at −80 °C for further analysis.
## Histopathology
After being fixed in $4\%$ paraformaldehyde for 24 h, the liver samples were dehydrated using an ethanol concentration gradient and then embedded in paraffin. These paraffin blocks were sliced into 5 μm sections, and the sections were stained with hematoxylin and eosin. The hepatic morphology was observed using a light microscopy (Nikon 80i, Tokyo, Japan).
## Biochemical assay of serum samples
Commercial assay kits were used to determine the triglyceride (TG; #A110-1-1), nonesterified free fatty acids (NEFA; #A042-1-1), total cholesterol (TC; #A111-1-1), total bile acid (TBA; #E003-2-1), high-density lipoprotein cholesterol (HDL-C; #A112-1-1), low-density lipoprotein cholesterol (LDL-C; #A113-1-1) and glucose (GLU; #A154-1-1) content, according to the manufacturer’s instructions (Nanjing JianCheng Bioengineering Institute, Nanjing, Jiangsu, China).
## Determination of hepatic metabolite concentration
Commercial assay kits were used to determine the TG (#A110-1-1), lipoprotein lipase (LPL; #A067-1-2), hepatic lipase (HL; #A067-1-2), TC (#A111-1-1), TBA (#E003-2-1) and total protein (TP; #A045-4-2) content, according to the manufacturer’s instructions (Nanjing Jiancheng Bioengineering Institute).
The 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR; #RX500243P) and very low-density lipoprotein (VLDL; #RX500284P) levels were detected using enzyme-linked immunoassay (ELISA) kits for swine from Quanzhou Ruixin Biological Technology Co., Ltd. (Quanzhou, Fujian, China), following the manufacturer’s instructions.
## RNA isolation and quantitative real-time polymerase chain reaction (PCR) analysis
Total RNA was extracted from the ileum (mucosal) and liver samples using the Total RNA Extraction Reagent (Vazyme Biotechnology, Nanjing, Jiangsu, China) and quantified using an ND-2000 micro spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). After the determination of RNA quality and concentration, 1 µg of total RNA was reverse-transcribed into complementary DNA (cDNA) using the HiScript III RT SuperMix Reagent (Vazyme Biotechnology), following the manufacturer’s instructions. The mRNA expression levels of specific genes were quantified via real-time polymerase chain reaction (PCR) using the SYBR qPCR Master Mix (Vazyme Biotechnology) and the QuantStudio 5 Real-Time PCR System (Thermo Scientific, Wilmington, DE, USA). The SYBR Green PCR reaction mixture consisted of 10 µL TB Green Premix Ex Taq, 0.4 µL ROX Reference Dye II, 2 µL cDNA template, 0.4 µL of each primer (total 0.8 µL, 10 µmol/L) and 6.8 µL of double-distilled H2O. The reaction conditions were as follows: pre-run at 95 °C for 30 s, 40 denaturation cycles at 95 °C for 10 s and annealing at 60 °C for 30 s. Each sample was run in triplicate. The relative mRNA expression levels were analyzed via the 2−ΔΔCt method after normalization against β-actin, and the results displayed a similar trend when GAPDH served as the housekeeping gene.
## Protein extraction and western blot assay
TP was isolated from the frozen liver samples using a lysis buffer containing protease inhibitors (Beyotime Institute of Biotechnology, Nantong, Jiangsu, China). The protein concentration was measured using a BCA Protein Assay Kit (Beyotime Institute of Biotechnology). Equal amounts of TP (20 µg) were subjected to electrophoresis in $4\%$–$20\%$ SDS-PAGE and then transferred to PVDF membranes activated by methanol. After blocking with $5\%$ fat-free dry milk in TBST ($0.05\%$ Tween-20, 100 mmol/L Tris–HCl, and 150 mmol/L NaCl, pH 7.5) at room temperature for 2 h, the membranes were incubated overnight at 4 °C with primary antibodies that target specific proteins, including β-actin (#20536-1-AP; Proteintech, Chicago, IL, USA), NR1H4 (#25055-1-AP; Proteintech), NR1H3 (#14351-1-AP; Proteintech), PPARα (#66826-1-Ig; Proteintech) and CYP7A1(#AF6657; Beyotime Institute of Biotechnology) and CYP27A1(#14739-1-AP; Proteintech). The blots were washed in TBST three times and incubated for 1.5 h at room temperature with a secondary antibody: alkaline phosphatase-conjugated goat anti-rabbit IgG or anti-mouse IgG (#BL023A and #BL021A; Biosharp, Hefei, Anhui, China). Finally, the blots were washed with TBST three times before protein detection using an enhanced chemiluminescence reagent (#BL520A; Biosharp) and visualisation on a ChemiDocTM Imaging System (BIO-RAD, Hercules, CA, USA). Protein band intensity was quantified using ImageJ 1.42 q software (NIH, Bethesda, MD, USA).
## 16S rRNA analysis of the ileal microbial community
Total bacterial DNA was extracted from the ileum chyme samples using the TIANamp Stool DNA Kit (Tiangen Biotech, Beijing, China), according to the manufacturer’s guidance. The V3–V4 region of the bacterial 16S rRNA genes was amplified using the specific primers 341 F/806R (341 F: 5’-ACTCCTACGGGAGGCAGCAG-3’; 806R: 5’-GGACTACHVGGGTWTCTAAT-3’). The PCR reaction involved a thermal cycle consisting of initial denaturation at 94 °C for 3 min, then 30 cycles of 94 ℃ for 30 s, 56 °C for 45 s, and 72 °C for 45 s, and a final extension step for 10 min at 72 °C. PCR enrichment was performed in a 50-µL reaction containing 30 ng template, fusion PCR primer and PCR master mix. The PCR products were purified with AmpureXP beads and eluted in an elution buffer. Libraries were qualified using the Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA). The validated libraries were sequenced using the Illumina MiSeq platform (BGI, Shenzhen, Guangdong, China), following the standard pipelines of Illumina and generating 2 × 300 bp paired-end reads.
Raw reads were filtered to remove adaptors and low-quality ambiguous bases, and then paired-end reads were added to tags by the Fast Length Adjustment of Short reads program (V 1.2.11) to generate the tags. Subsequently, the clean tags were assigned to operational taxonomic units with a threshold of $97\%$ identity by UPARSE (V 7.0.1090), and the chimeric sequences were identified and eliminated using UCHIME (V 4.2.40). Species annotation analysis was performed by an RDP Classifier (V 2.2) based on the Greengene database (V 201,305) with a minimum confidence threshold of $80\%$. The rarefaction curve, species accumulation curve and Shannon, Simpson and Chao indices were calculated using RStudio software (V 3.5.3) with the vegan package. To estimate beta diversity, principal coordinates analysis (PCoA) based on the Bray-Curtis distance and an analysis of similarities (ANOSIM) were conducted to compare the differences between the treatments using RStudio software (V 3.5.3).
## Targeted metabolome analysis of intestinal and liver bile acids (BAs)
The BA content of the ileal chyme and the liver was determined using ultra-high performance liquid chromatography–mass spectrometry (UPLC/MS, ACQUITY UPLC-Xevo TQ-S, Waters Corp., Milford, MA, USA). In brief, approximately 10 mg of each freeze-dried sample was added to an Eppendorf tube along with 10 µL internal standard, 190 µL acetonitrile/methanol (v/$v = 8$:2) and 25 mg pre-cooling grinding beads. The sample was homogenized, and the homogenate was centrifuged at 13,500 r/min for 20 min at 4 ℃ (Microfuge 20R, Beckman Coulter, Inc., Indianapolis, IN, USA). Next, 10 µL of the resultant supernatant was removed and diluted with 45 µL acetonitrile/methanol (v/$v = 8$:2) and ultrapure water. Then, 5 µL of the diluent was applied to the UPLC/MS system for BA quantification. An ACQUITY UPLC Cortecs C18 1.6 μm analytical column (2.1 mm × 100 mm) heated to 30 ℃ was used for chromatographic separation. The gradient system consisted of Solvent A (10 mmol/L ammonium acetate with $0.25\%$ acetate acid) and Solvent B (acetonitrile:methanol:isopropanol = 8:1:1) at a flow rate of 0.4 mL/min. The other parameters were set as follows: capillary voltage = 2.0 kV, ion source temperature = 150 ℃, desolvation temperature = 550 ℃, desolvation flow = 1000 L/h. BA standards were purchased from Steraloids, Co. Ltd (Newport, RI, USA) and TRC Chemicals, Co. Ltd (Toronto, ON, Canada). The mixed reference standards were obtained by dissolving each BA reference standard in methanol.
## Statistical analysis
The data were analyzed using SPSS 25.0 statistical software (ver. 25.0 for Windows, SPSS Inc., Chicago, IL, USA). Statistical differences in serum and hepatic biochemical indices, hepatic gene and protein expression levels and intestinal and hepatic BA contents were determined by one-way ANOVA followed by Tukey’s test when F was significant. The Kruskal–Wallis test was used to detect differences in the relative abundance of bacteria among these groups. A P-value < 0.05 was considered statistically significant. The results are presented as mean ± SE.
## Growth performance
The piglets in the IUGR-CON group had lower FBWs than those in the NBW-CON group ($P \leq 0.05$), indicating that IUGR decreased the FBW. In contrast, in the IUGR-CB group, C. butyricum supplementation improved ($P \leq 0.05$) the growth performance of these piglets, and the average daily gain (ADG) and FBW were increased by $41.60\%$ and $30.57\%$, respectively (Table 1).
Table 1Effect of supplemental C. butyricum on the growth performance of IUGR suckling piglets from 3 to 24 days of ageItems1NBW-CON2IUGR-CONIUGR-CBP-values123IBW, kg2.24 ± 0.031.51 ± 0.11*1.52 ± 0.12*< 0.001< 0.0010.998FBW, kg7.16 ± 0.195.66 ± 0.40*7.39 ± 0.26#0.0050.8470.001ADG, g/d234.11 ± 8.56197.32 ± 16.42279.40 ± 9.77*#0.1030.038< 0.001All data are presented as mean ± SE ($$n = 8$$). Significant difference is depicted as *$P \leq 0.05$ when compared with NBW-CON group, #$P \leq 0.05$ when compared with IUGR-CON group. Contrast: [1] NBW-CON versus IUGR-CON; [2] NBW-CON versus IUGR-CB; [3] IUGR-CON versus IUGR-CB1IBW, initial body weight; FBW, final body weight; ADG, average daily gain2NBW-CON, piglets with normal birth weight; IUGR-CON, piglets with intrauterine growth restriction; IUGR-CB, piglets with intrauterine growth restriction supplemented with Clostridium butyricum
## Histopathological analysis
As shown in Fig. 1, liver samples from IUGR-CON group piglets exhibited a congestive central vein, loosely arranged cords, dilated sinusoids and fewer lipid droplets in the hepatic lobule. However, C. butyricum supplementation effectively ameliorated these conditions, and normally oriented liver morphology was observed in the liver samples from IUGR-CB group piglets.
Fig. 1Effect of supplemental C. butyricum on hepatic histomorphology of IUGR suckling piglets. All samples were stained with hematoxylin and eosin (Low magnification: ×400, High magnification: ×800, Bars: 50 μm). NBW-CON, piglets with normal birth weight; IUGR-CON, piglets with intrauterine growth restriction; IUGR-CB, piglets with intrauterine growth restriction supplemented with Clostridium butyricum
## Alterations in the FA metabolites content and the corresponding enzymes in the serum and liver
As shown in Table 2, a higher ($P \leq 0.05$) level of serum NEFA and lower ($P \leq 0.05$) hepatic HL and TL levels were found in the IUGR-CON group compared to the NBW-CON group, indicating that IUGR induced these conditions. However, in the IUGR-CB group, C. butyricum treatment resulted in significantly less ($P \leq 0.05$) NEFA accumulation in the serum and elevated ($P \leq 0.001$) hepatic levels of TG and HL.
Table 2Effect of supplemental C. butyricum on the content of FA metabolites and their corresponding enzymes in the serum and liver of IUGR suckling pigletsItems1NBW-CON2IUGR-CONIUGR-CBP-values123 Serum TG, mmol/L1.27 ± 0.101.25 ± 0.181.25 ± 0.070.9930.9911.000 NEFA, µmol/L1.49 ± 0.141.94 ± 0.04*0.95 ± 0.11*#0.0270.005< 0.001 Liver TG, µmol/gprot89.66 ± 4.5374.37 ± 5.92108.83 ± 3.98*#0.0920.029< 0.001 HL, U/gprot580.11 ± 14.66502.69 ± 9.49*621.34 ± 5.99*#< 0.0010.045< 0.001 LPL, U/gprot679.73 ± 14.25675.03 ± 17.36694.09 ± 20.130.9800.8300.723 TL, U/gprot1259.84 ± 19.351177.72 ± 11.57*1315.43 ± 24.70#0.0180.128< 0.001 VLDL, mmol/gprot0.57 ± 0.040.52 ± 0.020.57 ± 0.020.5370.9960.587All data are presented as mean ± SE ($$n = 8$$). Significant difference is depicted as *$P \leq 0.05$ when compared with NBW-CON group, #$P \leq 0.05$ when compared with IUGR-CON group. Contrast: [1] NBW-CON versus IUGR-CON; [2] NBW-CON versus IUGR-CB; [3] IUGR-CON versus IUGR-CB1TG, triglyceride; NEFA, non-esterified fatty acid; HL, hepatic lipase; LPL, lipoprotein lipase; TL, total lipase; VLDL, very low-density lipoprotein2NBW-CON, piglets with normal birth weight; IUGR-CON, piglets with intrauterine growth restriction; IUGR-CB, piglets with intrauterine growth restriction supplemented with Clostridium butyricum
## Gene expression involved in FA uptake, synthesis and oxidation
Regarding FA uptake, piglets in the IUGR-CON group had lower ($P \leq 0.05$) CAV1 mRNA expression levels than piglets in the NBW-CON group. Piglets in the IUGR-CB group had significantly higher ($P \leq 0.05$) levels of CD36 and CAV1 mRNA expression compared to piglets in the IUGR-CON group (Fig. 2A).
Fig. 2Effect of supplemental C. butyricum on FA metabolism of IUGR suckling piglets. A The mRNA abundance of genes related to FA uptake and transport. B The mRNA abundance of genes related to FA synthesis. C The mRNA abundance of genes related to FA oxidation. D The protein levels of FXR and PPARα. The column and its bar represented the means value and SE ($$n = 8$$), respectively. Significant difference is depicted as *$P \leq 0.05$ when compared with NBW-CON, #$P \leq 0.05$ when compared with IUGR-CON group. FATP2, fatty acid transport protein 2; CD36, cluster of differentiation 36; CAV1, caveolin 1; FABP, fatty acid binding protein; SREBP1c, sterol regulatory element-binding protein 1c; ACC, acetyl-CoA carboxylase; FASN, fatty acid synthase; DGAT1, diacylglycerol transferase 1; DGAT2, diacylglycerol transferase 2; FXR, farnesoid X receptor; CPT1, carnitine palmitoyltransferase 1; PPARα, peroxisome proliferator-activated receptor α; LCAD, long-chain acyl-CoA dehydrogenase; ACOX, acyl-CoA oxidase; NBW-CON, piglets with normal birth weight; IUGR-CON, piglets with intrauterine growth restriction; IUGR-CB, piglets with intrauterine growth restriction supplemented with *Clostridium butyricum* Regarding FA synthesis, IUGR significantly decreased the gene expression of DGAT1 and DGAT2 (IUGR-CON group vs. NBW-CON group), and C. butyricum supplementation resulted in increased expression of ACC, DGAT1 and DGAT2 in the IUGR-CB group (Fig. 2B).
In terms of fatty acid oxidation (FAO), LCAD mRNA expression was significantly lower in the IUGR-CON group compared to that in the NBW-CON group. In contrast, compared to the IUGR-CON group, expression of FXR, CPT1, PPARα, LCAD and ACOX were all significantly higher in the IUGR-CB group ($P \leq 0.05$) (Fig. 2C).
## Protein expression related to FA transport and metabolism
As shown in Fig. 2D, the expression of PPARα was significantly lower ($P \leq 0.05$) in the IUGR-CON group compared to that in the NBW-CON group. In contrast, IUGR piglets treated with C. butyricum exhibited higher ($P \leq 0.05$) expression of FXR and PPARα than IUGR piglets that did not receive C. butyricum.
## Alterations in cholesterol and BA metabolites in serum and liver
As shown in Table 3, the IUGR-CON group piglets had significantly lower ($P \leq 0.05$) serum HDL-C and hepatic TBA concentrations and higher ($P \leq 0.05$) hepatic TC levels than the NBW-CON group piglets. In contrast, piglets that received C. butyricum had elevated ($P \leq 0.05$) serum HDL-C and hepatic TBA concentrations and lower ($P \leq 0.05$) hepatic TC levels, compared to the IUGR-CON group piglets.
Table 3Effect of supplemental C. butyricum on the content of TC and its metabolite in the serum and liver of IUGR suckling pigletsItems1NBW-CON2IUGR-CONIUGR-CBP-values123 Serum TC, mmol/L6.83 ± 0.575.28 ± 0.726.38 ± 0.570.2080.8650.443 HDL-C, mmol/L3.28 ± 0.172.47 ± 0.19*3.54 ± 0.23#0.0220.6360.003 LDL-C, mmol/L3.55 ± 0.312.80 ± 0.292.83 ± 0.230.1650.1860.997 Liver TC, mmol/gprot56.27 ± 3.6272.54 ± 2.54*58.11 ± 3.12#0.0040.9100.010 TBA, µmol/gprot7.12 ± 0.485.62 ± 0.33*7.21 ± 0.40#0.0410.9880.030All data are presented as mean ± SE ($$n = 8$$). Significant difference is depicted as *$P \leq 0.05$ when compared with NBW-CON, #$P \leq 0.05$ when compared with IUGR-CON group. Contrast: [1] NBW-CON versus IUGR-CON; [2] NBW-CON versus IUGR-CB; [3] IUGR-CON versus IUGR-CB1TC, total cholesterol; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; TBA, total bile acid2NBW-CON, piglets with normal birth weight; IUGR-CON, piglets with intrauterine growth restriction; IUGR-CB, piglets with intrauterine growth restriction supplemented with Clostridium butyricum
## Expression of genes associated with cholesterol and BA metabolism
Compared with the NBW-CON group, the IUGR-CON groups showed significantly higher ($P \leq 0.05$) SREBF2 mRNA expression. However, C. butyricum supplementation resulted in significantly lower ($P \leq 0.05$) SREBF2 expression and, at the same time, higher ($P \leq 0.05$) expression of genes involved in reverse cholesterol transport and cholesterol efflux, such as LXRα, ABCA1, SCARB1 and ABCG8 (Fig. 3A).
Fig. 3Effect of supplemental C. butyricum on cholesterol and BA metabolism of IUGR suckling piglets. A The mRNA abundance of genes related to cholesterol synthesis and efflux. B The mRNA abundance of genes related to BA synthesis and excretion. C The protein levels of LXRα, CYP7A1 and CYP27A1. The column and its bar represented the means value and SE ($$n = 8$$), respectively. Significant difference is depicted as *$P \leq 0.05$ when compared with NBW-CON, #$P \leq 0.05$ when compared with IUGR-CON group. SREBF2, sterol-regulatory element binding factor 2; LXRα, liver X receptor α; ABCA1, ATP-binding cassette transporter A1; ABCG1, ATP-binding cassette transporter G1; ABCG5, ATP-binding cassette transporter G5; ABCG8, ATP-binding cassette transporter G8; SCARB1, scavenger receptor B class I; CYP7A1, cholesterol 7 α-hydroxylase; CYP27A1, cholesterol 27 α-hydroxylase; CYP7B1, Cytochrome P450 Family 7 Subfamily B Member 1; CYP8B1, Cytochrome P450 Family 8 Subfamily B Member 1; BSEP, bile salt export pump; MRP2, multidrug resistance associated protein 2; NBW-CON, piglets with normal birth weight; IUGR-CON, piglets with intrauterine growth restriction; IUGR-CB, piglets with intrauterine growth restriction supplemented with *Clostridium butyricum* Regarding BA metabolism, the IUGR-CON group exhibited significantly lower ($P \leq 0.05$) CYP27A1 mRNA expression compared to the NBW-CON group. However, C. butyricum supplementation resulted in higher ($P \leq 0.05$) expression of genes related to BA synthesis, such as CYP7A1, CYP27A1 and CYP7B1, and upregulation ($P \leq 0.05$) of genes associated with BA excretion, such as BSEP and MRP2 (Fig. 3B).
## Expression and enzymatic activity of proteins involved in cholesterol metabolism and BA synthesis
Piglets in the IUGR-CON group exhibited lower ($P \leq 0.05$) HMGCR activity and CYP27A1 expression in the liver compared to piglets in the NBW-CON group. Piglets in the IUGR-CB group exhibited not only elevated ($P \leq 0.05$) HMGCR activity, but also higher ($P \leq 0.05$) expression of LXRα, CYP7A1 and CYP27A1 (Fig. 3C).
## Microbial composition of the ileal chyme samples
As is shown in Additional file 2: Table S2, there was no significant difference noted in the α-diversity among the three groups, including in the Sobs index, Chao index, Ace index and Shannon index. However, the IUGR-CB group’s Simpson index was lower ($P \leq 0.05$) than that of the NBW-CON group. Although no obvious separation was observed between the NBW-CON and IUGR-CON groups, C. butyricum treatment did make a difference ($P \leq 0.05$), as shown in the PCoA (Additional file 4: Fig. S1A) and ANOSIM results (Additional file 4: Fig. S1B).
At the phylum level (Additional file 4: Fig. S1C), Firmicutes, Proteobacteria and Bacteroidetes predominantly constituted the ileal microbiota of the piglets, and no significant difference was found among the groups. At the genus level (Additional file 4: Fig. S1D), Lactobacillus, Veillonella and Actinobacillus were the dominant genera in the NBW-CON group, while in the IUGR-CON group, Lactobacillus, *Streptococcus and* *Escherichia accounted* for the majority of the bacteria. In the IUGR-CB group, the Lactobacillus, Actinobacillus and *Escherichia* genera dominated. Compared to the NBW-CON group, the IUGR-CON group had a relatively higher abundance of Streptococcus, Enterococcus and Moraxella ($P \leq 0.05$); however, piglets that received C. butyricum had clearly less ($P \leq 0.05$) Streptococcus, Enterococcus, Rothia, Moraxella and Acinetobacter compared to piglets in the IUGR-CON group (Fig. 4).
Fig. 4Differences in the relative abundance (> $0.1\%$) of the piglets’ ileal microbiota at the genus level. The column and its bar represented the means value and SE ($$n = 6$$), respectively. Significant difference is depicted as *$P \leq 0.05$ when compared with NBW-CON, #$P \leq 0.05$ when compared with IUGR-CON group. Streptococcus, Enterococcus, Rothia, Moraxella and Acinetobacter are all opportunistic pathogens. Thereinto, *Streptococcus and* Enterococcus are BSH-producing microbes. NBW-CON, piglets with normal birth weight; IUGR-CON, piglets with intrauterine growth restriction; IUGR-CB, piglets with intrauterine growth restriction supplemented with Clostridium butyricum
## Intestinal and hepatic BA profiles
The principal component analysis (PCA) of the ileum samples revealed that there were differences in the ileal BA profiles of the three groups (Fig. 5A). Taurohyocholic acid (THCA), taurohyodeoxycholic acid (THDCA), glycohyocholate (GHCA) and glycohyodeoxycholic acid (GHDCA) accounted for the largest proportion of conjugated BAs in the ileum, while hyocholic acid (HCA), hyodeoxycholic acid (HDCA), and chenodeoxycholic acid (CDCA) represented the major unconjugated BAs in the ileum (Additional file 5: Fig. S2A). Compared to the NBW-CON group piglets, the IUGR-CON group piglets had significantly lower ($P \leq 0.05$) ratios of conjugated BAs, such as THCA, GHCA, taurocholic acid (TCA) and glycocholic acid (GCA), and higher ($P \leq 0.05$) levels of unconjugated BAs, such as HDCA, 6-ketolithocholic acid (6-ketoLCA) and deoxycholic acid (DCA) (Fig. 5C, D). As a result, the ratio of conjugated BAs to unconjugated BAs in the ileum of IUGR-CON group piglets decreased significantly (Fig. 5B). Piglets that received C. butyricum had dramatically higher ($P \leq 0.05$) levels of conjugated BAs (THCA, GHCA, taurochenodeoxycholic acid [TCDCA] and TCA), lower ($P \leq 0.05$) levels of unconjugated BAs (6-ketoLCA), and as a result, a higher ratio between conjugated and unconjugated BAs (Fig. 5B, C, D).
Fig. 5Effect of supplemental C. butyricum on ileal BA contents of IUGR suckling piglets. A Principal component analysis (PCA) of ileal BAs. B The ratio of the content of conjugated BAs to unconjugated BAs. C Differential metabolites of conjugated BAs. D Differential metabolites of unconjugated BAs. The column and its bar represented the means value and SE ($$n = 6$$), respectively. Significant difference is depicted as *$P \leq 0.05$ when compared with NBW-CON, #$P \leq 0.05$ when compared with IUGR-CON group. THCA, taurohyocholic acid; GHCA, glycohyocholate; TCDCA, taurochenodeoxycholic acid; TCA, taurocholic acid; GCA, glycocholic acid; HDCA, hyodeoxycholic acid; 6-ketoLCA, 6-ketolithocholic acid; DCA, deoxycholic acid. NBW-CON, piglets with normal birth weight; IUGR-CON, piglets with intrauterine growth restriction; IUGR-CB, piglets with intrauterine growth restriction supplemented with *Clostridium butyricum* The PCA of the liver samples showed that there was no significant difference among the three groups and that the majority of the hepatic BAs were conjugated BAs (Fig. 6A). GHDCA, GHCA, glycochenodeoxycholic acid (GCDCA), THCA, tauroursodeoxycholic acid (TUDCA), TCDCA and GCA were the primary conjugated BAs found (Additional file 5: Fig. S2B). It was also found that piglets in the IUGR-CON group had significantly lower ($P \leq 0.05$) TUDCA levels than piglets in the NBW-CON group. In addition, the THCA, TUDCA and TCDCA levels were higher ($P \leq 0.05$) and the glycoursodeoxycholic acid (GUDCA) level was lower ($P \leq 0.05$) in the IUGR-CB group compared to the IUGR-CON group (Fig. 6B).
Fig. 6Effect of supplemental C. butyricum on hepatic BA contents of IUGR suckling piglets. A Principal component analysis (PCA) of ileal BAs. B Differential metabolites of hepatic BAs. The column and its bar represented the means value and SE ($$n = 6$$), respectively. Significant difference is depicted as *$P \leq 0.05$ when compared with NBW-CON, #$P \leq 0.05$ when compared with IUGR-CON group. THCA, Taurohyocholic acid; TUDCA, tauroursodeoxycholic acid; TCDCA, taurochenodeoxycholic acid; GUDCA, glycoursodeoxycholic acid. NBW-CON, piglets with normal birth weight; IUGR-CON, piglets with intrauterine growth restriction; IUGR-CB, piglets with intrauterine growth restriction supplemented with Clostridium butyricum
## Discussion
A great deal of evidence shows that the BW of IUGR neonates is significantly lower than that of normal neonates in mammals, and the syndrome has an adverse impact on health over a long period [27]. In the present study, the serum GLU levels (Additional file 3: Table S3) and the FBWs of the IUGR-CON group piglets were significantly lower than those of the NBW-CON group piglets. It is well known that, maintaining serum GLU levels within an appropriate range is necessary for an organism’s metabolic and energy systems to function effectively [28, 29]. Therefore, we could infer that the IUGR-CON group piglets were in a low energy state during the suckling period, and as a result, their growth performance was restricted. Indeed, malnutrition is considered as a leading cause of growth restriction in mammals. When the lacking factor is replenished, spontaneous catch-up growth usually occurs, and brings the individual back to its original growth trajectory [30]. In our study, C. butyricum treatment resulted in significantly higher serum GLU levels (Additional file 3: Table S3) compared to the levels in the IUGR-CON piglets, indicating a recovery of the energy supply. As a consequence, the FBWs of the IUGR-CB piglets caught up. Given that the IBWs of IUGR-CB piglets were significantly lower than those of the NBW-CON piglets, the ADGs of IUGR-CB piglets were even higher than those of the NBW-CON piglets. Similarly, other studies have concluded that C. butyricum supplementation can effectively improve the growth performance of broilers, weanling pigs and Holstein heifers [31–33].
As a central regulator of lipid homeostasis, the liver is responsible for the de novo synthesis, oxidation and export of FAs; it also controls the biosynthesis and efflux of cholesterol [34, 35]. Maldevelopment of the liver has been observed in IUGR newborns, and this study’s findings of severe damage to the hepatic morphological structure in IUGR infants aligns with those of a previous study [5]. This damage may in turn lead to considerable dysfunction in the lipid metabolism system. Indeed, higher TC levels and lower TBA levels were observed in the livers of the IUGR-CON piglets. BAs are the end products of cholesterol catabolism, and the conversion of cholesterol to BAs accounts for the daily turnover of a major fraction of cholesterol in mammals [36]. These results indicate that IUGR could reduce the transformation of BAs and lead to an excessive accumulation of cholesterol.
Although no significant difference in TG levels was found in either the liver or serum samples, the serum NEFA concentration was notably higher in the IUGR-CON piglets. There is growing evidence that the accumulation of NEFAs is closely associated with a series of health problems, such as obesity, insulin resistance and vascular disease [37–39]. Thus, the elevated NEFA level is likely to have negative effects on the growth and development of these piglets.
In addition, the activity of hepatic HL and the level of serum HDL-C were both lower in the IUGR-CON piglets. HL plays a critical role in the hydrolysis of TGs and promotes the uptake of HDL-C in circulating blood [40, 41]. HDL-C is involved in the reverse cholesterol transport (RCT) pathway, via which excess cholesterol can be transported from the periphery to the liver for clearance [42]. Together, these findings suggest that the ability to clear excess lipids was weakened in the IUGR piglets, which may have increased the risk of developing diseases related to lipid accumulation.
On the contrary, the morphological structure of the liver was normalized in the IUGR-CB piglets. The addition of C. butyricum also resulted in less deposition of excess lipids, such as TC and NEFAs, and a simultaneous higher efflux of lipids due to high levels of hepatic TBA, HL and serum HDL-C. Similar effects of C. butyricum have been confirmed in HFD mice, indicating that C. butyricum intake could effectively improve the HFD-induced accumulation of lipid droplets in hepatocytes and decrease the content of hepatic TC and NEFAs in mice [15]. Intriguingly, we also found that hepatic TG levels in the IUGR-CB group were elevated compared to those in the other two groups. We inferred that this was likely due to a type of feedback mechanism triggered by a high cholesterol level. Because cholesterol esters are less toxic than free cholesterol, the promotion of FA synthesis plays a role in cholesterol homeostasis, with FAs being used as substrates for cholesterol esterification [43]. Hence, as shown above, supplemental C. butyricum could effectively regulate the disordered lipid metabolism of IUGR suckling piglets.
To further explore the molecular mechanism of lipid regulation utilized by C. butyricum, we detected the levels of genes and proteins associated with FA and cholesterol metabolism. As an important energy substrate, plasma NEFA can provide fuel for mitochondria, the engine of the body, to generate adenosine triphosphate (ATP) [44]. The uptake of circulating FAs by the liver is largely dependent on three major FA transporters located in the hepatocyte plasma membrane: fatty acid transport proteins (FATP), cluster of differentiation 36 (CD36) and caveolins [45, 46]. Following uptake, hydrophobic FAs cannot freely diffuse in the cytosol and must instead be shuttled between different organelles by fatty acid binding proteins (FABP) [45]. In this study, although no significant difference was observed in the expression of FATP2, CD36 and FABP1 between the NBW-CON and IUGR-CON groups, the expression of CAV1 mRNA in the IUGR-CON piglets was markedly lower than that in the NBW-CON piglets. However, treatment with C. butyricum resulted in not only higher CAV1 expression, but also higher CD36 expression. Given that CAV1 contributes to lipid trafficking and lipid droplet formation and that CD36 facilitates long-chain FA (LCFA) transport [46, 47], it could be inferred that the IUGR-CON piglets could not obtain enough materials to generate ATP for their growth and development. However, C. butyricum intervention could effectively rectify this issue.
The de novo biogenesis of FAs is mainly controlled by sterol regulatory element-binding protein 1c and its downstream targets ACC and FASN [34]. Thereinto, ACC is the first rate-limiting enzyme that converts acetyl-CoA to malonyl-CoA, and FASN is a key lipogenic enzyme that catalyzes the terminal steps of FAs synthesis [48, 49]. The newly synthesized FAs are then used in TG synthesis, and DGAT1 and DGAT2 catalyze the final step [50]. In the current study, IUGR had no great impact on the de novo synthesis of FAs; however, it reduced the storage of FAs as non-toxic TGs (IUGR-CON group vs. NBW-CON group). In contrast, IUGR piglets treated with C. butyricum exhibited increased biosynthesis of FAs and TGs. The reason for this phenomenon could be that IUGR impaired the uptake of FAs, which are an important substrate for TG synthesis. Nevertheless, C. butyricum supplementation effectively corrected this and simultaneously promoted FA synthesis. Previous studies have similarly reported that the addition of C. butyricum could increase the expression of genes related to FA synthesis in chickens [14, 16].
Following uptake, FAs are utilized by hepatocytes to generate ATP by means of β-oxidation. As the rate-limiting enzyme of FAO, carnitine palmitoyltransferase I (CPT1) catalyzes the conversion of acetyl-CoAs into acyl-carnitines, following which they can cross membranes to enter the mitochondria [51]. PPARα is an FA-activated nuclear receptor that plays a key role in the transcriptional regulation of genes involved in peroxisomal and mitochondrial FAO, such as ACOX and LCAD [52–54]. In line with the decreased uptake of FAs, the expression of PPARα and its target gene LCAD were down-regulated in the IUGR-CON group. Hence, we could infer that the elevated serum NEFA levels probably resulted from the weakened uptake and utilization of FAs in the IUGR-CON piglets. Given the involvement of FAO in energy generation, the growth performance of the IUGR-CON piglets could have been restricted by adverse effects on FAO. However, C. butyricum treatment resulted in higher expression of FXR and PPARα and its target genes involved in FAO. As a nuclear receptor, FXR is mainly expressed in the liver and intestine and has a comprehensive effect on lipid metabolism [55]. Similarly, Wang et al. found that C. butyricum supplementation could stimulate peroxisomal FA β-oxidation, possibly through the FXR–PPARα–ACOX pathway in hens [14]. Another study with human cells also found that FXR activation induced the expression of PPARα and its downstream genes involved in FAO [56]. It is interesting to note that although mitochondrial FAO was impaired in the IUGR piglets, C. butyricum addition improved peroxisomal and mitochondrial FAO both. Substantial evidence supports the notion that mitochondria and peroxisomes exhibit a close functional interplay in the β-oxidation of FAs to maintain lipid homeostasis [57]. Thus, we concluded that the C. butyricum treatment greatly improved FAO in the IUGR piglets, and as a result, the serum GLU level was raised and more energy was produced for their growth. To obtain more materials for FAO, the uptake of FAs was accordingly boosted, which likely reduced the serum NEFA content in the IUGR-CB piglets compared to the IUGR-CON piglets and even the NBW-CON piglets.
In cholesterol metabolism, LXRs work together with the sterol regulatory element-binding protein 2 (SREBP2) pathway to maintain cellular and systemic sterol levels [58]. On the one hand, LXRs can facilitate the elimination of excess cholesterol by stimulating biliary cholesterol excretion through the target genes ABCG5 and ABCG8 [59]. On the other hand, SREBP2 can boost cholesterol biosynthesis by activating the transcription of the gene that encodes the rate-limiting enzyme HMGCR [60]. The higher expression of SREBF2 and the activity of HMGCR in the IUGR-CON group compared with that in the NBW-CON group suggested that IUGR promotes cholesterol synthesis, whereas the addition of C. butyricum downregulated cholesterol synthesis by decreasing SREBF2 expression and upregulating its efflux by elevating the expression of LXRα and its downstream target ABCG8. LXRα is one isoform of the LXR family that is highly expressed in metabolically active tissues, such as the liver and intestine.
Moreover, LXRα also plays a critical role in promoting RCT, through which excess cholesterol in peripheral tissues can be transferred to HDL and then transported to the liver for BA synthesis and excretion [58]. In this process, downstream genes of LXRα, such as ABCA1, ABCG1 and SR-BI, work together to drive the assembly of HDL to initiate RCT [61–63]. Then, the excess cholesterol transported by HDL-C is used for BA synthesis, which is critical for maintaining cholesterol homeostasis and preventing the accumulation of cholesterol in the liver [36].
BAs are synthesized by multi-step reactions catalyzed in hepatocytes via two distinct routes: the “classical” (neutral) pathway and the “alternative” (acidic) pathway. The classic pathway is initiated by 7α-hydroxylation of cholesterol catalyzed by the rate-limiting enzyme CYP7A1, followed by further transformations of the steroid nucleus and oxidative cleavage of the side chain involving CYP8B1 [64]. The alternative pathway is initiated by sterol 27-hydroxylase (CYP27A1). This reaction is followed by oxysterol 7α-hydroxylation, which is primarily mediated by CYP7B1 [65]. Finally, the synthesized BAs are secreted through the bile canalicular membrane by two ABC transporters (BSEP and MRP2) into the canalicular lumen [66]. Consistent with the variation in hepatic TBA levels, the expression of CYP27A1 and CYP7B1 was lower in the IUGR-CON group compared to that in the NBW-CON group. In the IUGR-CB group, expression of CYP7A1 and CYP27A1 and its downstream gene CYP7B1 was markedly higher, and BSEP and MRP2 expression were both increased accordingly. These results revealed that the reduced BA levels in the IUGR-CON group were likely the result of an impaired alternative pathway, and that C. butyricum treatment could effectively restore BA content to normal levels by promoting the classic and alternative pathways. A study that focused on oxysterol 7α-hydroxylase, an important enzyme in the alternative pathway, confirmed the quantitative importance of the alternative pathway in early life in humans [67], suggesting that the alternative pathway might be the major route of BA synthesis in infants. Given the specific period of suckling, malfunction of the alternative pathway would consequently lead to considerable disruption of cholesterol metabolism. Moreover, previous studies have similarly indicated that increases in CYP27A1 activity could downregulate cholesterol synthesis through the SREBP pathway as well as enhance the efflux and elimination of cholesterol via LXR [68].
There is a plethora of evidence confirming that the composition of the gut microbiota can have profound effects on the host [69, 70]. In our study, no significant difference was found in the microbial α-diversity among the three groups except for a decreased Simpson index in the IUGR-CB group compared to the NBW-CON group. Hence, it could be inferred that the addition of C. butyricum affected the homogeneity of the gut microbiota by modulating its composition. To further investigate the connection between the change in the gut microbiota and the effect of lipid regulation in the IUGR-CB group, we analyzed the differences in the bacteria among the groups at the phylum and genus levels. Although no significant difference was found at the phylum level, piglets in the IUGR-CON group had significantly more opportunistic pathogens, such as Streptococcus, Enterococcus and Moraxella, which increases the risk of developing an inflammatory response and associated impairment of normal liver function [71–73]. Intriguingly, C. butyricum treatment resulted in not only lower relative abundance of the bacteria mentioned above but also lower relative abundance of Rothia and Acinetobacter, which are also considered opportunistic pathogens that could have negative effects on the host’s health [74, 75]. Hence, the modulation of the gut microbiota caused by C. butyricum might have effectively protected the IUGR piglets against pathogen invasion, and this is supported by the observed recovery of congestion in the liver portal vein and sinusoids.
Of note, among the changed microbes, *Streptococcus and* Enterococcus are BSH-producing microbes; BSH catalyzes the deconjugation of glycine- or taurine-conjugated BAs to form unconjugated BAs [76, 77]. As a result, the ileal BA profiles of the IUGR-CON piglets were altered compared with those of the NBW-CON piglets, and the profiles were characterized by lower levels of conjugated BAs and higher levels of unconjugated BAs. It is well known that BAs can function as endogenous signaling molecules by binding to BA receptors, such as FXR and LXRα, to regulate BA homeostasis in enterohepatic circulation and to modulate cholesterol and TG metabolism [36, 78]. In the current study, C. butyricum treatment was found to elevate the proportion of conjugated BAs in the ileum, and the THCA, TCDCA and TCA levels were dramatically increased. THCA is a known LXRα agonist, and TCA and TCDCA are known FXR agonists [23, 79]. Therefore, these signaling molecules might be transported to the liver via enterohepatic circulation and then play an important role in regulating lipid metabolism. To confirm this, we also analyzed the BA profile in the liver. In line with the results derived from the ileum, C. butyricum supplementation increased the levels of the LXRα agonist THCA and the FXR agonist TCDCA. Meanwhile, GUDCA, an FXR antagonist [80], was also decreased in the IUGR-CB group compared with the IUGR-CON group. Hence, FXR and LXRα may have been simultaneously activated to affect lipid metabolism in the liver.
Although FXR activation can repress BA synthesis, it relies on the effect of small heterodimer partner (SHP). The importance of SHP in the feedback regulation of BA synthesis was demonstrated in SHP−/− mice, in which the repression of CYP7A1 was dismissed and the size of the BA pool was enlarged [81]. In our study, despite the activation of FXR, no significant difference in SHP expression was observed in the IUGR-CB group compared to the IUGR-CON group. As a result, BA synthesis may not have been repressed. Conversely, the synthesis of BAs may have been upregulated by the activation of LXRα. As a type of hydrophilic BA used to treat hepatobiliary disorders, TUDCA can penetrate into the cell membrane and help transfer cholesterol from the cell membrane to HDL [82, 83]. A previous study showed that TUDCA treatment could effectively decrease serum and hepatic TC levels and increase the mRNA expression of CYP27A1 in a model of cholesterol gallstones [84]. Similarly, in our study, and consistent with the lower hepatic TUDCA levels in the IUGR piglets, the serum HDL-C level was lower and the hepatic TC level was higher in the IUGR-CON group than in the NBW-CON group. However, C. butyricum supplementation resulted in significantly higher hepatic TUDCA levels and, at the same time, promoted cholesterol efflux by increasing the serum HDL-C level to drive the transport of cholesterol and upregulating the expression of CYP27A1 to accelerate BA synthesis (Fig. 7).Fig. 7Supplemental C. butyricum could effectively improve lipid disorders of IUGR suckling piglets. C. butyricum treatment modulated the gut microbiota to reduce the relative abundance of opportunistic pathogens in IUGR piglets. Therein, *Streptococcus and* Enterococcus are BSH-producing microbes, so that some conjugated BAs function as signaling molecules were increased in the ileum. Via enterohepatic circulation, these signaling molecules could be transported to the liver to activate LXRα and FXR. The activation of LXRα could promote the synthesis of FAs and the transformation of cholesterol to BAs, and the activation of FXR could increase the β-oxidation of FAs and the excretion of BAs.
## Conclusion
Based on the findings of this study, we concluded that providing C. butyricum to IUGR suckling piglets might result in modulation of the gut microbiota; specifically, a reduction in the relative abundance of BSH-producing microbes, such as *Streptococcus and* Enterococcus. Thus, the levels of some conjugated BAs that function as signaling molecules were increased in the ileum. Via enterohepatic circulation, these signaling molecules could be transported to the liver and further regulated hepatic lipid metabolism by activating LXRα and FXR. As a result, the lipid metabolism was normalized and the growth performance was improved in the IUGR suckling piglets.
## Supplementary Information
Additional file 1: Table S1. Sequences for real-time PCR primers. Additional file 2: Table S2. Effect of supplemental C. butyricum on alpha diversity of ileal microbiota in IUGR suckling piglets. Additional file 3: Table S3. Effect of supplemental C. butyricum on serum GLU of IUGR suckling piglets. Additional file 4: Fig. S1. Effect of supplemental C. butyricum on the microbial structure of the ileum in IUGR suckling piglets. Additional file 5: Fig. S2. Effect of supplemental C. butyricum on BAs composition of the ileum and liver.
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|
---
title: Comparison of the efficacy of intravitreal Anti-VEGF versus intravitreal dexamethasone
implant in treatment resistant diabetic Macular Edema
authors:
- Hakan Koc
- Atilla Alpay
- Suat Hayri Ugurbas
journal: BMC Ophthalmology
year: 2023
pmcid: PMC10009964
doi: 10.1186/s12886-023-02831-6
license: CC BY 4.0
---
# Comparison of the efficacy of intravitreal Anti-VEGF versus intravitreal dexamethasone implant in treatment resistant diabetic Macular Edema
## Abstract
### Purpose
Comparison of the efficacy of monthly anti-VEGF versus dexamethasone (DEX) implant in patients with diabetic macular edema (DME) whose macular edema persists despite three doses of anti-VEGF therapy.
### Materials and methods
This retrospective study included 94 eyes of 94 patients with central macular thickness (CMT) > 300 μm despite previously receiving three doses of anti-VGEF (aflibercept or ranibizumab) injections between January 2014 and January 2019. The patients were divided into four groups. The first and second groups were the patients who received three more doses of initial anti-VGEF treatment after the initial anti-VGEF treatment. The third and fourth groups were patients switched to intravitreal dexamethasone implants. Patients were followed up every month for six months after the injection. The primary outcome measures were best-corrected visual acuity (BCVA), central macular thickness (CMT), and intraocular pressure (IOP) at six months.
### Results
The mean age of the patients included in the study was 64.64 ± 7; there were 58 men ($61.7\%$) and 36 women ($38.3\%$). There was no statistically significant difference between the groups regarding age, stage of retinopathy, and lens status. When CMT, BCVA, and IOP were assessed among the four groups at the end of the sixth month, no statistical difference between the groups was found. There was no need for medical intervention despite the statistically significant increase in IOP at the end of the sixth month compared to the third month in the dexamethasone implanted groups. In contrast to the decrease in CMT, which was statistically significant in all four groups at the end of the sixth month compared to the third month, the increase in BCVA was not statistically significant in any of the four groups at the end of the sixth month.
### Conclusion
According to the results of our study, there is no superiority between continuing with existing anti-VEGF or switching to a dexamethasone implant after three doses of anti-VEGF.
## Introduction
Diabetic retinopathy (DRP) is a specific microvascular complication of Diabetes Mellitus (DM) and is the leading cause of vision loss worldwide in middle-aged and economically active people [1]. The leading cause of visual loss in diabetic patients is diabetic macular edema (DME). Inflammation, angiogenesis, and oxidative stress are involved in the pathogenesis of DME, which is mostly caused by interleukin (IL)-6, -8, and cytokines such as monocyte chemotactic protein and vascular endothelial growth factor (VEGF) [2]. Intravitreal anti-VEGF injections have improved visual acuity and reduced retinal thickness in DME eyes [3]–[4]. Nonetheless, macular edema persisted in 32–$66\%$ of eyes treated with injections for at least six months, and visual acuity declined in general [3]. Therefore, additional treatments are needed for eyes that do not sufficiently respond to anti-VEGF medication.
Intravitreal steroids are utilized in the treatment of DME because they inhibit VEGF secretion and vascular permeability while stabilizing the lysosomal membranes and blood-retinal barrier [5]. Dexamethasone implant (Ozurdex; Allergan Inc., Irvine, CA) is a slow-release dexamethasone delivery system designed for intravitreal administration that was recently introduced as a treatment option for DME [6]. Studies have also revealed that intravitreal Dexamethasone may be beneficial for DME patients who do not respond well to anti-VEGF therapy since corticosteroids act on distinct targets than anti-VEGF medicines [7].
## Study population
The study was conducted in accordance with the Helsinki Declaration’s tenets, and the local ethics committee’s approval was required before the study could begin. Each patient provided an informed consent form before intravitreal injection. Between January 2014 and January 2019, 94 eyes of 94 patients with NPDR or early PDR with a central macular thickness (CMT) greater than 300 microns despite three doses of anti-VEGF therapy for persistent diabetic macular edema were assessed retrospectively and included in the study. When both eyes passed the inclusion criteria, only the right eye was included since there was a propensity for the outcome measurements from the two eyes of the same participant to be positively associated. Before receiving the injection, each patient was informed of the procedure’s benefits, dangers, and potential side effects and granted informed consent. 18 years and above with Type 2 DM who had previously received three monthly anti-VEGF injections but still had CMT values above 300 μm were enrolled in the study.
## Criteria for exclusion
High-risk PDR.Pregnancy. Uncontrolled hypertension. Retinal vascular disease, Age-related macular degeneration, presence of epiretinal membrane in OCT.Uveitis, Glaucoma. History of pars plana vitrectomy. Severe cataracts. History of Yag laser capsulotomy within six months. Patients who underwent laser photocoagulation were not included in the study.
## Study design
The patients were divided into four groups. Group 1, patients who received an additional three doses of Aflibercept after three doses of Aflibercept treatment (AFL + AFL); group 2, patients who received an additional three doses of ranibizumab after three doses of ranibizumab treatment (RAN + RAN), group 3, patients who underwent intravitreal Dexamethasone after three doses of Aflibercept treatment (AFL + DEX), group 4, patients who received intravitreal Dexamethasone (RAN + DEX) after three doses of ranibizumab treatment were patients. Detailed ophthalmologic examinations of the patients were performed. The best corrected visual acuity (BCVA) with the Snellen chart, anterior segment examinations with a biomicroscope, intraocular pressures (IOP) with a Goldmann applanation tonometer, and posterior segment examinations using 90D non-contact lenses after pupil dilatation was performed. CMT was measured by OCT (Heidelberg Engineering, Heidelberg, Germany). For the analysis between the groups, the values obtained in the sixth month after the injection in each of the four groups were calculated and evaluated. All of the patients included in the study with the diagnosis of DME were evaluated with Fundus Fluorescein Angiography (FFA), and macular ischemia was ruled out.
## Treatment protocol
Before the injection, the conjunctiva and skin were cleansed with $5\%$ and $10\%$ povidone-iodine, respectively. In blepharos, the sterile eye cover was concealed and worn. The upper temporal region was chosen as the injection site, and topical proparacaine was used prior to the injection. Anti-VEGF and dexamethasone implants were injected into the vitreous at a distance of 4 mm from the limbus in phakic patients and 3.5 mm from the limbus in pseudophakic patients using their respective injector systems. We assessed post-injection light detection and IOP. After the injection, all patients received topical moxifloxacin $0.5\%$ prophylaxis four times per day for one week and were called for infection throughout the first postoperative week. All patients were reviewed at one week, one month, two months, three months, four months, five months, and six months after injection. At each review visits, all patients underwent comprehensive ophthalmologic tests. The BCVA, CMT, and IOP were documented. Patients who missed their monthly follow-up appointments were eliminated from the study.
## Statistical analysis
SPSS 19.0 (SPSS Inc., Chicago, IL, USA) was used for statistical analysis. The Shapiro-Wilk test was used to determine whether numerical variables fit the normal distribution. Descriptive statistics were expressed as numbers and percentages for categorical data, while mean, standard deviation, and median (minimum-maximum) were used for numerical variables. The Chi-square test was used to examine the categorical variable differences between the groups. A Kruskal-*Wallis analysis* of variance was used to compare the four groups in terms of numerical variables. The measurement values between the two dependent groups were compared using the Wilcoxon-marked sequence(s) test. $p \leq 0.05$ was considered significant.
## Results
The demographic characteristics of the patients included in the evaluation are summarized in Table 1.
There was no statistically significant difference between the four groups regarding age, stage of retinopathy, laterality, or lens status. ( phakic, pseudophakic). ( $p \leq 0.05$) When the mean visual acuity, mean central macular thickness and mean intraocular pressure were evaluated after three doses of anti-VEGF treatment, there was no statistically significant difference between the four groups. ( $p \leq 0.05$) Table 2.
## İntragroup analysis
Visual acuity after three doses of anti-VEGF treatment (3rd month) and six doses of anti-VEGF treatment or third month (6th month) of DEX implant treatment was evaluated using intragroup analysis. There was no statistically significant difference between the mean visual acuity in the third month and the mean visual acuity in the sixth month in any of the four groups. ( $p \leq 0.05$) The mean central macular thickness after three doses of anti-VEGF treatment (3rd month) and six doses of anti-VEGF treatment or the third month (6th month) of DEX implant treatment was evaluated using intragroup analysis. There was a statistically significant difference between the mean central macular thickness in the third month and the mean central macular thickness in the six month in all four groups.($p \leq 0.05$) The mean intraocular pressure after three doses of anti-VEGF treatment (3rd month) and six doses of anti-VEGF treatment or the third month (6th month) of DEX implant treatment was evaluated using intragroup analysis. There was a statistically significant difference between the mean intraocular pressure in the third month and the mean intraocular pressure in the six month in groups that switched to Dexamethasone implant.($p \leq 0.05$) None of these individuals, however, required medical or surgical treatment.
## Intergroup analysis
For the analysis between the groups, the values obtained in the sixth month after the injection in each of the four groups were calculated and evaluated.
When the values obtained in mean visual acuity, mean central macular thickness, and mean intraocular pressure were compared between the four groups, there was no statistically significant difference between the groups at six months. ( $p \leq 0.05$) Table 4.
OCT images of some patients in the groups are shown in Fig. 1.
Fig. 1 OCT images of patients A1. OCT image before treatment A2. OCT image after 3 doses of AfliberceptA3. OCT image after 6 doses of Aflibercept B1. OCT image before treatmentB2. OCT image after 3 doses of ranibizumab B3. OCT image after 6 doses of ranibizumabC1. OCT image before treatment C2. OCT image after 3 doses of AfliberceptC3. OCT image at 3months of Dexamethasone implant D1.OCT image before treatmentD2. OCT image after 3 doses of ranibizumab D3. OCT image at 3 months of Dexamethasoneimplant
## Discussion
Diabetic macular edema (DME) is a common consequence of diabetic retinopathy (DRP) and the leading cause of vision loss among persons of working age in developed nations [8]–[9]. Numerous studies indicate a correlation between VEGF level and retinopathy activity and suggest that VEGF is the principal angiogenic agent responsible for the development of diabetic retinopathy and maculopathy [10]–[11]. With the discovery of VEGF’s function in the pathophysiology of DME, anti-VEGF medicines have become the standard treatment.
In the “Study of Safety and Efficacy of ranibizumab in Diabetic Macular Edema” (RESOLVE) and “Study of ranibizumab for Diabetic Macular Edema” (RISE and RIDE) investigations, patients who received injections of 0.3 mg and 0.5 mg of ranibizumab were compared to the control group. After 12 months, the groups receiving intravitreal ranibizumab had a significant improvement in visual acuity and a significant decrease in central macular thickness [12]. In the study titled “An exploratory study of the safety, tolerability, and bioactivity of a single intravitreal injection of vascular endothelial growth factor Trap-Eye in patients with diabetic macular edema,“ the efficacy and safety of intravitreal Aflibercept were assessed. Six weeks following the injection, the central macular thickness and mean letter gain in visual acuity decreased [13]. Compared to other studies, the central macular thickness in the AFL + AFL group and the RAN + RAN group decreased statistically significantly in the sixth month compared to the third month. However, the increase in visual acuity was not statistically significant in the sixth month compared to the third month, unlike other studies.
Corticosteroids stabilize lysosomal membranes and the blood-retina barrier while diminishing VEGF secretion and vascular permeability. For this reason, corticosteroid medications are utilized to treat DME [5]. In a study examining the changes in the amounts of inflammatory and angiogenic cytokines in the humoral aqueous after intravitreal injection of triamcinolone and bevacizumab in patients with DME, IL6, inducible protein 10, monocyte chemoattractant protein, platelet-derived growth factor AA, and VEGF were observed to be significantly decreased in eyes injected with triamcinolone, only VEGF has been shown to be reduced in eyes injected with bevacizumab [14]. In another study, it was stated that corticosteroids in DME may be more meaningful than anti-VEGF therapy, which is effective only on the part of the inflammatory cascade [15]. Intravitreal corticosteroids in DME; It can be considered if anti-VEGFs are contraindicated, if there is poor compliance with repetitive anti-VEGF applications and if there is resistance to anti-VEGFs [16]. In the study of Vujosevic et al., *Dexamethasone is* effective in cases of increased foveal autofluorescence [17]. In the study of Vural et al., it was concluded that dexamethasone implant responds better than anti-VEGF treatment in DME cases with subretinal fluid [18]. In the MEAD study, 0.7 mg dexamethasone was assigned at random to 0.35 mg dexamethasone and placebo groups. It was determined that the intravitreal dexamethasone group had a greater decrease in central macular thickness and letter gain. In addition, it was observed that nearly one-third of dexamethasone-treated patients required treatment for intraocular hypertension [19].
In the study conducted by Totan et al., after intravitreal dexamethasone administration to patients who received three doses of 2.5 mg intravitreal bevacizumab at 6-week intervals and whose central macular thickness was greater than 275 microns, central macular thickness decreased significantly in the first, third, and sixth months. Additionally, the average visual acuity and intraocular pressure increased significantly in the first and third month [20]. In the study of Zhioua et al., intravitreal dexamethasone was administered to 12 patients whose central macular thickness continued to be 300 microns and above, despite six consecutive months of ranibizumab, and they found that dexamethasone was influential on the central macular thickness and visual acuity [21]. Simsek et al. applied the DEX implant to patients with central macular thickness greater than 300 microns despite at least six doses of ranibizumab treatment and showed that the intravitreal DEX implant significantly improved visual acuity and central macular thickness values in patients with DME resistant to anti-VEGF therapy [22]. Yorgun et al. showed that intravitreal dexamethasone implantation resulted in significant improvement in BCVA and reduction in CMT in patients with persistent DME who did not respond to three consecutive injections of ranibizumab [23].
In the study by Maturi et al. ( Protocol U), the group that continued to receive 0.3 mg ranibizumab after three doses of 0.3 mg ranibizumab was compared to the group that received a Dexamethasone implant. Although the decrease in mean central macular thickness and increase in mean intraocular pressure were more pronounced in the group that was switched to dexamethasone after 24 weeks, there was no difference in mean visual acuity [7].
In a meta-analysis study by Khan et al., it was reported that the DEX implant applied to patients with refractory DME despite the use of anti-VEGF agents improves vision and may be a treatment alternative for patients with DME who have a low response to anti-VEGF agents [24].
Ozata et al. demonstrated that dexamethasone implantation raised BCVA and decreased CMT in DME patients refractory to sequential intravitreal ranibizumab therapy over the initial three months. They reported that intravitreal dexamethasone implantation could be a viable alternative treatment for DME that is resistant [25].
Busch et al. observed that in patients with central macular thickness > 300 microns after three doses of ranibizumab treatment, the group that switched to a Dexamethasone implant had better visual and anatomical results than the group that continued with ranibizumab [26].
In our study, at the conclusion of the sixth month, we compared the groups that continued with three doses of anti-VEGF after three doses of anti-VEGF treatment and the groups that were switched to intravitreal dexamethasone implants. The decrease in central macular thickness, the rise in intraocular pressure, and the rise in visual acuity were not statistically significant. Despite the statistically significant increase in intraocular pressure between the third and sixth months in the groups implanted with dexamethasone, the patients did not require anti-glaucoma medication.
## Strengths and limitations
This study has disadvantages such as being retrospective, having a small number of patients in the groups, being unable to evaluate inflammatory markers, and having an average follow-up period of 6 months. However, the absence of a similar study conducted on four different groups is also important for our study. Our study should be supported by prospective, long-term follow-up and further studies with large patient groups. New molecules with fewer side effects and longer acting are needed for better therapeutic results.
## Conclusion
According to the results of this study, there is no superiority between continuing with the current anti-VEGF treatment or switching to a dexamethasone implant after three doses of anti-VEGF, as there is no statistically significant difference between the mean visual acuity and central macular thickness at six months. According to the results of our research, after three doses of anti-VEGF (3rd month), to apply for a medication change (to switch to dexamethasone implant treatment) should be selected according to the patient (presence of glaucoma, compliance with treatment, etc.) and cost. Dexamethasone implant has an advantage over anti-VEGF in that it needs fewer injections, and its cost is cheaper than three doses of anti-VEGF. In addition, while the rise in intraocular pressure was statistically significant in the groups that moved to dexamethasone, it is also important for the transition to dexamethasone because this condition does not need medical or surgical treatment.
Table 1Demographic characteristics of the dataAFL + AFLRAN + RANAFL + DEXRAN + DEXP valueAge62.68 ± 6.964.74 ± 7.664.27 ± 8.066.9 ± 5.60.466Stage of Retinopathy (NPDR/PDR)$\frac{20}{522}$/$\frac{417}{515}$/60.705Laterality R/L$\frac{13}{1212}$/$\frac{1413}{912}$/90.736Lens Status(Phakic/Psodophakic)$\frac{15}{1016}$/$\frac{1011}{1110}$/110.226 Table 2Comparison of treatment groups after 3 doses of anti-VEGF (3rd month) in terms of evaluation parametersAFL + AFLRAN + RANAFL + DEXRAN + DEXP ValueVisual Acuity (LogMAR)0.59 ± 0.260.64 ± 0.320.8 ± 0.40.85 ± 0.430.260Central Macular Thickness (µm)403.6 ± 70.4411.5 ± 84413.6 ± 88.1418.1 ± 880.964Intraocular Pressure (mmHg)15.3 ± 2.715.4 ± 2.914.7 ± 2.916.2 ± 3.20.392 Table 3Comparison of intragroup mean visual acuity, mean central macular thickness and mean intraocular pressure values after 3 anti-VEGF (3 months) and 6 anti-VEGF post or DEX 3 months (6 months)AFL + AFLRAN + RANAFL + DEXRAN + DEXVisual Acuity (LogMAR) after 3 anti-VEGF (3rd month)0.59 ± 0.260.64 ± 0.320.8 ± 0.40.85 ± 0.436 Anti-VEGF or DEX 3rd month Average Visual Acuity (LogMAR) (6th month)0.49 ± 0.220.57 ± 0.380.62 ± 0.380.80 ± 0.43P value 3rd month*6th monthVisual Acuity (LogMAR)0.1320.2840.0640.704Central Macular Thickness (µm) (3rd month) after 3 anti-VEGF403.6 ± 70.4411.5 ± 84413.6 ± 88.1418.1 ± 886 Post-anti-VEGF or DEX 3rd month Central Macular Thickness (µm) (6th month)291.6 ± 54.4293.1 ± 79.5320 ± 103.8332.5 ± 96.8P value 3rd month*6th month Central Macular Thickness (µm) 0.000 0.000 0.001 0.000 Intraocular Pressure (mmHg) (3rd month) after 3 anti-VEGF15.3 ± 2.715.4 ± 2.914.7 ± 2.916.2 ± 3.26 Post-anti-VEGF or DEX 3rd month Intraocular Pressure (mmHg) (6th month)15.9 ± 3.515.59 ± 415.9 ± 318.1 ± 3.8P value 3rd month*6th month Intraocular Pressure (mmHg)0.5420.834 0.013 0.027 Table 4Evaluation of mean visual acuity, mean central macular thickness and mean intraocular pressure between groups at 6 months after injectionAFL + AFLRAN + RANAFL + DEXRAN + DEXP valueAverage Visual Acuity LogMAR (6th month)0.49 ± 0.220.57 ± 0.380.62 ± 0.380.80 ± 0.430.159Central Macular Thickness (µm) (6th month)291.6 ± 54.4293.1 ± 79.5320 ± 103.8332.5 ± 96.80.295Intraocular Pressure (mmHg) (6th month)15.9 ± 3.515.59 ± 415.9 ± 318.1 ± 3.80.109
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|
---
title: Blood glucose trajectories and incidence of diabetes mellitus in Ugandan people
living with HIV initiated on dolutegravir
authors:
- Frank Mulindwa
- Barbara Castelnuovo
- Nele Brusselaers
- Robert Bollinger
- Joshua Rhein
- Mutebi Edrisa
- Allan Buzibye
- Willington Amutuhaire
- George Yendewa
- Sarah Nabaggala
- Eva Laker Agnes Odongpiny
- Ronald Kiguba
- Aisha Nakawooza
- Simon Dujanga
- Martin Nabwana
- Jean-Marc Schwarz
journal: AIDS Research and Therapy
year: 2023
pmcid: PMC10009965
doi: 10.1186/s12981-023-00510-6
license: CC BY 4.0
---
# Blood glucose trajectories and incidence of diabetes mellitus in Ugandan people living with HIV initiated on dolutegravir
## Abstract
### Background
Following reports of anti-retroviral therapy (ART) experienced Ugandan people living with HIV (PLHIV) presenting with diabetic ketoacidosis weeks to months following a switch to dolutegravir (DTG), the Uganda Ministry of Health recommended withholding DTG in both ART naïve and experienced PLHIV with diabetes mellitus (T2DM), as well as 3-monthly blood glucose monitoring for patients with T2DM risk factors. We sought to determine if the risk of T2DM is indeed heightened in nondiabetic ART naïve Ugandan PLHIV over the first 48 weeks on DTG.
### Methods
Between January and October 2021, 243 PLHIV without T2DM were initiated on DTG based ART for 48 weeks. Two-hour oral glucose tolerance tests (2-h OGTT) were performed at baseline, 12, and 36 weeks; fasting blood glucose (FBG) was measured at 24 and 48 weeks. The primary outcome was the incidence of T2DM. Secondary outcomes included: incidence of pre-Diabetes Mellitus (pre-DM), median change in FBG from baseline to week 48 and 2-h blood glucose (2hBG) from baseline to week 36. Linear regression models were used to determine adjusted differences in FBG and 2hBG from baseline to weeks 48 and 36 respectively.
### Results
The incidence of T2DM was 4 cases per 1000 PY ($\frac{1}{243}$) and pre-DM, 240 cases per 1000 person years (PY) ($\frac{54}{243}$). There was a significant increase in FBG from baseline to week 48 [median change from baseline (FBG): 3.6 mg/dl, interquartile range (IQR): − 3.6, 7.2, p-value (p) = 0.005] and significant reduction in 2hBG (2hBG: − 7.26 mg/dl, IQR: − 21.6, 14.4, $$p \leq 0.024$$) at week 36. A high CD4 count and increased waist circumference were associated with 2hBG increase at week 36.
### Conclusion
We demonstrated a low incidence of T2DM in Ugandan ART-naïve patients receiving DTG. We also demonstrated that longitudinal changes in BG were independent of conventional risk factors of T2DM in the first 48 weeks of therapy. Restricting the use of dolutegravir in Ugandan ART naïve patients perceived to be high risk for diabetes mellitus may be unwarranted.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12981-023-00510-6.
## Background
In the early anti-retroviral therapy (ART) era, nucleoside reverse transcriptase inhibitors (NRTIs) including didanosine, zidovudine, stavudine, or lamivudine were coupled with non-nucleoside reverse transcriptase inhibitors (NNRTIs) [1, 2]. Some of these combinations were linked to metabolic complications including metabolic syndrome, lipodystrophy and diabetes mellitus (T2DM) [3–5]. Since then, anti-retroviral therapy (ART) has typically included NNRTIs, protease inhibitors (PIs) and lately the preferred integrase strand transfer inhibitors (INSTIs) as anchor drugs coupled with largely metabolically safe NRTIs like tenofovir [6, 7].
After multiple countries reported primary resistance to NNRTIs above the recommended $10\%$ threshold, the World Health Organisation (WHO) recommended the use of dolutegravir (DTG), a later generation integrase inhibitor as part of first and second line therapy [8, 9]. Multiple prospective studies reported a very good safety profile of DTG leading to high tolerability, enhanced efficacy and a high genetic barrier to resistance [10–13]. Since then, multiple countries have adopted DTG-based ART as first line therapy in programmatic settings, especially in sub-Saharan Africa where the burden of HIV is highest [14, 15].
Uganda adopted DTG-based ART use in 2018, pioneered by selected centres including the Makerere University Infectious Diseases Institute (IDI). Patients were actively switched from NNRTIs and PIs to DTG-based ART [16]. In the first year of use, the IDI reported sixteen cases of patients presenting with diabetic ketoacidosis (DKA) a few weeks to months after being switched to DTG preceded by weight loss, a phenotypical sign of insulin deficiency [17]. Fifteen of the patients were ART exposed and one, ART naïve. One shortcoming of the report was that the patients’ diabetes status was unknown at the time of switch. Following that and other anecdotal field reports, the Uganda Ministry of Health (MoH) recommended avoiding the use of DTG in Ugandan ART naïve and experienced PLHIV known to have diabetes mellitus and withholding DTG in patients who develop type-2 diabetes mellitus (T2DM). Additionally, the guidelines encouraged three monthly blood glucose (BG) monitoring for patients with one or more of these factors; age ≥ 45 years, BMI ≥ 24 kg/m2 and history of hypertension at baseline [18].
Multiple similar case reports of accelerated hyperglycaemia have been published [19–24]. Large population cohort studies have however given conflicting reports about the association between integrase inhibitor use and incident diabetes mellitus [25–27]. It is still unclear whether patients who present with accelerated hyperglycaemia post-initiation of DTG-based ART have a special predisposition to developing T2DM, or if DTG truly increases risk of incident T2DM at population level [28]. In the Ugandan setting, where resources are limited, the adoption of these restrictive guidelines to the use of DTG has implications. There may be missed opportunities in starting certain patient groups on DTG as well as possible over-screening for diabetes in populations perceived to have an increased risk for DM.
The reported cases of accelerated hyperglycemia that informed the Ugandan MoH guidelines were largely ART experienced patients whose diabetes status was unknown at the time of the switch to DTG. From that evidence, the Uganda MoH guidelines that followed thereafter on the use of DTG were restrictive to both ART naïve and experienced PLHIV. We set out to determine if the risk of diabetes is indeed heightened in nondiabetic Ugandan ART naïve patients in the first 48 weeks of DTG based ART. We also assessed fasting and 2-h oral glucose tolerance test (OGTT) blood glucose time course changes over 48 weeks.
## Study design and setting
The GLUMED (Glucose metabolism changes in Ugandan PLHIV on Dolutegravir) study was a prospective cohort study at the Kisenyi Health Center IV HIV clinic in Uganda’s capital city, Kampala. This clinic has a total of 12,000 active PLHIV in care and the HIV program is supported by the Makerere University Infectious Diseases Institute with funding from the Center for Disease Control (CDC) and the U.S. President's Emergency Plan for AIDS Relief (PEPFAR).
## Study participants and study processes
Between January and October 2021, ART naïve PLHIV ≥ 18 years starting DTG based ART were screened for study inclusion. Pregnant women and very sick patients deemed unable to undergo a 2 h–75 g oral glucose tolerance test (2 h-OGTT) were excluded. Criteria for further exclusion during follow up included: new pregnancy and poor adherence to ART (adherence < $85\%$ determined by pill count and self-reporting [18]). Patients with poor adherence were excluded to ascertain exposure to DTG based ART.
After consenting, patients were scheduled for review in 24–48 h after an overnight fast of 8–12 h. Baseline demographic, clinical and social data were collected which included: age, sex, CD4 count, body mass index (BMI), level of education, area of residence, blood pressure, waist circumference, tuberculosis status, smoking status, physical activity measured by the Global Physical Activity Questionnaire (GPAQ), alcohol consumption measured by the Alcohol Use Disorders Identification Test (AUDIT), serum creatine and serum lipid profiles. Fasting blood glucose (FBG) was measured after which patients were given an oral solution containing 75 g of glucose to be taken within five minutes. Blood glucose was measured at 30, 60, 90 and 120 min from the time of ingestion of the glucose solution using ACCU-CHECK™ glucometers from Roche diagnostics [29]. Patients found to have a normal 2 h-OGTT (FBG < 126 mg/dl and 2-h blood glucose (2hBG) < 200 mg/dl) were enrolled for 48-week follow up on tenofovir/lamivudine/dolutegravir (TDF/3TC/DTG) as recommended by the Uganda National HIV treatment guidelines [18]. Enrolled patients received the same adherence and positive living counselling package as the other patients in the Kisenyi HIV clinic before ART initiation.
Enrolled patients were prospectively followed up with BMI, waist circumference, adherence counselling, assessment of concurrent medications and clinical assessments at 12, 24, 36 and 48 weeks. Repeat 2 h-OGTT was performed at 12 and 36 weeks while FBG was measured at 24 and 48 weeks. ART adherence was evaluated on every clinical visit using self-reports and pill counts as recommended by the Uganda MoH guidelines [30].
## Outcomes
The primary outcome of the study was incidence of T2DM. Secondary outcomes were: incidence of pre-DM and median changes in FBG from baseline to 48 weeks and 2hBG from baseline to 36 weeks. T2DM was defined as a FBG ≥ 126 mg/dl or 2hBG ≥ 200 mg/dl. Pre-DM was defined as a FBG of 100 mg–125 mg/dl or 2hBG between 140 and 199 mg/dl [31].
## Statistical analysis
Data were collected by a clinical team including a study doctor, nurse and lab technician. Double entry of data was performed with external data quality assurance provided by the IDI study monitoring team. Data were exported for statistical analyses. CD4 cell count, age, serum creatine, serum lipids, changes in BMI and BG were reported as continuous variables and the rest of the variables as categorical data. BMI was categorized according to the WHO into: underweight (< 18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2) and obesity (≥ 30 kg/m2) [32]. Waist circumference was categorized according to the WHO cut offs; Normal (≤ 94 cm) [Men (M)]; ≤ 80 cm [Women (W)], Increased risk of cardiometabolic complications [95–102 cm (M); 81–88 cm (W)] and substantially increased risk of cardiometabolic complications [> 102 cm (M); > 88 cm (W)] [33]. Blood pressure was categorized as: normal (< $\frac{120}{80}$ mmHG), pre-hypertension (120–$\frac{139}{80}$–89 mmHG) and hypertension (≥ $\frac{140}{90}$ mmHG) according to the Joint National Committee 8 (JNC-8) guidelines [34]. Participants were staged into HIV clinical stages 1, 2, 3 and 4 according to the WHO [35]. Physical activity was reported as meeting WHO recommendations on physical activity (≥ 600 Metabolic Equivalent of Task (MET) minutes per week) or not meeting WHO recommendations on physical activity (< 600 MET minutes per week) [36]. Virologic suppression was categorized according to the Uganda HIV treatment guidelines into suppressed viral load (VL) < 1000 copies/ml and non-suppressed (VL ≥ 1000 copies/ml) [30]. Laboratory normal ranges for serum creatine, fasting low density lipoproteins (LDL), high density lipoproteins (HDL) and total cholesterol (TC) were 0.72–1.24 mg/dl, 0–127.6 mg/dl, 34.8–56.07 mg/dl and 0–200 mg/dl respectively [37].
We compared characteristics between study participants who completed follow-up to those who did not. Categorical data were presented as proportions while continuous variables were presented as medians with their corresponding interquartile ranges (IQR). Post-baseline FBG and 2hBG was compared with the baseline using Wilcoxon signed rank test.
Multiple linear regression models were used to determine adjusted differences in blood glucose change between baseline and week 48 for FBG or baseline and week 36 for the 2hBG. Variables that had p-values < 0.1 or those with known biological plausibility and clinical significance were included in the multivariable model. Baseline BG was adjusted for in both models. Statistically significant differences were tested at a p-value of less than 0.05 and all p-values were two-sided. All analyses were done using Stata Release 17.0.
## Results
Out of the 435 patients screened for enrollment, 309 patients ($71.0\%$) were enrolled with 243 patients completing 48 weeks of follow up ($21.7\%$ drop-out) (Fig. 1).Fig. 1Study participant enrollment schema
## Baseline demographic and clinical characteristics of the study participants
Of the 243 patients enrolled, 140 ($58\%$) were female. The median age of the participants was 31 years (IQR:27–38) with a median CD4 cell count of 318 cells/mm3 (IQR 163–524). Majority [$$n = 192$$ ($79\%$)] of the patients were in HIV clinical stage 1 with 35 ($14\%$), 14 ($5.8\%$), 2 ($0.8\%$) being in HIV clinical stages 2, 3 and 4 respectively. Nine ($3.7\%$) patients had an established diagnosis of active tuberculosis diagnosed at enrollment into HIV care. Overall, $61\%$ had a normal BMI, $23.5\%$ were overweight, $10\%$ underweight and $5.8\%$ obese. Most ($81\%$) of the patients self-reported as meeting WHO recommendations on physical activity for health. The median serum creatine, fasting LDL, fasting HDL and fasting total cholesterol at baseline were: 0.83 mg/dl, 78.1 mg/dl, 31.7 mg/dl and 136.1 mg/dl respectively. At 24 weeks of follow up, 238 patients ($99.6\%$) had viral suppression (Table 1).Table 1Baseline clinical and demographic characteristics of the study populationCharacteristicTotal (%)Age (years), median (IQR)31 [27, 38]Sex Female140 (57.6) Male103 (42.4)Baseline CD4 cell count (cells/mm3), median (IQR)318 [163, 524]Level of education Uneducated3 (1.2) Primary129 (53.1) Secondary99 (40.7) Tertiary12 (4.9)Religion Christian188 (77.4) Muslim55 (22.6)Residence Rural18 (7.4) Urban225 (92.6)Employment No34 [14] Yes209 [86]*Marital status* Single135 (55.6) Married108 (44.4)*Tuberculosis status* No symptoms203 (83.5) TB suspect31 (12.8) TB disease9 (3.7)Baseline blood pressure (mmHG) Normal BP175 [72] Pre-hypertension48 (19.8) Hypertension20 (8.2)HIV clinical stage Stage 1192 [79] Stage 235 (14.4) Stage 314 (5.8) Stage 42 (0.8)Body Mass Index (BMI) (kg/m2) Underweight (< 18.5)24 (9.9) Normal (18.5–24.9)148 (60.9) Overweight (25.0–29.9)57 (23.5) Obese (≥ 30)14 (5.8)Waist circumference (cm) Normal160 (65.8) Increased risk of cardiometabolic complications45 (18.5) Substantially increased risk of cardiometabolic complications38 (15.6)*Smoking status* Smoker14 (5.8) Non-smoker229 (94.2)Physical activity (MET minutes) GPAQ < 600 MET minutes46 (18.9) GPAQ ≥ 600 MET minutes197 (81.1)Alcohol consumption No consumption139 (57.2) Low risk alcohol consumption67 (27.6) Hazardous alcohol consumption19 (7.8) Risk of alcohol dependence18 (7.4)24-week viral loads (copies/ml) (Proxy baseline VL) Virologically suppressed238 (99.6) Unsuppressed Viral load1 (0.4)Change in BMI from baseline to week 48, median (IQR)1.1 (0, 2.3)Laboratory investigations, median (IQR) Creatinine (mg/dl)0.83 (0.72, 0.95) LDL (mg/dl)78.1 (59.6, 94.0) HDL (mg/dl)31.7 (25.9, 39.8) Total cholesterol (mg/dl)136.1 (117.9, 158.6) Triglycerides (mg/dl)90.4 (69.1, 117.8)MET, metabolic equivalent of task; IQR, interquartile range; LDL, low density lipoproteins; HDL, high density lipoproteins; GPAQ, global physical activity questionnaire; VL, viral load; BMI, body mass index There were not significant baseline study characteristic differences between patients who completed 48 weeks of follow up and those who did not (Additional file 1: Table S1).
## Incidence of pre- diabetes and diabetes Mellitus
The incidence of DM was 4 cases per 1000 PY ($\frac{1}{243}$, diagnosed at 36 weeks). The incidence of pre-DM was 240 cases per 1000 person-years (PY) ($\frac{54}{243}$). Of the 54 patients diagnosed with pre-DM, 40 patients had a subsequent visit after pre-DM diagnosis (diagnosed between 12 and 36 weeks) and majority $\frac{32}{40}$ ($80\%$) reverted to a normal blood glucose state within the next 12 weeks.
## Fasting and 2-h OGTT blood glucose trajectories over 48 weeks
There was an insignificant reduction in median FBG at 12 weeks followed by a steady rise through 24 weeks, 36 weeks to 48 weeks. The median FBG at 48 weeks was significantly higher than that at baseline (FBG: 3.6, IQR: − 3.6, 7.2, $$p \leq 0.005$$) (Fig. 2).Fig. 2Box plots of changes in median fasting blood glucose over 48 weeks There was a significant decrease in the median 2hBG at 12 weeks (2hBG: − 9.0, IQR − 27.0, 5.4, $p \leq 0.0001$). Thereafter, was a slight increase to 36 weeks. Despite the rise in 2hBG from week 12 to week 36, the median 2hBG at 36 weeks was significantly lower than at baseline (2hBG: − 7.2, IQR: − 21.6, 14.4, $$p \leq 0.024$$) (Fig. 3).Fig. 3Box plots of changes in median 2-h OGTT blood glucose over 48 weeks
## Factors associated with changes in blood glucose
A higher baseline CD4 cell count was significantly associated with increase in 2hBG at week 36 [adjusted median change in 2hBG: 0.01, $95\%$ confidence interval (CI): 0.0002–0.021, p-value (p) = 0.045]. Additionally, having a waist circumference corresponding to increased risk of cardiometabolic complications at baseline was associated with an increase in 2hBG at week 36 (adjusted median change in 2hBG: 12.85, $95\%$ CI 4.80–20.89, $$p \leq 0.002$$). There were no factors associated with increase in FBG at 48 weeks (Table 2).Table 2Factors associated with changes in fasting and 2-h blood glucose over the follow up periodFasting blood glucose (0–48 weeks)2-h blood glucose (0–36 weeks)Characteristic*Crude change ($95\%$ CI)p-valueAdjusted change ($95\%$ CI)p-valueCrude change ($95\%$ CI)p-valueAdjusted change ($95\%$ CI)p-valueAge − 0.11 (− 0.25, 0.04)0.1520.6 (− 0.10, 0.22)0.486 − 0.08 (− 0.61, 0.46)0.7760.08 (− 0.32, 0.49)0.680Sex FemaleRef. Ref. Ref. Ref. Male − 2.59 (− 5.06, − 0.12)0.040 − 2.24 (− 4.98, 0.50)0.108 − 3.60 (− 10.73, 3.53)0.3213.25 (− 4.20, 10.70)0.391Baseline CD4 cell count0.0001 (− 0.005, 0.006)0.9700.002 (− 0.005, 0.005)0.9430.006 (− 0.006, 0.019)0.3130.01 (0.0002, 0.021)0.045Tuberculosis status No symptomsRef. Ref. TB suspect2.07 (− 1.82, 5.97)0.295 − 4.93 (− 17.02, 7.16)0.423 TB disease0.55 (− 6.11, 7.22)0.8701.24 (− 18.29, 20.76)0.901Baseline blood pressure Normal BPRef. Ref. Ref. Ref. Pre-hypertension − 2.98 (− 5.69, − 0.27)0.031 − 0.89 (− 3.62, 1.83)0.5190.19 (− 9.96, 10.34)0.970 − 3.15 (− 10.72, 4.41)0.412 Hypertension − 2.55 (− 7.11, 2.00)0.2700.99 (− 4.30, 6.28)0.712 − 5.04 (− 18.88, 8.80)0.4744.00 (− 8.15, 16.14)0.517HIV clinical stage Stage 1Ref. Ref. Ref. Stage ≥ 21.49 (− 1.67, 2.876)0.2382.67 (− 2.11, 7.82)0.262 − 18.00 (− 23.87, 3.210)0.312 − 2.24 (− 13.01, 7.432)0.317Body Mass Index (BMI) Underweight (< 18.5)Ref. Ref. Ref. Ref. Normal (18.5–24.9)0.07 (− 4.54, 4.68)0.9772.25 (− 1.98, 6.48)0.295 − 11.00 (− 25.58, 3.58)0.139 − 3.18 (− 14.75, 8.39)0.588 Overweight (25.0–29.9) − 2.94 (− 7.76, 1.89)0.232 − 2.05 (− 7.32, 3.22)0.444 − 7.62 (− 23.53, 8.29)0.346 − 8.07 (− 21.36, 5.23)0.233 Obese (≥ 30) − 2.88 (− 10.00, 4.23)0.426 − 1.77 (− 8.97, 5.43)0.628 − 10.97 (− 27.08, 5.14)0.181 − 12.91 (− 28.08, 2.26)0.095Waist circumference NormalRef. Ref. Ref. Ref. Increased risk of cardiometabolic complications − 0.49 (− 3.36, 2.39)0.7381.81 (− 1.79, 5.42)0.3225.18 (− 4.46, 14.82)0.29112.85 (4.80, 20.89)0.002 Substantially increased risk of cardiometabolic complications − 1.95 (− 5.47, 1.58)0.2773.56 (− 0.50, 7.62)0.0862.50 (− 6.35, 11.34)0.57913.79 (4.14, 23.45)0.005Smoking status Non-smokerRef. Ref. Smoker0.55 (− 5.32, 6.42)0.85310.722 (− 4.56, 26.00)0.168Physical activity GPAQ < 600 MET minutesRef. Ref. Ref. Ref. GPAQ ≥ 600 MET minutes − 1.25 (− 4.46, 1.96)0.444 − 2.72 (− 5.60, 0.16)0.0643.75 (− 4.99, 12.48)0.399 − 2.87 (− 10.50, 4.76)0.459Alcohol consumption No consumptionRef. Ref. Low risk alcohol consumption − 1.32 (− 4.12, 1.48)0.3530.86 (− 6.70, 8.42)0.823 Hazardous alcohol consumption − 1.65 (− 5.54, 2.24)0.40514.93 (2.88, 26.99)0.015 Risk of alcohol dependence4.23 (− 2.12, 10.58)0.1913.48 (− 14.49, 21.46)0.703Change in BMI from baseline to Week 480.01 (− 0.61, 0.62)0.9850.11 (− 0.43, 0.64)0.698 − 0.95 (− 3.19, 1.29)0.4051.08 (− 0.71, 2.88)0.235Laboratory investigations Creatinine (mg/dl) − 8.59 (− 14.71, − 2.46)0.006 − 2.04 (− 10.63, 6.55)0.698 − 12.10 (− 26.33, 2.12)0.095 − 9.19 (− 22.3, 3.93)0.169 LDL (mg/dl) − 0.02 (− 0.07, 0.03)0.470 − 0.03 (− 0.17, 0.11)0.679 HDL (mg/dl)0.03 (− 0.05, 0.11)0.4390.10 (− 0.16, 0.36)0.467 Total cholesterol (mg/dl) − 0.004 (− 0.04, 0.04)0.8510.04 (− 0.08, 0.16)0.510 Triglycerides (mg/dl) − 0.003 (− 0.03, 0.02)0.8070.09 (0.03, 0.16)0.0050.06 (− 0.0005, 0.112)0.052Bold values indicate statistically significant p values ($p \leq 0.05$)CD4, cluster of differentiation 4; BP, blood pressure; BMI, Body Mass Index; GPAQ, Global Physical Activity Questionnaire; HDL, high density lipoproteins; LDL, low density lipoprotein*Variables that had p-values < 0.1 or those with known biological plausibility and clinical significance were included in the multivariable model. Baseline blood glucose was adjusted for in both models
## Discussion
In this study, we determined that over 48 weeks of DTG-based ART, the incidence of T2DM was low [4 cases per 1000 PY ($0.4\%$)] with the majority of the patients diagnosed with pre-DM having transient increase in FBG/2hBG.
The incidence rate reported in our study is similar to the rates reported in the safety data of two DTG efficacy randomized controlled trials (RCTs) with ART naïve PLHIV in Cameroon and South Africa. The ADVANCE (96-week follow up) and NAMSAL (48-week follow up) studies reported incidences of $0.9\%$ and $0.3\%$ with controls on NNRTIs having $0.3\%$ and $0\%$ respectively [38, 39]. Patients from these two studies had mean ages of 33 and 36 years respectively slightly above the 31 years in our study. Other DTG efficacy trial safety data with ART naïve patients from Europe and North America reported similar rates of incident diabetes. The FLAMINGO and SINGLE trials reported incidence rates of $0.4\%$ and $0.5\%$ with controls having $0.8\%$ and $0.2\%$ respectively over 48 weeks of follow up [40, 41]. These RCTs had primarily virologic outcomes with BG reported in safety data using Division of AIDS grading of hyperglycemia. In two large population cohort studies with PLHIV from North America and median follow up time of 24–36 months, the incidence of diabetes in patients on DTG was $2\%$ and $1.7\%$ compared to $3.6\%$ in patients on NNRTI and $1\%$ in patients on PIs, slightly above what we determined in our study population [25, 26]. Of note, these two studies were retrospective database cohorts with different criteria used for T2DM diagnosis including: HBA1C, fasting blood glucose, random blood glucose and being on diabetes drugs versus our study which was a prospective study with a strict criterion for T2DM diagnosis.
From the Makerere University Infectious Diseases Institute report, over 12 months the reported incidence of T2DM was 4·7 per 1000 PY in the case (ART naïve and exposed PLHIV started on DTG) versus 0·32 per 1000 PY in the control group (PLHIV on other first line regimens without DTG). Almost all ($\frac{15}{16}$) of the T2DM cases were ART exposed before the switch to DTG. One limitation of that report was that the diabetes statuses of patients were not known at the time of ART switch and the cases reported were the overtly symptomatic DKA patients hence a possible underestimation of the burden of hyperglycemia. The possible underestimation of the burden of incident T2DM may partially have informed the MoH guidelines. In contrast, our study recruited only patients free of T2DM at baseline with prospective proactive screening for T2DM irrespective of symptoms using a strict criterion for T2DM diagnosis hence may better represent the incidence estimates in the naïve group, which is low.
In our study, there was a drop in both FBG and 2hBG in the first 12 weeks of follow-up, followed by a consistent increase up to 48 weeks. Despite the 48-week FBG being significantly higher than at baseline, the 2hBG at 36 weeks was less. The trajectories suggested a steady increase in BG after 12 weeks suggesting that with longer follow up there could be a consistent significant increase in both fasting and 2hBG. Multiple factors interact in regulating blood glucose. These include: basal insulin secretion, post prandial insulin secretion, hepatic gluconeogenesis, insulin resistance and insulin clearance [42–44]. Systemic inflammation is known to impair insulin signaling at end organs leading to insulin resistance as well as insulin production. There is evidence documenting reduction in systemic inflammation when ART is introduced in PLHIV [45, 46]. Some studies have even suggested the reduced inflammation is even more evident in patients on integrase inhibitors compared to other ART anchors drugs [47, 48]. The initial improvement in 2hBG may be explained by an initial reduction in systemic inflammation in the first 3 months of therapy. With stabilization on ART, patients get a ‘return to health phenomenon’ where their appetite, weight and functionality improve, all factors known to impair insulin sensitivity in the long term. This could explain the subsequent rising trend in BG after week 12.
From our analysis, both FBG and 2hBG changes between baseline to 48 weeks were not associated to conventional risk factors for diabetes including: baseline BMI, physical inactivity and increase in BMI over the follow up period, apart from increased waist circumference which was associated with increase in 2hBG at 36 weeks. In the Ugandan HIV treatment guidelines, these are the parameters used to stratify T2DM risk to determine which patients to screen for T2DM every 3 months [49]. Additionally, patients tended to have improvement in glucose measurements in the first 3–6 months, the same period recommended for intensified monitoring for patients with risk factors to incident T2DM.
Our study had various limitations. We did not have a comparator group hence we could only describe the natural history of glucose changes in our cohort but not conclusively attribute the changes in BG to exposure to dolutegravir. It was a single center study in an urban area hence the results may not be generalizable to the general PLHIV population in Uganda. Much as the patients that were excluded during the study did not have significantly different clinical and demographic characteristics compared to those that completed the study, the dropout rate was high which could lead to under- or over-estimation of our end point depending on the outcome of the dropout participants. At enrollment, very sick patients were excluded, the same group of patients that may have more heightened systemic inflammation with different glucose metabolism trends on introduction of ART. Despite the limitations, our study had a clearly defined metabolic end point with a strict and sensitive criterion for the diagnosis of T2DM making our results reliable [50]. We screened patients for factors known to compound glucose metabolism and evaluated if these had significant impact on the reported BG trends. To the best of our knowledge, this was the first study since the roll out of DTG in Uganda documenting the incidence of T2DM and describing the BG changes in the first year of treatment.
## Conclusion
We demonstrated that the incidence of T2DM in Ugandan ART naïve patients over 48 weeks of DTG anchored ART is low. Nevertheless, there was a trend towards rising blood glucose after week 12. We also demonstrated that longitudinal changes in BG were independent of conventional risk factors of T2DM in the first 48 weeks of therapy. Restrictive use of DTG in Ugandan PLHIV perceived to have a high risk for T2DM may be uncalled for.
## Supplementary Information
Additional file 1. Table S1: *Sensitivity analysis* comparing baseline characteristics of participants who dropped out and those who did not.
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|
---
title: Interaction effects between sleep disorders and depression on heart failure
authors:
- Tianshu Fan
- Dechun Su
journal: BMC Cardiovascular Disorders
year: 2023
pmcid: PMC10009973
doi: 10.1186/s12872-023-03147-5
license: CC BY 4.0
---
# Interaction effects between sleep disorders and depression on heart failure
## Abstract
### Background
Sleep disorders and depression were recognized as independent risk factors for heart failure, whether their interaction effects also correlated with the risk of heart failure remains elusive. This study was to explore the interaction effects between sleep disorders and depression on the risk of heart failure.
### Methods
This was a cross-sectional study that included data from 39,636 participants in the National Health and Nutritional Examination Survey (NHANES) database. Poisson regression model was applied to evaluate the associations of depression or sleep disorders with heart failure. The relative excess risk of interaction (RERI), attributable proportion of interaction (API) and synergy index (SI) were used to measure whether the interaction effects between depression and sleep disorders on heart failure was statistically significant.
### Results
The risk of heart failure was increased in people with sleep disorders [risk ratio (RR) = 1.92, $95\%$ confidence interval (CI): 1.68–2.19) after adjusting for confounders including age, gender, body mass index (BMI), race, marital status, education level, annual family income, drinking history, smoking history, diabetes, hypertension and stroke. The risk of heart failure was elevated in patients with depression after adjusting for confounders (RR = 1.96, $95\%$CI: 1.65–2.33). Patients with depression and sleep disorders were associated with increased risk of heart failure after adjusting for confounders (RR = 2.76, $95\%$CI: 2.23–3.42). The CIs of interactive indexes RERI was -0.42 ($95\%$CI: -1.23–0.39), and API was -0.15 ($95\%$CI: -0.46–0.16), which included 0. The CI of interactive indexes SI was 0.81 ($95\%$CI: 0.54–1.21), which contained 1.
### Conclusion
Depression and sleep disorders were independent risk factors for heart failure but the interaction effects between depression and sleep disorders on the occurrence of heart failure were not statistically different.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12872-023-03147-5.
## Background
Heart failure is a progressive and symptomatic syndrome that has been recognized as one of the main global health problems [1]. Nearly 5.7 million adults > 20 years suffered from heart failure and the estimated prevalence is about $10\%$ in people age > 65 years [2]. Patients with heart failure require lifelong medical treatment and great health care, and this results in a high premature mortality [3]. Heart failure decreases the quality of life in patients and brings heavy burden to the society [4]. Given the high prevalence and cost of heart failure, increasing emphasis has been put on exploring the factors associated with heart failure. A better understanding of modifiable risk factors and their interaction effects on heart failure is vital for the prevention of this disease.
Sleep disorders including sleep apnea, insomnia, restless legs, and others are frequently identified in patients with heart failure [5, 6]. Almost $75\%$ of heart failure patients were reported to have sleep disorders [7]. Sleep duration and sleep disorders were revealed to be associated with increased risk of heart failure [8, 9]. Another modifiable risk factor for heart failure might be depression in patients. Depression is a common comorbidity in heart failure and approximately $30\%$ of heart failure patients suffer from depression and even more have depressive symptoms [10]. Multiple evidence indicated that depressive symptoms were risk factors for heart diseases [11]. Several studies also revealed that depression not only reduces the quality of life and increases the re-hospitalization rate of heart failure patients, but also increases the morbidity and mortality of heart failure and affects the prognosis of these patients [12, 13]. Growing numbers of studies showed that depression and sleep disorders had bidirectional relationship with each other [14]. Several researchers also found that the interaction effects between depression and sleep disorders affected the occurrence of stroke and type 2 diabetes [15]. At present, sleep disorders and depression were recognized as independent risk factors for heart failure, whether their interaction effects also correlated with the risk of heart failure remains elusive.
Due to the high mortality and morbidity of heart failure, an updated and careful management of different aspects that characterize the disease such as stratifying factors associated with heart failure is essential for correctly clinical managing patients [16]. Previous studies have explored the well-known clinical, laboratory and instrumental characteristics that might influence heart failure [17, 18], the demographic characteristics such as age, gender, and body mass index (BMI) also have different influences and deserve specific insights and clarifications [19–21]. At this scope, we performed the subgroup analysis to investigate the interaction effects between sleep disorders and depression on the risk of heart failure in participants with different demographic characteristics.
In the current study, we aimed to explore the interaction effects between sleep disorders and depression on the risk of heart failure based on the data from the National Health and Nutritional Examination Survey (NHANES) database. The independent as well as the bidirectional relationship between sleep disorders and depression were respectively investigated to deeply evaluate the associations of sleep disorders and depression with the risk of heart failure. We also stratified the analysis in terms of demographic characteristics such as age, gender, BMI and marital status.
## Study design and population
This was a cross-sectional study collected the data of 39,636 participants from NHANES database. NHANES is a survey collecting the data of nationally representative samples in the United States each year and recording the detailed demographic information and comprehensive nutrition data on dietary intake, anthropometric measurements, as well as blood samples by standardized interviews and direct examination of participants [22]. In the current study, after excluding participants without the data on sleep disorders, depression questionnaires and others, 30,406 subjects were finally included.
## Data collection
The data of all subjects were collected including the age (years), gender, BMI (kg/m2), race (Mexican American, Hispanic, non-Hispanic White, non-Hispanic Black, or others), marital status (married, widowed, divorced/separated, or unmarried), education [Junior high and below, High school/General Equivalent Diploma (GED), Junior college or above], annual family income (< $20,000 or ≥ $20,000), drinking history, smoking history, diabetes mellitus, stroke, hypertension, sleep disorders, depression, depression severity (no, moderate, moderate to severe and severe), and heart failure. All participants were divided into the heart failure group ($$n = 977$$) and non-heart failure group ($$n = 29$$,429).
## Definitions of variables
Heart failure in patients was defined as the outcome in this study, which was determined by a response of “yes” to the household interview question asking whether they had been told to have congestive heart failure in the “Medical Conditions” module of “Questionnaire Data” in the NHANES. Sleep disorders were defined based on a response of “yes” to the question asking them whether they had been told to have sleep disorders by doctors or professional health workers. Depression was measured by the Patient Health Questionnaire 9 (PHQ-9) and PHQ-9 scores ≥ 10 was defined as depression [23]. The severity of depression was determined based on the PHQ-9 scores, including no depression (0–9), moderate depression (10–14), moderate to severe depression (15–19), and severe depression (20–27) [24]. The reliability or validity of the PHQ-9 were assessed and the Cronbach Alpha coefficient of PHQ-9 was 0.846447 after standardization.
## The additive interaction effects model
Three indexes including relative excess risk of interaction (RERI), attributable proportion of interaction (API), and synergy index (SI) were used to assess the interaction effects between sleep disorders and depression on the risk of heart failure based on the addictive model. RERI = R11-R10-R01 + 1: represents the difference between the sum of the combined effects of the two factors and the sum of the separate effects. It also represents the risk degree of interaction effects in comparison with all other factors except the two factors. API = RERI/R11: represents the proportion of total effects attributed to interaction. SI = R11 (R10 × R01): the meaning is the same as RERI. No interaction effects were shown when 0 was included in the confidence intervals (CIs) of RERI and API and 1 was involved in the CI of SI.
## Statistical analysis
The Shapiro–Wilk was applied for measuring the normality of the measurement data. The measurement data with normal distribution were described as Mean ± SD and comparisons between groups were subjected to t test. Non-normal distributed data were shown as [M (Q1, Q3)] and differences between groups were compared via Mann–Whitney U rank sum test. The enumeration data were described as n (%). Chi-square test (χ2) or Fisher’s exact probability method was used for comparison between the groups. Poisson regression model was applied to evaluate the associations of depression or sleep disorders with heart failure. Model 1 was the unadjusted crude model. Model 2 adjusted for demographic characteristics including age, BMI, marital status and gender. Model 3 adjusted for variables with statistical difference between heart failure group and non-heart failure group including age, gender, race, marital status, education, annual family income, drinking history, smoking history diabetes mellitus, hypertension and stroke. Stepwise regression analysis was applied in Model 3. Sensitivity analysis was performed in the data before and after deleting the missing data to explore whether the missing values influenced the results. RERI, API and SI were used to assess whether the interaction effects between depression and sleep disorders on heart failure was statistically significant. All statistical tests were performed by two-sided test and $P \leq 0.05$ were considered to be statistically significant. SAS 9.4 was used for statistical analysis, R 4.20 software was used to draw the forest plot, and GraphPad was applied to draw the graph showing the risk ratios (RR) of interaction effects term.
## The characteristics of all participants
The data of 39,636 participants were extracted from NHANES database. Among them, 25 people missed the data on sleep disorders, 5579 subjects had no data on depression questionnaires and 3626 persons missed other data, and 30,406 subjects were finally included. The detailed screen process was shown in Fig. 1. The median age of all the participants was 49 years. 14,998 ($49.33\%$) of the subjects were male. The average BMI was 29.25 kg/m2. 4679 people were Mexican–American, accounting for $15.39\%$, 2748 persons were Hispanic, accounting for $9.04\%$, 13,503 participants were non-Hispanic White, accounting for $44.41\%$, 6485 people were non-Hispanic Black, accounting for $21.33\%$, and 2991 participants were other ethnicities, accounting for $9.84\%$. 7831 patients had sleep disorders, accounting for $25.75\%$, and 2634 people had depression, accounting for $8.66\%$. Among patients with depression, 1642 patients were moderate depression, accounting for $5.40\%$, 709 people were moderate to severe depression, accounting for $2.33\%$, and 283 persons were severe depression, accounting for $0.93\%$. 977 patients had heart failure, accounting for $3.21\%$ (Table 1).Fig. 1The screen process of participants in this studyTable 1Comparisons of the characteristics between patients with and without heart failureVariableTotal ($$n = 30$$,406)GroupStatistical magnitudePHeart failure ($$n = 29$$,429)Non-heart failure ($$n = 977$$)Age, years, M(Q1, Q3)49 [34, 64]48 [34, 63]69 [60, 77]$Z = 30.471$ < 0.001Gender, n (%)χ2 = 21.986 < 0.001 Male14,998 (49.33)14,444 (49.08)554 (56.70) Female15,408 (50.67)14,985 (50.92)423 (43.30)BMI, kg/m2, Mean ± SD29.25 ± 7.0029.15 ± 6.9232.29 ± 8.56t = -11.160 < 0.001Race, n(%)χ2 = 96.403 < 0.001 Mexican American4679 (15.39)4603 (15.64)76 (7.78) Hispanic2748 (9.04)2678 (9.10)70 (7.16) non-Hispanic White13,503 (44.41)12,985 (44.12)518 (53.02) non-Hispanic Black6485 (21.33)6222 (21.14)263 (26.92) others2991 (9.84)2941 (9.99)50 (5.12)Marital status, n (%)χ2 = 346.640 < 0.001 Married15,801 (51.97)15,337 (52.12)464 (47.49) Widowed2354 (7.74)2142 (7.28)212 (21.70) Divorced/separated4389 (14.43)4205 (14.29)184 (18.83) Unmarried7862 (25.86)7745 (26.32)117 (11.98)Education, n (%)Z = -8.028 < 0.001 Junior high and below7093 (23.33)6788 (23.07)305 (31.22) High school/GED7076 (23.27)6804 (23.12)272 (27.84) Junior college or above16,237 (53.40)15,837 (53.81)400 (40.94)Annual family income, $, n (%)χ2 = 152.884 < 0.001 < $20,0006482 (21.32)6118 (20.79)364 (37.26) ≥ $20,00023,924 (78.68)23,311 (79.21)613 (62.74)Drinking history, n (%)20,952 (68.91)20,351 (69.15)601 (61.51)χ2 = 25.748 < 0.001Smoking history, n (%)13,882 (45.66)13,273 (45.10)609 (62.33)χ2 = 113.169 < 0.001Diabetes mellitus, n (%)4021 (13.22)3591 (12.20)430 (44.01)χ2 = 833.809 < 0.001Stroke, n (%)1127 (3.71)930 (3.16)197 (20.16)χ2 = 766.006 < 0.001Hypertension, n (%)14,073 (46.28)13,337 (45.32)736 (75.33)χ2 = 342.6158 < 0.0001Sleep disorders, n (%)7831 (25.75)7357 (25.00)474 (48.52)χ2 = 273.488 < 0.001Depression, n (%)2634 (8.66)2451 (8.33)183 (18.73)χ2 = 129.320 < 0.001Depression severity, n (%)$Z = 11.450$ < 0.001 No27,772 (91.34)26,978 (91.67)794 (81.27) Moderate1642 (5.40)1537 (5.22)105 (10.75) Moderate to severe709 (2.33)653 (2.22)56 (5.73) Severe283 (0.93)261 (0.89)22 (2.25)BMI Body mass index, GED General equivalent diploma
## Comparisons of the characteristics between patients with and without heart failure
The median age (69 years vs 48 years, $Z = 30.471$, $P \leq 0.001$), and median BMI (32.29 kg/m2 vs 29.15 kg/m2, $t = 11.160$, $P \leq 0.001$) of participants with heart failure were higher than those without. The proportions of males ($56.70\%$ vs $49.08\%$, χ2 = 21.986, $P \leq 0.001$), subjects with annual family income < $20,000 (χ2 = 152.884, $P \leq 0.001$), patients with smoking history ($62.33\%$ vs $45.10\%$, χ2 = 113.169, $P \leq 0.001$), patients with diabetes (χ2 = 833.809, $P \leq 0.001$), patients with stroke ($20.16\%$ vs $3.16\%$, χ2 = 766.006, $P \leq 0.001$), patients with sleep disorders ($48.52\%$ vs $25\%$, χ2 = 273.488, $P \leq 0.001$), patients with depression ($18.73\%$ vs $8.33\%$, χ2 = 129.320, $P \leq 0.001$) and different depression degrees ($Z = 11.450$, $P \leq 0.001$) in the heart failure group were higher than the non-heart failure group. The percentages of people with different education levels (Z = -8.028, $P \leq 0.001$) and drinking history ($61.51\%$ vs $69.15\%$ χ2 = 25.748, $P \leq 0.001$) in people with heart failure were lower than those without heart failure. The differences concerning race (χ2 = 96.403, $P \leq 0.001$) and marital status (χ2 = 346.640, $P \leq 0.001$) between people with and without heart failure were statistically significant (Table 1).
## Associations of sleep disorders or depression with heart failure
As observed in Fig. 2, the risk of heart failure was 2.21 times increase in patients with sleep disorders in the adjusted model for age and sex (RR = 2.21, $95\%$CI: 1.94–2.51). After adjusting for age, sex, BMI, race, marital status, education level, annual family income, drinking history, smoking history, diabetes, hypertension and stroke, the risk of heart failure was 1.92-fold increase in people with sleep disorders (RR = 1.92, $95\%$CI: 1.68–2.19). The risk of heart failure was 2.53-fold increase in patients with depression after adjusting for age and sex (RR = 2.53, $95\%$CI: 2.14–2.99). The risk of heart failure was 1.96-fold increase in patients with depression compared with those without (RR = 1.96, $95\%$CI: 1.65–2.33) after adjusting for age, sex, BMI, race, marital status, education level, annual family income, drinking history, smoking history, diabetes, hypertension and stroke (Fig. 2). For most categories of age, BMI, marital status and gender, the risk ratios for sleep disorders and depression were statistically significantly (greater than 1.0) in most cases. Exception was in underweight group. The detailed information of the subgroups was exhibited in Figs. 3 and 4.Fig. 2Forest plot showing the association between sleep disorders or/and depression and heart failureFig. 3Forest plot showing the association between sleep disorders and heart failure in people with different demographic characteristicsFig. 4Forest plot showing the association between depression and heart failure in people with different demographic characteristics
## Interaction effects between sleep disorders and depression on heart failure
The additive interaction effects terms of sleep disorders and depression were established, including no depression and no sleep disorders, no depression and sleep disorders, depression and no sleep disorders, depression and sleep disorders. The detailed sample size of each interaction effects term was displayed in Table 2. Patients with depression and sleep disorders were associated with increased risk of heart failure after adjusting for age and gender (RR = 3.68, $95\%$ CI: 2.99–4.54), or adjusting for age, gender, BMI, race, marital status, education level, annual family income, drinking history, smoking history, diabetes, hypertension and stroke (RR = 2.76, $95\%$CI: 2.23–3.42) (Fig. 2). For most categories of age, BMI, marital status and gender, the risk ratios for people with sleep disorders and depression were statistically significantly (> 1.0) in most cases (Fig. 5). Sensitivity analysis depicted that there was no statistical difference of the results between the data before and after deleting the missing values (Supplementary Table 1). The CIs of interactive indexes RERI was -0.42 ($95\%$CI: -1.23–0.39), and API was -0.15 ($95\%$CI: -0.46–0.16), which included 0. The CIs of interactive indexes SI was 0.81 ($95\%$CI: 0.54–1.21), which contained 1 (Table 3). These indicated that the interaction effects between sleep disorders and depression on heart failure was not statistically significant (Fig. 6). Subgroup analysis concerning the demographic characteristics exhibited no statistical differences in terms of the interaction effects between depression and sleep disorders on the risk of heart failure (Table 3).Table 2The detailed sample size of each interaction effects termHeart failureDepressionSleep disorderORYesNoDepression (Yes)Depression (No)Yes122352YesR11R10No14125945Yes61442NoR01R00No103921,033Fig. 5Forest plot showing the association of sleep disorders and depression with heart failure in people with different demographic characteristicsTable 3Interaction effects between sleep disorders and depression on heart failureRERI (CI: $95\%$)API (CI: $95\%$)SI (CI: $95\%$)Total-0.42 (-1.23, 0.39)-0.15 (-0.46, 0.16)0.81 (0.54, 1.21)Age > 65-1.18 (-2.54, 0.18)-0.37 (-0.84, 0.10)0.65 (0.41, 1.04) 30–65-0.46 (-1.47, 0.54)-0.22 (-0.74, 0.30)0.70 (0.33, 1.51)BMI Underweight1.68 (-5.53, 8.88)0.49 (-0.86, 1.83)3.19 (0.01, 721.80) Normal2.29 (-0.54, 5.11)0.43 (0.05, 0.82)2.14 (0.83, 5.55) Overweight-1.23 (-3.09, 0.63)-0.50 (-1.41, 0.41)0.54 (0.21, 1.40) Obesity-0.57 (-1.59, 0.45)-0.22 (-0.64, 0.20)0.74 (0.43, 1.25)*Marital status* Married-1.05 (-2.53, 0.42)-0.36 (-0.94, 0.21)0.64 (0.35, 1.18) Others-0.06 (-0.98, 0.86)-0.02 (-0.39, 0.34)0.96 (0.54, 1.73)Gender Male-0.73 (-1.95, 0.49)-0.26 (-0.74, 0.22)0.71 (0.41, 1.25) Female-0.03 (-1.06, 1.01)-0.01 (-0.41, 0.39)0.98 (0.52, 1.85)CI Confidence interval, BMI Body weight index, RERI Relative excess risk, API Attributable proportion of interaction, SI Synergy indexFig. 6Interaction effects between sleep disorders and depression on heart failure after adjusting for confounders
## Discussion
In the present study, 30,406 eligible participants were enrolled from NHANES, including 977 people with heart failure and 29,429 without heart failure. The results depicted that depression and sleep disorders were independently associated with increased risk of heart failure. No synergic and additive interaction effects between depression and sleep disorders on the occurrence of heart failure was obtained. The findings of this study might make it more clear about the effects between depression and sleep disorders on the occurrence of heart failure, and help the clinicians to make appropriate interventions on patients with depression or/and sleep disorders.
Depression is a chronic medical illness affecting thoughts, mood, and physical health, which decreases the ability of individuals to function in their daily life [25]. A review by Celano et al. uncovered that depression was associated with the development and progression of heart failure via mediating the physiologic and behavioral mechanisms [26]. Another prospective observational study including about 2 million healthy adults demonstrated that depression was prospectively associated with an $18\%$ increased risk of heart failure [27]. These findings gave support to the results of our study, which showed that depression was a risk factor for the occurrence of heart failure. This may be because patients with depression was linked to the hypothalamic–pituitary–adrenal gland dysfunction, increased pro-inflammatory and pro-thrombotic factor activity, reduced heart rate variability and physical inactivity [28]. Depressive symptoms and major depression were associated with elevated levels of inflammatory biomarkers such as C-reactive protein (CRP), interleukin (IL)-1, IL-6, tumor necrosis factor-alpha (TNF-α) and monocyte chemoattractant protein-1 (MCP-1) [29]. The immune system and inflammation were reported to be involved in the pathogenesis of heart failure [30]. To early screen out patients with depression can timely provide proper treatments such as anti-depressive medications. In the current study, sleep disorders were recognized to be associated with a higher risk of heart failure. Javaheri et al. discovered that insomnia, especially when accompanied by short sleep duration was linked with increased risk of heart failure [31]. A prospective population-based study reported that obstructive sleep apnea had a twofold increase in the risk of heart failure [32]. The potential mechanisms might be that sleep disorders including sleep-disordered breathing is associated with increased sympathetic activation, vagal withdrawal, altered haemodynamic loading conditions, and hypoxaemia, which is one of the most common risk factor for cardiac failure [33]. To improve the quality of sleep in general population might be a strategy for the prevention of heart failure. Subgroup analysis showed that depression and sleep disorders were associated with higher risk of heart failure in both patients aged ≥ 65 years and 30–65 years, married or others, males or females. These suggested that patients with sleep disorders or depression should be cautious of the risk of heart failure despite the age, marital status and gender. As for underweight patients, no significant association was found between depression or sleep disorders and heart failure. This maybe because the sample size in underweight group was small [underweight group ($$n = 475$$) vs normal BMI ($$n = 8521$$) vs overweight ($$n = 9945$$) vs obesity ($$n = 11$$,465)].
In this study, the interaction effects between depression and sleep disorders on heart failure were also explored, and no synergic interaction effects between depression and sleep disorders was identified on the occurrence of heart failure. This maybe because the disease course of heart failure was progressive and long [34], and the interaction effects of depression and sleep disorders on heart failure was not significant during the long disease course. The interaction effects between depression and sleep disorders on heart failure might be more obvious if more relevant clinical biomarkers were including in the subgroup analysis. Inflammation plays an important part in depression, sleep disorders and heart failure [35, 36], and depression and sleep disorders might be linked to heart failure through the pathway of inflammation. Depression and heart failure also share some mechanisms and risk factors, including dysregulation of platelet reactivity, neuroendocrine function, arrhythmias, high-risk behaviors, and social factors [10]. Severe depression was reported to be associated with diastolic dysfunction and left ventricular hypertrophy, which increase the risk of heart failure [37]. For patients with depression, early interventions should be provided to decrease the risk of heart failure in those patients. Interestingly, we found in people with normal BMI, there might be interaction effects of depression and sleep disorders on heart failure. This maybe because abnormal BMI might involve in more mechanisms associated with the occurrence of heart failure [38], and the interaction effects of depression and sleep disorders on heart failure might be not significant.
This study explored the interaction effects between depression and sleep disorders on heart failure, which obtained convincing results based on the nationally representative NHANES database with a large sample size. Several limitations existed in our study. Firstly, this was a cross-sectional study, which could only identify the associations but not the causal relationship between depression or sleep disorders and heart failure. Secondly, the history of sleep disorders and heart failure were based on the self-reported data of participants in the NHANES, which might cause bias. Thirdly, subgroup analysis was only conducted in terms of gender, race and age, more heart failure related subgroups should be performed to clearly identify the interaction effects between depression and sleep disorders on heart failure in different populations. In the future, more case–control studies on deeply exploring the interaction effects of depression and sleep disorders on heart failure were required to verify the findings in the current study.
## Conclusions
Our study analyzed the effect of depression and sleep disorders on the risk of heart failure based on the data of 30,406 participants from NHANES. The findings revealed that depression and sleep disorders were independent risk factors of heart failure but the interaction effects between depression and sleep disorders were not statistically different on the occurrence of heart failure. The results of our study revealed the co-existing of sleep disorder and depression does not seem to have a synergistic effect on the occurrence of heart failure.
## Supplementary Information
Additional file 1: Table1. Comparisons of the results between the data before and after deleting themissing values.
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---
title: FGF19 increases mitochondrial biogenesis and fusion in chondrocytes via the
AMPKα-p38/MAPK pathway
authors:
- Shiyi Kan
- Caixia Pi
- Li Zhang
- Daimo Guo
- Zhixing Niu
- Yang Liu
- Mengmeng Duan
- Xiahua Pu
- Mingru Bai
- Chenchen Zhou
- Demao Zhang
- Jing Xie
journal: 'Cell Communication and Signaling : CCS'
year: 2023
pmcid: PMC10009974
doi: 10.1186/s12964-023-01069-5
license: CC BY 4.0
---
# FGF19 increases mitochondrial biogenesis and fusion in chondrocytes via the AMPKα-p38/MAPK pathway
## Abstract
Fibroblast growth factor 19 (FGF19) is recognized to play an essential role in cartilage development and physiology, and has emerged as a potential therapeutic target for skeletal metabolic diseases. However, FGF19-mediated cellular behavior in chondrocytes remains a big challenge. In the current study, we aimed to investigate the role of FGF19 on chondrocytes by characterizing mitochondrial biogenesis and fission–fusion dynamic equilibrium and exploring the underlying mechanism. We first found that FGF19 enhanced mitochondrial biogenesis in chondrocytes with the help of β Klotho (KLB), a vital accessory protein for assisting the binding of FGF19 to its receptor, and the enhanced biogenesis accompanied with a fusion of mitochondria, reflecting in the elongation of individual mitochondria and the up-regulation of mitochondrial fusion proteins. We then revealed that FGF19-mediated mitochondrial biogenesis and fusion required the binding of FGF19 to the membrane receptor, FGFR4, and the activation of AMP-activated protein kinase alpha (AMPKα)/peroxisome proliferator-activated receptor-gamma coactivator 1 alpha (PGC-1α)/sirtuin 1 (SIRT1) axis. Finally, we demonstrated that FGF19-mediated mitochondrial biogenesis and fusion was mainly dependent on the activation of p-p38 signaling. Inhibition of p38 signaling largely reduced the high expression of AMPKα/PGC-1α/SIRT1 axis, decreased the up-regulation of mitochondrial fusion proteins and impaired the enhancement of mitochondrial network morphology in chondrocytes induced by FGF19. Taking together, our results indicate that FGF19 could increase mitochondrial biogenesis and fusion via AMPKα-p38/MAPK signaling, which enlarge the understanding of FGF19 on chondrocyte metabolism.
Video Abstract
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12964-023-01069-5.
## Introduction
Mitochondria play a vital role in chondrocyte metabolism because they not only provide the indispensable adenosine triphosphate (ATP) for chondrocytes [1] but also directly participate in many cellular physiological activities by changing their biogenesis [2]. The homeostasis of mitochondrial biogenesis is maintained by a mitochondrial quality control (MQC) system [3]. MQC mainly preserves functional mitochondria by controlling the homeostasis of the fission–fusion process, and even removes redundant non-functional mitochondria [4]. Mitochondrial fission is mainly driven by dynamin-related protein 1 (Drp1), a cytoplasmic dynamin guanosine triphosphatase (GTPase) [5], and mitochondrial fission protein 1 (Fis1) [6]. Drp1 is dynamically recruited to the outer mitochondrial membrane (OMM) and then oligomerized in a ring-like structure and drives membrane constriction in a GTP-dependent manner. Fis1 serves as a membrane-anchor that could also regulate mitochondrial fission through interaction with Drp1 and other fission components in mitochondria [7]. For mitochondrial fusion, it is controlled by 2 mitofusins (Mfn1 and Mfn2) [8] and dominant optic atrophy 1 (Opa1) [9]. Mfn1 and Mfn2 mediate the fusion of OMMs and then Opa1 mediates the fusion of the inner mitochondrial membrane (IMM). The outer membranes of two mitochondria are tethered by Mfns. GTP binding and/or hydrolysis induces a conformational change of Mfns, resulting in increased mitochondrial docking and membrane contact sites. Following OMM fusion, the interaction between OPA1 and cardiolipin (CL) on either side of the membrane tethers the IMMs, which drives IMM fusion by OPA1-dependent GTP hydrolysis. Mitochondrial fusion promotes the exchange of important components among mitochondria, especially mitochondrial deoxyribonucleic acid (mtDNA), and ensures the continuity of mitochondrial function [10]. Mitochondria adjust their number and mitochondrial network morphology in cells by coordinating the cycle of mitochondrial fission and fusion. These dynamic changes further regulate mitochondrial functions and determine cell metabolism [11].
Fibroblast growth factors (FGFs) are a type of cytokine that plays an important role in regulating organic growth, development, maturation and disease [12]. They include a total of 22 members of 7 subfamilies. FGF19, belonging to the FGF19 subfamily including FGF19, FGF21 and FGF23, was first found to be expressed in the human cartilage in 1999 [13] and then it is recognized to be one of the predominant FGF ligands present in developing human cartilage [14]. These reports indicate its potential role in chondrocyte development and homeostasis. Previous evidence has confirmed that FGF19 signalling is crucial to glucose metabolism [15]. It could increase energy consumption and glucose utilization by increasing the cyclic-AMP response binding protein (CREB)-peroxisome proliferator-activated receptor-gamma coactivator 1 alpha (PGC-1α)-signalling cascade. Previous studies have shown that mitochondria are the potential targets of FGF19. FGF19 has been shown to increase energy homeostasis by increasing fatty acid delivery to mitochondria in the liver [16]. In white adipose tissue, FGF19 levels are correlated with the mitochondrial number [17]. FGF19 can prevent excessive palmitate-induced dysfunction of differentiated mouse myoblast cells by protecting mitochondrial function [18]. These results suggest that FGF19 may work as a potential mediator of mitochondrial metabolism. Besides, the receptors of FGF19 include fibroblast growth factors receptor 1c, 2c, 3c and 4 (FGFR1c, 2c, 3c & 4), but FGFR4 is considered to be the primary receptor due to its high affinity for FGF19 [19]. Report also indicates the binding of FGF19 to its receptor FGFR4 requires the participation of β-Klotho (KLB), a co-receptor to achieve high affinity [20]. FGF19 has been reported to form a dimer with the β-Klotho monomer via its C-terminal tail at 1:1 ratio [21]. Till now, although we realize the importance of FGF19 in the development and maturation of cartilage, there is a lack of evidence that FGF19 regulates cartilage behavior, especially mitochondrial changes.
Cartilage is a special structure composed of dense extracellular matrix (ECM), mainly including type II collagen and proteoglycan, and highly differentiated cells called chondrocytes [22]. *In* general, chondrocytes are localized in a relatively low-oxygen environment that energy producing is vital for them. Mitochondrial dysfunction could break the balance between glycolysis and oxidative phosphorylation (OXPHOS) in chondrocytes, reducing ATP production substantially [23]. Thus, in the current study, we aim to explore the effect of FGF19 on the mitochondrial fission–fusion process in chondrocytes by characterizing the morphology of the mitochondria network and its fission–fusion mediator proteins, and its underlying bio-mechanism.
## Chondrocyte isolation
The tissue materials used in the current study were obtained according to ethical principles and the protocol was firstly approved before the experiments began by our Institutional Review Board (No. WCHSIRB-OT-2020-048). Chondrocytes were isolated from 0 to 3 days’ newborn C57 mice as previously described [24]. In brief, the chondrocytes from cartilage of the knee joints were collected by $0.25\%$ trypsin digestion for 30 min at 37 °C and $0.2\%$ type II collagenase (No. C6885, Sigma, MO, USA) digestion for about 16–18 h at 37 °C till the cartilage tissue mass was completely digested. The isolated chondrocytes were filtered and cultured in $10\%$ FBS DMEM (No. D6429, HyClone, Logan, UT, USA). We used the chondrocytes at passage 1–2.
## ATP assay
ATP concentrations were tested with enhanced ATP assay kit (No. S0027, Beyotime, Shanghai, China) according to the manufacturer’s protocol as previously described [25]. Cells were lysed with ATP lysis buffer (200 μl of lysate per well in 6-well plates) and centrifuged at 15, 000 g for 5–10 min at 4 °C. The lysates were collected and stored at -20℃. Before the ATP test, 100 μl ATP working solution (ATP test solution: ATP test dilution = 1: 5) was added to 1.5 ml EP tubes and incubated for 3–5 min at room temperature (RT). Next, the lysates were transferred to 100 μl of ATP working solution and mixed quickly. The amount of luminescence emitted was measured with a luminometer (Synergy HTX Multi-Mode Microplate reader, BioTek Instrument, WI, USA) immediately. The luminescence data were normalized to the control sample protein amounts. The statistical program GraphPad Prism 8 was used to process the data and images.
## Mitochondrial staining of living cells
Cell Navigator™ Mitochondrion Staining Kit (No.22667, AAT Bioquest, CA, USA) was used to stain mitochondria in living chondrocytes. Briefly, cells were cultured in petri dishes specified for confocal laser microscopy (1,000 cells per dish, Glass Bottom Cell Culture Dish, Φ15mm, No. 801002, NEST, Jiangsu, China). FGF19 (200 ng/ml, No.100-32, PEPRO TECH, USA) and/or KLB (200 ng/ml, 2619-KB-050, R&D Systems, USA) at a 1:1 ratio were added into the culture media as the experimental group and continued to incubate for 72 h. Thaw all the components of Cell Navigator™ Mitochondrion Staining Kit at RT before starting the experiment. 2 µl of 500X Mitolite™ Orange (Component A) was added into 1 ml of Live Cell Staining Buffer (Component B) to make a working solution. 200 µl working solution was added to the petri dishes and incubate at 37 °C for 30–120 min. Fluorescence was detected at Ex/Em = $\frac{540}{590}$ nm (TRITC filter set). Replace the dye loading solution with phosphate-buffered saline (PBS, 1 ×). Then the cells were fixed in $4\%$ paraformaldehyde for 20 min and rinsed with PBS for three times. After being penetrated by $0.5\%$ Triton X-100 (Beyotime, Shanghai, China) for 15 min. Nuclei were counterstained with 4′,6-diamidino-2-phenylindole (DAPI; D9542, Sigma, USA) and the cytoskeleton was stained with phalloidin (FITC, A12379, Thermo, MA, USA). The immunofluorescence images were observed through a confocal laser scanning microscope (FV3000, Olympus, Tokyo, Japan).
## RNA sequencing and bioinformatics analysis
Chondrocytes (at 1 × 106 cells per well) were treated by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml) for 72 h and harvested by trypsin digestion. Then the cells were sent for RNA sequencing at Shanghai Lifegenes Biotechnology CO., Ltd (Shanghai, China) as previously described [26]. Total RNA was extracted from chondrocytes using Trizol reagent (Catalog#15596026, Thermo Fisher Scientific, Waltham, MA), and the quantification was performed with an RNA Nano 6000 assay kit (Bioanalyzer 2100 System, Agilent Technologies, CA). Illumina NeoPrep system was applied to purify and fragment the mRNAs, synthesize cDNAs, and amplify the targets. Sequencing was accomplished with the Illumina NovaSeq 6000 platform, and the raw data were obtained by matching reference genome using HISAT2 v2.1.0. The data were reported in Fragments Per Kilobase of exon model per Million mapped fragments (FPKM). Pheatmap was generated by online R package.
## Transmission electron microscopy (TEM)
The cell pellets in agarose piece were treated with $1\%$ OsO4 solution for 1 h at 4 °C, helping to provide an enhanced contrast to TEM images. Samples were further processed for dehydration, infiltration and embedding into LX-112 resin with serial changes into following solutions: $25\%$ ethanol at RT for 15 min, $50\%$ ethanol at RT for 15 min, $75\%$ ethanol at RT for 15 min, $95\%$ ethanol at RT for 15 min, $100\%$ ethanol at RT for 15 min, twice; Ethanol: LX-112 (3:1) at RT for 30 min, Ethanol: LX-112 (1:1) at RT for 30 min, Ethanol: LX-112 (1:3) at RT for 30 min; pure LX-112 at RT for 60 min, twice. Finally, the samples were transferred in pyramid tip mold (Ted Pella; 10585) and polymerized at 60 °C for 72 h. Semi-thin sections (1 μm) were cut using an ultra-microtome (Leica EM UC7) after attaching the pyramid on mounting cylinders (Ted Pella; 10580) and stained with toluidine blue to identify the position of cells. Ultra-thin sections (70–100 nm) were cut and collected on 200 mesh grids. The grids were stained with $1\%$ uranyl acetate, at RT for 10 min, followed by Reynolds lead citrate, at RT for 5 min. Sections were examined with a JEM-1400FLASH electron microscope (JEM-1400FLASH, JEOL, Tokyo, Japan), at 80 kV, using the AMT-600 image capture engine software. Images were transferred to photoshop software for final processing.
## Western blotting
The specific procedure followed our published paper [27]. Briefly, cells at 5 × 105 per well (six-well plate) were lysed as one amount of a sample. Equal amounts of protein extracts were separated on $10\%$ SDS-polyacrylamide gel electrophoresis, and then transferred into a PVDF membrane (IPVH00010, Millipore, Massachusetts, USA) at 200 mA for 2 h. PVDF membranes were blotted with $5\%$ skim milk for 1 h and then the blots were probed overnight with primary antibodies overnight at 4 °C (mouse anti-β-actin, 1:1,000, sc-47778, Santa Cruz Biotechnology, Santa Cruz, USA; rabbit anti- citrate synthase (CS), 1:1000, No.383932, ZEN BIO, Chengdu, China; rabbit anti-AMPKα-1, 1:1,000, No.380431, ZEN BIO, Chengdu, China; rabbit anti-phospho-AMPK alpha 1 (Ser496), 1:1,000, No. R26252, ZEN BIO, Chengdu, China; rabbit anti-PGC-1α:1,000, No.381615, ZEN BIO, Chengdu, China; rabbit anti-SIRT1, 1:1,000, No. R25721, ZEN BIO, Chengdu, China; rabbit anti-Mfn1, 1:1,000, No.509880, ZEN BIO, Chengdu, China; rabbit anti-Mfn2, 1:1000, No.340604, ZEN BIO, Chengdu, China; rabbit anti-Opa1, 1:1000, No.382025, ZEN BIO, Chengdu, China; rabbit anti-ERK$\frac{1}{2}$, 1:1,000, ab17942, Abcam, Cambridge, UK; rabbit anti-p-ERK$\frac{1}{2}$, 1:1,000, No.4370, Cell Signaling Technology, Boston, USA; rabbit anti-p38/MAPK, 1:1,000, No.340697, ZEN BIO, Chengdu, China; rabbit anti-pp38 (Thr180/Tyr182), 1:1,000, No.9211, Cell Signaling Technology, Boston, USA; rabbit anti-JNK, 1:1,000, No. R22866, ZEN BIO, Chengdu, China; rabbit anti-phospho-JNK (Thr183/Tyr185), 1:1,000, No.340810, ZEN BIO, Chengdu, China). Membranes were washed with TBST, and the homologous secondary antibody (anti-mouse, m-IgGКBP-HRP, 1:5,000, sc-516102; anti-rabbit, IgG-HRP, 1:5,000, sc-2357, Santa Cruz Biotechnology, Santa Cruz, USA) was incubated with the membrane for 2 h. The Immobilon ® Western (P90719, Millipore, Massachusetts, USA) kit was used to visualize immune complexes, and the protein expression levels were analysed with Image J software (NIH). β-actin was used as an internal control.
## RNA extraction and quantitative real-time polymerase chain reaction (qPCR)
Total RNA was extracted from the chondrocytes using RNeasy Plus mini kit (No. 73404, Qiagen, Shanghai, China) and then reverse-transcribed to complementary DNA (cDNA) using a first-strand cDNA synthesis kit (K1621, Thermo, MA, USA) according to manufacturer’s instruction. The SYBR Premix Ex Taq II PCR Kit (RR820A, TAKARA, Dalian, China) was used to perform qPCR on an ABI 7300 instrument (Applied Biosystems, Shanghai, China). Primer sequences were designed with basic local alignment search tool (BLAST), and the sequences were as follows: glyceraldehyde-3-phosphate dehydrogenase (GAPDH), 5′-AGGTTGTCTCCTGCGACTTCA-3′ (forward) and 5′-CCAGGAAATGAGCTTGACAAA-3′ (reverse); FGFR4, 5′-AAGGTGGTCAGTGGGAAGTCTG-3′ (forward) and 5′-CAGAGGCCTCAAGGGACAAAG-3′ (reverse). *Each* gene sample was repeated based on three copies. The cycle threshold (CT) values were normalized to GADPH and calculated using the 2−∆∆Ct method.
## Immunofluorescence and confocal laser scanning microscopy (CLSM)
Immunofluorescence staining was performed as previously described [28]. Briefly, Cells were cultured in petri dishes specified for confocal laser microscopy for 12 h. Then FGF19 (200 ng/ml) and/or KLB (200 ng/ml) were added to the culture media as the experimental group and continued to incubate for 24 h. Then the cells were fixed in $4\%$ paraformaldehyde for 20 min and rinsed with PBS for three times. After being penetrated by $0.5\%$ Triton X-100 for 15 min, the samples were blocked with $1\%$ bovine serum albumin (BSA) for 1 h. Cells were then incubated with the antibodies (rabbit anti-CS, 1:1000; rabbit anti-phospho-AMPK alpha 1 (Ser496), 1:1,000; rabbit anti-PGC-1α, 1:1,000; rabbit anti-Mfn2, 1:1,000; rabbit anti-Opa1, 1:1,000; rabbit anti-pp38 (Thr180/Tyr182), 1:1,000) overnight at 4 °C. The secondary antibody was Alexa Fluor® 647 (10 μg/ml, Alexa Fluor ®647, Life Technology, Grand Island, NY, USA). Nuclei were counterstained with DAPI and the cytoskeleton was stained with FITC. Two different antibodies, conjugated with two different fluorochromes (i.e., Alexa Fluor® 488 antibody: green fluorescence; Alexa Fluor® 647 antibody: red fluorescence) were used in double-fluorescence labeling. The immunofluorescence images were observed through a confocal laser scanning microscope FV3000.
## Inhibitor treatments
P38 pathway inhibitor SB203580 (No. A8254, APExBIO Technology, TX, USA) was prepared in dimethyl sulfoxide (DMSO) (No.196055, MP Biomedicals, OH, USA) as stock solutions and the treatment procedure was followed our previous report [29]. Chondrocytes were pre-incubated by SB203580 (10 µM) for 2 h prior to the addition of FGF19. DMSO was added to the cell culture medium as a control.
## Statistical analysis
All protein bands and immunofluorescence images were quantified using optical density (OD) value and fluorescent intensity by ImageJ software (ImageJ2, NIH, Bethesda, MD, USA). Data were presented as the mean ± SD of at least three independent experiments (n ≥ 3) and plotted with Graph Pad Prism. The significant difference analyses were all based on Student T-test. In each analysis, the critical significance level was set to be $$p \leq 0.05.$$
## FGF19 increases the mitochondrial biogenesis
To explore the influence of FGF19 on the biological behaviors of mitochondria, we first used TEM to observe the morphological change of mitochondria in chondrocytes induced by FGF19 with the help of β Klotho (KLB), a vital accessory transmembrane glycoprotein for assisting the binding of FGF19 to its receptor [20]. We found that FGF19 at 200 ng/ml could significantly increase the mitochondrial biogenesis as indicated in Fig. 1a. Quantification confirmed that the number of mitochondria in chondrocytes in the FGF19 + KLB group was significantly enhanced relative to that of the single FGF19 group or the KLB control group (Fig. 1b). To further confirm the number change of mitochondria in living chondrocytes, we then used mitochondria staining kit for living cells (Cell Navigator™ Mitochondrion Staining) and performed immunofluorescence. The results revealed that the mitochondria number was significantly enhanced in FGF19-treated living chondrocytes in the presence of KLB (Fig. 1c). Linear quantification of fluorescence intensity (red) also showed that the number of mitochondria was increased and the distribution of mitochondria was broader in the cytoplasm region of living chondrocytes by FGF19 (Fig. 1d). The increase of mitochondrial biogenesis is usually accompanied with the generation of ATP products [30]. Thus, intracellular ATP products were tested by enhanced ATP assay kit in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml) at 48 h. Results confirmed that the intracellular ATP products in chondrocytes were considerably increased by FGF19 (Fig. 1e). By using western blotting, we detected the expression of citrate synthase (CS), one of the key enzymes of aerobic respiration in mitochondria, was up-regulated in chondrocytes induced by FGF19 (200 ng/ml) in the presence of KLB (200 ng/ml) (Fig. 1f, g). Taking together, these results indicated that FGF19 could increase the mitochondrial biogenesis and thus promote energy generation. Fig. 1FGF19 induces a transient increase in mitochondrial number and an enhanced generation of ATP products. a Representative TEM images showing the changes of mitochondrial number in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). The images were chosen based on three independent experiments ($$n = 3$$). Orange arrows indicated individual mitochondrion. b Quantification of mitochondrial number (per cell) in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). Quantitative analyses of the mitochondrial number were based on nine cells (per group) from three independent experiments ($$n = 3$$). c Representative immunofluorescent staining showing the number changes of mitochondria in living chondrocytes induced by FGF19 (200 ng/ml) in the presence of KLB (200 ng/ml) for 72 h. The images were chosen based on three independent experiments ($$n = 3$$). Red, individual mitochondrion; Green, F-actin; Blue, nucleus. d Linear quantification of fluorescence intensity of mitochondrion number in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml) by Image Pro Plus 6.0. e ATP assay showing the increase of intracellular ATP products in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). The results were based on three independent experiments ($$n = 3$$). f Representative western blotting showing the expression change of CS in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). The images were chosen based on three independent experiments ($$n = 3$$). g Quantification of CS by western blotting in (f). The data in b are shown as box (from 25, 50 to $75\%$) and whisker (minimum to maximum values) plots. The significant difference analysis in b, e and g was based on Student T-test
## FGF19-induced mitochondrial biogenesis accompanies with a fusion of mitochondria
We performed RNA sequencing to precisely explore the associated gene changes in mitochondrial metabolism of chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). Genes shown in red are upregulated and genes in green are downregulated in the pheatmap (Fig. 2a and gene information in Additional file 1: Table S1). We analyzed the expression of all changed genes and screened 22 changed mitochondrion-related genes in chondrocytes induced by FGF19 in the presence of KLB. Among them, mitochondrial fusion genes, Mfn1, Mfn2 and Opa1, were substantially upregulated, which indicates that FGF19 enhances the expression of mitochondrial fusion genes in chondrocytes. By using western blotting, we then confirmed the protein changes of Mfn1, Mfn2 and Opa1 in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml) at 72 h. As shown in Fig. 2b, FGF19 significantly upregulated the expression of Mfn1, Mfn2 and Opa1 in chondrocytes. Quantitative analysis confirmed the significant increase in mitochondrial-fusion proteins in chondrocytes induced by FGF19 (Fig. 2c). Since mitochondrial fission–fusion is a dynamic process [4], we also detected the protein expression of mitochondrial fission-related proteins, Drp1 and Fis1, by western blotting in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml) (Additional file 1: Figure S1). Results showed that FGF19 did not significantly change the expressions of Drp1 and Fis1 in chondrocytes induced by FGF19 at 200 ng/ml in the presence of β-Klotho (200 ng/ml). We next used TEM to explore the fission–fusion change of mitochondrial morphology in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). The results showed that FGF19 could elongate the individual mitochondrial morphology in chondrocytes (Fig. 2d), especially, the boxed images in yellow showing the elongation of mitochondrial morphology in chondrocytes induced by FGF19. The schematic diagram showed the morphological changes of individual mitochondria transferred from regularly shaped and circular to irregular and elongated. Further, we analyzed the mitochondrial morphology with Image J (Fig. 2e). Quantitative results confirmed a significant increase in spreading area (in nm2), perimeter in 2D (in nm), aspect ratio (major to minor axis) and Feret’s diameter (longest distance in one single mitochondrion) of individual mitochondria and a significant decrease of circularity (rated by 4π × area/perimeter2) and roundness (rated by 4 × area/π × major axis2) of individual mitochondria in chondrocytes induced by FGF19. Together, FGF19 could also mediate fission–fusion process of mitochondria by characterizing the enhancement of fusion proteins and elongation of mitochondrial morphology. Fig. 2FGF19 promotes the elongation of mitochondrial morphology by up-regulating the expression of mitochondrial fusion proteins. a RNA sequencing showing the change of mitochondrial metabolism-related genes in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). Three pairs of samples were obtained from three independent cell isolates ($$n = 3$$), namely, samples 1, 1′, 1′′and 1′′′, samples 2, 2′, 2′′and 2′′′, and samples 3, 3′, 3′′and 3′′′. The data were present as log2(FPKM + 1). FPKM, Fragments per kilobase of exon model per million mapped fragments. b Representative western blotting showing the expression changes of Opa1, Mfn1 and Mfn2 in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). The images were chosen based on three independent experiments ($$n = 3$$). c Quantification of Opa1, Mfn1 and Mfn2 by western blotting in b was performed to confirm these protein changes ($$n = 3$$). d Representative TEM images showing the changes of mitochondrial network’s morphology in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). The images were chosen based on three independent experiments ($$n = 3$$). Cyan arrows indicated the elongation of mitochondrial morphology. Schematic diagram illustrated that elongation was correlated with mitochondrial fusion. e Measurements of mitochondrial network’s morphology in d by Image J. Quantitative analyses of mitochondrial network’s morphology were based on three independent experiments ($$n = 3$$). The data in e were shown as box (from 25, 50 to $75\%$) and whisker (minimum to maximum values) plots. The significant difference analysis in c and e was based on Student T-test
## FGF19 enhances the mitochondrial biogenesis and fusion via up-regulation of AMPKα signaling.
It is widely recognized that FGF19 can bind to FGFR1, 2, 3 and 4 receptors but has a high affinity for FGFR4 with the help of KLB [20]. In order to explore the gene expressions of FGFRs in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml), we analyzed RNA sequencing and the results were shown in the form of pheatmap (Fig. 3a and gene information in Additional file 1: Table S2). *The* gene expression of FGFR1 and FGFR4 were significantly increased by FGF19 in the presence of KLB, and moreover, the expression of FGFR4 was much higher than that of FGFR1 in the chondrocytes. Then, we performed qPCR and western blotting to affirm the change of FGFR4 expression in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). The results in Fig. 3b showed that FGF19 could significantly increase the gene expression of FGFR4 in chondrocytes by qPCR and the up-regulation of FGFR4 gene in the FGF19 + KLB group was remarkably enhanced relative to that without the help of KLB (the single FGF19 group). The protein expression of FGFR4 was also increased in chondrocytes induced by FGF19 (Fig. 3c). With the help of KLB, FGFR4 in the FGF19 + KLB group showed a higher expression than that in the single FGF19 group. Fig. 3FGF19 increases the mitochondrial biogenesis by up-regulating the expression of AMPKα signalling related proteins in chondrocytes. a RNA sequencing showing the change of FGFRs genes in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). Three pairs of samples were obtained from three independent cell isolates ($$n = 3$$), namely, samples 1, 1′, 1′′and 1′′′, samples 2, 2′, 2′′and 2′′′, and samples 3, 3′, 3′′and 3′′′. The data were present as log2(FPKM + 1). b q-PCR showing the gene changes of FGFR4 in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). The results were based on three independent experiments ($$n = 3$$). c Representative western blotting showing the expression change of FGFR4 in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). The images were chosen based on three independent experiments ($$n = 3$$). d Representative western blotting showing the expression changes of AMPKα, p-AMPKα, PGC-1α and SIRT1 in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). The images were chosen based on three independent experiments ($$n = 3$$). e Quantifications of AMPKα, p-AMPKα, PGC-1α and SIRT1 by western blotting in (d). f Representative immunofluorescent staining showing the change in the distribution of p-AMPKα in chondrocytes induced by FGF19 (200 ng/ml) in the presence of KLB (200 ng/ml) for 72 h. The images were chosen based on three independent experiments ($$n = 3$$). Red, p-AMPKα; Green, F-actin; Blue, nucleus. g Quantification of fluorescence intensity of p-AMPKα in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). The data were based on at least eight cells from three independent experiments. h Representative immunofluorescent staining showing the change in the distribution of PGC-1α in chondrocytes induced by FGF19 (200 ng/ml) in the presence of KLB (200 ng/ml) for 72 h. The images were chosen based on three independent experiments ($$n = 3$$). Red, PGC-1α; Green, F-actin; Blue, nucleus. i Quantification of fluorescence intensity of PGC-1α in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). The data were based on at least eight cells from three independent experiments. The data in g and i were shown as box (from 25, 50 to $75\%$) and whisker (minimum to maximum values) plots. The significant difference analysis in b, e, g and i was based on Student T-test AMPKα signalling directly regulates the biogenesis of mitochondria through the AMPKα-PGC-1α-SIRT1 axis, a putative mitochondrial biogenesis relevant signalling [30]. From western blotting, we found that the expression of AMPKα, p-AMPKα, PGC-1α and SIRT1 was up-regulated in chondrocytes by FGF19 (Fig. 3d). Quantitative analysis of these proteins further confirmed the increase in AMPKα-PGC-1α-SIRT1 signalling in chondrocytes induced by FGF19 in the presence of KLB (Fig. 3e). As the phosphorylation of AMPKα and activation of PGC-1α play a vital role in mitochondrial biogenesis. We further performed immunofluorescent staining to explore the expression and distribution of p-AMPKα and PGC-1α in chondrocytes induced by FGF19 (Fig. 3f–i). The results showed that FGF19 could increase the expression of p-AMPKα and PGC-1α. The expression of p-AMPKα was notably accumulated in the nuclear region (Fig. 3f) while the expression of PGC-1α was increased in whole cytoplasm of chondrocytes (Fig. 3h). Quantification of fluorescent intensity (per cell) confirmed the increase of p-AMPKα and PGC-1α in chondrocytes induced by FGF19 in the presence of KLB (Fig. 3g, i). Taking together, these results indicated that FGF19 could enhance the biogenesis and fusion by up-regulation of AMPKα signalling.
## FGF19 enhances mitochondrial biogenesis and fusion through p38/MAPK pathway
To determine the key cytoplasmic pathways related to FGF19-mediated mitochondrial biogenesis and fusion, we analyzed the RNA sequencing data and screened out all changed kinases involving classical pathways. These kinases were clustered by pheatmap (Fig. 4a and gene information in Additional file 1: Table S3). It showed that most of the kinases were related to MAPK signaling. In particular, MAP kinases such as Dusp4 and Dusp2 were shown to be significantly enhanced in chondrocytes. We then performed western blotting to confirm the changes of ERK/p-ERK, p38/p-p38 and JNK/p-JNK in chondrocytes induced by FGF19 (Fig. 4b). Among them, we found that the enhancement of total p38 and p-p38 were higher than the other two. Quantitative analysis confirmed a significant increase in total p38 and p-p38 but the increase of ERK/p-ERK and JNK/p-JNK was not as obvious as p38/p-p38 in chondrocytes induced by FGF19 in the presence of KLB (Fig. 4c and Additional file 1: S2). We further used immunofluorescent staining to explore the expression and distribution of p-p38 in chondrocytes induced by FGF19 in the presence of KLB (Fig. 4d, e). From the CLSM images, we found that FGF19 could increase the expression of p-p38 in the cytoplasm of chondrocytes, especially in the nuclear region (Fig. 4d). Quantification of total fluorescent intensity confirmed the increased expression of p-p38 in chondrocytes induced by FGF19 in the presence of KLB (Fig. 4e).Fig. 4FGF19 activates p38/MAPK signalling in chondrocytes. a RNA sequencing showing the changes in the expression of MAPK-related mediators in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). Three pairs of samples were obtained from three independent cell isolates ($$n = 3$$), namely, samples 1, 1′, 1′′and 1′′′, samples 2, 2′, 2′′and 2′′′, and samples 3, 3′, 3′′and 3′′′. The data were present as log2(FPKM + 1). b Representative western blotting showing the expression change of ERK, p-ERK, p38, p-p38, JNK and p-JNK in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). The images were chosen based on three independent experiments ($$n = 3$$). c Quantification of p38 and p-p38 by western blotting in (b). d Representative immunofluorescent staining showing the change in the expression and distribution of p-p38 in chondrocytes induced by FGF19 (200 ng/ml) in the presence of KLB (200 ng/ml) for 72 h. The images were chosen based on three independent experiments ($$n = 3$$). Red, p-p38; Green, F-actin; Blue, nucleus. e Quantification of fluorescence intensity of p-p38 in chondrocytes induced by FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). The data were based on nine cells from three independent experiments. The data in e were shown as box (from 25, 50 to $75\%$) and whisker (minimum to maximum values) plots. The significant difference analysis in c and e was based on Student T-test
## Inhibition of p38/MAPK attenuates AMPKα signalling and impairs the biogenesis and fusion of mitochondria induced by FGF19
To further determine the importance of p38/MAPK in regulating the expression of AMPKα signalling, SB203580, a specific inhibitor of p38/p-p38 signalling, was utilized [31]. We detected the expressions of p38/p-p38, AMPKα/p-AMPKα, PGC-1α and Sirt1 in chondrocytes induced by FGF19 (200 ng/ml) in the presence of KLB (200 ng/ml) after pretreatment of SB203580 (10 µM) for 2 h (Fig. 5a). From western blotting, we revealed that SB203580 could effectively impair the up-regulation of p38/p-p38 and also attenuated the mitochondrial biogenesis proteins including AMPKα/p-AMPKα, PGC-1α and Sirt1. Quantitative analysis further confirmed a significant decrease in the expression of p38 pathway and AMPKα signalling in chondrocytes induced by FGF19 (200 ng/ml) in the presence of KLB (200 ng/ml) after pretreatment of SB203580 (10 µM) (Fig. 5b). We then used immunofluorescent staining to show the expression and distribution of p-AMPKα and PGC-1α (Fig. 5c–e). The results showed that the expressions of p-AMPKα (Fig. 5c) and PGC-1α (Fig. 5d) were largely reduced in chondrocytes induced by FGF19 (200 ng/ml) in the presence of KLB (200 ng/ml) after pretreatment of SB203580 (10 µM). Fluorescence quantification further confirmed the changes of p-AMPKα and PGC-1α (Fig. 5e).Fig. 5Inhibition of p38 attenuated FGF19-enhanced AMPKα activity. a Representative western blotting showing the expression change of p38, p-p38, AMPKα, p-AMPKα, PGC-1α and SIRT1 in chondrocytes induced by SB203580 (10 µM) in the presence of FGF19 (200 ng/ml) and KLB (200 ng/ml). The images were chosen based on three independent experiments ($$n = 3$$). b Quantification of p38, p-p38, AMPKα, p-AMPKα, PGC-1α and SIRT1 by western blotting in (a). c Representative immunofluorescent staining showing the change in the distribution of p-AMPKα in chondrocytes induced by SB203580 (10 µM) in the presence of FGF19 (200 ng/ml) and KLB (200 ng/ml). The images were chosen based on three independent experiments ($$n = 3$$). Red, p-AMPKα; Green, F-actin; Blue, nucleus. d Representative immunofluorescent staining showing the change in the expression and distribution of PGC-1α in chondrocytes induced by SB203580 (10 µM) in the presence of FGF19 (200 ng/ml) and KLB (200 ng/ml). The images were chosen based on three independent experiments ($$n = 3$$). Red, PGC-1α; Green, F-actin; Blue, nucleus. e Quantification of fluorescence intensity of p-AMPKα and PGC-1α in chondrocytes induced by SB203580 (10 µM) in the presence of FGF19 (200 ng/ml) and KLB (200 ng/ml). The data in e were shown as box (from 25, 50 to $75\%$) and whisker (minimum to maximum values) plots. The significant difference analysis in b and e was based on Student T-test Next, we explored the role of p38/MAPK in regulating biogenesis and fusion of mitochondria. We detected the protein mediators in the fission–fusion process (Additional file 1: Figure S3 and 6a). The results showed that inhibition of p38 did not significantly change the expression of FGF19-induced mitochondrial fission proteins, i.e., Drp1 and Fis1 (Additional file 1: Figure S3), but indeed decreased the expression of FGF19-induced mitochondrial fusion proteins, i.e., Opa1, Mfn1 and Mfn2 (Fig. 6a, b). Further, we performed immunofluorescence and found the impairment of Opa1 (Fig. 6c) and Mfn2 (Fig. 6d) in chondrocytes induced by FGF19 (200 ng/ml) in the presence of KLB (200 ng/ml) after pretreatment of SB203580 (10 µM). Fluorescence quantification further confirmed the changes of Opa1 and Mfn2 (Fig. 6e).Fig. 6Inhibition of p38 decreases the expressions of mitochondrial fusion proteins induced by FGF19 in chondrocytes. a Representative western blotting showing the expression change of Opa1, Mfn1 and Mfn2 in chondrocytes induced by SB203580 (10 µM) in the presence of FGF19 (200 ng/ml) and KLB (200 ng/ml). The images were chosen based on three independent experiments ($$n = 3$$). b Quantification of Opa1, Mfn1 and Mfn2 by western blotting in (a). c Representative immunofluorescent staining showing the change in the expression and distribution of Opa1 in chondrocytes induced by SB203580 (10 µM) in the presence of FGF19 (200 ng/ml) and KLB (200 ng/ml). The images were chosen based on three independent experiments ($$n = 3$$). Red, Opa1; Green, F-actin; Blue, nucleus. d Representative immunofluorescent staining showing the change in the distribution of Mfn2 in chondrocytes induced by SB203580 (10 µM) in the presence of FGF19 (200 ng/ml) and KLB (200 ng/ml). The images were chosen based on three independent experiments ($$n = 3$$). Red, Mfn2; Green, F-actin; Blue, nucleus. e Quantification of fluorescence intensity of Opa1 and Mfn2 in chondrocytes induced by SB203580 (10 µM) in the presence of FGF19 (200 ng/ml) and KLB (200 ng/ml). The data were based on at least eight cells from three independent experiments. f Representative immunofluorescent staining showing the changes of morphology mitochondrial network in living chondrocytes induced by SB203580 (10 µM) in the presence of FGF19 (200 ng/ml) and KLB (200 ng/ml) for 72 h. Image J shows the change of mitochondrial network morphology analysis in cyan boxes. The images were chosen based on three independent experiments ($$n = 3$$). Red, mitochondrial network; Blue, nucleus. g Quantification of mitochondrial number (per cell) and mitochondrial elongated number (per cell) in chondrocytes induced by SB203580 (10 µM) in the presence of FGF19 (200 ng/ml) by Image J. Quantitative analyses were based on three independent experiments ($$n = 3$$). The data in e and g were shown as box (from 25, 50 to $75\%$) and whisker (minimum to maximum values) plots. The significant difference analysis in b, e and g were based on Student T-test Finally, to confirm the role of p38/MAPK in controlling mitochondrial network morphology, we applied mitochondrial living cell staining (Fig. 6f, g). From CLSM, we observed that SB203580 could significantly decrease the FGF19-enhanced mitochondrial number, and moreover, it could sharply reduce the mitochondrial network morphology formed by FGF19 (cyan boxes). Quantitative analysis confirmed that about a $50\%$ decrease in the total change of mitochondrial number (per cell) and a $60\%$ decrease in the number of mitochondrial elongation (per cell) in chondrocytes induced by SB203580 (Fig. 6g).
Together, the inhibition of p38/MAPK could decrease the expression of AMPKα signalling, and thus impair the biogenesis and fusion of mitochondria by characterizing mitochondrial fusion proteins, and mitochondrial network morphology in living chondrocytes.
## Discussion
It has been well established that cartilage is an avascular, non-lymphatic, and non-innervated tissue composed of ECM and chondrocytes [32]. Chondrocytes, the only mature cell type, in cartilage are surrounded by a relatively low-oxygen extracellular environment. In chondrocytes, glycolysis and OXPHOS are both present, and the ATP produced by OXPHOS makes up about $25\%$ of the total energy in chondrocytes [23]. Nonetheless, reports have confirmed that OXPHOS is a kind of more effective method of ATP synthesis [33]. OXPHOS thus plays a significant role in the energy metabolism of chondrocytes. Four ETC complexes (respiratory chain complexes I–IV) and complex V (ATP synthase) of the respiratory chain, which is located on the IMM, produce ATP during the OXPHOS process [34]. Mitochondrial dysfunction impairs ATP generation and further interferes with the repair process against cartilage degradation [35]. For these reasons, mitochondria are indispensable energy-producing organelles in the OXPHOS of chondrocytes. In this study, we discovered that FGF19 could greatly boost the production of the key enzyme, CS, for aerobic respiration, as well as intracellular ATP products (Fig. 1e–g). Our findings also showed that FGF19 might increase mitochondrial biogenesis by enhancing the number of functional mitochondria in chondrocytes (Fig. 1a–d). These results suggest that FGF19 is involved in mitochondrial biogenesis and fusion and enlarge our understanding of chondrocyte metabolism induced by growth factors.
FGFs family members, in particular the members of the FGF19 subfamily, are a type of cytokines that play an important role in regulating cellular energy homeostasis and mitochondrial function [36]. Previous researches indicated that FGF19 played a pivotal role in glucose metabolism. According to reports, FGF19 not only improves hepatic gluconeogenesis and glucose catabolism by activating the CREB-PGC-1α signalling cascade pathway, but also enhances glycogen synthesis by increasing glycogen synthetase (GS) activity [16]. Martinez et al. also found that FGF19 levels are correlated with the mitochondrial number in white adipose tissue [17]. In addition to FGF19, the FGF19 subfamily also includes FGF21 and FGF23. FGF21 was reported as a biomarker with high sensitivity for predicting mitochondrial disease in muscle [37]. Moreover, deletion of the fission-related protein DRP1 from the mice liver disrupted mitochondrial fission, which would further promote the expression of FGF21 [38]. Furthermore, it was discovered that FGF21 activates the AMPK-SIRT1-PGC-1α pathway to regulate mitochondrial fission–fusion, increase mitochondrial biogenesis, and promote mitochondrial function [39]. Another FGF19 subfamily member, FGF23, could enhance mitochondrial function by upregulating CS activity [40]. FGF23 treatment increased peroxisome proliferator-activated receptor δ (PPAR-δ) mRNA levels and improved mitochondrial function. Other FGF subfamily members may also affect mitochondrial function. For instance, it has been suggested that FGF13 may improve mitochondrial function in primary cortical neurons [41]. Interestingly, we also found that FGF19 could significantly change the mitochondrion-related gene expression in chondrocytes (Fig. 2a). The regulation of chondrocyte mitochondria by FGF19 extends our understanding of FGF19. Since FGF21 and FGF23 are both FGF19 subfamily members, we are interested in whether the same changes in mitochondria will occur by the induction of FGF19. The mitochondrial metabolism induced by FGF19 may also be similar in other subfamily members. More detailed studies are needed to confirm this assumption.
The mitochondrial network keeps the proper balance between fission and fusion, which helps to maintain dynamic homeostasis of mitochondrial biogenesis [42]. In addition to controlling mitochondrial biogenesis, fission and fusion proteins may also control mitochondrial bioenergetics. Traditionally, mitofusins drive the fusion of outer mitochondrial membrane and regulate the shape of mitochondrial cristae structure [43]. However, it is reported that the master regulator effect of PGC-1 on mitochondrial biogenesis may require or may be mediated by Mfn2. Mfn2 overexpression activated mitochondrial metabolism by increasing the expression of several subunits of OXPHOS complexes in muscle cells and the connection between Mfn2 and mitochondrial metabolism has been also demonstrated using loss-of-function studies [44]. The function of Mfn2 in mitochondrial energy metabolism was also demonstrated in Mfn2 knockdown mouse embryonic fibroblasts that Mfn2 affects mitochondrial energy metabolism by inhibiting the expression of complexes I, II, III and V and reducing mitochondrial membrane potential [45]. The deletion of Mfn2 causes a deficiency in coenzyme Q that leads to electron transport chain (ETC) dysfunction and a decrease in ATP production. Opa1 resides and works in the IMM after the Mfn$\frac{1}{2}$ proteins are anchored in the OMM. *In* general, the crucial determinants of bioenergetic efficiency depend on the cristae structure on the IMM. The change in the morphology of mitochondria is inevitably related to IMM remodeling. OPA1 inactivation significantly alters the mitochondrial morphology, resulting in scattered mitochondrial fragments and disordered mitochondrial cristae [46]. On the other hand, OPA1 overexpression can favor the assembly and stability of respiratory chain supercomplexes (RCS) by changing cristae shape [47]. The change between these mitochondrial fission–fusion proteins and mitochondrial biogenesis in chondrocytes was the main focus of the current work. And we found that mitochondrial fusion-related proteins Mfn1, Mfn2 and Opa1 were significantly enhanced by FGF19 (Fig. 2a–c), which was accompanied with mitochondrial biogenesis (Fig. 1). Moreover, elongated mitochondria were reported to have more cristae structure and higher ATP synthase activity [1]. Additionally, mitochondrial fusion could lead to the elongation of the mitochondrial network under physiological conditions [48]. Thus, mitochondria with an elongated morphology are regarded to be more bioenergetically efficient. As the study has demonstrated, FGF19 stimulated the fusional changes of mitochondria in chondrocytes (Fig. 2d, e). We discovered for the first time that FGF19 could upregulate mitochondrial biogenesis and mitochondrial fusion process by regulating fusion-related proteins in chondrocytes and thus promote the elongation of mitochondria in chondrocytes.
Mitochondria provide energy and are involved in several metabolic activities through various signalling pathways. It is well-known that the AMPK pathway is associated with mitochondria. AMPK could sense the changes in the energy status of cells and adapt mitochondrial function by regulating its biogenesis, MQC and dynamics [30]. Conversely, a deficiency of mitochondrial biogenesis could decrease the phosphorylation of AMPK. In the current study, we provided solid evidence to prove that FGF19 stimulation could enhance mitochondrial biogenesis and fusion via up-regulating AMPKα signalling (Fig. 3d–i). The level of mitochondrial biogenesis may be related to the level of AMPK phosphorylation. As reported that the decline of p-AMPK further leads to the depression of NAD+ -dependent deacetylase SIRT-1 and the mitochondrial biogenesis master regulator PGC-1α. The activation of PGC-1α not only leads to its translocation from the cytoplasm to the nucleus, but also upregulates the transcription of genes that are important for mitochondrial OXPHOS [49]. As we found in this study, SIRT-1 and PGC1α expression was also upregulated in chondrocytes by FGF19 (Fig. 3f–i). These results confirm that FGF19 enhances mitochondrial biogenesis and fusion by upregulating AMPKα signalling. It is reported that FGF19 related downstream signalling pathway mainly includes the MAPK, the phosphatidylinositol 3-kinase- (PI3K-) AKT, the phospholipase C (PLC) γ-protein kinase C (PKC), and the signal transducer and activator of transcription (STAT) pathway [50]. Among them, MAPK signalling, as a canonical FGFs family signalling pathway, has been confirmed to be an important downstream pathway in maintaining the homeostasis of cartilage [36]. Studies on the development of craniofacial cartilage in zebrafish have found that estrogen may disrupt the bone-related MAPK signalling pathway by affecting FGF19 [51]. And according to our research, most of the changed kinases involving classical pathways kinases were related to MAPK signalling in chondrocytes induced by FGF19 (Fig. 4a). Hence, MAPK signalling may be a vital pathway in enhancing mitochondrial biogenesis and fusion in chondrocytes with FGF19 treatment. We also discover that FGF19 activated the MAPK subfamilies p-ERK, p-JNK, and p-p38 with p-p38 being the most significant (Fig. 4b, c). Besides, p38 is generally considered an active regulator in chondrogenesis and chondrocyte differentiation [52]. Hence, we also provided evidence to validate mitochondrial biogenesis and fusion process in chondrocytes were mediated by p-38/MAPK signalling pathway. Inhibition of p38/MAPK attenuates AMPKα signaling (Fig. 5) and further impairs the biogenesis and fusion of mitochondria induced by FGF19 (Fig. 6). For these reasons, p-38/MAPK signalling pathway is one of the most important pathways that are activated by FGF19. However, this could not be the only pathway that can modulate mitochondrial fusion by FGF19 since the expression of mitochondrial fusion-related proteins was not completely abrogated by using SB203580. We assume that there may be other signal pathways involved in the regulation of mitochondrial fusion in chondrocytes. It will be interesting to identify which pathways are also involved in the process of mitochondrial fission–fusion by FGF19 in future studies (Fig. 7).Fig. 7The schematic diagram showing how FGF19 mediates mitochondrial fusion in chondrocytes. In the present study, the result indicates that FGF19 binds to FGFR4, activates the p38/MAPK signaling and AMPKα signaling, and resultantly induces the mitochondrial biogenesis and fusion in chondrocytes Inflammatory joint diseases, such as osteoarthritis (OA), are characterized by metabolic disorders. In OA, chondrocytes rapidly change their metabolic pathways in the process of OA disease [53]. Therefore, exploring the mechanism of chondrocyte metabolism may provide potential new therapeutic strategies for the treatment of OA and other inflammatory joint diseases. Previous researches have verified that FGFs could cause cell metabolic disorders and work as key participants in morphogenesis, angiogenesis, neoplastic and several diseases [54]. For instance, FGF21 was found to be related to glucose and lipid metabolism [55]. FGF23 was reported to be involved in phosphate and vitamin D metabolism [56]. FGF2 and FGF18 were revealed to participate in cartilage remodeling [57]. And FGF20 was verified to be associated with cartilage pathology [58]. As for FGF19, it was recognized to be an important growth factor in cell metabolism and cartilage development, because it acted as a critical metabolic regulator in bile acid biosynthesis [59], gallbladder filling [60], glucose metabolism [37] and skeletal muscle development [61]. Besides, FGF19 was also reported to play a key role in growth plate development [14] and morphogenesis during craniofacial development [51]. Therefore, exploring the change of FGF19-mediated cellular metabolism in chondrocytes enlarges our understanding of the physiology and pathology of cartilage and chondrocytes.
In summary, we demonstrated that FGF19 promotes the process of mitochondrial fusion and elongates the morphology of mitochondrial network in chondrocytes and revealed the potential mechanism of mitochondrial fusion mediator proteins regulation in chondrocytes. These findings enhance our understanding of the molecular mechanisms of mitochondrial dynamics in chondrocytes and provide a new potential for therapeutic targets for the management of cartilage diseases.
## Supplementary Information
Additional file 1. Figure S1. FGF19 do not significantly change the expressions of mitochondrial fission-related proteins in chondrocytes. Figure S2. Quantitative analysis of western blotting in Fig. 4b indicates that FGF19 induces a higher expression of p-p38 signalling than the other two ones, p-Erk and p-JNK in chondrocytes in the presence of β-Klotho. Figure S3. Inhibition of p38 changes the expression of FGF19-induced mitochondrial fission proteins in chondrocytes. Table S1. RNA sequencing showing the change of mitochondrial metabolism-related genes in chondrocytes treated with FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). Table S2. RNA sequencing showing the change of FGFRs genes in chondrocytes treated with FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml). Table S3. RNA sequencing showing the changes in the expression of MAPK-related mediators in chondrocytes treated with FGF19 at 200 ng/ml in the presence of KLB (200 ng/ml).
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|
---
title: 'Screening behaviors for diabetic foot risk and their influencing factors among
general practitioners: a cross-sectional study in Changsha, China'
authors:
- Nan Zhao
- Jingcan Xu
- Qiuhong Zhou
- Juanyi Hu
- Wenjing Luo
- Xinyi Li
- Ying Ye
- Huiwu Han
- Weiwei Dai
- Qirong Chen
journal: BMC Primary Care
year: 2023
pmcid: PMC10009976
doi: 10.1186/s12875-023-02027-3
license: CC BY 4.0
---
# Screening behaviors for diabetic foot risk and their influencing factors among general practitioners: a cross-sectional study in Changsha, China
## Abstract
### Background
Diabetic foot is a serious complication of diabetes with a high disability and mortality rate, which can be prevented by early screening. General practitioners play an essential role in diabetic foot risk screening, yet the screening behaviors of general practitioners have rarely been studied in primary care settings. This study aimed to investigate foot risk screening behaviors and analyze their influencing factors among general practitioners.
### Methods
A cross-sectional study was conducted among 844 general practitioners from 78 community health centers in Changsha, China. A self-designed and validated questionnaire was used to assess the general practitioner’s cognition, attitude, and behaviors on performing diabetic foot risk screening. Multivariate linear regression was conducted to investigate the influencing factors of risk screening behaviors.
### Results
The average score of diabetic foot risk screening behaviors among the general practitioners was 61.53 ± 14.69, and 271 ($32.1\%$) always or frequently performed foot risk screening for diabetic patients. Higher training frequency (β = 3.197, $p \leq 0.001$), higher screening cognition (β = 2.947, $p \leq 0.001$), and more positive screening attitude (β = 4.564, $p \leq 0.001$) were associated with more diabetic foot risk screening behaviors, while limited time and energy (β=-5.184, $p \leq 0.001$) and lack of screening tools (β=-6.226, $p \leq 0.001$) were associated with fewer diabetic foot screening behaviors.
### Conclusion
The score of risk screening behaviors for the diabetic foot of general practitioners in Changsha was at a medium level. General practitioners’ diabetic foot risk screening behaviors may be improved through strengthening training on relevant guidelines and evidence-based screening techniques, improving cognition and attitude towards foot risk screening among general practitioners, provision of more general practitioners or nurse practitioners, and user-friendly screening tools.
## Background
Diabetic foot consists of infection, ulceration, and destruction of the foot tissues that are mainly caused by peripheral neuropathy and peripheral arterial disease [1, 2]. Diabetic foot ulcer (DFU) is one of the most common and devastating chronic complications of diabetes, with a global prevalence of $6.3\%$ among diabetic patients. DFU is the leading cause of hospitalization, amputation, reduced mobility, loss of social participation, and lower quality of life in people with diabetes [3]. It has been reported that every 20 s in the world a lower limb was amputated due to a diabetic foot, and the mortality rate after amputation is as high as $50\%$ [5–7]. Additionally, diabetic foot causes tremendous physical and psychological suffering to the patient and place a huge burden on the individual, family, and society due to increased healthcare costs. The disease burden of diabetic foot is ranked in the top 10 of all medical conditions [4].
Prevention of diabetic foot is more important than treatment. Regular screening and timely identification of risk factors for diabetic foot is the most cost-effective way to prevent diabetic foot. Studies have demonstrated that early risk identification and prevention can prevent half of diabetic patients from developing foot ulcers or amputations [6, 7]. It is thus crucial to carry out diabetic foot screening to facilitate early detection, prevention, diagnosis, and treatment of diabetic foot [8–12]. The American Diabetes Association (ADA) guidelines recommend [13] that all patients with diabetes should be screened for risk factors of ulcers and amputation, the feet should be checked at each visit, and a comprehensive foot assessment should be performed at least once a year. The International Working Group on the Diabetic Foot’s (IWGDF) “Practical Guidelines on the Prevention and Management of Diabetic Foot Disease [2019]” also gives clear recommendations on the content and frequency of diabetic foot risk screening and provides a risk grading system for diabetic foot [14].
However, the overall status of screening is not optimistic, the foot risk screening rate of diabetic patients in different countries ranges from 15.7 to $64.8\%$ [15–17]. A multi-center cross-sectional study in Spain found that $56.4\%$ of patients with diabetes underwent foot screening, of which $39.5\%$ underwent 10 g monofilament (10 g-MF) exploration, $45.8\%$ underwent palpation of the dorsal foot artery, and $10.1\%$ received ankle-brachial index test [15]. A Canadian survey of 13,388 people with diabetes showed that 7,277 ($53\%$) had at least one foot exam by a healthcare provider in the past year [16]. According to the Scottish Diabetes Survey from 2019, $56.7\%$ of people with type 1 diabetes and $64.8\%$ with type 2 diabetes had received foot risk screening within 15 months [17]. Compared with these countries, Australia has a lower rate of foot screening for diabetics, a study for Primary Care found that only $45\%$ of people with diabetes said they would take off their diabetic shoes and socks for a foot risk screening when visiting a doctor [18]. Studies in different countries have shown that primary medical staff play a very important role in the foot risk screening of diabetic patients [15–17]. China has the largest population with diabetes in the world. It is reported that $57.1\%$ of Chinese diabetic patients were at high risk of diabetic foot, yet only $15.7\%$ of them have performed regular foot risk screening, a huge gap exists between the high-risk and low risk screening rates of diabetic foot among diabetic patients [19]. In recent years, the number of general practitioners in primary care settings in China has gradually increased due to policy encouragement [20], as the main force of primary medical institutions, general practitioners act as the goalkeeper for the implementation of primary prevention of diabetic foot and thus are required to master diabetic foot risk screening [15, 17, 20].
*Although* general practitioners play a vital role in diabetic foot risk screening, we found only a few surveys on community health care workers’ perceptions of diabetic foot screening [21], yet little is known about whether and how general practitioners implemented diabetic foot risk screening among diabetic patients. To the best of our knowledge, only one study has investigated the rate of foot screening practiced by general practitioners. However, this study just asked general practitioners whether to perform diabetic foot ulcer screening and ignored the contents and methods of specific screening. There is also a paucity of studies on the influencing factors of risk screening behaviors for diabetic foot among general practitioners. Meanwhile, we also did not find studies related to the specific risk screening behavior for diabetic foot and its influencing factors among other health practitioners. Understanding the status of foot risk screening behavior and its influencing factors is of great importance for constructing foot screening interventions and improving the awareness of foot screening for diabetic patients among primary medical staff [15–17]. At the same time, it provides a theoretical basis for improving foot risk screening behavior. Therefore, this study aimed to understand the current level and specific screening behavior of diabetic foot risk screening for diabetic patients by general practitioners in Changsha, China, and to analyze its influencing factors. The findings of our study will provide useful and important guidance for improving diabetic foot risk screening for diabetic patients in primary care, so as to prevent the occurrence of diabetic foot and improve the outcomes of diabetic patients.
## Ethics approval
The study received ethical approval from the Institutional Review Board of the Xiangya Hospital Medicine Ethics Committee, Central South University [202,103,024] and all participants provided consent to participate online.
## Study design, participants, and procedure
This study conducted a cross-sectional study in Changsha, Hunan Province of China from April 12 to 20, 2021. Our target population was all general practitioners working in all community health centers in Changsha City, Hunan Province, China. The inclusion criteria included: [1] Engaged in diabetes management for more than 1 year; [2] Qualified as a general practitioner. The *Exclusion criteria* included: [1] Failed to work normally due to illness, pregnancy, or other reasons; [2] Refused to be investigated.
The study was approved by the Institutional Review Board of the Xiangya Hospital Medicine Ethics Committee, Central South University (No. 202,103,024), and was in accordance with the Declaration of Helsinki. The survey was conducted through an online survey platform named the “questionnaire star” with a QR code link, with the support of the administrative department in charge of managing the community health service center in Changsha, China, we obtained approval and help from each of the 78 community health centers, then we sent the QR code link to an online questionnaire to eligible general practitioners via Wechat (Most widely used social app in China). All participants were informed of the purpose, benefits, risks, and significance of the study, and gave electronic informed consent. Participation was voluntary, and responses were anonymous. In the meantime, this online questionnaire improves the quality of the questionnaire collection by setting up mandatory questions, limiting the target object, and submitting the same IP address only once.
Finally, we approached 964 general practitioners to participate in the online questionnaire and received 867 questionnaires. After excluding 23 invalid questionnaires with response time less than 180s (according to the data of the pilot test), we finally received 844 valid questionnaires, with a response rate of $87.55\%$.
## General Information Questionnaire
A self-designed general information sheet was used to collect the general practitioner’s basic demographic information such as gender, age, years of working, and education. We also asked about the training frequency that the general practitioner received on diabetic foot risk screening in the last two years. In addition, we asked about the potential barriers the general practitioner may encounter that prevent them from performing diabetic foot risk screening on their diabetic patients, which included limited time and energy, lack of screening tools, expenses not covered by health insurance, and patients not cooperating.
## Diabetic Foot Risk Screening Cognitive, attitudinal, behavioral questionnaire
Because there was no mature scale before. A self-designed Diabetic Foot Risk Screening Cognitive, Attitudinal and Behavioral Questionnaire was used to assess the general practitioner’s cognition, attitude, and behaviors on performing diabetic foot risk screening on diabetic patients in their routine work. The questionnaire was developed based on the International Working Group on the Diabetic Foot (IWGDF) [14] “Practical Guidelines on the prevention and management of diabetic foot disease [2019]”, and the “Consensus on Diabetic Foot Basic-level Screening and Prevention in China [2019] " [20]. The questionnaire was evaluated by experts familiar with the subject and purpose of the questionnaire. We recruited 5 experts, including 3 general practitioners and 2 experts in the field of diabetic foot, all of whom have worked for more than 10 years. Experts assessed whether the information was understandable and whether there was any improvement needed. After all the modifications were completed, the questionnaire will be sent back to experts to review whether the items were essential, useful, or unnecessary. For that purpose, experts assessed each item on a 4-point Likert scale ranging from 1 to 4 for clarity and relativity. Once this was done content validity indexes (CVI) were calculated for each question. The CVI of each item, both for relevance and clarity, was calculated by the ratio of the number of responses “3” or “4” in relation to the total number of responses to the item. The content-related validity of the item (CVI) reached 0.80. The scale-level content validity item (S-CVI) was 0.80. The questionnaire was also pilot-tested among a sample of 135 community general practitioners and demonstrated good internal consistency, with a Cronbach’α coefficient of 0.77.
The questionnaire consists of 15 items under three subscales: cognition, attitude, and behavior, it contains single-choice or multiple-choice, and the scores for each item are standardized, with 4 points for each item; 4 points for correct answers to multiple-choice questions, and 0 points for incorrect answers, the score of multiple-choice questions = (actual number of correct options/total number of correct options) × 4. The score of cognition of diabetic foot screening and behavior of diabetic screening transformed into a standard score for better comparison, using the following formula: standard score = (actual score/ highest possible score) × 100. The standard score was further classified into three categories: <60 as poor, 60–79 as medium, and > 80 as good [22].
Cognition of diabetic foot risk screening There are 9 items in the cognition subscale that assess the respondents’ knowledge and awareness of diabetic foot risk screening including screening objects and frequency, screening content and methods, and the knowledge of patient foot self-management. Among them, items 1–5 were “yes-no” questions, with each “yes” answer assigned 4 points, and each “no” answer assigned 0 point. The 6-9th item is a multiple-choice question, each chosen method was scored 4 points. The original total score of the cognition subscale ranges from 0 to 36.
The attitude towards diabetic foot risk screening The attitude towards diabetic foot risk screening was assessed by one question asking whether the respondents thought it necessary to carry out diabetic foot screening among diabetic patients in the communities, with optional answers being “strongly necessary”, ‘necessary” and “not necessary”.
The behavior of diabetic foot risk screening There are 5 items in the behavior subscale that assess the respondents’ diabetic foot risk screening behaviors in their routine practice. Including screening frequency, the content and methods of risk screening, information risk level, guidance for regular screening or referral, and foot self-management education. The item about respondents’ behavior of foot screening content is a multiple-choice question, and the rest items were rated on a 5-point Likert scale from 0 “occasionally/never” to 4 “always”. In addition, the commonly used methods of peripheral neuropathy and peripheral vascular lesions are not scored because there are no clear and correct answers. The original total score of the behavior subscale ranges from 0 to 20.
The questionnaire took approximately 4 min to complete, and detailed information on the questionnaire was listed in Supplement 1.
## Data analysis
All data analyses were conducted using SPSS 26.0. Continuous variables were described by means (standard deviations) for normal distributions and medians (quartiles) for non-normal distributions. Two independent sample t-tests and one-way analysis of variance were used for group comparisons, and significant results from univariate analysis were included in multiple linear regression analysis to explore factors influencing diabetic foot risk screening behavior. P values were 2-tailed and $p \leq 0.05$ was considered to be statistically significant.
## Diabetic foot screening behaviors
The average score of the diabetic foot risk screening behaviors was 61.53 ± 14.69, which was at a medium level overall. The specific implementation is shown in Table 1.
Table 2 further shows the tools and methods used for peripheral neuropathy and peripheral vascular disease. For peripheral neuropathy, the top 3 most frequently used methods were asking about symptoms ($59.6\%$), pinprick ($55.7\%$), and temperature sense ($31.3\%$), while the top 3 least frequently used methods were Ipswich Touch Test (IPTT) ($15.0\%$), Sensory threshold determination ($8.3\%$), and Nerve conduction rate test ($4.1\%$). For peripheral vascular disease, the most used method was palpating the dorsal/posterior tibial artery ($50.9\%$), while the least used method was angiography ($11.5\%$).
Table 1Implementation of routine risk screening for diabetic footScreening itemsImplementation rateScreening Frequency (times) Always64 ($7.6\%$) Frequently207 ($24.5\%$) Sometimes329 ($39.0\%$) Rarely239 ($28.3\%$) Never5 ($0.6\%$) Screening content Blood sugar750 ($88.9\%$) History of diabetes comorbidities/complications639 ($75.7\%$) General information634 ($75.1\%$) Smoking history622 ($73.7\%$) Foot skin599 ($71.0\%$) Peripheral neuropathy568 ($67.3\%$) Foot hygiene585 ($69.3\%$) History of foot ulcer/amputation (toe)571 ($67.7\%$) Habits of wearing shoes and socks517($61.3\%$) Peripheral vascular disease512 ($60.7\%$) Foot care education498 ($59.0\%$) Ankle joint activity489 ($57.9\%$) Foot deformity435 ($51.5\%$) Inform risk level Always205 ($24.3\%$) Frequently314 ($37.2\%$) Sometimes195 ($23.1\%$) Rarely112 ($13.3\%$) Never18 ($2.1\%$) Guidance for regular screening or referral Always239 ($28.3\%$) Frequently293 ($34.7\%$) Sometimes213 ($25.2\%$) Rarely77 ($9.1\%$) Never22 ($2.6\%$) Foot self-management education Always152 ($19.2\%$) Frequently226 ($26.8\%$) Sometimes202 ($23.9\%$) Rarely156 ($18.5\%$) Never98 ($11.6\%$) Table 2Methods used for screening peripheral neuropathy, peripheral vascular diseaseItemN (%)Peripheral Neuropathy Screening Methods Ask about symptoms503(59.6) Pin prick470(55.7) Temperature sense264(31.3) Ankle reflex259(30.7) 10 g-MF203(24.1) Tuning fork 128 Hz148(17.5) IPTT127(15.0) Sensory threshold determination70(8.3) Nerve conduction rate test35(4.1) Other4(0.5)Peripheral Vascular Disease Screening Methods Palpate the dorsal/posterior tibial artery430(50.9) Intermittent claudication or rest pain418(49.5) Ankle Brachial Index172(20.4) Color Doppler ultrasonography128(15.2) Angiography97(11.5) Other1(0.1)
## Sample characteristics and association with diabetic foot risk screening behaviors
Table 3 shows the sample characteristics and comparisons of diabetic foot risk screen behaviors among participants with different sample characteristics. Among the 844 general practitioners, 404 were male ($47.9\%$) and 440 ($52.1\%$) were female. The largest proportion concentrated in the 30–39-year-old age group. The average working year was 16.74 ± 9.35 years, with the largest proportion concentrated in the > 20 years group. The largest proportion of people has an education level of an undergraduate degree ($57.2\%$). In terms of diabetic foot risk screening, the largest proportion of people received equal or more than 3 screening training in the last two years ($42.5\%$), had a medium level of screening cognition ($62.7\%$) and thought it strongly necessary to carry out risk screening ($61.6\%$).
As for the major obstacle to diabetic foot risk screening, the most frequently mentioned obstacle was “limited time and energy” ($78.6\%$), followed by “Expenses not covered by health insurance” ($57.9\%$) and “Patient not cooperating” ($57.3\%$). About half of the participants ($50.8\%$) listed “Lack of screening tools” as a major obstacle.
A further comparison of diabetic foot risk screen behavior scores by sample characteristics showed that diabetic foot risk screen behaviors varied significantly by age, work duration, training frequency, cognition, and attitude of risk screening, as well as the four major obstacles of risk screening. Participants with higher training frequency, good level of risk screening cognition, and thought risk screening was strongly necessary had higher scores of diabetic foot risk screening behaviors. Participants who reported “yes” to each of the four major obstacles had significantly lower diabetic foot risk screening scores than those who reported “no”.
Table 3Univariate analysis of diabetic foot risk screening behaviorItemN (%)Scoret/F/HPGender0.1630.686 Male Female404($47.9\%$)440($52.1\%$)61.75 ± 14.6061.34 ± 14.79Age2.9430.032 18–29 30–39 40–49 ≥ 5088($10.4\%$)349($41.4\%$)304($36.0\%$)103($12.2\%$)63.86 ± 13.3460.22 ± 15.6362.90 ± 14.4059.99 ± 12.79Work duration (years)2.8100.025 <5 6–10 11–15 16–20 >2096($11.4\%$)177($21.0\%$)156($18.5\%$)143($16.9\%$)272($32.2\%$)65.453 ± 13.1259.63 ± 14.9060.45 ± 15.6461.70 ± 15.3362.90 ± 13.95Education0.2770.842 Secondary school and below College Undergraduate Master’s and above133($15.8\%$)222($26.3\%$)483($57.2\%$)6($0.7\%$)60.97 ± 14.7461.05 ± 13.3261.89 ± 15.2863.57 ± 16.45Training Frequency (last two years)34.1110.000 0 1 2 ≥ 3218($25.8\%$)167($19.8\%$)100($11.8\%$)359($42.5\%$)55.47 ± 14.4257.64 ± 14.3863.49 ± 13.4766.49 ± 13.45Screening cognition32.0480.000 Poor Medium Good236($28.1\%$)530($62.7\%$)78($9.2\%$)56.54 ± 14.6861.31 ± 14.0265.28 ± 14.82Screening attitude27.2620.000 No need Necessary Strongly necessary7($0.8\%$)317($37.6\%$)520($61.6\%$)54.69 ± 14.1657.24 ± 14.1964.24 ± 14.37Major obstacleExpenses not covered by health insurance5.6660.018 Major obstacle Expenses not covered by health insurance Yes No489 ($57.9\%$)355 ($42.1\%$)60.51 ± 14.8562.95 ± 14.37 Patient not cooperating Yes No484 ($57.3\%$)360($42.7\%$)60.43 ± 14.8663.02 ± 14.346.4560.011 Limited time and energy Yes No663 ($78.6\%$)181($21.4\%$)60.11 ± 14.5266.78 ± 14.1630.3710.000 Lack of screening tools Yes No429 ($50.8\%$)415 ($49.2\%$)58.34 ± 15.5664.83 ± 12.9543.2070.000
## Influencing factors of diabetic foot risk screening behavior
The results showed that training frequency in the last two years, the cognition of diabetic foot risk screening, the attitude toward diabetic foot risk screening, limited time and energy, and lack of screening equipment were significant influencing factors of diabetic foot risk screening behaviors (Table 4). Higher training frequency (β = 3.197, $p \leq 0.001$), higher screening cognition (β = 2.947, $p \leq 0.001$), and more positive screening attitude (β = 4.564, $p \leq 0.001$) were associated with more diabetic foot risk screening behaviors, while limited time and energy (β=-5.184, $p \leq 0.001$) and lack of screening tools (β=-6.226, $p \leq 0.001$) were associated with fewer diabetic foot risk screening behaviors.
Table 4Multiple linear regression analysis of diabetic foot risk screening behaviorItemRegression coefficientsstandard errorstandard regression coefficientt P 39.2673.145-12.4850.000Training Frequency (last two years)3.1970.3620.2738.8400.000Screening cognition2.9470.6580.1404.4810.000Screening attitude4.5640.9030.1575.0550.000Limited time and energy-5.1841.106-0.145-4.6860.000Limited screening tools-6.2260.905-0.212-6.9030.000*$R = 0.487$, R2=0.237, adjusted R2=0.230, $F = 12.899$, $P \leq 0.05$;
## Discussion
To our best knowledge, this cross-sectional study is the first to investigate specific diabetic foot risk screening behavior based on the guidelines and its influencing factors among general practitioners. The results showed that the general practitioners’ diabetic foot risk screening behaviors in Changsha, China were at a moderate level, with an average standard score of 61.53 ± 14.69, and only $32.1\%$ of general practitioners reported always or frequently screened their diabetic patients for diabetic feet, which was lower than the reported $69\%$ in South Africa [23]. In fact, $69\%$ of the reported frequency of screening in general practice in South Africa was the result of one year after the implementation of the intervention. Therefore, some targeted interventions are needed to be taken to improve the screening behavior of general practitioners in China.
The ADA recommends components of annual diabetic foot risk screenings should emphasize the importance of peripheral neuropathy and peripheral vascular disease [24]. But our results showed low screening rates in peripheral neuropathy, and peripheral vascular disease, despite high screening rates in other items such as blood sugar, diabetes comorbidities and complications, and general information. It may be due to the screening of diabetic peripheral neuropathy and peripheral vascular disease, which requires the use of certain technology and equipment and requires more time. At the same time, the high screening rates in blood sugar, diabetes comorbidities, medical history, and so on, may be related to the requirement in China’s National Basic Health Service Code for the health management of diabetic patients to routinely ask for medical history, monitor blood glucose, and understand common complications or comorbidities. Therefore, it is necessary to further enhance the awareness of general practitioners on peripheral neuropathy and peripheral vascular disease, and foot skin screening. We recommended that the national health administration should put peripheral neuropathy and peripheral vascular disease into the routine management norms of diabetic patients in primary health care.
Given that peripheral neuropathy and peripheral arterial disease are the two leading causes of diabetic foot, we further investigated the specific methods used by general practitioners to screen for these two lesions. As for the specific methods used in screening peripheral neuropathy, we found generally low rates of 10 g-MF, IPTT, and Tuning fork 128 Hz, although these methods have been demonstrated to be suitable, inexpensive, effective, and easy-to-use tools for foot screening in primary hospitals [25–27]. International Working Group on the Diabetic Foot (IWGDF) [14] “Practical Guidelines on the prevention and management of diabetic foot disease [2019]” also clearly recommends that primary hospitals should choose these screening tools (i.e., 10 g-MF, 128 Hz Tuning fork, and IPTT) to screen for peripheral neuropathy, but less than $25\%$ of general practitioners in this study had used them. Especially for IPTT, previous studies have shown that it is an easy-to-use tool that can effectively screen diabetic foot in the absence of other tools [8], that only $15.0\%$ of general practitioners in this study used IPTT for foot screening. This may be mainly due to the lack of relevant knowledge and training for this research subject. Therefore, the IWGDF guidelines should be further promoted among general practitioners in China, and the training of general practitioners on screening methods should be strengthened to timely update their knowledge and skills.
Good behavior is determined by correct knowledge and a positive attitude [28]. Our study showed that the frequency of training in the last two years, cognition, and attitude toward diabetic foot screening were the main factors influencing screening behaviors. It means that the more training the general practitioners participated in, the higher level of screening cognition they had, and the more positive screening attitudes they held, the better screening behaviors they would have. What is less optimistic is that more than half of the general practitioners had never or rarely received relevant training. Previous studies have also shown that although the medical staff’s willingness to be trained was high, they received insufficient standardized training on diabetic foot from primary medical institutions [29]. This indicates the necessity and importance of increasing general practitioners’ knowledge of diabetic foot screening to improve their screening behaviors. Similar to knowledge-attitude-behavior theory, knowledge is the foundation of behavior, relevant administrative departments, and medical institutions should carry out standardized training related to diabetic foot screening and management, such as establishing standardized simple screening processes and norms, tour lectures on the interpretation of guidelines and consensus and strengthen assessments. This training may improve screening cognition, promote positive screening attitudes, and ultimately improve screening behavior.
In addition, lack of time and energy and limited equipment was also found to be important factors affecting general practitioners’ foot screening. Previous studies in different countries have also shown that limited time during healthcare professionals’ consultations may result in foot assessments being overlooked [30–32], as macrovascular complications and HbA1c monitoring were the primary focus in busy practices. Although the number of general practitioners is constantly growing in China, it still cannot meet the screening needs of large populations of diabetic patients just like in other developing countries, and thus general practitioners may not be able to provide each patient with a full range of services [33]. But how to solve the problem? We suggested, on the one hand, health administrative departments at all levels and community health centers shall strengthen the allocation of general practitioners and include the ratio of manpower in the scope of medical institution evaluation; on the other hand, we should also learn from the successful experience of other countries and train nurse practitioner to undertake diabetic foot screening in combination with national conditions to make up for the shortage of general practitioners [34–36]. In addition, the insufficient screening equipment also reflects the need to further strengthen the quality management of diabetes foot prevention and control in community medical institutions. It is suggested that the provision of basic screening equipment for important complications of diabetes should be included in the terms of inspection and review of community medical institutions. At the meantime, future studies also could develop a more simple and rapid diabetic foot risk screening scale suitable for primary care facilities or develop artificial intelligence technology to help primary care facilities to screen efficiently.
## Strengths and limitation
This study was the first to investigate the specific diabetic foot risk screening behavior base on the guidelines and to analyze its influencing factors among general practitioners. Our finding provides empirical evidence of behavioral responses towards diabetic foot risk screening among general practitioners, the large study sample size and various potential influencing factors examined in the analysis models stand as the strengths of this study.
This study also has several limitations. First, the cross-sectional design makes it impossible to establish causal associations between diabetic foot screening behaviors and their influencing factors. Future longitudinal study designs are needed for causal relationships. Second, the general practitioners in the study were all recruited from Changsha and may not represent general practitioners from other areas of China. A future national multi-center survey is needed to get a more representative sample. It is noteworthy mentioning that the sample size recruited in this study is large and covers all of the community health centers in Changsha, China. Third, the evaluation of general practitioners’ cognition, attitude, and behaviors on performing diabetic foot risk screening was based on participants’ self-report, which may be subject to recall bias and also may not truly reflect their cognition, attitude, and behaviors. Future more objective indicators are needed to get a more accurate assessment. Fourth, we didn’t find any difference in the frequency of training attendance by age of the physician, which may be because we only collected their training information over the last two years instead of since employment to reduce recall bias. The total frequency of training they received since employment may affect their attitudes and behaviors toward diabetic foot risk screening, which warrants further investigation in future research.
## Conclusion
In conclusion, the general practitioners’ diabetic foot risk screening behaviors in Changsha, China were at a moderate level, indicating more efforts are needed to improve screening among general practitioners. Diabetic foot risk screening behaviors were affected by training frequency, the cognition of diabetic foot risk screening, the attitude toward diabetic foot risk screening, limited time and energy, and limited screening equipment. These findings provide important guidance for future intervention programs to improve diabetic foot risk screening behaviors, which may be realized through strengthening training on relevant guidelines and evidence-based screening techniques, improving cognition and attitude, provision of more general practitioners or nurse practitioners, and user-friendly risk screening tools.
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|
---
title: Understanding school food systems to support the development and implementation
of food based policies and interventions
authors:
- Maria Bryant
- Wendy Burton
- Niamh O’Kane
- Jayne V. Woodside
- Sara Ahern
- Phillip Garnett
- Suzanne Spence
- Amir Sharif
- Harry Rutter
- Tim Baker
- Charlotte E. L. Evans
journal: The International Journal of Behavioral Nutrition and Physical Activity
year: 2023
pmcid: PMC10009978
doi: 10.1186/s12966-023-01432-2
license: CC BY 4.0
---
# Understanding school food systems to support the development and implementation of food based policies and interventions
## Abstract
### Background
Schools provide opportunities to improve the quality of children's diet, whilst reducing inequalities in childhood diet and health. Evidence supports whole school approaches, including consistency in food quality, eating culture and food education. However, such approaches are often poorly implemented due to the highly complex environments in which schools operate. We aimed to develop a school food systems map using a systems thinking approach to help identify the key factors influencing primary school children’s dietary choice.
### Methods
Eight workshops were conducted with 80 children (from schools from varying locations (region of England/UK; urban/rural), deprivation levels and prioritisation of school food policies)) and 11 workshops were held with 82 adult stakeholders across the UK (principals, teachers, caterers, school governors, parents, and local and voluntary sector organisations) to identify factors that influence food choice in children across a school day and their inter-relationships. Initial exploratory workshops started with a ‘blank canvas’ using a group model building approach. Later workshops consolidated findings and supported a wider discussion of factors, relationships and influences within the systems map. Strengths of the relationship between factors/nodes were agreed by stakeholders and individually depicted on the map. We facilitated an additional eight interactive, in-person workshops with children to map their activities across a whole school day to enable the production of a journey map which was shared with stakeholders in workshops to facilitate discussion.
### Results
The final ‘CONNECTS-Food’ systems map included 202 factors that were grouped into 27 nodes. Thematic analysis identified four key themes: leadership and curriculum; child food preference; home environment; and school food environment. Network analysis highlighted key factors that influence child diet across a school day, which were largely in keeping with the thematic analysis; including: 'available funds/resources', 'awareness of initiatives and resources', 'child food preference and intake', 'eligibility of free school meals', 'family circumstances and eating behaviours', 'peer/social norms', 'priorities of head teachers and senior leaders'.
### Conclusions
Our systems map demonstrates the need to consider factors external to schools and their food environments. The map supports the identification of potential actions, interventions and policies to facilitate a systems-wide positive impact on children’s diets.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12966-023-01432-2.
## Background
Around $30\%$ of foods and drinks consumed by children are consumed during the school day (Nathan et al. 2019); providing an opportunity for schools to improve dietary quality and reduce inequalities in obesity and health. This is important, as dietary intake consistently fails to meet government recommendations (e.g. [1]). Children aged 4–11 years consume above recommended intakes of free sugars and saturated fat and inadequate amounts of fibre, vitamin D and fruits and vegetables. Furthermore, substantial dietary inequalities exist in the UK, particularly affecting those living in the most deprived households, who are twice as likely to have obesity [2] and less likely to achieve dietary guidelines [3, 4]).
The WHO’s Health Promoting Schools framework advocates a whole school approach to promoting health [5]. In England, current government initiatives include mandated food based school food standards [6] and similar standards are mandated in other nations (e.g. [7, 8]. Previous research that has explored the impact of such legislated food and nutrient-based standards has highlighted the potential positive impact on primary school children's dietary intake [9, 10]. However, non-mandated recommendations, including ‘whole school approaches to food’ have an under-realised potential to improve children’s eating habits within and outside of school [11], with evidence indicating poor implementation (failure to engage parents and no consideration of sustainability) and evaluation (limited data on long-term effects, system adaptation or contextual factors) [10, 12]. This is an area gaining increased interest, including within the UKs Levelling Up White paper. Notwithstanding the political uncertainties, this is a policy paper which recommends that schools not only improve their whole school approaches to food, but provide a statement of this on their websites [13]. These ‘whole school approaches’ advocate a systems approach to the provision and education surrounding food, including the promotion of a consistent food culture in schools (how food is provided and the ethos around celebration foods [14], food policy (such as regulations on packed lunches) and education (healthy food practices within the curriculum).
Application of systems thinking is well established in many policy areas, but the use of systems approaches to improve population health including food environments is relatively new [15]. Key aspects of complex systems thinking are identification of connections and strengths of relationships between different parts of the system and the need to see the system from many different points of view [15]. Although a systems approach is considered useful for designing policies that take account of the complexities involved, it does lead to large amounts of inherent unpredictability [16]. Nevertheless, some countries, including the UK, encourage systems approaches in public health policy due to their potential benefits [4].
There are many competing priorities and demands within schools and it is not clear how whole school approaches to food fit within a broader context of school based health promotion. There is further complexity found within the wider educational system, in which decision making is often linked to the delegation of funding and responsibility to schools, and the increase in numbers of independent academies, and changes in the wider food system beyond the school environment. Whilst a number of initiatives for enhancing food environments within schools exist, uptake has been low [17], limiting the potential for demonstrable impacts on diet and health. Thus, schools would benefit from active support to deliver effective policies/guidance which support whole school approaches to food.
Systems-led work in schools in Canada suggests that there are three key factors that may influence the ability of school food interventions to have an impact, including the actions of key staff (“Actors and Elements”), the implementation of different school food policies (“System Regulation and Interconnections”) and priorities of stakeholders (“Purpose and Values”) [18]. This is likely to resonate with school food systems in the UK, where existing implementation evaluation of the School Food Plan suggests that the skill and will of head teachers is a strong predictor of success [19]. Further evidence suggests that government incentives and commitment from multiple stakeholders is required to achieve a higher uptake of guidance of school plans [20]. Case studies highlighted by ‘what works well’ within the School Food Plan offer some additional insight to guide optimisation of the whole schools approaches, but there has been a lack of evaluation of potential impacts [20]. Development of interventions to optimise school food provision and consumption requires an understanding of local and wider influences on the school food system and potential levers to shape them [21]. However, the paucity of research on systems approaches to school food has potentially hindered the development, and evaluation, of whole school approach interventions.
This study attempted to fill the gap in evidence within school food system through the development of a primary school food systems map. This was intended to highlight key factors influencing children’s food choice across a school day in the UK, in order to support the design of school food interventions or policies, including those that support the implementation of whole school approaches to food. This adopted a co-design approach alongside key stakeholders in order to identify complex and non-linear pathways through which decision making occurs in schools, including key organisational components and/or political pathways for successful implementation of whole school approaches to food. Central to this was the decision making at the level of the child. Hence, we were interested in mapping the system to allow for the identification of opportunities within the system that could influence child food choice via whole school approaches to food.
## Aim
To build a systems map of influences on school children’s food choice throughout the school day, performing network analysis to describe relationships, complexity, interactions and potential adaptations through our mapping activities—generating theories and assumptions required to support future intervention development in this setting.
## Study design
We used a group model building approach [22, 23] to develop our systems map. This is a participatory approach in which a group of stakeholders with differing perspectives are brought together to build a shared understanding of a complex system. In addition to stakeholder workshops, eight separate workshops were also planned with primary school children to provide an understanding of key experiences throughout a child's school day with the potential to influence food choice either directly (i.e. via the offer of food) or indirectly (e.g. exposure to foods/food marketing). These ‘journey mapping’ workshops were interspersed with workshops with school stakeholders to support discussions. Earlier, ‘phase 1’ workshops were exploratory in nature and elicited factors that influenced the child’s journey across the school day and their inter-relationships, resulting in the development of an initial systems map. Later, confirmatory, ‘phase 2’ workshops consolidated and refined the findings, enabling the development of the final systems map.
Ethical approval for the study was received from the University of York Department of Health Sciences’ Research Governance Committee (ref HSRGC/$\frac{20210}{428}$/A) and we used the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist to guide our approach and reporting (Tong, 2007).
## Site selection and participant recruitment
Research was undertaken in four regions of the UK (Leeds, Bradford, Newcastle, Belfast) led by a principal investigator (PI) in each site (SA/CE/JW/SS). However, as the majority of adult stakeholder workshops were delivered remotely, relevant stakeholders outside of these areas were also invited to take part (e.g. national and local organisations with a key role in school food). All childhood workshops were planned to be delivered in person. The following eligibility criteria were applied:
## Child workshop: Inclusion criteria
Children from any year group were eligible. We engaged with existing school councils / food ambassadors / global champions to enable a breadth of engagement across the school
## Stakeholder workshop: Inclusion criteria
Public and academy schools across a range of differing demographics with single and multiple form entrySchool stakeholders to include: head teachers, teaching staff, catering staff, school governors, and parentsExternal catering stakeholders to include: representatives from catering and/or procurement services, and food supply chain agents (producers, distributors)External businesses (as appropriate) including local businesses
## Child workshop: Exclusion criteria
Children whose families did not provide consent for them to take part
## Stakeholder workshop: Exclusion criteria
Private and specialist schools Stakeholders were invited to take part via direct communication with head teachers from schools within each area. We also promoted the study using poster/leaflet and through direct invitations from those that had already agreed to take part (snowballing method [24]. Social media (Twitter) and existing networks (e.g. GENIUS school food network (https://geniusschoolfoodnetwork.com/)) were then used to invite stakeholders from outside of the immediate schools that had an interest in school food. All workshop participants were required to provide written informed consent prior to taking part. There was no limit placed on the number of people recruited to each workshop, although we anticipated 10–12 people per workshop. Further, stakeholders across the four regions were able to attend any workshop (i.e. they were not restricted to workshops that were organised within their region). Children were invited to take part by school teachers and written informed consent was obtained from parents / guardians prior to workshops. All child workshops were delivered during a school day, within two schools in each region.
## Sampling
A sampling framework was applied in attempt to ensure a diversity of the eight planned schools according to area level deprivation (at least four schools situated within the highest quintiles of deprivation from the index of multiple deprivation), with a range in geography (urban and rural locations) and the level of school engagement with school food initiative (defined as either having an existing school food strategy or not) (Additional file 3).
## Child journey mapping workshops
Journey mapping is typically used in healthcare to map the patient journey and inform service improvement [25, 26]. For this study, a card based activity was performed with children by two members of the research team to build a picture of a typical day in the life of a primary school child by asking children to talk about key timepoints throughout the previous day. This information was used Although dietary choices were the main focus of the research, facilitators did not make them the main focus of the discussion in order to remove potential anxiety around describing personal food habits and to allow for the identification of a broader range of events during a day which have the potential to influence food choice (e.g. exposure to food marketing during the journey to school, attending sports clubs etc.). In each workshop, children were asked to pick a card that denoted a particular time in the day (e.g. waking up and getting ready for school, lunch time, during lessons etc.) and asked to describe what they did the day before at this time point. Once the child had responded to their card, the question was opened out to all children. During discussions, responses were mapped onto a whiteboard at the appropriate time in the day by the support facilitator.
## Exploratory systems mapping workshops with stakeholders
We originally planned to deliver eight × 90 min workshops in total, four exploratory and four confirmatory, although this was updated to seven and three workshops respectively with agreement within the team, as the exploratory workshops continued to elicit new information beyond the planned four sessions. These initial exploratory workshops used a ‘blank canvas’ approach to identify factors that influence food choice by children during the school day using a group model building approach [22].
At the start of the exploratory workshops, participants were introduced to the systems mapping concept, the aim of the workshop and informed that they would be helping to develop a map of the school food system. Journey maps produced from the child workshops were presented to prompt discussion. In the first half of the workshop, participants were divided into small groups (ranging from 2 – 6 people) and given two minutes to think independently of 1–3 factors that influenced child food choice/dietary habits throughout the school day. Participants then shared these factors with the rest of their small group in turn and these were recorded by a study note taker on an interactive whiteboard (Google Jamboard) using the sticky note function. Discussions around the factors were then held, with participants asked to expand on how they felt that the factors influenced child food choice dietary habits. The wider group was then reconvened in a plenary discussion where participants were asked to comment on each other's choices. In the second half of the exploratory workshops, participants, in small groups again, were asked to consider if any of the factors were related to each other and if so, how (including the direction of relationship). Discussions during this activity were captured on the same interactive whiteboard by drawing directional arrows between the sticky notes. Additional factors revealed during this discussion were also added to the jamboard.
## Development of initial systems map
Following all exploratory workshops, interactive whiteboards completed during the workshops were reviewed and an overall list of factors that stakeholders believed influence child dietary choice and food habits was compiled. Recordings of the workshops were reviewed to ensure no factors had been overlooked. These were then thematically grouped by two members of the research team (WB & NOK), with each group being assigned a theme name and descriptive summary. Overarching themes were also identified and agreed initially by WB & NOK and then by the rest of the team. Theme names were entered onto a matrix using Microsoft Excel. One member of the research team (WB) again reviewed data collected via interactive whiteboards and discussions during the workshops to identify themes that were connected to one another, and the direction of the relationships. Linked themes were represented visually on the matrix which was reviewed by a second member of the research team (NOK). The STICKE map builder application (Deakin University 2019 STICKE [software] (build 640—Oct 2019) [Accessed 2021]) was used to develop the initial systems map. Each theme was represented by a node on the map and each connection depicted with a directional arrow. Overarching themes were represented as a colour coded domain to assist with interpreting the map. Once the initial map was produced, it was reviewed by the rest of the research team to “sense check” and ensure it was an accurate reflection of workshop discussions.
## Confirmatory workshops
Confirmatory workshops led by a session chair (with two session facilitators) were carried out with stakeholders to consolidate findings and support a wider discussion of factors, relationships and influences within the emergent systems map. Participants were divided into small groups and asked to look in detail at the initial systems map developed from earlier workshops. We applied a focused approach, where participants were asked to review specific areas of the map in turn, followed by the map as a whole, and discuss whether they agreed with the factors/nodes depicted, proposed relationships, and the direction of the relationships. In addition, participants were asked to consider the strength of the relationships between factors (high, medium or low) and whether any nodes or potential relationships were missing.
## Final systems map development
Following completion of the confirmatory workshops, recordings of the workshops were reviewed to ensure all new data captured during discussions were included within the existing nodes and domains on the initial version of the map. This led to the initial thematic groupings to be refined resulting in a revised relationship matrix and a revised version of the map. During the review of workshop recordings, discussions around relationship strengths were also drawn out, allowing relationships to be coded as high, medium, or low in strength on the theme/relationship matrix. New connections identified during workshop discussions were also incorporated. Following refinement of the map, it was again reviewed by the wider research team to ensure it coherently depicted concepts and relationships identified during workshops and by a selection of the workshop stakeholders (Headteacher $$n = 4$$; local authority food leads $$n = 3$$; catering representatives $$n = 3$$; representatives of school food organisations $$n = 2$$; dietician $$n = 1$$) (who were later involved in using the map to co-design an implementation intervention to support whole school approaches to food). Following this, the final version of the’CONNECTS-Food’ systems map was produced.
## Network analysis
A network analysis was performed to understand and describe relationships, complexity and interactions with the school food systems map developed via the workshops [27]. The theme/relationship matrix developed during development of the systems map was exported as a CSV file and imported into R statistical software as an adjacency matrix. The matrix included edge weights 1—4, with a weight of 1 indicating that there is a relationship between the nodes but the weight is unknown. Weights 2—4 presented low, medium, and high strength relationships respectively.
Within R, iGraph was used for the network analysis by generating a directed graph from the adjacency matrix. We calculated the network density, reciprocity, centrality, and the mean path length. We also calculated the betweenness centrality, in and out degree, and the degree for each of the nodes. Betweenness centrality measures the number of paths between any two nodes that go through the other nodes. Therefore, betweenness centrality in this case provides an indication of how important a node is for connecting together the different influences on children’s food choices. In degree is a count of the number of relationships into a given node from other nodes. Out degree is the number of relationships out from a given node into the other nodes.
We also clustered the network using the cluster edge betweenness algorithm in iGraph to split the network into communities [28]. This clustering method is useful as it can take into account both the direction and weights of the edges. The clusters therefore represent factors that more closely influence each other in the network.
In addition to the clustering of the network we calculated the 'in' coreness of the directed graph by doing a core-periphery analysis using the k-core decomposition algorithm provided by iGraph graph.coreness [27, 29]. This allowed us to determine the core of the network by the connections into the different factors. In this case there are three layers to the core-periphery analysis: [1] A central core of factors that have many relationships into each other; [2] A middle layer that is not as highly connected, but has connections going into the central core; [3] An outer layer of factors that can be considered as more peripheral.
The results of the network analysis are presented in Table 2. Results show that there are a many factors that rank highly on most network measures, including: 'available funds resources', 'awareness of initiatives and resources', 'child food preference and intake', 'eligibility of free school meals', 'family circumstances and eating behaviours', 'peer social norms', 'priorities of headteachers and senior leaders', 'school food policy and culture', 'school packed lunch uptake', and 'skills passion of cook and lunch staff'. All of these factors scored highly on betweenness and degree measures; suggesting these factors are viewed as significant to children's food choice. Seven out of these 10 highly ranked factors also cluster together into cluster six, along with two other factors that score highly, 'child hunger clues' and 'quality of school food provision' (Fig. 2). Broadly, factors within cluster six describe the potential for children to access healthy food, as they are often either about preferences, availability, or resources (either directly in the form of money, or indirectly in the form of the allocation/prioritisation of resources).Table 2Results of network analysisNode NameBetweennessDegreeIn DegreeOut DegreeClustersCorenessAvailable funds resources90.1792711Awareness of initiatives and resources72.6752382Child food preferences and intake107.371814462Child hunger cues29.0065162Eligibility of free school meals96.9084461Environmental prompts9.5383591Extent of food incorporation in curriculum24.3364262Extent of DFE Ofsted monitoring0.0050530Family circumstances and eating behaviours218.90146862Local authority buy in26.3392721Mode of travel to school0.50312110Offer provided by catering companies11.20422131Parent perception of school food quality and value0.0087162Parental attitudes to school food policy33.00312101Peer social norms69.8396362Priorities and skills of teachers14.8362472Priorities of headteachers and senior leaders141.501551042Priorities of school governors9.5031251Quality of school food provision37.20127562School dining experience11.3395462School food policy and culture90.70149562School lunch menu1.0085362School packed lunch uptake130.00119262Skills passion of cook and lunch staff72.1792761Training provision and pay25.83431131Urban vs rural location0.00808120Use of breakfast after school club0.0021161Bolded rows indicate factors which were ranked highly on most measures. To note, absolute values are based on the total number of factors; thus, they do not represent any given cut-offsFig. 2CONNECTS-Food Cluster Map The core-periphery analysis conducted on the directed network, groups factors into the network core taking the direction of the relationship into account. This produced a core of 14 factors (coreness ‘2’ in Table 2) at the centre of the network shown in Fig. 3, (10 of which are also present in cluster 6). These 14 central core factors also relate to the ability for children to access good food, and additionally include awareness factors such as ‘extent of food incorporation in curriculum’ and ‘parent perception of school food quality and value’. A further 10 factors group around, or influence, this central core (coreness ‘1’ in Table 2), which are broadly more external, such as ‘priorities of school governors’, training provision and pay’, and ‘environmental prompts’ and ‘resource availability’. Fig. 3CONNECTS-Food Coreness Map
## Child workshops
In-person workshops were conducted with 80 children from eight schools across four locations with representation according to rurality: (4 = rural, 4 = urban), deprivation (5 = school's IMD < 8, 3 school’s IMD ≥ 8 (or NI equivalent)) and prioritisation of school food policies ($$n = 2$$ schools yes, $$n = 6$$ schools unknown) between May–July 2021. A journey map was created after the first workshop and built upon in subsequent workshops so that the information shared by children was available to present in all stakeholder workshops (which were interspersed with child workshops). Children shared their experiences across a whole range of activities within a school day. This enabled us to get a sense of the factors that had a role in child food choice from the perspective of the child. Details of this work will be published separately; but in summary, key discussion points centred around the impact of rurality (particularly related to factors that children experienced on their journey to school), eating behaviours at home, school eating environment and preferences for school or packed lunch. The full range of activities reported by children was provided to stakeholders to enable them to consider multiple factors that might influence child food choice across the day.
## Adult stakeholder workshops
Remote workshops were conducted with 81 adult stakeholders within the same period across 11 sessions. This included an ‘out of hours’ workshops to accommodate those who could not attend workshops during working hours and a workshop attended only by teaching assistants from one of the participating schools who were also not able to attend any of the planned sessions. Participants included those who were based within the four regions, in addition to national school food stakeholders and included representatives from teaching staff, caterers/food producers, lunch time staff, headteachers, governors and parents (Table 1).Table 1Workshop participantsGroup concept mapping workshops—participant typeN = 81Teaching staffN = 23 (Age range 25–64; 22 female, 1 male; 18 white British, 4 Asian/British Asian, 1 undeclared ethnicity)Catering/lunch staffN = 17 (Age range 35–64; 17 female; all white British/Irish)ParentN = 11 (Age range 35–64; 9 female, 2 male; all white British/Irish)School governorN = 7 (Age range 35–65 +; 7 female; all white British)HeadteacherN = 3 (Age range 50–64; 2 female, 1 male; all white British)Food producer/distributorN = 1 (Age range 35–49; female, white British/Irish)Other (including representatives from local authorities, civil servants, school food organisations, nutritionists/dieticians)$$n = 19$$ (Age range 18–65 +; 14 female, 4 male; all white British/Irish)
## Systems map
Our systems mapping workshops identified 202 factors which were grouped into 27 thematic nodes. These are represented in our final CONNECTS-Food school food systems map (Fig. 1).Fig. 1CONNECTS-Food School Food Systems Map
## Thematic analysis
Thematic analysis identified four key themes: [1] leadership and curriculum; [2] child food preference; [3] home environment; and [4] school food environment (additional file 2). The leadership and curriculum domain comprises nine factors, (e.g. priorities of headteachers and senior leaders, school food policy and culture and the extent of Department for Education (DfE) and Office for Standards in Education, Children's Services and Skills (Ofsted) monitoring). Child food preference comprises four factors which include child food preference and intake, environmental prompts and peer/social norms. The home environment comprises seven factors, which include family circumstances and eating behaviours and parental attitudes to school food policies, and finally, the school food environment domain comprises seven factors, including the quality of school food provision, the school dining experience and the skills and passion of lunchtime staff and cooks. Although it was attempted during confirmatory workshops to ascertain strengths of the relationships between all factors, in practice, this was not possible, as some relationship strengths were difficult to quantify, resulting in an incomplete data set. Where relationships strengths were estimated by stakeholders, these data were used in the network analysis for clustering purposes.
## Discussion
Our ‘CONNECTS-Food’ systems map provides an in-depth understanding of the primary school food system through the eyes of a range of key stakeholders. By highlighting factors with potential influence on children’s diets, the map also supports the identification of leverage points which could be used to influence the system it depicts in ways that could promote healthier diets. This includes consideration of both school level and external factors that influence the diets of children. Such information will support the design of future interventions to improve the school food environment. For example, the map has already been used to co-design resources for schools to implement whole school approaches to food in the UK (www.connects-food.com) in combination with the Action Scales Model [30]. In this, the map was used by the co-design team to identify key factors that are likely to influence child diet in schools (e.g. prioritisation of food within senior leaders). Once identified, the map was used to estimate what other factors might need to change to influence this factor (i.e. via relationships within the map). In the case of leadership prioritisation, the map suggests that external monitoring and priorities of school governors are likely influencers. Further, the map tells us that an intervention that is able to impact on school food prioritisation by leadership is likely to have a direct impact on factors such as the quality of food, the school dining experience, training provision and pay, and child food preferences (Additional file 1).
Our network analysis identified 10 key factors that were consistent across all metrics, including those that significantly link to other factors (betweenness), and those that were central to children's food choice (degree). Many of these clustered together and were related to food preferences, availability, or resources (either directly in the form of money, or indirectly in the form of the allocation/prioritisation of resources). This was consistent with our thematic analysis, in which key themes linked to leadership (enabling and prioritisation of resources), child food preferences (food choice), environmental norms (availability) and peer/social norms (food choice). The extensive number of key factors also supports the concept of whole school approaches to food, where a number of initiatives are most likely to have the greatest impact at disrupting the system; in this case, to positively influence child food choice across a school day. The analysis of the network should not be viewed in isolation. The different analytical methods and network statistics should be combined with the qualitative analysis and views of domain experts, to form a more holistic view of what factors are important. One potentially interesting finding from the core-periphery analysis is that the factors in the system that can be more easily influenced (such as ‘available funds resources’, ‘local authority buy in’, and ‘training provision and pay’) have grouped together into the middle layer (coreness ‘1’ in the table). This suggests that targeting a number of these factors together might provide an effective way of influencing the highly connected core of the network.
There has been limited stakeholder engagement work mapping school food systems in the UK, though previous work with School Food Trust staff working in this area has identified key factors that contribute to whether children eat a healthy lunch at school [31]. Our work builds upon this, by extending the focus of interest beyond healthy food provision to identification of key areas with a whole school approach to food. To the best of our knowledge, this is the first primary school food systems map for children aged 4 to 11 years that has been developed alongside key stakeholders in this area. Our thematic and network analysis findings also support (and extend) the systems work conducted within the Canadian school food system in which leadership, school food policies and priorities of stakeholders were deemed key factors [18]. Our CONNECTS-Food systems map builds on this by considering wider external factors that influence children’s food choice in schools; notably the influence of family circumstances, which has the potential to directly and indirect influence via multiple pathways (e.g. social norms, school meal uptake and free school eligibility).
Although there have been calls for the adoption of whole school approaches to food from the WHO [5], from national governments [6, 7, 13, 32] and other organisations [20, 33] here is limited evidence that this is embedded within the majority of schools. Given the potential extent of interventions associated with delivering whole school approaches to food, a lack of implementation is likely due to the associated perceived burden and cost against a background of lack of macro-level support and policy enforcement [18, 34]. However, research in this area suggests that, when well implemented, whole school approaches can have a substantial impact on diet, health [35] and food insecurity [36]. One example comes from the Daire randomised controlled trial, in which implementation of a multi-component school food intervention, ‘Nourish’, led to significant improvement in child emotional and physical health compared to non-intervention control schools [37]. Activities were delivered over a 2.5—5 month period and included improvements to the food environment, increased exposure to locally produced foods, sensory education and support for school food policy implementation. This demonstrates encouraging support for whole school approaches to food, though longer-term implementation and follow-up are needed to confirm sustainable impact. Other research evaluating a UK national systems based initiative called ‘Food for life’ indicates that children based at schools that have adopted the scheme eat more fruit and vegetables and are more likely to have a school meal [38], and systematic review evidences shows that implementation of school food environment policies can have a positive impact on diet quality in children [39, 40].
Our CONNECTS-Food systems map highlights some of the same factors previously hypothesised to influence the adherence to English School Food Standards [41] (which may be considered as a proxy to diet quality). These included factors within the physical environment (e.g. full production kitchens), support from head teachers, training for catering staff, low school prices and connections with the local authorities [41–43]. Other research has indicated that primary schools that adopt a whole school approach are more successful in adhering to the school food standards [9]. Ongoing research is being conducted in this area, including the FUEL study, which includes research to capture variation in the implementation of how the School Food Standards and the degree to which both of these has an impact on pupils’ dietary intake and dental health [44].
There are many strengths to this work. Perhaps most notable, we worked with a wide and extensive range of stakeholders from a variety of regions of the UK in the design and development of the system’s map including primary school children. Our group model building approach allowed all stakeholders to provide input and the online process further supported this by allowing a wider range of people to feed into the work remotely. However, given that stakeholders volunteered their time to take part in the workshops, it is recognised that they may have a bias view that does not necessarily represent the views of others. Further, as we were not able to fully clarify the strength of the relationships for all factors in our map, there are opportunities for future research to add greater depth in understanding of the school food system.
Interspersing our findings from the child in-person workshops within our adult stakeholder sessions (via a journey mapping process) also ensured that the child's voice was prominent throughout. However, whilst our online approach facilitated a wider range of stakeholder views, with good consideration of the relationship between factors, it was challenging for stakeholders to conceptualise the ‘strength of relationships’ in the given time. Had workshops been in-person, sufficient time would have been provided to facilitate this; however, we aimed to reduce online meeting fatigue via relatively short workshop session timings. Lastly, it is worth noting that our work was focused on primary schools. Although there are gaps in the evidence with regards to school food systems all schools (e.g. secondary/high schools) the approach to understand these would likely differ and would result in a different map, given how different these settings are. This remains to be a gap that we recommend is explore in future research.
## Conclusions
The CONNECTS-Food system map extends the current understanding of complexity within school food and key factors that influence children’s diets by highlighting how these relate to one another. This also enables us to explore the multiple social determinants of children’s diets, including family circumstances, social networks, peer influence, economic inequality and social capital. Importantly, doing this via a systems lens provides an opportunity to develop interventions that may have a positive impact on school food systems. Only focussing on one area, such as quality of school meals, is less likely to provide sustainable impact unless the whole school day and all stakeholders are focussed on the same goals. Achieving this can not be underplayed given the lack of resources and increased competing pressures within schools; however lessons can be learned from those who have managed to overcome barriers to successfully implement system wide, whole school approaches to food [15, 45–47]. Given the urgency of addressing the food system more widely, there is value in implementing these approaches so that both population and environmental health are considered [48, 49].
## Supplementary Information
Additional file 1. Workshop participants. Additional file 2. CONNECTS food systems map: Domain, node and theme summaries. Additional file 3. Sampling frame for recruitment.
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|
---
title: Using the RE-AIM framework to evaluate the feasibility of a parent-focused
intervention targeting childhood obesity
authors:
- Daniel Briatico
- Kristen C. Reilly
- Patricia Tucker
- Jennifer D. Irwin
- Andrew M. Johnson
- Erin S. Pearson
- Dirk E. Bock
- Shauna M. Burke
journal: Pilot and Feasibility Studies
year: 2023
pmcid: PMC10009980
doi: 10.1186/s40814-023-01248-8
license: CC BY 4.0
---
# Using the RE-AIM framework to evaluate the feasibility of a parent-focused intervention targeting childhood obesity
## Abstract
### Background
Childhood obesity remains a serious public health concern. Community-based childhood obesity treatment interventions have the potential to improve health behaviors and outcomes among children, but require thorough evaluation to facilitate translation of research into practice. The purpose of the current study was to determine the feasibility of a community-based, parent-focused childhood obesity intervention (“C.H.A.M.P. Families”) using the RE-AIM framework, an evaluation tool for health interventions.
### Methods
A single-group, non-randomized, repeated measures feasibility study was conducted. Participants ($$n = 16$$ parents/caregivers of 11 children with obesity) completed a 13-week parent-focused education intervention. The intervention consisted of three main components: (a) eight group-based (parent-only) education sessions; (b) eight home-based (family-centered) activities; and (c) two group-based follow-up support sessions for parents and children. The five dimensions of RE-AIM—reach, effectiveness, adoption, implementation, and maintenance—were assessed using various measures and data sources (e.g., child, parent/caregiver, costing, census) obtained throughout the study period. Outcome variables were measured at baseline, mid-intervention, post-intervention, and at a 6-month follow-up.
### Results
Overall, the C.H.A.M.P. Families intervention reached approximately $0.09\%$ of eligible families in London, Ontario. Despite the small number, participants were generally representative of the population from which they were drawn, and program participation rates were high (reach). Findings also suggest that involvement in the program was associated with improved health-related quality of life among children (effectiveness/individual-level maintenance). In addition, the intervention had high fidelity to protocol, attendance rates, and cost-effectiveness (implementation). Lastly, important community partnerships were established and maintained (adoption/setting-level maintenance).
### Conclusions
Based on a detailed and comprehensive RE-AIM evaluation, the C.H.A.M.P. Families intervention appears to be a promising parent-focused approach to the treatment of childhood obesity.
### Trial registration
ISRCTN Registry, Study ID ISRCTN 10752416. Registered 24 April 2018.
## Key messages regarding feasibility
What uncertainties existed regarding the feasibility?
While there is evidence available in support of parent-focused childhood obesity treatment interventions, information related to program implementation and sustainability in community-based settings is lacking and/or underreported in the current literature. What are the key feasibility findings?
The findings outlined in this study provide preliminary support for the feasibility of C.H.A.M.P. Families and suggest that this parent-focused, community-based childhood obesity program could lead to improvements in children’s health-related quality of life. What are the implications of the feasibility findings for the design of the main study?
Via a comprehensive examination of RE-AIM dimensions, the feasibility findings presented in this manuscript can be used to inform the development and implementation of future community-based pediatric obesity interventions. We also expect that these findings will inform the planning and design of a future RCT conducted with a larger and more diverse group of children and families.
## Background
Obesity is widely recognized as one of the most significant health problems affecting children in the twenty-first century [1]. In recent decades, the prevalence of overweight and obesity in children has increased dramatically, affecting approximately 340 million children worldwide [1]. Accompanying the prevalence of obesity among children is the associated health risks including a myriad of physical [2–4] and psychological health sequelae [4–6], as well as considerable economic implications [7].
Seminal research conducted by Epstein and colleagues in the early 1980s established the key role that parents play in family-based pediatric weight management interventions [8, 9]. Since these landmark studies, researchers have targeted parents as the principal agents of change in childhood overweight/obesity programs [10–13]. For example, a longitudinal study by Golan and Crow [2004] compared a child-only childhood obesity intervention to a parent-only intervention and found that weight reductions at a 7-year follow-up were greater in children from the parent-only treatment group [14]. Similarly, in 2006, Golan and colleagues compared the effectiveness of obesity interventions targeting parents exclusively versus those that included both parent and child, and found that the parent-only intervention was superior with regard to reductions in adiposity among children [15].
In addition to these early studies, numerous controlled trials have confirmed the beneficial effects of parent-focused interventions in pediatric weight management [16]. In fact, recent reviews of the literature support the conclusion that parent-only childhood obesity interventions are at least as effective as [17, 18], and potentially more effective than, parent-child and child-only approaches [19]. Furthermore, some reviews have found that parent-only interventions were more cost-effective and less resource-intensive than family-focused interventions [16, 20].
While the literature supports the efficacy of parent-focused interventions targeting childhood overweight and obesity [10–20], published papers in this area contain limited information about the key items necessary to effectively translate research into community settings [21]. The RE-AIM framework addresses such factors, vital to program generalizability and dissemination, through five evaluative dimensions–reach, effectiveness, adoption, implementation, and maintenance [22, 23]. RE-AIM provides a systematic means of evaluating an intervention’s potential for public health impact and widespread application, placing emphasis on both internal and external validity [22, 23].
In 2015, Jang, Chao, and Whittemore conducted a systematic review, using the RE-AIM framework, of childhood obesity treatment interventions targeting parents as agents of change [24]. Results showed that all seven of the randomized controlled trials included in the review lacked full reporting of the RE-AIM components [24]. The overall proportion of studies reporting on each dimension ranged from $23.8\%$ (maintenance) to $78.6\%$ (reach). Reporting on items within the effectiveness ($60.7\%$), adoption ($47.6\%$), and implementation ($47.6\%$) dimensions was moderate amongst the studies included in the review [24]. The least reported RE-AIM items across studies were adoption rate ($0.0\%$), methods of identifying the target delivery agent ($0.0\%$), quality-of-life measures ($14.3\%$), and estimates of costs and sustainability ($0.0\%$).
In 2008, members of our research team developed, designed, implemented, and assessed a child-centered, community-based pilot intervention targeting childhood obesity (ISRCTN #13143236 [25]). The Children’s Health and Activity Modification Program (i.e., “C.H.A.M.P.”) was a 4-week family-based intervention delivered to children in a unique camp-based format. Using RE-AIM metrics, C.H.A.M.P. was found to be a potentially effective and feasible community-based childhood obesity treatment program [25]. Participation in the program was associated with several positive outcomes for children including improvements in body composition, health-related quality of life (HRQoL), and physical activity self-efficacy [25, 26]. Furthermore, focus group data revealed that parents [27] and children [28] had positive perceptions of the program. Some parents noted that future programs should include supplementary education and opportunities for parents, as well as greater parental accountability and involvement [27]. Interestingly, children also expressed a desire for increased support and involvement from their parents in order to initiate and maintain health behavior changes at home [28].
Based on the compelling empirical evidence in support of parent-focused childhood obesity treatment interventions [10–20], and key findings from the original C.H.A.M.P. pilot program [25–28], Burke and colleagues developed and implemented a second community-based pilot intervention entitled “C.H.A.M.P. Families.” This program was designed to target childhood overweight and obesity using a novel, parent-focused, group-based approach [29]. Given the paucity of studies in this area that have thoroughly reported on RE-AIM dimensions [24], the purpose of the present study was to: (a) apply the RE-AIM framework to evaluate the feasibility of this parent-focused childhood obesity intervention; and (b) explore the impact of the program on children’s HRQoL within the effectiveness dimension of RE-AIM.
## Methods
C.H.A.M.P. Families was a single-group, non-randomized, repeated measures feasibility study (ISRCTN #10752416). A detailed overview of the study protocol and theoretical foundation is published elsewhere [29]. For the purpose of the present study, a brief overview of the intervention and relevant measures is provided below.
## Participant eligibility
Parents and caregivers in London, Ontario (city population in 2017, when the study took place, was ~383,822 [30]), were eligible to participate in C.H.A.M.P. Families if: (a) they had a child between the ages of 6 and 14 years with a body mass index (BMI) ≥ 85th percentile for age and sex [31]; (b) at least one parent/caregiver agreed to take part in the study; and (c) both the child and parent(s) could speak, read, and understand English.
## Sample size
Given this was a feasibility study focused predominantly on the research and intervention process [32], a formal sample size calculation was not considered necessary [33]. Recognizing that sample sizes in such studies should be sufficient to gather the data needed to address feasibility-related questions and outcomes [34, 35], the sample size in the present study (outlined below) was deemed sufficient.
## Procedure
C.H.A.M.P. Families consisted of: (a) eight 90-min group-based (parent-only) education sessions delivered over the course of 13 weeks; (b) eight home-based (family-centered) activities; and (c) two group-based follow-up (“booster”) sessions for parents and children. Parent education sessions covered a range of topics related to child and family health (e.g., healthy eating, physical activity, screen time, family dynamics and communication, mental health) and were delivered by a number of experts and health professionals. As outlined in detail by Reilly and colleagues [2018], parent sessions were developed using evidence-based strategies grounded in social cognitive theory [36–39], motivational interviewing [40, 41], and group dynamics [42–44]. The C.H.A.M.P. Families program was free of charge, and on-site childing-minding activities were provided for all children (including siblings) during each parent session. The two family-focused booster sessions (for parents, children, and additional family members) were held 3- and 6-months post-intervention, and were designed to reinforce concepts delivered throughout the formal intervention in a fun, family-friendly atmosphere. Researchers also offered follow-up support via email and telephone up to 6-months post-intervention, after which formal contact with participants ceased. Ethical approval for all study procedures was obtained from the Health Sciences Research Ethics Board at Western University (project ID #108826). Written informed consent and assent were obtained from parents and children, respectively, prior to program involvement.
## Data collection
Parents and children completed several research assessments at four time points: baseline (i.e., ≤ 4-week pre-intervention); mid-intervention (i.e., week 6); post-intervention (i.e., week 13); and at a 6-month follow-up. For the purpose of the present study, only measures pertinent to the RE-AIM dimensions and HRQoL are presented and discussed in this section. See Table 1 for RE-AIM dimension definitions, measures, and data sources. As noted above, a detailed description of all study outcomes and measures has been published elsewhere [29].Table 1Definitions, measures, and data sources for participant characteristics and RE-AIM dimensionsOutcome(s)DefinitionMeasure(s)Data source(s)Demographic variablesCharacteristics of sample populationPrimary parent: e.g., age, sex, ethnicity, marital status, education level, household income, relationship to childDemographic surveyChild: e.g., age, sex, years child has lived with obesityDemographic surveyRE-AIM dimensions [22, 23]ReachThe absolute number, proportion, and representativeness of individuals/centers who are willing to participate in a given initiativeEligibility criteriaScreening formEligible target population estimation (valid denominator)Statistics Canada Census, 2016Number of families registered for the programProject coordinator recordsNumber of families who were eligible but did not participateProject coordinator recordsRecruitment strategiesResearch recordsIdentification of facilitators and barriers to recruitmentProject coordinator recordsEffectivenessThe impact of an intervention on important outcomes, including potential negative effects, quality of life, and economic outcomesShort-term attrition rates (baseline to post-intervention)Education session attendance recordsReasons for attritionProject coordinator recordsChildren’s health-related quality of life (child and parent-proxy reports; baseline to post-intervention)Pediatric Quality of Life Inventory 4.0 [45]AdoptionThe absolute number, proportion, and representativeness of settings and intervention agents who are willing to initiate a programStaffNumber of intervention agents approached to participateResearch recordsRoles and credentials of intervention agentsResearch recordsSettingSetting criteria for implementing C.H.A.M.P. FamiliesResearch recordsNumber of settings approached to implement C.H.A.M.P. FamiliesResearch recordsImplementationThe intervention agents’ fidelity to the intervention’s protocol, including consistency of delivery as intended, time and cost of the intervention, and program adaptationsFidelity to study protocolResearch/project coordinator recordsIntervention adaptationsResearch/project coordinator records13-week program attendance rateEducation session attendance recordsHomework completion rateHomework recordsFinancial costs of the interventionResearch recordsMaintenanceThe extent to which a program or policy becomes institutionalized or part of the routine organizational practices and policiesLong-term attrition rates (post-intervention to 6-month follow-up)Project coordinator recordsIndividualChildren’s health-related quality of life (child and parent-proxy reports; post-intervention to 6-month follow-up)Pediatric Quality of Life Inventory 4.0 [45]SettingPerceptions of intervention agents regarding the program and interest in future involvementAnecdotal reports
## Reach
To determine representativeness, participant demographics were compared to census demographics1 for the City of London, Ontario, in 2017 [30]. Records of all program inquiries were used to determine the participation rate and most effective recruitment methods via descriptive statistics of categorical variables (i.e., frequencies and proportions).
Data regarding the number of potentially eligible families within the defined target population were not available for the area in which the program was offered. However, as of 2016, there were 41,585 children aged 5–14 living in London, Ontario [30]; of which, approximately $30\%$ were considered to have been living with overweight/obesity [50]. Taking into account these data [30, 50], and recognizing the uncertainty as to whether these individuals would have been eligible for the program, it is estimated that C.H.A.M.P. Families reached approximately $0.09\%$ of families with children between the ages of 5 and 14 living with overweight/obesity in the London, Ontario, community.
Based on Statistics *Canada data* for the city in which the program took place [30], families appeared to be similar to the population from which they were drawn in terms of ethnicity ($72.7\%$ and $80.0\%$ identified as Caucasian in C.H.A.M.P. Families and London, respectively), income (median household income was $70,000–$79,999 and $62,011 CAD, respectively), and employment status ($81.8\%$ and $75.6\%$ employed, respectively). With regard to self-reported sex, only five parents ($31.3\%$) and four children ($44.4\%$) identified as male. While females marginally outnumber males in London, Ontario (i.e., $52\%$ female and $48\%$ male [30]), male parents and male children were underrepresented in the current study.
As for participant recruitment, the strategies used to recruit eligible families, from most to least effective, were radio advertisements ($$n = 4$$ families, $33.3\%$), word of mouth ($$n = 3$$ families, $25.0\%$), newspaper advertisements ($$n = 2$$ families, $16.7\%$), social media ($$n = 2$$ families, $16.7\%$), and physician referrals ($$n = 1$$ family, $8.3\%$). It should be noted that one family indicated that they heard about the program from two sources (i.e., radio and Internet/social media).
## Effectiveness and Individual-Level Maintenance
C.H.A.M.P. Families was delivered in a real-world community setting. Thus, we evaluated effectiveness (rather than efficacy) via an examination of changes in children’s HRQoL. Children’s HRQoL was measured using the Pediatric Quality of Life Inventory (PedsQL 4.0 [45]). This inventory ($$n = 23$$ items) has been found to be valid and reliable, and consists of a child self-report component as well as a proxy report completed by a parent/guardian [45]. The inventory is used to assess four dimensions of children’s quality of life (i.e., physical, emotional, social, and school), which were aggregated in accordance with the scoring manual to generate two summary scores (i.e., a Physical Health Summary Score and Psychosocial Health Summary Score) for subsequent analysis [45].
Given the nature of our study (and small sample size), a quasi-experimental single-subject design with intersubject replication was used to examine the HRQoL data, wherein each participant served as their own control [46, 47]. Potential relationships between the intervention and HRQoL were identified through the intensive and prospective study of individuals over time (i.e., repeated measures [46, 47]). Single-subject data were examined through visual analysis of both level (i.e., change scores from baseline to post-intervention and post-intervention to 6-month follow-up) and trend (i.e., slope from baseline to 6-month follow-up [46, 47]). An increase in score (level) or slope (trend) represents a positive outcome and indicates an improvement in children’s HRQoL throughout the study period. Minimal clinically important differences2, considered to represent the smallest difference in a score that is perceived to be beneficial for the individual [49], are reported in the results section.
As noted above, a total of nine parent-child dyads completed the full intervention; thus, only these families were included in the HRQoL analysis. There were no cases of missing data across all assessment points for these nine families.
## Adoption
Data and detailed descriptions (i.e., roles, credentials, demographic information, and/or representativeness where applicable) of delivery settings and intervention agents (i.e., researchers, setting staff, guest speakers) involved in the implementation of the program were recorded by the research team and analyzed via descriptive statistics of categorical variables (i.e., frequencies and proportions).
One primary community-based delivery setting (the YMCA) was involved in the implementation of C.H.A.M.P. Families. A central YMCA location was selected to host the 13-week intervention based on its suitability and family-friendly environment, as well as an ongoing working relationship and prior involvement in the original C.H.A.M.P. program [25]. A boardroom was provided, at no cost, in which the eight group-based parent education sessions were delivered. Two additional rooms were also provided to host the end-of-program focus groups.
All of the intervention agents (i.e., program staff/personnel; $$n = 29$$) who were approached to participate in the design and delivery of the 13-week intervention agreed to do so. Seven of these individuals ($24.1\%$) were co-investigators/researchers who contributed to the design and development of C.H.A.M.P. Families, 12 ($41.4\%$) were considered content experts (i.e., researchers, health professionals, and/or other experts in the area[s] of interest) responsible for delivering the parent-focused education sessions, and six ($20.7\%$) provided support services (i.e., reception, child-minding).
Three months after the conclusion of the formal intervention period, the first family-focused booster session was held at a local not-for-profit organization focused on enhancing and promoting food education and literacy among children and families. Delivery agents included three professional chefs and three volunteers (representing $20.7\%$ of 29 intervention agents) from this organization to facilitate the 2-h family event. At 6-months post-intervention, the second booster session was held at a local obstacle course center focused on physical activity and physical literacy. Two staff members from this organization ($6.9\%$ of 29 intervention agents) facilitated this booster session for parents and children.
## Implementation
Records detailing anticipated and actual planned program activities/components were kept by the research team to determine whether the intervention was delivered as intended. Take-home (i.e., family homework) activities were also assessed for completion and recorded as a measure of participants’ use of implementation strategies. Sign-in sheets supplied at the parent education and booster sessions were used to track participant attendance and retention. While most study-related data were collected from the primary parent only, researchers recorded the attendance of all family members and invited them to join the education sessions, booster sessions, and post-intervention focus groups. Lastly, costs associated with the development, implementation, and delivery of the program were documented in detail (including in-kind contributions).
All ($100\%$) of the parent-focused education sessions ($$n = 8$$), home-based goal-setting activities ($$n = 8$$), and follow-up booster sessions ($$n = 2$$) were implemented as planned. Additional resources, developed in response to parent requests (e.g., C.H.A.M.P. Families Community Resources Handbook), were provided to participants throughout the intervention. As a result, more resources were distributed than originally planned. The completion rate of the eight assigned home-based goal setting worksheets was, on average, one ($12.5\%$ of assigned worksheets) per family.
As noted above, attendance records and homework completion logs were reviewed to assess participants’ use of intervention and implementation strategies (i.e., individual-level implementation). The average attendance rate for the parent-only educational sessions was $78.9\%$. Program attendance was found to be higher among participants who had a secondary caregiver attend the sessions with them (MAttendance = $97.5\%$ or $\frac{7.8}{8.0}$ sessions) than for participants who did not have a secondary caregiver present at sessions (MAttendance = $62.5\%$ or $\frac{5.0}{8.0}$ sessions). Lastly, average parental attendance for the family-focused booster sessions was $46.7\%$ for the first session and $60.0\%$ for the second session.
The C.H.A.M.P. Families budget was divided into four general cost categories: personnel ($19,150.00 CAD); research and recruitment ($7,550.00 CAD); equipment and supplies ($3,900.00 CAD); and knowledge dissemination ($3,800.00 CAD). In total, the funds required to design and implement C.H.A.M.P. Families, excluding external researcher and graduate student funding received from grants and awards, were approximately $34,400.00 CAD.
## Maintenance
Maintenance is assessed at both individual and setting levels [22, 23]. As noted above, individual-level maintenance was considered and assessed as part of program effectiveness. Setting-level maintenance was assessed via the use of anecdotal reports of interest from individuals who were approached and took part in the C.H.A.M.P. Families intervention at a setting/delivery level (i.e., intervention agents).
## Participants
Twenty-three parents representing 25 children inquired about the program during the 4-month recruitment period. Of these, 17 parents ($74.0\%$) were assessed for eligibility. Of the 15 families who were deemed eligible, 11 families ($73.3\%$) agreed to participate in the program. In total, 16 parents representing 11 children (six dyads and five triads) enrolled. At baseline, the self-reported age of the primary parents ($$n = 11$$) and children ($$n = 11$$) ranged from 30 to 52 years (MAge = 42.0 years, SD = 6.4), and 6 to 14 years (MAge = 9.5 years, SD = 2.0), respectively. All children had a BMI ≥ 95th percentile for age and sex (BMI-z scores ranged from 1.74–2.75; MBMI-$z = 2.20$, SD = 0.3); see Table 2 for an overview of demographic information for parents and children. Table 2Baseline demographic information for parents* and children involved in C.H.A.M.P. FamiliesDemographic variablen (%)Parent ($$n = 11$$)Age (mean ± SD)42 (6.4)Sex Female10 [90] Male1 [10]Ethnicity Caucasian8 [73] Arab2 [18] Egyptian1 [9]*Marital status* Married8 [73] Common law1 [9] Separated1 [9] Single1 [9]Educational attainment University degree7 [64] College diploma2 [18] Trades certificate1 [9] High school diploma1 [9]Employment Full-time6 [55] Part-time3 [27] Unemployed2 [18]Household income ≥ $100,0004 [36] $70,000–$79,9992 [18] $60,000–$69,9992 [18] $50,000–$59,9991 [9] $30,000–$39,9991 [9] $20,000–$29,9991 [9]Child ($$n = 11$$)Age (mean ± SD)9.5 (2.0)Sex Female7 [64] Male4 [36]BMI-z (mean ± SD)2.2 (0.3)Weight issue (mean ± SD)~4.9 yrs. ( 2.5)Note: *Demographic information was only collected from the self-identified primary parent in each family Figure 1 provides an overview of the participation and attrition rates for C.H.A.M.P. Families, including reasons for ineligibility and dropout. One parent-child dyad withdrew from the program at week 4, and another was lost to follow-up at week 6 of the 13-week intervention, resulting in a total of nine families who completed the formal intervention. Fig. 1Consolidated Standards of Reporting Trials (CONSORT) diagram for intervention recruitment and attritionNote: *Of the 11 self-identified primary parents, 5 attended the educational and/or booster sessions with a secondary parents/caregiver (i.e., parent or stepparent)
## Child-reported HRQoL
With regard to level, seven of nine children ($77.8\%$) reported an increase in physical HRQoL scores across the 13-week intervention. From post-intervention to the 6-month follow-up, only one child ($11.1\%$) reported an increase in physical HRQoL, although five ($55.6\%$) children maintained a minimal clinically important difference (i.e., a change of 6.66) across the entire study period. Similarly, six of nine children ($66.7\%$) reported an increase in psychosocial HRQoL scores from baseline to post-intervention. From post-intervention to the 6-month follow-up, four of nine children ($44.4\%$) reported an increase in psychosocial HRQoL, and six children ($66.7\%$) maintained a minimal clinically important difference (i.e., a change of 5.30). Change scores from baseline to post-intervention and post-intervention to 6-month follow-up (for child and parent-proxy reports) are displayed in Tables 3 and 4.Table 3Change scores for child- and parent-reported physical health-related quality of life (HRQoL)* for children across study time points (summary change scores)Child-reported physical HRQoLParent-proxy ratings of children's physical HRQoLParticipantIDBaseline–Post-interventionPost-intervention–Follow-upBaseline–Post-interventionPost-intervention–Follow-up1+15.62−9.37+12.50−15.622+28.13−6.25+18.750.003+21.87−12.500.00−6.254+3.12+9.38+12.50−6.255+3.130.00+3.12+9.386+28.12−6.25+28.12+3.1370.00−3.12−12.52+6.378−6.25−34.37−40.63+40.639+40.620.00−12.05+9.37Mean (SD)14.93 (15.84)−6.94 (12.08)1.09 (20.64)4.53 (15.87)Note: *Changes in HRQoL were determined using the PedsQL4.0 assessment tool [45]Table 4Change scores for child- and parent-reported psychosocial health-related quality of life (HRQoL)* for children across study time points (summary change scores)Child-reported psychosocial HRQoLParent-proxy ratings of children's psychosocial HRQoLParticipantIDBaseline–Post-interventionPost-intervention–Follow-upBaseline–Post-interventionPost-intervention–Follow-up1+18.33+6.67+18.34−31.672+28.34−3.34+23.33−6.663+10.00+6.66+8.30−8.3340.000.00+3.33+6.675+16.67−2.03+16.67−13.336+6.670.00+11.67−12.747−10.00+5.00+16.67−23.338+16.66−13.33+6.66−1.669−3.34+20.000.00+1.67Mean (SD)9.26 (12.14)2.18 (9.11)11.66 (7.69)−9.93 (12.06)Note: *Changes in HRQoL were determined using the PedsQL4.0 assessment tool [45] As for trend, six of nine children ($66.7\%$) reported positive changes in physical HRQoL across the entire study period. Positive trends in child-reported psychosocial HRQoL were observed for seven of nine children ($77.8\%$) from baseline to 6-month follow-up. Trends of individual child-reported summary scores are displayed in Tables 5 and 6. Corresponding graphed data can be found in Figs. 2 and 3 in the Appendix. Table 5Trends from baseline to 6-month follow-up for child- and parent-reported physical health-related quality of life (HRQoL)* for children (summary scores)Trends from baseline to 6-month follow-upParticipant IDChild-reported physical HRQoLParent-proxy ratings of children's physical HRQoL1+1.12−1.122+1.45+1.343+0.22−0.674+1.45−0.3450.00+1.456+1.45+2.467−0.22−0.558−3.91+1.569+3.01−0.08Mean (SD)0.51 (1.93)0.45 (1.26)Note: *Changes in HRQoL were determined using the PedsQL4.0 assessment tool [45]Table 6Trends from baseline to 6-month follow-up for child- and parent-reported psychosocial health-related quality of life (HRQoL)* for children (summary scores)Trends from baseline to 6-month follow-upParticipant IDChild-reported psychosocial HRQoLParent-proxy ratings of children's psychosocial HRQoL1+2.32−1.372+1.73+1.193+1.07−0.124−0.06+1.075+1.15+0.066+0.30−0.477−0.12+0.718+0.12+0.839+1.13+0.34Mean (SD)0.85 (0.85)0.25 (0.82)Note: *Changes in HRQoL were determined using the PedsQL4.0 assessment tool [45]
## Parent-reported HRQoL
Insofar as level is concerned, five of nine parents ($55.6\%$) reported an increase in their child’s physical HRQoL during the formal 13-week intervention. From post-intervention to the 6-month follow-up, five of nine parents ($55.6\%$) reported an increase in their child’s physical HRQoL, three of which were considered minimal clinically important differences (i.e., a change of 6.92). Moreover, eight of nine parents ($88.9\%$) reported an increase in child psychosocial HRQoL from baseline to post-intervention. At 6-month follow-up, two parents ($22.2\%$) reported an increase in psychosocial HRQoL from the post-intervention scores, although only one was considered a minimal clinically important difference (i.e., a change of 5.49). Change scores for parent-reported child HRQoL from baseline to post-intervention and post-intervention to 6-month follow-up are displayed in Tables 3 and 4.
Pertaining to the observed trend in parent-reported child physical HRQoL, four of nine parents ($44.4\%$) reported positive changes across the full study period (i.e., from baseline to 6-month follow-up). Increases in trend scores for parent-reported child psychosocial HRQoL were reported by six of nine parents ($66.7\%$) across the study period. Trends of individual parent-proxy summary scores are displayed in Tables 5 and 6.
## Setting-Level Maintenance
Because C.H.A.M.P. Families was a feasibility study focused primarily on the implementation of a 13-week community-based intervention [32], long-term maintenance was not assessed. However, anecdotal reports from individuals representing organizations who took part in (at a setting/delivery level) the C.H.A.M.P. Families intervention expressed a keen interest in participating in future projects.
## Discussion
The purpose of this study was to use the RE-AIM framework [22, 23] to determine the feasibility of C.H.A.M.P. Families, a parent-focused intervention targeting childhood obesity. While the program had limited reach (~$0.09\%$ of eligible families living in the city in which the intervention took place), the participation rate of families who inquired about the program and were eligible was high. Parents who enrolled in C.H.A.M.P. Families ($$n = 16$$) were predominantly female, Caucasian, married, employed, and had some form of postsecondary education. Bearing in mind the small sample size, it was found that participant ethnicity, employment status, and median household income were generally representative of the broader community [30]. C.H.A.M.P. Families was designed to address a number of challenges to participation and retention that have been noted in the childhood obesity literature [51, 52]; the program was offered to participants at no cost, had few exclusion criteria, utilized low intensity and timely implementation strategies (i.e., 12 h over 13 weeks), and included complimentary parking and child minding for all children (including siblings).
While data for numerous health-related outcomes (e.g., standardized body mass index [BMI-z], physical activity levels and sedentary time) were collected as part of the larger study [29], children’s HRQoL was used as the indicator of effectiveness (i.e., from baseline to post-intervention) and individual-level maintenance (i.e., from post-intervention to the 6-month follow-up) in this study for several reasons. First, HRQoL has been recognized as an important consideration in the childhood obesity treatment literature [53, 54], as well as within the context of the RE-AIM framework [22, 23]. Second, while C.H.A.M.P. Families was designed specifically for parents of children with obesity (and children’s BMI-z was indeed a primary outcome [29]), the intervention was created, as outlined in the program philosophy, “…to improve family health behaviors and communication by enhancing the knowledge and confidence of parents in a group-based environment that is safe, supportive, inclusive, and positive.” Therefore, rather than focusing on weight loss, parents and families were supported in an effort to make lifelong healthy choices and behavior changes that were sustainable and realistic. Third, and in line with the previous reason for utilizing HRQoL as an important measure of effectiveness, it has been suggested that focusing primarily on anthropometric outcomes in childhood obesity research may be problematic and even detrimental to children’s health and well-being [55]. Instead, encouraging healthy behaviors alongside positive communication may result in better health outcomes [56].
The majority of children in the present study reported increases in both physical and psychosocial HRQoL summary scores over the 13-week program. As for individual-level maintenance, while the majority of children reported reduced scores in both physical and psychosocial health at the 6-month follow-up, minimal clinically important differences remained for some children’s physical and psychosocial HRQoL summary scores ($$n = 5$$ and 6 out of 11, respectively). The fact that C.H.A.M.P. Families may have had a lasting (i.e., ≥ 6 months) minimal clinically important impact on psychosocial HRQoL for some children is noteworthy, as Tsiros and colleagues [2009] have suggested that psychosocial functioning among children may be more resistant to change than physical functioning [54]. These findings are also in line with other pediatric obesity studies showing that HRQoL scores among children with obesity tend to increase during behavioral-based treatments, and such improvements are generally maintained (although often lower than post-intervention scores) up to 1-year follow-up [25, 54]. Moreover, the apparent positive impact of the C.H.A.M.P. Families program on child-reported HRQoL is particularly important given the intervention was delivered to parents only, without child involvement. Whether this reflects the effectiveness of the educational intervention, the motivation of parents to improve their child’s health and HRQoL, or a combination of these and other factors, requires further investigation.
Generally speaking, parents reported lower child HRQoL scores than children, with fewer reported clinically important improvements in both physical and psychosocial HRQoL summary scores from baseline to the 6-month follow-up. Differences in parent and child HRQoL scores are not uncommon in the literature [57] and are important to acknowledge as it has been suggested that parents’ perceptions of their child’s HRQoL often influence treatment-seeking behaviors [58].
With regard to adoption and setting-level maintenance, $100\%$ of the individuals and community organizations approached to participate in the design and/or delivery of C.H.A.M.P. Families agreed to take part. Designing interventions that include a variety of community-based organizations and account for stakeholder priorities can improve community partner involvement and program sustainability [59, 60]. As such, all intervention agents who served as members of the core research team or were approached to deliver aspects of the program were involved to some extent in the planning, development, and/or delivery of the intervention. As C.H.A.M.P. Families was delivered predominantly by highly specialized health professionals and support personnel, an important point of consideration is that while most of these sessions were provided free of charge, sustained adoption/integration in community-based settings—if delivered in the same format by the same individuals—may pose a challenge.
A considerable strength of C.H.A.M.P. Families was that numerous community partnerships, vital to the success of the program, were initiated and maintained. For example, the local YMCA was fundamental in providing a safe, family-friendly venue for program delivery and recreation-based child minding. Furthermore, the successful adoption of the program within the local community, and its potential for setting-level sustainability, is reflected in continued community-based support of C.H.A.M.P.-related projects aimed at the treatment of childhood obesity [25].
In terms of implementation, although the percentage of planned activities implemented was high across the 13-week intervention, completion of the eight home-based activities was low. Focus groups conducted with parents during the final C.H.A.M.P. Families session revealed that time constraints were perceived as a barrier to health behavior change among participants [61]. Thus, it is possible that the home-based activities were viewed as an additional burden beyond the time commitment already required for the intervention.
With regard to program adherence, overall parental attendance was high, with waning retention across the two booster sessions. The high level of adherence to the formal intervention is noteworthy as adherence and attrition issues are commonly cited as challenges in childhood obesity intervention research, particularly for programs that target parents [16, 18, 24]. The theoretical foundation of C.H.A.M.P. Families, including the use of evidence-based group dynamics strategies and motivational interviewing techniques [29], coupled with regular participant contact, may have played a role in promoting program adherence.
Lastly, another aspect of implementation that merits discussion is the cost associated with developing and implementing a community-based program such as C.H.A.M.P. Families. Reporting the costs of an intervention is considered important when attempting to enhance program translation [62]. Unfortunately, there is a lack of reported implementation costs in the parent-focused childhood overweight and obesity literature [24]. Our results show that it was possible to implement a parent-focused, community-based childhood obesity intervention at a relatively low cost, without extensive external funding, which aligns with evidence suggesting that parent-only childhood obesity interventions are typically less expensive and require fewer resources than those that involve children directly [16, 20]. Such findings certainly lend support to the idea that a program similar to C.H.A.M.P. Families may be sustainable, translatable, and cost-effective to implement in a community setting.
## Limitations and future directions
Notwithstanding the apparent positive impact and potential of this parent-focused program, several limitations should be noted. First, despite extensive recruitment efforts, the sample size of C.H.A.M.P. Families was small (11 children and 16 parents/caregivers at baseline). Although participants appeared to be representative of the population in which the intervention took place, the low sample size and resultant single-subject analyses conducted preclude any possibility for generalization of findings. As noted by Reilly and colleagues [2018], future attempts to enhance recruitment for parent-focused, community-based pediatric obesity interventions could include a longer recruitment period, enhanced program messaging and marketing, greater child involvement, and additional family-based activities [29].
The single community-based setting used for C.H.A.M.P. Families, as well as high specificity of trained staff involved in the delivery of the program, could also limit generalizability. As the formal 13-week intervention was implemented at a single site, reporting of setting comparison information (e.g., reasons for participation vs. non-participation) was not possible. Moreover, translation to other locations, particularly rural and remote settings, may pose additional challenges. Families living in such areas may have reduced access to health-related services [63] and face unique geographical burdens including transportation issues, extreme weather, and food insecurity [64].
Given the apparent feasibility and preliminary effectiveness of C.H.A.M.P. Families, next steps include the design, implementation, and evaluation of a randomized controlled trial (RCT) to test the efficacy and cost-effectiveness of the intervention [65]. While C.H.A.M.P. Families was strategically designed as community-based, lifestyle intervention for parents of children with obesity, Reilly and colleagues [2019] noted that some parents who took part in the intervention felt that their children would have benefited from increased participation in the program [61]. Given the ample literature supporting family-based interventions in which parents are the primary agents of change [10–20], establishing a balance between program design and effectiveness/efficacy and participant preferences is imperative. Ensuring that parents and families are ready to commit to a parent-focused intervention will be an important aspect of the successful implementation and sustainability of future pediatric obesity initiatives. Additionally, to maximize translation and scalability of the C.H.A.M.P. Families program, it is important to consider design, recruitment, and implementation strategies to better serve and target more diverse geographic areas and populations. Lastly, iterative application of the RE-AIM framework in both planning and evaluation can inform meaningful adaptations, enhancing the reach, effectiveness, and potential adoption of future interventions [23, 66, 67].
## Conclusion
In short, C.H.A.M.P. Families holds promise as a parent-focused treatment intervention for children with obesity. The current paper includes a comprehensive examination of, and detailed reporting on, key elements within each dimension of the RE-AIM framework. Together, these findings provide important and pragmatic information which can be used to inform the development and implementation of community-based pediatric obesity programs. It is also expected that the findings herein will inform the design and delivery of a future RCT conducted with a larger, more diverse group of children and families. Moving forward, researchers should consider the use of RE-AIM in both the planning and evaluation stages of interventions targeting childhood obesity.
## Appendix
Figures 2 and 3Fig. 2Graphed data and trends for child-reported health-related quality of life (HRQoL)Fig. 3Graphed data and trends for parent-reported children’s health-related quality of life (HRQoL)
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|
---
title: The effect of dichotomization of skewed adjustment covariates in the analysis
of clinical trials
authors:
- Alan Herschtal
journal: BMC Medical Research Methodology
year: 2023
pmcid: PMC10009982
doi: 10.1186/s12874-023-01878-9
license: CC BY 4.0
---
# The effect of dichotomization of skewed adjustment covariates in the analysis of clinical trials
## Abstract
Baseline imbalance in covariates associated with the primary outcome in clinical trials leads to bias in the reporting of results. Standard practice is to mitigate that bias by stratifying by those covariates in the randomization. Additionally, for continuously valued outcome variables, precision of estimates can be (and should be) improved by controlling for those covariates in analysis. Continuously valued covariates are commonly thresholded for the purpose of performing stratified randomization, with participants being allocated to arms such that balance between arms is achieved within each stratum. Often the thresholding consists of a simple dichotomization. For simplicity, it is also common practice to dichotomize the covariate when controlling for it at the analysis stage. This latter dichotomization is unnecessary, and has been shown in the literature to result in a loss of precision when compared with controlling for the covariate in its raw, continuous form. Analytic approaches to quantifying the magnitude of the loss of precision are generally confined to the most convenient case of a normally distributed covariate. This work generalises earlier findings, examining the effect on treatment effect estimation of dichotomizing skew-normal covariates, which are characteristic of a far wider range of real-world scenarios than their normal equivalents.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12874-023-01878-9.
## Introduction
Two-arm randomized clinical trials with a continuous valued outcome may be analysed using a linear regression model to test for association between the dichotomous intervention (independent variable), and the outcome (dependent variable). As with all tests for association between an intervention and an outcome, it is important to adjust for any baseline covariates believed a priori to be associated with the outcome [1–6]. This protects against bias due to baseline imbalance and increases the precision of treatment effect estimates.
When the baseline covariate to be controlled for is either categorical or ordinal, a common approach for this adjustment consists of two steps. Firstly, each level of the baseline covariate is regarded as a separate stratum and the randomisation is stratified such that the desired study-wide allocation ratio is honoured in each stratum individually. This stratified randomization approach pre-empts any incidental imbalance in the covariate between arms which may arise in simple randomization due to sampling variability.
Then, at the analysis stage, the baseline covariate is controlled for by including it as an additional independent variable in the model. This partitions the variance between the baseline covariate and the intervention and thus yields a more precise estimate of the treatment effect. Failing to adjust for stratification variables in analysis leads to models which overestimate standard error, and thus overestimate confidence interval width, underestimate type 1 error, and reduce power [1, 5].
The procedure above is easily implemented for categorically valued baseline covariates such as gender or ethnicity, or ordinal ones such as disease stage. However, when the baseline covariate to be adjusted for is continuously valued, such as patient age or BMI, no naturally occurring strata exist, and more variability exists in approach [7]. Creating artificial strata by thresholding the baseline covariate at pre-defined bin boundaries is attractively simple, as it allows the stratified randomization to proceed in the same way as for the categorical or ordinal covariate above. Although the decision as to how many bins to threshold into and what the bin boundaries should be introduces a certain arbitrariness into the adjustment, it is nonetheless widespread practice, and often a simple dichotomization at a somewhat arbitrarily chosen value close to the median is deployed. Thus, for example, prior to inclusion as covariates in a model, age may be dichotomized as < 55 vs. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document}≥ 55 years, BMI as < 30 vs. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document}≥ 30 kg/m2, and continuously valued gene-based risk scores may be summarized as ‘high’ vs. ‘low’, based on a pre-determined threshold.
Unfortunately, when it comes to analysis, oftentimes the stratification variables are included in the model using the same dichotomization that was used in the stratification. It is well documented that this leads to substantial additional imprecision in treatment effect estimates, and is subject to all the same drawbacks as omitting the stratification variable from analysis altogether, only to a lesser extent [8–10].
Analytic approaches to understanding and quantifying the deleterious effect of covariate dichotomization in the literature have focussed, for simplicity and convenience, on the case where the covariate of concern is normally distributed [4]. However, non-normally distributed covariates arise frequently in the analysis of medical data in particular, and are a subject of increasing interest in clinical trials. It is well documented that anthropomorphic measures such as BMI [11, 12] and weight [13], lipid measurements such as triglycerides [14], biomarker measurements, and commonly used measures in medical domains as diverse as opfthalmology [15] and cardiology [16] all display substantial right skew.
Log-transformation, the most commonly used method of normalizing right skewed data, is inflexible and in many cases will either over- or under-correct for the skewness. More flexible normalization methods such as the Box-Cox transformation [17] have been used with some success in normalizing skewed anthropomorphic data [18] but come at the cost of potentially undermining the assumed linear relationship between the covariate and the outcome variable. It is thus of considerable interest to extend findings on the effect of covariate dichotomization from the case of normally distributed covariates to that of skew-normal ones.
## Method
Consider a test for association between a dichotomous indicator variable \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$z$$\end{document}z representing treatment (intervention vs. standard) and a continuously valued outcome variable \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y$$\end{document}y, controlling for a skew-normal (SN) distributed covariate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x purportedly associated with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y$$\end{document}y. Assuming a linear relationship between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y$$\end{document}y, the following model may be considered:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y=\alpha +\gamma z+\beta x+\varepsilon$$\end{document}y=α+γz+βx+εwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon \sim N\left(0,{\sigma }_{\varepsilon }\right)$$\end{document}ε∼N0,σε and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x \sim SN\left(\varphi,\omega,\lambda \right)$$\end{document}x∼SNφ,ω,λ. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varphi$$\end{document}φ and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\omega$$\end{document}ω are the location and scale parameters of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x respectively. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda$$\end{document}λ controls the skewness. If \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda =0$$\end{document}λ=0 the normal distribution is retrieved. The variance of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}xis given by [19, 20].2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\sigma }_{x}^{2}={\upomega }^{2}\left(1-\frac{2{\lambda }^{2}}{\pi \left(1+{\lambda }^{2}\right)}\right)$$\end{document}σx2=ω21-2λ2π1+λ2 The test for a treatment effect is formulated as a hypothesis test with null hypothesis of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma =0$$\end{document}γ=0 against a 1-sided alternative (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma >0$$\end{document}γ>0 or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma <0$$\end{document}γ<0), or a 2-sided alternative (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma \ne 0)$$\end{document}γ≠0). The precision of the estimator of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}γ, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat{\gamma }$$\end{document}γ^, affects the power of the hypothesis test, the confidence interval width and the p-value. It is thus of interest to assess the effect of dichotomization of the covariate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x on the precision of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat{\gamma }.$$\end{document}γ^. To this end, we compare the following three models:i)the full model as presented in Eq. 1;ii)a restricted model, in which the covariate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x is omitted;iii)a ‘partially restricted’ model, in which \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x is dichotomized before inclusion in the model.
The full model takes advantage of all the information available in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x, whereas the restricted model does not use \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x at all. The partially restricted model resides somewhere between these extremes. Measuring the precision of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat{\gamma }$$\end{document}γ^ under the partially restricted model relative to the full and restricted models tells us just how much information is lost by dichotomization of a SN covariate when estimating a treatment effect.
We denote the total sample size by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n$$\end{document}n, and consider a 1:1 allocation ratio, (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n/2$$\end{document}n/2 participants per arm).i)Full model *From analysis* of variance theory [21] we have that for the linear model in Eq. 1, the variance of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat{\gamma }$$\end{document}γ^, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V\left(\widehat{\gamma }\right)$$\end{document}Vγ^, may be expressed as\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V\left(\widehat{\gamma };m{}_{\mathrm{f}}\right)={\sigma }_{\varepsilon }^{2}/{S}_{zz}$$\end{document}Vγ^;mf=σε2/Szzwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$m{}_{\mathrm{f}}$$\end{document}mf represents the full model and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${S}_{zz}$$\end{document}*Szz is* the sum of squared errors for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$z$$\end{document}z:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${S}_{zz}= \sum_{$i = 1$}^{n}{\left(z{}_{i}-\overline{z }\right)}^{2}$$\end{document}Szz=∑$i = 1$nzi-z¯2 Encoding the arm indicator \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$z{}_{i}$$\end{document}zi as 0 (standard care) or 1 (intervention), for 1:1 randomization, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{z }=\frac{1}{2}$$\end{document}z¯=12, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\left(z{}_{i}-\overline{z }\right)}^{2}=\frac{1}{4} \forall i$$\end{document}zi-z¯2=14∀i, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${S}_{zz}=\frac{n}{4}$$\end{document}Szz=n4 and thus\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V\left(\widehat{\gamma };m{}_{\mathrm{f}}\right)={4\sigma }_{\varepsilon }^{2}/n$$\end{document}Vγ^;mf=4σε2/nii)Restricted model Because we are considering a randomized study, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$z$$\end{document}z can be expected to be independent. In this case, if the covariate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x is omitted from the model altogether, the mean component of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta x$$\end{document}βx term will be absorbed into the intercept \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α and the variance component of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta x$$\end{document}βx term, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }^{2}{\sigma }_{x}^{2}$$\end{document}β2σx2, will be absorbed into the error term, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon$$\end{document}ε, whose standard error under the restricted model will be referred to as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\sigma }_{\upvarepsilon }}^{^{\prime}}$$\end{document}σε′.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\sigma }_{\upvarepsilon }}^{^{\prime}} = \sqrt{{\sigma }_{\varepsilon }^{2}+{\beta }^{2}{\sigma }_{x}^{2}}$$\end{document}σε′=σε2+β2σx2
Using \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$m{}_{\mathrm{r}}$$\end{document}mr to denote the restricted model, and using the expression for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\sigma }_{x}^{2}$$\end{document}σx2 in Eq. 2,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V\left(\widehat{\gamma };m{}_{\mathrm{r}}\right)=4\left({\sigma }_{\varepsilon }^{2}+{\beta }^{2}{\omega }^{2}\left(1-\frac{2{\lambda }^{2}}{\pi \left(1+{\lambda }^{2}\right)}\right)\right)/n$$\end{document}Vγ^;mr=4σε2+β2ω21-2λ2π1+λ2/niii)Partially restricted model We now consider the effect of dichotomizing SN distributed covariate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x prior to including it in the model.
The SN distribution may be expressed as [22].\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f\left(x;\varphi,\omega,\lambda \right)=\frac{2}{\omega }\phi \left(\frac{x - \varphi }{\omega }\right)\Phi \left(\lambda \frac{x -\varphi }{\omega }\right)$$\end{document}fx;φ,ω,λ=2ωϕx-φωΦλx-φω\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi \left(.\right)$$\end{document}ϕ. represents the standard normal distribution and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Phi \left(.\right)$$\end{document}Φ. is its cumulative distribution. When \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda =0$$\end{document}λ=0 the normal distribution is recovered.
For notational convenience, without loss of generality, we temporarily restrict analysis to the special case of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varphi =0$$\end{document}φ=0 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\omega =1$$\end{document}ω=1. The expected value of a doubly truncated standard SN random variable can then be expressed in terms of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda$$\end{document}λ and the lower and upper standardized truncation points, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β respectively [21, 23, 24].3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E\left(x;\lambda | \alpha <x<\beta \right)= - \frac{f\left(\beta;\lambda \right)-f\left(\alpha;\lambda \right)}{F\left(\beta;\lambda \right)-F\left(\alpha;\lambda \right)}+{\omega }^{2}\sqrt{\frac{2{\lambda }^{2}}{\pi \left(1+{\lambda }^{2}\right)}}\frac{\Phi \left(\beta \sqrt{1+{\lambda }^{2}}\right)-\Phi \left(\alpha \sqrt{1+{\lambda }^{2}}\right)}{F\left(\beta;\lambda \right)-F\left(\alpha;\lambda \right)}$$\end{document}Ex;λ|α<x<β=-fβ;λ-fα;λFβ;λ-Fα;λ+ω22λ2π1+λ2Φβ1+λ2-Φα1+λ2Fβ;λ-Fα;λ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f\left(.;\lambda \right)$$\end{document}f.;λ and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F\left(.;\lambda \right)$$\end{document}F.;λ are the distribution and cumulative distribution functions respectively of the standard SN distribution with skewness parameter \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda$$\end{document}λ, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Phi \left(.\right)$$\end{document}Φ. is the cumulative distribution function of the standard normal.
Consider that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x may be partitioned into two component random variables. The first, denoted by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{d}$$\end{document}xd, represents the dichotomized \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x. For \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x below the dichotomization threshold \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u$$\end{document}u, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{d}$$\end{document}xd is set to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{-}$$\end{document}u-, the mean of all values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x below \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u$$\end{document}u. For \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x above \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u$$\end{document}u, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{d}$$\end{document}xd is set to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{+}$$\end{document}u+, the mean of all values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x above \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u$$\end{document}u. The second random variable, denoted by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{r}$$\end{document}xr, is the “residual” of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x around \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{d}$$\end{document}xd, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{r} = x - {x}_{d}$$\end{document}xr=x-xd. Setting the lower and upper values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{d}$$\end{document}xd in this way achieves independence of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{d}$$\end{document}xd and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{r}$$\end{document}xr, such that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V\left(x\right)=V\left({x}_{d}\right)+V\left({x}_{r}\right)$$\end{document}Vx=Vxd+Vxr. Proof of this can be found in Additional file 1: Appendix 2.
To calculate the variance of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{d},$$\end{document}xd, we require the mean of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x above and below the dichotomization point (singly truncated means), as well as the overall (untruncated) mean. These are arrived at by setting the boundary points \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β in Eq. 3 to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha =-\infty$$\end{document}α=-∞ and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta =u$$\end{document}β=u or to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha =u$$\end{document}α=u and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta =\infty$$\end{document}β=∞ for the singly truncated means, and to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha =-\infty$$\end{document}α=-∞ and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta =\infty$$\end{document}β=∞ for the untruncated mean. By definition, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f\left(-\infty;\lambda \right)=f\left(\infty;\lambda \right)=0$$\end{document}f-∞;λ=f∞;λ=0, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F\left(-\infty;\lambda \right)=0, F\left(\infty;\lambda \right)=1$$\end{document}F-∞;λ=0,F∞;λ=1, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Phi \left(-\infty \right)=0,\Phi \left(\infty \right)=1$$\end{document}Φ-∞=0,Φ∞=1. For dichotomization threshold \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u$$\end{document}u, we have\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E\left(x|-\infty <x<u\right)= - \frac{f\left(u;\lambda \right)}{F\left(u;\lambda \right)}+\sqrt{\frac{2{\lambda }^{2}}{\pi \left(1+{\lambda }^{2}\right)}}\frac{\Phi \left(u\sqrt{1+{\lambda }^{2}}\right)}{F\left(u;\lambda \right)}$$\end{document}Ex|-∞<x<u=-fu;λFu;λ+2λ2π1+λ2Φu1+λ2Fu;λand\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E\left(x| u<x<\infty \right)= \frac{f\left(u;\lambda \right)}{1-F\left(u;\lambda \right)}+\sqrt{\frac{2{\lambda }^{2}}{\pi \left(1+{\lambda }^{2}\right)}}\frac{1-\Phi \left(u\sqrt{1+{\lambda }^{2}}\right)}{1-F\left(u;\lambda \right)}$$\end{document}Ex|u<x<∞=fu;λ1-Fu;λ+2λ2π1+λ21-Φu1+λ21-Fu;λ The undichotomized mean is also easily derived as\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E\left(x\right)= \sqrt{\frac{2{\lambda }^{2}}{\pi \left(1+{\lambda }^{2}\right)}}$$\end{document}Ex=2λ2π1+λ2 We represent the percentile at which dichotomization occurs as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0<\tau <1$$\end{document}0<τ<1, i.e. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau =F\left(u;\lambda \right)$$\end{document}τ=Fu;λ.
Scaling by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\omega$$\end{document}ω to retrieve the more general case of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x \sim SN\left(\varphi,\omega,\lambda \right)$$\end{document}x∼SNφ,ω,λ, the variance of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{d}$$\end{document}xd may be calculated using the above relationships for the truncated means and the identities \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Var[X]=E\left[{X}^{2}\right]-{E\left[X\right]}^{2}$$\end{document}Var[X]=EX2-EX2 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{Var}\left(X\right)=\frac{1}{n}{\sum }_{$i = 1$}^{n}{\left({x}_{i}-\mu \right)}^{2}$$\end{document}VarX=1n∑$i = 1$nxi-μ2.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V\left({x}_{d}\right)={\frac{{\omega }^{2}}{\tau \left(1-\tau \right)}\left(f\left({F}^{-1}\left(\tau \right)\right)+\sqrt{\frac{2{\lambda }^{2}}{\pi \left(1+{\lambda }^{2}\right)}}\left(\tau -\Phi \left({F}^{-1}\left(\tau \right)\sqrt{1+{\lambda }^{2}}\right)\right)\right)}^{2}$$\end{document}Vxd=ω2τ1-τfF-1τ+2λ2π1+λ2τ-ΦF-1τ1+λ22 Since \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V\left(x\right)=V\left({x}_{r}\right)+V\left({x}_{d}\right)$$\end{document}Vx=Vxr+Vxd, we have partitioned the variance attributable to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x into a component attributable to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{d}$$\end{document}xd and another attributable to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{r}$$\end{document}xr. We may now calculate the variance of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat{\gamma }$$\end{document}γ^ under the partially restricted model as follows.
After partitioning \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x into components \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{d}$$\end{document}xd and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{r}$$\end{document}xr, the model may be expressed as:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y=\alpha +\gamma z+\beta {x}_{r}+\beta {x}_{d}+\varepsilon$$\end{document}y=α+γz+βxr+βxd+ε Arguing analogously as for the restricted model, since \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{r}$$\end{document}xr is independent of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$z$$\end{document}z, if the covariate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{r}$$\end{document}xr is omitted from the model, then the mean component of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta {x}_{r}$$\end{document}βxr term will be absorbed into \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α and the variance component of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta {x}_{r}$$\end{document}βxr term, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }^{2}\left(V\left(x\right)-V\left({x}_{d}\right)\right)$$\end{document}β2Vx-Vxd, will be absorbed into the error term, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon$$\end{document}ε, whose standard deviation will now be referred to as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\sigma }_{\upvarepsilon }\mathrm{^{\prime}}\mathrm{^{\prime}}$$\end{document}σε′′.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\sigma }_{\upvarepsilon }\mathrm{^{\prime}}\mathrm{^{\prime}} = \sqrt{{\sigma }_{\upvarepsilon }^{2}+{\beta }^{2}\left(V\left(x\right)-V\left({x}_{d}\right)\right)}$$\end{document}σε′′=σε2+β2Vx-Vxd Denoting the partially restricted model by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${m}_{\mathrm{p}}$$\end{document}mp,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V\left(\widehat{\gamma };{m}_{\mathrm{p}}\right)=4\left({\sigma }_{\upvarepsilon }^{2}+{\beta }^{2}\left(V\left(x\right)-V\left({x}_{d}\right)\right)\right)/n$$\end{document}Vγ^;mp=4σε2+β2Vx-Vxd/n The reduction in variance associated with the estimator for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat{\gamma }$$\end{document}γ^ when going from the restricted model (covariate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x omitted altogether) to the full model (covariate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x included in raw form) can be derived by subtraction.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V\left(\widehat{\gamma };{m}_{\mathrm{r}}\right)-V\left(\widehat{\gamma };{m}_{\mathrm{f}}\right)=4{\beta }^{2}{\sigma }_{x}^{2}/$$n = 4$${\beta }^{2}{\omega }^{2}\left(1-\frac{2{\lambda }^{2}}{\pi \left(1+{\lambda }^{2}\right)}\right)/n$$\end{document}Vγ^;mr-Vγ^;mf=4β2σx2/$$n = 4$$β2ω21-2λ2π1+λ2/n
The reduction in variance associated with the estimator for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat{\gamma }$$\end{document}γ^ when going from the restricted model to the partially restricted model (with covariate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x included in dichotomized form) can be similarly derived.4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V\left(\widehat{\gamma };{m}_{\mathrm{r}}\right) -V\left(\widehat{\gamma };{m}_{\mathrm{p}}\right)=4{\beta }^{2}{\frac{{\omega }^{2}}{\tau \left(1-\tau \right)}\left(f\left({F}^{-1}\left(\tau \right)\right)+\sqrt{\frac{2{\lambda }^{2}}{\pi \left(1+{\lambda }^{2}\right)}}\left(\tau -\Phi \left({F}^{-1}\left(\tau \right)\sqrt{1+{\lambda }^{2}}\right)\right)\right)}^{2}/n$$\end{document}Vγ^;mr-Vγ^;mp=4β2ω2τ1-τfF-1τ+2λ2π1+λ2τ-ΦF-1τ1+λ22/n We will refer to the ratio between these two variance differences as the ‘dichotomization efficiency’, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D$$\end{document}D.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D=\frac{V\left(\widehat{\gamma };{m}_{\mathrm{r}}\right) - V\left(\widehat{\gamma };{m}_{\mathrm{p}}\right)}{V\left(\widehat{\gamma };{m}_{\mathrm{r}}\right) - V\left(\widehat{\gamma };{m}_{\mathrm{f}}\right)}$$\end{document}D=Vγ^;mr-Vγ^;mpVγ^;mr-Vγ^;mf5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D=\frac{1}{\tau \left(1-\tau \right)}\left(f\left({F}^{-1}\left(\tau \right)\right)+\sqrt{\frac{2{\lambda }^{2}}{\pi \left(1+{\lambda }^{2}\right)}}\left(\tau -\Phi \left({F}^{-1}\left(\tau \right)\sqrt{1+{\lambda }^{2}}\right)\right)\right)}^{2}/\left(1-\frac{2{\lambda }^{2}}{\pi \left(1+{\lambda }^{2}\right)}\right)$$\end{document}$D = 1$τ1-τfF-1τ+2λ2π1+λ2τ-ΦF-1τ1+λ$\frac{22}{1}$-2λ2π1+λ2 Detailed derivations of the expressions in Eqns. 4 and 5 are presented in Additional file 1: Appendix 1.
## Real-world data
The prevalence and extent of skewness in real-world data was explored using publicly available summary statistics on BMI, weight and lipid measurements from the US Center of Disease Control and Prevention (CDC) [25–27]. Using the provided percentile values for the variable being summarized, we used Maximum Likelihood Estimation (MLE) to find the SN parameter values that optimize the fit. Results are presented in Table 1. BMI and *Weight data* are specific to people aged 20. All lipid parameter data are for people aged 20–74 between 1976 and 1980.Table 1MLE parameters for fitting a SN model to common anthropomorphic and lipid measurementsDataLocationScaleShape (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varvec{\lambda}})$$\end{document}λ)Male BMI19.45.514.55Female BMI17.76.667.49Male Weight57.9193.51Female Weight46.817.75.19Total Cholesterol16365.72.19HDL Cholesterol32.117.52.93non-HDL Cholesterol11667.72.2Fasting Triglycerides49.611511 The CDF of a random variable is a function with argument ‘x’ which provides the probability of obtaining a value smaller than x. In Fig. 1 it was decided to present graphs of the CDF rather than the probability density function (PDF), which is the derivative of the CDF, because the publicly available datasets provided values at non-equally spaced percentiles, which makes presentation of the PDF cumbersome and difficult to interpret. Figure 1 shows that the SN model fit the data extremely well in six of the eight cases, and moderately well in the other two (Female BMI and Fasting Triglycerides). In contrast, the normal model achieved very good fit for just two of the eight cases (Total Cholesterol and non-HDL Cholesterol). Table 1 shows that in all eight cases the amount of skewness was at least moderate (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda >2$$\end{document}λ>2), and in three cases it was substantial (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda >5$$\end{document}λ>5). This highlights the prevalence of skewed data in medical datasets and the importance of considering implications for analysis. In all cases the skewness was to the right \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left(\lambda >0\right)$$\end{document}λ>0.Fig. 1Cumulative Distribution Functions (CDF’s) for common anthropomorphic and lipid measurements with SN densities with parameters determined by MLE overlaid
## Results
We see from Eq. 5 that the dichotomization efficacy is a function of just two parameters, the SN shape parameter \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda$$\end{document}λ and the dichotomization percentile \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ. Equation 5 may then be used to graph the dichotomization efficacy as a function of these parameters. Figure 2 shows results for a range of realistic shape parameters (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda)$$\end{document}λ), with the dichotomization percentile ranging from 0.1 to 0.9. Figure 3 shows the distribution functions for the same range of shape parameter values, chosen to cover those observed in the real-world data summarized above. Fig. 2Dichotomization Efficiency as a function of Proportion below the Dichotomization Threshold for a range of shape parameters and dichotomization thresholdsFig. 3Distribution functions of standard skew-normal distributions for a range of shape parameters Figure 2 shows that the loss of efficiency when dichotomizing a continuously valued covariate is similar for a SN distributed covariate as is the case for a normally distributed covariate (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda =0)$$\end{document}λ=0). As for the normal case, the loss of efficiency is substantial and should be avoided if at all possible. However, if dichotomization is necessary, advice regarding the best cut-point at which to dichotomize in order to mitigate this loss should consider the likely skew in the data. For data with little or no skew, the ideal cut-point is at the median, with little additional loss so long as the cut-point remains in the percentile range 0.35, 0.65. However, when skew becomes substantial (> 5), this advice changes. The ideal cut-point becomes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim \frac{2}{3}$$\end{document}∼23 and the acceptable range runs from ~ 0.5 to ~ 0.8. Table 2 shows the cut-point that optimises the loss of precision, as well as the range of cut-points such that the additional loss of precision is kept within modest bounds, as percentiles of the covariate being dichotomized. Table 2Optimal dichotomization cut-point, as well as minimum and maximum cut-points that avoid substantial additional loss of precision (taken as keeping the dichotomization efficacy > 0.6), as a function of the shape parameter of the SN distributionShapeOptimalMinimumMaximum00.50.350.6520.590.440.7350.660.480.81100.670.480.82200.670.480.82
## Simulation
Analytic findings were corroborated using simulation as follows. A dichotomously valued variable represented the trial arm (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$z$$\end{document}z). A continuously valued covariate (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x), designed to have a relationship with the outcome as described below, was controlled for. The proportion below the dichotomization threshold, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ, was set to values ranging from 0.1 to 0.9 in increments of 0.1, and 500 datasets were generated at each setting of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ to ensure sufficient accuracy in simulation-based estimates. The expected difference between arms (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}γ) was set to 15 units, and the standard error of the residuals was set to 30 units, which gives a moderate effect size of ½). The sample size per dataset was set to 100 per arm, large enough to obviate any small sample effects. The strength of the relationship between the covariate and the outcome variable was set by choosing a value of 20 for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β. For each dataset at each of the above settings of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ, three models were generated: the full model; the restricted model; and the partially restricted model. These were used to empirically calculate the dichotomization efficiency as a function of the proportion below the dichotomization threshold. Theoretical values based on Eq. 5 are shown in Fig. 4 (black dashed curve), and simulation-based point estimates and their $95\%$ CI’s from the 500 runs are shown as points with error bars. Fig. 4Confirmation of analytic findings by simulation, for low and high values of skewness (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda =2, 10)$$\end{document}λ=2,10), and different values of the error standard deviation [10, 20] *It is* of note that the calculation of the dichotomization efficiency (Eq. 5) involves a division in which the denominator is a random variable. That being the case, simulation runs where the denominator has a low value due to sampling variation have high variance and thus increase the standard errors in the estimate of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D$$\end{document}D. To circumvent this, point estimates and $95\%$ CI’s for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D$$\end{document}D were calculated by regressing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V\left(\widehat{\gamma };{m}_{\mathrm{r}}\right) - V\left(\widehat{\gamma };{m}_{\mathrm{p}}\right)$$\end{document}Vγ^;mr-Vγ^;mp on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V\left(\widehat{\gamma };{m}_{\mathrm{r}}\right) - V\left(\widehat{\gamma };{m}_{\mathrm{f}}\right)$$\end{document}Vγ^;mr-Vγ^;mf and estimating \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D$$\end{document}D from the slope of the regression line.
## Discussion
The development above is valid where the covariate to be controlled for is linearly associated with the outcome variable. Deviations from this assumption will change results. If the nature of the non-linearity is such that the dichotomisation threshold coincides with a natural ‘change point’ (i.e. a near discontinuity) in the covariate – outcome relationship, then the deleterious effects of dichotomisation may be ameliorated, or even reversed. However, such change points are rare in nature, and given that dichotomisation thresholds are not usually chosen with this in mind, such an occurrence would be purely serendipitous. Since the nature of possible non-linearities is diverse, and any attempted transformation (logarithmic, quadratic, square-root, sigmoid) will likely only partly capture it, a full investigation of their effect is considered beyond the scope of this work.
It is of note that the dichotomization efficiency for the case of a normal covariate is analogous to that demonstrated in Senn [28] for dichotomization of a normally distributed outcome variable. However, in the case of dichotomization of a covariate, the dichotomization efficiency multiplies the maximum possible gain in efficiency, which would be achieved when the covariate is left in its raw form.
Taking practical advantage of the findings in this work requires that a method to estimate the parameters of the SN distribution be available. There are a number of ways in which this can be done. One is to find the maximum likelihood estimates of the parameters using a simplex method such as that of Nelder and Mead [29]. This is the approach which was taken for estimating the parameters of the publicly available CDC datasets discussed in the Real-World Data subsection above. Alternatively, Thiuthad and Pal [30] present an approximation based on the method of moments. An R package [31] to perform this parameter estimation based on the method of Fernandez and Steel [32] is also available.
It is of interest to compare this work to a related work by Kahan and Morris [1]. Kahan and Morris consider a somewhat different but nonetheless related scenario, in which paired continuous valued data are analysed using an independent groups t-test to test for a difference between groups. They show that by ignoring the pairing when conducting the t-test, the model estimated variance of the treatment difference is inflated by a factor of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\left(1-\uprho \right)}^{-1}$$\end{document}1-ρ-1, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\uprho$$\end{document}ρ is the correlation between the group means induced by the pairing. We can equivalently represent a paired t-test as a bivariate linear regression with treatment assignment as the predictor variable, controlling for a second categorical variable representing the participant. By assigning each participant to both of the treatment conditions, we effectively stratify by participant in the randomization, with exactly 2 observations in each stratum, one for each treatment condition. Then, by including the participant indicator in the regression at the analysis stage, this stratification variable is controlled for. Such a model is equivalent to a paired t-test, and a model which fails to control the participant indicator is equivalent to an independent groups t-test. There is a direct analogy between the relationship between the paired and independent groups t-tests, and the relationship between the full model and the restricted model in this current work. The first step in the current work – comparison of the full model to the restricted model, is exactly analogous to that of Kahan and Morris, except that in this current work the covariate to be controlled for is a continuous valued SN covariate (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x) as opposed to being a participant indicator. The next step, which constitutes the main message of this work, is to determine what proportion of this loss in efficiency is ‘recouped’ by including the dichotomized \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x in the model (partially restricted model) rather than its raw value.
## Conclusion
We have found that the ratio of the additional variability incorporated into the treatment effect estimate under a model with a dichotomized SN covariate to that incorporated under a model with the same covariate omitted altogether is a function only of two parameters – the proportion below the dichotomization boundary, and the shape parameter of the SN covariate, which controls the skewness. We have provided an analytic expression for this ratio which can be easily computed using any standard statistical software package. We have further shown that dichotomization of a SN covariate has a similar effect on efficiency to that of dichotomization of a normal covariate. We have also shown that in real-world medical data the amount of skewness is often substantial and that, should dichotomisation be unavoidable, this changes advice regarding the optimal dichotomization cut-point from being at the median to being at approximately the 67th percentile (for right-skewed data).
## Limitations
Computation of the dichotomization efficiency depends on calculation of the cumulative distribution function of the SN distribution for which there is no closed form expression. However, it can be expressed in terms of Owen’s function [33], for which fast and accurate computational algorithms are well established [34].
The findings are asymptotically valid for large sample sizes, regardless of whether randomisation was simple, or stratified by the dichotomized covariate. For small sample sizes, findings are approximate. However, for reasonable sample sizes the magnitude of the inaccuracy is very small (of order \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{n-1}{n}$$\end{document}n-1n with sample size \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n$$\end{document}n). Results shown in the Simulation section show that with sample sizes as small as 100 per arm, theoretical calculations match empirical findings with high accuracy.
## Software implementation details
All simulation code was written in the R programming language, version 4.1.0 [35]. Regressions used the glm function in the ‘stats’ package and all graphs were produced using the ggplot2 [36] package. Densities and cumulative densities of the skewed-normal distribution were calculated using the ‘sn’ package [37]. MLE estimation was performed using the bbmle package [38].
## Supplementary Information
Additional file 1.
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|
---
title: Identification of immune cells infiltrating in hippocampus and key genes associated
with Alzheimer’s disease
authors:
- Chenming Liu
- Sutong Xu
- Qiulu Liu
- Huazhen Chai
- Yuping Luo
- Siguang Li
journal: BMC Medical Genomics
year: 2023
pmcid: PMC10009990
doi: 10.1186/s12920-023-01458-2
license: CC BY 4.0
---
# Identification of immune cells infiltrating in hippocampus and key genes associated with Alzheimer’s disease
## Abstract
Alzheimer’s disease (AD) is the most prevalent cause of dementia and is primarily associated with memory impairment and cognitive decline, but the etiology of AD has not been elucidated. In recent years, evidence has shown that immune cells play critical roles in AD pathology. In the current study, we collected the transcriptomic data of the hippocampus from gene expression omnibus database, and investigated the effect of immune cell infiltration in the hippocampus on AD, and analyzed the key genes that influence the pathogenesis of AD patients. The results revealed that the relative abundance of immune cells in the hippocampus of AD patients was altered. Of all given 28 kinds of immune cells, monocytes were the important immune cell associated with AD. We identified 4 key genes associated with both AD and monocytes, including KDELR1, SPTAN1, CDC16 and RBBP6, and they differentially expressed in 5XFAD mice and WT mice. The logistic regression and random forest models based on the 4 key genes could effectively distinguish AD from healthy samples. Our research provided a new perspective on immunotherapy for AD patients.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12920-023-01458-2.
## Background
Alzheimer’s disease (AD) is an age-related neurodegenerative disorder that primarily involves memory decline and executive dysfunction. The main features of AD are abnormal aggregation of extracellular amyloid plaques and hyperphosphorylation of neuronal tau, which lead to synaptic loss and neuronal atrophy [1]. Experts now believe that, like other common chronic diseases, AD is caused by a combinational factor [2], including age, environment, genetics, or specific susceptibility genes [3–5]. Cardiovascular disease, diabetes, obesity, and diet are generally considered to be factors that increase the risk of AD [6–8]. Activated microglia and astrocytes in AD patient brains usually have higher levels of inflammatory markers, which are generally distributed around amyloid plaques and neurofibrillary tangles [9, 10]. The role of immune response in the brain of AD patients may be bidirectional. On the one hand, pathogenic substances such as cell debris and protein aggregates can be eliminated by phagocytosis of microglia and astrocytes; On the other hand, persistent neuroinflammation is a chronic response of the innate immune system to neurological changes, and the sustained activation of glial cells causes harm to the nervous system [11]. In cell culture studies, activated microglia could produce harmful substances, which may damage neurons [12–14]. Another feature of AD is the impairment of the blood brain barrier (BBB), and a compromised BBB might increase the permeability of immune cells and peripheral tissue molecules, which could lead to neurodegeneration [15]. Both peripheral macrophages and neutrophils can infiltrate the brain of AD patients through the BBB and induce the activated innate immune response in AD patients [16–18]. In addition, activated T cells are also found in the brain of AD patients, where they could release inflammatory factors[19–21]. Amyloid β (Aβ), which aggregates alone, has been found to be a powerful complement activator [22]. Activation of the complement system in AD patients results in the production of allergenic toxins that further promote inflammation [23], cytokine-induced APP production, and higher Aβ production due to increased APP amounts [24, 25]. Although numerous studies have shown that inflammation plays a vital role in the pathogenesis of AD, the identification of immune cells closely related to AD and the molecular mechanisms of AD pathogenesis requires further elucidation.
In this study, we assess the level of immune infiltration from the hippocampus based on the expression of given immune cell genes by single sample gene set enrichment analysis (ssGSEA), and revealed the differences in the immune infiltration of hippocampal tissue in AD and healthy samples. We identified key genes from highly correlated co-expression modules, which were closely associated with disease and immune cells. This study laid the foundation for further finding effective targets for curing AD and developing immunomodulatory regimens for effective treatment of AD.
## Data preprocessing and immune infiltration assessment
At first, we used “hippocampus” and “Alzheimer’s disease” as keywords to search the datasets in the GEO database, and we found the GSE5281 and GSE48350 datasets, which were both from the GPL570 platform. The ssGSEA could assess the infiltration of 28 immune cells for each AD and control sample through GSEA package [26]. We retained the immune cells with significant differences as traits for subsequent analysis (p value < 0.05).
## Weighted gene co-expression network analysis (WGCNA)
At first, we used the limma package to normalize the raw data of all samples, and then we removed genes containing NA. We used WGCNA package to construct a gene co-expression network to find key modules and module genes [27]. Genes were clustered based on the phase dissimilarity machine. The division of modules was based on the high topological overlap of genes within the modules [28]. We selected the modules associated with disease for subsequence analysis. *For* genes within modules, we further screened based on gene significance (GS) and module importance (MM). *The* genes with high MM and high GS were described as the central module genes, which were strongly associated with disease and candidate immune cell. In our study, the central module genes were the genes in the candidate module with |MM| > 0.8 and |GS| > 0.2.
## Functional enrichment analysis
Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of central module genes were conducted by clusterProfiler [29] and ReactomePA packages [30].
## Analysis of protein–protein interaction (PPI) network and identification of key genes
The Search Tool for the Retrieval of Interacting Genes (STRING) online tool [31] was used to analyze the PPI of central module genes with the default parameters. Then we used the cytoHubba plugin [32] of the Cytoscape (version 3.8.2) to identify the key genes [33]. The cytoHubba provides 12 analysis algorithms to calculate hub genes in protein interaction network graphs, we used five of which to identify key genes in the PPI network, including Degree, Edge Penetration Component (EPC), Maximum Neighborhood Component (MNC), Density of Maximum Neighborhood Component (DMNC) and Maximum Group Centrality (MCC). We regarded the intersection of top10 genes, which were obtained by cytoHubba’s five algorithms as the key genes, the VennDiagram package was used to visualize these results [34].
## Validation of the key genes
We constructed logistic regression model and random forest model by the intersection genes of cytoHubba’s five algorithms to explore the correlation between disease and key genes. We randomly divided all samples into test and training cohort according to the proportion of $\frac{3}{7}$, we generated logistic regression model and random forest model in the training cohort and validated the performance of the models in the test cohort. The receiver operating characteristic (ROC) curves and confusion matrix were used to assess the validity of the models [35].
## Animals
5.5-month-old heterozygous 5XFAD mice are housed in Tongji University Animal Center under standard conditions. Aβ42 began to accumulate in the brains of 5XFAD mice at 1.5 months of age [36]. There are many Aβ plaques in the hippocampus at 5.5 months of age. In this study, 5.5-month-old 5XFAD mice were euthanized and hippocampal tissue was isolated for subsequent experiments. Both AD and control groups contained two female mice and two male mice.
## RNA extraction and quantitative real-time PCR (qRT-PCR)
The total RNA of the hippocampus in all mice were extract by RNAiso Plus (9109, TaKaRa, China). According to the manufacturer’s instructions, qRT-PCR was performed by the AceQ Universal SYBR qPCR Master Mix (Q511-02, Vazyme, China). *All* genes’ expression levels were normalized to β-Actin by the comparative CT method (2−ΔΔCt). Table 1 showed the sequences of all RNA primers. Table 1The primer sequences used for RT-qPCR.GenesSequencesβ-actinForward: CTAAGGCCAACCGTGAAAAGReverse: ACCAGAGGCATACAGGGACAKdelr1Forward: GTGGTGTTCACTGCCCGATAReverse: AACTCCACCCGGAAAGTGTCSptan1Forward: ACAAGGACCCCACCAACATCReverse: GCCTTGACAGCATCCTCACTCdc16Forward: CCTGTGTCTTGGTTTGCGGTReverse: TCTCCACAGCGAAGGAATGCRbbp6Forward: TTAGCATGAGCGAGTGGGACReverse: ACAACGAAGGACCCTAAGGC
## Statistical analysis
All data were visualized and analyzed by GraphPad Prism 8. T test was used to compare expression level between the AD and WT groups and p value < 0.05 were considered statistically significant.
## Gene set enrichment analysis (GSEA)
Based on the median expression levels of key genes, we divided all samples into high and low expression groups, and GSEA was performed to explore hallmark pathways between the two groups [37]. We used p value < 0.05 and p-adjust < 0.25 as the screening criterion for statistically significant.
## Construction of mRNA-miRNA-lncRNA network
To further explore the miRNA and lncRNA regulatory networks associated with key genes, we constructed an mRNA-miRNA-lncRNA network based on the key genes screened in the previous result. At first, based on the “multiMiR” package [38], we used experimentally validated data to explore miRNAs associated with key genes. After obtaining miRNAs that interact with key genes, we used the starBase database to explore lncRNAs that interact with miRNAs [39]. The lncRNAs that interact weakly with miRNAs are removed. Our screening criteria were that miRNA-lncRNA expression was negatively correlated in more than four cancers and validated by more than three clip-seq experiments. Finally, we used Cytoscape for visualization of the mRNA-miRNA-lncRNA network.
## Data processing
This study procedure was conducted methodically based on the steps outlined in the flow diagram (Fig. 1). Based on the search for keywords in Materials and Methods, we downloaded two datasets, GSE5281 and GSE48350, from the GEO database. As we mainly focused on the changes in transcriptome data of hippocampal tissue, we selected the data of hippocampal tissue, GSE5281 containing 23 samples and GSE48350 containing 62 samples. The R software was used to process the raw expression profiles of these two datasets, and the limma package was used to normalize the raw data [40]. The batch effects of these two datasets were processed by the “sva” package [41].Fig. 1Flow chart of this study
## Immune infiltration in the hippocampus of AD patients might be altered
As described in methods and materials, ssGSEA was performed on 29 AD samples and 56 control samples to assess the scores of 28 immune cells (Fig. 2A, B). Our results indicated that the scores of activated B cell, activated CD8 T cell, CD56 bright natural killer cell, effector memory CD8 T cell, eosinophil, immature B cell, macrophage, memory B cell, monocyte, myeloid derived suppressor cell, natural killer cell, natural killer T cell and type 17 T helper cell were significantly different between AD and healthy groups ($p \leq 0.05$), indicating that the level of immune cell infiltration might be altered in the hippocampus of AD patients. Fig. 2 Immune infiltration analysis. A Boxplot of the enrichment score of 28 immune cells in each AD and healthy sample. B The immune cells with significant differences between AD and healthy samples
## Monocytes were the important immune cell associated with AD in the hippocampus
To identify disease-associated immune cell types associated with disease, we constructed gene co-expression modules using WGCNA. We first normalized the data from the datasets and subsequently removed genes containing NA. 2971 genes were eligible for further analysis. We built a scale-free (scale-free R2 > 0.85) co-expression network using soft threshold power β = 12 (Additional file 1: Fig. S1). These 2971 genes were clustered into 10 different color modules (Fig. 3A, B). Then, we analyzed the correlation between each module and immune cell types or sample types (AD and control) (Figure B). As a result, the green module was positively correlated with AD but negatively correlated with monocytes, and in contrast, the pink module was negatively correlated with AD but positively correlated with monocytes ($p \leq 0.05$). Additionally, monocytes exhibited highly correlation with both pink and green module. These results suggested that monocytes infiltrating the hippocampus might be the important immune cell associated with AD. Fig. 3 Identification of key modules correlated with AD and monocytes in the datasets through WGCNA. A Cluster dendrogram of all genes. B The heatmap showed relationship of each module and traits. C Scatterplot of genes in the pink module. D Scatterplot of genes in the green module
## Pink and green central module genes were mainly involved in lipid metabolism, notch signaling pathway and material transport by Golgi-associated vesicles transport.
13 and 55 genes with high connectivity (|MM| > 0.8 and |GS| > 0.2) were screened from pink and green modules, respectively (Fig. 3C, D), which were considered as central module genes. To clarify the biological processes of the pink and green central module genes, we conducted GO, KEGG and Reactome enrichment analysis. According to our selection criteria, as for GO, the pink central module genes were mainly involved in misfolded protein reactions, proteasome-mediated proteolytic metabolic processes and low-density lipoprotein particle metabolism (Fig. 4A). The KEGG analysis suggested the pink central module genes were mainly involved in type 1 diabetes mellitus, legionellosis and endocrine and other factor-regulated calcium reabsorption (Fig. 4B). Reactome analysis demonstrated that pink central module genes were mainly involved in wnt signaling pathway and lipid metabolism (Fig. 4C). The same analysis was also performed on the green module genes. The GO analysis suggested that the 55 green central module genes were mainly enriched in histone modification and Golgi-associated vesicles transport (Fig. 4D). KEGG analysis revealed that the 55 green central module genes were mainly involved in thyroid hormone signaling pathway, notch signaling pathway, lysine degradation and C-type lectin receptor signaling pathway (Fig. 4E). As for Reactome analysis, green central module genes were mainly focused on notch signaling and the transport of substances between the Golgi and the endoplasmic reticulum (Fig. 4F). As a result, based on the frequency of terms, the pink central module genes were mainly affected lipid metabolism, and the green central module genes were mainly affected notch signaling pathway and material transport by Golgi-associated vesicles transport. Fig. 4 Functional enrichment analysis of pink and green central module genes. A The GO result of central pink module genes. B The KEGG result of central pink module genes [42]. C The Reactome result of central pink module genes. D The GO result of central green module genes. D The KEGG result of central green module genes [42]. F The Reactome result of central green module genes
## KEDLR1, SPTAN1, CDC16 and RBBP6 were identified as key genes associated with AD and monocytes
As for all the 68 central genes in the pink and green modules, we explored the PPI of these genes by STRING database, and the result was shown in Fig. 5A. The five algorithms of the cytoHubba, including EPC, MCC, MNC, DMNC and Degree, were used to process the PPI network to identify the top 10 genes (Table 2). KEDLR1, SPTAN1, CDC16 and RBBP6 were regarded as the key genes associated with monocytes and AD, which were the common genes identified by the five algorithms, respectively (Fig. 5B). Correlation analysis showed that KDELR1, SPTAN1 and RBBP6 were positively associated with AD and negatively associated with monocytes, while CDC16 was negatively associated with AD and positively associated with monocytes (Fig. 5C).Fig. 5 Multiple algorithms identified 4 key genes associated with AD and monocytes. A The PPI network of central genes in the pink and green modules. B A Venn diagram between five algorithms of cytoHubba. The coincident part represents the four genes (KDELR1, SPTAN1, CDC16 and RBBP6) identified by all five algorithms. The lines between nodes in the PPI network diagram represent the interactions between the nodes. C The correlations between 4 key genes and monocytes and AD. D ROC curve of logistic regression model could distinguish AD and control samples. E Confusion matrix of the logistic regression model in test cohort. F ROC curve of the RF model could distinguish AD and control samples. G Confusion matrix of the RF model in test cohort To validate correlation between KEDLR, SPTAN1, CDC16 and RBBP6 and AD occurrence, we constructed logistic regression model and random forest model. The area under curve (AUC) of logistic regression model was 0.789 ($95\%$ CI = 0.641–0.938), and the AUC of RF model was 0.828 ($95\%$ CI = 0.688–0.878) (Fig. 5D, F).
The results of confusion matrix were shown in Fig. 5E, G, and the accuracy and recall of the models were shown in Table 3. These results suggested the logistic regression model and random forest model based on KDELR1, SPTAN1, CDC16 and RBBP6 can distinguish AD patients from healthy samples. Then, we verified the expression values of these 4 genes between the two groups and found that they were significantly different in AD and healthy group (Fig. 6). In summary, multiple algorithms verified KDELR1, SPTAN1, CDC16 and RBBP6 were the key genes corelated with AD.Fig. 6 The expression value of the four genes in AD and control samples. A–D The expression value of KDELR1, SPTAN1, CDC16 and RBBP6 in AD ($$n = 29$$) and control ($$n = 56$$) samples. Statistical analysis was performed by t test We also validated the relative mRNA levels of Kdelr1, Sptan1, Cdc16 and Rbbp6 in 5XFAD mice and WT mice. Compared with 5XFAD mice, the relatively mRNA levels of Kdelr1, Sptan1, Cdc16 and Rbbp6 were significantly increased in WT mice (Fig. 7).Fig. 7 The validation of 4 key genes in 5XFAD and WT mice. A–D The relative mRNA levels of Kdelr1, Sptan1, Cdc16 and Rbbp6 in WT and 5XFAD mice. ns $p \leq 0.05$, *$p \leq 0.05$, **$p \leq 0.01$ and ***$p \leq 0.001.$ *Statistical analysis* was performed by t testTable 2The top 10 genes identified by five algorithms of the cytoHubba, including EPC, MCC, MNC, DMNC and DegreeDegreeMNCDMNCMCCEPC1COPB1COPB1CDC16COPB1COPB12KDELR1CLTARBBP6KDELR1KDELR13CDC16KDELR1RNF4CDC16SPTAN14KAT2AASH1LFBX011SPTAN1TMED95SETD2SETD2TMED9CLTACLTA6RBBP6SPTAN1KDELR1RBBP6SETD27CLTACDC16GAKFBX011CDC168FBX011RBBP6SPTAN1SETD2RBBP69SPTAN1RNF4ARHGAP1TMED9KPNB110ASH1LFBX011KAT2AASH1LASH1LTable 3The confusion matrix index of logistic regression and random forest modelsIndexLogistic regression modelForest modelTest cohortTest cohortPrecision0.77270.8333Recall0.71910.7895
## GSEA revealed that lipid metabolism and immune response play important roles in AD
On the basis of the expression value of these 4 key genes, we performed GSEA to explore the potential pathways. We found that samples with high expression of KDELR1, SPTAN1, CDC16 and RBBP6 were enriched for adipogenesis, fatty acid metabolism, glycolysis, mTORc1 signaling, MYC targets V1 and protein secretion proteolysis (Fig. 8). In addition, samples with high expression of KDELR1 were enriched in four other gene sets including apical surface, hedgehog signaling, oxidative phosphorylation and UV response up (Fig. 8A), while samples with high expression of SPTAN1 were enriched in UV response down (Fig. 8B). Apical surface, *Cholesterol homeostasis* and UV response down were also significantly enriched in samples with high expression of CDC16 (Fig. 8C). Coagulation, interferon alpha response and interferon gamma response gene sets was significantly enriched in samples with high expression of RBBP6 (Fig. 8D). It has been shown that dysregulation of lipid metabolism is associated with aging, alterations in lipid rafts and brain lipid peroxidation levels[43]. Our results showed the core role of KDELR1, SPTAN1, CDC16 and RBBP6 in the lipid metabolism and immune response. Fig. 8 The GSEA results of 4 key genes. A–D The GSEA results of the group with high expression of KDELR1, SPTAN1, CDC16 and RBBP6
## Construction of key gene-related mRNA-miRNA-lncRNA network
For a further understanding of the role of key genes in AD occurrence, we built mRNA-miRNA-lncRNA network based the four key genes. Firstly, 12 miRNAs interacting with key genes were found in the multiMiR database based on four key genes KDELR1, SPTAN1, CDC16 and RBBP6, and then 38 lncRNAs interacting with 12 miRNAs were identified in the starBase database based on our screening criteria (Fig. 9). Thus, we obtained the mRNA-miRNA-lncRNA regulatory network of 4 key genes (containing 54 nodes and 60 edges). These interacting RNAs may be key mechanisms affecting the pathogenesis of AD.Fig. 9The mRNA-miRNA-lncRNA regulatory network of 4 key genes
## Discussion
Microglia are the brain-resident immune cells, and many studies regards Aβ-associated mononuclear phagocytes as microglia [44]. There are now evidences that blood-derived monocytes can infiltrate the brain of AD patients through the BBB [45, 46]. In cell cultures incorporating Aβ42, the percentage of monocytes/macrophages (M/M) is significantly higher and M/M express chemokines to promote their migration through the BBB [47]. Monocytes recruited in the brain can phagocytose Aβ in the brain parenchyma [48]. In addition, not only Aβ in central nervous system can be removed, but also Aβ that spreads from the brain to the periphery can be captured and phagocytosed by peripheral monocytes. In this study, we analyzed transcriptomic data from hippocampus of AD patients in GEO database. We revealed the difference immune cell types in hippocampus between AD patients and healthy controls. In addition, we identified the pink and green modules are the key modules closely related to AD.
Based on the PPI network and cytoHubba, we identified 4 key genes associated with monocytes and AD, including KDELR1, SPTAN1, CDC16 and RBBP1, and found that these 4 genes differentially expressed in 5XFAD transgenic mice and WT mice. The GSEA and mRNA-miRNA-lncRNA network based on these 4 key genes further confirmed the possibility of these key genes affecting AD.
KDELR1, KDEL endoplasmic reticulum protein retention receptor 1. It could regulate integrated stress responses (ISR), and promote the naive T-cell survival in vivo [49], and regulates T-cell homeostasis through PP1 (protein phosphatase) [50]. KDELR1 is also one of the candidate molecules associated with neurodevelopmental disorders [51], suggesting it may be one of the key molecules associated with the occurrence of AD. SPTAN1, spectrin alpha, non-erythrocytic 1, is essential for myelin formation [52]. Patients with SPTAN1 mutations have also been found to present with peripheral neuropathy, severe dyslexia, and executive function difficulties [53]. SPTAN1 is downregulated in the hippocampus of patients with medial temporal lobe epilepsy(MTLE), which is usually involved in drug-resistant seizures and cognitive deficits[54]. Therefore, we believe that SPTAN1 is also a key potential molecule associated with Alzheimer’s disease. CDC16, cell division cycle 16, functions as a protein ubiquitin ligase. Together with other proteins, CDC16 forms a protein complex containing the Tre2-Bub2-Cdc16 (TBC) structural domain, the protein that belongs to the Rab-specific GTPase-activating protein (GAP) and is highly conserved in eukaryotes [55]. The TBC and LysM Domain containing (TLDc) proteins containing the structural domain of TBC1 domain family member 24 (TBC1D24) are associated with neurodevelopmental disorders and are mainly involved in the oxidative stress response [56, 57]. Therefore, we speculate that CDC16 may also be one of the key molecules affecting neurodevelopment in AD. RBBP6, retinoblastoma binding protein 6. In various human cancers, RBBP6 is involved in the regulation of cell cycle and apoptosis [58]. However, the role of RBBP6 has not been studied in AD, and it may be a new target related to AD pathology. What’s more, we performed the GSEA and mRNA-miRNA-lncRNA regulatory network to have a more comprehensive knowledge of the roles of key genes in AD.
To sum up, the current study initially assessed the abundance of immune cells in the hippocampus and identified monocytes were associated with AD. We identified and verified 4 key genes associated with the occurrence of AD by multiple methods, and also revealed the signaling pathways associated with immune response in AD, which might provide new insights for immunological studies in AD pathology.
## Supplementary Information
Additional file 1: Fig. S1. The selection of the soft-thresholding power β.
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|
---
title: The association between tea consumption and blood pressure in the adult population
in Southwest China
authors:
- Ying Zhao
- Chengmeng Tang
- Wenge Tang
- Xuehui Zhang
- Xiaoman Jiang
- Zhuoma Duoji
- Yixi Kangzhu
- Xing Zhao
- Xiaohe Xu
- Feng Hong
- Qiaolan Liu
journal: BMC Public Health
year: 2023
pmcid: PMC10010002
doi: 10.1186/s12889-023-15315-5
license: CC BY 4.0
---
# The association between tea consumption and blood pressure in the adult population in Southwest China
## Abstract
### Objectives
Prior research on the effect of tea consumption on blood pressure (BP) generated inconsistent findings. The objective of this study was to explore the effects of different types of tea consumption on BP.
### Methods
We included 76,673 participants aged 30–79 from the baseline data of the China Multi-Ethnic Cohort (CMEC) study. Binary logistic regression was used to analyze the influences of different types of tea consumption on the risk of hypertensive BP. Moreover, multiple linear regression was used to examine the association between tea drinking and BP.
### Results
Tea consumption was associated with a reduced risk of hypertensive BP by $10\%$ (AOR: 0.90, $95\%$CI: 0.86–0.94). While dark tea was related to a 1.79–5.31 mmHg reduction in systolic blood pressure (SBP) and a 0.47–1.02 mmHg reduction in diastolic blood pressure (DBP), sweet tea, regardless of the duration, frequency, or amount of consumption, significantly was associated with a reduced SBP by 3.19–7.18 mmHg. Green tea also was associated with a reduced SBP by 1.21–2.98 mmHg. Although scented tea was related to reduced SBP by 1.26-2.48 mmHg, the greatest effect came from the long duration (> 40 years:β=-2.17 mmHg, $95\%$CI=-3.47 mmHg --0.87 mmHg), low frequency (1–2 d/w: β = -2.48 mmHg, $95\%$CI=-3.76 mmHg–-1.20 mmHg), and low amount (≤ 2 g/d: β=-2.21 mmHg, $95\%$CI=-3.01 mmHg–-1.40 mmHg). Additionally, scented tea was correlated to a decrease in DBP at the frequency of 1–2 d/w (β=-0.84 mmHg, $95\%$CI=-1.65 mmHg–-0.02 mmHg). Drinking black tea only was associated with lowered SBP. The protective effect of black tea on SBP was characterized by the long-duration (> 15 years, -2.63–-5.76 mmHg), high frequency (6–7 d/w, -2.43 mmHg), and medium amount (2.1-4.0 g/d, -3.06 mmHg).
### Conclusion
Tea consumption was associated with lower SBP and a reduced risk of hypertensive BP. The antihypertensive effect varies across types of tea consumed.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-023-15315-5.
## Introduction
As a common chronic non-communicable disease, hypertension has become a global challenge to public health [1]. Due to the rapid growth of the aging population, the number of people with elevated blood pressure increased by $90\%$ from 1975 to 2015, with the majority of the increase occurring in low-income and middle-income countries or regions [2]. The prevalence of hypertension in China showed an upward trend [3], increasing from $13.6\%$ to 1991 to $27.9\%$ in 2015 [4]. Elevated blood pressure is significantly associated with a higher risk of cardiovascular disease and kidney-related diseases [5–8]. It has become a major cause of death and is linked to the reduction of disability-adjusted life-year[9]. However, once the diastolic blood pressure (DBP) reduces by 2 mmHg, the incidence of coronary heart disease and stroke can decrease by $6\%$ and $15\%$, respectively [10]. A systematic review of 48 randomized clinical trials showed a $10\%$ reduction in the risk of cardiovascular events for every 5 mmHg reduction in systolic blood pressure (SBP)[11].Therefore, effective prevention and control of hypertension are of crucial significance to public health.
Tea is a popular beverage worldwide, especially in Asia [12]. The association between tea consumption and incident hypertension, however, remains uncertain. Several studies have shown that drinking tea was associated with a reduced risk of hypertension, type 2 diabetes, and cardiovascular disease[13–15]. The protective effect is due primarily to the antihypertensive active ingredients, such as the tea polyphenols[16, 17]. Due to the different degrees of fermentation, the antihypertensive active ingredients in tea vary. The main antihypertensive component of green tea is catechins. In black tea, most catechins are oxidized into the thearubigins and theaflavins with weak antioxidant capacity during the fermentation process [18, 19]. In addition, the amount, duration, and frequency of tea consumption may also affect the antihypertensive effect[20–22]. On the other hand, while some studies showed that tea consumption was not associated with a reduced risk of hypertension[23–25], others even demonstrated an increased risk[26]. In addition, most of these studies focused on green tea and black tea. Rarely did they investigate the association of other types of tea consumption with blood pressure[20, 27]. The lack of population representativeness, relatively small sample size, and limited types of tea might be responsible for these inconsistent results. Therefore, it is necessary to explore the effects of different types of tea consumption and tea consumption habits on blood pressure in a large sample of the population to make scientific recommendations for lowering blood pressure.
China has a long history of tea consumption with a sizable tea-drinking population. According to the 2011 China Health and Nutrition Survey, the rates of tea drinkers in urban and rural areas were $46.5\%$ and $33.0\%$, respectively [28]. As the center of the origin of tea trees, tea consumption is high in Southwest China. According to the China Patient-Centered Evaluative Assessment of Cardiac Events (PEACE) Million Persons Project, the prevalence of hypertension among adults in western China was $40.60\%$ in 2014–2017, which was lower than the national prevalence of $44.72\%$[29]. But it is not clear whether tea consumption plays a role in the low prevalence of hypertension. Thus, exploring the association between tea consumption and hypertension in Southwest China can help establish such an association in the population. Moreover, Southwest *China is* home to multiple ethnic groups and several types of tea. In addition to green tea and black tea, dark tea, scented tea, and sweet tea are also popular, making the region an ideal location to evaluate the effects of different types of tea on blood pressure.
Based on the baseline data of the China Multi-ethnic cohort (CMEC) study, this study aimed [1] to explore the relationships between different types of tea consumption and hypertensive BP; [2] to analyze the effects of different tea consumption habits (duration of tea consumption, frequency of tea consumption, and amount of tea consumption) by different types of tea consumption on DBP and SBP; [3] to explore the modification effect of demographics and lifestyle factors on the association between tea consumption and hypertensive BP in a large population in southwest China, a low-income and middle-income region.
## Participants
A multistage, stratified cluster sampling technique was employed to conduct the China Multi-ethnic Cohort (CMEC) study in 5 provinces in Southwest, including Sichuan, Chongqing, Tibet, Guizhou, and Yunnan provinces. The baseline survey of CMEC was fielded from May 2018 to September 2019. It enrolled a total of 99,556 participants aged 30–79 (few of the Tibetan participants were younger than 30 years old). The specific investigation methods have been described elsewhere [30]. Participants were excluded from this study if they had [1] any physician-diagnosed hypertension (considering that hypertensive patients might change the habit of tea consumption), [2] any physician-diagnosed cardiovascular disease (considering that cardiovascular disease patients might change the habit of tea consumption), or [3] didn’t have three blood pressure measures. After these exclusions, 76,673 participants were included (Fig. 1). All the participants signed an informed consent form before data collection. Ethical approval for this study was obtained from the Sichuan University Medical Ethical Review Board (K2016038, K2020022).
## Data collection
The data used in this study came from the electronic questionnaire and medical examination of the CMEC baseline survey. The electronic questionnaires were conducted through face-to-face interviews, and tablet computers with a CMEC application (CMEC App) were used to record questionnaire information. The CMEC App, developed by the research team, featured an automatic recording function. The interviewers were recruited from local universities or colleges with medical backgrounds. All interviewers were trained before conducting the interviews. Through a unified and standardized process, each investigation lasted for 30–45 min. The questionnaire included sociodemographic characteristics, lifestyle questions (e.g., smoking, alcohol consumption, tea consumption, dietary habits, and physical activity), physician-diagnosed diseases, family history of diseases, reproductive information, and psychological conditions.
## Hypertensive blood pressure
The blood pressure was measured by an electronic sphygmomanometer, which was calibrated before use. The process of measurement followed the standard procedure of the American Heart Association[31]. Before the measurement was taken, all participants were asked to not smoke, drink (alcohol, coffee, and tea), and exercise for at least 30 min. When measuring, all participants were required to keep an upright seated position. A total of 3 SBP and DBP measures were taken and recorded. Participants who met one of the following criteria were considered as hypertensive status. The criteria were: [1] the average measured SBP of 3 times ≥ 140 mmHg and [2] the average measured DBP of 3 times ≥ 90 mmHg.
## Tea consumption
Variables for tea consumption contained the status, type, duration, frequency, and amount of tea consumed in the past or present.
For the status of tea consumption, participants were asked: “Have you ever drunk tea every week for more than six months? ( Yes/ No)”. Participants who answered “Yes” were queried further about other variables of tea consumption. For the type of tea consumed, participants were asked: “What kind of tea do you most commonly drink?”. Responses included green tea, scented tea, dark tea, sweet tea, black tea, oolong tea, yellow tea, and white tea. The last three kinds of tea are consumed by a small number of participants, therefore, they are combined and collectively referred to as other tea in the analysis that follows. Dark tea included brick tea and Pu’er tea. Sweet tea was black tea with milk added. For the duration of tea consumption, both the past drinkers and current drinkers were asked about the age when they began to drink tea. Duration of tea consumption was obtained by subtracting the age of starting drinking from the age at which the tea drinkers stopped drinking or the study time for the current drinkers. Duration of tea consumption was recoded into “no”, “≤ 15 years”, “16–40 years”, and “>40 years”. The frequency of tea consumption was ascertained by one survey question: “How many days did you drink tea per week on average in the past year?” The answers were “no”, “1–2 d/w”, “3–5 d/w”, and “6–7 d/w”. For the amount of tea consumption, participants were asked: “When drinking tea, how many times do you add new tea in one day?” and “How much do you usually add each time? ( g/d)”. The amount of tea consumed was calculated by the equation: daily amount= (the times of adding new tea + 1)× the weight of tea added each time. The amount of tea consumption was recoded into “no”, “≤ 2.0 g/d”, “2.1-4.0 g/d”, “4.1-8.0 g/d”, and “≥ 8.1 g/d”.
## Covariates
The covariates included age (“30–39”, “40–49”, “50–59”, “60–69”, and “70–79”), sex (“male” and “female”), ethnicity (“Han”, “Dong”, “Bouyi”, “Yi”, “Miao”, “Bai”, and “Tibetan”), marital status (“married”, “divorced”, “widowed”, and “single”), education (“illiteracy”, “primary”, “middle school”, and “college”), occupation (“employed”, “unemployed”, and “retired”), family income (“<12,000 RMB/year”, “12,000–19,999 RMB/year”, “20,000–59,999 RMB/year”, “60,000–99,999 RMB/year”, “100,000-199,999 RMB/year”, and “≥200,000 RMB/year”), BMI (“normal”, “overweight”, and “obesity”), non-sedentary metabolic equivalent (MET) (“low”, “middle”, and “high”), smoking status (“no”, “current” and “quit”), alcohol use status (“no”, “occasionally” and “frequently”), salt intake (g/w), vegetables intake (g/w), fruits intake (g/w), dairy intake (g/w), physician-diagnosed diseases (“no” and “yes”), and family history of hypertension (“no” and “yes”). Physician-diagnosed diseases referred to whether participants had other chronic diseases or cancer, except for hypertension. The family history of hypertension was indicated by the participant’s direct relatives who had been diagnosed with hypertension by doctors.
## Data analysis
The Chi-square test was conducted for univariate analysis. The covariates that were statistically significant ($P \leq 0.05$) in the univariate analysis were used in the subsequent analysis as adjustment variables. The association between tea consumption and blood pressure was analyzed in two parts. In the first part, hypertensive BP was used as the dependent variable and a series of binary logistic regression models were developed to estimate the effects of the status and types of tea consumption net of statistical controls. Adjusted odds ratios (AORs) and $95\%$ confidence intervals (CI) were reported. In the second part, multiple linear regression was used to estimate the associations between types of tea consumption and the levels of SBP and DBP.
## Stratified analysis
To examine whether demographics and lifestyle factors affect the association between tea consumption and hypertensive BP, we performed a stratified analysis, stratified by age, sex, alcohol use status, smoking status, BMI, and salt intake. In each stratified analysis, the models were controlled for all other covariates except those used for stratification. We also conducted multiple linear regression analyses on the relationships of different types of tea with change values of BP according to age, sex, alcohol use status, smoking status, BMI, and salt intake.
Stratified analyses suggested that the association between the status of tea consumption and hypertensive BP was modified by sex, BMI, smoking status, alcohol use status, age, and salt consumption. That is, compared with those who never drank tea, tea consumption was associated with a lowered risk of hypertensive BP for women (AOR: 0.89, $95\%$CI: 0.83–0.96), those with normal-weight (AOR: 0.90, $95\%$CI: 0.84–0.97) or overweight (AOR: 0.91, $95\%$CI: 0.84–0.97), those who never smoked (AOR: 0.91, $95\%$CI: 0.86–0.97) or never drank alcohol (AOR: 0.84, $95\%$CI: 0.78–0.90), and those who were over 50 years old of age (AOR: 0.87, $95\%$CI: 0.82–0.92). Among participants with above-average salt intake, the protective effect of tea consumption on hypertensive BP was weakened. The associations between different types of tea consumption and hypertensive BP were also modified by sex, BMI, smoking status, alcohol use status, age, and salt consumption (Fig. 3). The relationships between consuming behaviors of different types of tea and BP stratified by sex, BMI, smoking status, alcohol drinking status, age, and salt was shown in Table S2 (Supplementary material).
## Sensitivity analysis
To assess the robustness of the results, we performed a series of sensitivity analyses, including participants with physician-diagnosed cardiovascular disease and physician-diagnosed hypertension and cardiovascular disease. The association between the duration and amount of tea consumption as continuous variables and the levels of SBP and DBP were evaluated with a restricted cubic spline.
R (version 3.6.3) was utilized for all statistical analyses, and $P \leq 0.05$ was considered statistically significant.
The results of the analyses for participants with physician-diagnosed cardiovascular disease were consistent with those reported above. Green tea, scented tea, dark tea, sweet tea, and black tea all linked to lower risk of hypertensive BP (Fig S1, Supplementary material). Similarly, the results of the analyses for participants with physician-diagnosed hypertension and cardiovascular disease also showed tea consumption was associated with a protective effect on hypertensive BP (Fig S2, Supplementary material). Finally, the results of restricted cubic spline analyses indicated that the gradual decrease in SBP correlated with the increase in drinking duration of green tea, scented tea, dark tea, sweet tea, and black tea, and an L-shaped or U-shaped pattern was observed between SBP and the amount of tea consumed (Fig S3-7, Supplementary material).
## General prevalence
The sociodemographic characteristics of the participants are displayed in Table 1. Among 76,673 participants, 25,315 ($33.01\%$) reported drinking tea, with the proportion of green tea consumption at $47.39\%$, dark tea consumption at $19.57\%$, scented tea at $17.86\%$, sweet tea at $9.64\%$, and black tea at $2.80\%$. Compared with non-tea drinkers, tea drinkers were more likely to be male, overweight, smokers, had a history of other diseases, and lower salt intake. Among the drinkers, the duration of tea drinking was centered around 16–40 years, $75.80\%$ drank tea almost every day, and the amount of tea they drank was, by and large, less than 4.0 g/d. The green tea and scented tea drinkers were likely to be Han Chinese, whereas the dark tea and sweet tea drinkers were likely to be Tibetan (Table 1). The number of participants with hypertensive BP was 14,624 ($19.07\%$), and they were more likely to be male, widowed, retired, overweight or obesity, had a lower education level and lower family income, had a lower physical activity level, and had more likely to smoke and drink alcohol. In addition, those with higher salt intake and lower fruit and dairy intake had a higher prevalence of hypertensive BP (Table S1, Supplementary material).
## The association between tea consumption and hypertensive BP
Overall, tea consumption was associated with $10\%$ lower risk of hypertensive BP net of confounders (AOR: 0.90, $95\%$CI: 0.86–0.94). Compared with non-drinkers, participants who drank green tea (AOR: 0.94, $95\%$CI: 0.89–0.99), scented tea (AOR: 0.91, $95\%$CI: 0.83–0.98), dark tea (AOR: 0.74, $95\%$CI: 0.66–0.83), sweet tea (AOR: 0.78, $95\%$CI: 0.66–0.91) and black tea (AOR: 0.81,$95\%$CI: 0.65-1.00) were associated with a lower risk of hypertensive BP (Fig. 2).
Fig. 1Flowchart for participants selection Table 1Baseline characteristics of the study participants by type of tea consumptionCovariatesType of tea consumptionOverall p NeverGreen teaScented teaDark teaSweet teaBlack teaOther tea Age group (years, %) < 0.00130–3911,083(21.56)1,642(13.69)676(15.96)1,251(25.26)722(29.58)183(25.77)152(23.17)15,709(20.49)40–4917,292(33.64)4,268(35.58)1,522(33.67)1,558(31.46)809(33.14)304(42.82)210(32.01)25,963(33.86)50–5912,898(25.09)3,498(29.16)1,204(26.64)1,304(26.33)623(25.52)141(19.86)149(22.71)19,817(25.85)60–697,735(15.05)1,963(16.36)829(18.34)676(13.65)236(9.67)59(8.31)103(15.70)11,601(15.13)70–792,389(4.65)625(5.21)289(6.39)164(3.31)51(2.09)23(3.24)42(6.40)3,583(4.67) Sex (%) < 0.001Male15,203(29.58)7,642(63.70)3,148(69.65)2,001(40.40)950(38.92)403(56.76)353(53.81)29,700(38.74)Female36,194(70.42)4,354(36.30)1,372(30.35)2,952(59.60)1,491(61.08)307(43.24)303(46.19)46,973(61.26) Ethnicity (%) < 0.001Han29,862(58.10)8,581(71.53)4,244(93.89)841(16.98)19(0.78)447(62.96)479(73.02)44,473 (58.00)Dong4,658(9.06)567(4.73)27(0.60)9(0.18)0(0.00)47(6.62)32(4.88)5,340(6.96)Bouyi4,092(7.96)408(3.40)31(0.69)20(0.40)1(0.04)17(2.39)17(2.59)4,586(5.98)Yi4,084(7.95)646(5.39)11(0.24)53(1.07)1(0.04)23(3.24)5(0.76)4,823(6.29)Miao3,627(7.06)438(3.65)20(0.44)7(0.14)1(0.04)29(4.08)6(0.91)4,128(5.38)Bai3,423(6.66)1,259(10.50)16(0.35)88(1.78)0(0.00)69(9.72)4(0.61)4,859(6.34)Tibetan1,651(3.21)97(0.81)171(3.78)3,935(79.45)2,419(99.10)78(10.99)113(17.23)8,464 (11.04) *Marital status* (%) < 0.001Married45,816(89.14)10,985(91.57)4,148(91.77)4,404(88.92)2,219(90.91)641(90.28)581(88.57)68,794 (89.73)Divorce2,204(4.29)410(3.42)213(4.71)202(4.08)51(2.09)39(5.49)39(5.95)3,158(4.12)Widowed2,794(5.44)473(3.94)119(2.63)191(3.86)82(3.36)10(1.41)23(3.51)3,692(4.82)Single582(1.13)128(1.07)40(0.88)156(3.15)89(3.65)20(2.82)13(1.98)1,028(1.34) Education (%) < 0.001Illiteracy12,627(24.57)1,919(16.00)403(8.92)2,792(56.37)1,305(53.46)102(14.37)104(15.85)19,252 (25.11)Primary12,584(24.48)3,079(25.67)1,039(22.99)1,229(24.81)807(33.06)115(16.20)146(22.26)18,999 (24.78)Middle school19,917(38.75)5,305(44.23)2,475(54.76)689(13.91)299(12.25)271(38.17)281(42.84)29,237 (38.13)College6,269(12.20)1,692(14.11)603(13.34)243(4.91)30(1.23)222(31.27)125(19.05)9,184 (11.98) Occupation (%) < 0.001Employed45,125(87.88)10,351(86.35)3,442(76.22)4,597(92.87)2,363(96.80)618(87.04)545(83.21)67,041 (87.51)Unemployed2,351(4.58)475(3.96)382(8.46)119(2.40)29(1.19)43(6.06)35(5.34)3,434(4.48)Retired3,875(7.55)1,161(9.69)692(15.32)234(4.73)49(2.01)49(6.90)75(11.45)6,135(8.01) Family income (RMB /year) (%) < 0.001< 12,0008,744(17.03)1,787(14.93)419(9.28)782(15.79)507(20.77)69(9.75)75(11.45)12,383 (16.17)12,000–19,9999,182(17.89)1,895(15.83)544(12.05)1,327(26.80)809(33.14)80(11.30)108(16.49)13,945 (18.21)20,000–59,99918,592(36.22)4,494(37.54)1,762(39.04)2,020(40.80)819(33.55)199(28.11)220(33.59)28,106 (36.70)60,000–99,9997,705(15.01)1,867(15.59)973(21.56)411(8.30)189(7.74)128(18.08)109(16.64)11,382 (14.86)100,000–199,9995,793(11.28)1,480(12.36)648(14.36)296(5.98)86(3.52)158(22.32)109(16.64)8,570 (11.19)>=200,0001,321(2.57)449(3.75)167(3.70)115(2.32)31(1.27)74(10.45)34(5.19)2,191(2.86) BMI(kg/m2) (%) < 0.001Normal28,522(55.58)6,584(54.95)2,094(46.37)1,960(39.68)623(25.60)353(49.72)250(38.23)40,386 (52.76)Overweight17,414(33.93)4,195(35.01)1,846(40.88)1,954(39.56)1,464(60.15)274(38.59)302(46.18)27,449 (35.86)Obesity5,382(10.49)1,202(10.03)576(12.75)1,025(20.75)347(14.26)83(11.69)102(15.60)8,717 (11.39) MET (%) < 0.001Low11,635(22.74)2,647(22.18)1,548(34.42)1,918(38.84)889(36.64)170(24.08)211(32.26)19,018 (24.92)Middle26,075(50.96)6,078(50.93)2,159(48.00)2,044(41.39)1,135(46.78)399(56.52)329(50.31)38,219 (50.08)High13,453(26.29)3,210(26.90)791(17.59)976(19.77)402(16.57)137(19.41)114(17.43)19,083 (25.00) *Smoking status* (%) < 0.001No42,830(83.33)6,410(53.43)2,037(45.07)4,040(81.57)1,808(74.07)439(61.83)438(66.77)58,002 (75.65)Current6,969(13.56)4,710(39.26)2,020(44.69)670(13.53)530(21.71)229(32.25)176(26.83)15,304 (19.96)Quit1,598(3.11)876(7.30)463(10.24)243(4.91)103(4.22)42(5.92)42(6.40)3,367(4.39) Alcohol use status (%) < 0.001No30,472(59.29)5,167(43.07)1,628(36.02)3,832(77.37)1,632(66.86)275(38.73)266(40.55)43,272 (56.44)Occasionally16,253(31.62)3,953(32.95)1,524(33.72)793(16.01)661(27.08)302(42.54)260(39.63)23,746 (30.97)Frequently4,672(9.09)2,876(23.97)1,368(30.27)328(6.62)148(6.06)133(18.73)130(19.82)9,655 (12.59) Salt (g/w, mean ± SD) 47.52 ± 28.6445.91 ± 27.5242.74 ± 24.4143.32 ± 33.0843.52 ± 33.6242.28 ± 26.5642.71 ± 28.2046.50 ± 28.74< 0.001 Vegetables (g/w, mean ± SD) 2,227.71 ± 1,416.932,197.89 ± 1,433.732,401.19 ± 1,516.651,655.93 ± 1,459.111,146.90 ± 1,366.482,189.56 ± 1,522.612,319.35 ± 1,720.952,162.39 ± 1,450.08< 0.001 Fruits (g/w, mean ± SD) 854.48 ± 799.88913.66 ± 813.56894.90 ± 811.07650.16 ± 729.30575.48 ± 877.84967.34 ± 879.90894.91 ± 882.93845.42 ± 806.07< 0.001 Dairy (g/w, mean ± SD) 367.18 ± 574.19368.40 ± 577.94605.88 ± 691.37477.99 ± 616.77276.34 ± 423.35553.02 ± 664.07501.68 ± 647.71388.64 ± 586.38< 0.001 Family history of hypertension (%) < 0.001No39,523(76.9)9,090(75.78)3,394(75.09)4,200(84.80)1,986(81.36)519(73.10)512(78.05)59,224(77.24)Yes11,874(23.1)2,906(24.22)1,126(24.91)753(15.20)455(18.64)191(26.90)144(21.95)17,449 (22.76) Physician-diagnosed disease (%) < 0.001No43,100 (83.86)9,681(80.70)3,443(76.17)3,975(80.25)1,861(76.24)561(79.01)482(73.48)63,103(82.30)Yes8,297(16.14)2,315(19.30)1,077(23.83)978(19.75)580(23.76)149(20.99)174(26.52)13,570 (17.70) Duration of tea consumption (years) (%) < 0.001≤ 15-3,485(29.06)1,353(29.94)523(10.56)139(5.69)325(45.77)320(48.78)6,164(24.35)16–40-6,782(56.55)2,601(57.56)2,724(55.00)1,268(51.95)304(42.82)246(37.50)13,940 (55.08)> 40-1,725(14.38)565(12.50)1,706(34.44)1,034(42.36)81(11.41)90(13.72)5,206(20.57)Frequency of tea consumption (d/w) (%)< 0.0011–2-1,465(12.49)550(12.66)312(6.39)77(3.16)114(16.38)109(17.33)2,631(10.63)3–5-1,784(15.22)848(19.52)372(7.62)97(3.99)133(19.11)120(19.08)3,360(13.57)6–7-8,476(72.29)2,947(67.83)4,199(85.99)2,260(92.85)449(64.51)400(63.59)18,761 (75.80) Amount of tea consumption (g/d) (%) < 0.001≤ 2.0-2,713(22.75)1,453(32.16)909(18.36)1,204(49.32)147(20.85)194(29.66)6,621(26.25)2.1-4.0-3,708(31.09)1,306(28.91)899(18.16)462(18.93)210(29.79)176(26.91)6,768(26.83)4.1-8.0-3,020(25.32)966(21.38)1,310(26.46)256(10.49)149(21.13)133(20.34)5,847(23.18)≥ 8.1-2,486(20.84)793(17.55)1,832(37.01)519(21.26)199(28.23)151(23.09)5,989(23.74) Hypertensive BP (%) < 0.001No41,714(81.16)9,374(78.14)3,502(77.48)4,230(85.40)2,109(86.40)598(84.23)522(79.57)62,049(80.93)Yes9,683(18.84)2,622(21.86)1,018(22.52)723(14.60)332(13.60)112(15.77)134(20.43)14,624 (19.07) Fig. 2Association between type of tea consumption and hypertensive BP. ( Note: AORs ($95\%$CIs) were adjusted for age, sex, ethnicity, marital status, education, occupation, family income, BMI, MET, smoking status, alcohol use status, salt intake, vegetable intake, fruits intake, dairy intake, family history of hypertension, and physician-diagnosed diseases)
## The association between tea consumption and blood pressure
Compared with never drinkers, drinking green tea, scented tea, dark tea, sweet tea, and black tea was associated with a reduced the average level of SBP, whereas drinking scented tea and dark tea significantly was related to a reduced the average level of DBP. But the reduction in DBP was not observed for tea drinkers who consumed green tea, sweet tea, or black tea (Table 2).
Drinking green tea was correlated with reduced SBP, and the degree of reduction varied by the duration, frequency, and amount of tea consumption. The longer duration of tea drinking was statistically associated with the greater reduction in SBP. For example, tea consumption for more than 40 years was associated with a 2.98 mmHg ($95\%$CI=-3.74 mmHg–-2.21 mmHg) reduction in SBP. In terms of frequency, low and high frequency tea consumption groups were correlated with a 1.35 mmHg ($95\%$CI=-2.15 mmHg–-0.55 mmHg) and 1.76 mmHg ($95\%$CI=-2.14 mmHg–-1.39 mmHg) decrease in SBP, respectively. In the low-dose group, the antihypertensive effect of green tea was the best, and the weakened effect of antihypertensive was associated with the increased amount of tea consumption. The reduction in SBP was in the range of 1.21–1.85 mmHg. Unlike other types of tea, green tea showed to be associated with a mild increase in DBP. However, these effects were only observed in short-middle-term drinking(≤ 40 years), medium and high-frequency drinking(> 2d/w), and medium and high amount drinking(≥ 4.1 g/d). The increase in blood pressure was small, no more than 0.7 mmHg (Table 2).
Drinking scented tea was associated with a 1.26-2.48 mmHg reduction in SBP significantly, and the greatest antihypertensive effect appeared in the low-frequency tea consumption group. That is, when the frequency of tea consumption was 1–2 d/w, and the amount was less than 2.0 g/d, it was associated with the greatest decrease in SBP, by 2.48 mmHg ($95\%$CI=-3.76 mmHg–-1.20 mmHg) and 2.21 mmHg ($95\%$CI=-3.01 mmHg–-1.40 mmHg), respectively. The longer duration of scented tea consumption was associated a more significant reduction in SBP. For instance, the duration of tea consumption more than 40 years was associated with a reduction in SBP of 2.17 mmHg ($95\%$CI=-3.47 mmHg --0.87 mmHg). The effect of scented tea on DBP was only observed in those who consumed scented tea 1–2 days per week (β=-0.84 mmHg, $95\%$CI=-1.65 mmHg–-0.02 mmHg) (Table 2).
Regardless of the duration, dark tea consumption was linked to a reduced SBP by a range of 1.79–4.19 mmHg. When dark tea consumption was 1-2d/w and 6-7d/w, it was associated with a 2.41 mmHg ($95\%$CI=-4.13 mmHg–-0.70mmHg) and 2.90 mmHg ($95\%$CI=-3.58 mmHg–-2.21 mmHg) reduction in SBP, respectively, whereas it was related to a 0.47 mmHg ($95\%$CI=-0.91 mmHg–-0.03 mmHg) decrease in DBP when dark tea consumption was and 6-7 d/w. When drinking ≤ 2.0 g of dark tea per day, it was correlated the best antihypertensive effect. The amount of reduction in SBP was 5.31 mmHg ($95\%$CI=-6.41 mmHg–-4.20 mmHg) (Table 2).
Sweet tea, regardless of the duration, frequency, or amount consumed, was correlated to a reduced SBP by a range of 3.19–7.18 mmHg. When drinking sweet tea for more than 40 years, the frequency was 3-5d/w and the amount was 2.1-4.0 g per day, it was linked to a 5.64 mmHg ($95\%$CI=-6.83 mmHg–-4.45 mmHg), 6.26mmHg ($95\%$CI=-9.34 mmHg–-3.18 mmHg), and 7.18 mmHg ($95\%$CI=-8.74 mmHg–-5.62 mmHg) decrease in SBP, respectively. However, when drinking sweet tea for more than 40 years and the amount of tea consumed was ≤ 2.0 g/d, sweet tea was connected with a slight DBP boosting effect: an increase in DBP by 1.60 mmHg and 1.38 mmHg, respectively (Table 2).
Drinking black tea was only associated with lowered SBP. Long-duration, high-frequency, and medium-amount drinking groups were associated with a significant reduction in SBP. When drinking black tea for more than 15 years, and the frequency was 6–7 d/w, it was connected with an SBP reduction of 2.43-5.76 mmHg. Moreover, drinking black tea less than 8.0 g/d was associated with an SBP reduction of 2.67-3.06 mmHg (Table 2).
Table 2The association between type of tea consumption and SBP/DBPGreen teaScented teaDark teaSweet teaBlack teaβ($95\%$CI)β($95\%$CI)β($95\%$CI)β($95\%$CI)β($95\%$CI) Outcome = SBP Duration of tea consumption (years) ≤ 15-0.24(-0.77,0.30)-1.42(-2.25,-0.59)-2.66(-3.99,-1.33)-5.35(-7.96,-2.73)-0.65(-2.31,1.02)16–40-1.88(-2.29,-1.47)-1.88(-2.51,-1.25)-1.79(-2.55,-1.02)-5.08(-6.20,-3.97)-2.63(-4.36,-0.91)> 40-2.98(-3.74,-2.21)-2.17(-3.47,-0.87)-4.19(-5.12,-3.26)-5.64(-6.83,-4.45)-5.76(-9.07,-2.45) Frequency of tea consumption (d/w) 1–2-1.35(-2.15,-0.55)-2.48(-3.76,-1.20)-2.41(-4.13,-0.70)-5.18(-8.64,-1.71)-0.59(-3.39,2.20)3–5-0.70(-1.43,0.04)-1.26(-2.30,-0.23)-0.33(-1.92,1.27)-6.26(-9.34,-3.18)-2.14(-4.73,0.44)6–7-1.76(-2.14,-1.39)-1.82(-2.41,-1.22)-2.90(-3.58,-2.21)-5.30(-6.26,-4.33)-2.43(-3.86,-1.01) Amount of tea consumption (g/d) ≤ 2.0-1.85(-2.44,-1.25)-2.21(-3.01,-1.40)-5.31(-6.41,-4.20)-5.29(-6.42,-4.15)-2.72(-5.17,-0.26)2.1-4.0-1.67(-2.19,-1.15)-1.60(-2.44,-0.75)-4.61(-5.69,-3.53)-7.18(-8.74,-5.62)-3.06(-5.13,-0.99)4.1-8.0-1.32(-1.90,-0.74)-2.14(-3.13,-1.16)-0.29(-1.26,0.67)-6.61(-8.60,-4.62)-2.67(-5.13,-0.22)≥ 8.1-1.21(-1.85,-0.57)-0.72(-1.80,0.37)-1.81(-2.67,-0.96)-3.19(-4.69,-1.69)-0.22(-2.35,1.91) Outcome = DBP Duration of tea consumption (years) ≤ 150.55(0.21,0.89)0.31(-0.21,0.84)-0.76(-1.61,0.09)1.41(-0.26,3.08)-0.25(-1.31,0.81)16–400.31(0.05,0.58)0.08(-0.33,0.48)-0.96(-1.45,-0.47)-0.53(-1.25,0.18)-0.54(-1.64,0.56)> 400.30(-0.18,0.79)-0.14(-0.97,0.69)0.45(-0.14,1.05)1.60(0.84,2.36)-0.62(-2.73,1.49) Frequency of tea consumption (d/w) 1–20.08(-0.43,0.59)-0.84(-1.65,-0.02)-0.43(-1.52,0.67)-0.18(-2.39,2.04)-1.19(-2.98,0.59)3–50.53(0.07,1.00)0.16(-0.50,0.82)-1.02(-2.04,0.00)-0.46(-2.43,1.51)-0.09(-1.74,1.55)6–70.44(0.20,0.68)0.37(-0.01,0.75)-0.47(-0.91,-0.03)0.55(-0.07,1.16)-0.24(-1.15,0.66) Amount of tea consumption (g/d) ≤ 2.00.33(-0.05,0.71)0.05(-0.46,0.56)0.31(-0.40,1.02)1.38(0.65,2.11)-1.20(-2.76,0.37)2.1-4.00.11(-0.23,0.44)0.13(-0.41,0.67)-1.02(-1.72,-0.33)-0.76(-1.75,0.24)-0.73(-2.04,0.59)4.1-8.00.65(0.29,1.02)-0.06(-0.69,0.56)-0.26(-0.88,0.35)-0.45(-1.72,0.82)0.11(-1.45,1.67)≥ 8.10.51(0.10,0.92)0.56(-0.13,1.26)-0.86(-1.41,-0.31)0.06(-0.90,1.02)0.26(-1.09,1.62)Note: Adjusted for age, sex, ethnicity, marital status, education, occupation, family income, BMI, MET, smoking status, alcohol use status, salt intake, vegetable intake, fruits intake, dairy intake, family history of hypertension, and physician-diagnosed diseases. β indicates unstandardized partial regression coefficient Fig. 3Associations between different types of tea consumption and hypertensive BP according to sex, BMI, smoking status, alcohol use status, age, and salt intake. ( Note: AORs ($95\%$CIs) were adjusted for age, sex, ethnicity, marital status, education, occupation, family income, BMI, MET, smoking status, alcohol use status, salt intake, vegetable intake, fruits intake, dairy intake, family history of hypertension, and physician-diagnosed diseases)
## Discussion
It is the first study on the association between tea consumption and blood pressure in southwest China. In this large community-based study, we found statistically significant and net associations between different types of tea consumption (green tea, scented tea, dark tea, sweet tea, and black tea) and reduced SBP in the adult population (aged 30–79) in Southwest China. Furthermore, we found that dark tea consumption was significantly associated with a lower DBP in the same study population. In the pages that follow, we reiterate these findings in detail.
Our study found that tea consumption was associated with a reduced risk of hypertensive BP, which was consistent with the majority of previous research findings[27, 32–34]. For example, a meta-analysis of 25 randomized controlled trials showed that habitual tea consumption was significantly associated with reduced blood pressure[22]. A large cohort study in China also reported that habitual tea consumption was associated with a decreased risk for incident hypertension (by $14\%$ with HR = 0.86, $95\%$CI: 0.80–0.91) and a lowered risk for blood pressure progression (by $17\%$ with OR = 0.83, $95\%$CI: 0.79–0.88). These findings suggest that not only did habitual tea drinking was associated with the reduced risk of hypertensive BP but also provided a preventive effect against blood pressure progression[35]. Another study of 4,579 elderly people in Jiangsu province of China found that habitual tea consumption was negatively associated with the prevalence of hypertension and SBP level[32]. On the other hand, a cohort study of Iranian adults found that tea consumption was not associated with a lowered risk of hypertension after six years of follow-up[24]. A more inconsistent finding came from a Chinese cohort study of 59,693 subjects, which found that habitual tea consumption was associated with a slightly higher risk of hypertension after 7.1 years of follow-up[26]. The reasons for these inconsistent results may be related to differences in the study population and measurement methods of tea consumption.
This study found that green tea consumption was associated with a $6\%$ lower risk of hypertensive BP. Green tea has the best SBP-lowering associated effect when the consumption is long-term, low in amount, and less frequent. Green tea is unfermented and contains more catechins than other teas. Previous studies have shown that catechins can improve vascular endothelial function by promoting NO production and enhancing NO bioavailability, thereby reducing blood pressure[36]. Green tea catechins also protect blood vessels by inhibiting angiotensinase production[37]. Like previous study findings[26], this study also generated an inconsistent result. That is, green tea was associated with a slight increase in DBP (no more than 0.7mmHg) with short duration, medium or high frequency, and medium or high amount of consumption. This unexpected finding might be related to the caffeine contained in green tea.
Drinking dark tea was associated with a reduced risk of hypertensive BP by $26\%$. In effect, dark tea consumption was associated with a significantly reduction in both SBP and DBP, which was not found in other teas, except that scented tea correlated to slightly reduced the DBP in the low-frequency group. Dark tea belongs to post-fermented tea. During the fermentation process, catechins are converted into the theabrownie, which leads to changes in its biological activity [38]. Previous studies found that dark tea had the effects of improving hyperlipidemia and reducing the risk of diabetes [39, 40]. There are two explanations for why dark tea consumption can reduce BP. First, dark tea had stronger antioxidant properties and could protect the vascular endothelium from damage by reactive oxygen species and free radicals [41, 42]. Second, unlike other teas, the blood pressure reduction mechanism of dark tea does not depend on the vascular endothelium. Instead, it inhibites Ca2 + influx to reduce vasoconstriction by blocking voltage-dependent calcium channels [43].
When the dosage of tea consumption was in the range of 2.1-4.0 g/d, sweet tea associated with a reduced SBP by 7.18mmHg. Paradoxically, sweet tea was associated with a slight increase in DBP when the consumption endured for more than 40 years and the amount was less than 2.0 g per day. The effect of sweet tea to increase DBP might be related to its special preparation method. Sweet tea is made by adding milk to black tea, which may partially explain a slight increase in DBP. Previous studies found that adding milk to black tea increased both SBP and DSP, but the exact mechanism was unclear[44].
Scented tea was also associated with lower DBP, but only at the low frequency of consumption. Scented tea is made from dried flowers and green tea. Scented tea contains chlorogenic acid and anthocyanins [45]. Laboratory evidence indicates that anthocyanins can induce eNOS expression in vascular endothelial cells through the Src-ERK$\frac{1}{2}$-Sp1 signaling pathway, promote NO production [46], inhibit the activity of the angiotensin-converting enzyme [47], and regulate aldosterone activity, thus producing a hypotensive effect [48]. As natural phytochemicals, the polyphenols and flavonoids in tea has additive and synergistic antioxidant activities [49], and the combined health effects were greater than that of a single substance. Therefore, the antihypertensive effect of scented tea might be better than that of single tea.
Our stratified analysis showed that the association between tea consumption and the risk of hypertensive BP was susceptible to demographic and lifestyle influences. Consistent with Tong et al. ’s study, which found that green tea consumption was inversely correlated with five-year blood pressure changes in Chinese adults, but smoking attenuated the effect[50], our study indicated that tea consumption was associated with a reduced risk of hypertensive BP in nonsmokers, but not in smokers. In addition, our study results showed that tea drinking was associated with a further reduction in the risk of hypertensive BP if healthy lifestyles were practiced, such as no smoking or no drinking. It is well established that unhealthy lifestyle behaviors such as smoking, drinking alcohol, or a high-salt diet were related to a higher risk of high blood pressure, thereby attenuating the protective effect of tea consumption on blood pressure.
## Strengths and limitations
There are several strengths and limitations in this study. First, the large sample, multi-ethnic natural cohort study has a better representation of the population, which increased the credibility and generalizability of the study findings. Second, this study explored the antihypertensive effects of five types of tea consumption on SBP and DBP, including green tea, dark tea, scented tea, sweet tea, and black tea. The detailed analyses of the associations between frequency, duration, and amount of tea consumption and blood pressure were adjusted for various confounders, which allowed us to offer specific recommendations about tea consumption. Third, stratified and sensitivity analyses were conducted to explore the association between tea consumption and hypertensive BP by different sociodemographic characteristics and lifestyle variations, which made the results more robust and generalizable.
There are several study limitations as well. First, this study might suffer from an unspecified amount of recall bias associated with self-reported data. However, our carefully designed research protocol, such as the questionnaire development and interviewing technique might have helped us to minimize this possible bias. Second, the information about the tea brewing method was not collected and analyzed. Different brewing methods entail differences in time and temperature, which could have changed the bioactive ingredient of tea to affect the association between tea consumption and blood pressure [19]. Third, the content of the substance in tea was not measured in this study, as such, the confounding effect of caffeine was not identified and excluded. Finally, this was a cross-sectional study, which would not allow us to establish a causal link between tea consumption and blood pressure. Such a causal relationship can be confirmed by randomized controlled trials or panel studies in the future.
## Conclusion
Tea consumption is associated with a protective effect on blood pressure by lowering the risk of hypertensive BP by $10\%$. However, the protective effects vary across the type of tea consumed. Dark tea is related to lower SBP irrespective of duration and frequency of consumption. Long duration of green tea, scented tea, black tea, and sweet tea consumption is associated with decreased SBP, but the antihypertension effects vary in frequency, amount, and types of tea consumed.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1
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---
title: Association of the triglyceride–glucose index with coronary artery disease
complexity in patients with acute coronary syndrome
authors:
- Shiqiang Xiong
- Qiang Chen
- Yu Long
- Hong Su
- Yan Luo
- Hanxiong Liu
- Yingzhong Chen
- Qiao Feng
- Xiufen Peng
- Maoling Jiang
- Xiuqiong Yu
- Zhen Zhang
- Lin Cai
journal: Cardiovascular Diabetology
year: 2023
pmcid: PMC10010005
doi: 10.1186/s12933-023-01780-0
license: CC BY 4.0
---
# Association of the triglyceride–glucose index with coronary artery disease complexity in patients with acute coronary syndrome
## Abstract
### Aim
The triglyceride–glucose (TyG) index has been shown to be an independent predictor for the progression and prognosis of coronary artery disease (CAD). Whether the TyG index predicts the severity of CAD in patients presenting with acute coronary syndrome (ACS) remains unknown.
### Methods
A total of 1,007 individuals presenting with ACS undergoing coronary angiography were stratified according to the tertiles of the TyG index and The Synergy Between Percutaneous Coronary Intervention (SYNTAX) score (SYNTAX score ≤ 22 versus SYNTAX score > 22). CAD complexity was determined by the SYNTAX score.
### Results
After adjusting for multiple confounding factors, the TyG index was still an independent risk factor for mid/high SYNTAX scores (SYNTAX score > 22, OR 2.6452, $95\%$ CI 1.9020–3.6786, $P \leq 0.0001$). Compared with the lowest tertile of the TyG (T1) group, the risk for a mid/high SYNTAX score in the T2 and T3 groups was 2.574-fold higher (OR, 2.574; $95\%$ CI 1.610–4.112; $P \leq 0.001$) and 3.732-fold higher (OR, 3.732; $95\%$ CI 2.330–5.975; $P \leq 0.001$), respectively. Furthermore, there was a dose‒response relationship between the TyG index and the risk of complicated CAD (SYNTAX score > 22; nonlinear $$P \leq 0.200$$). The risk for a mid/high SYNTAX score in the T2 and T3 groups was significantly higher in normoglycemia, prediabetes mellitus, and diabetes mellitus subgroups.
### Conclusions
A higher TyG index was associated with the presence of a higher coronary anatomical complexity (SYNTAX score > 22) in ACS patients, irrespective of diabetes mellitus status. The TyG index might serve as a noninvasive predictor of CAD complexity in ACS patients and could potentially influence the management and therapeutic approach.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12933-023-01780-0.
## Introduction
The Synergy Between Percutaneous Coronary Intervention (SYNTAX) score, a comprehensive angiographic tool that takes into account anatomic risk factors, is the best-known scoring system to grade the complexity of coronary artery disease (CAD) and objectively guide decision-making between coronary artery bypass grafting surgery and percutaneous coronary intervention (PCI) in patients with complex CAD [1]. According to the SYNTAX score, CAD patients are categorized as low (≤ 22), intermediate (23 to 32), or high risk (≥ 33) [2]. Patients with higher SYNTAX scores reflect more complex disease and are at higher potential risk of major adverse cardiovascular events [2, 3]. However, the calculation of the SYNTAX score depends on the findings of invasive coronary angiography. Noninvasively assessing the severity of CAD prior to coronary angiography could be beneficial for early stratification and possibly alter the therapeutic approach and management of patients with acute coronary syndrome (ACS).
Mounting evidence demonstrates that insulin resistance plays a crucial role in the development and pathogenesis of cardiovascular disease [4]. A high level of insulin resistance not only is associated with an increased risk of atherosclerotic cardiovascular disease but is also correlated with a higher risk of adverse cardiovascular events [5]. Recently, the triglyceride–glucose (TyG) index, derived from triglyceride and fasting blood glucose, has been shown to be a reliable surrogate marker of insulin resistance [6]. A recent large-scale prospective study suggested that the TyG index is an independent predictor for the progression of coronary artery calcification, especially in individuals without heavy coronary artery calcification at baseline [7]. Moreover, an increased TyG index has been shown to be independently associated with higher risks of atherosclerotic cardiovascular diseases, including myocardial infarction [8], and worse prognosis in patients with ACS, irrespective of diabetes mellitus [9].
Thus, the aim of this study was to investigate the angiographic severity of CAD encountered across the TyG index continuum and determine the association between the TyG index and the SYNTAX score in patients with ACS.
## Study population
We enrolled 1,007 patients hospitalized at the Third People's Hospital of Chengdu (Sichuan, China) undergoing coronary angiography who were diagnosed with ACS from July 2018 to December 2020. Individuals with incomplete key variables, including the SYNTAX score and the TyG index variables, were excluded. This retrospective observational cohort study was approved by the local ethics committee and strictly adhered to the Declaration of Helsinki, with informed consent waived due to its retrospective nature.
## Data collection and definitions
Data on sociodemographic characteristics, medical history, smoking status, laboratory examination, and medical and procedural information of participants were collected from the electronic medical records. Previous medical history data included a history of PCI, hypertension, diabetes mellitus, atrial fibrillation, stroke, and chronic obstructive pulmonary disease. ACS was defined as including unstable angina (UA), non-ST segment elevation myocardial infarction (NSTEMI), or ST segment elevation myocardial infarction (STEMI) [10].
Peripheral venous blood samples from patients were collected after overnight fasting (> 8 h). Laboratory parameters, including fasting blood glucose (FBG), triglycerides (TG), total cholesterol (TC), low-density lipoprotein-C (LDL-C), high-density lipoprotein-C (HDL-C), cardiac troponin T (cTnT), serum creatinine (Scr), and brain natriuretic peptide (BNP), were measured by standard biochemical techniques in the Clinical Laboratory of the Third People's Hospital of Chengdu. The left ventricular ejection fraction (LVEF) was determined by the two-dimensional modified Simpson’s method.
The TyG index was calculated as ln [fasting TG (mg/dL) × FBG (mg/dL)/2] [11]. A web-based online calculation tool (http://syntaxscore.com/) was used to calculate the SYNTAX score from the preprocedural angiograms by two independent cardiologists who were blinded to the study protocol and baseline clinical characteristics.
## Statistical analysis
Categorical data are described as counts and percentages (%) and were compared via the chi-square or Fisher’s exact test when appropriate. Continuous data are described as the mean with standard deviation or median with interquartile range and were compared via Student’s t test or the Mann–Whitney U test, respectively. For comparisons across the tertiles of the TyG index, the one-way analysis of variance and the Kruskal–Wallis test for parametric and nonparametric variables were used for continuous variables, respectively, and the chi-square test was performed for categorical data.
Spearman’s correlation analysis was used to investigate the correlation between the SYNTAX score and other parameters. A logistic regression analysis was adopted to analyze the association between the TyG index and the angiographic severity of CAD (SYNTAX score ≤ 22 versus SYNTAX score > 22). After checking for collinearity, the variables with an unadjusted P value of < 0.05 were included in the multivariate model. The results are described as odds ratios (ORs) with $95\%$ confidence intervals (CIs). Model I was adjusted for age, body mass index (BMI), hypertension, and diabetes mellitus. Model II was adjusted for age, BMI, hypertension, diabetes mellitus, heart rate (HR), BNP and Scr. Restricted cubic splines (RCS) were performed to evaluate the dose‒response relationship between the baseline TyG index and CAD severity.
The area under the receiver operating characteristic (ROC) curve (AUROC) were calculated to determine the diagnostic performance of the TyG index in detecting the severity of CAD in patients with ACS.
All statistical analyses were carried out with R version 4.0.2 software (R Foundation for Statistical Computing, Vienna, Austria) and SPSS version 26.0 software (IBM Corporation, New York, NY, USA). A P value < 0.05 was considered statistically significant.
## Baseline characteristics
The average age of the 1,007 patients ($28.2\%$ were female) with confirmed ACS (UA, NSTEMI, and STEMI) who underwent PCI was 66.55 ± 11.41 years. The baseline characteristics based on tertiles of the TyG index (T1, TyG ≤ 8.67; T2, 8.67 < TyG ≤ 9.18; T3, TyG > 9.18) are shown in Table 1. Compared with patients in the T1 group, patients with a higher TyG index tended to have a higher prevalence of diabetes mellitus, multivessel disease (MVD), left main lesion, and chronic total occlusion, increased levels of FBG, TG, TC, LDL-C, the TyG index and SYNTAX score, and a larger number and length of stents. Table 1The baseline characteristics based on tertiles of the TyG indexVariableT1 ($$n = 341$$)T2 ($$n = 331$$)T3 ($$n = 335$$)P valueAge, years67.75 ± 11.6465.80 ± 11.4066.07 ± 11.130.055Female, n (%)72 (21.1)87 (26.3)125 (37.3) < 0.001BMI, kg/m223.63 ± 2.8824.69 ± 2.7324.64 ± 2.83 < 0.001Smoking, n (%)189 (55.4)196 (59.2)168(50.1)0.061Previous PCI, n (%)32 (9.4)25 (7.6)27 (8.1)0.674COPD, n (%)22 (6.5)19 (5.7)14 (4.2)0.414Hypertension, n (%)216 (63.3)219 (66.2)220 (65.7)0.714Diabetes mellitus, n (%)51 (15.0)118 (35.6)191 (57.0) < 0.001AF, n (%)22 (6.5)20 (6.0)24 (7.2)0.839Previous Stroke, n (%)28 (8.2)25 (7.6)24 (7.2)0.874SBP, mmHg131.28 ± 19.96132.73 ± 22.32132.70 ± 22.000.602HR, bpm76.21 ± 14.1378.14 ± 15.3378.84 ± 14.790.056cTnT, pg/ml32.53 (11.18, 899.45)30.47 (11.50, 534.50)64.79 (13.48, 1178.00)0.094BNP, pg/ml109.90 (46.45, 300.85)95.5 (31.6, 242.75)118.75 (39.6, 379.85)0.028Scr, umol/L77.60 (66.25, 90.30)77.00 (65.60, 91.60)73.90 (61.30, 93.10)0.335FBG, mmol/L5.44 ± 1.206.46 ± 1.798.97 ± 3.58 < 0.001TG, mmol/L0.99 ± 0.291.61 ± 0.422.22 ± 0.89 < 0.001TC, mmol/L4.09 ± 1.094.59 ± 1.294.75 ± 1.19 < 0.001HDL-C, mmol/L1.22 ± 0.331.12 ± 0.261.12 ± 0.27 < 0.001LDL-C, mmol/L2.49 ± 0.822.87 ± 0.952.95 ± 0.86 < 0.001AMI, n (%)174 (51.0)171 (51.7)193 (57.6)0.168Diagnosis, n (%)0.137 UA167 (49.0)160 (48.3)142 (42.4) NSTEMI65 (19.1)81 (24.5)83 (24.8) STEMI109 (32.0)90 (27.2)110 (32.8)*Angiographic data* MVD, n (%)204 (59.8)228 (68.9)253 (75.5) < 0.001 LM, n (%)4 (1.2)23(6.9)27 (8.1) < 0.001 Calcified lesions, n (%)41 (12.0)41(12.4)56 (16.7)0.144 Thrombosis, n (%)24 (7.0)24(7.3)36 (10.7)0.149 Long lesion, n (%)132 (38.7)149(45.0)177 (52.8)0.001 CTO, n (%)57 (16.7)69(20.8)82 (24.5)0.045 Number of stents1.32 ± 0.751.42 ± 0.881.63 ± 0.99 < 0.001 Length of stents, mm34.12 ± 22.7337.02 ± 26.5043.94 ± 29.75 < 0.001 bSS11.00 (7.00, 17.5)13(8.00, 20.00)16.00 (10.00, 23.50) < 0.001 TyG index8.30 ± 0.308.95 ± 1.159.54 ± 0.32 < 0.001The groups were stratified by the tertiles of the TyG index (T1, TyG ≤ 8.67; T2, 8.67 < TyG ≤ 9.18; T3, TyG > 9.18). BMI body mass index, COPD chronic obstructive pulmonary disease, AF atrial fibrillation, SBP systolic blood pressure, HR heart rate, BNP brain natriuretic peptide, *Scr serum* creatinine, FBG fasting blood glucose, TG triglyceride, TC total cholesterol, HDL-C high density lipoprotein, LDL-C low density lipoprotein, UA unstable angina, STEMI ST-segment elevation myocardial infarction, NSTEMI non-ST-segment elevation myocardial infarction, MVD multivessel disease, LM left main disease, CTO chronic total occlusion, bSS baseline SYNTAX score, TyG index, the triglyceride–glucose index. Data are presented as mean ± SD, median (IQR) or n (%) According to the SYNTAX score, patients were categorized into low (SYNTAX score ≤ 22) and mid/high risk (SYNTAX score > 22) groups as shown in Additional file 1: Table S1. Patients with a SYNTAX score > 22 were older and had higher prevalence rates of hypertension, diabetes mellitus, AMI, MVD, left main lesion, calcified lesions, thrombosis, long lesion, and chronic total occlusion, increased levels of HR, cTnT, BNP, Scr, FBG, TG, and the TyG index, and a larger number and length of stents.
## Association between the TyG index and severity of CAD
The univariate logistic regression analysis revealed that age, BMI, previous history of hypertension and diabetes mellitus, HR, BNP, Scr, FBG, TGs, and the TyG index were potential risk factors (univariate $P \leq 0.05$) for a mid/high SYNTAX score (SYNTAX score > 22, Table 2). FBG and TG, being components of the TyG index, were not included in the multivariable logistic regression model in order to avoid any potential interactions. After checking for collinearity, the potential risk factors were used as variables in the multivariate model, and the results showed that the TyG index was an independent predictor of the mid/high SYNTAX score (SYNTAX score > 22, OR 2.6452, $95\%$ CI 1.9020–3.6786, $P \leq 0.0001$).Table 2Univariate and multivariate logistic regression analysis for predicting a mid/high SYNTAX scoreVariablesUnivariate analysisMultivariate analysisOR$95\%$ CIP valueOR$95\%$ CIP valueAge, years1.03661.0211–1.0523 < 0.00011.03031.0135–1.04740.0004Female, n (%)1.09370.7743–1.54490.6113BMI, kg/m20.92970.8788–0.98360.01130.93340.8772–0.99320.0295Smoking, n (%)0.83920.6100–1.15450.2814Previous PCI, n (%)0.91620.5120–1.63960.7683Hypertension, n (%)1.42821.0126–2.01430.04221.34370.9187–1.96530.1277Diabetes mellitus, n (%)1.47901.0737–2.03710.01660.89300.6150–1.29660.5520AF, n (%)1.65300.9384–2.91180.0819SBP, mmHg0.99660.9892–1.00400.3675HR, bpm1.01081.0005–1.02120.04041.00490.9941–1.01580.3714cTnT, pg/ml1.00001.0000–1.00010.0863BNP, pg/ml1.00051.0003–1.0007 < 0.00011.00041.0002–1.00060.0009Scr, umol/L1.00211.0007–1.00340.00311.00130.9999–1.00260.0677TyG index2.41871.8081–3.2357 < 0.00012.64521.9020–3.6786 < 0.0001FBG, mmol/L1.09111.0377–1.14710.0007TG, mmol/L1.79991.4548–2.2270 < 0.0001TC, mmol/L1.10620.9758–1.25390.1148HDL-C, mmol/L1.08400.6414–1.83220.7632LDL-C, mmol/L1.06840.9847–1.15930.1119Abbreviations as shown in Table 1. PCI percutaneous coronary intervention, Data are presented as mean ± SD, median (IQR) or n (%) Logistic regression models were further constructed to demonstrate that the TyG index was significantly associated with CAD severity ($P \leq 0.001$, Table 3). When analyzed as a continuous variable, the TyG index was significantly related to a mid/high score (SYNTAX score > 22, OR: 2.419; $95\%$ CI 1.808–3.236, $P \leq 0.001$). Using the T1 group as a reference, the risk of a mid/high SYNTAX score for the T2 and T3 groups was 2.110-fold higher (OR, 2.110; $95\%$ CI 1.361–3.270; $P \leq 0.001$) and 3.112-fold higher (OR, 3.112; $95\%$ CI 2.042–4.744; $P \leq 0.001$), respectively. After adjusting for age, BMI, hypertension, diabetes mellitus, HR, BNP and Scr, the TyG index as a categorical variable was still an independent hazard factor for a mid/high SYNTAX score (OR, 2.645; $95\%$ CI 1.902–3.679; $P \leq 0.001$). Compared with the T1 group, the risk for a mid/high SYNTAX score in the T2 and T3 groups was 2.574-fold higher (OR, 2.574; $95\%$ CI 1.610–4.112; $P \leq 0.001$) and 3.732-fold higher (OR, 3.732; $95\%$ CI 2.330–5.975; $P \leq 0.001$), respectively. Table 3Associations between the TyG index and complexity of CADNon-adjustedModel IModel IIOR ($95\%$ CI)P valueOR ($95\%$ CI)P valueOR ($95\%$ CI)P valueTyG index2.419 (1.808–3.236) < 0.0012.719(1.965–3.762) < 0.0012.645 (1.902–3.679) < 0.001T1Ref. Ref. Ref. Ref. Ref. Ref. T22.110 (1.361–3.270) < 0.0012.480(1.571–3.917) < 0.0012.574 (1.610–4.112) < 0.001T33.112 (2.042–4.744) < 0.0013.671(2.320–5.809) < 0.0013.732 (2.330–5.975) < 0.001None, non-adjusted model. Model I was adjusted for age, BMI, hypertension, diabetes mellitus, Model II was adjusted for age, BMI, hypertension, diabetes mellitus, heart rate, BNP and serum creatinine. CAD coronary artery disease The spearman’s correlation analysis found that there was a statistically significant but weak positive correlation between the TyG index and the SYNTAX scores ($r = 0.22$, $P \leq 0.001$, Fig. 1). Compared with patients in the T1 group, the proportion of patients with a SYNTAX score > 22 was larger in participants with a higher TyG index (Fig. 2). Additionally, the results of the RCS showed a dose‒response relationship between the TyG index and the risk of a mid/high SYNTAX score (Fig. 3; Nonlinear $$P \leq 0.200$$).Fig. 1Correlation of the SYNTAX score with the TyG index. Spearman’s correlation analysis found that there was a significant positive correlation between the TyG index and the SYNTAX scores ($r = 0.22$, $P \leq 0.001$). SYNTAX score Synergy Between Percutaneous Coronary Intervention score, TyG index triglyceride–glucose indexFig. 2Comparison of the SYNTAX score according to the TyG index tertiles. The proportion of patients with a SYNTAX score ≤ 22 and SYNTAX score > 22 in patients presenting with acute coronary syndrome stratified according to the tertiles of the TyG index. SYNTAX score Synergy Between Percutaneous Coronary Intervention score, TyG index triglyceride–glucose indexFig. 3RCS for the odds ratio of a mid/high SYNTAX score. RCS restricted cubic spline, OR odds ratio, SYNTAX score Synergy Between Percutaneous Coronary Intervention score
## The predictive performance of the TyG index for complex coronary lesions
The AUROC of the TyG index was significantly higher than that of fasting blood glucose (0.631 [$95\%$ CI 0.588–0.674] vs. 0.574 [$95\%$ CI 0.528–0.621], $$P \leq 0.0095$$) and was greater than that of TG with no statistical significance (0.631 [$95\%$ CI: 0.588–0.674] vs. 0.613 [$95\%$ CI 0.567–0.659], $$P \leq 0.2651$$) (Fig. 4) (Additional file 1: Table S2). These results demonstrated the TyG index has the highest predictive value for predicting coronary anatomical complexity (SYNTAX score > 22) in patients with ACS, when compared to either FBS or TG alone. Fig. 4ROC curves for predicting a mid/high SYNTAX score. The area under the ROC curve of the TyG index, FBS, and TG for predicting a mid/high SYNTAX score (> 22) was 0.631 ($95\%$ CI 0.588–0.674, $P \leq 0.001$), 0.574 ($95\%$ CI 0.528–0.621, $$P \leq 0.002$$), and 0.613 ($95\%$ CI 0.567–0.659, $P \leq 0.001$), respectively. SYNTAX score Synergy Between Percutaneous Coronary Intervention score, TyG index triglyceride–glucose index, ROC curve receiver operating characteristic curve, FBG fasting blood glucose, TG triglyceride
## Associations between the TyG index and the severity of CAD in subgroups stratified by different glucose metabolism status
Subgroup analyses were also conducted to investigate the associations between the TyG index and the severity of CAD in patients according to different diabetes status, including those with normoglycemia ($$n = 363$$), prediabetes mellitus ($$n = 284$$), and diabetes mellitus ($$n = 360$$) (Table 4). When adjusted for age, BMI, hypertension, heart rate, BNP, and serum creatinine in model II, the TyG index as a continuous variable was an independent risk factor for a mid/high SYNTAX score in individuals with normoglycemia (OR, 2.902; $95\%$ CI 1.453–5.797; $$P \leq 0.003$$), prediabetes mellitus (OR, 2.321; $95\%$ CI 1.213–4.441; $$P \leq 0.011$$), and diabetes mellitus (OR, 2.666; $95\%$ CI 1.585–4.486; $P \leq 0.001$). Compared with the T1 group, the risk for a mid/high SYNTAX score in the T2 and T3 groups was significantly higher in all subgroups, irrespective of diabetes mellitus status. Table 4Associations between the TyG index and severity of CAD in different glucose metabolism statusNon-adjustedModel IModel IIOR ($95\%$ CI)P valueOR ($95\%$ CI)P valueOR ($95\%$ CI)P valueNG TyG index2.159 (1.140–4.092)0.0182.843(1.442–5.606)0.0032.902(1.453–5.797)0.003 T1Ref. Ref. Ref. Ref. Ref. Ref. T22.140(1.088–4.208)0.0272.705(1.335–5.480)0.0062.654 (1.282–5.492)0.009 T32.562(1.105–5.945)0.0283.221(1.334–7.779)0.0093.544(1.446–8.686)0.006Pre-DM TyG index2.091(1.126–3.884)0.0202.095(1.123–3.910)0.0202.321(1.213–4.441)0.011 T1Ref. Ref. Ref. Ref. Ref. Ref. T21.412(0.642–3.103)0.3911.503(0.675–3.348)0.3191.789 (0.771–4.147)0.176 T32.669(1.261–5.648)0.0102.784(1.300–5.964)0.0083.309(1.481–7.391)0.004DM TyG index2.577 (1.591–4.175) < 0.0012.957 (1.782–4.907) < 0.0012.666 (1.585–4.486) < 0.001 T1Ref. Ref. Ref. Ref. Ref. Ref. T22.730 (0.986–0.755)0.0533.595(1.262–10.241)0.0173.143 (1.084–9.113)0.035 T33.351 (1.262–8.903)0.0154.867(1.772–13.365)0.0024.029(1.445–11.231)0.008None, non-adjusted model. Model I was adjusted for age, BMI, hypertension, diabetes mellitus, Model II was adjusted for age, BMI, hypertension, diabetes mellitus, heart rate, BNP and serum creatinine. CAD coronary artery disease, NG normoglycemia, Pre-DM prediabetes mellitus, DM prediabetes mellitus
## Discussion
The present study shows that a higher TyG index independently predicts the presence of a higher coronary anatomical complexity (SYNTAX score > 22) in patients with ACS undergoing coronary angiography, irrespective of diabetes mellitus status. Our findings suggest that a higher insulin resistance represented by the TyG index makes the patients more susceptible to severe coronary lesions.
The adverse effect on the clinical prognosis of more extensive and complex CAD has been confirmed in many studies. Fumiaki et al. demonstrated that a mid/high SYNTAX score (≥ 23) could predict increased risks of major cardiovascular events (HR 1.36; $95\%$ CI 1.07–1.75, $$P \leq 0.01$$) over 5 years in patients from the BARI-2D trial [3]. Additionally, higher SYNTAX scores were significantly associated with more favorable outcomes of revascularization compared with medical therapy among patients suitable for coronary artery bypass grafting surgery [3]. Higher SYNTAX scores also predicted a particular therapeutic benefit from coronary artery bypass grafting surgery compared with PCI in the SYNTAX trial [12]. The most recent clinical guideline for coronary artery revascularization recommends that using the SYNTAX score to assess CAD complexity in patients with multivessel CAD may be useful to guide revascularization [13]. Nevertheless, the application of the SYNTAX score in early treatment decisions in patients with ACS is less clear, because its calculation has to depend on the findings of invasive coronary angiography.
The key finding of this study is that higher levels of the TyG index predict more extensive and complex coronary anatomical lesions in patients with ACS, irrespective of diabetes mellitus status, and this index score can be determined in a non-invasive manner. Previously, although Wang et al. found a significant association between the TyG index and the incidence of MVD, the results were only significant in patients with prediabetes mellitus [14]. Additionally, Lee et al. reported that the TyG index was associated with an increased risk of coronary artery stenosis in asymptomatic subjects with type 2 diabetes mellitus, and the degree of coronary artery stenosis was not quantified [15]. Notably, in the present study, a significantly higher complexity of CAD, including MVD, left main lesion, calcified lesions, thrombosis, long lesion, and chronic total occlusion increased with increasing TyG index levels. Further analysis demonstrated that the associations between the TyG index and the severity of CAD were significant in both diabetic and nondiabetic individuals. These findings indicate that the TyG index might serve as a predictor of CAD severity in patients with ACS prior to undergoing coronary angiography.
Mounting epidemiological evidence suggests that insulin resistance constitutes an independent prognostic predictor in CAD [16], but insulin resistance or its surrogate marker has not been included in any risk prediction tools, such as the GRACE score or the SYNTAX score. Previous studies have shown that prediction models combining anatomical and clinical factors such as the SYNTAX II score and clinical residual SYNTAX score could improve the discriminative ability for a better risk assessment [17–19]. One of our previous studies also highlighted that adjustment of the residual SYNTAX score by the TyG index significantly improves the predictive accuracy for adverse cardiovascular events in patients with type 2 diabetes mellitus undergoing PCI [11]. Therefore, we suggest that the TyG index could be added to a preexisting risk prediction model to enhance its discriminate ability for patients with CAD in future studies.
Although the detailed mechanism underlying the association between the TyG index and cardiovascular disease is not fully illustrated, the TyG index has been regarded as a valuable indicator linked to insulin resistance and cardiovascular disease. Chronic hyperglycemia and dyslipidemia induced by insulin resistance contribute to the development of cardiovascular disease [5]. A higher level of TyG index has been shown to be associated with an increased risk of cardiovascular diseases in the general population [8, 20–22], and is an independent predictor of poor prognosis in different cohorts undergoing PCI [23–29]. One of our previous studies demonstrated that the TyG index could provide additional predictive ability on the top of residual SYNTAX score in predicting intermediate‑term major adverse cardiovascular events after PCI in patients with diabetes mellitus [11]. Recently, Wang et al. revealed that the TyG index was an independent risk factor for multi-vessel coronary artery disease in individuals with prediabetes mellitus, but not in those with normoglycemia or diabetes mellitus [14]. Meanwhile, another literature reported that the association between the TyG index and multi-vessel coronary artery disease was significant in patients with diabetes mellitus [30]. In line with previous studies, the current research supports this notion by more thoroughly demonstrating that the TyG index could independently predict a mid/high SYNTAX score (≥ 23) in patients with ACS, irrespective of diabetes mellitus status. Taken together, these findings indicated that a higher insulin resistance represented by the TyG index makes individuals more susceptible to severe coronary lesions and unfavorable outcomes.
Recently, several cardiovascular outcome trials have demonstrated that therapies aimed at improving insulin resistance are a promising intervention for diabetic patients at risk of experiencing adverse cardiovascular events [31]. Pioglitazone, a potent insulin sensitizer, has been shown to reduce atherosclerotic progression (based on PERISCOPE and Chicago studies) and the rate of cardiovascular events (according to the IRIS and PROactive randomized prospective cardiovascular outcome trials) [32–35]. Glucagon-like peptide-1 analogues have been shown to reduce the risk of major adverse cardiac events and have a direct impact on cardiac mortality in advanced atherosclerosis [36, 37]. The potential cardioprotective effect of glucagon-like peptide-1 analogues is partially attributed to their direct effects on vascular redox state and changes in insulin resistance [37]. Therefore, treatment of insulin resistance may contribute to the amelioration of coronary lesions and clinical prognosis.
## Limitations
This is a single-center, observational study with a relatively small sample size and that only enrolled the Chinese population. The results should be interpreted cautiously and further verified by multicenter and large sample size studies. Additionally, because of the inevitable inherent disadvantage of retrospective studies, a causal relationship between the TyG index and CAD complexity could not be concluded from this study; therefore, these findings need to be verified by a prospective study. Additionally, the feasibility of calculating TyG Index using blood samples collected after an overnight fasting (> 8 h) before coronary angiography is limited in some patients undergoing an emergent coronary angiography, particularly those with STEMI.
## Conclusions
The present study demonstrated a significantly positive relationship between the TyG index and the SYNTAX scores in patients with ACS undergoing coronary angiography. A higher TyG index independently predicted the presence of a higher coronary anatomical complexity (SYNTAX score > 22) in patients with ACS, irrespective of diabetes mellitus status. Our findings suggest that the TyG index could be used as a predictor of CAD severity and could potentially influence the management and therapeutic approach. Novel therapies aimed at improving insulin resistance may contribute to the amelioration of coronary lesions and clinical prognosis.
## Supplementary Information
Additional file 1: Table S1. The baseline characteristics based on tertiles of the baseline SYNTAX score. Table S2. Comparisons of the area under the ROC curves of the TyG index, FBG and TG.
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|
---
title: Single-cell analyses reveal distinct expression patterns and roles of long
non-coding RNAs during hESC differentiation into pancreatic progenitors
authors:
- Hai-Tao Luo
- Qian He
- Wei Yang
- Fei He
- Jun Dong
- Chao-Feng Hu
- Xiao-Fei Yang
- Ning Li
- Fu-Rong Li
journal: Stem Cell Research & Therapy
year: 2023
pmcid: PMC10010006
doi: 10.1186/s13287-023-03259-x
license: CC BY 4.0
---
# Single-cell analyses reveal distinct expression patterns and roles of long non-coding RNAs during hESC differentiation into pancreatic progenitors
## Abstract
### Background
Deep understanding the differentiation process of human embryonic stem cells (hESCs) is essential for developing cell-based therapeutic strategy. Substantial efforts have been made to investigate protein-coding genes, yet it remains lacking comprehensive characterization of long non-coding RNAs (lncRNAs) during this process.
### Methods
hESCs were passaged every 5–6 days and had maintained stable karyotype even until the 50th generation. Pancreatic progenitor specification of in vitro differentiation from hESCs was performed and modified. The nuclei were stained with 4,6-Diamidino-2-phenylindole (DAPI). Droplet-based platform (10X Genomics) was applied to generate the single-cell RNA sequencing (scRNA-seq) data. The quality of the filtered read pairs was evaluated by using FastQC. Batch effects were removed using the size factor method. Dimension reduction and unsupervised clustering analyses were performed using Seurat R package. The Monocle 2 and MetaCell algorithms were used to order single cells on a pseudotime course and partition the scRNA-seq data into metacells, respectively. Co-expression network was constructed using WGCNA. Module- and hub-based methods were adopted to predict the functions of lncRNAs.
### Results
A total of 77,382 cells during the differentiation process of hESCs toward pancreatic progenitors were sequenced. According to the single-cell map, the cells from different time points were authenticated to constitute a relatively homogeneous population, in which a total of 7382 lncRNAs could be detected. Through further analyzing the time course data, conserved and specific expression features of lncRNAs during hESC differentiation were revealed. Based upon pseudotime analysis, 52 pseudotime-associated lncRNAs that grouped into three distinct expression patterns were identified. We also implemented MetaCell algorithm and network-based methods to explore the functional mechanisms of these lncRNAs. Totally, 464 lncRNAs, including 49 pseudotime-associated lncRNAs were functionally annotated by either module-based or hub-based methods. Most importantly, we demonstrated that the lncRNA HOTAIRM1, which co-localized and co-expressed with several HOX genes, may play crucial role in the generation of pancreatic progenitors through regulation of exocytosis and retinoic acid receptor signaling pathway.
### Conclusions
Our single-cell analyses provide valuable data resources for biological researchers and novel insights into hESC differentiation processes, which will guide future endeavors to further elucidate the roles of lncRNAs.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13287-023-03259-x.
## Background
The self-renewal and pluripotency features of human embryonic stem cells (hESCs) have made them to be valuable resources for basic scientific research and provided remarkable promises in translational medicine [1–4]. hESCs are able to differentiate diverse cell lineages both in vitro and in vivo through a series of defined developmental paths, which allows scientists to investigate the molecular mechanisms of early cell fate decisions [4, 5]. Furthermore, due to the therapeutic potential for diabetes and for application in drug discovery, in vitro differentiation of hESCs into pancreatic progenitor cells, which are on course to become functional beta-like cells, has received much attention over the past decades [4, 6, 7]. Nevertheless, the mechanisms underlying hESCs differentiation and the therapeutic efficiency remain largely unknown. By contrast, several problems such as unexpected cell growth, low differentiation efficiency, and the risk of teratoma formation have occurred. Therefore, more comprehensive and systematic studies to investigate the transcriptome of cells in the development of hESCs are desired.
Long non-coding RNAs (lncRNAs) that are defined as transcripts longer than 200 nucleotides (nt) with little or no protein-coding potential have emerged as important regulators in a variety of cellular developmental and differentiation processes and are closely related to major human diseases, such as diabetes [8, 9]. Recent studies have indicated that lncRNAs appear as regulators for ESC self-renewal and pluripotency [10–12]. Furthermore, the number of lncRNAs is cell-specific and dynamically regulated during β cell differentiation and maturation, indicating that lncRNAs could be potential regulators of lineage-specific differentiation or specialized cellular functions [13, 14]. However, the global expression patterns and regulatory mechanisms of lncRNAs during the early stage of hESCs remain poorly understood and need to be addressed systematically.
Recently, although high-throughput single-cell RNA sequencing (scRNA-seq) has been applied to characterize cell types during human beta-cell and islet cell differentiation [15–17], single-cell lncRNA profiling of the differentiation process of hESCs to pancreatic progenitor seems to have not been reported. Here, we apply scRNA-seq and computational approaches to generate a single-cell transcriptome map of the early stage of hESC differentiation toward pancreatic progenitors and perform the systematical analysis to globally characterize the expression dynamics and functional roles of lncRNAs.
## Cell culture
Human embryonic stem-cell lines (H9) were obtained from Cell Bank of the Shanghai Institutes for Biological Sciences of the Chinese Academy of Sciences (Order Number: 18-1-1522) and authenticated using short tandem repeat (STR) analysis (GENETIC TESTING BIOTECHNOLOGY Co., Ltd.). hESCs were maintained in feeder-free cell culture medium mTeSR™1(STEMCELL Technologies, #85850). hESCs were passaged every 5–6 days using ReLeSR™ (STEMCELL Technologies, #05873) and had maintained a stable karyotype even until the 50th generation (Beijing Cellapybio Biotechnology Co., Ltd.).
Procedures for pancreatic progenitor specification in vitro differentiation from hESCs were performed and modified according to previously protocols [18, 19]. Briefly, hESCs were dissociated into single cells by TrypLE™ (ThermoFisher, 12604021) and re-suspended in DMEM/F-12 (ThermoFisher, 11330057). After centrifuging at 300 g for 5 min, cell pellets were re-suspended in mTeSR™1 with 10 μM Y-27632. The differentiation was conducted 24 h later by changing the induction media. The media changes were described as follows. Day 1:RPMI1640 supplemented with 100 ng/ml Activin, 50 ng/ml WNT3a and 1:2000 ITS. Day 2–3: RPMI1640 supplemented with 100 ng/ml Activin, $0.2\%$ FBS and 1:1000 ITS. Day 4–6: RPMI1640 supplemented with $0.5\%$ FBS, 0.25 mM Vitamin C, 1:1000 ITS and 50 ng/ml KGF. Day7–9: DMEM supplemented with $0.5\%$ FBS, 0.25 mM Vitamin C, 50 ng/ml KGF, 2 μM RA, 1:200 B27, 0.25 μM Sant1, and 100 ng/ml Noggin.
## Immunofluorescence and image analysis
The prepared cells were twice-washed with 0.1 mM phosphate-buffered saline (PBS) and then cross-linked by $4\%$ paraformaldehyde for 20 min at room temperature. After another wash with 0.1 mM PBS, the cells were incubated with $10\%$ BSA and $0.5\%$ Triton X-100 in PBS for 1 h. Primary antibodies (anti-SOX17, Abcam/ab84990, 1:1000, and anti-FOXA2, R&D/AF2400, 1:500) were then added and incubated at 4 °C overnight. The next day, the cells were washed with 0.1 mM PBS three times and followed by incubation with secondary antibodies (1:1000) conjugated with a fluorophore at room temperature for 2–3 h. The nucleus was then stained by using 4,6-Diamidino-2-phenylindole (DAPI).
The fluorescence expression of SOX17, FOXA2, and DAPI was detected using the Leica DMi8 system (S/N 434713, objective lenses 20×, Fluorescence Filters: Blue for DAPI, Green for SOX17 and Red for FOXA2) and the Leica DFC7000 T camera/detector. The images were acquired with Leica Application Suite X software and then analyzed using ImageJ2x software. Briefly, the background was subtracted at the value of 10 from these raw images (resolution 1920 × 1440 pixels), and the individual color channels were then merged to assess the colocalization of SOX17 an FOXA2 expression in the nuclei. No further downstream processing or averaging that enhances the resolution of the images was conducted. The immunofluorescence analysis experiment was repeated three times independently.
## Single-cell library preparation and sequencing
Droplet-based platform (10X Genomics) was used to generate the scRNA-seq data in current study according to the manufacturer’s instructions in the Chromium Single-Cell 3’ Reagents Kits v2 User Guide. The single-cell suspension from each time point was washed twice with 1 × PBS + $0.04\%$ BSA. The loaded cell numbers were about 10,000 for each sample, that were confirmed with TC20™ Automated Cell Counter. The cells were then partitioned into the Gel Beads-in-Emulsion (GEM) along with Gel Beads coated with oligos in the 10X Genomics Chromium Controller machine. In each GEM, polyadenylated RNAs were captured by poly-dT oligos and then were reverse transcribed, amplified, and barcoded (including cell-specific and transcript-specific barcodes). Library quality and concentration were assessed using the Agilent 2100 Bioanalyzer (Agilent Technologies). Libraries were run on the Illumina Hiseq X with 150 bp paired-end reads.
## Quality control (QC) and pre-processing of scRNA-seq data
Raw scRNA-seq reads were pre-processed using Trimmomatic software [20] with the parameters: SLIDINGWINDOW: 4:10; TRAILING:3; ILLUMINACLIP: adapter.fa: 2: 0:7. The quality of the filtered read pairs was evaluated using FastQC. Clean reads from each cell were mapped to the human reference genome (GRCh38) and quantified using the 10X Cell Ranger package (version 2.1.0, 10 × Genomics). Low-quality or doublet cells were filtered for each sample according to the following criteria: [1] the cells were filtered if the number of total UMI counts was lower the medians of all cells minus 3 × the median absolute deviation (MAD); [2] cells were filtered out if the total number of expressed genes was lower than 2000 or higher than the medians of all cells plus 3 × the MAD; [3] cells were filtered out if the proportion of reads mapped to mitochondrial genes was larger than $5\%$ or higher than the medians of all cells minus 3 × the MAD. For cells from all samples, the size factor was computed based on a pooling and deconvolution strategy as implemented in the R package named ComputeSumFactors with the sizes ranged from 80, 100, 120 to 140 [21]. Then, the counts of each cell were normalized by dividing the counts by the size factor.
## Dimension reduction and clustering
Based on scRNA-seq expression data, we performed dimension reduction and unsupervised clustering analysis using Seurat R package (version 3.1.5) [22]. *The* genes that expressed in at least 3 cells were retained. The count matrix was normalized using NormalizeData function with default parameters. Then, FindVariableGenes function was used to identify highly variable genes (HVGs) that were subsequently used for PCA dimension reduction. The top fifteen principal components were selected according to elbow method and used for graph-based clustering. Cell clusters were identified and projected into 2D spaces using UMAP.
## Differential expression and functional enrichment analysis
Differentially expressed genes (DEGs) were identified using FindMarkers function with Wilcoxon rank sum test as implemented in Seurat. DEGs with adjusted P value less than 0.01, fold-change ≥ 2, and detected in a minimum fraction of 0.25 cells were retained. GO term enrichment analysis were done on DEGs using DAVID with default parameters.
## Re-analysis of human SC-islet and pancreatic islet scRNA-seq data
Read counts of from human SC-islet and pancreatic islet were obtained from previous publications [15, 16]. Data integration, batch effect normalization, dimensionality reduction, and unsupervised clustering analysis were performed as described above.
## Pseudotime analysis
The Monocle 2 (version 2.14.0) [23] was used to order single cells on a pseudotime course during hESC differentiation. *The* genes with expression value more than 0.1 and expressed in more than 10 cells were subjected to differential expression analysis. *The* genes with q value < 1E−4 were selected as ordering genes and used for pseudotime calculation. The Discriminative Dimensionality Reduction with Trees (DDRTree) algorithm was used for dimension reduction. To explore the different expression patterns of lncRNAs during hESC differentiation, the lncRNAs that significantly expressed along pseudotime were identified by “differentialGeneTest” function, which were then clustered into three distinct expression patterns based on k-means cluster method.
## Partitioning the scRNA-seq data into metacells
MetaCell algorithm (version 0.3.41) [24] was used to partition the scRNA-seq data into metacells. After removing mitochondrial genes (annotated with the prefix “MT-”), the remaining genes whose scaled variance (variance/mean on down-sampling) more than 0.08 were used to compute cell similarities. Two balanced K-NN similarity graph was constructed by using the parameter $K = 100.$ Then, 500 bootstrap iterations with resampling $75\%$ of the cells in each iteration were performed in the resampling procedure. The minimum metacell size was set to 30. The metacells and the cells involved in them were projected into 2D spaces by “mcell_mc2d_plot_by_factor” function.
## Co-expression network construction
The expression profiles of both protein-coding and lncRNA genes across all metacells were used to construct co-expression network, in which genes are represented as nodes, and two genes are linked by an edge (undirected) if they are co-expressed significantly.
*Only* genes with expressional variance ranked in the top $75\%$ were retained for co-expression network construction. The Pearson correlation coefficients for each gene pair were calculated. The significance of correlations between gene pair was evaluated by Fisher’s asymptotic test using R package WGCNA library of R and adjusted by Bonferroni multiple test correction using R package Multtest. *Only* gene pairs that met the following criteria were regarded as co-expressed and connected by edges: [1] Adjusted P value < 0.01; [2] Pearson correlation coefficient more than 0.7; [3] Pearson correlation coefficient ranked in the top or bottom $0.5\%$ for each gene.
## LncRNA function prediction based on module- and hub-based methods
Based on co-expression network constructed above, we predicted lncRNA functions by both model- and hub-based methods [25, 26]. Genes in the same network modules are closely connected with each other and may act as functional programs to play similar functions. The Markov cluster algorithm (MCL) was used to identify co-expressed modules in the network. If the protein-coding genes involved in a co-expressed module are significantly enriched for at least one GO term (adjusted P value < 0.01), the lncRNAs involved in this module would be assigned the same GO functions. For hub-based method, the functions of hub lncRNAs were predicted based on the functional enrichments (adjusted P value < 0.01) of their immediate neighboring protein-coding genes.
## Large-scale scRNA-seq of the early stage of hESC differentiation
To systematically characterize the transcriptomics of the early stage of hESC differentiation toward pancreatic progenitors, we performed droplet-based scRNA-seq (10X Chromium) by taking samples at four time points (day 0, day 2, day 4, and day 9) based on established protocols [18, 19] (Fig. 1A and Additional file 1: Table S1). After sequencing and quality control, high-quality transcriptomic data from 40,190 cells were obtained, including 6178 hESCs (day 0), 7615 cells from day 2, 13,082 cells from day 4, and 13,315 cells from two replicated samples of day 9 (marked as Day 9-R1 and Day 9-R2). For each sample, doublet cells and low-quality cells were filtered out if the number of expressed genes was higher than the medians of all cells plus 3 × the median absolute deviation or lower than 2000, respectively. On average, we detected 4391 genes (from 2001 to 8772) expressed in each individual cell (Fig. 1B). Sequencing depth and the number of detected genes were comparable across samples (Fig. 1B). To assess batch effects, the overall quality and gene expression profiles between two replicated samples of day 9 were compared. The results showed that cells from each batch were evenly distributed on the uniform manifold approximation and projection (UMAP) (Fig. 1C) and the highly correlated gene expression profiles (the Pearson correlation coefficient was 0.99) of the two batches (Fig. 1D), proving minimal batch effect in the present study. In addition, all samples showed highly correlated gene expression profiles with Pearson correlation coefficients were within the 0.90–0.97 range (Additional file 11: Fig. S1). Altogether, these results confirmed the validity and reasonable technical variability of our scRNA-seq data. Fig. 1Overview of single-cell sequencing data of hESC early differentiation. A Experimental workflow of single-cell sequencing. scRNA-seq was performed by taking samples at day 0, day 2, day 4, and day 9. B. The number of genes expressed in each sample. C. UMAP plot of single cells from two replicated samples of day 9. D. Scatter plot showing the gene expression correlation between two replicated samples of day 9. E. PCA plot of cells from five samples. F–G. UMAP plot of cells from five samples. Cells are colored by clusters (F) or samples (G) Next, dimension reduction and low-dimensional visualization of the scRNA-seq time series were performed by principal component analysis (PCA) based on the genes with high variance and expression across cells. Obviously, principal component (PC) 1 mainly discriminated day 0–4 from day 9, while PC2 captured the differences among hESCs, day 2, and day 4, with day 2 located in the middle of the axis (Fig. 1E).
Moreover, we performed uniform manifold approximation and projection (UMAP) analysis and clustered all cells together by using Seurat. In total, we defined 13 clusters that grouped into six cell groups featured by the expression of known marker genes and sample information, including hESCs (cluster 4 and 7), mesendoderm cells (cluster 1 and 9), definitive endoderm cells (cluster 2, 5, 6, and 8), ISL1+ progenitors (cluster 0, 3, and 11), and two groups of intermediate cells (cluster 10 and cluster 12) (Fig. 1F, G). Markedly, cells of hESC group (G1_hESC_day0) were dominant from day 0 and expressed well-known stem-cell markers including POU5F1, NANOG, and SOX2 (Fig. 2A). The second cluster, G2_ME_day2, was characterized by the high expression of the mesendoderm markers such as FGF4 and WNT3 and predominantly composed of cells from day 2 (Fig. 2A). The third group, G3_DE_day4, characterized by specific expression of SOX17 and high expression of FOXA2 (Fig. 2A), was mainly composed of definitive endoderm cells, which were also confirmed by immunofluorescence staining for SOX17 and FOXA2 (Fig. 2B and Additional file 12: Fig. S2). The fourth group, G4_IP_day9, was annotated as ISL1+ progenitor cells for the expression of ISL1 and HNF1B (Fig. 2C), which are of significance for the development of endocrine progenitors [27, 28], and mainly composed of cells from two replicated samples of day 9.Fig. 2Expression profiles and functional enrichments of marker genes across time points identified during hESC differentiation. A. UMAP visualization of the expression of well-defined marker genes of different stages in hESC differentiation. B. The representative immunofluorescence staining for definitive endoderm cells with antibodies against SOX17 and FOXA2. DAPI serves as a nucleus indicator. The individual color channels were merged to assess the colocalization of SOX17 an FOXA2 expression in the nuclei. Scale bars, 200 μm. C. Dot plot showing the average and percentage expression of well-defined marker genes in different stages or cell types. Genes are colored according to their mean expression level. Diameter denotes fractional expression. SC-α, SC-β, and SC-EC represent stem-cell-derived α, β, and enterochromaffin cells, respectively. D. Heatmap showing the expression of the top ten marker genes of each time point. lncRNA genes are indicated by arrows. E. Enriched GO terms of marker genes of each time point. F. UMAP visualization of the expression of genes related to endoderm formation. G. Violin plots showing the expression of lncRNA genes that listed in the top 10 marker genes of each time point To take a global view of the expression pattern of marker genes, we combined the analysis results of our data with previous studies [15, 16] and evaluated the expression levels of well-known marker genes across in vitro β-cell differentiation and human main pancreatic islet cell types. The results showed that all marker genes were dynamically expressed along the cell developmental stages (Fig. 2C), which may reflect the dynamic features of cell differentiation pathways.
To further investigate the pathways or molecular events during the early differentiation of hESCs, functional enrichment analysis based on differently expressed genes (DEGs) among cell groups were performed (Fig. 2D and Additional file 2: Table S2, Additional file 3: Table S3). Expectedly, we found that genes specifically expressed in each cell group were significantly enriched for the expected biological functions (Fig. 2E). For instance, genes that are specifically expressed in hESCs were significantly enriched in stem-cell functions such as somatic stem-cell population maintenance ($$P \leq 8.77$$E−08). And the major biological processes enriched in mesendoderm cell groups were related to cell division ($$P \leq 3.84$$E−04), oxidation–reduction process ($$P \leq 0.002$$) and Wnt signaling pathway ($$P \leq 0.003$$), in accordance with the status of cells at this stage. Genes related to endoderm formation were enriched in definitive endoderm cell group ($$P \leq 2.32$$E−04), which also evidenced by the high and specific expression of DUSP4, LHX1 and EOMES (Fig. 2F). Furthermore, glycolytic process and anterior/posterior pattern specification were significantly enriched in ISL1+ progenitor cells ($$P \leq 2.32$$E−05 and 4.32E−05, respectively) (Fig. 2E), consistent with the properties of this cell group.
Apparently, we revealed that a portion of marker genes were lncRNAs whose functions in hESC differentiation process have not been well elucidated (Fig. 2D). For instance, four lncRNA genes (RP11-1144P22.1, FOXD3-AS1, LINC01356 and HOTAIRM1) were listed in the top 10 most significant differentially expressed genes during the early stage of hESC differentiation (Fig. 2G). These discoveries prompted us to conduct further analyses to globally explore the expression patterns and putative roles of lncRNAs during the differentiation of hESCs toward pancreatic progenitors.
## Highly expressed lncRNAs shown both conserved and specific expression features
To depict the expression profiles of lncRNAs during the hESC differentiation, we first checked the number of expressed lncRNAs at single-cell levels. The results showed that a total of 7382 lncRNAs could be detected (on average 149 lncRNAs per cell) in our scRNA-seq data (Additional file 13: Fig. S3), enabling us to perform further analyses. Furthermore, the expression level and frequency of lncRNAs were evaluated and an average of 128 lncRNA genes were found to express in at least $25\%$ of cells (Fig. 3A). Intriguingly, among the top ten highly expressed lncRNAs, some were commonly expressed in all samples of hESC early differentiation and the others were expressed in a stage-specific manner (Fig. 3B, C). For example, as the top two highly expressed lncRNAs, MALAT1 and ZSAF1, were expressed in $99\%$ of cells, while as the third and fourth highly expressed lncRNAs, RP11-148B6.1 and LINC01356, were expressed in only $67\%$ and $65\%$ of cells, respectively, and specifically expressed in day 0–4 (Fig. 3C). In particular, HOTAIRM1 as the sixth highly expressed lncRNA was exclusively expressed in day 9 (Fig. 3C). In conclusion, these results may imply distinct roles of these lncRNAs during the early stage of hESC differentiation. Fig. 3Conserved and specific expression features of lncRNAs. A. The plots showing the percentage of expressing cells against the mean expression level for lncRNA genes. The top 10 expressed lncRNAs were labeled. B. The top 20 highly expressed lncRNAs. Genes are ordered according to their mean expression levels. C. UMAP visualization of the expression of top 10 highly expressed lncRNA genes. D. UMAP plot of stem-cell-derived islet cells. E. UMAP plot of human pancreatic cells. F. UMAP visualization of gene expression level of the indicted genes To further assess the expression features of these lncRNAs in the late stage of hESC differentiation toward β-cells and in human pancreatic islets, computational analyses were performed by using scRNA-seq data generated by previous studies [15, 16]. The cell cluster results in current study were in accordance with those in the original papers (Fig. 3D, E). According to the cell cluster annotations, we found that MALAT1 was conserved expressed during the whole process of hESC differentiation and across the main pancreatic islet cell types (Fig. 3F). As expected, HOTAIRM1 was highly and specifically expressed in PDX1 progenitor cells. Additionally, the lncRNA MEG3 was discovered to be specifically expressed in pancreatic β-cells (Fig. 3F), which has been validated by multiple previous studies [29–31].
## Distinct lncRNA expression patterns during hESC early differentiation
Effective differentiation of hESCs requires genome-wide gene specific expression at different developmental stages. To more comprehensively characterize the distinctive patterns of lncRNA expression, we further carried out scRNA-seq of approximately 30,000 cells at daily intervals from day 5 to 9 by using the same protocol as described above (Fig. 4A). After quality control, 10,537 cells from day 5, 6687 cells from day 6, 8107 cells from day 7, 5036 cells from day 8, and 6825 cells from day 9 (as control and marked as Day 9-C) were obtained and used for further analyses (Additional file 4: Table S4). Through unsupervised clustering of all qualified cells (77,382 cells in total) generated by current study, the single-cell map of hESC early differentiation was reconstructed (Fig. 4B). The cells from Day 9-C in close proximity to Day 9-R1 and Day 9-R2, demonstrating the low degree of variation among different batches. Fig. 4Characterization of lncRNA expression patterns based on pseudotime analysis. A. Experimental design for studying the pseudotime-associated lncRNAs. B. UMAP plot of cells from all samples. C. Violin plots of cell pseudotime across all samples. D. Heatmap showing the relative expression of pseudotime-associated lncRNAs along pseudotime axis. E. Relative expression of representative lncRNAs for each pattern with cells ordered along the pseudotime axis Next, we performed *Monocle analysis* [23, 32] to order cells and infer the pseudotime (hypothetical timeline) of each cell. As shown in Fig. 4C, the inferred pseudotimes were highly consistent with the hESC differentiation time point during which the cells were collected. For example, hESC stage (day 0) exhibited the lowest pseudotime and cells from different stages exhibit a progressive differentiation pseudotime (Fig. 4C).
Based on pseudotime results, we further investigated the global lncRNA expression patterns during hESC differentiation. In total, 52 lncRNA genes were found to be strikingly differentially expressed (adjusted P value < 0.01) along the pseudotime axis and were further grouped into three distinct expression patterns (Fig. 4D and Additional file 5: Table S5). Specifically, 17 lncRNAs were involved in pattern I and highly expressed in day 6 to day 9, represented by HOTAIRM1, KCNQ1OT1, HOXB-AS1, and CRNDE, implying their potential functions in initiating the gene regulatory program toward ISL1+ progenitor cells (Fig. 4E). 16 lncRNAs grouped in pattern II highly expressed at the beginning period of hESC differentiation, represented by LINC00662, RP11.132A1.3, LINC00678, and RP11.69I8.2, indicating their putative roles in the stemness maintenance of hESC (Fig. 4E). 19 lncRNAs, such as RP11.445F12.1, RP11.380D23.2, GATA6.AS1, and RP3.428L16.2 in pattern III showed upregulated expression in day 4–5 and downregulated expression in other stages, (Fig. 4E). These lncRNAs may contribute to the differentiation of definitive endoderm cells. Collectively, these results suggested that the lncRNAs for each pattern could be orchestrated and served as the functional program to regulate the hESC differentiation.
## Dissecting the functional roles of lncRNAs during hESC differentiation based on co-expressed modules and hub-based sub-networks
To further clarify the potential functions and regulatory mechanisms of those pseudotime-associated lncRNAs (the lncRNAs involved in different expression patterns along the pseudotime as described above), we performed computational analysis by constructing “coding–non-coding” co-expression network based on gene expression correlations [25, 26]. To minimize the variance and noises of lncRNA expressions across single cells, we decomposed our scRNA-seq data into metacells that were defined as homogeneous cell groups by pooling together cells with the similar transcriptional states using a series of algorithms implemented in MetaCell package [24]. We totally identified 730 metacells with on average 96 cells involved in each metacell (Fig. 5A and Additional file 14: Fig. S4). According to the 2D projection and composition of metacells, the results derived from MetaCell algorithm were in accordance with the results of Seurat (Fig. 5A and Additional file 15: Fig. S5). Moreover, the expression patterns of several pseudotime-associated lncRNAs identified above such as HOTAIRM1, LINC01356, and RP11-771K4.1 were further confirmed by analyzing metacell marker genes (Additional file 16: Fig. S6).Fig. 5Functional annotation of lncRNAs using Metacell algorithm and co-expression network method. A. 2D projection of 730 metacells (metacell map). B. Visualization of co-expression network. Green nodes represent protein-coding genes and red nodes represent lncRNA genes. C. Gene–gene correlation heatmap for genes involved in co-expressed modules. D. Sub-network visualization of module 13. The lncRNAs genes (red) mentioned in the main text were marked by rectangles. E. Functional enrichment results of model 13 In view of gene expression values derived from MetaCell method, we calculated the Pearson correlation coefficients and adjusted P values for each gene pair and then constructed the co-expression network using weighted gene co-expression network analysis (WGCNA) method (Fig. 5B) [25, 26]. The resulting co-expression network totally comprised of 6669 protein-coding genes and 591 lncRNA genes that were connected by 200,412 edges, including 29,675 coding-lncRNA edges, 167,511 coding-coding edges, and 3226 lncRNA-lncRNA edges (Additional file 6: Table S6). The protein-coding genes in the network that had at least one GO term were adopted to predict the functions of lncRNAs. On average, there were 50 protein-coding partners connected with each lncRNA gene (the mean of Pearson correlation coefficient was 0.78) in the co-expression network (Additional file 17: Fig. S7).
Next, The Markov cluster algorithm (MCL) was applied to authenticate co-expressed gene modules in network. In total, 27 modules were identified through the use of a custom pipeline that comprised both protein-coding and lncRNA genes and significantly enriched for at least one biological function (Figs. 5C and Additional file 7: Table S7). Accordingly, 366 lncRNAs including 32 pseudotime-associated lncRNAs were functionally annotated (Additional file 8: Table S8 and Additional file 9: Table S9), some of which were in accordance with previous findings. For instance, the lncRNA CRNDE involved in Module 13 which including 78 protein-coding and 18 lncRNAs and significantly enriched for “transforming growth factor (TGF) beta receptor signaling pathway” (adjusted P value = 1.56E-24), whose predicted functions were in line with the previous reports that CRNDE was significantly upregulated after TGFβ1 treatment and contributed to cell proliferation (Fig. 5D, E and Additional file 9: Table S9) [33, 34]. Notably, CRNDE as a pseudotime-associated lncRNA was highly enriched in day 6 and day 9 during hESC differentiation and its enriched functions such as “secretion” (adjusted P value = 5.30E−21) and “exocytosis” (adjusted P value = 1.97E−13) were consistent with the expected cell functions at this stage (Fig. 5E). In addition, several antisense lncRNAs were involved in the module whose host genes have been demonstrated to have relationships with cell differentiation, cell secretion or cell fate decision (Fig. 5D). For example, the host gene of HMGN3-AS1, HMGN3, has been identified as a key regulator in glucose homeostasis especially in glucose-stimulated insulin secretion [35, 36]. Interestingly, a lncRNA MEG3 that was specifically expressed in human pancreatic β cells as described above was found in this module (Figs. 5D). These results indicated that the lncRNAs and protein-coding genes involved in the same modules may partly reflect the complex gene interactions or regulations during hESC differentiation process.
To further clarify the functions of individual lncRNAs in a more targeted way, we adopted hub-based prediction method by assigning functions to hub lncRNAs based on the functional enrichments of their connected protein-coding genes. Through multiple filtration processes as described in Materials and methods section, 342 lncRNA genes (including 47 pseudotime-associated lncRNAs) with at least 10 neighboring protein-coding genes which significantly enriched at least one GO term were functional annotated accordingly (Additional file 10: Table S10). For instance, as a pseudotime-associated lncRNA that highly expressed in day 6 and day 9 during hESC differentiation, HOTAIRM1 connected with 105 protein-coding and 13 lncRNA genes (Fig. 6A). As shown in Fig. 6B, HOTAIRM1 is located in the homeobox A (HOXA) gene cluster (between HOXA1 and HOXA2 locus) and was co-expressed with several HOX genes including HOXA1, HOXA2, HOXA3, HOXB1, HOXB2, and HOXB3. Based upon hub-based method, HOTAIRM1 was assigned functions such as “regulation of exocytosis” (adjusted P value = 1.73E−77), “retinoic acid receptor signaling pathway” (adjusted P value = 4.53E−28), and “anterior/posterior pattern specification” (adjusted P value = 9.74E−12) (Fig. 6C and Additional file 10: Table S10), which is consistent with previous findings that the transcription of HOTAIRM1 was induced by retinoic acid and the HOX gene cluster played crucial roles in cell differentiation and early embryonic development [37–40]. Interestingly, the prediction results of HOTAIRM1 were further validated by a recent study, which revealed that HOTAIRM1 could contribute to HOXA gene activation by regulating three-dimensional chromatin organization [41]. Obviously, the antisense lncRNA HOXA-AS2 that co-expressed and co-located with HOTAIRM1 also acted as pseudotime-associated lncRNA and showed the similar function annotations and expression patterns with HOTAIRM1 (Fig. 6B–D). In addition, the pseudotime-associated lncRNA PCAT14 was co-expressed with 14 protein-coding genes that significantly enriched “exocytosis” (adjusted P value = 7.12E−18) and “proteolysis” (adjusted P value = 1.08E−11) related processes (Fig. 6E, F and Additional file 10: Table S10). Notably, both HOTAIRM1 and PCAT14 were confirmed as the critical regulators in cancer by multiple previous studies [42–45], but less is known about their regulatory roles during hESC development. Fig. 6Examples of lncRNA annotations based on hub-based method. A. Sub-network visualization of HOTAIRM1 and its co-expressed genes. Green nodes represent protein-coding genes and red nodes represent lncRNA genes. The hub gene HOTAIRM1 was marked by yellow circle. HOTAIRM1 as well as its co-expressed and co-located genes were marked by rectangle. B. The genomic view of HOTAIRM1 and its co-expressed and co-located genes. The genomic view was generated by UCSC genome browser. C, D. Functional annotations of HOTAIRM1 and HOXA-AS2. E, F. The co-expressed sub-network and functional annotations of PCAT14 By combining the lncRNA functional annotation results of both module- and hub-based methods, the functions of 464 lncRNAs in total were predicted, 244 of which were calculated by both methods (Additional file 9: Table S9 and Additional file 10: Table S10). Moreover, $94\%$ ($\frac{49}{52}$) of pseudotime-associated lncRNAs were functional annotated. The main prediction results of the lncRNAs were similar between the two methods.
## Discussion
hESC differentiation involves a series of changes in cell transcriptome with a complex spatial pattern. The molecular characterization of cell groups from different stages of hESC differentiation based on protein-coding genes agreed well with previous reports [6, 28]. Although a number of protein-coding genes and transcription factors have been demonstrated as crucial regulators in hESC differentiation process, little is known about the physiological roles of lncRNAs in this process. The current study was carried out to address this issue.
Many studies have been performed on lncRNA genes during proliferation and differentiation processes of hESCs by using bulk RNA analyses [11–13], but the characteristics of lncRNAs at single-cell level are still poorly understood. Since one of the major challenges in scRNA-seq analysis is batch effect, which will have an impact on downstream analysis and may lead to the false interpretation of the data. To minimize the batch effect, a pooling and deconvolution strategy was adopted to normalize the counts of all cells. According to the single-cell maps, the cluster results of cells from three samples of day 9 were highly comparable with the cells from different batches, which enabled us to conduct integrative analyses among these data. By utilizing computational analysis, we identified 7382 lncRNAs that were expressed in scRNA-seq data and on average 149 lncRNAs could be detected at single-cell level. Moreover, a portion of lncRNAs display stage-specific expression, while some lncRNAs are ubiquitous transcripts that were highly expressed across all time points during hESC differentiation such as MALAT1. Furthermore, Monocle was used to order cells from hESCs to day 9 and identified 52 pseudotime-associated lncRNAs that grouped into three distinct expression patterns. These findings suggested that the lncRNAs involved in different expression patterns may be dynamically regulated to make contribution to hESC differentiation.
To unveil the functional roles of lncRNAs in regulating hESC differentiation based upon our scRNA-seq data, we adopted network-based method (gene co-expression network) that has been proved to be an effective way to mine the functions of unknown genes [25, 26]. In comparison with bulk RNA sequencing, it is a challenging task to accurately evaluate the correlations for each gene pair at single-cell level, due to the variance of RNA capture efficiency and technique noise among cells from scRNA-seq data. To address this issue, we used MetaCell algorithm that partitioned the scRNA-seq data into metacells [24], which enabled us to more robustly and accurately analyze the gene expression levels, especially for those lowly expressed lncRNAs. The normalized gene expression values across all metacells were applied to construct the “coding–non-coding” co-expression network. Although the true biological relationship between connected genes involved in the network is still unclear, it has been shown that highly correlated genes generally have similar functions, implying the functional association of co-expressed genes. Therefore, the connections between lncRNAs and protein-coding genes can be considered putative biological interactions, and the putative functions of lncRNAs could be predicted by their co-expressed protein-coding genes. Accordingly, both module- and hub-based methods were adopted to annotate the lncRNA functions and a number of results obtained from the two methods were coherent, strengthening the accuracy of the prediction results. The functions of several lncRNAs have been validated in previous studies. For example, an endoderm-specific lncRNA DEANR1 can positively regulate expression of the endoderm factor FOXA2 and plays a key role in human endoderm differentiation [46]. Nevertheless, among the 464 lncRNAs with assigned functions, 49 were pseudotime-associated lncRNAs identified in this study, whose regulatory mechanisms are worth further validating by biological experiments.
Overall, we provide a detail map of single-cell profiling of the early stage of hESC differentiation and systematic analyses of lncRNA roles in this process. Of note, our scRNA-seq data were generated by droplet-based technology with oligo-dT-primer that could only be used to analyze polyadenylated (ployA) transcripts. However, the polyA[-] lncRNAs that remain largely unexplored were absent in current studies. Therefore, the sequencing data of 77,382 single cells as valuable resource lay the ground work for further studies. The functions and interactions of lncRNAs, which were associated with hESC differentiation, would be beneficial in designing experiments to further validate their regulatory mechanisms. Our findings will facilitate to comprehensively understand models of cellular network and enable us to navigate the regulatory landscape underlying the differentiation of hESCs.
## Conclusion
In this study, we conducted scRNA-seq experiments of 77,382 cells to comprehensively characterize the transcriptome of the early stage of hESC differentiation at single-cell level and further performed computational analysis to identify the expression patterns as well as putative functions of lncRNAs.
## Supplementary Information
Additional file1. Table S1: Sequencing statistics of single cell samples. Additional file2. Table S2: List of differentially expressed genes during hESC differentiation. Additional file3. Table S3: Functional enrichments of differentially expressed genes during hESC differentiation. Additional file4. Table S4: Statistics of scRNA-seq data. Additional file5. Table S5: List of pseudotime-associated lncRNAs. Additional file6. Table S6: The Pearson correlation coefficients of gene pairs in co-expression network. Additional file7. Table S7: The statistics of co-expression modules. Additional file8. Table S8: The list of lncRNAs involved in each module. Additional file9. Table S9: The functional annotation results of co-expression modules. Additional file10. Table S10: The functional annotation results of lncRNAs based on hub-based method. Additional file11. Fig. S1: Scatter plots showing the gene expression correlation across four time points. The axes represent log2 (Read count + 1). The lower half of the matrix shows the Pearson correlation coefficients (R) for the comparisons in the upper half. Additional file12. Fig. S2: Immunofluorescence staining of different batches for definitive endoderm cells with antibodies against SOX17 and FOXA2. DAPI serves as a nucleus indicator. The individual color channels were merged to assess the colocalization of SOX17 an FOXA2 expression in the nuclei. Scale bars, 200 μm. Additional file13. Fig. S3: The number of lncRNA genes expressed in five scRNA-seq samples. Additional file14. Fig. S4: The number of cells involved in metacells. Additional file15. Fig. S5: The composition of cells from different time points. Additional file16. Fig. S6: Heatmap of marker genes of metacells. Marker lncRNA genes are indicated by arrows. Additional file17. Fig. S7: Subnetwork of lncRNAs and their co-expressed protein-coding genes. Green nodes represent protein-coding genes and red nodes represent lncRNA genes.
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|
---
title: 'The relationship between diabetes-related knowledge and kidney disease knowledge,
attitudes, and practices: a cross-sectional study'
authors:
- Asem Badran
- Anas Bahar
- Mohammed Tammam
- Sami Bahar
- Amani Khalil
- Amer A. Koni
- Sa’ed H. Zyoud
journal: BMC Public Health
year: 2023
pmcid: PMC10010008
doi: 10.1186/s12889-023-15390-8
license: CC BY 4.0
---
# The relationship between diabetes-related knowledge and kidney disease knowledge, attitudes, and practices: a cross-sectional study
## Abstract
### Background
Diabetes mellitus (DM) is one of the main noncommunicable diseases encountered in primary health care clinics. DM is considered one of the most common causes of chronic kidney disease (CKD). In this study, we aimed to assess the knowledge, attitudes, and practices (KAP) of patients with DM on the early detection and prevention of CKD, determine its relationship with other variables, and examine the relationship between KAP scores for the prevention and early detection of CKD and the Michigan Diabetic Knowledge Test.
### Methods
We collected data from 2 Nablus primary healthcare centers using a questionnaire that contains three sections: sociodemographic section, questions related to DM, and CKD screening index, which is formed of three scales. We used the Michigan Diabetic Knowledge Test (MDKT) to assess the knowledge of diabetic patients.
### Results
The study was carried out among 386 diabetic patients with a mean age of 57.62 ± 12.4 years (ranging from 28 to 90). The median (interquartile range) was 11 (8–14) for the knowledge scale, 56 (52–59) for the attitude scale, and 30 (26–33) for the practice scale. In the multiple linear regression, only patients under 55 years old ($$p \leq 0.012$$), with normal BMI ($$p \leq 0.030$$), high educational level ($p \leq 0.001$), high monthly income ($$p \leq 0.020$$), and MDKT test score ($$p \leq 0.007$$) were significantly associated with higher knowledge score. Furthermore, patients who were over or equal to 55 years old ($$p \leq 0.007$$), had a high monthly income ($$p \leq 0.016$$), used a single oral diabetic drug ($$p \leq 0.003$$), had a total number of medications less than 4 ($$p \leq 0.010$$), and had a high knowledge and MDKT test were significantly associated with a higher attitude score. Finally, a patient with normal BMI ($$p \leq 0.002$$), city residency ($$p \leq 0.034$$), high educational level ($$p \leq 0.003$$), less frequent tobacco use ($p \leq 0.001$), last HbA1c ($$p \leq 0.023$$) and greater knowledge, attitude, and MDKT score were significantly associated with better practices toward CKD prevention and early detection.
### Conclusion
Regarding KAP analysis, higher practice scores for the prevention and early detection of CKD were significantly associated with patients with normal BMI, being city residents, high educational level, less tobacco use, last HbA1c below 7, and higher knowledge, attitude, and MDKT score.
## Background
Diabetes mellitus (DM) is one of the main noncommunicable diseases encountered in primary care clinics. It is estimated that approximately 1 in 11 adults worldwide have diabetes, and $90\%$ of them have type 2 DM [1]. The prevalence of type 2 DM in Palestine in 2010 was $15.3\%$ and is expected to be $23.4\%$ in 2030 [2], which is too high compared to the global prevalence of diabetes in 2015, which was $8.8\%$, and is expected to rise to $10.4\%$ by 2040 [3]. However, there may be a significant decrease in the prevalence of DM if risk factors for it are controlled, especially obesity. It was suggested that a $2.8\%$ reduction in diabetes prevalence could be achieved if obesity trends start to decline by $5\%$ in 5 years [2].
Chronic kidney disease (CKD) is defined by Kidney Disease Improving Global Outcomes (KDIGO) and the National Kidney Foundation (NKF) Kidney Disease Outcomes and Quality Initiative (K/DOQI) as kidney damage (either functional or structural abnormalities of the kidneys) or a GFR < 60 ml/min/1.73 m2 for more than three months [4].
DM is considered one of the most common causes of CKD [5], which usually develops after long-standing poorly controlled DM. Nevertheless, many other factors can contribute to the development of CKD, including hypertension, urinary tract infections, nephrolithiasis, acute kidney injury (AKI), family history of CKD, old age, smoking, obesity, and nonsteroidal anti-inflammatory drugs (NSAIDs) [6].
In Palestine, there are a few studies on the prevalence of CKD among diabetic patients; one study showed that $23.6\%$ of patients with type 2 DM in the North West Bank have CKD in different stages [7].
*In* general, there is a lack of awareness about possible risk factors, so the majority of CKD cases will not be recognized clinically or will be diagnosed at a later stage. Furthermore, it has been proven that the diagnosis of CKD will be delayed in patients with a positive attitude and solid knowledge and practices [8]. To avoid this, it is recommended to screen for CKD for early detection and treatment [9].
As DM and CKD still pose problems and it is possible to detect or delay the onset of CKD early, it is important to evaluate the knowledge, attitudes, and practice (KAP) of patients with DM related to the early detection and prevention of CKD. However, a literature review did not reveal studies among patients with DM in Palestine, so it is justified to establish a study on it.
We aimed to assess the KAP of patients with DM regarding early detection and prevention of CKD, determine its relationship with other variables, including clinical and sociodemographic factors, and examine the relationship between KAP scores for the prevention and early detection of CKD and the Michigan Diabetic Knowledge Test.
## Study design
A cross-sectional study is used to assess the KAP of DM patients with regard to early detection and prevention of CKD using a newly developed index known as the CKD screening index. This index contains three scales regarding knowledge, attitudes, and practice, and its validity and reliability were ensured [8, 10].
## Study population
The study was held in Nablus, Palestine. Two primary healthcare centers were included, the Al-Makhfiya and Hiwara centers.
These primary care centers share a common fund provided by the Palestinian government. They are the main centers for primary care for most patients, including patients with DM, in the city of Nablus and surrounding villages, where patients are provided with essential medical care and drugs. The participant was chosen from registered patients and received treatment as diabetic patients in those primary care centers.
## Sample size and sampling procedure
The approximate number of patients who visited the diabetes clinics in the Nablus health center was 2000. The sample size was calculated using an online Raosoft sample size calculator (http://www.raosoft.com/samplesize.html). The minimum effective sample size was 323, assuming a $5\%$ margin of error, a $95\%$ confidence interval, and a response distribution of $50\%$. This study was conducted on 398 diabetic patients. We recruited participants who were conveniently available using the nonprobability sampling technique.
## Inclusion and exclusion criteria
Patients 18 years or older who could read and/or understand Arabic and had a diagnosis of DM for at least six months were included. However, we excluded patients with CKD diagnoses and conditions affecting their cognition, including stroke and mental illnesses. Additionally, patients with missing data were excluded from the final analysis.
## Data collection instrument
We used a questionnaire containing only one type of question that is close-ended. All subjects were interviewed in primary care centers by medical students who are familiar with the CKD screening index [8, 10].
It contained four sections. The first is the sociodemographic section, which contains a question on age, residency, sex, marital status, employment, educational level, monthly income, weight, and height. Body mass index (BMI) was calculated using the equation (weight “in kg”/height^2 (in m^2) and classified into underweight if less than 18.5, normal if greater than or equal to 18.5 to 24.9, overweight if greater than or equal to 25 to 29.9, obese if greater than or equal to 30, and morbid obese if greater than or equal to 40 [11]. Age was divided into two groups according to a previous study: 18–54 years and 55 years old and above [12].
The second section contained factors related to DM, such as duration of the disease, presence of comorbidities, type of therapy (monotherapy/combination, use of insulin), total number of medications, HbA1c and smoking status. The duration of DM was divided into two groups: below seven years and seven years or older [12]. The HbA1c reading was divided into two groups: below 7 and 7 or greater [12].
The third section contains the CKD screening index, which is divided into three different scales. The knowledge scale consists of 24 items regarding general knowledge of CKD, including its definition, risk factors, signs, symptoms, and complications. The attitudes scale, composed of 15 items, was used to assess the attitudes of diabetic patients toward CKD signs and symptoms and the ability to seek appropriate social and medical help related to their concerns. The practice score consists of 12 items that evaluate the health practices of each patient to prevent CKD. The validity and reliability of this screening index were examined and guaranteed [8] and used in its Arabic edition in another study [13].
The fourth section is the Michigan Diabetic Knowledge Test (MDKT). Its validity and reliability were evaluated [14] and used in its Arabic edition in previous studies [15–17]. We obtained developer permission to use it. We used the modified MDKT test, which contains the first 14 questions in the English edition of the online questionnaire (http://diabetesresearch.med.umich.edu/peripherals/profs/documents/svi/DKT2_with_answers.p-f) that are appropriate to assess knowledge of diabetes. It was a multiple-choice question that had only one single correct answer for each. It covers different aspects of diabetes, in which each question evaluates the patient’s knowledge of each. The correct answer was given one point, zero points for an incorrect answer, and the total score was 14. Therefore, more correct answers meant greater knowledge of DM.
## Ethical issues
All aspects of the study protocol, including access to and use of patient clinical information, were authorized by the Institutional Review Boards (IRBs) of An-Najah National University and the Palestinian Health Authority.
## Statistical analysis
Data were entered and analysed using the Statistical Package for Social Sciences program version 26 (IBM-SPSS 26). Data are expressed as the means ± SDs for continuous variables and as frequencies and percentages for categorical variables. The Kolmogorov–Smirnov test was performed to assess the normality of continuous data. The median (interquartile range (IQR)) and mean rank were used for variables that were not normally distributed. We used the Mann‒Whitney U test or Kruskal–Wallis test to detect and compare the differences between the medians of nonparametric data. The significance level was established at a p value of < 0.05. Additionally, we used multiple linear regression analysis for all univariate variables, which are significant in further evaluating the relationship between the patients’ KAP scores towards prevention of CKD, MDKT test scores, and the main clinical and sociodemographic variables. Internal consistency was assessed using Cronbach’s alpha for all CKD screening index subscales and MDKT.
## Sociodemographic and clinical characteristics
The total number of diabetic patients who participated and were interviewed was 398. Only 386 samples were included, 11 patients were excluded due to missing data, and one was newly diagnosed (less than six months). Sociodemographic and clinical characteristics are shown in Table 1. The mean age was 57.62 ± 12.4 years (range 28–90), and $58.5\%$ of the subjects were ≥ 55 years old. Most of the subjects were male (n 211; $54.4\%$), married ($75.4\%$), city residents ($60.9\%$), employed ($58.8\%$), and reached at least high school or more ($62.7\%$). The mean BMI was 28.13 ± 3.86. DM was diagnosed in less than seven years in most patients ($$n = 223$$; $57.8\%$), the majority of them used only one oral drug for DM (n 240; $62.2\%$), and most patients used insulin ($$n = 240$$; $62.2\%$). Most patients had no chronic diseases other than DM ($$n = 181$$; $46.9\%$). The mean for the last HbA1c measurement was 8.18 ± 1.55 (Table 1).
Table 1Sociodemographic and clinical characteristics of 386 patients with DMVariableTotal: $$n = 386$$ (%) Age category (years) < 55160(41.5)≥ 55226 (58.5) Gender Male211(54.7)Female175 (45.3) BMI category Normal84 (21.8)Overweight181 (46.9)Obese121 (31.3) Residency Refugee camp32 (8.3)Village119 (30.8)City235 (60.9) *Marital status* Married291 (75.4)Widow52 (13.5)Divorced19 (4.9)Unmarried24 (6.2) Educational level No formal education45 (11.7)Elementary school99 (25.6)High school137 (35.5)Collage/University105 (27.2) Employment Employed227 (58.8)Unemployed159 (41.2) Monthly income (NIS a) Low (< 2000)177 (45.9)Moderate (2000–5000)160 (41.5)High (> 5000)49 (12.7) Smoking Yes165 (42.7)No221 (57.3) Duration of DM (years) < 7223 (57.8)≥ 7163 (42.2) Number of oral medications for DM Mon therapy240 (62.2)Multi therapy71 (18.4)No oral medications75 (19.4) Use of insulin Yes240 (62.2)No146 (37.8) Last HbA1c < 771 (18.4)≥ 7315 (81.6) Comorbidities Yes205 (53.1)No181 (46.9) Total number of chronic diseases (other than DM) 0181 (46. 9)1110 (28.5)267 (17.4)328 (7.2) Total number of medications other than DM medications < 4303 (78.5)≥ 483 (21.5)Abbreviations: BMI: body mass index, NIS: New Israeli shekel, HbA1c: hemoglobin A1ca 1NIS equals 0.31US Dollar Approximately $47.9\%$ ($$n = 185$$) of patients had hypertension, $8.5\%$ ($$n = 33$$) had ischemic heart disease, $8.3\%$ ($$n = 32$$) had congestive heart failure, $8\%$ ($$n = 31$$) had rheumatoid arthritis, $5.4\%$ ($$n = 21$$) had asthma, and $6.7\%$ ($$n = 26$$) had other diseases, including inflammatory bowel disease ($$n = 10$$) and COPD ($$n = 3$$).
## Knowledge
The participants’ mean score on the knowledge scale was 11.27 ± 4.6, and the correct responses ranged from (0–24). The median and interquartile range of knowledge was 11 (8–14). The Cronbach’s alpha was 0.788, indicating good internal consistency. Furthermore, only $41.5\%$ of diabetic patients knew that CKD is irreversible. Most patients ($62.2\%$) did not know that smoking increased the risk of CKD. Furthermore, a higher percentage of patients did not realize that CKD can affect their concentration ($76.6\%$) or their sleep pattern ($68.9\%$), that it can cause muscle aches and pain, especially during the night ($70.7\%$), or that it can cause skin dryness and itchiness ($73.6\%$). More than half of the patients ($55.7\%$) did not know that a procedure requiring contrast injection as cardiac catheterization could affect their kidney function.
Finally, only approximately $32.9\%$ of the patients knew that CKD has five stages and that each stage requires special medical care, but most patients ($58\%$) did not know that the final stage of CKD would require lifelong dialysis.
## Attitude
The participants’ mean score on the attitude scale was 55.61 ± 5.52, ranging from 39 to 72. The median of the attitude scale was 56 (52–59). The Cronbach’s alpha coefficient was 0.648, indicating an acceptable level of internal consistency [18, 19].
Participants were generally more likely to agree or strongly agree on positive attitudes or beliefs toward CKD. For example, most patients ($89.1\%$) visited a healthcare specialist if they felt any signs or symptoms of CKD. Furthermore, most patients agree or strongly agree that it is important for health to exercise and eat a balanced diet ($88.6\%$), and regular check-ups with their doctor will make them less concerned about their health ($72.3\%$).
Finally, $78.2\%$ of patients agree or strongly agree that their healthcare provider has to give them more information about CKD.
## Practice
The mean score on the practice scale was 29.59 ± 5.19, ranging from 13 to 42. The median of the attitude scale was 30 (26–33). The Cronbach’s alpha coefficient was acceptable, 0.747.
Generally, most of the patient’s responses were against positive practices most of the time or always and toward negative practices such as smoking or drinking alcohol. However, only $31.1\%$ responded mostly or always to a balanced diet, and $17.3\%$ responded to regular physical exercise.
## Michigan diabetic knowledge test (MDKT)
The mean scores on the MDKT test were 6.7 ± 2.75, ranging from 1 to 13. The median was 7 (5–9). The internal consistency was 0.601, indicating an acceptable Cronbach’s alpha coefficient [18, 19].
There was an obvious lack of knowledge about diabetes, and the diet among diabetic patients ($55.4\%$) had an incorrect answer about the food that was highest in carbohydrates. ( $63.5\%$) did not know the effect of unsweetened fruit juice on blood glucose. A total of $52.1\%$ did not know that HbA1c reflected their blood glucose over the last three months.
In terms of Spearman correlation between the three KAP scales and the MDKT test, there was a positive correlation between the practice score and the level of knowledge of CKD ($r = 0.292$; $p \leq 0.001$). Furthermore, knowledge about CKD positively correlated with knowledge about DM assessed by the MDKT test ($r = 0.215$; $p \leq 0.001$). Furthermore, the practice score was associated with a moderate positive correlation with knowledge of DM assessed by the MDKT test ($r = 0.151$; $$p \leq 0.003$$). On the other hand, the attitude score was associated with a moderate negative correlation with knowledge about DM assessed by the MDKT test (r = -0.318; $p \leq 0.001$).
## Characteristics of patients that associated with the knowledge score
In the bivariate analysis, higher knowledge scores were significantly associated with patients less than 55 years of age, normal BMI, city resident, unmarried, high educational level, employed, high income, no comorbidities, and used less than four medications other than DM drugs (Table 2). However, in the analysis with multiple linear regression, only patients under 55 years old ($$p \leq 0.012$$), normal BMI ($$p \leq 0.030$$), high educational level ($p \leq 0.001$), high income ($$p \leq 0.020$$) and higher MDKT test score ($$p \leq 0.007$$) were significantly associated with higher knowledge score (Table 3).
Table 2The median knowledge score of 386 diabetic patients related to the prevention and early detection of chronic kidney diseaseVariableTotal: $$n = 386$$ (%)Median knowledge score a[Q1-Q3]Mean rankP value b Age category (years) < 55160(41.5)12 (10–16)224.37 < 0.001 c ≥ 55226 (58.5)10(8–14)171.65 Gender Male211(54.7)11(8–14)183.430.051cFemale175 (45.3)11(9–15)205.64 BMI category Normal84 (21.8)12(9–14)203.43 0.043 d Overweight181 (46.9)11(9–15)202.94Obese121 (31.3)11(8–13)172.48 Residency Refugee camp32 (8.3)12(9–15)206.39 < 0.001 d Village119 (30.8)9(7–13)151.85City235 (60.9)12(10–15)212.84 *Marital status* Married291 (75.4)11(9–14)197.47 0.002 d Widow52 (13.5)10(5–13)143.64Divorced19 (4.9)12(9–17)220.89Unmarried24 (6.2)13(11–15)231.65 Educational level No formal education45 (11.7)10(6–12)139.73 < 0.001 d Elementary school99 (25.6)9(7–11)133.02High school137 (35.5)12(9–14)204.78Collage/University105 (27.2)13(11–17)258.85 Employment Employed227 (58.8)12(9–15)211.58< 0.001cUnemployed159 (41.2)10(7–14)167.69 Monthly income (NIS e) Low (Less than 2000)177 (45.9)11(8–14)171.88 < 0.001 d Moderate (2000–5000)160 (41.5)12(9–14)202.65High (More than 5000)49 (12.7)13(10–17)241.71 Smoking Yes165 (42.7)11(9–14)186.790.306cNo221 (57.3)11(8–15)198.51 Duration of DM (years) < 7223 (57.8)11(9–15)199.070.250c≥ 7163 (42.2)11(8–14)185.88 Number of oral medications for DM Mon therapy240 (62.2)11(8–14)189.250.108dMulti therapy71 (18.4)11(8–14)182.70No oral medications75 (19.4)12(10–15)217.33 Use of insulin Yes240 (62.2)11(8–14)188.890.297cNo146 (37.8)11(9–15)201.07 Last HbA1c < 771 (18.4)12(10–15)214.820.074c≥ 7315 (81.6)11(8–14)188.70 Comorbidities Yes205 (53.1)10(8–13)175.51 0.001 c No181 (46.9)12(10–15)213.87 Total number of chronic diseases (other than DM) 0181 (46.9)12(10–15)213.87 < 0.001 d 1110 (28.5)11(9–14)193.45267 (17.4)11(8–14)179.34≥ 328 (7.2)8(6–10)95.89 Total number of medications other than DM medications < 4303 (78.5)12(9–15)201.59 0.006 c ≥ 483 (21.5)10(8–13)163.96Abbreviations: BMI: body mass index, NIS: New Israeli shekel, HbA1c: hemoglobin A1ca Knowledge scale contains 24 items (range 0–24, the higher the score, the better knowledge)b cut-off level of significance was 0.05c Mann‒Whitney U test was used to detect statistical significanced Kruskal‒Wallis test was used to detect statistical significancee 1NIS equals 0.31US Dollar Table 3Characteristics of diabetic patients that are associated with the knowledge score related to prevention and early detection of chronic kidney disease in multiple linear regressionVariables aUnstandardizedcoefficients (B)Standardizedcoefficients (Beta)P value b$95\%$ confidenceinterval for B Constant 9.122 < 0.001 6.393 to 11.851 Age category (years) -1.177− 0.126 0.012 -2.088 to − 0.265 BMI category − 0.631− 0.099 0.030 -1.201 to − 0.061 Residency c 0.3720.0520.275− 0.297 to 1.040 *Marital status* c − 0.188− 0.0220.647− 0.624 to 0.388 Educational level c 1.3470.285 < 0.001 0.837 to 1.857 Employment c 0.4020.5460.462− 0.671 to 1.475 Monthly income (NIS e) 0.9180.137 0.020 0.148 to 1.687 Comorbidities c − 0.766− 0.0830.327-2.301 to 0.770 Total number of chronic diseases (other than DM) − 0.832− 0.1700.090-1.774 to 0.128 Total number of medications other than DM medications − 0.081− 0.0070.907-1.447 to 1.284 MDKT test 0.2300.137 0.007 0.064 to 0.396R: 0.504; R Square: 0.254; Adjusted R Square: 0.232; Std. Error of the Estimate: 4.04045Abbreviations: BMI: body mass index, NIS: New Israeli shekel, HbA1c: hemoglobin A1c, MDKT: Michigan Diabetes Knowledge Testa Multiple linear regression was done on each factor with a p value < 0.05b cut-off level of significance was 0.05c dichotomous variable is used to represent the nominal variables
## Characteristics of patients that associated with attitude score
In bivariate analysis, higher attitude scores were significantly associated with patients who were 55 years old or older, refugees in camps, high income, patients with DM less than seven years duration, using a single oral drug for DM, and less than four medications other than DM drugs (Table 4). Furthermore, in multiple linear regression, we found that patients who were older than or equal to 55 years of age ($$p \leq 0.007$$) had a high monthly income ($$p \leq 0.016$$), used a single oral diabetic drug ($$p \leq 0.003$$), had a total number of medications less than 4 ($$p \leq 0.010$$) and had high knowledge and MDKT test scores, which were significantly associated with a higher attitude score (Table 5).
Table 4The median score for the attitude of 386 diabetic patients related to the prevention and early detection of chronic kidney diseaseVariableTotal: $$n = 386$$ (%)Median knowledge score[Q1-Q3]Mean rankP value Age category (years) < 55160(41.5)55(50–59)180.100.047c≥ 55226 (58.5)56(52–59)202.99 Gender Male211(54.7)55(51–59)187.130.217cFemale175 (45.3)56(52–59)201.19 BMI category Normal84 (21.8)55(51–60)191.160.947dOverweight181 (46.9)56(52–59)195.45Obese121 (31.3)56(52–59)192.21 Residency Refugee camp32 (8.3)59(56–61)246.34 0.003 d Village119 (30.8)57(52–59)204.88City235 (60.9)55(51–58)180.54 *Marital status* Married291 (75.4)56(52–59)200.300.189dWidow52 (13.5)56(50–59)176.25Divorced19 (4.9)52(50–61)177.74Unmarried24 (6.2)55(49–57)160.90 Educational level No formal education45 (11.7)55(51–59)179.620.340dElementary school99 (25.6)55(52–59)187.28High school137 (35.5)57(52–59)207.12Collage/University105 (27.2)55(52–59)187.54 Employment Employed227 (58.8)55(51–59)189.480.396cUnemployed159 (41.2)56(52–59)199.25 Monthly income (NIS a) Low (Less than 2000)177 (45.9)55(51–59)178.69 < 0.001 d Moderate (2000–5000)160 (41.5)55.5(52–59)191.65High (More than 5000)49 (12.7)59(55–63)253.03 Smoking Yes165 (42.7)56(52–60)196.470.651cNo221 (57.3)56(52–59)191.28 Duration of DM (years) < 7223 (57.8)57(52–60)205.96 0.010 c ≥ 7163 (42.2)55(51–58)176.46 Number of oral medications for DM Mon therapy240 (62.2)57(53–60)213.55 < 0.001 d Multi therapy71 (18.4)55(51–58)172.16No oral medications75 (19.4)52(50–57)149.55 Use of insulin Yes240 (62.2)56(51–59)190.890.555cNo146 (37.8)56(52–59)197.79 Last HbA1c < 771 (18.4)56(51–58)187.760.631c≥ 7315 (81.6)56(52–59)194.79 Comorbidities Yes205 (53.1)55(51–59)189.430.444cNo181 (46.9)56(52–59)197.11 Total number of chronic diseases (other than DM) 0181 (46.9)56(52–59)198.110.730d1110 (28.5)56(52–59)195.50267 (17.4)55(51–59)182.95≥ 328 (7.2)54(51–60)181.05 Total number of medications other than DM medications < 4303 (78.5)56(52–59)203.55 0.001 c ≥ 483 (21.5)53(51–57)156.82Abbreviations: BMI: body mass index, NIS: New Israeli shekel, HbA1c: hemoglobin A1can Attitude scale is 15 items on a 5-point Likert-type scale (range 15–75, the higher score, the better attitudes)b Cut-off level of significance was 0.05c Mann‒Whitney U test was used to detect statistical significanced Kruskal‒Wallis test was used to detect statistical significancee 1NIS equals 0.31US Dollar Table 5Characteristics of diabetic patients that are associated with attitude score related to prevention and early detection of chronic kidney disease in multiple linear regressionVariables aUnstandardizedcoefficients (B)Standardizedcoefficients (Beta)P value b$95\%$ Confidenceinterval for B Constant 57.564 < 0.001 55.404 to 59.724 Age category (years) 1.5420.138 0.007 0.426 to 2.658 Residency c − 0.621− 0.0720.135-1.437 to 0.195 Monthly income (NIS e) 0.9510.119 0.016 0.177 to 1.725 Duration of DM (years) − 0.0020.0000.997-1.110 to 1.105 Types of medications for DM c -1.008− 0.145 0.003 -1.680 to − 0.336 Total number of medications other than DM medications -1.706− 0.127 0.010 -2.999 to − 0.414 Knowledge score 0.1710.143 0.005 0.051 to 0.292 MDKT test − 0.525− 0.261 < 0.001 − 0.722 to − 0.329R: 0.438; R Square: 0.192; Adjusted R Square: 0.175; Std. Error of the Estimate: 5.02076Abbreviations: NIS: new Israeli shekel, DM: diabetes mellitus, MDKT: Michigan Diabetes Knowledge Testa Multiple linear regression was done on each factor with a p value < 0.05b cut-off level of significance was 0.05c Dummy coding was used to represent nominal variablesD Knowledge scale contains 24 items (range 0–24, the higher the score, the better knowledge)
## Characteristics of patients associated with the practice score
In the bivariate analysis, a higher practice score was significantly associated with patients who were less than 55 years of age, had normal BMI, were city residents, were unmarried, had a high educational level, were employed, had a high income, were smokers, and had HbA1c less than 7 (Table 6). In an analysis with multiple linear regression, we found that normal BMI ($$p \leq 0.002$$), city residency ($$p \leq 0.034$$), high educational level ($$p \leq 0.003$$), smoking status ($p \leq 0.001$), last HbA1c ($$p \leq 0.023$$) and higher knowledge, attitude, and MDKT score were significantly associated with better practices toward the prevention and early detection of CKD (Table 7).
Table 6The median score for the practice of 386 diabetic patients related to the prevention and early detection of chronic kidney diseaseVariableTotal: $$n = 386$$ (%)Median knowledge score[Q1-Q3]Mean rankP value Age category (years) < 55160(41.5)30(27–34)210.33 0.012 c ≥ 55226 (58.5)29(26–32)181.58 Gender Male211(54.7)30(26–33)194.670.821cFemale175 (45.3)30(26–33)192.09 BMI category Normal84 (21.8)30(27–35)214.920.001dOverweight181 (46.9)30(27–33)204.26Obese121 (31.3)28(25–32)162.54 Residency Refugee camp32 (8.3)30(25–32)176.55 0.001 d Village119 (30.8)29(24–32)164.16City235 (60.9)30(27–34)210.67 *Marital status* Married291 (75.4)30(26–33)199.56 < 0.001 d Widow52 (13.5)27(25–30)140.09Divorced19 (4.9)27(25–31)165.82Unmarried24 (6.2)34(28–36)257.69 Educational level No formal education45 (11.7)25(22–29)104.67 < 0.001 d Elementary school99 (25.6)29(26–32)176.45High school137 (35.5)30(26–33)192.60Collage/University105 (27.2)32(29–35)248.82 Employment Employed227 (58.8)31(27–34)216 < 0.001 c Unemployed159 (41.2)28(25–32)161.38 Monthly income (NIS a) Low (Less than 2000)177 (45.9)29(25–32)167.87 < 0.001 d Moderate (2000–5000)160 (41.5)30(27–33)202.51High (More than 5000)49 (12.7)32(30–37)256.66 Smoking Yes165 (42.7)31 (27–35)218.55 < 0.001 c No221 (57.3)29 (26–32)174.79 Duration of DM (years) < 7223 (57.8)30(26–33)195.490.682c≥ 7163 (42.2)29(26–33)190.78 Number of oral medications for DM Mon therapy240 (62.2)30(26–33)194.130.114dMulti therapy71 (18.4)28(26–32)172.75No oral medications75 (19.4)31(27–33)211.12 Use of insulin Yes240 (62.2)30(26–33)200.410.118cNo146 (37.8)29(26–33)182.14 Last HbA1c < 771 (18.4)32(29–34)235.06 0.001 c ≥ 7315 (81.6)29(26–33)184.13 Comorbidities Yes205 (53.1)30(26–33)189.600.464cNo181 (46.9)30(26–34)197.92 Total number of chronic diseases (other than DM) 0181 (46.9)30(26–34)197.920.767d1110 (28.5)30(27–33)195.21267 (17.4)30(25–33)182.97≥ 328 (7.2)29(27–32)183.39 Total number of medications other than DM medications < 4303 (78.5)30(27–33)196.500.312c≥ 483 (21.5)29(25–33)182.56Abbreviations: BMI: body mass index, NIS: New Israeli shekel, HbA1c: hemoglobin A1cA The practices scale contains 12 items (the core ranged from 12 to 48; the higher score, the better practice)b Cut-off level of significance was 0.05c Mann‒Whitney U test was used to detect statistical significanced Kruskal‒Wallis test was used to detect statistical significancee1 new Israeli shekel (NIS) equals 0.31US Dollar Table 7Characteristics of diabetic patients that are associated with practice scores related to prevention and early detection of chronic kidney disease in multiple linear regressionVariables aUnstandardizedcoefficients (B)Standardizedcoefficients (Beta)P value b$95\%$ Confidenceinterval for B Constant 21.226 < 0.001 15.414 to 27.038 Age category (years) 0.2160.0210.672− 0.785 to 1.217 BMI category -1.005− 0.140 0.002 -1.644 to − 0.366 Residency c 0.8020.100 0.034 0.061 to 1.544 *Marital status* c 0.3310.0540.248− 0.232 to 0.893 Educational level c 0.8760.165 0.003 0.294 to 1.459 Employment c − 0.013− 0.0010.984-1.229 to 1.204 Monthly income (NIS e) 0.7210.0960.109− 0.162 to 1.604 Smoking -1.806− 0.172 < 0.001 -2.799 to − 0.813 Last HbA1c -1.390− 0.104 0.023 -2.587 to − 0.194 Knowledge score 0.2200.196 < 0.001 0.107 to 0.334 Attitude score 0.0930.099 0.042 0.003 to 0.183 MDKT test 0.2170.115 0.027 0.025 to 0.409R: 0.527; R Square: 0.278; Adjusted R Square: 0.254; Std. Error of the Estimate: 4.48175Abbreviations: BMI: body mass index, NIS: new Israeli shekel, HbA1c: hemoglobin A1c, MDKT: Michigan Diabetes Knowledge Testa Multiple linear regression was done on each factor with a p value < 0.05b cut-off level of significance was 0.05C dummy coding was used to represent nominal variables
## Characteristics of the patient that are associated with the MDKT score
In the bivariate analysis, the characteristics significantly associated with higher MDKT scores were male gender, city residents, unmarried patients, high education level, employed, with moderate to high income, no insulin use or oral medication for DM, duration of less than seven, years, HbA1c less than seven and presence of a single comorbid disease (Table 8). In an analysis with multiple linear regression, we found that city residency ($$p \leq 0.001$$), a high education level ($p \leq 0.001$), employment status ($$p \leq 0.005$$), monthly income ($p \leq 0.001$), longer duration of DM ($p \leq 0.001$) and no insulin use ($p \leq 0.001$) were significantly associated with a better score on the MDKT test (Table 9).
Table 8Michigan Diabetes Knowledge Test of 386 patients with DMVariableTotal: $$n = 386$$ (%)Median knowledge score[Q1-Q3]Mean rankP value Age category (years) < 55160(41.5)7(5–9)200.700.283c≥ 55226 (58.5)7(4–9)188.40 Gender Male211(54.7)7(5–9)204.56 0.032 c Female175 (45.3)6(4–8)180.17 BMI category Normal84 (21.8)6(4–8)170.240.073dOverweight181 (46.9)7(4–9)196.24Obese121 (31.3)7(5–9)205.55 Residency Refugee camp32 (8.3)6(4–7)139.77 < 0.001 d Village119 (30.8)6(4–8)167.61City235 (60.9)7(5–9)213.93 *Marital status* Married291 (75.4)7(5–9)199.06 0.043 d Widow52 (13.5)6(4–8)162.93Divorced19 (4.9)6(4–7)157.32Unmarried24 (6.2)7(6–9)220.96 Educational level No formal education45 (11.7)6(4–8)157.10 < 0.001 d Elementary school99 (25.6)6(4–8)177.70High school137 (35.5)6(4–8)171.90Collage/University105 (27.2)8(7–10)252.18 Employment Employed227 (58.8)7(5–9)207.08 0.004 c Unemployed159 (41.2)6(4–8)174.12 Monthly income (NIS a) Low (Less than 2000)177 (45.9)7(5–9)196.02 < 0.001 d Moderate (2000–5000)160 (41.5)7(5–9)208.75High (More than 5000)49 (12.7)4(4–7)134.61 Smoking Yes165 (42.7)7(4–9)187.880.390cNo221 (57.3)7(5–9)197.69 Duration of DM (years) < 7223 (57.8)6(4–8)175.6 < 0.001 c ≥ 7163 (42.2)7(6–9)217.99 Number of oral medications for DM Mon therapy240 (62.2)6(4–9)179.63 0.003 d Multi therapy71 (18.4)7(5–9)204.99No oral medications75 (19.4)8(6–9)227.03 Use of insulin Yes240 (62.2)6(4–8)174.87 < 0.001 c No146 (37.8)7(6–9)224.13 Last HbA1c < 771 (18.4)7(6–10)227.49 0.004 c ≥ 7315 (81.6)7(4–9)185.84 Comorbidities Yes204 (52.8)7(4–9)196.270.602cNo182 (47.2)7(5–9)190.36 Total number of chronic diseases (other than DM) 0181 (46.9)7(5–9)190.36 0.031 d 1110 (28.5)7(5–9)210.95267 (17.4)7(5–9)194.76≥ 328 (7.2)5(3–8)142.20 Total number of medications other than DM medications < 4303 (78.5)7(4–9)189.500.176c≥ 483 (21.5)7(5–9)208.08Abbreviations: BMI: body mass index, NIS: New Israeli shekel, HbA1c: hemoglobin A1c, DM: diabetes mellitusA Knowledge scale contains 24 items (range 0–24, the higher the score, the better knowledge)b Cut-off level of significance was 0.05c Mann‒Whitney U test was used to detect statistical significanced Kruskal‒Wallis test was used to detect statistical significancee1 new Israeli shekel (NIS) equals 0.31 US Dollar Table 9Characteristics of diabetic patients that were associated with Michigan Diabetic Knowledge related to prevention and early detection of chronic kidney disease in multiple linear regressionVariables aUnstandardizedcoefficients (B)Standardizedcoefficients (Beta)P value b$95\%$ Confidenceinterval for B Constant 5.259 < 0.001 4.058 to 6.461 Gender − 0.412− 0.0750.141− 0.962 to 0.137 Residency c 0.6350.149 0.001 0.245 to 1.024 *Marital status* c − 0.163− 0.0500.287− 0.463 to 0.138 Educational level c 0.6180.219 < 0.001 0.318 to 0.917 Employment c − 0.966− 0.173 0.005 -1.633 to − 0.299 Monthly income (NIS e) -1.061− 0.266 < 0.001 -1.521 to-0.602 Duration of DM 1.0000.180 < 0.001 0.476 to 1.524 Types of oral medications 0.3280.0950.068− 0.024 to 0.677 Use of insulin 1.3120.232 < 0.001 0.778 to 1.849 Last HbA1c − 0.362− 0.0510.270-1.006 to 0.282 Total number of chronic diseases other than DM − 0.068− 0.0240.626− 0.343 to 0.207R: 0.513; R Square: 0.263; Adjusted R Square: 0.242; Std. Error of the Estimate: 2.39498Abbreviations: NIS: new Israeli shekel, DM: diabetes mellitus, HbA1c: hemoglobin A1cA multiple linear regression was done on each factor with a p value < 0.05b cut-off level of significance was 0.05c Dummy coding was used to represent nominal variables
## Discussion
CKD has become a serious global health problem [20], and it is important to have good and reliable data on the knowledge, attitudes, and practices of patients at risk of developing CKD. However, CKD could be prevented or its progression could be slowed by using a three-level strategy. First, we should begin with primary prevention, which includes public education and modified risk factors. The second level, secondary prevention, includes screening and slowing disease progression. The third level, tertiary prevention, includes optimal management of patients with CKD.
Few studies about KAP among diabetic patients are available on the early detection and prevention of CKD. These include research done in Jordan that developed and used the CKD screening index, a reliable, valid, and generalizable index used to assess the KAP in CKD prevention and early detection [10]. Thus, we used this index in Nablus, Palestine, to assess how healthy practices to prevent or detect CKD early are affected by good knowledge and positive attitudes.
In our study, there was a great lack of knowledge about CKD and its signs, symptoms, and risk factors, as the mean knowledge score was 11.27 out of 24 compared to a study conducted in Jordan that showed a mean score of 19.27 out of 24 using the same screening index [10] and a mean score of 18.55 out of 30 in another study in Palestine [13]. We found that a higher practice score was significantly associated with being less than 55 years old, having a normal BMI, being a city resident, having a higher income and educational level, and having HbA1c of less than seven. In regression analysis, we found that a higher practice score for CKD prevention and early detection was significantly associated with normal BMI, being a city resident, high educational level, less tobacco use, last HbA1c below 7, higher knowledge, good attitude, and higher MDKT. The better practices among these patients can be attributed to a higher knowledge of diabetes, as seen in the MDKT test, and a higher knowledge about possible signs, symptoms, and risk factors for developing CKD. The higher the knowledge about diabetes evaluated on the MDKT test, the more likely the patient will comply with his medications and improve self-care and awareness of possible complications of DM, including CKD [21].
Our study found an obvious lack of knowledge about diabetes and its diet among diabetic patients using the MDKT test, similar to a study in Saudi Arabia showing that only $21.6\%$ of patients had good knowledge about diabetes [21]. The mean score was low at $47.8\%$ (6.7 out of 14 with a standard deviation of 2.75), which is close to a study conducted in the United Arab Emirates (UAE) that showed mean scores of $55\%$ in 2016, $55.5\%$ in 2001 and $68.2\%$ in 2006 [22].
Poor knowledge can challenge attitudes and practices, as a previous study showed that knowledge about cardiovascular health improves attitudes and practices [23]. Therefore, if patients had sound knowledge about diabetes, they would have good attitudes and practices regarding secondary prevention, such as chronic kidney diseases. Specifically, human beings’ practices depend on their attitudes regarding each behavior, and these attitudes are built on what they know about the outcomes [24].
In addition, there were no relationships between the age of the patient and his knowledge of diabetes. However, another study showed a slight decrease in knowledge with increasing age [22]. We also found that patients with higher education levels are more likely to have a better knowledge about diabetes than in a study in the UAE [22]. Multiple linear regression shows that a city resident with a high income and educational level using insulin therapy with a longer duration of DM is significantly associated with better knowledge about DM. A similar study using the MDKT test also showed that patients with a longer duration of DM, insulin use, and higher educational levels are significantly associated with better knowledge about DM [21].
As we said, most patients will visit their doctors if they have any signs or symptoms of CKD. Unfortunately, due to the lack of knowledge about the signs and symptoms of CKD, this will not be applicable. Therefore, this attitude will not benefit the early detection of CKD without adequate education about CKD.
Finally, incorrect assumptions about risk factors, signs and symptoms, disease stages, and related management plans can explain why patients present late for medical help. Therefore, it is encouraged to have a good education program and screening protocols to prevent and detect CKD. This approach improved medical outcomes among patients diagnosed with CKD [25].
## Strengths and limitations
This study has a strong point. It is one of the few studies related to KAP to prevent and detect CKD early. To our knowledge, this is the first study in Palestine specifically for diabetic patients. Furthermore, it is a multicenter study, making it more representative of diabetic patients in Nablus. In addition, medical students familiar with these scales were given the forms of data collection and scales, which will reduce the risk of misunderstood or missing data. Finally, the KAP was measured using a screening index developed, tested, and validated by Khalil and Abdalrahim in 2014, making the results convenient and reliable [10].
Despite the strengths, there are limitations regarding the generalizability of these results. For example, the sample was restricted only to patients who regularly followed up in the Nablus primary health care center at the Al-Makhfia and Hiwara primary health care centers and was limited to patients who visited these outpatient clinics, meaning that it may not represent patients who cannot reach the clinics due to serious illnesses. In addition, although we have attempted to minimize the recall and selection biases, they are still limitations of the current study. Furthermore, only close-ended questions were included in the questionnaire, which would decrease the ability to identify the underlying reasons for certain results. Another limitation was that cross-sectional studies could not generally determine the cause-effect and temporal relationships between sociodemographic and clinical characteristics and KAP scores. Furthermore, the sample size was small, which might not be sufficient to identify the real differences that are considered statistically significant in some sociodemographic characteristics and clinical factors. Additionally, some variables, such as the type of diabetes medication that the patient used, were not analysed. Finally, we did not have information on the patient’s current renal function, which could have helped to develop and assess the relationship between the current renal status and their level of KAP score on early detection and prevention.
## Conclusion
*Patients* generally have poor knowledge about DM, its diet, and its complications, making them susceptible to complications and a poor disease prognosis. We found that a higher practice score for prevention and early detection was significantly associated with normal BMI, being a city resident, high educational level, less tobacco use, last HbA1c below seven, and higher knowledge, attitude, and MDKT. Although patients have some information about CKD, most have incorrect assumptions about signs and symptoms and risk factors related to CKD. Therefore, they are unaware of the behaviors that can protect against CKD and the importance of early detection. Therefore, it is strongly recommended that patients improve their knowledge of the signs and symptoms and risk factors for CKD, and educating them about incorrect practices may increase the risk of CKD. This can be achieved through national educational programs focusing on patients with an increased risk of CKD as DM in this case and on the general population beginning early in the school population to establish baseline knowledge and positive attitudes and practices. It is also recommended to provide a screening protocol for high-risk patients for early detection of CKD to reduce or halt the progression of the disease.
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|
---
title: 'Pericytes protect rats and mice from sepsis-induced injuries by maintaining
vascular reactivity and barrier function: implication of miRNAs and microvesicles'
authors:
- Zi-Sen Zhang
- Yi-Yan Liu
- Shuang-Shuang He
- Dai-Qin Bao
- Hong-Chen Wang
- Jie Zhang
- Xiao-Yong Peng
- Jia-Tao Zang
- Yu Zhu
- Yue Wu
- Qing-Hui Li
- Tao Li
- Liang-Ming Liu
journal: Military Medical Research
year: 2023
pmcid: PMC10010010
doi: 10.1186/s40779-023-00442-2
license: CC BY 4.0
---
# Pericytes protect rats and mice from sepsis-induced injuries by maintaining vascular reactivity and barrier function: implication of miRNAs and microvesicles
## Abstract
### Background
Vascular hyporeactivity and leakage are key pathophysiologic features that produce multi-organ damage upon sepsis. We hypothesized that pericytes, a group of pluripotent cells that maintain vascular integrity and tension, are protective against sepsis via regulating vascular reactivity and permeability.
### Methods
We conducted a series of in vivo experiments using wild-type (WT), platelet-derived growth factor receptor beta (PDGFR-β)-Cre + mT/mG transgenic mice and Tie2-Cre + Cx43flox/flox mice to examine the relative contribution of pericytes in sepsis, either induced by cecal ligation and puncture (CLP) or lipopolysaccharide (LPS) challenge. In a separate set of experiments with Sprague–Dawley (SD) rats, pericytes were depleted using CP-673451, a selective PDGFR-β inhibitor, at a dosage of 40 mg/(kg·d) for 7 consecutive days. Cultured pericytes, vascular endothelial cells (VECs) and vascular smooth muscle cells (VSMCs) were used for mechanistic investigations. The effects of pericytes and pericyte-derived microvesicles (PCMVs) and candidate miRNAs on vascular reactivity and barrier function were also examined.
### Results
CLP and LPS induced severe injury/loss of pericytes, vascular hyporeactivity and leakage ($P \leq 0.05$). Transplantation with exogenous pericytes protected vascular reactivity and barrier function via microvessel colonization ($P \leq 0.05$). Cx43 knockout in either pericytes or VECs reduced pericyte colonization in microvessels ($P \leq 0.05$). Additionally, PCMVs transferred miR-145 and miR-132 to VSMCs and VECs, respectively, exerting a protective effect on vascular reactivity and barrier function after sepsis ($P \leq 0.05$). miR-145 primarily improved the contractile response of VSMCs by activating the sphingosine kinase 2 (Sphk2)/sphingosine-1-phosphate receptor (S1PR)1/phosphorylation of myosin light chain 20 pathway, whereas miR-132 effectively improved the barrier function of VECs by activating the Sphk2/S1PR2/zonula occludens-1 and vascular endothelial-cadherin pathways.
### Conclusions
Pericytes are protective against sepsis through regulating vascular reactivity and barrier function. Possible mechanisms include both direct colonization of microvasculature and secretion of PCMVs.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s40779-023-00442-2.
## Background
Sepsis and associated multiple organ dysfunction syndrome (MODS) are major causes of mortality in patients with combat injuries [1]. Since vascular dysfunction is a core feature of MODS, many of the treatment strategies focus on vascular dysfunction in patients with sepsis [2, 3]. Despite of decreasing incidence of traumatic sepsis in hospitals over the past two decades, however, the mortality rate in trauma patients remains at ~ $30\%$ [4–6].
Pericytes, a group of perivascular cells, are distributed throughout arterioles, capillaries, and venules [7, 8], and perform vascular-stabilizing and tension-controlling functions [9]. Dysfunction of pericytes contributes to the pathogenesis of a variety of diseases, including diabetic retinopathy, cardiovascular diseases, neurodegenerative diseases and strokes [10, 11]. Pericytes interact with endothelial cells via specific adhesion points, adhesion plaques, gap junctions, and tight junctions [12], and play an important role in endothelial barrier development and integrity maintenance [13]. For instance, within the blood–brain barrier, pericytes contribute to endothelial barrier integrity [14]. Pericyte loss has been shown to aggravate diabetes-induced microvascular dysfunction [15]. A study by Avolio et al. [ 16] suggested that myocardial pericytes facilitate heart neovascularization during myocardial injury. Additionally, pericyte degeneration leads to changes in cerebrovascular hemodynamics [17]. Collectively, these findings suggest that pericytes play an essential role in maintaining the vascular barrier function and regulating blood flow. However, whether pericytes exert a protective effect on vascular reactivity and barrier function upon sepsis is unknown.
We conducted a series of experiments to investigate the vascular reactivity and barrier functions of pericytes upon sepsis. To this end, we generated platelet-derived growth factor receptor beta (PDGFR-β)-Cre + mT/mG transgenic mice and vascular endothelial cell (VEC)-specific connexin 43 (Cx43) knockout (Tie2-Cre + Cx43flox/flox) mice to obtain green fluorescent protein (GFP)-labeled pericytes and to determine the effect of Cx43 on pericyte colonization. Pharmacological depletion of pericytes, cultured mesenteric VECs, and vascular smooth muscle cells (VSMCs) were used to investigate the underlying mechanisms.
## Animals
Sprague–Dawley (SD) rats were obtained from the Animal Center of Research Institute of Surgery, Army Medical University (Chongqing, China). PDGFR-β-Cre + mT/mG transgenic mice were generated by cross-breeding PDGFR-β-Cre mice (B-CM-004 on the C57BL/6 background, Biocytogen, Beijing, China) with R26mT/mG mice (007576, the Jackson Laboratory, Bar Harbor, ME, USA). PDGFR-β-Cre mice were crossed with mT/mG reporter mice in which Cre-mediated excision resulted in GFP expression. Genotypes were confirmed by polymerase chain reaction (PCR) followed by sequencing (Additional file 1: Fig. S1a, b). Tie2-Cre mice were acquired from Nanjing University (000125, Jiangsu, China). Cx43 in 129S7 Cx43flox/flox mice (008039, the Jackson Laboratory) were conditionally knocked out by Cre/loxP recombinase. Tie2-Cre + Cx43flox/flox mice were generated by cross-breeding Cx43flox/flox mice with Tie2-Cre mice, as previously reported [18, 19]. Genotypes were confirmed by PCR analysis followed by sequencing (Additional file 1: Fig. S1c, d). A total of 896 rats, 144 PDGFR-β-Cre + mT/mG transgenic mice, 104 wild-type (WT) mice (C57BL/6), and 40 Tie2-Cre + Cx43flox/flox mice were used in this study. The study protocol was approved by the Research Council and Animal Care and Use Committee of the Army Medical Center, Army Medical University (AMUWEC20188914). Experiments were conducted in accordance with the Guide for the Care and Use of Laboratory Animals issued by the US National Institutes of Health (NIH Publications, 8th Edition, 2011).
## Pericyte depletion animal model
CP-673451 (S1536, Selleckchem, Houston, TX, USA), a selective PDGFR-β inhibitor, was used to deplete pericytes, as previously described [20]. Briefly, rats received CP-673451 at a dosage of 40 mg/kg per day or vehicle (polyethylene glycol 400) for 7 consecutive days via gastric gavage.
## Preparation of rat and mouse sepsis models
Adult SD rats and mice (8–12 weeks of age) were anesthetized with sodium pentobarbital (45 mg/kg intraperitoneally). Sepsis was induced by cecal ligation and puncture (CLP) or intravenous infusion of lipopolysaccharide (LPS, *Escherichia coli* serotype O111:B4, Sigma), as described previously [21, 22].
For CLP-induced sepsis, laparotomy was performed, and the cecum was exposed, ligated, and punctured 1 cm from the distal end with a triangular needle for rats and three punctures (23-gauge needle) for mice. Feces were allowed to flow into the abdominal cavity. Upon completion of the surgery, the rats and mice were returned to home cages and allowed ad-libitum access to food and water. For LPS-induced sepsis, LPS was injected into the caudal vein at a dosage of 10 mg/kg. Animal subjects with mean arterial pressure at < 70 mmHg or > $30\%$ reduction at 12 h after CLP or LPS administration were used for subsequent experiments. The success rate of the modeling was $89\%$ in this study.
## Isolation and cultivation of VECs and VSMCs
VECs and VSMCs were obtained from the mesenteric veins and arteries of SD rats via enzymatic digestion. Before each experiment, VECs and VSMCs (3–5 passages) were serum-starved for 24 h.
## Isolation, cultivation, and identification of pericytes
Pericytes were isolated and cultured as previously described [23]. Morphological characterization was performed using phase contrast microscopy, and immunofluorescence characterization was performed using confocal laser scanning microscopy (CLSM; SP5II, Leica Microsystems, Wetzlar, Germany). The cells were verified with primary antibodies against PDGFR-β (ab32570, Abcam, Cambridge, UK), nerve/glial antigen 2 (NG-2; ab5320, Merck Millipore, Burlington, MA, USA), CD146 (ab75769, Abcam), α-smooth muscle actin (α-SMA; ab7817, Abcam), and platelet endothelial cell adhesion molecule (CD31; ab24590, Abcam).
For flow cytometry, cells were labeled with directly conjugated antibodies, including NG-2-PE, CD146-PE, PDGFR-β-PE, CD31-PE and IgG-PE (all from BD Biosciences, Franklin Lakes, NJ, USA). Samples were analyzed using the high-sensitivity imaging flow cytometer Amnis ImageStream MK II (ISX).
## Pericyte transplantation
Animal subjects in the control group received conventional treatment of sepsis animals, including fluid resuscitation [lactated Ringer’s solution (LR) 35 ml/kg], vasopressor (dopamine 1.75 mg/kg), and antibiotics (cefuroxime sodium, 100 mg/kg) at 12 h after CLP [6]. Pericytes were primed for 24 h with or without polyinosine–polycytidylic acid [Poly(I:C), 20 μg/ml; P1530, Sigma], and infused at a dosage of 1 × 106 pericytes slowly in 200-μl saline via the femoral vein at 12 h after sepsis. To visualize pericyte colonization, pericytes were transfected with Cx43 shRNA adenovirus (PCCx43−down) or control adenovirus expressing GFP (PCvehicle, Genechem Technologies, Shanghai, China).
## Vascular reactivity
The abdomen was opened via a midline incision. The ileocecal portion of the mesentery was gently exteriorized and mounted on a transparent plastic stage under moist condition at 37 °C. Single unbranched arterioles without obvious bends, with diameters ranging from 30 to 50 μm, and lengths of approximately 200 µm were used to determine responses to norepinephrine (NE) at 10–7 to 10–4 mol/L and acetylcholine (Ach) at 10–3 mol/L [24]. Changes in arteriole diameter were recorded with a video camera (OLYMPUS, DP21, Tokyo, Japan) and analyzed using Image-Pro Plus 5.0 software (Media Cybernetics Inc., Rockville, MD, USA). Contraction was calculated as (DBaseline – DNE)/DBaseline × $100\%$; dilation was calculated as (DAch10−3 – DNE10−4)/(DBaseline – DNE10−4) × $100\%$ (Additional file 2: Video S1).
## Vascular permeability
Fluorescein isothiocyanate (FITC)–bovine serum albumin (BSA) was used to evaluate albumin leakage across the mesenteric venular wall using inverted intravital microscopy (C11440, Hamamatsu, Shizuoka, Japan). Briefly, under anesthesia, the rat abdomen was opened via a midline incision. The ileocecal portion of the mesentery (10 to 15 cm from the caudal mesentery) was exteriorized and mounted on a transparent plastic stage under moist condition at 37 °C. Fluorescence intensity in the venules (IV) and perivenular interstitium (IP) was recorded at 0, 1, 3, and 6 min after an intravenous injection of FITC–BSA (50 mg/kg) using Image-Pro Plus 5.0 software. FITC–BSA leakage was estimated by dividing IP by IV, and the ratio of FITC–BSA leakage at a given time point to that of the baseline was designated as the ratio of FITC–BSA leakage at that point. For the mesenteric microvessel networks measurement, 50 mg/kg FITC–BSA was injected intravenously and allowed to circulate for 5 min; the fluorescence intensity of FITC–BSA was recorded and the FITC–BSA+ area per vessel was quantified.
## Immunohistochemistry of rat mesenteric microvessels
Mesenteric tissues were harvested and fixed in $4\%$ paraformaldehyde at 4 °C overnight. Samples were extensively washed with phosphate-buffered saline (PBS) and incubated for 30 min at 37 °C with $0.1\%$ Triton X-100 in PBS, and blocked with $5\%$ BSA prior to incubation with antibodies against PDGFR-β, NG-2, vascular endothelial (VE)-cadherin (555289; BD Biosciences), zonula occludens-1 (ZO-1; 33–9100, Invitrogen, Waltham, MA, USA), and CD31.
## Electron microscopy
The mesenteric venules were fixed in $3\%$ glutaraldehyde in 0.1 mol/L PBS for 20 min, cut into blocks smaller than 1 mm3 and then post-fixed by immersion in the same fixative for 1 h at room temperature and then overnight at 4 °C. Samples were incubated in $1\%$ osmium tetroxide in 0.1 mol/L PBS for 2 h at 4 °C, dehydrated, and then embedded in Epon 812. Ultrathin sections were stained with uranyl acetate and lead citrate for observation using transmission electron microscopy (TEM; JEM 1400, JEOL Ltd., Tokyo, Japan).
## Quantification of microvesicles
Microvesicles were isolated from culture supernatant with successive centrifugations [25]. Briefly, the medium was centrifuged at 1500 g for 5 min. The supernatant was centrifuged at 16,000 g for 1 h. The pellet was resuspended in 1 ml PBS and centrifuged at 16,000 g for 45 min. The process was repeated twice, and the final pellet was suspended in PBS and stored at -80 °C until use. Negative staining and ultrathin sections were used for TEM analysis.
For negative staining, microvesicle preparations were measured as previously described [26]. Briefly, the microvesicles preparations were fixed in $2.5\%$ glutaraldehyde in PBS at 4 °C for 24 h. After rinsing twice with 0.1 mol/L PBS, samples were post-fixed in $1\%$ OsO4 at room temperature for 70 min. After rinsing thrice with 0.1 mol/L PBS, samples were dehydrated using a series of graded ethanol. Finally, the samples were embedded in Epon 812 and 100 nm sections were prepared on grids. The microvesicles were analyzed using TEM (JEM 1400, JEOL Ltd.).
For scanning electron microscopy (SEM) analysis, the microvesicles preparations were fixed with $2.5\%$ glutaraldehyde overnight, washed 2–3 times with 0.1 mol/L PBS, dehydrated with a series of graded ethanol (5 min in $30\%$, 5 min in $50\%$, 10 min in $70\%$, 10 min in $90\%$, and twice for 10 min in absolute ethanol), and dried with CO2 using the critical point method with a dryer. Dried samples were covered with a 10-nm gold layer and scanned using a Zeiss Crossbeam 340 electron microscope.
For dynamic light scattering (DLS) analysis, the microvesicles preparations were suspended in 1 ml of PBS and then loaded into a cuvette for DLS analysis using a Zetasizer Nano ZS (Malvern Instruments, Ltd., Worcestershire, UK) at room temperature with a 633-nm He–Ne laser automatic attenuator. Each sample was measured at least three times.
For flow cytometry analysis, 10 μl of 0.2 μm, 0.5 μm, or 0.8 μm standard microbeads were added to 100 µl of PBS, respectively. Microvesicles preparations were suspended in 100 μl of PBS; 10 μl of 10 × Annexin V-binding buffer (10 mmol/L HEPES, pH 7.4, 140 mmol/L NaCl, 2.5 mmol/L CaCl2) and 5 μl of APC-Annexin V were added to microvesicle preparations. Annexin V was used to detect total microvesicles. After incubation for 25 min in the dark at room temperature, the samples were analyzed using the high-sensitivity imaging flow cytometer Amnis ImageStream MK II (ISX).
## Endocytosis of PCMVs by VSMCs and VECs
PKH-26 (MiNi26-1KT, Sigma), a red fluorescent dye that binds to the lipid bilayer, was used to label PCMVs [27]. Briefly, PCMVs were stained with PKH-26 dye in 0.4 ml of diluent C fluid for 5 min at room temperature. An equal volume of PCMV-depleted serum was used to stop the labeling reaction. Next, 5 ml of serum-free medium was added, and unbound PKH-26 was removed using centrifugation at 20,000 g for 40 min. VECs and VSMCs were subsequently incubated with 5 µl of labeled PCMVs for 0, 4, and 12 h in glass-bottom cell culture dishes at 37 °C and then washed with PBS. The uptake of labeled PCMVs by VECs and VSMCs was determined using CLSM.
## Co-culture of VSMCs and VECs with PCMVs
Transendothelial electrical resistance (TEER) and penetration rate in VECs were measured as previously described [28]. Briefly, VECs were seeded on inserts (100,000 cells per well) in a 6-well culture plate (0.4-μm pore size, 3450, Corning Inc., Corning, NY, USA). After 12 h exposure to LPS (2 μg/ml), the VECs in the PCMV group were incubated with PCMVs (2 × 106/ml) for 24 h. TEER was determined using a voltohmmeter (World Precision Instruments Inc., Sarasota, FL, USA) at 30 min interval. For penetration rate, FITC–BSA (10 μg/ml; A9771, Sigma) was added to the inserts, and 200 μl of the supernatant was collected every 10 min with 200 μl fresh basal medium. The penetration rate was calculated based on the total supernatant fluorescence OD/FITC–BSA stain fluorescence OD.
## Contraction in cultured VSMCs
Contraction of cultured VSMCs was determined as previously described [29]. Briefly, VSMCs (3–5 passage) were plated on collagen-coated polyethylene terephthalate cell culture inserts (3 μm pore, 3452, Corning) in 24-well culture plates. The lower compartment of the Transwell was filled with 600 μl of medium and cultured for 48 h. After 12 h LPS exposure, the VSMCs in the PCMV group were incubated with PCMVs (2 × 106) for 24 h. The contractile response of VSMC to NE was determined by measuring the infiltration ratio of FITC–BSA.
## 3D cell culture and assessment of contact area
For 3D imaging, GFP-VECs/VSMCs (control adenovirus) were used to distinguish VECs/VSMCs exhibiting GFP, whereas pericytes were labeled with mCherry to distinguish pericytes exhibiting red fluorescence. Briefly, GFP-VECs/VSMCs were cultured with mCherry-pericytes (control adenovirus) or PCCx43−down (Cx43 shRNA adenovirus) for 24 h. GFP-VECs/VSMCs and mCherry-pericytes were observed within a 3D volume viewer using CLSM.
## Quantification of miRNAs
miRNAs were extracted from the PCMVs using the miRCute™ RNA Isolation Kit (RP5301, BioTeke, Beijing, China). Reverse transcription (RT)-PCR was performed using the All-in-One™ miRNA qPCR Detection Kit (GeneCopoeia, Rockville, MD, USA) on a C1000™ Thermal Cycler Real-Time PCR system from Applied Biosystems (Bio-Rad, Hercules, CA, USA). The induction was calculated using the Ct method as follows: ΔΔCt = (Ct target miRNA − Ct U6), and the final values were determined using 2−ΔΔCt.
## miRNA transfection
Cells were transfected with a miR-$\frac{145}{132}$ inhibitor, a miR-$\frac{145}{132}$ mimic or a control miRNA (EF013, GeneCopoeia) [30]. miR-$\frac{145}{132}$-downregulated PCMV [miR-$\frac{145}{132}$[-]PCMV], miR-$\frac{145}{132}$-upregulated PCMV [miR-$\frac{145}{132}$(+)PCMV], or vehicle-PCMV were isolated from the supernatant of miR-$\frac{145}{132}$[-]-pericyte and miR-$\frac{145}{132}$(+)-pericyte or vehicle-pericyte.
## Western blotting analysis
Western blotting analysis was performed using antibodies against the following: PDGFR-β (1:1000, ab32570, Abcam), NG-2 (1:1000, ab129051, Abcam), VE-cadherin (1:1000, ab231227, Abcam), ZO-1 (1:1000, ab190085, Abcam), p-MLC20 (1:2000, M6068, Sigma), MLC20 (1:2000, 3672, Cell Signaling Technology, Danvers, MA, USA), Sphk2 (1:2000, PA5-99,720, Thermo Fisher Scientific, Waltham, MA, USA), S1PR1 (1:2000, ab11424, Abcam), S1PR2 (1:2000, PA5-72,868, Thermo Fisher Scientific), and β-actin (1:7000, A5441, Sigma). Bands were detected with fluorescent secondary antibodies and quantified using the Odyssey CLx Infrared Imaging System (LI-COR, Lincoln, NE, USA).
## Statistical analysis
The results are expressed as mean ± standard deviation for the indicated number of experiments. Student’s t-test was used for statistical analysis between two groups, and one-way analysis of variance (ANOVA) for comparisons involving three or more groups, followed by Tukey’s post hoc test for pairwise comparisons. Survival data were analyzed using the log-rank test (Kaplan–Meier curves). All statistical analyses were conducted using the SPSS software (version 11.0). Statistical significance was set at $P \leq 0.05$ (2-sided for all analyses).
## Pericyte loss and destruction contribute to vascular hyporeactivity and vascular leakage following sepsis
Pericytes were severely damaged following sepsis in CLP- and LPS-treated rats. Immunofluorescence staining showed significantly decreased expression of pericyte markers (NG-2 and PDGFR-β) in the mesenteric microvascular networks and mesenteric venules at 12 and 24 h after either CLP or LPS in rats ($P \leq 0.01$, Fig. 1a, Additional file 1: Fig. S2a). Decreased expression of NG-2 and PDGFR-β was also evident in Western blotting analysis ($P \leq 0.01$, Additional file 1: Fig. S2b). Electron microscopy showed that pericytes were tightly ensheathed within the endothelium in the sham control group, but severe desquamation, swelling and destruction in the mesenteric venules and retina (Fig. 1b, Additional file 1: Fig. S2c), and diapedesis in the mesenteric venules at 24 h in CLP rats (Fig. 1b).Fig. 1Sepsis induces pericyte loss, vascular hyporeactivity and leakage in rats. a Mesenteric microvascular networks from CLP and LPS (10 mg/kg)-induced sepsis at 6, 12 and 24 h were stained for NG-2 (pericyte marker; green), PDGFR-β (pericyte marker; green), and CD31 (VEC marker; red). Pericyte coverage rate of endothelium was quantified by analyzing percentage of CD31+ capillaries opposed to NG-2+ and PDGFR-β+ PCs ($$n = 8$$ rats). Scale bars: 100 μm. b TEM observation of ultrastructural changes of pericyte in mesenteric venules at 24 h after CLP and LPS administration (yellow arrowheads indicate pericyte loss and swelling, *indicate erythrocyte diapedesis). Scale bars: 2 μm. c Changes in vascular response of mesenteric arterioles to NE and Ach in vivo ($$n = 8$$ rats). d Vascular leakage of mesenteric venules measured by the appearance of intravenously injected FITC–BSA and quantitation of FITC–BSA+ vessel ($$n = 8$$ rats). Scale bars: 50 μm. e Representative TEM images of tight junctions in mesenteric venules after CLP and LPS administration at 24 h (green arrow indicate the tight junction, red arrowheads indicate the endothelial fragments and disrupted VEC junctions). Scale bars: 1 μm. PC pericyte, CLP cecal ligation and puncture, LPS lipopolysaccharides, NG-2 nerve/glial antigen 2, PDGFR-β platelet-derived growth factor receptor beta, VEC vascular endothelial cell, RBC red blood cell, L lumen, NE norepinephrine, Ach acetylcholine, MA mesenteric arteriole, TJ tight junction, TEM transmission electron microscopy. Data shown as mean ± SD. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$ vs. Sham (one-way ANOVA) Responses of mesenteric arterioles to both NE and Ach were significantly decreased at 12 and 24 h ($P \leq 0.05$, Fig. 1c). The expression of p-MLC20, a regulatory protein of vascular smooth muscle in the superior mesenteric artery (SMA), was significantly decreased ($P \leq 0.05$, Additional file 1: Fig. S2d). Permeability of the mesenteric venules and microvascular networks started to increase at 1 min after LPS exposure, and became more pronounced at 6 min ($P \leq 0.01$, Fig. 1d, Additional file 1: Fig. S2e). Electron microscopy showed disrupted continuity and integrity in the VECs of mesenteric venules (Fig. 1e) and severe swelling in the retina at 24 h after sepsis (Additional file 1: Fig. S2c). Immunohistochemical staining of ZO-1 and VE-cadherin revealed defective tight and adhesion junctions at 24 h after sepsis (Additional file 1: Fig. S2f). Western blotting analysis showed decreased expression of ZO-1 and VE-cadherin in superior mesenteric veins (SMVs) at 12 and 24 h in CLP and LPS rats ($P \leq 0.01$, Additional file 1: Fig. S2g).
Similar to SD rats, fluorescent-labeled pericytes in PDGFR-β-Cre + mT/mG transgenic mice also exhibited significant destruction and desquamation after sepsis (Additional file 1: Fig. S3a). Electron microscopy revealed severe damage to pericytes in the mesenteric venules and retina of mice after sepsis (Additional file 1: Figs. S3b). Changes in vascular reactivity and permeability were consistent with those in septic rats (Additional file 1: Fig. S3c-f).
## Pericyte transplantation protects vascular reactivity and barrier function in septic rats
Next, we examined the potential effects of transplantation of cultivated pericytes (Additional file 1: Fig. S4a-c) and function-enhanced [Poly(I:C) pre-treatment] pericytes [Poly(I:C)PC] in septic rats.
## Effect of pericyte transplantation
Pericyte transplantation (1 × 106) increased the 72-h survival rate in septic rats [$50.0\%$ ($\frac{8}{16}$) vs. $18.8\%$ ($\frac{3}{16}$) in the conventional treatment group and $6.3\%$ ($\frac{1}{16}$) in the sepsis group, $P \leq 0.01$, Fig. 2a]. GFP-labeled pericytes (Additional file 1: Fig. S4d) were found to colonize in the mesenteric venules at 6, 12, 24 and 36 h after pericyte transplantation [106], with the highest rate of colonization at 24 h (Fig. 2b, Additional file 1: Fig. S4e). Notably, the transplanted pericytes were embedded within the vascular endothelium of mesenteric venules (Fig. 2c).Fig. 2The transplanted pericytes improve the vascular hyporeactivity and leakage after sepsis. a Effects of transplanting different amount of exogenous pericytes on animal survival ($$n = 16$$ rats). Intravital microscopy (b, red arrows indicate GFP-PC) and immunofluorescence (c) by CLSM were used to monitor the GFP-PC location on mesenteric venules at 24 h after transplantation of exogenous pericytes [106]. Scale bars: 50 μm. d Mesenteric microvascular networks were stained for NG-2, PDGFR-β, and CD31 at 24 h after resuscitation ($$n = 8$$ rats). Scale bars: 100 μm. e Changes in vascular response of mesenteric arterioles to NE and Ach in vivo after sepsis in rats ($$n = 8$$). f Vascular leakage of mesenteric venules measured by the appearance of intravenously injected FITC–BSA and quantitation of FITC–BSA+ vessel ($$n = 8$$ rats). Scale bars: 50 μm. g Immunohistochemistry for ZO-1 and VE-cadherin in mesenteric venules. Scale bars: 20 μm. h Representative TEM images of tight junctions in mesenteric venules (green arrows indicate the tight junction, *indicate the erythrocyte diapedesis). Scale bars: 1 μm. NG-2 nerve/glial antigen 2, PDGFR-β platelet-derived growth factor receptor beta, CT conventional treatment, CLSM confocal laser scanning microscopy, PC pericyte, Poly(I:C)PC polyinosine-polycytidylic acid pre-treatment pericyte, NE norepinephrine, Ach acetylcholine, MA mesenteric arteriole, ZO-1 zonula occludens-1, VE-cadherin vascular endothelial cadherin, VEC vascular endothelial cell, RBC red blood cell, TJ tight junction, L lumen, TEM transmission electron microscopy. Data shown as mean ± SD. ** $P \leq 0.01$, ***$P \leq 0.001$ vs. Sham; ##$P \leq 0.01$, ###$P \leq 0.001$ vs. Sepsis; &&$P \leq 0.01$, &&&$P \leq 0.001$ vs. Sepsis + CT (one-way ANOVA) Pericyte transplantation improved vascular reactivity and barrier function in septic rats ($P \leq 0.05$, Additional file 1: Fig. S4f–i); observed effects were noticed as early as 6 h, and reached plateau at 24 h. Both NG-2 and PDGFR-β were upregulated in the pericyte and Poly(I:C)PC groups ($P \leq 0.01$; Fig. 2d, Additional file 1: Fig. S5a, b). The conventional treatment group exhibited only marginal improvements in vascular function. Protective effects were significant in both the pericyte and Poly(I:C)PC groups ($P \leq 0.01$ vs. both the sepsis and conventional treatment groups, Fig. 2e-f, Additional file 1: Fig. S5c, d).
Pericyte transplantation improved the integrity of tight and adhesion junctions (Fig. 2g) as well as the ultrastructure of tight junctions (Fig. 2h, Additional file 1: Fig. S5e). The expression levels of p-MLC20 in SMA, ZO-1 and VE-cadherin in SMV were significantly increased ($P \leq 0.01$, Additional file 1: Fig. S5f, g).
## Pericyte colonization is associated with Cx43
To examine the potential role of Cx43 in the colonization of microvessels by transplanted pericytes, we conducted a series of experiments in Cx43 shRNA adenovirus-infected pericytes (PCCx43−down) and VEC Cx43-knockout mice (Tie2-Cre + Cx43flox/flox mice). The results showed significantly lower number of colonizing PCCx43−down in the mesenteric microvascular networks than in the pericyte group (Fig. 3a). PCCx43−down transplantation showed decreased protective effect on vascular reactivity and barrier function in septic rats, compared to pericyte group ($P \leq 0.05$, Fig. 3b, c; Additional file 1: Fig. S6a-c).Fig. 3Transplanted pericytes regulate vascular reactivity and permeability via Cx43 after sepsis. a Immunofluorescence by CLSM was used to monitor the PCCx43−down colonization on mesenteric microvascular networks in septic rats. Scale bars: 100 μm. b Changes in vascular response of mesenteric arterioles to NE and Ach in vivo after PCCx43−down transplantation ($$n = 8$$ rats). c Vascular leakage of mesenteric venules measured after PCCx43−down transplantation ($$n = 8$$ rats). Scale bars: 50 μm. d Immunofluorescence by CLSM was used to monitor the GFP-PC location on mesenteric venules at 24 h after sepsis in Tie2-Cre + Cx43flox/flox mice. Scale bars: 20 μm. e Changes in vascular response of mesenteric arterioles to NE and Ach in vivo after sepsis in Tie2-Cre + Cx43flox/flox mice ($$n = 8$$). f Vascular leakage of mesenteric venules measured after sepsis in Tie2-Cre + Cx43flox/flox mice ($$n = 8$$). Scale bars: 20 μm. g 3D projection images of contact area on 24 h in the pericytes-VSMCs/VECs culture at a 1:9 pericyte:VSMCs/VECs ratio (PC group: pericyte with no-treatment; PCCx43−down group: infection of pericytes with shRNA adenovirus targeting Cx43; PCvehicle group: infection of pericytes with control adenovirus). Pericytes, VSMCs/VECs and nuclei are shown in red, green and blue, respectively. Scale bars: 20 μm. CLSM confocal laser scanning microscopy, PC pericyte, NE norepinephrine, Ach acetylcholine, MA mesenteric arteriole, VECs vascular endothelial cells, VSMCs vascular smooth muscle cells. Data shown as mean ± SD. $$P \leq 0.05$, $$$P \leq 0.01$ vs. PC or PC (WT) (one-way ANOVA) In comparison to the WT mouse control, the extent of transplanted pericyte colonization as well as the associated protective effects on vascular reactivity and vascular barrier function were significantly reduced in Tie2-Cre + Cx43flox/flox mice ($P \leq 0.05$, Fig. 3d-f, Additional file 1: Fig. S6d).
In the 3D VSMCs/pericytes or VECs/pericytes co-culture, pericytes formed direct connection with VSMCs or VECs via Cx43 (Additional file 1: Fig. S6e). Knockdown of Cx43 in pericytes reduced the contact area ($P \leq 0.05$, Fig. 3g; Additional files 3–8: Videos S2-S7) and number of cells in contact with each other ($P \leq 0.01$, Additional file 1: Fig. S6f).
## Pericyte-depleted rats are recapitulated by CP-673451
Repeated CP-673451 treatment for 7 consecutive days, a PDGFR inhibitor, reduced the amount of vascular pericytes within the rat mesentery and decreased the expression of pericyte markers (NG-2 and PDGFR-β) in mesenteric microvascular networks (Additional file 1: Fig. S7a, b). Electron microscopy revealed reduced pericyte in mesenteric venules (Additional file 1: Fig. S7c). In contrast, the heart, lung, liver, and kidney functions were not altered. CP-673451 treatment for 14 consecutive days also damaged heart and kidney functions in SD rats in addition to depletion of pericytes in the mesenteric microvessels (Additional file 1: Fig. S7d, e). Accordingly, the 7-day-CP-673451 treatment regimen was used in subsequent experiments.
## Pericyte depletion aggravates sepsis-induced vascular hyporeactivity and vascular leakage, which is rescued by pericyte transplantation
Repeated CP-673451 treatment (40 mg/kg) for 7 d resulted in pericyte loss within the mesenteric microvascular networks. Both NG-2 and PDGFR-β were down-regulated ($P \leq 0.001$, Additional file 1: Fig. S8). Vascular reactivity and vascular barrier function were markedly impaired ($P \leq 0.001$, Additional file 1: Figs. S9 and S10). Pericyte transplantation restored pericyte coverage, vascular reactivity, and barrier function to normal levels (Additional file 1: Figs. S8-S10).
In septic rats, pericyte depletion aggravated sepsis-induced pericyte loss ($P \leq 0.01$, Additional file 1: Fig. S8), vascular hyporeactivity and permeability damage ($P \leq 0.01$, Additional file 1: Figs. S9 and S10). Electron microscopy revealed fragmentation of the endothelial cell membrane, disruption of tight junctions, and erythrocyte diapedesis within the mesenteric venules (Additional file 1: Fig. S10d). Pericyte transplantation rescued the poor pericyte coverage ($P \leq 0.01$, Additional file 1: Fig. S8), damaged vascular reactivity ($P \leq 0.01$, Additional file 1: Fig. S9) and barrier function ($P \leq 0.01$, Additional file 1: Fig. S10). The rescue effects were more pronounced with Poly(I:C)-treated pericytes ($P \leq 0.01$, Additional file 1: Figs. S8-S10).
## Role of PCMVs in cultured VSMCs and VECs in vitro
Although pericyte colonization was highest 24 h after transplantation, protective effects on vascular function were observed within the first 6 h. Considering our previous study showing that pericytes can release microvesicle to deliver connective tissue growth factors to VECs and promote their proliferation [31], we speculated that the early effects caused by pericytes are associated with PCMV release. Indeed, pericytes secreted PCMVs with characteristics similar to other cell-derived microvesicles (for example, 100–1000 nm in diameter) (Fig. 4a, Additional file 1: Fig. S11a). Poly(I:C)-treated pericytes released even more PCMVs. PKH-26-labeling suggested that PCMVs entered VECs and VSMCs in a time-dependent manner (Additional file 1: Fig. S11b). Additionally, PCMV (2 × 106 microvesicles/ml, 12 h after LPS stimulation) incubation alleviated LPS-induced damage to VSMC contractile function and VEC barrier function. PCMVs also improved the contractile response of VSMCs to NE and the expression of p-MLC20 ($P \leq 0.05$, Fig. 4b, Additional file 1: Fig. S11c), while improving the integrity of VECs and expression of ZO-1 and VE-cadherin ($P \leq 0.05$, Fig. 4c, Additional file 1: Fig. S11c). The protective effects of Poly(I:C)-induced PCMVs on VSMC contractile function and VEC barrier function were even stronger. Fig. 4PCMVs regulate the contractile response of VSMCs and barrier function of VECs after sepsis. a Identification of PCMV. ( i-ii) Representative TEM micrographs of microvesicle isolated from pericyte; (iii) Representative SEM micrographs of microvesicle observed from pericytes; (iv) PCMV diameter measured by DLS analysis. b Role of PCMVs and Poly(I:C)PCMVs (2 × 106 microvesicles/ml) on the contractile response of rat VSMC to NE at 12 h after LPS (2 μg/ml) stimulation ($$n = 8$$ cells). c Role of PCMVs and Poly(I:C)PCMVs on the barrier function of rat VECs after LPS administration. i PCMVs and Poly(I:C)PCMVs were added into rat VECs, and TEER of each group was measured ($$n = 3$$ cells); ii PCMVs and Poly(I:C)PCMVs were added into VECs, and FITC–BSA penetration of each group was measured ($$n = 8$$ cells); iii VECs treated with PCMV were analyzed by immunofluorescence for ZO-1. Scale bars: 20 μm. d Changes in vascular response of mesenteric arterioles to NE and Ach in vivo after PCMV transplantation ($$n = 8$$ rats). Scale bars: 50 μm. e Vascular leakage of mesenteric venules measured after PCMV transplantation ($$n = 8$$ rats). Scale bars: 50 μm. PC pericyte, PCMV pericyte-derived microvesicle, TEM transmission electron microscopy, SEM scanning electron microscopy, VECs vascular endothelial cells, VSMCs vascular smooth muscle cells, LPS lipopolysaccharides, TEER transendothelial electrical resistance, ZO-1 zonula occludens-1, NE norepinephrine, Ach acetylcholine, MA mesenteric arteriole. Data shown as mean ± SD. ** $P \leq 0.01$, ***$P \leq 0.001$ vs. Normal control or Sham; ##$P \leq 0.01$, ###$P \leq 0.001$ vs. LPS or Sepsis (one-way ANOVA)
## Role of PCMVs in septic rats in vivo
Similar to the in vitro findings, PCMV infusion (2 × 107 microvesicles per rat, at 12 h after sepsis [31]) significantly improved the vascular reactivity and barrier function in septic rats at 24 h ($P \leq 0.05$, Fig. 4d, e; Additional file 1: Fig. S11d-g). The vasoconstriction and dilation responses of the mesenteric arteriole to NE were significantly increased. Vascular permeability and integrity of ZO-1 and VE-cadherin were also improved.
## Role of miRNAs transferred by PCMVs to VSMCs and VECs
To elucidate whether the protective effects of PCMVs on vascular function were attributable to miRNAs, we screened the main miRNAs related to VSMCs (miR-1, miR-15b, miR-143, miR-145, miR-147, and miR-503) and VECs (miR-23b, miR-125, miR-126, and miR-132) in PCMVs. miR-132 and miR-145 were more abundant in the PCMVs generated by pericytes and Poly(I:C)-treated pericytes (Additional file 1: Fig. S12a). To further determine whether miR-132 and miR-145 in PCMVs regulate the contractile response of VSMCs and barrier function of VECs, miR-$\frac{145}{132}$ inhibitors and mimics were used to obtain miR-$\frac{145}{132}$downregulated-PCMV [miR-$\frac{145}{132}$[-]PCMV] and miR-$\frac{145}{132}$upregulated-PCMV [miR-$\frac{145}{132}$(+)PCMV], respectively. Results showed that miRNA-145 and miRNA-132 acted on VSMCs and VECs to protect vascular reactivity and barrier function, respectively. In cultured VSMCs, miR-145(+)PCMV enhanced the effects of PCMVs on VSMCs ($P \leq 0.01$), whereas miR-145[-]PCMV attenuated the protective effect of PCMVs on VSMCs ($P \leq 0.01$). However, miR-132(−/+)PCMV had no effect on the constriction response of VSMCs (Fig. 5a, b; Additional file 1: Fig. S12b). In contrast, in cultured VECs, miR-132(+)PCMV enhanced the protective effect of PCMVs on the VEC barrier function ($P \leq 0.001$), whereas miR-132(−)PCMV attenuated VEC barrier function ($P \leq 0.01$). miR-145(−/+)PCMV did not affect VEC barrier function (Fig. 5c, d; Additional file 1: Fig. S12c-e).Fig. 5PCMVs carry miR-145 and miR-132 to play coordinated effects on the VSMCs and VECs. a Effects of different types of PCMVs on contractile response of rat VSMC to NE after LPS administration ($$n = 8$$ cells). b Western blotting analysis of p-MLC20, Sphk2, S1PR1 and S1PR2 from VSMCs treated with different types of PCMVs ($$n = 3$$ cells). c Different types of PCMVs were added into rat VECs, and FITC–BSA penetration of each group was measured ($$n = 8$$ cells). d Western blotting analysis of ZO-1, VE-cadherin, Sphk2, S1PR1 and S1PR2 from VECs treated with different types of PCMVs ($$n = 3$$ cells). e–f Western blotting analysis of p-MLC20, ZO-1 and VE-cadherin from VSMCs and VECs with S1PR1 inhibition (W146) and S1PR2 inhibition (JTE013) ($$n = 3$$ cells). LPS lipopolysaccharides, PC pericyte, PCMV pericyte-derived microvesicle, VECs vascular endothelial cells, VSMCs vascular smooth muscle cells, Sphk2 sphingosine kinase 2, S1PR1 sphingosine-1-phosphate receptor 1, p-MLC20 phosphorylation of myosin light chain 20, ZO-1 zonula occludens-1, VE-cadherin vascular endothelial cadherin, Ad-SK2 adenovirus-mediated overexpression of Sphk2, Negative infection of VSMCs or VECs with negative control adenovirus. Data shown as mean ± SD. *** $P \leq 0.001$ vs. Normal control; ##$P \leq 0.01$, ###$P \leq 0.001$ vs. LPS; $$$P \leq 0.01$ vs. LPS + PCMV; @@$P \leq 0.01$ vs. Ad-SK2 (one-way ANOVA) Dual-luciferase reporter assay indicated that miR-132 and miR-145 acted on Sphk2 mRNA in VECs and VSMCs, respectively (Additional file 1: Fig. S12f). Briefly, the expression of Sphk2 was significantly increased upon LPS exposure. Experiments using Sphk2 overexpression in VSMCs and VECs, S1PR1 inhibitor W146 and S1PR2 inhibitor JTE013 suggested that high Sphk2 expression antagonized the protective effect of PCMVs, whereas S1PR1 inhibition restored the protective role of PCMVs on p-MLC20 in VSMCs. In VECs, overexpression of Sphk2 antagonized the protective effect of PCMVs, whereas inhibition of S1PR2 restored its endothelial barrier protective roles (Fig. 5e, f).
## Discussion
Sepsis is defined as a life-threatening organ dysfunction caused by dysregulated host response to infection, with high morbidity and mortality rates [32]. Vascular hyporeactivity and leakage are key pathophysiologic features that cause MODS [2, 3]. A variety of treatment novel strategies have been proposed, but only a few have demonstrated sufficient therapeutic efficacy [33, 34]. Moreover, some of these strategies are inherently problematic. For example, vasoconstrictors (e.g., NE and arginine vasopressin) increase vascular reactivity but can also cause endothelial cytoskeleton contraction and subsequent aggravation of vascular leakage [33, 34]. Results from the current study indicate that pericyte transplantation can protect sepsis-induced vascular dysfunction by colonizing and covering microvessels and releasing microvesicles. Treatment with Poly(I:C)-treated pericytes elicited stronger effects than untreated pericytes. Importantly, Cx43 was found to play a crucial role in pericyte microvasculature colonization and coverage. miR-145 and miR-132 were identified as the key factors carried by PCMVs that contribute to the regulatory and protective roles in vascular contractile and barrier functions (Fig. 6).Fig. 6A schematic diagram of the protective role of pericytes in sepsis. After sepsis, pericyte desquamation, increased expression of endothelial S1PR2 and decreased ZO-1 and VE-cadherin are associated with vascular endothelial barrier breakdown; increased expression of S1PR1 and decreased p-MLC20 in VSMCs are associated with the vascular hyporeactivity. After pericyte transplantation, pericytes colonize in the mesenteric vein and form direct contact with endothelial cells to form a gap junction. Pericytes also secreted microvesicles (MVs) containing miR-$\frac{145}{132}$ to VSMCs and VECs to produce additional protective effects. miR-145 mainly acts on VSMCs to improve the vascular reactivity via inhibiting the expression of Sphk2 and S1PR1, and increasing the expression of p-MLC20. miR-132 mainly acts on VECs to improve the barrier function via inhibiting the expression of Sphk2 and S1PR2, and increasing the expression of ZO-1 and VE-cadherin. PC pericyte, PCMV pericyte-derived microvesicle, VEC vascular endothelial cell, VSMC vascular smooth muscle cell, p-MLC20 phosphorylation of myosin light chain 20, Sphk2 sphingosine kinase 2, S1PR1 sphingosine-1-phosphate receptor 1, ZO-1 zonula occludens-1, VE-cadherin vascular endothelial cadherin Pericytes are pluripotent cells embedded in the vascular basement membrane and play key roles in the regeneration of microvessels and regulation of local blood flow. However, pericytes also contribute to the regulation of contractile and angiogenic functions, exhibit immune-promoting properties, and regenerate the cell types that constitute the tissue in which they exist [7, 8]. Pericyte transplantation thus could facilitate tissue repair, including the skeletal muscle, heart and bone tissues, after injury. For instance, Munroe et al. [ 35] reported that pericyte transplantation improved skeletal muscle recovery following hindlimb immobilization. Alvino et al. [ 36] observed that allogeneic pericytes improved myocardial vascularization and reduced interstitial fibrosis in a swine model of acute myocardial infarction. Konig et al. [ 37] found that transplantation of pericytes from adipose tissue promoted the healing of critical-sized bone defects. However, no previous studies have reported whether pericytes are protective against sepsis-induced vascular dysfunction. In the current study, we found that sepsis damaged endogenous pericytes in microvessels and that loss or chemical depletion of pericytes exacerbated vascular hyporeactivity and leakage. Pericyte transplantation protected both rats and mice from sepsis-induced vascular hyporeactivity and vascular leakage.
Pericytes are connected to endothelial cells by several types of intercellular junctions, including tight junctions, gap junctions, and adhesion plaques [13]. In particular, gap junctions (Cx$\frac{43}{30}$) play important roles in the connection of pericytes and VECs and allow direct communication between neighboring cells via diffusion of nutrients, metabolites, secondary messengers, ions and various other molecules [38]. The current study suggested a new mechanism by which Cx43 regulates the colonization of transplanted pericytes, and extended our current knowledge beyond the traditional role of Cx43 in direct cell–cell communication through gap junctions [39]. We hypothesized that transplanted pericytes could form gap junction via Cx43 and promote endothelial barrier function.
Optimal colonization of pericytes was observed 24 h after transplantation. However, improved vascular function was detected as early as 6 h, suggesting additional mechanisms that are not dependent on pericyte colonization. Indeed, PCMVs exerted protective effects primarily by transferring miRNA-145 and miRNA-132 to VSMCs and VECs, respectively. miR-145 and miR-132 have important roles in vascular leakage, maturation and reparative angiogenesis [40, 41]. In particular, miR-145 is highly enriched in VSMCs and promotes the contractile phenotype, thus playing a crucial role in several cardiovascular diseases, including hypertension and coronary artery disease [42]. miR-132 is highly conserved and abundantly expressed in normal VECs, where it regulates VEC proliferation and migration. miR-132 also plays a key role in promoting VEC angiogenesis and maintaining vascular integrity [43]. The current study suggested that pericytes may secrete PCMVs and transfer miR-145 and miR-132 to VSMCs and VECs to protect vascular reactivity and vascular barrier function in septic rats and mice.
The protective effects elicited by PCMV-associated miR-145 and miR-132 could involve the Sphk2/S1PR1/p-MLC20 pathway in VSMCs and Sphk2/S1PR2/ ZO-1 and VE-cadherin pathway in VECs, respectively. The S1P signaling pathway plays a key role in regulating endothelial barrier function and angiogenesis via the G protein-coupled receptors S1PR1 and S1PR2 [44]. S1P can also counteract pericyte loss and microvessel disassembly during sepsis [45]. S1PR1 was originally identified as an abundant transcript in endothelial cells that contributes to the regulation of endothelial cell cytoskeletal structure, migration, capillary-like network formation, and vascular maturation [46]. S1PR2 is highly expressed in neuronal cells and VSMCs and has been implicated in various biological processes, including cell migration, contraction and differentiation, by regulating the expression of smooth muscle differentiation genes [47]. Under normal conditions, S1PR1 and S1PR2 are primarily expressed in VECs and VSMCs, respectively. S1PR2 expression is increased in VECs where it aggravates vascular permeability upon inflammation; S1PR1 is increased in VSMCs and decreased in vascular reactivity in cardiovascular disorders [48]. Sphk2 catalyzes the phosphorylation of sphingosine to S1P, and participates in inflammation, endoplasmic reticulum stress, and apoptosis [49]. We found increased expression of both Sphk2 and S1PR1 in VSMCs upon LPS exposure. miR-145 released by PCMVs inhibited Sphk2 and S1PR1, and restored the contractile function of VSMCs by upregulating p-MLC20. In contrast, miR-132 released by PCMVs inhibited Sphk2 and S1PR2, and restored the barrier function of VECs by upregulating ZO-1 and VE-cadherin expression.
Poly(I:C), a dsRNA analog and inducer of interferon, has broad-spectrum antiviral and immunoregulatory effects. Mesenchymal stem cell (MSC)-based therapy is a promising approach for many critical diseases, such as graft-versus-host disease, autoimmune diseases, and kidney, liver, and heart injury, owing to their prominent ability in immune regulation [50]. Poly(I:C) has been shown to reduce the immunogenicity of MSCs and enhance their paracrine functions [51]. In animal models of CLP-induced sepsis, Poly(I:C) improves the immunosuppressive properties of MSCs and animal survival by inhibiting miR-143 [52]. Poly(I:C)-pretreated MSCs also enhance the antimicrobial effects of microvesicles in injured lungs [53]. In the current study, Poly(I:C)-stimulated pericytes produced higher amount of PCMVs with higher levels of miR-145 and miR-132 and elicited superior protective effects on vascular reactivity and barrier function following sepsis.
The current study has several limitations. First, pericytes are broadly distributed but only mesenteric microvessels were examined in this study. Second, we only assessed the role of LPS and CLP in pericyte damage and loss without elucidating the mechanisms underlying LPS- and CLP-induced pericyte damage and loss. Third, although the relationship between Cx43 and pericyte colonization was clearly demonstrated, the underlying mechanisms remain unknown. Fourth, miR-145 and miR-132 could be produced by cells other than VECs and VSMCs; whether endogenous miR-145 and miR-132 from other sources also contribute to the protective action of pericytes in sepsis requires further investigation. Fifth, considering the high mortality of PDGFR-β knockout mice, we used CP-673451, a PDGFR-β inhibitor, to deplete pericytes based on a previously published protocol [20]. Importantly, we found that pericytes in the mesenteric microvascular networks were successfully depleted by 7 d of CP-673451 treatment without noticeably damaging the function of other vital organs. Prolonged treatment, however, produced significant effects beyond the mesenteric microvascular networks. Also, the extent of pericytes depletion with CP-673451 treatment was not complete. Finally, although two control groups (a sepsis control and a conventional treatment control) were included, all experiments were conducted in rodents. Further investigation with large animals (e.g., non-human primates) are warranted.
## Conclusions
In summary, the present study showed that pericyte loss and structural destruction contribute to vascular hyporeactivity and leakage upon sepsis. Pericyte transplantation could protect the contractile and barrier functions of vasculature via colonization and direct coverage as well as PCMV release.
## Supplementary Information
Additional file 1. Fig. S1 Production of PDGFR-β-Cre + mT/mG transgenic mice and Tie2-Cre + Cx43flox/flox mice. Fig. S2 Sepsis induces pericyte loss, vascular hyporeactivity and leakage. Fig. S3 Sepsis induces pericyte loss, vascular hyporeactivity and leakage in PDGFR-β-Cre + mT/mG transgenic and WT mice. Fig. S4 Primary pericyte identification, the number of pericyte colonization at different times, and the effect of pericyte transplantation on vascular reactivity and barrier function in septic rats within 6 h. Fig. S5 Pericyte transplantation increases the pericyte coverage and improves the vascular functions at 24 h after sepsis. Fig. S6 Effect of Cx43 on vascular reactivity and permeability of VSMCs/VECs through Cx43. Fig. S7 Depletion of pericytes with CP-673451. Fig. S8 Pericyte depletion aggravates sepsis-induced pericyte loss, which in turn is rescued by pericyte transplantation. Fig. S9 Pericyte depletion aggravates sepsis-induced vascular hyporeactivity, which in turn is rescued by pericyte transplantation. Fig. S10 Pericyte depletion aggravates sepsis-induced vascular leakage, which in turn is rescued by pericyte transplantation. Fig. S11 PCMVs improve the contractile response of VSMCs and barrier function of VECs after sepsis. Fig. S12 PCMVs carry miR-145 and miR-132 to VSMCs and VECs to play orchestrate effects on the contractile response of VSMCs and barrier function of VECs. Additional file 2. Video S1: Vascualr reactivity of sham group. Additional file 3. Video S2: PC VSMC.Additional file 4. Video S3: PC Cx43 down VSMC.Additional file 5. Video S4: PC vehicle VSMC.Additional file 6. Video S5: PC VEC.Additional file 7. Video S6: PC Cx43 down VEC.Additional file 8. Video S7: PC vehicle VEC.
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|
---
title: Prospective insight into the role of benzyl propylene glycoside as a modulator
of the cGAS-STING signaling pathway in the management of nonalcoholic fatty pancreas
animal model
authors:
- Reda Albadawy
- Amany Helmy Hasanin
- Sara H. A. Agwa
- Shaimaa Hamady
- Reham Hussein Mohamed
- Eman Gomaa
- Mohamed Othman
- Yahia A. Yahia
- Amani Mohamed Abdel Ghani
- Marwa Matboli
journal: Biological Research
year: 2023
pmcid: PMC10010022
doi: 10.1186/s40659-023-00423-8
license: CC BY 4.0
---
# Prospective insight into the role of benzyl propylene glycoside as a modulator of the cGAS-STING signaling pathway in the management of nonalcoholic fatty pancreas animal model
## Abstract
### Background
Nonalcoholic fatty pancreatitis (NAFP) is one of the metabolic syndrome manifestations that need further studies to determine its molecular determinants and find effective medications. We aimed to investigate the potential effect of benzyl propylene glycoside on NAFP management via targeting the pancreatic cGAS-STING pathway-related genes (DDX58, NFκB1 & CHUK) and their upstream regulator miRNA (miR-1976) that were retrieved from bioinformatics analysis.
### Methods
The rats were fed either normal chow or a high-fat high-sucrose diet (HFHS), as a nutritional model for NAFP. After 8 weeks, the HFHS-fed rats were subdivided randomly into 4 groups; untreated HFHS group (NAFP model group) and three treated groups which received 3 doses of benzyl propylene glycoside (10, 20, and 30 mg/kg) daily for 4 weeks, parallel with HFHS feeding.
### Results
The molecular analysis revealed that benzyl propylene glycoside could modulate the expression of the pancreatic cGAS-STING pathway-related through the downregulation of the expression of DDX58, NFκB1, and CHUK mRNAs and upregulation of miR-1976 expression. Moreover, the applied treatment reversed insulin resistance, inflammation, and fibrosis observed in the untreated NAFP group, as evidenced by improved lipid panel, decreased body weight and the serum level of lipase and amylase, reduced protein levels of NFκB1 and caspase-3 with a significant reduction in area % of collagen fibers in the pancreatic sections of treated animals.
### Conclusion
benzyl propylene glycoside showed a potential ability to attenuate NAFP development, inhibit pancreatic inflammation and fibrosis and reduce the pathological and metabolic disturbances monitored in the applied NAFP animal model. The detected effect was correlated with modulation of the expression of pancreatic (DDX58, NFκB1, and CHUK mRNAs and miR-1976) panel.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s40659-023-00423-8.
## Background
Although non-alcoholic fatty pancreas (NAFP) was reported early in the 1930s, our knowledge about this disease is still in its infancy and perceived as a relatively new condition [1]. NAFP is described as pancreatic fat cumulation without significant alcohol intake [2]. It was considered a benign incidental finding, and therefore its clinical consequences were ignored. The prevalence of NAFP ranges from 16 to $35\%$ and is increasingly associated with obesity, insulin resistance (IR), deterioration of beta-cell function and metabolic syndrome which might lead to the development of diabetes and pancreatitis [1]. Therefore, its early detection may help to diagnose prediabetic patients to reduce the rising morbidity and mortality due to diabetes mellitus.
Although the shared association between NAFP and non-alcoholic fatty liver disease (NAFLD), the implicating mechanisms still unclear and has led researchers to hypothesize comparable etiologies of NAFP and NAFLD [3]. The metabolic stress in NAFP, including insulin resistance and obesity, can stimulate severe acinar cell injury resulting in progressive acinar cell death, and acute pancreatitis, which acts as a trigger for various signaling mechanisms including the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway [4]. The cGAS-STING pathway was found to be activated in acute pancreatitis and can induce cell injury by activating inflammation and by disturbing glucose and lipid metabolism [5, 6]. STING activation also affects several signaling cascades resulting in the induction of the nuclear factor kappa beta (NF-kB) to produce proinflammatory cytokines and activate fibrogenesis [7]. Therefore, more exploration into this signaling mechanism might help in identifying novel therapies for NAFP disease.
Dysregulation of microRNA (miRNA) may impact the function and status of various tissues, like the pancreas [8–10], and liver [11–13], contributing to metabolic disorders associated with obesity and insulin resistance-linked diseases including NAFP. miRNAs play a very important role as key regulators of inflammation, insulin signaling, and glucose and lipid metabolism. However, information about the mechanisms of their implication in NAFP progression remains nearly limited, due to the ability of miRNAs to simultaneously affect several gene/pathway networks [14]. This integrated gene (mRNAs)—miRNAs regulatory interaction may provide new early non-invasive diagnostic biomarkers and identification of therapeutic strategies for NAFP. Obviously, bioinformatic analysis facilitates the identification of new candidate RNA species and their interactions as biomarkers for disease screening, diagnosis, and therapy [15].
Emerging studies have reported that benzyl propylene glycoside (Rosavin), a main constituent of the Rhodiola Rosea plant, possesses several pharmacological effects such as anti-inflammatory, anti-adipogenic and hepato-protective effects on metabolic syndrome and related disorders [16–18]. The underlying mechanisms behind these effects may involve inhibition of NF-kB, reducing cell death, inhibition of adipogenesis, and modulation of miRNA expression [19], and this suggests that the miRNA may be a target for benzyl propylene glycoside treatment. However, its effect on NAFP is not clearly illustrated.
Based on the all previously discussed data, we aimed to investigate the potential therapeutic efficacy of benzyl propylene glycoside on NAFP management via targeting the pancreatic cGAS-STING pathway-related genes (DDX58, NFκB1 & CHUK) and their upstream regulator miRNA (miR-1976) that were retrieved from bioinformatics analysis in NAFP animal model.
## Benzyl propylene glycoside—miR-1976 in-silico interaction prediction
The miRNA1976 secondary structure modelling showed MFE for thermodynamic ensemble of -31.85 kcal/mol which was further used to obtain the 3D model (Fig. 1A). Docking scores were recorded as in Table 1. Benzyl propylene glycoside (Rosavin) predicted interactions were sketched as an interaction per nucleotide and type of bond for the top 10 poses (Additional file 1: Fig. S1). HDock calculated confidence score was over 0.5 for one pose with a docking score of − 151.9 and a calculated RMSD of 40.24 (Fig. 1B).Fig. 1A 3D structure of miR-1976 as predicted. U: Uridine, G: Guanosine, C: Cytosine, and A: Adenosine. B Benzyl propylene glycoside-miR-1976 ranked 1 interaction. Green interaction: Hydrogen bonding. Pink interaction: T-shaped pi–pi interaction. Orange interaction: pi-pi anionic interactionTable 1Rosavin-miRNA1976 docking poses as computed by HDock serverRank12345678910Docking score− 151.90− 148.36− 148.21− 147.51− 146.05− 145.32− 142.85− 141.85− 141.14− 139.81Confidence score0.50950.49180.49110.48760.48030.47660.46430.45930.45580.4492Ligand RMSD (Å)40.2415.7213.8143.4112.3811.7938.3310.3941.629.85
## Effect of 8 weeks of HFHS feeding on body weight and blood biochemical parameters
Feeding experimental rats with an HFHS diet for 8 weeks (Table 2) has resulted in a significant increase ($p \leq 0.001$) in body weight, HbA1C%, the levels of lipid profile markers, fasting serum glucose, and insulin as well as insulin resistance represented by HOMA-IR, compared to the Sham animals. In addition, a significant elevation in the level of serum amylase and lipase was also recorded in HFHS-fed animals. The results indicated that the HFHS-challenged animals exhibited signs of dyslipidemia and pancreatic damage. Table 2Body weight and blood parameters after 8 weeks of HFHS feedingShamHFHS-8 weekInitial body weight, g160.3 ± 8.08164.7 ± 8.96Final body weight, g190.8 ± 8.08355.3 ± 9.33*TC (mg%)123.8 ± 11.68227.3 ± 7.076*TG (mg%)49.07 ± 4.53145.5 ± 13.6*HDL (mg%)59.33 ± 3.6438.07 ± 2.84*LDL (mg%)54.27 ± 12.74160.3 ± 7.86*Glucose (mg%)100.7 ± 11.07280.3 ± 28.08*Insulin (μU/ml)4.907 ± 0.8915.08 ± 1.16*HOMA-IR1.223 ± 0.2610.42 ± 1.160*HbA1C%4.28 ± 0.809.94 ± 0.91*Lipase (U/L)354 ± 52.893523 ± 441.8*Amylase (U/L)1006 ± 94.219143 ± 1221*Values are mean ± SD; number = 15 rats/each group. Obtained from sample t-test*$p \leq 0.001$ vs Sham
## Effect of benzyl propylene glycoside treatment on body weight and blood biochemical parameters
As shown in Table 3, body weight was significantly higher at the end of 12 weeks in untreated HFHS-fed rats than in Sham animals. After 4 weeks from benzyl propylene glycoside treatment, the body weight was significantly attenuated in HFHS rats (R-20 & R-30) compared to the NAFP group. Feeding animals an HFHS diet for the entire 12 weeks (NAFP group) caused a significant upsurge in the levels of serum glucose, insulin, and HbA1C% compared to the normal chow-fed rats (Sham group). Therefore, untreated NAFP rats presented a higher HOMA-IR ($p \leq 0.001$) than those of the Sham group. Serum levels of TG, TC, and LDL-C, but not HDL-C, also significantly ($p \leq 0.001$) increased in NAFP animals compared to the animals of the Sham group. On the other hand, animals of the three treated groups displayed a significant correction in all previous variables compared to the untreated NAFP group in a dose-dependent manner. Table 3Ingredients, and energy content of the normal chow and high-fat and high-sucrose (HFHS) diets [22]Diet ingredients, g/kgNormal chowHFHSLard–180Sucrose100300Casein140160Starch620.7220.7Fiber5050Cholic acid2.52.5Soybean oil4040Vitamin mix & Mineral mix4545L-cysteine1.81.8Energy kcal/g3.814.71Carbohydrate %75.744.2Protein %14.913.7Lipid %9.442.0 *Regarding serum* amylase and lipase, a highly significant elevation in the levels of these enzymes was recorded in animals in the untreated NAFP group compared to the Sham group. Daily injection with benzyl propylene glycoside for four weeks, caused a significant reduction in the levels of serum amylase and lipase, in comparison with the NAFP group. This ameliorative effect was more prominent in both the R-20 and R-30 groups.
As compared to the HFHS-8 week group, the benzyl propylene glycoside-treated groups (R-20 and R-30) showed a significant reduction in all previously mentioned biochemical variables that indicated Benzyl Propylene Glycoside has the potential to restore the initial pathological changes induced by HFHS feeding. Moreover, the results demonstrated that the four additional weeks of the HFHS diet resulted in more severe damage in NAFP animals.
## Histological observations
The light microscopic examination of H&E sections of the pancreas of the Sham group revealed normal acinar arrangement with basal basophilia and apical acidophlia and the acinar cells have basal open phase nuclei (Fig. 2). The NAFP group showed loss of the normal lobulation of the pancreas. Large areas of pancreatic parenchyma were occupied by fat cells with noticeable areas of fat necrosis. Some intact acini were seen in between fat tissue. Meanwhile, the remaining acini appeared distorted, vacuolated. Rounded structures of variable size were detected which might be regenerating acini. Areas of intense mononuclear cellular infiltration and oedema were also noticed. The interlobular and intralobular connective tissues were relatively thickened. Some blood vessels showed fibrin clots with margination and pavementation of inflammatory cells. While pancreas of R-10 group showed focal structural changes in some lobules as well as interlobular and intralobular connective tissue. The affected acinar cells showed variable structural changes. Some acinar cells were lightly stained with loss of basal basophilia and apical acidophilia. Others showed vacuolated cytoplasm with pyknotic nuclei within oedematous areas. In group R-20, the pancreatic lobules and the pancreatic acini were closely packed, however, noticeable areas of pancreatic affection were still present. There were focal areas of disorganized acini. Localized areas of mononuclear cellular infiltration were still noticed. In R-30 group, pancreas showed normal structure with tightly packed pancreatic acini and thin interlobular septa. Most acini were formed of normal acinar cells with basal basophilia and apical secretory granules and vesicular nuclei. Few acini showed hyalinized cytoplasm. Fig. 2H&E-stained pancreas sections of A; Sham group showed closely packed pancreatic acini with basal basophilia and apical acidophilia and the acinar cells have basal open phase nuclei, B, C; NAFP group showed loss of pancreatic architecture, fat deposition among distorted acini (f), a congested and dilated blood vessel (↑), and fibrin clot with inflammatory cells margination and pavementation. The adjacent pancreatic acini were distorted, and some acinar cells showed vacuolated cytoplasm and pyknotic nuclei (▲) with edematous clear areas in-between the acini (*), D; R-10 group showed focal areas of loss of architecture and the acini in the affected areas showed variable structural changes. Some acini attained lightly stained cells with loss of basal basophilia and apical acidophilia and other acinar cells showed vacuolated cytoplasm and pyknotic nuclei (▲) with area of oedema and inflammatory cell infiltration (*), E; R-20 group showed the pancreatic lobules and the pancreatic acini are closely packed. Thin interlobular septa can be seen. There were focal areas of structural changes at the periphery of pancreatic lobules, where some acini attained pale vacuolated cytoplasm, (*), and F; R-30 group showed most of the pancreatic acini attained numerous and closely packed zymogen granules and the nuclei are basal and vesicular. [ Magnification: 200x] In Masson’s trichrome stained sections (Fig. 3) showed progressive increase of collagen fibers deposition in all groups to be maximum in NAFP. In the treated group, collagen fibers were still noticed surrounding the blood vessels and thickened interlobular septa in both groups R-10 and R-20, but they were more pronounced in R-10. Despite of that, collagen fibers were apparently less than those in R-30. These results were confirmed by the statistical study. Morphometric and statistical study for area percentage of collagen fibers (Fig. 3F) revealed significant increase in NAFP in relation to other groups. In the treated group R-10 there was a significant increase as compared to the control. However, R-20 showed significant decrease as compared to NAFP group. Fig. 3Masson trichrome-stained pancreas sections of A; Sham group showed minimal green color of collagen fibers in between the closely packed pancreatic acini, B; NAFP group showed marked green collagen fibers deposition in the interlobular septa and in between the destructed acini, C; R-10 group collagen fibers deposition in the interlobular septa and in between the destructed acini, D; R-20 group showed collagen fibers especially around blood vessels and distorted acini, and E; R-30 group showed mild collagen fibers deposition around acini. [ Magnification: 200x]. F The mean area % of collagen deposition (± SD) in the Sham and the experimental groups ($$n = 6$$): *$P \leq 0.001$ vs the Sham group; ###$P \leq 0.001$, and ##$P \leq 0.01$ vs NAFP group. aP < 0.05 vs R-10. bP < 0.05 vs R-20. Measurements were taken from three different sections obtained from each animal. Moreover, five haphazardly selected non-overlapping fields were examined for each section
## The effect of benzyl propylene glycoside on the expression of the pancreatic selected RNA species
Results showed a significant elevation in the expression of pancreatic DDX58, NFκB1 and CHUK mRNAs with a significant reduction in the expression level of miR-1976 in the untreated NAFP group compared to the Sham group ($p \leq 0.001$), Fig. 4. Meanwhile, the administration of benzyl propylene glycoside at its two higher dosages 20 & 30 (R-20 and R-30) significantly reduced the significant upregulation in the expression of pancreatic mRNA species manifested in untreated NAFP group animals. Moreover, the data were coupled with a significant increase in the expression of miR-1976 in the treated groups (R-20 and R-30) compared to the untreated NAFP group. Fig. 4Effect of benzyl propylene glycoside on the expression of the pancreatic selected RNA species A DDX58. B NFκB1 C CHUK. D miR-1976. Values are mean ± SD; $$n = 8$$ rats/each group. *** $P \leq 0.001$ and **$P \leq 0.01$ vs Sham group; ###$P \leq 0.001$ vs NAFP group. aP < 0.05 vs R-10. bP < 0.05 vs R-20. One-way ANOVA followed by Tukey’s multiple comparison test RQ, relative quantification
## The effect of benzyl propylene glycoside on the pancreatic NFκB1 and Caspase-3
As shown in Fig. 5A–E, caspase-3-stained sections revealed minimal reaction in acinar cells in R-30 group. However moderate reaction was noticed in both the cytoplasm of acinar cells and the rounded structures in R-20. Positive reaction was distinguished in most of acinar cells in NAFP group. Morphometric and statistical study for area percentage of Caspase-3 positive cells reaction revealed significant elevation in NAFP and R-10 in comparison to other groups. Fig. 5A–E Caspase-3 immunohistochemistry-stained pancreas sections of A; Sham group showed minimal reaction for caspase-3 among acinar cells, B; NAFP group showed extensive positive reaction for caspase-3, C; R-10 group showed positive reaction for caspase-3 in destructed areas, D; R-20 group showed moderate positive reaction for caspase-3 in destructed areas, and E; R-30 group showed minimal positive reaction for caspase-3. [ Magnification: 200x]. F; The mean area percentage of Caspase-3 positive cells (± SD) in the Sham and the experimental groups ($$n = 6$$): *$P \leq 0.001$ vs the Sham group; ###$P \leq 0.001$ vs NAFP group. aP < 0.05 vs R-10. bP < 0.05 vs R-20 Nuclear factor kappa-stained sections (Fig. 6A–E) revealed minimal reaction in acinar cells in R-30 group. However moderate reaction was noticed in both the acinar cells and the rounded structures in R-20. Maximum Positive reaction was distinguished in most cells in NAFP group. Morphometric and statistical study for area percentage of nuclear factor kappa stained \ cells revealed significant elevation in R-10 group in comparison to other treated groups. Fig. 6A–E NFκB1 immunohistochemistry-stained pancreas sections of A; Sham group showed minimal reaction for NFκB1 among acinar cells, B; NAFP group showed extensive positive reaction for NFκB1, C; R-10 group showed positive obvious reaction for NFκB1 among distorted pancreatic acini, D; R-20 group showed moderate positive reaction for NFκB1 in destructed areas, and E; R-30 group showed minimal positive reaction for NFκB1. [ Magnification: 200x]. F; The mean area percentage of NFκB1 positive cells (± SD) in the Sham and the experimental groups ($$n = 6$$): *$P \leq 0.001$ vs the Sham group; ###$P \leq 0.001$ vs NAFP group. aP < 0.05 vs R-10. bP < 0.05 vs R-20
## Discussion
Nonalcoholic fatty pancreatitis (NAFP) considers one of the manifestations of metabolic syndrome that needs further studies to determine molecular determinants of this disorder and find effective medications [1]. Emerging data showed that insulin resistance and dysregulation of the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway are the major driving forces for acute pancreatitis and fibrogenesis in NAFP progression [5]. Thus, herein, we constructed a mRNAs (DDX58, NFκB1& CHUK)—(miR-1976) panel linked to metabolic syndrome and pancreatic cell dysfunction as well as be enrolled in the cGAS-STING pathway via in silico data analysis. Then we evaluated the potential ameliorative effects of benzyl propylene glycoside (Rosavin) treatment, the main constituent of the Rhodiola Rosea plant, on NAFP management and its effects on the constructed RNA panel in the NAFP animal model.
One of the primary mechanisms that explain the incidence of the fatty pancreas is the infiltration of adipocytes into the pancreatic tissue. Obesity and increased body weight are the major contributing factors to this condition. Adipose tissue is an endocrine organ as it emits signals to different organs. During weight gain, the storage of fat in adipose tissue is overridden, resulting in the excess lipid is deposited in visceral and peripheral non-adipose organs including the pancreas [20]. Therefore, fatty infiltration of the pancreas is detected as ectopic adipocytes infiltrating the pancreatic tissue where fats deposit in adipocytes in the pancreatic tissue inducing pathological disorders such as insulin resistance and pancreatic cell injury and ultimately resulting in pancreatitis [21].
Accordingly, all previously discussed data can illustrate the results we obtained. We have used a high-fat and high-sucrose (HFHS) feeding as a representative experimental animal model of NAFP disease. Accumulating studies have investigated the impacts of HFHS diet on experimental animals, and it has been concluded that consumption of this diet induces obesity and insulin resistance [22–24]. In the current study, this nutritional model nearly covered the spectrum of the pathological and metabolic disturbances associated with NAFP. The HFHS diet feeding resulted in increased body weight, hyperglycemia, hyperinsulinemia, insulin resistance, and dyslipidemia in the untreated NAFP group. The animals also showed degrees of pancreatitis manifested by large areas of fat cells with noticeable areas of fat necrosis as well as areas of intense mononuclear cellular infiltration and oedema were also noticed in the pancreatic sections, and elevated serum levels of amylase and lipase.
Recently, it was reported that the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway can be activated by lipotoxicity-induced pancreatic cell injury [25]. cGAS-STING signaling pathway is a crucial regulator of immune responses [26] and plays an important role in glucolipid metabolic disorders and was found to be activated in the animals fed a high-fat diet, and its gene silencing reversed metabolic dysfunction, insulin resistance, and inflammation [27]. Activated STING can stimulate the phosphorylation of interferons (IRFs). Phosphorylated IRF regulates the expression of target genes, including DEAD Box Protein 58 (DDX58) & Nuclear Factor Kappa B Subunit 1 (NFκB1), to activate diverse downstream signaling pathways and promote the expression of inflammatory and fibrotic genes [25]. Therefore, STING can promote cellular inflammation in several pathological conditions like insulin resistance.
The current results are consistent with the previously discussed data where a significant increase in the expression level of the pancreatic cGAS-STING pathway-related genes (DDX58 and NFκB1) in the untreated HFHS-fed animals (NAFP group) compared to the Sham group ($p \leq 0.001$). The results were propped by the results of histological and immunohistochemistry assay which showed a significant prevalent maximum positive immunostaining for NFκB1 marker, coupled with large areas of inflammatory cells infiltration in the pancreatic tissue, in comparison with the Sham group. Parallel studies have also confirmed that NFκB1 and DDX58 are upregulated in pancreatic cell injury [28–30]. Moreover, DDX58 was reported to be one of the genes that were differentially expressed in obese patients with type two diabetes mellitus [31].
The NF-KB activation requires the activity of the upstream serine/threonine protein kinase alpha (IKKα) which is encoded by the CHUK (conserved helix-loop-helix ubiquitous kinase) gene. The phosphorylation of nuclear factor-kB inhibitor (IKB) by IKKα results in its degradation and activation of NFκB1 [32]. The results of the present study revealed a significant upregulation in the expression of the pancreatic CHUK in the NAFP group compared to the Sham group.
Increased expression of caspase-3 has been observed in the absence of apoptosis. Caspase-3 may be implicated in processes other than apoptosis where it can participate in inflammatory responses by cleaving and activating cytokines [33]. Moreover, it was also reported that increased caspase-3 in the high-fat diet-fed animal was associated with a significant elevation in hepatic expression of inflammatory cytokines indicating that increased apoptosis could be an insulting mechanism in hepatic inflammation [34]. Consistently, our results displayed a marked increase in caspase-3 protein expression in the pancreas of NAFP model rats compared to the Sham group.
Herein, all observed disturbances in the untreated NAFP animals were significantly adjusted by treatment of the experimental animals with benzyl propylene glycoside daily for four weeks. The recent studies on benzyl propylene glycoside showed that it exhibits anti-oxidative [35], anti-cancer [36], and anti-inflammatory effects [37]. The toxicity of benzyl propylene glycoside has been previously assessed and shown to have a hepatoprotective effect and can alleviate kidney damage [17, 38, 39]. An emerging study that evaluated the toxicity of Rhodiola components showed that LD50 > 5000 mg/kg b.w., considers safe for consumption. This study also showed that long-term administration of Rhodiola doses (100, 250, and 500 mg/kg b.w.) for 28 days didn’t cause any toxic effects in experimental animals. Moreover, all the parameters related to the liver, and kidney were not affected [40].
It was also reported that benzyl propylene glycoside can attenuate cell injury and fibrosis through inhibition of NF-kB and decreasing the production of pro-inflammatory and fibrotic cytokines [41]. Benzyl propylene glycoside can also improve cellular immunity by inhibiting tissue apoptosis [37].
The results of the current study were in accordance with the published data. The results revealed that the daily treatment with benzyl propylene glycoside had beneficial actions on the progression of NAFP. It significantly improved the lipid panel, decreased the body weight, lowered the serum insulin and glucose levels, ameliorated the insulin resistance status, and decreased the serum level of lipase and amylase. Surprisingly, as compared to the HFHS-8 week group, the benzyl propylene glycoside-treated groups (R-20 and R-30) revealed significant decreases in all detected biochemical variables that showed benzyl propylene glycoside has the potential to prevent the progression of exocrine pancreatic damage and can recover the initial pathological changes induced by HFHS feeding. Moreover, the applied treatment decreased the expression level of cGAS-STING pathway-related genes, DDX58, NFκB1 & CHUK, coupled with decreased the protein expression of pancreatic inflammatory NFκB1 and caspase-3 as compared with the untreated NAFP animals. Normal structure with tightly packed pancreatic acini and thin interlobular septa with a significant decrease in area percentage of collagen fibers were also detected in the treated groups (R-30) when compared to the NAFP model group. The revealed results indicate that benzyl propylene glycoside could improve pancreatic tissue injury via modulating and inhibiting the cGAS-STING pathway.
The currently identified biomarkers in the early diagnosis of NAFP are insufficient and poorly known. Thus, novel non-invasive biomarkers and precise therapeutic targets are required urgently. MicroRNAs (miRNAs) are a class of small non-coding RNAs that modulate the expression of protein-coding genes [42]. They can be detected in body fluids, like blood and urine, and changes in their levels have been associated with several diseases therefore they can be utilized as diagnostic biomarkers [43]. Accumulating evidence shows that modulation of miRNA expression could be one of the regulatory mechanisms behind the ameliorative activities of benzyl propylene glycoside [19]. miRNAs also play crucial roles in the function and survival of pancreatic cells and have been found to regulate the adaptive responses of pancreatic cells in conditions like obesity and pancreatitis [42, 44].
In the present study, the miRWalk database was utilized to retrieve the upstream regulators, miR-1976, for the selected three mRNAs (CHUK, NFκB1, and DDX58). Regarding the in-silico study of benzyl propylene glycoside-miR-1976 interaction, the confidence scores showed slight significance for successful binding for the first pose with a confidence score > 0.5. However, the high deviation in the RMSD suggests the decreased probability of binding which needs further experimental proof of direct binding [45]. Nevertheless, the alteration of miR-1976 maybe due to indirect effect of benzyl propylene glycoside on miR-1976. Previous emerging studies have demonstrated miR-1976 role as a prognostic indicator and tumor suppressor in non-small lung cancer progression [46]. Moreover, it was reported that the miR-1976 knockdown significantly inhibited cell apoptosis and increased cell proliferation [47]. miR-1976 was also found to be one of the specific downregulated exosomal-miRNA signatures related to pancreatic lesions [48]. Interestingly, the functional enrichment analysis of miR-1976 revealed that it is highly linked to inflammatory cGAS-STING-related and fibrogenic pathways including NF-KB signaling, TGF signaling, and TNF signaling pathways.
Herein, DDX58, NFκB1, and CHUK were screened as target genes of miR-1976 using the mirwalk3 database. miR-1976 can regulate the expression level of these genes via binding to their 3ʹUTR resulting in post-transcriptional inhibition or their degradation [47]. Accordingly, the results showed that there was a significant decrease in the expression level of pancreatic miR-1976 in the untreated NAFP group, in comparison with the Sham control. While benzyl propylene glycoside administration significantly increased its expression, compared to the NAFP group.
Taken all together (Fig. 7), we hypothesized that HFHS-induced lipotoxicity (untreated NAFP) downregulated the expression of miR-1976 which could not exert its inhibitory action on its target genes thereby upregulating the expression of pancreatic DDX58, NFκB1, and CHUK mRNAs. Activating the cGAS-STING signaling pathway stimulated diverse downstream signaling pathways, promoted the expression of inflammatory responses (NFκB1 and Caspase-3), increased the area percentage of collagen fibers (fibrosis), and increased the serum level of lipase and amylase. Consequently, increasing pancreatic cell injury and pancreatitis progression. On treatment, the benzyl propylene glycoside increased the expression of miR-1976 and inhibited the expression of its target genes (DDX58, NFκB1, and CHUK). Inhibiting the cGAS-STING signaling pathway reversed metabolic dysfunction, ameliorated insulin resistance, decreased body weight and obesity, and reduced inflammation, and fibrosis observed in the untreated NAFP group. Fig. 7Summary and schematic representation of the study hypothesis. HFHS-induced lipotoxicity (untreated NAFP) downregulated the expression of miR-1976 and thereby upregulated the expression of pancreatic DDX58, NFκB1, and CHUK mRNAs. Activating the cGAS-STING signaling pathway stimulated diverse downstream signaling pathways including promoting the expression of inflammatory and fibrotic responses (NFκB1, Caspase-3, and increase in area percentage of collagen fibers). On benzyl propylene glycoside treatment, increased expression of miR-1976 inhibited the expression of its target genes (DDX58, NFκB1, and CHUK). Inhibiting the cGAS-STING signaling pathway reversed pathological disturbances manifested by decreased inflammation and fibrosis observed in the untreated NAFP. HFHS: high fat and high sucrose diet, NAFP: non-alcoholic fatty pancreas The present study may help in better understanding the etiology and pathophysiology of the non-alcoholic fatty pancreas disease (NAFP) and also provides useful information regarding potential molecular targets for NAFP treatment. However, benzyl propylene glycoside may not yet be a suitable fundamental mode of therapy until further preclinical trials are performed.
## Conclusion
Benzyl propylene glycoside has demonstrated a potential ability to attenuate NAFP development, inhibit pancreatic cell inflammation and fibrosis and reduce the pathological and metabolic disturbances monitored in the applied NAFP animal model. The detected effect was correlated with upregulation of the expression of pancreatic DDX58, NFκB1, and CHUK mRNAs and downregulation of the expression of pancreatic miR-1976.
## Drugs and materials
Sodium pentobarbital was obtained from Sigma Aldrich (St. Louis, Missouri, USA). Rosavin (benzyl propylene glycoside) was supplied from Aktin Chemicals, Inc (Cat. #. APC-380, China).
## Animals and treatment
The handling and experimentation protocols were reviewed and approved by the Research Ethics Committee (Number; MoHP0018122017, 1017), Faculty of Medicine, Benha University. The experimental study was performed according to the Declaration of Helsinki guidelines. Male Wistar rats (150–170 g), were housed in cages under standard controlled conditions (12 h light/dark cycles and 21 ± 2 °C) and randomly grouped into normal chow-fed rats (Sham group, $$n = 8$$) and high-fat high-sucrose-fed rats (HFHS), Table 4, as a nutritional model for NAFP induction [22]. After 8 weeks of dietary intake, blood samples were drawn to evaluate the effect of the HFHS diet manipulation on the experimental animals. The HFHS-fed animals were then subdivided into 4 groups ($$n = 8$$ for each group): untreated HFHS group (NAFP model group) and three benzyl propylene glycoside (rosavin)-treated groups, R-10 group, R-20 group, and R-30 group (Fig. 8). In these treated groups, the rats injected intraperitoneally with10 mg, 20 mg, and 30 mg rosavin/kg body weight, respectively for 4 weeks parallel with HFHS diet [17]. The normal chow-fed rats were given vehicle $0.9\%$ saline intraperitoneally. Table 4The effect of benzyl propylene glycoside on body weight and blood biochemical parametersParametersGroupsShamNAFPHFHS-8 weekR-10R-20R-30Initial body weight, g160.5 ± 9.40163.8 ± 10.23162.7 ± 8.62164.5 ± 6.83161.7 ± 7.47158.2 ± 7.41Final body weight, g221.7 ± 9.33451.8 ± 14.44*353.8 ± 10.23421.8 ± 42.37321.3 ± 31.14###a268.2 ± 33.37###abTC (mg%)117.3 ± 15.31273.7 ± 10.57*225.8 ± 8.04*##246.8 ± 9.96#206 ± 8.32###a131.2 ± 20.81###δabTG (mg%)50.23 ± 5.37208.4 ± 23.91*143 ± 11.97*##131 ± 27.83###88.6 ± 13.73###δa61.24 ± 5.76###δaHDL-C (mg%)58.67 ± 4.126 ± 4.13*37 ± 2.76*##42 ± 2.48###45.8 ± 1.40###δ50.51 ± 3.89###δaLDL-C (mg%)44.18 ± 11.55206 ± 9.13*160.8 ± 7.25*##181.9 ± 10.68##δ137.2 ± 14.57###δa80.11 ± 10.59###δabGlucose (mg%)101.3 ± 13.9392 ± 39.01*279 ± 37.77*##260 ± 28.95###151.3 ± 7.94###δa125.6 ± 15.56###δaInsulin (µU/ml)5.07 ± 1.1517.78 ± 1.09*15.24 ± 1.28*#14.85 ± 1.09##7.37 ± 1.71###δa6.037 ± 1.26###δaHOMA-IR1.38 ± 0.2717.25 ± 2.46*10.75 ± 1.25*##9.547 ± 1.39###2.775 ± 0.75###δa1.85 ± 0.36###δaHbA1C%4.28 ± 0.9411.32 ± 1.51*9.883 ± 1.22*8.6 ± 0.74##6.3 ± 0.6###δa5 ± 1.19###δaLipase (U/L)350 ± 56.573868 ± 166.7*3613 ± 486*3478 ± 511946.7 ± 133.5###δa595.5 ± 49.73###δaAmylase (U/L)983.7 ± 111.910,927 ± 1273*9245 ± 1504*9185 ± 1611#2931 ± 478.9###δa1190 ± 208.2###δabValues are mean ± SD; number = 8 rats/each group. One-way ANOVA followed by Tukey’s multiple comparison test*$p \leq 0.001$ vs Sham###$p \leq 0.001$##$p \leq 0.01$ and#$p \leq 0.05$ vs NAFP groupδp < 0.05 vs HFHS-8 weekap < 0.05 vs R-10bp < 0.05 vs R-20Fig. 8Flowchart showing the experimental design of the study. NAFP: nonalcoholic fatty pancreas; HFHS: high fat and high sucrose
## Euthanasia and blood and pancreas tissue collection
All the experimental rats were monitored daily for body weight. At the end of the experimental period (12 weeks), the experimental rats were anesthetized with a single dose of sodium pentobarbital (45 mg/kg, intraperitoneally) [49] and blood samples were rapidly obtained from the retro-orbital vein. Serum was then obtained by centrifugation (1200 g for 10 min) and stored at − 20 °C for the biochemical analyses. The pancreas was carefully removed, weighed, and then rapidly fixed in freshly prepared $10\%$ neutral buffered formaldehyde for analysis by light microscopy.
## Lipid profile markers and glycated hemoglobin (HbA1C)
Total cholesterol (TC), HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), triglycerides (TG), fasting serum glucose and glycated hemoglobin (HbA1C) were quantitatively determined by the multifunctional biochemistry analyzer (AU680, Beckman Coulter Inc., Brea, CA, USA).
## Nonalcoholic fatty pancreas (NAFP)-model markers
Serum insulin was measured using a rat sandwich ELISA kit purchased from Invitrogen (Cat. NO. ERINSX10, Waltham, Massachusetts, USA) according to the manufacturer’s instructions. Serum lipase and amylase were measured using commercial kits obtained from Erba Diagnostics (Miami, Florida, USA) according to the protocol supplied with the respective kits. Homeostasis model assessment-insulin resistance (HOMA-IR) was calculated using the following formula: HOMA-IR = [fasting serum insulin (µU/ml) x fasting serum glucose (mg%)]/405 [50].
## Tissue preparation
The buffered formalin-fixed pancreatic samples were dehydrated using an ascending concentration of alcohol, cleared using methyl benzoate, and mounted in paraffin blocks. Sections were cut at a thickness of 5 μm and stained using hematoxylin and eosin (H&E) and Masson's trichrome stain for the detection of collagen fibers. Other paraffin sections were cut and placed on positively charged slides and were exposed to immune reaction for caspase 3 monoclonal antibody (Cat. No. CPP32 4-1-18, Invitrogen, Waltham, MA, USA) and NFκB1 antibody (Cat. No. BS-3300R, Bioss Antibodies, Woburn, MA, USA). The positive reactions for the caspase 3 and NFκB1 immune-histochemical technique appeared as brown nuclear and cytoplasmic reactions. Negative controls were performed according to the same protocol, but without the usage of the primary antibody. Positive control was performed using a section of tonsils. Finally, the slides were counterstained using Mayer’s hematoxylin. Positive controls were carried out according to the same protocol [51].
## Morphometric study
The morphometric study was done using an image analyzer Leica Q win V.3 program installed on a computer which connected to a Leica DM2500 microscope (Wetzlar, Germany). Pancreatic slides from all groups were evaluated by morphometric study. Evaluations were obtained from five different slides taken from each rat. Five non-overlapping fields were selected haphazardly and examined for each slide. The pancreatic slides were used to measure: I-The mean area percentage (%) of collagen fibers in Masson's trichrome stained sections at objective lens X 20.
II- The mean area percentage (%) of positive reaction of caspase-3 and NFκB1 sections (X20).
## Retrieval of the mRNAs-miRNAs panel
The RNAs species that are related to NAFP development and implicated in obesity and insulin resistance were searched for. Firstly, the differentially expressed genes (mRNAs) associated with pancreatic injury were screened through the Gene Expression Omnibus (GEO) (www.ncbi.nlm.nih.gov/geo/, accessed on 22 Oct 2021) [52]. The screened mRNAs were further filtered according to their significant differential expression (Additional file 1: Fig. S2), their pancreatic tissue-specific expression (Additional file 1: Fig. S3), and their links to the cGAS-STING signaling pathway. From the filtered mRNAs, DEAD Box Protein 58 (DDX58), Nuclear Factor Kappa B Subunit 1 (NFκB1), and Conserved Helix-Loop-Helix Ubiquitous Kinase (CHUK) were selected as they were validated by other microarray databases (Additional file 1: Fig. S4) and by reviews [53–57] to be related to metabolic syndrome and pancreatic cell dysfunction diseases. The selected genes were also mapped and visualized through the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (https://www.genome.jp/kegg/, accessed on 22 Oct 2021) to be enrolled in the cGAS-STING pathway (Additional file 1: Fig. S5). The pathway enrichment analysis using Enrichr (http://amp.pharm.mssm.edu/Enrichr, accessed on 22 Oct 2021) [58] was primarily enriched in cGAS-STING and NF-kappa B signaling pathways. The top ten terms for pathway enrichment are shown in Fig. 9A. Based on the STRING tool (http://stringdb.org, accessed on 22 Oct 2021) [59], the protein–protein interaction (PPI) between the three selected genes showed a high confidence level with a combined score > 0.7 (Fig. 9B).Fig. 9A Top 10 items of KEGG pathways for the three selected genes shown in the bar chart according to p value obtained with (http://amp.pharm.mssm.edu/Enrichr). B The protein–protein interaction (PPI) between the three selected genes using the String tool (http://stringdb.org; version 11.0). C) The interaction between the selected genes with the retrieved miR-1976 using miRWalk 3.0 (http://mirwalk.umm.uni-heidelberg.de/). D Workflow of bioinformatics Set Up Secondly, miRWalk 3.0 (http://mirwalk.umm.uni-heidelberg.de/, accessed on 22 Oct 2021) was utilized for the retrieval of miRNAs interacting with the three selected mRNAs. miR-1976 (Fig. 9C) was found to target the 3 selected mRNAs with a score ˃ 0.9 (Additional file 1: Fig. S6). DIANA tools mirPath (http://www.microrna.gr/miRPathv3, accessed on 22 Oct 2021) was then used to track pathways of miR-1976. Interestingly, miR-1976 was detected to be related to cGAS-STING-related pathways (Additional file 1: Fig. S7).
All in all, the mRNAs (DDX58, NFκB1& CHUK)—(miR-1976) panel was constructed.
## Molecular docking analysis: benzyl propylene glycoside (Rosavin)—miR-1976 in-silico interaction prediction
The molecular docking between the upstream regulator miR-1976 and rosavin was performed. Rosavin ligand was obtained from PubChem with ID: 9,823,887. The miRNA1976 sequence was extracted from the miRbase database with accession number: MI0009986. The secondary structure was computed using RNAFold under ViennaRNA package (Version 2.4.18) [60]. The minimum free energy (MFE) of the secondary structure was computed at 37 °C. The secondary structure was subjected to 3D modelling using RNAComposer web server [61, 62]. The 3D model was used for docking using HDock software which models the protein using two algorithms: template-based and ab initio modelling [63]. The docked forms are ranked upon their docking scores, Root mean standard deviation and confidence score according to the HDock manual. The predicted Rosavin-miRNA1976 interaction for the top 10 poses were calculated using BIOVIA Drug Discovery Studio Visualizer 2021 (version 21.1.0.20298).
## Total RNA extraction and quantitative polymerase chain reaction (qPCR)
Total RNA, involving mRNAs and miRNAs, extraction from the 60 mg of frozen pancreas tissue samples was performed using a miRNEasy extraction kit (Qiagen, Hilden, Germany, Cat. No. 217004) according to the protocol supplied with the kit. NanoDrop (Thermo scientific, USA) was utilized to assess the concentration and purity of total RNA and the purity of the isolated RNAs was adjusted to be 1.8–2 (A260/A280). The RNA extracted from the pancreas tissues was then reverse transcribed into complementary DNA using miScript II RT (Cat. No. 218161, Qiagen, Germany).
Relative expression of the selected RNAs species in the pancreatic tissue samples was assessed using a Quantitect SYBR Green Master Mix Kit (Qiagen, Germany, Cat. No. 204143) for DDX58, NFκB1, and CHUK mRNAs and miScript SYBR Green PCR Kit (Qiagen, Germany, Cat no. 218073) for miR-1976 miRNA. Real-time (RT)-qPCR was conducted on 7500 Fast System (Applied Biosystems, Foster City, USA). The GAPDH and SNORD72 were used as housekeeping genes. The primers list used herein was obtained from Qiagen, Germany (Additional file 1: Table S1). The relative quantification of RNA expression was calculated using RQ = 2 –ΔΔCt formula [64].
## Statistical analysis
GraphPad Prism software, version 8.0 (Inc., CA, USA) was utilized to perform the Statistical analyses. The distribution normality of the data was analyzed using the Kolmogorov–Smirnov test. Data are represented as the mean ± standard deviation (SD). Differences among groups were analyzed by one-way analysis of variance (ANOVA) for statistical significance, followed by Tukeyʼs test.
## Supplementary Information
Additional file 1: Figures S1. The docking poses of Rosavin-miRNA1976 interaction. Figures S2. The significant differential expression of the selected candidate genes (DDX58, NFκB1, and CHUK) in pancreatic injury using the Expression Atlas database. Figures S3. Validation of the significant expression of the candidate genes/proteins (DDX58, NFκB1, and CHUK) in the pancreatic tissue. Figure S4. Validation of the implication of DDX58, NFκB1, and CHUK in metabolic syndrome and pancreatic cell dysfunction diseases. Figure S5. The visualization of the selected DDX58, NFκB1, and CHUK genes in the cGAS-STING pathway through KEGG pathway database. Figure S6. Validation of the interaction between the selected m-RNAs and the retrieved miR-1976 from mirwalk3. Figure S7. Validation of the relation of miR-1976 to cGAS-STING-related pathways through DIANA tools mirPath 3. Table S1. List of primer assays.
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|
---
title: The association of dietary insulin load and dietary insulin index with body
composition among professional soccer players and referees
authors:
- Mohammad Beba
- Mohammad Gholizadeh
- Mohammad Sharifi
- Tohid Seifbarghi
- Kurosh Djafarian
journal: BMC Sports Science, Medicine and Rehabilitation
year: 2023
pmcid: PMC10010033
doi: 10.1186/s13102-023-00635-1
license: CC BY 4.0
---
# The association of dietary insulin load and dietary insulin index with body composition among professional soccer players and referees
## Abstract
### Background
There has been limited research undertaken about the association of dietary insulin load (DIL) and dietary insulin index (DII) with body composition in non-athletic adults, however, to the best of our knowledge *No previous* study has investigated such an association in an athletic population.
### Purpose
The aim of this study was to explore the association of DII and DIL with body compositions in male and female soccer players and referees.
### Methods
The cross-sectional study was conducted on 199 professional male and female soccer players and referees. A 147-item semi-quantitative food frequency questionnaire (FFQ) was adopted to congregate the participants’ dietary data. Body composition was measured using InBody to gain a detailed understanding of fat mass, percent body fat (PBF), lean mass, percent muscle mass (PMM), and bone mineral content (BMC). Waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR) were obtained from all participants. Other body composition parameters include a body shape index (ABSI), abdominal volume index (AVI), body adiposity index (BAI), body roundness index (BRI), conicity index (CI), weight-adjusted waist index (WWI) and waist-to-hip-to-height ratio (WHHR) were calculated using a particular defined formula.
### Results
Results of multiple linear regression revealed that there is a significant association between DIL and BMI ($$P \leq 0.04$$) in < 18 male soccer players, CI ($$P \leq 0.04$$) and WWI ($$P \leq 0.03$$) in ≥ 18 female soccer players, PBF ($$P \leq 0.02$$), PMM ($$P \leq 0.01$$) and WWI ($$P \leq 0.01$$) in ≥ 18 female soccer players. Nevertheless, no significant associations between DIL and body composition parameters were found in the referees. Additionally, there is a significant association between DII and BMC ($$P \leq 0.02$$) in male soccer referees, however, no significant associations were found in young soccer players and female athletes.
### Conclusion
This study demonstrates that DIL is positively associated with BMI, CI, and WWI in male soccer players and PBF, and WWI in female soccer players. Although, there was an observed negative association between DIL and PMM in females. In addition, a significant negative association between DII and BMC was observed in male soccer players.
## Introduction
The food insulin index (FII) directly represents the quantity of postprandial insulin secretion after the consumption of a specific food [1]. FII is utilized to demonstrate the ratio of insulin response after a meal to an iso-energetic food’s (such as glucose or white bread) insulin response[1]. Furthermore, dietary insulin load (DIL) and dietary insulin index (DII), are two indices that emblematize insulin response to the total diet [2]. Adherence to unhealthy dietary patterns that induce excessive insulin release lead to beta cell dysfunctions and increase cells’ oxidative stress [3–5]. Diets rich in refined carbohydrates emerged as a strong factor associated with postprandial glucose levels and insulin response [6, 7]. High insulin response accompanies higher fat deposition, elevated lipid profile, and insulin resistance [8, 9]. Insulin resistance stems from the inability of insulin to transport glucose to tissues which may exert an influence on body composition [10, 11]. Insulin resistance proved to be a consequence of disparate non-communicable diseases such as diabetes, obesity, cancers, and cardiovascular diseases [12, 13].
Body composition interprets various elements in the human body [14]. Obesity is commonly evaluated and classified by body mass index (BMI) [15]. Nevertheless, mortality and cardio-metabolic risk factors can differ among individuals with the same BMI [16, 17]. It seems that BMI is not a precise indicator to assess the odds of mortality and non-communicable disease risk factors. Furthermore, fat distribution and muscle mass are more accurate indicators for health and morbidity than BMI alone [18, 19]. The most prominent fat depot is visceral adipose tissue, which is implicated in unbalanced lipid profile, fasting blood glucose, and metabolic syndrome, but be that as it may, subcutaneous adipose tissue can also be protective [20, 21]. Moreover, increased body fat and sedentary lifestyles cause mitochondrial dysfunction and insulin resistance[22, 23].
Exercise reduces the process of sarcopenia (decreasing muscle mass and increasing body fat) by protecting muscle mass and reducing fat deposition. This mechanism diminishes many inflammatory factors and insulin resistance [24, 25].
Exercise improves protein synthesis rate and helps to maintain fat-free mass [26, 27]. Besides, it increases muscle function, enhances insulin responsiveness, leads to GLUT-4 expression, and promotes oxidative capacity. In addition, exercise is demonstrated the greatest impacts on oxidative fiber and several cytokines, adipokine such as leptin, as well as increasing fatty acids oxidation and decreasing muscle fat depositions. [ 28, 29]. Soccer is more enjoyable and sought-after than other kinds of training. Consistent exercise upholds muscle insulin sensitivity and increases adaptive response by promoting muscle size, capillarization, morphology, and protein composition. Whereas, adaptation protects insulin sensitivity and has a health-promoting effect. Animal studies have presented that exercise elevates insulin-stimulated glucose uptake via the AMPK-dependent form. [ 28, 30]. Some studies have shown an increasing whole-body insulin sensitivity in exercise [30, 31]. Up to now, far too little attention has been paid to the association of DIL and DII with body composition.
Some previous studies performed in this area found a significant correlation between dietary insulin index and dietary insulin loud with insulin resistance[32]. It has previously been observed that postprandial insulin showed an unfavorable effect on body composition in young adulthood [33]. More exercise increases insulin-sensitizing and protects fat-free body mass by activation of AMPK [30]. So far, however, the relevance and association of body composition with dietary insulin index and dietary insulin have remained unclear. Therefore, The aim of this study is to explore the relationship between dietary insulin load and dietary insulin index with body compositions indices (BMI, fat percent, fat-free mass (FFM), fat mass, percent body fat, lean mass, percent muscle mass, bone mineral content, Waist-to-hip ratio (WHR), Waist-to-height ratio (WHtR), waist circumference, hip circumference (cm), a body shape index (ABSI), Abdominal volume index (AVI), Body adiposity index (BAI), Body roundness index (BRI), Weight-adjusted waist index (WWI) and Waist-to-hip-to-height ratio (WHHR)) among professional soccer players and referees.
## Study population and design
The cross-sectional study was carried out among 199 elites (11 males and 22 females) and sub-elite (13 males and 24 females) soccer players and elite referees (90 males and 39 females) in Iran, during the early stages of the 2019–2020 competitive season. Elite and sub-elite (the national under-18 soccer players) soccer players as well as elite soccer referees from all divisions, under the directive of The Football Federation Islamic Republic of Iran, were recruited for this study. Data including participants’ demographics (age, gender, and education), physical activity, medical history, anthropometric measurements, and dietary intake were gathered via a face-to-face interview with mentioned athletes. Informed consent was obtained from all the participants and their legal guardians. This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by the Ethics Committee of Tehran University of Medical Sciences; (Ethic number: IR.TUMS.VCR.REC.1398.729)]. Subjects were all given verbal and written communication about the study before signing an informed consent form.
Convenience sampling was used for this study, and all players and referees who agreed to participate in the present study were included in the study. We used Brooke L Devlin’s study to calculate the required sample size [34]. Fat-free mass (FFM) was reckoned to be the largest sample size among other variables. Therefore, we calculated the study power based on this variable. Thus, with a power of $80\%$, type I error of 0.05, desired confidence interval (CI) of 0.95, and effect size (d) of 0.86, the minimum required sample size was estimated to be 11 subjects, but since we were working on different categories of athletes (under/over 18 years, male/female, soccer players/referees) and on account of over- or under-reporting by some individuals and ruling out the possibility of missing some information, the eventual sample size consisted of 199 subjects. The main reason both soccer players and referees were analyzed together was that, while the soccer players play the most influential role in this popular sport[35, 36], the soccer referees also play a critical role in the modern era[37–40]. Furthermore, the physical activity level of soccer referees during a match has been estimated to be around 10-12 km, with 4–$18\%$ of this match distance covered at speeds faster than 13-15 km/h[41], which is almost equal to what is observed in midfield players[35–42]. Collection of all information related to anthropometric indices, demographic and lifestyle factors include dietary intake and physical activity took place at the Medical Committee of the Football Federation Islamic Republic of Iran.
## Assessment of Dietary intake
A 147-item semi-quantitative food frequency questionnaire (FFQ) validated in the Tehran Lipid and Glucose Study (TLGS) was adopted to congregate the participants’ dietary data. The validity and reliability of this 147-item semi-quantitative questionnaire have been published elsewhere [43]. This questionnaire elicits food intakes of the past year; subsequently, dietary intake data was then entered into Nutritionist IV software modified for Iranian foods to estimate nutrient intake composition. Average energy, macro and micro-nutrient intakes were also acquired.
## Dietary insulin load (DIL) and dietary insulin index (DII) calculation
Food insulin index (FII) is the area under the curve that causes insulin surge within 2 h after consuming 1000 kj (239 kcal) of a specific food divided by the area under the curve after consuming 1000 kj of a reference food (e.g. white bread). The FII for each food item was procured from previous studies by Holt et al., Bao et al., and Bell et al. [ 1, 2, 44]. Since the FII of all Iranian dishes was not available in the food list of mentioned studies, the FII of similar food items was used. We used the following formula to calculate the insulin load of each food: Insulin load of a given food = FII of that food × the energy content of that food per 1 gram (kcal) × an amount of that food consumed in a day (gr/day), which is proposed by Nimptsch et al. [ 45]. Total dietary insulin load for each participant was computed by summing the insulin load of all foods consumed by that participant. Finally, the DII for each participant was calculated by dividing the DIL by the total energy consumed by that person.
## Assessment of body composition
Body composition was measured using the InBody 570 (InBody Co., Ltd. in Seoul, Korea), and analyzed to quantify fat mass, percent body fat, lean mass, percent muscle mass, and bone mineral content. The InBody 570 uses three different frequencies (5 kHz, 50 kHz, 500 kHz) at each of five segments (right arm, left arm, trunk, right leg, and left leg). Calibration took place as per manufacturer guidelines. Participants’ measurements were taken after an overnight fast and rest, without exercise, on the morning of the scan. Participants were required to empty their bladders and be minimally clothed before each scan. Athletes were advised not to consume caffeinated beverages at least 4 h before and drink at least 2–4 glasses of water 2 h before scanning. The software automatically analyzed scans. Body weight was measured with subjects in light clothing, upshot, using a digital scale (Seca 808, Germany) to the nearest 0.1 kg, whilst height was assessed using a wall-mounted stadiometer (Seca, Germany) to the nearest 0.1 cm. BMI was calculated by dividing weight (kg) by the square of height(m). Waist circumference (WC) was measured at the midpoint of the lowest rib and iliac crest at the end of expiration using a non-elastic measuring tape (Seca 201, Germany) to the nearest 0.1 cm. Hip circumference (HC) was measured at the widest point over the buttocks using a measuring tape to the nearest 0.1 cm. Waist-to-hip ratio (WHR) and Waist-to-height ratio (WHtR) were obtained by dividing the waist circumference (cm) by the hip circumference (cm) and height (cm), respectively. Other body composition parameters include A body shape index (ABSI), Abdominal volume index (AVI), Body adiposity index (BAI), Body roundness index (BRI), Conicity index (CI), Weight-adjusted waist index (WWI) and Waist-to-hip-to-height ratio (WHHR) were calculated using a specific formula published by Chang et al[46].
## Assessment of physical activity
A 7-item (short form) International Physical Activity Questionnaire (IPAQ) was employed to ascertain the participants’ physical activity levels. The validity and reliability of this questionnaire have been described and confirmed elsewhere [47]. This questionnaire asks the participants about the types of physical activities performed in the preceding 7 days. Individuals were divided into 3 groups in terms of physical activity: Low activity: This group does not meet any of the criteria for subsequent groups. Average: Having any type of physical activity (light, moderate or heavy) for 5 days or more in a week to meet 600MET/minute/week. High activity: Having any type of physical activity (light, moderate or heavy) for 7 days a week to meet 3000MET/minute/week.
## Statistical methods
The R Studio software (Version 2022.07.1) [48] was used for all statistical analyses and statistical significance was set at $p \leq 0.05.$ Descriptive statistics (Frequencies, cross-tabulation, and Chi-square value) were used to elucidate the primary features of the data. Participants’ general characteristics were compared across tertiles of Dietary Insulin Load (DIL) and Dietary Insulin Index (DII) using an analysis of variance (ANOVA) for continuous variables. Pearson’s correlation coefficient was used to discern the correlation between DIL and DII with the measures of body composition. To identify associations between DIL and DII with body composition parameters (Body Mass Index (BMI), Percent Body Fat (PBF), Percent Muscle Mass (PMM), Waist to Hip Ratio (WHR), Waist to Height Ratio (WHtR), Bone Mineral Content (BMC), A body shape index (ABSI), Abdominal volume index (AVI), Body adiposity index (BAI), Body roundness index (BRI), Conicity index (CI), Weight-adjusted waist index (WWI) and Waist-to-hip-to-height ratio (WHHR)), multivariate regression models were created, with adjustment for potential covariates such as age, gender, and physical activity. The power of $80\%$, type I error of 0.05, desired confidence interval (CI) of 0.95, and effect size (d) of 0.86 was used for the statistical analyses.
All variables were tested for normality via the Kolmogorov-Smirnov statistic and visual assessment of histograms, and appropriate statistical tests were subsequently conducted. Data are presented as percentages, means, and standard deviations.
## Results
*The* general characteristics of soccer players and referees are indicated in Table 1. However, to better display the results, it was decided to show the characteristics of participants across the tertiles of DIL and DII are demonstrated in Table 2A and Table 2B, respectively. All 199 volunteers partook in the present study, consisting of 113 ($56.8\%$) males and 86 ($43.2\%$) females. The mean age of participants was 29.38 ± 8.53 years, of which 36 ($18.1\%$) were under 18 and 163 ($81.9\%$) were over 18 years of age. Of all the participants, 70 ($35.2\%$) were soccer players and 129 ($64.8\%$) were soccer referees. Mean physical activity was 3003.75 ± 1834.97 MET/min/week, and, according to this, 144 ($72.4\%$) obeyed a moderate physical activity, and 55 ($27.6\%$) followed a high physical activity lifestyle. A significant difference in mean DIL, age, post position, CI, total calorie intake, carbohydrate intake, protein intake, and fat intake is apparent from the tertiles of DIL. Results of Tukey’s test quite revealed a significant difference between tertile 1, tertile 2, and tertile 3 in the case of mean DIL, while, regarding age and post position, there is a significant difference among tertiles. CI was also significantly different in tertiles 1 and 3. Total calorie, carbohydrate, protein, and fat intake were also significantly different among all tertiles of DIL. No significant differences in the mean and frequency of other characteristics were evident ($P \leq 0.005$). Additionally, results of Post *Hoc analysis* on DII illustrated that there is a significant difference between mean DII, physical activity, BMI, WC, AVI, and fat intake. Mean DII was significantly different across all tertiles of DII. Further, physical activity, BMI, WC, and AVI were significantly different between tertile 1 and tertile 2 of DII. Furthermore, total fat intake was also different among tertile 1 and tertile 3 of DII, according to Tukey’s test. There were no significant differences in the mean and frequency of other characteristics ($P \leq 0.005$).
Table 1General characteristics of soccer players and refereesVariablesSoccer playersSoccer referees Male ($$n = 23$$) Female ($$n = 47$$) Male ($$n = 90$$) Female ($$n = 39$$) MeanSDMeanSDMeanSDMeanSD Mean DIL a 209542.4963824.56155340.0172999.52136276.2753411.98159335.35126806.33 Mean DII a 53.204.8451.477.6652.767.6949.877.06 Age (year) a < 18 years ≥ 18 years 18.2912 ($52\%$)11 ($48\%$)3.3619.9324 ($51\%$)23 ($49\%$)4.6135.4790 ($100\%$)4.1433.2839 ($100\%$)4.23 Physical activity b Moderate High 20 ($87\%$)3 ($13\%$)40 ($85\%$)7 ($15\%$)52 ($58\%$)38 ($42\%$)32 ($82\%$)7 ($18\%$) Physical activity a MET/Min/Week2348.291195.552730.192098.073242.861527.9031782338.89 Height (cm) a 176.415.89167.176.69177.926.05164.495.44 Weight (kg) a 68.177.6359.436.5874.566.6159.456.07BMI (kg/m2)a21.821.6421.221.8823.481.6022.032.28 BFM (kg) a 9.163.2312.263.2612.773.2015.174.09 PBF (%) a 13.434.4020.604.8117.043.7525.295.14 FFM (Kg) a 59.007.0647.145.9561.795.5344.274.22 PMM (%) a 86.574.4179.404.8182.953.7474.695.14 WC a 75.164.4974.844.6982.064.6276.175.33 HC a 95.433.5492.393.5898.333.1793.263.83 WHR a 0.780.030.810.040.830.030.810.03 WHtR a 0.420.020.440.030.460.020.460.03 ABSI a 0.070.0030.070.0030.070.0020.070.002 AVI a 11.631.3011.471.3513.701.4511.871.63 BAI a 22.761.7124.852.8423.492.0726.292.82 BRI a 2.090.412.460.532.680.462.720.64 CI a 1.110.041.150.041.160.041.160.04 WWI a 9.120.439.720.419.510.379.880.39 WHHR a 0.440.020.480.030.460.020.490.02 BMC (kg) a 3.430.442.820.403.500.382.640.28 Calorie intake (Kcal) a 3952.571208.273055.981532.862634.831107.823195.892457.89 Carbohydrates (gr/d) a 628.66204.20458.53252.45404.42177.40516.53622.96 Protein intake (g/d) a 146.2036.44121.4064.08108.7053.97115.4541.26 Fat intake (g/d) a 112.1941.9892.5647.5375.6533.7492.9241.76ABSI = A body shape index, AVI = Abdominal volume index, BAI = Body adiposity index, BFM = Body fat mass, BMC = Bone mineral content, BMI = Body mass index, BRI = Body roundness index, CI = Conicity index, FFM = Fat free mass, HC = Hip circumference, PBF = Percent body fat, PMM = Percent muscle mass, WC = Waist circumference, WHR = Waist to hip ratio, WHHR = Waist to hip to height ratio, WHtR = Waist to height ratio, WWI = Weight-adjusted-waist-indexa Values are expressed as mean ± SD, by using one-way ANOVAb Values are reported as total and percentage, by using cross-tabulation and Chi-square test Table 2AGeneral characteristics of participants across tertiles of Dietary Insulin Load (DIL)Dietary Insulin LoadP-valueTertile1($$n = 66$$)Tertile2($$n = 67$$)Tertile3($$n = 66$$)Total($$n = 199$$)MeanSDMeanSDMeanSDMeanSD Mean DIL a 92950.2115733.17140767.9515677.28228597.5399586.04154038.2181,089 < 0.001 Age (year) a < 18 years ≥ 18 years 31.658 ($12.1\%$)58 ($87.9\%$)7.5308 ($11.9\%$)59 ($88.1\%$)8.6826.4720 ($30.3\%$)46 ($69.7\%$)8.6429.3836 ($18.1\%$)163 ($81.9\%$)8.53 0.002 Gender b Male Female 38 ($57.6\%$)28 ($42.4\%$)36 ($53.7\%$)31 ($46.3\%$)39 ($59.1\%$)27 ($40.9\%$)113 ($56.8\%$)86 ($43.2\%$)0.81 Post b Soccer players Soccer referees 15 ($22.7\%$)51 ($77.3\%$)22 ($32.8\%$)45 ($67.2\%$)32 ($49.2\%$)33 ($50.8\%$)70 ($35.2\%$)129 ($64.8\%$) < 0.001 Physical activity b Moderate High 45 ($68.2\%$)21 ($31.8\%$)49 ($73.1\%$)18 ($26.9\%$)50 ($75.8\%$)16 ($24.2\%$)144 ($72.4\%$)55 ($27.6\%$)0.61 Physical activity a MET/Min/Week3133.731750.472873.881720.293005.622.37.923003.751834.970.71 Height (cm) a 172.639.45172.367.79172.878.03172.628.410.94 Weight (kg) a 67.639.9367.379.9566.999.3167.339.690.93BMI (kg/m2) a22.642.1222.581.8822.202.1322.472.040.41 BFM (kg) a 13.033.6912.972.9712.054.4912.693.770.25 PBF (%) a 19.555.9319.464.3718.126.6319.055.730.27 FFM (Kg) a 54.610.1254.399.3654.939.2054.649.520.055 PMM (%) a 80.445.9380.544.3781.886.6480.955.730.27 WC a 79.035.4978.845.9577.335.8878.405.800.18 HC a 95.774.2295.764.1795.314.5895.624.310.77 WHR a 0.820.030.820.030.810.040.810.030.09 WHtR a 0.450.030.450.020.440.030.450.030.11 ABSI a 0.0750.0020.0750.0030.0740.0030.0750.0030.34 AVI a 12.751.6712.711.8412.261.7812.571.770.21 BAI a 24.383.0324.402.2524.032.6524.272.660.66 BRI a 2.630.532.610.492.460.612.570.550.14 CI a 1.160.031.160.041.140.051.150.04 0.03 WWI a 9.640.399.630.379.480.549.580.450.059 WHHR a 0.4780.0280.4780.0240.0470.0360.4750.030.22 BMC (kg) a 3.150.553.160.503.200.543.170.530.85 Calorie intake (Kcal) a 1881.36399.792735.88479.564389.962050.083001.061610.45 < 0.001 Carbohydrates (gr/d) a 277.2065.81413.9781.23707.44486.04465.94336.75 < 0.001 Protein intake (g/d) a 76.4821.08112.6725.04163.3561.42117.4853.50 < 0.001 Fat intake (g/d) a 58.3618.2881.6523.44122.1347.0587.3541.40 < 0.001 ABSI = A body shape index, AVI = Abdominal volume index, BAI = Body adiposity index, BFM = Body fat mass, BMC = Bone mineral content, BMI = Body mass index, BRI = Body roundness index, CI = Conicity index, FFM = Fat free mass, HC = Hip circumference, PBF = Percent body fat, PMM = Percent muscle mass, WC = Waist circumference, WHR = Waist to hip ratio, WHHR = Waist to hip to height ratio, WHtR = Waist to height ratio, WWI = Weight-adjusted-waist-indexa Values are expressed as mean ± SD, by using one-way ANOVAb Values are reported as total and percentage, by using cross-tabulation and Chi-square testP-value is considered significant at < 0.05 Table 2BGeneral characteristics of participants across tertiles of Dietary Insulin Index (DII)Dietary Insulin IndexP-valueTertile1($$n = 66$$)Tertile2($$n = 67$$)Tertile3($$n = 66$$)Total($$n = 199$$)MeanSDMeanSDMeanSDMeanSD Mean DII a 44.433.6751.311.6760.104.6951.947.32 < 0.001 Age (year) a < 18 years ≥ 18 years 28.7713 ($19.7\%$)53 ($80.3\%$)8.9330.3710 ($14.9\%$)57 ($85.1\%$)7.8928.9713 ($19.7\%$)53 ($80.3\%$)8.7729.3836 ($18.1\%$)163 ($81.9\%$)8.530.50 Gender b Male Female 32 ($48.5\%$)34 ($51.5\%$)42 ($62.7\%$)25 ($37.3\%$)39 ($59.1\%$)27 ($40.9\%$)113 ($56.8\%$)86 ($43.2\%$)0.23 Post b Soccer players Soccer referees 26 ($39.3\%$)40 ($60.7\%$)17 ($25.37\%$)50 ($74.63\%$)27 ($40.9\%$)39 ($39.1\%$)70 ($35.2\%$)129 ($64.8\%$)0.32 Physical activity b Moderate High 51 ($77.3\%$)15 ($22.7\%$)44 ($65.7\%$)23 ($34.3\%$)49 ($74.2\%$)17 ($25.8\%$)144 ($72.4\%$)55 ($27.6\%$)0.30 Physical activity a MET/Min/Week2679.191411.143510.782325.522813.611538.023003.751834.97 0.01 Height (cm) a 171.718.44172.888.67173.278.16172.628.410.54 Weight (kg) a 65.169.4368.589.3968.2310.0267.339.690.08BMI (kg/m2)a21.932.0722.811.9722.672.0122.472.04 0.02 BFM (kg) a 12.033.3013.034.3912.993.5012.693.770.22 PBF (%) a 18.705.2419.126.3519.325.5919.055.720.81 FFM (Kg) a 53.139.3655.559.1455.2310.0154.649.520.28 PMM (%) a 81.305.2380.876.3680.675.6080.955.730.81 WC a 76.825.3379.296.0579.105.7578.405.80 0.02 HC a 94.634.1996.264.1495.944.4895.624.310.06 WHR a 0.810.0340.820.0360.820.0410.810.0380.10 WHtR a 0.4470.0300.4590.0330.4560.0300.4540.0320.10 ABSI a 0.0750.0030.0750.0020.0750.0030.0750.0030.95 AVI a 12.081.6012.851.8412.791.7712.571.77 0.02 BAI a 24.182.7924.472.7824.152.4024.272.660.74 BRI a 2.450.522.640.582.600.532.570.550.10 CI a 1.140.0461.150.0461.160.0491.150.0470.27 WWI a 9.550.439.600.459.610.469.580.450.71 WHHR a 0.4730.0290.4760.0290.4760.0320.4750.0300.78 BMC (kg) a 3.080.503.210.513.210.573.170.530.25 Calorie intake (Kcal) a 3177.351479.9031762109.802647.181000.153001.061610.450.09 Carbohydrates (gr/d) a 470.18242.67513.87498.37413.05170.66465.94336.750.22 Protein intake (g/d) a 126.6358.86120.3459.74105.4237.28117.4853.500.06 Fat intake (g/d) a 102.0348.0986.6538.8173.383187.3541.40 < 0.001 ABSI = A body shape index, AVI = Abdominal volume index, BAI = Body adiposity index, BFM = Body fat mass, BMC = Bone mineral content, BMI = Body mass index, BRI = Body roundness index, CI = Conicity index, FFM = Fat free mass, HC = Hip circumference, PBF = Percent body fat, PMM = Percent muscle mass, WC = Waist circumference, WHR = Waist to hip ratio, WHHR = Waist to hip to height ratio, WHtR = Waist to height ratio, WWI = Weight-adjusted-waist-indexa Values are expressed as mean ± SD, by using one-way ANOVAb Values are reported as total and percentage, by using cross-tabulation and Chi-square testP-value is considered significant at < 0.05 The correlations between DIL/DII and body composition parameters are presented in the order in Table 3A and Table 3B. Our crude model showed significant correlations between DIL and PBF ($r = 0.60$, $$P \leq 0.04$$), PMM (r = -0.61, $$P \leq 0.04$$), WHR ($r = 0.67$, $$P \leq 0.02$$), ABSI ($r = 0.67$, $$P \leq 0.02$$), CI ($r = 0.72$, $$P \leq 0.01$$) and WWI ($r = 0.66$, $$P \leq 0.02$$) in ≥ 18 male soccer players, and PBF (r = -0.49, $$P \leq 0.02$$), PMM ($r = 0.49$, $$P \leq 0.02$$), WHR (r = -0.43, $$P \leq 0.04$$) and WWI (r = -0.53, $$P \leq 0.01$$) in ≥ 18 female soccer players. Conversely, we did not find any significant correlation between DIL and body composition parameters among other athletes. A significant correlation was likewise obvious between DII and BAI ($r = 0.64$, $$P \leq 0.01$$) in < 18 male soccer players, CI ($r = 0.41$, $$P \leq 0.04$$) in < 18 female soccer players, and BMC ($r = 0.24$, $$P \leq 0.01$$) in male soccer referees. On the other hand, no evidence of a considerable correlation between DII and body composition parameters among other athletes was detected.
Table 3ACorrelation between Dietary Insulin Load (DIL) and measures of body composition among different categories of athletesCategories of AthletesBMIra(P)bPBFra(P)bPMMra(P)bWCra(P)bHCra(P)bWHRra(P)bWHtRra(P)bABSIra(P)bAVIra(P)bBAIra(P)bBRIra(P)bCIra(P)bWWIra(P)bWHHRra(P)bBMCra(P)b < 18 male soccer players -0.34(0.24)-0.39(0.17)0.39(0.17)-0.40(0.16)-0.37(0.20)-0.25(0.41)-0.34(0.25) 0.12 (0.68)-0.41(0.16)-0.17(0.56)-0.34(0.25)-0.22(0.46)-0.16(0.59)-0.12(0.68)-0.35(0.23) ≥ 18 male soccer players 0.07(0.82) 0.60 (0.04 *) -0.61 (0.04 *) 0.45(0.16)-0.01(0.97) 0.67 (0.02 *) 0.54(0.08) 0.67 (0.02 *) 0.42(0.19)0.17(0.61)0.54(0.08) 0.72 (0.01 *) 0.66 (0.02 *) 0.57(0.06)-0.18(0.58) < 18 female soccer players -0.18(0.38)0.02(0.92)-0.01(0.93)-0.13(0.51)-0.17(0.41)-0.02(0.92)-0.11(0.59)0.06(0.77)-0.13(0.53)-0.10(0.61)-0.10(0.61)-0.02(0.92)-0.00(0.99)0.00(0.98)-0.09(0.66) ≥ 18 female soccer players -0.26(0.24) -0.49 (0.02 *) 0.49 (0.02 *) -0.32(0.14)-0.09(0.68) -0.43 (0.04 *) -0.32(0.13)-0.17(0.43)-0.31(0.16)-0.15(0.50)-0.32(0.14)-0.36(0.09) -0.53 (0.01 *) -0.38(0.07)0.40(0.06) Male soccer referees 0.01(0.88)-0.08(0.45)0.08(0.44)-0.11(0.29)-0.03(0.75)-0.13(0.21)-0.4(0.66)-0.14(0.16)-0.10(0.31)0.08(0.41)-0.04(0.66)-0.13(0.19)-0.08(0.44)-0.03(0.77)-0.06(0.56) Female soccer referees -0.070.65-0.050.720.050.720.040.80-0.050.720.130.430.010.910.200.200.030.84-0.070.650.010.940.110.480.080.600.070.660.000.99ABSI = A body shape index, AVI = Abdominal volume index, BAI = Body adiposity index, BFM = Body fat mass, BMC = Bone mineral content, BMI = Body mass index, BRI = Body roundness index, CI = Conicity index, FFM = Fat free mass, HC = Hip circumference, PBF = Percent body fat, PMM = Percent muscle mass, WC = Waist circumference, WHR = Waist to hip ratio, WHHR = Waist to hip to height ratio, WHtR = Waist to height ratio, WWI = Weight-adjusted-waist-indexa ‘r’: Pearson correlation coefficientb P: significant valueEffect size = 0.86, confidence interval = 0.95*. Correlation is significant at the 0.05 level (2-tailed) Table 3BCorrelation between Dietary Insulin Index (DII) and measures of body composition among different categories of athletesCategories of AthletesBMIra(P)bPBFra(P)bPMMra(P)bWCra(P)bHCra(P)bWHRra(P)bWHtRra(P)bABSIra(P)bAVIra(P)bBAIra(P)bBRIra(P)bCIra(P)bWWIra(P)bWHHRra(P)bBMCra(P)b < 18 male soccer players 0.46(0.11)0.05(0.86)-0.05(0.85)0.12(0.67)0.28(0.34)-0.08(0.78)0.30(0.31)-0.21(0.48)0.15(0.62) 0.64 (0.01 *) 0.30(0.31)-0.09(0.75)0.02(0.94)0.13(0.67)0.00(0.99) ≥ 18 male soccer players -0.29(0.37)-0.14(0.66)0.14(0.66)-0.22(0.50)-0.00(0.99)-0.32(0.33)-0.48(0.12)-0.13(0.69)-0.23(0.49)-0.55(0.07)-0.49(0.12)-0.20(0.54)-0.36(0.27)-0.50(0.11)0.18(0.58) < 18 female soccer players -0.05(0.81)0.21(0.30)-0.22(0.30)0.29(0.17)-0.06(0.76)0.38(0.06)0.17(0.41)0.38(0.06)0.27(0.19)-0.26(0.21)0.17(0.41) 0.41 (0.04 *) 0.28(0.18)0.21(0.31)-0.01(0.95) ≥ 18 female soccer players 0.30(0.16)0.30(0.16)-0.30(0.16)0.12(0.57)0.09(0.67)0.13(0.55)0.08(0.71)-0.20(0.36)0.12(0.57)0.01(0.94)0.08(0.72)0.27(0.22)0.31(0.15)0.06(0.76)-0.20(0.36) Male soccer referees 0.12(0.22)-0.06(0.56)0.06(0.57)0.16(0.11)0.17(0.10)0.09(0.40)0.03(0.75)-0.01(0.90)0.17(0.10)-0.11(0.30)0.03(0.75)0.07(0.49)-0.02(0.81)-0.07(0.48) 0.24 (0.01 *) Female soccer referees -0.02(0.86)0.04(0.79)-0.04(0.78)0.13(0.43)0.03(0.84)0.18(0.26)0.05(0.73)0.23(0.14)0.13(0.43)-0.09(0.56)0.05(0.73)0.14(0.37)0.07(0.64)0.05(0.74)0.17(0.30)ABSI = A body shape index, AVI = Abdominal volume index, BAI = Body adiposity index, BFM = Body fat mass, BMC = Bone mineral content, BMI = Body mass index, BRI = Body roundness index, CI = Conicity index, FFM = Fat free mass, HC = Hip circumference, PBF = Percent body fat, PMM = Percent muscle mass, WC = Waist circumference, WHR = Waist to hip ratio, WHHR = Waist to hip to height ratio, WHtR = Waist to height ratio, WWI = Weight-adjusted-waist-indexa ‘r’: Pearson correlation coefficientb P: significant valueEffect size = 0.86, confidence interval = 0.95*. Correlation is significant at the 0.05 level (2-tailed) With regards to previous studies, Age, gender, and physical activity were foremost covariates and differences were observed in our data. Therefore, our final model was adjusted for age, gender, and physical activity. Results of multiple linear regression revealed that there is a significant association between DIL and BMI ($$P \leq 0.04$$) in < 18 male soccer players, CI ($$P \leq 0.04$$) and WWI ($$P \leq 0.03$$) in ≥ 18 male soccer players, PBF ($$P \leq 0.02$$), PMM ($$P \leq 0.01$$) and WWI ($$P \leq 0.01$$) in ≥ 18 female soccer players. Nevertheless, no significant associations between DIL and body composition parameters were found in the referees. Additionally, this study showed a significant association between DII and BMC ($$P \leq 0.02$$) in male soccer referees, however, no significant associations were found in young soccer players and female athletes. More comprehensive information about the associations between DIL/DII and body composition parameters is presented in Table 4A and Table 4B, respectively. According to the significant relationship between some body composition parameters and dietary insulin load, it can be claimed that controlling dietary carbohydrates can be considered as a strategy to improve body composition in soccer players and referees.
Table 4AThe association between Dietary Insulin Load (DIL) and body composition adjusted for potential covariatesCategories of AthletesBody composition parametersDietary Insulin LoadUnstandardized β coefficientSEP-value < 18 male soccer players ($$n = 12$$) BMI (kg/m 2) -0.000.00 0.04* PBF (%) -0.000.000.45 PMM (%) 0.000.000.45 WC (cm) 0.000.000.45 HC (cm) -0.000.000.06 WHR -0.000.000.78 WHtR -0.000.000.27 ABSI 0.000.000.68 AVI -0.000.000.15 BAI -0.000.000.41 BRI -0.000.000.26 CI -0.000.000.94 WWI 0.000.000.83 WHHR 0.000.000.80 BMC(kg) -0.000.000.07 ≥ 18 male soccer players ($$n = 11$$) BMI (kg/m 2) -0.000.000.99 PBF (%) 0.000.000.11 PMM (%) -0.000.000.11 WC (cm) -0.000.000.11 HC (cm) -0.000.000.90 WHR 0.000.000.06 WHtR 0.000.000.19 ABSI 0.000.000.07 AVI 0.000.000.37 BAI 0.000.000.89 BRI 0.000.000.17 CI 0.000.00 0.04* WWI 0.000.00 0.03* WHHR 0.000.000.09 BMC(kg) -0.000.000.62 < 18 female soccer players ($$n = 24$$) BMI (kg/m 2) -0.000.000.59 PBF (%) 0.000.000.89 PMM (%) -0.000.000.90 WC (cm) -0.000.000.86 HC (cm) -0.000.000.70 WHR 0.000.000.93 WHtR -0.000.000.69 ABSI 0.000.000.74 AVI -0.000.000.90 BAI -0.000.000.47 BRI -0.000.000.72 CI 0.000.000.99 WWI -0.000.000.87 WHHR -0.000.000.90 BMC(kg) 0.000.000.92 ≥ 18 female soccer players ($$n = 23$$) BMI (kg/m 2) -0.000.000.26 PBF (%) -0.000.00 0.02* PMM (%) 0.000.00 0.01* WC (cm) -0.000.000.25 HC (cm) -0.000.000.83 WHR -0.000.000.10 WHtR -0.000.000.21 ABSI -0.000.000.61 AVI -0.000.000.27 BAI 0.000.000.59 BRI -0.000.000.21 CI -0.000.000.15 WWI -0.000.00 0.01* WHHR -0.000.000.13 BMC(kg) 0.000.000.06 Male soccer referees ($$n = 90$$) BMI (kg/m 2) 0.000.000.67 PBF (%) -0.000.000.39 PMM (%) 0.000.000.39 WC (cm) -0.000.000.26 HC (cm) -0.000.000.87 WHR -0.000.000.13 WHtR -0.000.000.68 ABSI -0.000.000.06 AVI -0.000.000.28 BAI 0.000.000.27 BRI -0.000.000.68 CI -0.000.000.10 WWI -0.000.000.33 WHHR -0.000.000.72 BMC(kg) -0.000.000.59 Female soccer referees ($$n = 39$$) BMI (kg/m 2) -0.000.000.97 PBF (%) 0.000.000.82 PMM (%) -0.000.000.82 WC (cm) 0.000.000.35 HC (cm) 0.000.000.99 WHR 0.000.000.11 WHtR 0.000.000.46 ABSI 0.000.000.09 AVI 0.000.000.39 BAI -0.000.000.89 BRI 0.000.000.49 CI 0.000.000.16 WWI 0.000.000.25 WHHR 0.000.000.30 BMC(kg) -0.000.000.91ABSI = A body shape index, AVI = Abdominal volume index, BAI = Body adiposity index, BFM = Body fat mass, BMC = Bone mineral content, BMI = Body mass index, BRI = Body roundness index, CI = Conicity index, FFM = Fat free mass, HC = Hip circumference, PBF = Percent body fat, PMM = Percent muscle mass, WC = Waist circumference, WHR = Waist to hip ratio, WHHR = Waist to hip to height ratio, WHtR = Waist to height ratio, WWI = Weight-adjusted-waist-indexA multiple linear regression model was created with adjustment for age, gender and physical activity levelP-value is considered significant at < 0.05Effect size = 0.86, confidence interval = 0.95 Table 4BThe association between Dietary Insulin Index (DII) and body composition adjusted for potential covariatesCategories of AthletesBody composition parametersDietary Insulin IndexUnstandardized β coefficientSEP-value < 18 male soccer players ($$n = 12$$) BMI (kg/m 2) 0.050.090.57 PBF (%) -0.060.250.80 PMM (%) 0.060.250.80 WC (cm) -0.090.280.75 HC (cm) 0.050.230.82 WHR -0.000.000.48 WHtR 0.000.000.89 ABSI 0.000.000.49 AVI -0.020.080.80 BAI 0.130.070.12 BRI 0.000.020.88 CI -0.000.000.56 WWI -0.000.020.80 WHHR -0.000.000.95 BMC(kg) -0.000.020.86 ≥ 18 male soccer players ($$n = 11$$) BMI (kg/m 2) -0.040.170.80 PBF (%) -0.200.270.47 PMM (%) 0.210.270.46 WC (cm) -0.110.420.79 HC (cm) 0.120.330.73 WHR -0.000.000.43 WHtR -0.000.000.23 ABSI 0.000.000.59 AVI -0.030.120.79 BAI -0.190.140.23 BRI -0.040.020.22 CI -0.000.000.55 WWI -0.030.020.20 WHHR -0.000.000.08 BMC(kg) 0.030.040.49 < 18 female soccer players ($$n = 24$$) BMI (kg/m 2) 0.030.060.63 PBF (%) 0.170.160.31 PMM (%) -0.170.160.31 WC (cm) 0.240.160.15 HC (cm) 0.060.120.60 WHR 0.000.000.19 WHtR 0.000.000.39 ABSI 0.000.000.26 AVI 0.060.040.16 BAI -0.040.070.53 BRI 0.010.010.40 CI 0.000.000.16 WWI 0.010.010.41 WHHR 0.010.010.56 BMC(kg) 0.000.010.58 ≥ 18 female soccer players ($$n = 23$$) BMI (kg/m 2) 0.020.040.61 PBF (%) 0.060.100.52 PMM (%) -0.060.100.52 WC (cm) -0.000.120.98 HC (cm) -0.020.070.72 WHR 0.000.000.80 WHtR 0.000.000.67 ABSI -0.000.000.42 AVI -0.000.030.94 BAI -0.060.090.51 BRI -0.000.010.63 CI 0.000.000.35 WWI 0.000.000.38 WHHR 0.000.000.74 BMC(kg) -0.000.000.42 Male soccer referees ($$n = 90$$) BMI (kg/m 2) 0.020.020.18 PBF (%) -0.010.040.72 PMM (%) 0.010.040.73 WC (cm) 0.110.060.08 HC (cm) 0.070.040.09 WHR 0.000.000.29 WHtR 0.000.000.62 ABSI 0.000.000.96 AVI 0.030.020.07 BAI -0.020.020.31 BRI 0.000.000.62 CI 0.000.000.36 WWI 0.000.000.97 WHHR 0.000.000.58 BMC(kg) 0.010.00 0.02* Female soccer referees ($$n = 39$$) BMI (kg/m 2) -0.010.050.79 PBF (%) 0.030.100.72 PMM (%) -0.030.100.72 WC (cm) 0.090.100.37 HC (cm) 0.000.080.96 WHR 0.000.000.12 WHtR 0.000.000.63 ABSI 0.000.000.07 AVI 0.020.030.38 BAI -0.030.060.58 BRI 0.000.010.63 CI 0.000.000.23 WWI 0.000.000.42 WHHR 0.000.000.48 BMC(kg) 0.000.000.50ABSI = A body shape index, AVI = Abdominal volume index, BAI = Body adiposity index, BFM = Body fat mass, BMC = Bone mineral content, BMI = Body mass index, BRI = Body roundness index, CI = Conicity index, FFM = Fat free mass, HC = Hip circumference, PBF = Percent body fat, PMM = Percent muscle mass, WC = Waist circumference, WHR = Waist to hip ratio, WHHR = Waist to hip to height ratio, WHtR = Waist to height ratio, WWI = Weight-adjusted-waist-indexA multiple linear regression model was created with adjustment for age, gender and physical activity levelP-value is considered significant at < 0.05Effect size = 0.86, confidence interval = 0.95
## Discussion
Given our results, there is distinctly discovered a significant positive correlation between dietary insulin load (DIL) and different body composition parameters including body mass index (BMI) in < 18 male soccer players, conicity index (CI), weight-adjusted waist index (WWI) in ≥ 18 male soccer players, percent body fat (PBF) and negative correlation with percent muscle mass (PMM) in ≥ 18 female soccer players. Conversely, body composition parameters had no overall significant correlation with DIL in referees. This novel finding concerning soccer players and referees is unprecedented. Altogether, our novel finding declares Sports, specifically, playing soccer affect body compositions and consequently accounted for better DIL, lower PBF, and higher PMM. Regarding previous studies, the decrease in the level of fat mass is linked to the reduction of free fatty acid in circulation; this process limits the access of skeletal muscle tissue to free fatty acid [49]. In addition, studies have shown that higher plasma insulin levels are associated with a higher percentage of body fat [50].
Insulin secretion occurs in response to the food, which directly reflects the dietary insulin index. Also, in comparison to the dietary glycemic index and glycemic load, the dietary insulin index is more suitable to quantify the relationship of insulin exposure and non-communicable diseases. Furthermore, insulin secretion primarily takes place after carbohydrate intake and even the combination of protein and carbohydrate plays a role in insulin secretion. This combination synergically leads to a raising insulin concentration and decreasing glycemia. Although fat does not reduce insulin response, it does lower glycemia [2, 45]. Some in vitro studies have shown that higher levels of IGF-1 play a role in the proliferation of preadipocytes. This mechanism causes body fat formation. Also, lipogenesis betides with absorbing cellular glucose by IGF-1 stimulation in preadipocytes and adipocytes and inhibiting lipolysis in body fat mass. We hypothesize that insulin resistance and increased IGF-1 concentrations predispose to postprandial insulinemic spikes and are related to fat accumulation in adipocytes [33].
Consistent with our findings, prior studies reported no significant association between DII with overweight and obesity in men, but, this association was significant in women [51]. Furthermore, a study among Iranian adults indicated that DIL and DII had no relationship with the risk of metabolic syndrome [52]. Moreover, some studies have conclusively established that there is a significant correlation between CI and fasting insulin levels among healthy premenopausal women [53]. In addition, Maysa et al. showed a significant correlation between insulin actions and insulin sensitivity in soccer players with type 2 diabetes [28]. Unlike our finding, Nassis et al. attempted to perform a study on obese and overweight with aerobic training, and they did not find any association between 12 weeks of aerobics training with body fat and body weight. However, they found that aerobic training ameliorates metabolic abnormalities in children [54]. Meng-Meng Liu et al. in accord with our results corroborated a significant correlation between insulin release at each phase and WHR [55].
The strength of our study is the novelty and lack of prior exploration of the association between DII and DIL with body compositions among soccer players and referees in both sexes. In addition, the determined sample size was considered very large in order to prevent over- or under-reporting by some individuals and rule out the possibility of missing some information.
Well-qualified analyses were controlled for various probable confounders to accomplish an independent association between DII and DIL with body compositions. Nevertheless, several limitations have to be deemed, first, based on its cross-sectional nature, causal inference is precluded and relies on a specific time period, which can contain assorted misinterpretations. Prospective studies are required to clarify their cause-and-effect relationship. Secondly, the existence of some unknown confounding factors should not be disaffirmed; they can erroneously affect the results. Also, the more reliable instrument for measuring body compositions is Dual-Energy X-Ray Absorptiometry (DEXA) and ‘Skinfolds’ methods than BIA. Furthermore, it is recommended that future studies consider this item for improved reliability in measuring body compositions [56, 57].
## Conclusion
This study demonstrates that DIL is positively associated with BMI, CI, and WWI in male soccer players and PBF, and WWI in female soccer players. Although, there was an observed negative association between DIL and PMM in males. In addition, a significant negative association between DII and BMC was observed in male soccer players.
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|
---
title: Deep eutectic solvent-based manganese dioxide nanosheets composites for determination
of DNA by a colorimetric method
authors:
- Jia Xu
- Yuan Yang
- Juan Du
- Hui Lu
- Wenqi Gao
- Hongjian Gong
journal: BMC Chemistry
year: 2023
pmcid: PMC10010034
doi: 10.1186/s13065-023-00922-5
license: CC BY 4.0
---
# Deep eutectic solvent-based manganese dioxide nanosheets composites for determination of DNA by a colorimetric method
## Abstract
### Background
Nucleic acid is the carrier of genetic information and the keymolecule in life science. It is important to establish a simple and feasible method for nucleic acid quantification in complex biological samples.
### Methods
Four kinds of hydrogen bond acceptors (choline chloride (ChCl), L-carnitine, tetrabutylammonium chloride (TBAC) and cetyltrimethylammonium bromide (CTAB)) were used to synthesize deep eutectic solvents (DESs) with hexafluoroisopropanol (HFIP). DESs based manganese dioxide (MnO2) nanosheets composites was synthesized and characterized. DNA concentration was determined by a UVVis spectrometer. The mechanism of DNA-DES/MnO2 colorimetric system was further discussed.
### Results
The composite composed of DES/MnO2 exhibited excellent oxidase-like activity and could oxidize 3,3’,5,5’ -tetramethylbenzidine (TMB) to produce a clear blue change with an absorbance maximum at 652 nm. When DNA is introduced, the DNA can interact with the DES by hydrogen bonding and electrostatic interactions, thereby inhibiting the color reaction of DES/MnO2 with TMB. After condition optimization, ChCl/HFIP DES in 1:3 molar ratio was used for the colorimetric method of DNA determination. The linear range of DNA was 10–130 µg/mL and exhibited good selectivity.
### Conclusion
A colorimetric method based on DES/MnO2 was developed to quantify the DNA concentration. The proposed method can be successfully used to quantify DNA in bovine serum samples.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13065-023-00922-5.
## Introduction
Deep eutectic solvents (DESs), an emerging class of environmentally friendly solvents are formed by hydrogen bond acceptors (HBAs) and hydrogen bond donors (HBDs) in an appropriate ratio. The formation of strong hydrogen bonds leads to a melting point lower than that of each individual component [1]. DESs possess numerous excellent properties including low volatility, ease of storage, stable physical and chemical properties, and good biocompatibility [2, 3]. In addition, the physical or chemical properties of DESs can be tuned by selecting HBAs and HBDs species [4, 5]. Owing to their excellent biocompatibility, DESs have been widely applied in the partitioning of biomass, such as proteins and, nucleotides, and for improving the efficiency of enzymatic reactions [6–11].
Nucleic acid, the carrier of genetic information, is a crucial molecule in life sciences. High-purity nucleic acids are the foundation of studies in clinical trials, genomics, food safety and other fields [12, 13]. However, real samples of nucleic acid usually contain impurities such as metal ions and proteins, which interfere with the reliability of the experimental analysis. Consequently, establishing a convenient and simple method for the accurate quantification of nucleic acids in complex biological samples is of great significance. DESs have been used as green substitutes for traditional organic solvents for nucleic acid extraction from aqueous solutions [8, 9, 11, 14]. In addition, Mondal et al. reported the solubility of DNA in DESs and confirmed the chemical and structural stability of DNA after six months of storage in DESs comprising glycerol and ethylene glycol [15]. Sharma et al. reported that hydrogen bonding is the major driving force that promotes the dissolution of DNA in DESs [16]. A recent and promising improvement in DES-based DNA purification approaches is the use of a combination of nanomaterials [2, 17–19].
Manganese dioxide (MnO2) is a functional transition metal oxide and its nanosheets have unique properties, such as a high specific surface area and oxidase-mimicking activity [20, 21]. It has been applied in sensing technology [22, 23], cell imaging [24], magnetic resonance imaging [25], and biomedical analysis [26–28]. 3,3’,5,5’-Tetramethylbenzidine (TMB) is a commonly used chromogenic substrate that can change from colorless to blue in the presence of MnO2 nanosheets with oxidase-like activity [29]. A colorimetric method based on MnO2 nanosheets/TMB has been reported for the detection and quantification of target compounds and biomacromolecules, including glucose, pesticides, metal ions, antibacterial agents, and nucleic acid [14, 20–22, 29, 30].
Hexafluoroisopropanol (HFIP) is a perfluorinated alcohol with a high density and strong hydrophobicity [31]. HFIP has proven to be an excellent HBD for preparation of high-density HFIP-based DESs with various HBAs [4, 32]. At present, HFIP-based DESs have been successfully employed in the purification of pesticides, anthraquinones, and dyes [4, 31, 33], but they are also used as environmental reaction media [34]. In this study, HFIP-based DESs combined MnO2 nanosheets were synthesized and employed for the quantification of DNA for the first time. Choline chloride (ChCl) was selected as the HBA to synthesize the DES with HFIP. The DNA quantification procedure was based on the colorimetric reaction between DES/MnO2 and TMB. We demonstrated that this method could accurately quantify DNA from bovine serum samples.
## Materials
Cetyltrimethylammonium bromide (CTAB), sodium acetate (NaAc), anhydrous acetic acid, KMnO4, NaOH, (NH4)2SO4, K2HPO4, KH2PO4, Na2CO3, Na2HPO4 and Na2SO4 were purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). Salmon sperm DNA sodium salt, morpholine ethanesulfonic acid (MES), HFIP, ChCl, L-carnitine, tetrabutylammonium chloride (TBAC), and TMB were purchased from Macklin (Shanghai, China). Bovine serum was purchased from Haoyang Biological Manufacture Co., Ltd. (Tianjin, China). All other reagents were of analytical grade and were commercially available. Deionized (DI) water (18.25 MΩ) was used in all the experiments.
## Instrumentation
The surface modification of the obtained DES and DES/MnO2 was investigated using a Nicolet 470 fourier transform infraed (FT-IR) spectrometer (Thermo Fisher Scientific, USA) in a KBr pellet at room temperature. Nuclear magnetic resonance (1 H NMR) spectra were obtained using an Avance III 400 MHz spectrometer (Bruker, Germany) and the morphology of the MnO2 nanosheets was observed using a JEM-2100 transmission electron microscope (TEM) (JEOL, Japan). Thermal gravimetric analysis (TGA) was performed using TG 209F1 (NETZSCH, Germany). A PHI5000 VersaProbe (PHI, Japan) was used for X-ray photoelectron spectroscopy (XPS) analysis. The zeta potential and dynamic light scattering (DLS) were analyzed using a Zeta sizer Nano ZS90 (Malvern, England). Agitation and extraction were performed using an UXI orbital shaker (Huxi, China). The concentration of the DNA solution was determined using a UV-1600PC ultraviolet-visible (UV-Vis) spectrophotometer (XIPU, China). The obtained MnO2 nanosheets were dried using an XMTD-8222 vacuum dryer (Jinghong, China). The obtained DES-MnO2 was dried in a ZX-LGJ-1 A freeze dryer (Zhixin, China).
## Preparation of DES
Four types of DESs (ChCl/HFIP, L-carnitine/HFIP, TBAC/HFIP, and CTAB/HFIP) were synthesized by stirring a designed amount of HBAs and HFIP in a 150 mL thick-walled pressure-resistant flask at an appropriated temperature until a homogeneous transparent liquid was formed. After optimization, a DES composed of ChCl/HFIP at a 1:3 molar ratio was prepared.
## Preparation of MnO2 nanosheets
MnO2 nanosheets were synthesized according to a previous reported method [35, 36]. 20 mg KMnO4 was accurately weighed and transferred to a 50 mL conical flask, dissolved in 18 mL of DI water, and stirred for 1 h at room temperature. Subsequently, 60 mg of CTAB was added to the flask and stirred continuously until a stable emulsion was formed. Next, 2 mL of 0.1 mol/L MES was poured into the mixture and reacted for 6 h. Finally, the MnO2 nanosheets were washed three times with DI water. After centrifuging at 12,000 rpm for 5 min, the MnO2 nanosheets were dried at 60 °C under vacuum.
## Preparation of DES/MnO2
DES/MnO2 was prepared using a previously reported method [14] with some modifications. Briefly, 20 mg of MnO2 nanosheets were dispersed in 2 mL of methanol and 0.5 mL of synthesized ChCl/HFIP DES and the mixture was ultrasonicated for 2 h at room temperature. The resulting solution was centrifugated at 5000 rpm for 10 min and washed three times with methanol. Finally, the DES/MnO2 solid was collected by vacuum freeze-drying.
## Colorimetric reaction of DES/MnO2 and TMB
50 µL of TMB (2 mg/mL) was dissolved in 1800 µL NaAc (pH 4.0). Subsequently, 150 µL DES/MnO2 of different concentrations were added to this above mixed solution and shaken on an incubator shaker for 30 min at room temperature. Finally, the resulting solution was measured at 652 nm by UV-Vis spectrometer.
## Colorimetric determination of DNA concentration
Next, 150 µL of DES/MnO2 (0.1 mg/mL) was added to 1750 µL NaAc (pH 4.0) aqueous solution. Therefore, 50 µL of DNA solutions with different concentrations was added to the mixed solution. After the addition of 50 µL TMB (2 mg/mL), the mixture was shaken for 30 min at room temperature. Finally, the absorbance of the resulting solution was measured at 652 nm using a UV-Vis spectrometer.
An aqueous solution of 150 µL DES/MnO2 (0.1 mg/mL), 50 µL TMB (2 mg/mL), and 1850 µL NaAc (pH 4.0) was prepared to conduct selectivity experiments. Various non-specific proteins, carbohydrates, and salts were selected to replace DNA and were added to the prepared aqueous solution for the DNA selectivity test. The mixture was then shaken for 30 min at room temperature. Finally, the absorbance of the resulting solution was measured at 652 nm using a UV-Vis spectrometer.
To explore the utility of the DES/MnO2-TMB system, a colorimetric quantitative analysis of DNA was performed under optimal conditions, and a standard curve was plotted. The difference in absorbance increased with an increase in DNA concentration until it eventually reached a plateau (the image depicts the gradual lightening of the solution color) (Fig. 5). Furthermore, the absorbance difference (ΔA), where ΔA denotes the difference in absorbance of the DES/MnO2-TMB system before (A0) and after (A) the addition of DNA, exhibited a good linear relationship with DNA concentration in the range of 10–130 µg/mL, and the linear equation was $y = 2.019$x + 0.004 (R2 = 0.996). The adsorption of DNA onto the surface of DES/MnO2 was mainly attributed to electrostatic interactions and hydrogen bonding between the phosphate group of DNA and the cationic part of the DES. With the addition of DNA adsorbed on the surface of DES/MnO2, the colorimetric reaction of DES/MnO2 with TMB was inhibited [21].
Fig. 5Absorbance intensity of DES/MnO2-TMB system at different concentrations of DNA and inset show the color change photographs
## Preliminary studies
ChCl, L-carnitine, TBAC, and CTAB were selected as HBA, and HFIP was selected as the HBD. To determine the extraction potential of the proposed DESs for DNA extraction, six inorganic salts ((NH4)2SO4, K2HPO4, KH2PO4, Na2CO3, Na2HPO4, and Na2SO4) were used as phase separation inducers. A system of 0.5 mL DES (ChCl/HFIP, L-carnitine/HFIP, TBAC/HFIP, and CTAB/HFIP) and 0.8 g inorganic salts ((NH4)2SO4, K2HPO4, KH2PO4, Na2CO3, Na2HPO4, and Na2SO4) were prepared in 5 mL of aqueous solution. The molar ratio of HBAs to the HFIP was 1:2. DNA (10 µg/mL) was added to investigate the extraction performance of the two-phase system. After separating into two phases, the bottom phase was removed and detected at 260 nm using a UV detector. The extraction results are summarized in Table S1. It can be seen that the DES comprising ChCl and HFIP was suitable for DNA extraction.
Figure S1 shows the effect of the ChCl:HFIP molar ratio on DNA extraction. A system involving 0.5 mL DES with different molar ratios (1:1.5, 1:2, 1:3, and 1:4) and 0.8 g Na2SO4 was prepared in 5 mL of aqueous solution. It was clear that DNA extraction increased with the molar ratio varying from 1:1.5 to 1:3 and thereafter a declined at molar ratio of 1:4. In conclusion, a DES comprising ChCl and HFIP in a 1:3 molar ratio was suitable for DNA extraction.
## Characterization of DES and DES/MnO2
FT-IR spectra and 1 H NMR were used to characterize the synthesized DESs. As shown in Fig. S2 the stretching vibration peaks of O-H in pure HFIP and ChCl were observed at 3424 cm− 1 and 3293 cm− 1, respectively, which shifted to a lower wavenumber of 3165 cm− 1 in ChCl/HFIP. The shift of the –OH stretching vibration indicated the existence of hydrogen bonding between ChCl and HFIP. In addition, no new peaks were detected, demonstrating that no chemical reaction occurred during DES synthesis. As shown in Fig. S3, the 1 H NMR of ChCl/HFIP is as follows: δ 4.60 (s, 1 H), 4.01 (dd, 2 H), 3.52 (m, 2 H), 3.21 (d, 9 H). These results verified that the HFIP/ChCl DES was successfully synthesized.
The high-resolution TEM image of the prepared MnO2 nanosheets (Fig. 1a) revealed the presence of large two-dimensional sheet-like structures, which provided a large surface area for the reaction with TMB, a chromogenic substrate. Figure 1b shows the FT-IR characterization spectrum of the MnO2 nanosheets, DES, and DES/MnO2, where the MnO2 nanosheets exhibited a distinct band at 554 cm− 1, which was attributed to Mn-O and Mn-O-Mn. The DES/MnO2 spectrum revealed the presence of some characteristic peaks of DES, such as the absorption peaks at 2850 cm− 1 and 2920 cm− 1, attributed to C-H, and the absorption peaks at 1173 cm− 1 and 1190 cm− 1 attributed to C-O. These results indicate the successful modification of the MnO2 nanosheets by DES. TGA of the MnO2 nanosheets and DES/MnO2 (Fig. 1c) was performed to determine the mass percentages of the DES in the composites. The decomposition of the DES occurred at 225 °C with a mass loss of approximately $14\%$, indicating that the DES successfully modified the surface of the nanosheets at a grafting rate of approximately $14\%$. Figure 1d shows the high-resolution XPS profile of the DES/MnO2. The N 1s spectrum (Fig. 1e) confirmed the presence of DES. Moreover, as shown in Fig. 1f, the two characteristic peaks with binding energies of 654.16 eV and 642.68 eV were attributed to the Mn 2p$\frac{1}{2}$ and Mn 2p$\frac{3}{2}$ of MnO2, respectively. The XPS spectra also indicated the successful synthesis of DES/MnO2.
Fig. 1(a) The TEM image of MnO2 nanosheets; (b) FT-IR spectra of MnO2 nanosheets, ChCl/HFIP DES and DES/MnO2; (c) TGA of the MnO2 nanosheets and DES/MnO2; (d) The XPS full scan spectrum of DES/MnO2; (e) The XPS spectrum of N 1s; (f) The XPS spectrum of Mn 2p DLS and zeta potential measurements were used to investigate the mechanism underlying the detection of DNA by DES/MnO2. The zeta potential of the pure DNA, MnO2 nanosheets, DES/MnO2 and DNA-DES/MnO2 were recorded. As shown in Fig. 2a, the zeta potential of the MnO2 nanosheet was − 23.77 mV. After combining with DES, the zeta potential of DES/MnO2 was approximately − 19.57 mV, which is slightly higher than that of the pure MnO2 nanosheets. It was proven that the HFIP/ChCl DES was positively charged. Thus, the negatively charged DNA can bind to the DES through electrostatic interactions and thereafter adsorb onto the surface of DES/MnO2. In addition, HFIP contains a large number of hydroxyl groups and is selected as the HBD in the synthesis of DES, which can enhance the hydrogen bond interaction between DES/MnO2 and DNA. Therefore, the surface zeta potential of DNA-DES/MnO2 was − 22.9 mV, which is slightly lower than that of DES/MnO2. Figure 2b shows the DLS results. The particle size of the DES/MnO2 was approximately 342 nm. After combining with DNA, the size of the new aggregates was 459 nm, indicating that DNA-DES/MnO2 was formed.
Fig. 2Zeta potentials of DNA, MnO2, DES/MnO2 and DNA-DES/MnO2 (a), and sizes distribution of DNA, DES/MnO2 and DNA-DES/MnO2 (b)
## Measurement of the DES/MnO2 oxidase activity
TMB was selected as the substrate to investigate the oxidase activity because DES/MnO2 possess an oxidase-like activity and can directly oxidize TMB into oxidized TMB (oxTMB). Figure 3a shows neither a significant absorption peak (red) for DES/MnO2 nor a significant absorption peak for TMB from 400 to 800 nm (blue). However, owing to the oxidase-like activity of the MnO2 nanosheets, a deep blue color (characteristic absorption peak at 652 nm) was observed upon the binding of DES/MnO2 with TMB owing to the oxidation of the colorless TMB.
To verify the catalytic activity of DES/MnO2 further, different concentrations of DES/MnO2 (0–30 µg/mL) were reacted with TMB. The absorbance gradually increased with increasing DES/MnO2 concentration (Fig. 3b). However, the absorption intensity decreased when the concentration of DES/MnO2 was higher than 22 µg/mL, because TMB or oxTMB may have been denatured. Figure 3b shows a series of color changes. Furthermore, the absorbance signal increased linearly with an increase in the DES/MnO2 concentration in the range of 0–18 µg/mL, and the linear regression had an equation of $y = 0.128$x + 0.115 (R2 = 0.996).
Fig. 3UV absorption spectra of DES/MnO2 (line 1), TMB (line 2), DES/MnO2 + TMB (line 3) and inset show the corresponding solution color (a); the absorbance intensity of DES/MnO2-TMB system at different concentrations of DES/MnO2 and inset shows the corresponding visual changes in color (b). All the error bars were calculated by three independent experiment ($$n = 3$$) The colorimetric reaction of the DES/MnO2 composites with TMB under different pH conditions was thereafter evaluated (Fig. 4), and the strongest absorbance response was detected at pH 4.0, which was selected as the optimal pH.
Fig. 4Effect of the pH
## Specificity
To investigate the specificity of this method for DNA detection, the absorption spectral response of the DES/MnO2-TMB system to various interfering substrates (non-specific proteins, carbohydrates, and salts) was studied (Fig. 6). The first column shows the absorption intensities of the DES/MnO2-TMB system without the addition of DNA or other interfering substances. RNA had a greater effect on the absorption intensity, whereas proteins such as bovine serum albumin (BSA), hemoglobin, and cytochrome C had a weaker effect. This is primarily because RNA has a structure similar to that of DNA, resulting in a similar inhibitory effect. Consequently, when testing samples containing both DNA and RNA, masking or pre-treatment steps are required.
Fig. 6Effect of interfering factors on DES/MnO2 + TMB and inset show the color change photographs of DES/MnO2-TMB system with different interfering factors
## Application to real samples
To evaluate the viability of our designed assay for practical applications, DES/MnO2 was used to detect DNA in bovine serum. Different concentrations of DNA standard solution were spiked into the bovine serum samples to examine the recovery. The analytical results are summarized in Table 1. The recoveries were within the range of 102.73-$107.08\%$ for the three known concentrations of added DNA, and the relative standard deviation (RSD) was less than $3.63\%$. These results demonstrate the potential application of the proposed colorimetric method for the detection of DNA in real samples.
Table 1Determination of DNA in real sample of bovine serum ($$n = 3$$)Added DNA (µg/mL)Detected DNA (µg/mL)Recovery (%)RSD (%)20.0021.42107.083.6360.0061.29102.162.40100.00102.73102.732.04
## Conclusion
Herein, we report the synthesis of a DES/MnO2 composite that efficiently catalyzes TMB. The composition and molar ratio of DESs were evaluated and DES composed of ChCl and HFIP with molar ratio of 1:3 was suitable for DNA extraction. The addition of DNA to the system significantly inhibited the colorimetric reaction and reduced the absorbance of DES/MnO2-TMB owing to hydrogen bonding and electrostatic interactions between DNA and the DES. Consequently, a colorimetric method based on DES/MnO2 was developed to quantify the DNA concentration. This method exhibited good linearity and specificity and could be used to determine DNA concentration in a simple and rapid manner. Consequently, it exhibits potential for application in DNA detection.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Table S1 The extraction efficiency of DESs with inorganic salts for the DNA extraction. Fig. S1 The effect of ChCl:HFIP molar ratio on DNA extraction. Fig. S2 FT-IR spectra of ChCl/HFIP DES. Fig. S3 1H NMR spectra of ChCl/HFIP DES. Table S2 *The data* for Fig. 6.
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|
---
title: 'Depression, cardiometabolic disease, and their co-occurrence after childhood
maltreatment: an individual participant data meta-analysis including over 200,000
participants'
authors:
- Camille Souama
- Femke Lamers
- Yuri Milaneschi
- Christiaan H. Vinkers
- Serena Defina
- Linda Garvert
- Frederike Stein
- Tom Woofenden
- Katharina Brosch
- Udo Dannlowski
- Henrike Galenkamp
- Ron de Graaf
- Vincent W. V. Jaddoe
- Anja Lok
- Bas B. van Rijn
- Henry Völzke
- Charlotte A. M. Cecil
- Janine F. Felix
- Hans J. Grabe
- Tilo Kircher
- Karim Lekadir
- Margreet ten Have
- Esther Walton
- Brenda W. J. H. Penninx
journal: BMC Medicine
year: 2023
pmcid: PMC10010035
doi: 10.1186/s12916-023-02769-y
license: CC BY 4.0
---
# Depression, cardiometabolic disease, and their co-occurrence after childhood maltreatment: an individual participant data meta-analysis including over 200,000 participants
## Abstract
### Background
Childhood maltreatment is associated with depression and cardiometabolic disease in adulthood. However, the relationships with these two diseases have so far only been evaluated in different samples and with different methodology. Thus, it remains unknown how the effect sizes magnitudes for depression and cardiometabolic disease compare with each other and whether childhood maltreatment is especially associated with the co-occurrence (“comorbidity”) of depression and cardiometabolic disease. This pooled analysis examined the association of childhood maltreatment with depression, cardiometabolic disease, and their comorbidity in adulthood.
### Methods
We carried out an individual participant data meta-analysis on 13 international observational studies ($$n = 217$$,929). Childhood maltreatment comprised self-reports of physical, emotional, and/or sexual abuse before 18 years. Presence of depression was established with clinical interviews or validated symptom scales and presence of cardiometabolic disease with self-reported diagnoses. In included studies, binomial and multinomial logistic regressions estimated sociodemographic-adjusted associations of childhood maltreatment with depression, cardiometabolic disease, and their comorbidity. We then additionally adjusted these associations for lifestyle factors (smoking status, alcohol consumption, and physical activity). Finally, random-effects models were used to pool these estimates across studies and examined differences in associations across sex and maltreatment types.
### Results
Childhood maltreatment was associated with progressively higher odds of cardiometabolic disease without depression (OR [$95\%$ CI] = 1.27 [1.18; 1.37]), depression without cardiometabolic disease (OR [$95\%$ CI] = 2.68 [2.39; 3.00]), and comorbidity between both conditions (OR [$95\%$ CI] = 3.04 [2.51; 3.68]) in adulthood. Post hoc analyses showed that the association with comorbidity was stronger than with either disease alone, and the association with depression was stronger than with cardiometabolic disease. Associations remained significant after additionally adjusting for lifestyle factors, and were present in both males and females, and for all maltreatment types.
### Conclusions
This meta-analysis revealed that adults with a history of childhood maltreatment suffer more often from depression and cardiometabolic disease than their non-exposed peers. These adults are also three times more likely to have comorbid depression and cardiometabolic disease. Childhood maltreatment may therefore be a clinically relevant indicator connecting poor mental and somatic health. Future research should investigate the potential benefits of early intervention in individuals with a history of maltreatment on their distal mental and somatic health (PROSPERO CRD42021239288).
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12916-023-02769-y.
## Background
Childhood maltreatment is a major public health concern [1]. Robust evidence shows that childhood maltreatment is associated with a twofold to threefold increased risk of depression in adulthood [2, 3], and this association is likely causal [4]. Depression risk is increased after experiencing any type of maltreatment, although emotional abuse and neglect seem to be particularly strong predictors [5]. The effect of childhood maltreatment on depression has also been suggested to be sex-specific, with stronger associations in females than in males [6], yet evidence is limited.
Beyond mental health, childhood maltreatment is also linked to cardiometabolic diseases in adulthood. Exposure to maltreatment in childhood is associated with an increased incidence of cardiovascular diseases (incidence rate ratio = 1.71) and type 2 diabetes (incidence rate ratio = 2.13) [7]. There seems to be a dose–response relationship; the greater the number of experienced maltreatment types, the higher the risk of cardiometabolic diseases [8]. Although this association is mostly similar across sexes, emotional neglect appears more strongly related to cardiovascular disease in females [9].
Extensive evidence shows that cardiometabolic disease and depression co-occur and are bidirectionally linked [10, 11]. Meta-analyses indicate that $29\%$ of patients with myocardial infarction [12] and 18–$32\%$ of those with diabetes have comorbid depression [13]. Depressed individuals also have a 64–$80\%$ higher risk of developing cardiovascular disease [14, 15]. The high risk of co-occurrence, or comorbidity, is possibly explained by common underlying mechanisms. Childhood maltreatment could be a shared risk factor prompting a cascade of mechanisms leading to these diseases. In fact, depression and cardiovascular disease have a shared genetic vulnerability [16], possibly associated to biological pathways [17] such as inflammation, hypothalamic–pituitary–adrenal (HPA) axis dysregulations, and dysfunction of the autonomic nervous system; and behavioral pathways such as physical inactivity, unhealthy diet, smoking, and drinking, that may be further stimulated by childhood maltreatment, giving rise to more comorbidity between depression and cardiometabolic diseases.
Despite this evidence, no one has yet directly compared the increase in depression prevalence with the increase in cardiometabolic disease prevalence after childhood maltreatment. Investigating the associations of childhood maltreatment with depression and cardiometabolic disease in the same samples with harmonized variable definitions and a uniform handling of covariates is novel and enables the comparison of effect sizes. Additionally, although childhood maltreatment potentially activates biological and behavioral risk pathways that are shared for depression and cardiometabolic disease, no one has yet established whether childhood maltreatment is associated with the comorbidity of these diseases in adulthood. Because comorbid depression and cardiometabolic disease involve a heavier disease burden and mortality than each disease individually [18], characterizing the association with comorbidity would facilitate efforts to develop appropriate and efficient approaches to this major public health issue.
In this study, we conducted a large-scale individual participant data (IPD) meta-analysis to investigate the association of childhood maltreatment with depression, cardiometabolic disease and their comorbidity in adults. We also explored the role of childhood maltreatment type, lifestyle factors, and sex in these associations.
## Cohorts and participants
This IPD meta-analysis is an effort of the EarlyCause consortium [19], which investigates the association between early-life stress and comorbid depression and cardiometabolic outcomes. Studies within and outside the consortium were selected based on the consortium network. Study inclusion criteria were as follows: having retrospective reports on childhood maltreatment (at least physical and emotional abuse) before the age of 18 and having data on depression and/or cardiometabolic diseases in adulthood. Studies were excluded if participants were younger than 18 years at assessment time. In total, 13 studies from Germany, the Netherlands, the UK, and the USA were included.
Three studies were case–control studies with an overrepresentation of individuals with affective disorders: the Marburg-Münster Affective Disorders Cohort Study (MACS) [20], the Netherlands Study of Depression and Anxiety (NESDA) [21], and the Netherlands Study of Depression in Older persons (NESDO) [22]. Ten studies were population-based cohort studies: the mothers and partners of the Avon Longitudinal Study of Parents and Children (ALSPAC, see Additional file 1: Sect. 1) [23–25], the mothers of Generation R Study (GenR, see Additional file 1: Sect. 2) [26], the Healthy Life in an Urban Setting (HELIUS) study [27], the first wave of the Midlife in the United States (MIDUS) study [28], the first and second Netherlands Mental Health Survey and Incidence Studies (NEMESIS-1 and NEMESIS-2) [29, 30], two independent cohorts of the Study of Health In Pomerania (SHIP-Trend baseline assessment and SHIP-Legend) [31], and the UK Biobank (UKBB) [32, 33]. Each cohort study was approved by local ethics committees and all participants provided informed consent. This research project was pre-registered on the international prospective register of systematic reviews PROSPERO in March 2021 (reference CRD42021239288). The PRISMA-IPD guidelines [34] were followed except for the systematic review-related steps which were not applicable in this research.
## Childhood maltreatment
The main exposure was the presence of childhood maltreatment in any of the following categories: physical, emotional, and/or sexual abuse, before the age of 18. Physical and emotional maltreatment were defined by the following: [1] self-reported history of regular or more frequent abuse (“often true”, “very often true”, “regularly”, “often”, or “very often” frequency ratings depending on the instrument) when a frequency assessment was available or [2] self-reported history of abuse in case of a dichotomous assessment. Sexual abuse was defined by the report of at least one occurrence of sexual abuse in childhood. Cases of childhood maltreatment were identified when criteria were met for either maltreatment type. Neglect was not included in the definition of childhood maltreatment because participating studies either did not assess physical and emotional neglect ($$n = 6$$) or assessed them in discrepant manners. Specific measures and criteria used to code childhood maltreatment (absent vs. present) in each study are described in Additional file 1: Table S1 [35–38].
## Depression
The presence of depression was defined by the following: [1] the presence of a lifetime (eight cohorts) or current (one cohort) diagnosis of major depressive disorder assessed with (semi-)structured clinical interviews or [2] current depressive symptomatology (four cohorts) assessed with self-report scales using validated clinical cut-offs. Cohort-specific measures and criteria used to identify depression cases (absent vs. present) are described in Additional file 1: Table S2 [39–48].
In sensitivity analyses, the depression definition was extended, including information on self-reported current use of antidepressants (Anatomical Therapeutic Chemical (ATC) codes starting with N06A, N06AA, N06AB, N06AF, N06AG, and N06AX) to identify additional depression cases.
## Cardiometabolic disease
The presence of cardiometabolic disease was based on self-reports of a lifetime clinical diagnosis of a non-congenital cardiovascular disease (see Additional file 1: Table S3 for a complete list) and/or diabetes mellitus (absent vs. present). Cardiovascular disease and diabetes were selected as cardiometabolic diseases because of their known co-occurrence with depression [15, 49], their high prevalence and major impact on public health [50, 51], and their consistent assessment across cohorts.
In sensitivity analyses, we additionally tested strict (limited to heart/cardiac diseases) and broad (also including blood pressure and other heart and peripheral vascular problems; see Additional File 1: Table S3 for complete list) definitions of cardiovascular disease. Furthermore, the cardiometabolic disease definition was extended, including information on self-reported current use of related medications (ATC codes C01, C03, C04, C05, C07, C08, C09, and C10) to identify additional cases of cardiometabolic disease.
## Comorbidity status
Comorbidity status was based on depression and cardiometabolic disease status. It comprised four levels: 0 = absence of depression and cardiometabolic disease (heathy controls), 1 = depression only, 2 = cardiometabolic disease only, 3 = comorbidity of depression and cardiometabolic disease.
In sensitivity analyses, the definition of comorbidity status was adjusted based on the strict (model 8) and broad (model 9) definitions of cardiovascular disease and on definitions of depression and cardiometabolic disease incorporating current medication use (model 10).
## Covariates
Sociodemographic covariates sex, age, and educational attainment were assessed at the earliest timepoint available. Sex and age were based on either self-reports or municipal registries, and educational attainment was based exclusively on self-reports. Educational attainment was harmonized across cohorts and countries by using the International Standard Classification of Education (ISCED) 2011 [52] and categorized in three levels: ISCED 0–2 corresponds to no education, early childhood education, primary and lower secondary education; ISCED 3–4 corresponds to upper secondary education and post-secondary non-tertiary education; and ISCED 5–6-7–8 corresponds to short-cycle tertiary education, bachelor, master, and doctor or equivalent levels. In addition, ethnicity was entered as a sociodemographic covariate in all analyses of HELIUS due to its design-specific oversampling of participants from different ethnic groups (Dutch, Ghanaian, Moroccan, Surinamese, and Turkish). Lifestyle covariates included self-reported current smoking status, weekly alcohol consumption, and weekly physical activity. Smoking status was assessed consistently across cohorts (current smoking vs. no current smoking). Alcohol consumption and physical exercise assessments varied across cohorts and specifications are described in Additional File 1: Table S4 [53].
## Statistical analyses
A two-step IPD meta-analysis was carried out [54]. In the first step, cohorts applied a standardized protocol for data harmonization to create the required variables and carry out statistical analyses estimating the associations between childhood maltreatment and the different outcomes. In the second step, we meta-analyzed cohorts’ aggregate effect sizes with random-effects models using inverse-variance weighting. We chose random-effects models to pool the aggregate effect sizes because these effect sizes are drawn from different populations. Cohorts with cell count < 5 across exposure and outcome categories were excluded from the meta-analyses. Heterogeneity parameters Q, I2, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\tau }^{2}$$\end{document}τ2 were calculated. Scripts of the two steps can be found on the Github EarlyCause repository (see Additional File 1: Sect. 3).
## Main models
The main models assessed the association of childhood maltreatment with (model 1) depression (vs. no depression) and (model 2) cardiometabolic disease (vs. no cardiometabolic disease) using binomial logistic regressions and (model 3) comorbidity status (absence of disease vs. depression only vs. cardiometabolic disease only vs. comorbidity) using multinomial logistic regression. Subgroup analyses were carried out to explore whether differences in cohorts’ depression assessments possibly explained effect size heterogeneity in model 3. All models were adjusted for sociodemographic covariates. Lifestyle factors were additionally included in the model to examine their impact on the association of childhood maltreatment with comorbidity status (model 4). Analyses were then stratified by sex to check the consistency of results in males and females (models 5a and 5b). Additionally, the association of types of childhood maltreatment (physical abuse, emotional abuse, and sexual abuse) with the four-level comorbidity status was investigated in a multinomial logistic multiple regression model (model 6). Finally, we examined the role of maltreatment severity by creating a new variable, number of maltreatment types (0 type vs. 1 type vs. 2 or more types of childhood maltreatment) and testing its association with comorbidity status in a multinomial logistic regression model (model 7).
## Sensitivity analyses
We carried out sensitivity analyses to check the consistency of the results obtained in the main model 3. First, we alternatively applied different definitions (strict and broad) of cardiovascular disease (models 8 and 9, respectively). Then, we extended the definition of depression and cardiometabolic disease incorporating information on the use of related medications (model 10).
Analyses were conducted on participants with complete data on childhood physical and emotional abuse, as well as on depression and/or cardiometabolic disease. For cohorts with $20\%$ or more cases with missing lifestyle values in model 4 compared to the sample used in model 3, missing lifestyle values were imputed (see Additional File 1: Sect. 4 and Table S5 for detailed explanations). For cohorts with less than $20\%$ missingness on lifestyle factors, cases with missing lifestyle values were excluded from the model. High missingness in lifestyle covariates (in particular smoking status) applied in model 4 was limited to two out of nine total cohorts. Although participants with these missing covariates represented only $2.1\%$ of the total sample size of the pooled model 4, we decided a priori to impute lifestyle covariates when their missingness caused an important loss of data since we aimed to compare estimated associations from models with (model 4) and without lifestyle covariates (model 3). The statistical software R version 4.0.5. ( packages “metafor” version 3.0–2 [55] and “meta” version 5.2–0 [56]) was used to carry out the analyses. Statistical significance level was set at $p \leq 0.05$, two-sided.
Results were similar in sensitivity analyses applied to comorbidity status model 3 when using different operational definitions of cardiovascular disease based on stricter or broader definition (models 8 and 9; Table 2) or different definitions of cardiometabolic diseases and depression additionally including information on medication (model 10; Table 2).
## Results
This study includes 13 cohorts, with a combined sample size of 217,929 participants. Weighted mean age across studies was 52.4 years. Three studies were case–control studies with a higher prevalence of depression only (weighted mean $52.4\%$) compared to the 10 population-based cohort studies (weighted mean $19.1\%$). The weighted mean prevalence of cardiometabolic disease was $5.1\%$, and the weighted mean prevalence of comorbidity was $2.1\%$. Cohort-specific information can be found in Table 1.Table 1Descriptive statistics of the participating cohortsCohortStudy typeNMean age (SD)Female (%)CM (%)Dep. only (%)Card. only (%)Comorbidity dep. and card. (%) ALSPAC, mothersPB392729.2 (4.4)100.010.816.72.50.8ALSPAC, partnersPB207632.0 (5.1)0.07.97.73.90.8GenR, mothersPB399241.4 (4.5)100.010.95.22.50.3HELIUSPB20,82044.2 (13.2)57.513.111.58.52.4MACSCC167735.3 (13.1)63.847.442.30.51.6MIDUSPB598846.7 (12.8)52.120.49.911.02.0NEMESIS-1PB706041.1 (12.2)53.315.815.72.50.4NEMESIS-2PB646944.3 (12.5)55.215.418.33.61.3NESDACC297741.9 (13.1)66.432.858.93.35.6NESDOCC50870.6 (7.3)64.829.147.88.122.6SHIP-LegendPB188257.2 (13.4)53.212.57.522.13.7SHIP-TrendPB404251.5 (15.3)51.610.99.219.34.9UKBBPB156,51155.9 (7.7)56.611.321.74.32.0Total217,929Abbreviations: N sample size, CM Childhood maltreatment, Dep Depression, Card Cardiometabolic disease, PB Population-based, CC Case-controlAge was recorded at baseline for most cohorts. Exceptions were for GenR mothers, for which age at the assessment when children were 9 years old was used; for ALSPAC partners for which age at 16 weeks of gestation was used, and for SHIP-Legend for which age at the assessment wave SHIP-Start-2 was used [31]
## Association of childhood maltreatment with depression, cardiometabolic disease, and comorbidity
The odds of having depression increased almost three folds in those with a history of childhood maltreatment compared to those without (model 1, OR [$95\%$ CI] = 2.82 [2.40; 3.30], Fig. 1A). This positive association was seen in all cohorts, albeit with significant heterogeneity across studies ($Q = 70.44$, $p \leq .001$, I2 = $91.8\%$, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\tau }^{2}$$\end{document}τ2 = 0.07). Adults with a history of childhood maltreatment, as compared to those without, were also more likely to have a cardiometabolic disease (model 2, OR [$95\%$ CI] = 1.34 [1.23; 1.46], Fig. 1B). Effect sizes were relatively homogeneous across studies ($Q = 18.09$, $$p \leq 0.113$$, I2 = $27.1\%$, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\tau }^{2}$$\end{document}τ2 = 0.01). The results of all meta-analyzed models are shown in Table 2.Fig. 1Forest plots of the random-effects models of the association of childhood maltreatment with depression (A) and cardiometabolic disease (B). CM, childhood maltreatment. Dep., depression. Card., cardiometabolic disease. OR, odds ratio. CI, confidence interval. Note. Squares represent effect sizes of individual studies. Their size reflects the precision of the estimate based on the random-effect model. The diamond represents the pooled effect size across studies in the center of the diamond, and the lower and upper $95\%$ confidence interval limits at the left and right side of the diamondTable 2Overview of results of meta-analyzed modelsModel specificationkOutcome levelsnPooled OR [$95\%$ CI]Main modelsBinomial regressions1. Association of childhood maltreatment with depressiona13No depression115,959refDepression39,9102.82 [2.40; 3.30]2. Association of childhood maltreatment with cardiometabolic diseasea13No cardiometabolic disease162,511refCardiometabolic disease15,4211.34 [1.23; 1.46]2a. Outcome: diabetes mellitus112No diabetes168,943refDiabetes73301.35 [1.19; 1.53]2b. Outcome: cardiovascular diseasea11No cardiovascular disease165,502refCardiovascular disease92161.39 [1.26; 1.54]Multinomial logistic regressions3. Association of childhood maltreatment with comorbidity statusa9Healthy controls99,191refDepression only34,3602.68 [2.39; 3.00]Cardiometabolic disease only90511.27 [1.18; 1.37]Comorbidity35653.04 [2.51; 3.68]4. Model 3 + adjustment of lifestyle factorsb9Healthy controls90,166refDepression only30,5442.57 [2.28; 2.89]Cardiometabolic disease only81811.25 [1.15; 1.36]Comorbidity31752.99 [2.46; 3.63]5a. Model 3 in males onlya7Healthy controls46,327refDepression only10,4022.92 [2.51; 3.40]Cardiometabolic disease only53141.13 [1.01; 1.27]Comorbidity15822.90 [2.37; 3.53]5b. Model 3 in females onlya8Healthy controls49,571refDepression only23,1672.59 [2.30; 2.91]Cardiometabolic disease only34381.41 [1.26; 1.57]Comorbidity19423.49 [2.79; 4.36]6. Model 3 with predictorsa = Physical abuse6Healthy controls88,347crefDepression only32,198c1.53 [1.41; 1.66]Cardiometabolic disease only8481c1.45 [1.24;1.71]Comorbidity3411c2.06 [1.78; 2.39]Emotional abuseHealthy controls88,347crefDepression only32,198c2.87 [2.56; 3.22]Cardiometabolic disease only8481c1.29 [1.11; 1.49]Comorbidity3411c[2.16; 3.83]Sexual abuseHealthy controls88,347crefDepression only32,198c1.66 [1.56; 1.76]Cardiometabolic disease only8481c1.09 [1.01; 1.17]Comorbidity3411c1.60 [1.19; 2.15]7. Model 3 with cumulation of maltreatment types as predictora = 1 type (vs. 0 type)6Healthy controls88,347crefDepression only32,198c2.32 [2.22; 2.41]Cardiometabolic disease only8482c1.17 [1.07; 1.28]Comorbidity3411c2.37 [1.87; 3.01]2 or more types (vs. 0 type)Healthy controls88,347crefDepression only32,198c5.14 [3.93; 6.72]Cardiometabolic disease only8482c1.83 [1.49; 2.26]Comorbidity3411c5.96 [3.59; 9.90]Sensitivity analyses—multinomial logistic regressions8. Model 3 with outcome = comorbidity status based on strict definition of cardiovascular diseasea9Healthy controls101,203refDepression only35,2792.66 [2.37; 2.98]Cardiometabolic disease only70301.28 [1.18; 1.40]Comorbidity26433.09 [2.54; 3.75]9. Model 3 with outcome = comorbidity status based on broad definition of cardiovascular diseasea11Healthy controls81,817refDepression only28,6122.84 [2.41; 3.35]Cardiometabolic disease only27,1781.11 [1.06; 1.17]Comorbidity10,3643.00 [2.69; 3.36]10. Model 3 with outcome = comorbidity status based on medication intake in addition to reports of diagnosesa7Healthy controls78,989refDepression only31,2662.83 [2.27; 3.53]Cardiometabolic disease only16,8961.13 [1.00; 1.26]Comorbidity65172.76 [2.28; 3.35]aMinimal adjustment: correction for age, sex, and education levelbMinimal + additional adjustment: correction for age, sex, education level, smoking status, alcohol consumption, and physical activitycThese sample sizes are specific to each outcome level and across all predictors and predictor levelsAbbreviations: k number of studies included, n sample size used in model, OR Odds ratio, CI Confidence interval, ref outcome reference category in logistic regressionsAn overview of the cohorts included in each meta-analyzed model can be found in Additional file 1: Table S6 *The analysis* of the association of childhood maltreatment with comorbidity status (model 3) was restricted to nine cohorts ($$n = 146$$,167) due to crosstab cells with less than five cases for some outcomes in four cohorts. In these nine cohorts, adults with a history of childhood maltreatment had twice higher odds of depression only (OR [$95\%$ CI] = 2.68 [2.39; 3.00], Fig. 2) and also higher odds of cardiometabolic disease only (OR [$95\%$ CI] = 1.27 [1.18; 1.37], Fig. 2). The effect size for cardiometabolic disease was around half of the effect size for depression only. The strongest association was found for comorbid depression and cardiometabolic disease (OR [$95\%$ CI] = 3.04 [2.51; 3.68]), Fig. 2), and this positive association was statistically significant in most cohorts. Since the UKBB represented the largest dataset in the pooled analysis ($$n = 98$$,619, $67.5\%$ of participants, see Additional File 1: Table S7 for weights in pooled estimate), we re-ran the meta-analysis excluding the UKBB and observed that results remained largely similar (OR [$95\%$ CI] depression only = 2.67 [2.33; 3.06], OR [$95\%$ CI] cardiometabolic disease only = 1.29 [1.15; 1.44], OR [$95\%$ CI] comorbidity = 2.82 [2.43; 3.27]). These results were mostly consistent with results of the UKBB only (OR [$95\%$ CI] depression only = 2.66 [2.54; 2.78], OR [$95\%$ CI] cardiometabolic disease only = 1.26 [1.13; 1.40], OR [$95\%$ CI] comorbidity = 4.11 [3.73; 4.53]), although the odds of comorbidity seemed to be slightly higher in the UKBB than in the other cohorts. Subgroup analyses showed that results were largely unaffected by depression assessment type (see Additional File 1: Table S8) and by current vs. lifetime depression (see Additional File 1: Sect. 5). Additionally, we exploratively ran a post hoc test to evaluate whether childhood maltreatment was more strongly associated with comorbidity than with the individual diseases. Since the UKBB was the largest sample for which we had direct access to the individual-level data, we used that sample to calculate the following two odds ratios after childhood maltreatment: depression only vs. comorbidity and cardiometabolic disease only vs. comorbidity. Instead of using the outcome level “absence of depression and cardiometabolic disease” as reference category as in the previous calculations of odds ratios, we used the outcome level “comorbidity” as new reference category to statistically test whether childhood maltreatment was more strongly associated with comorbidity than with the single diseases. We found that the association of childhood maltreatment with depression only (OR [$95\%$ CI] = 0.65 [0.59; 0.71]) and cardiometabolic disease only (OR [$95\%$ CI] = 0.31 [0.27; 0.35]) were significantly smaller than with comorbidity. Fig. 2Forest plot of the random-effects model of the association of childhood maltreatment with depression only, cardiometabolic disease only, and comorbidity. Note. Number of cases, weights and odds ratios of each cohort can be found in Additional file 1: Table S7. OR: odds ratio. CI, confidence interval. Note. Squares represent effect sizes of individual studies. Their size reflects the precision of the estimate based on the random-effect model. The diamond represents the pooled effect size across studies in the center of the diamond, and the lower and upper $95\%$ confidence interval limits at the left and right side of the diamond Additional adjustment for smoking status, alcohol consumption, and physical activity (model 4) did not substantially change the associations of childhood maltreatment with depression only (OR [$95\%$ CI] = 2.57 [2.28; 2.89]), cardiometabolic disease only (OR [$95\%$ CI] = 1.25 [1.15; 1.36]), and comorbidity (OR [$95\%$ CI] = 2.99 [2.46; 3.63]), highlighting the independence of these associations from lifestyle factors. Similar associations to those obtained in the main model 3 were observed for males (model 5a) and females (model 5b). However, the association with cardiometabolic disease only seemed stronger in females than in males (OR [$95\%$ CI] in females = 1.41 [1.26; 1.57], in males = 1.13 [1.01; 1.27], with OR difference $z = 2.77$, $$p \leq .006$$; Table 2, models 5a and 5b). All types of childhood maltreatment (physical, emotional, and sexual abuse) were associated with increased odds of developing depression only, cardiometabolic disease only, and comorbidity, although physical and emotional abuse were particularly strong predictors of the comorbidity (Table 2, model 6). Finally, although all numbers of maltreatment types were related to comorbidity status, there seemed to be a dose–response relationship: When two or more types of childhood maltreatment were experienced, the odds of (comorbid) depression and cardiometabolic disease exceeded the odds found after one type of maltreatment only (Table 2, model 7).
## Association of childhood maltreatment with (comorbid) depression and cardiometabolic disease
This study used data from 13 international cohorts involving 217,929 persons to systematically investigate the association of childhood maltreatment with (comorbid) depression and cardiometabolic disease in adulthood. In order to obtain a consistent set of aggregate data across cohorts, individual participant data were harmonized and cohort-level analyses were standardized. Main findings show that adults with a history of childhood maltreatment, as compared to those without, are 1.27 times more likely to have cardiometabolic disease only and 2.68 times more likely to have depression only. The largest difference between maltreated and non-maltreated individuals was found for the co-occurrence of both conditions: Maltreated individuals were 3.04 times more likely to suffer from comorbid depression and cardiometabolic disease in adulthood. Post hoc analyses showed that this association was larger than the ones for either disease alone. Results remain similar in sensitivity analyses using different outcome ascertainment definitions, suggesting findings are robust.
Our results are in line with findings from existing meta-analyses. Two relatively recent meta-analyses [2, 3] evaluated the association of childhood maltreatment history with depression and found that childhood maltreatment was associated with 2.03 ($95\%$ CI = [1.37; 3.01]) and 2.81 ($95\%$ CI = [2.35; 3.36]) increased odds of depression in adulthood, using pooled samples of 4579 [2] and 26,536 [3] participants, respectively. The current study found similar heightened odds of depression (OR [$95\%$ CI] = 2.82 [2.40; 3.30]) after childhood maltreatment, based on by far the largest sample size ($$n = 155$$,869). Additionally, although age was demonstrated to moderate this association [3], pooled effect sizes from the previous meta-analyses were either based on study-level effect sizes with inconsistent handling of covariates or on raw data excluding covariate adjustments. In contrast, the current research facilitated cohort-level analyses in a systematic manner, enabling the estimation of pooled effect sizes adjusting for important sociodemographic and lifestyle covariates. Although the effect size reported in Li et al. [ 2] is slightly smaller than the one in the current study, the difference may be explained by the definition of Li et al. ’s exposure variable: Childhood maltreatment was based on official records, which are more likely to suffer from underreporting than retrospective self-reports. Despite this difference in assessment, the consistent direction of findings increase confidence in the validity of maltreatment self-reports. Further, a previous meta-analysis [57] of 29 studies ($$n = 247$$,393) showed that cumulative childhood adversity was moderately related to cardiometabolic disease in adulthood (OR [$95\%$ CI] = 1.36 [1.27; 1.46]). Although the exposure (an index including at least two adverse childhood experiences) and outcome (cardiometabolic disease including metabolic syndrome) definitions slightly differ from the ones of the current meta-analysis, results align closely (our findings: OR [$95\%$ CI] = 1.34 [1.23; 1.46]). Finally, because the current meta-analysis consistently adjusted associations for the same covariates, it provides the unique possibility to directly compare the increased odds of each disease after maltreatment. The results show that, compared to non-maltreated individuals, maltreated adults are 2.68 times more likely to suffer from depression and “only” 1.27 times more from cardiometabolic disease. Although linked to both, childhood maltreatment is therefore more strongly related to depression than to cardiometabolic disease in adulthood.
A striking result is that the odds of comorbid depression and cardiometabolic disease after childhood maltreatment (OR [$95\%$ CI] = 3.04 [2.51; 3.68]) are higher than for each disease alone. Although previous studies report that depression and cardiometabolic disease tend to co-occur, the current meta-analysis is the first study to investigate and support the relationship between childhood maltreatment and the co-occurrence of depression and cardiometabolic disease in adulthood. This association is possibly explained by the early-life stress triggering mechanisms common to both depression and cardiometabolic disease. Previous research suggests that childhood maltreatment activates interrelated biological and behavioral pathways [17] potentially leading to adverse health outcomes. Because childhood maltreatment occurs during a critical period for brain neuroplasticity, it may dysregulate stress-related neural circuits [58, 59]. Longitudinal studies show that childhood maltreatment is associated with structural and functional neural changes [60]. Among others, these changes may subsequently dysregulate neuroendocrine and immune systems. The HPA axis may be hyper- or hypo-activated due to impaired glucocorticoid receptor function and inflammation levels may be elevated [1, 61]. Although behavioral pathways are also hypothesized to contribute to poor health outcomes in people with childhood maltreatment [17], our results show that the associations of childhood maltreatment with comorbidity status survive adjustment for smoking, alcohol consumption and physical activity; suggesting that the increased likelihood of (comorbid) depression and cardiometabolic disease after maltreatment does not exclusively depend on one’s lifestyle. Additionally, other non-biological factors (i.e., disease severity, age at diagnosis, working conditions) may also explain the strong association observed between childhood maltreatment and comorbidity and should be investigated. Lastly, the higher odds of comorbidity than single diseases after childhood maltreatment may be explained by the fact that depression and cardiometabolic disease have a bidirectional feedforward loop [10]. Both diseases likely magnify each other in a reinforcing vicious cycle, which is further stimulated by childhood maltreatment and its related biological, psychosocial and behavioral consequences.
## Differential effects of sex and maltreatment types
We carried out additional analyses to explore how the associations between childhood maltreatment and (comorbid) depression and cardiometabolic disease varied across sex and maltreatment types. Both in males and females, childhood maltreatment was associated with more (comorbid) depression and cardiometabolic disease. Associations between childhood maltreatment and comorbidity status were mostly similar across males and females. However, females showed a slightly stronger association than males for cardiometabolic disease only (males: OR [$95\%$ CI] = 1.13 [1.01; 1.27], females: OR [$95\%$ CI] = 1.41 [1.26; 1.57]). Evidence from the literature on that matter is inconsistent [9, 57], and conclusions should therefore be drawn carefully. Further analyses were carried out to test the relationship between maltreatment types and (comorbid) depression and cardiometabolic disease. Because multi-type maltreatment is common [62], the different maltreatment types were entered as multiple predictors within the same model to obtain average estimates of the association between each maltreatment type with comorbidity status while accounting for the co-occurring experience of other types of maltreatment. Findings show that all maltreatment types were independently associated with (comorbid) depression and cardiometabolic disease. Zooming in on specific outcomes, depression only was particularly strongly associated with emotional abuse. Cardiometabolic disease only and comorbidity were particularly strongly associated with physical and emotional abuse. Previous research findings support our results: Physical and emotional abuse are stronger predictors of depression and cardiovascular disease than sexual abuse [3, 9]. Alternatively, the estimated associations of sexual abuse with the disease outcomes may be harder to detect because of the relatively low prevalence of sexual abuse compared to the other types of abuse [63] or because milder forms of sexual abuse are picked up, for instance from the population-based studies. Lastly, findings endorse a dose–response relationship of childhood maltreatment severity—here operationalized as the number of maltreatment types—with (comorbid) depression and cardiometabolic disease. This converges with previous evidence on various health outcomes [64, 65].
## Strengths and limitations
This study has several strengths. First, the meta-analysis gathered 13 international cohorts including 217,929 individuals from European countries and the USA. Second, the systematic methodology used with the two-step individual participant data design has essential advantages [54]. It enables the standardization of analyses across studies (i.e., harmonization of variables and consistent covariate adjustment of estimates) and increases the quality of aggregate data entering the meta-analysis. Third, the variety of cohorts involved (e.g., case–control and population-based studies; cohort oversampling persons with migration background) and comprehensiveness of the analyses carried out (e.g., sensitivity analyses with different outcome definitions, effects of different maltreatment types, stratified analyses across sex) strengthens the robustness of the findings across settings.
This study also has limitations. In some cohorts, especially those with younger samples, the prevalence of comorbid depression and cardiometabolic disease was low (weighted mean $2.1\%$), leading to some studies being excluded of the multinomial regression analyses due to small cell count. This is likely because cardiometabolic events usually happen in later life [66]. The prevalence of comorbidity increases with age as seen in the oldest cohort NESDO with the highest rate of comorbidity ($22.6\%$). The relatively high average age across cohorts (52.4 years old) may have thereby facilitated finding existing associations. Despite the difference in outcome prevalence across cohorts of different ages, the associations found for depression and cardiometabolic disease are consistent across younger (e.g., ALSPAC mothers and partners) and older (e.g., NESDO and SHIP-Legend) cohorts. Therefore, there is no obvious indication of a differential effect of age. An alternative explanation for the low prevalence of comorbidity may be survival bias where patients with severe depression and cardiometabolic disease have died or are too ill to participate in the studies. Nevertheless, even after excluding studies with too few comorbidity cases from the multinomial regression analyses, the total sample used to investigate the association with comorbidity status still amounted to 146,167 individuals. Another limitation is that meta-analyzed associations of childhood maltreatment with depression and comorbidity showed non-negligible heterogeneity. However, we used random-effect models which, by definition, assume the included studies have different true effect sizes, and thereby account for heterogeneity in calculating pooled estimates. The heterogeneity could not be explained by differences in depression assessment but other factors could have possibly caused this divergence (e.g., study design, age, cultural differences in stigma reporting childhood maltreatment) and should be further investigated. Additionally, as with every assessment type, the reliance on self-reports has its set of limitations. Cardiometabolic diseases were assessed with self-reported diagnoses, which may be perceived as biased. However, previous research show that cardiometabolic disease assessment (self-reports vs. medical records) does not influence the association found between childhood maltreatment and cardiometabolic disease [9]. Childhood maltreatment was also assessed with self-reports. It has been suggested that depression may negatively bias someone’s recall of their childhood experiences [67] in which case, self-reports may spuriously inflate the association found between childhood maltreatment and depression as well as comorbidity. Recent evidence from published and unpublished research [67, 68] highlights the marginal susceptibility of maltreatment self-reports to negative recall bias as well as their temporal stability irrespective of depression diagnosis. In order to test this in the current study, we compared the associations found in population-based cohorts using lifetime vs. current depression assessments (see Additional file 1: Sect. 5) and found no evidence of negative recall bias. In addition, analyses were exclusively carried out on individuals with available data on childhood maltreatment. This may have introduced some bias as maltreatment non-response might be associated with the disease outcomes [8]. Moreover, the current study’s assessment of maltreatment was limited to experiences of abuse because neglect was assessed so differently across cohorts that we could not harmonize. Yet, childhood neglect is an important early-life stressor potentially affecting depression and cardiometabolic outcomes in adulthood and should be investigated in future studies. Furthermore, although the current study focusses on the comorbidity of depression with cardiovascular disease and diabetes, other comorbid psychiatric and somatic diseases may also be activated by early-life stress pathways and warrant further investigation. Another limitation concerns the fact that the current study did not test the role of maltreatment timing. Although a recent meta-analysis shows no evidence of consistent sensitive periods of childhood maltreatment linked to various health outcomes [69], future studies with detailed timing information are needed to determine with more certainty whether timing of childhood maltreatment exposure matters for (comorbid) depression and cardiometabolic disease. Finally, a last limitation concerns the unknown timeline of events. Although depression and cardiometabolic disease likely have their onset after—and we believe are caused by mechanisms stemming from—childhood maltreatment, the current study only articulates associations and does not inform about causality.
## Implications
The current findings have clinical implications. First, our results raise awareness on the association between early-life stress and distal psychiatric and somatic health. Second, this study may be a first step in the process of preserving the health of individuals with a history of childhood maltreatment. If future evidence supports that childhood maltreatment triggers a cascade of mechanisms leading to (comorbid) depression and cardiometabolic disease, early intervention could prevent the dysregulation of biological stress systems and preserve the health of individuals with a history of childhood maltreatment. For instance, standard psychotherapy has been shown to effectively reduce depression severity in individuals with a history of childhood maltreatment [70]. One could therefore consider providing trauma-focused psychotherapy to help victims of maltreatment process the stress evoked by the trauma, or pharmacotherapy aiming to regulate biological stress systems, subsequently promoting somatic and mental health. In addition to individual interventions, societal action is an opportunity to prevent these comorbid diseases. Recent influential work emphasizes that promoting fair distribution of income, protecting work conditions, fostering gender equity, decreasing discrimination, and improving social cohesion/support have a great potential to prevent early-life stress, and in turn (comorbid) depression and cardiometabolic disease [71, 72].
## Conclusions
In sum, adults with a history of childhood maltreatment are more likely to suffer from depression and cardiometabolic disease than those without a history of childhood maltreatment. Notably, childhood maltreatment is more strongly associated with the comorbidity of the two diseases than with each disease alone suggesting shared mechanisms. Since childhood maltreatment appears to be a relevant indicator linking poor mental and somatic adult health, the findings emphasize the need for the fields of pediatrics, psychiatry, cardiology, and endocrinology to collaborate in efforts to improve health outcomes.
## Supplementary Information
Additional file 1. Section 1. ALSPAC participants. Section 2. GenR additional information. Section 3. R-script of analyses. Section 4. Imputation of lifestyle variables. Section 5. Associations with current vs. lifetime depression diagnoses. TableS1. Childhood maltreatment assessment overview. Table S2. Depressionassessment overview. Table S3. Definition of cardiovascular disease. Table S4. Alcohol consumption and physical activity assessment overview. Table S5. Pooled associations of childhood maltreatment with comorbidity status after adjusting for lifestyle factors (model 4), according to three different imputation strategies. Table S6. Overview of cohorts included in each meta-analyzed model. Table S7. Number of cases, weights and odds ratios of thecohorts in meta-analyzed model 3. Table S8. Results of meta-analyzed model 3 per subgroup of studies based on depression assessment type.
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|
---
title: A retrospective cohort study of tamoxifen versus surgical treatment for ER-positive
gynecomastia
authors:
- Weili He
- Weidong Wei
- Qing Zhang
- Rongzhao Lv
- Shaohua Qu
- Xin Huang
- Juan Ma
- Ping Zhang
- Hening Zhai
- Ningxia Wang
journal: BMC Endocrine Disorders
year: 2023
pmcid: PMC10010038
doi: 10.1186/s12902-023-01310-9
license: CC BY 4.0
---
# A retrospective cohort study of tamoxifen versus surgical treatment for ER-positive gynecomastia
## Abstract
### Background
Gynecomastia is a common condition in clinical practice. The present study aimed to review the clinical data of ER-positive gynecomastia patients treated by tamoxifen (TAM) versus surgery and discussed the clinical effects of the two treatment strategies.
### Method
We retrospectively collected the clinical indicators of patients with unilateral or bilateral gynecomastia who received treatment at our hospital between April 2018 and December 2021. Depending on the treatment received, the patients were divided into TAM and surgery groups.
### Result
A total of 170 patients were recruited, including 91 patients in TAM group and 79 patients in surgery group. The age of the patients differed significantly between the TAM and surgery groups ($P \leq 0.01$). The estrogen level was closer in patients with stable and progressive disease, but significantly different in patients of glandular shrinkage in TAM group ($P \leq 0.01$). The proportion of patients achieving stable disease was higher among those with clinical grade 1–2. Among patients classified as clinical grade 3, the proportion of patients achieving glandular shrinkage of the breast was higher after TAM treatment ($P \leq 0.05$). The age and length of hospital stay were significantly different in patients undergoing open surgery than minimally invasive rotary cutting surgery and mammoscopic-assisted glandular resection ($P \leq 0.01$). Patients had significantly different complications including mild postoperative pain, hematoma, nipple necrosis, nipple paresthesias and effusions among the surgery subgroups (all $P \leq 0.05$). The estrogen level and the type of surgery were significantly different between the surgical recurrence and non-recurrence subgroups ($P \leq 0.05$). The difference in the thickness of glandular tissues upon the color Doppler ultrasound also reached a statistical significance between the two groups ($$P \leq 0.050$$). An elevated estrogen level was a factor leading to TAM failure. Among surgical patients, the thickness of glandular tissues, estrogen level, and type of surgery performed were risk factors for postoperative recurrence (all $P \leq 0.05$).
### Conclusion
Both treatment strategies can effectively treat gynecomastia, but different treatment methods can benefit different patients. TAM treatment is more beneficial than surgery for patients who cannot tolerate surgery, have a low estrogen level, and are clinical grade 1–2. Surgery treatment is better than TAM for patients of clinical grade 3. Different surgery options may lead to different complications. Patients with a greater glandular tissue thickness and a higher estrogen level were shown to have a higher risk of recurrence.
## Background
Gynecomastia is a common medical condition characterized by abnormal development of breast tissues and abnormal hyperplasia of breast connective tissues in men, which results from an estrogen and androgen imbalance due to physiologic or pathologic factors. Gynecomastia can occur unilaterally or bilaterally, typically presenting with abnormal enlargement of the breasts in men [1]. About $25\%$ of boys and men have physiologic gynecomastia, especially teenage boys. Physiologic gynecomastia is usually benign [1–3]. Pathologic gynecomastia is usually caused by a hormonal imbalance, medications, endocrine diseases, chronic conditions, or systemic diseases [1, 4]. Estrogen level fluctuation or poor estrogen metabolism is an important factor affecting breast health in men [5]. Oral estrogen receptor antagonists, such as tamoxifen (TAM) and clomiphene, are common agents for gynecomastia treatment and can relieve breast pain and hyperplasia [6, 7]. Medications closely related to gynecomastia include spironolactone, human growth hormone (hGH), estrogen, human chorionic gonadotrophin (hCG), anti-androgens, and GnRH analogues [8]. These medications should be used carefully in the clinic for the sake of its close regulations of the human body. Chronic health conditions, including thyroid disorders, hypogonadism, and renal insufficiency, are also common causes of gynecomastia [1, 4]; however, if gynecomastia is secondary to other diseases, the underlying diseases will need treatment.
Consolation, continuous follow-up observation, and lifestyle changes (e.g., weight loss) are usually used to control gynecomastia [1, 4, 9]. Due to psychological and cosmetic considerations, medications and surgical treatment are becoming increasingly preferred among patients [1, 4, 9]. Several studies have recommended the use of TAM, an effective agent for gynecomastia [6, 10]. In one cohort study, TAM was shown to achieve complete remission in $90\%$ of gynecomastia patients [11]. Another study recommended the use of TAM at an early stage of gynecomastia. Surgical intervention is the preferred option for gynecomastia patients with a course of disease longer than 12 months [12]. Leung et al. [ 13] recommended surgery for drug-resistant gynecomastia patients. Serretta et al. reported that TAM prevents bicalutamide-induced gynecomastia and breast pain [14]. To date, however, there have been few reports comparing the efficacy of TAM and surgical treatment for gynecomastia.
We determined the estrogen level in gynecomastia patients and compared the efficacy of TAM and surgical intervention. We also determined the expression of estrogen receptor (ER) in surgical patients and discussed the clinical treatment strategy for ER-positive gynecomastia patients.
## Study design
This study was approved by our institutional ethical review committee. We retrospectively collected the clinical data of 236 patients with unilateral or bilateral gynecomastia who received treatment at our hospital between April 2018 and December 2021. The patients chose the treatment strategies for gynecomastia after knowing detailed information on their clinical features and the two different treatments. Depending on the treatment the patient chose and received, the patients were divided into TAM and surgery groups. TAM was prescribed at a dose of 10 mg orally once daily for 3–6 months consecutively. These patients were followed up after discontinuation of TAM within 6 months to check whether there was recurrence. The following surgeries were performed: subcutaneous mastectomy with preservation of the nipple-areola complex via a peri-areolar incision; subcutaneous mastectomy by vacuum rotary cutting; and mammoscopic-assisted subcutaneous mastectomy with preservation of the nipple-areola complex. Data were collected of the following indicators: patient age; breast nodule grading based on the physical examination; results of color Doppler ultrasound; estrogen level; type of surgery performed; postoperative complications; and length of hospital stay. ER expression, a pathologic indicator, was determined. The patients receiving surgeries were followed up within 6 months after operations. During the follow-up period, the degree of patient satisfaction and recurrence were included for analysis. Recurrence was detected by color ultrasound during follow-up.
## Inclusion and exclusion criteria
The inclusion criteria were as follows: [1] The patients satisfied the diagnostic criteria for gynecomastia. Specifically, the patients had breast pain and discomfort. Breast nodules were palpable on physical examination with clear boundaries, a rough texture, and good mobility. The majority of breast nodules were located below or around the areola. There was tenderness pain, but no adhesions to the skin. The color Doppler ultrasound findings were consistent with gynecomastia. [ 2] The patients were > 18 years of age and willing to receive medications or strongly requested medications or surgical treatment. The patients signed the written consent form. [ 3] The patients had no severe heart, liver, kidney, and hematologic diseases, or any surgical contraindications. [ 4] The patients had no significant adverse reactions to oral TAM. [ 5] The patients received complete clinical and pathologic treatment. The exclusion criteria were as follows: confirmed cases of male breast cancer; no TAM or surgical treatment; intolerant to oral TAM; and surgical contraindications.
## Grading method for breast nodules
The glandular tissue thickness in the areola was determined by color Doppler ultrasound [15] and the maximum diameter along the direction of the vertical thickness was determined. The glandular tissue thickness based on color Doppler ultrasound is considered the gold standard, and gynecomastia was diagnosed if the glandular tissue thickness was ≥ 2 mm. Simon’s classification for the clinical grading of gynecomastia was used [16]. Depending on the degree of breast enlargement and skin redundancy, gynecomastia was divided into three grades: grade 1, minor breast enlargement with no skin redundancy; grade 2, moderate breast enlargement without skin redundancy; and grade 3, marked breast enlargement with skin redundancy.
By referring to the published literature [17], the TAM group was further divided into two sub-groups (patients achieving glandular shrinkage or a stable disease [collectively defined as responsive]; and patients with progressive disease [defined as unresponsive]). The surgical group was also divided into two sub-groups (patients without surgical recurrence [defined as responsive] and patients with surgical recurrence [defined as unresponsive]).
## ER detection
ER expression was determined using the EnVision method with ready-to-use ER (clone no.: EP1; Dako, Copenhagen, Denmark). The tissue sections were dewaxed and subjected to antigen retrieval (97 ℃ for 30 min). Then, the sections were washed in PBS 4 times for 3 min each time. The working solution of primary antibodies was added dropwise to incubate the sections for 60 min, followed by washing with PBS four times for 3 min each time. The sections were further incubated with FLEX/Mouse (LINKER; DAKO, Denmark), which was added dropwise for 15 min, followed by washing with PBS four times for 3 min each time. The sections were then incubated with FLEX/HRP (secondary antibody), which was added dropwise for 30 min. The sections were washed with PBS four times for 3 min each time. The incubation was continued for 3–5 min by adding 3,3’-diaminobenzidine (DAB; DAKO, Denmark) dropwise to the sections. The incubation duration varied with the staining intensity. After color development, the tissues were washed, soaked in hematoxylin for 5 min, then rinsed with running water. Differentiation was done using $1\%$ hydrochloric acid-ethanol for 1–10 s. Next, the sections were washed with running water. After returning to blue for 4–5 s, the sections were washed with running water. Next, the counterstained sections were successively dehydrated in $70\%$, $85\%$, and $95\%$ ethanol for 1 min. Finally, the tissue sections were dried and sealed with neutral balsam.
## Statistical analysis
All statistical analyses were performed using SPSS 18.0 software. For univariate analysis, the chi-square test with a four-fold table was used to analyze the correlation between the data. For multivariate analysis, multivariate logistic analysis was used to analyze the correlation between surgical recurrence and clinical indicators.
## Baseline indicators
A total of 236 male patients with gynecomastia were identified. According to the above inclusion and exclusion criteria, 66 patients who did not receive TAM or surgical treatment were excluded. Therefore, 170 patients were included. There were 91 patients in the TAM group and 79 patients in the surgery group. The following surgeries were performed: subcutaneous mastectomy with preservation of the nipple-areola complex via the peri-areolar incision ($$n = 36$$); subcutaneous mastectomy by vacuum rotary cutting ($$n = 39$$); and mammoscopic-assisted subcutaneous mastectomy with preservation of the nipple-areola complex ($$n = 4$$).
The comparison of baseline indicators between the two groups is shown in Table 1. Patients in the TAM group were older than patients in the surgical treatment group (average age: 36.40 vs. 28.85 years, $P \leq 0.01$). The proportion of patients with clinical grade 2 was significantly higher than patients with clinical grades 1 and 3 in both groups. Patients in the surgical treatment group were younger than patients in the TAM group. The proportion of patients with clinical grade 3 was significantly higher in the surgical treatment group than the TAM group ($$P \leq 0.043$$). The two groups of patients did not differ significantly with respect to glandular tissue thickness (91 mm vs. 79 mm) and estrogen level (37.60 vs. 40.99) ($P \leq 0.05$).
Table 1Baseline indicatorsIndicatorsTAM groupSurgery groupP-valueNumber9179Age(year)36.4028.850.000clinical stagingGrade 17($7.7\%$)8($10.1\%$)0.043Grade 264($70.3\%$)41($51.9\%$)Grade 320($22.0\%$)30($38.0\%$)Glandular tissue thickness(mm)91790.172Estrogen level37.6040.990.351
## Correlation between the efficacy of TAM treatment and the clinical indicators
The correlation between the efficacy of TAM treatment and the clinical indicators was shown in Table 2. Ninety-one patients were treated with TAM, 21 of whom achieved glandular shrinkage, and 41 had stable disease and 29 had progressive disease based on efficacy evaluation. The choice of TAM treatment was not correlated with age or glandular tissue thickness based on color Doppler ultrasound findings; however, the glandular tissues were thicker in patients achieving glandular shrinkage after medication ($P \leq 0.05$). The estrogen levels were similar in patients with stable and progressive disease, but significantly different in patients of glandular shrinkage in TAM group ($P \leq 0.01$). The clinical grade was also correlated with the efficacy of TAM treatment. The proportion of patients achieving stable disease was higher among those with clinical grade 1-to-2. Among patients classified as clinical grade 3, the proportion of patients achieving glandular shrinkage of the breast was higher after TAM treatment ($P \leq 0.049$).
Table 2The correlation between the efficacy of TAM treatment and the clinical indicatorsIndicatorsefficacy of TAM treatmentP-valueΧ2glandular shrinkagestableprogressiveNumber2141290.083Age(year)2941210.358Glandular tissue thickness(mm)9.117.417.960.063Estrogen level46.7434.5131.000.001clinical stagingGrade 1043Grade 21832140.0499.539Grade 31154
## Correlation between the type of surgery and clinical indicators
Seventy-nine patients completed surgical treatment, including 36 patients who underwent subcutaneous mastectomy with preservation of the nipple-areola complex via the peri-areolar incision, 39 patients underwent subcutaneous mastectomy by vacuum rotary cutting, and 4 patients underwent mammoscopic-assisted subcutaneous mastectomy with preservation of the nipple-areola complex. The results of correlation analyses between the indicator data are shown in Table 3. The patients undergoing open surgery were significantly older in age compared with the other two groups. The patients undergoing minimally invasive rotary cutting surgery and the mammoscopic-assisted glandular resection were comparable in age ($$P \leq 0.032$$). The patients undergoing open surgery had a longer hospital stay; however, there was no significant difference in the length of hospital stay between patients undergoing minimally invasive rotary cutting surgery and mammoscopic-assisted glandular resection ($P \leq 0.01$). The type of surgery was not significantly correlated with glandular tissue thickness based on color Doppler ultrasound, the estrogen level, ER expression, and clinical staging ($P \leq 0.05$). Mild pain was more common after minimally invasive procedures; however, a greater proportion of patients were suffering from moderate pain after open surgery ($P \leq 0.01$). Postoperative hematomas were more common after minimally invasive procedures ($$P \leq 0.040$$). Among patients undergoing open surgery, nipple necrosis ($$P \leq 0.01$$), nipple paresthesias ($$P \leq 0.07$$), and postoperative effusions were the most common complications ($$P \leq 0.02$$).
Table 3The correlation between the type of surgery and clinical indicatorsIndicatorsSurgery typesP-valueΧ2open surgeryminimally invasive rotary cutting surgerymammoscopic-assisted glandular resectionNumber363940.100Age(year)31.7226.7723.250.032hospital stay4.672.442.50.000Glandular tissue thickness(mm)9.2478.23610.20.268Estrogen level39.2042.3331.520.206ER expression70.3167.8777.500.686Clinical stagingGrade 14400.2295.619Grade 216261Grade 316113Pain levellight153100.00016.882mild2184Hematomasno18730.04011.286yes18321Nipple necrosisno233740.00112.670yes1320Nipple paresthesiasno102520.0079.937yes26142Effusionsno62210.00212.976yes30173
## Correlation between surgical recurrence and clinical indicators
Among the 79 surgical patients, the correlations between surgical recurrence and clinical indicators were analyzed, as shown in Table 4. Twenty-six patients relapsed after surgery; 53 patients did not relapse. The two groups of patients were comparable in age and clinical grade ($P \leq 0.05$); however, the two groups differed significantly with respect to the estrogen level ($P \leq 0.01$) and the proportion of patients undergoing each type of surgery ($$P \leq 0.012$$). The difference in the glandular tissue thickness based on the color Doppler ultrasound also reached statistical significance between the two groups ($$P \leq 0.050$$). Thus, we concluded that patients with a greater glandular tissue thickness and a higher estrogen level had a higher risk of recurrence. The recurrence rate was higher in those undergoing a minimally invasive procedure.
Table 4Correlation between surgical recurrence and clinical indicatorsIndicatorsRecurrenceNo recurrenceP-valueNumber2653Age(year)29.5527.420.346Glandular tissue thickness(mm)10.238.090.050Estrogen level48.4137.360.009ER expression70.9669.740.595Clinical StagingGrade 117Grade 211300.092Grade 31416open surgery630Surgical typesminimally invasive rotary cutting surgery19200.012mammoscopic-assisted glandular resection13
## Correlations between ER and different indicators
ER expression was not significantly correlated with glandular tissue thickness, clinical grade, or estrogen level (Fig. 1).
Fig. 1The correlation of ER expression with glandular tissue thickness (A), estrogen level (B), and clinical grade (C)
## Results of multivariate analysis
Logistic regression analysis was performed to determine the impact of each clinical indicator on efficacy. The estrogen level and type of treatment had an influence on efficacy ($P \leq 0.05$). An elevated estrogen level was a factor leading to TAM failure ($P \leq 0.01$; Table 5). Among the surgical patients, logistic regression analysis was performed to assess the correlation between each clinical indicator and surgical recurrence. The glandular tissue thickness, estrogen level, and type of surgery performed were risk factors leading to postoperative recurrence (all $P \leq 0.05$; Table 6).
Table 5Logistic regression analysis was performed to determine the impact of each clinical indicator on efficacyIndicatorsP-valueOR$95\%$ intervalAge(year)0.6830.9950.969–1.021Glandular tissue thickness(mm)0.5491.0720.854–1.346Clinical Staging0.8961.0840.323–3.633Estrogen level0.0071.0351.009–1.061Grouping0.0000.0970.043–0.216 Table 6Logistic regression analysis was performed to determine the impact of each clinical indicator on recurrence in surgery groupIndicatorsP-valueOR$95\%$ intervalAge(year)0.5100.9770.911–1.048Glandular tissue thickness(mm)0.0191.6131.082–2.459Clinical Staging0.2800.3410.048–2.406Estrogen level0.0201.0741.006–1.075ER expression0.2570.9780.941–1.016Surgery types0.0143.7401.307–10.697
## Discussion
Gynecomastia is a breast disease that specifically involves males. The incidence of gynecomastia has been on the rise due to lifestyle changes and the increasing prevalence of obesity and diabetes [18, 19]. Androgen abuse, use of drugs promoting male breast development, and exposure to environmental endocrine disruptors are considered reasons for the increasing prevalence of gynecomastia [19]. Gynecomastia may also be a clinical manifestation of male breast cancer [20, 21]. This fact highlights the need for clinical management of gynecomastia.
The male hormones most often studied concerning gynecomastia include estrogens, progestogens, androgens, luteinizing hormone, and growth hormone. Thyroid hormones have also been the focus in some studies. According to most studies, abnormal estrogen levels can result in male breast development for the following reasons [1–5, 21]: [1] endocrine disorders and estrogen-androgen imbalance; [2] abnormal estrogen metabolism; [3] elevation of the estrogen level induced by exogenous drug uptake, such as drugs for urinary tract diseases and prohibited substances; [4] hypersensitivity to ERs, leading to mammary gland hyperplasia; and [5] excessive androgen consumption or abnormal endocrine function. Estrogen receptor (ER), a member of the nuclear receptor superfamily, mediates various effects of estrogen. ER is also closely related to the differentiation and proliferation of mammary epithelial cells and tumorigenesis [5, 21, 22]. TAM is an ER antagonist that competitively binds to ER in breasts. TAM reduces a series of effects induced by estrogen by inhibiting estrogen binding to the ER [6, 7, 10–12]. These studies have also demonstrated that TAM can be used as an effective agent in gynecomastia [6, 7, 10–12]. It has also been shown that males are tolerant of TAM with few adverse events reported [23]. TAM works by binding to the ER, which partially explains the variability of responsiveness to TAM [5]. Among patients receiving TAM treatment in the present study, those achieving a more substantial decrease in glandular tissue thickness had a higher estrogen level. By contrast, patients with similar glandular tissue thicknesses also had comparable estrogen levels. The above differences were statistically significant. Multivariate analysis also showed that the efficacy of TAM treatment was significantly correlated with the estrogen level. Moreover, the efficacy of TAM treatment was also significantly correlated with clinical grade. The patients receiving TAM treatment were older in the present study. Thus, we recommend TAM for elderly patients who cannot tolerate surgery, have a low estrogen level, and clinical grade 1–2.
Gynecomastia mainly presents with breast enlargement and pain, which negatively impacts the quality of life. In addition, patients with gynecomastia are prone to anxiety and other psychological problems. Because of psychological and cosmetic considerations, many patients choose to undergo surgical treatment [1, 4, 9]. This is especially the case for patients with clinical grade 2–3. The surgery usually involves a resection of the hyperplastic mammary glands to improve the appearance of the enlarged breasts and dispel patients’ worries and concern [12, 13]. Conventional open surgery can achieve complete resection of hyperplastic mammary glands, although causing conspicuous scars in the breasts, and prolonging the hospital stay and time to healing. Many patients have severe pain after conventional open surgery along with other adverse reactions, including edema, bruises, postoperative effusions, or nipple necrosis [24, 25]. The minimally invasive procedure is less traumatic and shortens the hospital stay and time to healing. The patients are more satisfied with a minimally invasive procedure, although the problems of postoperative hematomas, effusions, gland residue, and postoperative recurrence still exist [24, 25]. Mammoscopic-assisted glandular resection is a safe and feasible procedure for treating gynecomastia, causing few adverse reactions except for mild pain and edema. Mammoscopic-assisted glandular resection is often described as having a long learning curve and highly-skilled surgeons, which pose obstacles to the wider clinical application of this procedure [26, 27]. The present study included an insufficient number of patients undergoing mammoscopic-assisted glandular resection for an efficacy evaluation of the procedure. In the present study, the patients receiving an open surgery were older in age, had a prolonged stay in hospital, and suffered from moderate pain after surgery compared with the other two surgical procedures. Those patients undergoing open surgery had more frequent adverse reactions, including nipple necrosis and effusions, the findings of which are in agreement with previous reports [24, 25]. The minimally invasive rotary cutting surgery resulted in a shorter length of hospital stay and less severe pain compared with the other two procedures. Hematoma was a common postoperative complication. However, the minimally invasive rotary cutting surgery was associated with a higher risk of surgical recurrence, which was consistent with the previous reports [24, 25]. Therefore, we recommend open surgery for patients with more redundant skin and clinical grade 2–3; however, the minimally invasive rotary cutting technique is preferred for patients with clinical grade 1–2 and a smaller scope of glandular hyperplasia. The surgical patients were further divided into recurrence and non-recurrence subgroups. The two subgroups were not significantly different in age and clinical grade; however, the glandular tissue thickness was smaller in the non-recurrence group than the recurrence group. The estrogen level was also considerably lower in the non-recurrence group than the recurrence group. Given the above findings, we believe that patients with a greater glandular tissue thickness and a higher estrogen level are at a higher risk of recurrence. In addition, recurrence was more common after the minimally invasive procedure. Therefore, we suggest that the type of surgery should be chosen based on the clinical grade, glandular tissue thickness, estrogen level, and the patient’s willingness to undergo treatment.
Thus far, the minimally invasive rotary cutting technique is more widely accepted among patients. Young patients usually have a stricter requirement for breast appearance, a higher acceptance of new technologies, and achieve more rapid postoperative healing [24, 28, 29]. In the present study, such patients tended to have a higher estrogen level and less significant breast enlargement. Specifically, these patients had hyperplastic glands in the areola and no redundant skin, and the size of resection was small. The minimally invasive procedure is guided by B-mode ultrasound, which offers an accurate localization of the hyperplastic glands. The area of resection with the minimally invasive procedure is limited, which increases the risk of residual glands [24, 25, 28, 29]. Postoperative estrogen level monitoring is necessary for these patients. Oral TAM should be prescribed for ER-positive patients with a high estrogen level and preoperative glandular thickening to prevent postoperative recurrence.
## Limitations
In the present study, we excluded teenage males < 18 years of age for the following reasons: oral TAM may cause greater side effects among adolescents; and transient mammary gland development caused by hormonal changes is reversible to a certain degree. The exclusion of adolescent males, however, might result in biases. Moreover, the present study was only conducted in a single center. Multi-center studies with a larger sample size with long-term follow up are warranted.
## Conclusion
Our study results show that TAM and surgery treatments can effectively treat gynecomastia, but an individualized treatment regimen is recommended for these patients based on their clinical features. TAM is more beneficial than TAM for patients who cannot tolerate surgery, have a low estrogen level, and are clinical grade 1–2. Surgery treatment is better than TAM for patients of clinical grade 3. Different surgery options may lead to different complications. The patients with a greater glandular tissue thickness and a higher estrogen level were shown to have a higher risk of recurrence.
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|
---
title: 'Parity and hypertension risk in couples: does number of parity matter: findings
from Tehran Lipid and Glucose Study'
authors:
- Maryam Rahmati
- Marzieh Saei Ghare Naz
- Fereidoun Azizi
- Fahimeh Ramezani Tehrani
journal: BMC Public Health
year: 2023
pmcid: PMC10010040
doi: 10.1186/s12889-023-15397-1
license: CC BY 4.0
---
# Parity and hypertension risk in couples: does number of parity matter: findings from Tehran Lipid and Glucose Study
## Abstract
### Background and aims
As reported, hypertension (HTN) plays a leading role in explaining mortality worldwide, but it still has many confounding factors. This study explored whether the number of parity and age matters for HTN among couples from the Tehran Lipid and Glucose Study (TLGS).
### Methods
This study was conducted on 2851 couples from TLGS. All the variables were collected based on the standard protocol. The participants were categorized into four and five categories according to the number of parity (childless, one, two, three, or more parities) and age (18-30y, 30-40y, 40-50y, 50-60y, and 60-70y), respectively. Spline regression models via log link function for the binary outcome and linear link function for continuous outcomes were applied to evaluate the effect of interaction term age and parity categories on the desired outcome.
### Results
Among the total of 2851 pairs, $2.3\%$ had no child, $9.5\%$ had 1 child, $38.4\%$ had 2 children, and $49.8\%$ had ≥ 3 children. The adjusted risk ($95\%$ CI) of HTN in females aged 40-50y with 1 child, 2 and ≥ 3 children compared to no child were 1.14(1.04, 1.26), 1.05(1.01, 1.10), 1.12(1.07, 1.17), respectively ($p \leq 0.05$). Moreover, in those aged 50-60y with 2 and ≥ 3 children, the risk of HTN significantly increased by $4\%$. In females aged 60-70y with ≥ 3 children compared to those without children, the risk of HTN increased by $2\%$. For males aged 30-40y with 2 children compared to the no child group, the adjusted risk of HTN increased by $17\%$, while for those with ≥ 3 children in the same age group, this risk significantly decreased by $13\%$. Moreover, in males aged 30-40y with 2 children, risk ratio of HTN increased by $17\%$, but in males with ≥ 3 children, it decreased by $13\%$ and in those in the same groups but aged 40-50y the risk increased by $6\%$ and $11\%$, respectively.
### Conclusion
Our findings suggest that gender, childlessness, having one child, and multi-parity had different impacts on HTN. Further research is needed to confirm our findings.
## Highlights
• Gender, childlessness, having one child, and multi-parity had different impacts on HTN.
• The increased risk of HTN was observed among females aged 40-50y with 1 child, 2 and ≥ 3 children compared to females with no child.
• In females aged 50-60y with 2 and ≥ 3 children, and in females aged 60-70y with ≥ 3 children the risk of HTN increased compared those without children.
• In males aged 30-40y with 2 children compared to the no-child group the risk of HTN increased.
## Introduction
Hypertension (HTN) is one of the most prevalent risk factors for non-communicable diseases (NCD) [1]. It represents no warning signs or symptoms, so is mainly known as a silent killer [2]. HTN per se acts as the main risk factor for atherosclerosis, renal disease, stroke, and peripheral arterial diseases [2]. Although the exact cause of HTN in most cases is unknown [3], it is well established that the conditions that increase a person's risk of developing HTN include genetic factors, ageing, stress, unhealthy diet, physical inactivity, tobacco, and alcohol use, obesity, and pollution [3, 4]. Further, reproductive factors have been found to be associated with HTN [5].
Parity has long been seen as a condition primarily affecting females. However, childbearing could also affect the health status of males. Metabolic and hormonal factors alter during normal pregnancy [6]. Apart from the link between physiological and pathological changes during pregnancy and the later development of diseases [7, 8], other factors such as behavioral patterns related to childbearing could affect the health of parents [9, 10]. Recent studies have shown that childbearing may affect males’ and females’ health. The previous meta-analysis demonstrated the J-shaped dose–response relationship between a number of parity and cardiovascular diseases (CVD) [11]. Previous studies have indicated that the number of parity may also be associated with cognitive function [12], HTN [13], and chronic kidney disease (CKD) [14]. Some part of this association was explained by biological alterations in the endocrine and immune system during pregnancy [14]. As family members share a common environment and are under similar circumstances, genetic factors are an important factor that could influence health [15]. Consequently, having children may lead to significant health changes over the life course in both females and males.
Thus far, limited prior studies have investigated the association between the number of parity and HTN risk in females [16–18]. These studies have yielded conflicting results. Despite the growing number of studies, controversy still exists about positive or inverse associations between the number of parity and HTN among females and males. Gender is a matter in hypertension development and it is well established that males had consistently higher blood pressure (BP) and were at greater risk for HTN [19, 20]. Hormonal differences play an important role in these gender differences [20]. In this study, we strive to illustrate the impact of number of parity on the risk of HTN in females and males in different age groups. To separate the non-biological effects of parenthood from the biological effects of pregnancy, we also investigated the effects of fatherhood on HTN risk. Recognizing the high-risk group of couples during different life stages could provide an opportunity for family-based interventions.
## Method
This study was conducted using data from Tehran Lipid and Glucose Study. The protocol of this prospective cohort study was designed according to the World Health Organization (WHO)-recommended model for non-communicable diseases (NCD) surveillance [21]. The population of the TLGS study was selected by multistage stratified cluster random sampling technique from urban district 13 of Tehran, the capital of the Islamic Republic of Iran. This population was representative of the overall population of Tehran at the beginning (1999–2001) of the study. Informed written consent was obtained from all the participants. In this study, all the family members were invited to participate in the study. In total, 15,005 participants aged ≥ 3y were invited to the TLGS-specific data-gathering center. A detailed description of the rationale, design, and methodology of the TLGS study has been previously published [22].
All methods were carried out in accordance with relevant guidelines and regulations with the Declaration of Helsinki Informed consent was obtained from all subjects.
## Study population
In this analysis, the participants were selected from the last examination phase (2015–2018) of the TLGS. For the current study, 2851 couples were included in the analysis. The age variable ranged from 18 to 70 years and was categorized into five groups (18-30y, 30-40y, 40-50y, 50-60y, and 60-70y).
## Measures
All the participants were studied by trained physicians according to the standard protocol. Information for demographic and clinical variables was obtained using a standard and validated questionnaire. Smoking status in this study was considered as past, current, and never user. Anthropometric, laboratory, and clinical assessments were performed based on the TLGS measurement protocol [22]. All the blood analyses were carried out at the TLGS research laboratory. Details of the laboratory measurements, including fasting blood glucose (FBS) levels, triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and total cholesterol (TC), have been reported previously [22].
Blood pressure (BP) was measured after a 15-min rest in the sitting position; moreover, two systolic and diastolic blood pressure measurements were taken on the right arm using a standardized mercury sphygmomanometer (calibrated by the Iranian Institute of Standards and Industrial Researches). Hypertension was defined as hypertension diagnosed by a physician, the current use of antihypertensive drugs, or systolic blood pressure (SBP) of ≥ 140 mmHg or final diastolic blood pressure (DBP) of ≥ 90 mmHg [23].
Parity is defined as the number of live births for females and the number of children for males. This information was gathered through individual interviews at the time of the survey. Based on the evidence, the self-reported number of parity had high validity [24]. For the data analysis of the present study, the number of parity was categorized into four groups (0, 1, 2, and ≥ 3).
## Statistical analysis
Continuous variables were checked for normality based on the one-sample Kolmogorov–Smirnov test and were presented as mean (standard deviation) if they had a normal distribution, or median with an inter-quartile range (IQ25-75) for the variables with skewed distribution. Categorical variables were presented in number and percentage. Characteristics of the participants were compared between the parity categories by applying ANOVA or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\chi^2$$\end{document}χ2 test for continuous and categorical data, respectively. Appropriate postdoc analysis was used for pairwise comparisons. The Kruskal–Wallis test was applied to compare the variables with skewed distribution. The spline regression models via log link function for the binary outcome and linear link function for continuous outcomes were applied to evaluate the effect of interaction term age and parity categories on the desired outcome. The marginal means for SBP and DBP and marginal probability for HTN status were plotted for both males and females based on the different categories of age. These models were also adjusted for the potential risk factors: BMI, smoking status, physical activity, education, TG, and HDL. We used covariate-adjusted spline regression within a cross-sectional framework. This model allows for flexible consideration of non-linear age-associated patterns while accounting for traditional covariates and interaction effects. Statistical analysis was performed using the software package STATA (version 13; STATA Inc., College Station, TX, USA) and the significance level was set at $P \leq 0.05.$
## Result
Of a total of 2851 pairs, aged 18–70 years, 65 ($2.3\%$) had no child, 270 ($9.5\%$) had 1 child, 1094 ($38.4\%$) had two children and 1422 ($49.8\%$) had 3 or more children (Fig. 1). In this study, there were significant differences between the mean (SD) ages of females with no child 38.5(14.8), and females with two children 45.4 (8.1) and three or more children 53.0(8.5) ($p \leq 0.05$). Similarly, there were significant differences between the mean (SD) ages of males with no child 44.4(15.4) and males with two 50.6(8.4) and three or more children 58.1(8.6) ($p \leq 0.05$). Moreover, there were significant differences between the mean (SD) of BMI in females with no child 27(5.7), and females with two 29.1(4.8) and three or more children 30.9(5.1) ($p \leq 0.05$). While there were no significant differences between the BMI of males with different numbers of children ($p \leq 0.05$) (Table 1). Characteristics of participants were presented in Table 1.Fig. 1Flowchart of the study. * Abbreviations: TLGS, Tehran lipid and glucose study; SBP, systolic blood pressure; DBP: Diastolic blood pressureTable 1Characteristics of participantsCharacteristicFemaleP-value dMaleP-value dNumber of childrenNumber of childrenNo($$n = 65$$)One($$n = 270$$)Two($$n = 1094$$)Three and more($$n = 1422$$)No($$n = 65$$)One($$n = 270$$)Two($$n = 1094$$)Three and more($$n = 1422$$)Age, yeara38.5(14.8)13,1439.2(10.7)23,2445.4 (8.1)3453.0(8.5) < 0.00144.4(15.4)13,1444.9(10.4)23,2450.6(8.4)3458.1(8.6) < 0.001Smoking statusc(Past or current)7 (11.1)13,1414(5.3)56(5.2)3443(3.0)0.00225(39.1)130(49.4)509(48.8)674(48.8)0.5Educationc(Diploma and upper)37(56.9)13,14126(46.7)23,24424(38.8)34409(28.8) < 0.00129(44.6)131(48.5)24501(45.8)34503(35.4) < 0.001WHtRa0.56(0.10)140.57(0.07)23,240.59(0.07)340.64(0.08) < 0.0010.55(0.08)0.54(0.06)23,240.56(0.06)340.57(0.06) < 0.001BMI (kg/m2) a27(5.7)13,1428.0(4.9)23,2429.1(4.8)3430.9(5.1) < 0.00127.7(6.6)26.8(4.3)27.4(4.2)27.2(4.1)0. 1Appropriate physical activityc13(20.3)12,13105(38.9)24372(34.2)34405(28.7) < 0.00119(29.2)13,1491(34.5)23,24447(42.0)571(42.7)0.02SBP(mmHg) a113.8(15.1)14110.4(14.4)24111.2(15.1)34119.8(18.8) < 0.001123.0(20.0)117.4(16.0)24119.2(16.1)34125.2(20.0) < 0.001DBP(mmHg) a75.4(8.9)74.9(9.8)2475.7(9.2)3477.9(9.9) < 0.00178.5(12.4)79.1(10.1)80.0(9.8)80.0(11.0)0.4FBS (mg/dl) a91.9(29.4)14,24,3492.9(17.7)96.3(26.6)107.7(39.7) < 0.001103.2(30.5)103.2(35.2)24104.7(34.1)34110.3(40.4)0.001TG (mg/dl) b108(84–139)13,14112(78–163)23,24123(87.5–170)34140(101.5–188)0.001160(98–205)156(107–209)146(105–205)142(103–197)0.4TC (mg/dl) a195.2(48.2)189.8(40.7)24196.5(40.4)200.1(42.3)0.001192.4(44.7)193.6(41.5)192.6(40.4)190.9(41.0)0.6LDL-C (mg/dl) a121.9(36.5)113.8(34.1)24118.9(34.1)120.5(37.0)0.04118.8(40.9)118.2(33.9)117.5(34.1)116.9(36.1)0.9HDL-C (mg/dl) a48.9(12.7)49.6(11.8)49.8(11.9)3448.4(11.0)0.0341.0(11.3)40.8(10.8)41.8(9.8)41.2(9.9)0.3Incidence HTNc15(23.1)1459(21.8)24266(24.3)34654[46] < 0.00120(30.8)1475(27.8)23,24400(36.6)34687(48.3) < 0.001Ever used blood pressure medicationc6(9.2)1422(8.2)24120(11.0)34352(24.9) < 0.00111(16.9)27(10.0)24143(13.1)34298(21.1) < 0.001Abbreviations: WHtR Waist-to-Height Ratio, BMI Body mass index, FBS Fasting blood glucose, HTN Hypertension, SBP Systolic blood pressure, DBP Diastolic blood pressure, TG Triglyceride, LDL-C Low-density lipoprotein cholesterol, HDL-C High-density lipoprotein cholesterol, TC *Total cholesterola* Mean (SD)b median(Q25-Q75)c n (%)d ANOVA test, Kruskal–Wallis test, or Pearson's Chi-squared test was used as appropriate; and post hoc comparisons were identified by superscripts 1,2,3,41: No child; 2: one child; 3: two children; 4: three and more children Figures 2 and 3 show the predictive marginal means (unadjusted and adjusted) of SBP and DBP and the predictive probability plot for HTN status for different age and parity categories (18-70y). Overall, according to the adjusted plot, SBP differed slightly between groups (among males and females aged 18-60y) while the most notable change of adjusted marginal means (AMM) of SBP was observed between the age group of 60-70y among females with one child and males with no child. Moreover, females aged 60-70y with 1 child and females with 2 children experienced the highest and lowest AMM of DBP. Also, we observed differences in terms of a predictive marginal probability for HTN between groups. Young males (18-30y) with multiple children and older males (60-70y) with a child experienced a high probability of HTN.Fig. 2Predictive marginal means plot for SBP (a unadjusted, b adjusted) and DBP (c unadjusted, d adjusted) based on different categories of age and parity for males and females obtained from regression spline modelFig. 3Predictive marginal probability plot for HTN status (a unadjusted, b adjusted) based on different categories of age and parity for males and females obtained from logistic regression spline model Table 2 shows unadjusted (superscript a) and adjusted (superscript b) risk ratio (RR) and $95\%$ Confidence Intervals (CIs) for HTN among females and males based on age and parity categories, respectively. Table 2Risk ratio and $95\%$ Confidence Intervals (CI) for hypertension among female and male in different categorizes of age and parityVaribleFemaleMaleUnadjusted RR($95\%$ CI)p-valueAdjusted RR($95\%$ CI)p-valueUnadjusted RR($95\%$ CI)p-valueAdjusted RR($95\%$ CI)p-valueAged18-30yRef: no-childRef: no-childRef: no-childRef: no-child 1 child1.20(0.96, 1.50)0.091.16(0.94, 1.44)0.151.28(0.67, 2.45)0.441.33(0.67, 2.63)0.40 2 children1.17(0.94, 1.46)0.151.14(0.92, 1.41)0.211.29(0.68, 2.42)0.421.31(0.67, 2.54)0.42 ≥ 3 childeren1.20(0.96, 1.49)0.091.16(0.94, 1.43)0.141.37(0.73, 2.56)0.311.42(0.73, 2.74)0.29Aged:30-40yRef: no-childRef: no-childRef: no-childRef: no-child 1 child0.99(0.87, 1.12)0.880.99(0.87, 1.12)0.921.16(1.00, 1.36)0.041.14(0.97, 1.34)0.09 2 children1.11(1.01, 1.23)0.031.10(0.99, 1.22)0.051.11(0.99, 1.25)0.061.17(1.02, 1.35)0.02 ≥ 3 childeren1.01(0.88, 1.14)0.921.00(0.87, 1.14)0.970.88(0.77, 0.99)0.040.87(0.78, 0.99)0.03Aged:40-50yRef: no-childRef: no-childRef: no-childRef: no-child 1 child1.14(1.03, 1.25)0.0061.14(1.04, 1.26)0.0040.99(.92, 1.06)0.880.99(.92, 1.07)0.92 2 children1.06(1.02, 1.10)0.0021.05(1.01, 1.10)0.0051.06(1.01, 1.10)0.0061.06(1.02, 1.11)0.004 ≥ 3 childeren1.13(1.08, 1.19) < 0.0011.12(1.07, 1.17) < 0.0011.13(1.07, 1.20) < 0.0011.11(1.04, 1.17) < 0.001Aged:50-60yRef: no-childRef: no-childRef: no-childRef: no-child 1 child1.06 (0.99, 1.14)0.071.06(0.98, 1.14)0.111.12(1.05, 1.19) < 0.0011.11(1.05, 1.18) < 0.001 2 children1.04(1.01, 1.09)0.011.04(1.01, 1.09)0.011.03(1.01, 1.06)0.011.03(1.00, 1.05)0.02 ≥ 3 childeren1.04(1.02, 1.06) < 0.0011.04(1.02, 1.06) < 0.0011.02(1.00, 1.05)0.031.04(1.01, 1.06)0.001Aged60-70yRef: no-childRef: no-childRef: no-childRef: no-child 1 child1.05(0.97, 1.14)0.221.06(0.95, 1.20)0.250.92(0.82, 1.035)0.170.95(0.85, 1.07)0.47 2 children1.00(0.92, 1.08)0.951.00(0.91, 1.09)0.981.03(.99, 1.06)0.051.04(1.01, 1.08)0.007 ≥ 3 childeren1.03(1.01, 1.05)0.0011.02(1.00, 1.04)0.011.03(1.01, 1.04)0.0011.03(1.01, 1.04)0.002Adjusted for potential risk factors: BMI, smoking status, physical activity, education, TG and HDL Furthermore, in the adjusted model, the risk of HTN in females aged 40-50y with 1 child, 2 and ≥ 3 children compared to no child were 1.14($95\%$ CI, 1.04, 1.26; $$P \leq 0.004$$), 1.05($95\%$ CI, 1.01, 1.10; $$P \leq 0.005$$), 1.12($95\%$ CI, 1.07, 1.17; $P \leq 0.001$), respectively. Moreover, in those aged 50-60y with 2 (1.04($95\%$ CI, 1.01, 1.09; $$P \leq 0.01$$)) and ≥ 3 children (1.04($95\%$ CI, 1.02, 1.06; $P \leq 0.001$)), the risk of HTN increased by $4\%$. Also, in the adjusted model, for females aged 60-70y with ≥ 3 children compared to those without children, the risk of HTN increased by $2\%$ (1.02($95\%$ CI, 1.00, 1.04; $$p \leq 0.01$$)). For males aged 30-40y with 2 children compared to no child group, the adjusted risk of HTN increased by $17\%$ (1.17($95\%$ CI, 1.02, 1.35; $$P \leq 0.02$$), however for those with ≥ 3 children in the same age group, this risk decreased by $13\%$ (0.87 ($95\%$ CI, 0.78, 0.99; $$P \leq 0.03$$)). Moreover, males aged 40–50 years with 2 (1.06($95\%$ CI, 1.02, 1.11; $$P \leq 0.004$$)) and ≥ 3 (1.11($95\%$ CI, 1.04, 1.17; $p \leq 0.001$)) children showed an increasing risk of HTN compared to no child in the same group. Also, in males aged 50–60 y, we found that having a child was associated with an increased risk of HTN. Additionally, in males aged 60-70y, we found that compared to the childless males, the risk of HTN was 1.04 ($95\%$ CI, 1.01, 1.08; $$p \leq 0.007$$) and 1.03($95\%$ CI, 1.01, 1.04; $$p \leq 0.002$$) in those males with 2 and ≥ 3 children, respectively (Table 2 and Fig. 4).Fig. 4Risk ratio plot of HTN in terms of interaction between Parity number and age in females and males based on (a) unadjusted and (b) adjusted model. Adjusted for potential risk factors: BMI, smoking status, physical activity, education, TG and HDL. The risk ratios (RRs) are plotted on a floating absolute scale. Vertical lines indicate the corresponding $95\%$ confidence intervals (CIs)
In the unadjusted GLM model, the risk of HTN increased by $19\%$ in males than females (RR: 1.19; $95\%$ CI: (1.11, 1.27), p-value < 0.001). The adjusted model also showed that the risk of HTN increased by $13\%$ in males than in females (RR: 1.13; $95\%$ CI: (1.05, 1.23), p-value = 0.002).
## Discussion
This study attempted to examine whether parity provokes an impact on HTN in couples with different age groups. In this population-based study, we found that regardless of age group, higher parity (≥ 3 parity) was associated with a higher risk of HTN among both females and males. After adjustment for potential risk factors, among the females aged 60-70y with one child, SBP increased compared to childless females. While for males aged 60-70y, having children significantly decreased the mean of SBP compared to the childless males. Meanwhile, childless females or those with one child had higher SBP than the males in either group (60-70y). In terms of prediction DBP, there were no significant differences between the groups. Differences in mean SBP and DBP within the age categories were generally negligible.
Nowadays, the BP trajectory has an upward trend in low- and middle-income countries [14]. Regardless of this trajectory, HTN is a well-known common risk factor for CVDs, CKD, and associated events [25, 26]. Additionally, HTN and elevated SBP are the leading global burden of NCD risk factors and the causes of mortality worldwide [27, 28]. *Multiple* genetic, epigenetic, environmental, and social factors are determinants of HTN [29]. There is a growing number of evidence that highlights the role of reproductive history on health later in life [30, 31]. From the biological point of view, reproduction in a female’s life is costly in terms of physiological adaptation [32]. The exact underlying mechanism of association of HTN and the number of parity remains poorly understood. Part of this association could be explained by biological modifiers related to the physiological alterations related to normal pregnancy; this association could be exacerbated by pregnancy abnormalities [8, 33]. Becoming a parent could change the female’s and male’s life in both positive and negative ways [34]. Moreover, among males, the non-pregnancy-related pathway contributes to the underlying pathway of association between parity and mortality [35]. Hence, the number of parity could influence SBP, DPB, and HTN in both males and females via different pathways.
We found that females aged 60-70y without a child and females with one child had higher SBP than the males in either group. The association between parity and HTN has been reported in several previous studies [16–18] with inconclusive results. However, limited studies have comprehensively examined the association between parity and BP in both males and females. In line with our work, a previous study on 133 African women found that parity > 5 could act as a risk factor for HTN [16]. Additionally, in the Iranian population, the result of a study showed that women with ≥ 3 parity are at increased risk for HTN [18]. Another study demonstrated that HTN among Norwegian males and females aged 40-80y without a child or with a child was more common than among females and males with 2 children [36]. However, in contrast to our study, an Italian study reported that parity was not associated with HTN during postmenopausal [17]. In a previous study, the association between parity and HTN and metabolic syndrome in postmenopausal women was not confirmed. It is proposed this is related to the physiological changes related to menopause and ageing to some extent [13], while Moazzeni et al. [ 2020] found a J-shaped association between parity and CVD [37]. The impact of parenting on the overall health of males and females may be influenced by several factors including gender, women’s role in the decision-making process, and family unit. Moreover, ordinary risk factors including smoking, diet, and physical activity play a role [38–40].
Our result showed that for males aged 60-70y, having children significantly decreased the mean of SBP compared to the males without a child. However, among females aged 60–70 years with one child, SBP increased compared to the females without a child. It is well known that subfertility and nulliparity per se are known as risk factors for CVD in women [41]. The great impact of parenting, especially at an older age, may provide a sense of security and support for parents which could decrease their feeling of loneliness [42]. Evidence shows the association of nulliparity and low parity with poor health behaviors, lack of social support, and subsequent adverse health outcomes [43]. Moreover, findings from the British Women’s Heart and Health Study and the British Regional Heart Study revealed that the prevalence of CHD in women and men with no child or only one child increased [44]. It should be noted that some childless couples might suffer from reproductive-related disorders (such as infertility, experiencing complications of pregnancy, polycystic ovary syndrome, etc.). Some of these medical conditions may also adversely their general health status [44, 45]. Evidence showed that, there is regional fertility difference in different locations among fertile couples [46, 47]. What is more, the regional differences in reproductive patterns can impact of the association between parenthood and cardio-metabolic risk factors [48]. It should be noted that sex differences could also contribute to explaining the underlying mechanism in differences in HTN in both males and females. In this study, females were younger than males and their smoking status, lipid profile, and FBS were more favorable than males.
Further, the result of a study on the rural women of Bangladesh demonstrated that in females with 1 parity, DBP was lowest; but, in females with > 2 parity as well as in females without parity, DBP was elevated. This association increased in females without parity after 45y [49]. However, in our study, there was no significant association between parity and DBP. The results also showed that, among males, as parity and age increased, the adjusted marginal means of SBP and DBP increased. However, among childless males and females aged 60-70y, the mean of SBP and DBP was higher than that among males and females with one child. A Swedish study evaluated the metabolic profile of childless males and observed an elevated risk of CVD [50]. Married males without children, which might reflect infertility conditions, demonstrated a higher risk of cardio-metabolic diseases [51]. The diversities in these studies might be due to the differences in the classification of variables and adjustment for relevant characteristics and other methodology-related factors. In light of the high smoking rate among males, it is possible that the effect of parity was obscured by the influence of smoking on BP. Beyond the physiological changes of pregnancy in females and the interplay of genetic and environmental factors in both sexes, factors related to childrearing could play a role in the later health of parents [43].
Parenthood across different stages of life can strain psychological well-being and may influence health [34]. A recent study demonstrated that the effect of ageing posed a greater effect on SBP and DBP of males than the females in age groups 40–49 and 30–39 years [52]. Healthy behavior in parenthood during different life stages may face a paradox. Additionally, lifestyle risk factors related to the child-rearing process may contribute to the formation of an obesogenic environment and subsequent CVD risk in both males and females [44]. Obligations related to promoting the overall well-being of children, providing for their financial demands, and planning family diets based on the interests of children’s needs could result in promoting or deterring effects on the parents’ health [53]. Every lifetime stress exposure (such as stress related to the low socioeconomic status, occupational stress, and stressful aspects of the social environment) act as risk factor for HTN [54]. Alternatively, socioeconomic and lifestyle-related factors associated with HTN risk might differ between both sexes, and different age groups across various categories of the number of parity.
Nonetheless, this study has some limitations and strengths that need to be considered in conjunction with the results. The main strength of the present study is its methodology which uses a population-based study data set with a large sample size of couples and reliable assessment of variables. Furthermore, the cardio metabolic variables were measured directly. In this study, we included different age range of couples with various number of children. Our results were less likely to be influenced by selective recall bias since the distribution of the number of children for both sexes was similar. Another limitation of this study is that we did not have information on the genetic factors and some important lifestyle factors affecting the risk of HTN. This study was also limited by not considering some influential factors such as nutrition, economic situation, family norms, and psychosocial health. This study was restricted to only married individuals, and widowed or divorced persons were not included.
In this study, we have considered the effect of higher BMI in the adjusted models. Besides that, the effect of obesity on HTN in both males and females was significant. One of the well-recognized risk factors for HTN is obesity. In fact obesity via enhancing the activity of renin–angiotensin–aldosterone system and the sympathetic nervous system [55]. It seems that biological changes related to pregnancy and lifestyle factors could affect the risk of developing metabolic syndrome components in women [56].
Further comprehensive well designed longitudinal studies assessing various influential factors are recommended to investigate the possible mechanisms linking parity and HTN. More frequent HTN screening would be advisable among childless couples. Identifying and preventing HTN cases would be highly impactful. History taking of number of children and representing the consulting program for couples is a potentially affordable and cost-effective approach for the prevention of HTN. This approach especially in low and middle-income countries where HTN remains largely undiagnosed and uncontrolled is the most cost-effective.
## Conclusion
We observed that, in both males and females, having children and being childless were associated with HTN. Our study presented novel findings for the association between parity and HTN among couples with various numbers of parity within different age groups. As HTN and elevated BP are the leading causes of mortality in males and females, it is increasingly important to develop a strategy for health promotion and disease prevention. Recognizing the potential association of parity with HTN could help identify high-risk couples.
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|
---
title: 'Modeling extracellular stimulation of retinal ganglion cells: theoretical
and practical aspects'
authors:
- Kathleen E Kish
- Scott F Lempka
- James D Weiland
journal: Journal of Neural Engineering
year: 2023
pmcid: PMC10010067
doi: 10.1088/1741-2552/acbf79
license: CC BY 4.0
---
# Modeling extracellular stimulation of retinal ganglion cells: theoretical and practical aspects
## Abstract
Objective. Retinal prostheses use electric current to activate inner retinal neurons, providing artificial vision for blind people. Epiretinal stimulation primarily targets retinal ganglion cells (RGCs), which can be modeled with cable equations. Computational models provide a tool to investigate the mechanisms of retinal activation, and improve stimulation paradigms. However, documentation of RGC model structure and parameters is limited, and model implementation can influence model predictions. Approach. We created a functional guide for building a mammalian RGC multi-compartment cable model and applying extracellular stimuli. Next, we investigated how the neuron’s three-dimensional shape will influence model predictions. Finally, we tested several strategies to maximize computational efficiency. Main results. We conducted sensitivity analyses to examine how dendrite representation, axon trajectory, and axon diameter influence membrane dynamics and corresponding activation thresholds. We optimized the spatial and temporal discretization of our multi-compartment cable model. We also implemented several simplified threshold prediction theories based on activating function, but these did not match the prediction accuracy achieved by the cable equations. Significance. Through this work, we provide practical guidance for modeling the extracellular stimulation of RGCs to produce reliable and meaningful predictions. Robust computational models lay the groundwork for improving the performance of retinal prostheses.
## Introduction
Retinal prostheses aim to provide artificial vision for blind people, using an implanted neurostimulation device [1]. They have been used to treat profound vision loss caused by retinal degenerative diseases, such as retinitis pigmentosa and age-related macular degeneration [1]. The devices are implanted in or near the eye to target inner retinal neurons (ganglion and bipolar cells) that survive, even during late-stage disease [1]. Retinal prostheses have been shown to improve users orientation and mobility, and allow them to locate high-contrast objects [1]. However, the visual acuity possible with artificial vision remains limited, with a best-reported value of $\frac{20}{400}$ [2]. To improve outcomes, it is critical to understand the fundamental mechanisms underlying the electrical excitation of retinal neurons. Computational models provide a tool to investigate how various anatomical and biophysical factors contribute to retinal activation, and to design improved stimulation paradigms and devices. In this work, we used a two-part technique to model the electrical stimulation of retinal tissue. We used finite element analysis to model the current flow from the stimulating electrodes. We then coupled the calculated electric fields with multi-compartment cable models, to capture the membrane dynamics of target neurons.
The most widely used family of models describing retinal ganglion cell (RGC) biophysics was initially developed by Fohlmeister, Miller and colleagues at the University of Minnesota [3–6]. This seminal model was based on whole cell patch clamp recordings from neurons in the tiger salamander retina [3]. They derived cable equations describing ion channel gating kinetics by fitting a model to experimental data, using the same mathematical structure as Hodgkin and Huxley [7]. In 2010, Fohlmeister et al adapted the original model to describe mammalian RGC membrane dynamics using patch clamp data from rat and cat retinal neurons [6]. Importantly, they included four ion channels in the RGC membrane (sodium, calcium, calcium-activated potassium, and delayed rectifier potassium). They used temperature as an independent variable to determine the ion channel distribution across various cellular regions and calculate experimental Q10 values.
The Fohlmeister cable equations have been the basis for hundreds of modeling projects. Despite their widespread use, documentation of RGC model structure and parameters is sparse and inconsistent. Model implementation can influence performance and simplifying assumptions may limit the significance of the model predictions. In this paper, we provide detailed instructions for creating a mammalian RGC cable model, and share all code necessary to replicate the model. Next, we investigated several practical aspects of modeling the extracellular stimulation of RGCs. A critical question is the extent to which the neuron’s three-dimensional (3D) shape will influence model predictions. We conducted sensitivity analyses to study how dendrite representation, axon trajectory, and axon diameter influence membrane dynamics and corresponding activation thresholds. Another practical consideration is how to maximize computational efficiency. When modeling a large number of neurons (e.g. a densely populated retina [8]), runtime remains a limiting factor in spite of modern computing resources. First, we optimized the spatial and temporal discretization of our cable model. Second, we applied several simplified threshold prediction theories to compare their prediction accuracy with our RGC cable model. The overall aim of this work was to provide shareable tools for modeling extracellular stimulation of RGCs, to investigate how morphometric factors influence model predictions, and to test several strategies for decreasing computation time.
## RGC membrane dynamics
We defined the biophysical properties of our RGC cable model following previous work [6]. We set the temperature of the simulation environment to 37.1 °C. The cytoplasmic (axial) resistivity was 136.6 Ω cm and the membrane capacitance was 1 µF cm−2 across all compartments [6]. The overall differential equation governing membrane voltage was: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{align*}&{C_{\text{m}}}\left({\frac{{{\text{d}}V}}{{{\text{d}}t}}} \right) + {\bar g_{{\text{Na}}}}{m^3}h\left({V - {E_{{\text{Na}}}}} \right) + \left({{{\bar g}_{\text{K}}}{n^4} + {g_{{\text{K,Ca}}}}} \right)\left({V - {E_{\text{K}}}} \right)\\ & \quad + {\bar g_{{\text{Ca}}}}{c^3}\left({V - {E_{{\text{Ca}}}}} \right) + {\bar g_{{\text{pas}}}}\left({V - {E_{{\text{pas}}}}} \right) = {I_{{\text{stim}}}}\end{align*}\end{document}CmdVdt+gˉNam3hV−ENa+gˉKn4+gK,CaV−EK+gˉCac3V−ECa+gˉpasV−Epas=Istim For the voltage-gated ion channels (Na+, K+, Ca2+), the channel state variables (m, h, n, c) are governed by equations of the form: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{align*}\frac{{{\text{d}}x}}{{{\text{d}}t}} = - \left({{\alpha _x} + {\beta _x}} \right)x + {\alpha _x}.\end{align*}\end{document}dxdt=−αx+βxx+αx.
The voltage-dependent rate equations for each state variable are shown in table 1. From Fohlmeister et al [2010], the equations were adjusted by the Q10 values corresponding to 37.1 °C (see [6], table 2).
Unlike the other ion channels, the calcium-activated potassium channel (KCa +) is ligand-gated according to the following equation: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{align*}{g_{{\text{K,Ca}}}} = {\bar g_{{\text{K,Ca}}}}\frac{{{{\left({{{\left[{{\text{C}}{{\text{a}}^{2 + }}} \right]}_i}/{{10}^{ - 6}}{\text{M}}} \right)}^2}}}{{1 + {{\left({{{\left[{{\text{C}}{{\text{a}}^{2 + }}} \right]}_i}/{{10}^{ - 6}}{\text{M}}} \right)}^2}}}.\end{align*}\end{document}gK,Ca=gˉK,CaCa2+i/10−6M21+Ca2+i/10−6M2.
Calcium ion concentration is driven by a pump mechanism, with the equation, where F is Faraday’s constant (96 489 °C) and r is the depth (0.1 µm) at which the calcium ion concentration [Ca2+] i is measured: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{align*}\frac{{{\text{d}}{{\left[{{\text{C}}{{\text{a}}^{{\text{2 + }}}}} \right]}_i}}}{{{\text{d}}t}} = \frac{{ - 3{I_{{\text{Ca}}}}}}{{2Fr}} - \frac{{\left({{{\left[{{\text{C}}{{\text{a}}^{2 + }}} \right]}_i} - {{10}^{ - 7}}{\text{M}}} \right)}}{{1.5{\text{ms}}}}.\end{align*}\end{document}dCa2 + idt=−3ICa2Fr−Ca2+i−10−7M1.5ms.
Each ion has a unique Nernst potential, defined in table 2. These potentials are constant, except for calcium.
The maximum ion channel conductance values, listed in table 3, represent the ion channel density in each region. The RGC axon is subdivided into four distinct regions [6, 9]. The sodium channel band (SOCB) has an ion channel density 3–5× higher than the neighboring regions, making it prone to excitation [9, 10]. Since Fohlmeister et al defined a range of appropriate conductance values, we adopted exact parameters following Raghuram et al [11]. Other physical characteristics of each region (e.g. length, diameter) are provided in table 4.
We implemented our multi-compartment cable model and governing equations using NEURON v 7.7 in a Python simulation environment [13]. All code used to build the RGC cable model is online and freely available at: https://github.com/Kathleen-Kish/Retinal_Ganglion_Cell.
## Applying extracellular voltage
To predict the RGC response to extracellular stimulation, we used the finite element method (FEM) to determine the extracellular potential produced by the stimulating electrode at each compartment of the cable model (Ve) [14]. The geometry of our simplified 3D model is shown in figure 1. We conducted finite element analysis in COMSOL Multiphysics Version 5.6 (Stockholm, Sweden) using the AC/DC electric currents (ec) module. This physics interface is used to compute the electric field, current, and potential in conductive media. For these simulations, we represented the active electrode as a 1 A current terminal (surface boundary condition), and assigned bulk tissue conductivities to each domain. We designated the bottom boundary of the sclera as an electrical ground (0 V). We obtained the tissue conductivities and layer thicknesses from prior work [15–17]. Using COMSOL, we generated a physics-controlled finite element mesh with extra fine element size. We used a quasi-static solver to calculate the electric potential (φ) distribution throughout the mesh. This solver employed the conjugate gradient method to solve Laplace’s equation, shown below:
**Figure 1.:** *Finite element model configuration. The rectangular model had an overall size of 25 × 17 × 2.18 mm (figure not to scale). The cross-section shown is in the x–z plane. The schematic also shows a sample location for an RGC cable model with a shallow (1), medium (2), and deep (3) soma. All RGC axons extend in the +x direction, constituting the nerve fiber layer.*
After solving the potential fields generated by the stimulation, we interpolated the spatially dependent FEM solution to find the extracellular potential at the center of each neuron compartment. These V e values were exported from COMSOL as a.txt file and imported to Python. To couple the cable model with the applied extracellular potentials, we used the ‘extracellular’ mechanism in NEURON (xraxial = 1 × 109 S cm−1, xg = 1 × 1010 S cm−2, and xc = 0 µF cm−2). Since biological tissue conductivities are predominantly linear at retinal stimulation frequencies (5–100 Hz [1]), we scaled the extracellular potentials calculated for a 1 A current by the time-dependent stimulus pulse parameters, and integrated over time to calculate the membrane voltage response [18]. We chose electrode size and stimulus pulse parameters (biphasic, cathodic-first, 0.45 ms/phase) to match the Argus II device [19]. A version of our COMSOL model including the 3D geometry is posted on GitHub, and a full version including the mesh and solution is available upon request.
## Cell morphometry
Naturally, RGCs exhibit substantial variations in both somatodendritic and axonal morphometry [20]. To study the effects of cell morphometry modifications on model behavior, we conducted several sensitivity analyses.
## Dendritic arbor
First, we studied the importance of including a dendritic arbor as part of an RGC cable model when modeling extracellular stimulation. An RGC model published by Schiefer and Grill does not include any dendrites [21]. On the other hand, many recent publications include a full branched dendritic morphology traced from ex vivo images [6, 22–25]. Werginz suggests that using a simplified equivalent cylinder to represent the dendrites produces nearly identical results as a more complex model [26].
To reconcile the conflicting approaches in previous studies, we designed an experiment comparing an RGC model with a full branched dendritic arbor, an RGC model with an equivalent cylinder used to represent the dendrites, and an RGC model with no dendrites (figure 2). To identify a suitable dendrite tracing, we searched the Neuromorpho database with keywords ‘human’ and ‘ganglion’ [27]. Two publications were identified, one containing five intrinsically photosensitive RGCs [28] and the other containing forty-seven RGCs from the mid and peripheral retina [12]. We selected a single cell tracing (ID: NMO_110421) with a mid-size dendritic field (168 × 183 µm). This is consistent in size with a parasol cell found 2–4 mm from the fovea [29] and would be reasonably targeted by electrodes implanted in the mid-peripheral region [30]. To create the equivalent cylinder model, we used a short vertical dendritic section (length = 10 µm, diameter = 4 µm) attached to a longer horizontal dendritic section (length = 1620 µm, diameter = 2 µm), following the methods described in prior work [26]. The equivalent cylinder approximates the somatodendritic membrane as a lumped impedance by matching the surface area of the original model (11 560 µm2) [31].
**Figure 2.:** *Multiple representations of the dendritic arbor in an RGC cable model. (a) Full branched morphology traced from a human cell [12]. The dendritic field size is 168 × 183 µm in the x–y plane. (b) Equivalent cylinder model. The cylinder extends for 810 µm in both the +x and −x direction after descending 10 µm in the −z direction. (c) Simple model with no dendrites.*
We compared the response of the models to extracellular stimulation from a disc electrode [32]. We placed the electrode at a height of 50 μm above the retina, which is within the range of electrode-retina distances for current clinical devices [30, 33]. We moved the electrode in a two-dimensional grid (1 × 1 mm) above the soma, with a 50 µm step-size. We calculated the action potential threshold at each electrode location using a bisection algorithm (with convergence of 0.1 µA).
## Axon trajectory
Next, we investigated the RGC axon pathway as it ascends from the soma and enters the nerve fiber layer. Anatomical studies show that axon trajectories vary among cells [34]. Prior models have made assumptions about the axon; for example, that it follows a 90° circular arc [21, 35] or ascends linearly [9, 36].
For this analysis, we systematically explored the influence of RGC axon trajectory on activation threshold. We calculated the path of an ellipse with one vertex at the soma and the other at the nerve fiber layer [37]. We adjusted the distance between ellipse vertices to create variable curvature and steepness (figure 3). We tested RGCs with multiple soma depths (35, 55, 75 µm) to represent natural variations [38]. We compared the response of the models to extracellular stimulation from a disc electrode. We placed the electrode at a height of 50 μm above the retina, and moved it horizontally along the length of the axon. We calculated action potential threshold at each electrode location using a bisection algorithm (with convergence of 0.1 µA).
**Figure 3.:** *Multiple representations of the RGC axon as it ascends from the soma to the nerve fiber layer. The sodium channel band is plotted with a thicker line for visualization. (a) Shallow soma, 35 µm below retinal surface and 20 µm below nerve fiber layer. (b) Medium soma, 55 µm below retinal surface. (c) Deep soma, 75 µm below retinal surface.*
## Axon diameter
As described in table 4, RGC axons are divided into several regions that vary in both physical dimensions and ion channel densities. Early studies identified that RGC axons have a narrow segment with an average length of 75 µm, surrounded by a larger diameter region on either side [39]. More recently, Fried et al used immunochemical staining to identify an SOCB adjacent to the narrow segment, densely populated with voltage-gated Na+ channels, that is on average 40 µm long [10]. Experimental thresholds are lowest when the stimulating electrode is placed over this band [10].
Axon diameter in all four regions has been inconsistent in prior computational models. When measured anatomically, RGC axon diameter has been generally shown to scale with cell size [6, 11]. We conducted a sensitivity analysis to investigate the effect of axon diameter on membrane dynamics and action potential threshold for extracellular RGC stimulation. We placed the disc electrode in three locations: directly above the soma, above the SOCB (150 µm offset in the +x direction), and above the distal axon (500 µm offset in the +x direction). We adjusted the axon hillock diameter between 2–4 µm and the narrow region diameter 0.6–1 µm [11]. We tapered the SOCB diameter to connect these two regions smoothly [9]. In all simulations, we recorded membrane voltage at all compartments to classify the action potential propagation behavior. We calculated action potential threshold at each electrode location using a bisection algorithm (with convergence of 0.1 µA).
## Model run time
*In* general, more complex models require higher computation time. To maximize efficiency, it is beneficial to find the simplest model that produces reliable predictions. We investigated two strategies for improving the run time of our RGC cable model. First, we conducted a sensitivity analysis to identify the minimum temporal and spatial resolutions necessary to provide consistent predictions. Second, we examined the ability of simplified techniques, such as the activating function (AF), to predict activation threshold.
## Spatial and temporal discretization
Multi-compartment cable models predict a spatiotemporally continuous solution for the membrane voltage by solving at a finite number of points in space (sections) and time. NEURON discretizes time and space to solve the relevant partial differential equations using a backward Euler integration method [40]. As a result, the integration time step (Δt) and the spatial interval between sections (Δx) both contribute to the solution accuracy. *In* general, the runtime of a cable model is directly proportional to the product of Δt and Δx [40].
To establish a ‘ground truth’ solution, we set Δx = 1 µm and Δt = 1 µs and calculated the action potential threshold using a bisection algorithm. From there, we incrementally increased the value for Δx until activation threshold changed by more than 0.1 µA, documenting the change in computation time. We adjusted Δx independently for each cell region and tested multiple electrode positions to ensure robust results. Then, we similarly increased the value for Δt incrementally, documenting the change in activation threshold and computation time.
## AF for threshold prediction
The second spatial derivative of extracellular potential along an unmyelinated axon, called the AF (AF = ∂2V e /∂x2), drives a neuron’s response to an applied stimulus [41, 42]. The maximum value of the AF represents the point of peak depolarization on the cell membrane, thus, the most probable action potential initiation site (figure 4). The AF has also been used as a predictor for threshold [43–46]. This approach represents a significant reduction in computational demands because it does not require solution of the RGC membrane voltage.
**Figure 4.:** *(a) Example of the extracellular potential (V
e) generated along an RGC cable model during epiretinal stimulation with a disc electrode. Extracellular potentials are negative because the cathodic phase of the pulse causes activation. Compartment 0 represents the soma and compartment 610 represents the end of the axon; dendritic compartments are not shown. (b) Activating function (AF), calculated as the second spatial derivative of V
e along the RGC. The red dot represents the AF peak, hypothesized to be a predictor for the site of action potential initiation. The neuron’s location with respect to the electrode is shown in figure 5.*
While the AF approach is appropriate for a uniform axon, its validity remains unclear for neurons with non-uniform ion channel density and physical dimensions, such as RGCs. Werginz et al found that while the AF predicts the RGC membrane’s initial response to stimulation, uneven axial current flow during the stimulus pulse can limit its relevance [36]. Esler et al suggested using a weighted AF (AFw) to linearly approximate the cellular integration of transmembrane currents [46]. In this framework, the AF value at each neural compartment (ƒn) is multiplied by a weight value (wn) representing its influence on the SOCB. The authors found that the sum (∑ƒn·wn) across a certain number (n) of compartments could accurately predict RGC firing behavior [46].
We tested the ability of both the AF and weighted AF (AFw) to predict threshold for our RGC cable model. We used two methods to calculate the AF. First, we used Python’s gradient operator to calculate the second spatial derivative of the applied potential field along the trajectory of the RGC. To produce a smooth function with this technique, we discretized the finite element tetrahedral mesh with a cubic (third-order) shape function in COMSOL. Secondly, we implemented a passive RGC model in NEURON and used the ‘i_membrane’ variable as a proxy for the AF. This variable measures the net transmembrane current density for a given compartment. The transmembrane current (mA cm−2) in response to the applied extracellular potential field after one time step ($t = 5$ µs) is proportional to the AF, while also incorporating differences in axial resistance (R a) caused by non-uniform section diameter. Finally, to calculate the weighted AF (AFw), we followed the methods described by Esler et al [46]. We calculated individual compartment weights (wn) by finding the relative SOCB depolarization resulting from injecting 1 nA current into each compartment, using a passive model. We tested the sum (∑ƒn·wn), varying $$n = 1$$ to $$n = 50$$ compartments and ordering compartments from highest to lowest weight [46].
To determine the accuracy of these simplified threshold predictors, we calculated their value for 441 uniquely positioned RGCs (figure 5). We used full cable model solutions to determine the threshold electrode current amplitude and corresponding extracellular potential along the RGC. Then, we defined the AF threshold as the AF peak when applying the threshold V e vector. For a reliable predictor, AF threshold should fit an exponential curve based on electrode-axon distance [43, 44]. We quantified prediction accuracy by calculating the coefficient of determination (R 2) for the exponential curve. We calculated electrode-RGC distance from the center of the electrode to the center of the soma.
**Figure 5.:** *Position of RGC somata (black) beneath the stimulating electrode (red). Cell bodies are spaced 50 µm apart to span a 1 × 1 mm retinal area. Axons extend in the +x direction, as above. The RGC used as an example in figure 4 is highlighted blue.*
## Dendritic arbor representation influences activation threshold map
To characterize the effects of dendritic arbor on activation, we built three versions of an RGC cable model: [1] with a full-branched dendritic morphology traced directly from a human cell, [2] with an equivalent cylinder used to represent the dendrites, and [3] a simplified model with no dendrites (see figure 2). We compared the response of each cell model to extracellular stimulation. Figure 6 shows the distribution of activation thresholds (µA) in response to a biphasic stimulus pulse, when a disc electrode was moved in a 1 × 1 mm grid above each cell model. Compared to the full branched morphology (figure 6(a)), the equivalent cylinder representation (figure 6(b)) predicted the same absolute minimum threshold (21.7 ± 0.1 µA) located above the SOCB. However, the elongated dendritic geometry created a low threshold region spatially overlapping the equivalent cylinder. Removing the dendrites altogether (figure 6(c)) increased the threshold magnitude (absolute minimum: 26.4 ± 0.1 µA), especially when the electrode was near the soma. This model lacks the active ion channels on the dendritic membrane, which can integrate the voltage produced by the electrode and increase the overall excitability of the neuron. Based on these results, we used the full-branched dendritic morphology for our remaining analyses.
**Figure 6.:** *Threshold maps for various dendritic arbor representations: (a) Full-branched morphology traced from a human cell, (b) equivalent cylinder dendritic model, (c) simple model with no dendrites. Threshold values (µA) were measured by moving the stimulating electrode in a 1 × 1 mm grid above the RGC, with 50 µm step-size. Results are shown in the x–y plane, which is parallel to the disc electrode.*
## Axon curvature modulates activation threshold profile
We altered our RGC cable model by creating a series of elliptical axon trajectories with variable curvature and steepness, including several soma depths (see figure 3). Again, we compared their response to biphasic extracellular stimulation with a disc electrode. Figure 7 shows the profile of activation thresholds (µA) as we shifted the stimulating electrode horizontally along the length of each axon. The minimum threshold always occurred when the electrode was above the SOCB. *In* general, steeper trajectories with sharper curvature had lower activation thresholds, particularly when the electrode was near the soma. The maximum threshold decrease (between gradual and steep trajectories) was $37\%$, $53\%$, and $60\%$ for somata at depths of 35, 55, and 75 µm, respectively.
**Figure 7.:** *Threshold changes associated with varying axon trajectory for (a) shallow, (b) medium and (c) deep cell bodies. The plots show threshold (µA) as the stimulating electrode shifts horizontally along the length of the RGC axon. The soma is located at 0 µm, and the axon extends in the +x direction. Steeper trajectories are associated with darker shades; see figure 3 for more details. (d) For three RGC axons where the SOCB angle was 6°, the effect of SOCB-electrode distance on threshold. (e) For three axons where the SOCB-electrode distance was 140 µm, the effect of SOCB angle on threshold. Note that SOCB angle refers to incline in the x–z plane, for a line drawn between the first and last compartment, where 0° is horizontal and we calculated the SOCB-electrode distance from the center of the disc electrode to the center of the SOCB. Figures (d) and (e) are not to scale.*
Steeper trajectories may have lower activation thresholds for two reasons: smaller SOCB-electrode distance and greater SOCB angle. Both factors influence the electric field gradient across the SOCB, which drives activation. To separate these effects, we looked at two scenarios. First, we identified axons with an SOCB angle of 6°, and varying SOCB-electrode distance (figure 7(d)). Increasing the SOCB-electrode distance systematically increased threshold. Then, we identified axons with an SOCB-electrode distance of 140 µm, and varying SOCB angle (figure 7(e)). Increasing SOCB angle systematically decreased threshold. This supports the claim that threshold decrease for steep trajectories is due to a combination of SOCB-electrode distance and SOCB angle.
## Axon diameter affects action potential propagation
The final morphometry modification to our model was to change axon diameter (specifically in the axon hillock and narrow region) and evaluate action potential propagation. First, we adjusted the axon hillock diameter between 2 and 4 µm. Figure 8 shows the membrane voltage dynamics of our model as we decreased axon hillock diameter. In this analysis, we applied the threshold stimulus pulse with a disc electrode directly above the soma. When the axon hillock diameter was 4 µm, the action potential propagated initiated in the SOCB and propagated in both directions. When the axon diameter was 3 µm, there was a prolonged latency before the action potential invaded the soma. This provided time for the SOCB membrane to repolarize and experience a secondary ‘echo spike’, which propagated down the axon. When the axon hillock diameter was 2 µm, the action potential could not depolarize the somatodendritic membrane. The same behavior occurred when the stimulating electrode was located above the SOCB (150 µm offset) or distal axon (500 µm offset). As shown in figure 8, there was a minor decrease in activation threshold with axon hillock diameter when the electrode was above the soma. No change in threshold was observed when the electrode was above the SOCB (threshold: 22.5 ± 0.1 µA) or distal axon (threshold: 38.1 ± 0.1 µA).
**Figure 8.:** *Axon hillock diameter (black arrows) affects action potential propagation. The biphasic stimulation pulse is applied at 1 ms. In all cases, the action potential initiates in the SOCB. (a) When the axon hillock has a 4 µm diameter, the action potential propagates in both directions. (b) When the axon hillock has a 3 µm diameter, we observe an ‘echo spike’ in the axon. (c) When the axon hillock has a diameter of 2 µm, the action potential fails to invade the soma. For these simulations, the narrow region diameter was 0.8 µm.*
Adjusting the narrow region diameter between 0.6 and 1.0 µm caused analogous trends in the model. Action potentials propagated bi-directionally only when the narrow region diameter was greater than 0.8 µm. Narrow region diameter had no influence on activation threshold. In some situations, increasing Na+ and K+ conductance in the narrow region could counteract a smaller diameter. For example, setting \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\bar g_{{\text{Na}}}}$\end{document}gˉNa = 250 mS cm−2 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\bar g_{\text{K}}}$\end{document}gˉK = 125 mS cm−2 allowed for bidirectional propagation, even with a 0.6 µm narrow region.
## Optimized spatial and temporal discretization
At first, we built our multi-compartment cable model with a compartment length of 1 µm and integrated with a time step of 1 µs. We incrementally decreased the spatial and temporal resolution, measuring the change in activation threshold and computation time. Table 5 shows the spatial resolution of our final model by region. The ‘ground truth’ model contained 4260 sections, while the reduced model had 960 sections. Computation was 7.4 times faster for the spatially optimized model, with no differences in predicted threshold (±0.1 µA).
**Table 5.**
| Unnamed: 0 | Optimized section length (µm) |
| --- | --- |
| Dendrites | 10 |
| Soma | 4 |
| Axon hillock | 5 |
| SOCB | 5 |
| Narrow region | 5 |
| Axon | 5 |
Table 6 summarizes the effects of increasing the integration time step on simulation time required to calculate threshold for one neuron. We chose a value of Δt = 5 µs for our final model. Overall, performing this sensitivity analysis allowed us to solve the RGC cable equations 12.75 times faster, with a negligible influence on activation threshold.
**Table 6.**
| Timestep (µs) | Threshold change (%) | Run time (min) |
| --- | --- | --- |
| 1.0 | 0.0 | 43.4 |
| 2.0 | 0.0 | 21.7 |
| 5.0 | 0.0 | 8.7 |
| 10.0 | 1.12 | 4.6 |
| 20.0 | 3.6 | 2.2 |
| 50.0 | 10.56 | 1.1 |
## Simplified threshold predictors fall short of full cable model solutions
We assessed both an AF approach and a weighted AF (AFw) approach to reduce the computational demands required to predict threshold. We compared these simplified approaches to the threshold predictions generated with the full RGC cable model. First, we calculated the second spatial derivative by applying Python’s gradient operator twice to the extracellular potentials along the neuron. We calculated the AF threshold and spatial location of the AF peak for 441 uniquely positioned RGCs. Figure 9(a) shows AF threshold plotted against electrode-RGC distance. The exponential fit had an equation of $y = 0.67$ × 10−9.23x + 0.25 with R 2 = 0.04. The low coefficient of determination indicates poor threshold prediction accuracy. Figure 9(a) also indicates the spatial location of the AF peak using color. The AF peak was most often located at the axon hillock, due to its changing orientation with respect to the soma (e.g. soma aligned with z-axis, axon hillock begins shifting in x-direction). However, the peak occurred in the axon when it passed directly beneath the disc electrode. The distribution of peak locations contradicts the cable model, in which the action potential always initiated in the SOCB.
**Figure 9.:** *(a) AF threshold versus electrode-RGC distance, points are color-coded based on the spatial location of the AF peak. The black line shows the exponential fit: y = 0.67×10−9.23x + 0.25, R
2 = 0.04. (b) Instantaneous transmembrane current threshold versus electrode-RGC distance, points are color-coded based on the spatial location of the peak. The black line shows the exponential fit: y = 4.71×10−14.4x + 0.52, R
2 = 0.19. (c) Weighted activating function threshold (∑ƒn·wn) versus electrode-RGC distance, n = 25. (d) Neuron compartment centers, color-coded by compartment weight (w
n
). (e) Instantaneous transmembrane current at the SOCB at pulse onset. Each dot represents a single RGC soma, as in figure 5. The magnitude and polarity (indicated by the color) varies with neuron location.*
Using transmembrane current (mA cm−2) after one time step ($t = 5$ µs) as a substitute for the AF only produced modest improvements in prediction accuracy. We calculated the threshold and spatial location of the peak transmembrane current for 441 uniquely positioned RGCs. Figure 9(b) shows ‘i_membrane’ threshold plotted against electrode-RGC distance. The exponential fit had an equation of $y = 4.71$×10−14.4x + 0.52 with R 2 = 0.19. By accounting for non-uniform section diameter, the instantaneous transmembrane current incorporates differences in axial resistance (R a) and provides a better estimate of the AF peak. The peak was more likely to occur in the large diameter soma and less likely to occur in the narrow axon. However, the AF peak was still only located in the SOCB $30\%$ of the time with this method, which does not agree with the cable model.
The AFw approach involved scaling the AF by specified compartment weights and finding the sum across n compartments (∑ƒn·wn), ordering compartments from highest to lowest weight [40]. We first derived compartment weights (wn) by finding the SOCB depolarization resulting from injecting 1 nA current into each compartment, using a passive model, and normalizing [46]. Figure 9(d) shows the resulting weights, coded by color. We calculated AFw across $$n = 1$$–50 compartments for 441 uniquely positioned RGCs at threshold. Figure 9(c) shows weighted AF threshold (∑ƒn·wn) plotted against electrode-RGC distance when $$n = 25$.$ Notably, this approach generated both positive and negative values that were largely dependent upon the AF polarity at the SOCB. The polarity varied based on the neuron’s location with respect to the stimulating electrode (figure 9(e)). As a result, we did not see a consistent value emerge at threshold, no matter how many compartments were included in the sum.
## Discussion
This work had three main outcomes. We provided a functional guide for modeling extracellular RGC stimulation. We described how morphometric factors influence model predictions, adding to the prior work. Finally, we determined temporal and spatial resolutions that optimize run time versus accuracy.
A prior study by Werginz et al analyzed the effect of multiple morphometric properties on extracellular stimulation thresholds using tracings from over 100 mouse αRGCs [47]. Soma diameter (15–25 µm), dendritic field diameter (150–500 µm), and axon hillock length (10–50 µm) had no meaningful effect on activation thresholds. SOCB length (15–45 µm) significantly influenced activation thresholds. In this study, we addressed three additional factors: dendritic arbor complexity, axon trajectory, and axon diameter.
RGC dendrites contain active ion channels that contribute to action potential generation [4, 48]. Prior models have made simplifications including eliminating dendrites or using an equivalent cylinder representation [21, 26]. Our sensitivity analysis showed that including a full branched dendritic morphology was important to produce reliable activation threshold maps. We limited our analysis to a single parasol cell in the mid-peripheral region [12, 29]. Prior work demonstrated that increasing dendritic field diameter from 150 µm to 500 µm had no significant influence on threshold [47]. Therefore, while including a full branched dendritic morphology is important for future models, the dendritic field diameter of parasol cells is unlikely to influence predictions. On the other hand, developing models of midget cells, which are prevalant in the foveal region and have much smaller dendritic arbors, should be investigated [49]. Unfortunately, no human midget cell tracings were available on the Neuromorpho database at the time of this publication.
RGC axons ascend from a soma in the inner retina to the nerve fiber layer, and the path they take varies naturally among cells [34]. Prior models have made various assumptions about the curvature of this path [9, 21, 35, 36]. Our sensitivity analysis revealed that RGC axon trajectory influences activation thresholds, specifically due to the orientation and distance of the SOCB in relation to the stimulating electrode. Regardless, the lowest thresholds always occurred when the electrode was above the SOCB, consistent with experimental results [10]. Furthermore, the average threshold profile is similar across all soma depths. These results demonstrate that future models should incorporate a range of axon trajectories or use the average trajectory and clearly state their assumptions.
The diameter of RGC axons (including their various subregions) has not been consistent in prior models. In our cable model, axon diameter had a minimal effect on activation threshold, but did influence action potential propagation. Prior models of intracellular current injection have similar findings. A model of neocortical pyramidal neurons established that axon hillock diameter can influence the efficacy with which a spike invades the soma [50]. Sheasby and Fohlmeister found that the diameter of the narrow region can influence whether an action potential will propagate uni-directionally or bi-directionally [5]. Importantly, cells with a high somatodendritic surface area and low axonal surface area can experience increased latency for an axonal spike to enter the soma or even failure of a spike to enter the soma altogether [5]. Our sensitivity analysis showed that for action potentials to propagate bi-directionally with extracellular stimulation, we must similarly avoid a large impedance (i.e. surface area) mismatch between the axon and soma. Based on experimental evidence, we believe that action potential propagation in both the orthodromic and antidromic direction is most realistic. Specifically, calcium imaging data shows somatic activation in response to axonally initiated spikes [51]. Therefore, the surface area of the axon (which is directly proportional to diameter) must be large enough to depolarize the somatodendritic surface area, given certain ion channel densities. For our particular cell tracing, an axon hillock diameter of at least 3 µm and narrow region diameter of at least 0.8 µm were necessary. Additionally, there was a small range of axon diameters that caused an axonal ‘echo spike’ (see figure 8(b)). It is difficult to garner experimental evidence about whether echo spikes occur in nature or are simply an artifact of the cable model. Therefore, it may be up to a modeler’s discretion whether to permit them in a simulation.
We also investigated two strategies for improving the computational efficiency of our RGC cable model. First, we optimized the temporal and spatial resolutions. The optimized section lengths are summarized in table 5. We found an ideal integration timestep of 5 µs. Computation time is proportional to the product of Δt and Δx, allowing our optimized cable model to run 12.75 times faster without impacting threshold predictions. Given that the original full-resolution cable model took 112 min to run, this reduction can be significant depending on the number of neurons included in the simulation.
Secondly, we found that simplified threshold prediction techniques could not reliably replicate the predictions generated with our full RGC cable model, in agreement with Werginz et al who observed that spikes are generally generated in the SOCB [36]. The varying ion channel densities and diameters between cell regions violate the assumptions of the AF framework that require homogeneous cell properties [41]. Figure 10 exemplifies how the AF, which is proportional to the instantaneous transmembrane current, does not clearly predict cell firing behavior. In figure 10(a), we see an example where the SOCB is hyperpolarized at the pulse onset, but the transmembrane current changes direction mid-pulse. By the end of the cathodic phase, current is peaking in the SOCB, instigating spike initiation. This behavior is caused by the high SOCB conductance (ion channel density) compared to neighboring regions. Figure 10(b) shows a comparison where the AF peak is located in the SOCB to begin with, and subsequent depolarization and spike initiation occur more rapidly. *In* general, the AF predicts the initial membrane response, and largely depends on where the cell is located in relation to the stimulating electrode (see figure 9(e)). However, for RGCs, this relationship could not reliably indicate what would happen by the end of the pulse, due to disproportionate axial current flow into the SOCB. We could not overcome these unpredictibilities, even by using a weighted AF as suggested by Esler et al [46]. Perhaps this disparity is because they drew conclusions based on a limited number of RGC locations, reducing the variability of AF shape.
**Figure 10.:** *Example of RGC firing behavior that cannot easily be explained by the AF. (a) The left plot shows membrane voltage over time, with the red arrow indicating the initial hyperpolarization of the SOCB. Despite this hyperpolarization, the action potential still initiates in the SOCB. The middle plot shows the transmembrane current for each compartment at the time of the pulse onset (proportional to AF). The SOCB compartments are highlighted red, and SOCB current is negative. The right plot shows spatial transmembrane current at the end of the pulse. The SOCB compartments are highlighted red, and SOCB current has changed polarity. (b) Example where the SOCB is depolarized right away. The first plot shows membrane voltage over time. The second plot shows spatial transmembrane current at the pulse onset (proportional to AF), with the SOCB compartments highlighted in red. Immediately, SOCB current is positive. Overall, these plots highlight the challenges of using simplified threshold predictors derived from second spatial derivative of the extracellular potential field for non-uniform axons. (c) Location of the neurons in (a) and (b) with respect to the stimulating electrode.*
There were several limitations of this work. First, we did not analyze the effects of changing regional maximum ion channel conductances. In all simulations, we used the constant values provided in table 3. These values are consistent with experimentally derived ion channel densities across four mammalian RGCs [6]. However, future work could include a more systematic analysis of ion channel conductance, spanning across the physiological range. Additionally, we limited our model to epiretinal stimulation, in which the stimulating electrode is located directly above the nerve fiber layer. In doing so, we considered only direct RGC activation, disregarding indirect activation via other cells (bipolar, amacrine) in the retinal network. For subretinal electrodes, it may be important to include a network model. Finally, RGC structural changes (e.g. dendritic field reduction, neurite sprouting) that may be induced by retinal degeneration were not included in our model [52, 53]. The presence and severity of these structural changes likely depends on the stage of disease progression and retinotopic location [52, 53].
For the purpose of this work, we calculated the extracellular potentials generated by the stimulating electrode using finite element analysis in COMSOL. In certain situations, a researcher may not have access to this software or may want to simplify their simulations. It is possible to calculate extracellular potential generated by a disc electrode using a simplified equation, as described by Wiley and Webster [54]. However, using this approach would disregard the effects of non-uniform conductivity for various tissue types. Alternatively, an open-source finite element analysis software (e.g. FEBio, Netgen) could be used.
## Conclusion
Through this work, we aim to provide practical guidance for modeling the extracellular stimulation of RGCs to produce reliable and meaningful predictions. Additionally, we intend to increase the accessibility of these methods by sharing our code. Reliable computational models lay the groundwork for improving the performance of retinal prostheses. They allow for the design of novel stimulation paradigms and hardware, which could ultimately improve the quality of life for millions suffering from retinal degenerative diseases.
## Data availability statement
All data that support the findings of this study are included within the article (and any supplementary files).
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|
---
title: 'Supporting the parent‐to‐child transfer of self‐management responsibility
for chronic kidney disease: A qualitative study'
authors:
- Ruth Nightingale
- Sue Kirk
- Veronica Swallow
- Gretl A. McHugh
journal: 'Health Expectations : An International Journal of Public Participation in
Health Care and Health Policy'
year: 2022
pmcid: PMC10010075
doi: 10.1111/hex.13693
license: CC BY 4.0
---
# Supporting the parent‐to‐child transfer of self‐management responsibility for chronic kidney disease: A qualitative study
## Abstract
### Introduction
As children with long‐term conditions (LTCs) mature, they are usually expected to assume responsibility from their parents for self‐management of their condition. Little is known about what supports families with this handover of responsibility, including the role of healthcare professionals (HCPs). This study aimed to explore what supports young people with chronic kidney disease (CKD) to assume self‐management responsibility and parents to relinquish control.
### Methods
A qualitative study, using a grounded theory approach was conducted. Individual and dyadic interviews and focus groups were carried out with 16 young people aged 13–17 years old with CKD, 13 parents, and 20 HCPs. Participants were recruited from two UK children's renal units.
### Findings
Building and maintaining trust, fostering positivity, learning from mistakes, forming partnerships and individualized support, facilitated the transfer of self‐management responsibility. However, HCPs' focus on developing partnerships with young people meant some parents felt excluded, highlighting uncertainty around whether support should be child‐ or family‐centred. Although tailored support was identified as critical, aspects of local service provision appeared to impact on HCPs' capacity to implement individualized approaches.
### Conclusion
This study has identified what supports the handover of responsibility, and, importantly, HCPs' current, and potential role in helping young people to assume responsibility for managing their LTC. Further research is needed to explore how HCPs' involvement balances child‐ and family‐centred care, and how HCPs can adopt personalized, strengths‐based approaches to help ensure the support that families receive is tailored to their individual needs.
### Patient or Public Contribution
Patient and public involvement was integrated throughout the study, with young adults with CKD and parents who had a child with CKD actively involved in the study's design and delivery.
## INTRODUCTION
Self‐management has become an increasingly important aspect of health care across all age groups, due to the growing prevalence of long‐term conditions (LTCs). 1 Although definitions of self‐management vary, Lorig and Holman 2 suggest it involves medical, role and emotional management to enable the individual ‘to manage the symptoms, treatment, physical and psychosocial consequences and lifestyle changes inherent in living with a chronic condition’ (p. 178). As children are usually dependent on, or share condition management with their parents, alternative terms such as ‘supported self‐management' and ‘responsibility sharing’ have been used in childhood LTCs, 3, 4 As children mature, they are expected to assume responsibility from their parents for self‐management of their LTC. 5 However, this expectation has been challenged, and studies suggest that for some families, shared parent‐child management is preferable to the young person managing their LTC independently. 6, 7 Healthcare policy and research focuses on adolescence and the transition between child and adult services as the main developmental phase to acquiring self‐management skills. 8, 9 Consequently, healthcare professionals (HCPs) tend to view the assumption of self‐management responsibility as a process that starts when the young person is around 13 years old and ends with the transfer to adult services. 10 Studies suggest, however, that families can start this process at an earlier developmental stage, 6, 11, 12 and some guidelines recommend that HCP support to develop self‐management skills should start in early childhood. 4 This uncertainty around the optimal time for children to assume responsibility is compounded by studies highlighting adolescents' difficulties engaging in self‐management, resulting in adverse consequences for their health. 13 Additionally, the conflation between children's age and competency and the tendency of HCPs to view children as a homogenous group, 14, 15 underlines the need for individualized support with the transfer of responsibility.
An integrative review that explored the parent‐to‐child transfer of self‐management responsibility found that this transfer was a complex, individualized process. 16 The review identified how children and parents adopted various strategies to facilitate the transfer of responsibility, but there was limited evidence about the approaches used by HCPs and ambivalence around what was helpful. Where research explored what supported children to assume responsibility, this was primarily from the perspectives of children and parents; the views of HCPs were noticeably absent. Due to this gap in the literature, the review suggested further research was needed with all key stakeholders, including children, parents and HCPs, to gain a better understanding of the transfer process and what supports families with the handover of responsibility.
Research around the transfer of self‐management responsibility has mostly focused on diabetes and asthma, two of the most prevalent childhood LTCs. 16, 17 As LTCs differ in severity and self‐management demands can vary, a condition‐specific approach can be useful when studying the parent‐to‐child transfer of responsibility. 18 Therefore, this study focused on chronic kidney disease (CKD), a complex LTC related to irreversible kidney damage, with a wide range of causes and complications. 19 Children with CKD can be classified by stages 1–5, based on the rate at which the kidneys filter waste products; stage 5 indicates end‐stage kidney disease, which means renal replacement therapies, such as dialysis or kidney transplantation, are needed. 20 Although CKD shares some self‐management tasks with other LTCs, condition‐specific demands include renal diets, fluid restrictions or targets and dialysis, either carried out in a hospital or home setting. In the United Kingdom, 13 specialist renal centres manage the care of children with CKD stages 3–5. 21 As the majority of CKD management tasks are undertaken outside of the renal centre (e.g., in the child's home or school), and because CKD is a lifelong condition, child and family assumption of management responsibility is critical.
Studies suggest children, especially during adolescence, experience difficulties engaging in CKD self‐management. 22 Adolescents have higher levels of kidney transplant loss compared to younger children and adults 23 and less than $20\%$ of adolescents on dialysis were perceived by HCPs to have assumed self‐management responsibility at transfer to adult care. 24 While the literature suggests the parent‐to‐child transfer of self‐management responsibility is an important aspect of children's development, there is limited research on this transfer process involving children with CKD, and, crucially, how the process can be supported. Therefore, this study aimed to address this gap by exploring what supports young people with CKD to assume self‐management responsibility and parents to relinquish control.
## METHOD
The study used a constructivist grounded theory methodology. 25 Grounded theory is useful in exploratory research, as it aims to construct a theory that offers in‐depth understanding and explains the phenomenon being studied. 26
## Sampling and recruitment
Participants were recruited from two UK children's kidney units. Purposive sampling was initially used as the aim was to achieve maximum variation in relation to [1] young people aged 13–18 years old with CKD stages 3–5 and their parents/carers, and [2] HCPs from a range of disciplines in the renal multidisciplinary team. As the study progressed, theoretical sampling was used to sample young people with CKD stages 3–4, to generate data to elaborate and refine the emerging categories. One clinician from each of the kidney units identified potential participants and gained consent for R. N. to provide them with study information. A total of 49 participants took part in the study comprising 16 young people (Table 1), 13 parent/carers (11 mothers, 1 step‐father, 1 carer) and 20 HCPs (5 renal paediatricians, 4 nurses, 4 social workers, 3 clinical psychologists, 3 play workers, 1 dietitian).
**Table 1**
| Young people's characteristics | Girls (n = 9) | Boys (n = 7) | Total |
| --- | --- | --- | --- |
| Age | | | |
| 13 | 1.0 | 2.0 | 3.0 |
| 14 | 1.0 | 3.0 | 4.0 |
| 15 | 2.0 | 1.0 | 3.0 |
| 16 | 4.0 | 1.0 | 5.0 |
| 17 | 1.0 | 0.0 | 1.0 |
| Ethnicity | | | |
| White | 4.0 | 3.0 | 7.0 |
| South Asian | 3.0 | 2.0 | 5.0 |
| Black | 2.0 | 1.0 | 3.0 |
| Other | 0.0 | 1.0 | 1.0 |
| CKD stage/treatment | | | |
| Pre‐emptive transplant | 0.0 | 3.0 | 3.0 |
| Dialysis | 4.0 | 3.0 | 7.0 |
| In‐centre haemodialysis | 1.0 | 3.0 | 4.0 |
| Home dialysis | 3.0 | 0.0 | 3.0 |
| Transplant | 5.0 | 1.0 | 6.0 |
## Data collection
Semi‐structured interviews and focus groups were conducted to generate data. Young people and parents were offered the opportunity to be interviewed together or separately, and HCPs participated in either individual interviews or focus groups (Table 2).
**Table 2**
| Method | Type of participant/number | Length (range, in minutes) |
| --- | --- | --- |
| Individual interview (n = 21) | Young people = 7 Parents = 4 HCPs = 10 | 24–78 |
| Paired interview (n = 9) | Young people/parent dyads = 9 | 46–93 |
| Focus group (n = 2) | 13 HCPs | |
| Focus group (n = 2) | Focus group A = 9 × HCPs (renal paediatricians = 3; clinical psychologists = 2; social workers = 2; nurse = 1; play worker = 1). 3 of these HCPs also took part in an individual interview | 46 |
| Focus group (n = 2) | Focus group B = 4 × HCPs (social workers = 2; clinical psychologist = 1; play worker = 1) | 54 |
R. N. conducted all data collection, although the larger focus group (A) was co‐facilitated by V. S. Interviews and focus groups took place in person in the family home or hospital setting, or by telephone and were guided by a topic guide. As part of theoretical sampling, topic guides were revised as the study progressed (Supporting Information: 1). Interviews and focus groups were digitally recorded and transcribed verbatim. To address some of the methodological and ethical issues of conducting research with children, task‐based methods were used to generate data. 27 For example, in later interviews, participants were asked to consider the suggestions generated during earlier interviews around what supported the transfer of responsibility. Each individual suggestion was written on a piece of card, which was handed to participants, with the request that they consider each suggestion in turn.
## Data analysis
Data collection and analysis were conducted concurrently, using an iterative, inductive process. Initial codes were developed by line‐by‐line coding, with the aim of identifying actions and processes in the data. Focused coding, in conjunction with constant comparison, involved evaluating the initial codes to identify analytical, and theoretical categories. 25 A supplementary approach was used to analyse how interaction contributed to data generation in the paired interviews and focus groups. 28 NVivo11 was used to code and manage data. To ensure trustworthiness and credibility, reflexivity and regular discussion between authors were incorporated into the analytic process.
## Patient and public involvement (PPI)
PPI was integrated throughout the study, with two young adults with CKD and two parents of young people with CKD involved in the study's design and delivery. Table 3 summarizes the PPI at different stages of the study.
**Table 3**
| Stage of study | Advice sought | Methods |
| --- | --- | --- |
| Initial research idea/before study started | Relevance of research idea; study methods; plain English summary for funding application | Online meeting |
| Initial research idea/before study started | Relevance of research idea; study methods; plain English summary for funding application | Email |
| Applying for ethical approval | Participant information leaflets | Email |
| Data collection | Topic guides and task‐based methods used during interviews | Face‐to‐face meeting |
| Data collection | Topic guides and task‐based methods used during interviews | Email |
| Data analysis | Discussion of study findings | Online meeting |
| Dissemination | Plain English summary of study findings for participants | Email |
The impact of PPI on the study was manifold. For example, during discussions, none of the PPI contributors used the term ‘self‐management’, instead describing young people ‘being in control’ and ‘taking charge’ of their health care; this had a significant impact on the language used with participants throughout the study, especially during data collection. PPI contributors' advice to change some of the language and design of the participant information leaflets made the leaflets more accessible and, through provision of improved information, potentially supported participants to make an informed decision about whether to participate. 29 Topic guides were revised based on feedback to: ask additional questions to explore other aspects of self‐management PPI contributors thought relevant; alter existing questions so they were easier to understand; and adjust the order of the questions. Consultation with PPI contributors about study findings suggested the emergent categories and theory resonated with their own experiences of the transfer of CKD self‐management responsibility.
## Ethical issues
Participants were provided with age/developmentally appropriate information, and all provided informed assent/consent. Participants were assured of confidentiality and anonymity. In the data extracts presented, participants are identified by the type of participant (young person, parent, HCP) and the participants' numerical study identifier (1–20). The young person's age and gender are included in the data extracts to provide contextual information.
## RESULTS
A grounded theory, shifting responsibilities, was constructed from the data, consisting of a core category (shifting responsibilities) and two connected subcategories (developing independence and making changes). Further details about the grounded theory, core category and subcategories have been reported previously. 12 This paper focuses on a specific aspect of the second subcategory, making changes, to explain how young people's, parents' and HCPs' adjustments to their behaviour and communication supported the parent‐to‐child transfer of self‐management responsibility. This included behaviour and communication that: built and maintained trust; formed partnerships; fostered positivity; supported learning from mistakes, and was responsive to young people's and parents' individual preferences and needs (Figure 1). A gradual transfer, developing a routine, and connecting with others with CKD were also perceived to support the transfer process and have been described elsewhere. 12
**Figure 1:** *Supporting the parent‐to‐child transfer of self‐management responsibility*
## Building and maintaining trust
Young people, parents and HCPs perceived trust was needed for the transfer of self‐management responsibility. This included trusting relationships between young people and their parents and between the young person–parent dyad and HCPs. Additionally, some young people suggested trusting themselves was an aspect of assuming responsibility. When young people were able to demonstrate they could consistently engage in self‐management, parents started to trust that their child could be relied on to perform self‐management:It became a habit, I got good at taking them [medication], there was that trust. Then I stopped taking them. I think that trust is there again, but when I stopped taking them, I was obviously not being responsible. ( YP1, 14‐year‐old girl) Parents adjusted their behaviour and communication as trust was built with their child; for example, they reduced how often they reminded their child to take their medication or monitored their renal diet. Trust was essential for parents to feel able to relinquish control. However, as the quotation above highlights, maintaining trust was difficult, especially if the young person disengaged, even temporarily, from self‐management. When trust was lost, the transfer process was disrupted as parents tended to reassume responsibility and an increased level of control.
Some HCPs' perceived they had a role in supporting parents to start trusting their child was able to self‐manage. This included identifying opportunities where the young person would be able to demonstrate to their parents that they could be trusted, such as connecting themselves safely to their dialysis machine, or following their renal diet when outside the family home:With the diet, one thing happens at home, and another thing happens at school or when they're out with their friends. One way that I tackle it, is for them to take more responsibility for what happens when they're not at home first. If they can show their parents that they're managing well when they're out on their own, and the parents can trust them to make the right decisions, then that shows them that they are capable of managing … it's trying to build up the trust between the child and their parents. ( HCP2) Trusting relationships between the young person–parent dyad and HCPs were perceived as supporting the transfer of responsibility:If they trust in you, I think that's very helpful. I've looked after most of these people for the last 14 years, I'm a familiar face. We've got a relationship, we've built up trust over time, that really helps. To analyse the problems, the young person has got to be open first. ( HCP8) This quotation suggests that trust needed to be two‐way, that HCPs needed to be able to trust families, as well as young people and parents trust HCPs. There was a sense that as young people assumed self‐management responsibility, they needed to be ‘open’ with HCPs, which was more likely if there was a trusting relationship. Some young people described how being able to trust themselves, or having confidence in their ability to manage their condition, was part of assuming responsibility. Their accounts suggested that this impacted how much their parents were able to trust them and relinquish control:I can't trust myself with food, because I like a lot of food that I'm not supposed to eat. Sometimes I won't be able to contain myself from not eating it. My mum, she cares too much about me to stop reminding me about the things I eat, so she won't hand me that responsibility that easily. ( YP15, 15‐year‐old boy) Approaches used to support young people to trust themselves and develop self‐confidence, included HCPs and parents acknowledging when young people were managing their condition. This will be discussed in more detail in Section 3.3, fostering positivity.
## Forming partnerships
Partnerships between the young person, their parents and HCPs were perceived to support the transfer of self‐management. Young people and parents described how ‘teamwork’, which included undertaking self‐management activities together, supported young people to become increasingly involved in managing their CKD. HCPs adopted a range of approaches to encourage partnership including directing communication primarily at young people rather than parents; exploring young people's concerns and their motivation to assume responsibility; joint goal‐setting; findings solutions together; acting as an advocate for the young person and helping young people to negotiate with their parents around the transfer of responsibility. Young people appeared to value being treated as an equal; they described how interactions with HCPs that encouraged partnership, supported their assumption of responsibility:It's a two‐way thing. They [HCPs] want your take on it, because they don't want to be saying things and then me leave and be, ‘Forget that. I'm not doing that’. They ask our opinions, how it would work. They are very supportive in that way. It's your opinions and their opinions, but they mostly want your take on it, so you can help them understand. I like the independence, they're treating me like an adult rather than a kid. ( YP1, 14‐year‐old girl) Although most HCPs encouraged young people to attend appointments on their own, there were conflicting views among HCPs around how much parents should be included and whether they were a barrier or facilitator to young people taking responsibility for condition management. The few young people who had attended appointments on their own appreciated having the opportunity to focus on issues important to them and talk more openly with HCPs, compared to when their parents were present. Parents, however, appeared more ambivalent about HCPs' decisions to include or exclude them from consultations; while they seemed to accept that HCPs forming a partnership with their child was a necessary stage in their child assuming responsibility, they also struggled with relinquishing control. Some HCPs emphasized the need to partner with the young person–parent dyad and perceived parents' involvement was critical to supporting young people to assume responsibility:It does need to be in tandem because they are closely entwined. The danger of doing it in isolation is that the young person comes home and goes, ‘Mum I've talked to this nurse, I want to take my own meds’, and the parent goes, ‘No bloody way!’ Unless you're doing it together, I mean it could work, but it's going to be more successful if you're doing it as a combined approach. ( HCP17)
## Fostering positivity
Young people, parents and HCPs described how the transfer of responsibility was often a difficult process, in particular when young people struggled to integrate self‐management into their daily life. Therefore, behaviour and interactions that fostered positivity, such as acknowledging when the young person had been able to manage their condition, and focusing on what was going well, were perceived to support the transfer of self‐management. Parents, in particular, emphasized the importance of keeping positive, even when their child was struggling with self‐management:Sometimes she'll [child] say, ‘I'm doing well with my tablets, aren't I?’ I'll be like, ‘Oh, yes’. I try to be positive about it, but I can't say if she's had any tablets yesterday. I try to look at the positive stuff, she could be a lot worse than what she is, behaviour wise, but it is a concern to me. ( Parent7, 16‐year‐old girl) HCPs' accounts suggested they were aware of the need to acknowledge a young person's strengths. However, there was a sense this rarely happened, as appointments tended to focus on problems, including the young person's difficulties with assuming responsibility:Sometimes patients do nine tasks out of ten really well, but the focus in clinic will be on the one they're not doing, which is disheartening on the young person, because they probably really tried, and it's the one thing that they've not managed to stay on top of. Conversations tend to be so negative, that it puts them right off trying again. Somebody needs to say, ‘Well done for doing your medicine, turning up today, engaging in your healthcare, but we need to work a little bit on…’. ( HCP1) *As this* extract suggests, HCPs making changes to their interactions with young people to acknowledge what they had achieved and provide positive feedback was perceived to support young people's motivation to continue engaging in self‐management.
## Supporting learning from mistakes
When young people had difficulties with assuming self‐management responsibility, learning from mistakes was perceived to be helpful. Some young people acknowledged the impact on their health when they stopped engaging in self‐management, and this prompted them to resume responsibility:I definitely learnt from my mistake. I keep my water bottle near now. I make sure I'm keeping on top of things. I have all my medications properly, and check and double‐check that I've got all my medications. ( YP14, 16‐year‐old girl) Although parents were aware of the potential risks of their child making mistakes with self‐management, they accepted making mistakes was ‘normal’ and could provide opportunities for their child to learn:I'd tell parents with teenage children, when they make mistakes, let them see. Let them understand that sometimes they will make mistakes. Don't teach them there's no mistake, no, then you make them so rigid, let them be free with you. Tell them it's a mistake and this is the repercussion, so they know. ( Parent3, 15‐year‐old girl) *As this* extract suggests, acknowledging that young people would make self‐management mistakes could potentially encourage young people to be ‘free’ or honest with their parents when they were struggling with self‐management. HCPs accounts also indicated how learning from mistakes could facilitate the transfer of responsibility. Some HCPs described discussing with families how mistakes could provide opportunities for young people to develop an understanding of the consequences of their self‐management decisions:*Being a* teenager is about making mistakes, it's learning from your mistakes. But we don't want them to make mistakes that cause them harm … I talk to the family, I say making mistakes is the learning process, let them make mistakes safely, not letting them make any mistakes is not safe. ( HCP8) However, as this quotation highlights, the emphasis was on making mistakes ‘safely’ due to the awareness that some self‐management mistakes could have a significant impact on the young person's health.
## Individualizing support
Young people, parents' and HCPs' accounts suggested that the transfer of responsibility was completely individualized to each family. Contextual issues, such as the: young person's chronological and developmental age; family relationships and physical and social environment, interacted with and influenced the transfer process. A young person's progression through the CKD stages and the condition‐specific self‐management requirements, such as starting dialysis or receiving a kidney transplant, were also perceived to impact the young person's assumption of responsibility and parents' willingness to relinquish control. During a dyadic interview, a 16‐year‐old girl and her mother discussed how responsibility shifted after she had received a transplanted kidney: Young person: Before my transplant I was responsible for taking my tablets of an evening, and you would just know. You wouldn't even—, Parent: She only took two tablets. She took them at night and at that point I never used to check in. Now and again I used to say, ‘Have you taken your tablets?’ when I said goodnight, but it's not like it is now. I think it's the importance of the tablets, because tacrolimus [immunosuppressive medication], if you forget it, it's massive … I was a lot more slapdash then. ( YP8, Parent 8, 16‐year‐old girl) HCPs accounts indicated they were aware that the transfer of responsibility was experienced differently by each family. The importance of individualizing support to each family's needs was discussed in focus groups, as HCPs generated ideas around what facilitated the transfer of self‐management: HCP8: For some people, meeting other patients would be hell, for some it would be great … there isn't one size that fits all. HCP1: It's tailoring it. Like you say, some people wouldn't engage, some don't like digital technology, but they'd like the face‐to‐face. It's finding what fits.
Although HCPs believed support needed to be individualized, national and local transition guidance around young people moving from paediatric to adult services, underpinned service provision. Consequently, HCP involvement in the transfer of responsibility tended to start when young people were around 13 years old and finished when they transferred to adult services. Some HCPs accounts revealed their frustration that the young person's chronological age, rather than their ability to self‐manage, determined when they moved to adult services: HCP8: We are driven by age … that drives when we do transition rather than the patient. HCP11: It depends as well where you work. We have a [NHS] Trust that mandates that we move patients over at the age of 16 … but there are other Trusts where between 16 and 19, young people are offered a choice, ‘Do you want to go to paediatric services, or move up to adult services?’ So how we practise as clinicians is dictated by the management who decide how they want to do things within this Trust. ( A National Health Service (NHS) *Trust is* an organizational unit in England and Wales that provides health services, and generally serves either a geographical area or a specialized function).
These extracts highlight potential tensions between HCPs' belief in the need for individualized support and what they were able to provide in practice.
## DISCUSSION
Previous studies have explored the parent‐to‐child transfer of self‐management responsibility, but little is known about what support young people and parents' need as responsibilities shift. 16 This study contributes to knowledge by identifying what facilitates this transfer process, and, importantly, HCPs' current and potential role in helping both young people to assume responsibility, and parents relinquish control. Findings suggest there were similar views among young people, parents and HCPs about what supported the transfer of responsibility. However, some tensions appeared to be evident, in particular around the formation of partnerships between HCPs and young people that excluded parents, and the provision of individualized support. By highlighting what facilitates the transfer of responsibility, study findings both support and extend the existing literature, and have implications for practice.
Behaviour and communication that built and maintained trust were perceived to help the transfer process. This finding supports existing research that found parents needed to trust their child to relinquish control. 30, 31 However, by exploring HCPs' perspectives, this study extends the current understanding of the HCP role, suggesting HCPs could contribute to the development of trusting parent‐child relationships. Previous studies recognized that situations, when the child was away from the family home (e.g., to attend school, or socialize with friends), could be anxiety‐provoking for parents, and therefore, recommendations were made that HCPs should provide reassurance to parents about their child's self‐management ability. 32 In contrast, this study's findings suggest that by actively identifying situations when the young person has the opportunity to demonstrate to their parents they can be trusted to engage in self‐management, HCPs can help build and maintain trust. The importance of trusting relationships between young person–parent dyads and HCPs has been highlighted in previous research. Sullivan‐Bolyai et al. 33 found parents lost trust in HCPs when HCPs believed the deterioration in young people's health was a consequence of parents' transferring responsibility to the child before they were ready. The inclusion of HCPs in this study, however, extends knowledge in this area by suggesting trust is two‐way, as young people–parent dyads need to trust HCPs, and HCPs need to trust families.
Some young people in this study believed they needed to be able to trust themselves to assume self‐management responsibility. This suggestion that young people with CKD benefit from developing confidence and belief in their own ability aligns with the concept of self‐efficacy. 34 Although the literature proposes that enhancing self‐efficacy can facilitate young people assuming responsibility, 18, 35, 36 there is limited empirical research to support this. Colver et al. 9 suggest HCPs should encourage self‐efficacy and recommend further research ‘to identify the most effective and efficient ways to promote young people's knowledge and confidence in the management of their LTC’ (p. 77). By identifying approaches that can support young people's belief in their self‐management ability, such as fostering positivity and connecting with others with CKD, 12 this study's findings have implications for practice.
Young people, parents' and HCPs' perceived partnerships supported the transfer of responsibility. Previous research has highlighted the importance of collaborative child–parent relationships, as young people are more likely to learn self‐management from their parents, rather than HCPs. 22, 37 Participants' accounts suggest that HCPs formed partnerships primarily with young people, rather than the young person–parent dyad, as they perceived this encouraged young people to assume responsibility. As reported previously, HCPs tended to view the transfer of responsibility as part of the transition between child and adult services. 12 Consequently, UK transition guidance shaped HCP involvement, including the importance of young people attending clinic appointments without their parents. 8, 38 Consistent with previous research, young people in this study valued meeting with HCPs on their own, as they felt more able to talk openly without their parents present. 22 While some parents were positive about their children attending appointments without them, others struggled with being excluded and wanted to be kept informed. 39 The conflicting views among HCPs about whether parents are a facilitator or barrier to the transfer process and parents' ambivalence about their inclusion or exclusion from consultations, 40 extend the debate around whether HCP involvement should be child‐ or family‐centred. 41 Although it has been recommended that triadic collaboration is fostered between young people, parents and HCPs during the transfer of responsibility, 9, 42 only a few HCPs in this study seemed to view parents as supporting the assumption of responsibility. Therefore, few aimed to form a partnership with the young person–parent dyad. The uncertainty around how HCPs balance child‐ and family‐centred care during the parent‐to‐child transfer of responsibility indicates further research is needed.
Parents and HCPs perceived the transfer of responsibility was supported by fostering positivity. Only a few previous studies exploring diabetes self‐management identified positive reinforcement and offering rewards as helping young people to assume responsibility. 33, 43 However, as neither of these studies included HCP participants, further research exploring how HCPs can adopt a ‘strengths‐based approach’, as recommended by UK transition guidance, is needed. 38 Consistent with previous studies, young people learnt from making mistakes with self‐management. 44, 45 Parents and HCPs were aware, however, that some mistakes could have a significant impact on the young person's health and, as a result, there was ambivalence about learning through trial and error. 46 Although the existing literature recommends HCPs increase opportunities for experiential learning so young people can learn from the mistakes they make, 33 there is limited evidence to suggest that HCPs have utilized this strategy. Potentially due to including HCP participants, this current study extends knowledge in this area, finding that HCPs discussed with parents how making mistakes ‘safely’ was part of their child assuming responsibility.
In this study, HCPs described the importance of tailored support to meet the individual needs of young people and parents. Previous literature has discussed the need for HCPs to consider children as individuals and avoid having a uniform policy around when, and how the transfer of responsibility occurs. 15, 47 However, as a consequence of UK transition guidance underpinning HCPs support to young people assuming responsibility, HCP involvement tended to be service‐led, rather than based on family needs. 12 This highlights a potential tension between HCPs' beliefs that support needs to be individualized and what occurs in practice. Although guidance recommends HCPs adopt individualized or personalized approaches, 1, 4 there is limited evidence around how HCPs use these approaches in practice to support the transfer of responsibility. Further research to explore how HCPs construct and implement individualized support to facilitate the parent‐to‐child transfer of responsibility is needed.
## Strengths and limitations
Having PPI to advise on the design and conduct of this study was a major strength and impacted on the quality and relevance of its findings. An equal focus on HCPs' perspectives, alongside those of young people and parents, assisted with gaining an in‐depth and holistic understanding of what supports young people to assume self‐management responsibility. Although there was diversity in the sample, especially in relation to young people's age, ethnicity and CKD stage/treatment and HCPs' discipline, selection bias may have occurred due to reliance on clinicians in the kidney units for recruitment. Diversity could have been increased further through the recruitment of a greater number of fathers. Dyadic and focus groups can generate unique ethical and practical challenges, as power relations and family/group dynamics can potentially inhibit some participants from speaking. 48, 49 However, adopting techniques such as task‐based methods, the researcher aimed to encourage young people and ‘quieter’ group members to contribute to discussions.
## CONCLUSION
This study has explored what supports the parent‐to‐child transfer of self‐management responsibility for CKD. Study findings have contributed to knowledge, and, importantly, have identified HCPs' current and potential role in facilitating young people to assume responsibility and parents to relinquish control. These new insights have implications for practice, highlighting how families would benefit from individualized support that helps to: build and maintain trust, form partnerships that include parents, foster positivity and support learning from mistakes. Conflicting views around whether parents are a barrier or a facilitator to young people assuming responsibility indicate further research is needed to understand how HCPs can balance child‐ and family‐centred care when supporting the transfer process. Finally, further research to explore how HCPs can adopt personalized and strengths‐based approaches in practice would help ensure the support that families receive is tailored to their individual needs.
## CONFLICT OF INTEREST
The authors declare no conflict of interest.
## ETHICS STATEMENT
Approval was obtained from the UK Health Research Authority [226365], a National Health Service (NHS) Research Ethics Committee (18/YH/0210) and the NHS Trust Research and Development Departments.
## DATA AVAILABILITY STATEMENT
Research data are not shared.
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|
---
title: Experiences of peer navigators implementing a bilingual multilevel intervention
to address sexually transmitted infection and HIV disparities and social determinants
of health
authors:
- José A. Robles Arvizu
- Lilli Mann‐Jackson
- Jorge Alonzo
- Manuel Garcia
- Lucero Refugio Aviles
- Benjamin D. Smart
- Scott D. Rhodes
journal: 'Health Expectations : An International Journal of Public Participation in
Health Care and Health Policy'
year: 2023
pmcid: PMC10010095
doi: 10.1111/hex.13698
license: CC BY 4.0
---
# Experiences of peer navigators implementing a bilingual multilevel intervention to address sexually transmitted infection and HIV disparities and social determinants of health
## Abstract
### Introduction
Sexually transmitted infections (STIs) and human immunodeficiency virus (HIV) disproportionately affect young gay, bisexual and other men who have sex with men (GBMSM) and transgender women of colour. We explored the experiences of community‐based peer navigators (‘Community Navigators’) who participated in Impact Triad, a bilingual multilevel intervention developed by our community‐based participatory research partnership to reduce STIs and HIV and address social determinants of health (e.g., employment, education, social support and discrimination) among young GBMSM and transgender women of colour.
### Methods
Individual in‐depth interviews were conducted with 15 Community Navigators who participated in Impact Triad. Themes were identified through constant comparison.
### Results
Community Navigators' mean age was 31.4 years. Seven were self‐identified as African American/Black, 5 as Latine, 2 as multiracial/multiethnic, 1 as Asian American, 10 as cisgender men, 4 as transgender women and 1 as gender nonbinary. Thirteen themes emerged in three domains: [1] key aspects of the Community Navigator role (e.g., desire to serve as a community resource, the importance of being part of the communities in which one was working, the value of having an official role, being connected to other Community Navigators to problem‐solving and sustaining intervention aspects long‐term); [2] experiences implementing Impact Triad (e.g., engaging community members, meeting prioritized needs, building trust, using social media, increasing awareness and knowledge and challenges related to COVID‐19) and [3] lessons learned for future interventions (e.g., facilitating access to broader resources, building additional skills and increasing interactions among Community Navigators).
### Conclusion
Interviews identified important learnings about serving as Community Navigators and implementing Impact Triad that can guide future efforts to address STI/HIV disparities and social determinants of health through community‐based peer navigation.
### Patient or Public Contribution
Throughout this intervention trial, our partnership worked collaboratively with a study‐specific community advisory board (CAB) comprised primarily of young GBMSM and transgender women of colour. Members of this CAB participated in all aspects of the trial including trial design, intervention development, recruitment and retention strategies, data collection and analysis, interpretation of findings and dissemination.
## Sexually transmitted infection (STI) and human immunodeficiency virus (HIV) disparities
There is an urgent need to address disparities related to STIs and HIV in the United States. STI and HIV rates are highest among young persons, 1, 2 and gay, bisexual and other men who have sex with men (GBMSM) and transgender women, and particularly those who are persons of colour, also carry a disproportionate burden of STIs and HIV. For example, GBMSM comprise approximately $4\%$ of the US adult male population but $43\%$ of all syphilis cases in the country and $86\%$ of all HIV diagnoses among men, 2, 3, 4 and if current rates persist, one in two African American/Black and one in four Latine GBMSM may be diagnosed with HIV during his lifetime. 5 (The term ‘Latine’ uses a gender‐neutral ‘e’, which replaces the gendered endings ‘a’ and ‘o’ as in ‘Latina’ and ‘Latino’, and is similar to ‘Latinx’; this term is increasingly used within Latine LGBTQ communities). It is estimated that $14\%$ of transgender women in the country are living with HIV, with prevalence rates as high as $44\%$ among African American/Black and $26\%$ among Latine transgender women. 6 Furthermore, the US South has high STI rates compared to other regions of the country 1, 7 and has been referred to as the ‘new’ and ‘latest’ US HIV epicentre. 8
## Social determinants of health
The issues contributing to STI and HIV disparities experienced by young GBMSM and transgender women of colour are complex. Social determinants that influence the health of these communities include individual, sociocultural, environmental, system and policy factors. For example, GBMSM and transgender women, particularly those who are persons of colour, are more likely than heterosexual and cisgender counterparts to experience limited access to employment and education and face high rates of discrimination in a range of settings including health care, workplaces and schools. 9, 10, 11, 12 In addition, employment status, education level and related factors such as poverty, income and health insurance coverage have been associated with STI and HIV incidence and outcomes. 13, 14 There has been a call to broaden the focus of STI and HIV prevention efforts to address these ‘upstream’ factors that profoundly impact sexual health, in addition to individual‐level factors such as increasing correct and consistent condom use and STI and HIV screening. 15, 16
## Community‐based participatory research (CBPR) and community‐based peer navigation
CBPR, which engages community members and community organizations as partners in all phases of the research process, 17 has been identified as an important approach to understanding and reducing disproportionate STI and HIV burdens. 18, 19 Community‐based peer navigation leverages natural helping within existing social networks and has been used in STI and HIV prevention efforts with promising results. 20, 21, 22, 23, 24 *There is* a need for further development and evaluation of community‐based peer navigation interventions to address STIs and HIV as well as contributing social determinants using CBPR.
## Centers for Disease Control and Prevention (CDC) Community Approaches to Reducing Sexually Transmitted Diseases (CARS) initiative and the Impact Triad intervention
CARS is a unique initiative of the US CDC that promotes the use of community engagement to increase STI and HIV prevention, screening and treatment and address related social determinants of health within communities disproportionately affected by STIs and HIV. The initiative focuses on identifying and implementing innovative community‐driven strategies that leverage community assets. 25 As part of the CARS initiative, our CBPR partnership developed and tested Impact Triad, a bilingual (English and Spanish), multilevel intervention designed to reduce STIs and HIV and improve social determinants of health among young GBMSM and transgender women of colour in a high‐incidence community within the US South.
## Purpose
A better understanding of the implementation of community‐based peer navigation interventions is essential to strengthening future STI and HIV prevention efforts among communities facing health disparities. As a component of the process evaluation of Impact Triad, we qualitatively explored the experiences of community‐based peer navigators within the intervention, using individual in‐depth interviews.
## Development of Impact Triad
The Impact Triad intervention trial was conducted by the North Carolina Community Research Partnership. This partnership is a long‐standing CBPR partnership comprised of community members, community organization and clinic representatives and academic researchers. 18 *Throughout this* trial, our partnership worked collaboratively with a 15‐member community advisory board (CAB) comprised primarily of young GBMSM and transgender women of colour.
We first conducted a community‐driven needs assessment to identify and better understand needs and priorities related to STI and HIV prevention, screening and treatment and social determinants of health among young GBMSM and transgender women of colour locally, as well as existing community assets. Through this process, the CAB and CBPR partnership prioritized four social determinants of health as particularly salient for young GBMSM and transgender women of colour and their risk for STIs and HIV: employment, education, social support and discrimination. Based on needs assessment findings, the CAB and CBPR partnership developed intervention strategies to reduce STIs and HIV and improve these social determinants of health and integrated these strategies into the Impact Triad intervention, the details of which are described elsewhere. 26 Briefly, Impact Triad includes three primary multilevel strategies: community‐based peer navigation, use of social media and antidiscrimination trainings for community organization staff. The intervention involved training young GBMSM and transgender women of colour as community‐based peer navigators, known as ‘Community Navigators’ in English and ‘Navegantes Comunitarios’ in Spanish, to carry out helping activities with members of their social networks in the community within the context of their daily lives. In addition to one‐on‐one and group‐level in‐person helping to share information and resources related to STI and HIV prevention, screening and treatment and prioritized social determinants of health, Community Navigators created and updated intervention‐related social media accounts (e.g., Facebook and Instagram) and used their own social media accounts for messaging with social network members. Community Navigators were also involved in the development of brief online video testimonials designed to raise consciousness among community organizations about the challenges and barriers young GBMSM and transgender women of colour face when accessing services related to STIs and HIV and social determinants of health and ways to facilitate access (https://www.youtube.com/channel/UCd7gOGhBerT0w1CTq5BwMcQ).
## Community Navigator recruitment and training
Fifteen young GBMSM and transgender women of colour identified as informal leaders among their communities were recruited and trained to serve as Impact Triad Community Navigators. To be eligible to serve as a Community Navigator, a participant was 18 years of age or older; self‐identified as a person of colour; self‐identified as a man or as a transgender woman; reported sex with men and provided informed consent.
Community Navigators completed four 4‐h training sessions, in two cohorts of seven to eight Community Navigators each. They were trained to serve as [1] health advisors to provide information to social network members to meet needs and priorities related to STI and HIV prevention (e.g., condom and pre‐exposure prophylaxis [PrEP] access and use), screening and treatment, as well as social determinants of health (e.g., employment and education), offering guidance on where and how to access available services; [2] opinion leaders to bolster healthy and reframe unhealthy norms and expectations related to STIs and HIV or social determinants of health (e.g., social support) and [3] community advocates to bring the voices of young GBMSM and transgender women of colour to local community organizations by sharing feedback for improvement based on the perspectives of social network members. Community Navigators were trained to use an adapted version of the ‘ask‐advise‐assist’ model, 27 represented by the acronym ‘IMPACT’ in English or ‘IMPACTO’ in Spanish with each letter representing a step in the natural helping process. A low‐literacy wallet‐sized reminder card was developed to serve as a ‘cheat sheet’ for Community Navigators outlining these steps to support others. 26 Upon graduation, Community Navigators received a framed certificate of training completion, an identification badge, a t‐shirt and bag with the Impact Triad intervention logo and intervention materials (e.g., condoms, penis models and informational brochures) to carry out their work within their social networks and communities of young GBMSM and transgender women of colour. Community Navigators worked informally and formally with members of their social networks for 12 months and met monthly as a group with one another and with members of the CBPR partnership in convenient locations within trusted community organizations to plan, coordinate and evaluate their activities.
Community Navigators were provided a $50 stipend for each training session and each month of the 12‐month intervention implementation. They also were provided $50 per month to compensate for transportation costs.
## Qualitative interviews
After the conclusion of intervention implementation, individual in‐depth interviews were conducted with all 15 Impact Triad Community Navigators. Standardized interview guides were developed in English and Spanish with careful consideration of wording, sequence and content to explore experiences with the intervention and current issues affecting the health of young GBMSM and transgender women of colour. Abbreviated sample items from the guide are outlined in Table 1.
**Table 1**
| Impact Triad project |
| --- |
| Tell me about how you became involved with the Impact Triad project. |
| When you became a Community Navigator, was your role how you thought it would be? What was different than you expected? |
| What did you like about being a Community Navigator? |
| What was hard about being a Community Navigator? |
| Training |
| How well prepared were you to serve as a Community Navigator? What other training would have helped you? |
| What suggestions for other topics should we have included? |
| How new for you was the information you received? |
| Recruitment |
| Tell me how you recruited the young gay and bisexual men and transgender women of colour who are part of your social network to participate in Impact Triad. |
| Community Navigator experience |
| What are some challenges that you faced as you began working with other young gay and bisexual men and transgender women of colour in your community? |
| What were some of the things that made it easier to talk to other young gay and bisexual men and transgender women of colour about sexual health? To talk about social determinants of health, such as employment, education, social support and discrimination? What were some of the things that made it difficult? |
| What were the most popular topics that young gay and bisexual men and transgender women wanted to discuss? What topics did they not seem comfortable talking about? |
| How did you use social media to work with young gay and bisexual men and transgender women of colour? |
| What do you think worked well with social media? |
| What challenges did you have using social media? Were you able to work them out? |
| Think back to before Impact Triad started. Are the topics that you and your friends talk about now any different? How have the ways your friends provide support to one another changed? |
| How were the monthly meetings with the Impact Triad coordinators and the other Community Navigators? |
| PrEP |
| How do the people who were part of your group feel about PrEP? |
| How interested are they in PrEP? |
| What barriers do they face to using PrEP? |
| Alcohol and drug use |
| How common is alcohol use among the people who were part of your group? Among other young gay and bisexual men and transgender women of colour? |
| How about drug use? What types of drugs do they use? |
| How common is it to use alcohol when having sex? |
| How common is it to use drugs when having sex? What drugs are used during sex? |
| What does alcohol and drug use have to do with risky sexual behaviours? |
| Social determinants of health |
| In Impact Triad, we focused on employment, education, social support and discrimination as social determinants of health. What else affects the health and well‐being of young gay and bisexual men and transgender women of colour? |
| How big of an issue is housing for young gay and bisexual men and transgender women of colour? How big of an issue is access to food? How do these issues affect their health? |
| Individual perceptions and empowerment |
| What do you think healthy sexuality means to young gay and bisexual men and transgender women of colour in our local community? Have your ideas of what healthy sexuality looks like changed since participating in Impact Triad? |
| What do you think would be good ideas or ways to prevent STIs and HIV among young gay and bisexual men and transgender women of colour living in your community? What do you think would be good ideas or ways to improve social determinants of health? |
| Do you think you will continue your work as a Community Navigator? Why? |
Because of the COVID‐19 pandemic, interviews were conducted via telephone. Two trained bilingual members of our CBPR partnership conducted the interviews in English ($$n = 12$$) and Spanish ($$n = 3$$). Interviews averaged 45 min in length. All Community Navigators provided written consent and were compensated $50 for participating in an interview. Human subject protection and oversight were provided by the Wake Forest University School of Medicine Institutional Review Board.
## Data analysis and interpretation
All interviews were digitally recorded and transcribed. Constant comparison, an approach to grounded theory, was used to analyse interview data. Constant comparison combines qualitative coding with simultaneous comparison; initial observations are continually refined throughout data collection and analysis. 28 Three analysts read and reread interview transcripts, compared and contrasted content categories based on each analyst's interpretation of the data and identified emerging themes. After preliminary themes were developed, analysts came together with other CBPR partnership members via WebEx (a videoconferencing platform) in multiple meetings to discuss and reconcile final themes using an iterative process. Themes were also presented to the CAB in a meeting via WebEx for refinement and validation; CAB members were invited to respond to the themes and offered insights regarding themes that resonated with their own experiences and themes that they considered priorities for informing future efforts to improve the health of young GBMSM and transgender women of colour. At each stage in the analysis and interpretation process, a consensus approach was used, resolving discrepancies through discussion.
## Community Navigator characteristics
Key characteristics of the Impact Triad Community Navigators at the time of enrolment in the intervention trial are presented in Table 2. The average Community Navigator age was 31.4 years. Seven Community Navigators ($47\%$) identified as African American/Black, 5 as Latine ($33\%$), 2 as multiracial/multiethnic ($13\%$) and 1 as Asian American ($7\%$); 10 identified as cisgender men ($67\%$), 4 as transgender women ($27\%$) and 1 as gender nonbinary ($7\%$); 10 identified as gay ($67\%$), 4 as heterosexual ($27\%$) and 1 as bisexual ($7\%$); 4 had a high school diploma or lower ($27\%$) and 11 had at least some college ($73\%$); 5 were in school full‐time ($33\%$) and 12 were employed ($80\%$).
**Table 2**
| Characteristic | Mean (SD) or n (%) |
| --- | --- |
| Age (years) | 31.4 (8.4) |
| Racial/ethnic identity | Racial/ethnic identity |
| African American/Black | 7 (47) |
| Hispanic/Latine | 5 (33) |
| Multiracial/multiethnic | 2 (13) |
| Asian American | 1 (7) |
| Gender | Gender |
| Cisgender man | 10 (67) |
| Transgender woman | 4 (27) |
| Gender nonbinary | 1 (7) |
| Sexual orientation | Sexual orientation |
| Gay | 10 (67) |
| Heterosexual | 4 (27) |
| Bisexual | 1 (7) |
| Education level | Education level |
| Less than a high school diploma or equivalent | 1 (7) |
| High school diploma or equivalent | 3 (20) |
| Some college | 5 (33) |
| 2‐year degree | 1 (7) |
| 4‐year degree | 4 (27) |
| Master's degree, professional degree or more | 1 (7) |
| Student status | Student status |
| Not in school | 10 (67) |
| In school full‐time | 5 (33) |
| Employment status | Employment status |
| Employed | 12 (80) |
| Disabled and not working | 2 (13) |
| Unemployed | 1 (7) |
## Qualitative findings
Thirteen themes emerged from the interviews and were organized into three domains: [1] key aspects of the Community Navigator role, [2] experiences implementing the Impact Triad intervention and [3] lessons learned for future interventions.
## Key aspects of the Community Navigator role
Community Navigators identified several important aspects of fulfilling their role within Impact Triad. These themes, along with selected quotations, are presented in Table 3.
**Table 3**
| Desire to serve as a community leader |
| --- |
| Community Navigators valued serving as leaders and as resources to address social determinants of health within their communities of identity. |
| I liked getting to know other Latinos, and that I learned more about STDs and helped the community to find resources that helped them to live better. (Participant [P] 15) |
| I am educated and went to college, but I never felt like I achieved anything. After being a Community Navigator I feel like I have something to give to the community and it's great. (P11) |
| Importance of being part of the communities in which one was working |
| Being members of the communities in which they worked was fundamental to trust building and successful intervention implementation by Community Navigators. |
| We keep it one hundred, real, and hit the street, because that is what people respect and is how we earn trust, because we are demonstrating how we are part of this world. (P12) |
| Yo creo que mi credibilidad—ellos creen en mí—y que soy parte de la comunidad, eso fue lo que me ayudó. [I believe that my credibility—they put their faith in me—and that I am part of the community, that is what helped me out.] (P2) |
| Value of having an official role |
| Having official titles and roles contributed to Community Navigators' self‐assurance and validated their work in communities. |
| Podíamos hablar de temas de sexualidad sin que fuera tabú. Y era más fácil teniendo el respaldo de una institución y un programa para resolver algunas de las preguntas que tenían algunos de mis amigos y les daba pena hacer. [We were able to discuss topics about sexuality without it being taboo. It was also easier having the backing of an institution and program to assist in answering the questions that some of my friends had but were shy to ask.] (P1) |
| I was extremely well prepared and was ready to become a Community Navigator for all the good, the bad, ugly, and different. We all prepared for everything no matter what happens. (P11) |
| Connections built with other Community Navigators |
| Being connected to the other Community Navigators across gender identities, sexual orientations and racial/ethnic identities increased a sense of belonging, resource sharing, creativity and problem solving. |
| Well for me, I am a helper. I have always been an advocate for the transgender community but didn't have the opportunity to be part of a group before, so having that opportunity was even better than what I thought it would be. It was very special. (P6) |
| I think [the monthly Community Navigator meetings] were probably the most impactful for me, honestly. Connecting with other Navigators and being able to talk about things that we had experienced. (P7) |
| Sustaining intervention aspects long‐term |
| Community Navigators sustained aspects of the intervention after implementation officially ended. |
| I will continue work as a Community Navigator because it isn't a job but is something that needs to be done, even if I am not paid for it. It just needs to be done. (P10) |
| I think I will always be a Community Navigator, formally or informally, because I know something that I can always use and I will always be involved doing something to help others. (P7) |
## Desire to serve as a community leader
Community Navigators expressed that they valued serving as leaders and as resources to address social determinants of health within their communities. Among the Community Navigators, there was a common sense of intrinsic motivation to engage, be involved with and help their communities of identity—LGBTQ communities and communities of colour. Community Navigators reported that they were drawn to the Impact Triad intervention because of the opportunity to gain knowledge to share within their social networks and because they recognized the importance of both promoting sexual health and addressing social determinants that can make it difficult for community members to take care of their health. Community Navigators also appreciated that they made a genuine impact, which furthered their desire to continue being leaders within their social networks and communities. They felt pride in their roles as Community Navigators, saw themselves as fulfilling a need for social support within their communities, enjoyed building leadership skills that could potentially create opportunities for future careers (e.g., public speaking skills) and demonstrated a desire to continue utilizing the knowledge and training they received postintervention.
## Importance of being part of the communities in which one was working
Community Navigators emphasized that being members of the communities in which they worked was fundamental to trust building and successful intervention implementation. Community Navigators had shared experiences and identities and felt a sense of belonging with others in their communities and were known and seen as leaders in their communities before becoming involved with Impact Triad. These connections led to increased initial levels of rapport with community members within Community Navigators' social networks, facilitated the process of recruiting social network members to participate in the intervention and were helpful for further building trust to have conversations about a wide array of health‐related topics.
## Value of having an official role
Community Navigators reported that having official titles and roles that were linked to well‐known and well‐respected institutions (i.e., Wake Forest University School of Medicine and Triad Health Project, an established AIDS service organization in Greensboro, North Carolina), and having received formal training through these institutions, provided a sense of reassurance that helped to facilitate relationship building with members of their communities. As a consequence, Navigators felt more confident in their knowledge about sexual health and social determinants and perceived that they were more trusted to talk with others within the community about sensitive health topics. In addition, Community Navigators highlighted the importance of having a badge indicating their role in the intervention and emphasized that receiving a stipend for their time furthered their sense of legitimacy and validated the work they performed to assist their communities.
## Connections built with other Community Navigators
Community Navigators shared that being connected to the other Community Navigators across gender identities, sexual orientations and racial/ethnic identities increased a sense of belonging, resource sharing, creativity and problem solving. Community Navigators expressed that monthly meetings with the other Community Navigators and intervention coordinators were a time and place for growth because they brought together diverse experiences and perspectives and provided an opportunity to exchange ideas with one another, as well as discuss challenges and ways to overcome them. Navigators noted that it was valuable to know that other Community Navigators, including those with different gender identities, sexual orientations and/or racial/ethnic identities than their own, had found themselves in similar situations and to learn from one another. Thus, these conversations fostered an environment where Community Navigators felt welcome to express themselves and their ideas about promoting health within their communities through engagement and education. While each Community Navigator carried out helping activities independently with their social network members, the Community Navigators also worked as a team by providing social support to one another. These connections took place both in meetings and more informally in one‐on‐one interactions outside of meetings (including via social media) and continued after implementation ended.
## Sustaining intervention aspects long‐term
Community Navigators reported hoping to continue their roles to improve sexual health and social determinants within their communities after intervention implementation officially ended. Many expressed a conviction to share the knowledge they had gained through the intervention, stressing that their social network members and other community members continued to come to them for information. Community Navigators reported continuing to engage in helping activities such as condom distribution and disseminating information through social media because the need within their communities persisted and because it felt natural to continue to draw on the training and resources provided by Impact Triad.
## Experiences implementing the Impact Triad intervention
Another set of themes related to Community Navigators' experiences implementing Impact Triad. These themes and selected quotations are presented in Table 4.
**Table 4**
| Engaging community members |
| --- |
| Community Navigators identified effective and innovative strategies to engage community members. |
| Con las actividades, al momento de juntarnos también teníamos comida, refrescos, botana. A veces aprovechábamos que había algún evento deportivo importante o programas como los Oscar. [With activities, when we gathered we would bring food, drinks, snacks. Sometimes we would take advantage of big sporting events or programs like the Oscars to get together.] (P1) |
| I posted fliers and updated social media about when there was going to be a testing event at Triad Health Project, and just let them know what events were happening and what we were doing. (P8) |
| Les di la prioridad a mis amigos a los que yo sabía que podrían estar tal vez en más riesgo, tuvieran menos información o que en conversaciones privadas me habían comentado que nunca se habían hecho la prueba del VIH. O algunos amigos que podrían no estar utilizando condones o otros métodos de prevención como PrEP. [I gave priority to my friends that I thought would be most at risk, or had less information, or in private conversations had mentioned to me that they had never been tested for HIV. Or other friends who might not be utilizing condoms or other prevention methods such as PrEP.] (P1) |
| Meeting prioritized needs |
| Community Navigators met social network members' prioritized needs by educating and supporting them to access and use local resources. |
| I helped my social network members to find some resources. For instance, most of the guys that I invited didn't know how to get condoms, how to get PrEP, or how to make an appointment to get STD testing. So that was very helpful, because every time they were asking me for more information and more condoms and things like that. (P15) |
| Some of my friends didn't even know where to get condoms or medications or that there are educational resources available. It's good to have associations with organizations like Triad Health Project or FaithAction [immigrant‐serving community organization in Greensboro, NC]. It made them interested or willing to talk more about it. (P15) |
| Getting the information out there is important because I find that a lot of people in our communities don't even know that PrEP exists. (P6) |
| We learned about continuing one's education, employment opportunities, and stuff like that. I think it's better if [social network members] hear from someone they know about the resources available for them. (P9) |
| Building trust |
| Communicating about sexual health and addressing stigma and misinformation were difficult but overcome by building and nurturing trust. |
| It's important to support each other, help people with whatever they need, have open conversations, and build trust, because when you build trust you can change and improve people's lives. (P15) |
| Yes, my friends definitely feel more comfortable now talking to me about sex, how to put a condom on correctly, and things like that. It's not awkward anymore. They feel completely comfortable talking about STDs and PrEP and some of them asked me to go with them to get tested and asked me questions about resources, and I have been able to give out materials with the information they need. (P6) |
| When [social network members] start to trust you, it's easier to talk about anything, so trust made it easier. (P7) |
| Using social media |
| Use of social media complemented personal interaction to further facilitate dissemination of information and support. |
| I was making posts about testing events and testing sites, informing [my social network members] about different STIs and also about jobs, job fairs, and places that may be hiring. Also about housing. I used Facebook, Grindr, and Jack'd. (P4) |
| I think social media helped us so a lot more people could be reached. I don't go to many places so being on social media allows me to get out in the world when I am not going to do it in person. (P9) |
| I used social media to share what I think is important and to show information and resources that I come across. I like to say from my own experiences and other people's experiences too, what people go through, so there is someone seeing it. (P13) |
| I posted on my Snapchat stories, usually about where to go and get tested and about getting tested regularly. I posted on Facebook on my timeline and created stories. (P14) |
| Increasing awareness and knowledge |
| Intervention successes included increased awareness of prevalent health disparities and increased knowledge about sexual health and about resources to address disparities. |
| I told them all about the ‘sexual pandemic’ and then they became curious. I taught the group how to wear a condom correctly, and about how to be safe or get PrEP if they need it. (P14) |
| I educated [my social network] about some of the bad statistics that often face the LGBTQ communities such as HIV rates, unemployment, and things like that that affect us. (P6) |
| Challenges related to COVID‐19 |
| The COVID‐19 pandemic hindered implementation and social media became the main strategy to reach social network members. |
| It was way more difficult to continue with the communication. I guess that's the blessing of social media but, yes, [the pandemic] changed everything. (P12) |
| It made it very different because I feel certain things worked better in person, like teaching people how to put a condom on. [The pandemic] made it a little difficult. (P6) |
| [The pandemic] definitely impacted how many people I was able to reach, because I spread the message in the halls at school just talking to people and it affected my ability to do that. (P14) |
| La pandemia afectó a mi red social mucho porque nos dejamos de reunir, y la intimidad y el espíritu del grupo de estar reunidos en un solo lugar, la convivencia y la confianza que se crea en el grupo, se pierde y se vuelve más individual. [The pandemic affected my social network a lot since we stopped getting together, and the intimacy and group spirit from gathering in the same place, the atmosphere of trust that was created within the group, this was all lost and everything became more individualized.] (P1) |
## Engaging community members
Community Navigators shared that they had identified effective and innovative strategies to engage community members. Community Navigators utilized various strategies to reach the individuals within their social networks. At the beginning of intervention implementation, Community Navigators focused their recruitment efforts on social network members who they perceived as being more vulnerable or at risk and as needing social support and emphasized to these social network members the benefits of their participation. Through this process, Community Navigators were able to reach a broad range of individuals, including engaging nongay identifying MSM by building trust to overcome concerns about having their sexual behaviours exposed. In addition, Community Navigators used creative ways to share information about health and local resources through social media posts and by making in‐person events and gatherings as welcoming and comfortable environments as possible for social network members.
## Meeting prioritized needs
Community Navigators reported meeting social network members' prioritized needs by educating and supporting them to access and use local resources. Community Navigators noted that their role provided them with insight that allowed them to pinpoint needs and priorities within the community. Community Navigators were able to effectively link social network members with the resources they needed, which helped social network members stay engaged in the intervention. Through communication and interaction with social network members, Community Navigators identified and filled knowledge gaps and guided social network members to resources that would meet their needs related to social determinants (e.g., employment and education) and sexual health (e.g., how to access condoms and PrEP and how to make an appointment for STI or HIV screening).
## Building trust
Community Navigators emphasized that communicating about sexual health and addressing stigma and misinformation were difficult but overcome by building and nurturing trust. Many of the Community Navigators reported initial challenges in communication with social network members related to sensitive topics due to stigma and discomfort. Though Community Navigators had their social network members' trust more generally, they had to work on building further trust specifically around sexual health. To surpass this barrier, Community Navigators fostered an open and safe environment and worked to normalize talking about sexual health. To make social network members feel more comfortable, Community Navigators eased into conversations about sexual health, first starting with a topic that was less sensitive or emphasizing common experiences that the Community Navigators and social network members shared, including feelings of stigma. Social network members opened up to the Community Navigators and to one another over time, and conversations about sexual health happened more naturally over the course of intervention implementation as Community Navigators continued to promote a sense of community within their social networks.
## Using social media
Community Navigators described using social media to complement personal interaction to further facilitate dissemination of information and support. Community Navigators reported that personal interactions were an important part of the intervention because they provided a more personable approach, particularly during the early stages of implementation and when recruiting individuals within their social networks to participate. Social media was also an essential tool utilized by Community Navigators throughout the course of the intervention in addition to these personal interactions, and after the COVID‐19 pandemic began many Community Navigators adapted their interactions to rely solely on social media. Community Navigators posted on their social media accounts about resources or community events, and these posts benefited not only their immediate social network but also extended their reach to their social network members' larger networks and individuals whom they would not have interacted with otherwise. Expanding reach in these ways had a dual impact on Community Navigators' work by strengthening both their confidence as Community Navigators and their rapport within the community. In addition, Community Navigators noted other advantages of social media, including that it was a direct and instant form of communication between Community Navigators and social network members and, in some cases, made conversations about sensitive topics easier and more comfortable than in face‐to‐face communication.
## Increasing awareness and knowledge
Community Navigators considered important intervention successes to include increased awareness of prevalent health disparities and increased knowledge about sexual health and about resources to address disparities. Community Navigators expressed that, among their social networks, there was a general lack of awareness of how young GBMSM and transgender women of colour, and particularly those in the US South, were disproportionately affected by STIs and HIV and by social determinants such as unemployment. When educating social network members about these disparities many Community Navigators reported witnessing a ‘light bulb’ moment where social network members internalized information about sexual health and local employment and education resources.
Community Navigators also successfully promoted broader definitions of sexual health, noting that within their communities sexual health had typically been equated with condom use or abstinence only. Through participating in Impact Triad, Community Navigators and members of their social networks came to understand sexual health as also involving screening for STIs and HIV, access to treatment and other prevention strategies such as PrEP.
## Challenges related to COVID‐19
Community Navigators reported that the COVID‐19 pandemic hindered implementation and impacted Community Navigators in various ways. Because of the need for physical distancing, social media became the primary way the Community Navigators communicated with their social network members. Through social media Community Navigators maintained contact with their social networks, but Community Navigators expressed that members of their social networks felt isolated from one another. The connectedness Community Navigators fostered within their social network groups was reduced by the pandemic. In‐person interactions were postponed, and the closeness and ‘spirit’ or morale of the groups were negatively affected. Furthermore, Community Navigators faced additional challenges teaching social network members about certain sexual health topics such as how to correctly use condoms when they could not carry out these activities in person. However, Community Navigators were resilient and adapted quickly to utilizing social media to continue to maintain relationships with participants and engage and teach them.
## Lessons learned for future community navigation interventions
Finally, Community Navigators highlighted learnings to inform future similar interventions. Related themes and selected quotations are presented in Table 5.
**Table 5**
| Facilitating access to broader resources |
| --- |
| The Impact Triad intervention should be expanded to facilitate access to a wider range of resources. |
| I think financial aspects, access to education, resources, transportation, and education are all important. (P5) |
| I think we should work more on housing, because I have learned about other programs that are available for housing. (P4) |
| Without access to food, it will affect your mental health and your physical health. It's a real problem, and then what you are willing to do to get food or what you are willing to do to get housing can affect your future. The problem is disproportionately bigger [among GBMSM and transgender women of color] than other populations. (P13) |
| Building additional skills |
| Community Navigator training should include activities to develop a more in‐depth and diverse skillset, including more practice communicating about sensitive topics and promoting healthy coping strategies. |
| It would be good to learn more about how to talk or how to deal with people who use drugs and also how to talk to people who are newly diagnosed with HIV. (P11) |
| It would help to have training about having difficult conversations and how to be honest with people without making them feel that you are judging them. (P7) |
| I think we need to do more with trauma and mental health, honestly. A good way to help sexual health is to talk about trauma because we live in a society that bombards us with traumatic things all the time. (P13) |
| Increasing interactions among Community Navigators |
| Future iterations of Impact Triad should foster more interaction among Community Navigators. |
| Sería bueno poder reunirnos con navegantes por años de otros proyectos que nos ayuden a buscar nuevas oportunidades y conocimiento para los navegantes. [It would be good to get together with Navigators that have worked for years on other projects who could help us find new opportunities and provide advice for new Navigators.] (P2) |
| As Community Navigators, we had our meetings every month, and we talked about any challenges, shared our thoughts, and provided some feedback. It was really friendly, to be honest. But I would like to have more interactions with the whole group instead of just once a month. (P15) |
## Facilitating access to broader resources
Community Navigators reported that the Impact Triad intervention should be expanded to facilitate access to a wider range of resources. Throughout intervention implementation, Community Navigators heard from their social network members that it would be beneficial to receive information about additional resources related to social determinants of health, such as transportation, food insecurity, financial literacy, housing and substance use, which also contribute to sexual risk. Other barriers such as access to health care more generally, including insurance coverage and mental health care, were also suggested for inclusion as outcomes in future iterations of Impact Triad. Community Navigators noted that young GBMSM and transgender women of colour in different geographical areas experienced different needs, highlighting the importance of expanding the resources shared through the Impact Triad intervention. Community Navigators also suggested potentially adapting the intervention to reach broader communities, such as non‐LGBTQ communities, or to meet the unique needs of specific subgroups such as bisexual men.
## Building additional skills
Community Navigators indicated that future trainings should include activities to develop a more in‐depth and diverse skillset, including more practice communicating about sensitive topics and promoting healthy coping strategies. Community Navigators shared that their training prepared them well for interacting within their social networks but that they experienced challenges in a few areas. Community Navigators felt they would benefit from more skill‐building, particularly through role‐playing, that emphasized effective ways to talk about sensitive and complex topics, such as how to engage in dialogue around a recent positive HIV diagnosis. Many Community Navigators noted that they found it difficult to have conversations about mental health, healthy coping strategies and how to overcome fear among social network members to access services when needed. They noted that more training would be useful to build their skills and increase their self‐efficacy to have these difficult conversations.
## Increasing interactions among Community Navigators
Community Navigators expressed that future iterations of Impact Triad should foster more interaction among Community Navigators. Community Navigators reported that they formed a support network with each other during the monthly meetings with one another, during which they shared ideas, challenges and successes. Many expressed a desire for a greater frequency of these meetings to discuss barriers and receive feedback. To further increase interaction and learning opportunities among Community Navigators, Community Navigators suggested that in future iterations of the intervention, former Community Navigators could be invited to mentor newer Community Navigators and share their experiences, thus expanding the network of trained Community Navigators and building on real‐life experiences of serving as Community Navigators.
## DISCUSSION
Analysis of in‐depth interviews with the Impact Triad Community Navigators both highlighted the ways that intersecting factors magnify health risks among GBMSM and transgender women of colour and illustrated the strengths of the intervention in raising awareness and disseminating information pertaining to sexual health and social determinants of health. One of the greatest contributing factors to the success of the Impact Triad intervention was the Community Navigators' ability to forge and nurture connections and relationships with members of their social networks and then build on this foundation to discuss sensitive topics with confidence and increase awareness, knowledge and use of local resources among these social network members. This finding aligns with existing literature that points to the potential of community‐based peer navigators to reach marginalized communities affected by health disparities and to connect community members to services because community‐based peer navigators understand community needs and strengths on a personal level and reflect the ways that members of their communities communicate and interact. 29, 30, 31 The desire expressed by Community Navigators to sustain their work to continue to improve the overall health of their communities further illustrates the potential impact of the intervention.
Another factor playing a role in the success of Impact Triad was the Community Navigators' flexibility and ability to pivot and adapt to the unprecedented circumstances brought by the onset of the COVID‐19 pandemic. Initially, Community Navigators' interactions with members of their social networks included in‐person communication, which suddenly became no longer possible during the early phases of the pandemic. Adapting to the pandemic led to increased integration and utilization of social media platforms to cultivate and strengthen relationships. Shifting to an entirely virtual implementation of Impact Triad modified previous perceptions of how to connect with individuals and was consistent with documented changes in the ways in which networks provide social support in the context of COVID‐19 with shifts away from physical interaction and toward the use of technology. 32 In some ways, these changes increased accessibility, allowing Community Navigators to overcome logistical barriers to engagement based on locations, schedules and work demands; these learnings could also greatly benefit research efforts in rural and other more isolated communities. However, restrictions due to COVID‐19 did affect camaraderie among Community Navigators and members of their social networks. Community Navigators noted that there was a personal aspect of face‐to‐face contact that social media could not replicate. Nonetheless, our findings demonstrate that the versatility of social media is a major asset in conducting intervention research. Other intervention studies in recent years have reported making similar adaptations due to COVID‐19, 33, 34, 35 and future studies should continue to explore ways to achieve the connection typically built and reinforced through in‐person interaction in a virtual setting.
Interviews also identified implications for future iterations of the Impact Triad intervention to seek to further address and improve the quality of life among young GBMSM and transgender women of colour. A common thread across Community Navigators' experiences involved the intricate ways that interconnected social determinants impact health. To have a more profound impact, future efforts can expand the domains of resources and education provided by Community Navigators by incorporating into Community Navigator training information on how to address needs such as housing, food insecurity, transportation and others, in addition to employment, education, social support and discrimination. Furthermore, leveraging the experiences of existing Community Navigators to provide mentorship and support to future Community Navigators can lead to an increasingly cohesive group dynamic and greater community capacity. For example, future Community Navigator trainings and monthly meetings could include components in which Community Navigators from previous cohorts are present to share their experiences, offer ‘tips’, and answer questions from new Community Navigators.
## Limitations
It is possible that social desirability bias could have influenced Community Navigators' responses to interview items. Given that interview data were collected and analysed by members of our CBPR partnership that had developed and implemented the Impact Triad intervention, Community Navigators may have felt inclined to describe Impact Triad and their experiences with the intervention in ways that were positive and less comfortable sharing constructive criticism or difficulties they had faced as part of Impact Triad. We tried to mitigate this possibility and encourage open and honest responses by ensuring confidentiality and emphasizing that there were no right or wrong answers and that Community Navigators were experts in their own experiences. Furthermore, interviews were conducted by CBPR partnership members who were not directly responsible for training and supporting the Community Navigators.
## CONCLUSIONS
These interviews identified important learnings about serving as Community Navigators and implementing Impact Triad and provided information to guide future efforts to address STI and HIV disparities and critical social determinants of health through community‐based peer navigation. Further research is warranted given that community‐based peer navigation remains an understudied yet promising approach to health promotion and disease prevention among communities and populations experiencing health disparities.
## CONFLICT OF INTEREST
The authors declare no conflict 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: 'Understanding support systems for Parkinson''s disease management in community
settings: A cross‐national qualitative study'
authors:
- Dia Soilemezi
- Ana Palmar‐Santos
- M. Victoria Navarta‐Sánchez
- Helen C. Roberts
- Azucena Pedraz‐Marcos
- Anita Haahr
- Dorthe Sørensen
- Line K. Bragstad
- Ellen G. Hjelle
- Silje Bjørnsen Haavaag
- Mari Carmen Portillo
journal: 'Health Expectations : An International Journal of Public Participation in
Health Care and Health Policy'
year: 2022
pmcid: PMC10010098
doi: 10.1111/hex.13691
license: CC BY 4.0
---
# Understanding support systems for Parkinson's disease management in community settings: A cross‐national qualitative study
## Abstract
### Background
Health and social care systems face difficulties in managing multimorbidity, disease burden and complex needs in long‐term conditions such as Parkinson's disease.
### Objective
This study aimed to develop a European understanding of how health and social care professionals can collaborate with stakeholders from different organizations and sectors to enhance the management of Parkinson's disease in a community setting by identifying the existing gaps in this process and how people with Parkinson's disease and their family carers could benefit from these partnerships.
### Methods
A mixed‐methods sequential study was conducted in Denmark, Norway, Spain and the United Kingdom. The findings from the qualitative phase are presented. Individual semistructured interviews were analysed using Braun's and Clarke's thematic analysis. A meta‐ethnography approach was used to analyse and synthesize cross‐national findings.
### Results
A total of 41 healthcare professionals and 39 stakeholders from different disciplines and sectors were interviewed in the four countries. The participants acknowledged a lack of awareness of available resources and poor communication between the different support systems in the management of Parkinson's disease. To promote multiagency collaborations, the participants highlighted the need to organize services along the Parkinson's disease journey, patient involvement and strategic involvement of carers in organizing resources and Parkinson's disease care pathways. According to the participants, the benefits from multiagency partnerships could lead to an enhanced continuity of care and specialized knowledge, mobilization of resources in the community, personalized support and improved access to services.
### Conclusions
Policymakers are called upon to create formal structures that facilitate multisectoral collaborations to promote an integrated system of care for the management of Parkinson's disease in the community. To address this challenge, we propose five strategies showing how organizations can work together to optimize the use of resources and enhance the management of Parkinson's disease throughout the illness trajectory.
### Patient or Public Contribution
Patient and Public Involvement groups made up of stakeholders, healthcare professionals, patients with Parkinson's disease and family carers participated in the design of the study, the development of the interview guides and the validation of the findings.
## INTRODUCTION
The overall number of people diagnosed with Parkinson's Disease (PD) has been growing progressively globally. In 2019, approximately 8.5 million individuals had received a PD diagnosis. 1 This estimation is expected to increase to 12 million people in 2050, 2 indicating that compared to other neurological conditions, PD has the fastest‐growing rate in most countries.
Previous evidence has shown the direct and indirect costs associated with the management of PD, which affects both patients and family carers in relation to hospital admissions, medication, nonmotor symptoms and productivity loss. 3 The consequences of PD on an individual level may result in the need for continuous support to manage multiple aspects of everyday life, including mobility, work, medication, safety, social life and emotional stability. 3 In addition, cognitive deterioration in the person with PD (PwPD) may involve a financial burden for the PwPD, the family carer and the health and social care system. 4, 5, 6 Thus, support is needed from a long‐term perspective and often increases with the progression of the illness.
Life with PD usually takes place in the community, where PwPD and their family carers have to learn to cope with the PD and its consequences. 7 Current guidelines contain information regarding medication, symptom management, patient and professional relationships and communication and assessments. 8 However, with a clear focus on an acute episodic model of care, healthcare services are under pressure and may neglect nonbiomedical consequences of PD (biographical disruptions, negative emotions, strained relationships, nonmotor symptoms and a restriction of meaningful activities), which constitute the most essential burden for patients and families and are the leading causes of hospital re‐admissions and a poor quality of life. 7, 9, 10 Furthermore, the existing National Healthcare Systems' personalized self‐care plans and tools fail to capture how people live with and adjust to PD from the PwPD's and the family carers' perspectives. These demands on health and social care systems globally and the limited resources lead to gaps in the care pathways related to manage multimorbidity, disease burden and complex needs and to reach disadvantaged populations, which are understood in this study to be those having immigrant status and/or an ethnic minority background, being older, being socially vulnerable, living with disabilities due to long‐term conditions and being a caregiver. 11 Self‐management programmes for long‐term conditions are evolving and are now increasingly seen as a collective initiative involving personal networks and other community resources, which go beyond those traditionally known as formal services. 12 Consequently, this work builds on new understandings of how stronger collaborations between the levels of care and additional support can enhance existing self‐management approaches for PD on a community level 13, 14 while also reaching disadvantaged areas through more integrated action plans. 11, 12 Previous research has shown that community resources such as voluntary organizations can improve health outcomes through broader forms of support that include the provision of information, physical or social activities, and are better able to reach disadvantaged populations compared with the health and social care services. 15, 16 Furthermore, European recommendations 8, 17 are taking a strategic leap when it comes to placing patients and their families at the centre of decision‐making processes and also regarding the importance of involving various agents in the management of long‐term conditions, including PD. Nevertheless, despite these initiatives, the relationships between agencies are still not clearly established or understood. The lack of awareness of what support is available in the community can lead to an overlap in activities, limited use of community resources and action‐planning gaps. 18, 19, 20 Understanding how systems of support for PD management in the community work is essential to enhance the reach of services. Moreover, it is paramount to identify the successful initiatives used by different countries and to learn from established good practices.
In response to the previously mentioned knowledge gaps, the overall aim of this paper is to develop a European understanding of how health and social care professionals can collaborate with stakeholders from different organizations and sectors to enhance the management of PD in the community, and to identify the existing gaps in the collaboration and the potential benefits for PwPD and their family carers.
In particular, the following research questions will be answered: Q1. How could professionals and stakeholders from different levels of care and sectors work together to improve PD management in the community?
Q2. What are the gaps in the collaboration?
Q3. What could the potential benefits of partnerships for PD management be?
## Study design and setting
This article presents the qualitative phase of a sequential mixed‐methods study conducted in Denmark, Norway, Spain and the United Kingdom. This study is part of the OPTIM‐PARK project, which aims to enhance the process of living with PD by designing multisectoral care pathways to optimize the use of community resources across European countries. In this paper, we report findings from the qualitative phase, which is part of the development stage of The UK Medical Research Council framework for developing and evaluating complex interventions. 21 A strength of this study is the Patient and Public Involvement (PPI) from all countries in different phases to maximize the relevance, applicability and transferability of the findings. The study was reported using the *Consolidated criteria* for reporting qualitative research (COREQ) (see Supporting Information: File 1).
## Participants
A purposeful sampling of health and social care professionals and stakeholders was chosen in each participating country. A total of 40 participants were selected to ensure a broad representation of profiles in each group: [1]Health professionals from different disciplines that provide support directly or indirectly to PwPD and family carers. The exclusion criteria were an unwillingness to participate in the project or they were not involved in the direct care or support of PwPD.[2]Stakeholders from different sectors that directly or indirectly impact in the management of PD and the development of care pathways for PD or other long‐term conditions. The exclusion criteria were an unwillingness to participate in the project or a lack of involvement in their role in the strategic planning of community PD care.
Participants were recruited through the strategies shown in Figure 1. Two healthcare professionals and five stakeholders decided not to participate in the interview due to lack of time.
**Figure 1:** *Strategies for recruiting healthcare professionals and stakeholders*
## Data collection
Semistructured individual interviews were conducted between April and October 2020 and supported by an interview guide (Table 1), which was developed by all partners (Table 2) and refined by the PPI groups in Spain and the United Kingdom. Interviews initially took place face to face ($$n = 16$$), although due to the Covid‐19 pandemic, the majority had to be carried out by telephone ($$n = 30$$) and video conference ($$n = 34$$). The interviewers (all women) in all countries (Table 2) had extensive experience in conducting in‐depth qualitative interviews.
All the participants were also asked to complete a sociodemographic form. The recorded interviews lasted between 32 and 118 min, with an average of 60 min.
## Data analysis
All the interviews were transcribed and analysed following Braun's and Clarke's 22 thematic analysis combining deductive and inductive approaches (see Figure 2). 23, 24, 25, 26 *The analysis* started with an inductive approach with several readings and the categorization of the full transcripts of the professional interviews from Spain and stakeholders interviews from the United Kingdom to provide a framework of analysis connected to the research questions that the other participating countries could follow. All the countries completed their national analyses following the framework provided using a deductive approach and also created additional codes/themes whenever relevant using an inductive approach. An excel database for each analysed group of participants including codes, themes, quotes and a description of the themes was created and shared among all countries.
**Figure 2:** *Analysis process*
All the interviews were analysed in the original language of each country, and country‐specific reports were written in English explaining the process followed, and included the findings with quotes for each particular group of participants. A total of 81 themes and 186 subthemes emerged from the analysis across all countries.
Once the country‐specific reports and findings were received, a cross‐national comparison was initiated, which involved multiple readings and discussions across teams towards an analytic synthesis. A meta‐ethnography approach (lines of argument synthesis) was applied, 27 which helped to interpret and explain the findings across groups and countries, not in an attempt to create generalizations, but to ensure translation from one qualitative case study to another. Using the lines of argument strategy, 27 the most powerful constructs representing the entire data set from all countries were identified. This led to an agreed conceptual framework that incorporated a network of interconnected themes that are presented in the results and enhanced understanding of the phenomenon under study. This process led to comparative cross‐national synthetic constructs elaborated in the discussion.
## Ethical considerations
Following required ethical approval, the participants received a study invitation and were informed of the plans to maintain the participants' confidentiality and anonymity. They all signed an informed consent form. The participants were then allocated a study number and all the names were removed from the analysis and the written national reports.
## RESULTS
In total, 41 healthcare professionals and 39 stakeholders were interviewed across the four countries (Table 3). Most participants were women comprising professionals ($85.4\%$) and stakeholders ($79.5\%$), with an average age of 48.5 and 51.1 years, respectively. In relation to the profile of professionals, many nurses ($31.7\%$), physicians ($26.8\%$) and therapists ($34.1\%$) participated. The role played by the stakeholders in their organization was mainly managerial ($48.7\%$) and direct work with other groups, people with PD or carers ($30.1\%$). Moreover, at least $30\%$ of the participants from each country met the requirement of working actively and directly with vulnerable groups.
**Table 3**
| Unnamed: 0 | Professionals | Professionals.1 | Professionals.2 | Professionals.3 | Stakeholders | Stakeholders.1 | Stakeholders.2 | Stakeholders.3 | Total PROF | Total STH |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | UK (n = 10) | Spain (n = 11) | Norway (n = 10) | Denmark (n = 10) | UK (n = 11) | Spain (n = 10) | Norway (n = 9) | Denmark (n = 9) | 41 | 39 |
| Female | 8 | 10 | 10 | 7 | 10 | 8 | 5 | 8 | 35 | 31 |
| Male | 2 | 1 | 0 | 3 | 1 | 2 | 4 | 1 | 6 | 8 |
| Age | 48 (35–63) | 45.5 (31–62) | 48.2 (28–67) | 52.6 (37–60) | 50.6 (38–74) | 54 (42–67) | 51.7 (38–77) | 49.6 (39–59) | 48.5 (28–67) | 51.5 (38–77) |
| Years in this position | 21.6 (11–41) | 22.5 (6–42) | 12.9 (5–20) | 22.2 (5–35) | 9 (0.17–24) | 12.9 (2–43) | 8.6 (1–16) | 7.3 (1.5–20) | 19.9 (5–42) | 9.5 (0.17–43) |
| Years working with people with PD level | 8.5 (1–29) | 18.2 (1–39) | 7.5 (1–20) | 11.1 (3–20) | | | | | 14.9 (3–42) | |
| National | | | | | 6 | 4 | 7 | 2 | | 19 |
| Regional | | | | | | 5 | 2 | 1 | | 8 |
| Local | | | | | 5 | 1 | | 4 | | 10 |
| Others | | | | | | | | 2 | | 2 |
| Professionals' profiles | | | | | | | | | | |
| Nursea | 2 | 4 | 4 | 3 | | | | | 13 | |
| Physicianb | 3 | 4 | 1 | 3 | | | | | 11 | |
| Speech and language therapist | 2 | | 1 | 1 | | | | | 4 | |
| Physiotherapist | 2 | 1 | 1 | 2 | | | | | 6 | |
| Occupational therapist | | 1 | 2 | 1 | | | | | 4 | |
| Clinical Psychologist | 1 | | | | | | | | 1 | |
| Social worker | | 1 | | | | | | | 1 | |
| Training instructor | | | 1 | | | | | | 1 | |
| Role in the organization | | | | | | | | | | |
| Research | | | | | 2 | 3 | | | | 5 |
| Direct workc | | | | | 5 | 1 | 2 | 4 | | 12 |
| Managerial | | | | | 3 | 5 | 6 | 5 | | 19 |
| Policy | | | | | | 1 | | | | 1 |
| Other: Strategic lead—service design team | | | | | 1 | | 1 | | | 2 |
A total of two themes and five categories emerged from the cross‐national analysis and these are presented below (for additional quotes, see Supporting Information: File 2).
## Towards more connected systems of support
This theme describes the existing gaps and challenges in the health and social care systems, and the fragmented communication and support in PD management perceived by both professionals and stakeholders. It also covers the benefits that multiagency partnerships could potentially bring to the care and support systems in the four countries, according to the participants.
## Staff capacity and training
The stakeholders and professionals identified difficulties in the current systems of support such as an increased workload and overstretched services. The interviewees in the four participating countries discussed the increase in demand and caseload, the reduction in commissioned services and a reduced consultation time, which have all impacted the way the care and support is delivered. As a result, PwPD may call upon alternative support systems such as the family, voluntary organizations and other services to cover the care that the health system cannot provide. Most stakeholders and some professionals in some countries considered that the voluntary organizations were in a better position than the healthcare professionals and had more time to cover informational, social and emotional needs. Both the professionals and stakeholders perceived that the involvement of alternative support systems in PD management was largely dependent on each country's formal system and available funding, the changing political landscape and the individual's commitment to sustain the available support in a specific region, and also that there was a lack of a formal organizational structure and co‐ordination between sectors and organizations. It's only volunteers that work with these things, so it depends on what resources are available locally. In some places, there is a person or someone very passionate about something that becomes something big there because someone has a lot of energy to do it, and in other places it can be different. ( NO‐SH‐003) Some of the benefits identified by all countries from potential multiagency collaborations were the complementary roles in care and support provision. The professionals (Denmark [DK], Spain [SP], United Kingdom [UK]) and stakeholders (SP, UK, Norway [NO]) highlighted the specific advantages of collaborating with community organizations and the voluntary sector, such as organizing social activities, for example, walking groups, theatre and dance, which could provide peer support, a feeling of belonging and being part of a community, something that the clinical community cannot provide. In any case, identifying other hubs of support in the community was seen as a great opportunity to promote the PwPD's independence from the overstretched health system. I quite often will suggest just the [name of organisation] website […] actually Parkinson's cafes give people the opportunity to come together once a month, to have a chat, to get some support. ( UK‐SH‐008) In addition, the professionals from all countries commented on the staff's lack of PD specialized clinical skills, from primary care, community services and health centres, which could potentially lead to clinical misjudgements. Many participants indicated the need for education to improve care and support. The participants from Norway and Denmark shared current training opportunities, for example, the Parkinson Net model in Norway, and in Denmark, passionate professionals often educate other professionals about PD symptoms and care. Health centres have very few patients with PD, so I have actually been out teaching at several of the centres, just to give them the most basic knowledge about PD. ( DK‐HCP‐002) Moreover, in an attempt to foster a multiagency or more connected model of care, it was suggested that all parties could share training resources and best practices to complement each other and ensure continuous professional development. As such, all the agencies could benefit from the existing resources and expertise and avoid duplication, while addressing existing training gaps. The stakeholders and professionals from all of the countries agreed that linking up multidisciplinary and multisectoral teams might facilitate potential continuity of care, better management and knowledge mobilization, which is currently missing. Partnerships were also considered as a path to accessing specialized care that was not formally established in PD care pathways. You don't see a social worker going with the doctor or nurse for a home visit. When, well, yes, it is important for each one to make their report, okay, but also to see the relationships a bit, right? […] the representatives of the institutions have to negotiate and reach agreements. ( SP‐SH‐002)
## Awareness of, and communication between, support systems
Health and social care professionals in all four countries acknowledged that they were not always aware of local resources and support that were available as these were constantly changing and very diverse. This made it difficult and frustrating for professionals to navigate the existing resources and to check if the services were still available in their region. An additional issue raised by the interviewees in all countries was that some people could be missing out on the support available because they choose not to be part of the local Parkinson's association. Our association is of great importance to those who choose to and do sign up; those who choose to participate. Because some choose not to, they can be difficult to get in touch with. And they are probably the ones who need it most, right? ( DK‐HCP‐010) The participants in all of the countries identified that working in silos contributed significantly to the fragmentation of support and communication in PD management. In many cases, due to the bureaucracy and lack of communication between professionals and the different sectors, it has been difficult for clinicians to maintain an overview of the PwPD's history, for example, the admissions, discharges and follow‐ups were not communicated between clinicians. The community services are not necessarily told if, for example a PwPD falls, and he gets physiotherapy in a private clinic. Then he might tell the physiotherapist, but that information never goes any further. ( DK‐SH‐004) In discussing the potential benefits of working together, the participants in Norway, the United Kingdom and Spain shared examples of past, existing or ideal collaborations, such as when PD nurses had worked closely with consultants and local PD groups, the collaborations between health and social care services and family. According to the participants, reducing the burden of PwPD, maximizing clinical time and thus improving care might be some of the potential benefits from shared information record systems. In addition, sharing communication channels could be cost‐effective and time‐saving. All the participants agreed that effective communication between the levels of care and sectors could create a more connected system, with decision‐making processes involving treatment management being shared between patient, carers and different professionals. It would be a great advance, to create a truly multidisciplinary team […] we would have a fluid communication that could avoid making the patient dizzy, that the problems are not solved, that ends up in the hospital or in the ER hours and hours, using a resource that is not necessary at that time. ( SP‐HCP‐001)
## Managing the complexity and support needs of a neurodegenerative disease
This theme captures the complexity of care and support for PwPD and their families to address their increasing vulnerability and social, mental and health needs at various stages of PD. It also illustrates the potential benefits of multiagency, and across organization partnerships throughout the PD journey, such as enhanced support to PwPD and their families through their active engagement.
## Timely, meaningful and broader support
Issues concerning the inconsistent support and the lack of long‐term sustainability of the management of PD were discussed by the interviewees in all countries. These inconsistencies could be due to both the geographical location and the complex needs of the PD journey. For example, Norwegian health professionals highlighted that in some municipalities, PwPD did not receive personalized support due to the remote geography of the country. Some [professionals] have a pure Parkinson nurse position and can be reached all week, from Monday to Friday, some can be reached once a week, while others can only be contacted for a few hours per week. And sadly, this differs greatly from place to place. There is no standardized plan for this. ( NO‐SH‐004) The participants in Denmark and Spain noted the lack of support towards people living with advanced stages of the condition, for example, cognitive decline, and end‐of‐life care. I would like that there were more resources available for PwPD in the later stages. They are often forgotten. We have offers for all other stages, but in the later stages … arghh … I think something is missing. ( DK‐SH‐005) This postcode ‘lottery’ and lack of standardized provision of support were perceived as potentially creating health inequalities. Professionals from different disciplines and sectors in all countries acknowledged the imperative input to support PwPD in a long‐term perspective, from diagnosis to the advanced stages and criticized the poor management of mental health issues. We definitely had some patients who desperately needed psychological help […] who were really struggling with coming to terms with the diagnosis, that you know, suffered significant anxiety and depression. ( UK‐HCP‐007)
## Patient involvement and engagement
Management of the complexity and support needs along the PD journey could not be achieved without patient involvement and their continuous engagement. The lack of patient involvement in the design of services was mentioned by all countries, except the participants from Norway, where PPI in both public and voluntary organizations is well established, and where the PPI representatives are viewed as essential partners. In other countries, PwPD may not be involved in co‐production of care, or treatment plans, and several of the professionals had not considered this.
Many of the participants also experienced a lack of interest in and a low attendance to some of the available support services by PwPD. A barrier to engage in certain resources according to the Norwegian and the UK stakeholders was that the support available was not always flexible and responsive to the individual's particular needs. The professionals from all countries noted that language and location were potential barriers to the attendance to some resources. Professionals also discussed the need for different types of support to appeal to PwPD in different illness stages, from diagnosis to bereavement, and preferences, for example, social groups with many elderly people may not be appealing to younger PwPD, or those who have not accepted their diagnosis may consider it stigmatizing or a forecast of future deterioration.you can send out a letter to say the department is changing […] feedback from our patient group was what the hell is this? there was a big lesson learning there, in terms of any literature that we are going to send out to patients, we probably need to get patients to read it before we send it out! ( UK‐SH‐011) Building partnerships between disciplines, sectors, PwPD and FC could lead to personalized PD care and ensure continuous engagement with the decision‐making processes. This approach could enable PwPD to be partners and gain sense of control over their PD (self) management. He is an active patient, that is, you as a health professional will accompany him, you will help him to cope well with his illness, but the one who has to manage his illness is him. ( SP‐HCP‐001)
## Support to and involvement from carers
Although family carers were generally considered to be a relevant support in managing the complexity of PD, professionals in the United Kingdom and stakeholders in Spain acknowledged that carers were not always involved in designing and implementing care plans with the PwPD, and that carer engagement should start early. All countries acknowledged the need for support to the informal carers, who provide the care and may experience severe stress. We use the carers; we don't take over the tasks that they have. If carers become exhausted and there is a need for assistance, then that's what we're working towards rather than us starting to relieve carers so that they won't get worn out. ( NO‐HCP‐09) A potential outcome from multiagency partnerships could be proactively offering more support for carers, rather than solely reactively. Carers often lacked the initial knowledge and skills to deal with PD but could be quite resourceful and were proactively seeking help to access information and community/formal resources. Moreover, carers could be signposted to professional services and community resources to prevent burden and stress and offer opportunities for respite time if required.the family and carers are really active at that diagnosis point and that wasn't really featuring in our service offer … we hadn't realised that family and carers were actually the people doing all the information seeking at that moment. ( UK‐SH‐010)
## DISCUSSION
This qualitative study has shown a European understanding of how health and social care professionals and other stakeholders from different agencies and organizations can work together to enhance PD management in the community, what the existing gaps in this process are and how people with PD and family carers could benefit from these partnerships.
The main gaps in PD care identified in our study by the participants were overstretched services, lack of awareness of available resources and support, a limited trained workforce, disjointed services and fragmented communication, inconsistent and limited support, in particular, in mental health issues and advanced stages, and poor patient and carer involvement. Identifying these barriers to multiagency partnerships in PD management is an essential step in planning strategies to address them in European health systems. Recent studies 6, 28, 29, 30 have also identified three of these barriers, the lack of interdisciplinary management and ongoing support, especially regarding psychological needs, and advanced stages, and the fragmentation of health and social care in other countries.
To address these gaps, our main findings from health and social care professionals and stakeholders are integrated in Figure 3, which proposes five strategies and four underpinning mechanisms that could make it easier for different organizations to work together to improve PD management in a community setting.
**Figure 3:** *Strategies and mechanisms to sustain a more connected system of care for better PD management in the community. PD, Parkinson's Disease.*
The first strategy is to have the right staff capacity and resources to implement integrated systems of care for PD management. This includes the staff having sufficient training to obtain specialist PD skills, which is the second strategy. To achieve these multiagency collaborations, a macro‐level formal structure to formalize partnerships and care pathways for the PD management in the community might be proposed by policymakers. This could result in shifting priorities towards individualized care and a common vision and agreed agendas. At a meso level, co‐ordination is proposed that will enhance the connections between agencies, levels of care, professionals, the voluntary sector, community organizations, PwPD and family carers and help to navigate and mobilize resources to overcome the staff shortages. These connections could be achieved by an awareness of the roles and resources, specialized training, shared communication systems, complementing expertise and sharing best practice, of the creation of community hubs and identifying PD champions/navigators.
The relevance of creating formal partnerships involving all agencies, that is, the voluntary sector, the community, PWPD and family carers, in PD management has not yet been explored. However, according to the WHO and some comprehensive community‐based programmes, 17, 20, 31, 32, 33 a multisectoral approach has previously been shown to provide benefits in addressing health problems and reducing health inequalities through sharing objectives, pooling resources and optimizing them by avoiding duplication of activities. Furthermore, a multisectoral approach could facilitate two changes: an increase in the number of healthcare professionals who specialize in PD, and community care as the major context for PD management. According to previous studies, 28, 34, 35 these changes are essential to improve care for PwPD, especially the most vulnerable, the elderly, to reduce unexpected hospital admissions, carer burden, costs, pressure on the medical system and to enhance the patient's experience and their quality of life. This important change in PD management, from the care delivered mainly in hospitals towards care in the community and in the patients' home, is needed in many countries to achieve a patient‐centred perspective and to address health and nonhealth needs. 28, 35 *It is* in the community context that PwPD face multiple motor and nonmotor symptoms including cognitive decline 6 and where PwPD and their family carers face the adjustment process to their new personal, familiar, social and professional roles. 5, 36 Hence, it is important that all health and social care professionals involved in PD management acquire specialist training in PD and an in‐depth knowledge of the role of the different disciplines involved. 28 The training delivered to multidisciplinary teams in the Dutch ParkinsonNet to increase specialization in PD is an example that has shown improvements in patient outcomes and care costs. 37 The third strategy is effective communication between and across services, organizations, PwPD and their families and awareness of what support is available (see Figure 3). The strategy identified in our study is in line with previous international studies that have demonstrated that working with community organizations (beyond the healthcare system) is associated with better health outcomes in people with long‐term conditions. 13, 31 However, there is a gap in these studies as this has not been studied in PD. We propose that individual assessments of social support, from individual social networks and neighbourhoods, and participation in community organizations and the voluntary sector could also bring benefits for PwPD in terms of self‐management and health outcomes. Moreover, improving communication between health and social care professionals, regarding the levels of care, community organizations, the voluntary sector and PwPD and family carers, should be a priority for policymakers to foster multisectoral collaboration and integrated systems of care for PD management. 29, 31 The fourth strategy is individualized care along the PD journey that promotes timely, meaningful and wider support. The management of PD through this model is paramount to address care fragmentation, poor interdisciplinary care and promote timely access to services and therapies. 28, 38 To promote individualized care throughout the PD journey, it is essential to identify in healthcare a single point of access or a care coordinator, which is an urgent need according to PwPD 28, 29 and long‐term guidelines. 8 The care coordinator, or single hub, could play a leading role in the assessment process of each person, liaise and work with all health and social care services, the voluntary sector and community organizations and ensure that all referrals to any service or organization start working well for the person. 8, 29 The final strategy is to reach to PwPD and their families to ensure meaningful involvement and continuous engagement. We propose, from a micro‐level perspective, that PwPD and their families can become valuable partners that can influence these partnerships and advocate personalized support by their continuous engagement, involvement in clinical decision‐making and the management of their condition and preferred support.
In addition, it is proposed that the PwPD, and their family carers if appropriate, are involved in their needs assessment, as it has been highlighted in other long‐term conditions. 8 We also propose the need to include the family carers in these assessments to identify any caring, physical and mental health needs. 8 Fostering self‐management for PwPD is also paramount for a person‐centred approach but also requires ensuring educational and support opportunities. 29, 39 The adoption of this model may result in positive outcomes that are relevant to services, organizations, healthcare professionals, PwPD and their family carers, as described above and shown in Figure 3. Future research should explore the implementation of a multisectoral approach for PD management in a particular context. Future development of tools that help healthcare professionals and stakeholders connect, share resources and optimize communication could also constitute a breakthrough to a more personalized, integrated and cost‐effective PD care.
## Limitations
Although we have found important commonalities across country findings, we also acknowledge the existence of cultural differences and the variety of health and social care systems, as well as the use of both inductive and deductive thematic analyses, which could lead to a loss of national findings. However, the wide experience of researchers who undertook all interviews, the involvement of at least two researchers in each country in all analyses, the application of the meta‐ethnography approach (lines of argument synthesis) and the validation from the PPI groups have minimized this.
To our knowledge, this is the first exploratory study across four European countries engaging with a variety of participants to understand how different agencies can collaborate to enhance PD management in a community setting. This study has provided new insights and understanding that could facilitate changes across other countries with established healthcare systems and encourage a more connected system of care in PD and other long‐term conditions.
## CONCLUSIONS
Policymakers are called upon to create formal structures that facilitate multisectoral collaborations between healthcare, social care, community organizations, the voluntary sector and other agents to promote an integrated system of care for PD management in community settings. To address this challenge, five strategies of how different organizations can work together to enhance the management of the different needs throughout the PD journey and the optimization of the resources of the health and social care are proposed.
## AUTHOR CONTRIBUTIONS
Dia Soilemezi: Formal analysis; investigation; methodology; validation; visualization; writing – original draft; writing – review & editing. Ana Palmar‐Santos: Formal analysis; investigation; methodology; validation; visualization; writing – original draft; writing – review & editing. M. Victoria Navarta‐Sánchez: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; validation; visualization; resources; writing – original draft, writing – review & editing. Helen C. Roberts: Conceptualization; formal analysis; funding acquisition; investigation; methodology; validation; visualization; writing – review & editing. Azucena Pedraz‐Marcos: Formal analysis; Investigation; Methodology; Validation; Visualization; Writing – original draft; Writing – review & editing. Anita Haahr: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; validation; visualization; resources; writing – review & editing. Dorthe Sørensen: Formal analysis; investigation; methodology; validation; visualization; writing – review & editing. Line K. Bragstad: Data curation; formal analysis; investigation; methodology; resources; validation; visualization; writing – review & editing. Ellen G. Hjelle: Formal analysis; investigation; methodology; validation; visualization; writing – review & editing. Silje Bjørnsen Haavaag: Formal analysis; investigation; methodology; validation; visualization; writing – review & editing. Mari Carmen Portillo: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; validation; visualization; supervision; writing – original draft; writing – review & editing. All authors have contributed to the manuscript substantially and have agreed to the final submitted version.
## CONFLICT OF INTEREST
The authors declare no conflict of interest.
## ETHICS STATEMENT
This study obtained the ethical approval from the required ethics committees: University of Southampton—IRAS number: 265184; Research Ethics Committee in Hospital Universitario La Princesa number: 3995, CEIm $\frac{02}{20}$; Norwegian Centre for Research Data reference number: 986940. Participants gave informed consent before taking part in this study.
## DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
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|
---
title: '‘It''s like a never‐ending diabetes youth camp’: Co‐designing a digital social
network for young people with type 1 diabetes'
authors:
- Kerstin Ramfelt
- Boel Andersson Gäre
- Ann‐Christine Andersson
- Christina Petersson
journal: 'Health Expectations : An International Journal of Public Participation in
Health Care and Health Policy'
year: 2022
pmcid: PMC10010099
doi: 10.1111/hex.13690
license: CC BY 4.0
---
# ‘It's like a never‐ending diabetes youth camp’: Co‐designing a digital social network for young people with type 1 diabetes
## Abstract
### Introduction
Living with a chronic condition such as type 1 diabetes (T1D) affects everyday life and support from others experiencing a similar situation can be helpful. A way to receive such support is to use an online network where people can connect and share experiences. Research has described the benefits of using such tools for connecting patients. The aim of this study was to describe the co‐design of a social network for young people with T1D and to describe their experiences when using this network.
### Methods
A co‐design approach was used, following three steps adapted from Sanders and Stappers [2008]. In all, 36 adolescents with T1D participated. Data in the form of recordings and notes from telephone interviews, workshops and focus groups were collected and then analysed using content analysis. Numerical data from the digital platform were also used.
### Findings
For the interpersonal values, supporting, learning and relating to emerge, the framework of the network must be appealing and user‐friendly. The limits of time and place are eliminated, and there is a possibility for many more to join in.
### Conclusion
Co‐design ensures that what stakeholders think is important forms the basis for the design. The interpersonal values that are promoted are ones that only the exchange of lived knowledge and experience can generate. It is complementary to the support that healthcare professionals can offer; thus, this kind of social network is important for improved, coproduced care.
### Patient or Public Contribution
The participants in the present study were persons living with T1D. They were active co‐creators from the start to the end. An adult person with experience of living with T1D was involved as an advisor in the research team when drafting the manuscript.
## INTRODUCTION
Type 1 diabetes (T1D) is a noncurable chronic condition that requires treatment and ongoing self‐management ‘$\frac{24}{7}$’. Adolescents with diabetes and their caregivers spend less than $1\%$ of their time a year visiting a diabetes healthcare provider. 1 Knowledge about the disease and the need for support are important factors in adolescents' lives; having a chronic condition may lead to the feeling of being different. 2 Between the ages of 11 and 15, most young people perform much of the daily diabetes management, and in mid‐teens (ages 15–17) take responsibility almost entirely on their own. 3 Living with T1D has a huge impact on adolescents' daily life, 4 and they troubleshoot and make decisions in day‐to‐day activities on their own; therefore, they seek out and understand the importance of getting support from others in a similar situation. 5, 6, 7 Even though healthcare professionals are aware of this need, it may be difficult to organize groups within the healthcare system and even if they do, few young people join in. 8 Consequently, using other forms more focused on young persons, such as online communities, could represent one solution.
## Online social networks
An online social community or network involves people sharing experiences and supporting each other in online activities. 9, 10 Recent research describes the benefits of using different tools for connecting patients with chronic conditions to their peers. 11 For example, young women with T1D found comfort in receiving social support on forums on the Internet from others in the same situation; it helped them maintain a balanced view of their lives and to manage life transitions. 12 Adolescents and young adults with T1D who use social media in their everyday lives achieved better control compared to patients who did not use social media. 13 In the United States, $23\%$–$39\%$ of young people seek peers online. 14 Moreover, online social networks could be useful tools for patients and/or their caregivers to learn about blood glucose devices and receive technological assistance from other members. Through closed groups, members may help others in the network by spreading awareness about the condition itself, and providing emotional support and/or technical assistance when building on members' shared experiences. 15 Existing research exploring diabetes online communities shows that people with T1D seek out diabetes online networks because it is challenging to identify a peer in real life. The shared experience has been mentioned as the most frequent topic discussed in several studies, and the sense of normality and validation of lived experiences are also central. 10 The enthusiasm about using networks goes beyond information support and adds the value of emotional solidarity, shared feelings and experiences. 16 The immediate response and orientations from other members give opportunity to acknowledge the community as a safe space. This is not possible in the contacts with healthcare providers. 17 A recent scoping review of diabetes online communities reported promising results showing several benefits and relatively few negative outcomes. This points to the importance of participatory frameworks with the inclusion of users in the design and in the parameter‐setting stages, to better capture community elements and potentially increase social validity and usability of future networks. 10 This indicates that the users should be involved in the design process when developing a social network. The aim of this study was to describe the co‐design process of a social network for young persons with T1D and to describe the experiences of using this network.
## Participants
A co‐design approach was used involving joint exploration and articulation of needs and solutions. 18, 19 A sample of convenience 20 was created, in which three outpatient children's departments, serving about 700 patients, in the southeast of Sweden were involved. The inclusion criteria were young persons between 13 and 17 years of age diagnosed with T1D at least 1 year before the initiative (Table 1). Twelve persons from each department were included ($$n = 36$$). Diabetes nurses at the participating departments provided contact information to parents whose adolescent children fulfilled the criteria. Members were informed that participation was voluntary, and that they could withdraw at any time without explanation, following the Declaration of Helsinki. Approval was given by the Swedish Ethical Review Authority (D.nr $\frac{2018}{449}$‐32). Co‐designers were, in addition to the young persons, two researchers with experience of working with diabetes care, a software developer and two community managers from a health tech company.
**Table 1**
| Step | Activity | Invited | Participated | Age and background information | Gender |
| --- | --- | --- | --- | --- | --- |
| I | Telephone interviews | N = 36 | 36 | 13–17 years (median 15) | 18 girls |
| I | Telephone interviews | N = 36 | 36 | Range of years since diagnose 1–14 years (median 5) | 18 boys |
| I | Workshops | N = 36 | 21a | 13–17 years | 16 girls |
| I | Workshops | N = 36 | 21a | 13–17 years | 5 boys |
| III | Summarizing focus group interview or Telephone interviews | N = 36 | 6 | 13–17 years | 5 girls |
| III | Summarizing focus group interview or Telephone interviews | N = 36 | 3 | 13–17 years | 1 boy |
| III | Summarizing focus group interview or Telephone interviews | N = 36 | 3 | 13–17 years | 3 girls |
## Procedure
The process of co‐design followed three steps, adapted from Sanders and Stappers 19 (Figure 1). In the first step, telephone interviews were conducted to map out the general use of social media. These formed the basis for the semistructured workshops, where content and structure were enclosed for the online social network. The second step consisted of the iterative process of developing and testing the social network, and in the third step, a focus group was conducted to describe the experiences of using the network.
**Figure 1:** *Description of the co‐design process adapted from Sanders and Stappers
19
including a timeline for each step*
## Step I
Young persons with T1D were interviewed separately by phone (Table 1). The interviews were semistructured according to an interview guide (Table 2). Interviews were recorded and transcribed verbatim. The purpose was to understand these persons' use of social media and their ideas for developing a network for young persons with T1D. Then, they were invited to one of three semistructured workshops led by a software manager and two community managers. 21 Of these, 21 persons (hereinafter referred to as members) participated in the workshops. The members had a meal together, while the software manager coordinated the discussion about habits of using social media in general: when, how, where and why they used different types of social media. The following discussion was then focused on what the intended social network would be like, including the content, technical features and user‐friendly functions. The discussion also focused on how the members wanted to use such a network and what type of content was important. Members wrote post‐it notes, which were grouped based on subject areas and clustered into different subjects, and were later used to create different areas within the social network. Two researchers observed the conversation and took notes.
**Table 2**
| Question area | Questions |
| --- | --- |
| Background questions | Age, duration of diabetes diagnosis |
| Background questions | What kind of social media do you use today and how do you use them? |
| Thoughts about a social network for youths with T1D | If there was a social network for youths with T1D: what would be in it for you? |
| Thoughts about a social network for youths with T1D | What functions would you prefer? |
| Thoughts about a social network for youths with T1D | What would make you use such a network? |
| Thoughts about a social network for youths with T1D | Do you believe in the idea about such a network? |
| Thoughts about a social network for youths with T1D | Why? |
| Thoughts about a social network for youths with T1D | Why not? |
| Thoughts about a social network for youths with T1D | What are your thoughts about sharing your experiences of diabetes with others? |
| Thoughts about a social network for youths with T1D | What do you think about adults' presence in such a network? |
| Question about participation in the co‐designing process | Would you like to be involved in designing such a network together with other young people with diabetes and a software manager? |
## Step II
A framework for social networks was used and was adjusted based on the members' views and requirements described in Step I. An invitation was sent to all 36 members to test the network over 12 weeks. During these weeks, an iterative process of developing content and structure and adjusting for errors was performed simultaneously. Members could create their own posts, write their opinions and views about the content and interact with each other. The Community Manager also posted questions, news and polls to engage members. All new posts led to a notification by email and a push notification in the app in members' smartphones. Members could comment on other persons' posts and they could ‘clap’, which had the same function as ‘like’ on other social media. There was also an ‘eye’ symbol with a number indicating how many members had read the post. Before the members got access to the community, each member was paired with another member as a ‘buddy’. Each buddy‐couple received tasks to discuss; the community manager initiated these tasks. This buddy function was a way to meet the need for individual support described in previous research. 22 Data were collected from the community platform following the number of posts, comments and likes for members and the community manager.
## Step III
After the test period, all members were invited to a focus group interview led by the community managers. This was recorded and transcribed verbatim (Table 1). Two researchers observed and took notes during the focus group. The members were asked about their experiences, views and ideas for improvement of the network. The purpose of this focus group was to describe the members' experiences of participating in the development of the network and using the network.
## Data analysis
The telephone interviews in Step I were read several times to make sense of the data. Then, the text was coded by the first author and entered into a spreadsheet. The codes were grouped into subcategories by the first and last author. A thematic analysis was performed using an inductive approach strongly linked to the raw data. 23 *Numerical data* (Step II) from the community platform were compiled. The focus group interview (Step III) was analysed using inductive content analysis. 24 First, meanings and sentences were assigned a code. In the open coding phase, codes were grouped into categories and sorted under higher‐order headings. Similar subcategories formed higher‐order categories. To achieve trustworthiness, the other authors in the research group read and discussed the analysis.
## RESULTS
In the telephone interviews (Step I), members described incentives for becoming a member of a social network, such as the opportunity to connect with others and share experiences together. Another incentive could be to provide and receive information and support. Furthermore, important prerequisites were described, especially the presence of an adult, that the network has many members and that all members have their own experience of T1D. The workshop's discussions focused on functions, opinions and the members' habits around using apps, the internet and smartphones. This formed the basis for designing the social network. Members stated the importance of simplicity, such as easy access and a clear overview. Quotes illustrating the participants' opinions are presented in Table 3.
**Table 3**
| Incentives and prerequisites | Quotes from interviews (Step I) |
| --- | --- |
| Connecting | I think it's a good idea, because then you can write with each other about the disease if you don't want to share it with someone else who might not understand it |
| T1D only | It feels good to know that it is only for those with diabetes. It might feel better then. |
| Support | I think it would be really, really nice, because since there aren't very many people with diabetes that you know, it would be great to have everyone gathered in one place because you sometimes feel quite alone |
| Share experiences | Maybe more that you post things there about tips and advice that you yourself think work |
| Presence of an adult | It might be more special if it's only for young people, but it's good if there are adults who can keep an eye in case something bad is shown |
After clustering the subjects, five areas of different subjects emerged (news about diabetes, free questions, missions, polls and challenges—the last three created by the community manager). Then, members formulated rules for the social network. These included using a positive tone in the posts and avoiding negative reactions that could be interpreted as offensive. All members agreed that the community manager should be responsible for coordinating content, outsourcing assignments and polls and questions during the testing period.
All 36 members were invited to join the test period (Step II) and 33 logged in to the network. The members were encouraged to report any technical problems, which contributed towards improving the functionality simultaneously. Most posts and interactions were initiated by the community manager ($87\%$). The posts concerning diabetes‐specific questions and information about diabetes received most feedback from the members. There was almost no contact between the buddie couples. Some of the members tried to get in touch but received no response from their buddy.
To describe experiences of using the platform and of being a member of the network (Step III), all members were invited to a focus‐group discussion. The content analysis of this discussion revealed two categories: framework and possible interpersonal values (Figure 2 and Table 4). Quotes to illustrate the categories are presented in Table 4. The framework included ‘functionality’, exemplified by aspects such as accessibility and that the interface should be attractive irrespective of the devices used (smartphone, tablet or computer). Polls, posts and how notifications should be presented were discussed and also what kind of functionality was desirable. The members did not find any use for the buddy function, but it was argued that this kind of functionality may better suit members recently diagnosed with T1D.
**Figure 2:** *Results obtained from the three steps* TABLE_PLACEHOLDER:Table 4 The second part about the framework was described as ‘access and rules’. The rules were about how to engage in the network and which members should be allowed. For example, members preferred that young adults were invited into the network. Issues about transition into adult care and how to prepare for moving away from parents could be important topics to raise in an extended network. The need for more activity from members was expressed since ongoing activity is important for a social network to serve its purpose. Another important issue was about not allowing parents to be part of the network, because their presence may deter members from engaging.
The second category included the interpersonal values that can be developed while on an online social network (see Table 4 and Figure 2). One of the interpersonal values described is about relating; that members share the same experiences and can understand each other in a deeper way. They described several examples in everyday life that those with their own experiences of living with diabetes could understand. It could be the feeling of having a bad day (such as a stubborn high blood sugar) and how others describe that feeling and relate to it. Another value was about supporting, that members could bolster each other. This was illustrated by the solidarity that they could offer each other when they feel a sense of hopelessness. This way of giving and receiving hope was expressed in terms of not being alone in the struggle with the condition in everyday life. Members pointed out that there is a lower risk of a judgemental attitude between those who share the same experience, which then added to the feeling of support. Finally, ‘co‐learning’ was mentioned, describing how members could learn different things from each other's lived experiences. This was more implicit, exemplified by the network being a platform for sharing concrete examples of how to do things, for example, what to consider when drinking alcohol and advice concerning food and physical activity. Members could learn from these concrete examples and apply them to their own situation.
Being a member on a social network has potentials for young persons with T1D. However, to accomplish those potentials described as interpersonal values in the present study, the network need an appropriate framework. When all these things come together, it could be considered as a safe space to meet others with the same experiences. It is not restricted to any physical place or an organized group of selected members. The limits of time and place are eliminated and there is a possibility for many more to join in. This was described by the members as ‘participating in a never‐ending diabetes youth camp’. Due to the nature of a virtual network, facilitated by a community manager, the safe space for meetup becomes a reality. A camp is limited to time and place and is therefore not available to everyone.
## DISCUSSION
To our knowledge, this study is the first to use a co‐designed approach with young persons with T1D to create a digital network. Young people have a lot to contribute to the design as they can describe their incentives for using digital social media and how they use it.
The result shows that members want the presence of adults on the site as a prerequisite for the safe space that they value. This is in accordance with the idea of ‘facilitated networks’ as a possible configuration of value‐creating services. 25 Teenagers can be empowered by sharing lived experiences with young adults with T1D, 26 which was confirmed in our study. Members place a high level of trust in their peers and follow their advice about lifestyle changes, 11 which indicates the importance of the presence of healthcare professionals in the network, to minimize the risk of incorrect advice being shared, which could otherwise be a risk. 13 Members expressed the need for functionality, access and rules. Others have described that online networks need mentors, guides and moderators to be present. Such a presence can provide the necessary structure and direction to shift a negative or unproductive social networking process into one that could positively influence social support. 27 In the current social network, the risk of bad behaviour and misinformation has been minimized by the presence of a community manager.
Continuous interactions within the network are needed, which are dependent on members' engagement and the willingness to share personal experiences. 27 A critical minimum number of members has been confirmed by White et al., 28 who report that a rapid increase in membership and level of participation in the network indicates motivation and increases the possibilities for exchange of information. The low level of activity from and between members in this study may indicate that the test is too small or that the group was too homogeneous. Even if there is a small percentage of members who are active on a forum, it is worth keeping in mind that persons who choose not to post content or comment on others' postings may still benefit from observing or being part of the community. 29 We found that membership in a social online network offers a platform to both seek out and provide tailored social support around diabetes management. Our results about the interpersonal values; supporting and co‐learning as incentives for being a member in the network is confirmed by previous studies. 16, 29 Members highly value being part of a digital social network since it increases knowledge, improves self‐care and reciprocates emotional support. 10, 29, 30 Knowledge about T1D can form the basis for successful self‐management. 27 Belonging to a social network where members can receive emotional support and mutual reciprocity and being part of other members' lived experiences are important driving forces for using diabetes online social networks. 10 To conclude, the findings in this study are consistent with previous literature about online networks for persons with T1D and provide a more in‐depth understanding of the nature of online social networks. The results can be applied to a wider perspective of online networks to foster peer‐to‐peer support for other chronic conditions as well. The use of co‐design adds the value of directing content and structure to meet the needs of potential new members in future networks. It also ensures that structure and content are designed based on what users consider important and not based on assumptions made by others.
## Methodological considerations
A strength of this study was that we could work iteratively, due to the close collaboration between members, technology developers, community managers and researchers, although the several steps of data collection in the co‐design process were time‐consuming. Further, a potential limitation is that people who agree to participate may not be representative of the population. Members were recruited by the diabetes nurse, and we cannot control if the diabetes nurse may have asked specific persons to join the study due to special characteristics. This may have affected the sampling procedure. The members that were included in the present study had several years of experience of living with T1D, which may have influenced the design of the network. The needs can vary depending on how long a person has lived with diabetes. In addition, we started with a rather small group of members, which resulted in a low degree of activity in the network. On the other hand, this was part of the learning process and further cycles with a larger group of users are called for. The network was based on an existing technology framework, which may have affected the members' creativity in the design of features and appearance.
## CONCLUSIONS
We identified the potential advantages of joining a network for adolescents with T1D. Relating, supporting and learning together is something that the exchange of lived knowledge and experience can generate. This cannot be provided by healthcare professionals. By using co‐design, it was possible to straightaway build on what the young persons described as important. The participation of a facilitating healthcare professional was considered necessary by the users, to make the network a safe space to share and learn from. For future research, we recommend exploring the content in the network that could provide information about what is important for a wider group of young persons living with diabetes, and to also use this as a channel for patient feedback to diabetes teams to enable them to make improvements towards better, coproduced care.
## CONFLICT OF INTERESTS
The authors declare that there are no conflict of interests.
## DATA AVAILABILITY STATEMENT
The focus group and interview data (transcripts) that support the study conclusions are unavailable for public access because informed consent to share the complete transcripts outside of the research team was not obtained from the study participants.
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|
---
title: 'Exploring patients'' perspectives of gestational diabetes mellitus screening
and counselling in Ontario: A grounded theory study'
authors:
- Emma Ruby
- Sarah D. McDonald
- Howard Berger
- Nir Melamed
- Jenifer Li
- Elizabeth K. Darling
- Jon Barrett
- Joel G. Ray
- Michael Geary
- Beth Murray‐Davis
journal: 'Health Expectations : An International Journal of Public Participation in
Health Care and Health Policy'
year: 2023
pmcid: PMC10010101
doi: 10.1111/hex.13708
license: CC BY 4.0
---
# Exploring patients' perspectives of gestational diabetes mellitus screening and counselling in Ontario: A grounded theory study
## Abstract
### Introduction
Gestational diabetes mellitus (GDM) is associated with adverse perinatal outcomes. Approaches to screening for GDM continue to evolve, introducing potential variability of care. This study explored the impact of these variations on GDM counselling and screening from the perspectives of pregnant individuals.
### Methods
Following a Corbin and Strauss approach to qualitative, grounded theory we recruited 28 individuals from three cities in Ontario, Canada who had a singleton pregnancy under the care of either a midwife, family physician or obstetrician. Convenience and purposive sampling techniques were used. Semi‐structured telephone interviews were conducted and transcribed verbatim between March and December 2020. Transcripts were analysed inductively resulting in codes, categories and themes.
### Results
Three themes were derived from the data about GDM screening and counselling: ‘informing oneself’, ‘deciding’ and ‘screening’. All participants, regardless of geographical region, or antenatal care provider, moved through these three steps during the GDM counselling and screening process. Differences in counselling approaches between pregnancy care providers were noted throughout the ‘informing’ and ‘deciding’ stages of care. Factors influencing these differences included communication, healthcare autonomy and patient motivation to engage with health services. No differences were noted within care provider groups across the three geographic regions. Participant experiences of GDM screening were influenced by logistical challenges and personal preferences towards testing.
### Conclusion
Informing oneself about GDM may be a crucial step for facilitating decision‐making and screening uptake, with an emphasis on information provision to facilitate patient autonomy and motivation.
### Patient or Public Contribution
Participants of our study included patients and service users. Participants were actively involved in the study design due to the qualitative, patient‐centred nature of the research methods employed. Analysis of results was structured according to the emergent themes of the data which were grounded in patient perspectives and experiences.
## BACKGROUND
Gestational diabetes mellitus (GDM) is one of the most frequent metabolic disturbances of pregnancy, affecting up to $20\%$ of pregnant individuals in Canada. 1 Factors contributing to the rising rates of GDM in Ontario include variations in screening approaches and diagnostic thresholds, and increased incidence of delayed childbearing, obesity, and excess gestational weight gain. 2, 3, 4, 5 Two approaches to GDM screening are endorsed by Diabetes Canada (DC) and the Society of Obstetricians and Gynecologists of Canada (SOGC): the ‘preferred’ two‐step, method of a nonfasting, 50 g oral glucose challenge test (50 g OGCT) followed by a fasting 75 g oral glucose tolerance test (75 g OGTT) upon abnormal results, as well as an ‘alternate’ method of the one‐step, fasting 75 g OGTT. 6, 7, 8 A randomized controlled trial, comparing the incidence of GDM between screening approaches, revealed a nearly doubled incidence rate of GDM in the group who underwent a one‐step 75 g OGTT, when compared to the group who was administered a 50 g OGCT followed by a 100 g OGTT ($16.5\%$ vs. $8.5\%$, respectively). 9 However there is a lack of consensus between regulatory bodies on the optimal GDM screening approach. For example, DC and the SOGC recommendations differ from that of the International Association of the Diabetes and Pregnancy Study Groups (IADPSG), which supports the use of the one‐step 75 g OGTT with lower diagnostic cut‐off values. 7, 8, 10, 11 Comparatively, the American College of Obstetricians and Gynecologists (ACOG) recommends the use of an OGTT with a higher glucose load (100 g) and a longer assessment period (3 h). 11, 12 A recent randomized controlled trial examined the rate of GDM diagnosis across groups with different diagnostic threshold criteria, with the lower glycemic criteria group reporting over double the rate of GDM diagnoses compared to the higher glycemic criteria group ($15.3\%$ vs. $6.1\%$, respectively). 13 There has also been debate over universal versus risk‐based GDM screening. The SOGC and DC shifted from selective screening in the late 1990s to recommending universal screening. 6 Differences and uncertainty of diagnostic thresholds between approaches, the most appropriate glucose load, the number of abnormal values required to determine a GDM diagnosis, the importance of early trimester and postpartum screening, as well as whether to practice risk‐based or universal screening, has led to discrepancies in the true prevalence of GDM in Canada. 6
From the patient's perspective, GDM care includes a range of experiences such as counselling, screening, diagnosis, management and postpartum follow‐up. Patients who received a GDM diagnosis have reported feelings of self‐blame, failure, confusion, and fear, signifying uncertainty and guilt. 14, 15, 16 Lack of time and continuity of care have been identified as barriers to communication between patients and healthcare providers (HCPs); however, discussion of the GDM condition, associated risks and potential outcomes have been shown to promote greater acceptance of the diagnosis for the patient. 14 Despite evidence indicating patient motivation to protect the health of their baby, many challenges impeding positive behaviour change and treatment compliance have been reported. 17 These have included a lack of access to GDM services, financial barriers, lack of communication with HCPs and poor adherence to lifestyle changes such as diet and exercise. 17 Given the multifaceted nature of GDM for patients, the aim of this study was to explore the impact of variations in GDM counselling and screening from the perspective of patients.
## METHODS
We conducted a qualitative, grounded theory study with pregnant participants to explore their experiences of gestational diabetes screening and counselling practices. We sought to recruit participants from various geographic locations in the province, and who received care from different antenatal care providers. Patients who had had a singleton birth within the 5 months before data collection, who also received antenatal care from a midwife (MW), family physician (FP) or obstetrician (OB) within Hamilton, Ottawa or Sudbury, Ontario, Canada, were eligible to participate. Semi‐structured interviews took place between March 2020 and December 2020 and were conducted over the telephone for approximately 30–45 min. Convenience and purposive sampling were used for recruitment, using social media and posters within the community. These sampling methods were chosen based on the limited geographic regions targeted and the ability to identify participants with lived experiences with the research topic. Additionally, given the lack of strict selection criteria and the qualitative methodology employed, these sampling techniques were most appropriate.
The geographic regions identified for inclusion were selected to increase subpopulation variability and to utilize existing contacts to assist in recruitment. A minimum of three patients who received care from each health profession and from each geographic region was identified as the desired sample size (totalling 27 participants) based on experience with similar studies by the research team, but with the intention of continuing recruitment until we reached saturation. 18, 19 An interview guide was developed by our research team and utilized during the interview process, using a mix of open‐ and closed‐ended questions to elicit the participant's perspectives on their experiences with GDM counselling and screening (see Supporting Information: S1).
In keeping with grounded theory, as described by Corbin and Strauss, data analysis began at the same time as data collection, to make use of the iterative process of constant comparison. 20, 21 Interviews were transcribed verbatim and entered into Nvivo 11 software. 22 *Data analysis* began with open coding. Initial open coding of three transcripts was completed by three independent researchers to ensure consistency and agreement in the coding process. 23 Next, codes were grouped to form axial codes which provided a framework from which the open codes could be synthesized into hierarchically structured categories. 24, 25, 26, 27 Lastly, during the selective coding process, further grouping was completed to form themes that, when brought together, generated a theory grounded in the data. 27 Interim analyses were shared at team debrief meetings, and the Principal Investigator reviewed the coding at each stage of analysis. 23 The research team was comprised of students and experts from a variety of disciplines, including midwifery, maternal‐foetal medicine, obstetrics and health research methodology. Investigator triangulation was used to review, validate and come to an agreement on disputed codes between researchers. These approaches were employed to minimize bias, strengthen credibility and add breadth to the emerging phenomena. 28
## RESULTS
A total of 28 participants were included. Demographic characteristics were obtained and are presented below (Table 1).
**Table 1**
| Characteristics | Antenatal care provider | Antenatal care provider.1 | Antenatal care provider.2 | Antenatal care provider.3 |
| --- | --- | --- | --- | --- |
| Characteristics | Midwife (n = 12) | Obstetrician (n = 10) | Family physician (n = 6) | Total (n = 23a) |
| Geographic region | Geographic region | Geographic region | Geographic region | Geographic region |
| Hamilton | 5 | 3 | 2 | 10 |
| Ottawa | 4 | 4 | 3 | 11 |
| Sudbury | 3 | 3 | 1 | 7 |
| Maternal age (years) | Maternal age (years) | Maternal age (years) | Maternal age (years) | Maternal age (years) |
| 15–24 | 0 | 0 | 1 | 1 |
| 25–34 | 6 | 6 | 1 | 13 |
| 35–44 | 3 | 4 | 2 | 9 |
| Ethnic or cultural origin(s) | Ethnic or cultural origin(s) | Ethnic or cultural origin(s) | Ethnic or cultural origin(s) | Ethnic or cultural origin(s) |
| East Asian | 0 | 2 | 0 | 2 |
| White | 9 | 8 | 4 | 21 |
| Hispanic | 0 | 1 | 0 | 1 |
| Other | 0 | 0 | 0 | 0 |
| Highest level of education | Highest level of education | Highest level of education | Highest level of education | Highest level of education |
| High school | 0 | 2 | 1 | 3 |
| Bachelor's degree | 4 | 5 | 2 | 11 |
| Master's degree | 4 | 1 | 1 | 6 |
| Postgraduate certificate | 0 | 1 | 0 | 1 |
| Doctorate | 1 | 0 | 0 | 1 |
| College diploma | 0 | 1 | 0 | 1 |
| Past live births, including most recent (#) | Past live births, including most recent (#) | Past live births, including most recent (#) | Past live births, including most recent (#) | Past live births, including most recent (#) |
| 1 | 6 | 8 | 3 | 17 |
| 2 | 3 | 1 | 1 | 5 |
| 3 | 0 | 1 | 0 | 1 |
| BMI (kg/m2) | BMI (kg/m2) | BMI (kg/m2) | BMI (kg/m2) | BMI (kg/m2) |
| 18–24 | 5 | 4 | 4 | 13 |
| 25–30 | 4 | 0 | 0 | 4 |
| 30–35 | 0 | 2 | 0 | 2 |
| 35–40 | 0 | 3 | 0 | 3 |
| >40 | 0 | 1 | 0 | 1 |
| Neonatal birth weight (lbs) | Neonatal birth weight (lbs) | Neonatal birth weight (lbs) | Neonatal birth weight (lbs) | Neonatal birth weight (lbs) |
| <6 | 1 | 1 | 1 | 3 |
| 6–7 | 3 | 1 | 1 | 5 |
| 7–8 | 2 | 5 | 1 | 8 |
| 8–9 | 2 | 2 | 1 | 5 |
| >9 | 1 | 1 | 0 | 2 |
| Gestational age at delivery (weeks + # days) | Gestational age at delivery (weeks + # days) | Gestational age at delivery (weeks + # days) | Gestational age at delivery (weeks + # days) | Gestational age at delivery (weeks + # days) |
| <35 | 1 | 0 | 0 | 1 |
| 35–37 + 6 | 1 | 2 | 1 | 4 |
| 38–39 + 6 | 2 | 7 | 0 | 9 |
| ≥40 | 4 | 1 | 3 | 8 |
| Diagnosis of GDM in most recent pregnancy (Yes/No) | Diagnosis of GDM in most recent pregnancy (Yes/No) | Diagnosis of GDM in most recent pregnancy (Yes/No) | Diagnosis of GDM in most recent pregnancy (Yes/No) | Diagnosis of GDM in most recent pregnancy (Yes/No) |
| Yes | 2 | 1 | 0 | 3 |
| No | 7 | 9 | 4 | 20 |
Our findings are summarized in the themes arising from the data: ‘informing oneself’, ‘deciding’ and ‘screening’ (Figure 1). We found that all participants, regardless of geographical region, or antenatal care provider, moved through these three steps during the GDM counselling and screening process.
**Figure 1:** *Categorization of participants' experiences with GDM counselling and screening practices. GDM, gestational diabetes mellitus; HCP, healthcare provider.*
## Informing oneself
The theme of ‘informing oneself’ reflected the first stage of the patient's care experience. This theme was underpinned by the factors that influenced the understanding of GDM, particularly pertaining to screening, prevention and management.
Accessing GDM resources was influenced by the availability of sources outside of direct antenatal care to improve participants' understanding and satisfaction with their GDM care experiences. External sources included websites, pamphlets, information sessions led by hospital staff, prenatal classes, independent research, conversations with family or friends and Facebook pregnancy groups. One person articulated:I like it to be in a paper format, like a pamphlet, or some type of brochure. When I started going just to my OB/GYN, they would say, ‘here's some additional resources that you can read more about it’. That would have been really helpful with the gestational diabetes as well. ( P2, FP) Positive experiences with GDM information provision were described as factors that strengthened participants' knowledge acquisition, reduced pregnancy anxiety, and enabled participants to develop positive lifestyle habits through diet management and exercise. Negative experiences associated with a perceived lack of GDM information provision included feelings of frustration due to limited knowledge acquisition, as well as uncertainty in dietary and lifestyle modifications before and between testing:I remember I started to change my diet, but then I wondered, is this going to impact the [GDM] test? If I get negative, would I go back to my old diet, being that I wasn't sure whether I should make the changes before, or would I have to wait until after the test? ( P3, MW) Participants' relationship with their antenatal care provider was an important consideration in obtaining GDM information. Some participants felt that the counselling they received was thorough, whereas others felt it was minimal. *In* general, more comprehensive counselling among patients in midwifery care was noted.
A range of discussion topics covered during counselling was reported and included: adverse outcomes, screening options such as the OGTT or the OGCT, risk factors associated with GDM and logistical considerations of the screening procedure such as the timing of fasting if required, and the type of sugar beverage administered.
Most participants, regardless of the care provider, reported feeling that they received sufficient information and were able to ask questions as needed. Many participants expressed appreciation for a provider who was accessible, supportive, demonstrated a calm demeanour and listened attentively. However, many reported that they would have liked to receive more counselling on specific topics, including prevention and management of GDM, signs and symptoms to be aware of, customized diet recommendations, and how to best prepare for the testing procedure:I don't know if there's preventative measures that you can take to just prevent developing it. That would be helpful in terms of your diet or exercise. I don't feel like I got that information. ( P3, MW) Lack of communication emerged as a barrier to accessing information, including restrictive timelines and protocols for appointments, lack of follow‐up regarding the participant's GDM test results and inability to discuss health concerns due to restrictions in the scope of practice, or lack of educational training to provide patients with requested information.
## Deciding
The theme of ‘deciding’ explored factors in the decision‐making process pertaining to GDM screening, including participant beliefs, values and healthcare autonomy. Prior beliefs about GDM and personal values regarding knowledge acquisition were explored; many participants viewed knowledge as a tool to prevent potential medical complications in pregnancy.
Almost all participants expressed pro‐universal GDM screening value statements. Beliefs that informed this included the following: (a) GDM is largely an asymptomatic condition, (b) GDM can affect anyone, regardless of risk profile, (c) screening is a minimally invasive and low‐risk procedure (the benefits outweigh the harms) and (d) screening improves one's awareness and motivation for the health of themselves and their developing baby:From what I've seen, it's pretty random. I've seen people who are quite thin and healthy, people who aren't so healthy. Because of the effects it can have on the baby, I think it's important to be screened, because you don't necessarily know until it might be a bit late, and be causing significant effects. ( P2, OB) The degree to which participants were involved in the decision‐making process was particularly influential within this stage. Decisions included whether or not to be screened for GDM; choice of screening parameters such as the OGTT or OGCT; and the gestational time period to complete screening.
Being provided with an agency to make decisions was articulated as being very important for participants. For those who received care from a midwife, most reported that GDM screening was presented as optional and that they were able to make an informed decision based on the information provided. This was summarized best by one client, who stated:With the midwife, she definitely made everything an option because she just wanted me to have more of an informed choice. So she told me ‘these are the tests that we highly recommend’, but really it was always up to me whether or not I wanted to do a test. ( P9, MW) For those who were not presented with GDM screening as ‘optional’ they described being instructed to be screened for GDM as it was simply ‘the thing to do’. One participant described this clearly when they stated, ‘I don't think it was presented to me as an option. It was presented to me as everyone gets screened, so I should get screened’ (P5, OB).
Furthermore, when asked about perceptions of the differences in counselling practices between antenatal care providers, many expressed the general belief that midwives have more time to provide comprehensive counselling, offering more opportunities for the client to make an informed decision:OBs and FPs have such little time to sit and discuss things. Based on my experience of how it's gone in the past, I feel like there wouldn't be as much discussion and more just ‘you need to do this screen, here is the [requisition], go and do it’. ( P10, MW) Attitudes and motivation for engaging with health services were factors that influenced the level of importance that participants placed on healthcare autonomy. Some participants placed a significant emphasis on personal autonomy in their healthcare decisions. The participants that expressed the importance of making autonomous decisions were largely clients of midwives:I think it's important for us that we are provided with information and that we are able to make the decision. We are supposed to live in a society where we are not forced to do things that we are not comfortable with. I think by having a midwife and them always making sure that they are informing my decisions, it's an awesome thing and obviously very empowering knowing that you are able to make these decisions on your own. ( P12, MW) Receptivity to screening was influenced by the risk factors that participants presented with and the extent to which they expressed concerns about its impact on the health of themselves and their babies. Many participants for whom screening was presented as optional ultimately expressed their receptivity to being tested given their risk factors:[Screening] was available to me, and my OB thought it was a good idea based on the fact that I am older, I am overweight … and obviously with my family history of type 2. ( P7, OB)
## Screening
The last theme was related to experiences of completing the GDM screening test. Factors influencing the participant's access to testing were key determinants in their satisfaction with the screening process. Logistical barriers included challenges in obtaining childcare, inconvenient location of the laboratory, difficulty in scheduling time off work and transportation challenges. Individual reactions and experiences that presented barriers included distaste or aversion for the sugar beverage administered, difficulty coordinating fasting times before the test, emesis or nausea and discomfort with in‐person assessment during the COVID‐19 pandemic.
Factors that enabled screening were the absence of financial cost, ease of coordinating fasting times and taking time off work, the ability to attend the laboratory in a convenient location and on the weekend, a supportive partner that could transport the participant to and from their appointment, available childcare and minimal physical discomforts such as nausea.
Variations in screening practices included gestational timing, the type of testing approach recommended, and the locations where screening was offered. A few participants received earlier screening in their pregnancy due to the presence of risk factors: ‘My baby was trending quite large for my third pregnancy, so I did get screened earlier … I think given my BMI and whatnot … they just wanted to check and make sure that I didn't have it’ (P1, OB).
While most participants were screened within the recommended window of 24 to 28 weeks of gestation, a few individuals reported screening between 20 and 24 weeks gestation. No participants reported screening past 28 weeks. Of the 28 participants interviewed, 23 received the nonfasting 50 g OGCT; less than half required the follow‐up 75 g OGTT. The remaining five received the one step, fasting 75 g OGTT. Participants from rural areas experienced more limitations in lab capacities and screening times compared to cities.
## DISCUSSION
This study explored the experiences of GDM counselling and screening from the perspectives of patients who received antenatal care from either a midwife, family physician or obstetrician in Hamilton, Sudbury or Ottawa, Ontario. The goal of this paper was to provide a qualitative analysis to explore the impact of variations in screening guidelines and changing patient population trends related to GDM counselling and screening practices.
Our findings highlight the progression of an individual's experience engaging with GDM health services through three stages: ‘informing oneself’, ‘deciding’ and ‘screening’. The findings within the stage of ‘informing oneself’ aligned with literature that supports the importance of comprehensive and personalized care provision according to the lived experiences and preferences of the individual. 14, 29, 30 For example, much of the literature that explored patient perceptions GDM diagnosis highlighted feelings of self‐blame, failure, confusion and anxiety. 14, 29, 30 These negative feelings were largely attributable to a lack of communication with their care provider, self‐perceptions of risk factors and a lack of information regarding adverse outcomes. 14, 29, 30 Our participants also expressed how lack of communication, support or information provision impacted their experience.
Our findings highlight how autonomy and empowerment were tools for facilitating screening uptake and changes in health‐seeking behaviours. 31, 32 For many participants, the autonomy that they had in decision‐making reflected their confidence in, or motivation for complying with, their care provider's recommendations. For example, many midwifery clients expressed strong motivation for being an active participant in the decision‐making process. These participants were more likely to value informed choice approaches, and were generally more expressive about their healthcare desires than those who received care from a physician. Instead, those receiving physician‐led care expressed enthusiasm to comply with their providers recommendations if it meant protecting the health of their baby.
We also found that factors such as reactions to the screening test and logistical considerations in accessing laboratory services, at the individual, organizational and health systems levels influenced participants' experiences. Barriers to obtaining screening reported in the literature were consistent with those expressed by participants in our study, including time restraints, inconvenient locations and transportation challenges. 17, 33 In alignment with the literature, our findings indicated that there is a need for GDM care to be provided in a manner that is comprehensible, personalized and accessible, to best accommodate the lifestyle choices of diverse patient populations. 30, 34 In particular, our study highlighted the importance of knowledge sharing as a facilitator in the decision‐making process. 14, 29, 30, 35 Knowledge sharing is a reciprocal process that promotes patient empowerment, and encourages humility of the provider to foster a relationship built on mutual respect and rapport. 35 Given the evolution of screening guidelines in recent years, care providers have had to regularly integrate these changes into their clinical practice. We had hypothesized that patients would be aware of and possibly confused by the variations in screening guidelines over time and this would be reflected in patient data. However, the findings showed the minimal impact of screening guidelines inconsistencies on the experiences of participants. Instead, logistical challenges, accessibility of the screening and personal preferences arose as primary influences on the participants' experiences.
One of the aims of this study was to explore the differences, if any, in the GDM counselling and screening practices of antenatal care providers across professions and across geographic regions within Ontario. Our findings revealed that while there were considerable differences in the participants' counselling experiences across care provider groups, there were very few differences across geographical regions. For example, those who received care from a midwife offered similar sentiments regardless of their geography, highlighting consistencies in professional philosophies across geographic regions. However, we acknowledge the restrictions of our sample to three geographic regions, which may not reflect the spectrum of individual and professional philosophies across the country.
The interpretation of access was also important to consider within the context of our study. Access can be conceptualized by reciprocal interaction between health structures and the ‘consumer’. 36 It encompasses both accessibilities of providers, organizations, institutions and health systems to provide services, as well as the abilities of the consumer to receive such services, such as the ability to perceive, seek, reach, pay for and engage with health services. 36, 37 As highlighted in our findings, information provision was a key factor in subsequent decision‐making and GDM screening uptake. However, to adequately interpret our findings, we must consider the multitude of agents that impact an individual's access to healthcare. 36, 37 Lastly, the authors acknowledged that patient self‐selection of care providers was a key consideration in the findings. Those who selected to have midwifery‐led care may be inherently different that those who selected physician‐led care, particularly with respect to desired autonomy during decision‐making. 38 Our study was unique in that it was one of the first to explore the experiences of patients seeking services pertaining to GDM counselling and screening, and compare across antenatal healthcare services, in a Canadian context. Furthermore, this study uniquely highlighted the direct impact that policy‐level guidelines have on patients and providers. Given the lack of qualitative evidence on this topic, the findings from this study provide valuable insight into what factors patients are most impacted by when seeking GDM counselling and screening.
Strengths of our study included the multidisciplinary nature of our team and our recruitment approaches to maximize participant variation to reflect the diversity that exists within the greater Ontario population, and to explore the range of social, cultural, economic and environmental factors that contribute to the experiences of health‐seeking patients. This enabled a range of perspectives that formed the basis through which comparisons of clinical practices across health sectors could be made. 39 A limitation of our study was potential selection bias, given that those who volunteered to participate may be more willing to do so based on their personal beliefs about the topic. Furthermore, demographic characteristics were not obtained from five participants due to data collection documentation errors, presenting another source of bias. Additionally, our sample size reflected a lack of ethnic diversity, consisting of majority Caucasian identifying participants, with minimal to no participants from other ethnic groups. This may have further contributed to selection bias, with other populations not being well represented in our data. The COVID‐19 pandemic may have also presented possible selection bias which may have impacted recruitment for this study given the uncertainty in restrictions and research protocols. Also, during this time emergency alternatives to GDM screening protocols were published and may have introduced another variation in care. 40 Lastly, language was also a limitation as interviews were only conducted in English. 41
## CONCLUSION
Our findings indicate that patients engage in GDM counselling and screening with a motivated mindset to protect the health of their babies. During the process of informing/deciding/screening, the informing stage and knowledge acquisition were crucial steps for facilitating decision‐making and screening uptake. However, there were differences in the perceptions of the comprehensiveness of GDM counselling between antenatal care providers. The desire for patients to be active participants in decision‐making is a reflection of their selection of care providers. Useful next steps to improve the patient experience include training for health professionals, and the creation of patient information resources that are adapted to the needs, preferences and lifestyles of patients, as well as a greater emphasis on information provision to facilitate patient autonomy.
## CONFLICT OF INTEREST
The authors declare no conflict of interest.
## ETHICS STATEMENT
Ethical approval was obtained from the Hamilton Integrated Research Ethics Board (HiREB Project: 7916). All participants provided consent before participation in the study.
## DATA AVAILABILITY STATEMENT
The data sets generated and/or analysed during the current study are not publicly available due to the lack of consent from the study participants to share the data publicly but are available from the corresponding author at a reasonable request.
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|
---
title: '‘To me, it''s ones and zeros, but in reality that one is death’: A qualitative
study exploring researchers'' experience of involving and engaging seldom‐heard
communities in big data research'
authors:
- Piotr Teodorowski
- Sarah E. Rodgers
- Kate Fleming
- Naheed Tahir
- Saiqa Ahmed
- Lucy Frith
journal: 'Health Expectations : An International Journal of Public Participation in
Health Care and Health Policy'
year: 2023
pmcid: PMC10010102
doi: 10.1111/hex.13713
license: CC BY 4.0
---
# ‘To me, it's ones and zeros, but in reality that one is death’: A qualitative study exploring researchers' experience of involving and engaging seldom‐heard communities in big data research
## Abstract
### Background
Big data research requires public support. It has been argued that this can be achieved by public involvement and engagement to ensure that public views are at the centre of research projects. Researchers should aim to include diverse communities, including seldom‐heard voices, to ensure that a range of voices are heard and that research is meaningful to them.
### Objective
We explored how researchers involve and engage seldom‐heard communities around big data research.
### Methods
This is a qualitative study. Researchers who had experience of involving or engaging seldom‐heard communities in big data research were recruited. They were based in England ($$n = 5$$), Scotland ($$n = 4$$), Belgium ($$n = 2$$) and Canada ($$n = 1$$). Twelve semistructured interviews were conducted on Zoom. All interviews were audio‐recorded and transcribed, and we used reflexive thematic analysis to analyse participants' experiences.
### Results
The analysis highlighted the complexity of involving and engaging seldom‐heard communities around big data research. Four themes were developed to represent participants' experiences: [1] abstraction and complexity of big data, [2] one size does not fit all, [3] working in partnership and [4] empowering the public contribution.
### Conclusion
The study offers researchers a better understanding of how to involve and engage seldom‐heard communities in a meaningful way around big data research. There is no one right approach, with involvement and engagement activities required to be project‐specific and dependent on the public contributors, researchers' needs, resources and time available.
### Patient and Public Involvement
Two public contributors are authors of the paper and they were involved in the study design, analysis and writing.
## INTRODUCTION
Patient and public involvement and engagement (PPIE) has become embedded in health research and within the NHS, 1 and is used in healthcare services 2 to put the public perspective at the centre of the discussion 3 and improve professionalism among medical practitioners. 4 It helps to align priorities shared by researchers and the public 5 and it helps researchers understand the lived experience of patients and the public. 6 *There is* also an ethical argument that those who pay (taxpayers) should have a say on how their services and research are shaped. 7 We follow the National Institute for Health and Care Research (NIHR) definition of public involvement and engagement. 8 Public involvement in research means that work is ‘being carried out “with” or “by” members of the public rather than “to,” “about” or “for” them’. We use the term ‘public contributor’ to describe this role. Conversely, public engagement stands for activities ‘where information and knowledge about research is provided and disseminated’.
## Big data
There are multiple definitions of big data in the literature. 9 *In this* paper, we define big data research as reusing routinely collected medical data for research purposes. This can happen by linking large medical data sets from various sources. When initially collecting medical data, the public (or the researcher) might not be aware that their data may be later reused for research. Many big data research studies use opt‐out consent, where patients need to inform someone, usually their medical provider, that they do not want their medical data to be reused for research.
Public support is needed for these projects to be able to take place, 10 and a systematic review has shown that the public generally supports the reuse of their medical data. 11 However, they can be concerned that their data might be misused, for example, sold to private companies. 12 PPIE can assist in alleviating these concerns. 13 Hill et al. 14 found that talking about and explaining the research process around big data improved their study participants' support in reusing their medical data. Public contributors can also contribute to the decision process on who can access medical data for research purposes, thus ensuring that a social licence exists. 15 Social licence is more than meeting legal requirements and requires public trust that researchers will conduct their work ethically. 13 Poor governance can lead to a deterioration of the social licence. 16
## Seldom‐heard communities
In addition to the ‘usual’ public, it is important to capture the voices of groups in our communities who are less frequently heard. Successful PPIE requires the inclusion of seldom‐heard communities, 5, 17, 18 and researchers should aim to include them, 19 but how to do it in a meaningful way remains challenging. 20, 21, 22 Such communities are often easy to ignore, but not including them can make research findings ungeneralizable to all parts of society and miss the nuances of experiences specific to those groups 23 and will not provide solutions for all communities. 24 PPIE should be inclusive of and accessible to everyone. 5 Not including seldom‐heard voices can reflect the power structures at play and perpetuate health inequalities. This is important as these communities might experience poorer social and health outcomes. For example, the Covid‐19 pandemic disproportionately affected people from ethnic minorities. 25 The terminology and definitions in this area are contested. Some of the terms used include hard‐to‐reach, 23, 26 seldom‐heard, 27, 28 seldom‐listened, 29 peripheral voices, 30 marginalized 31, 32 and underserved. 33 The key characteristic of these definitions is that these communities are less included in research than other groups in mainstream society. Within the UK legal context, the Equality Act 2010 uses the term ‘protected characteristics’. These are age, disability, gender reassignment, marriage or civil partnership, pregnancy and maternity, race, religion or belief, sex and sexual orientation. The Act provides antidiscrimination laws and embeds requirements for diversity and inclusion for public bodies but is not always directly applied to research. However, it can be influential in how researchers approach diversity in their work. 18 We will use ‘seldom‐heard’ as this shifts the responsibility for inclusion to researchers rather than blaming the public, as implied by the ‘hard‐to‐reach’ wording. Their inclusion (or a lack of it) is not a fault of these communities. 33 When presenting the results, we kept the original terms used by participants when quoting them. However, we recognize that use of any terms might not necessarily represent how these communities would like to be described.
## Research aim
Despite understanding the importance of PPIE, there is limited knowledge of how this can be effectively facilitated in big data research. 34 A previously published system logic model identifying key elements of PPIE in big data research recognized the inclusion of seldom‐heard communities as a key component, 34 and therefore, there is a need to understand how to ensure all voices are included.
This paper explores researchers' experiences of involving and engaging seldom‐heard communities in big data research.
## Theoretical position
This study adopts social constructionism as its theoretical lens when understanding and analysing data. 35 We believe that multiple realities and perspectives exist among researchers. These are subjective and socially constructed and thus depend on participants' cultural, political and historical backgrounds. Researchers (and thus their work) are shaped by their relationships with public contributors. From the social constructionism perspective, the dynamics of social interactions are essential to understand how new knowledge is achieved. 36 Thus, in our analysis, we focused on the processes around PPIE rather than its structures.
Social constructionism can be used to justify a more collaborative form of inquiry. 35 This can be achieved by conducting research together with the public contributors. Collaborative work can be seen among our participants who involve the public in their work but also in our project, as we involved two public contributors as co‐researchers.
## Participants and data collection
Alongside big data researchers, we included facilitators of PPIE activities in big data projects. Facilitators (some of whom might be qualified researchers) are in charge of the overall organization of the PPIE progress; they co‐ordinate, organize and facilitate activities and act as intermediaries between researchers and public contributors. 37 They often are recruited at research institutions to support specific big data research projects. Throughout the paper, for clarity, we will refer to both groups as researchers. All participants had to have an experience of involving or engaging seldom‐heard communities or aiming to reach them.
We recruited through Twitter, bulletins and established networks within big data research such as Health Data Research UK. Interested participants contacted the author for further details and to register their interests. Interviews were conducted on Zoom between March and June 2022. Interviews were later transcribed and anonymized, with all participants assigned pseudonyms.
A semistructured interview guide was developed to elicit participant experiences of PPIE with seldom‐heard communities. We also included an opportunity for them to speak about communities that they planned to reach or tried to engage but were unsuccessful. After the first interview, co‐authors met to reflect on the topic guide. One follow‐up question on what participants perceived as a seldom‐heard community was added to the topic guide.
Only limited demographics were collected from participants to protect their anonymity. Twelve participants took part in the study. We reached data saturation when no new themes appeared in our analysis. 38 Participants were based in England ($$n = 5$$), Scotland ($$n = 4$$), Belgium ($$n = 2$$) and Canada ($$n = 1$$). The majority were women ($$n = 11$$) and there was one man. Their experience of research and PPIE ranged from two and a half years to 20 years, with an average of 9 years. We also asked them to describe themselves as researchers ($$n = 6$$) or facilitators ($$n = 9$$), although they could have chosen both options. Six participants were from an ethnic minority background.
## Data analysis
We conducted a reflective inductive thematic analysis. 39, 40, 41 This method allowed us to identify patterns across all interviews systematically. Thus, we unpacked the realities experienced by researchers. We used both semantic and latent coding. Semantic coding shows more explicit patterns within the data and stays as close as possible to what participants said. This allowed us to share specific practical examples of PPIE strategies. Latent coding provided more implicit and interpretive reflection on the data. Initially, one interview was coded jointly by three authors (P. T., S. A., N. T.) in Word. Then, the author (P. T.), an experienced qualitative researcher, inductively coded the remaining interviews, supported by NVivo 12. We met as a team on multiple occasions to discuss the data analysis and develop and refine further themes.
Public contributors can be meaningfully involved in qualitative analysis 42 and trained to conduct reflexive thematic analysis. 43 Two authors (S. A. and N. T.) who are public contributors received training in reflexive thematic analysis (focusing on being reflective, coding process and refining themes). They were involved in the designing the study, initial coding and developing and refining the themes. They are both experienced public contributors and S. A. also acts as the Data Ambassador for Care and Health Informatics theme within the Applied Research Collaboration North West Coast. This role involved raising awareness and knowledge about big data research. Two authors (S. A. and N. T.) also took part in an exercise reflecting on how their backgrounds influenced what they perceived in the data and what they brought to the analysis.
The research‐active authors also reflected on their academic backgrounds (P. T., K. F., S. E. R. and L. F.). Research team consisted of qualitative researchers with experience of involving and engaging the public, and those who conducted research in big data. These different perspectives allowed us to bring distinct views to the data analysis and furthered our understanding of the experiences of our participants.
## FINDINGS
We present four themes that explore how researchers involved and engaged seldom‐heard communities in big data research: [1] abstraction and complexity of big data, [2] one size does not fit all, [3] working in partnership and [4] empowering the public contribution. All themes appeared throughout all interviews, which provides an indication that these experiences were commonly shared among participants (even if participants were based in different countries). We have provided additional quotes in Supporting Information: Appendix 1 that offer further examples of how the participants involved and engaged seldom‐heard communities around big data research.
## Abstraction and complexity of big data
Big data can be an abstract and difficult topic to explain to the public. Participants said that conversations about big data include technical, specialist's vocabulary, jargon, references to legislation and regulations. Researchers found it challenging to discuss the complexity of this kind of research with public contributors in lay terms:*Big data* is a really complex environment to navigate both in terms of the research, but also in terms of like the regulatory aspects and legislative aspects. ( Sophia) Sometimes, the difficulty in explaining big data research impacted on participants' experience of involving the public. Public contributors can have a role in advising (or deciding) if researchers may access routinely collected health data for research purposes. Here, the public contribute to the governance groups of these initiatives. Researchers who worked with these groups found it hard to explain to the public the purpose of big data research. They struggled to contextualize the concept of big data to the public if it did not directly refer to the public contributors' health condition or a topic that might interest them. The following extracts illustrate that challenge as the participant refers to bringing public contributors to support big data infrastructure:Project (…) was just looking at the infrastructures of big data. It was really challenging to actually put that into a context that was relevant to members of the public; they kind of said ‘well we don't even know if you want us to be involved, we don't really see how we can be because this is all to do with linking up datasets with each other and it's all very technical, and it's not really anything to do with our living experience as patients or as members of the public’. So that was that was quite a hard project actually to think about. ( Sienna) *It is* not only public contributors who can be confused by big data jargon. Some participants who were not data researchers said that their familiarity with the topic was more akin to the public contributors rather than data researchers they worked with on the project. They might feel uncomfortable asking questions or requesting clarification. The public contributors often were more confident in asking these kinds of questions. This was seen as a very positive element of PPIE by a participant:I'm sometimes really pleased when [public contributors] ask questions. Because I'm like oh, good, I don't know if I could have asked that, but so I'm really pleased that you did. I probably should have known that, but I don't, so I'm glad you asked it. ( Robyn) Participants felt that promoting the benefits of big data research, being transparent in how data are used and building trust with the public would ensure that some negative media stories around big data research could be counteracted. They believed that overall, the general public would be supportive of data sharing to improve healthcare. They recognized the need for effective communication between researchers and the public. In individual projects, they suggested training and supporting the public contributors around big data research but described it as a slow and time‐consuming process. One of the things that we really do is kind of work with our staff to make sure that they are able to explain it in kind of like plain English. If we were to have a session about something like trusted research environments, which can be kind of like a technical. Then we would work with staff to actually plan the presentations (…) to make sure that the language is right, we also hold drop‐in sessions once a month so that members of the public that we work with can come in and say ‘I have a question’. (…) And so we bring in some of our more technical staff because I have no technical knowledge myself. ( Harriet)
Participants spoke about how communication must continue outside the research projects and involve the broader community. The public contributors involved in big data research are essential to helping further engagement with their communities. As they become more familiar with big data research, their knowledge can be utilized to engage with the general public and raise awareness of big data research. They can help explain what big data research is about, its benefits and how it works. Here, a participant speaks about explaining in lay terms a technical term related to data:When it comes to data and infrastructure and things, it can be very complex. There's lots of big words like pseudonymisation [laughing] and things like this, so we worked with the public members to create this animation, which gives a snapshot of what the project's like and it's an accessible snapshot. ( Robyn) This theme shows that talking about big data can be complex and challenging. However, there was an agreement that PPIE around big data research takes the researcher away from numbers and allows them to bring a human face to the data. The following excerpt explains this:I love doing this type of analysis of, you know, hitting the buttons and seeing the graphs come up and seeing results. It's really exciting, but you miss that contact with people. And having that PPI group, there was a really good way for me to touch base and think about what the numbers meant. And think about the stories behind some of the data. And connect it to people's lived experience and I think that's really important. (…) To me, it's ones and zeros, but in reality that one is death. So it's really important to have that in front of your mind, and I think that brings it home when you've got a group of people in front of you who are really interested in what you're doing and to whom it could potentially make a difference. ( Zoe)
## One size does not fit all
This theme elicits the need for researchers to be flexible and often innovative when involving public contributors in big data research. Participants did not have one prescription on how to successfully work with the public contributors.
How PPIE looked in the participants' work differed based on the project needs, public interest or experiences. Public contributors can be involved in different roles within projects around big data research. These included contributing to the review of the data access process and as co‐investigators or members of advisory groups for specific projects. The following quote shows how public contributors can assist with decisions over whether and how researchers can access routinely collected medical data for research purposes. That's a group of around eight members of the public who we meet with on a quarterly basis to get their views on our kind of engagement plans (…) and also to get them to become more part of our project approval process is something they've been really keen to do, so we're looking at our kind of review process. Researchers who want access to routinely collected health and social care data puts their applications in and it goes through a rigorous, multistage approval process and one of those that we're looking to do is to have the public voice within that so their vote, their part of it would be an assessment of the public value of the projects that come in. ( Alex) Participants said that public contributors can have a much more active role and co‐share responsibilities with researchers:We have two co‐leads. One of them is myself and but the other one is a member of the public, so that from the very beginning, I am working very closely with [the public contributor] so that we can kind of shape this programme together, making sure that the public views are fed in right from the very beginning and as part of that we've also got a leadership (…) and so in this leadership team, it's half public contributor, patient‐public contributors and the other half would be kind of like professionals such as myself. ( Harriet) How to work with each community might depend on their needs. Many participants spoke about the need to understand the specific community that they were planning to work with. Here, a participant suggests a pre‐engagement engagement to understand what PPIE should look like:It's just really interesting about doing that pre‐work to set up the scope and the scale of the engagement work and then to set up the environment that would be the safe as possible, so it's almost like a pre‐engagement engagement where you're really setting up the safe environment to allow for good public engagement to happen for diverse members. ( Victoria) Who represents seldom‐heard communities differed among participants. Participants often spoke about aiming to be reflective of the community. However, they recognized that it was not always possible (or feasible) to reach everyone who might potentially contribute. They admitted that because of their recruitment methods, limited resources or time, the public contributors who were generally involved often represented a limited range of demographics. Each community is different and might require different PPIE strategies. They argued that the recruitment should be specifically tailored to the group they wanted to reach. The communities that were most often involved in PPIE were generally white and elderly. The seldom‐heard communities they wanted to involve included ethnic minorities, people experiencing homelessness, traveller communities or different age groups (especially younger people). However, they also wanted to reach people with particular health conditions or improve male representation. The following quote illustrates how participants perceived their role in encouraging diversity:We do try to reach out to seldom‐heard groups. We are currently undertaking an audit of our group to see how, where we're lacking, 'cause I suppose within the patient and public involvement there tends to be a certain type of person who volunteers and has got the time. So tend to be retired, tend to be white more often than not, and so we are keen to widen our demographic (…) we're not just interested in ethnicity (…) it tends to be quite a lot of women as well that volunteer, so you know, increasing, men, also increasing our younger population. ( August)
## Working in partnership
PPIE is not conducted in a silo. The participants worked with others (organizations, charities, public services and public contributors) with the aim of being inclusive and to reach more diverse communities, especially around big data research. This theme explores these different actors' roles in successful PPIE.
These partnerships have the potential to fill the gaps in researchers' understanding of local communities. Some participants recognized that researchers themselves could be a hard‐to‐reach group. Meetings can be held during working hours or be otherwise inaccessible to public contributors. Others recognized that the diversity of their teams is important and might reflect how well they involve and engage communities. I think while we don't have as much diversity as we could in our staff, it's harder for us to communicate or share those messages or understand the groups that we're trying to reach. ( Arabella) Charities and organizations already provide existing links with the community and offer that bridge for researchers to reach the seldom‐heard groups. They can assist with recruitment and engagement strategies. However, there is a risk that a researcher will not necessarily improve the diversity of their group but rather take over the demographic composition of the group they engaged with, as this participant explains:So it was mainly about because I was kind of piggybacking on a charity, on several charities groups. It was down to who they had picked up and they were already actually meeting via *Zoom this* charity, so I kind of inherited their diversity or degree of diversity. ( Zoe) However, as much as these partnerships can be helpful, establishing them is not easy. It can be time‐consuming to build that trust with the charity, and participants recognized that this needs to be an ongoing relationship that should benefit both parties.
Some participants also said that that relationship could be confusing to the potential public contributors if there is more than one research team working on that project (and thus trying to involve them). The following extract shows how one of the participants struggled to get some patient groups involved because they already had been working with other researchers:I contacted several [patient groups] in [the city] to see if they would be interested in doing some PPI workshops with them or telling them a bit more about the research we're doing. (…) They didn't necessarily know that they it was the right thing for them at the time, but also they'd had so many researchers getting in contact with them that it's they said it's just really difficult for us to choose who we work with and if they've already got a relationship with somebody else. Then they may choose to work with them obviously instead. ( Sienna) Researchers can act as facilitators of PPIE or bring in trained experts (who might not necessarily be familiar with big data research). The facilitators' role is to act as this connecting bridge during work, an intermediary between researchers and the public contributors. What we are trying to do is bring these people on board and explain to us what it is, and we try to turn it into more lay language and sometimes with [public contributors], engage them to have a conversation so that they can actually challenge the experts rather than us doing it. So we are more of an inbetweener in that sense. ( Kimberly) PPIE is also about involving individual public contributors. Participants often spoke about how interested and passionate public contributors can become about their involvement. These partnerships require working together and respecting each other. Some participants spoke highly of public contributors they worked with:And one thing that I think that is often forgotten is about [public] members is that they are just, they're not just patients or they're not just a member of the public. These are very talented, very skilled people. You know they've got their own life skills. You know they've got their own careers. They've got all of the skills and knowledge from that, and I think it's great that they want to volunteer with us and help share some of that. ( Robyn) Only when truly working in partnership with public contributors can it lead to their empowerment. This is the focus of the next theme.
## Empowering the public contribution
Participants felt that for involvement to be successful, there must be a power balance between researchers and the public contributors. Empowerment gives public contributors the ability to contribute to the involvement process fully. This can be achieved through ongoing support and ensuring that they become more familiar with big data research or projects that they are involved in. As the following qoute illustrates, this is a continuing process. Giving a sort of chance for people to ask questions, which was the nice thing about that project is that it wasn't a one‐off, people could go away, look up something for themselves and then they could come back and be like what's this and they'd post a link and then we'd come back and answer those questions. So it was quite a nice kind of two‐way in that sense. ( Drew) Most participants felt that public contributors need to be supported at each stage of the involvement process but also recognized that this can be time‐consuming and requires additional work. Some suggested an open‐door policy where public contributors could reach researchers anytime and thus also feel like a part of the team. WhatsApp groups for public contributors can be a safe place to discuss the project further. Public contributors should receive training or induction both around the project and PPIE (especially if they are involved in a research project for the first time). One of the techniques that supported the public in understanding the jargon around big data research was a ‘live dictionary’, which could be updated as people asked questions throughout the lifetime of the research project. But one of the things that we've created is an ongoing glossary. And if there's any words or phrases that the [public] members don't understand, it's a case of pop it into that glossary, and someone will answer it for them. ( Robyn) However, participants recognized that not all training can be equally helpful and that some institutional resources were more bureaucratic and could potentially discourage people from being involved. This is illustrated by the following quote talking about the focus on training offered by the academic institution to new public contributors involved in the research:[The training] is quite formal and it's about like the whole university obviously it's not about big data, it's not really keyed towards seldom like heard groups or different types of groups, so I think there's other types of training that could still be useful for people, even if it's just, you know, stories of being involved that are from people who are more like them. So I think it could be a little bit of a little bit tailored, and some of it's very dry if I'm honest. ( Zoe) After receiving all this training and support, some participants felt that there is a danger that the public contributors start offering more of an expert view rather than a lay person perspective. There is a fine balance between understanding the project enough to be able to provide a nuanced contribution and where public contributors become what one can describe as ‘usual suspects’ of people who keep getting involved and thus become more like professionals. One participant spoke of a successful approach to dealing with this challenge:it is a really fine line between building their knowledge to get involved and becoming an expert in that and kind of losing that public perspective (…) to kind of help with that; we do also have members of the public in a role for only specific amount of time. So, for example, now [advisory board]. They're only there three years, and then we kind of refresh the board, so with that, we're constantly bringing in that kind of like newer public perspective as well. ( Harriet)
Empowerment must be felt in practice and involvement needs to be genuine. Public contributors must feel that they make a difference. In the ‘one size does not fit all’ theme, a researcher spoke about the public contributors' panel assessing if researchers can access medical data for research purposes. The participant described how the public contributors perceived this and how it could be expanded for more empowerment:‘Do you agree with our decisions over whether these were approved or not?’ And in the main, they aligned with what the decisions had been, but on a couple of occasions, they were like ‘we don't see the public value in doing this. It's not well explained’, so is either it wasn't when explained or the public value wasn't there, and so that going more of a point of challenge for them and made it quite clear that they wanted to be part of the genuine process of review. ( Alex) Participants pointed out that only when there is a real sense of empowerment can public contributors' involvement impact positively on the research projects. There are multiple ways by which public contributors can shape projects. Participants named the following contributions: ensuring that the research questions address the public interest, co‐analysing study results, advising if researchers' ideas and thoughts are on the right track (e.g., appropriate wording used or right engagement strategy put in place) and public contributors doing sense‐checking and contributing to potential engagement strategies with the broader public. The following quote shows the variety of involvement and its impact:Extremely impactful, (…), it's actually led to changes in the direction of our work, but in cases where that hasn't necessarily happened, that they've been more supportive of what we're kind of thinking and it has changed the way that some are kind of like thinking about the topic of public trust and public confidence, for example, and we only ever used to think like the wording that we would use as an organisation was we need to earn public trust. We need to build public trust but then through the [advisory board] through exploring that a bit more, we've kind of changed our way of thinking, so it's more about demonstrating trustworthiness in the use of data and building public confidence. ( Harriet) This theme has shown that public involvement should not be an afterthought and needs to be a genuine (but often time‐ and resource‐consuming) process that can have a significant impact on researchers' work. This can be especially seen in the following extract:*It is* difficult to do really well, and it takes a lot of time and a lot of resources, and I think people underestimate that. I also think there's a culture towards PPI as a tick box. ( Penelope)
## DISCUSSION
Our findings have shown that talking about big data ‘with’ (rather than ‘to’) public contributors can be challenging, but that PPIE can be meaningful for both researchers and public contributors. The findings elicited how researchers and their research can benefit from involving and engaging seldom‐heard communities. Table 1 summarizes the key recommendations. This adds to the previous literature on meaningfully including a diverse range of communities 44 and is relevant to other areas of health and social care research. PPIE requires time and resources, 45 and not all communities are often equally involved. 46 However, our participants have shown that inclusion around big data research (because of the complexity of the topic) takes additional time and resources to succeed (even in contrast to other health research). This can be seen in extra activities such as a ‘pre‐engagement engagement’, which was suggested as a baseline for successful working with the community. Our findings challenge the perspectives of some researchers who believe that public contributors rarely care about or can understand big data research and thus are not able to be involved in decisions around whether medical data can be reused for research. 47 Involving and engaging seldom‐heard communities in big data might be more challenging than in other forms of health research but it is important as big data research offers an opportunity to reduce health disparities. 48 Without seldom‐heard voice input, this might not happen.
**Table 1**
| 1.Provide information in lay language and, where not possible, explain in simple English. Ensure that these explanations are available at any point to the public contributor (e.g., through an online dictionary). |
| --- |
| 2.Rotate public contributors on a ‘big data panel’ every 3 years to bring in new ideas and lay perspectives. |
| 3.Reach out to new communities for at least 50% of the new attendees, potentially using charitable/partner organizations to help. |
| 4.Identify relevant seldom‐heard communities for each project. |
| 5.Consider strategies to add additional diversity on multiple characteristics (e.g., LGBTQ+ and ethnic minority, or disability). |
| 6.Adequate and ongoing training/support for PAs should be provided to empower them so that they can truly contribute. |
The findings confirm that defining a group as a seldom‐heard group is context‐specific. 33, 49 The participants named numerous types of seldom‐heard communities involved and engaged within the context of their work. Researchers should reflect on who would be the most seldom‐heard group within the context of their study and recognize that this might include more than one community. The concept of superdiversity 50, 51 could provide researchers with further guidance on moving away from looking at a single characteristic (e.g., ethnicity) of the community and focusing instead on diversity within diversity. This would ensure that the needs of communities within communities are considered.
Researchers need to take time to plan PPIE well as they design their projects. NIHR guidance 33, 52 recognizes this and recommends working with communities on a long‐term basis. Our findings have shown the importance of building and maintaining relationships with organizations, especially charities. This confirms previous research that shows that links to the third sector are crucial in building trust. 53, 54, 55 They often act as gatekeepers but also have the potential to act as a partner. There is, however, a risk that researchers would not reach many communities as they might be limited to the partner organization's level of diversity.
There is a growing trend to establish a pool of volunteers interested in participating in PPI activities. 56 This approach might appeal to those who have time, resources and feel comfortable with working with institutions. However, this risks public contributors becoming ‘usual suspects’ of people who are involved regularly and thus not providing new contributions. There is the danger that they will become more expert than researchers themselves, thus no longer providing lay experiences and views in the project. There remains a contentious issue: how to strike a balance between public contributors being capable of contributing fully but also retaining a lay perspective. 57 One of our participants suggested the need to change public contributors on advisory boards every 3 years. This offers a solution to deal with the challenge of ‘usual suspects’ and brings a fresh public perspective but adds more work on the part of the researchers to recruit, provide training and support new public contributors on the project. The other option is to sense‐check any work with the broader public.
Researchers should also ensure that any involvement is not tokenistic and enables power‐sharing between researchers and the public contributors. 21 *There is* no one ‘right’ way to do it, and the approach depends on the project's needs (or resources) and the public contributors' interests. However, their interests should not be confused with their understanding of the topic, and researchers should provide training to improve public contributors' knowledge, thus facilitating their ability to contribute. This genuine empowerment was seen as crucial among our participants when discussing big data research with public contributors. Although not mentioned by our participants, some public contributors, for example, coming from Indigenous communities, might also require researchers to respect their values to feel truly empowered. 58
## STUDY LIMITATIONS
The study participants came from diverse communities, for example, various ethnic minority backgrounds. However, we did not record if they are a part of other seldom‐heard communities, for example, LGBTQ+ or people living with disabilities. We only explored the perspectives of the researchers, and there is a possibility that the public contributors (including those coming from seldom‐heard communities) would have a different view on their PPIE activities around big data research. As big data is a fast developing and diverse research area, new ways of involving and engaging will emerge, so future research should further explore how researchers involve and engage public contributors and how concepts of super diversity could be utilized.
## CONCLUSION
Our study explored how researchers involve and engage public contributors (especially seldom‐heard communities) in a meaningful way in big data research. The findings highlight that there is no one right approach to doing PPIE and that PPIE strategies are project‐specific and depend on the public contributors, researchers' needs, resources and time available. We encourage others to reflect on their involvement strategies and hope that these results will support researchers who want to involve more seldom‐heard communities in complex research topics such as big data.
## CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest.
## ETHICS STATEMENT
We received ethical approval to conduct this study from the ethics committee at University of Liverpool under the number 10063.
## DATA AVAILABILITY STATEMENT
Anonymized data are available upon reasonable request.
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|
---
title: 'Increased low frequency fluctuation in the brain after acupuncture treatment
in CSVDCI patients: A randomized control trial study'
authors:
- Nan Yang
- Sina Chen
- Shuxue Liu
- Shuiqiao Ling
- Lidian Chen
journal: Frontiers in Neuroscience
year: 2023
pmcid: PMC10010105
doi: 10.3389/fnins.2023.1125418
license: CC BY 4.0
---
# Increased low frequency fluctuation in the brain after acupuncture treatment in CSVDCI patients: A randomized control trial study
## Abstract
### Background
Cerebral small vessel disease (CSVD) is one of two cognition-impairing diseases. Acupuncture (Acu) is a flexible treatment with few adverse effects and is thus widely used to treat neurological problems.
### Methods
We recruited a total of 60 patients and assigned them to two groups ($$n = 30$$ each group). During the study, some participants were excluded by quality control, and a total of 44 subjects (25 Acu and 19 controls) were completed to investigate the therapeutic efficacy of acupuncture on CSVD cognitive impairment (CSVDCI). The following demographic and clinical variables were compared between the two groups: gender, age, education, smoking, alcohol, Montreal cognitive assessment (MoCA), symbol digit modalities test (SDMT), verbal fluency test (VFT), digit span task (DST), Boston naming test (BNT) scores, and amplitude of low-frequency fluctuation (ALFF) under the typical band (0.01–0.08 Hz). Mixed effect analysis was utilized to test for differences between the two groups before and after the treatment.
### Results
Following acupuncture treatment, the Acu group scored higher on MoCA, SDMT, VFT, DST, and BNT compared to controls ($P \leq 0.05$). The brain regions showing substantially greater ALFF values in the Acu group were the right inferior temporal gyrus, left middle occipital gyrus, left superior occipital gyrus, left insula, bilateral postcentral gyrus, right superior parietal gyrus, right cerebellum, right precuneus, and right precentral gyrus ($P \leq 0.005$, no correction). The ALFF values in the right inferior temporal gyrus ($$P \leq 0.027$$), left middle occipital gyrus ($$P \leq 0.005$$), left superior occipital gyrus ($$P \leq 0.011$$), and right superior parietal gyrus ($$P \leq 0.043$$) were positively associated with MoCA.
### Conclusion
We found that acupuncture modulates the functional activity of temporal, occipital, and parietal regions of the brain in CSVDCI patients.
## 1. Introduction
CSVDCI is the most common cause of vascular cognitive impairment (VCI), accounting for approximately $74\%$ of all occurrences (Rao et al., 2009). It is not commonly known that CSVDCI is an important subtype of VCI due to its quiet onset and lack of prominent clinical features (Brookes et al., 2014), also representing the primary clinical manifestation of CSVD. CSVDCI shows a similar pattern of cognitive decompensation to the VCI, which is characterized by reduced executive function, attention, and information processing speed, with relatively intact memory function in the early stages and gradual development of dementia (Chen et al., 2019). In addition, cognitive impairment worsens with the development of the disease, which severely impacts patient quality of life.
Early management of CSVDCI can enhance cognitive performance and patient quality of life, while partially slowing the course of cognitive decline. Several conservative treatments, including the use of an acetylcholinesterase inhibitor and N-methyl-d-aspartate (NMDA) receptor antagonist were proposed for the symptomatic treatment of dementia (Arvanitakis et al., 2019). However, a limited number of targeted drugs effectively improved cognitive function in CSVDCI patients (Pantoni, 2010), with minimal efficacy for dementia (Bath and Wardlaw, 2015). Interestingly, acupuncture has been widely used in China as a complementary alternative treatment for dementia, and also been accepted for VCI treatment in Western medicine (Ji et al., 2021). The main advantage of acupuncture is the lower incidence of adverse effects that characterize pharmaceutical approaches (NIH Consensus Conference, 1998; Kim et al., 2019). Importantly, clinical randomized trials demonstrated the short-term impact of acupuncture on cognitive function in VCI patients (Yang et al., 2014; Yang et al., 2019; Huang et al., 2021).
With an increasing understanding of the etiological basis of CSVD in the elderly population, inflammatory responses have been associated with its development and progression (Li et al., 2020). Wang et al. demonstrated that acupuncture attenuates inflammation-related cognitive impairment in experimental vascular dementia (VD) by inhibiting the miR-93-mediated TLR4/MyD88/NF-κB signaling pathway (Wang et al., 2020). In addition, acupuncture reduces oxidative stress and inflammation associated with TXNIP, plays a neuroprotective role in VD rats (Du et al., 2018), enhances cognitive function and induces neuroprotective effects against inflammation in CCH rats by activating α7nAChR and the JAK2-STAT3 pathway (Cao et al., 2021).
Various ancient and modern acupuncture publications showed the Shenting (GV24) and Baihui (GV20) are vital distal acupoints associated with the cure of dementia, dizziness, headache, among other brain diseases. Huang et al. [ 2015] conducted a meta-analysis study that included 1,637 subjects with post-stroke cognitive impairment (PSCI), and found that integrating Shenting and Baihui acupuncture with computer-assisted cognitive training significantly improves attention deficits in stroke patients. Similar results were found in a randomized controlled trial of 2 × 2 factorial design conducted by Yang S. et al. [ 2014].
Resting-state functional magnetic resonance imaging (MRI) has been extensively employed to investigate the functional mechanisms underlying a variety of neurological diseases, and may also provide insights on the ability of acupuncture to improve cognitive performance (Cai et al., 2018). Measurements such as functional connectivity (FC) and degree centrality (DC) were created to mimic the brain network (Park and Friston, 2013). Zang et al. [ 2007] suggested ALFF to estimate regional brain activity and found it could represent the activity of different brain regions at the resting state. In addition, abnormal ALFF levels were found in people with cognitive problems and abnormal brain function, a powerful determinant of cognitive decline (Li et al., 2021; Wang et al., 2021; Zhang J. et al., 2021), and different brain regions, including the parietal, insular and cingulate regions. This is significantly correlated with cognitive function in patients with subcortical vascular cognitive dysfunction, which may lead to decreased cortical activation (Li et al., 2015). CSVDCI patients with cerebral microbleeds (CMBs) have altered spontaneous brain activity of the default, sensorimotor, and fronto-parietal lobe networks, that may impact potential neurophysiological mechanisms of intrinsic brain activity (Feng et al., 2021).
Since 1990, an increasing number of studies used imaging to explore the physio-pathological mechanisms of acupuncture for the treatment of disease (Dhond et al., 2007). Acupuncture improves cognitive function in patients with Parkinson and increases ALFF values of the default network, visual network, and insular lobe. This has led to the hypothesis that acupuncture can activate the cerebellum-thalamus-cortex loop by regulating the spontaneous activity of the brain in key regions, a neurophysiological mechanism to improve cognitive dysfunction (Li Z. et al., 2018). Moxibustion therapy can improve the cognitive function of patients with mild cognitive impairment by adjusting the ALFF values of the default, visual and subcortical networks (Lai et al., 2022), and might thus reveal the brain regions involved in cognitive function improvement through acupuncture.
Here, we examined the differences in ALFF values between the Shenting/Baihui acupoints and conventional drug treatment in CSVDCI patients, before and after treatment (in 12 weeks), to uncover the associated neural mechanisms.
## 2.1. Participants
CSVDCI patients were enrolled at the Zhongshan Hospital of Traditional Chinese Medicine from July 1st 2017 to July 30th 2019. The protocol was approved by the research ethics committee of the Zhongshan Hospital of Traditional Chinese Medicine (reference: 2017ZSZY-LLK-219). We recruited CSVDCI patients at the neurology outpatient and inpatient departments. All participants signed an informed consent form prior to enrollment.
Patients with the following conditions were considered eligible: (i) age between 40 and 80 years, (ii) comply with diagnostic imaging criteria for cerebral small vessel disease and vascular cognitive impairment, (iii) MoCA score between 10 and 26, (iv) not receiving regular acupuncture treatment for the recent six months.
Patients with the following conditions were excluded: (i) cognitive dysfunction caused by macrovascular, cardiogenic cerebral embolism, (ii) patients with severe speech, vision, or hearing impairments or mental disorders that impact cognitive examinations, (iii) cognitive dysfunction caused by neuropsychological disorders (e.g., depression), (iv) illiterates that could not cooperate with cognitive examinations, (v) prior alcohol and drug abuse experience, (vi) combination of serious diseases, including of the cardiovascular, hepatic, nephrology, endocrine system and hematopoietic systems, (vii) participating in other clinical trials.
The CSVD patients were diagnosed according to the Neuroimaging Standards for Research into Small Vessel Disease (Wardlaw et al., 2013). Specifically, the diagnostic standard for imaging of CSVD included: (i) Recent small subcortical infarct: Axial views showing an infarct diameter smaller than 20 mm, which could be larger than 20 mm in the coronal or sagittal views, (ii) Lacunes of presumed vascular origin: round or ovoid, 3–15 mm in diameter, distributed in subcortical regions, filled with the same signals as cerebrospinal fluid (CSF), (iii) white matter hyperintensity (WMH) of presumed vascular origin: abnormal brain white matter (WM) signals, lesions of variable size, showing a high signal on the T2-weighted or T2-weighted FLAIR images. ( iv) Perivascular space: the signal of perivascular space was the same as that of the CSF in all MRI sequences. The shape was linear when the image plane ran parallel to the blood vessels and round or oval when running perpendicular to the vessels, usually smaller than 3 mm in diameter, (v) Cerebral microbleeds, which were defined as the following changes in the images obtained with T2*-weighted gradient-echo sensitive to magnetizing effects. For example: [1] small round or oval, clear boundary, homogeneity, lack of signal focus; [2] diameter of 2–5 mm (maximum 10 mm) and lesion surrounded by the brain parenchyma; [3] brain atrophy: reduced brain volume not associated with specific focal lesions, such as trauma and cerebral infarction.
The sample size was estimated using the Gpower3.1 software. The MoCA total score was used as the main impact indicator. Based on previous studies (Wang et al., 2016), which estimated the MoCA difference for VCI patients treated with acupuncture as 5.5 ± 2.2, and the MoCA difference for the control group as 3.1 ± 1.8. The Gpower3.1 software estimated the effect value for acupuncture to improve cognitive function in VCI patients to be 1.194045, whereby we set the α value to 0.01, the Power (1-β) value to 0.9, and the effect value to 1.194045, which was calculated using a sample size of 23 cases per group. With a shedding rate of $20\%$, we predicted a total sample size of 56, with 28 cases per group.
A total of sixty patients were enrolled after screening for eligibility, and were randomly allocated to either the acupuncture or conventional treatment groups. At the baseline, all patients underwent fMRI. We removed 11 and 5 patients from the conventional and acupuncture groups, respectively, due to excessive head motion or rejection of the second fMRI scan.
## 2.2. Protocol
This study represents a randomized controlled trial using fMRI scans to assess the effect and mechanisms of acupuncture treatment on CSVDCI. Participants completed fMRI scans and cognitive function assessments at the baseline. We randomly divided the participants into two groups, one receiving acupuncture at the Shenting and Baihui acupoints combined with conventional treatment, and the other receiving conventional treatment only. After treatment, fMRI scans and cognitive function assessments were performed again (Figure 1). The acupuncture treatment lasted for approximately 40 min.
**FIGURE 1:** *Workflow and group inclusion/exclusion criteria.*
## 2.3. Blind
A random number generator with SPSS 22.0 statistical software was used by a researcher specializing in random assignment to derive 60 random numbers and generate a random assignment sequence. The cards with the random numbers, groupings, and interventions were then concealed in airtight, opaque envelopes and kept securely by this researcher. This person was not allowed to participate in the recruitment screening, outcome assessment and statistical analysis of this study.
## 2.4.1. Acupuncture treatment
With the thumb and forefinger holding the needle handle, the doctor alternately twists the needle body clockwise and counterclockwise to make it rotate quickly (180–300 times/min), and continues twisting for 2–3 min. After this, twist once every 10 min (following Deqi), and keep the needle for 40 min. Participants received acupuncture treatment once a day, for five days a week over a total of 12 weeks of intervention. Our selected points are Shenting and Baihui. Shenting is on the head, 0.5 inch straight up from the middle of the front hairline. The Baihui point is located at the intersection of the median line at the top of the head and the line connecting the tips of the two ears. The acupuncture treatments were performed by Yang Xiaoyan, an associate chief physician who practices acupuncture for more than 10 years.
## 2.4.2. Conventional treatment
Conventional treatment included donepezil tablets to improve cognition, aspirin to anti-platelet aggregation, atorvastatin calcium tablets to regulate lipid levels, in addition to blood pressure and blood glucose control according to the patient’s underlying disease, and each patient participated in modern cognitive rehabilitation training.
## 2.5. Cognitive assessment
Before and after the treatment, all participants completed a cognitive assessment. ( i) The MoCA includes eight cognitive domains: visual-spatial and executive functions, naming, memory, attention, language, abstraction, delayed recall, and orientation. The total score and the score of each cognitive domain were recorded following previous studies (O’Driscoll and Shaikh, 2017). For education levels lower than 12 years, we added 1 point to the total score. ( ii) SDMT (Silva et al., 2018): participants were asked to convert nonsensical symbols into numbers within 90 s, while we recorded the number of correct answers, which were given one point each. ( iii) VFT (Sutin et al., 2019) consisted of three parts, including semantic, phonetic, and motor fluency. Participants were asked to say the corresponding words within one minute as required, and the sum of the three groups of correct numbers was the total score. ( iv) DST (Leung et al., 2011) consisted of two parts, digit forward and digit backward. During the test, participants were asked to simultaneously remember two numbers read by the researcher, with one digit per second starting with the first set. ( v) The BNT (Durant et al., 2021) test provided 30 graphs, with the number of correctly named graphs representing the total score. We also collected information on sociodemographic background, medication, and disease history. The cognitive assessors were Huang Xiaohuang and Ling Shuiqiao, both physicians are at the level of attending physician or higher and have at least 5 years of training in cognitive aspects of therapy.
## 2.6. Imaging data acquisition
All images were obtained using a GE 3T MRI scanner with an 8-channel phased-array head coil. The participants were requested to keep their eyes closed, relax but not fall asleep, and minimize head movement during the scanning. Functional images were collected with a gradient echo-planar imaging (EPI) sequence with the following parameters: repetition time (TR) = 2,000 ms, echo time (TE) = 30 ms, flip angle (FA) = 90°, field of view (FOV) = 240 mm × 240 mm, slice thickness = 3.5 mm, inter-slice gap = 0.7 mm, data matrix = 64 × 64, 33 interleaved axial slices coving the whole brain, and 240 volumes acquired in about 8 min. In addition, high resolution brain structural images were acquired using a T1-weighed 3D BRAVO sequence with the following parameters: TR = 8.0 ms, TE = 3.0 ms, FA = 12°, data matrix = 256 × 256, FOV = 256 mm × 256 mm, slice thickness = 1 mm, and 188 sagittal slices covering the whole brain. The conventional T1-weighted and T2-weighted FLAIR images were acquired for clinical assessment. All MRI images for each participant were acquired in the same session.
## 2.7. Data pre-processing
The fMRI data were preprocessed using the DPARSF toolbox1 based on MATLAB. Before pre-processing the data, we visually inspected both brain functional and structural images, and excluded the datasets with significant signal dropouts, distortion, and other quality problems. The pre-processing procedure included: [1] removing the first 10 volumes to keep the magnetization equilibrium; [2] performing slice-timing and head-movement correction to remove effects caused by these factors; [3] conducting a linear co-registration between functional and structural images for each participant; [4] regressing the signals of the WM and CSF, and head-movement parameters (Friston-24 model); [5] performing a non-linear transformation between structural and template brain images of the Montreal Neurological Institute (MNI) space; normalizing functional images into the MNI space with a 3 mm3 × 3 mm3 × 3 mm3 voxel size; and smoothing with a Gaussian kernel of 5 mm full width at half maximum (FWHM), and [6] performing temporal band-pass filtering for the typical band (0.01–0.08 Hz). This study discarded the fMRI data for subjects with head motion displacement > 3 mm or rotation > 3° in any axis (x, y, and z-axis). Data pre-processing was performed by Chen Sina.
## 2.8. ALFF analysis
We first performed voxel-wise Fast Fourier Transform (FFT) for each participant to convert the filtered time series into the frequency domain to obtain the power spectrum. Since the power at a given frequency is proportional to the square of the magnitude of that frequency component, we calculated the square root of the power spectrum at each frequency and the average square root in the typical frequency band (0.01–0.08 Hz) at each voxel. This averaged square root was taken as ALFF (Zang et al., 2007), which was assumed to reflect the absolute intensity of spontaneous brain activity.
## 2.9.1. Demographic and cognitive assessment
A χ2-test was used to test between-group differences in gender. A t-test was used to test between-group differences in age. The Mann–Whitney U test was used to evaluate the education level, smoking, and alcohol consumption history between groups. The statistical significance level was set at $p \leq 0.05.$ *Statistical analysis* was conducted using SPSS (version 22.0). Continuous variables of MoCA without normal distributions were analyzed using the Mann-Whitney U test. VFT, SDMT, BNT, and DST with normal distribution were analyzed using an independent t-test.
## 2.9.2. ALFF and brain-cognitive correlation
The between-group differences test in ALFF was conducted using PALM and implemented in the DPARSF toolbox (see text footnote 1). In the calculations, a general linear model (GLM) was applied, and gender, education, and age factors were regressed. The significance level was set at $P \leq 0.005.$ Permutation tests and multiple comparison corrections were applied to all statistics. Mixed effect analysis with a whole brain mask was utilized while examining group differences, which involved a comparison of the ALFF maps.
For each group, the ALFF maps were assessed using paired t-test. The significance level was set at a corrected two-tailed P value <0.05. Corrections for multiple testing were done using the threshold free cluster enhancement (TFCE) and family wise error (FWE) methods with the DPABI package.
Mean ALFF values of the obtained regions with significant group differences were extracted. Pearson’s correlation analysis was performed to examine the association between ALFF values and MoCA changes. All statistical analyses were performed using SPSS and a statistical significance level of $P \leq 0.05.$
## 3.1. Demographics and acupuncture effects on cognition
Table 1 shows the demographic characteristics of all participants in each group. There were no significant differences in demographic variables between the two groups ($P \leq 0.05$). After statistical analysis, the results also showed no statistically significant differences in MoCA, SDMT, VFT, DST, and BNT scores between the two groups of subjects at the baseline level ($P \leq 0.05$). Further details are shown in Table 2. Compared with pre-intervention, the acupunture group showed better improved than the control group as measured by the MoCA, SDMT, VFT, DST, and BNT scores after intervention (Table 3).
## 3.2.1. Between-group analysis
Comparing the ALFF differences before and after the intervention in the two groups, we found several brain regions with significantly higher ALFF values in the treatment group compared to controls, including the right inferior temporal gyrus, the left middle occipital gyrus, the left superior occipital gyrus, the left insula, the bilateral postcentral gyrus, the right superior parietal gyrus, the right cerebellum, the right precuneus, and the right precentral gyrus ($P \leq 0.005$). Details are shown in Figure 2 and Table 4.
**FIGURE 2:** *Volumetric results of the subtracted ALFF values mix effect analysis between the acupuncture and control groups. Subtracted ALFF values before and after the intervention was extracted separately from ACU and CON groups and mix effect analysis was performed to compute the difference for the treatment effect. Warmer colors represent higher ALFF changes in the acu-group compared to the con-group. Peak coordinates refer to the Montreal Neurological Institute (MNI) atlas. a, the right inferior temporal gyrus; b, the right precuneus; c, the left postcentral gyrus; d, the left precentral gyrus; e, the right postcentral gyrus; f, the right superior parietal gyrus; g, the left middle occipital gyrus; h, the left insula; i, the right cerebellum.* TABLE_PLACEHOLDER:TABLE 4
## 3.2.2. Longitudinal analysis
Paired t-test results (TFCE and FWE multiple comparisons corrected $P \leq 0.05$ and cluster size > 200 voxels) showed that, when compared with pre-treatment, acupuncture at Shenting and Baihui showed increased ALFF values in the bilateral middle/superior/inferior frontal gyrus and the left caudate and putamen (Table 5 and Figure 3). In the control group, we found no significant differences before or after treatment.
## 3.3. Association between the changes in ALFF and MoCA after acupuncture
Correlation analysis showed differences in ALFF values in the right inferior temporal gyrus, left middle occipital gyrus, left superior occipital gyrus, and right superior parietal gyrus in the acupuncture group were significantly positively correlated with change of MoCA ($P \leq 0.05$; Figure 4).
**FIGURE 4:** *Correlations between brain measures and treatment performances. The x-axis represents the difference in MoCA scores before and after treatment, while the y-axis represents ALFF values differences before and after treatment.*
## 4. Discussion
We investigated cognitive function alterations (including MoCA, DST, VFT, SDMT, and BNT) in CSVDCI patients before and after acupuncture treatment, and performed neurology mechanisms voxel-based analysis of MRI-derived ALFF maps. According to our findings, and in contrast to conventional treatments, acupuncture at Shenting and Baihui significantly improved the cognitive function of patients. Importantly, we found an increase in spontaneous activity in regional brain areas, such as the right inferior temporal gyrus, left middle occipital gyrus, left superior occipital gyrus, and right superior parietal gyrus.
By comparing ALFF changes with those observed in the control group, we found that acupuncture combined with conventional treatment increased ALFF values of the right inferior temporal gyrus, left middle occipital gyrus, left superior occipital gyrus, left insula, bilateral postcentral gyrus, right superior parietal gyrus, right cerebellum, right precuneus, and right precentral gyrus in CSVDCI patients. This suggests that acupuncture at Shenting and Baihui may improve cognitive function by enhancing neuronal excitability in some brain regions of CSVDCI patients.
The inferior temporal gyrus is located in the temporal lobe. The structures in the medial temporal lobe, including the hippocampus, the internal olfactory and perirhinal cortex, and parietal hippocampal cortex, are important elements of long-term memory processing (Lech and Suchan, 2013). Wu et al. [ 2017] found that the combination of acupuncture and conventional treatment significantly improves motor and cognitive functions in stroke patients, and increased Reho values in the middle temporal gyrus. According to the authors, acupuncture may have a specific mechanism of action. PET technology showed that acupuncture points, such as Baihui, significantly increased glucose metabolism in the temporal and frontal lobes, improving cognitive function (Huang et al., 2007). Combined with our findings, the evidence supports that acupuncture of Shenting and Baihui significantly improve temporal lobe glucose metabolism levels, enhance energy supply, and increase local neuronal activity in CSVDCI patients, thus improving temporal lobe related cognitive functions.
Both the middle and superior occipital gyrus are part of the occipital lobe, an essential component of the visual center that transmits spatial information to the parietal lobe, which conveys the integrated spatial information to the prefrontal lobe, eventually forming spatial memory in the prefrontal area (Andersson et al., 2019). Previous studies found that the size of the white matter lesion volume in the occipital lobe in MCI patients is negatively correlated with cerebral blood flow, suggesting that decreased cerebral blood flow in the occipital lobe may lead to lesions in occipital lobe structures and to a decrease in cognitive function (Kim et al., 2020). According brain neuroimaging studies meta-analysis (Cao et al., 2020), the occipital lobe plays a role in the pathophysiology of dementia, suggesting it should be a target region for scalp acupuncture for treating dementia. Acupuncture of Shenting and Baihui improved executive function and visuospatial localization in CSVDCI patients, and their improvement was also correlated with improved spontaneous activity in the occipital lobe region.
The parietal cortex is an interesting part of the association cortex. Throughout modern neuroscience research, this region has been associated with a wide range of sensory, motor, and cognitive functions (Freedman and Ibos, 2018). Functional magnetic resonance imaging has been widely used to study the effects of acupuncture on neural activity. A study on functional MRI in MCI patients suggested that acupuncture increases functional connectivity between the parietal lobe and other cognitively relevant areas (Tan et al., 2017). Conversely, acupuncture increased Reho values of the parietal lobe in MCI patients. Hence, it is possible that that acupuncture also improves the regional homogeneity of different delicate structures in the parietal gyrus and increases spontaneous brain activity (Liu et al., 2014). Zhang J. et al. [ 2021] found that acupuncture reorganizes cognition-related brain areas, including the inferior frontal gyrus, and the temporal, parietal, and occipital lobes, and modulates post-stroke function and structural plasticity.
Acupuncture is widely used to cure post-stroke hemiplegia, cognitive dysfunction, anxiety, depression, among others (Wang et al., 2018; Du et al., 2020; Zhang et al., 2021). Li A. et al. [ 2018] explored the activating effects of acupuncture on the brain of healthy individuals using fMRI techniques and found it activates the postcentral gyrus, the precuneus, and the temporal and occipital lobes. Our results further validate these findings and reinforce the fact that acupuncture positively impacts spontaneous activity in various brain regions of CSDVDCI patients. Specifically, significant brain responses were observed after acupuncture stimulation at Shenting and Baihui, as well as improved ALFF values of the right inferior temporal gyrus, left middle occipital gyrus, superior occipital gyrus, and right superior parietal gyrus, which were positively correlated with an improvement in cognitive function.
## 5. Limitations
[1] The sample size was limited because this was a single-center study and screening for contraindications to MRI scanning was inadequate, resulting in some patients being unable to participate in the examination due to e.g., the presence of dentures, excessive head movement, and other factors. [ 2] In addition to cognitive dysfunction, CSVDCI patients also present with limb dysfunction, such as movement delays and mild hemiparesis, but our study did not evaluate such patients.
## 6. Conclusion
Acupuncture of Shenting and Baihui effectively improves cognitive brain function in CSVDCI patients. This may be related to an increase in spontaneous activity in local brain regions and changes in ALFF values at the right inferior temporal gyrus, left middle occipital gyrus, left inferior occipital gyrus, and left superior parietal gyrus.
## 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 Institutional Review Board of Zhongshan TCM hospital (ClinicalTrials.gov identifier: 2017ZSZY-LLK-219). The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
## Author contributions
NY and LC designed the study. NY, SC, LC, SXL, and SQL collected the data. NY and SC analyzed the data and prepared 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.
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|
---
title: Demonstration of doxorubicin's cardiotoxicity and screening using a 3D bioprinted
spheroidal droplet-based system†‡
authors:
- Raven El Khoury
- Salma P. Ramirez
- Carla D. Loyola
- Binata Joddar
journal: RSC Advances
year: 2023
pmcid: PMC10010162
doi: 10.1039/d3ra00421j
license: CC BY 3.0
---
# Demonstration of doxorubicin's cardiotoxicity and screening using a 3D bioprinted spheroidal droplet-based system†‡
## Abstract
Doxorubicin (DOX) is a highly effective anthracycline chemotherapy agent effective in treating a broad range of life-threatening malignancies but it causes cardiotoxicity in many subjects. While the mechanism of its cardiotoxic effects remains elusive, DOX-related cardiotoxicity can lead to heart failure in patients. In this study, we investigated the effects of DOX-induced cardiotoxicity on human cardiomyocytes (CMs) using a three-dimensional (3D) bioprinted cardiac spheroidal droplet based-system in comparison with the traditional two-dimensional cell (2D) culture model. The effects of DOX were alleviated with the addition of N-acetylcysteine (NAC) and Tiron. Caspase-3 activity was quantified, and reactive oxygen species (ROS) production was measured using dihydroethidium (DHE) staining. Application of varying concentrations of DOX (0.4 μM–1 μM) to CMs revealed a dose-specific response, with 1 μM concentration imposing maximum cytotoxicity and 0.22 ± $0.11\%$ of viable cells in 3D samples versus 1.02 ± $0.28\%$ viable cells in 2D cultures, after 5 days of culture. Moreover, a flow cytometric analysis study was conducted to study CMs proliferation in the presence of DOX and antioxidants. Our data support the use of a 3D bioprinted cardiac spheroidal droplet as a robust and high-throughput screening model for drug toxicity. In the future, this 3D spheroidal droplet model can be adopted as a human-derived tissue-engineered equivalent to address challenges in other various aspects of biomedical pre-clinical research.
A unique 3D bioprinted cardiac spheroidal droplet model used to test the cytotoxic effects of DOX.
## Introduction
We adopted a 3D cardiac spheroidal droplet model in this study to test the toxic effects of doxorubicin on cardiomyocytes (CMs)1,2 to overcome challenges associated with conventional modes of drug delivery. In preclinical studies, researchers have used 2D CM cultures as a prevalent method to assess drug response and cardiotoxicity.3 However, 2D cultured CM models lack cell–cell and cell–matrix interactions and fail to mimic the in vivo microenvironment of the native heart. This increases the need for in vitro 3D cardiac tissue models for more effective drug toxicity testing and pharmaceutical assays.1 3D bioprinting is an additive manufacturing process involving biomaterials, living cells, and active biomolecules aiming to fabricate structures that mimic natural tissue characteristics and an extracellular matrix (ECM) environment capable of sustaining cell adhesion, proliferation, and differentiation.4 Many studies have used biomaterials such as alginate, gelatin, collagen, fibrinogen, and hyaluronic acid. CMs seeded in such hydrogel-based scaffolds retain cardiac, and other cell specific-functions as these scaffold-based systems can provide an ideal 3D culture environment for CMs and other cardiac cells.5–14 *In a* recently published study, a unique droplet-based extrusion printing approach was performed to 3D bioprint cardiac spheroids using a CELLINK-BIO X printer. This technique was further scaled up to a high throughput 96-well array set-up with a six-axis robotic arm using a 3D bioprinter (BioAssemblyBot). This study produced morphologically consistent 3D spheroidal droplets with significant porosity and a large degree of pore interconnectivity. Moreover, the scaffolds retained structural fidelity after 28 days confirming their use in long-term in vitro cell culture studies. Rheological studies performed on these 3D spheroidal droplets were found to emulate Young's modulus of the native cardiac tissue making this an attractive model for in vitro studies. Cell viability quantification showed a steady turnover of cells in the scaffolds for up to 14 days, and the percent (%) heterocellular coupling (HC) between CMs and cardiac fibroblasts (CFs) was ∼$80\%$ in the 3D spheroidal droplets. This led to the fabrication of a high-throughput 3D cardiac tissue model that can be applied for studying drug effects on cardiac cells.1 *For this* study, we hypothesized that by using such a 3D bioprinted cardiac spheroidal model, we would be able to build a robust high-throughput 3D model for drug toxicity testing.
The anthracycline drug, doxorubicin (DOX), is one of the most potent antineoplastic agents used to treat various malignancies, including lymphoma, leukemia, and other solid tumors.15,16 Its use has been restricted due to its cardiotoxic effects, especially in patients at different stages of heart failure. Several hypotheses have been proposed regarding the mechanisms of DOX-induced cardiomyopathy.17 *The* generation of ROS is one route by which DOX harms the myocardium. Furthermore, free radical oxygen and lipid peroxidation play other essential roles in the pathogenesis of DOX-induced cardiomyopathy. In addition, it has been reported that apoptosis plays a significant role in the development of heart failure in humans by inducing autophagy in cardiomyocytes.3,15,16,18 Based on such an existing premise, we adopted a 3D bioprinted cardiac spheroidal model for testing the effects of cardiotoxicity induced by DOX.1 To do this, CMs were 3D bioprinted in a high throughput fashion inside of all wells in a 96-well plate. After confirmation of cardiac toxicity with DOX, quantitatively via a tetrazolium salt assay (MTS) and qualitatively by conducting a live/dead assay, we then aimed to mitigate these cytotoxic effects using two well-known antioxidants, N-acetyl cysteine (NAC) and Tiron. Furthermore, we quantitatively assessed the proliferation trends of CMs using a flow cytometer, analysed the activation of the caspase-3 pathway, and the release of ROS in the presence of DOX and the antioxidants (Tiron and NAC).
This study yielded 3D spheroidal droplet scaffolds specifically tailored to study the induced cytotoxic effects of DOX in vitro. We anticipate the use of this 3D bioprinted cardiac cell model to facilitate early-phase drug development in preclinical studies with sufficient versatility to evaluate the responses of various drugs and small molecules more efficiently at a relatively low cost and in a high throughput manner.
## Reagents and chemicals
Gelatin type A (MP Biomedicals LLC, USA, Cat. no. 901771) and medium viscosity alginic acid (MP Biomedicals LLC, USA, Cat. no. 154724) were used to fabricate the hydrogel scaffolds used in this study. Calcium chloride crosslinking solution was produced from calcium chloride dihydrate (Fisher Chemical, Germany, and CAS. no. 10035-04-8) and phosphate-buffered saline (PBS) 10× solution (Thermo Fisher Scientific, USA, Cat. no. 70011069). AC16 human cardio-myocytes (ATCC, Manassas, VA) were cultured and expanded in Dulbecco's modified *Eagle medium* (Sigma, Germany, Cat. no. D6434) containing 2 mM l-glutamine (EMD Millipore, Germany, Cat. no. TMS-002-C), $12.5\%$ FBS (EMD Millipore, Germany, Cat. no. ES-009-B) and 1× penicillin–streptomycin solution (EMD Millipore, Germany, Cat. No. TMS-AB2-C), $0.25\%$ trypsin-EDTA (Thermo Fisher Scientific, USA Cat. no. 25200056). 96 round-bottom well plates (Thermo Fisher Scientific, USA, Cat. no. 12-565-212) were used for bioprinting and cell culture. Doxorubicin hydrochloride powder (Sigma, Germany, CAS-no: 25316-40-9) was used as a cardiotoxic agent. N-Acetyl-cysteine (Sigma, Germany, CAS-no: 616-91) was obtained from Sigma-Aldrich and Tiron (Thermo Fisher Scientific, USA, Cat. No. 174140250) were used as ROS scavenging agents. MTS Assay Kit (Colorimetric) (ab197010) was procured from Abcam, MA, USA and the CellTiter 96® Aqueous One Solution Cell Proliferation Assay (Promega, USA, Cat. no. G3582) was used to determine cell viability. Hanks' Balanced Salt Solution (HBSS) (Thermo Fisher Scientific, USA, Cat. no. 88284) was used for culture washes. The LIVE/DEAD® Viability/Cytotoxicity Kit (Thermo Fisher, USA, Cat. no. L3224) was used to image viable and dead cells. The caspase-3 Colorimetric Assay Kit (NucView® 488 Caspase-3 Assay Kit for Live Cells, USA, Cat. no. 30029-T) was purchased from Biotium (USA) to detect cellular apoptosis. CellTrace Violet, proliferation dye (Invitrogen, USA, Cat. no. C34557) was used for tracking cell proliferation, and dihydroethidium (DHE) was used as a superoxide indicator (Thermo Fisher Scientific, USA, and Cat. no. D11347).
## DOX and Tiron/NAC solution preparation
10 g of DOX was dissolved in 1.72 mL of DMSO to reconstitute a stock solution of 10 mM according to the manufacturer's protocol.19,20 To induce cardiotoxicity in the 3D spheroidal droplets with CMs, four different stock concentrations of DOX (40 μM, 60 μM, 80 μM, and 100 μM) were prepared and 2 μL of each stock solution was added to 200 μL of culture media. DOX induces the production of ROS and antioxidants, such as NAC and Tiron can mitigate ROS-related cytotoxic effects.21,22 *From a* 200 mM Tiron/NAC stock solution, 1 μL and 3 μL were added to 200 μL of culture media to prepare a solution of 1 mM and 3 mM respectively. Similarly, from a 500 mM Tiron/NAC stock solution, 2 μL and 3.2 μL were added to 200 μL culture media to form a solution of 5 mM and 8 mM respectively and from a 613 mM stock solution, 3.3 μL and 4.9 μL were added to form a solution of 10 mM and 15 mM respectively. To ensure their homogeneous diffusion into the 3D bioprinted scaffolds and in the 2D samples, the agents were added 24 h prior to the addition of DOX.23–25 MTS measurements were recorded on a microplate reader (BioTek Synergy H1, CA, USA) on days 1, 3, and 5. The blank samples included the 3D scaffolds only (no cells) with DOX. For a more effective comparison, both positive (in the presence of DOX) and negative (in the absence of DOX) 2D control samples were included.
## Cell culture
AC16 human CMs (passages 3–4) were cultured in Dulbecco's modified Eagle's complete growth medium supplemented with $10\%$ fetal bovine serum (FBS) and $1\%$ penicillin–streptomycin. Before 3D bioprinting, cells were harvested by trypsinization and mixed with the alginate–gelatin hydrogel to constitute a final cell seeding density of 1 × 106 cells per 1 mL of bioink (approximately 50 spheroidal droplets/1 mL bioink; 20 000 cells per spheroidal droplet). Cultures were incubated with a complete growth medium and maintained in a humidified atmosphere of $95\%$ air and $5\%$ CO2 at 37 °C. The initial cell seeding density used in this study was 20 000 cells per 2D well and 20 000 per 3D spheroidal droplet.1
## Bioink preparation
An optimized protocol for bioink preparation was based on an alginate/gelatin scaffold that was developed and reported in a previous published study from our group.1 Briefly, under aseptic conditions, $2\%$ w/v gelatin and $3\%$ w/v alginate were dissolved in Milli-Q water respectively under constant stirring.1,5,26 The mixture was next allowed to rest and dissolve for 16–24 h at room temperature and centrifuged at 1200 rpm for 5 min to remove the remaining air bubbles. Before cell printing, gels were additionally UV-sterilized for 15 min, after which they were loaded into a 3 mL syringe (CELLINK, Blacksburg, VA, USA).
## Biofabrication of 3D constructs and culture
A 3D spheroid with a diameter of 2 mm was designed using SolidWorks® software. Using CELLINK BIO X (Blacksburg, VA, USA), the temperature-controlled printer head was used to place the droplets inside a 96-round-bottom well plate. Printing parameters are shown in Table 1, below. 5 μL of 80 mM CaCl2 sterile solution was added to the bottoms of each of the wells. The resultant spheroidal droplets were further cross-linked with an additional 75 μL of CaCl2 post-printing while being placed on a Belly Dancer Shaker (IBI SCIENTIFIC, Iowa, USA) for 15 min at a speed of 4.5 (au).
## MTS standard curve for cardiomyocytes
CM cell viability was determined using CellTiter 96 Aqueous One Solution Cell Proliferation Assay kit from Promega (Madison, WI). The culture medium was removed, and the MTS tetrazolium salt was prepared and added in the ratio of 1: 10 (MTS solution: media) where the samples were left in the incubator ($5\%$ CO2 and 37 °C) for 4 hours according to the manufacturer's protocol.5,27,28 Absorbance was recorded on a microplate reader (BioTek Synergy H1, CA, USA) at 490 nm. Using the calibration curve, the number of live cells was determined, and the percentage of surviving cells was compared with that of the control sample from the equations shown below: The linear best-fit equation for 3D spheroidal droplets used was:1y = 3.1 × 10−5x + 0.035 The linear best-fit equation for 2D samples used was:2y = 3.6 × 10−5x + 0.0743 To plot a standard curve, varying concentrations of CMs were used to determine the MTS value for each concentration and construct a best fit calibration curve for both 3D bioprinted spheroidal cell droplets and 2D cell culture samples. This enabled us to quantify the number of live cells via its corresponding linear equation derived using MATLAB by entering the variable “y” as the OD value and calculating “x” as the number of viable cells for both 3D and 2D samples using the above two eqn [1] and [2]28,29 and percent cell viability (% CV) was deduced using eqn [3].
## Live/dead assay
Live/dead cytotoxicity assay assessed cell survival following the manufacturer's protocol. NAC and Tiron were added to the 3D and 2D samples, 24 h prior to the addition of DOX. Based on the intracellular esterase activity and plasma membrane integrity, calcein AM was used to stain live cells in green exclusively. In contrast, the ethidium homodimer dye was used to stain only the compromised plasma membranes of dead cells by binding to nucleic acids exhibiting a red fluorescence dye. Images were acquired with an inverted Zeiss microscope (Zeiss, AXIO, Germany) using the filter set, 43 DsRed (ex533–558 nm/em570–640 nm) to observe dead cells and 38 green fluorescent Prot (ex450–490 nm/em500–550 nm) to observe live cells. Percent cell viability was quantified using the eqn [4] below:4
## In vitro caspase-3 activity assay
Activation of the caspase-3 (Cas-3) pathway is considered a pivotal event during cell apoptosis; therefore, Cas-3 activity was determined using NucView® 488 Cas-3 substrate; a permeable fluorogenic caspase substrate for identifying Cas-3 upregulation within live cells.30,31 The substrate comprises of a fluorogenic DNA dye coupled with a DEVD (Asp–Glu–Val–Asp) substrate element specific for caspase-3 and was prepared according to the manufacturer's protocol. The DEVD/Cas-3 recognition subunit is non-fluorescent until cleaved. During apoptosis, the substrate enters the cytoplasm by crossing the cell membrane, where it is cleaved by Cas-3. The dye, NucView®488, enters the cell nucleus where it binds with DNA and fluoresces green at 488 nm, expressing apoptosis. 15 mM of Tiron/NAC was added to the 3D and 2D samples 24 h prior to the addition of DOX, and cas-3 activity was quantified using a microplate reader (BioTek Synergy H1, CA, USA) on days 1 and 3 and high magnification images were acquired using an LSM 700 confocal microscope (ZEISS LSM, Germany).
## Dihydroethidium (DHE) staining
To determine the level of ROS production in DOX-induced AC16 CMs, intracellular oxidant production levels in CMs were measured using DHE fluorescence following the manufacturer's protocol. NAC and Tiron were added to the 3D and 2D samples 24 h prior to the addition of DOX, and at each time point samples were washed with Hanks' balanced salt solution (HBSS) and incubated with DHE for 30 min at 37 °C. The cells were washed 3 times, and mean fluorescent intensity readings were taken using a microplate reader (BioTek Synergy H1, CA, USA) on days 1 and 3. Images were taken using an inverted Zeiss microscope (Zeiss, AXIO, Germany) using the filters, 43 DsRed (em533–558 nm/ex570–640 nm) to observe ROS and 49 DAPI (ex335–383 nm/em420–470 nm) as an overall nuclear stain.
## Assessment of cell viability with DOX and NAC using flow cytometry
Flow cytometric analysis was performed using Beckman Coulter Gallios Flow Cytometer (Beckman Coulter, CA, USA). CMs were pre-stained using Cell Trace Violet (CTV) proliferation kit (Invitrogen, CA, USA) according to the manufacturer's protocol and were treated with their respective doses of NAC and DOX, as described earlier. On day 1 and day 3, the 3D spheroidal droplets with cells were cut using a blade, and cells were extracted using Miltenyi gentleMACS Dissociator (Miltenyi Biotec, MA, USA) using a Multi tissue Dissociation Kit-1 by running the Multi_B program according to the manufacturer's protocol. For 2D samples, cells were detached using trypsin-EDTA. Cells were fixed with $4\%$ paraformaldehyde (PFA) for 15 min at room temperature, then added to their assigned FACS analysis falcon tubes, and analysed using excitation and emission wavelengths of 405 and 450 nm, respectively. Negative controls included freshly isolated non-stained cells and positive controls were pre-stained with CTV.1,5,32
## Quantitative reverse transcriptase chain reaction (qPCR) analysis
To compare the gene expression and integrity between the CMs present in the 3D bioprinted spheroidal hydrogels to CMs cultured on 2D surfaces, qPCR was performed. After 5 days of culture, scaffolds were washed using 1× PBS, and cells were extracted following previously published methods.33 The extracted mixture was then centrifuged at 400 g for 10 min yielding the cell pellet.
Total RNA extraction was carried out using RNeasy® Plus Mini Kit (QIGEN, Germany) according to the manufacturer's instructions. Extracted RNAs were quantified by NanoDrop OneC spectrophotometer (ThermoFisher Scientific, MA, USA), and the absorbance ratios at $\frac{260}{280}$ nm and $\frac{260}{230}$ nm were measured to control RNA purity as shown in Table 2, below.
Total RNA (50 ng) was reverse transcribed to cDNA using the First Strand cDNA Synthesis Kit (OriGene Technologies Inc., MD, USA) in a volume of 20 μL, according to the manufacturer's instructions. Extracted cDNA was quantified by NanoDrop OneC spectrophotometer (ThermoFisher Scientific, MA, USA), and the absorbance ratios at $\frac{260}{280}$ nm and $\frac{260}{230}$ nm were measured.
The RT-qPCR reactions were performed in Quantstudio 3 (ThermoFisher Scientific, MA, USA). The cardiomyocytes isolated from 2D and 3D samples ($$n = 3$$ each) were placed in qPCR tubes, with optical strip caps 3 (ThermoFisher Scientific, MA, USA) in a reaction volume of 20 μL. To avoid sample contamination and primer-dimer formation that could produce false positive results, no template control was used. *The* genes analysed were selected based on vendor's recommendations. *The* gene expression of GJA1 (Connexin 43) was studied as the target gene of interest and GAPDH was used as the reference gene (control); the access gene and the sequence primers are shown in the Table 3. The reaction started with a 10 min initial denaturation step at 95 °C, 40 cycles of 95 °C for 15 s and 60 °C for 15 s according to the protocol provided by Origene. The quantification cycle (CT) values were automatically calculated by the qPCR instrument software Quantstudio 3 (ThermoFisher Scientific, MA, USA). A statistical algorithm was used to evaluate quantitative gene expression using the comparative CT method (2-ΔΔCT).34 Average CTs of GAPDH was used as endogenous control and the stability of the target genes was expressed as Ct values of each candidate gene normalized with GAPDH.
## DOX affects the viability and proliferation of CMs in a dose-responsive manner
Four different concentrations of DOX (0.4 μM, 0.6 μM, 0.8 μM, and 1 μM) were used to study the cardiotoxic effects on CMs using the 3D bioprinted spheroidal model, and the results were compared with 2D models. In Fig. 1 (for 3D and 2D results), MTS assay trends revealed that the OD values progressively reduced with respect to the control group (no DOX). Since the OD value is a measure of metabolic activity, it indicates the number of live cells per sample. Thus a lower OD value reflects a lower number of viable cells present in a sample and vice versa. For the 3D bioprinted spheroidal droplets, as shown in ESI, Table 1(A),† the OD value decreased from 0.90 ± 0.02 on day 1 to 0.52 ± 0.04 on day 5 ($$p \leq 0.001$$) when 0.4 μM of DOX was added, from 0.85 ± 0.05 on day 1 to 0.43 ± 0.03 on day 5 when 0.6 μM of DOX was added ($$p \leq 0.02$$) and from 0.77 ± 0.06 at day 1 to 0.12 ± 0.02 on day 5 when 0.8 μM of DOX was added ($$p \leq 0.005$$). With 1 μM of DOX, the OD value decreased from 0.71 ± 0.03 on day 1 to 0.002 ± 0.001 on day 5 ($$p \leq 0.015$$) but in the samples where DOX was not added, no statistical significance was observed between day 1 (0.93 ± 0.03) and day 5 (0.90 ± 0.04) values with $p \leq 0.05.$ Results showed as the concentration of DOX was increased, the OD value was decreased correlating with a lesser number of viable cells confirming the cardiotoxic effects caused by DOX on CMs.
**Fig. 1:** *Dose responsive effects of DOX on CMs. Optical density measurements for MTS assay of CMs treated with increasing concentrations of DOX during 5 days of culture. (A) 3D bioprinted spheroidal droplets. (B) 2D samples. Optical density measurements for MTS assay of CMs treated with increasing concentrations of DOX during 5 days of culture. The actual cell numbers used to generate % CV are shown in Fig. S1-b and S8-b.†*
For the 2D CMs models, as shown in ESI, Table 1(B),† the OD value decreased from 1.02 ± 0.04 on day 1 to 0.72 ± 0.04 on day 5 ($$p \leq 0.035$$) when 0.4 μM of DOX was added, from 1 ± 0.05 on day 1 to 0.56 ± 0.04 on day 5 when 0.6 μM of DOX was added ($$p \leq 0.025$$) and from 0.90 ± 0.06 at day 1 to 0.22 ± 0.02 on day 5 when 0.8 μM of DOX was added ($$p \leq 0.005$$). With 1 μM of DOX, the OD value decreased from 0.81 ± 0.05 on day 1 to 0.011 ± 0.003 on day 5 ($$p \leq 0.03$$) but in the samples where DOX was not added, no statistical significance was observed between day 1 (1.08 ± 0.05) and day 5 (1.08 ± 0.05) values with $p \leq 0.05.$ These trends correlated well with results from 3D samples.
To calculate the % cell viability (% CV) (eqn [1]) of CMs in each sample, the linear best-fit equations (eqn [2] and [3]) from the MTS standardization assay were used. For the 3D bioprinted spheroidal droplets (Fig. 1A/ESI, Table 1A†), the % CV decreased from 97 ± $2\%$ on day 1 to 56 ± $4\%$ on day 5 when 0.4 μM DOX was added, from 91 ± $5\%$ on day 1 to 46 ± $3\%$ at day 5 when 0.6 μM DOX was added, from 82 ± $6\%$ at day 1 to 10 ± $2\%$ at day 5 when 0.8 μM DOX was added and from 75 ± $3\%$ at day 1 to 0.22 ± $0.11\%$ at day 5 when 1 μM of DOX was added. These values confirmed the OD values from MTS assay reported earlier. All the trends were statistically significant between the varying time points studied.
For the 2D models (Fig. 1B/ESI, Table 1B†), % CV of CMs decreased from 94 ± $4\%$ on day 1 to 64 ± $4\%$ on day 5 when 0.4 μM DOX was added, from 92 ± $5\%$ on day 1 to 48 ± $3\%$ on day 5 when 0.6 μM DOX added, from 82 ± $6\%$ at day 1 to 15 ± $1\%$ at day 5 when 0.8 μM of DOX was added and from 73 ± $5\%$ at day 1 to 1.02 ± $0.28\%$ at day 5 when 1 μM DOX was added. The actual number of live CMs was derived from the best-fit curve using their corresponding OD values found in ESI, Fig. S1† (corresponding to 3D samples) and ESI, Fig. S2† (corresponding to 2D samples). Images showing the diffusion of DOX into the hydrogel scaffolds can be found in ESI, Fig. S3.† All the trends were statistically significant between the varying time points studied.
The effect of DOX on CMs was seemingly more pronounced in 3D scaffolds as confirmed by our results. This can be attributed to the intensified distribution of DOX inside the hydrogel's mesh network and its anchorage to the polymer's backbone that constitutes the bioink35,36 in contrast to 2D cell models where molecules can diffuse freely throughout the system.37
## Reversal of DOX-induced cardiotoxicity on CMs with the addition of NAC and Tiron
DOX induces myocardial damage to the heart via the elevation of ROS.38–40 In an attempt to mitigate the cytotoxic effects caused by doxorubicin on CMs in the 3D spheroidal droplet model, six different concentrations of NAC and Tiron were initially tested. Preliminary MTS assay data for 3 mM, 5 mM, and 10 mM of antioxidants can be found in ESI, Fig. S4–S7† respectively. Quantitative analysis using a colorimetric assay with increasing concentrations of Tiron and NAC (Fig. 2A and B) in the presence of 1 μM DOX is depicted during 5 days of culture. For the 3D bioprinted spheroidal droplets, OD values (ESI, Tables 2A and B†) decreased from 0.77 ± 0.06 to 0.24 ± 0.03 ($$p \leq 0.002$$) and from 0.78 ± 0.06 to 0.47 ± 0.02 ($$p \leq 0.015$$) after 5 days of culture when 1 mM and 8 mM of Tiron were added respectively. But with the addition of 15 mM of Tiron, no statistically significant difference was observed between day 1 (0.91 ± 0.05) and day 5 (0.83 ± 0.05) (Fig. 3) with $p \leq 0.05.$ With NAC, the OD value also dropped from 0.74 ± 0.03 to 0.25 ± 0.04 ($$p \leq 0.025$$) and from 0.77 ± 0.04 to 0.50 ± 0.03 ($$p \leq 0.005$$) after 5 days of culture when 1 mM and 8 mM of the antioxidant were added to the 3D spheroidal droplet (Fig. 2A). But with the addition of 15 mM of NAC, no statistically significant difference was observed between day 1 (0.90 ± 0.02) and day 5 (0.86 ± 0.02) with $p \leq 0.05.$
**Fig. 2:** *Quantitative analysis depicting the effects of supplementing Tiron/NAC on CMs. Optical density measurements for MTS assay of CMs treated with increasing concentrations (1 mm, 8 mm, and 15 mm) of (A) Tiron and (B) NAC with 1 μm DOX in using 3D spheroidal droplets and (C) Tiron and (D) NAC with 1 μm DOX in 2D samples.* **Fig. 3:** *Live/dead assay analysis representing the effects of supplementing Tiron/NAC on cms using 3D spheroidal droplets. Representative fluorescence images of live/dead staining of cms treated with increasing concentrations (1 mm, 8 mm, and 15 mm) of Tiron and NAC respectively and 1 μm DOX. Live cells are stained in green by calcein AM and dead cells stained in red by ethidium homodimer after 1 day and 5 days of culture in 3D cultures (A and B) and 2D cultures (C and D). The scale bar corresponds to 100 μm.*
To further calculate the number of viable to non-viable CMs present, the linear best-fit equations were used. % CV decreased from 82 ± $6\%$ to 24 ± $3\%$ ($$p \leq 0.0105$$) and from 83 ± $6\%$ to 50 ± $2\%$ ($$p \leq 0.025$$) after 5 days of culture when 1 mM and 8 mM of Tiron (Fig. 2A) were added respectively to the 3D spheroidal droplets. But with the addition of 15 mM of Tiron, no statistically significant difference was observed in % CV between day 1 (98 ± $5\%$) and day 5 (92 ± $6\%$) (ESI, Table 2A†) with $p \leq 0.05.$
With 1 mM and 8 mM of NAC (ESI, Table 2B†), the % CV dropped from 78 ± $3\%$ to 25 ± $4\%$ and from 82 ± $4\%$ to 54 ± $3\%$ respectively after 5 days of culture. But with the addition of 15 mM of NAC (ESI, Table 2B†), % CV remained relatively stable between day 1 (97 ± $2\%$) and day 5 (95 ± $2\%$) with $p \leq 0.05.$ A similar trend was observed in CMs cultured in 2D plates with 1 mM, 8 mM, and 15 mM of Tiron (Fig. 2C and D). While OD trends and % CV were comparable between the 3D bioprinted spheroidal droplet scaffolds and CMs cultured in 2D cell cultures, 3D scaffolds potentially serve as a desirable microenvironment for CMs providing mechanical support and necessary biochemical cues for optimal cell, proliferation, and function.41,42 The actual number of live CMs derived from the best fit curve can be found in ESI, Fig. S8† (for 3D models) and ESI, Fig. S9† (for 2D models) respectively. All the trends were statistically significant between the varying time points studied.
## Confirmation of the cardioprotective effects of tiron and NAC in the presence of DOX
To further illustrate the cardioprotective effects of NAC and Tiron in the presence of DOX, live/dead images were acquired and quantified as depicted in Fig. 3A and B (3D models) and C and D (2D) respectively. To visualize such effects on cardiac cells, quantitative analysis was performed on the acquired images and results indicated that the % CV of CMs in the 3D bioprinted spheroidal droplets decreased from 85 ± $13\%$ on day 1 to 42 ± $13\%$ on day 5 ($$p \leq 0.015$$) (ESI, Table 3A†) when 1 mM Tiron was added, and from 89 ± $13\%$ on day 1 to 52 ± $6\%$ on day 5 ($$p \leq 0.025$$) when 8 mM of Tiron was added. But with the addition of 15 mM of Tiron, no statistically significant difference was observed in % CV between day 1 (95 ± $11\%$) and day 5 (93 ± $9\%$) with $p \leq 0.05.$ With the addition of 1 mM of NAC (ESI, Table 3C†), % CV decreased from 84 ± $12\%$ on day 1 to 52 ± $4\%$ ($$p \leq 0.04$$) on day 5 and from 94 ± $11\%$ on day 1 to 54 ± $3\%$ ($$p \leq 0.002$$) on day 5 when 8 mM NAC was added. But with the addition of 15 mM of NAC, no statistically significant difference was observed in % CV between day 1 (93 ± $14\%$) and day 5 (96 ± $4\%$) with $p \leq 0.05.$
For the 2D control samples treated with NAC and Tiron (Fig. 3C and D), the % CV of cardiomyocytes decreased from 94 ± $7\%$ on day 1 to 57 ± $9\%$ on day 5 ($$p \leq 0.03$$) when 1 mM of Tiron (ESI, Table 3B†) was added and from 89 ± $13\%$ on day 1 to 43 ± $9\%$ on day 5 ($$p \leq 0.005$$) when 8 mM of Tiron was added. But with the addition of 15 mM of Tiron, no statistically significant difference was observed in % CV between day 1 (89 ± $22\%$) and day 5 (95 ± $6\%$) with $p \leq 0.05.$ With 1 mM of NAC (ESI, Table 3D†), % CV decreased from 90 ± $7\%$ on day 1 to 56 ± $8\%$ ($$p \leq 0.015$$) on day 5 and from 90 ± $12\%$ on day 1 to 49 ± $12\%$ ($$p \leq 0.035$$) on day 5 when 8 mM NAC was added. But with the addition of 15 mM of NAC, no statistically significant difference was observed in % CV between day 1 (96 ± $5\%$) and day 5 (95 ± $6\%$) with $p \leq 0.05.$ *Quantitative analysis* of representative live–dead images acquired when 3 mM, 5 mM, and 10 mM of NAC and Tiron were added can be found in ESI, Fig. S10 and S11† for 3D and 2D samples respectively. Images of negative control hydrogel scaffolds with DOX acquired using 43 DsRed filter can be found in ESI, Fig. S12.† % CV data collected from the live–dead assay further corroborates the cardioprotective role of Tiron and NAC against the induced cardiotoxic effects of doxorubicin as shown by other published research articles.43,44 The OD trends and % CV were comparable between the 3D bioprinted spheroidal droplet scaffolds and CMs cultured in 2D cell cultures validating the former. Because the 3D bioprinted spheroidal scaffolds serve as a desirable microenvironment for cells, in the future, other cell types can be seeded in the 3D bioprinted spheroidal scaffolds and utilized for drug screening and cytotoxicity testing.
## Mechanistic insights on DOX-induced cardiotoxicity
To gain insight into the cardiotoxic effects induced by DOX on CMs and how those were mitigated by the addition of 15 mM of Tiron and NAC; Fig. 4, we examined caspase-3 activity in the 3D bioprinted spheroids (A, B) and in 2D samples (C, D). Experimental groups included CMs samples with DOX, AOs (Tiron & NAC), and NucView488 Cas-3 substrate (SUB), while control samples included CMs with caspase-3 inhibitor (Ac-DEVD-CHO), AOs (Tiron & NAC), and NucView488 Cas-3 substrate (SUB). Using a microplate reader, a significant difference ($$p \leq 0.001$$) was observed in the measured mean fluorescence units (MFU) expressing Cas-3 activity for the 3D bioprinted spheroidal droplets (Fig. 4A and B) between the group that contained DOX + SUB (6216 ± 823) and the groups that had Tiron + SUB (2829 ± 386); NAC + SUB (3172 ± 520); CM + SUB (3254 ± 335); inhibitor + SUB (3448 ± 404), DOX + NAC + SUB (3553 ± 430) and DOX + Tiron + SUB (3637 ± 362) at day 1 with $$p \leq 0.005.$$ A similar trend in MFU was observed on day 3 but was not significant compared to day 1 ($p \leq 0.05$).
**Fig. 4:** *Evaluation of the caspase-3 activity of CMs. (A) In the 3D bioprinted spheroidal droplets representing fluorescence images of experimental and control groups of caspase-3 activated cardiomyocytes after adding 1 μm DOX and AOs compared to the control group captured on day 3 of culture. (B) Bar chart illustrating their relative mean fluorescence intensity. *p values were found to be all statistically different. The scale bar corresponds to 100 μm. (C) 2D samples representing fluorescence images of experimental and control groups of caspase-3 activated cardiomyocytes after adding 1 μm DOX and Tiron/NAC compared to the control group captured on day 3 of culture. (D) Bar chart illustrating their relative mean fluorescence intensity. *p values were found to be all statistically different. The scale bar corresponds to 80 μm.*
For 2D samples, a significant difference ($$p \leq 0.025$$) was observed in the MFU expressing cas-3 activity (Fig. 4C and D) between the group that contained DOX + SUB (6974 ± 841) and the groups that contained Tiron + SUB (3796 ± 363); NAC + SUB (3881 ± 531); CMs + SUB (3841 ± 321); inhibitor + SUB (3901 ± 399); DOX + NAC + SUB (4242 ± 417); and DOX + Tiron + SUB (4334 ± 371). A similar trend in MFU was observed on day 3 but was not significant compared to day 1 ($p \leq 0.05$). The NucView488 Cas-3 substrate, which was used to measure Cas-3 mediated apoptosis on CMs, was shown to have the highest emitted fluorescence in the group in which DOX was only added and the least fluorescence with Tiron/NAC or the Cas-3 inhibitor. This implied the initiation of the apoptosis pathway in CMs triggered by the caspase cell signalling pathway via DOX administration. But with the addition of strong antioxidants such as Tiron & NAC, cell survival was stimulated under such conditions and counteracted the effects of DOX.45 The thickness of hydrogel scaffolds can interfere with the passage of emitted fluorescent light, which is reflected in a slight but consistent decrease in average intensity among the experimental and control groups.46
## Confirmation of oxidative stress post-DOX addition
Oxidative stress is a major player in DOX-induced cardiotoxicity. Moderate levels of ROS are vital for standard signal transduction processes, but elevated levels have been shown to be involved in various pathological conditions. Therefore, to study the effects of supplementing 1, 8, and 15 mM of NAC on CMs in the presence of 1 μM DOX, we examined DOX-induced oxidative stress using DHE. After oxidation, the superoxide indicator DHE binds with the cell's DNA, staining its nucleus a bright fluorescent red. Representative fluorescence images are shown in Fig. 5A and C respectively for 3D and 2D samples. From results depicted in A (3D samples) and C (2D samples), CMs exposed to DOX alone demonstrated a significant increase in fluorescence when compared to groups where NAC was supplemented. The increase in MFU is due to an increase in ROS production in samples where NAC was not supplemented and therefore indicated higher levels of DOX-induced oxidative stress. When compared to control samples (no DOX), quantitative analysis showed a 34-fold, 17-fold, and 3-fold increase at day 1 and a 48-fold, 27-fold and 4-fold in the MFU at day 3 of CMs in 3D bioprinted samples (Fig. 5B) with $$p \leq 0.002.$$
**Fig. 5:** *NAC antagonized doxorubicin-induced oxidative stress. Representative fluorescence images of experimental and control groups of CMs treated with 1, 8, and 15 mm of NAC and 1 μm DOX on day 3 of culture in 3D samples (A) and 2D samples (C). Bar chart illustrating intracellular ros production based on the relative mean fluorescence intensity (B). *p values were found to be all statistically different (p < 0.05). The scale bar corresponds to 100 μm in the 3D samples (B) and 2D samples (D).*
In 2D samples, quantitative analysis showed a 39-fold, 24-fold, and 4-fold increase on day 1 and a 39-fold, 14-fold and 2-fold in the mean fluorescent intensity on day 3 of CMs (Fig. 5D) when 1, 8, and 15 mM of NAC was added respectively. Hence, a reduction in MFU intensity was due to a decrease in ROS generation. The administration of AOs such as NAC significantly mitigated the DOX-induced oxidative stress compared to untreated groups. Data for intracellular ROS production when 1, 8, and 15 mM of Tiron were used can be found in ESI, Fig. S13.†
## Flow cytometric-based cell proliferation analysis on CMs
To analyse the proliferation trends of cardiomyocytes with DOX, CMs were prestained with CTV dye (ex405 nm/em450 nm) and extracted at each time point, and analysed based on the concept of dye dilution. This permitted us to study the trends of CMs proliferation seeded in the 3D bioprinted spheroidal droplets and in 2D samples (Fig. 6). While *Tiron is* a vitamin E analog and NAC is a non-toxic glutathione precursor, both are considered antioxidants that help protect cells from the damage caused by free radicals,47,48 therefore in this experiment only NAC-supplementation was studied. Experimental samples included CMs exposed to varying doses of only NAC (1, 8, and 15 mM; Fig. 6A–F for 3D samples/2D samples) and 1 μM DOX, whereas the positive control group (Fig. 6G–H for 3D samples/2D samples) included samples with DOX only (no NAC), and the negative control (Fig. 6I–J for 3D samples/2D samples) consisted of neither (no DOX, no NAC).
**Fig. 6:** *Analysis of the effect of DOX and NAC using FACS analysis within the 3D bioprinted spheroidal droplets and 2D cells culture samples. Cardiomyocytes were prestained with celltrace violet (CTV) and mixed with the bioink prior to 3d bioprinting. Cells were extracted from the scaffolds from the experimental and control groups and analyzed using a flow cytometer. Representative graphs (A, C, E, G, and I) indicate the %CTV+ after 1 day of culture and (B, D, F, H, and J) after 3 days of culture.*
As shown in Fig. 6K, the average of %CTV+ of CMs at day 1 for 3D samples when 1 mM (91.87 ± $5.3\%$), 8 mM (88.07 ± $7.63\%$), 15 mM (87.75 ± $8.56\%$) of NAC was added respectively were not significant ($p \leq 0.05$) when compared to the positive (88.27 ± $9.79\%$) and negative (87.02 ± $4.82\%$) control group indicating a steady rate of proliferation of live CMs extracted from the 3D bioprinted spheroidal droplet at day 1.
On day 3, the average of %CTV+ of CMs at day 1 when 1 mM (88.84 ± $3.83\%$), 8 mM (93.11 ± $1.37\%$), 15 mM (88.16 ± $7.81\%$) of NAC and the positive control (87.29 ± $2.99\%$) were all statistically significant ($$p \leq 0.003$$) to the negative control (78.77 ± $0.4\%$) indicating that CMs did not significantly proliferate when 1 μM of DOX was added with varying concentration of NAC when compared to normal conditions. Moreover, the average change in %CTV + when 1 mM of NAC (−3.29 ± $0.02\%$), 8 mM NAC (5.72 ± $0.07\%$), 15 mM NAC (0.47 ± $0.008\%$), the positive control (−1.12 ± $0.01\%$) were all statistically significant ($$p \leq 0.015$$) to the negative control sample (−9.48 ± $0.05\%$).
For 2D samples, the average of %CTV+ (Fig. 6K) of CMs at day 1 when 1 mM (93.83 ± $1.97\%$), 8 mM (93.66 ± $0.98\%$), 15 mM (89.35 ± $5.14\%$) of NAC was added respectively were not significant ($p \leq 0.05$) when compared to the positive (92.64 ± $0.32\%$) and negative (89.22 ± $4.71\%$) control group indicating a steady rate of proliferation of live CMs at day 1.
On day 3, the average of %CTV+ of CMs at day 1 when 1 mM (94.19 ± $0.17\%$), 8 mM (94.04 ± $1.64\%$), 15 mM (93.72 ± $1.96\%$) of NAC and the positive control (94.21 ± $2.12\%$) were all statistically significant ($$p \leq 0.001$$) to the negative control (74.67 ± $1.03\%$) indicating that CMs did not significantly proliferate when 1 μM of DOX was added with varying concentration of NAC when compared to normal conditions. Moreover, the average change in %CTV + when 1 mM of NAC (0.38 ± $0.02\%$), 8 mM NAC (0.41 ± $0.007\%$), 15 mM NAC (4.89 ± $0.03\%$), the positive control (1.69 ± $0.02\%$) were all statistically significant ($$p \leq 0.025$$) to the negative control sample (−16.17 ± $0.04\%$). Shown in ESI, Table 4† are the average %CTV values expressed by cells in 3D and 2D platforms on days 1 and 3.
Results indicate that with 1 mM, 8 mM and 15 mM of NAC cells were not proliferating based on the concept of dye dilution between day 1 and day 3 compared to the negative control sample where DOX was not added. Positive controls included freshly isolated and prestained CMs with CTV while negative controls included freshly isolated and non-stained cells analysed using FACS (ESI, Fig. S14†).
## Gene expression and evaluation of CMs using qPCR
The GJA1 gene delivers instructions for the transcription of a protein called connexion 43, one component of a large family of connexion proteins. Moreover, connexions play a major role in cell-to-cell communication by forming channels, or gap junctions.49 In an attempt to study the expression of GJA1, a gene from which CX-43 protein is translated, in the 3D bioprinted spheroidal droplets in comparison with 2D controls, CMs were extracted using both models on day 5 and analysed using a thermocycler. As shown in Fig. 7, results indicated statistical significance enhancement of the expression of the GJA1 gene between the 3D spheroidal droplet and the 2D control samples ($p \leq 0.05$).
**Fig. 7:** *qPCR analysis. Relative expression levels of GJA1 in the 2D control samples and the 3D spheroidal droplets normalized to GAPDH.*
## Discussion
Doxorubicin is a chemotherapeutic drug used to treat numerous diseases. However, patients experience its cardiotoxic effects limiting its use as an effective drug. Despite extensive research, the mechanisms by which doxorubicin kills cardiomyocytes have been elusive, and the exact mechanisms remain unknown.50,51 Doxorubicin-induced regulated cardiomyocyte death pathways include autophagy,52 ferroptosis,43 necroptosis,52,53 pyroptosis,54 and apoptosis.52,55 *Autophagy is* a homeostatic dynamic process by which cellular components, including organelles, are sequestered into membrane vesicles called autophagosomes which fuse with lysosomes for degradation under normal and stress conditions.52 Such conditions are caused by doxorubicin, and autophagy may be activated during doxorubicin treatment. While doxorubicin can stimulate autophagy, the deregulation of autophagy leads to uncontrolled cardiomyocyte death.55 Studies have shown that doxorubicin originally induces autophagy but then blocks it, resulting in the build-up of un-degraded autophagosomes. Therefore, the suppression of lysosomal proteolysis results in an accumulation of un-degraded vesicles, which leads to increased ROS production and CMs death.48,52,55 Another pathway that causes DOX-induced cardiotoxic effects is known as ferroptosis.43 *It is* characterized by the build-up of iron lipid peroxides, a significant source of ROS. In addition, DOX treatment increases the iron pool, especially in the mitochondria, which can be detrimental to cells. Doxorubicin also triggers another form of cell death known as necroptosis.52,53 *While apoptosis* and autophagy are considered “programmed cell death,” necrosis is regarded as “unprogrammed” due to deregulated activity involving the secretion of death-signalling cytokines. Pyroptosis is an inflammatory cell death and is widely recognized in the pathogenesis of cardiovascular diseases.54 *It is* accompanied by activating inflammasomes and caspase pathways, mainly caspase 3. The apoptotic pathway is the most studied programmed cell death pathway in DOX-induced cardiotoxicity. DOX treatment causes excess oxidative stress and mitochondrial damage triggering cell death pathways through the activation of caspase 9, which cleaves and activates caspase 3. DOX also activates apoptosis by other mechanisms, including upregulation of p53 resulting in extrinsic and intrinsic apoptosis.52,55 Doxorubicin has been well-characterized in lowering cell viability, possibly the most significant aspect of cardiotoxicity. The dose-dependent cardiotoxic effects of doxorubicin are well documented and revealed even at low cumulative doses.50,51 Patients prescribed DOX are at potential risk of its asymptomatic cardiotoxic side effects, such as elevated stress in the left ventricular wall leading to arrhythmias, heart failure to heart transplantation.48,55 The principal proposed mechanism of doxorubicin-induced cardiotoxicity is increased oxidative stress. *The* generation of ROS is the general route by which doxorubicin harms the myocardium. Furthermore, oxidative stress is associated with cardiomyocyte death, contributing to the doxorubicin-induced cardiotoxicity.38,39 Doxorubicin appears to cause damage to the mitochondria generating ROS and increasing superoxide formation by increasing endothelial nitric oxide synthase promoting intracellular hydroxide formation.56,57 Many studies have found various reasons behind DOX's cardiotoxicity, with a common factor whereby induces oxidative stress resulting in excessive ROS generation.58 Moreover, ROS production or oxidative stress promotes apoptosis and necrosis in cardiomyocytes developing severe cardiomyopathy.59,60 Oxidative stress from exposure to hydrogen peroxide (H2O2) and reactive oxygen species causes apoptosis in several cells and organ tissues, including cardiomyocytes.61 Cardiomyocytes exposed to DOX undergo apoptosis, and this effect is primarily attributed to the formation of oxygen free radicals and its intercalation into DNA and disruption of topoisomerase-ii-mediated DNA repair. Therefore, a treatment with various antioxidants has been proposed to mitigate cardiotoxicity caused by doxorubicin. NAC and Tiron have been identified as possible antioxidants effective at impeding apoptosis triggered by reactive oxygen species.62 While the ROS-scavenging role of NAC is evident, the process for their regulation of apoptosis is still ambiguous.63 The inhibition of apoptosis by antioxidants such as N-acetyl cysteine or vitamin E can further mitigate the outcomes of oxidative stress in the DOX-induced apoptosis.64,65 While cardiomyocytes are the target cell type of DOX-induced cardiotoxicity, we were particularly interested in assessing the activation of initiator caspases such as caspase-3 activity and the release of free radicals, which can cause oxidative damage to myocytes and lead to apoptosis and cell death.38,59 *With maximum* activity shown in the group with added doxorubicin, a lower activity was demonstrated in groups with NAC. In the present study, treatment of AC16 cardiomyocytes with antioxidants such as NAC and Tiron alleviated the DOX-induced oxidative stress in the 3D bioprinted spheroidal droplets and 2D samples by inhibiting the apoptotic pathway and decreasing the number of apoptotic cells.
## Conclusion
In this study, our key objective was to compare the traditional method for evaluating cytotoxicity using 2D tissue cultures with a developed 3D bioprinted spheroidal droplet model for high throughput testing. For this, we utilized a previously fabricated and optimized 3D bioprinted cardiac spheroidal model and evaluated the cardiotoxic effects of doxorubicin against AC16 cardiomyocytes. Unlike cell suspensions and tissue culture cellular monolayers, tissue-engineered constructs have a 3D structure to superiorly simulate the substantial impact that cell-to-cell and cell-to-matrix interactions influence cell behaviour in in vivo tissue and organ systems; a feature that 2D cell and tissue cultures cannot emulate well. In the future, the parameters of the spheroidal droplet model can be additionally optimized to express a wider range of human-derived tissue-engineered equivalents allowing the examination of various cells and their interactions in a more biomimetic environment. While 3D cell culture systems offer a better way of representing human tissue in vitro, the 3D bioprinted cardiac model can be utilized for other drug screening and drug cytotoxicity assays to evaluate how cells are affected by drugs, disease, or injury.
## Author contributions
Conceptualization, R. E. K, B. J.; formal analysis, R. E. K; investigation, R. E. K, C. L., S. R.; methodology, R. E. K, B. J.; project administration and supervision, B. J.; visualization, R. E. K.; writing original draft, R. E. K., C. L., B. J.; writing review and editing
## Conflicts of interest
There are no conflicts to declare.
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|
---
title: 'Trends of national and sub-national burden attributed to kidney dysfunction
risk factor in Iran: 1990-2019'
authors:
- Seyed Aria Nejadghaderi
- Sahar Saeedi Moghaddam
- Mohammad Keykhaei
- Parnian Shobeiri
- Negar Rezaei
- Nazila Rezaei
- Mohsen Naghavi
- Bagher Larijani
- Farshad Farzadfar
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10010168
doi: 10.3389/fendo.2023.1115833
license: CC BY 4.0
---
# Trends of national and sub-national burden attributed to kidney dysfunction risk factor in Iran: 1990-2019
## Abstract
### Background
Kidney dysfunction is a risk factor for cardiovascular disease and chronic kidney disease. Herein, we aimed to describe the attributable burden of kidney dysfunction at the national and sub-national levels in Iran.
### Methods
The Global Burden of Disease (GBD) 2019 data were extracted on the deaths, disability-adjusted life years (DALYs), years of life lost, and years lived with disability attributed to the risk factor of kidney dysfunction by age and sex at the national and provincial levels from 1990-2019. Also, risk exposure was reported by summary exposure value (SEV) with a range of 0 to 100. The estimated values were based on a comparative risk assessment framework.
### Results
In 2019, the age-standardized death rate and age-standardized DALYs rate attributable to kidney dysfunction were 58.2 ($95\%$ uncertainty interval of 48.8-68.1) and 1127.2 (981.1-1282.7) per 100,000 population in Iran, respectively. Also, the Sistan and Baluchistan province (1729.3 [1478.3-2006.4]) and the province of Tehran (681.9 [571.4-809.8]) had the greatest and lowest age-standardized DALYs rates, respectively. Nationally, SEVs increased from 22.8 to 26.2. The age-standardized burden attributable to kidney dysfunction had a positive association with age advancement. The attributable age-standardized deaths and DALYs rates in all socio-demographic index regions decreased from 1990-2019. Also, the highest and lowest attributable age-standardized DALYs rates of kidney dysfunction came from ischemic heart disease and peripheral artery disease in 2019, respectively.
### Conclusion
Although the attributed age-standardized DALYs and death rates decreased from 1990-2019, risk exposure increased and remains a crucial risk factor in Iran. Therefore, policymakers should consider preparing a preventive program that takes into account different levels of prevention of kidney dysfunction.
## Introduction
Kidney dysfunction is a risk factor for many diseases such as cardiovascular diseases, chronic kidney disease, and even cancers (1–5). Kidney dysfunction accounted for 76.5 million disability-adjusted life years (DALYs) and 3.1 million deaths in 2019 worldwide [6]. Also, the risk exposure for kidney dysfunction showed a significant annual percent change of $0.35\%$ between 1990 and 2019 [7]. The diagnosis and treatment of kidney dysfunction in its early stages, especially among individuals with pre-existing diabetes, can prevent further life-threatening consequences and medical costs, and can increase quality-adjusted life years [8, 9].
As a middle-income country in the Middle East region, Iran has faced emerging challenges in the path toward overcoming non-communicable diseases; $78.1\%$ of all diseases burden were attributed to such diseases in 2019 [10]. According to a meta-analysis conducted on studies up to the end of 2017, the prevalence of chronic kidney disease (CKD) in the *Iranian* general population was $15.1\%$, which was greater than the global average [11]. The prevalence of CKD was 1.7 times greater in females than males in Iran ($18.8\%$ vs. $10.8\%$) [11]. Furthermore, more than 1,145,000 DALYs were attributable to CKD and the highest numbers were due to unknown etiologies, diabetes mellitus, and hypertension, respectively [12]. The prevalence of CKD is increasing in Iran, similar to the global trend, and imposing high levels of morbidity and costs [13, 14].
Many studies have reported the burden of kidney disorders, especially CKD, and associated risk factors in national and provincial levels in Iran (12, 15–18). Also, a recent study reported the burden of different diseases in Iran in 2019 and its changes from 1990-2019 [19]. To the best of our knowledge, impaired kidney function as a risk factor for different diseases has not been evaluated. As a result, we aimed to report the most recent data on the attributable burden of kidney dysfunction as a risk factor in Iran and its 31 provinces from 1990 to 2019 based on the findings of the Global Burden of Disease (GBD) 2019 study.
## Overview
We used the national and sub-national data on the burden of kidney dysfunction in Iran from 1990-2019 provided by the GBD project. The GBD study is a comprehensive project that measures the burden of injuries and both communicable and non-communicable diseases. In the GBD 2019 study, data on the burden of 369 injuries and diseases and 87 risk factors in 204 countries and territories between 1990 and 2019 were provided [7, 20]. The details of the methodology used for the estimation of the burden of diseases and risk factors are available elsewhere [7, 20]. The GBD study was in accordance with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) [21].
## Data sources
A comparative risk assessment (CRA) framework was used in the GBD study in which the risk factors were categorized into four levels based on common features of each risk factor. Briefly, the CRA framework utilized a six-step comparative risk assessment process to estimate the burden of risk factors: 1. Risk-outcome pair inclusion; 2. Exposure risk estimation; 3. Exposure level estimation; 4. Determining the counterfactual level of exposure; 5. Estimation of theoretical minimum risk exposure level value; and 6. Calculation of population-attributable fractions. Kidney dysfunction was a metabolic risk factor. Also, the burden of diseases attributable to kidney dysfunction, including cardiovascular diseases, chronic kidney disease, and gout, was evaluated [7]. The estimated deaths, DALYs, years of life lost (YLLs), and years lived with disability (YLDs) associated with kidney dysfunction as a risk factor, and cause-specific attributable burdens in males, females, and both sexes in eight age groups in Iran and 31 provinces from 1990 to 2019 were utilized. Details on the results of the present study were deposited online at http://ghdx.healthdata.org/gbd-results-tool [22].
## Definitions
Kidney dysfunction is defined as an estimated glomerular filtration rate (eGFR) of less than 60 ml/min/1·73m2 or an albumin-to-creatinine ratio (ACR) greater than or equal to 30 mg/g. Based on urinary ACR and eGFR, kidney dysfunction is divided into four categories, including: Gout was defined as the presence of monosodium urate (MSU) crystals or a tophus in addition to at least six out of twelve gout findings proposed by the American College of Rheumatology [20]. Ischemic heart disease was included as acute myocardial infarction or chronic ischemic heart disease (i.e., angina and asymptomatic ischemic heart disease following myocardial infarction) [20]. Ischemic stroke was defined as “an episode of neurological dysfunction caused by focal cerebral, spinal, or retinal infarction” [20]. The definition of peripheral artery disease was an ankle-brachial index (ABI) of less than 0.9 [20]. Intracerebral hemorrhage was defined as “a focal collection of blood within the brain parenchyma or ventricular system that is not caused by trauma” [20].
The socio-demographic index (SDI) is a composite measure of the level of development which includes incomes per capita, average educational attainment for those aged ≥15, and total fertility rates under the age of 25. It ranges from 0 (low development) to 1 (high development) or is classified into five quintiles, including low, low-middle, middle, high-middle, and high [23].
The summary exposure value (SEV) is “a measure of a population’s exposure to a risk factor that takes into account the extent of exposure by risk level and the severity of that risk’s contribution to disease burden”. It takes a value from $0\%$ (no excess risk exposure) to $100\%$ (the highest risk exposure) [24]. The SEV is calculated by the following formula: Where RR(x) is risk ratio at level x of exposure, RRmax is the highest risk ratio where more than $1\%$ of population are exposed, P(x) is the density of exposure, and l and u are the lowest and highest levels of exposure, respectively.
## Data processing and statistical analysis
In order to describe the time trend of attributable burden to kidney dysfunction, age-standardized rates and their estimated percentage changes were calculated from 1990 to 2019 for deaths, DALYs, YLLs, and YLDs. We classified age into eight categories, including<20, 20-54, 55-59, 60-64, 65-69, 70-74, 75-79, and 80 plus. The age-standardized rates have been presented as per 100,000 population. For each point estimate, the $95\%$ uncertainty interval ($95\%$ UI) has also been presented. The uncertainty intervals (UIs) were defined as the 25th and 975th values of the ordered draws. The R programming software version 3.6.1 (R Foundation for Statistical Computing, Vienna, Austria) was used for implementing statistical analysis [25].
## National attributed burden
In 2019, the age-standardized death rate (ASDR) and age-standardized DALYs rate attributable to kidney dysfunction were 58.2 ($95\%$ UI: 48.8 to 68.1) and 1127.2 (981.1 to 1282.7) per 100,000 population in Iran, respectively. By sex, the ASDR in females decreased from 75.1 (61.5 to 89.7) to 58.2 (48.0 to 68.7) and in males from 80.8 (66.8 to 96.5) to 58.4 (49.0 to 68.8) between 1990 and 2019. Moreover, the age-standardized DALYs rates attributable to kidney dysfunction were 1077.5 (933.4 to 1226.1) and 1179.4 (1023.7 to 1351.6) per 100,000 population in females and males in 2019, respectively (Table 1).
**Table 1**
| Measure | Age, Metric | Year | Year.1 | Year.2 | Year.3 | Year.4 | Year.5 | % Change (1990 to 2019) | % Change (1990 to 2019).1 | % Change (1990 to 2019).2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Measure | Age, Metric | 1990 | 1990 | 1990 | 2019 | 2019 | 2019 | % Change (1990 to 2019) | % Change (1990 to 2019) | % Change (1990 to 2019) |
| Measure | Age, Metric | Both | Female | Male | Both | Female | Male | Both | Female | Male |
| Deaths | Attributed all ages number | 15111 (12902 to 17507) | 6868 (5843 to 7973) | 8242 (6922 to 9598) | 35987 (30559 to 41889) | 17356 (14522 to 20245) | 18631 (15793 to 21799) | 138.2 (116.9 to 159.1) | 152.7 (126.4 to 180.9) | 126 (103.9 to 148.1) |
| Deaths | Attributed age-standardized rate (per 100,000) | 78.4 (65.0 to 93.1) | 75.1 (61.5 to 89.7) | 80.8 (66.8 to 96.5) | 58.2 (48.8 to 68.1) | 58.2 (48.0 to 68.7) | 58.4 (49.0 to 68.8) | -25.8 (-31.7 to -20.5) | -22.4 (-30.6 to -12.9) | -27.8 (-33.7 to -22.0) |
| DALYs | Attributed all ages number | 445178 (387847 to 502678) | 200663 (176741 to 226572) | 244514 (208327 to 281379) | 790836 (692731 to 897947) | 368995 (321289 to 415886) | 421841 (367827 to 483023) | 77.6 (64.2 to 91.3) | 83.9 (69.6 to 100.3) | 72.5 (57.0 to 90.0) |
| DALYs | Attributed age-standardized rate (per 100,000) | 1596.7 (1377.8 to 1837.6) | 1502.4 (1297.0 to 1733.1) | 1673.4 (1419.8 to 1941.1) | 1127.2 (981.1 to 1282.7) | 1077.5 (933.4 to 1226.1) | 1179.4 (1023.7 to 1351.6) | -29.4 (-33.9 to -24.7) | -28.3 (-33.9 to -22.0) | -29.5 (-35 to -24.0) |
| YLLs | Attributed all ages number | 394008 (341358 to 447212) | 172332 (150351 to 195320) | 221675 (186173 to 255711) | 662716 (579714 to 752950) | 301612 (262948 to 341511) | 361104 (314310 to 414065) | 68.2 (55 to 82.8) | 75.0 (58.6 to 93.2) | 62.9 (46.9 to 81.1) |
| YLLs | Attributed age-standardized rate (per 100,000) | 1445.4 (1233.3 to 1674.7) | 1332.8 (1140.1 to 1542.8) | 1540.5 (1297.0 to 1794.3) | 959.0 (833.1 to 1097.4) | 900.9 (773.5 to 1031.8) | 1019.4 (881.4 to 1171) | -33.6 (-38.2 to -28.9) | -32.4 (-38.3 to -25.5) | -33.8 (-39.5 to -28.3) |
| YLDs | Attributed all ages number | 51170 (37040 to 66931) | 28331 (20733 to 37146) | 22839 (16492 to 29802) | 128120 (93566 to 166807) | 67383 (49342 to 87263) | 60737 (43975 to 80304) | 150.4 (131.6 to 170.3) | 137.8 (119.6 to 157.4) | 165.9 (143.9 to 190.7) |
| YLDs | Attributed age-standardized rate (per 100,000) | 151.3 (112.5 to 197.2) | 169.6 (125.6 to 219.8) | 132.9 (97.7 to 173.7) | 168.1 (123.8 to 219.8) | 176.6 (130.4 to 231.0) | 160.0 (116.9 to 210.6) | 11.1 (5.6 to 16.9) | 4.1 (-2.3 to 10.5) | 20.3 (13.9 to 26.2) |
From 1990 to 2019, both age-standardized DALYs rates and the ASDR were decreased by -$29.4\%$ (-$33.9\%$ to -$24.7\%$) and -$25.8\%$ (-$31.7\%$ to -$20.5\%$), respectively. There was a higher decrease among men than women in ASDR (-$27.8\%$ [-$33.7\%$ to -$22.0\%$] vs. -$22.4\%$ [-$30.6\%$ to -$12.9\%$]) and age-standardized DALYs rates (-$29.5\%$ [-$35.0\%$ to -$24.0\%$] vs. -$28.3\%$ [-$33.9\%$ to -$22.0\%$]), which were not statistically significant (Table 1). Over 1990-2019, there was an overall decreasing trend in the age-standardized DALY and death rates (Figure S1).
## Provincial attributed burden
In 1990, the provinces of Golestan, Kerman, and Ilam had the highest ASDR and age-standardized DALYs rates in Iran (Figure 1; Figure S2). Ilam, Golestan, and Sistan and Baluchistan had the highest ASDR values in 2019 of 89.1 (74.4 to 103.8), 79.5 (65.0 to 94.2), and 78.8 (65.2 to 94.4), respectively. Moreover, the highest age-standardized DALYs rates in 2019 were in the provinces of Sistan and Baluchistan (1729.3 [1478.3 to 2006.4]), Ilam (1652.1 [1444.0 to 1875.0]), and Golestan (1619.1 [1373.2 to 1862.2]), respectively (Figures 1 and S3). On the other hand, Tehran had the lowest values of ASDR and age-standardized DALYs rates in females, males, and both sexes in 1990 and 2019 (Figure 1; Table S1).
**Figure 1:** *Comparison of the age-standardized rate of deaths and disability-adjusted life years (DALYs) attributable to kidney dysfunction for both sexes between 1990 and 2019 in Iran by province.*
Among females, the Golestan province had the highest ASDR (93.3 [71.9 to 116.0]) and age-standardized DALYs rates (1972.6 [1553.3 to 2405.1) in 1990. Among males, the highest ASDR and age-standardized DALYs rates in 1990 were in Kerman (105.5 [81.3 to 131.3]) and Golestan (2201.8 [1714.5 to 2779.2]), respectively. In 2019, Ilam had the highest ASDR in both males (95.2 [77.2 to 113.2]) and females (81.0 [65.4 to 97.5]). In addition, the largest age-standardized DALYs rates in males (1802.2 [1463.5 to 2211.1]) and females (1662.2 [1357.8 to 1995.7]) were in Sistan and Baluchistan (Table S1).
From 1990-2019, Ardebil was the only province that had an increase in ASDR in males ($6.0\%$ [-15.6 to 32.6]). The change in age-standardized death rates ranged from -$4.7\%$ in Kohgiluyeh and Boyer-Ahmad to -$32.4\%$ in Khorasan-e-Razavi over this period in females. Also, the greatest decline in age-standardized DALYs rate values among males and females were in Kerman (-$40.1\%$) and Khorasan-e-Razavi (-$38.1\%$), respectively (Table S1; Figure S4).
## National and provincial exposure to risk
The risk exposure on a scale from 0 to 100 increased from 22.8 (16.7 to 30.0) in 1990 to 26.2 (19.9 to 33.7) in 2019 in Iran. The SEV for females was higher than males in 2019 (28.1 [21.5 to 35.7] vs. 24.4 [18.4 to 31.7]). The trend for kidney dysfunction risk exposure from 1990 to 2019 showed an increase of $15.0\%$ ($10.7\%$ to $20.9\%$) in Iran (Table 2).
**Table 2**
| Location | Year | Year.1 | Year.2 | Year.3 | Year.4 | Year.5 | % Change (1990 to 2019) | % Change (1990 to 2019).1 | % Change (1990 to 2019).2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Location | 1990 | 1990 | 1990 | 2019 | 2019 | 2019 | % Change (1990 to 2019) | % Change (1990 to 2019) | % Change (1990 to 2019) |
| Location | Both | Female | Male | Both | Female | Male | Both | Female | Male |
| Iran (Islamic Republic of) | 22.8 (16.7 to 30.0) | 24.7 (18.2 to 32.3) | 21.0 (15.2 to 28.2) | 26.2 (19.9 to 33.7) | 28.1 (21.5 to 35.7) | 24.4 (18.4 to 31.7) | 15.0 (10.7 to 20.9) | 13.9 (8.8 to 20.6) | 16.2 (11.7 to 22) |
| Alborz | 22.1 (16.2 to 29.4) | 24.7 (18.2 to 32.3) | 19.9 (14.3 to 26.8) | 25.8 (19.7 to 33.2) | 28.1 (21.5 to 35.7) | 23.7 (17.8 to 30.9) | 16.7 (11.7 to 23.9) | 13.9 (8.8 to 20.6) | 19.4 (12.7 to 28.4) |
| Ardebil | 19.9 (14.3 to 26.8) | 19.9 (14.3 to 26.8) | 19.9 (14.3 to 26.8) | 24.5 (18.3 to 31.9) | 24.5 (18.3 to 31.9) | 24.5 (18.3 to 31.9) | 22.8 (15.2 to 32.4) | 22.8 (15.2 to 32.4) | 22.8 (15.2 to 32.4) |
| Bushehr | 19.9 (14.4 to 27.1) | 19.9 (14.4 to 27.1) | 19.9 (14.4 to 27.1) | 23.6 (17.7 to 30.8) | 23.6 (17.7 to 30.8) | 23.6 (17.7 to 30.8) | 18.6 (11.7 to 26.9) | 18.6 (11.7 to 26.9) | 18.6 (11.7 to 26.9) |
| Chahar Mahaal and Bakhtiari | 18.6 (13.2 to 25.5) | 18.6 (13.2 to 25.5) | 18.6 (13.2 to 25.5) | 22.1 (16.2 to 29.3) | 22.1 (16.2 to 29.3) | 22.1 (16.2 to 29.3) | 18.5 (12.1 to 27.7) | 18.5 (12.1 to 27.7) | 18.5 (12.1 to 27.7) |
| East Azarbayejan | 20.9 (15.2 to 28.1) | 20.9 (15.2 to 28.1) | 20.9 (15.2 to 28.1) | 25.2 (19.1 to 32.8) | 25.2 (19.1 to 32.8) | 25.2 (19.1 to 32.8) | 20.7 (13.7 to 30.7) | 20.7 (13.7 to 30.7) | 20.7 (13.7 to 30.7) |
| Fars | 22.6 (16.7 to 29.8) | 22.6 (16.7 to 29.8) | 22.6 (16.7 to 29.8) | 25.7 (19.4 to 33.2) | 25.7 (19.4 to 33.2) | 25.7 (19.4 to 33.2) | 13.5 (8.1 to 20.4) | 13.5 (8.1 to 20.4) | 13.5 (8.1 to 20.4) |
| Gilan | 20.1 (14.4 to 27.3) | 20.1 (14.4 to 27.3) | 20.1 (14.4 to 27.3) | 24.0 (18.1 to 31.3) | 24.0 (18.1 to 31.3) | 24.0 (18.1 to 31.3) | 19.2 (12.1 to 29.1) | 19.2 (12.1 to 29.1) | 19.2 (12.1 to 29.1) |
| Golestan | 23.2 (17.3 to 30.3) | 23.2 (17.3 to 30.3) | 23.2 (17.3 to 30.3) | 26.1 (20.1 to 33.6) | 26.1 (20.1 to 33.6) | 26.1 (20.1 to 33.6) | 12.5 (7.2 to 19.7) | 12.5 (7.2 to 19.7) | 12.5 (7.2 to 19.7) |
| Hamadan | 19.6 (14.1 to 26.7) | 19.6 (14.1 to 26.7) | 19.6 (14.1 to 26.7) | 23.7 (17.7 to 31.1) | 23.7 (17.7 to 31.1) | 23.7 (17.7 to 31.1) | 20.7 (13.8 to 30.8) | 20.7 (13.8 to 30.8) | 20.7 (13.8 to 30.8) |
| Hormozgan | 19.7 (14.0 to 26.7) | 19.7 (14.0 to 26.7) | 19.7 (14.0 to 26.7) | 24.5 (18.3 to 32.2) | 24.5 (18.3 to 32.2) | 24.5 (18.3 to 32.2) | 24.3 (16.6 to 34.2) | 24.3 (16.6 to 34.2) | 24.3 (16.6 to 34.2) |
| Ilam | 21.3 (15.5 to 28.4) | 21.3 (15.5 to 28.4) | 21.3 (15.5 to 28.4) | 26.3 (20.1 to 33.9) | 26.3 (20.1 to 33.9) | 26.3 (20.1 to 33.9) | 23.5 (15.9 to 33.8) | 23.5 (15.9 to 33.8) | 23.5 (15.9 to 33.8) |
| Isfahan | 27.4 (19.9 to 35.5) | 27.4 (19.9 to 35.5) | 27.4 (19.9 to 35.5) | 29.7 (22.1 to 38.0) | 29.7 (22.1 to 38.0) | 29.7 (22.1 to 38.0) | 8.6 (4.2 to 13.8) | 8.6 (4.2 to 13.8) | 8.6 (4.2 to 13.8) |
| Kerman | 21.0 (15.3 to 28.2) | 21.0 (15.3 to 28.2) | 21.0 (15.3 to 28.2) | 24.5 (18.3 to 31.9) | 24.5 (18.3 to 31.9) | 24.5 (18.3 to 31.9) | 16.5 (10.9 to 24.1) | 16.5 (10.9 to 24.1) | 16.5 (10.9 to 24.1) |
| Kermanshah | 20.0 (14.3 to 27.2) | 20.0 (14.3 to 27.2) | 20.0 (14.3 to 27.2) | 24.1 (18 to 31.5) | 24.1 (18.0 to 31.5) | 24.1 (18.0 to 31.5) | 20.6 (14.0 to 29.7) | 20.6 (14.0 to 29.7) | 20.6 (14 to 29.7) |
| Khorasan-e-Razavi | 20.8 (15.1 to 28.1) | 20.8 (15.1 to 28.1) | 20.8 (15.1 to 28.1) | 23.4 (17.5 to 30.6) | 23.4 (17.5 to 30.6) | 23.4 (17.5 to 30.6) | 12.1 (6.5 to 19.4) | 12.1 (6.5 to 19.4) | 12.1 (6.5 to 19.4) |
| Khuzestan | 19.9 (14.2 to 27.1) | 19.9 (14.2 to 27.1) | 19.9 (14.2 to 27.1) | 23.9 (18.0 to 31.3) | 23.9 (18.0 to 31.3) | 23.9 (18.0 to 31.3) | 20.2 (13.6 to 29.9) | 20.2 (13.6 to 29.9) | 20.2 (13.6 to 29.9) |
| Kohgiluyeh and Boyer-Ahmad | 18.9 (13.3 to 25.9) | 18.9 (13.3 to 25.9) | 18.9 (13.3 to 25.9) | 22.9 (17.1 to 30.0) | 22.9 (17.1 to 30.0) | 22.9 (17.1 to 30.0) | 21.4 (14.3 to 30.8) | 21.4 (14.3 to 30.8) | 21.4 (14.3 to 30.8) |
| Kurdistan | 19.7 (14.2 to 26.8) | 19.7 (14.2 to 26.8) | 19.7 (14.2 to 26.8) | 23.9 (17.8 to 31.0) | 23.9 (17.8 to 31.0) | 23.9 (17.8 to 31.0) | 20.8 (14.1 to 29.8) | 20.8 (14.1 to 29.8) | 20.8 (14.1 to 29.8) |
| Lorestan | 19.4 (14.0 to 26.5) | 19.4 (14.0 to 26.5) | 19.4 (14.0 to 26.5) | 23.7 (17.8 to 31.3) | 23.7 (17.8 to 31.3) | 23.7 (17.8 to 31.3) | 22.3 (15.2 to 32.5) | 22.3 (15.2 to 32.5) | 22.3 (15.2 to 32.5) |
| Markazi | 20.7 (14.9 to 27.9) | 20.7 (14.9 to 27.9) | 20.7 (14.9 to 27.9) | 24.3 (18.2 to 31.8) | 24.3 (18.2 to 31.8) | 24.3 (18.2 to 31.8) | 17.3 (11.4 to 25.2) | 17.3 (11.4 to 25.2) | 17.3 (11.4 to 25.2) |
| Mazandaran | 20.5 (14.8 to 27.6) | 20.5 (14.8 to 27.6) | 20.5 (14.8 to 27.6) | 24.2 (18.2 to 31.8) | 24.2 (18.2 to 31.8) | 24.2 (18.2 to 31.8) | 18.3 (11.8 to 26.9) | 18.3 (11.8 to 26.9) | 18.3 (11.8 to 26.9) |
| North Khorasan | 19.4 (13.8 to 26.3) | 19.4 (13.8 to 26.3) | 19.4 (13.8 to 26.3) | 23.6 (17.5 to 31.0) | 23.6 (17.5 to 31.0) | 23.6 (17.5 to 31.0) | 21.8 (14.9 to 31.3) | 21.8 (14.9 to 31.3) | 21.8 (14.9 to 31.3) |
| Qazvin | 19.4 (13.9 to 26.5) | 19.4 (13.9 to 26.5) | 19.4 (13.9 to 26.5) | 23.9 (17.9 to 31.3) | 23.9 (17.9 to 31.3) | 23.9 (17.9 to 31.3) | 23.5 (15.7 to 33.8) | 23.5 (15.7 to 33.8) | 23.5 (15.7 to 33.8) |
| Qom | 20.3 (14.6 to 27.5) | 20.3 (14.6 to 27.5) | 20.3 (14.6 to 27.5) | 24.2 (18.2 to 31.5) | 24.2 (18.2 to 31.5) | 24.2 (18.2 to 31.5) | 19.0 (12.5 to 28.4) | 19.0 (12.5 to 28.4) | 19.0 (12.5 to 28.4) |
| Semnan | 20.7 (15.0 to 27.5) | 20.7 (15.0 to 27.5) | 20.7 (15.0 to 27.5) | 24.6 (18.5 to 31.7) | 24.6 (18.5 to 31.7) | 24.6 (18.5 to 31.7) | 18.9 (12.5 to 27.7) | 18.9 (12.5 to 27.7) | 18.9 (12.5 to 27.7) |
| Sistan and Baluchistan | 20.3 (14.7 to 27.4) | 20.3 (14.7 to 27.4) | 20.3 (14.7 to 27.4) | 25.1 (18.8 to 32.7) | 25.1 (18.8 to 32.7) | 25.1 (18.8 to 32.7) | 23.8 (16.3 to 33.6) | 23.8 (16.3 to 33.6) | 23.8 (16.3 to 33.6) |
| South Khorasan | 19.3 (13.8 to 26.3) | 19.3 (13.8 to 26.3) | 19.3 (13.8 to 26.3) | 23.3 (17.2 to 30.5) | 23.3 (17.2 to 30.5) | 23.3 (17.2 to 30.5) | 20.3 (13.6 to 30.1) | 20.3 (13.6 to 30.1) | 20.3 (13.6 to 30.1) |
| Tehran | 21.3 (15.6 to 28.3) | 21.3 (15.6 to 28.3) | 21.3 (15.6 to 28.3) | 23.3 (17.5 to 30.6) | 23.3 (17.5 to 30.6) | 23.3 (17.5 to 30.6) | 9.3 (4.7 to 15.5) | 9.3 (4.7 to 15.5) | 9.3 (4.7 to 15.5) |
| West Azarbayejan | 19.3 (13.8 to 26.3) | 19.3 (13.8 to 26.3) | 19.3 (13.8 to 26.3) | 23.3 (17.4 to 30.5) | 23.3 (17.4 to 30.5) | 23.3 (17.4 to 30.5) | 20.6 (13.7 to 30.8) | 20.6 (13.7 to 30.8) | 20.6 (13.7 to 30.8) |
| Yazd | 19.9 (14.4 to 26.9) | 19.9 (14.4 to 26.9) | 19.9 (14.4 to 26.9) | 24.3 (18.2 to 31.6) | 24.3 (18.2 to 31.6) | 24.3 (18.2 to 31.6) | 22.0 (14.5 to 32.0) | 22.0 (14.5 to 32.0) | 22 (14.5 to 32.0) |
| Zanjan | 19.2 (13.6 to 26.3) | 19.2 (13.6 to 26.3) | 19.2 (13.6 to 26.3) | 23.1 (17.0 to 30.4) | 23.1 (17.0 to 30.4) | 23.1 (17.0 to 30.4) | 20.4 (13.8 to 30.1) | 20.4 (13.8 to 30.1) | 20.4 (13.8 to 30.1) |
At the sub-national level, the risk exposure among both sexes in 2019 ranged from 22.1 (16.2 to 29.3) in Chahar Mahaal and Bakhtiari to 29.7 (22.1 to 38.0) in Isfahan. The lowest and highest change in risk exposure between 1990 and 2019 was in Isfahan ($8.6\%$ [$4.2\%$ to $13.8\%$]) and Hormozgan ($24.3\%$ [$16.6\%$ to $34.2\%$]), respectively (Table 2).
## Attributed burden of kidney dysfunction by age
The attributable age-standardized death, DALYs, YLLs, and YLDs rates per 100,000 population increased with age and had the highest values in the 80-years plus category in 1990 and 2019 in Iran. In 1990, the ASDR and age-standardized DALYs rates were higher in males than females up to 79 years old. While, in 2019, these measures were higher among males from birth up to 74 years old, and females aged 75 or older had higher rates (Figure 2). The increasing trends of the burden of age-standardized deaths and DALYs attributable to kidney dysfunction in provinces were similar to the national trend (Figure S5).
**Figure 2:** *Age-standardized rate of years of life lost (YLLs), years lived with disability (YLDs), deaths, and disability-adjusted life years (DALYs) attributable to kidney dysfunction in Iran in 1990 and 2019 by sex and age.*
## Attributed burden of kidney dysfunction by SDI
Overall, the ASDR and age-standardized DALYs rate values in 2019 were lower than in 1990 in almost all SDI quintiles. Golestan, with a DALYs rate of above 2,000 per 100,000 population, had the highest age-standardized DALYs rates in 1990 and 2000, whereas it decreased to lower than 2,000 in 2010 and 2019 (Figure S6). Furthermore, the highest ASDR, which was in Golestan and Kerman in 1990 and 2019 with almost 100 deaths per 100,000 population in 1990 and 2010, decreased to about 90 deaths per 100,000 population in Ilam in 2019 (Figure S7). In 2019, the ASDR in all SDI quintiles were near to each other, whereas the provinces of Tehran and Ilam, which had the lowest and highest ASDR, respectively, were in the high and high-middle SDI quintiles, respectively. Also, Sistan and Baluchistan, which was in the low SDI quintile, had the largest DALYs age-standardized rate (Figure 3).
**Figure 3:** *Age-standardized rate of years of life lost (YLLs), years lived with disability (YLDs), deaths, and disability-adjusted life years (DALYs) attributable to kidney dysfunction in Iran in 1990 and 2019 by sociodemographic index (SDI) quintiles and province.*
## Burden of diseases attributable to kidney dysfunction
The highest attributable age-standardized DALYs rate of kidney dysfunction came from ischemic heart disease (755.6 [559.8-975.5]), while peripheral artery disease (1.3 [0.7-2.2]) had the lowest attributable burden in 1990. Ischemic heart disease and CKD due to other and unspecified causes than kidney diseases had the highest attributable age-standardized DALYs rates among both sexes in Iran in 2019. Furthermore, the largest ASDR of kidney dysfunction was contributed by ischemic heart disease in 1990 and 2019 (43.9 [30.6-58.4] in 1990 and 31.7 [22.7-40.9] in 2019). The second most attributed cause of death was ischemic stroke in 1990 and CKD due to hypertension in 2019 (Figure 4).
**Figure 4:** *Age-standardized rate of deaths and disability-adjusted life years (DALYs) of underlying causes attributable to kidney dysfunction in 1990 and 2019 in Iran by sex.*
## Discussion
Our descriptive national and sub-national study on the attributed burden of kidney dysfunction showed that ASDR and age-standardized DALYs rates decreased over the 1990-2019 period, although risk exposure and age-standardized YLD increased. Sistan and Baluchistan, Golestan, and Ilam accounted for the highest attributable burdens in 2019. The attributed age-standardized death and DALYs rates had a positive association with age and were greater in males. Also, the age-standardized death and DALYs rates decreased in all SDI quintiles, while the low-middle SDI had the highest values in 2019. The highest deaths and DALYs resulted from ischemic heart disease.
Comparing the burden of kidney dysfunction in Iran with the regional and global levels in 2019 showed that Iran had lower ASDR and age-standardized DALYs rates than the Eastern Mediterranean Region of the World Health Organization (WHO) classifications (ASDR: 58.2 vs. 87.4 and age-standardized DALYs rate: 1127.2 vs. 1837.0), and also in comparison to North Africa and the Middle East out of 21 GBD regions (ASDR: 58.2 vs. 83.4 and age-standardized DALYs rate: 1127.2 vs. 1691.0). However, the attributable burden in Iran was the highest in the world (ASDR: 58.2 vs. 40.6 and age-standardized DALYs rate: 1127.2 vs. 945.3) [6]. The higher burden of kidney dysfunction in comparison to global values could be explained by a greater prevalence of risk factors for chronic kidney disease such as salt consumption, physical inactivity, overweight and obesity, and high blood pressure in Iran (5, 26–29). On the other hand, there were negative changes in the age-standardized DALYs and death rates attributable to kidney dysfunction in Iran, which could be as a result of improving medication and access to healthcare equipment within Iran over the last three decades.
The global SEV for kidney dysfunction increased from 20.6 (14.3 to 28.0) in 1990 to 22.7 (16.2 to 30.3) in 2019 with a significant annualized rate of change of $0.35\%$ over this period [7]. Our findings showed an increase from 22.8 to 26.1 from 1990 to 2019 for SEVs in Iran. The higher risk exposure of kidney dysfunction could be justified by a higher risk of exposure to previously mentioned risk factors like salt intake in addition to high fasting blood glucose levels in Iran compared to global levels [27, 30]. Also, our results showed that Isfahan and the Chahar Mahaal and Bakhtiari province had the highest and lowest SEVs, respectively. The prevalence of different risk factors attributable to kidney dysfunction, such as metabolic syndrome, high blood pressure, inadequate physical activity, and high levels of salt consumption, had discrepancies between the various provinces. Overall, this variation in the burden of these risk factors in addition to sex and age disparities in the provinces could have led to the discrepancies in SEVs.
Studies that were conducted in different provinces of Iran, including Tehran, Fars, Kerman, and Golestan showed that the burden of CKD was higher in females than males (15, 18, 31–33). On the other hand, the study by Ghafari et al. in urban and rural populations of Urmia revealed no significant difference between males and females in the prevalence of high serum creatinine ($$p \leq 0.13$$) and proteinuria ($$p \leq 0.44$$) as markers of impairment in kidney function [34]. Moreover, a cross-sectional study on 1,400 participants in Shahreza in Isfahan province revealed a higher frequency of micro/macro-albuminuria in females compared to males ($16.8\%$ vs. $15.0\%$) [35]. Also, low GFR was greater among females and had a significant positive association with age [35]. The Tehran Lipid and Glucose Study in 2009 on individuals aged 20 and over showed a higher prevalence of CKD in the ≥70 years old age group compared to the 20-39 year old group ($61.0\%$ vs. $3.5\%$) [15]. In addition, the prevalence rate among females was $23.0\%$ compared to $13.1\%$ in males [15]. The article by Khajehdehi et al. in Southern Iran showed a significant higher rate of stage 3-5 CKD in the elderly ($p \leq 0.001$) and in females compared to males ($14.9\%$ vs. $5.4\%$) [18]. Our results showed a higher rate of deaths and DALYs attributable to kidney dysfunction in males up to 74 years old. By increasing oxidative stress, promoting fibrosis, and inducing the activation of the renin-angiotensin-aldosterone system, male hormones are associated with worse CKD progression [36]. Therefore, this might justify the trend that measures were higher among males from birth up to 74 years old and in females aged 75 or older.
The findings of studies in Iran about the level of awareness of people about the three diseases of diabetes, blood pressure, and kidney disease have also shown that awareness of the studied population about diabetes and blood pressure was much higher than that of kidney disease, and long-term training concerning diabetes and blood pressure have, to a large extent, not been effective in aspects of prevention and treatment. Thus, more training and information about kidney disease is needed. Therefore, it is necessary to make people, doctors, and health workers aware of these risks, and able to receive and perform simple and low-cost screening tests such as urine tests and creatinine measurements; furthermore, there is need for effective policies for timely diagnosis and treatment to prevent the progression of the disease and its complications. Evidence suggests that the diagnosis of kidney disease and the availability of community centers which provide the point-of-care to identify and guide the management of patients locally are critical factors in kidney disease-related DALY rates [37, 38]. Dehghani et al. showed that of 9,781 participants aged 30–73 years-old were referred to community health centers of Iran, and $27.5\%$ had positive screening results for renal dysfunction requiring follow-up [39]. There are complex relationships between kidney disease, metabolic factors, and behavioral factors, such as nutritional habits. The results of an ongoing cohort study in Iran have shown that the Iranian dietary pattern is safe and not related to incidences of CKD, but that a high-fat, high-sugar diet may significantly ($46\%$) increase the likelihood of CKD occurrence, whereas a lacto-vegetarian diet may protect against CKD occurrence by $43\%$ [37]. A reorientation of food systems appears to be needed to achieve better health and environmental outcomes because of kidney function-related dietary patterns. Shifting to a healthier diet requires that the necessary foods be both available and affordable to low-income populations. Food choices depend on resource availability, cost, and access to quality food in the area.
To our best of knowledge, no study was conducted to explain the association of SDIs and the burden of kidney dysfunction in Iran and its provinces. As a critical insight from this study, we found that low-middle, middle, and high-middle SDI quintiles had higher attributable age-standardized death and DALYs rates, although they were close to other SDI quintiles. Globally, all SDI quintiles had a positive annualized rate of change values for kidney dysfunction, while the highest and lowest DALYs attributable to kidney dysfunction were in the middle and low SDI quintiles, respectively [7]. The discrepancies between the provinces of Iran in the measurements of SDI quintiles might be a result of the differences in access to health care facilities. For example, kidney dysfunction might be diagnosed in end-stages, resulting in increased rates of deaths and DALYs [40]. In the United States of America, there was a positive upward association between the age-standardized DALYs rates attributable to CKD and SDI from 2002-2016 in national and state levels except in Washington D.C. [41].
Appraising the attributable burden of kidney dysfunction, Bikbov et al. showed that kidney dysfunction accounted for $58.4\%$, $41.6\%$, and $0.003\%$ of CKD, cardiovascular disease, and gout, respectively, in 2017 worldwide [5]. Also, it was illustrated that ischemic heart disease and peripheral artery disease had the highest and lowest attributable DALYs originating from kidney dysfunction, respectively [5]. Other attributable causes of CKD that were represented were diabetes mellitus type 1 and 2, hypertension, and glomerulonephritis. In this regard, an observational study showed that a past history of cardiovascular diseases (OR= 1.47; $95\%$ CI: 1.27-1.69), hypertension (OR= 1.58; $95\%$ CI: 1.43-1.74), and diabetes (OR= 1.09; $95\%$ CI: 1.02-1.23) were associated with CKD development [17]. Furthermore, Saber et al. revealed that hypertension in addition to hypercholesterolemia and high low-density lipoprotein (LDL) were significantly associated with CKD ($p \leq 0.05$), whereas no significant association was found for diabetes mellitus [33].
## Strengths and limitations
A strength of our study was that it was one of the most comprehensive and up-to-date studies to describe the attributable burden to kidney dysfunction by age, sex, location, SDI, and its contributed diseases. Also, a measure indicating the exposure to risk factors called SEV was introduced for kidney dysfunction in Iran and its provinces. Nevertheless, this study had several limitations. First, the lack of data due to under-registration in certain provinces was a major limitation of this study. Second, data on urban and rural areas or various areas of large cities were not available, due to the differences in risk factors between rural and urban areas, they might have affected the interpretation of results [42]. It should be considered that most of the limitations were as a result of GBD methodology in data collection and the fact that we could not manipulate this. Nevertheless, the GBD project has one of the most comprehensive and recent datasets on Iran and its provinces in addition to regional and global levels.
## Conclusion
Although the attributed age-standardized DALYs and deaths decreased from 1990-2019, the risk exposure increased and remains a crucial risk factor in Iran. Kidney dysfunction not only due to CKD can increase mortality and morbidity, but also predispose individuals to cardiovascular disease. Therefore, policymakers should consider preparing a preventive program that takes into account different levels of prevention from kidney dysfunction. Moreover, increasing awareness and directing the attention of public health authorities and citizens to such programs could be effective.
## 2019 Iran Kidney Dysfunction Collaborators
Ashkan Abdollahi, Department of Medicine, Johns Hopkins University, Baltimore, MD, United States; Department of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran; Ali Ahmadi, Department of Epidemiology and Biostatistics, Shahrekord University of Medical Sciences, Shahrekord, Iran; Department of Epidemiology, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Sepideh Ahmadi, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Sudabeh Alatab, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Jalal Arabloo, Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran; Mohammad Arjomandzadegan, Infectious Diseases Research Center (IDRC), Arak University of Medical Sciences, Arak, Iran; Seyyed Shamsadin Athari, Department of Immunology, Zanjan University of Medical Sciences, Zanjan, Iran; Sina Azadnajafabad, Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Mohammadreza Azangou-Khyavy, Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Social Determinants of Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Nayereh Baghcheghi, Department of Nursing, Saveh University of Medical Sciences, Saveh, Iran; Sara Bagherieh, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Shirin Barati, Department of Anatomy, Saveh University of Medical Sciences, Saveh, Iran; Azizallah Dehghan, Department of Epidemiology and Community Medicine, Non-Communicable Diseases Research Center (NCDRC), Fasa, Iran; Ali Fatehizadeh, Department of Environmental Health Engineering, Isfahan University of Medical Sciences, Isfahan, Iran; Fataneh Ghadirian, Psychiatric Nursing and Management Department, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Maryam Gholamalizadeh, Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Ali Gholami, Department of Epidemiology and Biostatistics, Neyshabur University of Medical Sciences, Neyshabur, Iran; Non-communicable Diseases Research Center, Neyshabur University of Medical Sciences, Neyshabur, Iran; Kimiya Gohari, Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Department of Biostatistics, Tarbiat Modares University, Tehran, Iran; Hadi Hassankhani, School of Nursing and Midwifery, Tabriz University of Medical Sciences, Tabriz, Iran; Independent Consultant, Tabriz, Iran; Mohammad Jokar, Zoonotic Research Center, Islamic Azad University, Tehran, Iran; Department of Clinical Sciences, Jahrom University of Medical Sciences, Jahrom, Iran; Fatemeh Khorashadizadeh, Department of Epidemiology and Biostatistics, Neyshabur University of Medical Sciences, Neyshabur, Iran; Farzad Kompani, Children’s Medical Center, Tehran University of Medical Sciences, Tehran, Iran; Hamid Reza Koohestani, Social Determinants of Health Research Center, Saveh University of Medical Sciences, Saveh, Iran; Soleiman Mahjoub, Cellular and Molecular Biology Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran; Department of Clinical Biochemistry, Babol University of Medical Sciences, Babol, Iran; Ata Mahmoodpoor, Department of Anesthesiology and Critical Care, Tabriz University of Medical Sciences, Tabriz, Iran; Elaheh Malakan Rad, Pediatric Cardiology Unit, Tehran University of Medical Sciences, Tehran, Iran; Mohammadreza Mobayen, Burn and Regenerative Medicine Research Center, Guilan University of Medical Sciences, Rasht, Iran; Esmaeil Mohammadi, Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; Yousef Moradi, Social Determinants of Health Research Center, Kurdistan University of Medical Sciences, Kurdistan, Iran; Negar Morovatdar, Clinical Research Development Unit, Mashhad University of Medical Sciences, Mashhad, Iran; Maryam Noori, Student Research Committee, Iran University of Medical Sciences, Tehran, Iran; Hassan Okati-Aliabad, Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran; Ghazaleh Pourali, Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran; Quinn Rafferty, Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States; Sina Rashedi, Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Department of Cardiology, Tehran University of Medical Sciences, Tehran, Iran; Mahsa Rashidi, Department of Clinical Science, Islamic Azad University, Garmsar, Iran; Mohammad-Mahdi Rashidi, Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Social Determinants of Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Amirhossein Sahebkar, Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Biotechnology Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Seyed Afshin Shorofi, Medical-Surgical Nursing Department, Mazandaran University of Medical Sciences, Sari, Iran; Department of Nursing and Health Sciences, Flinders University, Adelaide, SA, Australia; Seyyed Mohammad Tabatabaei, Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran; Clinical Research Development Unit, Mashhad University of Medical Sciences, Mashhad, Iran; Majid Taheri, Trauma and Injury Research Center, Iran University of Medical Sciences, Tehran, Iran; Medical Ethics and Law Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Amir Taherkhani, Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran; Mazyar Zahir, Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Moein Zangiabadian, Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Iman Zare, Research and Development Department, Sina Medical Biochemistry Technologies, Shiraz, Iran.
## Authors note
This study is based on publicly available data and solely reflects the opinion of its authors and not that of the Institute for Health Metrics and Evaluation.
## Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: http://ghdx.healthdata.org/gbd-results-tool.
## Ethics statement
The studies involving human participants were reviewed and approved by Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran (IR.TUMS.EMRI.REC.1400.026). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
Please see supplementary material for more detailed information about individual author contributions to the research, divided into the following categories: providing data or critical feedback on data sources; developing methods or computational machinery; providing critical feedback on methods or results; drafting the manuscript or revising it critically for important intellectual content; and management of the overall research enterprise. Members of the core research team for this topic area had full access to the underlying data used to generate estimates presented in this article. All other authors had access to and reviewed estimates as part of the research evaluation process, which includes additional stages of formal review.
## 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.
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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.2023.1115833/full#supplementary-material
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|
---
title: LTA4H extensively associates with mRNAs and lncRNAs indicative of its novel
regulatory targets
authors:
- Tianjiao Ren
- Song Wang
- Bo Zhang
- Wei Zhou
- Cansi Wang
- Xiaorui Zhao
- Juan Feng
journal: PeerJ
year: 2023
pmcid: PMC10010175
doi: 10.7717/peerj.14875
license: CC BY 4.0
---
# LTA4H extensively associates with mRNAs and lncRNAs indicative of its novel regulatory targets
## Abstract
The RNA-binding metabolic enzyme LTA4H is a novel target for cancer chemoprevention and chemotherapy. Recent research shows that the increased expression of LTA4H in laryngeal squamous cell carcinoma (LSCC) promotes tumor proliferation, migration, and metastasis. However, its mechanism remains unclear. To investigate the potential role of LTA4H in LSCC, we employed the improved RNA immunoprecipitation and sequencing (iRIP-Seq) experiment to get the expression profile of LTA4H binding RNA in HeLa model cells, a cancer model cell that is frequently used in molecular mechanism research. We found that LTA4H extensively binds with mRNAs/pre-mRNAs and lncRNAs. In the LTA4H binding peak, the frequency of the AAGG motif reported to interact with TRA2β4 was high in both replicates. More notably, LTA4H-binding genes were significantly enriched in the mitotic cell cycle, DNA repair, RNA splicing-related pathways, and RNA metabolism pathways, which means that LTA4H has tumor-related alternative splicing regulatory functions. QRT-PCR validation confirmed that LTA4H specifically binds to mRNAs of carcinogenesis-associated genes, including LTBP3, ROR2, EGFR, HSP90B1, and lncRNAs represented by NEAT1. These results suggest that LTA4H may combine with genes associated with LSCC as an RNA-binding protein to perform a cancer regulatory function. Our study further sheds light on the molecular mechanism of LTA4H as a clinical therapy target for LSCC.
## Introduction
Laryngeal cancer is not only the most common malignancy of the head and neck, but also the second most common respiratory system tumor after lung cancer. 95 to $98\%$ of laryngeal cancers are squamous cell carcinomas (LSCC) (Zuo et al., 2016). Research shows that nearly 180,000 new cases of throat cancer and nearly 100,000 throat cancer deaths were reported worldwide in one year (Bray et al., 2018). Due to the proneness to recurrence and metastasis, the five-year overall survival rate for LSCC has been approximately $50\%$ in recent years (Cavaliere et al., 2021). Due to LSCC’s uncertain molecular mechanism, over $60\%$ of patients were already at late stages when the disease was discovered (Steuer et al., 2017). An increasing amount of research demonstrates that the formation and development of LSCC is related to molecular mechanisms. Studies have shown that the prognosis of patients with LSCC with down-regulated HLA class I antigen was worse (Ogino et al., 2006). It has also been reported that the ANXA1 interaction with FPR2/ALX promote proliferation and metastasis of LSCC (Gastardelo et al., 2014). Studies have revealed lincRNA HOTAIR is highly expressed in LSCC and promotes methylation of PTEN (Li et al., 2013). Hence, to better identify biomarkers and explore effective new therapeutic strategies, it is essential to reveal the LSCC’s carcinogenic mechanism.
Genes, miRNAs and lncRNAs all play key roles in tumor genesis and development. In recent years, researchers have found that many genes, miRNAs and lncRNAs are key factors of LSCC (Zhang et al., 2016). Gao et al. [ 2019] detected LTA4H expression in LSCC and normal tissue by qPCR. The results showed that LTA4H was significantly up-regulated in LSCC tissues than in normal tissue. A recent study screened 275 differential proteins associated with laryngeal cancer through PPI (protein-interaction) network analysis. GO function was significantly enriched in RNA processing and respiratory electron transport chains, and LTA4H was one of the up-regulated proteins (Peyvandi et al., 2018). More importantly, studies have demonstrated that increased LTA4H expression in LSCC is associated with a poor prognosis, and knockdown of LTA4H successfully suppresses the growth, invasion and migration of laryngeal carcinoma cells (Gao et al., 2019; Peyvandi et al., 2018). However, more study is necessary to fully understand the specific molecular mechanism of LTA4H in laryngeal carcinoma.
There is increasing evidence that LTA4H is overexpressed in many malignant cancers, which promotes cancer cell proliferation. For example, it has been identified that LTA4H is overexpressed in esophageal adenocarcinoma and through inflammation-augmenting effect and growth-stimulatory effect to promote carcinogenesis (Chen et al., 2004). Studies have also shown that LTA4H can enhance aminopeptidase and epoxide hydrolase activity to promote colon cancer growth (Jeong et al., 2009). Activation of 5-LOx/LTA4H can stimulate oral epithelial cell proliferation and inflammation, which is the main way to promote oral cancer (Guo et al., 2011; Sun et al., 2006).
As a zinc-dependent epoxide hydrolase and aminopeptidase, the active site of Leukotriene A4 hydrolase (LTA4H) can be the target action site of related inhibitors (Chen et al., 2004; Haeggström, 2004; Vo, Jang & Jeong, 2018). LTA4H is being investigated as a new target for cancer treatment due to its role in inflammatory response and tumor progression. As a hydrolase, LTA4H performs its classic biological functions, including chemotaxis, endothelial cell adhesion, and leukocyte activation, by acting on the last step of the arachidonic acid metabolic process (Haeggström, 2018; Oh & Olefsky, 2016; Snelgrove et al., 2010). As an aminopeptidase, it involves in inflammation and host defensed though grading proline-glycine-proline (PGP), a neutrophil chemokine that is also a biomarker for chronic obstructive pulmonary disease (Haeggström, 2004; Snelgrove et al., 2010). Furthermore, LTA4H is believed to function as an RBP in the post-transcriptional control of specific mRNAs (Castello et al., 2012b; Castello, Hentze & Preiss, 2015). RBPs not only interact with mRNAs directly, but also bound to proteins and other diverse RNAs to play crucial roles (Gerstberger, Hafner & Tuschl, 2014; Hamosh et al., 2005). A growing body of evidence show RBPs can promote cancer cell growth, angiogenesis, and metastasis by regulating numerous target genes related to tumor development (Kang, Lee & Lee, 2020). We hypothesized that LTA4H might interact with the RNAs of cancer-related critical genes at the transcriptional or post-transcriptional levels to control the expression of those genes, thus affecting the proliferation, invasion and metastasis of tumors (including LCSS) cells. However, whether LTA4H binds to mRNAs in cancer cells remains unclear. We hypothesized that LTA4H might interact with the RNAs of cancer-related critical genes at the transcriptional or post-transcriptional levels to control the expression of those genes.
To validate our hypotheses, we used improved RNA immunoprecipitation and sequencing (iRIP-Seq) method (Ke et al., 2021) on LTA4H in modal HeLa cells (Capouillez et al., 2009) to explore its RNA-binding characteristics in cancer cells. The finding demonstrates that LTA4H extensively binds to mRNAs/ pre-mRNAs and lncRNAs. And we have identified some crucial LTA4H-bound genes that regulate cancer development, like NEAT1, LINC00657, LTBP3 and ROR2. These results reveal the underlying molecular mechanisms of LTA4H as a clinical therapeutic target for LCSS, which has significant effects on diagnostic and therapeutic applications.
## Cloning and plasmid construction
Hot fusion primer pairs were created using CE Design V1.04. Each primer contained a 17–30 bp sequence from the pIRES-hrGFP-1a vector and a gene-specific sequence fragment.
F-primer: agcccgggcggatccgaattcATGCCCGAGATAGTGGATACCTG R-primer: gtcatccttgtagtcctcgagATCCACTTTTAAGTCTTTCCCCAC.
At 37 °C for 2 to 3 h, we digested the pIRES-hrGFP-1a vector with EcoRI and XhoI (NEB). The enzyme-digested vector was purified on a Qiagen column kit using $1.0\%$ agarose gel. HeLa cells’ total RNA was obtained using Trizol. Oligo dT primers were used to transcribe the purified RNA for cDNA. Following that, PCR amplification was used to synthesize the inserted fragment. PCR insert and linearized vector digested with EcoRI and XhoI (NEB) were combined in a PCR microtube and ligated with Vazyme’s ClonExpress® II One Step Cloning Kit (Vazyme, Nanjing, Jiangsu, China). Chemical transformation was used to introduce plasmids into *Escherichia coli* strains. We incubated cells overnight at 37 °C on LB agar plates containing 1uL/ml ampicillin. Finally, 28 cycles of colony PCR were performed on the backbone vectors using universal primers to screen colonies.
## Cell culture and transfections
The China Center for Type Culture Collection (CCTCC), Wuhan, Hubei, China provided human cervical carcinoma (CC) cell lines, HeLa (CCTCC@GDC0009). In Dulbecco’s modified Eagle’s medium (DMEM), which contains $10\%$ fetal bovine serum (FBS), 100 ug/mL streptomycin and 100 U/mL penicillin, we cultivated HeLa cells at 37 °C and $5\%$ CO2. Following the manufacturer’s instructions, HeLa cells were transfected using Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA). Transfected cells were collected after 48 h for RT-qPCR and western blot analyses.
## Assessment of gene overexpression
To evaluated the effect of LTA4H overexpression, we used GAPDH (glyceraldehyde 3-phosphate dehydrogenase) as a control gene. The synthesis of cDNA was carried out according to standard procedures, and we performed RT-qPCR using Bestar SYBR Green RT-PCR Master Mix (DBI Bioscience, Shanghai, China) on a Bio-Rad S1000. Additional file contains primer information. After normalizing to GAPDH mRNA concentration level, each transcript was quantified using 2-ΔΔCT method (Livak & Schmittgen, 2001). The GraphPad Prism software (San Diego, CA, USA) was then used for comparison with the paired Student’s t-test.
## Immunoprecipitation
HeLa cells were lysed on ice for 5 min with ice-cold lysis buffer (1 ×PBS, $0.5\%$ sodium deoxycholate, $0.1\%$ SDS, $0.5\%$ NP40) containing RNase inhibitors (Takara, 2313) and protease inhibitors (329-98-6; Solarbio). In order to remove cell debris, the mixture was forcefully shaken and centrifuged for 20 min at 13,000 × g at 4 °C. The centrifuged supernatant was incubated overnight at 4 °C with DynaBeads protein A/G bound to normal IgG or anti-Flag LTA4H antibody. The beads were washed twice with low salt washing buffer, high salt washing buffer and 1X PNK buffer solution respectively, and the samples were suspended in the Elution Buffer to extract RNA from the LTA4H-RNA complex.
## Western blot
Resuspend sample with 40 ul Elution Buffer 50 mM Tris-Cl (PH = 8.0), 10mM EDTA (PH = 8.0), $1\%$SDS; incubate it at 70 °C, 1,400 rpm for 20 min. The supernatant was put in a fresh EP tube. The complex was separated on a $10\%$ SDS-PAGE gel after being boiled in boiling water with 1X SDS sample buffer for 10 min. with TBST buffer (20 mM Tris-buffered saline and $0.1\%$ Tween-20) contained $5\%$ non-fat milk power, we diluted the primary antibody: flag antibody (1:2,000, F7425; Sigma), actin (1:2,000, 66CUSABIO). The membranes were soaked in the primary antibody incubation solution and incubated at room temperature for 1 h. The membranes were then soaked in the HRP-conjugated secondary antibody incubation solution and incubated at room temperature for 1 h. The enhanced chemiluminescence (ECL) reagent (170506; Bio-Rad, Hercules, CA, USA) was used to detect the binding secondary antibody (anti-mouse or anti-rabbit 1:10,000) (Abcam).
## iRIP-seq library preparation and sequencing
TRIzol (Invitrogen) was used to isolate the RBP-bound RNAs from the immunoprecipitation of anti-Flag. In accordance with the manufacturer’s instructions, complementary DNA (cDNA) libraries were prepared using KAPA RNA Hyper RNA binding protein connects the future Prep Kit (KK8541; KAPA). On the Illumina HiSeq X Ten platform, the cDNA libraries were sequenced for 150 bp paired-ends.
## Data analysis
Only uniquely mapped reads were used for the subsequent analysis after reads were matched onto the genome using TopHat 2 (Kim et al., 2013).“ABLIRC” strategy was utilized to determine the genomic locations where LTA4H binds (Xia et al., 2017). Peaks were formed from reads that had at least one base pair of overlap. Using computational simulation, reads with the same number and lengths as reads in peaks were generated randomly for each gene. For the purpose of generating random max peak height from overlapping reads, the outputting reads were further mapped to the same genes. The whole procedure was done 500 times. All observed peaks with heights greater than those of random maximum peaks (p-value 0.05) were chosen. The simulation independently analyzed the IP and input samples, removing the IP peaks that overlapped the input peaks. The peaks were used for motifs analysis with the Hypergeometric Optimization of Motif Enrichment (HOMER) software (Heinz et al., 2010).
## Functional enrichment analysis
GO term and KEGG path enrichment analysis was performed using KOBAS 2.0 server (Xie et al., 2011). According to the annotation information of peak associated gene, the GO Term of each gene was counted, and significance of each Term was analyzed by Benjamini–Hochberg FDR (BH) and hypergeometric test to determine the degree of enrichment.
## Reverse transcription qPCR validation
RT-qPCR was performed using total RNA from the iRIP-seq library preparation. Using the M-MLV Reverse Transcriptase (Vazyme), RNA was reverse transcribed into cDNA. Real-time PCR was carried out with the StepOne RealTime PCR System using the HieffTM qPCR SYBR® Green Master Mix (Low Rox Plus; Yeasen, Pudong, China). Denaturation at 95 °C for 5 min was followed by 40 cycles of denaturation at 95 °C for 15 s, annealing and extension at 60 °C for 30 s under PCR cycling conditions. PCR amplifications were carried out in triplicate for each sample.
## Statistical analysis
The statistical software SPSS 16.0 (Chicago, IL, USA) was used to manipulate the experimental data, which were all presented as mean standard deviation (SD). All experiments were run at least three times independently, and $P \leq 0.05$ was considered significant.
## Deregulated expression of LTA4H in various cancers
Previous researches have shown that LTA4H is significantly expressed in several malignancies and affects the initiation and growth of tumors (Chen et al., 2004; Guo et al., 2011; Jeong et al., 2009; Sun et al., 2006). In order to explore the relationship between LTA4H and laryngeal squamous cell carcinoma (LSCC), or more broadly head and neck squamous cell carcinoma (HNSCC), we first studied the expression level of LTA4H in LSCC tissues and normal tissues through The Cancer Genome Atlas (TCGA) database (Fig. 1A). Box plot and scatter diagram were used to display the expression levels of LTA4H (Transcripts Per Million (TPM)). According to the finding, LSCC tissues had lower levels of LTA4H than normal control tissues. The results obtained by Gao et al. [ 2019] with TCGA database are also LTA4H downregulation in LSCC. However, the author then detected LTA4H expression in LSCC and normal tissue by qPCR, and have proved that TA4H in LSCC tissues was significantly up-regulated (Gao et al., 2019). In addition, we also studied the association between LTA4H expression level and prognosis in HNSCC and normal tissues (Fig. 1B). A blue curve represented the low expression group, while a red curve represented the high expression group. A significant finding was that HNSCC patients with high expression of LTA4H had a poor prognosis. Therefore, the potential function of LTA4H in laryngeal squamous cell carcinoma deserves further study.
**Figure 1:** *Expression and survival analysis of LTA4H.(A) Transcription levels of LTA4H in LSCC and normal samples from The Cancer Genome Atlas (TCGA) database. (B) Survival analysis of LTA4H in HNSCC from The Cancer Genome Atlas (TCGA) database.*
## Characterization of the LTA4H-RNA interaction map by iRIP-seq analysis
To explore the potential function of LTA4H in LSCC, we obtained a LTA4H-bound RNA profile in modal HeLa cells by applying theiRIP-seq approach. The iRIP-seq is an advanced technique for studying RBPs, which achieves the precision of CLIP-seq to obtain both direct and indirect binding sites of protein and RNA accurately, whereas maintains the simplicity of RIP-seq. Labelled antibody and control antibody were used for immunoprecipitation, and two separately replicate experiments were performed. For immunoprecipitation, two separate iRIP repetitions were carried out using flag-tagged LTA4H. The western blots of both IP samples showed the presence of the protein Flag- LTA4H, but the IgG control did not (Fig. 2A). Then, we performed paired-end sequencing for the cDNA libraries using the Illumina HiSeq X Ten platform, and obtained the high-quality clean reads. After removing the adapter sequences and low-quality reads, we were left with 30,619,638 and 49,122,624 reads for IP group and input control of replicate 1, and 28,885,522 and 40,002,548 reads from those of the replicate 2 (Table S1). Next, using TopHat 2, we mapped the sequencing reads to reference genomes GRCh38 (Kim et al., 2013). About 77.23–$78.94\%$ of them were aligned and about 39.85–$83.65\%$ were matched uniquely. The uniquely mapped reads are overwhelmingly from mature mRNAs. The percentage of splice reads among the uniquely aligned reads was substantially greater in the IP sample compared with the control sample, indicating that LTA4H might have an involvement in the splicing role (Table S2).
**Figure 2:** *Transcriptome-wide identification of LTA4H binding targets using iRIP-seq method.(A) Western blotting analysis of LTA4H expression. (B) Scatter plot showing Pearson correlation between IP and input samples and between the two IP replicates. (C) Reads distribution across reference genome. Error bars represent mean ± SEM. *** p < 0.001, ** p < 0.01. (D) Venn diagram showing the overlap of LTA4H binding peaks obtained from two replicates of iRIP-seq. The peaks were called by ABLIRC algorithm. (E) Bar plot showed the classification of LTA4H targets in common in two replicates. (F) Distribution of peaks across reference genome. (G) The top ten over represented motifs in LTA4H binding peaks.*
Correlation analysis of IP and input samples by comparing Reads per kilo base of a gene per million reads (RPKM) (Mortazavi et al., 2008) of the same gene revealed transcripts were obviously enriched in IP samples than input control, which indicated that the specificity of the LTA4H-bound RNA was good (Fig. 2B). We also made correlation analysis between the two IP replicates, and the results showed that $R = 0.917$, which indicated that the two groups of IP samples had good repeatability (Fig. 2B). The results of these two groups of samples are almost the same, which indicated that the iRIP-seq experiment is reliable. The reads distribution across reference genomic regions showed LTA4H binding reads tend to concentrate in the CDS, the intron regions than input control reads, as well as in 3′ UTR, 5′ UTR and noncoding exons regions (Fig. 2C).
In order to eliminate the interference caused by gene expression quantity for predicting LTA4H specific binding sites, we adopted the ABLIRC method (Chi et al., 2009) to identify LTA4H-bound peaks precisely. There were 29,242 overlapping peaks in the two replication groups in Hela cells, indicating the overlap of peaks from the two sets of experiments is relatively high (Fig. 2D). Interesting, after sorting according to the number of reads on overlapping peaks, the top peaks were mainly located in mRNAs and lncRNAs (Fig. 2E). The results demonstrated that LTA4H has an extensive capability for RNA binding and may function as a regulator by interacting to mRNAs and lncRNAs. Peak distribution across reference genomic regions revealed that the LTA4H binding peaks located in the intron region accounted for a large proportion ($66.93\%$ and $63.20\%$), followed by CDS region (Fig. 2F).
Then, HOMER was employed to obtain sequence motif enriched within LTA4H peaks. The results showed UG-rich motif and GA-rich motif were presented as the first two motifs of LTA4H peaks of two replicates, which may be the key sites of LTA4H binding to its target (Fig. 2G). We found a high frequency of motif AAGG in both repeats in LTA4H binding peaks. It has recently been reported that TRA2B interacts with motif AAGG to promote cancer cell growth by disrupting gene expression processes associated with aging (Kajita et al., 2016). Our results suggest that LTA4H may interact with TRA2B for binding of the motif AAGG to regulate gene expression in cancerous cells.
In conclusion, the obtained LTA4H-binding RNA map will help our understanding of the overall regulatory mechanism of LTA4H-RNA association during gene expression in Hela cells.
## Analysis of pre-mRNA and mRNAs associated by LTA4H
Further, the LTA4H overlapped peak associated genes were compared to the Gene Ontology database for enrichment biological process analysis. We found that LTA4H-bound genes were involved in gene expression, mitotic cell cycle, viral replication and DNA repair (Fig. 3A). Next, the DEseq package was used to identify the LTA4H-bound differentially enriched genes (DEGs) (Anders & Huber, 2010). Among the 14,170 DEGs, there were 2,776 enriched genes and 11,394 non-enriched genes related to LTA4H. We constructed a volcanic map to show the significantly enriched genes associated with LTA4H, all of which are associated with oncogenesis, including lncRNAs NEAT1 and LINC00657, and mRNAs ROR2, LTBP3, HSP90B1 and EGFR (Fig. 3B). To explore the potential biological role of these enriched genes, we continued to analyze the enriched genes using the GO database, and it showed they were mainly involved in negative regulation of transcription, mitotic cell cycle, gene expression, and viral replication (Fig. 3C).
**Figure 3:** *Analysis of the targets bound by LTA4H.(A) The top 10 enriched GO biological processes of the LTA4H-bound genes by ABLIRC algorithm in two replicates. (B) Potential targets identified by DEseq. (C) The top 10 enriched GO biological processes of the LTA4H-bound genes by DEseq. (D) Venn diagram showing the overlap of LTA4H bound genes obtained from two replicates by ABLIRC algorithm and DEseq. (E) The top 10 enriched GO biological processes of the LTA4H- bound genes by both ABLIRC algorithm and DEseq. (F) The reads density landscape of LTA4H- binding peaks across LTBP3 (left). Quantification of LTBP3 expression by qRT-PCR using iRIP-seq data (right).*
Next, we performed an overlap analysis of LTA4H bound genes from ABLIRC algorithm and DEG from DEseq. Running DEseq identified fewer enriched genes, which were well overlapped by the LTA4H-bound genes by ABLIRC and resulted in 2425 overlapped genes (Fig. 3D). The results demonstrated a significant association between LTA4H-bound and enriched gene expression. GO analysis showed the 2425 overlapped genes were mainly clustered at gene expression, mitotic cell cycle, viral replication and DNA repair (Fig. 3E).
To further verify the presence of Flag-LTA4H protein binding on target genes, we next showed the distribution of reads binding location and coverage depth compared to Peak associated genes. The results across LTBP3 show the two replicates were consistent, and the IP groups were obviously biased towards the intron and exon regions than input control, which was the potential binding region of LTA4H on LTBP3 (Fig. 3F, left panel). And further, we used this gene to verify that it directly interacts with mRNA by RT-qPCR (Fig. 3F, right panel). In comparison to the control group, LTBP3 was considerably higher in the IP group. Similarly, mRNAs EGFR, ROR2 and HSP90B1 were distinctly enriched in IP samples compared to the input samples and the results of subsequent validation were as expected (Fig. S1). Taken together, our results suggest that LTA4H and mRNAs are closely interacted in Hela cells.
## Analysis of LTA4H-bound lncRNAs
We also conducted the reads density landscape for lncRNAs that LTA4H highly enriched. The results across the cancer-promoting gene LINC00657 show there are many LTA4H-bound peaks in IP compared with input (Fig. 4A, left panel). In the validation experiment, LINC00657 was found to be significantly enriched in IP1, and there was no obvious bias in IP2 compared with the control group, which was considered to be related to experimental error (Fig. 4A, right panel). It may also be that the peak site is not enriched in IP2, that is, LTA4H may not bind LINC00657 specifically, they just bind randomly. The exact mechanism of LTA4H binding to LINC00657 has not been fully clarified and needs further study. NEAT1 had an obvious bias in IP groups, and we know that dysregulation of NEAT1 plays a key carcinogenic role (Chen et al., 2015; Wang et al., 2016) (Fig. 4B, left panel). Besides that, NEAT1 has been verified to be significantly enriched in the IP group (Fig. 4B, right panel). In summary, we hypothesized that LTA4H is preferentially bound to genes related to tumor formation and progression.
**Figure 4:** *LTA4H binds to lncRNA involved in laryngeal squamous cell carcinoma.(A–B) The reads density landscape of LTA4H-binding peaks across lncRNAs (left). Quantification of LINC00657 and NEAT1 expression by qRT-PCR using iRIP-seq data (right).*
## Discussion
As an essential hydrolase for LTB4 production (Vo, Jang & Jeong, 2018), upregulated LTA4H has been found to be linked with various malignancies, such as colon, esophageal, and lung cancer (Chen et al., 2003a; Chen et al., 2004; Jeong et al., 2009). In The Cancer Genome Atlas (TCGA) database, the decrease in LTA4H levels in the LSCC was unexpected. We think it could be related to the small number of laryngeal squamous cell carcinoma TCGA database, leading to inconsistency with the previous experimental results (Gao et al., 2019; Peyvandi et al., 2018). The low expression of LTA4H in laryngeal cancer tissues was consistent with previous studies (Gao et al., 2019; Rodrigues-Lisoni et al., 2010). Importantly, further data showed that patients’ survival times were considerably shorter when their LTA4H expression was higher, suggesting that LTA4H may have a neoplastic role in HNSCC. On the one hand, numerous investigations have revealed that Leukotriene can control tumor growth by influencing interactions between the stromal cells and tumor epithelial cells, creating the favorable conditions for tumor genesis. So inflammatory mediators can be detected in the tumor microenvironment (Colotta et al., 2009; Wang & Dubois, 2010). On the other hand, two recent mRNA-interacting protein identification studies reported the activity of LTA4H binding to mRNA (Castello et al., 2012a; Castello, Hentze & Preiss, 2015). Thus, we speculate that LTA4H not only participates in the regulation of cancer through the inflammatory mediator pathway, but also controls the expression of cancer key genes by interacting with mRNA at the transcriptional or post-transcriptional level. However, to understand the specific mechanism of LTA4H in tumor cells, more study is necessary.
Herein, we used iRIP-seq to identify interactions between LTA4H and RNAs in Hela cells. We analyzed the binding characteristics of LTA4H as RNA binding protein binding to RNAs and found that IP groups were highly enriched comparing with input groups. This indicates that many pre-mRNAs /mRNAs are specifically bound by LTA4H, confirming the function of LTA4H binding RNA. Surprisingly, we found that LTA4H targets were not only enriched in mRNAs, but also in lncRNAs, suggesting that LTA4H was also involved in non-coding processes.
We also analyzed the peaks of LTA4H proteins by using the ABLIRC algorithm from iRIP-seq results. The binding peak of LTA4H was mainly enriched in the Intron region and CDS region, indicating that LTA4H has functional RNA targets. Importantly, GO results revealed that LTA4H-bound proteins were considerably overrepresented in pathways associated with cancer, including mitotic cell cycle, DNA repair, RNA splicing related pathways and RNA metabolism pathways. We know that genomic instability is a common feature of most cancer cells, and DNA damage affects genomic stability (Bröckelmann, De Jong & Jachimowicz, 2020; Negrini, Gorgoulis & Halazonetis, 2010). In addition, defective DNA repair can lead to a predisposition to cancer (Chen et al., 2003b). In eukaryotic cells, RNA splicing is a highly complex fine-tuning step in gene expression, while tumor genes are prone to deactivation mutations at splicing sites (Rhine et al., 2018). A study indicated that RBPs influence the development of various cancers by controlling the metabolism of many transcripts, which confirms the relevance of our findings (Pereira, Billaud & Almeida, 2017). These results gave us a new hint that LTA4H may bind to cancer-related lncRNAs and mRNAs and regulate their expression and splicing levels, which may be a previously unknown molecular regulatory mechanism of LTA4H in cancer.
In our study, we obtained six enriched genes associated with carcinogenesis from 14,170 DEGs. Among them, studies have been shown that mRNAs such as ROR2, LTBP3, HSP90B1, and EGFR have some close links with the occurrence and treatment of LSCC. Upregulated ROR2 and Wnt5a have shown to represent poor tumor stage and lymphatic metastasis in LSCC, suggesting that ROR2 was an independent prognostic factor (Zhang et al., 2017). Wnt5a, which interacts with RoR2 physically and functionally, has been demonstrated to be related to the growth of many different cancers (Asem et al., 2016; Oishi et al., 2003). Likewise, early-stage head and neck neoplasm patients with high levels of LTBP3 have a poor prognosis for survival (Deryugina et al., 2018). It has been shown that HSP90B1 is regulated by Mir-99a-3p to participate in the pathogenesis of head-neck cancer (Okada et al., 2019), and the highly expressed HSP90B1 represents the poor prognosis of many tumors, including breast cancer and lung cancer (Lin et al., 2020; Liu et al., 2019). EGFR knockdown suppressed LSCC cell growth, infiltration and migration, and EGFR inhibitors were proved to have anti-laryngeal cancer effects in vitro and in vivo (Ren, Wang & Qi, 2021; Yang et al., 2020). These results show that LTA4H interacting mRNAs are involved in regulating cancer proliferation, invasion, and metastasis in LSCC.
We also found LTA4H widely binds to lncRNAs such as NEAT1 and LINC00657, which have recently been investigated for a variety of cellular roles (Mercer, Dinger & Mattick, 2009; Wilusz, Sunwoo & Spector, 2009). Previous studies have reported that many lncRNAs interact with RBPs to play regulatory functions. For example, MALAT1 binds serine/arginine (SR) proteins to regulate alternative splicing (Tripathi et al., 2010). And NEAT1 can regulate transcription, miRNA processing and alternative splicing by binding RBP (Cooper et al., 2014; Imamura et al., 2014; Jiang et al., 2017). We also found some links between these targeted genes and laryngeal cancer and other similar cancers. NEAT1 levels were significantly upregulated in LSCC, which predicted a poor prognosis (Wang et al., 2016). Others have shown activation of NEAT1 expression also promoted proliferation, migration, and invasion of *Esophageal squamous* cell carcinoma (Chen et al., 2015). LINC00657 has been shown to play a regulatory function as an oncogene in ESCC (Sun et al., 2018). These results suggest that LTA4H may bind IncRNAs to participate in transcriptional and post-transcriptional regulation to promote cancer development and result in a poor prognosis. It could reveal a novel mechanism by which LTA4H regulates LSCC and may become a possible target for clinical treatment of laryngeal carcinoma. If further studies in LSCC cells and clinical samples clarify the regulatory function of LTA4H and lncRNA interaction, new ideas will be provided for clinical treatment. In addition, we plan to conduct functional studies of LTA4H in the future, such as comparing laryngeal cancer cells with cells that overexpress LTA4H/LTA4H knockdown, to validate the findings from the RIP-seq analyses.
## Conclusion
In summary, this is the first time we found that LTA4H preferentially binds to the mRNAs and IncRNAs of cancer-related functional pathway genes in tumor cells by iRIP-Seq experiments and shows enriched binding in specific intron and CDS regions of these genes. Therefore, we speculated that LTA4H not only participates in the regulation of cancer through the inflammatory mediator pathway, but also influences the production of different subtypes of proteins by binding the RNA of target genes to regulate the alternative splicing process, thus regulating the proliferation, migration, and invasion of LSCC. To better understand how LTA4H regulates the alternative splicing of target genes, more research should be done. Therefore, our discoveries could shed light on the underlying mechanism of LTA4H in promoting tumor development and provide a new potential anti-tumor target.
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|
---
title: Stress native T1 and native T2 mapping compared to myocardial perfusion reserve
in long-term follow-up of severe Covid-19
authors:
- Jannike Nickander
- Rebecka Steffen Johansson
- Klara Lodin
- Anton Wahrby
- Daniel Loewenstein
- Judith Bruchfeld
- Michael Runold
- Hui Xue
- Peter Kellman
- Henrik Engblom
journal: Scientific Reports
year: 2023
pmcid: PMC10010213
doi: 10.1038/s41598-023-30989-y
license: CC BY 4.0
---
# Stress native T1 and native T2 mapping compared to myocardial perfusion reserve in long-term follow-up of severe Covid-19
## Abstract
Severe Covid-19 may cause a cascade of cardiovascular complications beyond viral pneumonia. The severe inflammation may affect the microcirculation which can be assessed by cardiovascular magnetic resonance (CMR) imaging using quantitative perfusion mapping and calculation of myocardial perfusion reserve (MPR). Furthermore, native T1 and T2 mapping have previously been shown to identify changes in myocardial perfusion by the change in native T1 and T2 during adenosine stress. However, the relationship between native T1, native T2, ΔT1 and ΔT2 with myocardial perfusion and MPR during long-term follow-up in severe Covid-19 is currently unknown. Therefore, patients with severe Covid-19 ($$n = 37$$, median age 57 years, $24\%$ females) underwent 1.5 T CMR median 292 days following discharge. Quantitative myocardial perfusion (ml/min/g), and native T1 and T2 maps were acquired during adenosine stress, and rest, respectively. Both native T1 (R2 = 0.35, $p \leq 0.001$) and native T2 (R2 = 0.28, $p \leq 0.001$) correlated with myocardial perfusion. However, there was no correlation with ΔT1 or ΔT2 with MPR, respectively ($p \leq 0.05$ for both). Native T1 and native T2 correlate with myocardial perfusion during adenosine stress, reflecting the coronary circulation in patients during long-term follow-up of severe Covid-19. Neither ΔT1 nor ΔT2 can be used to assess MPR in patients with severe Covid-19.
## Introduction
The novel betacoronavirus SARS Coronavirus 2 has resulted in a global pandemic of coronavirus disease 2019 (Covid-19)1, and was primarily associated with respiratory disease and systematic inflammation as the main cause of morbidity and mortality. However, there is increasing evidence linking Covid-19 with cardiovascular disease (CVD)2. Covid-19 infection may induce endothelial dysfunction, microvascular inflammation and thrombosis via angiotensin converting enzyme 2 and secondary autoimmune responses, causing coronary microvascular dysfunction (CMD), which might serve as a mechanism for long-term CVD post-Covid-193. Myocardial perfusion during rest and pharmacological stress can be used to calculate the myocardial perfusion reserve (MPR) to assess the coronary circulation4, which can be performed with quantitative cardiovascular magnetic resonance (CMR) myocardial perfusion maps with an excellent agreement with positron emission tomography (PET)5,6.
Quantitative parametric pixelbased mapping by CMR has been developed to image both the longitudinal relaxation time constant (T1) and transverse relaxation time constant (T2)7. Both native T1 and T2 maps have been shown to identify acute myocardial inflammation causing edema and chronic pathologies with expanded interstitium where free water can distribute, and can be used for a range of myocardial pathologies8. Both native myocardial T1 and T2 depend on myocardial blood T1 and T2, which constitute a basis for T1 and T2 mapping to capture change in myocardial perfusion during stress without contrast agents9,10. Theoretically, the relative change in native T1 and T2, also called T1- and T2-reactivity (ΔT1 and ΔT2) (%), should portray the same physiology as MPR11. However, the relationship between native T1, native T2, ΔT1 and ΔT2 with myocardial perfusion and MPR during long-term follow-up in severe Covid-19 is currently unknown. Therefore, the aim of this study was to elucidate the relationships of the parameters native T1, native T2, ΔT1 and ΔT2 with quantitative myocardial perfusion and MPR during long-term follow-up in severe Covid-19 using CMR.
## Study population
Patients hospitalized at Karolinska University Hospital, Stockholm, due to severe Covid-19 ($$n = 40$$, age median 57 interquartile range [IQR] [50–65], $23\%$ females), were included from the project ”Follow-up of patients with severe Covid-19 pneumonia” (UppCov), aiming to characterize the long-term consequences of severe Covid-19 pneumonia in a comprehensible fashion. The patients underwent CMR at 1.5 T during long-term follow-up, between November 2020 and September 2021. Patients were eligible to be included in UppCov if discharged from the hospital at the intensive care unit and/or hospital wards for severe Covid-19, defined as respiratory failure with a higher demand of ventilatory support and oxygen (at least 5 l/min of oxygen flow rate).
Exclusion criteria for this CMR substudy included risk factors such as diabetes mellitus, myocardial infarction, aortic stenosis (AS), uncontrolled severe hypertension, atrial fibrillation and previous stroke. Patients that had undergone percutaneous coronary intervention and/or coronary artery bypass grafting or valvular surgery, or had contraindications for adenosine including chronic obstructive pulmonary disease and asthma or other standard safety contraindications such as renal failure or pacemaker were also excluded. Ethical approval was granted for all study procedures and all subjects provided written informed consent.
In total, 3 patients did not undergo adenosine stress CMR and were therefore excluded from the analysis. The remaining patients ($$n = 37$$) underwent adenosine-stress CMR scan 292 [207–367] days after discharge. All images were assessed with regards to image quality, and no patients were excluded due to poor image quality. Due to operator dependency, 1 patient did not obtain T2 maps and 1 patient did not obtain T2 maps at stress and were consequently excluded from analysis of native T2. Minimal (< 1 segment) late gadolinium enhancement (LGE) was found in 4 patients, and these patients were included in the analysis due to the limited extent of scarring. Baseline characteristics, including previous medical history, are presented in Table 1. CMR-findings are presented in Table 2, and stress findings in Table 3.Table 1Clinical characteristics of patients. Clinical characteristicsn = 37Female sex, n (%)9 [24]Age, years57 [51–65]Body height, cm175 [170–180]Weight, kg86 [80–100]BSA, m22.1 [2.0–2.3]Creatinine, mmol/l85 [69–101]EVF, %41 [40–47]Hs-TnT, ng/l51 [23–183]PAP, mmHga50 [40–55]Diabetes mellitus, n (%)1 (2.7)Atrial fibrillation, n (%)1 (2.7)Hypertension, n (%)0 [0]Pulmonary embolism, n (%)1 (2.7)Clinical characteristics presented as median (IQR) or absolute number (%).BSA body surface area, EVF erythrocyte volume fraction, Hs-TnT high-sensitive troponin T, PAP pulmonary artery pressure.aData missing for $$n = 26$.$Table 2CMR findings of the patients. CMR findingsn = 37LVEDV, ml158 [150–194]LVEDVI, ml/m2a79 [72–89]LVESV, ml73 [61–92]LVESVI, ml/m2a35 [29–43]LVSV, ml91 [73–102]LVSVI, ml/m2a45 [36–49]LVEF, %55 [49–59]LVM, g98 [74–120]LVMI, g/m2a46 [41–55]Heart rate rest, bpm70 [64–80]Heart rate stress, bpm89 [82–102]ECV, %25 [23–27] LGE, n (%)4 [11]CMR findings are presented as median [IQR].CMR cardiac magnetic resonance imaging, LGE late gadolinium enhancement, LVEDV left ventricular end-diastolic volume, LVESV left ventricular end-systolic volume, LVSV left ventricular stroke volume, LVM left ventricular mass, LVEF left ventricular ejection fraction, bpm beats per minute, ECV extracellular volume.aLVEDV, LVESV, LVSV and LVM were indexed to BSA, which was calculated according to the Mosteller formula17.Table 3Rest and stress findings of the patients. CMR findingsn = 37Normal values10Native T1, ms1006 [983–1027]998 (930–1050)Native T2, msa48 [46–50]48 (44–53)Stress native T1, ms1049 [1012–1073]1049 (960–1140)Stress native T2, msa52 [50–54]56 (50–60)∆T1, %4.6 [3.0–6.3]5.9 (1.3–11.0)∆T2, %†11.0 [3.8–14.0]15 (4.4–26.0)Myocardial rest perfusion, ml/min/g0.89 [0.76–1.1]0.9 (0.50–1.25)Myocardial stress perfusion, ml/min/g2.71 [2.27–3.39]3.4 (2.19–5.04)MPR3.1 [2.7–3.6]4.2 (2.2–6.3)CMR findings are presented as median [IQR]. Reference range was calculated as mean ± 2 standard deviations from10.MPR myocardial perfusion reserve.aData missing for $$n = 2$.$
## Image acquisition
CMR was performed at 1.5 T (MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany). The image acquisition protocol is summarized in Fig. 1. Full coverage retrospective ECG-gated balanced steady state free precession (SSFP) cine imaging was acquired in short-axis and three long-axis slices. Typical imaging parameters were flip angle (FA) 68 degrees, pixel size 1.4 × 1.9 mm2, slice thickness 8.0 mm, echo time (TE)/repetition time (TR) 1.19 ms/37.05 ms, matrix size = 256 × 144 and field of view (FOV) 360 × 270 mm2.Figure 1Image acquisition protocol. Scouts and cines were acquired first, followed by native T1 and native T2 mapping. The adenosine infusion was started, and after 3 min one native midventricular T1 map and T2 map was acquired prior to quantitative first pass perfusion imaging, following a bolus of contrast agent. The adenosine infusion was then terminated and after 10 min rest perfusion maps were acquired. Post contrast T1 maps for extracellular volumes maps were acquired after an additional 10 min. Modified from8.
Three short-axis slices (basal, midventricular, apical) were acquired using first-pass perfusion imaging rendering myocardial quantitative perfusion (ml/min/g) maps5, both during adenosine stress (Adenosin, Life Medical AB, Stockholm, Sweden, 140 microg/kg/min infusion) and at rest, following administration of an intravenous bolus of contrast agent (0.05 mmol/kg, gadobutrol, Gadovist, Bayer AB, Berlin, Germany). Perfusion maps were generated using the Gadgetron inline perfusion mapping software, freely available as an executeable12, computed based on a bistributed tissue exchange model13 that estimates arterial delay, perfusion, myocardial blood volme, permeability surface area and interstitial volume in resonable agreement with T1-based estimates of ECV12. Adenosine and contrast were administered in separate cannulas. Typical imaging parameters were: flip angle 50°, slice thickness 8.0 mm, TE/TR 1.04 ms/2.5 ms, bandwidth 1085 Hz/pixel, FOV 360 × 270 mm2 and saturation delay/trigger delay (TD) $\frac{105}{40}$ ms.
Five short-axis native T1 maps were obtained during rest using an ECG-gated modified look-locker inversion (MOLLI, 5 s(3 s)3 s) recovery prototype sequence. One midventricular short axis T1-map was acquired during adenosine stress. Typical imaging parameters included single shot SSFP in end-diastole, flip angle 35 degrees, pixel size 1.4 × 1.9 mm2, slice thickness 8.0 mm, imaging duration 167 ms, TE/TR 1.12 ms/2.7 ms, matrix size = 256 × 144 and FOV 360 × 270 mm2. Five ECV-maps at rest were generated from native T1-maps and post-contrast T1-maps and calibrated by the hematocrit14,15.
Five short-axis native T2 maps were acquired before adenosine stress, and one midventricular short axis T2-map during adenosine stress. T2-mapping was performed using a T2-prepared sequence. Typical imaging parameters included TE/TR 1.06 ms/2.49 ms, FA 70 degrees, pixel size 1.8 × 1.8 mm2, slice thickness 8.0 mm, acquisition window 700 ms, TD 483 ms and matrix size = 144 × 256.
## Image analysis
Cine images, quantitative perfusion maps, ECV-maps, T1- and T2-maps were analyzed with the software Segment16 (version 2.7 Medviso AB, Lund, Sweden) by carefully delineating the endo- and epicardial borders of the LV in the short-axis images, Fig. 2. To further avoid contamination from blood pool and adjacent tissues, respectively, a $10\%$ erosion margin within Segment was set for both endo- and epicardial borders for the export of the respective mapping values. Global native T1 rest, native T2 rest, myocardial perfusion rest, myocardial perfusion stress and ECV values were acquired by averaging all segments of a 16-segment model of the LV. Global native T1 stress, and native T2 stress were averaged from a 6-sector bullseye plot. Intra- and inter-observer variability was assessed on the parameters myocardial rest perfusion, rest native T1 and rest native T2. For intra-observer variability one observer re-analyzed 10 subjects, while inter-observer variability analysis was performed on all 37 subjects by two independent observers. Figure 2Examples of segmentations of endo- and epicardial borders. The image shows segmentations of the respetives maps: (A) stresss perfusion map, (B) rest perfusion map, (C) rest native T1 map and (D) rest native T2 map.
## Statistical analysis
Statistical analysis was performed using Microsoft Excel version 16 (Microsoft, Redmond, Washington, USA) and IBM SPSS Statistics (IBM SPSS Statistics 27, IBM, New York, USA). All data was assessed for normality using the Kolmogorov–Smirnov test. Continuous data was expressed as median and IQR and categorical data was presented as numbers and percentages. Quantification of myocardial perfusion, ECV, native T1 and T2 was performed in each slice, and global values per subject were acquired by averaging the values from all short-axis slices in each subject. MPR was calculated as the ratio of stress to rest myocardial perfusion (ml/min/g). ∆T1 (%), and ∆T2 (%) were calculated as (stress-rest)/rest × 100 (%) for native T1 and T2, respectively. The individual relationships at rest and stress between parameters of myocardial perfusion, MPR and parameters of native T1, native T2, ∆T1 and ∆T2, were assessed by linear regression. By combining rest and stress values in the same data set to maintain aggregated data for T1, T2 and myocardial perfusion, the relations between these pooled parameters were investigated. Inter- and intra-observer agreement was calculated for myocardial rest perfusion, MPR, rest native T1, rest native T2, ∆T1 and ∆T2, and presented as intra-class correlation coefficient (ICC). The significance level in all statistical analyses was defined as $p \leq 0.05.$
## Ethics, consent and permission
All study procedures were carried out in accordance with relevant guidelines and regulations as per the Declaration of Helsinki and Good Clinical Practice for involving human participants. The study was approved by the Swedish Ethical Review Authority, Dnr 2020-04329 and all patients provided written informed consent.
## Relationship between native T1, native T2 and myocardial perfusion
In the analysis of pooled rest and stress data, myocardial perfusion correlated with both native T1 (R2 = 0.35, $p \leq 0.001$) and native T2 (R2 = 0.28, $p \leq 0.001$), respectively, Fig. 3. The relationships between native T1, T2 and myocardial perfusion at rest and stress, respectively, are presented in Table 4. Native T1 at rest correlated moderately with myocardial stress perfusion (R2 = 0.20, $p \leq 0.01$), but did not correlate with myocardial rest perfusion. Native T1 at stress correlated moderately with myocardial stress perfusion (R2 = 0.17, $$p \leq 0.01$$), but not with myocardial rest perfusion. Native T2 displayed no correlation with myocardial rest nor stress perfusion. Figure 3Relationship between rest and stress native T1 and T2 and absolute myocardial perfusion. Scatterplots comparing native T1 and T2 with absolute rest (white) and stress (black) myocardial perfusion. Table 4Relationship of native T1 and T2 with myocardial rest and stress perfusion. Myocardial perfusion restR2, p-valueMyocardial perfusion stressR2, p-valueNative T1 rest0.03, $$p \leq 0.090.20$$, $p \leq 0.01$Native T1 stress0.03, $$p \leq 0.290.17$$, $$p \leq 0.01$$Native T2 rest0.05, $$p \leq 0.220.11$$, $$p \leq 0.06$$Native T2 stress0.005, $$p \leq 0.700.02$$, $$p \leq 0.40$$∆T1–0.04, $$p \leq 0.26$$∆T2–0.004, $$p \leq 0.71$$
## Relationship between ∆T1, ∆T2 and myocardial perfusion reserve
Native T1 and T2 did not correlate with MPR at rest nor stress, Table 5. ∆T1 and ∆T2 did not correlate with MPR.Table 5Relationship of native T1 and T2 with myocardial perfusion reserve. Myocardial perfusion reserveR2, p-valueNative T1 rest0.04, $$p \leq 0.25$$Native T1 stress0.06, $$p \leq 0.16$$Native T2 rest0.005, $$p \leq 0.70$$Native T2 stress0.002, $$p \leq 0.79$$∆T10.02, $$p \leq 0.36$$∆T20.000, $$p \leq 0.99$$
## Reproducibility
Intra- and inter-observer agreement are presented in Table 6 as ICC. Overall, there was an excellent agreement for native T1, native T2, myocardial perfusion, MPR, ΔT1 and ΔT2.Table 6Intra- and inter-observer variability presented as ICC and p-values. Native T1 restNative T2 restMyocardial perfusion restMPRΔT1ΔT2Intra-observer0.92, $$p \leq 0.0010.98$$, $p \leq 0.0010.99$, $p \leq 0.0010.99$, $p \leq 0.0010.96$, $p \leq 0.0010.97$, $p \leq 0.001$Inter-observer0.91, $p \leq 0.0010.96$, $p \leq 0.0010.95$, $p \leq 0.0010.99$, $p \leq 0.0010.95$, $p \leq 0.0010.82$, $p \leq 0.001$MPR myocardial perfusion reserve.
## Discussion
We have demonstrated that both native T1 and native T2 correlate with myocardial perfusion, reflecting the coronary circulation during follow-up of patients with severe Covid-19. The recent trend towards the use of non-contrast techiques to detect changes in the coronary circulation needs to be elucidated in several different clinical contexts and with different clinical parameters, and in this study neither ΔT1 nor ΔT2 correlated to MPR. Therefore, native T1 and T2 mapping seem to capture changes in perfusion, however, the findings of ΔT1 and ΔT2 suggest that non-contrast methods may not be clinically applicable for diagnoses made with MPR, such as obstructive CAD or CMD.
## Relationship between myocardial perfusion, native T1 and native T2
The use of native T1 during adenosine stress to capture the intravascular compartment of myocardial perfusion was first illustrated by Mahmod et al. in a mechanistic study of patients with AS with a blunted ΔT1 that normalized following intervention18. ΔT1 as a possible diagnostic measure has been reproduced in both ischemic heart disease19 and diabetes20. However, due to the lack of a quantitative reference method for comparison, these mechanistic studies did not show the relationship between native T1 and myocardial perfusion. We elucidated the relationships between myocardial perfusion and native T1, and native T2, respectively, in normal physiology10, displaying the physiological basis for native T1 and native T2 for non-contrast diagnosis of the myocardial microcirculation, however more data in clinical contexts are needed. Previous experimental work in animals predicts that tissue T1 depends on perfusion and regional blood volume21 which is supported by work in humans10,18,20, however given the relative low perfusion in humans there are still big knowledge gaps for the translation of stress native T1 into the clinical work. Everaars et al. compared native T1 mapping with 15O(H2O)PET myocardial perfusion in patients with suspected CAD, and showed a moderate correlation between rest and stress measurements of native T1 and myocardial perfusion19. In normal physiology this correlation is stronger10, which is supported by the findings of the current study. A strong relationship between native T1 and myocardial perfusion was also found in healthy subjects undergoing regadenosone stress22. Myocardial stress perfusion is the net result of perfusion and increase of myocardial blood volume across the entire coronary vasculature which may explain the relationship between native stress T1 and myocardial stress perfusion in all three studies. However, there was no correlation between myocardial rest perfusion and native T1 at rest in patients with suspected CAD19, reproduced by the results of the current study, in contrast to normal physiology10. Potential contributing factors to the difference between patients and healthy volunteers besides age and distributions of sex23, include choice of native T1 mapping sequence, physiology in the presence of pathology, sample size and differences in contrast agents24,25.
Native T2 also correlated with myocardial perfusion, as previously shown in normal physiology10. Unlike native T1, individual T2 rest and T2 stress did not have a significant correlation with myocardial rest or stress perfusion in neither the present study nor in normal physiology10. More studies on native T2 during stress is needed to further understand the mechanisms behind non-contrast imaging for diagnostic use.
## Relationship between MPR, native T1 and native T2
Native T1 during stress has previously been shown to moderately correlate with MPR in diabetic patients20. However, this has not been reproduced in the current study, or in patients with CAD19, and AS18. The present study found no correlation between MPR and ΔT1, as previously reported18,20, while Everaars et al. found a weak correlation19. Furthermore, there was no correlation between ΔT2 and MPR in the current study, suggesting that non-contrast techniques cannot capture the MPR. It could be hypothesized that ΔT1 or ΔT2 do not reflect the same physiology as MPR. Native T1 and T2 increase with free water content, which may have an intracellular or extracellular origin, including the intravascular and interstitial compartments18. It is still unclear if the change in native T1 and T2 during stress is primarily dependent upon the changes in myocardial blood volume, ECV or myocardial perfusion. While myocardial perfusion is closely linked to myocardial blood volume and ECV, their intrinsic relationships have not been completely elucidated. Furthermore, native mapping may not be able to differentiate between myocardial blood volume and myocardial perfusion, and there may be a need for a contrast agent to separate the contributions of myocardial perfusion and the myocardial blood volume and ECV10. However, the correlation between pooled rest and stress values of myocardial perfusion and native T1, and native T2, respectively indicate that the underlying physiology behind the parameters are closely related.
## Comparison with controls
In Table 3 it is evident that this overall post-covid population are within the normal values, adding additional knowledge of cardiac pathology in long-term follow-up of severe covid-19. However, the scope of the current study was not to compare the CMR findings with controls, but rather display the heterogeneity in multiparametric native stress imaging in different populations to add the current body of knowledge of native stress mapping. Furthermore, looking at the normal values (mean ± standard deviations) of ∆T1 and ∆T2, the reference ranges with MOLLI 5 s(3 s)3 s are wide, thus limiting the diagnostic potential of stress native multiparametric mapping. In a recent PET study using regadenosone it was shown that patients with post acute sequele of covid-19 syndrome (PACS) had an impaired endothelial-dependent vasodilator response compared to controls26. It has previously been shown that ∆T1 are mediated through both endothelial and non-endothelial dependent mechanism, while adenosine perfusion and MPR are solely mediated through non-endothelial dependent mechanism27. Given that PACS patients more often are affected by postural ortostatic tachycardia syndrome (POTS) together with the observed differences in adenosine and regadenosone response with regards to vasodilator response this might explain the differences in results between the two studies. Further research into the response in ∆T1 and ∆T2 between adenosine and regadensone with regards to endothelial and non-endothelial dependent mechanisms are needed both in covid-19 but also other diseases.
## Clinical outlook
The correlation between pooled native T1, T2 and myocardial perfusion, shows that native T1 and T2 mapping indeed can be used to capture changes in myocardial perfusion during stress without the need of contrast agent. Standardization of native T1 mapping protocols remains important to mitigate cofounders such as age and sex. This study is to the best of our knowledge the first to investigate the relation between native T2 and myocardial perfusion in the presence of pathology and present the correlation between ΔT2 and MPR. While there is potential in non-contrast methods during stress for assessing the microcirculation, given that there is no correlation with MPR the diagnostic potential may be limited. Further research is needed in different patient groups, and with different native T1 mapping protocols.
## Limitations
In this prospective cohort study of previously healthy 37 patients from a single center was included, which is likely reflected in the structurally normal hearts of the study population. Only one native T1 and native T2 map, respectively, were acquired during stress, however there was no differences in significant correlations between one vs five slices at rest (data not shown). Furthermore, one slice at stress does not cover the entire ventricle, why it would be interesting to perform full coverage native mapping in the future. Another limitation is the heart rate dependency associated with native mapping techniques, where Shortened MOLLI and MOLLI 5 s(3 s)3 s (used in this this study) are two different techniques to mitigate heart rate dependecy compared to other MOLLI sequences28.
## Conclusions
Native T1 and native T2 correlate with myocardial perfusion during adenosine stress, reflecting the coronary circulation in patients during long-term follow-up of severe Covid-19. Neither ΔT1 nor ΔT2 can be used to assess MPR in patients with severe Covid-19.
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|
---
title: 'Experiences and results from using a novel clinical feedback system in routine
stoma care nurse follow-up of patients with an ostomy: a longitudinal study'
authors:
- Kirsten Lerum Indrebø
- Anny Aasprang
- Torill Elin Olsen
- John Roger Andersen
journal: Journal of Patient-Reported Outcomes
year: 2023
pmcid: PMC10010226
doi: 10.1186/s41687-023-00573-z
license: CC BY 4.0
---
# Experiences and results from using a novel clinical feedback system in routine stoma care nurse follow-up of patients with an ostomy: a longitudinal study
## Abstract
Studies show that patients struggle with adjustment to the ostomy in a broad spectre of physical and psychosocial factors, and stoma care nurse follow-up is necessary. Mapping each patient`s most important challenges during a short outpatient follow-up consultation may be difficult. Thus, missing information on individual factors affecting adjustment and quality of life can result in underreported problems and unclear communication between patients and stoma care nurse. The study aimed to explore patient satisfaction and experiences using digital questionnaires before each consultation in stoma care nurse ostomy follow-up and to study adjustment to ostomy and health-related quality of life in routine follow-up 3,6 and 12 months after ostomy surgery. The study results showed that the patients were satisfied with their follow-up using questionnaires before consultations ($96\%$). Especially, they felt they received sufficient and individualised information, were involved in treatment decisions, and benefited from the consultations. Especially the life areas “daily activities”, having “knowledge and skills”, “health”, and the patient`s health-related quality of life improved during the first year after the operation. Thus, patient reported outcomes/clinical feedbacksystem is a promising method for follow-up of ostomy patients because it may promote better discussions during the consultation and tailor the patient’s adjustment trajectory more precisely than without such a system.
### Background
A faecal or urinary ostomy may be lifesaving. However, it involves significant bodily change, and the adjustment process to life with an ostomy includes a broad spectre of physical and psychosocial challenges. Thus, new interventions are needed to improve adaptation to living with an ostomy. This study aimed to examine experiences and outcomes using a new clinical feedback system with patient-reported outcome measures in ostomy care.
### Methods
In this longitudinal explorative study, 69 ostomy patients were followed by a stoma care nurse in an outpatient clinic, using a clinical feedback system postoperatively at 3, 6 and 12 months. The patients responded electronically to the questionnaires before each consultation. The Generic Short Patient Experiences Questionnaire was used to measure patient experiences and satisfaction with follow-up. The Ostomy Adjustment Scale (OAS) measured adjustment to life with an ostomy, and the Short Form-36 (SF-36) assessed the patient's health-related quality of life. Longitudinal regression models with time as an explanatory (categorical) variable were used to analyse changes. The STROBE guideline was applied.
### Results
The patients were satisfied with their follow-up ($96\%$). Especially, they felt they received sufficient and individualised information, were involved in treatment decisions, and benefited from the consultations. The OAS subscale scores for 'daily activities', 'knowledge and skills' and 'health' improved over time (all $p \leq 0.05$), as did the physical and mental component summary scores of the SF-36 (all $p \leq 0.05$). Effect sizes of changes were small (0.20–0.40). Sexuality was the most challenging factor reported.
### Conclusions
The clinical feedback system could be helpful because outpatient follow-ups for ostomy patients may be more tailored when clinicians use clinical feedback systems. However, further development and testing are needed.
## Background
Ostomy surgery is necessary for about 1900 people annually in Norway, owing to colorectal cancer, inflammatory bowel disease (IBD), infections, incontinence and several other diagnoses [1]. With an ostomy, the urine or faeces enter an external pouch on the abdomen, and patients must adjust to bodily changes after the operation [2]. These changes in appearance and bodily function can influence physical, psychological and social life [3–6], as well as health-related quality of life (HRQoL) [7, 8].
Sufficient knowledge and the skills to carry out ostomy care and psychological support are essential to adjusting to life with an ostomy and enjoying HRQoL. A study by Notter et al. suggests the importance of a high degree of individualised follow-up following ostomy [9]. The physical and psychosocial adjustment to body changes after an ostomy operation is an individual process that lasts for years. Thus, the patient needs a long time individualised follow-up. Several studies have shown that stoma care nurses (SCNs) are central in the education and long-term follow-up of stoma patients and that patients and SCNs need to communicate effectively according to the patient's needs. [ 3, 4, 9–14]. To promote the patient's adjustment to life with an ostomy, the SCN needs knowledge of the patient's experiences with having an ostomy in their everyday life.
Consequently, it would be helpful to allow each patient to prepare for follow-up consultations and bring their experiences, knowledge and challenges into their communication with the SCN. However, patients do not always know what to ask about, and the SCN may not always grasp their patients' struggles [9]. Unclear communication may result in problems that are underreported at consultations.
Several interventions promote better adjustment to ostomy and better QoL following ostomy surgery. For example, education programs, telephone or text message follow-up [15–17], face-to-face education sessions [18] and participating in ostomy self-care programs [19] have all been found useful. Another finding is that the communication between patients and SCN is a significant factor in the adjustment process [3]. Still, there is a gap in the literature on using patient-reported outcomes (PROs) in routine clinical consultations with ostomy patients. There is also a lack of longitudinal studies studying the adjustment process in patients who regularly follow up with SCNs.
A promising tool for preparing and conducting these consultations is utilizing electronic PROs to monitor the patient's treatment progress over time [20]. PROs can be easily implemented in a clinical feedback system (CFS) in patient consultations, using an electronic device displaying results with user-friendly graphs [21] (Fig. 1). The CFS can be used as a communicational tool to improve user involvement in treatment decisions and measure the patient's progress in treatment over time [20, 22–25].Fig. 1Ostomy adjustment system The current study aimed to explore experiences and results from a novel CFS in ostomy patients receiving SCN follow-up in a routine clinical setting. We report experiences and satisfaction with patient care using the CFS, patient trajectories of change in adjustment to life with an ostomy, and comparisons of generic HRQoL between patients with an ostomy and norm scores from a general population. We also report the experiences and reflections of the SCNs on the development and use of the CFS.
## Methods
In this longitudinal study, we included patients who had undergone urostomy, colostomy, or ileostomy operations attending the regular follow-up programme of the outpatient ostomy clinic at the Department of Surgery from September 2017 to December 2021. The inclusion criteria were age > 18 years; to have had a colostomy, ileostomy or urostomy for ≥ 3 months; and to be able to speak, read and write Norwegian. The SCNs considered whether the patients filled the inclusion criteria. Those who fulfilled the criteria received a written information letter about the study on three weeks of postoperative outpatient consultation. A written consent form was added to the information. The study included the participants for four years, and each patient was followed for 12 months postoperative". The study followed the STROBE guideline.
Our power calculation was based on a two-sided paired test (effect size = 0.4, a correlation between measures of 0.3, $90\%$ power, p ≤ 0.05), the results of which indicated that at least 68 paired observations would be required to detect reasonably robust $95\%$ confidence interval (CI) estimates of changes on the primary outcome of interest: Ostomy Adjustment Scale (OAS) [26]. No minimally important effect sizes have been defined for the OAS; thus, we relied on research and consensus for PRO measures in general [27].
## Clinical feedback system
Planning and implementing the new intervention for outpatient follow-up of ostomy patients using the CFS involved several components, including the selection of instruments, development of the digital version, user involvement, planning and implementation of the follow-up consultations, and the documentation of results in the patient's journal. A more detailed overview is available in the study protocol [28].
Three SCNs followed up the patients at the 3-, 6- and 12-month postoperative intervals in an outpatient clinic, using electronic PROs and a CFS [28]. The PROs had to reflect the patient's adjustment process, HRQOL, and important patient experiences and satisfaction with the outpatient follow-up consultations, and the scales must have been validated in Norway. The follow-up was conducted according to national recommendations for the follow-up of ostomy patients in Norway [29]. Participants completed an electronic sociodemographic and clinical form, the OAS and the Short Form-36 (SF-36), prior to or occasionally during their postoperative 3-, 6-, and 12-month consultations with the SCN. The patient and SCN discussed the answers during their consultation, worked together on new interventions and planned further follow-up. After each consultation, the patients responded to a paper version of the Generic Short Patient Experiences Questionnaire scale (GS-PEQ), and the SCN responded to an electronic form. Using a paper version could mean less patient burden because the patient did not need to log in with Bank Id again after the consultation to answer a digital questionnaire. The OAS was previously cross-culturally adapted in Norway according to guidelines for cross-cultural adaptation of self-report measures [30, 31]. The GS-PEQ was developed in Norway [32]. The CFS and its implementation plan for clinical practice have been described in detail elsewhere [28] (Fig. 1).
## Documentation of results in the patient's electronic journal
As ostomy follow-up was part of a research project, the questionnaires were not incorporated into the patient's electronic hospital journal. The patients' answers were reported as bars (SF-36), graphs (OAS) and reports (clinical forms) on their screens. The PROs and results from the clinical component of the consultation were documented in the patient's electronic journal, together with the interventions that the patient and SCN agreed on. The patient can read the SCN's report in the electronic journal.
## User involvement during the development process
The questionnaire package was discussed with the patient user panel and approved by them, focusing on the burden of answering 96 items and the experience of responding to the questionnaires at home. During the study, the SCNs discussed the development of the OAS subscales and patient expectations from a consultation, including using the CFS. Feedback indicated that using the questionnaires made it easier to discuss self-esteem/body image and psychological/existential factors, enabling them to be viewed as 'whole persons'.
## Electronic platform and security
Our in-house expertise on digital platforms and statistical programmes helped us communicate as precisely as possible in "technology language" with the private company developing the electronic version. In Norway, bankID is a system for the identification and storage of sensitive personal data. To access this data in the hospital, using a code device or a cell phone and having access to a mobile network is necessary. We account for the fact that some patients forgot to bring their bank ID code device with them. In addition, mobile signal strength varied in the region around the hospital where the research was done. Due to these limitations, access to bank ID information was not always feasible. Thus, we created a reserve solution giving the patient one-time login codes for each questionnaire.
## Sociodemographic and clinical forms
The sociodemographic and clinical forms were based on theory [29, 33] and the long-term experiences of SCNs in the follow-up of ostomy patients. The same forms were used in the Norwegian validation study of the OAS [31].
The form completed by the patients included items on age (continuous variable), gender (male or female), marital status (married/cohabiting or living alone, and education (low [< 13 years] or high [≥ 13 years]).
The form completed by the SCN included items on time since surgery (< 1 year or > 1 year), diagnosis (ulcerative colitis/Crohn's disease, cancer or other diseases) and ostomy type (colostomy, ileostomy, urostomy or two ostomies).
## Patient experiences scale
At the start of the study, we used a nonvalidated questionnaire about patient experiences and satisfaction with follow-up. During the study, we discovered a validated Norwegian scale, which reflected the patient's experiences and satisfaction with outpatient consultations very well. The two scales mainly contained the same areas, but due to recommendations to use validated scales, we decided to change the scale during the study, and the responses on the nonvalidated scale were not analysed.
## Generic short patient experiences questionnaire
The GS-PEQ was used to measure patient experiences. The scale contains questions about patient satisfaction and experiences with somatic outpatient services in Norway [34]. It includes 10 generic core items about dimensions of the patient's experiences in using specialist health care services. The areas covered by the scale are outcome (two items), clinician services (two items), user involvement (two items), incorrect treatment (one item) and information (one item). The answers are scored on a five-point response scale from 1 = 'Not at all', 2 = '*To a* small extent', 3 = '*To a* moderate extent', 4 = '*To a* large extent', and 5 = '*To a* very large extent'. In addition, 'Not applicable' was a response option. The 10 items in the GS-PEQ have been rated highly important and relevant in research [35]. The GS-PEQ items about what happened in the consultation were essential in evaluating SCN follow-up. The GS-PEQ was developed in Norway [32].
## Ostomy adjustment scale
The OAS is a 34-item multidimensional scale that measures a patient's subjective adaptation to physical, psychological and social changes after ostomy surgery. The OAS comprises seven subscales measuring adaptation to ostomy relating to daily activities, knowledge and skills, self-esteem/body image, psychological/existential aspects, health, health professionals and sexuality [36]. Notably, it includes items about employment status, leisure, trust in ostomy equipment, and general description of life with an ostomy. The scale also records patients' opinions on the instructions they received about their ostomy, their self-image and social functioning, their feelings about the ostomy, their relationship with health professionals and their sexuality in relation to it [37]. The OAS is scored on a Likert scale from 1 (strongly agree) to 6 (strongly disagree). We used a total mean score and subscores ranging from 1 to 6. A pragmatic thumb of rule based on clinical experience is that subscores higher than 4.35 indicated good adjustment, scores from 2.67 to 4.34 showed some challenges and scores from 1 to 2.66 indicated low adjustment[36]. Previous reports on the reliability and validity of the OAS demonstrated acceptable internal consistency and test–retest reliability [37–39]. Previous studies also support the instrument's construct validity [37–39]. Mary Ellen Olbrisch, the researcher who developed the instrument, permitted us to freely use the OAS. " The OAS was cross-culturally adapted in Norway according to guidelines for the cross-cultural adaptation of self-report measures" [31, 30].
In the current study, the participants responded electronically to single items before the consultation. We divided the OAS scale into clinically meaningful subscales during the study period and analysed our research results according to the subscales. To divide the OAS into subscales, SCNs and researchers first divided the scale into clinically meaningful subscales. After that, the model was statistically tested using confirmatory factor analysis [36].
## Short form-36
The SF-36 is a well-validated, generic health scale that measures outcomes (health phenomena) known to be the most directly affected by disease and treatment [40]. The SF-36 has eight subscales measuring physical functioning, physical role limitations, emotional role limitations, bodily pain, general health, vitality, social functioning, emotional role functioning and mental health. The instrument has two summary scores: a physical component score (PCS) reflecting the domains of physical function, physical role function, pain, and general health, and a mental component score (MCS) reflecting the domains of vitality, social function, emotional role functioning and mental health. The SF-36 scores are presented from 0 to 100, with higher scores reflecting better HRQoL. The Norwegian version of the SF-36 has satisfactory reliability and validity [41], and Norwegian population norm scores for the SF-36 stratified by age and gender were derived from a recent publication [42].
## The nurse's experiences and reflections on the PRO/CFS
Experiences of the time spent in each consultation were gathered from the SCN's appointment list in the hospital's administrative system. The SCN's experiences and reflections on using the CFS were discussed in meetings between the SCNs and the developers and summarized in reports. If necessary, minor adjustments in the intervention were made continuously. Some of the thoughts and lessons are presented further.
## Data analyses
The characteristics of the sample ($$n = 69$$) were presented as numbers and percentages, except for age which was presented as mean and standard deviation (SD). Data missing from the questionnaires was handled according to the procedures described for each questionnaire [37, 43]. The OAS and the SF-36 scores at 3, 6 and 12 months after the operation were presented as means with $95\%$ CIs. To study changes in the OAS and the SF-36 scores, longitudinal mixed-effect regression models with time as an explanatory variable were used, with exact two-sided p-values. A one-sample t-test was used to study differences in SF-36 scores in the patient group versus the general population. Effect sizes for change in OAS and SF-36 were calculated by subtracting the average scores between time points divided by the SD by the 3-month consultation. Effect sizes for differences in the SF-36 scores between the patients and the general population were calculated by subtracting the patients' average scores from the average population scores and dividing them by the SDs from the study population. All effect sizes were judged against the standard criteria proposed by Cohen [45] as follows: trivial (< 0.2), small (0.2 to < 0.49), moderate (0.5 to < 0.79), and large (≥ 0.8) [44]. In the analysis of patient experiences and satisfaction with care received, descriptive results (number and percentage) for each item of the GS-PEQ at 1-year follow-up were presented. SPSS software (version 25; IBM, Armonk, NY) was used for all analyses.
## Results
The sociodemographic and clinical data are presented in Table 1. Of the patients, 35 ($51\%$) responded to the questionnaires electronically from home, 17 ($24.6\%$) answered at the hospital just before the consultation and 17 ($24.6\%$) answered the questionnaires during the consultation. None used a paper version. The patients used approximately 20 min to answer the questionnaires. Each consultation lasted 1 h unless patients needed help answering, in which case the consultation was up to 1.5 h. All invited patients agreed to participate in the study (Tables 2 and 3), but it was not complete data on all measure points. Reasons for not answering at 3 or 6 months were technical problems, changes of appointments, and restrictions owing to the Covid-19 pandemic. The response rates at twelve Month measure were $100\%$ in the subscales "daily activities", "knowledge and skills", "self-esteem/body image", "psychosocial/existential and $97\%$ on health, $88\%$ on "health professionals and $64\%$ on "sexuality". Table 1Demographic and clinical characteristics ($$n = 69$$)VariableValueAge, mean years (range)62.71 (20–86)Gender, n (%)Women25 (36.2)Men44 (63.8)Marital status, n (%)Married/cohabitant46 (66.7)Living alone23 (33.3)Type ostomy, n (%)Ileostomy21 (30.4)Colostomy34 (49.3)Urostomy8 (11.6)Two ostomies (colo and uro)6 (8.7)Diagnosis n (%)Cancer41 (59.4)Inflammatory bowel disease15 (21.7)Other diseases or conditions13 (18.8)Education, n (%)Primary school/senior high school/college52 (75.4)University college/university16 (23.2)Missing1 (1.4)Table 2Short patient experiences questionnaire at 12 months follow-up: crude numbers ($$n = 48$$)ItemsNot at allTo a small extentTo a moderateextentTo a largeextentTo a verylarge extentNotapplicableDid the clinicians talk to you in a way that waseasy to understand?0008400Do you have confidence in the clinicians’professional competence?0004440Did you get sufficient information about yourdiagnosis/your afflictions?00011352Did you perceive the treatment you receivedas suited to your situation?00210360Were you involved in any decisions regardingyour treatment?0139341Did you perceive the institution’s work as wellorganised?00110316Do you believe that you were in any way giventhe wrong treatment (according to your ownjudgement)?3730053Overall, were the help and treatment you receivedat the institution satisfactory?00112341Not at allYes, but notso longYes, quitelongYes, muchtoo long–NotapplicableDid you have to wait before you were admittedfor services at the institution?3940400No benefitSmall benefitSome benefitGreat benefitHuge benefitNot applicableOverall, what benefit have you had from thecare at the institution?00318270Table 3Ostomy adjustment scores over timeScores3 months, mean ($95\%$ CI)6 months, mean($95\%$ CI)12 months, mean ($95\%$ CI)Effect size*p-Value **Sum score total4.44 (4.27, 4.67)4.62 (4.43, 4.81)4.72 (4.53, 4.90)0.330.008Daily activities4.06 (3.79, 4.30)4.30 (4.06, 4.54)4.42 (4.18, 4.65)0.360.008Knowledge and skills5.14 (4.87, 5.36)5.21 (4.98, 5.44)5.47 (5.26, 5.68)0.370.025Self-esteem/body image4.72 (4.51, 5.03)4.90 (4.66, 5.15)4.98 (4.74, 5.22)0.230.165Psychological/existential4.07 (3.82, 4.39)4.36 (4.09, 4.63)4.40 (4.05, 4.57)0.270.138Health4.97 (4.73, 5.29)4.92 (4.65, 5.18)5.32 (5.05, 5.55)0.340.016Health professionals5.40 (5.12, 6.67)5.41 (5.15, 5.67)5.34 (5.10, 5.59)0.060.889Sexuality2.88 (2.44, 3.41)3.20 (2.76, 3.66)3.21 (2.76, 3.65)0.200.481CI Confidence intervalNumber of observations: 3 months, $$n = 48$$; 6 months, $$n = 59$$; 12 months, $$n = 69$$*Effect sizes are based on the differences between the 3-month versus the 12-month scores divided by the standard deviation of the 3-month scores. Effect sizes < 0.2 are considered trivial, from 0.2 to < 0.5 are considered small, from 0.5 to < 0.8 as moderate and ≥ 0.8 as large**p-Values are for overall changes over time
## Patient experiences and satisfaction with PRO/CFS
Of the participants, 48 answered the GS-PEQ questionnaire, and the first 29 responded to a non-validated form about satisfaction with care. First, almost all the patients indicated that the SCN talked to them in a way that was easy to understand. Second, they received sufficient information about their diagnosis and condition. Third, all participants had confidence in the clinicians' professional competence. Fourth, the treatment was suited to their situation, and they were involved in any treatment decisions; and fifth, they reported a 'great' or 'huge' benefit from the care they received (Table 2).
## Adjustment to life with an ostomy
The participants showed significant improvement in OAS total sum score from 3- to 12 months postoperatively ($$p \leq 0.008$$), with an effect size for change of 0.30. The following subscale scores improved significantly from 3- to 12 months postoperatively: daily activities ($$p \leq 0.008$$), knowledge and skills ($$p \leq 0.025$$) and health ($$p \leq 0.016$$). The effect sizes of change were small for the OAS sum score and the subscales scores for daily activities, knowledge and skills, health, self-esteem/body image and psychological/existential, and were trivial for health professionals and sexuality. Scores for the sexuality subscale indicated challenges throughout the first year post-ostomy, and scores were not significantly better at 12 months. Thus, sexuality was the greatest patient-reported challenge (Table 3).
## Health-related quality of life
MCS and PCS showed significant positive change 12 months postoperatively compared with the 3- and 6-month scores, with small effect sizes. Results from the subscales of physical functioning, physical role functioning and emotional role functioning were significantly better at 12 months than at 3 and 6 months, but the effect sizes were small. In all other SF-36 subscales, the effect sizes of the changes were trivial (Table 4). Compared to norms for the Norwegian population, the PCS and MCS scores were lower at 12 months postoperatively, but the effect sizes were small. The effect sizes for the subscales were also small (physical functioning, physical role functioning and emotional role functioning) or trivial (bodily pain, general health, vitality, social functioning and mental health) (Table 5).Table 4Patients Short Form-36 scores over timeScores3 months, mean($95\%$ CI)6 months, mean($95\%$ CI)12 months, mean($95\%$ CI)Effect size*p-Value**Physical component score61.16 (55.75, 66.56)68.75 (63.71, 73.79)68.86 (62.06, 71.65)0.410.015Physical function68.76 (62.26, 75.26)77.28 (71.18, 83.39)75.04 (69.17, 80.91)0.310.011Physical role function35.01 (24.07, 45.95)50.94 (40.94, 60.95)51.45 (42.07, 60.83)0.43 < 0.001Pain73.07 (66.20, 79.94)80.86 (74.51, 87.21)74.67 (68.67, 80.68)0.060.054General health68.18 (62.20, 74.15)66.28 (60.69, 71.87)66.40 (61.07, 71.72)− 0.080.752Mental component score69.32 (64.17, 74.47)76.84 (72.08, 81.58)73.35 (68.88, 77.82)0.210.025Vitality53.14 (46.89, 59.39)58.18 (52.33, 64.03)56.33 (50.79, 61.86)0.140.260Social function76.88 (70.63, 83.13)82.88 (77.16, 88.60)80.97 (75.61, 86.34)0.160.217Emotional role function66.01 (55.60, 76.42)83.84 (74.34, 93.33)74.40 (65.64, 83.15)0.200.016Mental health81.29 (77.30, 85.27)82.78 (79.08, 86.47)81.68 (78.21, 85.15)0.030.747CI Confidence intervalNumber of observations: 3 months, $$n = 46$$; 6 months, $$n = 58$$; 12 months, $$n = 69$$*Effect sizes are based on the differences between the 3-month versus the 12-month scores divided by the standard deviation of the 3-month scores. Effect sizes < 0.2 are considered trivial, from 0.2 to < 0.5 are considered small, from 0.5 to < 0.8 as moderate and ≥ 0.8 as large**p-Values are for overall changes over timeTable 5Patients’ Short Form 36 scores at 12-months follow-up versus norm scoresScores12 months, mean (standard deviation)Norm scores, meanEffect sizep-ValuePhysical component score68.86 (22.18)74.66− 0.260.033Physical function75.04 (26.10)83.55− 0.330.009Physical role function51.45 (41.10)71.26− 0.48 < 0.001Pain74.67 (26.14)72.97− 0.070.591General health66.40 (22.58)70.860.200.106Mental component score73.35 (20.13)79.260.290.017Vitality56.33 (25.82)61.09− 0.180.130Social function80.97 (22.95)87.28− 0.170.026Emotional role function74.40 (37.98)86.59− 0.320.010Mental health81.68 (15.21)82.06− 0.030.836Norm scores were adjusted for age and gender to reflect the same distribution as the study sampleEffect sizes were calculated by subtracting the mean score of the population norm from the mean score of the patient group divided by the standard deviation of the patient group. Effect sizes < 0.2 are considered trivial, from 0.2 to < 0.5 are considered small, from 0.5 to < 0.8 as moderate and ≥ 0.8 as largeNumber of observations = 69
## Follow-up consultation
The procedure for the follow-up consultations was developed in detail before we started the project [28]. However, after four years of implementing the consultations, it was clear that their development was an ongoing process. The implementation of the consultations differed from patient to patient because they were tailored to each patient's answers to the questionnaires and individually adapted to the patient's preferences for discussing their challenges. Practical issues needed addressing, such as having an appropriate place to answer the questionnaires in the outpatient clinic waiting area. We had to remember to change the questionnaire availability time for patients who changed their appointment.
Altogether, using PRO/CFS in patient consultations made it easier for the patients to bring up and discuss difficult themes, especially self-esteem, existential/psychological challenges, and sexuality. We used the single-item version of OAS in the consultations. Using this version could, in some consultations, result in specific questions dealing with the same topic being discussed several times in the consultation. In the future, using subscales could be a promising method to avoid discussions about the same topic several times.
After the consultation, a paper version of the GS-PEQ may have resulted in a higher response rate because the patients did not have to log in with Bank ID again to answer the questionnaire. Due to login challenges, the consultation could last longer than planned. Therefore, in the future, the login procedure should be more straightforward.
## Discussion
This study reports the initial results of using a new CFS for people with an ostomy. User satisfaction was high, with $96\%$ of the patients reporting being satisfied to a large extent or to a very large extent with the help they received. Patient adjustment to life with an ostomy improved significantly from 3 to 12 months postoperatively on the subscales of daily activities, knowledge and skills, and health. Sexuality was clearly the most challenging life domain with little improvement over time. Overall, HRQoL, as measured with the SF-36 summary scores, improved significantly over time but remained slightly below general population norms 12 months after surgery. To our best knowledge, this is the first study of its kind in ostomy care. Thus, a direct comparison of our results with others is not feasible. Consequently, we compare our results with those from other studies that might be informative.
## Patient experiences and satisfaction with using the CFS
The high scores on the GS-PEQ and the OAS subscale for 'health professionals' indicate that the CFS is a promising communication tool in the nurse-led follow-up of ostomy patients. However, scores could also have been high because patients may have been 'eager to please' because their future follow-up may have been with the same SCN. Of the patients, $24.6\%$ responded to the OAS during the consultation, and their answers about their relationships with health professionals may have been less honest than those of patients answering before their consultation. Another factor was that the researcher was one of three SCNs conducting the follow-up. The use of PROs has been reported for other patient groups, such as in a longitudinal study among 100 home dialysis patients who received nurse-led outpatient follow-up every third month, including the reporting of electronically PROs before and after the consultations [45]. The study results indicated positive experiences for patients and nurses using PROs. Patients were satisfied with the nurses' assistance, and the level of satisfaction with care was stable over time. About $40\%$ reported that they felt more supported and had a better understanding of their situation and how they could improve it. In another nurse-led randomised controlled pilot trial among patients with diabetes [54], $32.1\%$ of participants stated that completing PROMs led to discussions of diabetes-related challenges that would not otherwise have occurred. However, a Swedish study [4] of regular 3-, 6- and 12-month postoperative follow-ups of ostomy patients without CFS also showed high OAS mean scores in the three single items about health professionals. Measuring patient experiences is challenging owing to the complexity of the consultation. For example, it may be difficult for the patient to separate their experiences of the instruments and methods used and the SCN's competence and ways of communicating and teaching. A future research option could be a randomised controlled study of patients receiving follow-up with CFS compared with patients subject to standard follow-up. However, our CFS intervention must first be further developed and tested at more ostomy clinics.
## Patients' trajectories during the first postoperative year
In the current study, the patients were enrolled three months after their ostomy surgery. We found that their OAS sum score improved from three months to one year postoperative. Comparing our findings with other studies on improving OAS and SF-36 scores during the first postoperative year is difficult because few longitudinal studies have used PRO/CFS. A case–control study from Denmark [15] studied the effect of an education programme on OAS sum scores from baseline (before hospital discharge) to 3- and 6 months postoperative. The OAS sum score was lower than in the current study at these points, possibly because of differences in the study population and follow-up schedules.
Our study showed small but significant effect sizes reflecting improvements in daily activities, knowledge and skills, and health areas between 3 and 12 months postoperatively. Self-esteem/body image and psychological/existential factors showed small effects, but these were not significant. One explanation could be that the greatest change happens between hospital discharge after surgery and three months postoperatively [15]. However, adjustment to living with bodily changes may be complex and lengthy because the various aspects of this influence each other. For example, the ostomy, the area surrounding it and its function may change owing to changes in behaviour or body shape. These may result from dietary changes, weight gain or loss, more physical activity, travelling, resumption of work and participation in new social settings. The patient continually learns how to prevent complications, such as parastomal hernia, leakage and sore skin, possibly changing their clothing style, and how to deal with unpleasant sounds from their ostomy in social settings. Other studies have shown that even patients living with an ostomy for several years lack the knowledge to manage leakage and sore skin [46]. One focus group among patients with 1 to 3 years of experience with colo- or ileostomy found that patients still did not feel comfortable with their new body [47]. Similar findings were also reported from another focus group study among six young people with ostomies owing to IBD [48]. The participants reported uneasy feelings about the ostomy, such as embarrassment and having to change their wardrobe to conceal the ostomy bag, causing them to feel different from their peers, restricted in activity and clothing choices, and experience loss of control. A study among colostomy patients showed that patients with high levels of knowledge and independence had higher psychosocial adjustment than those with less competence [49]. Of the participants in our study, $59.6\%$ had a cancer diagnosis, and most of the study population ($75\%$) were more than 60 years old. Although the studies mentioned above are not directly comparable to the current study, they indicate the complexity of the adjustment process, which may progress as small steps over several years.
## Patients' most significant challenges at 12 months postoperative
The OAS subscore for sexuality (mean score of 3.20 12 months postoperative) indicated that this was the most challenging area for patients in our study. Sexuality is a multidimensional theme, including physical factors such as diagnosis, treatment and health [50] and psychosocial factors such as changes in body image and psychological, social and emotional aspects [51, 52]. Lifesaving cancer treatment such as surgery and eventual radiation therapy may have side effects such as nerve damage, resulting in erectile dysfunction or dyspareunia from reduced sensibility or anatomical changes [50]. In the current study, $59.4\%$ of patients had cancer diagnoses. The nature of the study population could, therefore, be one explanation for low scores for sexuality. A Swedish study [4] also found low scores on the three OAS items about sexuality (item mean scores 2.1–3.9). Although the two studies are not comparable owing to different designs, most participants had cancer diagnoses in both, and low scores for sexuality were still demonstrated 12 months after the ostomy operation. Another explanation could be that patients and partners must adapt psychologically to bodily change, as shown in one review study [51]. For example, Vural et al. [ 52] studied the impact of ostomy on the sexual life of patients up to 5 years after surgery, and sexuality was still reported as a challenge. A longitudinal study among colorectal cancer patients found that patients with rectal cancer had marginally worse sexual function than those with other diagnoses, and it did not improve during the first six postoperative months. Body image distress was common, but this decreased significantly from baseline to 6 months [53]. This could explain the trivial improvements seen between 3 and 12 months postoperatively because adaptation processes are complex and may last several years. Several studies in a review study suggested a need for more counselling and education about sexuality [51], and another study indicates that SCNs need to know how patients wish to discuss sexuality [4]. Raising the topic of sexuality in consultations may be difficult for patients and SCNs. Thus, having a communication tool with which the patient can respond to concrete items about sexuality may be helpful.
Our study showed significant improvement in SF-36 scores for both sum scores (MCS and PCS) and the subscales of physical function, physical role function, pain, and emotional role function from 3 to 12 months postoperative. A previous Norwegian study also found lower SF-36 scores in the study population than in the general population, but effect sizes were small or trivial [8]. A Danish study found significantly better SF-36 scores six months postoperatively than at baseline in a patient group who attended a systematic education group than in those receiving standard follow-up. Those results were not compared to norms [15].
## Experiences from using the CFS in the ostomy outpatient clinic
Using questionnaires primarily made for research and not clinical may be somewhat challenging. In our study, patients responded to the OAS scale with single items and the answers were used in the subsequent consultation. We discussed the items with low scores first, and we had to improvise when items belonging to the same theme appeared several times and using single items in the clinic could be too complex. Thus, we divided the OAS into clinically meaningful subscales, including all the items in the scale [36] and used the subscales in our data analysis (Table 3).
A follow-up ostomy consultation has several components. Using the CFS was novel in that we had to seamlessly incorporate the answers shown on the screen during the consultation into the dialogue and simultaneously allow the patient to speak in their own words about everyday life with an ostomy. Using CFS in regular follow-up enables uncovering patient knowledge gaps and individual factors affecting their psychosocial health. The patient can respond to items on themes that may be difficult to raise otherwise during a consultation [54]. For example, the user panel's feedback indicated that using the questionnaires made it easier to discuss self-esteem/body image and psychological/existential factors, enabling them to be viewed as 'whole persons'. The patient and SCN can then communicate precisely to co-create new knowledge, gain insight, and share decisions [21]. Based on using PROs and clinical mapping, counselling and education may be more precise than without using such an instrument.
## Implications for practice and further use of CFS
The experiences from this study indicate that using CFS as a communication tool in the follow-up of ostomy patients is promising, as it may promote user involvement and prepare the SCN better for the consultation. Using single OAS items during the consultation was challenging, and we recommend that the tool is further developed using subscales instead of single items alone. Questionnaires that include subscales mirroring the patients' challenges, combined with recommendations and guidelines for follow-up and the SCNs' own experiences and knowledge, may enhance the follow-up consultation.
Another factor is the technology that can be enhanced, for example, by more accessible identification methods than BankID and by having items designed for response through mobile tablets. Accessing the questionnaires and answering them must be made as easy as possible so that patients can answer from home before their consultation. We also need to develop solutions for a better graphical presentation of the PROs during the consultation.
## Limitations and strengths
The current study had several limitations. First, the sample was limited, and the study was conducted in a single ostomy outpatient clinic. Second, the study lacked qualitative data about the patient's experiences and satisfaction with the PRO/CFS. Such data may have provided a more detailed view of CFS use in a clinical context. Third, we cannot claim that outcomes are better using CFS, owing to the study's observational design. The researcher (KLI) met some patients in the clinical follow-up consultation, which could be both a limitation and a strength. The limitation was that it could influence the patient's answers, especially on the GS-PEQ. A limitation was also that 29 of the 69 participants responded to a non-validated scale and those responses were not analysed.
A strength was the close collaboration between the developers of the CFS system and the clinic. Another strength was the long-term, continuous development of the CFS system in cooperation between patients, SCNs, researchers, and developers. Another strength was the general high response rate, except of the subscale “sexuality”, having a response rate of $64\%$.
## Conclusion
Our initial experiences and findings from using the CFS are promising, with SCNs suggesting that the CFS may lead to a greater in-depth discussion of patient challenges. Better technological solutions are required to enhance the CFS, such as finding other user-friendly but secure identification methods than BankID, improving the design for smartphone and tablet responses, and developing better summarised reports for documentation in the electronic patient journal. Further studies are needed to evaluate future versions of the CFS for this patient group.
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|
---
title: 'Comorbidity-stratified estimates of 30-day mortality risk by age for unvaccinated
men and women with COVID-19: a population-based cohort study'
authors:
- Husam Abdel-Qadir
- Peter C. Austin
- Atul Sivaswamy
- Anna Chu
- Harindra C. Wijeysundera
- Douglas S. Lee
journal: BMC Public Health
year: 2023
pmcid: PMC10010246
doi: 10.1186/s12889-023-15386-4
license: CC BY 4.0
---
# Comorbidity-stratified estimates of 30-day mortality risk by age for unvaccinated men and women with COVID-19: a population-based cohort study
## Abstract
### Background
The mortality risk following COVID-19 diagnosis in men and women with common comorbidities at different ages has been difficult to communicate to the general public. The purpose of this study was to determine the age at which unvaccinated men and women with common comorbidities have a mortality risk which exceeds that of 75- and 65-year-old individuals in the general population (Phases 1b/1c thresholds of the Centre for Disease Control Vaccine Rollout Recommendations) following COVID-19 infection during the first wave.
### Methods
We conducted a population-based retrospective cohort study using linked administrative datasets in Ontario, Canada. We identified all community-dwelling adults diagnosed with COVID-19 between January 1 and October 31st, 2020. Exposures of interest were age (modelled using restricted cubic splines) and the following conditions: major cardiovascular disease (recent myocardial infarction or lifetime history of heart failure); 2) diabetes; 3) hypertension; 4) recent cancer; 5) chronic obstructive pulmonary disease; 6) Stages $\frac{4}{5}$ chronic kidney disease (CKD); 7) frailty. Logistic regression in the full cohort was used to estimate the risk of 30-day mortality for 75- and 65-year-old individuals. Analyses were repeated after stratifying by sex and medical condition to determine the age at which 30-day morality risk in strata exceed that of the general population at ages 65 and 75 years.
### Results
We studied 52,429 individuals (median age 42 years; $52.5\%$ women) of whom 417 ($0.8\%$) died within 30 days. The 30-day mortality risk increased with age, male sex, and comorbidities. The 65- and 75-year-old mortality risks in the general population were exceeded at the youngest age by people with CKD, cancer, and frailty. Conversely, women aged < 65 years who had diabetes or hypertension did not have higher mortality than 65-year-olds in the general population. Most people with medical conditions (except for Stage 4–5 CKD) aged < 45 years had lower predicted mortality than the general population at age 65 years.
### Conclusion
The mortality risk in COVID-19 increases with age and comorbidity but the prognostic implications varied by sex and condition. These observations can support communication efforts and inform vaccine rollout in jurisdictions with limited vaccine supplies.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-023-15386-4.
## Background
It has been well-established that older age, male sex, and the presence of comorbidities are associated with higher mortality risks following diagnosis with the coronavirus disease of 2019 (COVID-19) [1–12]. Given COVID-19 incidence and mortality, and the precarious supply of COVID-19 vaccines globally [13–18], prioritization schemes will continue to be needed to triage vaccine delivery in early stages to people at the highest risk of death. When COVID-19 vaccines were first introduced in the United States, the Centre for Disease Control (CDC) Vaccine Rollout Recommendations [19] placed people aged 75 years and older in the Phase 1b stage, while Phase 1c extended eligibility to people aged 65–74 years and younger individuals with “underlying medical conditions which increase the risk of serious, life-threatening complications from COVID-19”.
However, this CDC approach treats “adults of any age” as being at increased risk of severe illness [20]. Thus, it does not account for differential risk between conditions, sex, or the multiplicative impact of older age on adverse outcomes among people with underlying medical conditions [21–26]. Other investigators have developed sophisticated risk prediction algorithms for mortality COVID-19 diagnosis [9, 27–32], but these do not lend themselves to simple implementation on a large scale by jurisdictions for vaccine prioritization. Furthermore, vaccine hesitancy remains an important stumbling block for vaccination in jurisdictions with adequate vaccine supplies. Unfortunately, the sociodemographic risk factors for chronic disease in the young overlap substantially with predictors of vaccine hesitancy [33–40]. Communication of risk for younger individuals can be hindered by lower absolute event rates, while relative risks can be harder to appreciate [41–45]. This has galvanized the development of alternate approaches for communication of risk for preventative intervention in younger patients [46–49].
Given these shortcomings, it would be desirable to provide relatively simple means to communicate how the risk of dying after being diagnosed with COVID-19 varies by age and sex for unvaccinated people living with comorbidity. Accordingly, we conducted a population-based cohort study of community-dwelling adults who developed COVID-19 before the availability of vaccines to quantify the incremental risk for death associated with underlying medical conditions as a function of age and sex. We specifically focused on cardiovascular disease, diabetes, hypertension, cancer, chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD) and frailty. We hypothesized that chronic medical conditions could elevate the risk of some, but not all, younger individuals to equal that of individuals aged > 65 years.
## Study design and population
Residents of Ontario (Canada’s most populous province) receive universal coverage for essential physician services and hospital-based care through the Ontario Health Insurance Plan (OHIP). This facilitates the conduct of population-based cohort studies using administrative health datasets that are linked using unique encoded identifiers and are analyzed at ICES (formerly Institute for Clinical Evaluative Sciences). Multiple algorithms have been validated to ascertain medical diagnoses using these administrative databases [50]. ICES is an independent, non-profit research institute funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care (MLTC), and a prescribed entity under Ontario’s Personal Health Information Protection Act (PHIPA), Sect. 45 of PHIPA. As such, the use of the data in this project is authorized under Sect. 45, approved by ICES’ Privacy and Legal Office, exempt from Research Ethics Board review, and does not require patient consent. All methods were carried out in accordance with locally relevant guidelines and regulations.
The Ontario Laboratories Information System (OLIS) was used to identify individuals aged ≥ 18 years with a positive reverse-transcription SARS-CoV-2 polymerase chain reaction (RT-PCR) test in Ontario between January 1 and October 31st, 2020, prior to the availability of vaccines. For people with more than one positive test, we retained the first positive test. The index date was that of collection of the qualifying SARS-CoV-2 swab. We excluded people with missing/invalid key data (age, sex, OHIP number), non-Ontario residents, OHIP coverage < 1 year before the SARS-CoV-2 test (for ascertainment of medical history), or an index positive SARS-CoV-2 test that was collected on a date when the individual was hospitalized (to limit our study to outpatients). We also excluded 5740 long-term care (LTC) residents (minimum age 20 years; maximum age 107 years) since they are already prioritized in the highest risk category globally (e.g., Phase 1a of the CDC framework). The remaining patients constituted our cohort of community-dwelling outpatients with COVID-19.
Our primary exposure was age. We also studied underlying medical conditions that have been shown to increase mortality risk in COVID-19 [1–12], affect a substantial proportion of the population, and are objective enough to be implemented by governments in vaccine prioritization policies: [1] major cardiovascular disease, defined as a recent (in past 5 years) myocardial infarction [51] or lifetime history of heart failure [52]; [2] diabetes [53]; [3] hypertension [54]; [4] cancer diagnosed within 5 years [55]; [5] COPD [56]; and [6] Stages 4/ 5 CKD (defined as dialysis-dependence or estimated glomerular filtration rate (eGFR) < 30ml/min/m2) [57]. For the analysis of CKD, we excluded individuals who were not dialysis-dependent and did not have creatinine measurements in the 2 years before contracting COVID-19. We also studied the Johns Hopkins ACG System binary frailty indicator [58] as a marker of global illness that can be applied widely across multiple health systems.
## Outcome
The primary outcome was death within 30 days of the positive SARS-Cov-2 test; this follow-up period after a positive PCR test is expected to capture most deaths directly attributable to COVID-19 [59, 60] while decreasing the likelihood of incorporating deaths due to other illnesses (which may be more likely for older patients with comorbidities).
## Statistical analysis
We fit univariable logistic regression models using Firth’s penalized likelihood approach to address potential bias in parameter estimates due to small sample and outcomes sizes in some age/condition strata [61, 62]. In these models, age was the only predictor, and modelled using a restricted cubic spline with knots at the 5th, 35th, 65th, and 95th percentiles, as suggested by Harrell to account for the non-linear relationship between age and our outcome [63]. Since separate curves are fit to each segment (i.e., range of ages), the model better reflects the relationship between age and death. The fitted model was used to estimate the risk of death within 30 days for 75- and 65-year-old individuals. These two probabilities were used as benchmarks against which to compare other subjects, since these are the age cut-offs used in Phases 1b and 1c respectively of the CDC’s COVID-19 Vaccine Rollout Recommendations. We then repeated the same logistic regression analyses, this time stratifying the cohort by sex and presence of the underlying medical conditions described above [62]. For each sex/comorbidity stratum, we determined the predicted risk of death within 30 days at all ages and identified the age at which individuals with the medical condition exceed the predicted benchmarks risk in the general population at age 65 years and 75 years. The age at which the benchmark risks were crossed were rounded up to the next integer for ease of presentation. All analyses were performed using SAS Enterprise Guide 7.1 (SAS Institute Inc., Cary, NC).
## Results
We studied 52,429 community-dwelling individuals who tested positive for SARS-CoV-2 between January 1 and October 31st, 2020 (Tables 1and Supplemental Fig. 1). Median age of our study population was 42 years [minimum, Q1, Q3, maximum 18, 29, 56, 104] years with 5,962 and 2,596 individuals 65- and 75 years and older, respectively; and 27,535 [$52.5\%$] were women. A total of 1,185 individuals ($2.3\%$) had major cardiovascular disease, 7,336 ($14.0\%$) had been diagnosed with diabetes, 12,275 ($23.4\%$) with hypertension, 966 ($1.8\%$) with recent cancer, and 2,466 ($4.7\%$) with COPD. Of 34,724 people ($66.2\%$ of cohort) whose CKD status could be ascertained, 350 ($0.7\%$) were classified as having advanced CKD, of whom 146 ($0.3\%$) were dialysis dependent. Using the Johns Hopkins indicator, 1,794 individuals ($3.4\%$) were classified as frail.
Table 1Baseline characteristics of study population by sexCharacteristicWomenMenOverallStandardized differencep-valueN = 27,535 $$n = 24$$,894 $$n = 52$$,429Age in years, median (Q1, Q3)43 [29, 56]42 [29, 56]42 [29, 56]0.03< 0.001Major cardiovascular disease (recent myocardial infarction* or lifetime history of heart failure)552 ($2.0\%$)633 ($2.5\%$)1,185 ($2.3\%$)0.04< 0.001Hypertension6,288 ($22.8\%$)5,987 ($24.0\%$)12,275 ($23.4\%$)0.030.001Diabetes3,679 ($13.4\%$)3,657 ($14.7\%$)7,336 ($14.0\%$)0.04< 0.001Recent cancer*563 ($2.0\%$)403 ($1.6\%$)966 ($1.8\%$)0.03< 0.001Chronic obstructive pulmonary disease (COPD)1,236 ($4.5\%$)1,230 ($4.9\%$)2,466 ($4.7\%$)0.020.015Stage $\frac{4}{5}$ chronic kidney disease (CKD)†167 ($0.6\%$)183 ($0.7\%$)350 ($0.7\%$)0.02<0.001Undetermined CKD status8,070 ($29.3\%$)9,635 ($38.7\%$)17,705 ($33.8\%$)0.2Chronic dialysis55 ($0.2\%$)91 ($0.4\%$)146 ($0.3\%$)0.03< 0.001Frailty‡1,020 ($3.7\%$)774 ($3.1\%$)1,794 ($3.4\%$)0.03< 0.001* Hospitalization (for myocardial infarction) or diagnosis (for cancer) within the prior 5 years† Defined as receiving chronic dialysis or eGFR < 30ml/min/m2* using the most recent serum creatinine result up to 2 years prior to positive SARS-CoV-2 test‡ Defined using the Johns Hopkins frailty indicator from The Johns Hopkins ACG ® System Version 10.0 Within 30 days following their positive SARS-CoV-2 test, 417 ($0.8\%$) people died, with the greatest death rates for both men and women among those with hypertension (78,$7\%$ and $85.9\%$, respectively) or classified as frail ($45.8\%$ and $60.4\%$, respectively) (Table 2). The predicted risk of death in the general population was $1.1\%$ at age 65 years and $3.4\%$ at 75 years. The estimated 30-day mortality risk increased with age ($1.7\%$ among 65–74 year olds, and $11.5\%$ among those 75 years and older), and was generally higher in men (Fig. 1). After stratifying by presence of underlying medical conditions, the estimated risk of death was generally higher in those with a comorbidity.
Table 2Baseline characteristics of study population relative to sex and mortality status at 30 days following the qualifying positive SARS-CoV-2 testCharacteristicMenWomenNo death within 30 days ($$n = 24$$,669)Death within 30 days ($$n = 225$$)Mortality rate (%) in men with conditionNo death within 30 days ($$n = 27$$,343)Death within 30 days ($$n = 192$$)Mortality rate (%) in women with conditionAge in years, median (Q1, Q3)41 [29, 56]81 [68, 88]-43 [29, 56]86 [79, 93]-Major cardiovascular disease (recent myocardial infarction* or lifetime history of heart failure)569 ($2.3\%$)64 ($28.4\%$)$10.1\%$496 ($1.8\%$)56 ($29.2\%$)$10.1\%$Diabetes3,558 ($14.4\%$)99 ($44.0\%$)$2.7\%$3,620 ($13.2\%$)59 ($30.7\%$)$1.6\%$Hypertension5,810 ($23.6\%$)177 ($78.7\%$)$3.0\%$6,123 ($22.4\%$)165 ($85.9\%$)$2.6\%$Recent cancer*380 ($1.5\%$)23 ($10.2\%$)$5.7\%$544 ($2.0\%$)19 ($9.9\%$)$3.4\%$Chronic obstructive pulmonary disease (COPD)1,165 ($4.7\%$)65 ($28.9\%$)$5.3\%$1,178 ($4.3\%$)58 ($30.2\%$)$4.7\%$Stage $\frac{4}{5}$ chronic kidney disease (CKD)†156 ($0.6\%$)27 ($12.0\%$)$14.8\%$139 ($0.5\%$)28 ($14.6\%$)$16.8\%$Undetermined CKD status9,629 ($39.0\%$)6 ($2.7\%$)$0.1\%$8,063 ($29.5\%$)7 ($3.6\%$)$0.1\%$Chronic dialysis80 ($0.3\%$)11 ($4.9\%$)$12.1\%$48 ($0.2\%$)7 ($3.6\%$)$12.7\%$Frailty‡671 ($2.7\%$)103 ($45.8\%$)$13.3\%$904 ($3.3\%$)116 ($60.4\%$)$11.4\%$* Hospitalization (for myocardial infarction) or diagnosis (for cancer) within the prior 5 years† Defined as receiving chronic dialysis or eGFR < 30ml/min/m2* using the most recent serum creatinine result up to 2 years prior to positive SARS-CoV-2 test‡ Defined using the Johns Hopkins frailty indicator from The Johns Hopkins ACG ® System Version 10.0 Fig. 1Predicted risk of 30-day death by age and sex. The shaded region indicates $95\%$ confidence intervals. The blue curves depict estimated risk in men, while the red curves depict risk in women Figures 2 and 3 illustrate the differences in risk of death at 30 days for men and women with and without underlying medical conditions. Overall, both men and women with a medical condition had higher 30-day death rates than those without, though a few exceptions were observed (i.e., among middle-aged men with versus without COPD and older women with versus without diabetes).
Fig. 2Predicted risk of 30-day mortality for men by presence of underlying medical conditions. Horizontal lines indicate the predicted risk of death in the general population at age 65 years and 75 years. Vertical lines highlight the age at which the risk for men with the medical conditions is equivalent to the general population aged 65- or 75- years Fig. 3Predicted risk of 30-day mortality for women by presence of underlying medical conditions. Horizontal lines indicate the predicted risk of death in the general population at ages 65 years and 75 years. Vertical lines highlight the age at which the risk for women with the medical conditions is equivalent to the general population aged 65- or 75- years Table 3 summarizes the age (rounded up to the next integer) at which men and women with a particular comorbidity had the same predicted risk of death at 30-days as the general population aged 65 years and 75 years of age. The benchmark risks were exceeded at the earliest age by people with CKD, cancer, and frailty. For example, the risk of death at 30 days in men with CKD at age < 40 years and women with CKD at age 45 years equalled the risk of 75-year-olds from the general population. In contrast, the risk of death at 30 days for women aged < 65 years who had diabetes or hypertension was not higher than the 30-day mortality risk of 65-year-olds in the general population.
Table 3Age at which the risk of death in men and women with an underlying medical condition exceeds the risk in the general population aged 65- and 75-years of age. The presented age has been rounded up to the next integerAge with equivalent risk to a 65-year-old in the general populationAge with equivalent risk to a 75-year-old in the general population Major cardiovascular disease (recent myocardial infarction* or lifetime history of heart failure) Men5966Women5062 Diabetes Men6171Women6778 Hypertension Men6371Women6775 Recent cancer* Men< 40‡66Women4668 Chronic obstructive pulmonary disease (COPD) Men6571Women6371 Stage $\frac{4}{5}$ chronic kidney disease (CKD)† Men< 40‡< 40‡Women< 40‡45 Frailty, as defined by Johns Hopkins indicator Men3965Women5069* Hospitalization (for myocardial infarction) or diagnosis (for cancer) within the prior 5 years† Defined as receiving chronic dialysis or eGFR < 30ml/min/m2* using the most recent serum creatinine result up to 2 years prior to positive SARS-CoV-2 test‡ Suppressed due to the small population size or number of events in the sex/condition stratum
## Discussion
In this population-based cohort study, we determined the estimated age at which a community-dwelling man or woman with underlying medical conditions will exceed the 30-day mortality risk of the typical person included in Phase 1b or 1c of the CDC recommendations based on age alone. While the risk of death after COVID-19 was higher in people with underlying medical conditions, the prognostic implications varied by sex and condition. The risk of 30-day death was generally higher in men than in women. The increase in risk incurred by the presence of CKD and recent cancer was higher than isolated diabetes or hypertension. Thus, it would be inappropriate to treat hypertension and diabetes as being equivalent to the other comorbidities studied when triaging vaccine rollout. The Johns Hopkins ACG System binary frailty indicator was a useful composite measure which can be applied by large health systems using administrative data for identification of individuals at higher risk for death with COVID-19 despite younger age.
Numerous publications have demonstrated that individuals with comorbidities are at higher risk for adverse outcomes following COVID-19 infection [1–12]. The comorbidities studied in our analysis have been among the most studied and most consistently linked to higher mortality with COVID-19 [1, 2, 6, 11]. In meta-analyses of studies prior to vaccine availability, reported risk estimates have ranged from 3.07 to 4.90 for CKD, 1.47 to 1.90 for cancer, and 2.25–3.05 for cardiovascular disease [1, 6, 11]. Male sex has also been consistently shown to increase the risk of adverse outcomes following COVID-19 infection [5, 8, 9], which may be related, in part, to the X-linked nature of the SARS-CoV-2 receptor [64]. The combination of multiple comorbidities increase risk even further, and this has been utilized to develop comprehensive models which predict the risk of death following COVID-19 infection with high accuracy [9, 27–32]. In a study of Veteran *Affairs data* in the United States, a model of nine risk factors including age, sex, diabetes, CKD and heart failure demonstrated a discriminative ability of $83.4\%$ compared to $74.0\%$ in a model using age alone, as in the CDC approach [65]. Furthermore, once population vaccination rates reach $50\%$, vaccine prioritization based on the model was estimated to result in $21.5\%$ fewer deaths than prioritization on the CDC phased approach [65].
However, while these studies have provided valuable information about factors associated with increased COVID-19 mortality, we are not aware of studies reporting their age-equivalent mortality risk. Additionally, the inferences from these studies about the impact of comorbidity and sex on the risk of dying from COVID-19 at different ages are challenging to communicate to the public in a transparent and easily understood manner [41–43]. The communication gap is expected to be largest for demographic groups that are at higher risk for COVID-19 and more susceptible to misinformation [66–68]. The profound impact of some comorbidities on mortality is important to communicate to younger individuals with vaccine hesitancy, which remains an important issue among people with comorbidity, particularly those living in communities in which medical comorbidity is more likely to emerge at a younger age [33–40]. Provision of visual cues and expressing risk in terms of relative age (e.g., “heart age”) improves communication of cardiovascular risk for younger individuals and is more likely to promote behaviour change than traditional methods of communicating cardiovascular risk [42–49, 69]. We present our data in an analogous approach, which we believe can be helpful for vaccine-hesitant individuals with comorbidities that confer higher risk of adverse outcomes following COVID-19.
Importantly, we showed that regardless of underlying medical condition, the mortality risk rises substantially with age, meaning that age should continue to be a key factor in the triage process. The presence of most underlying medical conditions in individuals under the age of 45 years did not elevate their risk to that of the general population at age 65 years, with the notable exception of Stage 4–5 CKD, and among men, recent cancer and frailty. In other words, the protective effects of younger age persisted despite the presence of underlying medical conditions for most people aged < 45 years.
Several limitations to our study are noted. By design, we adopted an approach prioritizing parsimony and simplicity by focusing on the presence/ absence of comorbidity without accounting for the severity of disease. Although $12.8\%$ of our study population had more than one of the conditions studied, we also did not study the combined impact of multiple comorbidities outside the frailty indicator for the same reasons. As a result, in our comparisons of risk among men and women with specific conditions versus without, it is possible that some differences may be attenuated because those without the condition have other conditions which also increase their risk. Another limitation was the small event counts for sex-specific strata of patients with cancer and CKD, which decreases precision of our estimates for these groups. Our results also do not apply to LTC residents, who should already be considered at highest risk regardless of age or comorbidities. Finally, since our study population was identified during the first eight months of the pandemic and prior to the appearance of variants of concern, such as Delta and Omicron, the absolute risk estimates by age may not be applicable to patients diagnosed with these newer variants; however, the higher mortality risks associated with CKD, cancer and frailty compared with hypertension and diabetes are likely applicable beyond the first wave though the relative risk associated with variant types differs.[70, 71] Additionally, the relative ages reported from our analyses remain valuable for communication of relative risks.
## Conclusion
The mortality risk in COVID-19 increases with age and comorbidity but the prognostic implications varied by sex and condition. The risk was generally higher for men, and the increase in risk associated with CKD, cancer, and frailty was higher than what was observed with hypertension and diabetes. We hope to improve communication by presenting the combined impact of patient age, sex, and comorbidity using visual scales and reporting the “equivalent age” at which individuals would have the same mortality risk as the average 65- and 75-year-old with COVID-19. We believe these observations can inform vaccine rollout and support communication efforts in jurisdictions with limited vaccine supplies.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1
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|
---
title: Efficacy of Low-Dose Aspirin in Colorectal Cancer Risk Prevention is Dependent
on ADH1B and ALDH2 Genotype in Japanese Familial Adenomatous Polyposis Patients
authors:
- Kanae Mure
- Hideki Ishikawa
- Michihiro Mutoh
- Mano Horinaka
- Takahiro Otani
- Sadao Suzuki
- Keiji Wakabayashi
- Toshiyuki Sakai
- Yasushi Sato
- Yasushi Sato
- Hisashi Doyama
- Masahiro Tajika
- Shinji Tanaka
- Takahiro Horimatsu
- Yoji Takeuchi
- Hiroshi Kashida
- Jun Tashiro
- Yasumasa Ezoe
- Takeshi Nakajima
- Hiroaki Ikematsu
- Shinichiro Hori
- Tetsuji Takayama
- Yoshio Ohda
journal: Cancer Research Communications
year: 2022
pmcid: PMC10010329
doi: 10.1158/2767-9764.CRC-22-0088
license: CC BY 4.0
---
# Efficacy of Low-Dose Aspirin in Colorectal Cancer Risk Prevention is Dependent on ADH1B and ALDH2 Genotype in Japanese Familial Adenomatous Polyposis Patients
## Abstract
Aspirin has gained great attention as a cancer preventive agent. Our previous study revealed that the low-dose aspirin prevents colorectal tumor recurrence in Japanese patients with colorectal adenomas and/or adenocarcinomas, whereas aspirin increases risks in smokers and has no effects on regular drinkers. Our recent study revealed that aspirin reduces polyp growth in Japanese patients with familial adenomatous polyposis (FAP). In this study, we have studied the association of genotypes of alcohol metabolizing enzymes (ADH1B and ALDH2) on aspirin's efficacy of suppressing polyp growth (≥5 mm) in a total of 81 Japanese patients with FAP. Our study revealed that aspirin showed significant preventive effects for patients with ADH1B-AA and AA+GA types [OR = 0.21; $95\%$ confidence interval (CI), 0.05–0.95, and OR = 0.31; $95\%$ CI, 0.10–0.95, respectively], and for patients with ALDH2-GG and GG+GA types (OR = 0.10; $95\%$ CI, 0.01–0.92, and OR = 0.29; $95\%$ CI, 0.09–0.94, respectively), but not for patients with ADH1B-GG and GA+GG types, and ALDH2-AA and GA+AA types. In addition, substantial preventive effects of aspirin were seen for patients with ADH1B-AA type who do not drink regularly (<3 times/week, OR = 0.11; $95\%$ CI, 0.02–0.78), where a statistically significant interaction between aspirin and ADH1B was observed (Pinteraction = 0.036). Results from this exploratory study strongly indicate that aspirin is beneficial in prevention of polyp growth for patients with FAP with ADH1B-AA and AA+GA types, and ALDH2-GG and GG+GA types. Taken together, we propose ADH1B and ALDH2 as candidate markers for the personalized prevention by aspirin.
### Significance:
Aspirin is beneficial to patients with FAP with ADH1B-AA and AA+GA types or ALDH2-GG and GG+GA types. ADH1B and ALDH2 genotypes can be the markers for the personalized prevention of colorectal cancer by aspirin.
## Introduction
Aspirin has gained great attention as a cancer preventive agent. Recently, we reported that low-dose aspirin prevents colorectal polyp growth in Japanese patients with familial adenomatous polyposis (FAP) without a history of colectomy. We conducted a randomized double-blind, placebo-controlled trial with a 2 × 2 factorial design to determine the individual and concerted effects of low-dose aspirin and mesalazine, a NSAID (J-FAPP Study IV). Aspirin has prevented polyp growth (OR = 0.37; $95\%$ confidence interval (CI), 0.16–0.86] but mesalazine showed no effect [1]. FAP is an autosomal dominant syndrome primarily caused by germline mutations in adenomatous polyposis coli (APC). Somatic mutations in APC have been observed in $80\%$ of colorectal adenomas and carcinomas, therefore FAP has been considered as a model for colorectal cancer [2]. FAP and sporadic colorectal cancer share risk factors such as genetic alterations and lifestyle factors (e.g., smoking and heavy alcohol drinking). Our previous study revealed that the low-dose aspirin prevents recurrence of colorectal tumor in Japanese patients with colorectal adenoma and/or adenocarcinomas (J-CAPP Study; ref. 3). In addition, aspirin increases polyp recurrence risks in smokers but has no effects on regular drinkers (≥3 times/week). Ethanol is oxidized by alcohol dehydrogenase 1B (ADH1B) to produce acetaldehyde, and acetaldehyde is further oxidized to acetate by aldehyde dehydrogenase 2 (ALDH2; ref. 4). A previous study has been shown that the enzymatic activities of ADH1B and ALDH2 are influenced by genotypes of ADH1B (rs1229984, A/G) and ALDH2 (rs671, G/A; ref. 5). ADH1B-AA rapidly metabolizes ethanol to acetaldehyde, whereas ADH1B-GG metabolizes slowly. ALDH2-GG metabolizes acetaldehyde, but ALDH2-AA is inactive. Interestingly, ADH1B-AA+GA and ALDH2-GA+AA genotypes are exclusively found in eastern Asian populations and are related to the frequency of upper digestive cancer [6]. To date, the relationships of these genotypes and colorectal cancer have not been established.
In this study, we have examined the correlation of ADH1B (rs1229984) and ALDH2 (rs671) genotypes with the aspirin's efficacy on preventing polyp growth in the patients with FAP, where their drinking status was also considered. *Several* genetic variants that affect the efficacy of aspirin have been reviewed previously [7, 8]. However, there has been no study that investigates the effects of ADH1B or ALDH2 genotypes on aspirin's efficacy.
## Trial Design and Patients’ Description
Patients were rerecruited from the single clinic which attended to the previous multicenter ($$n = 11$$; located throughout Japan), randomized, double-blind, placebo-controlled clinical trial used a 2 × 2 factorial design (J-FAPP Study IV; ref. 1). Details of trials of J-FAPP Study IV was described previously [1]. Briefly, the effects of administrating low-dose enteric-coated aspirin tablets (Bayaspirin, 100 mg/day) and/or mesalazine (Pentasa, 2 g/day) for 8 months were evaluated on inhibiting the growth of colorectal polyps in Japanese patients with FAP. Colorectal polyps (≥5 mm) were removed endoscopically prior to the trial. Patients took low-dose enteric-coated aspirin tablets (100 mg/tablet) and their placebo counterparts (Bayer Yakuhin, Ltd.) and/or mesalazine tablets (250 mg/tablet) and their placebo counterparts (Kyorin Pharmaceutical Co., Ltd.) until 1 week before the 8-month colonoscopy.
Polyps in ≥5.0 mm size that were detected as twice the diameter of the polypectomy snare (ZEMEX Co.) was removed and collected during colonoscopy for histologic examination. Patients with uncurable cancer; taking antithrombotic or anticoagulant agents; a history of stroke, including transient ischemic attack; and other diseases were excluded from the J-FAPP Study IV. Among patients who participated in the J-FAPP Study IV, all patients belonged to the single-center clinic were rerecruited and provided written informed consent prior to this study. This study followed the principles stated in the Declaration of Helsinki and was approved by the ethical committees for Analytical Research on the Human Genome of Wakayama Medical University (approval no. 117).
## Questionnaire and Genotyping
Patients were requested to provide information such as height, body weight, medical history, smoking status, alcohol consumption, and intake of any NSAID prior to the J-FAPP Study IV. The smoking habits were categorized into two groups (yes: currently smoking, no: never and formerly smoking). Alcohol drinking habits were categorized into two groups (regularly drinking: ≥3 times per week, nonregularly drinking: otherwise).
Venous blood was collected in a heparinized vacuum blood collection tube and blotted onto a Whatman FTA card (FTA elute microcard, GE Healthcare UK Limited). Genomic DNA was extracted by using DNA Extract All Reagents Kit (Thermo Fisher Scientific) from the 2 mm punched out FTA sample. TaqMan SNP Assays used in this study were purchased from Thermo Fisher Scientific (ADH1B, rs1229984, C_2688467_20, ALDH2, rs671, C_11703892_10). Genotypes of ADH1B and ALDH2 were examined on Step One Plus Real-Time PCR systems (Applied Biosystems).
## Statistical Analysis
Statistical analyses were performed using R version 4.0.3 [9]. Differences in age among four groups were analyzed by one-way ANOVA. Differences in age between administrating aspirin and placebo groups were analyzed by Student t test. Differences in the categorical variables such as sex, alcohol drinking, and smoking habits were analyzed by Fisher exact test. The effects of aspirin on polyp growth were analyzed by logistic regression analyses adjusted for age, sex, alcohol drinking and smoking habits, and mesalazine intake. ADH1B-GA+GG and ALDH2-GA+AA were used as the dominant model, and ADH1B-AA+GA and ALDH2-GG+GA were used as the recessive model. Multiplicative interactions between aspirin and ADH1B or ALDH2 genotypes were assessed in the logistic regression analyses. P values of <0.05 were considered statistically significant.
## Data Availability
The data generated in this study are available upon reasonable request from the corresponding author.
## Results
The basic characteristics of patients in this study are summarized in Table 1. There were no significant differences among groups, except a significant difference was seen in the distribution of regular drinking among the ALDH2 genotype. The majority of ALDH2-GA+AA types do not drink alcoholic beverages regularly (Supplementary Table S1).
**TABLE 1**
| Unnamed: 0 | Placebo: Placebo | Aspirin: Placebo | Placebo: Mesalazine | Aspirin: Mesalazine | P b | Placebo | Aspirin | P c |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| N | 19 | 18 | 22 | 22 | | 41 | 40 | |
| Age (years), mean (SD) | 33.2 (8.5) | 38.8 (12.6) | 36.8 (9.1) | 35.6 (11.5) | 0.424 | 35.1 (8.9) | 37.1 (12.0) | 0.401 |
| Sex, male (%) | 10 (52.6) | 10 (55.6) | 11 (50.0) | 12 (54.6) | 0.990 | 21 (51.2) | 22 (55.0) | 0.825 |
| Regularly drinking, yes (%)a | 5 (26.3) | 5 (27.8) | 3 (13.6) | 3 (13.6) | 0.536 | 8 (19.5) | 8 (20.0) | 1.000 |
| Smoking | 3 (15.8) | 2 (11.1) | 1 (4.6) | 1 (4.6) | 0.549 | 4 (9.8) | 3 (7.5) | 1.000 |
| ADH1B | ADH1B | ADH1B | ADH1B | ADH1B | ADH1B | ADH1B | ADH1B | ADH1B |
| AA | 15 (79.0) | 13 (72.2) | 15 (68.2) | 13 (59.1) | 0.396 | 30 (73.2) | 26 (65.0) | 0.729 |
| GA | 3 (15.8) | 4 (22.2) | 7 (31.8) | 9 (40.9) | | 10 (24.4) | 13 (32.5) | |
| GG | 1 (5.3) | 1 (5.6) | 0 (0.0) | 0 (0.0) | | 1 (2.4) | 1 (2.5) | |
| Dominant, GA+GG | 4 (21.1) | 5 (27.8) | 7 (31.8) | 9 (40.9) | 0.567 | 11 (26.8) | 14 (35.0) | 0.477 |
| Recessive, AA+GA | 18 (94.7) | 17 (94.4) | 22 (100.0) | 22 (100.0) | 0.348 | 40 (97.6) | 39 (97.5) | 1.000 |
| ALDH2 | ALDH2 | ALDH2 | ALDH2 | ALDH2 | ALDH2 | ALDH2 | ALDH2 | ALDH2 |
| GG | 9 (47.4) | 8 (44.4) | 14 (63.6) | 11 (50.0) | 0.580 | 23 (56.1) | 19 (47.5) | 0.266 |
| GA | 9 (47.4) | 7 (38.9) | 8 (36.4) | 9 (40.9) | | 17 (41.5) | 16 (40.0) | |
| AA | 1 (5.3) | 3 (16.7) | 0 (0.0) | 2 (9.1) | | 1 (2.4) | 5 (12.5) | |
| Dominant, GA+AA | 10 (52.6) | 10 (55.6) | 8 (36.4) | 11 (50.0) | 0.621 | 18 (43.9) | 21 (52.5) | 0.508 |
| Recessive, GG+GA | 18 (94.7) | 15 (83.3) | 22 (100.0) | 20 (90.9) | 0.220 | 40 (97.6) | 35 (87.5) | 0.109 |
The logistic regression analyses (adjusted for age, sex, alcohol drinking, smoking, mesalazine intake, and ADH1B and ALDH2 genotypes in the additive and recessive models) indicate that aspirin intake correlates with significantly reduced risks of poly growth (OR = 0.29; $95\%$ CI, 0.09–0.89; as shown in Table 2). ALDH2 genotype showed increased risk in the additive model (OR = 2.62; $95\%$ CI, 1.05–6.50). When drinking status was considered, aspirin intake showed no effects in the nonregular drinkers in any models. ALDH2 genotype showed increased risk in the additive model (OR = 2.93; $95\%$ CI, 1.12–7.71). Significant interaction between aspirin and ADH1B genotype was observed in patients who do not drink regularly (Pinteraction = 0.036). Detail results of all covariates were presented in Supplementary Table S2. Aspirin intake and ADH1B or ALDH2 genotypes showed no significant effects in regular drinkers (Supplementary Table S3).
**TABLE 2**
| (a) Additive model | (a) Additive model.1 | (a) Additive model.2 | (a) Additive model.3 | (a) Additive model.4 | (a) Additive model.5 | (a) Additive model.6 |
| --- | --- | --- | --- | --- | --- | --- |
| | All | All | All | Nonregular drinkersa | Nonregular drinkersa | Nonregular drinkersa |
| | ORb | 95% CI | P int c | OR | 95% CI | P int |
| Aspirin | 0.29 | 0.09–0.89 | | 0.35 | 0.09–1.29 | |
| ADH1B | 1.51 | 0.57–3.99 | 0.664 | 1.09 | 0.32–3.69 | 0.036 |
| ALDH2 | 2.62 | 1.05–6.50 | 0.184 | 2.93 | 1.12–7.71 | 0.181 |
| (b) Dominant model | (b) Dominant model | (b) Dominant model | (b) Dominant model | (b) Dominant model | (b) Dominant model | (b) Dominant model |
| | All | All | All | Nonregular drinkers | Nonregular drinkers | Nonregular drinkers |
| | OR | 95% CI | P int | OR | 95% CI | P int |
| Aspirin | 0.36 | 0.12–1.04 | | 0.46 | 0.13–1.55 | |
| ADH1B-GA+GG | 1.14 | 0.37–3.52 | 0.596 | 0.81 | 0.20–3.23 | 0.054 |
| ALDH2-GA+AA | 2.18 | 0.72–6.66 | 0.099 | 2.60 | 0.78–8.70 | 0.108 |
| (c) Recessive model | (c) Recessive model | (c) Recessive model | (c) Recessive model | (c) Recessive model | (c) Recessive model | (c) Recessive model |
| | All | All | All | Nonregular drinkers | Nonregular drinkers | Nonregular drinkers |
| | OR | 95% CI | P int | OR | 95% CI | P int |
| Aspirin | 0.29 | 0.09–0.92 | | 0.31 | 0.08–1.16 | |
| ADH1B-AA+GA | | | | | | |
| ALDH2-GG+GA | 0.14 | 0.02–1.19 | 0.143 | 0.15 | 0.02–1.30 | 0.111 |
When the effects of aspirin intake were analyzed by genotypes, significant reducing risks in patients with ADH1B-AA and AA+GA types (OR = 0.21; $95\%$ CI: 0.05–0.95, and OR = 0.31; $95\%$ CI: 0.10–0.95, respectively) were observed in the multivariate logistic analyses (Fig. 1). In patients with ALDH2-GG and GG+GA types, aspirin intake was also correlated with significant reducing risks (OR = 0.10; $95\%$ CI, 0.01–0.92, and OR = 0.29; $95\%$ CI, 0.09–0.94, respec-tively).
**FIGURE 1:** *Effects of ADH1B and ALDH2 genotypes on the aspirin's effects on suppressing growth of polyps 5 mm or larger. Crude ORs and 95% CIs for ADH1B genotypes (A), adjusted ORs and 95% CIs for ADH1B genotypes (B), crude ORs and 95% CIs for ALDH2 genotypes (C), adjusted ORs and 95% CIs for ALDH2 genotypes (D). Numbers appearing on the graph represent OR (black dot) and the 95% CI (bars). Adjusted ORs were estimated with adjustment for age, sex, alcohol drinking and smoking habits, and mesalazine intake in the logistic regression analyses. The y-axis is in logarithmic scale.*
Finally, subgroup analyses with regard to their drinking status were performed (Table 3). Aspirin intake showed significant reducing risks only in the ADH1B-AA type who do not drink alcoholic beverages regularly (OR = 0.11; $95\%$ CI, 0.02–0.78).
**TABLE 3**
| Unnamed: 0 | No | Yes | Total | ORa | 95% CI |
| --- | --- | --- | --- | --- | --- |
| Placebo | 20 | 13 | 33 | 1 | |
| Aspirin | 23 | 9 | 32 | 0.35 | 0.09–1.29 |
| ADH1B-AA | ADH1B-AA | ADH1B-AA | ADH1B-AA | ADH1B-AA | ADH1B-AA |
| | No | Yes | Total | OR | 95% CI |
| Placebo | 14 | 12 | 26 | | |
| Aspirin | 16 | 4 | 20 | 0.11 | 0.02–0.78 |
| ADH1B-GA+GG | ADH1B-GA+GG | ADH1B-GA+GG | ADH1B-GA+GG | ADH1B-GA+GG | ADH1B-GA+GG |
| | No | Yes | Total | OR | 95% CI |
| Placebo | 6 | 1 | 7 | | |
| Aspirin | 7 | 5 | 12 | 3.50 | 0.25–48.55 |
| ALDH2-GG | ALDH2-GG | ALDH2-GG | ALDH2-GG | ALDH2-GG | ALDH2-GG |
| | No | Yes | Total | OR | 95% CI |
| Placebo | 11 | 5 | 16 | | |
| Aspirin | 11 | 1 | 12 | 0.001 | <0.001–6.01 |
| ALDH2-GA+AA | ALDH2-GA+AA | ALDH2-GA+AA | ALDH2-GA+AA | ALDH2-GA+AA | ALDH2-GA+AA |
| | No | Yes | Total | OR | 95% CI |
| Placebo | 9 | 8 | 17 | | |
| Aspirin | 12 | 8 | 20 | 1.05 | 0.23–4.85 |
## Discussion
The direct association between aspirin and ADH1B and ALDH2 has only been seen as acute effects in in vitro study involving ethanol. After alcohol intake, aspirin inhibits ADH activities through noncompetitive fashion thereby increasing blood alcohol concentrations [10, 11], and ALDH2 through uncompetitive fashion [12]. In this exploratory study, a significant preventive effect of aspirin was detected in patients with FAP with ADH1B-AA and AA+GA types, ALDH2-GG and GG+GA types, and ADH1B-AA type who do not drink regularly. To our knowledge, this is the first study that assessed the effects of ADH1B and ALDH2 genotypes on efficacy of aspirin on suppressing polyp growth 5 mm or larger.
It is intriguing that a significant interaction between aspirin and ADH1B genotype was observed in patients who do not drink regularly, as well as aspirin showed a significant preventive effect on patients with ADH1B-AA without regularly drinking habit. These results imply that the aspirin's preventive effect is influenced by the ADH1B genotype most likely due to ADH1B metabolizing nontraditional substrates other than ethanol. Besides ethanol, ADH1B is known to oxidize endogenous aliphatic alcohol such as retinol and lipid peroxidation products that are associated with the development of colorectal cancer [13, 14]. Acetylation of Lys331 and Lys340 of ADH1B protein has been detected in high frequency for colorectal tumors that are paired with liver metastasis [15]. ADH1B is downregulated in colorectal cancer by myc [16], which is associated with hyperactivation of Wnt signaling. A lower expression of ADH1B at the mRNA level was also observed in adenomas compared with adjacent normal mucosa [14]. A recent study indicated that ADH1B is involved in the metabolic activity of adipose tissues that is associated with insulin resistance. Furthermore, in that study, ADH1B is linked to progression of type 2 diabetes mellitus which is a risk factor for colorectal cancer [17]. Taken together, these studies suggest that ADH1B plays an important role in the development of colorectal cancer in the absence of ethanol, that may relate to the aspirin's efficacy observed in our study.
In this study, significant effects of aspirin were also detected in patients with FAP with ALDH2-GG and GG+GA types, although ALDH2 showed increased risk in the additive model. Aspirin is known to inhibit COX-1 to suppress the production of arachidonic acid, and also reduce the extent of the iron-induced oxidative stress and lipid peroxidation and prevent release of toxic aldehydes (e.g., malondialdehyde and 4-hydroxynoneal, 4-HNE; ref. 18). Besides acetaldehyde derived from ethanol, ALDH2 also metabolizes these toxic aldehydes [19]. ALDH2 has shown to play a protective role in myocardial infarction via modulating the β-catenin/Wnt signaling [20]. It is highly possible that both aspirin and ALDH2 orchestrate against β-catenin/Wnt signaling. In addition, ALDH2-GA+AA genotype, which is exclusively highly populated in Eastern Asia, seems to have an important role against polyp growth in patients with FAP. To our knowledge there are no studies that accessed the effects of ALDH2 genotypes in patients with FAP to date. Further study is necessary to define the role of ALDH2 genotype in polyp growth.
There are some limitations in this study. First, because the sample size is small, we have not been able to access the interaction between aspirin and ADH1B in the recessive model. We also have not been able to investigate the effects of alcohol drinking. There is a caveat that we have possible selection bias in this study. Second, the detail information about duration of smoking or regularly alcohol drinking and other cofounders that might affect result was not available. We are currently preparing for a larger number of patients who are warranted to clarify the gene–environment interaction.
In conclusion, patients with FAP with ADH1B-AA and AA+GA types, and ALDH2-GG and GG+GA types can get benefit of aspirin's preventive effects on suppressing polyp growth. In other words, ADH1B and ALDH2 genotypes can be candidates for consideration of personalized prevention of colorectal cancer by aspirin.
## Authors’ Disclosures
K. Mure, H. Ishikawa, M. Mutoh, K. Wakabayashi, T. Sakai, and S. Tanaka report a grant from Japan Agency for Medical Research and Development during the conduct of the study. Y. Takeuchi reports personal fees from Olympus, Boston Scientific Japan, Daiichi-Sankyo, Miyarisan Pharmaceutical, Asuka Pharmacoceutical, AstraZeneca, EA Pharma, Zeria Pharmaceutical, Fujifilm, Kaneka Medical, and Kyorin Pharmaceutical outside the submitted work. No other disclosures were reported.
## Authors’ Contributions
K. Mure: Conceptualization, investigation, writing-original draft. H. Ishikawa: Resources, supervision, funding acquisition, investigation. M. Mutoh: Conceptualization, investigation. M. Horinaka: Conceptualization, investigation. T. Otani: Formal analysis. S. Suzuki: Formal analysis. K. Wakabayashi: Conceptualization. T. Sakai: Conceptualization. Y. Sato: Investigation. H. Doyama: Investigation. M. Tajika: Investigation. S. Tanaka: Investigation. T. Horimatsu: Investigation. Y. Takeuchi: Investigation. H. Kashida: Investigation. J. Tashiro: Investigation. Y. Ezoe: Investigation. T. Nakajima: Investigation. H. Ikematsu: Investigation. S. Hori: Investigation. T. Takayama: Investigation. Y. Ohda: Investigation.
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|
---
title: Ocular blood flow evaluation by laser speckle flowgraphy in pediatric patients
with anisometropia
authors:
- Takashi Itokawa
- Tadashi Matsumoto
- Saiko Matsumura
- Momoko Kawakami
- Yuichi Hori
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10010384
doi: 10.3389/fpubh.2023.1093686
license: CC BY 4.0
---
# Ocular blood flow evaluation by laser speckle flowgraphy in pediatric patients with anisometropia
## Abstract
### Purpose
To determine the differences and reproducibility of blood flow among hyperopic anisometropic, fellow, and control eyes.
### Methods
We retrospectively studied 38 eyes of 19 patients with hyperopic anisometropia (8.2 ± 3.0 years of age) and 13 eyes of eight control patients (6.8 ± 1.9 years). We measured the optic nerve head (ONH) and choroidal circulation using laser speckle flowgraphy (LSFG) and analyzed the choroidal mean blur rate (MBR-choroid), MBR-A (mean of all values in ONH), MBR-V (vessel mean), MBR-T (tissue mean), and sample size (sample), which are thought to reflect the ONH area ratio, area ratio of the blood stream (ARBS). We then assessed the coefficient of variation (COV) and intraclass correlation coefficient (ICC) and compared the differences among amblyopic, fellow, and control eyes in MBR, sample, and ARBS.
### Results
The ONH, MBR-A, MBR-T, and ARBS of amblyopic eyes were significantly higher than those of fellow eyes ($P \leq 0.01$, $P \leq 0.05$, and $P \leq 0.05$, respectively), and control eyes (MBR-A and ARBS, $P \leq 0.05$, for both comparisons). The sample-T (size of tissue component) in amblyopic eyes was significantly smaller than that in fellow and control eyes ($P \leq 0.05$). Blood flow in the choroid did not differ significantly between the eyes. The COVs of the MBR, sample, and ARBS were all ≤$10\%$. All ICCs were ≥0.7. The COVs of pulse waveform parameter fluctuation, blowout score (BOS), blowout time (BOT), and resistivity index (RI) in the ONH and choroid were ≤$10\%$.
### Conclusion
The MBR value of the LSFG in children exhibited reproducibility. Thus, this method can be used in clinical studies. The MBR values of the ONH in amblyopic eyes were significantly high. It has been suggested that measuring ONH blood flow using LSFG could detect the anisometropic amblyopic eyes.
## 1. Introduction
The increasing number of patients with refractive error, a known risk factor for amblyopia, has attracted worldwide attention [1]. If amblyopia due to refractive error is not treated at the appropriate time in childhood, good vision will not be achieved, resulting in amblyopia. The prevalence of amblyopia is related to income level, age, ethnicity, public awareness, and screening programs; specifically, amblyopia has shown higher prevalence in people with low income, aged over 20 or under 10 years, and located in Europe, Oceania and North America [2]. A recent study reported that amblyopia prevalence will increase from 99 million in 2019 to 221 million in 2040 [2]. Myopia is more common in Asia, while amblyopia is more common in Europe and North America. Although the distribution of refractive error varies across regions, management of childhood refractive error is becoming increasingly important [3]. The prevalence of amblyopia is reported to be 0.74–$4.3\%$, and the most frequent form is anisometropic amblyopia [3, 4].
Although anisometropic amblyopia occurs when differences in refractive values between eyes cause developmental disorders, resulting in one eye being amblyopic, it has been reported that there are also differences in ocular structure between the right and left eyes (5–7). In patients with anisometropic amblyopia, the amblyopic eye exhibits a shorter axial length, smaller optic nerve head (ONH) diameter, and thicker choroid [5, 6]. In previous studies pulsatile ocular blood flow (POBF) and color Doppler ultrasonography have been used to evaluate retrobulbar blood flow in anisometropic amblyopic eyes and reported that blood flow between amblyopic and fellow eyes did not differ significantly [8, 9].
Laser speckle flowgraphy (LSFG) is a non-invasive technique for measuring ocular blood flow (10–13), and the mean blur rate (MBR) is an indicator of ocular blood flow [14]. Many investigators have used LSFG to measure ocular blood flow in patients with glaucoma [14, 15], retinal vascular occlusion [16], or diabetic retinopathy [17]. LSFG has also been used to study the relationship between ocular blood flow and systemic diseases such as sleep apnea syndrome and chronic kidney disease [18, 19]. A recent study also reported that MBR and age were significantly correlated, and females have higher MBRs than males [20]. However, to the best of our knowledge, there are no published studies on blood flow using LSFG in patients with anisometropic amblyopia other than case reports [21]. We hypothesized that differences in ocular structure in patients with anisometropia also affect ocular hemodynamics. The purpose of the present study was to investigate the differences in ocular blood flow attributable to differences in ocular structure among amblyopic, contralateral, and control eyes after assessing the reproducibility of the LSFG measurement value.
## 2.1. Patients
This was a retrospective, cross-sectional observational study, and all patients visited Toho University Omori Medical Center between April 2015 and July 2022. This study was approved by the Ethics Committee of Toho University Omori Medical Center (#M22161) and registered in the University Hospital Medical Information Network (UMIN) (Registry No. UMIN000049300). This study adhered to the tenets of the Declaration of Helsinki. This study was presented on our institutional website and the right to opt out was provided to all parents. This retrospective study comprised 19 amblyopic eyes and their fellow eyes of 19 pediatric patients with hyperopic anisometropic amblyopia [12 males and seven females; 5–15 years of age; 8.2 ± 3.0 years (mean ± standard deviation (SD))] and 13 eyes of 8 pediatric control patients (five males and three females; 5–10 years of age; 6.8 ± 1.9). Hyperopic anisometropic amblyopia was defined as an interocular difference in the cycloplegic spherical equivalent (SE) of 2.00 diopters (D) between the amblyopic and fellow eyes. Moreover, patients with anisometropic amblyopic had a best-corrected visual acuity (BCVA) of $\frac{20}{20}$ or better vision due to treatment and did not have strabismus. Pediatric control patients who matched the axial length to the amblyopic eye were defined as those with a visual acuity of $\frac{20}{20}$ or better vision and did not have strabismus, anisometropic amblyopia, history of intraocular surgery, cataract, glaucoma, or retinal disorder. We excluded patients who were not cooperative enough for the LSFG examination.
## 2.2. LSFG examination
Although we used the LSFG-baby, a modified version of LSFG that enables measurements with the subject in a supine position, to measure blood flow at the ocular fundus in neonates [22, 23], LSFG was performed using the LSFG-NAVI™ (Nidek, Aichi, Japan) in this study. Before examination, the patient's pupils were dilated with $0.4\%$ tropicamide. The LSFG measurement method has been previously described in detail [10, 24]. The measurements were conducted three consecutive times, and the ONH and choroid areas were analyzed. All measurements were performed by the same examiner (TI). The LSFG used the MBR as an indicator of blood flow. After the margin of the ONH was manually set (Figure 1), we calculated the MBR and number of samples in the ONH using LSFG Analyzer software (v3.8.0.4; Softcare, Fukuoka, Japan). For the choroidal blood flow (MBR-choroid), a rectangular area (200 × 200 pixels) was analyzed between the fovea and ONH, avoiding large retinal vessels. We also divided the MBR-all (MBR-A: the mean of all values) in the ONH into the MBR-vessel (MBR-V: component of vessels in the ONH) and MBR-tissue (MBR-T: component of tissues in the ONH) and calculated these three parameters. We assumed that the number of measurement points reflects the ONH area and defined the number of measurement points in the ONH as the sample size, which is equal to the pixel size. We divided sample-all (Sample-A: the mean of all sample sizes) in the ONH into sample-vessel (Sample-V: size of vessel component) and sample-tissue (Sample-T: size of tissue component) and calculated these three parameters. The area ratio of the bloodstream (ARBS, %) was defined as the ratio of the vessel area in the ONH. The vessel area was separated using an automated definitive threshold (Figure 2) [17]. Nine pulse waveform parameters were calculated: fluctuation, skew, blowout score (BOS), blowout time (BOT), rising rate, falling rate, flow acceleration index (FAI), acceleration time index (ATI), and resistivity index (RI) [24].
**Figure 1:** *Red, high blood flow; Blue, low blood flow. The margin in the optical nerve head (ONH) was determined manually using a rubber band. (Right) eye (R), fellow eye. (Left) eye (L), amblyopic eye.* **Figure 2:** *(A) Color-coded map before calculating the area ratio of the blood stream (ARBS). (B) In calculation of the ARBS, the vessels were separated using the automated definitive threshold.*
## 2.3. Analysis of reproducibility
To determine intra-examiner reproducibility when measured three consecutive times, we assessed the reproducibility of the MBR, number of samples, ARBS, and nine pulse waveform parameters by determining the coefficient of variation (COV) and intraclass correlation coefficient (ICC).
## 2.4. Other clinical examinations
Systolic blood pressure (SBP, mmHg), diastolic blood pressure (DBP, mmHg), and heart rate [beats per minute (bpm)] were measured as systemic parameters. We also measured intraocular pressure (IOP, mmHg), axial length (AL, mm), cycloplegic SE, and BCVA as ocular parameters. IOP and cycloplegic SE were measured using a TONOREF 2™ device (Nidek, Aichi, Japan) and AL was measured using an optical biometer OA1000™ (Tomey, Aichi, Japan). The BCVA was measured at a 5 m distance. The mean arterial blood pressure (MABP, mmHg) and ocular perfusion pressure (OPP, mmHG) were calculated using the following formulas. MABP formula: DBP + (SBP – DBP)/3. OPP formula: ($\frac{2}{3}$MABP) – IOP.
## 2.5. Statistical analysis
All statistical analyses were performed using JMP ver. 14 software (SAS Institute, Cary, NC, USA). Chi-square tests were used to compare sex. The paired t-test was used to compare differences between the amblyopic and fellow eyes, and a non-paired t-test was used to compare differences between amblyopic and fellow eyes and control eyes. The correlation between the AL and SE and blood flow was analyzed by Pearson's correlation coefficient. All measurement values are expressed as the mean ± standard deviation (SD), and $p \leq 0.05$ were considered significant.
## 3. Results
In the control group, of the eight patients, three were measured only in one eye due to a lack of cooperation. Thus, the control group included 13 eyes of 16 possible eyes from the 8 pediatric control patients. Tables 1, 2 present the demographic data and clinical parameters. The SEs of amblyopic, fellow and control eyes were 4.91 ± 1.49, 1.81 ± 1.36, and 3.30 ± 2.03D, respectively. The difference in SE between amblyopic and fellow eyes was 3.11 ± 0.81D. The SEs in three groups differed significantly (amblyopic eye vs. fellow eye: $P \leq 0.0001$, paired t-test; control eye vs. amblyopic eye and fellow eye: $P \leq 0.05$ for both comparisons, non-paired t-test). The AL of the amblyopic, fellow, and control eyes was 21.41 ± 0.93, 22.46 ± 0.96 and 21.52 ± 0.60 mm. The AL in the fellow eye was significantly longer than that in the amblyopic and control eyes ($P \leq 0.01$ for both comparisons).
## 3.1. Blood flow
Table 3 shows the MBR, ARBS, and sample size results. The MBR-As of amblyopic, fellow, and control eyes were 26.1 ± 3.6, 23.4 ± 3.5 and 22.6 ± 3.5, respectively. The MBR-A of the amblyopic eye was significantly higher than that of the fellow and control eyes ($$P \leq 0.0001$$ and $$P \leq 0.0108$$, respectively, for both comparisons). The MBR-Ts of the amblyopic, fellow, and control eyes were 11.4 ± 2.1, 10.3 ± 1.7, and 10.9 ± 1.1, respectively. The MBR-T of the amblyopic eye was significantly higher than that of the fellow eye ($P \leq 0.05$). The MBR-V did not differ significantly. The ARBS values of amblyopic, fellow, and control eyes were 40.9 ± 6.7, 36.7 ± 6.2, and 34.9 ± $5.9\%$, respectively. The ARBS of the amblyopic eye was significantly higher than that of the fellow and control eyes, indicating the ratio of vessel components in the amblyopic eyes was higher than that in the fellow and control eyes ($P \leq 0.05$). The MBR-choroid did not differ significantly among the three groups. MBR-A, MBR-T, and MBR-V were not significantly correlated with SE and AL.
**Table 3**
| Unnamed: 0 | Amblyopic eyes | Fellow eyes | Control eyes | P-value (amblyopia vs. fellow) | P-value (amblyopia vs. control) | P-value (fellow vs. control) |
| --- | --- | --- | --- | --- | --- | --- |
| MBR-A | 26.1 ± 3.6 (22.4–27.8) | 23.4 ± 3.5 (21.7–25.0) | 22.6 ± 3.5 (20.5–24.8) | 0.0001 | 0.0108 | 0.5554 |
| MBR-V | 47.4 ± 5.4 (44.8–45.0) | 46.0 ± 6.1 (43.0–38.9) | 43.9 ± 5.8 (40.1–47.5) | 0.1287 | 0.0981 | 0.362 |
| MBR-T | 11.4 ± 2.1 (10.4–12.4) | 10.3 ± 1.7 (9.5–11.1) | 10.9 ± 1.1 (10.3–11.6) | 0.0154 | 0.4585 | 0.2273 |
| ABRS (%) | 40.9 ± 6.7 (37.7–40.1) | 36.7 ± 6.2 (33.7–39.7) | 34.9 ± 5.9 (31.3–38.5) | 0.0139 | 0.0149 | 0.4333 |
| Sample-A | 37,413 ± 6,154 (34,447–40,379) | 39,198 ± 7,873 (35,403–42,993) | 42,858 ± 8,265 (37,863–47,852) | 0.1344 | 0.0408 | 0.2153 |
| Sample-V | 15,202 ± 3,356 (13,584–16,819) | 14,411 ± 3,664 (12,644–16,177) | 15,700 ± 4,313 (13,093–18,036) | 0.3326 | 0.7161 | 0.3702 |
| Sample-T | 22,211 ± 4,641 (19,975–24,448) | 24,438 ± 5,773 (21,655–27,220) | 26,769 ± 7,655 (22,143–31,395) | 0.0378 | 0.0442 | 0.3335 |
| MBR-choroid | 4.0 ± 1.5 (3.3–4.7) | 4.2 ± 1.1 (3.7–4.8) | 4.0 ± 1.2 (3.3–4.7) | 0.7501 | 0.8002 | 0.9126 |
The sample numbers, which represent the size of the optic nerve, reflected in Sample-A of amblyopic, fellow, and control eyes were 37,413 ± 6,154, 39,198 ± 7,873, and 42,858 ± 8,265, respectively. Sample-A in the amblyopic eye was significantly higher than that in the control eye. The Sample-Ts of amblyopic, fellow, and control eyes were 22,211 ± 4,641, 24,438 ± 5,773, and 26,769 ± 7,655, respectively. Sample-T of the amblyopic eye was significantly smaller than those of the fellow and control eyes ($P \leq 0.05$, for both comparisons). Sample-V did not differ significantly among the amblyopic, fellow, and control eyes.
## 3.2. Reproducibility
Table 4 provides the COVs and ICCs for the MBR, sample, ARBS, and pulse waveform parameters in the ONH. The COVs for the MBR, sample, and ARBS were all ≤$10\%$, and the ICCs were all ≥0.7. Among the pulse waveform parameters, the COVs of fluctuation, BOS, BOT, and RI were ≤$10\%$. The ICCs of all pulse waveform parameters were <0.7, except for fluctuations.
**Table 4**
| Unnamed: 0 | Average | COV | ICC |
| --- | --- | --- | --- |
| Sample-A | 39,466 ± 7,547 (37,343–41,589) | 0.3 ± 0.9 | 1.0 |
| Sample-V | 15,034 ± 3,692 (13,996–16,072) | 9.7 ± 8.4 | 0.71 |
| Sample-T | 24,203 ± 6,087 (22,491–25,915) | 9.0 ± 18.0 | 0.83 |
| MBR-A | 24.2 ± 3.8 (23.2–25.3) | 5.9 ± 3.5 | 0.82 |
| MBR-V | 46.0 ± 5.8 (44.3–47.6) | 6.3 ± 3.9 | 0.71 |
| MBR-T | 10.9 ± 1.8 (10.4–11.4) | 6.7 ± 5.6 | 0.79 |
| ARBS | 37.8 ± 6.7 (35.9–39.7) | 8.4 ± 5.5 | 0.71 |
| FLuctuation | 11.1 ± 2.4 (10.4–11.8) | 9.1 ± 5.6 | 0.71 |
| Skew | 8.0 ± 1.5 (7.6–8.5) | 20.5 ± 18.5 | 0.23 |
| BOS | 80.6 ± 3.7 (79.5–81.6) | 2.6 ± 1.7 | 0.67 |
| BOT | 62.6 ± 3.4 (61.7–63.6) | 6.1 ± 3.8 | 0.16 |
| Rising rate | 13.4 ± 1.5 (12.9–138) | 11.0 ± 8.7 | 0.27 |
| Falling rate | 11.0 ± 0.8 (10.8–11.2) | 11.3 ± 11.3 | 0.07 |
| FAI | 3.5 ± 0.9 (3.2–2.7) | 16.7 ± 11.9 | 0.52 |
| ATI | 31.6 ± 5.4 (30.1–33.1) | 16.0 ± 11.5 | 0.32 |
| RI | 0.30 ± 0.32 (0.28–0.32) | 9.8 ± 6.2 | 0.65 |
The results of COV and ICC by MBR-choroid showed the same trend as the reproducibility in the ONH. The COV for the MBR choroid were ≤$10\%$, and the ICC were ≥0.7. Among the pulse waveform parameters, the COVs of BOS, BOT, falling rate, and RI were ≤$10\%$. The ICCs of all pulse waveform parameters were <0.7, except for the FAI.
## 4. Discussion
The findings of the present study demonstrate that measuring of ocular blood flow in pediatric patients using LSFG was reproducible to the same degree as for adults. In the ONH and choroid, the COVs of all MBR values, sample size, ARBS, and even pulse waveform parameters such as the BOS, BOT, and RI were ≤$10\%$. The ICCs of all MBRs, sample sizes, and ARBS scores were ≥0.7. In the ONH, the MBR-A of the amblyopic eye was significantly higher than that of fellow and control eyes. The MBR-T score of the amblyopic eye was significantly higher than that of the fellow eye. Sample-A of the amblyopic eye was significantly smaller than that of the control eye, and Sample-T was also significantly smaller than that of the fellow and control eyes. The ARBS was significantly higher in the amblyopic eyes than in the fellow and control eyes. Thus, the amblyopic eyes showed higher blood flow and smaller ONH size than the control eyes, but amblyopic and control eyes were not significantly different in AL.
This is the first study to confirm the reliability of ocular blood flow measurements using the LSFG in pediatric patients. In previous reproducibility studies in adult patients with glaucoma, the COVs ranged from 0.9 to $3.8\%$ and the ICCs ranged from 0.95 to 0.98 [14]. The COVs in patients measured in a supine position during surgery ranged from 3.1 to $6.9\%$ [25]; the COV for those in an upright position after being in a supine position was $6.7\%$ [26], and the COVs in neonates ranged from 7.7 to $9.7\%$ [23]. In the present study, the reproducibility of the MBR in the ONH was $5.9\%$, which was very close to that observed in studies of adult patients; due to reproducibility with an ICC of ≥0.7, our results suggest sufficient reliability of LSFG for clinical use.
In this study, reproducibility in terms of both the COV and ICC was not favorable regarding the skew, rising rate, falling rate, FAI, or ATI. Large deviations in the COVs and ICCs were observed in the BOS, BOT, rising rate, and falling rate. According to Tsuda et al., pulse waves such as those in fluctuation, skew, and FAI in ocular blood flow are highly sensitive to subtle changes [27]. In a study of neonates using LSGF-baby, Matsumoto et al. reported that reproducibility of COVs in pulse waves such as those in the fluctuation, skew, FAI, and RI could not be achieved; in pulse waves such as those in the BOS, BOT, rising rate, and falling rate, deviations in COVs and ICCs similar to those obtained in the present study were observed [23]. The likely reason for this may be that children have higher heart rates than adults, making them prone to subtle changes in sight lines and body movements at the time of measurement.
The ONH in the amblyopic eye was significantly smaller than that in the fellow and control eyes. Because the ONH sample size of the vessels was not significantly different, the difference in size of the ONH was attributed to the difference in size of the tissue. In fact, the size of the tissue in the amblyopic eye was significantly smaller than that in the fellow and control eyes, and the ARBS representing the proportion of vessels in the amblyopic eye was significantly higher than that in the fellow and control eyes. Some researchers have reported that the size of the ONH in anisometropic eyes is significantly smaller than that in fellow or control eyes (28–30). The results of the present study are consistent with these findings. Lempert speculated that optic nerve hypoplasia leads to a decrease in ONH size in amblyopic eyes and associated retinal nerve fiber layer (RNFL) thinning, which impairs the anterior visual pathway and reduces visual function [5]. However, Huynh and Wang reported that ONH size and RNFL thickness are associated in children, resulting in a small ONH that tends to thin the RNFL [31]. The thickness of the RNFL varies depending on the refractive error and axis length, and some reports have shown that there is no significant difference between the thickness of the RNFL in the amblyopic eye and the fellow eye, while others have reported that the amblyopic eye has a thicker RNFL (32–34). There are no reports of RNFL thinning in amblyopic eyes, and the fact that the size of the ONH in anisometropic amblyopic eyes is smaller than in fellow eyes and normal eyes is a structural feature of anisometropic amblyopic eyes rather than optic nerve hypoplasia.
In the current study, the amblyopic eye had a significantly higher MBR-A than the fellow and control eyes in the ONH group. Some past studies that compared retrobulbar blood flow, that is, ophthalmic artery and central retinal artery, in amblyopic and fellow eyes reported that retrobulbar blood flow did not differ significantly between amblyopic and fellow eyes, indicating that the blood flow supplied to the anisometric amblyopic eye and the fellow eye with different axial lengths are the same [8, 9]. Kobayashi et al. reported that, in normal eyes, blood flow in the ONH measured by LSFG did not differ significantly between eyes [35]. Therefore, the reason for the higher MBR-A in the amblyopic eyes in the current study is that the size of the tissue component in the ONH of the amblyopic eyes was significantly smaller, while the size of the vascular component did not differ significantly among eyes. As a result, the same amount of blood flow passed through the smaller tissue component, resulting in higher blood flow velocity in the tissue component and faster overall blood flow velocity in the ONH. The vascular density of the ONH measured by optical coherence tomography angiography (OCTA) was significantly lower in amblyopic eyes than in fellow eyes, which is different from the present result that there was no significant difference in the size of the vascular component between amblyopic eyes and fellow or normal eyes [36]. This discrepancy is because Sobral et al. enrolled patients with strabismic and anisometropic amblyopia, whereas we enrolled patients with anisometropic amblyopia without strabismus [36]. Moreover, they included children who had been treated but had not reached $\frac{20}{20}$, whereas we enrolled amblyopic eyes whose visual acuity had reached $\frac{20}{20}$ or better with treatment. In addition, the difference in the analysis method between OCTA and LSFG may also have affected this discrepancy, as OCTA analyzes the superficial vascular structure, but LSFG analyzes blood flow from the superficial layer to the area around the stromal plate [37, 38]. Although MBR measured by LSFG was significantly correlated with peripapillary relative intensity (PRI) and circumpapillary vessel density (spVD) by OCT A, OCTA has an advantage of detecting visualization of vascular structure in each layer, while LSFG (MBR and 9 pulse wave parameters) has an advantage of assessing physiological phenomenon such as vascular resistance and auto regulation of retinal microvascular circulation and defocus (39–43).
There was no significant difference in choroidal blood flow in this study. Hashimoto et al. reported by case report that, although the MBR was decreased in the amblyopic eye before treatment, the MBR increased with improvement in visual acuity after treatment, and the difference in MBR between both eyes became smaller [21]. Some researchers have reported that the choroidal thickness of the amblyopic eye is greater than that of normal eyes with the same axial length or fellow eyes. In a study that focused on the structure of the choroid, that is, lumen and stroma, the lumen was larger, the stroma was smaller, and the ratio of lumen/stroma was larger in amblyopic eyes before treatment than in fellow and normal eyes; however, after treatment, the lumen and stroma became smaller and larger, respectively, and the ratio of lumen/stroma was the same as that in fellow and normal eyes [44]. Changes in the choroidal structure that occur during treatment may affect choroidal blood flow. In this study, we analyzed choroidal blood flow in eyes with anisometropic amblyopia in which visual acuity was improved by treatment, resulting in the absence of significant differences among amblyopic, fellow, and normal eyes.
The present study had some limitations. First, no comparison between fundus photographs and the ONH area using OCTA could be performed. Instead, we calculated the area based on the sample size of the ONH. The sample size did not consider refraction and axial length, and further studies on sample size values are needed. Second, although choroidal blood flow may reflect choroidal structure, visual function, and pathophysiology of anisometropic amblyopia, in this study, choroidal blood flow was analyzed between the macula and ONH, but not in the macula, because multiple locations could not be measured due to insufficient cooperation for the examination. In the ONH, MBR was not significantly correlated with AL or SE. The ONH structure may have influenced this finding. Moreover, although past studies have investigated correlations between AL or SE and blood flow in patients with myopia and hyperopia, they have excluded amblyopia, whereas we enrolled patients with hyperopia and hyperopic anisometropic amblyopia. It was thought that these things influenced the result which was not indicate correlation. Thus, the relationship between ONH and choroidal structure, visual function, and blood flow in the ONH and macula should be investigated in the future by increasing the number of patients. Third, inter-examiner reproducibility could not be studied due to insufficient patient cooperation for the LSFG examination. In the future, inter-examiner reproducibility should be examined. Fourth, in this study, although we measured blood flow in pediatric patients, we need to investigate the blood flow not only pediatric patients but also adult patients with anisometropic amblyopia for improving the reliability of this study. Fifth, the number of subjects in this study was small. Because there was no report of evaluating in blood flow using LSFG in the anisometropic amblyopic eye excluding case report, we calculated sample size using previous report investigating ONH size among amblyopic, fellow and control eye. The size of ONH in each group was 2.57, 1.74, 1.55 mm, respectively. We calculated the sample size based on this previous study, and at least 28 eyes were required in total for this study design (α = 0.05, power $80\%$), indicating that the 49 eyes enrolled in this study constitute a reasonable sample size, but the sample size seems small when considered as a study of blood flow. Because we conducted this study retrospectively, in further study we prospectively need to investigate blood flow in enough number of patients with anisometropic eye [29].
## 5. Conclusion
In conclusion, we were able to measure ocular blood flow in pediatric patients, and our results suggest that good reproducibility was achieved for clinical use. Moreover, the MBR values of the ONH in amblyopic eyes were high, and ocular structural differences were observed. It has been suggested that measuring ONH blood flow using LSFG could detect ocular structural changes in anisometropic amblyopic eyes.
## 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 Institutional Review Board of Toho University Omori Medical Center (#M22161). Written informed consent from the participants' legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.
## Author contributions
TI and TM: design of the study. TI and MK: collection of data, management, analysis, and interpretation of the data. TI, TM, SM, and YH: preparation and review of 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.
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|
---
title: Specific cannabinoids revive adaptive immunity by reversing immune evasion
mechanisms in metastatic tumours
authors:
- Sarah Dada
- Samantha L. S. Ellis
- Christi Wood
- Lilian L. Nohara
- Carola Dreier
- Nicolas H. Garcia
- Iryna Saranchova
- Lonna Munro
- Cheryl G. Pfeifer
- Brett A. Eyford
- Suresh Kari
- Emmanuel Garrovillas
- Giorgia Caspani
- Eliana Al Haddad
- Patrick W. Gray
- Tunc Morova
- Nathan A. Lack
- Raymond J. Andersen
- Larry Tjoelker
- Wilfred A. Jefferies
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10010394
doi: 10.3389/fimmu.2022.982082
license: CC BY 4.0
---
# Specific cannabinoids revive adaptive immunity by reversing immune evasion mechanisms in metastatic tumours
## Abstract
Emerging cancers are sculpted by neo-Darwinian selection for superior growth and survival but minimal immunogenicity; consequently, metastatic cancers often evolve common genetic and epigenetic signatures to elude immune surveillance. Immune subversion by metastatic tumours can be achieved through several mechanisms; one of the most frequently observed involves the loss of expression or mutation of genes composing the MHC-I antigen presentation machinery (APM) that yields tumours invisible to Cytotoxic T lymphocytes, the key component of the adaptive cellular immune response. Fascinating ethnographic and experimental findings indicate that cannabinoids inhibit the growth and progression of several categories of cancer; however, the mechanisms underlying these observations remain clouded in uncertainty. Here, we screened a library of cannabinoid compounds and found molecular selectivity amongst specific cannabinoids, where related molecules such as Δ9-tetrahydrocannabinol, cannabidiol, and cannabigerol can reverse the metastatic immune escape phenotype in vitro by inducing MHC-I cell surface expression in a wide variety of metastatic tumours that subsequently sensitizing tumours to T lymphocyte recognition. Remarkably, H3K27Ac ChIPseq analysis established that cannabigerol and gamma interferon induce overlapping epigenetic signatures and key gene pathways in metastatic tumours related to cellular senescence, as well as APM genes involved in revealing metastatic tumours to the adaptive immune response. Overall, the data suggest that specific cannabinoids may have utility in cancer immunotherapy regimens by overcoming immune escape and augmenting cancer immune surveillance in metastatic disease. Finally, the fundamental discovery of the ability of cannabinoids to alter epigenetic programs may help elucidate many of the pleiotropic medicinal effects of cannabinoids on human physiology.
## Introduction
The ability of the adaptive immune system to seek and destroy emerging tumours is reliant upon immune surveillance by cytolytic T lymphocytes (CTLs) [1]. In perhaps one of the most fascinating molecular mechanisms in all biology, CTL recognize major histocompatibility class I (MHC-I) molecules that act as peptide receptors that have been loaded with small fragments of proteolytically generated foreign peptides, through a process termed antigen processing [1]. As a result of immunological tolerance mechanisms, CTL generally ignore healthy cells that display MHC-I loaded with self-peptides and rather focus on cells expressing MHC-I bound foreign antigens such as viral peptides or abnormal peptides, including cancer antigens.
During the evolution of cancers, genetic and epigenetic alterations occur that enable the cancers to become metastatic [2] and are referred to as a metastatic signature. A common form of metastatic signature is one that allows the cancer to evade the immune system. In the context of CTL recognition of MHC-I peptide complexes, there are various mechanisms acting exclusively or in concert, that underpin escape from immune surveillance. These include the absence or low expression of MHC-I molecules due mutations or epigenetic regulation, tumour-induced T-lymphocyte anergy, and/or defects in MHC-I antigen presentation machinery (APM) [3, 4].
MHC-I molecules are required for antigen presentation to CTLs, and the regulation of natural killer cells. Thus, alteration in the expression of surface MHC-I has been determined as an important tumour escape mechanism [1]. Under the negative selection of CTLs, this immune escape [1] (also termed immune-edited) phenotype can even reach a penetrance of $100\%$ in some carcinoma types [5, 6]. Since entry of processed peptides into the endoplasmic reticulum (ER) via transporters associated with antigen processing 1 and 2 (TAP-$\frac{1}{2}$) is required for the assembly of MHC-I peptide complexes, the loss of TAP-$\frac{1}{2}$ greatly contributes to a functional defect in the antigen processing and presentation pathway [1]. These phenotypic changes that appear at the clonal level are associated with malignant transformation (7–9) and allow malignant cells to evade immune surveillance by ultimately disabling the cells’ ability to present cell surface peptides. Tumour cells that have defects in the APM have a selective advantage compared to other tumour cells that retain a functional APM, conferring on them a greater metastatic potential. Several types of cancer, including breast cancer [10, 11], renal cell carcinoma [12], melanoma [13, 14], colorectal carcinoma [15], head and neck squamous cell carcinoma [16], cervical cancer [17], and finally prostate carcinoma show a clear correlation between MHC-I down-regulation and poor prognosis (18–20). The increasing frequency of immune escape tumour variants in many forms of metastatic cancers is a predictor of disease progression as well as predictor of poor patient outcome. Relatively few attempts have been made to treat metastatic disease by directly trying to overcome the APM deficits in immune escape tumour variants as a therapeutic modality. During our earlier studies, we revised the conclusions of Stutman [21], and formally demonstrated, for the first time to our knowledge, that T-lymphocytes are indeed required for cancer immune surveillance in vivo [1]. Specifically, animals genetically lacking T-lymphocytes lose the ability to survey and resist tumour expansion of even MHC-I expressing tumours. Furthermore, we demonstrated that functional expression of APM components in tumours is required to enable immune surveillance and the loss of APM components that often occurs in metastatic tumours, allowing them to grow and expand even in wild-type animals possessing a normal T-lymphocyte compartment [1]. We subsequently elaborated on this point by directly restoring APM expression in vivo using viral vectors that introduced the missing APM into tumours in animals with ongoing metastatic disease, resulting in a dramatic reduction of tumour growth. Thus, we recognized that the possibility to restore CTL recognition of metastatic carcinomas by complementation and replacing missing APM components may have a clinical application in cancer immunotherapies (1, 22–27). Intriguingly, APM deficiency is not exclusively regulated by defects or mutations in the APM genes, but it may be epigenetically regulated as well [28] and can be restored by treatment with histone deacetylase inhibitors (HDACi) such as trichostatin-A (TSA) [28, 29] or complementation with cytokines such as IL-33 [30, 31] or interferon gamma (IFN-γ) (1, 2, 22–27).
Based on these observations, we focused on the discovery of natural small molecules that may reverse immune escape and thereby improve tumour antigen recognition by the immune system with the goal of enhancing protective immune responses. Among the compounds promoting immune recognition that we identified were cannabinoids. For decades, cannabinoids have been reported to specifically inhibit cancer growth, but the mechanism remains undescribed [32]. However, perhaps slowing the pace of experimentally documenting the exact mechanism underlying their medicinal effect, cannabinoids are actually a diverse class of compounds that act on the brain and other tissues of the body by targeting cannabinoid receptors, other G protein coupled receptors, ion channels, transporters, as well as enzymes [33]. The phytocannabinoids, for example, are found in *Cannabis sativa* and other plants, and many of these natural products have demonstrated pharmacological properties. Endocannabinoids such as anandamide, on the other hand, are naturally produced in the body, and act as natural ligands for cannabinoid receptors [34]. Artificially manufactured synthetic cannabinoids also exhibit activity on cannabinoid receptors, while having structural similarity to naturally occurring cannabinoids. The most controversial form of these is the primary psychoactive compound in Cannabis, known as tetrahydrocannabinol (THC). However, it should be emphasized that more than 100 different cannabinoids have been isolated from Cannabis. Some cannabinoids have agonist activity while others have antagonist activity on the characterized cannabinoid receptors. Consequently, mixtures of cannabinoids, such as those found in Cannabis or in crude extracts of Cannabis, are likely to have contradictory activities that likely continues to obscure their true clinical potential. Furthermore, such natural preparations are notoriously difficult to reproducibly manufacture resulting in differences from batch to batch. Therefore, it may be advantageous for a purified or synthetic cannabinoid or cannabinoid derivative to be advanced for clinical development.
We have developed a method to indirectly screen for compounds that increase MHC-I expression in metastatic tumours. This approach has identified numerous cannabinoid compounds that increased MHC-I expression and promoted immune recognition of metastatic cancer cells.
## Cell culture
The murine lung carcinoma cell line, TC-1, was derived from primary lung epithelial cells of a C57BL/6 mouse that were immortalized using the amphotropic retrovirus vector LXSN16 carrying the Human Papillomavirus E6/E7 oncogenes and subsequently transformed with pVEJB plasmid expressing the activated human H-Ras oncogene [35]. The metastatic cell line, A9, is a derivative of TC-1 that was generated in vivo after an immunization strategy in animals bearing the original TC-1 parental cells to drive selection for clones with enhanced immunoresistance [36, 37]. In contrast to the parental TC1 cells, which display high expression of TAP-1 and MHC-I, A9 cells express nearly undetectable levels of MHC-I. Both of the aforementioned cell lines were cultured in Dulbecco’s modified Eagle’s medium (Gibco) containing $10\%$ fetal bovine serum (FBS, Gibco), 100 U/mL penicillin-streptomycin (Gibco) and incubated at 37°C in a $5\%$ CO2 humidified atmosphere. Other cell lines including murine 4T1, CT26, EMT6, Renca, B16-410, LLC, MC38, B16F10 and A20 and human COLO 205, SK-N-MC, SNU-C1, DLD-1, LS123, LS411N, LoVo, SK-MEL-2, NCI-H146, A431 and SK-MEL-2 were cultured as described above.
## Mice
OT1 mice (Strain #:003831, The Jackson Laboratory) contain transgenic inserts for mouse Tcra-V2 and Tcrb-V5 genes. The transgenic T cell receptor was designed to recognize ovalbumin peptide residues 257-264 (OVA257-264) in the context of H2Kb (CD8 co-receptor interaction with MHC class I) and this results in the development of MHC class I-restricted, ovalbumin-specific, CD8+ T lymphocytes (OT-I T lymphocytes).
## Flow cytometry
Cell lines were trypsinized ($0.05\%$; Gibco), washed twice with PBS (Gibco), and stained with allophycocyanin (APC)-conjugated anti-mouse H-2Kb antibody (1:200; Biolegend) suspended in 150 μL of FACS buffer (PBS + $2\%$ FBS) for 20 minutes at 4°C. Cells were washed with PBS twice and then resuspended in 200 μL FACs buffer containing 1 μL of 7-aminoactinomycin D (7AAD) viability stain (Biolegend). Flow cytometry was performed on a LSRII (BD Biosciences) and analysis was done using FlowJo software (BD; flow cytometry analysis software, version 6).
A flow cytometry assay was also developed for testing the MHC-I-inducing activity of IFN-γ and cannabinoids in human and mouse cancer cell lines. All cells were obtained from ATCC and were cultured as recommended by the supplier. Cells were seeded at subconfluent densities in 96-well flat bottom plates and, after 24 hours of incubation at 37°C, $5\%$ CO2 in a humidified chamber, culture wells were supplemented with test concentrations of cannabinoids, recombinant human or mouse IFN-γ (positive control, R&D Systems), or vehicle control ($1\%$ DMSO in culture medium). The treated plates were then incubated another 48 hr, after which cells were collected by centrifugation in the case of non-adherent cells, and treatment with TrypLE Express (ThermoFisher Scientific) in the case of adherent cells. Harvested cells were washed thrice with cold FACS buffer ($1\%$ BSA in PBS) and stained for one hr on ice. All human cell lines were stained with a 1:20 dilution of the FITC-conjugated, mouse anti-human pan-HLA W$\frac{6}{32}$ antibody (Life Technologies). Mouse cell lines 4T1, CT26, EMT6, Renca, and A20 were stained with 2.5 μg/ml of the FITC-conjugated mouse anti-mouse H-2Kd/H-2Dd MHC-I allotype antibody (clone 34-1-2S, BioLegend), while mouse cell lines B16-410, LLC, MC38, and A9 were stained with 10 μg/ml of the FITC-conjugated mouse anti-mouse H-2Kb/H-2Db MHC-I allotype antibody (clone 28-8-6, BioLegend). After washing the cells in FACS buffer, stained cells were quantitated by flow cytometry using a FACSCalibur (BD Biosciences), after which data were analyzed with FlowJo software. Viability was determined using the vital dye, SYTOX Red (ThermoFisher Scientific).
A variety of cannabinoids, including endo-, phyto-, and synthetic cannabinoids, all acquired from Cayman Chemical (Ann Arbor, Michigan, USA), were tested in the COLO 205 MHC-I induction assay. The 371 synthetic cannabinoids were arrayed as 10 mM stocks in DMSO in a 96-well plate screening format (Cayman #9002891). The endocannabinoids were provided as concentrated stocks in ethanol (AEA) or acetonitrile (2-AG), and the phytocannabinoids provided as either concentrated stocks in methanol or as dry powder which was reconstituted in methanol. All cannabinoid stocks were diluted with positive displacement pipettors under appropriate safety constraints into culture medium for assay of MHC-I induction activity. All Schedule I regulated cannabinoids were handled in a DEA-certified laboratory (USA) or PHAC-certified laboratory (Canada) with special exemption status. Cannabigerol (CBG) was stored as a stock solution in DMSO.
## Proxy cytolytic T lymphocyte assay
1x106 A9 cells were plated onto a 6 well plate in two mL of RPMI medium (Advanced RPMI-1640 Medium; # 12633020, Gibco), 100 U/mL of Penicillin-Streptomycin (P+S) (#15070063, Thermo Fisher), $1\%$ L-Glutamine (#25030081, Gibco), and $10\%$ FBS. A9 cells were treated with 55 μM (8.8 ng/ml) CBG, 167 μM (18.6 ng/ml) CCP, 5.9 nM (100 ng/ml) IFN-γ, or $1\%$ DMSO vehicle. After 24 hours at 37°C, $5\%$ CO2, the ovalbumin peptide, SIINFEKL (#257-264, Genscript), was added to the A9 cells. Following an additional incubation for 24 hours, CD8+ T cells were collected from OT1 mouse spleens. Spleens were minced and passed through a 100-micron cell strainer (#352260, Falcon). Red Blood Cell ACK lysis buffer (#A10492-01, Gibco) was used to remove red blood cells from the spleen isolate. CD8+ Untouched Mouse CD8+ lymphocytes (Dynabeads, #11417D, ThermoFisher Scientific) was used to enrich CD8+ T lymphocytes, as per the manufacture’s protocol. The medium was removed from the A9 cells and they were washed three times with PBS before fresh RPMI was added to the wells. An extra well of untreated A9 cells was counted and used as a baseline count. CD8+ T cells were counted and afterwards treated with carboxyfluorescein succinimidyl ester (CFSE; #79898, Biolegend) per the manufacturer’s protocol, before being co-cultured with A9 cells at a 1:1 or a 1:5 ratio of T lymphocytes tumour cells. For the positive control, T cells were stimulated 24 hours later using CD28 monoclonal antibody clone 37.51 (#14-0281-86, eBioscience) at a concentration of 5 μg/mL and CD3e monoclonal antibody clone 145-2C11 at 10 μg/mL, (eBioscience, #14-0031-86). CD8+ T lymphocytes and A9 cells were harvested for analysis in flow cytometry. CD8+ T lymphocytes were stained with CD8 PE-efluor 610 antibody (#60-0081-82, Invitrogen). A9 Cells were stained using PE H-2KB antibody (#12-5958-82, Invitrogen), and 7AAD viability dye (#420404, Biolegend) in FACS buffer. All mouse experiments were approved by the Animal Care Committee at UBC. Animals were maintained and euthanized under humane conditions in accordance with the guidelines of the Canadian Council on Animal Care.
## Cytokine secretion profile of A9 cells upon treatment of small molecules
All compounds, (Cannabigerol, IFN gamma) were dissolved in $1\%$ Dimethyl Sulfoxide (DMSO) (Catalog #276855, Sigma) in media ($1\%$DMSO). 1x106 A9 cells were plated onto a 6 well plate in two mL of DMEM media. Twenty-four hours after seeding, cells were cultured at the optimum concentrations of Cannabigerol 0.055 μmol, or 5.832x10-6 nmol IFN gamma or $1\%$ DMSO vehicle for 48 hours. Relative expression levels of 111 soluble mouse proteins including cytokines, chemokines and growth factors were evaluated using the Proteome Profiler Mouse XL cytokine array kit (R&D System, ARY028) following the manufacturer’s instructions. Spot densities on the array film were detected and quantified using Image J analysis software Image J protein analyzer add-on on a scanned version of the film. Quantification of the spot intensity in the arrays was conducted with background subtraction in ImageJ. To determine fold change, the treatment values of IFN gamma microarrays were divided by the DMSO negative control values of the concurring spots. A value of 1 was subtracted from the absolute value of this fold change, to correct the value of DMSO to “0”. The experiment was done in technical replicates. Key cytokines that showed changes in expression levels were further characterized by pathway analysis for over-represented pathway identification through Reactome Database release 65, Pathway Brower Version 3.5. The results displayed concerning the arraying conducted is an average of an N of 2.
## Reverse transcription and RT-qPCR
RNA was isolated using the RNeasy plus mini kit. RNA was reverse transcribed into cDNA using the superscript II reverse transcription kit (Catalog # 18064014, Invitrogen). Quantitative RT PCR was done using 10nm of primer and 1uL of BioRad SYBR Green master mix (Catalog#1725271, Biorad). RT-qPCR was done on 7500 Fast Real-Time PCR System from Applied Biosystems 40 cycles (95°C denaturing for 15 seconds, 60°C annealing for one minute).
## Processing of ChIP-seq data
Raw sequencing data was aligned to mouse reference genome (mm10) with BWA mem (v0.7.6a) with option (-M). Peak calling was done with MACS2 (v2.1.2) with FDR cutoff 0.01 and option (-f BAMPE) [38, 39]. During peak calling process, input samples were used as a background control.
## Overlap analysis of H3K27Ac peaks of DMSO, cannabigerol and IFN-gamma samples and Venn diagram
We used Intervene (v0.6.4) to generate a Venn diagram of the comparison with “–save-overlaps” option to obtain sample-specific or common binding regions [40]. Comparison of DMSO and IFN-gamma generated the region sets lost and gained which gained represent IFN-gamma specific and lost is DMSO specific. Further, gained and lost regions then intersected with Cannabigerol regions to create genomic region set “gained-Cannabigerol” and “lost-Cannabigerol” respectively.
## MHC-I downregulation is reversible by IFN-γ in human and murine cancer cell lines
It has been determined that only $30\%$-$40\%$ of lost MHC-I expression in tumours is due to a genetic lesion impacting the structural genes involved in APM (28, 41–43), suggesting that MHC-I expression might be restorable in cancers with intact but under-expressed APM genes. To test this possibility, we treated various human and mouse cancer cell lines with dose titrations of recombinant human or mouse IFN-γ, respectively. The cells were incubated with IFN-γ for 48 hrs in a humidified chamber at 37°C, stained with fluorescent haplotype-appropriate MHC-I antibody, then signal was determined by flow cytometry. Cancers represented in this experiment include brain (SK-N-MC), breast (4T1, EMT6), colorectal (COLO 205, SNU-C1, DLD-1, LS123, LS411N, LoVo, CT26, MC38), kidney (Renca), lung (NCI-H146, LLC, A9), lymphoid (A20), and skin (A431, SK-MEL-2, B16F10). IFN-γ induced MHC-I expression to varying degrees in a dose-dependent manner in $\frac{6}{10}$ ($60\%$) human (Figure 1A) and $\frac{8}{9}$ ($89\%$) mouse cell lines (Figure 1B). A9 was treated with a maximum dose of mouse IFN-γ of 1ng/ml. This was based on a previous titration and time course with IFN-γ to establish maximal induction. These numbers are consistent with the previously reported values [41, 44] and indicate that, while cell surface MHC-I may be downregulated, it can be induced in many human and murine cancers.
**Figure 1:** *Downregulation of MHC-I expression is reversible by in most human (A) and mouse (B) cancer cell lines. The MHC-1-low or -null cell lines were treated with a titration of human (A) or mouse (B) IFN-γ for 48 hr, washed, and MHC-I expression determined by flow cytometry. The pan-human HLA antibody, W6/32, was used to stain the human cell lines, and mouse cell lines were stained with the MHC-I allotype-specific 34-1-2S (4T1, CT26, EMT6, Renca, A20) or 28-8-6 (B16-410, LLC, MC38, A9) antibodies.*
## Cannabigerol induces MHC-I expression in mouse and human metastatic carcinomas
The high percentage of cancer cell lines with low but inducible APM (Figure 1) prompted us to examine if cannabinoids can also induce MHC-I in metastatic cells [45]. The effect of the cannabinoid, cannabigerol was examined and compared with the effect of IFN-γ on the metastatic murine lung carcinomas, A9 cell line. MHC-I protein upregulation in the presence of cannabigerol was nearly as great as with IFN-γ at a concentration of 0.055 μmol. This was determined using FACS and was statistically significant, with a p-value of less than 0.0001 between DMSO treated cells and cannabigerol treated metastatic cells at a concentration of 0.055 μmol while using an ordinary one-way ANOVA. ( Figure 2). We next examined if the induction of MHC-I in response to cannabigerol could be generalizable to human metastatic carcinomas and other murine metastatic carcinomas. Figure 3 illustrates the response of the cells after 48 hrs of treatment with a suboptimal and an optimal dose of the cannabinoid. The human colorectal cancer cell line COLO 205 was treated with 25 and 50 μM CBG (Figure 3A), and the mouse breast cancer cell line 4T1 was treated with 9.5 and 18.6 μM CBG (Figure 3B). Both cell lines respond with MHC-I upregulation in a dose-dependent manner. With ascending dosing, COLO 205 responded with 1.2- and 1.8-fold, and 4T1 displayed 1.6- and 2.4-fold increase in MHC-I levels. Shifts in MHC-I expression as those observed in (Figure 3) have been demonstrated to functionally reconstitute CTL recognition in a previous study conducted by Jefferies et al. [ 46], indicating that as few as 10 MHC-I peptide complexes are able to be recognized by CTL. As illustrated in the flow cytometry histograms, most if not all, cells in the population responded to cannabigerol treatment, driving a rightward shift of the peak.
**Figure 2:** *MHC-I downregulation is reversible by IFN-γ or cannabigerol in metastatic murine cells. The murine lung cancer cell line, A9 was treated with various doses of CBG and compared with induction by IFN-γ. MHC-I was measured by flow cytometry after 48 hr. In comparison to the vehicle control DMSO, significant induction (*) was demonstrated at 0.0275 µM and 0.055 µM of CBG (red) while cell viability (grey) was maintained above 70% for both concentrations of CBG. These data are representative of three independent experiments.* **Figure 3:** *Cannabigerol induces MHC-I expression in human and mouse metastatic carcinomas. (A) The human colorectal cancer cell line, COLO 205 was treated with 25 and 50 μM CBG and (B) the mouse breast cancer cell line 4T1 was treated with 9.5 and 18.6 μM CBG. In both experiments, MHC-I was measured by flow cytometry after 48 hr. Left panel: flow cytometry histogram. Right panel: mean fluorescent intensities (MFI) for each treatment condition; fold increase in MHC-I expression (treatment MFI/no treatment MFI) is indicated above each bar. These data are representative of three independent experiments.*
## Phytocannabinoids as a class induce MHC-I expression
Cannabigerol is only one of many cannabinoid-like molecules with potential biopharmaceutical activity. Because of its relatively modest potency in the MHC-I induction assay compared to IFN-γ (see Figures 1, 3), we set out to test additional cannabinoids. Phytocannabinoids are derived from certain plants, most notably Cannabis sativa, and endocannabinoids are naturally present in vertebrates. Both classes exert their physiological effects via the various receptors and pathways discussed above. We tested the two best characterized endocannabinoids, 2-arachidonoylglycerol (2-AG) and N-arachidonoylethanolamine (AEA, anandamide) along with a second phytocannabinoid, cannabidiol (CBD) for MHC-I-inducing activity. The endocannabinoids did not induce MHC-I expression above baseline in COLO 205 cells at any of the concentrations tested (Figure 4A), suggesting these molecules do not play a physiological role in regulating MHC-I. However, along with CBG, CBD was able to induce MHC-I expression by these cells, with EC50 values of 40.3 and 11.1 μM, respectively, in this experiment (Figure 4A). The bell shape of the CBD induction curve was unexpected but may relate to the health of the cells exposed to the higher CBD concentrations. We found that overall viability of the cell population decreased at the higher CBD levels and, while dead cells were gated out of the flow cytometry analysis, MHC-I induction may be compromised in the remaining viable cells. Supporting this possibility is the observation that pharmacological induction of ER stress reduced MHC-I gene product expression in a human airway epithelium cell line [47].
**Figure 4:** *Phytocannabinoids, but not endocannabinoids, induce MHC-I expression in a dose- and time-dependent manner. (A) The endocannabinoids 2-AG and AEA had no effect on MHC-I expression in COLO 205 cells as determined by flow cytometry, in contrast to the activity of the phytocannabinoids CBD and CBG after 48 hr of treatment. (B) An MHC-I induction time course shows that elevated MHC-I is first detectable at 24 hr in cultured COLO 205 cells treated with 15 µM CBD but has reached maximum expression by 24 hr in cells treated with 3 ng/ml IFN-γ. (C). Parallel dose titrations spotlight the dramatic impact of IFN-γ and CBD on MHC-I expression in COLO 205 cells. The titrations also clarify differences in potency and dose effect between the cytokine and the cannabinoid. The dose titrations were conducted in triplicate in the same experiment and are representative of numerous IFN-γ and CBD comparisons. Statistical analysis using a two-sided T-test with unequal variances revealed that every titration data point has a p-value of less than 0.005 vs. the untreated sample. MFI: mean fluorescent intensity.*
Comparison of the kinetics of MHC-I induction suggests that the mechanism by which CBD induces MHC-I in these cells is distinct from that invoked by IFN-γ (see Figure 1A). The first detectable increase of expression was at 24 hr in cells treated with CBD (15 μM), and MHC-I levels continued to rise at 48 hr (Figure 4B). In contrast, induction by IFN-γ (3 ng/ml) reached a maximum by 24 hr in the same experiment (Figure 4B). Further, parallel comparison of the expression of MHC-1 based on dose-dependent titrations of IFN-γ and cannabigerol in COLO 205 cells was able to demonstrate that cannabigerol is approximately $50\%$ as potent as interferon-γ (Figure 4C).
The striking differences in MHC-I-inducing activity between the endocannabinoids and the phytocannabinoids are reflected in their structures (see Table 1). The functional phytocannabinoids share structural similarities, as do the non-functional endocannabinoids, but the two classes of cannabinoids are structurally quite dissimilar. To determine if MHC-I induction is a feature common to phytocannabinoids as a chemical class, we tested 13 additional phytocannabinoids in the assay (Table 2). A nine-point, 1.4-fold dose titration ranging from 100 μM down to 6.8 μM was applied to each cannabinoid. Except for cannabicitran, all of the phytocannabinoids induced MHC-I expression in COLO 205 cells, with EC50 values ranging from 11 to >72 μM. Induction levels of two- to three-fold were typical, although four- to six-fold induction was noted for Δ8- and Δ9-tetrahydrocannabinol, cannabivarin, cannabidivarin, and cannabicyclol. Each cannabinoid, except for cannabicitran and cannabigerorcinic acid, caused cell death within the mid to upper range of the titration curve, as reflected in the LC50 values. Of all the phytocannabinoids tested, CBD displayed the highest selectivity index (5.4), reflecting the broadest window between biological activity (EC50) and toxicity (LC50).
## Specific synthetic cannabinoids can induce MHC-I expression in metastatic cancers
Over time, numerous compounds have been synthesized to interact with and modulate the endogenous cannabinoid receptors, either as agonists, inverse agonists, or antagonists [48, 49]. A library of 371 synthetic cannabinoids was screened to determine whether any of the compounds share the capacity of phytocannabinoids to induce MHC-I expression on COLO 205 cells. A pilot experiment on a subset of the library demonstrated that a number of the compounds were active in the range of 20-40 µM, so the entire library was initially screened at a single concentration (35 µM) for each compound. This screen showed that many of the synthetic cannabinoids can induce MHC-I expression in COLO 205 cells, with 53 of them achieving a three-fold or higher level of induction (Table 3).
The synthetic compounds in the library can generally be grouped into seven families based upon structural similarities (Tables 1, 3). All compounds within each family with at least a 3-fold level of MHC-I induction in the initial screen, as well as several compounds that did not reach the 3-fold threshold at 35 μM were tested in dose titrations spanning 6.8 to 100 μM in the COLO 205 assay. The EC50 values from these curves revealed that several of the synthetic cannabinoids are as potent as CBD. Structures of representative members of each structural family are illustrated in Table 1. While some structural similarities are apparent between the distinct synthetic families, and between the synthetic families and the phytocannabinoids, they are all dissimilar to the endocannabinoids. Also notable, most of the cannabinoids, both synthetic and plant-derived, show a bell-shaped MHC-I induction curve (Figure 4; Table 1), in which induction diminishes as cannabinoid concentrations increase past the maximum induction point. In most cases, this diminishment correlates with increasing toxicity of the compound to the cells. All compounds tested are listed by family with fold induction data in Supplemental Table 1.
**Table 3**
| Family | Molecular Weight | Compounds in Family | Compounds with ≥3X MHC-I Induction | Range of Induction (Fold) | EC50 (µM) |
| --- | --- | --- | --- | --- | --- |
| 1 | 300 – 500 | 131 | 20 | 0.6 – 5.6 | 13 |
| 2 | 300 – 410 | 85 | 14 | 0.5 – 6.8 | 13 |
| 3 | 330 – 440 | 67 | 13 | 1.1 – 7.0 | 27 |
| 4 | 500 – 550 | 2 | 2 | 4.0 – 5.8 | 23 |
| 5 | 300 – 390 | 23 | 1 | 0.9 – 3.2 | 10 |
| 6 | 290 – 420 | 15 | 1 | 1.1 – 4.2 | 23 |
| 7 | 295 – 415 | 26 | 2 | 0.17 – 3.17 | ND |
## Metastatic carcinomas treated with cannabigerol or IFN-γ reconstitute antigen processing recognized by MHC-I restricted T-lymphocytes
We next tested whether treating metastatic tumours with cannabinoids and pulsing them with the peptide that is recognized by a clonotypic T cell receptor expressed in MHC-I restricted CTL can facilitate recognition of the metastatic tumour (50–53). We have used this method together with the CFSE dilution assessment of T lymphocyte recognition and proliferation, that is an alternative to Chromium release assays [54]. Furthermore, this assay is used here as a proxy for recognition of antigen presentation of “tumour-specific” antigens in the context of MHC-I molecules by tumour-specific CTL.
In order to assess whether MHC-I induction by cannabinoids has the potential to enhance CD8+ CTL recognition of a cancer cell, we performed a CFSE T lymphocyte proliferation assay. As we have shown previously [31], an increase in H-2Kb on A9 cells corresponds with an increase in the tumor cells’ presentation of antigen to CD8+ T lymphocytes. In the present experiment, this elevated antigen presentation stimulates a concomitant increase in OT1 mouse (ovalbumin-peptide SIINFEKL-specific, H-2Kb restricted) CD8+ T lymphocytes proliferation is indicated by the successive peaks of CFSE dilution. Ex vivo, OT1 mouse CD8+ T lymphocytes were found to increase in proliferation following their co-culture with H-2Kb A9 metastatic tumours treated with SIINKFEKL and cannabigerol or IFN-γ for 48 hours prior to co-culture, suggesting that the OT-1 CD8+ T lymphocytes are activated, which may be expected to result in cytolytic activity against these cells (Figure 5).
**Figure 5:** *Cannabinoids treated metastatic carcinomas function as antigen presenting cells. (A) We used CD8+ T lymphocytes from SIINFEKL-primed OT1 mice that recognize and respond to SIINFEKL peptide presented on MHC class I of metastatic Murine A9 lung carcinomas. A9 cells were treated with 0.055 μmol of Cannabigerol, or 5.832x10-6 nmol mL of IFN-γ used as a positive control. The negative control is CD8+ T cells alone or untreated A9 cells pulsed with the SIINFEKL peptide from ovalbumin. T cells were labeled with CFSE proliferation dye, which is reduced within the OT1 progeny cells as the generation number increases as an indication of proliferation. (B) Statistical assessment based using the one-way ANOVA with Tukey’s multiple comparison test of CFSE assay demonstrates both cannabinoids and IFN-γ resurrect antigen presentation in metastatic carcinomas. A P value smaller than 0.05 was considered significant. Analysis of the CFSE proliferation carried out at cellular proliferation generation 3 demonstrates that both cannabinoid and IFN-γ treated CFSE contain OT-1 CD8+ T lymphocytes proliferated significantly more than the OT-1 CD8+ T lymphocytes alone or untreated A9 cells pulsed with SIINFEKL.*
The data was analyzed further as described in earlier studies [55]. The cell number resulting from OT-1 CD8+ T lymphocytes proliferation was counted and plotted. The data from all the groups at different generation times was plotted in GraphPad prism and Gaussian distribution was applied. IFN-γ and cannabigerol treated antigen presenting cell groups showed Gaussian distribution in triggering CSFE loaded OT-1 CD8+ T lymphocytes to proliferate. To compare the OT-1 CD8+ T lymphocytes proliferation from treatment groups, the total cell number from the different treatment groups at each cell generation was plotted and compared using one way ANOVA with Tukey’s multiple comparison test. Any P value smaller than 0.05 was considered significant. *At* generation 1, neither the cannabigerol nor IFN-γ-treated antigen presenting cells triggered OT-1 CD8+ T lymphocytes proliferation greater than the vehicle treatment. However, at generation 3, both IFN-γ and cannabigerol treated antigen presenting cells triggered T cells proliferation significantly more than the vehicle treated group (Figure 5B; Appendix Figure 1).
## Cytokine profile in cannabigerol treated metastatic murine carcinomas
Next, changes in the expression of chemokines, cytokines and related molecules in response to cannabigerol and IFN-γ treatment were explored. Both treatments were shown to have similar effects on a range of immune markers, including the upregulation of IL-28A/B, CCL22, FGF-21 (Figure 6C), and most interestingly IL-33 (Figure 6A) and the downregulation of IL-6 (Figure 6D), and IL-11 (Figure 6A). Finally, there was an increase in VEGFA (Figure 6D). Cannabigerol was also found to cause a change in cytokines involved in inflammation, migration, growth and differentiation, angiogenesis, immune regulation, leukocyte development and metabolism (Figure 6). However, certain markers were differentially regulated by the treatments, offering insights into the potential anti-cancer mechanisms specific to cannabigerol and IFN-γ. Cannabigerol-specific effects included the inhibition of angiopoietin-1, MMP3 and VCAM-1, indicating that its anti-cancer effects may be mediated by the modulation of vascular-immune interactions. Angiopoietin-1 is a secreted glycoprotein that binds to endothelial cell-specific tyrosine-protein kinase receptors to promote in vascular development and angiogenesis. Matrix metalloproteinase 3 (MMP3) is a protein involved in the degradation of components of the extracellular matrix (fibronectin, laminin, collagens III, IV, IX, and X, and cartilage proteoglycans), with a known role in tumour initiation [56]. Vascular cell adhesion molecule 1 (VCAM-1) is a cell surface sialoglycoprotein expressed by cytokine-activated endothelium, important for adhesion of leukocytes to endothelial cells and subsequent signal transduction. Angiogenesis and endothelial cell adhesion are generally thought to promote tumour formation and migration [57]. On the other hand, treatment with IFN-γ, but not cannabigerol, was associated with a reduction in low density lipoprotein receptor (LDLR), which may be an additional anti-cancer mechanism specific to IFN-γ [58]. Finally, these data suggest that cannabigerol and IFN-γ exert their anti-cancer properties via the inhibition of STAT3 (upregulated by IL-11 and LIF) and c-Jun/AP-1 (downregulated by Pentraxin2/SAP), respectively.
**Figure 6:** *Fold change of cytokine production in Metastatic Carcinomas treated with cannabigerol or in supernatant upon treatment of 0.00875 mg/mL cannabigerol (0.05529 μmol) and 0.1ug/mL (100ng/mL) (5.832x10-6 nmol) IFN-γ relative to DMSO (vehicle)-treated metastatic A9 cells. (A–C) Fold change of cytokines present on microarray (D) Cytokines implicated in the IL4, 10, and 13 pathways. Data was determined using Proteome Profiler Mouse XL cytokine array kit and Image J protein analyzer add-on. Pathway analysis was determined using Reactome Database release 65, Pathway Brower Version 3.5.9.*
## Functional annotation of H3K27ac marks induced by cannabinoids in an antigen processing deficient metastatic carcinoma
H3K27Ac epigenetic modifications are generally associated with transcriptional activation of gene and H3K27Ac ChIPseq is an established method to identify genes and pathways that are induced following treatment with a drug. To understand how HDAC activators can potentially alter immune evasion in situ we conducted H3K27Ac ChIPseq on DMSO, IFN-γ and cannabigerol treated cells. Functional Annotation of H3K27ac regions from all samples demonstrated that the most significant alterations in histone modifications were observed at intronic and intergenic sites suggesting cannabigerol and IFN-γ alter H3K27Ac at enhancer sites (Figure 7A). We also found $40\%$ of acetylation marks were located in H3K27ac regions which are observed commonly in all samples (Figure 7B). Interestingly, in this common region set, we observed that the effects of cannabigerol were similar to IFN-γ suggesting that both cannabigerol and IFN-γ increase overall acetylation levels to initiate immune response (Figure 7C). A pathway analysis of closest genes with respect to overlap of Gained/*Cannabigerol* gene sets < 0.01 FDR were filtered out (Figure 7D). These included cell senescence, Class-I MHC mediated antigen processing and presentation, immune response genes related to DAP12 receptors in NK cells. IL-12 mediated signaling events, interferon alpha (IFN-α) and interferon beta (IFN-β) and gamma (IFN-γ) signaling pathways and antigen processing cross-presentation pathway genes were all enriched. This reinforces the observation that cannabigerol can reverse the immune-escape phenotype in metastatic tumours.
**Figure 7:** *Functional annotation of H3K27ac marks induced by cannabinoids in an antigen processing deficient metastatic carcinoma. (A) Gene regions modified by H3K27ac. Regions were plotted. (B) H3K27ac peaks locations were compared. (C) H3K27 acetylation levels of common regions were higher than all other combinations of interactions. At common regions, cannabigerol induction demonstrates increased global acetylation similar to IFN-γ. (D) Pathway analysis of closest genes with respect to overlap of Gained/Cannabigerol gene sets < 0.01 FDR were filtered out. Functional Annotation of H3k27ac regions showed that cannabigerol and IFN-γ acetylation profile enriched on intergenic/intronic parts of the genome. DMSO and IFN-γ samples then annotated as DMSO only (Lost, n= 8588), DMSO-IFN-γ common (Common, n=39311) and IFN-γ only (Gained, n= 15886). DMSO and cannabigerol samples then annotated as DMSO only (n= 15972), DMSO-cannabigerol common (n=31927) and cannabigerol only (Gained, n= 16746).*
## Discussion
Cannabinoids have demonstrated biological and pharmacological effects, including pain reduction, inhibition of nausea, appetite induction, anxiety and depression reduction, among others [59]. Some of these activities are of benefit in cancer therapy, especially for reducing nausea, pain, and depression, but also for increasing appetite [60]. While there are some reports of a direct cytotoxic effect of cannabinoids on tumor cells [61], there are no publications that demonstrate MHC-I induction by cannabinoids. Given the public interest in this area, the identification of cannabinoids that possess “immune escape” reversing activities may have significant impact on cancer immunotherapy and wellness seeking.
Understanding the mechanisms that promote cancer metastasis is profoundly important, as metastatic cancers account for $90\%$ of all cancer deaths [1]. The cellular immune system plays an essential role in reducing cancer progression through immune surveillance. In the absence of functional antigen processing machinery, adaptive immune responses fail to limit the emergence of tumours. During endogenous antigen processing, resident proteins are broken down to peptides and loaded onto MHC-I molecules. These subsequently cycle to the plasma membrane, peptide in tow, to present their cargo to T cell receptors (TCR) expressed by CTL. The TCR recognize the precise combination of specific the peptide bound to MHC-I molecules with exquisite accuracy. *To* generate the peptides, exogenous proteins are degraded by proteasomes in the cytosol before being transported into the ER by TAP-1 and -2. In the ER, as a result of the concerted action of a number of molecular chaperone proteins, the peptides are loaded onto the MHC-I molecules before being transported to the cell surface (1, 22–27). Overall, this mechanism antigenically defines self and non-self, thereby allowing CTL to distinguish between normal cells and cancerous or virus-infected cells. Following this interaction that provide a cue for activation of the effector functions of CTL, a specific immune response can be initiated, which generally leads to the destruction of the cancerous or virus infected cells [23, 62, 63] but may also act as a powerful selective force for the diabolical emergence of virus or tumour antigen escape mutants (1, 22–27).
Many cancerous cells display down-regulated MHC-I cell surface expression but do not possess structural mutations in either MHC-I genes or β2-microglobulin (41, 64–67). Reduced MHC-I expression can result at least in part from the downregulation or mutation of other genes such as transporters (for example, TAP-1, TAP-2), proteasome components (LMP), and other accessory proteins involved in the antigen presentation and processing pathway. However, immune escape is not exclusively regulated by defects or mutations in APM genes but can also be epigenetically regulated and can be restored by treatment with histone deacetylase inhibitors (HDACi), such as TSA [29, 31]. With this in mind, we conducted a screen and found that (i) cannabinoids can reverse the immune escape phenotype of both human and murine metastatic tumours and (ii), metastatic tumours induced by cannabinoids can upregulate MHC-I expression and act as MHC-I antigen presenting cells to promote CD8+ T lymphocytes proliferation in vitro.
The molecular mechanisms linking cannabinoid administration to MHC-I induction remain to be fully defined. Cannabinoids are known to modulate G protein-coupled receptors (GPCR), transient receptor potential channel, and voltage-dependent membrane channel activity [33, 68, 69]. We found that engagement of the cannabinoid receptors, CB1R or CB2R, per se does not activate the MHC-I pathway, as neither of the endogenous cannabinoids, 2-AG and AEA, induced MHC-I expression by COLO 205, which expresses both cannabinoid receptors [70]. This suggests that other receptors also associated with cannabinoid signaling may be involved, such as the GPCRs GPR3, GPR6, GPR12, GPR18 and GPR55, serotonin receptors 5-HT1A and 5-HT2A, μ- and δ-opioid receptors, and the adenosine A3 receptor [68]. Phytocannabinoids can also activate transient receptor potential channels of the vanilloid subtype and voltage-gated sodium channels, which are expressed in various cancers. Cannabinoids also inhibit voltage-gated calcium channels, specifically the Cav1 and Cav3 families [69]. However, the fact that low micromolar concentrations of cannabinoids are required to induce MHC-I suggests that the molecules may act through a non-receptor-mediated mechanism. The distinct induction kinetics displayed by IFN-γ and CBD suggest that different pathways are invoked, although it is possible that IFN-γ acts downstream of CBD en route to MHC-I induction. The long lag period of 48 hr before robust MHC-I upregulation suggests that the induction by CBD depends upon activation of new gene expression. Consistent with this possibility, low micromolar levels of CBD have been found to regulate expression of cellular stress response genes in microglial and lung epithelial cells [71, 72], genes involved in neurotransmitter signaling in neural cells [73], and genes associated with cell proliferation and division and DNA repair in squamous cell carcinoma cells lines in head and neck cancers [74]. Interestingly, in a study examining gene expression in the A549 lung epithelial cell line infected with SARS-CoV-2, CBD was found to reverse the changes in gene expression induced by the virus and to upregulate genes that promote innate immunity such as receptors for IFN-γ and IFN-β and the signaling proteins STAT1 and STAT2 that transduce the interferon signal [71]. In a study performed by van Breeman et al. [ 75], cannabigerolic acid (CBG-A) and cannabidiolic acid (CBD-A) prevented infection of human epithelial cells by a pseudovirus expressing the SARS-CoV-2 spike protein and prevented entry of live SARS-CoV-2 into cells.
In this study, other cytokines of importance to cancer that were upregulated upon the use of cannabigerol included IL-28A, CCL22, FGF-21 (Figure 6C), and IL-33 (Figure 6A). IL-33 is termed an “alarmin” and its expression is associated with the upregulation of MHC- I and APM [30, 31] and is decreased during metastasis [30, 31]. The loss of IL-33 expression is also a predictor of poor outcome in kidney and prostate carcimonas [30, 31]. In the same context, it was observed that normal epithelial cells and MHC-I+ primary tumours express IL-33 [31] and endogenous IL-33 acts in an autocrine loop to induce MHC-I expression thereby insuring immune surveillance of normal epithelial cells and limiting the emergence of tumours by surveying primary tumours as well. The transcriptional link between these two genes has also been previously demonstrated by genetic complementation experiments where a recombinant IL-33 gene was reintroduced into metastatic cells resulting in a rescue of MHC-I expression and tumour recognition by CTL and reduction of tumour growth in vivo [30]. Furthermore, IL-33 is also known to be the hallmark cytokine for activating Group 2 innate lymphoid cells (ILC2s). We subsequently demonstrated ILC2 can mediate and enhance TH1 CTL responses and are directly involved in tumour immunosurveillance and elimination in mice [30]. Once ILC2s are functionally activated, they alter the tumour microenvironment triggering both innate and adaptive immune responses. We used genetic studies to conclusively demonstrate that ILC2s dramatically reduce the number of circulating tumour cells, resulting in the reduction of the metastasis of tumours [30]. These studies established a new, hiterto undescribed, form of immune escape mechanism involving the loss of IL-33 and muting ILC2 function in TH1 responses. The data in the present study describes the ability of cannabigerol to increase IL-33 expression and uncover a potential method by revive IL-33 expression and ILC2 function leading to enhanced CTL responses against tumours. Additionally, CCL22 is usually secreted in response to IFN-γ and TNF alpha or IL-4 [35] and is associated with the induction of chemotaxis of T lymphocytes by the binding to CCR4. The production of CCL22 by cannabigerol-treated metastatic cells could provide a method by which the T lymphocytes could be recruited into the tumour site.
Interestingly, we observed downregulation of IL-6 in cannabigerol-treated metastatic A9 cells (Figure 6D). IL-6 is associated with differentiation of naïve CD4+ lymphocytes against a specific antigen, and of differentiation of CD8+ naïve cells into CTLs [36]. Also of note, the cannabigerol-treated A9 cells also appeared to produce IL1RN, an IL-1 antagonist [37] and negative regulator of inflammation, providing an additional avenue for explaining the anti-inflammatory role of cannabinoids. Notably, There was also an increase in VEGFA expression in cannabigerol-treated A9 cells. VEGFA is a crucial gene for the formation of blood vessels and angiogenesis (VEGFA NIH) [38], during which the new blood vessels supply nutrients to a tumour and increase tumorigenesis [3]. In the future, it might be interesting examine if angiogenesis is altered in cannabigerol treated mice.
To further understand the mechanism by which cannabinoids may act to reverse immune escape of metastatic carcinomas, we conducted H3K27Ac ChIPseq study to examine H3K27Ac, an epigenetic mark associated with gene activation. Following treatment with a cannabigerol or IFN-γ. Functional annotation of regions marked by H3k27ac showed that both cannabigerol and IFN-γ acetylation profile enriched on intergenic/intronic parts of the genome. A common gene set was shared by cannabigerol and IFN-γ induction suggesting cannabinoids may share some of the attributes of IFN-γ. Finally, gene enrichments analysis highlighted genes involved in cell senescence, MHC-I mediated antigen processing and presentation, and immune response genes related to DAP12 receptors in NK cells. IL-12 mediated signaling events, IFN-α, IFN-β, and IFN-γ signaling pathways and antigen processing cross-presentation pathway genes were all enriched. This reinforces the observation that cannabinoids can reverse the immune-escape phenotype in metastatic tumours and supports the somewhat surprising conclusion that, in many ways, cannabigerol acts like IFN-γ, a master-regulator of TH1 responses.
While our data advance the potential of cannabinoids to reverse the immune editing and escape that are characteristic of metastatic cancer, two important factors warrant consideration. First, relatively high concentrations of cannabinoids are needed to induce MHC-I expression – e.g., an EC50 of 11 μM for CBD on COLO 205 cells – suggesting that patients will require high doses for the effect to manifest. Such levels of CBD should be safely achievable, as up to 45 μM CBD have been reported in the plasma of mice without severe adverse consequences [76]. High dosing is also well tolerated in humans, with subjects receiving up to 1500 mg/d of CBD reporting only mild adverse effects (77–80).
The second factor to consider is that cannabinoids have been reported to have anti-inflammatory and immunosuppressive properties. This raises the possibility that any pro-immunosurveillance benefit of cannabinoid treatment might be countered by a detrimental immunosuppressive effect. Some Cannabinoids have been reported to reduce antibody and T Lymphocyte responses and increased susceptibility to infection (81–85). In several in vitro and in vivo models of infection and autoimmunity, THC, CBD, cannabinol, and synthetic cannabinoids have all been found to alter immune function (86–88). These data should be considered with caution, however, because the source and physiological context from which experimental cells are derived may significantly impact how they respond to treatment. For example, peripheral blood mononuclear cells (PBMCs) from multiple sclerosis patients were found to be more sensitive to the anti-proliferative effect of CBD or THC than PBMCs from normal or cancer patients [89]. Furthermore, since nearly all the studies demonstrating anti-inflammatory and immunosuppressive properties of cannabinoids were conducted in cell culture or in rodent models, there is no clear demonstration of immunomodulatory effects of cannabinoids in humans [85]. In fact, as noted above, extensive clinical testing of high dose CBD (Epidiolex) in patients with Lennox-Gastaut or Dravet syndromes, two rare, severe forms of epilepsy, found minimal evidence of compromised immunity [77, 78]. Similarly, cancer patients treated with pharmacologically active doses of the synthetic THC analogs, dronabinol and nabilone, for chemotherapy-induced nausea or anorexia showed no signs of reduced immunity (90–94). Likewise, HIV patients treated with dronabinol for HIV wasting syndrome exhibited no increase in opportunistic infections [95]. Even advanced HIV patients at elevated risk for opportunistic infections and treated with dronabinol to ameliorate anorexia showed no greater incidence of infection than non-treated patients [96]. A possible immunologically beneficial effect of THC treatment in HIV subjects was noted in a non-human primate model: chronic administration of THC to male rhesus macaques infected with simian immunodeficiency virus (SIV; model for HIV infection) resulted in decreased viral load and increased lifespan compared to control animals [97]. With respect to cancer, several studies have shown that cannabinoids preferentially inhibit or kill human cancer cells in vitro [98]. In a xenograft mouse model used to study head and neck squamous cell carcinoma, CBD alone slowed tumor growth and synergized with cisplatin for a dramatic delay in tumor growth [74]. Whether this translates to a benefit for patients remains to be seen. Nevertheless, the preponderance of evidence indicates that administration of cannabinoids to cancer patients is unlikely to result in generalized immune suppression, supporting the possibility that the immunosurveillance-promoting activity of these molecules will prevail in these patients.
Recent advances in immunotherapy have significantly improved outcomes for some patients with cancer. Combination therapy of antibodies against the antagonistic co-inhibitory receptor Programmed death-1(PD-1) [99] and agonistic OX40 [100], that are currently under investigation [101]. Likewise, our data suggest that cannabinoids, similar to complementation with TAP genes [1] may potentiate immune-checkpoint blockade inhibitor activity by reversing immune escape and restoring tumour visibility to the adaptive immune system. The investigation of pure cannabinoid molecules rather than plant extracts or formulations in combination with immune- checkpoint blockade inhibitors may facilitate the use of cannabinoids in clinical practice.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number can be found below: *The data* discussed in this publication have been deposited in NCBI's Gene Expression Omnibus [102] and are accessible through GEO Series accession number GSE179897.
## Author contributions
Conceived Project: WAJ. Designed research: SD, LN, SE, CP, CW, RA, WAJ. Performed research: SD, LN, SE, NG, IS, LM, CD, PG, TM, NL, BE, RA, LT, EG. Analyzed data: SD, LN, SE, CD, NG, IS, LM, CP, SK, CW, PG, TM, NL, BE, RA, LT, GC, EAH, WAJ. Wrote paper: SD, SE, CW, WAJ. Edited paper: SD, LN, SE, CP, TM, NL, RA, GC, EAH, WAJ. All authors contributed to the article and approved the submitted version.
## Conflict of interest
WAJ was the founder and held financial interest in the University of British Columbia start-up, Pascal Biosciences. NG, CW, PG, and LT were employees of and hold a financial interest in Pascal Biosciences.
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/fimmu.2022.982082/full#supplementary-material
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|
---
title: 'Roles of gut microbiome in epilepsy risk: A Mendelian randomization study'
authors:
- Youjie Zeng
- Si Cao
- Heng Yang
journal: Frontiers in Microbiology
year: 2023
pmcid: PMC10010438
doi: 10.3389/fmicb.2023.1115014
license: CC BY 4.0
---
# Roles of gut microbiome in epilepsy risk: A Mendelian randomization study
## Abstract
### Background
Recent studies have suggested an association between gut microbiomes (GMs) and epilepsy. However, the GM taxa identified in different studies are variable. In addition, observational studies cannot indicate causality. Therefore, our study aimed to explore the causal association of GMs with epilepsy and identify the most influential GM taxa.
### Methods
We conducted a Mendelian randomization (MR) study using summary statistics from genome-wide association studies (GWAS) of 211 GM taxa and epilepsy. The GWAS summary statistics for 211 GM taxa (from phylum to genus level) were generated by the MiBioGen consortium, while the FinnGen consortium provided the GWAS summary statistics for epilepsy. The primary analytical method to assess causality was the inverse-variance weighted (IVW) approach. To complement the IVW method, we also applied four additional MR methods: MR-Egger, weighted median, simple mode, and weighted. In addition, we conducted sensitivity analyses using Cochrane’s Q-test, MR-Egger intercept test, MR-PRESSO global test, and leave-one-out analysis.
### Results
We evaluated the causal effect of 211 GM taxa (from phylum to genus level) on epilepsy, generalized epilepsy, and focal epilepsy. After using the Bonferroni method for multiple testing correction, Class Betaproteobacteria [odds ratio (OR) = 1.357, $95\%$ confidence interval (CI): 1.126–1.635, $$p \leq 0.001$$] and Order Burkholderiales (OR = 1.336, $95\%$ CI: 1.112–1.606, $$p \leq 0.002$$). In addition, 21 nominally significant causal relationships were also identified. Further, the MR-Egger intercept test and MR-PRESSO global test suggested that our MR analysis was unaffected by horizontal pleiotropy ($p \leq 0.05$). Finally, the leave-one-out analysis suggested the robustness of the results.
### Conclusion
Through the MR study, we analyzed the causal relationship of 211 GM taxa with epilepsy and determined the specific intestinal flora associated with increased epilepsy risk. Our findings may provide helpful biomarkers for disease progression and potential candidate therapeutic targets for epilepsy. In addition, in-depth analysis of large-scale microbiome GWAS datasets based on metagenomics sequencing is necessary for future studies.
## 1. Introduction
Epilepsy is a common, chronic neurological disorder characterized by sudden abnormal excessive ultra-synchronized neuron discharges that result in temporary involuntary brain dysfunction (Fisher et al., 2005). Globally, there are 70 million people with epilepsy, with the highest incidence in infants and the elderly, posing a tremendous social burden throughout the world (Collaborators, 2019; Thijs et al., 2019). Despite advances and innovations in antiepileptic medications, approximately one-third of the patients suffer from drug-resistant epilepsy (de Biase et al., 2019). Therefore, further insights into the pathogenesis and the exploration of novel therapeutic targets for epilepsy are required.
There is growing evidence that the gut microbiome (GM) can regulate host homeostasis, including cardiovascular function, metabolism, and immune/inflammatory response (Le Chatelier et al., 2013). Recent research has shown that GMs play a role in neuropsychiatric disorders (Iannone et al., 2019), as they regulate brain function and behavior via the microbiota-gut-brain axis (Johnson and Foster, 2018). Differences in GM taxa have been identified in epilepsy patients compared to controls (Dong et al., 2022). The ketogenic diet (KD) is a treatment approach for intractable epilepsy (D’Andrea Meira et al., 2019). During the KD treatment of drug-resistant epilepsy, the GM pattern was altered simultaneously (Lindefeldt et al., 2019). Consequently, GMs may be involved in the crosstalk between KD and epilepsy (Fan et al., 2019). Furthermore, researchers are investigating the possibility of using the change in GM composition as a surrogate marker for the efficacy of the KD treatment in patients with drug-resistant epilepsy (Thambi et al., 2020). However, the effect of various GM taxa on epilepsy has not yet been determined. The 16S rRNA and metagenomic sequencing are the most widely used methods for identifying GM taxonomic characteristics (Durazzi et al., 2021), providing the basis for identifying the potential role of GM taxa. Recent research has increasingly focused on the causal effects of GMs on epilepsy, particularly refractory epilepsy (Lum et al., 2020). In addition, perturbations for certain GM taxa levels have been reported to affect the activity of epileptic neurons (Darch and McCafferty, 2022).
Nevertheless, the specific contribution of various GM taxa to epilepsy warrants further exploration. Similar to randomized controlled trials (RCT), the Mendelian randomization (MR) study is a novel research method for exploring the causal association between exposure and outcome (Swanson et al., 2017). In MR studies, single nucleotide polymorphisms (SNPs) are considered instrumental variables (IVs) to estimate the causal association between exposures and the outcomes of interest (Burgess et al., 2017). SNPs conform to the principle of random assignment of genetic variants at meiosis, which avoids the effect of confounding factors and the potential impact of reverse causation since genetic variants precede the onset of disease (Lawlor et al., 2008). Therefore, the causal associations of exposure factors of interest to outcomes can be identified more rapidly by MR analysis compared to RCT. For example, a recent MR study by Cai et al. has identified several blood metabolites with potential causal associations with epilepsy (Cai et al., 2022). Here, we conducted an MR study using large-scale GWAS summary statistics of GMs and epilepsy to identify potentially influential GM taxa, which could provide confidence to some existing evidence and may yield new insights into the prevention and treatment of epilepsy.
## 2.1. Study design
The overall flow chart of this study is shown in Figure 1. MR studies are required to satisfy the following three assumptions: (i) IVs are strongly associated with exposure factors, (ii) IVs are independent of confounding factors, and (iii) IVs are solely associated with outcomes through exposure factors (Burgess et al., 2017). Specifically, we identified GM taxa that have a causal effect on epilepsy, generalized epilepsy, and focal epilepsy by performing a two-sample MR analysis. Our results were reported in accordance with the STROBE-MR guidelines (Skrivankova et al., 2021).
**Figure 1:** *Overall flow chart of this study.*
## 2.2. Data sources for the exposure
A study from the MiBioGen consortium analyzed the host genotypes and 16S fecal microbiomes rRNA gene sequencing profiles of 18,340 participants (Kurilshikov et al., 2021). This GWAS study examined 211 GM taxa (from genus to phylum level) and ultimately identified genetic variants associated with nine phyla, 16 classes, 20 orders, 35 families, and 131 genera. The GWAS summary statistics of GMs are available for download at1 (Swertz and Jansen, 2007; Swertz et al., 2010; van der Velde et al., 2019).
## 2.3. Data sources for the outcome
We obtained GWAS summary statistics for epilepsy from the FinnGen consortium R7 release2 (Kurki et al., 2022). In addition, we downloaded GWAS summary data for generalized epilepsy and focal epilepsy. Epilepsy diagnosis in FinnGen was based on G40 in the 10th version of the International Classification of Diseases (ICD). Cases of generalized and focal epilepsy were narrower endpoints of epilepsy under the strict definition. Table 1 shows the details of the exposure and outcome analyzed in this MR study.
**Table 1**
| Trait | Consortium | Samples | Case | Control |
| --- | --- | --- | --- | --- |
| Exposure | Exposure | Exposure | Exposure | Exposure |
| 211 GM taxa | MiBioGen | 18340 | / | / |
| Outcome | Outcome | Outcome | Outcome | Outcome |
| Epilepsy | FinnGen (R7) | 252026 | 8523 | 243503 |
| Generalized epilepsy | FinnGen (R7) | 302828 | 2197 | 300631 |
| Focal epilepsy | FinnGen (R7) | 301493 | 862 | 300631 |
## 2.4. Identification of IVs
SNPs strongly associated with each GM taxon were used as IVs in this MR study. Since the number of IVs obtained under the strict threshold ($p \leq 5$ × 10−8) was extremely minimal, we adopted a more comprehensive threshold ($p \leq 1$ × 10−5) to obtain relatively more IVs to achieve relatively robust results. In addition, to ensure each IV’s independence, SNPs within a window size of 10,000 kb at a threshold of r2 < 0.001 were pruned to mitigate linkage disequilibrium (LD). Then, palindromic SNPs and SNPs not present in the outcome were removed from the IVs. Finally, we calculated the F-statistic of IVs to assess the degree of weak instrumental bias. If the F-statistic >10, it was considered that no bias was caused by weak IVs (Pierce et al., 2011).
## 2.5. Statistical methods
The inverse variance weighted fixed-effect (IVW-FE) method or the IVW random effect (IVW-RE) method was used as the primary MR method for inferring causality. The choice of IVW-FE or IVW-RE was determined based on Cochrane’s Q heterogeneity test. The IVW method is an extension of the Wald ratio estimator based on the principles of Meta-analysis (Pagoni et al., 2019).
For each GM taxon, if the IVW method identified a causal association ($p \leq 0.05$), four additional MR methods, MR-Egger, weighted median, simple mode, and weighted mode, would be performed to supplement the IVW result (Bowden et al., 2016; Burgess and Thompson, 2017). The criterion for using the weighted median method is that at least $50\%$ of the SNPs must satisfy the premise that they are valid IVs (Bowden et al., 2016). The MR-Egger method provides unbiased estimates even when all selected IVs are multivariate (Burgess and Thompson, 2017). Finally, the results of causal associations were presented as odds ratios (OR) and $95\%$ confidence intervals ($95\%$ CI). The significance threshold was set at $p \leq 0.05.$ In addition, the Bonferroni method was used for multiple testing corrections. The threshold for various levels was $p \leq 0.05$/n, where n represents the number of taxa at a particular level.
Only exposure-outcome pairs with the same direction identified by all MR methods were considered to have a causal association. To test the stability of the causal association, we further performed several sensitivity analyses. First, the MR-Egger intercept test and MR-PRESSO global test were utilized to detect horizontal pleiotropy (Rees et al., 2017; Verbanck et al., 2018). In addition, the leave-one-out analysis was performed to assess the robustness of the results. Furthermore, we performed replicated MR analyses after excluding potential confounders from the IVs. Specifically, the confounders-related SNPs were retrieved from the PhenoScanner V2 database3 (Staley et al., 2016; Kamat et al., 2019), including education level (Wang et al., 2021), diabetes (Marcovecchio et al., 2015), obesity (Hafizi et al., 2017), and smoking (Yuan et al., 2021).
All analyses in this study were performed based on R software(version 4.2.1). The “TwoSampleMR” R package4 and the “MRPRESSO” R package5 were used in our MR study.
## 3.1. Details of IVs
Overall, 2,252 SNPs were identified as final IVs. These SNPs were classified according to five levels: phylum, class, order, family, and genus. Specifically, there were 102 IVs in 9 phyla, 179 IVs in 16 classes, 216 IVs in 20 orders, 383 IVs in 35 families, and 1,372 IVs in 131 genera. In addition, all IVs were more strongly associated with exposure than with outcome (pexposure < poutcome), and all F-statistics were greater than 10. Details of the IVs are presented in Supplementary Table S1.
## 3.2. MR analysis
First, we performed MR analysis to assess the causal association of 211 GM taxa at five levels with epilepsy. The results assessed by the IVW-FE showed that class Betaproteobacteria (ID: 2867), class Verrucomicrobiae (ID: 4029), order Burkholderiales (ID: 2874), order Verrucomicrobiales (ID: 4030), family Verrucomicrobiaceae (ID: 4036), genus Akkermansia (ID: 4037), genus Anaerotruncus (ID: 2054) and genus Ruminococcaceae UCG 014 (ID: 11371) were associated with an increased risk for epilepsy, while genus Eubacterium Xylanophilus Group (ID: 14375) and genus *Unknown genus* (ID: 826) were associated with a decreased risk for epilepsy (Figure 2A). Furthermore, the results of Cochran’s Q test indicated the absence of heterogeneity. After applying the Bonferroni correction, class Betaproteobacteria (ID: 2867) [OR = 1.357 (1.126, 1.635), $$p \leq 0.001$$] and order Burkholderiales (ID: 2874) [OR = 1.336 (1.112, 1.606), $$p \leq 0.002$$] remained risk factors for epilepsy.
**Figure 2:** *(A) Forest plot of GM taxa associated with epilepsy identified by IVW_FE method. (B) Forest plot of GM taxa associated with generalized epilepsy identified by IVW_FE method. (C) Forest plot of GM taxa associated with focal epilepsy identified by IVW_FE method.*
Subsequently, we further evaluated the causal association of 211 GM taxa with generalized epilepsy using the IVW-FE method. The results showed that phylum Actinobacteria (ID: 400) and genus Bifidobacterium (ID: 436) were associated with an increased risk for generalized epilepsy, while class Bacilli (ID: 1673), genus Coprobacter (ID: 949), genus *Unknown genus* (ID: 826) and genus *Unknown genus* (ID: 1868) were associated with a decreased risk for generalized epilepsy (Figure 2B). However, after Bonferroni correction, the causal effect of these GM taxa on generalized epilepsy was insignificant. Furthermore, the results of Cochran’s Q-test suggested heterogeneity in the MR analysis of Genus *Unknown genus* (ID: 1868); thus, the IVW random effect (RE) was applied to explain the causal association of this GM taxon with generalized epilepsy, with results indicating no causal association.
Finally, we assessed the causal association of 211 GM taxa with focal epilepsy using the IVW-FE method. The results showed that phylum Verrucomicrobia (ID: 3982), class Verrucomicrobiae (ID: 4029), order Verrucomicrobiales (ID: 4030), family Verrucomicrobiaceae (ID: 4036), genus Akkermansia (ID: 4037), genus Alloprevotella (ID: 961), and genus Sutterella (ID: 2896) were associated with an increased risk for focal epilepsy, while genus Clostridium Sensu Stricto 1 (ID: 1873) was associated with a decreased risk for focal epilepsy (Figure 2C). The results of Cochran’s Q test suggested no heterogeneity in the MR analysis. However, after Bonferroni correction, the causal effect of these GM taxa on generalized epilepsy was insignificant.
In addition, four additional methods, MR-Egger, weighted median, simple mode, and weighted mode, were performed to assess the causal effect of these GM taxa on epilepsy (Figure 3), generalized epilepsy (Figure 4), and focal epilepsy (Figure 5). Similarly, the results were parallel to the IVW results (Supplementary Figure S1). The heat map visualized the causal association of GM taxa identified in our study with epilepsy, generalized epilepsy, and focal epilepsy (Figure 6).
**Figure 3:** *Diverse Mendelian randomization (MR) results for 10 GM taxa causally associated with epilepsy.* **Figure 4:** *Diverse MR results for 5 GM taxa causally associated with generalized epilepsy.* **Figure 5:** *Diverse MR results for 8 GM taxa causally associated with focal epilepsy.* **Figure 6:** *Heat map of GM taxa causally associated with epilepsy, generalized epilepsy, and focal epilepsy identified by IVW method. Red represents risk factors, while blue represents protective factors.*
## 3.3. Sensitivity analysis
The results of the MR-Egger intercept test and MR-PRESSO global test showed that there was no horizontal pleiotropy (pMR-Egger intercept > 0.05 and global pMR-PRESSO > 0.05) in (i) IVs of 10 GM taxa associated with epilepsy (Table 2), (ii) IVs of 5 GM taxa associated with generalized epilepsy (Table 3), and (iii) IVs of 8 GM taxa associated with focal epilepsy (Table 4). In addition, the leave-one-out analysis indicated the robustness of the MR results since excluding any one IV did not shift the overall results (Supplementary Figure S2).
## 3.4. Replicated analysis after removing confounders-related IVs
Among the IVs of 10 GM taxa associated with epilepsy, rs4936098 was associated with obesity and rs2321387 with education level. In addition, among the IVs of 5 GM taxa associated with generalized epilepsy, rs12634544, rs182549, rs1397793, rs7570971, rs35344081, and rs35344081 were associated with obesity; rs182549 with diabetes mellitus; and rs2952251 with smoking. Furthermore, among the IVs of 8 GM taxa associated with focal epilepsy, rs4936098 was associated with obesity, and rs2321387 with education level. After removing these SNPs from the IVs, the causal associations of these GM taxa were re-evaluated by the IVW-FE method. The results showed that, except for phylum Actinobacteria (ID: 400), the causal effects of the above GM taxa remained significant (Table 5).
**Table 5**
| Exposure | Outcome | p-value | OR (95% CI) |
| --- | --- | --- | --- |
| Phylum | Phylum | Phylum | Phylum |
| Actinobacteria (ID: 400) | Generalized epilepsy | 0.132 | 1.318 (0.920, 1.889) |
| Verrucomicrobia (ID: 3982) | Focal epilepsy | 0.017 | 1.697 (1.101, 2.616) |
| Class | Class | Class | Class |
| Betaproteobacteria(ID: 2867) | Epilepsy | 0.001 | 1.381 (1.136, 1.679) |
| Verrucomicrobiae (ID: 4029) | Epilepsy | 0.030 | 1.186 (1.016, 1.384) |
| Bacilli (ID: 1673) | Generalized epilepsy | 0.025 | 0.718 (0.537, 0.960) |
| Verrucomicrobiae (ID: 4029) | Focal epilepsy | 0.008 | 1.900 (1.178, 3.063) |
| Order | Order | Order | Order |
| Burkholderiales (ID: 2874) | Epilepsy | 0.002 | 1.359 (1.121, 1.648) |
| Verrucomicrobiales (ID: 4030) | Epilepsy | 0.030 | 1.186 (1.016, 1.384) |
| Verrucomicrobiales (ID: 4030) | Focal epilepsy | 0.008 | 1.900 (1.178, 3.063) |
| Family | Family | Family | Family |
| Verrucomicrobiaceae (ID: 4036) | Epilepsy | 0.030 | 1.186 (1.016, 1.385) |
| Verrucomicrobiaceae (ID: 4036) | Focal epilepsy | 0.008 | 1.900 (1.178, 3.063) |
| Genus | Genus | Genus | Genus |
| Eubacterium Xylanophilus Group (ID: 14375) | Epilepsy | 0.014 | 0.816 (0.694, 0.959) |
| Akkermansia (ID: 4037) | Epilepsy | 0.030 | 1.186 (1.017, 1.385) |
| Anaerotruncus (ID: 2054) | Epilepsy | 0.011 | 1.238 (1.050, 1.459) |
| Ruminococcaceae UCG014(ID: 11371) | Epilepsy | 0.030 | 1.178 (1.016, 1.367) |
| Unknown genus (ID: 826) | Epilepsy | 0.004 | 0.819 (0.715, 0.939) |
| Bifidobacterium (ID: 436) | Generalized epilepsy | 0.036 | 1.389 (1.021, 1.890) |
| Coprobacter (ID: 949) | Generalized epilepsy | 0.044 | 0.787 (0.624, 0.993) |
| Unknown genus (ID: 826) | Generalized epilepsy | 0.005 | 0.682 (0.524, 0.889) |
| Akkermansia (ID: 4037) | Focal epilepsy | 0.008 | 1.899 (1.178, 3.062) |
| Alloprevotella (ID: 961) | Focal epilepsy | 0.025 | 1.499 (1.052, 2.137) |
| Clostridium Sensu Stricto 1(ID: 1873) | Focal epilepsy | 0.024 | 0.534 (0.310, 0.919) |
| Sutterella (ID: 2896) | Focal epilepsy | 0.032 | 1.715 (1.048, 2.806) |
## 4. Discussion
Our study comprehensively assessed the causal effect of 211 GM taxa (from phylum to genus level) on epilepsy and its sub-types. Finally, we identified a total of 23 causal relationships, of which 21 were nominal causal relationships, and two were strong causal relationships, thus highlighting the importance of GMs in epilepsy.
Accumulating evidence has suggested crosstalk between GMs and the central nervous system (CNS) (Cryan and Dinan, 2012). Investigations have shown that GMs play a vital role in the development of the enteric nervous system, blood–brain barrier, and glial cells, which are all important for cognitive development and behavior regulation (Braniste et al., 2014; Collins et al., 2014). Various neurological disorders, including multiple sclerosis (Jangi et al., 2016), autism (Mulle et al., 2013), Alzheimer’s disease (Jiang et al., 2017), and Parkinson’s disease (Parashar and Udayabanu, 2017), have been linked to intestinal dysbiosis. Recent findings also suggest that GMs may also play a role in epilepsy (Russo, 2022). Several studies have examined the effect of the KD, a treatment for refractory epilepsy, on GMs to explore the potential mechanisms of GMs in KD treatment (Lum et al., 2020). However, it is inconclusive which GM taxa have the most significant impact on epilepsy. As one-third of patients with epilepsy are diagnosed with refractory epilepsy (Dahlin and Prast-Nielsen, 2019), exploring biomarkers of epilepsy on the GMs level could offer promising alternative treatment options and potentially prevent the need for invasive treatments such as vagus nerve stimulation (VNS) or epilepsy surgery (Braakman and van Ingen, 2018).
Our study identified two strong causal relationships. Class Betaproteobacteria (OR = 1.357, $95\%$ CI: 1.126–1.635, $$p \leq 0.001$$) and Order Burkholderiales (OR = 1.336, $95\%$ CI: 1.112–1.606, $$p \leq 0.002$$) significantly elevated the epilepsy risk after Bonferroni correction. Burkholderiales, an order of Betaproteobacteria, was found to have a potential impact on epilepsy from our MR study, which was consistent with the findings of some previous investigations. For instance, Safak et al. identified the genus Delftia and genus Lautropia, which are members of Burkholderiales, to be significantly higher in the intestine of epilepsy patients versus healthy individuals (Safak et al., 2020). In addition, another genus of Burkholderiales, Sutterella, which was reported with increased intestinal abundance in adult patients with epilepsy (Dong et al., 2022), was also identified in our study to be nominally associated with an increased risk of focal epilepsy. The present MR study could provide evidence and confidence for the increased level of genera belonging to Order Burkholderiales in the intestines of epilepsy patients.
It’s important to note that the Bonferroni correction can result in false negatives. Our findings showed 21 GM taxa with nominal causal connections, but these correlations vanished after applying the Bonferroni correction. This may be due to the crosstalk between the gut-brain axis being usually coordinated by multiple factors and that the role of a single microbiota in the genus level in causing disease may not be as important as previously thought. In fact, several GM taxa with nominal causal relationships identified in this study corroborate the findings of previous research. For instance, Huang et al. revealed that patients with cerebral palsy and epilepsy contained a higher proportion, in comparison to healthy controls, of Bifidobacterium and Akkermansia (Huang et al., 2019). In addition, Gong and colleagues identified Bifidobacterium, Ruminococcaceae UCG 014, and Akkermansia at the genus level were increased in patients with epilepsy compared to healthy controls (Gong et al., 2020). Further, Lee et al. identified *Enterococcus faecium* (species of class Bacilli), *Bifidobacterium longum* (species of genus Bifidobacterium), and *Eggerthella lenta* (species of phylum Actinobacteria) as biomarkers for drug-resistant epilepsy (Lee et al., 2021). Although only nominal causal associations were identified at the genus level for these GM taxa, the coordination and crosstalk between various GM taxa remain worthy of in-depth study in the future.
The mechanisms involved in the relationship between GMs and epilepsy have not been fully determined. However, some evidence suggests potential mechanisms. ( i) Studies have reported that GMs can alter neurotransmitter levels such as glutamate, gamma-aminobutyric acid (GABA), 5-hydroxytryptamine (5-HT) (Mittal et al., 2017), as well as increase levels of cytokines, chemokines, such as TNF⍺ and MCP-1, lipopolysaccharides (LPS) which led to generalized immune activation or inflammation (Blander et al., 2017), contributing to the risk of seizures. ( ii) GMs have been demonstrated to interact with gut-derived metabolites, resulting in both beneficial and detrimental mechanisms for the central nervous system (Tran and Mohajeri, 2021). ( iii) In addition, GMs can affect the hypothalamic–pituitary–adrenal (HPA) axis (Sudo et al., 2004) and the levels of brain-derived neurotrophic factor (BDNF) (Maqsood and Stone, 2016), which promote seizure propensity. ( iv) GMs also regulate peripheral metabolites and central neurotransmitter metabolism, which affect seizure susceptibility (Lum et al., 2020). Nevertheless, the specific mechanism and crosstalk between different GM taxa remain to be verified by future studies.
The limitations of the present study should be noted: (i) Since the number of IVs fulfilling the strict threshold ($p \leq 5$ × 10−8) was extremely small, a relatively lenient threshold ($p \leq 1$ × 10−5) was adopted for screening IVs. ( ii) This study included individuals of essentially European ancestry, so extrapolating the findings to other populations is limiting. ( iii) The number of cases of the two subtypes of epilepsy under strict definition (generalized epilepsy and focal epilepsy) is relatively small, so future analysis based on a larger sample size of GWAS summary data is necessary to increase the confidence of the results. ( iv) The GM-related GWAS summary-level dataset included in this study was based on 16S rRNA sequencing, and thus further analysis based on large-scale studies with more advanced methods, such as metagenomics sequencing, is required in the future in order to evaluate the species-level. ( v) Current studies of GMs have focused only on bacteria; however, other types of GMs may also have potential functions.
## 5. Conclusion
Overall, by performing MR analysis of the causal effects of 211 GM taxa on epilepsy and its sub-types, we finally identified 21 nominal causal relationships and two strong causal relationships. Among them, Class Betaproteobacteria and Order Burkholderiales are significantly associated with increased epilepsy risk. However, it is essential to recognize that since the present study was conducted based on the GWAS summary-level dataset generated from 16S rRNA sequencing, further in-depth analyses based on more advanced large-scale studies generated from metagenomics sequencing are necessary. Nevertheless, our findings may provide helpful biomarkers for disease progression and potential candidate therapeutic targets for epilepsy.
## Data availability statement
Publicly available datasets were analyzed in this study. This data can be found at: https://mibiogen.gcc.rug.nl/, https://r7.finngen.fi/.
## Ethics statement
Publicly available de-identified data from participant studies approved by an ethical standards committee were used in this study. Therefore, no additional separate ethical approval was required for this study.
## Author contributions
YZ designed the study, analyzed the data, and wrote the manuscript. SC assisted in analyzing the data and revising the manuscript. HY critically read and edited the manuscript. All authors contributed to the article and approved the submitted version.
## Funding
This research was funded by the Natural Science Foundation of Hunan Province (2022JJ70069).
## 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/fmicb.2023.1115014/full#supplementary-material
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|
---
title: Tissue-Specific Sex Difference in Mouse Eye and Brain Metabolome Under Fed
and Fasted States
authors:
- Meghashri Saravanan
- Rong Xu
- Olivia Roby
- Yekai Wang
- Siyan Zhu
- Amy Lu
- Jianhai Du
journal: Investigative Ophthalmology & Visual Science
year: 2023
pmcid: PMC10010444
doi: 10.1167/iovs.64.3.18
license: CC BY 4.0
---
# Tissue-Specific Sex Difference in Mouse Eye and Brain Metabolome Under Fed and Fasted States
## Body
Sexual dimorphisms have been reported in vertebrate eyes, including photoreceptor cell distribution, visual acuity, color perception and disease susceptibility. The males have more relative number of L- and M-cone photoreceptors, thicker macula, stronger response to blue light stimuli, and greater sensitivity for fine detail and rapidly moving stimuli.1–5 The females have a higher density of lens epithelium and more irons in the retina and RPE.6–8 Men are known for the high prevalence of color blindness9; however, women are more susceptible to AMD, cataract, and glaucoma.10–13 Although sex differences in eye physiology and pathology are well-established, the biochemical basis for these sex differences in different eye tissues remains unknown.
The metabolome is a collection of metabolites such as carbohydrates, amino acids, nucleotides, fatty acids, and vitamins in the cell or tissue, serving as substrates, products, cofactors, or ligands for biochemical reactions, nutrient transport, and cell signaling.14 These metabolites are not only the products of metabolic gene and protein expression, but also reflect interactions with the environment such as microbiome, diet, and exposure.15–17 Quantifying metabolites by metabolomics is increasingly important in eye research to identify tissue-specific metabolism in healthy ocular tissues and mechanisms or biomarkers in ocular diseases.18–22 Notably, the ocular tissues have a specialized metabolism, which may underlie various ocular diseases that cause blindness. Like tumors, the neural retina has the Warburg effect or aerobic glycolysis to produce large amounts of lactate from glucose. Many mutations of metabolic genes in glycolysis, tricarboxylic acid (TCA) cycle, and nucleotide metabolism only cause retinal degeneration in humans.23–25 The RPE, a single layer of epithelial cells, resides between the neural retina and choroid circulation. RPE metabolism is critical to the survival of the neural retina. The defects in RPE metabolism are attributed to inherited retinal degeneration and AMD, the leading cause of blindness in the elder population.23,26,27 The lens is a transparent tissue that relies on nutrients, especially glucose, from the aqueous humor through the blood–aqueous barrier.28 Metabolic disturbance of lens metabolism can cause the loss of transparency or cataract, a common ocular disease in the elderly.29,30 Sex differences in glucose, lipid, and amino acid metabolism are well-studied in adipose tissue, muscle, and liver31–34; however, sex differences in eye metabolism have not been determined or appreciated.
In this study, we used a targeted metabolomics approach to quantify 133 metabolites covering major intermediates in the metabolism of glucose, amino acids, nucleotides, fatty acids, and vitamins in the neural retina, RPE, and lens from male and female mice under fed or fasted conditions. We also quantified the metabolites from the brain and plasma to identify common and tissue-specific sex differences in metabolism. We have found 97 sex-different metabolites and 64 metabolites show tissue-specific sex differences. Our findings demonstrate strong tissue-specific and sex-specific differences in eye metabolome, and these differences may implicate sex differences in eye physiology and susceptibility to diseases.
## Abstract
### Purpose
Visual physiology and various ocular diseases demonstrate sexual dimorphisms; however, how sex influences metabolism in different eye tissues remains undetermined. This study aims to address common and tissue-specific sex differences in metabolism in the retina, RPE, lens, and brain under fed and fasted conditions.
### Methods
After ad libitum fed or being deprived of food for 18 hours, mouse eye tissues (retina, RPE/choroid, and lens), brain, and plasma were harvested for targeted metabolomics. The data were analyzed with both partial least squares-discriminant analysis and volcano plot analysis.
### Results
Among 133 metabolites that cover major metabolic pathways, we found 9 to 45 metabolites that are sex different in different tissues under the fed state and 6 to 18 metabolites under the fasted state. Among these sex-different metabolites, 33 were changed in 2 or more tissues, and 64 were tissue specific. Pantothenic acid, hypotaurine, and 4-hydroxyproline were the top commonly changed metabolites. The lens and the retina had the most tissue-specific, sex-different metabolites enriched in the metabolism of amino acid, nucleotide, lipids, and tricarboxylic acid cycle. The lens and the brain had more similar sex-different metabolites than other ocular tissues. The female RPE and female brain were more sensitive to fasting with more decreased metabolites in amino acid metabolism, tricarboxylic acid cycles, and glycolysis. The plasma had the fewest sex-different metabolites, with very few overlapping changes with tissues.
### Conclusions
Sex has a strong influence on eye and brain metabolism in tissue-specific and metabolic state-specific manners. Our findings may implicate the sexual dimorphisms in eye physiology and susceptibility to ocular diseases.
## Animals
We purchased 12-week-old C57 BL/6J mice of both sexes from the Jackson Laboratory (Bar Harbor, ME, USA; stock #:000664). The fed group had ad libitum access to food, but the food was removed for 18 hours after 4 pm in the fasted group. All mouse experiments were performed in accordance with guidelines by the National Institutes of Health and ARVO Statement for the Use of Animals in Ophthalmic and Vision Research, and the protocols were approved by the Institutional Animal Care and Use Committee of West Virginia University.
## Isolation of Retina, RPE/Choroid, Lens, Brain, and Plasma
All mice were euthanized via quick cervical dislocation. Enucleated eyeballs were cleaned of the lingering fat and muscle tissue and dissected to isolate the retina, RPE/choroid, and lens as previously reported.21,35 Another technician quickly drew blood from the heart into microtubes with 10 µL of 0.5 mM EDTA and centrifuged at 3000 rpm at 4°C for 15 minutes to collect the supernatant to fresh microtubes. The whole brain tissue was rapidly dissected and snap frozen in liquid nitrogen. All the harvested samples were stored at −80°C before use.
## Metabolite Extraction and Preparation
Metabolites from the retina, RPE/choroid, lens, and brain were extracted with $80\%$ cold menthol together with internal stand norvaline (1 mM) as described.36,37 Plasma metabolites were extracted by mixing 10 µL of plasma with 40 µL of cold methanol with norvaline. The mix was centrifuged and 10 µL of supernatant was used for metabolite analysis. Protein concentrations from extraction pellets were measured for data normalization using MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/).38 All the metabolite extracts were dried before targeted metabolomics to run in the same batch.
## Targeted Metabolomics
Targeted metabolomics was performed as described in detail before with liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry.22,37 A total of 133 metabolites that cover major metabolic pathways were quantified (see detailed pathways and parameters in Supplementary Table S1). A Shimadzu LC Nexera X2 UHPLC coupled with a QTRAP 5500 liquid chromatography-mass spectrometry (AB Sciex, Hong Kong), and an Agilent 7890B/5977B gas chromatography-mass spectrometer (Agilent Technologies, Santa Clara, CA. USA) were used for metabolite analysis. The data were analyzed by MultiQuant 3.0.2 (AB Sciex) and Agilent MassHunter Quantitative Analysis Software.39
## Statistical Analyses
Multivariate analysis was performed with a supervised classification model partial least-squares discriminant analysis after pareto scaling using MetaboAnalyst 5.0. The comparison of specific metabolites was analyzed with Volcano plot with a P of less than 0.05 and fold changes of more than 1.3 or less than −1.3 ($P \leq 1.3$) for all figures.
## Sex Differences in Retinal Metabolism
To study the impact of sex differences on retinal metabolites, we analyzed the abundance of metabolites from mouse retinas in both fed and fasted states. Under fed conditions, a multivariate analysis with partial least squares discriminant analysis (PLSDA) showed a clear separation between the male and female retinas (Fig. 1A), demonstrating sex differences in retinal metabolism. Volcano plots showed that 32 metabolites increased and 3 metabolites decreased in female retinas compared with male retinas (Fig. 1B, Supplementary Table S2). The female retinas had fewer long-chain fatty acids (palmitate and stearic acid) and cysteine, but more increased metabolites in the metabolism of amino acid, nucleotide, and nicotinamide adenine dinucleotide phosphate (NADP) (Figs. 1B, 1C, Supplementary Table S2). Pantothenic acid (a vitamin precursor for coenzyme A [CoA] synthesis), trigonelline (methylated nicotinic acid in NAD metabolism), aminoadipic acid (an intermediate in lysine metabolism), oxidized glutathione, nicotinamide adenine dinucleotide phosphate, nicotinamide adenine dinucleotide phosphate hydrogen, cytidine diphosphate (CDP), and guanosine diphosphate (GDP) were among the top increased metabolites, indicating that the female retina has more active CoA synthesis and NAD(P) metabolism.
**Figure 1.:** *Sex differences in retinal metabolites in fed and fasted states. (A) PLSDA plots of mouse retinal metabolites from the fed state. (B) Volcano plot of retinal metabolites in the fed state (N = 5). (C) The number of changed metabolites in metabolic pathways in the fed state. (D) PLSDA plots of mouse retinal metabolites from the fasted state. (E) Volcano plot of retinal metabolites in the fasted state (N = 5). (F) The number of changed metabolites from the volcano plot in metabolic pathways in the fasted state. (G) The number of common and sex-specific changes in retinal metabolites in response to fasting in fasted versus fed in males or females, respectively. (H) The number of changed retinal metabolites in male mice in response to fasting. (I) The number of changed retinal metabolites in female mice in response to fasting. FC, fold change.*
Retinal metabolites were further separated between sexes in the PLSDA plots in the fasted state (Fig. 1D) and sex-different metabolites were decreased to eight when compared with fed state (Fig. 1E). Similar to the fed state, pantothenic acid was increased in the fasted female retina. The long-chain acyl-carnitines (palmitoylcarnitine and stearoylcarnitine) were increased, but the short-chain acyl-carnitine (propionylcarnitine), purine nucleoside (hypoxanthine and 1-methyladenosine), hypotaurine, and riboflavin were decreased in the female retina (Figs. 1D, 1E). Compared with the fed state, 44 metabolites in the male retina and 21 in the female retina were changed in the fasted state with 11 overlapping changes between the sexes (Fig. 1G, Supplementary Fig. 1, and Supplementary Tables S3, S4). Ketone bodies are known to increase as alternative fuels during fasting. Consistently, 3-hydroxybutyrate (3-HB) was increased by 5- to 6-fold in both sexes after fasting. Pantothenic acid and acyl-carnitines were also increased in both sexes, suggesting that fatty acid oxidation is activated. Serine, methionine and trigonelline were decreased in the retinas of both sexes (Figs. 1H, 1I, Supplementary Tables S3, S4). Despite these common changes, male and female retinas responded differently to fasting in nucleotide metabolism, amino acid metabolism, NAD(P) metabolism, TCA cycle and glycolysis (Figs. 1H, 1I, Supplementary Tables S3, S4).
## Sex Difference in RPE/Choroid Metabolism
Similar to the neural retina, metabolites from RPE/choroid showed distinct separation between males and females in PLSDA scores plot under either fed or fasted conditions (Figs. 2A, 2D), indicating sex differences in RPE metabolism. The volcano plot showed that 19 metabolites were significantly different between sexes under fed state, but the number of different metabolites was decreased to 9 in the fasted state (Figs. 2B, 2E, Supplementary Table S5). Pantothenic acid was the only metabolite that was increased in the female RPE/choroid in both fed and fasted states. Succinate, 4-hydroxyproline, β-alanine, and adenosine triphosphate were decreased in both fed and fasted states in the female (Figs. 2B, 2E). Under the fed state, the sex-different metabolites were mostly in the metabolism of amino acid, nucleotide, TCA cycle, lipid and pentose phosphate pathway; however, under the fasted state, there were fewer or no changes in those pathways (Figs. 2C, 2F, Supplementary Table S5). In response to fasting, male and female RPE/choroid showed the same number of changed metabolites with approximately one-half of them overlapping (Fig. 2G, Supplementary Fig. S2, Supplementary Tables S6, S7). Like the retinas, ketone bodies and acyl-carnitines were increased, whereas trigonelline and serine were decreased in both sexes in the fasted state (Figs. 2H, 2I, Supplementary Tables S6, S7). However, female RPE/choroid had more significant changes in TCA cycle metabolites and pantothenic acid than male RPE/choroid.
**Figure 2.:** *Sex difference in RPE metabolites in fed and fasted states. (A) PLSDA plots of mouse RPE metabolites from the fed state. (B) Volcano plot of RPE metabolites in the fed state. N = 5. (C) The number of changed metabolites in metabolic pathways in the fed state. (D) PLSDA plots of mouse retinal metabolites from the fasted state. (E) Volcano plot of retinal metabolites in the fasted state. N = 5. (F) The number of changed metabolites from the Volcano plot in metabolic pathways in the fasted state. (G) The number of common and sex-specific changes in RPE metabolites in response to fasting in fasted vs. fed in males or females respectively. (H) The number of changed RPE metabolites in male mice in response to fasting (I) The number of changed RPE metabolites in female mice in response to fasting. FC, fold change.*
## Sex Differences in Lens Metabolism
The PLSDA plot showed slight overlapping under the fed state but a clear separation of male and female lens metabolites in the fasted state (Figs. 3A, 3D). Forty-five metabolites were different in the fed state and 28 under the fasted (Figs. 3B, 3E, Supplementary Tables S8, S9). Twenty-two metabolites were different between males and females, independent of metabolic states. The female lens had higher glucose but lower antioxidative metabolites, including cystine, glutathione, and ascorbic acid, than the male, suggesting that the female lens may be more vulnerable to oxidative stress (Figs. 3B, 3E, Supplementary Tables S8, S9). Fasting-induced changes of 20 metabolites in the male and 19 in the female, with 12 metabolites changed in both sexes (Fig. 3G, Supplementary Fig. 3, Supplementary Tables 10, 11). Like the retina and the RPE, fasting increased 3-HB but decreased trigonelline and serine in the lens in both sexes. In the fasted lens, changed metabolites were primarily enriched in amino acid metabolism in both sexes. The male lens had more changes in nucleotide metabolism, while the female lens had more changes in lipid metabolism (Figs. 3H, 3I, Supplementary Tables S10, S11).
**Figure 3.:** *Sex differences in lens metabolites in fed and fasted states. (A) PLSDA plots of mouse lens metabolites from the fed state. (B) Volcano plot of lens metabolites in the fed state (N > 6). (C) The number of changed metabolites in metabolic pathways in the fed state. (D) PLSDA plots of mouse lens metabolites from the fasted state. (E) Volcano plot of lens metabolites in the fasted state (N = 9). (F) The number of changed metabolites from the volcano plot in metabolic pathways in the fasted state. (G) The number of common and sex-specific changes in lens metabolites in response to fasting in fasted versus fed in males or females, respectively. (H) The number of changed lens metabolites in male mice in response to fasting. (I) The number of changed lens metabolites in female mice in response to fasting. FC, fold change.*
## Sex Difference in Brain Metabolism
To decrease variation from different brain regions, we homogenized the whole brain to measure metabolites from the aliquot. PLSDA scores plot showed distinct separations of metabolites from male and female brains under either fed or fasted states (Figs. 4A, 4D). Fourteen sex-different metabolites were in the fed state and 15 in the fasted state (Figs. 4B, 4E, Supplementary Tables S12, S13). Pantothenic acid was higher and hypotaurine was lower in the female brain, regardless of metabolic states. Strikingly, the sex-different metabolites were highly enriched in glucose metabolism including glycolysis and glycogen, in the fed state but not in the fasted state (Figs. 4C, 4F). The female brain was more sensitive to fasting than the male brain and had three times more changes in metabolites after fasting (Fig. 4G, Supplementary Fig. 4, Supplementary Tables S14, S15). Similar to eye tissues, 3-HB was increased and trigonelline decreased in both sexes in fasted brains. However, the female brain had massive changes of metabolites in amino acid and glucose metabolism but not the male brain (Figs. 4H, 4I, Supplementary Tables S14, S15). These results suggest that the female brain is more sensitive to fasting and more flexible in fuel use than the male brain.
**Figure 4.:** *Sex difference in brain metabolites in fed and fasted states. (A) PLSDA plots of mouse brain metabolites from the fed state. (B) Volcano plot of retinal metabolites in the fed state (N = 5). (C) The number of changed metabolites in metabolic pathways in the fed state. (D) PLSDA plots of mouse brain metabolites from the fasted state. (E) Volcano plot of brain metabolites in the fasted state (N = 4). (F) The number of changed metabolites from the volcano plot in metabolic pathways in the fasted state. (G) The number of common and sex-specific changes in retinal metabolites in response to fasting in fasted versus fed males or females, respectively. (H) The number of changed retinal metabolites in male mice in response to fasting. (I) The number of changed retinal metabolites in female mice in response to fasting. FC, fold change.*
## Sex Difference in Plasma Metabolites
We analyzed plasma metabolites to investigate whether the sex-different metabolites in the eye and brain are from the circulation. Scores plots showed minor overlapping between male and female plasma in the fed state but clear separation in the fasted state (Figs. 5A, 5D). Sex-different metabolites from plasma were much less than those from the eye and brain, with nine and seven sex-different metabolites in the fed and fasted states, respectively (Figs. 5B, 5E). Like brain and eye tissues, pantothenic acid was higher in the female plasma in the fed state, suggesting that pantothenic acid is a common sex-different metabolite. We found 4-hydroxyproline, pyroglutamic acid, and aconitic acid to be lower in the female than male plasma in either fed or fasting states (Figs. 5B, 5E). Sex-different plasma metabolites were enriched in amino acid metabolism in both states, but the female plasma had overall more decreased metabolites than the male, especially in the fasted state (Figs. 5C, 5F). In response to fasting, female plasma had slightly more changed metabolites than males, with less than half of overlapping changes (Fig. 5G). Plasma glucose was significantly decreased after fasting in the data from both glucometer and targeted metabolomics. However, there was no difference in males and females in either metabolic state (Supplementary Fig. S5, Supplementary Tables S16, S17). Similar to other tissues after fasting, 3-HB and trigonelline changed, demonstrating that these two metabolites are common fasting-sensitive metabolites (Fig. 5H, %I, Supplementary Fig. 6, and Supplementary Tables S16, S17). Both sexes were enriched in the changes of amino acid metabolism, but the female had more decreased metabolites after fasting (Fig. 5H, SI). Overall, these results suggest that except for several metabolites such as pantothenic acid, 3-HB, and trigonelline, most of the sex-specific metabolic changes in the tissues may not directly from the systemic circulation.
**Figure 5.:** *Sex difference in plasma metabolites in fed and fasted states. (A) PLSDA plots of mouse plasma metabolites from the fed state. (B) Volcano plot of plasma metabolites in the fed state (N = 5). (C) The number of changed metabolites in metabolic pathways in the fed state. (D) PLSDA plots of mouse plasma metabolites from the fasted state. (E) Volcano plot of plasma metabolites in the fasted state (N = 5). (F) The number of changed metabolites from the volcano plot in metabolic pathways in the fasted state. (G) The number of common and sex-specific changes in plasma metabolites in response to fasting in fasted versus fed males or females, respectively. (H) The number of changed retinal metabolites in male mice in response to fasting. (I) The number of changed plasma metabolites in female mice in response to fasting. FC, fold change.*
## Common and Tissue-Specific Metabolic Changes in Different Sexes
Thirty-two metabolites were commonly changed in two or more tissues and 64 metabolites were tissue-specific (Figs. 6A, 6B). There were more common and tissue-specific metabolite changes in the fed than in fasted states. Pantothenic acid, hypotaurine, and 4-hydroxyproline were the top commonly changed metabolites. Under the fed state, the lens and retina had the most tissue-specific sex-different metabolites, followed by the RPE, brain, and blood (Fig. 6B). Under the fasted state, the number of these tissue-specific sex-different metabolites was decreased by 2- to 9-fold in the lens, RPE, and retina, whereas the tissue-specific metabolites were increased in the brain and plasma, demonstrating that the response to fasting is sex specific and tissue specific.
**Figure 6.:** *Common and tissue-specific changes of metabolites in different sexes. (A) A heat map of sex-different metabolites in two tissues and more in either fed or fasted state. (B) A heat map of tissue-specific sex difference in metabolites in fed or fasted. C3-Car, proliponylcarnitine; C4-Car, butyrylcarnitine; C5-Car, 2-methylbutyroylcarnitine; C6-Car, hexanoylcarnitine; C14-Car, myristoylcarnitine; C16-Car, pPalmitoylcarnitine; C18-Car, stearoylcarnitine; 3PG, 3-phosphoglyceric acid; R5P, ribose 5-phosphate; G6P, glucose 6-phosphate; PEP, 2-phosphoenolpyruvate; G3P, glycerol-3-phosphate; IPP, isopentenyl pyrophosphate; SAM, S-adenosylmethionine; XMP, xanthosine monophosphate.*
To reveal common and tissue and sex-specific responses to fasting, we grouped changed metabolites of different tissues after fasting in either male or female into commonly changed (>2 tissues in either sex, 62 metabolites) and tissue-specific changed metabolites (40 metabolites) (Figs. 7A, 7B). After fasting, 3-HB was increased, but trigonelline was decreased in all tissues. Fasting decreased the level of glucose in blood, the lens, and the female brain, but not the retina and RPE. However, the retina and RPE had more pronounced changes of acyl-carnitines than other tissues (Fig. 7A). The male retina and female brain had the highest number of tissue-specific metabolites, with 13 and 9, respectively. However, the female retina and male brain only had two and one tissue-specific changes (Fig. 7B), further supporting that there is a robust tissue-specific sex difference in metabolic response to fasting.
**Figure 7.:** *Common and sex-specific metabolites in different tissues in response to fasting. (A) A heat map of changed metabolites in two tissues and more in response to fasting in either males or females. (B) A heat map of tissue-specific changes of metabolites in response to fasting in either males or females. GSSG, oxidized glutathione; C11-Car, 4,8-dimethylnonanoylcarnitine; C2-Car, L-acetylcarnitine; NMA, N1-methylnicotinamide; G1P, glucose-1-phosphate; DHAP, dihydroxyacetone phosphate; G6P, glucose-6-phosphate; D-2HG, D-2-hydroxyglutarate.*
## Discussion
In this study, we have found common and tissue-specific sex differences in the metabolome of the eye and brain under different metabolic states. Pantothenic acid, a primary precursor of CoA, is a common female-enriched metabolite. Brain shows more sex-different metabolites in glycolysis, while ocular tissues show more differences in the metabolism of amino acid, lipid, nucleotide and TCA cycle. We also found that the male retina, female brain, and female RPE are more sensitive to nutrient deprivation. Our results suggest that there are fundamental sex differences in eye metabolism.
## Sex-Different Metabolites in Both Eye and Brain
Neurodegenerative diseases including Alzheimer's disease, Parkinson's disease, multiple sclerosis, and motor neuron disease often show sexual dimorphisms.40,41 Interestingly, many of these neurodegenerative disorders in the brain manifest earlier morphological or pathological changes in the eye, suggesting an intrinsic connection between the eye and the brain.42,43 Our study also showed that eye tissues and the brain shared 13 sex-different metabolites. Pantothenic acid is higher in the female brain, eye, and plasma, suggesting it is a common sex-different metabolite. In a human adult metabolomics study, pantothenic acid is increased in the female urine.44 Interestingly, pantothenic acid and CoA-dependent mitochondrial enzymes are decreased in brain regions of patients with Alzheimer's disease.45,46 However, dietary pantothenic acid intake is associated with increased cerebral amyloid β burden in patients with cognitive impairment.47 It will be interesting to investigate the role of pantothenic acid in sexual dimorphisms in neurological diseases and their ocular symptoms.
Remarkably, the lens shows more overlapping changes with the brain, particularly in glucose metabolism, than with the neural retina and RPE. Recent studies showed extensive similarities between neurons and lens fiber cells in cell morphology and gene expression.48,49 Similar to the brain, lens metabolism highly depends on glucose and the deficiency of glucose transporter 1 in lens epithelium can lead to cataract formation.50
## Tissue-Specific, Sex-Different Metabolites
Sex hormones play critical roles in regulating energy metabolism by modulating substrate metabolism, the permeability of retina–blood and brain–blood barrier, transcriptional factor bindings, and epigenetic regulation.34,40,51–53 The different signaling of sex hormones in the retina, RPE, lens, and brain regions52,54,55 may lead to differential gene expressions and nutrient availability. The differential expression of genes from sex chromosomes, sex-different sensitivity to insulin and different levels of adipokines can also contribute to tissue-specific metabolism in different metabolic states.56–59 *The retina* is metabolically demanding to maintain active visual transduction and renew daily shed outer segments.23,60 *The retina* primarily uses glucose but also prefers glutamate and aspartate for its metabolism.22,36 Female retinas have higher availability of amino acids including aspartate, short-chain acyl-carnitines, and metabolites in NAD(P)(H) metabolism, probably accounting for much fewer metabolite changes upon fasting compared with the male retinas. It remains to be determined the mechanisms that cause the higher nutrient availability in the female retina and their implications in the sex difference in retinal physiology and diseases.
Unlike the neural retina, RPE shows lower levels of metabolites in the female under both fed and fasted states. These decreased metabolites are mainly amino acids (histamine, taurine, carnosine, creatinine and beta-alanine) and mitochondrial intermediates (acetyl-CoA, succinate). Taurine, carnosine, and its precursor beta-alanine have beneficial antioxidant properties.61,62 A decrease in these amino acids may predispose the female RPE to oxidative damage. Consistently, female mice show more severe RPE damage and decreased retinal thickness under oxidative damage induced by sodium iodate.63,64 RPE mitochondria prefer to oxidize succinate from the retina to produce malate and fumarate for the retina.65–67 However, succinate is lower in the female RPE under both fed and fasted states. The female RPE may import less succinate from the retina and circulation or oxidize more succinate, resulting in sex differences in RPE mitochondrial metabolism.
The lens has the greatest number of sex-different metabolites among eye tissues. Except for cystine and adenine, most the changed metabolites, including mitochondrial intermediates, purine metabolites, acyl-carnitines, and amino acids, were lower in the female lens. Mouse lens transcriptome shows the sex-different gene expression in mitochondrial metabolism, amino acid transport, and acyl-CoA metabolism.68 *Lens mitochondria* exist only in anterior epithelial cells, providing approximately $30\%$ of the energy for the entire lens.69 The lens epithelial cells are critical to maintaining lens transparency through nutrient transport, metabolism, and synthesis. The dysfunction of lens epithelial cells can lead to female-prevalent cataracts.10 In human donor eyes, the female has a greater epithelial cell density than the male.6 The lens metabolism relies on nutrients from the aqueous humor. There are significant sex differences in human aqueous humor proteome and protein.70,71 However, sex-different metabolites in aqueous humor remain unclear; most aqueous humor metabolomics are sex matched without an analysis of the sex differences.72,73 Further studies on the metabolites of lens epithelial cells and aqueous humor will help understand the sex difference in lens metabolism and its implications in cataracts.
Our results show that the female brain has a more sensitive glucose metabolism in different metabolic states, which may be implicated in the sex differences in brain metabolism, physiology, and diseases. The brain relies on glucose as its primary source of energy. Human studies with positron emission tomography show that young women have higher cerebral blood flow and glycolysis than men.74,75 *Aerobic glycolysis* is positively correlated with brain aging,76 and the adult female brain shows a few more years of metabolic youthfulness than the male.74 However, this metabolic youthfulness starts to disappear in cognitively impaired patients such as Alzheimer's disease, probably owing to a higher rate of decline in glucose metabolism in female patients.77 Both human studies and animal models also show that one of the earliest signs of Alzheimer's disease is a decrease in cerebral glucose metabolism, and the disturbed glucose metabolism is associated with disease progression.78 These findings are consistent with our results that the female brain has higher glycolytic metabolites in the fed state and more sensitive in changes of glucose metabolism to nutritional stress, suggesting that early intervention to glucose metabolism may be important in the female under stressed conditions such as neurological diseases.
In conclusion, our findings demonstrate substantial sex differences in metabolic profiles in different eye tissues, supporting the urgent need to include animals and humans of both sexes in eye metabolomics research. We also find similarities in sex-different metabolites between the eye and brain, supporting that eye metabolomics can serve as an important window to reflect brain metabolism and diseases.
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|
---
title: 'dbAQP-SNP: a database of missense single-nucleotide polymorphisms in human
aquaporins'
authors:
- Rachana Dande
- Ramasubbu Sankararamakrishnan
journal: 'Database: The Journal of Biological Databases and Curation'
year: 2023
pmcid: PMC10010469
doi: 10.1093/database/baad012
license: CC BY 4.0
---
# dbAQP-SNP: a database of missense single-nucleotide polymorphisms in human aquaporins
## Abstract
Aquaporins and aquaglyceroporins belong to the superfamily of major intrinsic proteins (MIPs), and they transport water and other neutral solutes such as glycerol. These channel proteins are involved in vital physiological processes and are implicated in several human diseases. Experimentally determined structures of MIPs from diverse organisms reveal a unique hour-glass fold with six transmembrane helices and two half-helices. MIP channels have two constrictions formed by Asn-Pro-Ala (NPA) motifs and aromatic/arginine selectivity filters (Ar/R SFs). Several reports have found associations among single-nucleotide polymorphisms (SNPs) in human aquaporins (AQPs) with diseases in specific populations. In this study, we have compiled 2798 SNPs that give rise to missense mutations in 13 human AQPs. To understand the nature of missense substitutions, we have systematically analyzed the pattern of substitutions. We found several examples in which substitutions could be considered as non-conservative that include small to big or hydrophobic to charged residues. We also analyzed these substitutions in the context of structure. We have identified SNPs that occur in NPA motifs or Ar/R SFs, and they will most certainly disrupt the structure and/or transport properties of human AQPs. We found 22 examples in which missense SNP substitutions that are mostly non-conservative in nature have given rise to pathogenic conditions as found in the Online Mendelian Inheritance in Man database. It is most likely that not all missense SNPs in human AQPs will result in diseases. However, understanding the effect of missense SNPs on the structure and function of human AQPs is important. In this direction, we have developed a database dbAQP-SNP that contains information about all 2798 SNPs. This database has several features and search options that can help the user to find SNPs in specific positions of human AQPs including the functionally and/or structurally important regions. dbAQP-SNP (http://bioinfo.iitk.ac.in/dbAQP-SNP) is freely available to the academic community.
Database URL http://bioinfo.iitk.ac.in/dbAQP-SNP
## Introduction
Members of the superfamily of major intrinsic proteins (MIPs) can be found in all three kingdoms of life (1–3). In some species groups like plants and fungi, MIP homologs are present abundantly and in multiple numbers (4–6). In humans, 13 MIP homologs are present. Since aquaporin (AQP) is a prototype member of this family, these proteins are named as AQP0 to AQP12, and hence these human MIP homologs will be referred to as human AQPs hereafter. Sequence analyses and phylogenetic studies reveal that the human AQPs can be broadly classified into three classes [3, 7, 8]. Those which specifically transport water (AQP0, AQP1, AQP2, AQP4, AQP5, AQP6 and AQP8) belong to classical AQPs. The human MIP homologs that prefer to transport glycerol and other neutral solutes fall under the category of aquaglyceroporins (AQP3, AQP7, AQP9 and AQP10). AQP homologs belonging to the third category are called ‘Super AQPs’ (AQP11 and AQP12). MIPs are one of the well-characterized membrane protein families, and the structures of several MIPs have been determined from different species groups. Despite low sequence identity, MIPs from bacteria, yeast, plants and mammals adopt a unique hour-glass helical fold (Figure 1) with six transmembrane (TM) helices (TM1–TM6) and two half-helices (LB and LE) [1]. Two-fold rotational pseudo-symmetry is observed in the structure which is also reflected in MIP sequences. The two halves of an MIP sequence exhibit sequence similarity indicating gene duplication and fusion during evolution. The sequence motif Asn-Pro-Ala (NPA) is predominantly conserved in the majority of MIP members in both the half-helices LB and LE present, respectively, in the N- and C-terminal halves. The channel is also characterized by a narrow constriction formed by four residues called the aromatic/arginine selectivity filter (Ar/R SF). Experimental and computational studies demonstrate that both NPA motifs and Ar/R SFs have an important role in the transport and selectivity of solutes that are transported across the membrane (9–13). Residues near the NPA motif exhibit characteristic conservation in AQPs and aquaglyceroporins [14], and simulation studies reveal that specific interactions involving these residues could play important roles in the transport properties of MIP channels [15]. AQP homologs form tetramers under physiological conditions with each monomer having a functional aqueous pore [16].
**Figure 1.:** *The typical hour-glass fold adopted by MIP family members shown here corresponds to the human AQP4 (PDB ID: 3GD8; resolution: 1.8 Å). Only the backbone is shown in ribbon representation. The most conserved residue in each TM segment (TM1–TM6) and the two half-helices (LB and LE) are displayed and the locations of the conserved residues are indicated, and their generic numbers are also shown. In human AQP4, they correspond to E44 (1.50), G78 (2.50), N97 (LB.50), Q122 (3.50), E163 (4.50), G194 (5.50), N213 (LE.50) and P237 (6.50), and the residue numbering corresponds to the PDB ID: 3GD8. For details about the generic numbering scheme in AQPs, see the main text.*
Point mutations in human AQP homologs are known to cause various diseases and result in abnormal water homeostasis. The range of defects due to genetic variants includes misfolding, problems in tetramer assembly, failure to transport the substrates or protein targeting/sorting [17]. In this regard, single-nucleotide polymorphisms (SNPs) in several human AQPs have been investigated, and their associations with several diseases have been studied in specific populations. Several reports suggest an association among SNPs of classical AQPs and diseases and/or important physiological processes. SNPs of AQP1 and other genes were found to have an association in male patients with a history of priapism indicating AQP1’s involvement in important cellular processes such as cell adhesion and cell signaling [18]. A study involving male long-distance runners found that a genetic variant of AQP1 was found to be associated with acute body fluid loss [19]. The involvement of AQP1 polymorphisms has been shown to be important in water retention among patients with liver cirrhosis [20]. Wang et al. genotyped AQP2 and AQP9 polymorphisms in lung cancer patients and showed that they contribute to chemotherapy response [21]. Genotyping 10 polymorphisms of salivary samples from ∼700 individuals found an association among AQP1, AQP2, AQP5 and AQP6 genes and periodontitis and temporomandibular joint disorders [22]. Genotyping of seven AQP3 SNPs in early breast cancer patients indicated that AQP3 could be a potential prognostic marker [23]. Using water permeability assays, Sorani et al. have shown that four non-synonymous SNPs (nsSNPs) in AQP4-reduced water permeability [24]. With AQP4 playing a crucial role in maintaining the brain water balance, the authors suggested that these nsSNPs may have a significant role in diseases such as cerebral edema. AQP4 SNPs have also been associated with sleep quality, latency and duration, which suggested a relationship between sleep and brain Aβ-amyloid burden [25]. Larsen et al. established a link between AQP4 SNPs and non-rapid eye movement sleep [26]. The association of AQP4 SNPs with serum S100 calcium-binding protein B and schizophrenia has been investigated by Wu et al. [ 27]. The association of AQP4 SNPs has been investigated in sudden infant death syndrome, neuromyelitis optica, vascular depression, schizophrenia and intracerebral hemorrhage (28–32). Polymorphism in the AQP5 gene has been shown to have an association with a reduced risk of chronic obstructive pulmonary disease in the Chinese Han and European American populations [33, 34]. SNPs in AQP5 indicate that AQP5 and other genes are involved in the pathogenesis of caries [35, 36]. In studies conducted in patients with sepsis and early-stage breast cancer, the AQP5 promoter polymorphism was found to be associated with susceptibility to major adverse kidney events and progesterone receptor positivity, respectively [37, 38].
Polymorphisms in aquaglyceroporins have been shown to have a role in diseases such as Type 2 diabetes and hypertension. SNPs in the AQP7 gene have been shown to have an association with obesity and Type 2 diabetes in the Caucasian and Chinese Han populations [39, 40]. AQP7 SNPs have been shown to be involved in the risk of stroke in patients with hypertension [41]. The possible role of AQP8 SNPs has been suggested in the pathogenesis of polycystic ovary syndrome [42]. Studies on Thai postmenopausal women revealed an association of AQP9 SNPs with femoral neck bone mineral density [43]. The role of AQP4 and AQP9 SNPs in methylation of inorganic arsenic has been studied in Croatian–Slovenian pregnant and non-pregnant women [44]. Not much is known about the SNPs of the so-called Super AQPs.
Association studies of human AQP SNPs with several diseases and specific phenotypes are scattered in the literature. The database of SNP (dbSNP) has revealed that many hundreds of SNPs are found in human AQPs [45]. We have systematically analyzed the dbSNP and classified the SNPs in human AQP homologs according to their positions in the AQP structure, the nature of amino acid substitutions and the possible disease association. We have compiled these data in the form of a database (dbAQP-SNP) and made this resource freely available in the form of a database. This database will help to look for specific SNPs in human AQP homologs and aid in experimental design to understand the effect of SNPs on the structure and function of human AQPs.
## Materials and methods
Human reference genome sequences and reference protein sequences available from the RefSeq database [46] are used to identify the variations, respectively, in the human genome and the corresponding protein sequences. Although SNPs can be of different types, we only considered the missense variants in human AQPs, AQP0–AQP12. The dbSNP as updated in May 2019 was queried to collect and compile all the SNPs of human AQPs. The typical query used in the dbSNP was ‘aqp1’ (Gene Name) AND ‘missense variant’ (Function Class). Each dbSNP entry is assigned a unique Accession ID called Reference SNP ID (rsID) and contains information regarding the variation in the nucleotide, codon and the resulting change in the amino acid of the protein with respect to the reference sequence. We have used only the primary isoforms of human AQPs for determining the variations in the protein sequences (Supplementary Table S1). The predicted and minor isoforms were not considered. The search results were downloaded as a batch JavaScript Object Notation (JSON) file. For ease of further analysis, the data were processed into comma-separated values (CSV) files using the JSON module in Python. We removed the duplicates and the deprecated files in the search results. Similarly, we excluded results that contained non-sense and synonymous variations.
## Construction of the dbAQP-SNP database
The contents of the database containing information regarding SNPs of human AQPs are stored as CSV files since they are easy to store and upgrade. The website is maintained on an Apache HTTP server v2.4 (https://httpd.apache.org). The webpages are developed in HTML 5, and the JavaScript used is loaded through a content delivery network. Python Common Gateway Interface (CGI) scripts are used for dynamic webpages which are generated according to the user’s input. CGI modules are used to communicate between the webpages and the Python scripts at the backend. When the user submits a query, a CGI script is invoked which processes the query and generates an HTML response for the user to view.
Positions of the residues that are changed due to missense variants were visualized in the respective experimentally determined structures or models. The 3D representation of the residue under consideration in the structure was implemented using NGL viewer [47] embedded as JavaScript. The structure files in the Protein Data Bank (PDB) format for each residue variation, with the side chains of the variant and the reference residue, were generated using the ‘swapaa’ option available in UCSF Chimera v 1.14 [48]. For the side chains displayed in the edited structure, the Cα coordinates remain the same as that of the reference residue, and the rotamers for the rest of the side chain were generated using the Dunbrack library [49] available in UCSF Chimera.
## Results
We searched the dbSNP for missense variants of human AQPs as described in the Materials and methods section. Our search yielded 2798 SNPs that resulted in missense variations that are found in 13 human AQP homologs. The number of entries found varied across different human AQPs, from 173 to 353. Among AQP0–AQP12 homologs, AQP7 and AQP0 have the largest and least numbers of entries, respectively. These data have been organized into a database called dbAQP-SNP available freely at http://bioinfo.iitk.ac.in/dbAQP-SNP. We first describe the salient features of the database and then present an analysis of the SNPs found in the human AQP homologs.
## dbAQP-SNP database
We have compiled the details of missense mutations for the 2798 SNPs of 13 human AQP homologs, and the details of all these entries are available in the dbAQP-SNP database. The database is organized into different sections. Each entry has a unique ID along with the reference SNP ID (rsID). The page for each entry has several details such as variation in the codon, the residue number and the TM segment/loop in which it occurs, the original residue and the mutated residue as the result of SNP, details of NPA motifs and Ar/R SFs, the position of the codon at which the SNP occurs, the original codon and the codon modified due to SNP and the generic number of the residue which is substituted due to missense variation. The molecular plot of a human AQP with the substituted residue due to missense SNPs from a sample entry in the dbAQP-SNP database is shown in Figure 2.
**Figure 2.:** *Molecular plot of a specific human AQP with the residue in which missense SNPs occurred shown in ball-and-stick representation. The helices (TM1–TM6) and the half-helices (LB and LE) are displayed in different colors.*
## Different search options
The dbAQP-SNP database provides various search options (Keyword Search and Advanced Search) for the users to easily navigate the database. With the Keyword Search, the user can search the database using the unique dbAQP-SNP ID, rsID or RefSeq ID. In the ‘Advanced Search’ option, the user can provide multiple parameters to search at the same time by combining all the terms. For example, the user can search by protein, and this will help to find all SNPs associated with a specific human AQP homolog. One can also search the database for SNPs associated with a specific disease. The AQP structure is divided into many regions such as channel-facing and helix–helix interface (explained in the section “Substitutions due to SNPs in the context of structurally and/or functionally important regions”). The user can search all the SNPs that are found in specific structural regions of AQPs. As the NPA motif and Ar/R SF have been shown to be functionally important, a search option is available that will fetch all the SNPs that result in missense variation in these functionally important regions. The amino acids are divided into six groups (see the section “Pattern of missense SNP substitutions”) for the purpose of understanding the pattern of substitution. A search option is also implemented in which the user can get all the SNPs which results in the substitution of amino acids belonging to one of the six groups to any of the six groups. This will also help to find non-conventional substitutions such as hydrophobic to charged or small residues to aromatic residues. These different search options can also be combined in the ‘Advanced Search’ option. For example, the user can search for all SNPs found in AQP0 that occur in the helix–helix interface in which charged residues are substituted by aromatic residues. The screenshot of the dbAQP-SNP Search page is shown in Figure 3.
**Figure 3.:** *Screenshot of the dbAQP-SNP Search page.*
## Documentation and statistics page
The ‘Documentation’ page provides a brief introduction to human AQPs and explains the database, different search options, classification of amino acids and structural regions of AQP channels. The Statistics page presents the statistics for each of the 13 human AQPs and the occurrence of SNPs for each human AQP in different structural regions such as TM region, channel-facing, helix–helix interface, monomer–monomer interface and lipid-facing. The statistical data for all the human AQPs in which SNPs occur in the cytoplasmic, extracellular, loops and N- or C-terminal regions can also be found on the same page.
Overall, this will be a useful resource where SNPs in different human AQPs are compiled and this resource will be helpful to predict the missense variations that are likely to disrupt the structure and function and the hypothesis generated from this study can be tested experimentally. In the following sections, we have analyzed the data available in the database and looked into the pattern of amino acid substitutions due to missense SNPs and substitutions that occur in the structurally and/or functionally important regions such as NPA motifs, Ar/R SFs and helix–helix interfaces. The likely influence of unusual substitutions in the context of the function is also discussed.
## Pathogenicity due to SNPs in human AQPs
A missense variation can be pathogenic and can cause many types of diseases. A missense mutation can be deleterious if it results in ‘a genetic alteration that increases an individual’s susceptibility or predisposition to a certain disease or disorder’ (https://www.cancer.gov/publications/dictionaries/genetics-dictionary). The disease condition could be the result of loss/gain of function. Two such examples are Gly5.60 → Arg in AQP2 and Ile5.61 → Phe in AQP5 [50, 51]. Improper trafficking is another factor that will result in disease conditions. In the case of AQP2, a Glu residue at the C-terminus is replaced by Lys and this substitution results in protein being retained in the Golgi complex and the protein fails to reach the plasma membrane [52]. We have used the Online Mendelian Inheritance in Man (OMIM) database (http://www.omim.org) [53] and ClinVar available at the National Center for Biotechnology Information (www.ncbi.nlm.nih.gov/clinvar) [54] to ascertain the pathogenicity of SNPs in human AQPs in the current dataset. We have found 22 SNPs that are reported to be implicated in disease conditions, and they mainly occur in AQP0, AQP2 and AQP5. These diseases include cataract (AQP0), autosomal recessive nephrogenic diabetes insipidus (AQP1 and AQP2) and palmoplantar keratoderma (AQP5) and diminished glycerol release (AQP7). Most of the missense mutations in these cases involve non-conservative substitutions such as Glu → Gly, Thr → Arg, Pro → Leu, Gln → Pro, Thr → Met, Gly → Arg, Arg → Cys, Glu → Lys and Ala → Glu.
## Pattern of missense SNP substitutions
It is clear that there are examples of human AQPs in which non-conservative substitutions due to missense SNPs give rise to pathogenic conditions. These are only a small fraction of all known missense SNPs in human AQPs. With 2978 examples in human AQPs, we first wanted to find out the nature of amino acid substitutions as a result of missense variations. For this purpose, the amino acids were classified into six groups. The residues Gly, Ala, Thr, Ser and Cys, collectively grouped as small and weakly polar (SWP) residues, have been shown to be highly group conserved at the helix–helix interface of many TM helical proteins including AQPs (55–57). The other groups are aliphatic-hydrophobic (AH; Leu, Ile, Val and Met), aromatic (Phe, Tyr and Trp), charged (Lys, Arg, His, Asp and Glu) and neutral polar (NP; Asn and Gln). The sixth group has only one residue Pro. Proline is known to introduce kink in α-helices [58]. Hence, we considered Pro separately and did not group into any of the categories mentioned above. Such grouping of amino acids has been previously used in other studies [59, 60].
Our analysis has revealed some interesting observations. Table 1 summarizes the number of substitutions involving amino acids of all six groups. It shows that the highest number of substitutions involves the group of residues that are classified as SWP residues. There are 1086 examples in which SWP residues are substituted by another member of this group [427] or any residue from the other five groups [659]. We found 73 and 50 cases in which SWP residues are substituted by larger aromatic or proline residues, respectively. We found a significantly larger number of 242 examples that involve substitutions from SWP residues to charged residues. Surprisingly, very few examples [26] are found in which SWP residues are substituted by NP residues. As far as the individual residues within the SWP group are concerned, the maximum number of substitutions involved Ala (Ala-Thr: 125; Ala-Val: 115) and Gly (Gly-Ser: 72; Gly-Arg: 88).
**Table 1.**
| Groupa,b | SWP | AH | Charged | NP | Aromatic | Pro | Total |
| --- | --- | --- | --- | --- | --- | --- | --- |
| SWP | 427 | 268 | 242 | 26 | 73 | 50 | 1086 |
| AH | 188 | 457 | 55 | 18 | 79 | 60 | 857 |
| Charged | 116 | 30 | 182 | 120 | 62 | 28 | 538 |
| NP | 38 | 10 | 70 | 0 | 0 | 15 | 133 |
| Aromatic | 78 | 70 | 32 | 8 | 8 | 0 | 196 |
| Pro | 80 | 57 | 27 | 4 | 0 | 0 | 168 |
| Total | 927 | 893 | 610 | 176 | 222 | 153 | 2978 |
In the case of the AH group, the largest number of substitutions involves residues within the same group. There are 457 instances in which one residue from the AH group is substituted by another residue within the same group. AH residues due to SNPs are substituted to SWP residues in a larger number compared to any other group, and 188 AH to SWP substitutions constitute the second largest number of missense substitutions. Among the residues in the AH group, Leu-Phe: 56, Leu-Pro: 60, Ile-Thr: 61 and Met-Thr: 31 are some of the substitutions in which AH residues are replaced by residues from other groups.
When it comes to charged residues, maximum substitutions due to SNPs occur [182] within the same group. A significant number of substitutions occur between the charged residues and residues from the SWP [116] or NP [120] groups. Some of the notable substitutions in this group involve Glu (Glu-Lys: 37), Arg (Arg-Cys: 42; Arg-His: 42; Arg-Gln: 45) and His (His-Tyr: 22). The residues in the NP group are mostly substituted by residues from the charged group. The number of instances of Asn substituted by Ser from the SWP group is 30. There is not a single substitution of NP residues to aromatic residues.
As far as the residues in the aromatic group are concerned, Phe-Ser and Phe-Leu have the most number of 22 and 47 substitutions, respectively. The maximum number of substitutions involving proline includes Pro-Leu: 57 and Pro-Ser: 45. However, we have not found a single example in which aromatic residues are substituted by proline or vice versa. Thus substitutions due to SNPs involve small to big, neutral to charged, positively charged to negatively charged or hydrophobic to proline, and vice versa. Such substitutions are not generally considered as conservative substitutions. It would be interesting to see where exactly these substitutions occur in the 3D structure of human AQPs. This knowledge can help to predict whether such substitutions are likely to disrupt the structure and/or function.
## Substitutions due to SNPs in the context of structurally and/or functionally important regions
Structures of 6 out of 13 human AQPs have been determined experimentally, and they all have the characteristic hour-glass helical fold. We have examined the site of missense mutations by mapping them onto the structures. AQP structures from diverse organisms, including Eschericha coli, plants and mammals, have been determined experimentally [1]. Although these are sequentially distantly related, they all adopt a unique hour-glass helical fold. In the present study, we used the experimentally determined structures for the human homologs AQP1, AQP2, AQP4, AQP5, AQP7 and AQP10. Their respective PDB [61] IDs are 4CSK, 4NEF, 3GD8, 3D9S, 6QZI and 6F7H. For all other human AQPs, we downloaded the modeled structures from the MIPModDB database (http://bioinfo.iitk.ac.in/MIPModDB) developed in our laboratory [62]. The protocol used to build these models is described in detail in previous publications [1, 5, 55]. Since AQP11 and AQP12 are the most distantly related homologs, we compared the respective models downloaded from MIPModDB with the models predicted using AlphaFold [63, 64] which is based on a machine-learning approach. Superposition of the two models for both AQP11 and AQP12 indicates that the distantly related AQP11 and AQP12 also adopt the helical hour-glass fold (Supplementary Figure S1) and the differences are mainly found in the loop regions connecting the TM segments. The root-mean-square deviation (RMSD) of MIPModDB- and AlphaFold-modeled structures was calculated using ChimeraX [65]. The RMSD of AQP11 structures for 129 pruned atom pairs for AlphaFold and MIPModDB models is only 1.004 Å. The RMSD of AQP12 models derived from AlphaFold and downloaded from MIPModDB is 0.986 Å for 120 pruned atom pairs. In calculating the RMSD for pruned atom pairs, the conformationally dissimilar regions such as loop regions are excluded. This has clearly increased our confidence levels in the models available in the MIPModDB database.
There are also examples in which SNPs occurred at the N- or C-terminal regions which were not defined in the experimentally determined structures or were not included in the models. We found 607 SNPs in the N- or C- terminal ends and 474 of them could not be mapped on the structures due to the truncation of N- and C-terminal regions in the structures. Hence, they will not be discussed further.
To uniquely define the position of residues in any AQP structure, we have earlier proposed a structure-based generic numbering scheme [1] for comparing residue positions in diverse AQP sequences. In this scheme, the most conserved position in each of the six TM helical segments and the two half-helices are identified from a large number of MIP sequences. This conserved residue within a TM helix is given the number 50, and all other positions in the same TM helix are relative to this position. Thus, the 3.47 and 5.52 residue positions refer to the three and two residues positions preceding and succeeding the most conserved residues of the third and fifth TM helices, respectively. The most conserved residues in each of the TM helix and the half-helices are shown in Figure 1, and this generic numbering scheme will be used hereafter. A similar generic numbering scheme has been used for G-Protein Coupled Receptors and transporters [57, 66]. Very recently, a generic number scheme is proposed for AQPs also along the similar lines [67].
For each SNP that resulted in missense mutation, we examined where exactly they occurred in the structure. For this purpose, we divided the AQP structure into six different regions, namely, (i) channel-facing, (ii) helix–helix interface, (iii) monomer–monomer interface, (iv) lipid-facing, (v) exposed to the cytoplasm or (vi) exposed to the extracellular environment.
The NPA motifs and the residues forming the Ar/R SF are functionally important regions. Apart from these two regions, the classification of six regions with distinct structural features is based on the following criteria. To find out the membrane boundary in the AQP TM segments, we used the Orientation of Proteins in Membranes (https://opm.phar.umich.edu/) and Positioning of Proteins in Membranes (https://opm.phar.umich.edu/ppm_server) servers [68]. Residues are classified as channel-facing if both backbone and side-chain atoms face the channel interior. If the backbone of a residue in a TM helix is present within 4 Å of another TM helix, then this residue is considered as a residue at the helix–helix interface. If a residue of one monomer is present within 4 Å of another monomer, then this residue is considered to be present at the monomer–monomer interface. If a residue is present inside the TM region and not at the monomer–monomer interface or any of the other regions mentioned earlier, then it is considered as lipid-facing. If the residues are present outside the membrane region and toward the extracellular environment (or the cytoplasm), then these residues are considered to be exposed to the extracellular (or cytoplasmic) environment. The nature of residue changes in functionally important positions and other structural regions are discussed in the following subsections.
## NPA motifs
The NPA motif is highly conserved in AQPs in LB and LE half-helices and has been shown to be functionally important [9, 10]. It is one of the two narrowest regions in AQP channels. Hence, any change in this motif is likely to have repercussions on the transport properties of AQPs. Missense mutations due to SNPs have been observed in both NPA motifs. There are 33 and 23 instances in which substitutions occur in NPA motifs found in LB and LE half-helices, respectively, and they are summarized in Table 2. Simulation and experimental studies have shown that the side chain of Asn in NPA motifs prevents the transport of protons and cations in AQP channels (69–71). Asn in the NPA motifs is completely invariant in all human AQP homologs. Missense variation due to SNPs results in the substitution of Asn in the NPA motifs (LB.50 and LE.50 positions) to Thr, Ser, Asp, Ile and Lys. The replacement of Asn by residues such as Lys and *Ile is* most likely to affect the function of human AQPs AQP1, AQP9, AQP10 and AQP11 (Table 2).
**Table 2.**
| Functionally important regions | SNPs observeda |
| --- | --- |
| LB-NPA motif | AsnLB.50 → Thr (AQP2, AQP5, AQP6, AQP11), Ser (AQP2, AQP8, AQP9, AQP11), Asp (AQP9), Ile (AQP9, AQP11), Lys (AQP10)ProLB.51 → Thr (AQP0, AQP12), Gln (AQP1, AQP11), Leu (AQP1, AQP10, AQP11), Ala (AQP1), Ser (AQP1, AQP3), His (AQP5), Arg (AQP12)AlaLB.50 → Thr (AQP7)AlaLB.52 → Val (AQP3, AQP8, AQP10), Ser (AQP5), Thr (AQP6), Asp (AQP6), Gly (AQP7)ThrLB.52 → Ala (AQP12) |
| LE-NPA motif | AsnLE.50 → Thr (AQP0, AQP7), Lys (AQP1, AQP9), Ser (AQP2, AQP7, AQP10)ProLE.51 → Ser (AQP0, AQP4), Ala (AQP2, AQP5, AQP10), Arg (AQP5, AQP9), Leu (AQP5, AQP7)AlaLE.52 → Thr (AQP4, AQP8, AQP9), Val (AQP5, AQP10), Asp (AQP8)SerLE.52 → Phe (AQP7) |
| Ar/R SF | Phe2.49 → Cys (AQP0), Leu (AQP1), Ile (AQP2)His2.49 → Gln (AQP8), Tyr (AQP8)Tyr2.49 → Cys (AQP11)AlaLE.47 → Thr (AQP0, AQP4)CysLE.47 → Phe (AQP1), Trp (AQP2, AQP5, AQP6, AQP9), Tyr (AQP2)TyrLE.47 → Cys (AQP3)IleLE.47 → Thr (AQP10)ArgLE.53 → Leu (AQP0, AQP4, AQP7), His (AQP0, AQP2, AQP5, AQP6, AQP8), Cys (AQP0, AQP2, AQP5, AQP6, AQP8), Gly (AQP1), Trp (AQP1, AQP3, AQP7, AQP10), Gln (AQP1, AQP3, AQP4, AQP7, AQP9, AQP10), Ser (AQP6), Pro (AQP8, AQP9)His5.57 → Gln (AQP2), Arg (AQP4, AQP5), Tyr (AQP4)Gly5.57 → Arg (AQP7, AQP10), Ala (AQP7)Ile5.57 → Met (AQP8)Ala5.57 → Val (AQP9), Thr (AQP12)Val5.57 → Ile (AQP11) |
SNPs that give rise to missense variation lead to the substitution of proline residues (LB.51 and LE.51 positions) in NPA motifs by Thr, Gln, Leu, Ala, Ser, His and Arg. Proline in NPA motifs plays a structural role as the N-cap residue for the half-helices found in LB and LE loops. Hence, the substitution of proline by any other residue is likely to affect the helix stability. The proline residue in the NPA motif of LB is replaced by Ala in wild-type AQP7. Ala at LB.51 in wild-type AQP7 is substituted by Thr due to missense SNP. Since Thr has a hydroxyl group in its side chain, it will be interesting to see how this will affect the transport properties in AQP7.
The highly conserved Ala in the LB.52 position of NPA motifs is substituted by Cys and Thr in wild-type AQP11 and AQP12, respectively. Similarly, Ala in the LE.52 position is replaced by Ser in wild-type AQP7. All other human AQP homologs have Ala in LB.52 and LE.52 positions. Alanine’s substitution in NPA motifs to Thr, Ser, Asp, Val and Gly alters either the chemical nature of one of the constrictions in the channel or a bulkier residue like Val introduces further constriction in the channel. In the case of AQP12, the wild-type Thr at Position LB.52 is replaced by Ala, whereas Ser at LE.52 in AQP7 is substituted by the bulky aromatic residue Phe. The substitutions at LB.52 and LE.52, respectively, in AQP12 and AQP7 are likely to affect the transport properties. Hence, we can easily speculate that missense mutations in any of the positions corresponding to the conserved NPA motifs will have serious consequences that can compromise the transport properties of AQPs.
## Aromatic/arginine selectivity filter
The Ar/R SF forms the most important constriction in AQP channels. The Ar/R SF is formed by four residues, one each from TM2 and TM5 helices and two residues from the LE loop. In the generic numbering scheme, the positions of these residues are 2.49, 5.57, LE.47 and LE.53 [1]. Mutation and computational studies suggest that the replacement of residues belonging to the Ar/R SF results in different transport properties in AQP channels [11, 12, 72, 73]. As the name suggests, in 11 out of 13 human AQP homologs, *Arg is* present in the LE.53 position. In AQP11 and AQP12, *Arg is* replaced by Leu. With the exception of AQP10 and AQP12, aromatic residues Phe, His or Tyr are present in Position 2.49. In the other two positions, namely 5.57 and LE.47, many types of residues are found. SNPs result in substitutions in all four positions that form the Ar/R SF and are summarized in Table 2. Missense variations due to SNPs are found in the LE.53 position in which the functionally important *Arg is* replaced by residues that have completely different chemical and physical properties (Leu, His, Cys, Gly, Trp, Gln, Ser and Pro), implying that the selectivity and transport will be certainly compromised. As far as the 2.49 position is concerned, the aromatic residues Phe and Tyr are substituted by Cys (AQP0 and AQP11) or hydrophobic residues (AQP1 and AQP2). In the case of AQP8, the missense mutations due to SNPs result in the substitution of His2.49 by Gln or Tyr. The LE.47 position is occupied by Cys in five human homologs, namely, AQP1, AQP2, AQP5, AQP6 and AQP9. SNPs at this position result in the substitution of bulky aromatic residues. Such replacement is most likely to restrict the size of the substrate that will be transported through these AQP homologs. At Position 5.57, six human AQP homologs have His, three have Gly and the remaining have hydrophobic residues. In three human AQPs, His5.57 is replaced by Gln (AQP2), Arg (AQP4 and AQP5) and Tyr (AQP4), indicating that these changes could affect the function. The missense mutations due to SNPs give rise to the substitution of Gly5.57 in AQP7 and AQP10 to Arg, and this will surely impact the type of substrates that will be transported along these channels.
## Channel-facing residues
There are 287 missense mutations involving residues that can be characterized as channel-facing. Overall, $9\%$ of all the SNPs occur in the channel-facing positions, indicating that drastic changes in these positions are likely to affect the transport properties of AQPs. In addition to NPA motifs and residues that are part of the Ar/R SF, other positions from which side chains of residues directly point to the channel axis are likely to interact with the permeating substrates and could certainly influence the transport across the human AQP channels. Among all human AQPs, AQP2 and AQP7 have the maximum number of 33 and 30 instances, respectively, in which the channel-facing wild-type residues are replaced due to SNPs. Substitutions of channel-facing residues that can be considered as non-conservative are listed in Table 3. Some of the notable substitutions include those at Positions 1.53, 3.42, 4.65 and 6.62 in which charged residues Arg/Lys are replaced by aromatic or small residues. At Position 4.66, the negatively charged Asp in AQP6 is mutated to positively charged Arg, while the positively charged Lys1.69 is substituted by negatively charged Glu in AQP1. Similarly, Gly at Positions 1.61, 5.61 and 5.65 are substituted by bulkier or charged residues. As these substitutions at positions facing the channel drastically change the chemical and/or physical properties of the residues, we can speculate that they could alter the selectivity and transport of substrates compared to the wild-type proteins that will result in different phenotypes.
**Table 3.**
| Channel-facing residuesa | Missense mutations due to SNPsb |
| --- | --- |
| TM1 | Arg1.53 → Trp (AQP12); Phe1.57 → Ser (AQP5)Leu1.57 → Ser (AQP9), Pro (AQP10)Gly1.61 → Arg (AQP2, AQP8), Val (AQP7, AQP9), Glu (AQP9)Thr1.61 → Ile (AQP11); Ala1.65 → Asp (AQP1); Val1.65 → Gly (AQP9)Gln1.65 → Arg (AQP11); Tyr1.65 → Asn (AQP12), His (AQP12)Met1.68 → Thr (AQP7), Lys (AQP7); Lys1.69 → Glu (AQP1)Ile1.69 → Thr (AQP9) |
| TM2 | Ile2.45 → Thr (AQP2, AQP6), Ser (AQP5); Pro2.45 → Leu (AQP8)Tyr2.49 → Cys (AQP11); Leu2.53 → Pro (AQP0, AQP11)Ile2.53 → Thr (AQP1, AQP4), Ser (AQP4, AQP5); Thr2.53 → Ile (AQP6)Val2.57 → Gly (AQP7); Ile2.57 → Ser (AQP9), Asn (AQP10)Trp2.61 → Arg (AQP6); Gly2.61 → Val (AQP7), Asp (AQP7), Arg (AQP8)Ser2.64 → Arg (AQP2); Gly2.65 → Arg (AQP2) |
| LB | Val HB.53 → Asp (AQP0); Cys HB.57 → Tyr (AQP2); Met HB.57 → Thr (AQP10) |
| TM3 | Arg3.42 → Cys (AQP0, AQP6), Gly (AQP0), Trp(AQP5)Lys3.42 → Asn (AQP3, AQP4), Thr (AQP9); Thr3.42 → Met (AQP11) |
| TM4 | Leu4.57 → Pro (AQP6); Val4.61 → Asp (AQP9); Leu4.61 → Pro (AQP12)Thr4.65 → Ile (AQP2), Met (AQP7); Arg4.65 → Trp (AQP12), Gln (AQP12); Asp4.66 → Gly (AQP6) |
| TM5 | Ile5.45 → Thr (AQP4), Lys (AQP4)Ile5.49 → Thr (AQP2, AQP4, AQP6, AQP7, AQP9), Asn (AQP9), Ser (AQP9)Val5.53 → Asp (AQP8, AQP12); Gly5.57 → Arg (AQP10)Met5.61 → Thr (AQP0); Ile5.61 → Thr (AQP1)Gly5.61 → Val (AQP7, AQP11), Arg (AQP12)Gly5.65 → Arg (AQP2), Asp (AQP4), Val (AQP5) |
| TM6 | Phe6.62 → Ser (AQP5); Arg6.62 → Ser (AQP8) |
## Helix–helix interface
We have previously shown that the residues belonging to the SWP group are close to $95\%$ group conserved at the helix–helix interface of many membrane proteins including AQPs, formate/nitrite transporters, Sugars Will Eventually be Exported Transporters (55–57). The presence of such residues at the helix–helix interface helps to bring the TM helices close together for an optimal helix–helix packing interaction. As mentioned earlier, we define a residue at the helix–helix interface if the backbone of the residue of one helix is within 4 Ǻ of another helix. The maximum number of >850 SNPs occurs at the helix–helix interface. When we examined the human AQP SNPs that occur at the helix–helix interface, half of all SNPs in human AQP homologs involve SWP residues. Among them, 174 involve missense mutations that result in a change of one SWP to another SWP residue. In the remaining 254 cases, SWP residues at the helix–helix interface are substituted by bulkier hydrophobic or aromatic residues, indicating that the helix-bundle geometry is likely to be disrupted with such replacement. This may lead to the overall destabilization of the hour-glass fold typically found in AQP structures. Similarly in ∼50 SNPs, AH residues are substituted by SWP residues that may have an effect on the helix packing. The introduction of a charged residue requires another charged/polar residue within the TM helical domain so that the two residues can interact among themselves. Otherwise, their presence in a hydrophobic environment becomes unfavorable [74]. Hence, the substitution of any hydrophobic residue by a charged residue will be energetically not favorable within the TM region. The same is true when a charged residue is substituted by a hydrophobic residue in the middle of the hydrophobic environment. There are close to 164 entries in which charged residues are introduced at the helix–helix interface. However, we found <15 examples in which the missense mutation of charged residue leads to hydrophobic/aromatic/SWP residues. The introduction of a charged residue in the TM region or replacement of a charged residue by hydrophobic/aromatic residues will destabilize a membrane helix-bundle protein.
## Missense mutations in other positions
We also analyzed positions that occur at the monomer–monomer interface and lipid-facing positions. There are 371 entries that can be defined to fall at the interface of two monomers in the tetrameric arrangement of AQPs. It has been shown that the function of AQP monomers can be influenced by their interaction with the neighboring monomers in the tetramer assembly [75, 76]. Hence, missense variations due to SNPs at the monomer–monomer interface could potentially impact the transport properties of human AQP homologs. Another 294 SNPs are found in positions that are lipid-exposed. The majority of them involve AH residues. These are mostly substituted by another hydrophobic residue from the same group. These substitutions are likely to have little effect on the structure and function of AQPs.
Helices of AQP hour-glass-shaped helix-bundle extend beyond the lipid head–group region, and thus these are exposed to the cytoplasmic or extracellular side. We have looked at the positions of these exposed helical regions and found 122 and 85 cases, respectively, in which SNPs occur in these regions. The majority of these SNPs result in missense mutations involving SWP or charged residues. We have identified 718 SNPs that are found in the loop regions connecting the six TM helices. This is the second largest category after those found at the helix–helix interface. Not surprisingly, >500 of these SNPs involve residues that are classified as SWP, charged or NP. Hence, we speculate that these substitutions will not have any major consequence on the structure and/or function of the human MIPs.
SNPs were found at positions at the N- or C-terminal regions that are within the structurally defined regions. We found >40 and 90 examples in N- or C-terminal tails, respectively, in which missense mutations due to SNPs occur. Compared to functionally recognized regions such as NPA motifs and Ar/R SF residues, structural and/or functional consequences of missense variations due to SNPs will be difficult to speculate in N- and C-terminal regions. If the residues are known to undergo phosphorylation or other post-translational modifications (PTMs), then substitutions in these positions will have a significant effect on the function or regulation of these channel proteins. However, residues undergoing PTMs have to be clearly established.
## Discussion
Several studies have investigated the association between human AQP SNPs and health complications or diseases in specific populations (17–43). This includes more prevalent problems such as Type 2 diabetes, hypertension and obesity. Human AQP SNPs have been implicated in other health risks such as acute body fluid loss, liver cirrhosis, periodontitis, temporomandibular joint disorders, cerebral edema, sleep-related disorders, sudden infant death syndrome, neuromyelitis optica, vascular depression, schizophrenia, chronic obstructive pulmonary disease, pathogenesis of caries, adverse kidney events, polycystic ovary syndrome and femoral neck bone mineral density. Many of these missense SNPs that are found to be associated with diseases or health complications are not available in databases like OMIM and Humsavar, which are part of the UniProt/Swiss-Prot protein knowledgebase (https://www.uniprot.org).
Calvanese et al. considered 34 single amino acid polymorphisms found in human AQPs that are associated with genetic disorders and investigated the possible relationship between the structural defects and experimental phenotypes for 17 mutations [77]. The database MutHTP [78] has compiled details of disease-associated and neutral mutations from ∼5000 human TM proteins. As per the ‘Statistics’ page of this website, the total number of distinct disease mutations is 183 395, and the number of neutral mutations is 17 827. The mutation data were collected from different mutation databases such as Humsavar, SwissVar [79], 1000 Genomes [80], COSMIC [81] and ClinVar [54]. This implies that on average, there are ∼40 disease mutations per TM protein. We realize that some proteins may have higher disease mutations, and some may have a negligible number of disease mutations. Recently, a study by Iqbal et al. [ 60] identified 3D features associated with pathogenic (disease-associated) and population (benign) missense variants from 1330 disease-associated genes using 14 270 experimentally determined structures. The mutation data for this study was compiled from OMIM [53], Human Gene Mutation Database [82], ClinVar [54], Exome Aggregation Consortium [83] and Genome Aggregation Database [84]. The 3D features include the mutated amino acids’ physicochemical properties, structural context and functional features. Among the different functional classes they analyzed, four AQPs (AQP1, AQP2, AQP4 and AQP5) have been included under the ‘Transporter’ functional class. In all four AQP homologs, only 58 pathogenic missense variants are available, while population (neutral) missense variants are 542 in total out of which 447 have been mapped onto the 3D structure. Hence, the number of pathogenic mutants available in the mutation databases for the AQP family is in general very less. However, there are many reports associating SNPs with disease conditions as detailed in the Introduction section. In the current study, we have found >20 examples in the OMIM database in which AQP SNPs result in diseases. Most of these missense SNPs occur in the helix–helix interface or they are facing the channel interior, and one can safely assume that the substitutions due to SNPs could possibly interfere with the structure and/or function. Other possibilities include improper folding and targeting. The study by Igbal et al. showed that the 3D-mutational hotspots can be different across different protein structural and functional classes [60]. In this regard, the current study is very important as it has compiled the missense variants and classified functional features specific to AQP family members. As mentioned above, at present the number of recognized pathogenic variants for the AQP family is very less as evident from other studies. When more pathogenic variants are recognized, the current data with structural and functional classifications can be applied to come up with the prediction for human AQP missense substitutions.
In this study, a systematic search in the dbSNP yielded ∼2800 missense SNPs in all human AQP homologs. Hence, it is important to analyze the nature of the residue substitutions that can help to infer where the substitutions in the structure take place and what kind of substitutions are occurring. We are fully aware that not all SNPs will result in pathogenic conditions. However, it is important to know what kind of substitutions occur due to missense SNPs and whether or not these substitutions are likely to affect the structure and/or function of human AQPs. We analyzed all the missense SNPs of human AQPs to understand the pattern of substitutions. We divided the naturally occurring amino acids into six groups, and this classification is based on both chemical and physical properties. This analysis helped us to find out some of the unusual and non-conservative substitutions which included small to big, hydrophobic to charged and negatively charged to positively charged, and vice versa. Some substitutions are never observed. For example, we did not find even a single case in which aromatic amino acids are substituted by proline, and vice versa. We have also compared the pattern of substitutions found in our study and the data available in the MutHTP database [78]. A comparison between data obtained from mutation databases and the current study using SNP missense substitution data reveals that there are some notable differences in the pattern of amino acid substitutions. For example, in the present study, we have not found even a single example in which NP (Gln and Asn) residues are replaced by aromatic residues (Tyr, Trp and Phe). However, in MutHTP, many examples have been found in which *Asn is* replaced by Tyr in disease mutations, and a few cases of Asn → Tyr mutation were found in neutral mutations. Similarly, Pro to Aromatic substitutions and vice versa were not found in the present study. In MutHTP, there are some examples in which *Phe is* substituted by Pro in disease mutations.
Next, we wanted to find out where exactly these substitutions occur with respect to the structure. We found many examples of non-conservative substitutions in the functionally important NPA motif or Ar/R SF. Similarly, substitutions that are likely to disrupt the TM helix packing occur at the helix–helix interface. Building the dbAQP-SNP database and making it publicly available is the first step where researchers can use this resource to look at specific missense SNPs, the type of substitutions and the structural region in which these substitutions occur. Using the Advanced Search options, one can look at all the missense SNPs that occur in specific structural regions. One can also choose a particular human AQP homolog and focus on all missense SNPs to understand whether these SNPs are likely to result in pathogenic conditions. We hope that this resource will help to further advance our knowledge in associating SNPs in human AQPs with the structural and functional defects that may be pathogenic.
## Supplementary material
Supplementary material is available at Database online.
## Conflict of interest statement
None declared.
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|
---
title: Identification and validation of metabolism-related hub genes in idiopathic
pulmonary fibrosis
authors:
- Youjie Zeng
- Jun Huang
- Ren Guo
- Si Cao
- Heng Yang
- Wen Ouyang
journal: Frontiers in Genetics
year: 2023
pmcid: PMC10010493
doi: 10.3389/fgene.2023.1058582
license: CC BY 4.0
---
# Identification and validation of metabolism-related hub genes in idiopathic pulmonary fibrosis
## Abstract
Background: Idiopathic pulmonary fibrosis (IPF) is a fatal and irreversible interstitial lung disease. The specific mechanisms involved in the pathogenesis of IPF are not fully understood, while metabolic dysregulation has recently been demonstrated to contribute to IPF. This study aims to identify key metabolism-related genes involved in the progression of IPF, providing new insights into the pathogenesis of IPF.
Methods: We downloaded four datasets (GSE32537, GSE110147, GSE150910, and GSE92592) from the Gene Expression Omnibus (GEO) database and identified differentially expressed metabolism-related genes (DEMRGs) in lung tissues of IPF by comprehensive analysis. Then, we performed GO, KEGG, and Reactome enrichment analyses of the DEMRGs. Subsequently, key DEMRGs were identified by machine-learning algorithms. Next, miRNAs regulating these key DEMRGs were predicted by integrating the GSE32538 (IPF miRNA dataset) and the miRWalk database. The Cytoscape software was used to visualize miRNA-mRNA regulatory networks. In addition, the relative levels of immune cells were assessed by the CIBERSORT algorithm, and the correlation of key DEMRGs with immune cells was calculated. Finally, the mRNA expression of the key DEMRGs was validated in two external independent datasets and an in vivo experiment.
Results: A total of 101 DEMRGs (51 upregulated and 50 downregulated) were identified. Six key DEMRGs (ENPP3, ENTPD1, GPX3, PDE7B, PNMT, and POLR3H) were further identified using two machine-learning algorithms (LASSO and SVM-RFE). In the lung tissue of IPF patients, the expression levels of ENPP3, ENTPD1, and PDE7B were upregulated, and the expression levels of GPX3, PNMT, and POLR3H were downregulated. In addition, the miRNA-mRNA regulatory network of key DEMRGs was constructed. Then, the expression levels of key DEMRGs were validated in two independent external datasets (GSE53845 and GSE213001). Finally, we verified the key DEMRGs in the lung tissue of bleomycin-induced pulmonary fibrosis mice by qRT-PCR.
Conclusion: Our study identified key metabolism-related genes that are differentially expressed in the lung tissue of IPF patients. Our study emphasizes the critical role of metabolic dysregulation in IPF, offers potential therapeutic targets, and provides new insights for future studies.
## 1 Introduction
Idiopathic pulmonary fibrosis (IPF) is a progressive, life-threatening, chronic interstitial lung disease of unknown etiology (Noble et al., 2012). It is characterized by progressive scarring of the lung parenchyma, accompanied by a continuous deterioration of respiratory symptoms and a decline in lung function, ultimately leading to death (Raghu et al., 2018). Approximately two to 3 years is the median survival time for patients with IPF after diagnosis (Ley et al., 2011). There is a higher prevalence of IPF in the elderly, and the mean age of patients with IPF is around 65–70 years (Maher et al., 2021). The FDA currently approves two antifibrotic drugs (nintedanib and pirfenidone) for IPF, which only slow, not stop, fibrosis progression (Saito et al., 2019). IPF is currently curable only through lung transplantation (Shenderov et al., 2021). Despite identifying several candidate biomarkers for IPF, none of these markers have yet been translated into clinical practice (Ley et al., 2014). Thus, there is an urgent need to explore the pathophysiological mechanisms of IPF further and develop new targeted therapeutic strategies.
An increasing number of studies have recently demonstrated the role of metabolic dysregulation in IPF. For instance, Kang et al. reported altered glycolysis and glutamine metabolism in human lungs with severe IPF (Kang et al., 2016). Furthermore, proteomics studies revealed dysregulated levels of transcription factors NF-kB, PPARγ, and c-myc in bronchoalveolar lavage fluid (BALF) from IPF patients compared to healthy controls (Landi et al., 2014). Interestingly, these transcription factors have been reported to participate in numerous metabolic dysregulation mechanisms (Kauppinen et al., 2013; Jiang et al., 2017; Botta et al., 2018). In addition, lung fibroblasts and alveolar epithelial cells have been observed to display profibrotic phenotypes due to dysregulated lipid metabolism (Mamazhakypov et al., 2019). A recent review summarized the proteins dysregulated in IPF involving the renin-angiotensin-aldosterone system, hypoxia, oxidative stress, iron metabolism, dysregulated lipid metabolism, and mitochondrial alterations, highlighting the potential impact of metabolic dysregulation in IPF (Bargagli et al., 2020). Conclusively, there is an inescapable relationship between metabolic dysregulation and IPF, and the search for novel metabolism-related markers can help further understand the metabolism-related pathological molecular mechanisms of IPF. Rectifying these metabolic alterations is emerging as a promising new strategy for antifibrotic therapy.
Our study first analyzed GSE32537, GSE110147, GSE150910, and GSE92592 from the Gene Expression Omnibus (GEO) database and identified differentially expressed metabolism-related genes (DEMRGs) in the lung tissue of IPF patients. Subsequently, we conducted a functional enrichment analysis of DEMRGs. Then, we used two machine-learning methods, least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE), to identify six IPF signature genes as key DEMRGs: ENPP3, ENTPD1, PDE7B, GPX3, PNMT, and POLR3H. The expression of ENPP3, ENTPD1, and PDE7B was significantly upregulated in IPF patients’ lung tissue, and the expression of GPX3, PNMT, and POLR3H was significantly downregulated. Afterward, we predicted miRNAs regulating key DEMRGs using the miRWalk database, combining it with the GSE32538 dataset (miRNA microarray expression profiles of IPF) to construct a miRNA-mRNA regulatory network. Next, the relative levels of immune cells were assessed by the CIBERSORT algorithm, and the correlation of key DEMRGs with immune cells was calculated. Finally, we validated the expression patterns of six key DEMRGs by analyzing the external independent dataset GSE53845 and performing qRT-PCR.
## 2.1 Study design
Figure 1 shows the overall flow chart of this study. First, we performed differential expression analysis on four GEO gene expression profile datasets to identify common differentially expressed metabolism-related genes (DEMRGs) in IPF lung tissues. Subsequently, we performed a functional enrichment analysis for these common DEMRGs. Then, we identified key DEMRGs using two machine-learning algorithms. Finally, we constructed potential miRNA-mRNA regulatory networks for key DEMRGs, calculated the correlation of key DEMRGs with immune cell levels, and validated the expression of key DEMRGs in external GEO datasets and animal models.
**FIGURE 1:** *The overall flow chart of this study.*
## 2.2 Gene expression profile data
Our study obtained publicly available datasets from the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) database (Edgar et al., 2002). The GEO database is a public database that stores extensive publicly available high-throughput gene expression and other functional genomics datasets (Clough and Barrett, 2016). All datasets downloaded in this study stored gene expression at the mRNA level (array or high-throughput sequencing), and samples in the dataset were obtained from the lung tissue of IPF patients and healthy control individuals. First, we performed a comprehensive bioinformatics analysis of GSE32537, GSE110147, GSE150910, and GSE92592 to identify key DEMRGs. Then, using GSE32538, we constructed a miRNA-mRNA regulatory network of key DEMRGs. Finally, we validated the key DEMRGs in GSE53845 and GSE213001. Table 1 provides details of all the GEO datasets used in our study.
**TABLE 1**
| Accession number | Platform | Samples | Experiment type |
| --- | --- | --- | --- |
| GSE32537 | GPL6244 | 119 IPF lung tissues vs 50 healthy lung tissues | Array |
| GSE110147 | GPL6244 | 22 IPF lung tissues vs 11 healthy lung tissues | Array |
| GSE150910 | GPL24676 | 103 IPF lung tissues vs 103 healthy lung tissues | High throughput sequencing |
| GSE92592 | GPL11154 | 20 IPF lung tissues vs 19 healthy lung tissues | High throughput sequencing |
| GSE32538 | GPL8786 | 106 IPF lung tissues vs 50 healthy lung tissues | Array (miRNA) |
| GSE53845 | GPL6480 | 40 IPF lung tissues vs 8 healthy lung tissues | Array |
| GSE213001 | GPL21290 | 62 IPF lung tissues vs 41 healthy lung tissues | High throughput sequencing |
## 2.3 Screening of differentially expressed metabolism-related genes (DEMRGs)
The metabolism-related genes (MRGs) were obtained from the Molecular Signatures database (MSigDB, https://www.gsea-msigdb.org/gsea/msigdb) (Liberzon et al., 2011). Specifically, we first downloaded the KEGG gene set (c2. cp.kegg.v7.5.1. symbols.gmt) from the MSigDB, then searched for the keyword “metabolism” to obtain metabolism-related terms, and finally we integrated the genes within these selected metabolism-related trems, which were defined as MRGs to be used for subsequent analysis in our study. All metabolism-related terms and the genes within each term are shown in Supplementary Table S1.
GSE32537 and GSE110147 were based on the GPL6244 platform of Affymetrix Human Gene 1.0 ST Array. We used the RMA algorithm via the “oligo” R package for background correction and normalization of the raw data in the two datasets. Subsequently, differentially expressed genes were identified using the “limma” R package. GSE150910 and GSE92592 were RNA-seq datasets that were generated using the Illumina platform. We first downloaded their raw gene count matrix files. Then, we performed differential expression analysis on the gene expression matrix normalized by the vst function of the “Deseq2” R package. An adjusted p-value <0.05 was set as the threshold for identifying differentially expressed genes.
After acquiring the DEMRGs from each of the four GEO datasets, we used the Venn diagram to search for the common upregulated DEMRGs and the common downregulated DEMRGs. The “ggvenn” R package was applied to plot the Venn diagrams of common DEMRGs of the four datasets.
## 2.4 Functional enrichment analysis of DEMRGs
We performed an enrichment analysis of the common DEMRGs using the Database for Annotation, Visualization and Integrated Discovery (DAVID database, https://david.ncifcrf.gov/) (*Huang da* et al., 2009; Sherman et al., 2022). We performed three categories of enrichment analysis: Gene ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis, and Reactome pathway enrichment analysis. In addition, the GO enrichment analysis includes three sections: biological process (BP), cellular component (CC), and molecular function (MF). We downloaded the enrichment analysis results and defined the false discovery rate (FDR) < 0.05 as the significant enrichment threshold. In addition, we selected the top 10 most significantly enriched terms in each category and imported these results into the SangerBox platform to generate dot plots for visualization (Shen et al., 2022).
## 2.5 Screening of IPF key DEMRGs
To identify the most critical DEMRGs, we used two machine-learning algorithms: least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE). The LASSO algorithm is a regression analysis method that minimizes regression coefficients through successive shrinkage operations to reduce the possibility of overfitting, thereby reducing redundancy and eliminating uncorrelated genes from these analyses (Friedman et al., 2010). The SVM-RFE algorithm is a method for feature selection based on SVM that defines the minimum classification error and avoids overfitting and thus is frequently used to select the optimal genes (Duan et al., 2005). The LASSO and SVM-RFE algorithms were implemented respectively by the “glmnet” package and the “e1071”package in R software. By using the two machine-learning algorithms, two sets of DEMRGs can be obtained, and the overlapping genes of these two sets of DEMRGs will be identified as the IPF key DEMRGs.
## 2.6 Construction of miRNA-mRNA regulatory networks for key DEMRGs
We intend to investigate further the miRNAs that regulate these key DEMRGs, so we first performed a differential expression analysis of GSE32538 (IPF miRNA expression profile microarray) to obtain differentially expressed miRNAs (DEmiRNAs). The significance threshold was set at an adjusted p-value <0.05. Since the IDs of the miRNAs in this dataset were derived from an older version of miRBase, we updated the miRNA IDs using the miEAA 2.0 database (Kern et al., 2020). Subsequently, we predicted miRNAs that interacted with key DEMRGs using the miRWalk database (http://mirwalk.umm.uni-heidelberg.de/) (Sticht et al., 2018). If a DEmiRNA was present in the predicted miRNAs from miRWalk, it would be included in the final miRNA-mRNA regulatory network. Therefore, the upregulated DEmiRNAs were then intersected with the predicted miRNAs that interact with downregulated key DEMRGs, while the downregulated DEmiRNAs were intersected with the predicted miRNAs that interact with upregulated key DEMRGs. Finally, we visualized the miRNA-mRNA regulatory network in the Cytoscape software (v 3.9.1) (Shannon et al., 2003).
## 2.7 Immune infiltration analysis
We assessed the relative content of immune cells of each sample in the GSE32537 dataset using the CIBERSORT algorithm in R software (Newman et al., 2015). The CIBERSORT algorithm calculates the relative expression of 22 immune cells based on the “LM22”matrix downloaded from the CIBERSORT portal (http://cibersort.stanford.edu/). First, we evaluated the relative expression of immune cells in all samples and plotted a histogram of immune cell content for each sample. Subsequently, we compared the content of each immune cell between IPF patients and healthy controls and plotted a boxplot for visualization. The Shapiro-Wilk test was performed to examine the normality of data, and the t-test or Mann-Whitney Wilcoxon test was used to conduct comparisons between groups based on the results of normality test (Supplementary Table S2). Finally, we calculated the correlation between 6 key DEMRGs and M2 macrophage content in 119 IPF patients. All results were visualized using the “ggplot2” R package.
## 2.8 Validation of key DEMRGs in independent external datasets
To improve the confidence of the results, we validated the expression of key DEMRGs in two independent external datasets (GSE53845 and GSE213001). We compared the mRNA expression levels of the key DEMRGs between IPF patients and control groups. We performed the Shapiro-Wilk test to check the normality of the data before making comparisons between groups. Based on the normality results (Supplementary Table S3, S4), we used the t-test or the Mann-Whitney Wilcoxon test to compare differences between groups. A p-value of <0.05 was considered statistically significant. Gene expression comparisons between groups were analyzed and visualized using the “ggplot2” package in R software (Wickham, 2016).
## 2.9 Construction of IPF animal models
The animal study was approved by the Laboratory Animal Welfare Ethics Committee of Central South University. Mice of the C57BL/6 strain (Adult male, 20 ± 2 g) were purchased from Hunan SJA Laboratory Animal Co., Ltd. (Hunan, China). Mice were housed in pathogen-free conditions with a 12 h dark/light cycle and were given access to food and water without restriction.
Single tracheal instillation of bleomycin (BLM) was applied to construct the pulmonary fibrosis model (Moeller et al., 2008). Mice were randomly divided into two groups: 1) Sham group ($$n = 6$$): intra-tracheal instillation of 50 µL saline alone; 2) BLM group ($$n = 6$$): intra-tracheal instillation of 50 µL saline containing BLM (5 mg/kg). Before surgery, mice were anesthetized by intraperitoneal injection of $1\%$ sodium pentobarbital (50 mg/kg). All mice were euthanized 2 weeks after surgery, and their lung tissue was harvested.
## 2.10 Validation of key DEMRGs by qRT-PCR
Total RNA was extracted from lung tissue using TRIzol reagent (Invitrogen, Carlsbad, CA, United States), and qRT-PCR was performed using the ABI ViiA 7 real-time PCR system. GAPDH mRNA was used as an internal control for the key DEMRGs, and the relative fold differences were calculated using the 2−ΔΔCT method. Triplicates of all experiments were performed. Table 2 presents the qRT-PCR primer sequences utilized in our study.
**TABLE 2**
| Gene | Primer sequence (5'→ 3′) |
| --- | --- |
| ENPP3 | F: CAGCAACGGTGAAAGCAAAT |
| ENPP3 | R: CTGATGTAGTCCCTGTGGTAAAG |
| PDE7B | F: ACTCTGTTGTGTCACCTCTTC |
| PDE7B | R: GGTTGTGACCGTGGTAATCT |
| ENTPD1 | F: AACTGTCCACCGAACTGATAC |
| ENTPD1 | R: CCGATTGTTCGCTTTCCATTC |
| PNMT | F: GGGACGGGTTCTCATTGATATT |
| PNMT | R: CTGACGGTTGACTTCCAAGAA |
| POLR3H | F: CCAGGGCCTCTTTCATGTT |
| POLR3H | R: CTGCTCTGCCACCAGTATTT |
| GPX3 | F: CCTTTTAAGCAGTATGCAGGCA |
| GPX3 | R: CAAGCCAAATGGCCCAAGTT |
| GAPDH | F: GAGCATCTCCCTCACAATTC |
| GAPDH | R: GGGTGCAGCGAACTTTAT |
Relative expression levels of the key DEMRGs were plotted in a barplot using the GraphPad Prism 8 software. Based on the normality results calculated by Shapiro-Wilk (Supplementary Table S5), the differences between groups were calculated using the t-test or Mann-Whitney Wilcoxon test, and p-values <0.05 were considered statistically significant.
## 3.1 Identification of differentially expressed metabolism-related genes (DEMRGs)
We obtained 949 unique MRGs through MSigDB. Subsequently, we performed differential expression analysis on lung tissue samples from IPF patients and healthy control individuals from four GEO datasets (GSE32537, GSE110147, GSE150910, and GSE92592), and thus obtained the differentially expressed MRGs (DEMRGs) between IPF patients and healthy controls in each dataset. As a result, in GSE32537, GSE110147, GSE150910, and GSE92592, we detected 203, 279, 263, and 267 upregulated DEMRGs, respectively. In addition, we identified 336, 402, 231, and 211 DEMRGs that were downregulated in GSE32537, GSE110147, GSE150910, and GSE92592. The heat map shows the distribution of DEMRGs in the four datasets (Figures 2A–D). The red part of the heat map indicates the upregulated DEMRGs in IPF lung tissues, while the green part indicates the downregulated DEMRGs in IPF lung tissues. The Venn diagram shows that there were 51 common upregulated DEMRGs and 50 common downregulated DEMRGs in the four datasets (Figures 2E,F). These 101 common DEMRGs were used for subsequent analysis.
**FIGURE 2:** *Identification of DEMRGs in IPF. (A) Heatmap of DEMRGs in GSE32537 (203 upregulated and 336 downregulated DEMRGs). (B) Heatmap of DEMRGs in GSE110147 (279 upregulated and 402 downregulated DEMRGs). (C) Heatmap of DEMRGs in GSE150910 (263 upregulated and 231 downregulated DEMRGs). (D) Heatmap of DEMRGs in GSE92592 (267 upregulated and 211 downregulated DEMRGs). (E) The Venn diagram identified fifty-one commonly upregulated DEMRGs. (F) The Venn diagram identified fifty commonly downregulated DEMRGs.*
## 3.2 Gene ontology, KEGG pathway, and reactome pathway enrichment analysis
We performed a functional enrichment analysis of these 101 common DEMRGs through the DAVID database. Figure 3 shows the top 10 significantly enriched GO, KEGG, and Reactome pathway terms. The dot size indicates the number of DEMRGs enriched to the corresponding term, and the dot color indicates the enrichment significance of the corresponding term. In the BP category of the GO enrichment analysis, DEMRGs were mainly enriched in items such as “xenobiotic metabolic process”, “inositol phosphate dephosphorylation”, and “phosphatidylinositol dephosphorylation” (Figure 3A). In the CC category of the GO enrichment analysis, these genes were mainly enriched in items such as “cytosol”, “mitochondrial matrix”, and “endoplasmic reticulum membrane” (Figure 3B). In the MF category of the GO enrichment analysis, these genes were mainly enriched in items such as “oxidoreductase activity”, “phosphorus-oxygen lyase activity”, and “ATP binding” (Figure 3C). KEGG analysis showed that DEMRGs were likely related to “metabolic pathways”, “purine metabolism”, and “nucleotide metabolism” (Figure 3D). Reactome analysis indicated that DEMRGs were significantly enriched in “metabolism”, “biological oxidations”, and “metabolism of nucleotides” (Figure 3E).
**FIGURE 3:** *Functional enrichment analysis of DEMRGs. The dot size indicates the number of DEMRGs enriched to the corresponding term, and the dot color indicates the enrichment significance of the corresponding term. (A) The top 10 significantly enriched terms for Gene ontology biological process. (B) The top 10 significantly enriched terms for Gene ontology cellular component (C) The top 10 significantly enriched terms for Gene ontology molecular function. (D) The top 10 significantly enriched terms for the KEGG pathway. (E) The top 10 significantly enriched terms for the Reactome pathway.*
## 3.3 Identification of IPF key DEMRGs
In order to identify key DEMRGs, the LASSO regression analysis was used to screen the gene signatures for the 101 common DEMRGs (Figure 4A), yielding 23 gene signatures. Furthermore, ten gene signatures were obtained using the SVM-RFE for the 101 common DEMRGs (Figure 4B). Finally, the Venn diagram showed that there were six overlapping DEMRGs (ENPP3, ENTPD1, GPX3, PDE7B, PNMT, and POLR3H) among the 23 genes identified by LASSO and the ten genes identified by SVM-RFE, and thus these six overlapping DEMRGs were defined as key DEMRGs (Figure 4C).
**FIGURE 4:** *Identification of IPF key DEMRGs by using two machine-learning algorithms. (A) Twenty-three gene signatures were extracted via LASSO regression. (B) Ten gene signatures were extracted via SVM-RFE. (C) The Venn diagram identified six overlapping DEMRGs shared by LASSO and SVM-RFE. Therefore, the six overlapping DEMRGs were identified as key DEMRGs.*
## 3.4 Identification of miRNA-mRNA regulatory networks of key DEMRGs
After updating the miRNA IDs by the miEAA 2.0 database, we identified 59 upregulated miRNAs and 103 downregulated miRNAs in IPF from the GSE32538 dataset (Figure 5A). Using the miRWalk database, we identified 1,295 miRNAs predicted to interact with the upregulated key DEMRGs (ENPP3, ENTPD1, and PDE7B), and they had 42 overlapping miRNAs with the 103 downregulated DEmiRNAs (Figure 5B). In addition, 1,103 miRNAs predicted to interact with the downregulated key DEMRGs (GPX3, PNMT, and POLR3H) were identified through the miRWalk database, and they had 18 overlapping miRNAs with the 59 upregulated DEmiRNAs (Figure 5B). Except for PNMT, all other key DEMRGs have interactions with one or more overlapping miRNAs. Ultimately, a miRNA-mRNA regulatory network of 60 DEmiRNAs and 5 DEMRGs was constructed by Cytoscape software, with red representing upregulation and green representing downregulation (Figure 5C).
**FIGURE 5:** *Identification of miRNA-mRNA regulatory networks of Key DEMRGs. (A) Heatmap of DEmiRNAs in GSE32538 (59 upregulated and 103 downregulated DEmiRNAs). (B) Venn diagram showing the intersecting miRNAs between DEmiRNAs and the predicted miRNAs. (C) The metabolism-related miRNA-mRNA regulatory network contained 60 DEmiRNAs and 5 DEMRGs. Red nodes represent upregulated key DEMRGs or DEmiRNAs in IPF lung tissue, and green nodes represent downregulated key DEMRGs or DEmiRNAs in IPF lung tissue.*
## 3.5 Immune infiltration features of IPF
Figure 6A presents the distribution of immune cells in the lung tissue of 119 IPF patients and 50 healthy controls in the GSE32537 dataset. The relative levels of many immune cells differed significantly between IPF and controls (Figure 6B). We focused on M2 macrophages because they contribute to the fibrotic phenotype exacerbation (Wynn and Vannella, 2016). A significant increase in M2 macrophages was found in the lung tissue of patients with IPF (Figure 6B). In addition, we calculated the correlation between the expression levels of six key DEMRGs and the expression levels of M2 macrophages. To minimize the false positive rate, correlation analysis was conducted on only 119 IPF patients. Figure 6C indicates that M2 macrophage expression was positively correlated with the expression level of ENPP3 ($R = 0.28$, $$p \leq 0.0023$$). Therefore, ENPP3 might be potentially associated with increased levels of M2 macrophages in the IPF process.
**FIGURE 6:** *Immune cell infiltration in IPF. (A) Histogram of the proportion of each type of immune cell in the lung tissue of 119 IPF patients and 50 controls in the GSE32537 dataset. (B) Boxplot of the relative expression of each immune cell subtype between the IPF patients and healthy controls. (C) M2 macrophage expression was positively correlated with the expression level of ENPP3. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.*
## 3.6 Key DEMRGs exhibited the same expression pattern in the external datasets
We compared the expression levels of six key DEMRGs in IPF patients and controls in two independent external datasets (GSE53845 and GSE213001). According to the results, ENPP3, ENTPD1, and PDE7B were significantly upregulated in the lung tissues of IPF patients (Figure 7), while GPX3, PNMT, and POLR3H were significantly downregulated (Figure 7). These results were in accordance with those in the previous four datasets.
**FIGURE 7:** *The expression levels of six key DEMRGs were validated in two independent external datasets (GSE53845 and GSE213001): ENPP3, ENTPD1, and PDE7B were significantly upregulated in IPF lung tissue (p < 0.05), while GPX3, PNMT, and POLR3H were significantly downregulated in IPF lung tissue (p < 0.05).*
## 3.7 Validation of the key DEMRGs by qRT-PCR
According to the results of qRT-PCR, the expression levels of ENPP3, PDE7B, and ENTPD1 were elevated, while the expression levels of PNMT, GPX3, and POLR3H were decreased in the lung tissues of bleomycin-induced pulmonary fibrosis mice compared with the sham group (Figure 8). The results of qRT-PCR remained consistent with the bioinformatics analysis; therefore, these key DEMRGs may play an essential role in the progression of IPF.
**FIGURE 8:** *Validation of Key DEMRGs by qRT-PCR. Values represent means ± SD, n = 6/group. *p < 0.05; **p < 0.01; ***p < 0.001.*
## 4 Discussion
Our study aims to identify key metabolism-related genes of IPF. First, we performed the differential analysis of the four GEO public datasets (GSE32537, GSE110147, GSE150910, and GSE92592) and integrated metabolism-related genes from the MSigDB dataset, resulting in 51 DEMRGs that were commonly upregulated and 50 DEMRGs that were commonly downregulated in the four IPF datasets. Subsequently, we performed functional enrichment analysis on these 101 DEMRGs, and the results indicated that these genes were involved in various metabolism-related terms. Then, two machine-learning algorithms were utilized to screen the key DEMRGs, resulting in six genes (ENPP3, ENTPD1, PDE7B, GPX3, PNMT, and POLR3H) as key DEMRGs. We further combined the miRNA expression profile dataset of IPF and the predicting results of the miRWalk database to construct the miRNA-mRNA network regulating the key DEMRGs. Next, we performed an immune infiltration analysis and identified an elevated M2 macrophage level in IPF patients, which reflects the enhanced M2 polarization-mediated fibrosis phenotype. In addition, the mRNA expression of the key DEMRGs was validated in two external independent datasets (GSE53845 and GSE213001). Finally, the gene expression pattern was validated by qRT-PCR, demonstrating that the key DEMRGs might have potentially significant roles in IPF.
The immune cell infiltration results showed increased levels of M2 macrophages in the lung tissue of IPF patients. As the most abundant immune cells in the lung (approximately $70\%$), macrophages play a critical role in pulmonary fibrosis-related airway remodeling (Cai et al., 2014). Activated macrophages are usually divided into two categories, M1 macrophages (pro-inflammatory) and M2 macrophages (anti-inflammatory/pro-fibrotic) (Vasse et al., 2021). The ENPP3 and ENTPD1 encoded products can hydrolyze ATP. Thus the elevated levels of ENPP3 and ENTPD1 observed in our study lead to a decreased ATP level. Extracellular ATP increases the global inflammation level (Cauwels et al., 2014). Besides, we identified that the M2 macrophage expression was positively correlated with the expression level of ENPP3. Taken together, we have reason to believe that ENPP3 and ENTPD1 may play a role in macrophages. The enhanced macrophage M2 polarization might be partly through the upregulation of ENPP3 and ENTPD1, leading to a decrease in ATP levels, which produces an anti-inflammatory and pro-fibrotic phenotype and ultimately exacerbates IPF. However, the specific mechanism needs to be validated in further studies.
GPX3 encodes glutathione peroxidase 3, which is expressed mainly in the lung and kidney (Lubos et al., 2011). Recent studies have shown a strong link between reactive oxygen species and fibrosis (Richter and Kietzmann, 2016). NADPH oxidase 4-derived ROS has been reported to regulate TGF-beta1-induced myofibroblast differentiation, extracellular matrix production, and contractility. A recent study uncovered a therapeutic effect of ROS-responsive liposomes in IPF, further suggesting the significance of anti-oxidative stress in IPF treatment (Liu et al., 2022). Our study shows that GPX3 expression levels are decreased in IPF lung tissue, which leads to increased levels of oxidative stress and thus exacerbates the fibrotic phenotype. Therefore, GPX3 is expected to be a potential novel target for the anti-oxidative stress treatment of IPF.
PDE7B encodes a phosphodiesterase that hydrolyzes cAMP and downregulates its signaling effects (Sasaki et al., 2000). In addition, the products of PNMT increase adrenaline production, and activation of adrenoceptors increases cAMP synthesis (Torphy, 1994; Martin et al., 2001). The decrease in cAMP results in a reduction of PKA activity and an increase in PFK activity, leading to increased F2,6BP levels. In response to the rise in F2,6BP levels, gluconeogenesis is suppressed, and glycolysis is stimulated (Pernicova and Korbonits, 2014). The increasing cellular cAMP level inhibits pulmonary fibroblast proliferation and collagen synthesis (Liu et al., 2004). In addition, glycolysis is increased early and sustainably during myofibroblast differentiation (Xie et al., 2015). The glucose transporter protein 1-dependent glycolytic phenotype was significantly increased in the lungs of aged mice, which was essential for pulmonary fibrosis (Cho et al., 2017). Actually, β-adrenergic agonists/cAMP play a key role in IPF, and β-adrenergic receptor agonists/cAMP have been shown to have beneficial effects on alveolar injury, including protection from epithelial and endothelial cell damage, restoration of alveolar fluid clearance, and reduction of fibrotic remodeling (Sriram et al., 2021). Overall, the upregulation of PDE7B and downregulation of PNMT in the lung tissues of IPF patients identified in our study might conjointly result in decreased β2-AR agonist/cAMP levels, decreased PKA activity, and enhanced glycolysis, which induced excessive collagen production and fibrosis formation.
The advantage of this study is that we have identified key metabolism-related genes that are commonly differentially expressed in IPF lung tissue using multiple bioinformatics approaches and validation in animal models. *These* genes may be a potential focus for future research on IPF metabolic disorders. However, several shortcomings of our study need to be acknowledged. First, the general profile of the IPF population cohort and the healthy control population cohort in the original dataset was not identical; for example, the mean age of the case group in the original study of GSE32537 was 62.6 years, whereas the mean age of the control group was 47.5 years. Therefore, it is unclear whether these differential gene expressions could be influenced by age. Nevertheless, our findings were obtained based on the analysis and validation of multiple datasets, thus minimizing the effect of potential confounding factors. The second limitation of this study is that although the identified key DEMRGs are commonly differentially expressed in IPF lung tissues, the specific degree of their impact on IPF needs to be clarified. Therefore, it will be important to interpret the findings with caution until they are validated by functional experimental research, despite the fact that they were based on reliable bioinformatics data.
## 5 Conclusion
Overall, through a comprehensive analysis of public datasets and experimental validation, we identified key metabolism-related genes that are differentially expressed in the lung tissue of IPF patients. Our study emphasizes the critical role of metabolic dysregulation in IPF, offers potential therapeutic targets, and provides new insights for future studies.
## Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/; accession number: GSE32537, GSE110147, GSE150910, GSE92592, GSE32538, GSE53845, GSE213001).
## Ethics statement
The animal study was reviewed and approved by the Laboratory Animal Welfare Ethics Committee of Central South University.
## Author contributions
YZ designed the study, analyzed the data, and wrote the manuscript. JH and RG performed validation in animal models. SC assisted in analyzing the data and revising the manuscript. HY and WO critically read and edited 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.2023.1058582/full#supplementary-material
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|
---
title: Factors affecting do-not-resuscitate decisions among patients with amyotrophic
lateral sclerosis in Taiwan
authors:
- Mei-Hsing Chuang
- Jiunn-Rong Hsu
- Chia-Wei Hung
- Yu Long Hwang
- Chih-Ching Lee
- Hsiu-Yi Shen
- Fu-Kang Chang
- Li-Lin Kuo
- Saint Shiou-Sheng Chen
- Sheng-Jean Huang
journal: PLOS ONE
year: 2023
pmcid: PMC10010504
doi: 10.1371/journal.pone.0282805
license: CC BY 4.0
---
# Factors affecting do-not-resuscitate decisions among patients with amyotrophic lateral sclerosis in Taiwan
## Abstract
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease. Usually, patients survive for approximately 2–4 years after the onset of the disease, and they often die of respiratory failure. This study examined the factors associated with signing a “do not resuscitate” (DNR) form in patients with ALS. This cross-sectional study included patients diagnosed with ALS between January 2015 and December 2019 in a Taipei City hospital. We recorded patients’ age at disease onset; sex; presence of diabetes mellitus, hypertension, cancer, or depression; use of invasive positive pressure ventilator (IPPV) or non-IPPV (NIPPV); use of nasogastric tube (NG) or percutaneous endoscopic gastrostomy (PEG) tube; follow-up years; and number of hospitalizations. Data from 162 patients were recorded (99 men). Fifty-six ($34.6\%$) signed a DNR. Multivariate logistic regression analyses revealed that the factors associated with DNR included NIPPV (OR = 6.95, $95\%$ CI = 2.21–21.84), PEG tube feeding (OR = 2.86, $95\%$ CI = 1.13–7.24), NG tube feeding (OR = 5.75, $95\%$ CI = 1.77–18.65), follow-up years (OR = 1.13, $95\%$ CI = 1.02–1.26), and number of hospital admissions (OR = 1.26, $95\%$ CI = 1.02–1.57). The findings suggest that end-of-life decision making among patients with ALS may often be delayed. DNR decisions should be discussed with patients and their families during the early stages of disease progression. Physicians are advised to discuss DNR with patients when they can speak and to offer palliative care options.
## Introduction
Amyotrophic lateral sclerosis (ALS)—a neurodegenerative disease—affects both the upper and lower motor neurons. The clinical symptoms and disease progression vary greatly [1]. The disease incidence and prevalence are approximately 0.78–2.35 and 3.01–7.96 per 100 000 people, respectively [2]. The onset of ALS peaks between 50–75 years [1]. Its etiology is unclear—gene mutation, environmental factors, viruses, toxin exposure, and autoimmunity may be related [1–3]. Patients may have symptoms of muscle weakness, dysphagia, and dyspnea, while muscles for eye movement and the sphincter remain unaffected [1]. Usually patients survive for approximately 2–4 years after the onset; they often die of respiratory failure [1–3].
The incidence of ALS in *Taiwan is* approximately 0.33–0.44 per 100 000 people; its prevalence is approximately 2.31 per 100 000 people [4]. In Taiwan, patients with ALS have an annual crude mortality rate of approximately $14.7\%$–$19.7\%$ within five years of diagnosis [5]. Average survival can be improved with riluzole, ventilators, or gastrostomy [4–6]. To date, the pathogenesis of ALS is not fully understood, and no effective treatment has been found [1–6]. Treatment includes symptomatic treatment and palliative care [7–9].
A Polish study of patients with ALS in the locked-in state found that some patients maintain a high sense of well-being despite severe physical restrictions [10]. Caring for patients with ALS is medically and financially resource intensive. Furthermore, there is a considerable burden on caregivers [11, 12]. Most patients with ALS are conscious and have normal sensory function. As the disease progresses, they become bedridden owing to immobility, have difficulty swallowing and breathing, and need to rely on tube feeding and a breathing ventilator. Patients with end-stage ALS require a tracheotomy to survive, and they often feel fatigued, depressed, pained, and hopeless—as if their souls are imprisoned in an immobile body.
Although many patients take life-sustaining measures, it is not uncommon for some to wish for death. According to surveys conducted in India and the US, 18.9–$25\%$ of patients said they wanted to die and $5.7\%$ wanted to speed up their death [13–15]. A Dutch report stated that approximately $20\%$ of patients with ALS chose euthanasia [16]. Reasons included fear of suffocation, feeling there is no chance for improvement, loss of dignity, dependence on others, and fatigue [16]. A US survey found that $71.4\%$ (30 patients out of a sample of 42) of patients with ALS decided not to have cardiopulmonary resuscitation (CPR) administered [17].
Limited attempts have been made to identify the factors contributing to the signing of a DNR form in patients with ALS. Studies have reported that malnutrition, dementia, aspiration, very severe pneumonia, respiratory failure, albumin levels less than 3, Charlson Comorbidity Index higher than 2, and being transferred to the intensive care unit were independently associated with DNR orders among elderly people [18, 19]. In 2000, Taiwan promulgated the “Hospice Palliative Care Act” [20]. In this law, terminally ill patients refer to those who experience serious injuries or illness, have incurable diseases, or have medical evidence that shows that they have a fatal prognosis, in the near future. Additionally, this law stipulates that the terminal illness must be diagnosed by two physicians who are qualified and are specialists in the relevant field. If CPR is not to be performed, written consent is required from the patient or their closest relative, if the terminally ill patient has become unconscious or failed to clearly express his/her will [20]. CPR includes endotracheal intubation, chest compression, injection of resuscitation drugs, external defibrillation, artificial cardiac pacing, mouth-to-mouth ventilation, and ventilator use. Physicians provide palliative medical care to terminally ill patients according to their wishes [20]. The first ALS ward in Asia was established on October 15, 2006 at the Taipei City Hospital–Zhongxiao branch. A professional medical team took care of this group of patients. In the study hospital, the most common reasons for patients to be hospitalized were infectious diseases, dyspnea, and receiving percutaneous endoscopic gastrostomy (PEG). During hospitalization, various medical needs of patients are integrated by medical specialists. Patients may display improved quality of life and mental health. One study in Japan showed that communicating with patients and their families were important. This could help clinicians understand what patients require [21]. ALS does not have to be a terminal disease; however, when the condition is serious, the use resuscitation should coincide with their own wishes. This study examined the factors associated with signing a DNR form in patients with ALS.
## Sample
This cross-sectional study analyzed electronic inpatient and outpatient medical records of patients from a Taipei hospital between January 2015 and December 2019. Inclusion criteria were patients with an ALS diagnosis per the International Classification of Diseases (ICD) ninth revision (code 335.20) or ICD tenth revision (code G12.21). All patients were diagnosed by neurologists. The diagnoses were based on the revised El Escorial research diagnostic criteria for ALS [22]. Patients with ALS but not diagnosed by a neurologist were excluded.
Other factors recorded included basic information (onset age and sex); comorbidities (diabetes mellitus, hypertension, cancer, or depression); use of invasive positive pressure ventilator (IPPV) or non-IPPV (NIPPV); use of nasogastric tube (NG), PEG tube, or oral feeding; follow-up years; and number of hospitalizations since diagnosis. We also assessed whether a DNR form had been signed. This study was approved by the Human Research Ethics Review Committee of Taipei City Hospital (no: TCHIRB-10811001-E), and the need for informed consent was waived owing to the identification data of participants being removed before analysis.
## Statistical analysis
Statistical tests were performed using two-tailed tests, and p-values <.05 were considered significant. For descriptive statistics, chi-square and t-tests were conducted for categorical and numerical variables, respectively. For inferential statistics, a multivariable logistic regression analysis was conducted. Data analyses were performed using SAS 9.4 statistical software (SAS Institute, Inc., Cary, NC, USA).
## Results
From January 2015 to December 2019, there were 163 patients. Of which, 162 patients with ALS diagnosed by neurologists were recorded (99 men, $61.1\%$) and one patient was excluded as they were not diagnosed by a neurologist (Fig 1). About one-third had signed a DNR form (DNR group), while nearly two-thirds had not (non-DNR group). Participants’ characteristics are shown in Table 1. The two groups were similar regarding the prevalence of hypertension, cancer, and depression; however, mean follow-up years, use of ventilator, tube feeding, prevalence of diabetes, and number of hospitalizations were higher in the DNR group compared to the non-DNR group. The average duration from onset to signing a DNR was 6.38 years (year of signing the DNR minus the year of disease onset).
**Fig 1:** *Process of case enrollment.* TABLE_PLACEHOLDER:Table 1 We performed a collinearity analysis of all the variables; none of the univariate items were collinear. A univariate analysis of the factors associated with signing a DNR form in patients with ALS found that diabetes ($p \leq .01$), use of invasive ($$p \leq .01$$) or non-invasive ventilators ($$p \leq .003$$), use of NG tube feeding ($$p \leq .007$$), years of follow-up ($$p \leq .01$$), and number of hospitalizations ($p \leq .01$) were highly significant (Table 2). Variables that were significant in the univariate analysis were included in the multivariate analysis. A multivariate logistic regression analysis revealed that the factors associated with DNR included non-invasive ventilators, PEG tube feeding, NG tube feeding, follow-up years, and number of hospital admissions (Table 2).
**Table 2**
| Unnamed: 0 | Univariate analysis | Multivariate analysis |
| --- | --- | --- |
| Variables | ORb (95% CI) | AORc (95% CI) |
| Sex | Sex | Sex |
| Male | 0.87 (0.45–1.69) | 0.80 (0.34–1.88) |
| Female | 1 | 1 |
| Age of onset, years | 1.03 (0.99–1.05) | 1.03 (0.99–1.07) |
| Follow-up years | 1.10 (1.02–1.18)* | 1.13 (1.02–1.26)* |
| Respiration | Respiration | Respiration |
| IPPVd | 8.53 (2.95–26.65)* | 2.91 (0.69–12.24) |
| NIPPVe | 8.73 (3.30–23.10)* | 6.95 (2.21–21.84)* |
| No ventilator | 1 | 1 |
| Feeding tube | Feeding tube | Feeding tube |
| PEG f | 1.77 (0.83–3.76) | 2.86 (1.13–7.24)* |
| NGg | 4.36 (1.65–11.51)* | 5.75 (1.77–18.65)* |
| No tube feeding | 1 | 1 |
| Comorbidity | Comorbidity | Comorbidity |
| Diabetes mellitus | 4.80 (2.09–11.00)* | 2.65 (0.93–7.51) |
| Hypertension | 1.94 (0.98–3.85) | |
| Cancer | 5.94 (0.60–58.46) | |
| Depression | 0.70 (0.27–1.79) | |
| Number of hospitalizations | 1.49 (1.23–1.80)* | 1.26 (1.02–1.57)* |
## Discussion
Fifty-six people ($34.6\%$) had signed a DNR form (DNR group). This contradicts an American study in which $71.4\%$ (30 patients out of a sample of 42) had decided on a DNR, regardless of their level of respiratory insufficiency [17]. Furthermore, the related factors for the DNR-group patients in this study differed. We found that NG or PEG tube, number of hospitalizations, use of ventilator, and years of follow-up increased the probability of signing a DNR. Further, NG and PEG tubes were significant in the multivariate analysis. This is consistent with physicians’ clinical experience. When a patient has dysphagia and needs a tube feeding diet, they may consider signing a DNR form. However, IPPV was not significant in the multivariate analysis. The $95\%$ CI is relatively wide, which we speculate to be because of the small sample size. Doctors’ clinical experience allows them to know when a patient has difficulty breathing despite using NIPPV, and therefore, requires IPPV, they may consider signing a DNR form. The reason for the difference may be that physicians in the US discuss end-of-life decisions with patients during their first visit [17], while discussing death is still taboo in Taiwan. Compared to other diseases in Taiwan, a study reported that $19.6\%$ (66 out of 337) of the geriatric patients signed a DNR form during hospitalization [19].
Most patients with ALS signed a DNR when the disease was more severe. The average duration from onset to signing a DNR was 6.38 years. This hospital provided a professional medical team to take acute care of ALS patients; thus, patients’ survival period was relatively long. The study hospital also has a respiratory care unit for those who must rely on ventilators for a long time. Another reason was that Taiwan’s national health insurance covers all outpatient, inpatient, and hospice care for patients with ALS [23, 24]. Therefore, patients with ALS or their family usually decide whether to sign a DNR at the last minute.
Another reason may be that physicians do not inform patients of the need for a tracheostomy for future respiratory failure during the initial diagnosis [25, 26]. Until now, there is still no satisfactory treatment for ALS. For such patients, palliative care may be an appropriate treatment choice [7, 27, 28]. According to the experience of our doctors who care for ALS patients, after being judged as terminally ill by two doctors, the following two situations can be regarded as futile medical care: the first is when a patient has progressed from NIPPV to requiring IPPV, and the second is when the patient is dying and he is already on an IPPV. Both of these situations are extremely difficult moments for patients or their families to decide during. If the patient does not pre-declare not to do so, the physician will perform endotracheal intubation or CPR. Hospice palliative care is supportive medical care that is provided to help relieve terminally ill patients from physical, mental, and spiritual pain. Hospice training can help doctors discuss end-of-life decisions and future treatment plans [29, 30]. Patients with ALS may sustain cognitive impairments with disease progression [31–34], such as frontotemporal lobar degeneration and frontotemporal dementia [1]. Early discussion of end-of-life decisions can avoid delays owing to an unpredictable disease progression [17]. Therefore, after diagnosis, integrating advance care planning into the follow-up is recommended [28].
Taiwan’s National Health Insurance Bureau implemented the registration of the “DNR Willing Form” into the health insurance integrated circuit (IC) card. Hence, a physician can read patients’ “no CPR” on the IC card [35]. The most humane mode of care for terminally ill patients with ALS is palliative care. In this study, only one-third of ALS patients in the hospital signed a DNR form, and more than one-fifth of them used IPPV. It is suggested that doctors should discuss palliative care with patients at the right time so that patients are more comfortable and suffer less. Physicians and family members can assist the passing of the patient in a peaceful manner according to their wishes.
This study has several strengths, including providing insights into the association of patients’ functional status in their DNR decision making and considering several comorbidities to adjust for multiple confounding variables. Furthermore, patients were followed for up to five years. However, this study also has some limitations. First, we failed to consider patients’ and family members’ religion, educational level, socioeconomic status, serum albumin levels, and cognitive factors. Second, participants were limited to patients from only one hospital, which limits the generalizability of our results to all patient with ALS.
## Conclusion
The factors associated with DNR in patients with ALS included non-invasive ventilators, PEG tube feeding, NG tube feeding, follow-up years, and the number of hospital admissions. These factors all appeared in the late stage of the disease, suggesting delayed DNR decision making in patients with ALS. DNR decisions should be discussed with patients and their families during disease progression. We suggest physicians assist them in making appropriate care decisions, avoid futile medical care, and maintain patients’ end-of-life dignity. Furthermore, a referral to specialist palliative care is encouraged to improve patients’ quality of life.
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|
---
title: 'Exploring the content of the STAND-VR intervention: A qualitative interview
study'
authors:
- David Healy
- Emma Carr
- Owen Conlan
- Anne C. Browne
- Jane C. Walsh
journal: PLOS Digital Health
year: 2023
pmcid: PMC10010507
doi: 10.1371/journal.pdig.0000210
license: CC BY 4.0
---
# Exploring the content of the STAND-VR intervention: A qualitative interview study
## Abstract
Prolonged sedentary behaviour has been identified as a potential independent contributor to a number of chronic conditions as well as mortality. The integration of digital technology into health behaviour change interventions has been shown to contribute to increases in physical activity levels, reductions in time spent sedentary, reductions in systolic blood pressure and improvements physical functioning. Recent evidence suggests that older adults could be motivated to adopt a technology such as immersive virtual reality (IVR) due to the added agency it can potentially afford them in their lives through physical and social activities offered in IVR. To date, little research has attempted to integrate health behaviour change content into an immersive virtual environment. This study aimed to qualitatively explore older adults’ perspectives on the content of a novel intervention, STAND-VR, and how it could be integrated into an immersive virtual environment. This study was reported using the COREQ guidelines. Twelve participants aged between 60 and 91 years took part. Semi-structured interviews were conducted and analysed. Reflexive thematic analysis was the chosen method of analysis. Three themes were developed, “Immersive Virtual Reality: The *Cover versus* the Contents”, “Ironing Out the (Behavioural) Details” and, “When Two Worlds Collide”. These themes offer insights into how retired and non-working adults perceived IVR before and after use, how they would like to learn how to use IVR, the content and people they would like to interact with and finally, their beliefs about their sedentary activity and using IVR. These findings will contribute to future work which aims to design IVR experiences that are more accessible to retired and non-working adults, offering greater agency to take part in activities that reduce sedentary behaviour and improve associated health outcomes and, importantly, offer further opportunity to take part in activities they can ascribe greater meaning to.
## Author summary
Sedentary behaviour is defined as any waking behaviour that takes place in a sitting, lying, or reclining position each day while exerting little to no effort. Six or more hours of time spent sedentary each day has been associated with the development of a number of chronic conditions as well as mortality. Older populations tend to spend prolonged periods of time sedentary each day. Immersive virtual reality (IVR), a relatively new digital technology, offers new ways to be less sedentary which retired and non-working adults can potentially ascribe more meaning to, such as taking part in physical activities they enjoy as well as facilitating social connection. IVR is a computer technology that makes a person feel like they are somewhere else. The findings from this study describe how retired and non-working adults perceived IVR before and after use, how they would like to learn how to use IVR, the content and people they would like to interact with and finally, their beliefs about their sedentary activity and using IVR. These findings will inform the design of future virtual experiences that are tailored to retired and non-working adults’ needs and preferences.
## Introduction
Maintaining health and wellbeing into old age has become a priority in recent years as the number of people over the age of 65 rapidly increases [1]. Prolonged sedentary behaviour has been identified as a potential independent contributor to a number of chronic conditions as well as mortality [2–4]. Sedentary behaviour is defined as any waking behaviour which involves expending ≤ 1.5 metabolic equivalents while in a sitting, lying or reclining position [5]. Although clinical guidelines are yet to be established for prolonged sedentary behaviour, epidemiological evidence suggests that 6 or more hours of sedentary behaviour per day is associated with numerous morbidities and all-cause mortality as well as significant costs to public healthcare services [6,7]. Prolonged sedentary behaviour can be defined in a number of different ways. For this study, we focused on what point a cumulative number of sedentary bouts, accrued throughout a single day, are correlated with negative health outcomes over time–which is approximately 6-hours [6]. A sedentary bout can be described as a period of uninterrupted time spent in a sedentary position (i.e., sitting, lying, or reclining while awake) [5]. A conservative estimate of a single objectively measured sedentary bout is 10-minutes, with most sedentary behaviours “… observed within bout durations of <10 minutes…” [8]. Carson and colleagues [9] specifically reported that ≥20-minute prolonged sedentary bouts could be particularly harmful to adults with their study reporting associations between bouts of this length and higher insulin and lower diastolic blood pressure levels, in a large sample of 4935 adults aged between 20–79 years. Other findings from this study suggested that each additional 10 breaks per day were associated with health outcomes such as higher HDL-cholesterol and lower insulin levels, among other outcomes. They concluded that breaking up these bouts throughout the day could mitigate negative health outcomes such as high insulin and low diastolic blood pressure.
Systematic review evidence indicates that many older adults are spending greater than 6-hours per day in a sedentary position [7,10]–with objectively measured sedentary activity revealing that older adults are spending an average of 9.4 hours sedentary each day [10]–bringing them over the threshold for potential health risks that could impede their overall health and wellbeing into old age.
In the past 30 years, digital technology has become a central part of our lives, changing the way we approach many of today’s problems, including health promotion and behaviour change. Novel approaches to designing digital behaviour change interventions have been established to support the integration of health behaviour change content with digital technologies [11]. The person-based approach is one such approach which places the end-user at the centre of the design process and recommends iteratively designing suitable health behaviour change content that can be integrated with digital technology based on continuous participant feedback. Instances of intervention development using the person-based approach offer insight into the potential of digital technology in user-centred health behaviour change intervention development [12,13]. Past examples of studies which integrate digital technology with health behaviour change interventions have shown to increase physical activity levels, reduce time spent sedentary, reduce systolic blood pressure, and improved physical functioning [14].
Immersive virtual reality (IVR) can be defined as fully computer-generated environments that are displayed through a head-mounted display [15]. A synthesis of recent qualitative studies exploring older adults’ experiences and perceptions of IVR indicated that older adults could be motivated to adopt a technology such as IVR due to the added agency it can potentially afford them in their day-to-day lives [16]. This materialises in the form of various physical activities that are available in IVR, opportunities to travel to places around the world where it may otherwise be impossible to do so, connect with others who may not be available to meet physically through the embodiment of virtual avatars [17], as well as a variety of other meaningful experiences [16].
To our knowledge, little research has attempted to integrate health behaviour change content into an immersive virtual environment. Based on evidence now suggesting that older adults would be motivated to use such a technology [16], it is worth exploring if IVR could offer a new platform for digital health behaviour change interventions.
Using the person-based approach [11], an IVR behaviour change intervention was being developed by the study team while this study was being conducted. The behaviour change wheel guide to intervention development was utilised to develop the intervention content [18]. This process involved collating existing evidence from the literature to first understand the context within which prolonged sedentary behaviour takes place in older populations. After this understanding was established, a target behaviour was chosen to change; the determinants for which were identified and organised using the theoretical domains framework (TDF) [19]. The target behaviour chosen through this process was, taking part in meaningful non-sedentary activities in IVR. Through further identification of intervention functions and behaviour change techniques, intervention content was created [18]. This intervention content offers additional opportunities for retired and non-working adults to reduce the prolonged periods of time they spend sedentary each day using IVR. During the initial development of this content, it is important to understand the perspectives of the potential users with regards to the proposed content prior to integrating it with a virtual environment [11]. This study therefore aimed to explore retired and non-working adults’ experiences with IVR, their views on the STAND-VR (SedenTAry behaviour iNtervention Development using Virtual Reality) intervention content, and their views on using IVR to help reduce their time spent sedentary.
## Methods
This study was reported using the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines [20].
## Personal characteristics
The research team consisted of three health psychology researchers, one computer scientist and one general practitioner. The lead author (DH) conducted the interviews. At the time the interviews were conducted, DH had completed an undergraduate degree in applied psychology and a master’s degree in health psychology. DH was a PhD student completing research in the field of health psychology. He was a 25-year-old man. To date, DH had completed a systematic review and thematic synthesis exploring older adults’ experiences and perceptions of IVR [16]. DH was a frequent user of ubiquitous digital technologies such as smart phones, personal computers, and activity watches. DH also had three years of experience using IVR technologies by the commencement of this study.
## Relationship with participants
No relationship was established with the participants prior to study commencement. Participants knew that the interviewer was a PhD student exploring if IVR could be used to support retired and non-working adults over the age of 55 to reduce their time spent sedentary.
## Patient and public involvement
A patient and public involvement (PPI) panel was formed to contribute to the design of the study. The panel consisted of two retired adults over the age of 55. During the study design phase, the PPI contributors were invited to consult on the development of the interview schedule (see S1 Text). Their feedback led to significant changes to the wording of the interview schedule, to make the questions clearer and more accessible to the general public. The PPI contributors were also consulted about the wording and appearance of the study advertisement flyer.
## Theoretical framework
Reflexive thematic analysis was the chosen method of analysis for this study, providing an epistemologically and ontologically flexible approach to qualitative analysis. As a result, it is a method that can be used across a range of research contexts. The current study aimed to explore the content for a behaviour change intervention with retired and non-working adults. Therefore, this study is grounded in a critical realist ontology and contextualist epistemology [21], to allow for subjective meaning to be explored with each participant–such as their views on how comfortable the IVR equipment is or their preferences regarding goal setting–but also rooting this subjectivity within the context of a single reality [22]. This requires the researcher to interpret, to an extent, why each participant holds certain beliefs by considering forces such as cultural norms or physical capabilities. Furthermore, reflexive thematic analysis enables patterns to be generated across the data.
## Ethical statement
Ethical approval was granted for this study by the University of Galway Research Ethics Committee (application reference number: 2021.05.008). At the beginning of each interview session, participants were invited to read the participant information sheet which explained why this research was being conducted and what was involved in taking part. Once participants verbally confirmed that they understood everything on the participant information sheet and agreed to continue with the study, formal written consent was obtained from each participant.
## Participant selection
Purposive and convenience sampling as well as snowballing were employed to recruit participants for this study. The recruitment strategy and inclusion criteria were developed based on the aims of the study as well as the broader PhD research project. These are reported in Tables 1 and 2 below.
Potential participants were approached through several channels. A flyer was created containing information about the study, the lead author’s contact information and a prompt to make contact if interested. Members of the PPI panel assisted with recruitment by providing retirement organisations with the study flyer. Study flyers were posted on social media and national newsletters of various retirement organisations and other support groups for older adults. Twelve participants were included in the study. No participants who were contacted directly after providing their contact details refused to participate, and no participants dropped out. Recruitment continued until it was determined that information power had been reached [23].
## Setting
Data collection took place in a spacious, ventilated room on the University of Galway campus. A second researcher was present during the first IVR activity to mitigate any risks of falling during this activity. It was decided after this interview, however, that a second researcher would not be necessary as there were no safety risks that required their assistance.
## Data collection
The interview schedule was informed by systematic review data exploring older adults’ experiences and perceptions of IVR [16], the TDF [19], and the wider literature relevant to the study. Participants were asked to complete the “sitting time” section of the International Physical Activity Questionnaire [24] (see S1 Table), to confirm they spend six or more hours each day sedentary, as well as a demographic questionnaire (Tables 3 and 4, and S2 Text). Four participants described themselves as “not working” and the reasons given included medical conditions, child rearing and caregiving. No repeat interviews were carried out. An audio recording device was used to collect the interview data, which was then transcribed verbatim. Interviews lasted between 33 and 86 minutes. Field notes were collected after each interview in the form of a reflexive journal.
Using IVR during the interview session required participants to wear the equipment and interact with the computer-generated environments displayed to them through the head-mounted display using the hand-held controllers. The Meta Quest 2 was used to facilitate participants’ virtual reality experience [25]. A recent systematic review [16] indicated that Meta Quest equipment was suitable to be used with older adult cohorts. Participants experienced a virtual environment co-developed by the lead researcher in collaboration with a human-computer interaction researcher and a games developer. The training environment was named VR FOUNDations (Virtual Reality Familiarisation envirOnment for older adUlts with aND without dementia). Images of this training environment can be found in Fig 1 below.
**Fig 1:** *Images of the VR FOUNDations Training Environment.*
## Data analysis
One researcher (DH) coded and analysed the data in NVivo 20 [26], and circulated the results with the rest of the research team to be discussed and refined where necessary. In line with Braun and Clarkes’ steps for reflexive thematic analysis, initial codes were first developed, after which similar codes were organised into clusters or groups and finally, these groups were organised to form candidate themes [21,27]. Where relevant, subthemes were created under these themes.
Themes were generated from participant data [28]. However, as the interview schedule was, in part, derived from the TDF, the knowledge generated was generally within the scope of the constructs that make up this framework. Member checking of the interpretations made by the lead researcher during the write-up of the analysis was not carried out as it contradicts the ontological and epistemological positioning of reflexive thematic analysis [28], in which the researcher’s interpretations are made as a result of their subjective engagement with the collected data.
## Summary of results
Three themes were developed, “Immersive Virtual Reality: The *Cover versus* the Contents”, “Ironing Out the (Behavioural) Details” and, “When Two Worlds Collide”. These themes explore retired and non-working adults’ experiences with IVR, their views on the STAND-VR intervention content, and their views on using IVR to help reduce their time spent sedentary. As a large quantity of data was collected, the themes represent higher-level interpretations made during the analysis while design-specific findings are presented in the supporting information section (see S2 Table and S3 Table). These tables consist of design considerations for the STAND-VR virtual environment (S2 Table)–organised based on Abeele and colleagues’ [29] design guidelines for IVR development for older adults and the TDF (S3 Table) [19].
## Immersive virtual reality: The cover versus the contents
This theme illustrates participants’ views on IVR prior to use followed by their views after experiencing it during the interview session. The majority of participants had never used IVR before, but the few who had recounted a positive experience with the technology: “I find that the facility [a virtual environment] to be able to walk through your house, room by room, and, and the outside gardens, and upstairs and downstairs, an unbelievable experience to have”.
A pattern identified while exploring participants’ thoughts on IVR prior to use was their uncertainty around how this technology could be used in their own lives, particularly to reduce their sedentary behaviour: “I think when I use it [IVR] maybe… I really have to use it to see what is [sic] going to give me or what benefit it will be for me, you know?”.
Variations of this uncertainty were made by all participants prior to experiencing IVR. Some concerns were also raised prior to use, with a number of participants worried they would feel claustrophobic during the experience or would not be able to use the equipment. Despite these hesitancies, however, almost all participants were interested in finding out what IVR could offer as a tool to manage health, such as reducing sedentary behaviour, as well as a way of trying something that could be enjoyable. For example, prior to experiencing IVR during the session, one 72-year-old man with previous experience using IVR shared his thoughts on the potential for the technology, “it’s unbelievably good… opens a mind-boggling sphere of opportunities for older people”.
Participants’ impressions after experiencing IVR were generally positive, with words such as “spectacular” and “fantastic” used to describe it. Participants saw IVR as a novel type of learning environment which brought with it the opportunity to broaden their imagination in new and challenging ways. Participants were pleasantly surprised by the capabilities of IVR, not realising it could offer such an immersive experience, “Because it could be so vivid… I wasn’t expecting it to be… in as much detail as it was”, with many even finding it difficult to put the experience into words. In contrast, participants also felt a technology such as this could be misused: “I think it has the potential of, of wonderful stuff. It’s no more than the internet. It could… bring [you] to the end of the world, it could also bring you torture”.
The comparison to the internet here suggests that this participant is aware of IVR’s potential for good but also cautious about what unforeseen adverse effects a technology like this could have.
After experiencing IVR, participants shared that they were intrigued by feelings such as presence, immersion, and the added agency which IVR could simulate. Almost all participants felt physically present in the virtual environment. This feeling brought about a range of different reflections, with participants awe struck by the experience, amazed that a digital technology could make them feel like they are somewhere else, “that you could feel you’re, in a place that’s 1000s of miles away, maybe? It really is… It’s hard to believe that it can be done”. The feeling of being physically present in the virtual environment was universally received as a novel experience that brought about a sense of wonderment and for some participants it was seen as an opportunity to escape reality for a while, like a form of respite or retreat.
Many participants also acknowledged the added agency IVR brought about by its interactive nature. The hand-held controllers gave participants a form of “power” over their actions in the virtual environment, enabling them to interact with objects in similar ways to the real world, such as picking up different objects and moving them to different places. A common pattern across the responses of participants who discussed this phenomenon was that the more freedom, or fewer limits, they were afforded to explore the virtual environment on their terms, the more enjoyable and meaningful the experience was. In contrast, some participants experienced a reduced sense of agency as a result of the novelty of the immersive experience–with participants nervous about falling as they did not trust their footing when immersed in the virtual environment and blinded from the real world.
Participants also shared why they would return to IVR in the future and the conditions under which they would do so. At the beginning of the interview session, it was made clear to each participant that the aim of this project was to explore if they would be interested in using IVR to reduce their time spent sedentary. However, after trying the technology, some participants were only interested in using IVR to take part in activities they could ascribe meaning to, rather than to reduce their time spent sedentary, “I would use virtual reality simply because I would enjoy [it]. It would be nothing to do with being sedentary”. Participants also believed that persistent use would make them more proficient users of IVR, indicating that many believe this technology is one they can master. There were also a range of activities that participants were interested in experiencing in IVR (see S2 Table). Interestingly, a number of participants wanted these activities to be risky and stimulating, and more specifically, activities that they would be too frightened to do in reality. This feedback illustrates that participants see IVR as a means to broaden the range of activities they can take part in.
## Ironing out the (Behavioural) details
This theme focuses primarily on how best the IVR experience can be facilitated for retired and non-working adults. Feedback from this part of the analysis was mapped onto domains of the TDF where appropriate (see S3 Table) and also organised into new interpretations that stand outside of this framework.
Learning the Ropes. This subtheme focuses on the preferred conditions under which retired and non-working adults would like to learn how to use IVR. *Participants* generally found the IVR learning curve easy, “…I found it okay. Nothing complicated about it, really”. Most only needed a few minutes to become comfortable with the controllers and navigating the virtual environment. Most participants also indicated that they would prefer to learn how to use IVR with someone present who had experience using the technology. *Participants* generally felt this was necessary as a kind of “stand-by” support to make sure nothing goes wrong, rather than the need for any major assistance, “…you’re kind of vulnerable and you want I suppose you want someone there just in case something happens”. Participants gave up their sense of sight while in the virtual environment, making it critical for many of them to have someone physically present initially to get used to that experience. There was also wide support for accessible written instructions which users could reference if they were ever uncertain about any of the features of IVR. The various combinations of instructions suggested by participants can be found in the supported information section (see S4 Table).
A number of participants were also interested in learning how to use IVR in a group environment. Group learning was seen as a motivator for them to learn how to use IVR as well as a more enjoyable means of doing so: “I think maybe even presenting it to a group of people in a group. Because that way, you have a bit of interaction, and you can make it a fun thing”.
Importantly, participants believed other people who were also learning how to use IVR could be more approachable than a more experienced, and supposedly younger, facilitator alone–indicating a need for an approach where experienced and less experienced IVR users learn together. This form of social support appeared to be important for some participants when considering how best to learn how to use IVR. Some participants noted that they would like a graded learning experience, with various conditions attached to these (see Table 2). Of note, the learning environment they were presented during the interview (VR FOUNDations) was deemed sufficient, suggesting a similar template could be used in future iterations to introduce participants to IVR. Some participants stressed that IVR is more appropriate for practical activities, referring to refining the skills required to interact with the virtual environment, rather than information gathering–referring to the passive consumption of information; whether it be in video, audio or text format in the virtual environment. This conflicted with some aspects of the designed intervention, which had proposed providing health information on sedentary behaviour in IVR. Tailoring the way information is delivered to participants is therefore an important element to highlight here.
To Strategize or Not to Strategize. This subtheme explores feedback on different types of strategies retired and non-working adults could use to take part in meaningful non-sedentary activities. It considers whether strategizing is something retired and non-working adults would be interested in doing and if so, what kind of strategies they would be interested in adopting.
When asked about their thoughts on using strategies to change their sedentary behaviour, there was a variety of responses given (see S3 Table). This illustrates how complex the topic of strategies is, with a clear need for them to be tailored to the individual. A common pattern across these suggestions was that they did not involve the use of digital technology–although some were open to this mode of delivery (see S3 Table).
Views on goal setting were divided (see S3 Table). Participants who were in favour of goal setting believed that having goals to take part in IVR activities would motivate them and would provide a sense of achievement when goals were successfully completed. In contrast, other participants believed that goal setting would take away from the IVR experience as it would make it competitive when they are not competitive people. Participants also believed that simply being able to use the technology and take part in activities they enjoyed was achievement enough, without any need to create a specific goal to do so.
Some participants were in favour of monitoring their sedentary behaviour, using techniques such as reminders to take part in IVR activities to break up their sedentary bouts. The ways in which participants imagined how these would materialise varied, however. Participants were open to both digital solutions such as on-screen reminders and a digital activity logbook that appeared in the IVR device (see S3 Table) as well as more traditional reminders such as internal self-monitoring or a physical timetable.
What was universal across the diverse range of strategies suggested, was that activities participants take part in would need to be done on their terms, referring to when, or if, they wanted to do it, rather than being told to take part by some form of external que, “No, it takes away the freedom to either use it or just get on with something else and then go to it at a time when you’re completely relaxed…”.
Together is (mostly) Better. This subtheme explores retired and non-working adults’ thoughts on taking part in IVR activities with others. Furthermore, it explores how participants thought about being represented in the virtual environment and how they would interact with others in that environment.
A number of participants were not consciously aware of how they were represented in the virtual environment. Although they only embodied hands in the virtual environment, the common response to this was that they simply accepted this for what it was; it did not impede on their ability to interact with the virtual environment, “Oh, no, no. Once, once I got used to the idea [of just having hands], I actually forgot completely”. Many participants added that the hands enabled them to interact with the environment and that was all that mattered to them–they were a “tool” that gave them more “power”. Closely linked to agency discussed above, participants attributed their ability to successfully interact with the virtual environment, at least in part, to the representation of their hands. It represented the same way they would interact with physical objects in the real world and therefore appeared to be given value in the virtual world.
Although participants did not have the opportunity to engage with their own avatar or other avatars in a meaningful way in VR FOUNDations, when asked what they thought about the inclusion of avatars in the virtual environment in various ways, many were open to the idea (see S2 Table), stating they would be motivated to meet other people in IVR to share the experience together as it would be a way of enhancing social connection. In contrast, others believed an avatar was not necessary for individual experiences and even where it could be social, they thought it would probably lack the social cues to have meaningful social interactions with others embodied as avatars.
A number of participants suggested integrating a technology like IVR into community organisations. Some participants saw community organisations as a pivotal way to promote, teach how to use, and utilise IVR as another way of interacting with each other outside of meeting in person: “…one sensible approach then would be to come to a group like ours [retirement organisation], where you have maybe 100 old people in one group… And you’re getting a larger group of people who’d [at] least try it”.
Community organisations were seen as an engine through which IVR could be introduced and adopted by retired and non-working adults in the community. They are seen as a safe environment for retired and non-working adults to learn how to use IVR with each other and eventually interact with each other in it.
Offering and Influencing Opportunities to IVR Use. This subtheme explores what opportunities IVR offer retired and non-working adults with regards to reducing sedentary behaviour–as well as many other opportunities. It also explores what would influence retired and non-working adults’ ability to use the technology in the future. This subtheme bares strong links with the “opportunity” element of the COM-B model [18], exploring both social and physical barriers and enablers to IVR use.
The opportunities offered by IVR that were suggested by participants were diverse (see S2 Table). *Participants* generally saw it as a new tool to bring new positive change into their lives, offering them an escape from negative thinking, the opportunity to take part in activities they may be insecure to do in public, the opportunity to alleviate boredom and, an alternative to activities such as TV watching. The key in this theme is that participants saw opportunity with this technology. They saw the potential it had to enhance their lives in various ways that they would otherwise not have the opportunity to do: “…in recent years, I have developed arthritis in my hands, which has affected my grip… So I can see if… I could play my tennis on that and play with other people and satisfy that competitive streak and then also be active”.
With regards to opportunities influencing IVR use, all participants said they would have space in their home to set up the IVR equipment. The current price of the equipment was also not seen as an issue for those who commented on it. *In* general, when discussing any potential physical barriers to use with regards to opportunity, participants did not see any major ones. Some did stipulate, however, that they would require some social support when setting up the equipment. This is linked closely to previous comments made about needing someone else there in person to learn with–such as within a community organisation.
## When two worlds collide
This theme explores retired and non-working adults’ beliefs about sedentary behaviour and whether digital technology can or should be adopted by them to assist in reducing this behaviour.
Constructing Beliefs and Identity. This subtheme offers insight into how retired and non-working adults’ sense of identity relates to their beliefs about health and digital technology. It offers insight into these beliefs and how their identity has been shaped by various events throughout their lives.
It was clear that almost all participants understood that sedentary behaviour has negative effects on health, with some indicating that it is a common-sense belief. They observed the negative impact of prolonged sedentarism in their own lives with some sharing that they feel unwell or depressed when they spend too much time sedentary. Participants were also affected by the negative health outcomes for others in their lives who lived sedentary lifestyles. For some participants, there was a belief that physical activity made up for their time spent sedentary–regardless of whether they were sedentary for prolonged periods of time each day: “most of the group you’re going to meet [participants from the same retirement organisation] are not all that [sedentary] they’ll often be out walking quite a bit, they keep active”.
In this case, the participant was aware of the benefits of physical activity but were unaware of the potential negative effects of prolonged sedentary behaviour, which each of the participants reported taking part in. These beliefs highlight the importance of conveying the difference between physical activity or inactivity versus prolonged sedentary behaviour or non-sedentary behaviour. Thoughts about future health were also highlighted as a key motivator to take part in non-sedentary activities in the present: “I do kind of think, Gosh, in another 10 years what will I be like, will I be able to move at the same speed that I’m able to do at the minute?”.
The formation of participants’ identities in the context of their sedentary behaviour was also discussed during each interview. A pattern that developed across a number of participant accounts was the way time, and the age-related changes associated with time, shaped the way they identified with being sedentary or non-sedentary throughout their lives. Participants spoke about how consistent hard work across time instils and maintains non-sedentarism as part of their identity. They also shared how social support can help maintain such an identity and terms such as “retirement” attributed to them when they reach a certain age can be potentially stigmatising and influence their identity–with the suggestion that now is the time for them to “slow down” and rest more rather than being active. Additionally, closely linked to participants’ beliefs about their ability to use IVR equipment was how this in turn formed part of their identity. The responses from participants about their beliefs about capabilities and identity in this case suggests a temporal arrangement of the two–with the belief first forming through the encouragement of their use of IVR, as well as their actual use of it, and later the formation of this instilled belief as part of their identity.
Participants expressed that simply trying IVR strengthened their beliefs about their capabilities in using it. For many, using IVR broke down the prior assumption that they would not have the ability to do so: “I think, to reassure them [retired and non-working adults], that they they’re capable of doing it, like people are afraid, oh that’s too high tech for me… I think if they give it a go, and, you know, try it, and be open to change and open to new ideas”.
An important stipulation made by some participants was that IVR needs to first be presented to them in an accessible way. Participants pointed out that it is prior fears and uncertainties about their capabilities that would influence their decision to try it, so presenting IVR in a positive and accessible way was considered important.
Technology and Health: A Strange New World. This subtheme explores participants’ views and experiences with digital technology to date and what their preferences would be for delivering health information using digital technology, if at all.
Many participants shared their thoughts on their prior experience with digital technology, primarily discussing it in the context of health management. Some participants said they do not use digital technology to help manage their health as they would not have any meaningful use for it, and because they believed they are too old to benefit from it, “What’s the point at this stage of my life?”. *Participants* generally used digital technology to browse the internet and connect with others on social media. In contrast, others have found utility for digital technology to help manage their health, seeing it as means to give them more control and confidence. External factors influencing a few participants’ views on digital technology use included having younger people, who were perceived as more tech-savvy, available to assist them in using it as well as the pandemic which began in 2019 and forced some to adopt digital technology to do things like staying in touch with others, “…my wife can Zoom [online communication medium] now where she could barely click on a mouse before this [pandemic]”.
After experiencing IVR during the interview session, almost all participants indicated that they would be interested in trying it in the future to reduce their time spent sedentary. Participants’ reflections on this point generally indicated that IVR would act as a motivator to take part in non-sedentary activities due to the experiences it would offer them as well as simply acting as an additional outlet to be non-sedentary. However, some participants showed preference to other forms of non-sedentary activity and remained unconvinced of the benefits IVR for their health, “Well, at the moment, I’m not convinced… of the health benefits of it, let’s say”. Other participants shared that they would need more experience with IVR before deciding on whether it is something they would use.
Delivery preferences for receiving information about sedentary behaviour were also discussed. This is linked to the TDF element, knowledge, which is a posited determinant of health behaviour change that emphasizes the importance of individuals first needing to know why and how they need to change their behaviour before going about changing it. The majority of participants were open to receiving health information in IVR in a variety of different forms (see S4 Table). Some participants liked the idea of another avatar presenting health information as it would be more interesting. In contrast, others preferred the idea of receiving health information via a video in IVR rather than from an avatar. Participants also suggested other preferences for health information delivery outside of IVR (see S4 Table). Beyond the mode of delivery itself, participants emphasized that it is important for the information to be accessible, transparent, reliable and actionable. Participants saw their doctor, family members and friends as people who they would trust to receive this information from. *In* general, the central concept formed across each of these suggestions was that a tailored approach to health information delivery inside and outside IVR is necessary to facilitate everyone’s preferences.
## Main findings
During this study, retired and non-working adults were introduced to IVR and interviewed about this experience and their views on the STAND-VR intervention content. Through reflexive thematic analysis, three themes were generated which offered insights into how retired and non-working adults perceived IVR before and after use, how they would like to learn how to use IVR, the content and people they would like to interact with and finally, their beliefs about their sedentary activity and using IVR.
## In the context of existing research
IVR was an enigma to participants who had never tried it before; they needed to experience it before they could grasp what it had to offer. All participants were enthusiastic, in some way, about their experience with IVR and found it to be an accessible technology to use once tried–providing novel immersive experiences many had never encountered before. The uncertainty experienced prior to use is evident in previous research, where older adults were quite negative in their preconceptions of IVR [30–32], while others, similar to sentiments in this study, were unsure what it would be like but were interested to try it nonetheless [33]. The evidence suggests a need to ensure that IVR is presented in a transparent and accessible way to older populations, where a lack of understanding about the technology is exhibited. In the current study, participants found IVR easy to use. This is also evident in previous research, where participants navigated the IVR equipment and virtual environments with ease and competence, which increased with practice [34]. This shift from uncertainty about IVR and personal capability, to mastery with little effort, was also reported in a systematic review exploring older adults’ experiences and perceptions of IVR [16]. This change in perspective before and after use points to a temporal pattern evident in older adults’ experiences with IVR, with a general sense of uncertainty around what IVR is and in some cases, a lack of self-confidence in their ability to use it. Current evidence also highlights that once older adults have had the opportunity to use it, IVR is generally received as an accessible technology that offers novel and meaningful immersive experiences.
An interesting pattern evident across the data was specific participant motives for using IVR in the future. Participants were more interested in the meaning they could ascribe to IVR beyond the health benefits of reducing their sedentary behaviour. Participants’ primary interest in IVR was using it as a means of enjoyment rather than a means to reduce their time spent sedentary. Previous research has identified a similar phenomenon described as “incidental physical activity” [35]. This highlights that although the health-promoting activity may be conveyed formally as a health behaviour in the eyes of the intervention developer, for the participant, it is not seen this way; rather, the activity is simply something they enjoy, and any health-related outcomes are secondary for them.
A range of different instruction formats were recommended by participants regarding how they would like to learn about using IVR. The range suggested the importance of tailoring information provisions to the individual user’s needs, to ensure the technology and the experience are accessible to all. A number of participants highlighted that they would like someone present to help them to set up the equipment. This suggestion is also reported in previous research, where some participants shared that they would like someone present to reassure them that they are safe [36]. The IVR experience means users are completely blinded from reality. A common pattern across the literature is that this feature can be disconcerting for older adults and requires them to have someone present while acclimatising to this new experience.
A notable finding was the belief that passively receiving information through IVR would not be an efficient use of the technology, with one participant claiming that other modes of delivery such as YouTube and other web browsers are more effective ways of acquiring such information. This is an important finding as part of the STAND-VR intervention will be exploring how participants find receiving health information in IVR–which may relate to previous discussions about participants’ preconceptions of IVR. Specifically, although participants did not have an opportunity to receive health information in VR FOUNDations, the general views were that it would not be a useful way to receive it. There is little evidence on how older adults receive health information in IVR, with most of the research focusing on other interactive and passive activities such as sports, travel, and reminiscence, to name a few [30,37,38]. Some participants were interested in the idea of receiving health information from an avatar or video in IVR so this will be explored in more detail during the optimisation phase of intervention development [11]. Similarly, although participants did not have the opportunity to embody and observe their own avatar or others’, when asked about the idea of interacting with other avatars in IVR, some were sceptical that the experience would not be as meaningful as reality due to a lack of social cues, such as moving lips. This was also emphasised in previous research exploring social IVR where these social cues were missing, affecting participants’ experiences with other avatars negatively [39]. However, it is now possible to integrate features such as lip-syncing into avatars [40], enhancing the social experience for IVR participants. As such, it is another example of the importance of clarifying any misconceptions or uncertainty IVR participants might have prior to use.
Participants offered mixed responses to the intervention content presented to them. For example, the use of strategies to encourage and maintain the use of IVR for reducing sedentarism, such as goal setting and monitoring, were met with enthusiasm by some but for others, they were perceived as potential hindrances to the experience–making it a chore rather than something to look forward to. This view is also present in previous research exploring motivators and barriers to reducing sedentary behaviour in older adults [41]. Qualitative feedback from participants in this study highlighted that using strategies to encourage them to reduce their time spent sedentary would feel “artificial and false”. The evidence suggests that older adults need to feel that there is purpose or meaning to the strategy if they are to adopt it in their everyday lives.
Participants’ beliefs about social, physiological, and psychological factors influenced how they responded both to information about sedentary behaviour as well as their experiences with IVR. Social interaction was a pattern present throughout the analysis, with participants in favour of learning how to use IVR with more experienced people and other retired and non-working adults, as well as showing interest in taking part in IVR activities with others. This concurs with evidence synthesised in a systematic review exploring adults’ experiences with non-workplace sedentary behaviour interventions, where friends and family appear to act as a prompt, reminder, and motivation to take part in non-sedentary activities [42]. Participants also offered insights about their age and how their lifestyle is changing. A common pattern across the literature is older adults’ adoption of new activities which are suited to their ageing bodies; which generally meant less active, more sedentary pastimes than when they were younger [42]. With regards to certain misconceptions, some participants believed that the prolonged length of time they spend sedentary each day is not problematic as they believe they are exercising regularly outside of this activity. This same misconception, or distortion, can be seen in previous research [41], with systematic review evidence suggesting that this may be due to a lack of education regarding the effects of sedentary behaviour and how it differs from physical inactivity [42]. The argument that moderate to vigorous physical activity cannot mitigate the negative health outcomes of prolonged sedentary activity alone has been contested to a degree in recent years, however, with a recent harmonised meta-analysis including more than 44,000 middle-aged and older adults reporting that “about 30–40 min of MVPA per day attenuate the association between sedentary time and risk of death…” [43]. With this in mind, the information provided to people about sedentary behaviour must be considered further in light of this emerging evidence–emphasising the added importance of physical activity and the positive health outcomes associated with it.
## Implications and recommendations
The aim of this study was to explore older adults’ perspectives on the STAND-VR intervention content prior it’s integration into a virtual environment. As presented in the supporting information section (see S2 Table and S3 Table), based on participant feedback, practical improvements will be made to the STAND-VR intervention content and integrated into a virtual environment.
With regards to presenting health information to retired and non-working adults, a tailored approach appears to be necessary. Participants provided a variety of health information delivery preferences (see S4 Table), with some preferring video and audio to text and vice versa, for example. It is important to offer access to preferred health information delivery formats as participant responses suggest they are more likely to engage with some information platforms over others. In addition, and in line with previous findings [42], it is essential to convey health information about sedentary behaviour in a clear and accessible way and from a reliable source to reduce the likelihood of any confusion between time spent sedentary and time spent physically active. This will be accounted for in the next iteration of the STAND-VR intervention, with these various formats of health information delivery being explored during the next data collection phase.
In the context of existing research, there were several common patterns identified across the data analysed in this study and the findings from other studies. There appears to be a need to focus on what is meaningful to retired and non-working adults, rather than pushing the importance of the health benefits of certain activities [35,42]–which is in line with the chosen target behaviour of this intervention. This is important when designing health behaviour change interventions in the future as it suggests the framing of the intervention should be to emphasise what meaning the end-user can derive, beyond what health benefits it offers. Although physical health outcomes, specifically long-term health outcomes associated with reduced sedentary activity, are also important to this cohort, other experiences such as enhanced social connection and enjoyment need to be part of the activities that lead to these health outcomes.
The findings also point to the importance of empowering people through health behaviour change interventions. This can be seen through the mixed responses to strategies to reduce their time spent sedentary. Although some participants were open to the idea of using strategies such as goal setting and monitoring of their sedentary behaviour, others did not like the intrusive nature of these strategies in their lives. Participants wanted the choice to take or leave such strategies rather than it being prescribed to them. As such, we recommend offering agency in behaviour change interventions, where participants have a say in how such strategies, in this case in the form of behaviour change techniques, are applied and how they are used by them–which, similar to the above point, can empower end-users to derive more personal meaning from the intervention [41]. This agency further enables users to scrutinise how digital health interventions are personalised for them [44], bringing the role of the technology in their lives to the front of their minds, empowering them to reflect more critically on how and why they are using a technology such as IVR. This will be explored further in future work related to this intervention.
The value of social support when learning a new skill and changing a health behaviour was present in many of the interviews conducted and has important implications for this research. There was a substantial preference to have someone present to help participants set up the IVR equipment, although they found using the technology quite easy, regardless. The reassurance that they were safe provided by the interviewer likely influenced their ability to focus on learning how to use the technology–a preference which many participants shared in the post-interview sessions. Some participants suggested further that they would be interested in learning how to use IVR with other retired and non-working adults as they saw that as a supportive learning environment. Although IVR offers people the opportunity to take part in activities together from different locations, the initial learning experience seems to be one that retired and non-working adults would enjoy together.
## Strengths and limitations
This qualitative interview study was conducted following Braun and Clarkes’ six steps to reflexive thematic analysis [21], paying close attention to recent updates published by the authors on how to apply this approach in qualitative research [27,45,46]. The lead author engaged in reflexivity throughout the research process to offer further justification for decisions made regarding how data was collected, analysed, and reported. Supplementary files such as reflexive journal entries are pre-registered on the lead author’s open science framework repository.
Participants did not have the opportunity to design or observe full-body avatars during their IVR experience, making their responses to questions relating to avatar use purely hypothetical. This process will be explored further during the next data collection phase, where participants will have the opportunity to choose their own pre-designed avatar to embody as well as the opportunity to interact in a virtual environment with other retired and non-working adults. The demographic of the sample of participants who took part in this study was also relatively homogenous (see Tables 3 and 4). All participants described themselves as white Irish, the majority of whom had either third level education or post-secondary school professional training. Many participants also shared that the cost of the equipment would not be an issue. Although these perspectives are valuable, further research is needed to understand how people with other socio-economic capacities experience IVR and its potential impact on their sedentary activity [47].
## Conclusions
The findings of this study offer further insight into retired and non-working adults’ perspectives on IVR. Moving forward, these findings can contribute to future work which aims to design IVR experiences that are more accessible to retired and non-working adults, offering greater agency to take part in activities that reduce sedentary behaviour and improve associated health outcomes and, importantly, offer further opportunity to take part in activities they can ascribe greater meaning to.
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|
---
title: 'Leaving no one behind in health: Financial hardship to access health care
in Ethiopia'
authors:
- Yawkal Tsega
- Gebeyehu Tsega
- Getasew Taddesse
- Gebremariam Getaneh
journal: PLOS ONE
year: 2023
pmcid: PMC10010508
doi: 10.1371/journal.pone.0282561
license: CC BY 4.0
---
# Leaving no one behind in health: Financial hardship to access health care in Ethiopia
## Abstract
### Background
Financial hardship (of health care) is a global and a national priority area. All people should be protected from financial hardship to ensure inclusive better health outcome. However, financial hardship of healthcare has not been well studied in Ethiopia in general and in Debre Tabor town in particular. Therefore, this study aimed to assess the incidence of financial hardship of healthcare and associated factors among households in Debre Tabor town.
### Methods
Community based cross sectional study was conducted, from May $\frac{24}{2022}$ to June $\frac{17}{2022}$, on 423 (selected through simple random sampling) households. Financial hardship was measured through catastrophic (using $10\%$ threshold level) and impoverishing (using $1.90 poverty line) health expenditures. Patient perspective bottom up and prevalence based costing approach were used. Indirect cost was estimated through human capital approach. Bi-variable and multiple logistic regressions were used.
### Results
The response rate was $95\%$. The mean household annual healthcare expenditure was Ethiopian birr 12050.64 ($227.37). About $37.1\%$ ($95\%$CI: 32, $42\%$) of the households spend catastrophic health expenditure with a $10\%$ threshold level and $10.4\%$ of households were impoverished with $1.90 per day poverty line. Being old, with age above 60, (AOR: 4.21, CI: 1.23, 14.45), being non-insured (AOR: 2.19, CI: 1.04, 4.62), chronically ill (AOR: 7.20, CI: 3.64, 14.26), seeking traditional healthcare (AOR: 2.63, CI: 1.37. 5.05) and being socially unsupported (AOR: 2.77, CI: 1.25, 6.17) were statistically significant factors for catastrophic health expenditure.
### Conclusion
The study showed that significant number of households was not yet protected from financial hardship of healthcare. The financial hardship of health care is stronger among the less privileged populations: non-insured, the chronically diseased, the elder and socially unsupported. Therefore, financial risk protection strategies should be strengthened by the concerned bodies.
## Introduction
Universal health coverage (UHC), one (the overarching) target of Sustainable Development Goals (SDGs), ensures that all people receive quality essential health services they need without exposing them to financial hardship. Financial risk protection is at the core of universal health coverage and it is one priority area in Ethiopian health sector as indicated in Health Sector Transformational Plan two (HSTP II). It is achieved when there are no financial barriers(mainly due to direct out of pocket health expenditure) to access essential health services [1–3]. Out of pocket (OOP) health spending is defined as any spending incurred by a household when any member uses a healthcare, including promotive, preventive, curative, rehabilitative and palliative care. To access (high quality) health care, the household incurs direct medical and non-medical costs, indirect cost and intangible cost. These costs impose financial hardship to the households, and worst in low income countries like Ethiopia [1,2].
Financial hardship (FH) is measured through Catastrophic Health Expenditure (CHE) and Impoverishing Health Expenditure (IHE). These metrics are standards that used to monitor and track Sustainable Development Goal indicator 3.8.2 (SDG indicator 3.8.2) across United Nations (UN) member states. CHE is considered when healthcare spending exceeds a certain threshold (varied from $10\%$ to $40\%$) of household expenditure or income. From these thresholds, $10\%$(the lower threshold level) and $25\%$(the higher threshold level) are used in a joint report of World Bank(WB) and World Health Organization(WHO), a report in every 2 years, for monitoring and tracking SDG indicator 3.8.2. Whereas, IHE is considered when households’ health expenditure is making the households below a given poverty line (in our cases a World Bank $1.9 a day extreme poverty line) or further impoverish to extreme poverty [1,2,4].
Globally, the incidence of financial hardship of healthcare has been increased since 2000. For example, the incidence of CHE increased by $3.6\%$ annually, from 571 million in 2000 to 927 million in 2015 with $10\%$ threshold level. Similarly, the incidence of catastrophic health expenditure has increased from $12.7\%$ in 2015 to $13.2\%$ in 2017 at $10\%$ threshold level. CHE, as measured by SDG indicator 3.8.2, will continue to rise to the year 2030 if the share of out-of-pocket health spending continues at its current rate [1,2].
Furthermore, OOP healthcare costs lead more people falling into poverty. About 89.7 million individuals ($1.2\%$ of global population) were forced into extreme poverty (below $1.90 a day poverty line) and 98.8 million ($1.4\%$ of global population) were pushed below $3.20 a day poverty line and 183.2 million were pushed into poverty defined in relative terms (below $60\%$ of median daily per capita consumption or income in their country). At all of these poverty levels, lower and middle-income countries (LMICs) had the highest number and proportion of the world’s population with impoverishment due to OOP health spending [1,2].
These financial burden (CHE&IHE) contributes to socioeconomic disparities in access to essential healthcare services. The burden is directly proportional to the severity of the underlying health condition (ill individuals spend more). Households seeking care face barriers to access essential health services related to financial hardships. This leads to people delayed or forgone essential health services [2,5,6].
In the majority of LMICs, low health care resources and a lack of protection from catastrophic healthcare costs have led to an over-reliance on OOP health spending. Households who are dependent on OOP healthcare payment and who are unable to cope with the economic implications of illness are frequently pushed into poverty. Households in this scenario incur more financial obligations and lack the resources to meet other basic requirements such as food and education [7].
Moreover, in low-income countries, OOP health expenditures accounts for more than half of overall spending and more than one third in middle-income countries. According to World Health Organization (WHO), OOP payments push millions of households into absolute poverty each year, and many of them are at risk of catastrophic health expenditure since their OOP healthcare expenses are equivalent to or exceed $40\%$ of their income or expenditure. Many families forego services because of the direct and indirect health expenditures exceed their financial means. Because of the loss of income caused by disease, poor households become increasingly poorer, and overall quality of life suffers even more [8].
Catastrophic health payments are concentrated among the poor, including African countries. Inequities in access exist in Sub-Saharan African (SSA) countries as a result of income disparities and the level of OOP health expenditure within the country. The percentage of households suffering by catastrophic health care expenses has been proven to differ significantly among countries [9].
Since financial hardship of health care is a main challenge and a priority area of the health sector, Ethiopian healthcare financing reform has been implemented before 24 years, since 1998. For example, various financial hardship protection measures like fee waiver system, exempted services(e.g. maternal health services) and community based health insurance have been implemented in Ethiopia [10]. However, OOP health expenditures continue to be a considerable financial burden of households. For example, as per the latest national health account, the seventh Ethiopian Health Account (EHA), OOP health spending amounted to $31\%$ of the total health expenditure, which is unacceptably high and it is higher than that of the global recommended target, $20\%$ [11,12]. As a result, households often obliged to borrow money, sell their assets, reduce consumption of other basic needs to spend on healthcare expenditure and my forgone the healthcare services [13,14].
Our literature review indicted that financial hardship of health care can be affected by several factors[11, 12]. Based on the review, we developed a conceptual framework (Fig 1) that depicts the potential relationships between outcome (financial hardship of healthcare) and explanatory variables.
**Fig 1:** *Conceptual framework depicts relationships between financial hardship of health care and predictor variables.*
Evidence, on the magnitude of financial hardship of healthcare and its determinant factors at household level, is critical to ensure effective, equitable and affordable access to quality health services that will achieve the motto of “leave no one behind” as stated in SDG 3.8.2 and HSTP II. However, it has not been well studied in Ethiopia in general and in Debre Tabor town in particular. Therefore, the aim of this study is to assess the financial hardship of healthcare and its associated factors among households in Debre Tabor town, South Gondar zone, Ethiopia.
## Study design and period
Community based cross-sectional study design was conducted to assess financial hardship of healthcare and associated factors among households in Debre Tabor town from May $\frac{24}{2022}$ to June $\frac{17}{2022.}$
## Study area and setting
The study was conducted in Debre Tabor town, Amhara regional state of Ethiopia. Debre Tabor town is the capital of South Gondar Zone and has six kebeles with 19,624 households. The town has 84,382 populations of which 19,898 are in reproductive age group and 10,868 are children from age 6 to 59 months. The town is located at 108.6 kilo meters east of capital of Amhara state, Bahir Dar city. The town has one public hospital namely Debre Tabor comprehensive specialized hospital and three health centers namely Leul Alemayehu, Tabor and Debre Tabor health centers [15].
## Study population
All households in Debre Tabor town were the study populations.
## Sampling unit
The sampling unit of the study was households
## Study unit
The study unit of this study was household heads
## Inclusion criteria
All households lived in Debre Tabor town for 6 months and above were included in the study.
## Exclusion criteria
Household heads unable to respond due to different reasons were excluded from the study.
## Operational and term definitions
Financial hardship of healthcare: defined as a situation where the household is having difficulty to pay health care cost. It is measured through CHE and IHE Adult equivalent: All members of the household with adjusted calorie need requirement on the basis of age and sex [16].
Catastrophic health expenditure (CHE): spending greater than $10\%$ of household’s reported total expenditure for healthcare service [1,2].
Poverty line (PL): WB poverty line ($1.90 a day extreme poverty line)was used in this study [1,2].
Healthcare expenditure: The total household expenditure related to healthcare which included direct medical, direct non-medical and indirect costs [1].
Impoverishing health expenditure (IHE): When households pushed below $1.90 a day extreme poverty line because of their healthcare expenditure, it was considered as IHE [1].
Poverty gap (poverty gap index): How far households are from the poverty line (measures intensity of poverty) [17].
Wealth index: The composite measure of cumulative living standard of the household. It was measured by 35 items [12,18].
Chronic health condition: is a human health condition or disease that is persistent or otherwise long-lasting in its effects or a disease that comes with time [19]. The term chronic was used when the course of the disease lasts for more than three months Health insurance: in this study means community based health insurance(CBHI) [20].
Healthcare cost measurement
## Types of costs and their costing methods
There are two methods of costing approaches such as the prevalence and incidence approaches. The prevalence method is the commonest costing approach in studies and was used in this study [21,22].
We estimated the direct medical and nonmedical costs, and indirect costs. Since, intangible costs are difficult to measure, we did not measure the intangible costs. The direct medical costs included costs of registration cards, medications, imaging diagnostic tests, laboratory and bed incurred 12 months back the study conducted and direct non-medical costs include cost of transportation, cafeteria and lodging while seeking healthcare service both for the patient and the caregiver [23–25]. Bottom up (micro) costing approach was used based on average cost of health care services to estimate the direct costs of healthcare services [26,27].
Moreover, annual average expenditure on healthcare for each household was estimated by summing up all self-reported healthcare expenditures from May 2021 to May $\frac{24}{2022.}$ Similarly, all the expenditures for transportation, cafeteria and lodging was summed up based on the self-reported number of household members having history of illness and amount of money they incurred.
Data on indirect costs covered in this study included lost days (absenteeism) both for the patient and caregiver as per human capital approach.
For workers(payroll paid and merchants), monetary value of lost days was calculated by multiplying number of lost days with reported personal daily income (monthly income divided by 30). For non-payroll paid households, their reported annual household income was used.
## Measurement of catastrophic and impoverishing health expenditure
Wagstaff and van Doorslaer approach was used to measure CHE and IHE. This approach considers catastrophic health expenditure when the proportion of household’s health expenditure as a share of total household expenditure/income or nonfood expenditure exceeded a specific threshold level. The choice regarding the threshold to use in determining catastrophic health expenditure is arbitrary and has typically varied from $10\%$ to $40\%$ [28].
To calculate the catastrophic head count which is the percentage of households incurring catastrophic expenditures, we defined THE as total annual health expenditures for household i, TE total annual expenditure for household i, and FE for food expenditures for household i.
A household was considered to have catastrophic health expenditure if THE/TE surpassed a specified threshold, Z (in our case $10\%$ threshold level was used).
The catastrophic headcount (Hc) is the given by:- Hc=1N∑$i = 1$NEi [1] Where N is the sample size and Ei equals 1 if THE/TE > z and zero otherwise.
The headcount does not reflect the amount by which households exceed the threshold. Therefore, we used the catastrophic expenditure overshoot which captures the average degree by which health expenditures (as a proportion of total expenditure or non-food expenditure) exceed the threshold z. The overall overshoot (O) is given by:- $O = 1$N∑$i = 1$NOi [2] Where Oi = Ei ((THE/TE) − z).
Where Ei = ((THE/TE)-z) if (THE/TE)>z, and 0 otherwise.
The incidence (headcount) and the intensity (overshoot) of catastrophic expenditures are related through the mean positive overshoot (MPO) which captures the intensity of occurrence of catastrophic expenditures defined as overshoot divided by headcount: MPO=OH;O=H*MPO [3] Wagstaff and van Doorslaer also describe methods to adjust poverty measures on the basis of household expenditure net of OOP spending on health care[28]. The three measures of poverty include; Hpovpre=1N∑$i = 1$NPipre=μPpre [4] Where *Hpovpre is* poverty headcount before health payment and Pipre = 1 if Xi< PL and zero otherwise.
Where *Gpovpre is* prepayment poverty gap, gipre = PL-Xi if PL>Xi and zero otherwise.
Calculating the three measures requires setting a poverty line and assessing the extent to which health care payments push households below the poverty line. The World Bank poverty line 1.9 US dollar per person per day was converted to ETB based on average exchange rate (1USD = ETB 53) of September 2021 to August 2022 was used to estimate poverty levels before and after healthcare expenditure. Replacing all the pre-payment superscripts, ‘pre’ by the superscript ‘post’ gives the analogous post-payment poverty measurement.
The measures of poverty impact (PIH) of health expenditure are then simply defined as the difference between the pre-payment and post-payment measures, i.e.
## Sample size determination
Single population proportion formula was used to estimate the sample size, by taking the proportion $50\%$ of CHE at $10\%$ threshold level with confidence level of $95\%$ and degree of precision $5\%$ and non-response rate of $10\%$ is considered and then the total sample size was; n=Z(α/2)2*P(1‐q)d2 Where $$P \leq 50$$% $d = 0.05$ (degree of precision) and Z α/2 at $95\%$ confidence level = 1.96 By taking the above values, the sample size was n=(1.96)2*(0.5)(1−0.5)(0.05)2=384
Non-response rate (NRR) = $10\%$; Therefore; the number of households included in the study were 384*$10\%$NRR + 384 = 423.
## Sampling method and procedures
*Computer* generated simple random sampling method was used. The list of eligible households was obtained from urban health extension professionals and used as a sampling frame. Households were listed and coded (from 1 to 19,624). Then, households were selected using OpenEpi application computer generated simple random sampling method. Abrahajira Hospital 72[20] employees
## Survey instruments and data collection procedures
Structured questionnaire was used. The questionnaire was developed after reviewing various literatures. The survey instrument included categories aim to collect data on sociodemographic and socioeconomic characteristics, health profile and related characteristics of households, total expenditures of the household, total health expenditures and coping mechanisms of catastrophic health expenditure (S1 Text). A pretested and interviewer administered questionnaire was used. Two data collectors who have bachelor of degree (public health graduates) and one supervisor (MPH) were employed. Total annual (from May 2021 to May $\frac{24}{2022}$) health care and other household expenditures were collected from the head of each selected household. Each healthcare, food and non-food expenditures were summed up and total annual health expenditure, total annual food expenditure, total annual nonfood expenditure and total annual household expenditure, which used as a denominator to calculate catastrophic health expenditure, were determined. Furthermore, the wealth index assessing variables were adapted from Ethiopian DHS 2019 for urban area. About 35 questions assessing sanitation facility, drinking water source, housing condition and ownership of durable assets were asked to the household head.
## Data management and analysis
The collected data were checked for completeness. Then, data were coded, organized and entered into EpiData version 3.1 and exported to SPSS version 25 for analysis. Descriptive statistical analysis (frequencies and percent), bi-variable and multiple logistic regressions were conducted. In bi-variable logistic regression, variables having P-value of <0.2 with $95\%$ confidence interval were eligible to multiple logistic regression. The overall goodness fit of binary logistic regression model was checked by Hosmer and Lemeshow test (p-value >0.05). Assumptions of binary logistic regression such as multicollinearity and outliers were checked for the model. Adjusted Odds Ratio (AOR) with $95\%$ confidence intervals was estimated to assess the strength of the association, and a p-value of< 0.05 was used to declare statistically significant factors.
Wealth index was constructed using principal component analysis by SPSS. Wealth index construction question scores was derived using principal component analysis in that; 35 wealth status assessing variables from sanitation, housing condition, water source and household durable assets was computed. Variables having frequency of greater than $95\%$ and less than $5\%$ were excluded. In principal component analysis output of correlation matrix, values less than 0.1 and greater than 0.9 were removed from the analysis. After all, 12 variables were used to construct wealth index. The first component of the composite variables was used to estimate wealth status of households and ranked in ascending order.
## Data quality assurance
The structured questionnaires were prepared in English first and translated to Amharic with clear way for better understanding with respondents. Two data collectors with educational level of bachelor of degree (public health graduates) and one supervisor (MPH) were employed. Three days training was given for data collectors on the overall picture of questionnaires, how to collect the data and how to approach the respondents. Before actual data collection, pretesting on $5\%$ of the sample size was done at Woreta town. Close supervision of data collectors was done and data were checked for its completeness on daily basis.
## Ethical consideration
Ethical clearance was obtained from Institutional Review Board (IRB) of College of Medicine and Health Sciences, Bahir Dar University with the approval number of $\frac{459}{2022.}$ Prior to data collection, informed verbal consent was obtained from each study participants. The informed verbal consent was accepted by IRB since the study has minimal risk. The respondents were given full right to withdraw from the interview whenever they feel uncomfortable. Furthermore, confidentiality was kept by excluding name of the respondents from data collection tool and instead we used unique identification number as a code.
## Sociodemographic and socioeconomic factors
Four hundred two [402] household heads were interviewed, making a response rate of $95\%$. From which $69.4\%$ [279] of the households were led by male, the mean and standard deviation of age of household heads were 44.1±14.91 with minimum and maximum value of 20 and 100 respectively. About $40\%$ [161] of the household heads were found to be the age category of 31–45. About $99.5\%$ [400] and $90\%$ [362] of the household heads were Amhara and Orthodox Christian, respectively. From the participants, $10.7\%$ [43] were cannot read and write and $69.2\%$ [278] of them were married. About $75.6\%$ of the households had family size of less than or equal to 4. Regarding wealth status of the households, $19.9\%$, $20.1\%$, $19.9\%$, $21.4\%$, and $18.7\%$ of the households were fall in first, second, third, fourth and fifth quintiles respectively (Table 1).
**Table 1**
| Variables | Category | Frequency | Percent (%) |
| --- | --- | --- | --- |
| Sex of household head | Male | 279 | 69.4 |
| Sex of household head | Female | 123 | 30.6 |
| Age of household head | < = 30 | 92 | 22.9 |
| Age of household head | 31–45 | 161 | 40.0 |
| Age of household head | 46–60 | 90 | 22.4 |
| Age of household head | >60 | 59 | 14.7 |
| Marital status of household head | Married | 278 | 69.2 |
| Marital status of household head | Unmarried | 124 | 30.8 |
| Educational status of household head | No education | 43 | 10.7 |
| Educational status of household head | Read and write only | 31 | 7.7 |
| Educational status of household head | primary(1–8) | 52 | 12.9 |
| Educational status of household head | secondary(9–12) | 66 | 16.4 |
| Educational status of household head | College and above | 210 | 52.2 |
| Occupation of household head | Self employed | 195 | 48.5 |
| Occupation of household head | Government employed | 188 | 46.8 |
| Occupation of household head | Private sectors | 19 | 4.7 |
| Family size | < = 4 | 304 | 75.6 |
| Family size | >4 | 98 | 24.4 |
| Presence of U5 Children | Yes | 125 | 31.1 |
| Presence of U5 Children | No | 277 | 68.9 |
| Wealth status | Quintile 1 | 80 | 19.9 |
| Wealth status | Quintile 2 | 81 | 20.1 |
| Wealth status | Quintile 3 | 80 | 19.9 |
| Wealth status | Quintile 4 | 86 | 21.4 |
| Wealth status | Quintile 5 | 75 | 18.7 |
## Household annual consumption expenditure
The mean annual household expenditure (food expenditure: ETB47791.34 ($901.72) and nonfood expenditure: ETB42033.35 ($793.08)) was ETB89824.69 ($1694.81) with standard deviation of 45826.33($864.65). Whereas, the mean household annual healthcare expenditure was ETB12050.64 ($227.37) with the standard deviation of 25299.87($ 477.36) (Table 2).
**Table 2**
| HH Annual expenditure | N | Mean (ETB) | Std. Dev | Median |
| --- | --- | --- | --- | --- |
| Total household expenditure | 402 | 89824.69 | 45826.33 | 80548.0 |
| Household food expenditure | 402 | 47791.34 | 21061.86 | 43800.0 |
| Nonfood household expenditure | 402 | 42033.35 | 31141.96 | 33695.0 |
| Annual direct medical cost | 402 | 5036.97 | 10824.95 | 1096.0 |
| Registration card, | 402 | 174.02 | 492.463 | 50.0 |
| Medications | 402 | 2874.71 | 500.0 | 5980.98 |
| Imaging diagnostic test | 402 | 876.19 | 2434.26 | 0.0 |
| Laboratory | 402 | 812.24 | 200.0 | 2832.53 |
| Bed | 402 | 299.81 | 2360.39 | 2832.53 |
| Annual direct nonmedical cost | 402 | 865.1 | 3494.16 | 100.0 |
| Transport | 402 | 318.36 | 837.16 | 100.0 |
| Cafeteria | 402 | 481.69 | 3026.45 | 0.0 |
| Lodging | 402 | 65.05 | 345.1 | 0.0 |
| Indirect health cost (lost days) | 402 | 5622.1 | 13035.5 | 1996.5 |
| Total Health expenditure | 402 | 12050.64 | 25299.87 | 4120.5 |
## Health and health related characteristics
One or more household members sought modern healthcare in $83.8\%$ [337] of the households and from these, about $6.2\%$ [27] of the sick members have had referral history. The percentage of households which have at least one chronic health condition was $32.3\%$ and $21.9\%$ of the households sought healthcare from traditional healers (Table 3).
**Table 3**
| Variables | Category | Frequency | Percent (%) |
| --- | --- | --- | --- |
| Modern healthcare seek | Yes | 337 | 83.8 |
| Modern healthcare seek | No | 65 | 16.2 |
| Health institution type | Public | 228 | 56.7 |
| Health institution type | Private | 109 | 27.1 |
| Admission history | Yes | 35 | 8.7 |
| Admission history | No | 302 | 75.1 |
| Referral history | Yes | 27 | 6.7 |
| Referral history | No | 310 | 77.1 |
| Chronic health conditions | Yes | 130 | 32.3 |
| Chronic health conditions | No | 272 | 67.7 |
| Traditional healthcare seek | Yes | 88 | 21.9 |
| Traditional healthcare seek | No | 314 | 78.1 |
## Financial hardship of healthcare
About $37.1\%$ [149], $11.2\%$ [45] and $15.9\%$ [64] of the households encountered catastrophic health expenditure at $10\%$, $25\%$ and $40\%$ nonfood threshold levels, respectively. Moreover, $10.4\%$ [42] of the households were pushed below extreme poverty line ($1.90 a day extreme poverty line) because of healthcare expenditure. From participants with a history of referral, 27, 26($96.35\%$) of them experienced catastrophic health expenditure which attributes $17.45\%$ of catastrophic households. About ETB9527.21 ($179.76) and ETB11 848.68 ($223.56) were needed to bring the poor households to poverty line before and after healthcare expenditure, respectively. An additional ETB2321.47 ($43.80) was needed to bring the poor households to poverty level after expending for healthcare services (Table 4).
**Table 4**
| Variables | Measurements | At 10% threshold | At 25% threshold | At 40% threshold |
| --- | --- | --- | --- | --- |
| Catastrophic health expenditure | Catastrophic headcount (%) | 37.1 | 11.2 | 15.9 |
| Catastrophic health expenditure | Catastrophic overshoot | 20.05 | 7.32 | 12.51 |
| Catastrophic health expenditure | Mean positive gap (%) | 54.04 | 65.36 | 78.68 |
| | Measurements | Prepayment | Post payment | Discrepancy |
| Impoverishing health expenditure | Poverty headcount (%) | 70.4 | 80.8 | 10.4 |
| Impoverishing health expenditure | Poverty gap | 9527.21 | 11848.68 | 2321.47(24.37%) |
| Impoverishing health expenditure | Normalized poverty gap | 94.33 | 117.31 | 22.98 |
## Coping mechanisms of healthcare expenditure
Among the households, $99\%$ used own savings as a source of fund for healthcare cost. Moreover, $3.7\%$ and $5.5\%$ used selling household asset and borrowing as a coping mechanism for their health expenditure. About $22.6\%$ of the households were found to be insured with community based health insurance (CBHI) (Table 5).
**Table 5**
| Unnamed: 0 | Category | Frequency | Percent (%) |
| --- | --- | --- | --- |
| Insurance status | Insured | 91 | 22.6 |
| Insurance status | None insured | 311 | 77.4 |
| Main source of fund for healthcare cost | Own savings | 398 | 99.0 |
| Main source of fund for healthcare cost | Social support | 79 | 19.7 |
| Main source of fund for healthcare cost | Borrowing | 22 | 5.5 |
| Main source of fund for healthcare cost | Selling assets | 15 | 3.7 |
## Factors associated with catastrophic health expenditure
From bi-variable regression, about 15 variables were candidates ($p \leq 0.2$) for multiple logistic regression: These were sex of household head, age of household head, religion of household head, educational status of household head, occupation of household head, presence of under 5 children (U5C), family size, insurance status, hospitalization, health institution type, presence of chronic health conditions and seeking healthcare from traditional healers. Finally, from multiple logistic regression, age of household head, occupation of household head, insurance status, having social support, having chronic health conditions, sought healthcare from traditional healers were found to be statistically significant (at $p \leq 0.05$) factors for CHE.
For instance, households with a head of age in the interval between 31 and 45 years old were 2.5 times higher odds (AOR: 2.5, CI: 0.1.071, 5.82) to encounter catastrophic health expenditure than that of the households with a head in the age less than or equal to 30. Moreover, odds of facing CHE among households with a household head of age 60 and above was 4.213 (AOR: 4.213, CI: 1.23, 14.448) as compared to that of the households with a head whose age less than or equal to 30. Furthermore, the odds of catastrophic health expenditure among non-insured households was 2.188 (AOR: 2.188, CI: (1.037, 4.619) as compared to that of the insured households.
Additionally, households having members with chronic health conditions were 7.201 times higher odds (AOR: 7.201, CI: 3.64, 14.262) to be experienced catastrophic health expenditure as compared to that of households not having members with chronic health conditions. Likewise, households whose a member seek healthcare from traditional healers were 2.632 times higher odds (AOR: 2.632, CI: 1.372, 5.046) to encounter catastrophic health expenditure as compared to that of the households members not seeking healthcare from traditional healers.
Households which had no social support were 2.773 times higher odds (AOR: 773, CI: 1.246, 6.170) to face catastrophic health expenditure as compared to that of households having social support (Table 6).
**Table 6**
| Unnamed: 0 | Unnamed: 1 | CHE | CHE.1 | Unnamed: 4 | Unnamed: 5 |
| --- | --- | --- | --- | --- | --- |
| Variables | Category | No | Yes | COR(95%CI) | AOR(95%CI) |
| Sex of HH head | Male | 167 | 112 | 1 | 1 |
| Sex of HH head | Female | 86 | 37 | 0.642(0.408, 1.010) | 0.790(0.400, 1.560) |
| Age of Household head | < = 30 | 75 | 17 | 1 | 1 |
| Age of Household head | 31–45 | 107 | 54 | 2.226(1.198, 4.238) | 2.5(1.071, 5.821) * |
| Age of Household head | 46–60 | 49 | 41 | 3.691(1.888, 7.216) | 1.884(0.725, 4.897) |
| Age of Household head | >60 | 22 | 37 | 7.42(3.321, 15.636) | 4.213(1.23, 14.448) * |
| Religion | Orthodox | 224 | 138 | 1 | 1 |
| Religion | Others | 29 | 11 | 0.616(0.298, 1.292) | 0.504(0.182, 1.397) |
| Educational status of HH head | No education | 24 | 19 | 1 | 1 |
| Educational status of HH head | Read & write only | 20 | 11 | 0.695(0.269, 1.797) | 0.616(0.164, 2.319) |
| Educational status of HH head | Primary | 37 | 15 | 0.512(0.219, 1.198) | 0.443(0.128, 1.527) |
| Educational status of HH head | Secondary | 44 | 22 | 0.632(0.287, 1.392) | 0.761(0.220, 2.630) |
| Educational status of HH head | College and above | 128 | 82 | 0.809(0.417, 1.570) | 0.791(0.202, 3.099) |
| Occupation of HH head | Self employed | 135 | 60 | 1 | 1 |
| Occupation of HH head | Gov’t employed | 108 | 80 | 1.667(1.096, 2.536) | 0.809(0.312, 2.095) |
| Occupation of HH head | Private sectors | 10 | 9 | 2.025(0.783, 5.239) | 6.344(1.765, 22.80) * |
| Presence of U5C | No | 163 | 114 | 1 | 1 |
| Presence of U5C | Yes | 90 | 35 | 0.556(0.352, 0.879) | 0.786(0.405, 1.528) |
| Family size | < = 4 | 199 | 105 | 1 | 1 |
| Family size | >4 | 54 | 44 | 0.648(0.0.408, 1.03) | 0.881(0.440, 1.762) |
| Wealth status | Quintile 1 | 56 | 24 | 0.608(0.313, 1.181) | 0.637(0.230, 1.764) |
| Wealth status | Quintile 2 | 60 | 21 | 0.497(0.252, 0.978) | 0.640(0.243, 1.687) |
| Wealth status | Quintile 3 | 52 | 28 | 0.764(0.399, 1.464) | 0.961(0.400, 2.308) |
| Wealth status | Quintile 4 | 41 | 45 | 1.558(0.834, 2.910) | 1.126(0.490, 2.589) |
| Wealth status | Quintile 5 | 44 | 31 | 1 | 1 |
| Insurance status | Insured | 66 | 25 | 1 | 1 |
| Insurance status | None insured | 187 | 124 | 1.751(1.048, 2.925) | 2.188(1.037, 4.619) * |
| Chronic health conditions | Yes | 36 | 94 | 10.302(6.344, 16.73) | 7.201(3.64, 14.262) * |
| Chronic health conditions | No | 217 | 55 | 1 | 1 |
| Institution type | Public | 127 | 101 | 1 | 1 |
| Institution type | Private | 61 | 48 | 0.989(0.625, 1.367) | 1.481(0.793, 2.764) |
| Admission history | Yes | 9 | 26 | 4.204(1.904, 9.282 | 2,571(0,917, 7.209) |
| Admission history | No | 179 | 123 | 1 | 1 |
| Traditional healthcare seek | Yes | 42 | 46 | 1 | 1 |
| Traditional healthcare seek | No | 211 | 103 | 2.244(1.388, 3.626) | 2.632(1.372, 5.046) * |
| Social Support | Yes | 226 | 97 | 1 | 1 |
| Social Support | No | 27 | 52 | 4.487(2.662, 7.565) | 2.773(1.246, 6.170) * |
| Borrowing | Yes | 27 | 52 | 6.388(2.305, 3.626 | 2.722(0.723, 10, 255) |
| Borrowing | No | 226 | 97 | 1 | 1 |
## Discussion
This study aimed to assess financial hardship of health care in terms of the incidence of catastrophic health expenditure (CHE) and impoverishing health expenditure (IHE), including the determinants of CHE, among households in Debre Tabor town. The incidence of CHE was $37.1\%$ and the proportion of impoverished households due to health expenditure was $10.4\%$. This study implies that the financial hardship of health care is stronger among the less privileged populations: the non-insured, the chronically ill, the elder and socially unsupported. Moreover, avoiding impoverished health expenditure can reduce more than one tenth of poor households.
The incidence of CHE in the current study was higher than that of a previous study conducted, $\frac{2015}{2016}$, in Ethiopia which was $2.1\%$ [29]. The possible reason might be due to time and our study included indirect medical costs (lost days due to the illness) which were not considered in the previous study. The other probable reason might be due to the fact that the previous study used secondary data (from $\frac{2015}{16}$ HCE and WM survey).
Moreover, the incidence of catastrophic health expenditure in this study was higher compared with the studies conducted on CHE and impoverishment in households of persons with depression in 2019 and CHE for households of people with severe mental health disorder (SMD) in 2015 in rural Ethiopia which stated the incidence of CHE, $20\%$ and $20.3\%$ using $10\%$ threshold level, respectively [14]. The probable reason of this discrepancy might be due to the fact that the time of the current study used latest primary data whereas the previous studies were conducted since 2015.The other possible explanation may be escalation of health care cost due to the COVID-19 pandemic while conducting this study.
However, the incidence of catastrophic health expenditure in the current study was lower by half than that of the findings of the study conducted on economic burden of diabetic mellitus healthcare at Bahir Dar public hospitals in 2020 with the incidence of catastrophic health expenditure of $74.3\%$ using the same, $10\%$, threshold level [12]. The possible explanation for this difference might be due to the fact that the current study included insured households and non-ill household members, which may lower the incidence of the catastrophic health expenditure, that were not included in the previous study. The other possible reason might be the fact that the current study is conducted on households regardless of the diseases status of the members, whereas the previous study was conducted on diabetic patients, that indicates those households with the presence of household member with chronic conditions (e.g. DM) are prone to CHE.
Similarly, the incidence of catastrophic health expenditure in this study was lower than the findings of the study conducted on financial risk of seeking maternal and neonatal healthcare in southern Ethiopia in 2020 (incidence of CHE: $46\%$ at $10\%$ threshold level of total household expenditure) [30]. The possible reason might be due to the fact that mothers and neonates need more healthcare services in nature.
The incidence of CHE in this study was higher compared with study conducted at household level in African countries like Kenya, Uganda, Morocco and South Africa which stated the incidence of CHE(using $10\%$ threshold level) $10.7\%$, $14.2\%$, $1.77\%$ and $5\%$, respectively [8,31–33]. The possible reason this discrepancy might be due to contexts such as sociodemographic and socioeconomic characteristics are not the same.
The incidence of CHE in our study was also higher than that of the global monitoring for financial protection reports of 2019 and 2021 with incidences of CHE $12.7\%$ and $13\%$, respectively [1,2]. The probable reason behind the difference might be due to the differences on the scope and context of the studies and the global reports mainly relied on the national report which is secondary data.
Moreover, the percentage of the poverty impact of healthcare expenditure in the current study (IHE: $10.4\%$) was higher than that of similar studies, conducted on households, in national context, in Ethiopia in 2020 with IHE of $0.9\%$ [29], and conducted on diabetic mellitus patients in Bahirdar city public hospitals with IHE of $5\%$ [12], conducted on financial risk of seeking maternal and neonatal care, in southern Ethiopia, with IHE of $0.3\%$ [30] and conducted on patients with depression in Ethiopian rural households with IHE of $5.8\%$ [14]. The probable reasons behind this deference might be due to the fact that the current study included all household members standardized with adult equivalent size in in terms of sex and age where as the previous studies conducted on specific diseases. Moreover, the discrepancy may be the fact that the cost of life, including escalation of health care cost due to the COVID-19 pandemic, at the time of conducting this study is more costly than the previous.
In addition, the IHE in this study was higher than that of the studies conducted in various African countries like Kenya, Uganda, Morocco and South Africa with IHE were $2.2\%$, $2.7\%$, $1.11\%$ and $5\%$ [8,31–33], respectively. The possible explanation might be due to the fact that the difference in different poverty lines (e.g. Kenya used its national poverty line), sociodemographic and socioeconomic characteristics and the difference strategies used in Ethiopia and other African countries to protect their citizens from financial risk of seeking essential health services.
Furthermore, households led by heads with age 60 years and above were higher odds to spend catastrophic expenditure. This is supported by evidence in the study conducted on catastrophic health expenditure of SMD in rural Ethiopia and in Kenya in 2018 [30,32]. Likewise, non-insured households were more vulnerable to catastrophic health expenditure. This implied that health insurance is one way to safeguard households from financial risk of healthcare. This was supported by the study conducted in Kenya in 2018, which revealed that households with one member enrolled for health insurance were protected from catastrophic health expenditure [32].
Additionally, presence of chronic health conditions among household members had strong positive association with catastrophic health expenditure. This implied that chronic health conditions are the main source for financial risk for healthcare expenditure. This finding was supported by the evidence in the study conducted in southern Ethiopia, rural Ethiopia and Kenya [12,14,30,32].
The main limitation of this study was recall bias. Although, measures have been taken like triangulating self-reported health expenditure with the recipients, to reduce recall bias, it is still the limitation of this study.
## Conclusion
This study revealed that significant number of households in Debre Tabor town faced catastrophic health expenditure. The financial hardship of health care is stronger among the less privileged populations: the non-insured, the chronically ill, the elder and socially unsupported.
## Recommendations
Based on the key findings, we would like to recommend the following points for the respective concerned bodies
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|
---
title: 'Nutritional status and psychosocial stimulation associated with cognitive
development in preschool children: A cross-sectional study at Western Terai, Nepal'
authors:
- Prakash Sharma
- Chitra Bahadur Budhathoki
- Ram Krishna Maharjan
- Jitendra Kumar Singh
journal: PLOS ONE
year: 2023
pmcid: PMC10010513
doi: 10.1371/journal.pone.0280032
license: CC BY 4.0
---
# Nutritional status and psychosocial stimulation associated with cognitive development in preschool children: A cross-sectional study at Western Terai, Nepal
## Abstract
Quality education at the age of foundation to produce dynamic manpower is a public concern in developing countries including Nepal. Preschool children do not get proper care and support from their parents due to insufficient knowledge of proper feeding habits, nutrition status and methods of psychosocial stimulation, which may affect their proper cognitive development. This study aimed to identify the factors that influence cognitive development in preschool children aged 3–5 years in Rupandehi district of western Terai, Nepal. In this school based cross-sectional survey, a total of 401 preschool children were selected using a multistage random sampling technique. The study was conducted from 4th February to 12th April, 2021 in Rupandehi district of Nepal. Data on the children’s socio-economic and demographic status, level of psychosocial stimulation, nutritional status, and stage of cognitive development were collected through scheduled interviews and direct observation. Stepwise regression analysis was performed to determine the predictors of cognitive development in preschool children. A p-value less than 0.05 considered as statistical significance. Of 401 participants, $44.1\%$ had a normal nutritional status based on height for age Z-score (HAZ). Only $1.2\%$ of primary caregivers provided their children with high levels of psychosocial stimulation, and $49.1\%$ of children had a medium level of cognitive development. Furthermore, cognitive development in preschoolers is positively associated with nutritional status based on the height for age z score (β = 0.280; $p \leq 0.0001$), psychological stimulation from caregivers (β = 0.184; $p \leq 0.0001$), and advantageous castes/ethnicity (β = 0.190; $p \leq 0.0001$), but negatively associated with the child’s age (β = - 0.145; $$p \leq 0.002$$) and family type (β = -0.157; $$p \leq 0.001$$). Nutritional status and psychosocial stimulation appear to be major factors affecting cognitive development of preschoolers. Nutritional promotion strategies, as well as techniques for optimal psychosocial stimulation behavior, may play an important role in enhancing preschoolers’ cognitive development.
## Introduction
As a child grows, it gains mental skills like perception, memory, the ability to solve problems, and the ability to learn new languages and dialects [1]. All of the processes by which sensory inputs are reduced, transformed, elaborated, stored, recovered and utilized are considered to as cognitive processes [2], which may be influenced by biological factors such as heredity and normality of sensory organs, living environment, social interaction, stimulating environment, adequate nutrients, poverty, motivating factors and adequate learning opportunities [3,4]. The first few years of a child’s life are vulnerable time for cognitive development which affects future educational and occupational possibilities, and it may also decide a person’s risk of physical health in terms of obesity, malnutrition and mental-health issues [5].
Nutrition plays a critical role in cell proliferation, DNA synthesis, hormone metabolism, and the production of neurotransmitters, all of which contribute to improved mental health [6]. On the other hand, children’s culture, psychosocial stimulation, and living environment influence the cognitive development process [7]. Psychosocial stimulation, sociocultural factors, and realistic deficiency all affect the development of skills like cognitive, motor, perceptual, and language abilities [8]. Early nutritional feeding and a healthy psychosocial stimulation environment can lead to major changes in physical and mental development that affect children’s cognition, mood, and behavior [2,9].
A community-based cohort study in the United States examined the long-term effect of a randomized intervention based on nutrition, supplementation and psychosocial stimulation for malnourished children and found improved school attendance and learning performance [10]. In an experimental study carried out in an upper-middle income country, Jamaica reported that the group that received only nutrition and the group that received only psychosocial stimulation showed less cognitive development than the group that received both nutrition and stimulation [11]. Another cohort study in the same country compared the cognitive scores of children born to stunted and non-stunted parents, and discovered that the cognitive scores of the children born to stunted parents were lower, indicating that the effects of stunting on cognitive development continued in the subsequent generation of children [12]. Preschoolers’ cognitive development is significantly influenced by their diet and home environment, according to a cross-sectional study done in a Costa Rican refugee camp [13]. In the context of developing nations, Santosh et al. recommended that psychosocial stimulation and adequate nutritional status should both be intervened for proper cognitive development of children [14].
However, there is limited information on the determinants of early child development in low-income countries, especially rural sub-Saharan Africa and South Asia, where children are at high risk of not reaching their developmental potential due to the presence of many risk factors for poor cognitive development [1,3,9]. Very few studies have examined the relationship between nutrition status, psychosocial stimulation, and cognitive development, and they suggested a study focusing on the interactive effects of nutrients and psychosocial stimulation on the cognitive development of preschool children [15]. Similarly, there is a little practical discussion in the study literature of the challenges faced by the growing child in the setting of Nepal in the process of cognitive development. The rationale behind the study is to examine whether preschoolers’ nutritional status and psychosocial stimulation enhance their cognitive development.
Therefore, this study was conducted to assess the effect of nutritional status and psychosocial stimulation on cognitive development among preschool children aged 3–5 years in Western Terai of Nepal.
## Study design and setting
This study used a quantitative cross-sectional survey design with preschool children and their primary caregivers as respondents, in accordance with the post-positivist research paradigm of a single and objective reality. The study was conducted from 4th February to 12th April, 2021 in Rupandehi district of Nepal, that comprises one-metropolitan city, one municipality, and one rural municipality.
Located in Lumbini province, Rupandehi district has a total area of 1360 square kilometers and is divided into five electoral constituencies, ten provincial constituencies and sixteen local government units. It is also the birth place of Lord Buddha, lies in the western Terai of Nepal. The district had a population of 1,118,975 as of the most recent census, which was conducted in 2021. The population is diverse in terms of ethnicity, culture, and socio-economic background, and the district has a relatively high fertility rate, resulting in more children than in other places [16]. According to data obtained from the education division of the local government unit for the 2020 academic year, there were 14358 three- to five-year-olds children enrolled in 369 government funded early childhood development (ECD) centers [17].
## Participants and sampling approach
The study population consisted of preschool-aged children and their primary caregiver or mothers in the Rupandehi district. The study comprised three- to five-year-old children attended the ECD centre at the time of survey and whose mothers consented to their participation. Children were excluded if they had physical and mental problems that would affect the study. Raosoft’s sample size calculator was used to estimate a sample size based on the population size of 14358 three- to five-year children, an error margin of $5\%$, confidence level of $95\%$, and p (proportion of children who were deemed to be developing appropriately in the learning domain) as $82.4\%$ [18], with a design effect of 1.7. A minimum sample of 375 was computed and $7\%$ was added to account for non-responses, resulting in a sample size of 401. Multi-stage sampling was adopted to select the participants from Rupandehi district of Nepal. In the first stage, three local government units (one sub-metropolitan city, one municipality and one rural municipality) were selected at random in the district. These three local government units were chosen because they represent large urban and rural municipalities, resulting in a more representative sample. In the second stage, five ECD centres were chosen at random from a list provided by the education department in each local government unit. In the third stage, preschool children aged 3–5 years were selected using the population proportionate sampling (PPS) technique. Finally, a total of 401 preschool children (137 from sub-metropolitan city, 171 from municipality, and 93 from rural municipality) who were present at the EDC center at the time of the survey were enrolled in the study. Before enrolling children in the study, parental written consent was obtained. Participation rate between EDC center ranged from $97\%$ to $100\%$.
## Data collection
Data were collected through scheduled interviews and direct observation. The data included the children’s socio-economic and demographic status, level of psychosocial stimulation, nutritional status, and stage of cognitive development. The socio-economic and demographic components included nine variables. The gender of the child, the number of children in the household, age, family type, caste/ethnicity, mother tongue, father’s education, mother’s education, and economic situation were all taken into consideration. The economic ranking tool was adopted from Nepal Demographic and Health Survey 2016 (NDHS) [19] to determine economic status. The wealth status was measured based on household assets (chair, bed, radio, television, cassette player, mobile, car, motorcycle, bicycle etc.). A housing index was made by rating condition of the roof, floor and wall of the house, fuel used to cook, types of latrine, and availability of water supply. The sum of the wealth scores was then utilized to determine economic status. The score of wealth quartile (- 0.54 to +3.48) was divided by $25\%$ in each four categories: the poorest, poor, rich and the richest where <1.8 was poorest level of economic status. Likewise, from wealth quartile score >1.8 to <2.4 was categorized into poor, from >2.5 to <2.9 rich and >2.9+ were richest [17].
To determine the level of psychosocial stimulation, 37 questions based on cognitive stimulation, emotional stimulation, verbal responsiveness, avoidance of restriction and punishment, caregiver promotes child development, organization of physical and temporal environment, provision of appropriate play material, and opportunities for variety in daily stimulation were developed [3,20,21]. Out of thirty-seven questions, seven were eliminated in order to adjust Cronbach’s Alpha, and the final 28 questions were assessed with a binary response of either yes or no. One mark was assigned for a positive response and zero marks for a negative response. The total number and percentage of responses were calculated. Participants were divided into three levels of psychosocial stimulation: low (less than $52\%$), medium (between $53\%$ and $82\%$), and high (more than or equal to $83\%$) based on literature [22]. The height and weight of preschool children were measured at their respective EDC centers. A SAMSO branded LCD digital weighing scale ($L = 31$cm,$W = 29.4$cm, and $H = 2.65$cm) with a capacity of 150kg x 100g and a minimum weighing capacity of 0.82kg was used to measure body weight. To avoid reading parallax and ensure accurate and precise measurement, a portable child height measuring wooden board with a smooth gliding measuring slide/wedge that could be locked or had a friction feature was used. Over its entire length, the measuring slide wobbled only 0.2 cm, making it possible to take numerous precise readings. Five technical experts with bachelor’s degrees in a health-related field who were trained for the study protocol, tools, and data collection methods collected the data.
The age, height, and weight of children were measured in accordance with WHO standards for child growth [23]. Using WHO *Anthro plus* software [24] version 1.0.4, the Z-scores of the index for height-for-age Z-score (HAZ), weight-for-age Z-score (WAZ), and body mass index Z-score (BAZ) were calculated. The HAZ, WAZ, and BAZ scores of the children were then divided into three levels of nutrition status according to WHO nutrition guidelines: normal (score between -1 SD and +1 SD), moderate malnutrition (-2 SD to -1 SD; or +1 SD +2 SD), and severe malnutrition (less than -2 SD or greater than +2 SD) [24]. The cognitive development of children was assessed using a tool developed by Hema Pandey in 1992, [25] which was later given to the National Psychological Corporation of India with copyright registered in 2005. The assessment tool has six dimensions: conceptual skill, information, comprehension, memory, visual perception, and object vocabulary [25]. All six aspects of cognitive development were represented by 20 items with a maximum possible score of 65, ranging from one to eight for each item score [26]. All of these aspects occur during the pre-operational stage, which was initiated by Jean Piaget in his developmental psychology [27]. Only a few of the 20 items were adjusted to contextualize them for the current situation. The raw score obtained by three, four, and five-year-old children was converted into a standard score using the cognitive development tools’ guidelines [26]. The total score was converted into percentages for low (<$60\%$), medium (60–$79\%$), and high (≥ $80\%$) categories [22], in compliance with Nepal’s national education grading system. In the Nepali educational system, there are eight grades, which are designated as A+, A, B+, B, C+, C, D, and E based on scores of $90\%$ or higher, 80–$89\%$, 70–$79\%$, 60–$69\%$, 50–$59\%$, 40–$49\%$, 25–$39\%$ and <$25\%$, respectively [28,29]. Because of the wide range of grade levels, it was further classified into three categories: high (A and A+), medium (B and B+), and low (C+ and lower) [28].
## Reliability of tools
Tools for cognitive development, psychosocial stimulation and socio-economic and demographics were pre-tested among $10\%$ of respondents who were not included in the study. Tools were pre-tested to determine their understanding, time spent on each question, and consistency among related variables and acceptability [30]. Data collection assistants were intensively trained on the objective of the study and techniques of data collection. Two-day training was provided to the enumerators with a mock session. They were facilitated about the potential biases during data collection and some techniques such as probing questions, logic patterns, and other appropriate skills were instructed them. The reliability of the cognitive development tool was 0.9 and 0.8 for psychosocial stimulation assessed by use of Cronbach’s alpha test [31]. The subjects’ privacy and self-confidence were respected during the interviews and cognitive development tests.
## Statistical analysis
The data were entered into an Excel sheet and then transferred to the version 26.0 of IBM SPSS for statistical analysis. Data were coded and categorized according to the needs of the objectives and nature of the variables. Categorical variables were presented as numbers (n) and percentages (%), whereas continuous variables were represented as means and standard deviations (SD). The T-test and one-way ANOVA with post hoc analysis were conducted. A p-value was considered significant if it was less than 0.05. The Kolmogorov-Smirnov test was used to determine the normality of the data, and nine outliers were eliminated from the dependent variable to make it normally distributed. Considered OLS assumptions were: [1] Normality of residuals utilizing the Histogram and PP plot; [2] Homogeneity; and [3] Multicollinearity. Cognitive development was predicted by twelve variables: HAZ, WAZ, BAZ, psychosocial stimulation, economic status, types of family, caste/ethnicity, language, mother’s education, father’s education, number of children, and age.
For the regression analysis, there were five possible models built from the stepwise selection method. The results of ANOVA depicted that the entire model has a significant overall fit to the given set of observations. The values of F were gradually decreased from the first model (37.98) to the fifth model (19.38) and also they were all highly significant ($p \leq 0.01$) supporting them as the better models. The increased adjusted R square from first to last model as $08.8\%$, $13.5\%$, $15.9\%$, $17.5\%$ and $19.4\%$, respectively, and the decreased standard error of the estimate from the model first to last as 16.34, 15.91, 15.69, 15.54 and 15.36 respectively were also indicated a good sign of fitting of better models. Finally, the decreased values of Akaike’s information criterion (AIC) as 3320.70, 3302.11, 3292.87, 3286.13 and 3277.34 and Bayesian information criterion (BIC) 3332.62, 3318.01, 3312.74, 3309.98 and 3305.16 from model 1 to 5 provided similar support for the good model.
## Ethical consideration
The study received ethical approval from ethical review board of the Nepal Health Research Council (NHRC: No. 2078-$\frac{56}{2021}$). Furthermore, permission for the study was taken from the office of the Dean, Faculty of Education, Tribhuvan University, as well as the individual selected ECD centers. Written informed consent was obtained from the primary caregivers of preschool-aged children.
## Results
Distribution of sample characteristics is shown in Table 1. Of the 401 preschoolers, more than half ($52.6\%$) were from joint family, more than one third ($34.9\%$) were from advantageous caste or ethnicity, nearly a quarter ($23.9\%$) were from a non Dalit Terai caste, and more than half ($62.3\%$) spoke Nepali as their first language. In regard to education, $14.7\%$ of fathers and $23.7\%$ of mothers of preschoolers were illiterate. Almost one fourth of the participants were either the poorest, poor, rich, or the richest, and the majority ($71.6\%$) of parents had two or fewer children. Likewise, half of the children were male ($50.6\%$) and $45.6\%$ of them were five years old. The nutritional status of children measured by height for age z score (HAZ) showed that $44.1\%$ of preschool children had normal nutritional status, while the remainder were stunted, with $36.2\%$ moderate and $19.7\%$ severe. The prevalence of underweight children was higher, with $36.4\%$ in the moderate level and $18.5\%$ in the severe level for WAZ, whereas $45.1\%$ of the children were normal. The majority of the study’s children ($57.1\%$) fell into the normal category according to their BMI-for-age (BAZ) classification, followed by the moderate category with $31.2\%$ and the severe category with only $11.7\%$.
**Table 1**
| Characteristics | Category | N | % |
| --- | --- | --- | --- |
| Types of family | NuclearJoint | 190211 | 47.452.6 |
| Caste/ethnicity* | DalitJanajatiNon-Dalit Terai casteAdvantageous caste | 5311296140 | 13.227.923.934.9 |
| Mother tongue/language | NepaliBhojpuriMagarOther | 2501041631 | 62.325.94.07.7 |
| Father’s education | IlliterateBasic LevelSecondary and above | 59199143 | 14.749.635.7 |
| Mother’s education | IlliterateBasic LevelSecondary and above | 95181125 | 23.745.131.2 |
| Economic status | PoorestPoorerRichRichest | 10110010199 | 25.224.925.224.7 |
| Number of children | Less than or equal to twoMore than two | 287114 | 71.628.4 |
| Gender of children | MaleFemale | 203198 | 50.649.4 |
| Age of children | Three yearsFour yearsFive years | 39179183 | 9.744.645.6 |
| Height for age (HAZ) score of children (Stunted) | NormalModerateSevere | 17714579 | 44.136.219.7 |
| Weight for age (WAZ) score of children (Underweight) | NormalModerateSevere | 18114674 | 45.136.418.5 |
| Body mass index (BMI) for age (BAZ) score of children(Wasting/thinness) | NormalModerateSevere | 22912547 | 57.131.211.7 |
| Psychosocial stimulation by caregivers | LowMediumHigh | 1192775 | 29.769.11.2 |
| Cognitive development of children | LowMediumHigh | 37197167 | 9.249.141.6 |
Only $1.2\%$ of primary caregivers provided high levels of psychosocial stimulation, compared to $69.1\%$ who provided medium levels and $29.7\%$ who provided low levels. The greatest proportion of children ($49.1\%$) had a medium level of cognitive development, while $41.6\%$ had low levels of cognitive development and the remaining ($9.2\%$) high level of cognitive development.
## Cognitive development and determinant factors
Table 2 showed that the mean cognitive development score significantly differed by socio-economic and demographic, nutritional status, and psychological stimulation categories. There were significant differences in the mean cognitive development score by age category ($$p \leq 0.003$$), number of children ($p \leq 0.0001$), family types ($p \leq 0.018$), caste/ethnicity ($p \leq 0.0001$), mother tongue/language ($$p \leq 0.015$$), father’s education ($$p \leq 0.011$$), mother’s education ($$p \leq 0.0001$$), economic status ($$p \leq 0.016$$), psychosocial stimulation ($$p \leq 0.002$$), HAZ classification ($p \leq 0.0001$), WAZ classification ($p \leq 0.015$), and BAZ classification ($p \leq 0.022$).
**Table 2**
| Variables | Category | Number (%) | Mean | SD | P-value# |
| --- | --- | --- | --- | --- | --- |
| Age of children | Three yearsFour yearsFive years | 35(8.9)179(45.5)179(45.5) | 110.02101.6199.61 | 27.0012.8217.59 | 0.003*** |
| Number of children | Less than or equal twoMore than two | 282(71.8)111(28.2) | 102.38100.29 | 15.5315.97 | 0.0001*** |
| Types of family | NuclearJoint | 186(47.3)207(52.7) | 103.75100.02 | 14.6916.32 | 0.018* |
| Caste/ethnicity | DalitJanajatiNon-Dalit Terai casteAdvantageous caste | 52(13.2)110(28.0)94(23.9)137(34.9) | 97.6797.6997.83108.76 | 17.4615.1217.2116.34 | 0.0001*** |
| Mother tongue/language | NepaliBhojpuriMagarOthers | 245(62.3)101(25.7)16(4.1)31(7.9) | 103.3398.61106.5697.48 | 15.2315.6115.6117.32 | 0.015* |
| Father’s education | IlliterateBasic LevelSecondary and above | 59(15.0)195(49.6)139(35.4) | 99.7799.56105.02 | 13.6217.3917.67 | 0.011* |
| Mother’s education | IlliterateBasic LevelSecondary and above | 94(23.4)180(45.8)119(30.3) | 97.73100.25106.45 | 16.6316.9916.78 | 0.0001*** |
| Economic status | PoorestPoorRichRichest | 101(25.7)98(24.9)98(24.9)96(24.4) | 98.5899.18105.18103.28 | 15.5915.4717.2519.40 | .016* |
| Psychosocial stimulation by caregivers | LowMediumHigh | 116 (29.5)272 (69.2)5 (1.3) | 97.67103.40109.20 | 15.6715.3715.53 | 0.002*** |
| HAZ Classification | NormalModerateSevere | 172(43.8)142(36.1)79(20.1) | 105.42101.5493.00 | 16.5916.9215.74 | 0.0001*** |
| WAZ Classification | NormalModerateSevere | 176(44.8)143(36.4)74(18.8) | 102.65102.8196.35 | 15.8018.4616.82 | 0.015* |
| BAZ Classification | NormalModerateSevere | 224(57.0)122(31.0)47 (12.0) | 100.56100.79108.00 | 16.1718.7916.09 | 0.022* |
## Stepwise regression analysis and determinant factors of cognitive development
The results of the stepwise regression analysis showed that psychological stimulation from caregivers, advantageous castes/ethnicity, and nutritional status based on the height for age z score (HAZ) ($p \leq 0.01$) all had a positive and significant impact on cognitive development. The age of the child and living in a joint family, however, had a negative and significant effect ($p \leq 0.01$) [Table 3].
**Table 3**
| Predictors | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 |
| --- | --- | --- | --- | --- | --- |
| HAZ | 0.301**(0.723) | 0.323**(0.707) | 0.298**(0.706) | 0.284**(0.702) | 0.280**(0.695) |
| Advantageous castes/ethnicity | | 0.222**(1.656) | 0.194**(1.657) | 0.201**(1.643) | 0.190**(1.628) |
| Psychosocial stimulation by care givers | | | 0.165**(0.191) | 0.185**(0.191) | 0.184**(0.189) |
| Joint family | | | | -0.137**(1.615) | -0.157**(1.611) |
| Age of child | | | | | -0.145**(1.211) |
| Constant | 106.242**(1.128) | 102.099**(1.416) | 89.233**(3.991) | 89.831**(3.957) | 106.939**(6.725) |
| Adjusted R2 | 08.8% | 13.5% | 15.9% | 17.5% | 19.4% |
| Std. Error | 16.34 | 15.91 | 15.69 | 15.54 | 15.36 |
| F (P-value) | 37.98(P<0.01) | 30.78(P<0.01) | 25.05(P<0.01) | 21.28(P<0.01) | 19.38(P<0.01) |
| AIC♀ | 3320.70 | 3302.11 | 3292.87 | 3286.13 | 3277.34 |
| BIC♀ | 3332.62 | 3318.01 | 3312.74 | 3309.98 | 3305.16 |
According to this finding, nutritional status (HAZ) alone in model 1 can account for $8.8\%$ of a child’s cognitive development (β = 0.301 and adjusted R2 = 0.088). When advantageous caste/ethnicity was included in model 2, the contribution rose by $13.5\%$. The effect of psychosocial stimulation was shared by $15.9\%$ in model 3 and $17.5\%$ when family type was included in model 4. Finally, the five factors, namely nutritional status (HAZ), caste/ethnicity, psychosocial stimulation, family type, and child age, explained $19.4\%$ of a child’s cognitive development in model 5.
In the final multivariable regression model, a single unit change in height for age Z score (HAZ) resulted in a 0.280 unit change in cognitive development score (β = 0.280; $p \leq 0.0001$). Similar to this, an increase of one unit in psychological stimulation was associated with an increase of 0.184 units in the cognitive development score (β = 0.184; $p \leq 0.0001$). Additionally, changing caste/ethnicity categories resulted in a 0.190 unit change in cognitive development score (β = 0.190; $p \leq 0.0001$). Furthermore, the cognitive development score dropped by 0.157 units (β = -0.157; $$p \leq 0.001$$) when the family type changed from nuclear to joint. Similar to this, for every unit increase in pre-schooler age, the cognitive development score dropped by 0.145 units (β = - 0.145; $$p \leq 0.002$$).
## Discussion
The study revealed that the gender ratio was almost equal, about fifty percent was in marginal economic condition and most of them were undernourished. Almost half of them were in medium level of cognitive development and most of them received a medium level of psychosocial stimulation. The age of the child, number of child, types of family, caste/ethnicity, mother tongue, parental education, economic status, psychosocial stimulation and nutrition status [HAZ, WAZ, BAZ] were the main predictors of cognitive development of preschoolers in unadjusted analysis. In multivariate analysis, this study showed that nutrition status [HAZ], advantageous caste and psychosocial stimulation had significant and positive relationship with cognitive development. However, a significant but negative relationship was found with joint family and age of children.
Nutritional status (HAZ) appeared to be the most important and positive contributing predictor. When comparing with nutrition status HAZ, cognitive development in preschool children has a considerable impact since children who were undernourished have lower levels of cognitive growth. Cognitive development had a substantial impact on nutritional status as children with intermediate and severe nutrition status were reported to have low levels of cognitive development. It was associated with development and performance on social perception tests and visual-spatial abilities at 5 years of age [33] while studying the effect of vitamin B-12 in cognitive functioning among children from the Bhaktapur district of Nepal. Similarly, undernourishment and non-verbal IQ were found to be substantially associated with South-East Asian children aged 6 to 12 years [34]. In Bangladesh, a randomized controlled experiment comparing children aged 6 to 24 months with and without nutritional supplement revealed a significant cognitive development benefit of nutrition [35]. The most important takeaway from this study was that preschoolers with moderate or severe malnutrition showed lower cognitive development. It indicated that a high degree of cognitive development in preschool children was linked to a better nutritional status as undernourishment creates impaired growth and dysfunction of neurocognition [36].
Caste/ethnicity affected cognitive development of preschoolers. Advantageous caste children had higher cognitive development than disadvantageous caste. In India, children from high castes had higher educational outcomes than those from lower castes such as Dalits, Adivasi, Muslims, and children from privileged castes. The children from the advantageous castes had enjoyed over their friends from the disadvantageous castes [37]. It indicated that parents of disadvantageous castes (Dalit, non-Dalit Terai caste, and Janajati) seemed to have less attention for the cognitive development of their preschool children. This could be because most of them were illiterate and busy for their livelihood, and most ECD teachers were from advantageous castes who might not understand the feeling and be insensitive to those children [37].
Preschool children’s cognitive development was significantly influenced by the psychosocial stimulation they received from their caregivers. It appeared that the cognitive development of children who received high and medium levels of psychosocial stimulation was superior to that of those who have received low levels. A randomized trial in Bangladesh comparing children aged 6–24 months with and without stimulation demonstrated a significant ($$p \leq 0.037$$) effect of stimulation on cognitive development [38]. Cognitive development was strongly influenced by the types of families a child grows up in. Preschool children’s cognitive development was reported to be better in nuclear families than joint families. The role of the nuclear family had a smaller and favorable impact compared to HAZ, the advantageous caste, and psychological stimulation. Research in Cuba indicated that parents from a nuclear family provided more attention to their children’s education and all-round development than parents from other families, which had an impact on their children’s performance [39]. A higher level of parent-child interaction, maternal care, and children’s autonomy was also found to be associated with nuclear families [40]. It may be the reason that it was difficult to provide appropriate nutrition and good practice of psychosocial stimulation such as sufficient play materials and mother-child interaction in joint family [41] as insufficient nutrition and poor psychosocial stimulation were directly associated with cognitive outcomes [41]. This means that children from a nuclear family had a better chance of developing their cognitive potential.
The cognitive development of children was influenced by their age. While other factors influence preschoolers’ cognitive development, age alone has the least and significant negative impact on their progress. It was found that as preschoolers grew older, their cognitive development slowed down, which went against the theory behind this study which might be the reason they had to deal with the responsibility of caring for young babies and doing household work [25]. A similar conclusion was obtained in a longitudinal follow-up research with 79 children from birth to 3.6 years old, which discovered that children who were exposed to low and high biological risk and had less home stimulation had lower cognitive development [42].
## Strength and limitation of the study
The strength of this study was its large sample size of 401 preschoolers and primary caregivers. Dr. Hema Pandey’s cognitive development tool was shown to be successful following contextualization and pre-testing in this population. Dietary status, psychosocial stimulation, and cognitive development were categorized based on WHO nutrition guidelines and literature review. This study filled a knowledge gap on the cognitive development of children in Nepal and South Asia. The findings of this study may guide academics and policymakers in developing and implementing formal and non-formal curriculum and recommendations to promote the cognitive development of preschoolers. However, there were some limitations of this study that need to be considered. Since the study was cross-sectional the whole data were obtained only on the day of survey. The study was conducted among children who studied in government ECD centers, so it may not be generalized to other preschoolers. This study did not assess the IQ of mothers/primary caregivers, which may influence child care and stimulation. Further, some of the determinants of child development such as genetic factors, family conflict and violence were not considered. The efforts of ECD centers and ECD teachers regarding nutrition education and practice as well as psychosocial stimulation behavior were not addressed in this study, which could have influenced its findings. Additionaly, the psychosocial stimulation measurement tool was created for local use and the score has not yet been validated; however tertiles were used for the analysis to assess trend [43].
## Conclusions
This study showed that some socio-economic and demographic factors, including the children’s age, caste/ethnicity, and family type, were significantly associated with preschoolers’ cognitive development. Furthermore, the cognitive development of preschoolers was likely to be significantly influenced by nutritional status and psychosocial stimulation. In the current context of Western Terai, Nepal, nutritional promotion strategies along with techniques of optimal psychosocial stimulation behavior for preschoolers mothers from disadvantageous groups (Dalit, non-Dalit Terai caste, and Janajati caste/ethnicity), as well as those living in joint families, may be a significant contributor in enhancing the cognitive development of preschoolers. It is essential to conduct additional research with a strong design and a large sample size to establish cause and effect relationship between nutritional status with psychosocial stimulation and cognitive development of preschoolers.
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|
---
title: 'Health facility readiness to provide antenatal care (ANC) and non-communicable
disease (NCD) services in Nepal and Bangladesh: Analysis of facility-based surveys'
authors:
- Deependra K. Thapa
- Kiran Acharya
- Anjalina Karki
- Michelle Cleary
journal: PLOS ONE
year: 2023
pmcid: PMC10010536
doi: 10.1371/journal.pone.0281357
license: CC BY 4.0
---
# Health facility readiness to provide antenatal care (ANC) and non-communicable disease (NCD) services in Nepal and Bangladesh: Analysis of facility-based surveys
## Abstract
### Background
Antenatal care (ANC) visits provide an important opportunity for diagnostic, preventive, and curative services for non-communicable diseases (NCDs) during pregnancy. There is an identified need for an integrated, system-wide approach to provide both ANC and NCD services to improve maternal and child health outcomes in the short and long term.
### Objective
This study assessed the readiness of health facilities to provide ANC and NCD services in Nepal and Bangladesh, identified as low–and middle–income countries.
### Method
The study used data from national health facility surveys in Nepal ($$n = 1565$$) and Bangladesh ($$n = 512$$) assessing recent service provision under the Demographic and Health Survey programs. Using the WHO’s service availability and readiness assessment framework, the service readiness index was calculated across four domains: staff and guidelines, equipment, diagnostic, and medicines and commodities. Availability and readiness are presented as frequency and percentages, while factors associated with readiness were examined using binary logistic regression.
### Results
Of the facilities, $71\%$ in Nepal, and $34\%$ in Bangladesh reported offering both ANC and NCD services. The proportion of facilities which showed readiness for providing ANC and NCD services was $24\%$ in Nepal and $16\%$ in Bangladesh. Gaps in readiness were observed in the availability of trained staff, guidelines, basic equipment, diagnostics, and medicines. Facilities managed by the private sector or a Non-Governmental Organization, located in an urban area, with management systems to support the delivery of quality services were positively associated with readiness to provide both ANC and NCD services.
### Conclusion
There is a need to strengthen the health workforce by ensuring skilled personnel, having policy, guidelines and standards, and that diagnostics, medicines, and commodities are available/provided in health facilities. Management and administrative systems are also required, including supervision and staff training, to enable health services to provide integrated care at an acceptable level of quality.
## Background
Noncommunicable diseases (NCDs) are a leading cause of mortality, morbidity and disability, accounting for more than 41 million deaths ($71\%$ of all deaths) globally per year [1]. NCDs were responsible for 1.62 billion Disability Adjusted Life Years (DALYs) in 2019, an increase from $43.2\%$ in 1990 to $63.8\%$ of total DALYs in 2019 [2]. There has been a surge in the burden of NCDs in low- and middle-income countries (LMICs) due to the globalization of unhealthy lifestyles and the increased number and proportion of the ageing population. The burden of NCDs has disproportionately affected LMICs, with more than three quarters of NCD-related deaths occurring in these regions [3, 4]. A large disparity remains between high- and low-income countries regarding strategies to control NCDs, reliable and comprehensive data on risk factors, and surveillance systems [5]. Despite the increasing risk factor exposure, prevalence and mortality related to NCDs in LMICs, public health policy responses have been slow, limited, and inadequate.
Whilst people of all age groups, sexes and regions are affected by NCDs, these conditions are often overlooked, undiagnosed, and untreated among women [6]. NCDs contribute to more than 18 million deaths among women—more than two-thirds of all female deaths each year [7]. The burden of NCDs is also increasing among women of reproductive age, with NCDs becoming a significant cause of female death during childbearing age in LMICs [8]. The enduring myth that health issues among women are primarily related to their reproductive health has undermined recognition of the importance of NCDs affecting women.
Women with health conditions such as hypertension, anaemia, malnutrition, obesity and heart disease are at higher risk of pregnancy- and child-birth-related complications, increasing risks for the mother and her offspring. Almost $28\%$ of maternal deaths around the world are attributed to chronic health conditions exacerbated during pregnancy and childbirth [9]. Children of women with NCDs are also at increased risk of adverse health outcomes, including NCD conditions such as obesity, diabetes, stroke and cardiovascular diseases later in life. The intergenerational impact of NCDs during pregnancy can multiply the continuing NCD pandemic [10]. Mahajan et al. [ 11] identified several challenges for women in low-income countries, including a lack of awareness of NCDs and associated risk factors. A study in Tanzania, for example, showed almost $80\%$ of reproductive age women were not aware of their hypertensive condition [12].
Interventions designed to improve maternal and child health have primarily focused on the treatment and management of the presenting health condition during pregnancy, delivery, and the postpartum period, with limited attention given to the underlying causes [13]. The epidemiological transition to NCDs, including the burden of preventable maternal morbidity and mortality, is likely to hamper the achievement of sustainable development goals in LMICs. Recognizing the inextricable link between maternal and child health and NCDs, ensuring a more integrated and holistic approach to prevention and care at the primary health care level, is a requirement, especially in low resource settings. A critical strategy to enhance maternal and child health is to ensure a continuum of care by strengthening health systems and improving the quality of health care [14, 15].
Primary health care in most LMICs encompasses services related to maternal health during pregnancy, delivery, and the postnatal period. Antenatal care (ANC) coverage has increased substantially during the past decade. A study by Tikmani et al. [ 16] analysing trends of ANC visits in LMICs from 2011 to 2017 showed that almost all women attended an appointment at least once, and there was a significant increase in the proportion of women who made at least four ANC visits. The WHO’s updated ANC guideline recommends a minimum of eight antenatal care visits during pregnancy so that the well-being of mothers and newborns is ensured [17].
Whilst the Millennium Development Goals Report suggests significant progress in maternal and child health, there is still high maternal and neonatal mortality, mostly in LMICs, from preventable pregnancy- and birth-related complications [18, 19]. ANC visits provide a critical opportunity to diagnose and manage pregnancy-related complications to improve maternal and child health. In addition, ANC visits also provide opportunity for early screening of modifiable risk-factors and identification of pre-existing conditions [20]. Despite the evidence on the health and economic benefits of integrating maternal health and NCDs, the link between these services remains neglected, largely due to the traditional approaches that divide service delivery into communicable diseases, NCDs, and maternal and child health [10]. Maternal health programs in LMICs are well placed to integrate NCD care programs [21].
The need for an integrated, system-wide approach to ANC and NCDs to improve maternal and child health outcomes is well established. The integration of these services can ensure screening for NCDs, targeted interventions, and support for lifestyle modifications [22] as well as long-term population health. This may also improve maternal and child health outcomes in the short term. Readiness of health systems to provide both ANC and NCD services is important for successful integration of ANC and NCD services. To date, evidence of service readiness for ANC and NCD services is not well documented, with studies examining the readiness of health facilities to provide integrated services lacking.
The present study aimed to explore the readiness of health facilities to provide ANC and NCD services using data from health facilities surveys in two LMICs countries: Nepal and Bangladesh, and assess organizational factors associated with readiness to provide these services. Whilst these two countries have different cultures and practices in relation to healthcare, there are many challenges common to both as LMICs. Information on service readiness to provide ANC and NCD services in these two countries will provide an important insight for policy makers to integrate these services for improving health of women and general population in South Asia and other LMICs. This will also inform the further development of global aims to be achieved in the context of the Sustainable Development Goals (SDGs).
## Data source
This study used data from the Service Provision Assessments (SPA) conducted under the USAID’s Demographic and Health Surveys Program [23] which includes standardized health facility audit and health service provider interview data. The cross-sectional survey of health facilities provides comprehensive information on the availability and readiness of basic health care services for each country including child health, maternal and newborn care. SPA surveys provide essential data which enables health system to be monitored and strengthened in LMICs [23].
This study included two LMICs in South Asia: Nepal and Bangladesh, where recent Demographic and Health Program SPA surveys had been conducted. These surveys were the Nepal Health Facility Survey (NHFS) 2021, and the Bangladesh Health Facility Survey (BHFS) 2017. The health facility survey, globally called the SPA, provides nationally representative estimates, collecting information from health facilities managed by the government, non-governmental organizations (NGO) and private for-profit organizations across the country. Details of survey methodology and sampling strategy are available in the published survey reports; The Ministry of Health and population/Nepal et al. [ 24] for Nepal, and The National Institute of Population Research Training—NIPORT et al. [ 25] for Bangladesh.
The recent health facility surveys in the selected two countries collected information from 1576 health facilities in Nepal and 1524 in Bangladesh. Stand-alone HIV Testing and Counselling Centres (HTCs) in Nepal and community clinics in Bangladesh were excluded from the current study (Table 1).
**Table 1**
| Country | Survey Year | Total number of surveyed health facilities | Number of health facilities excluded† | Sample size for current study (after exclusion) |
| --- | --- | --- | --- | --- |
| Nepal | 2021.0 | 1576 | 11 | 1565 |
| Bangladesh | 2017.0 | 1524 | 1012 | 512 |
| Total | | 3100 | 1023 | 2077 |
## Dependent variable–readiness for integrating ANC and NCD services
The dependent variable in this study was readiness for providing ANC and NCD services at health facilities, defined as the availability of services and capacity of health facilities to provide both ANC and NCD services. The readiness index was calculated based on the scores of ANC and NCD service readiness. NCD service included any service offering diagnosis and/or management of diabetes, cardiovascular diseases, and chronic respiratory diseases. Based on the WHO Service Availability and Readiness Assessment (SARA) Manual [23], we identified four domains of service readiness for ANC and NCD, which included staff and guidelines, equipment, diagnostic, and medicines and commodities. The different tracer items for each specific domain of ANC and NCD are provided in Table 2.
**Table 2**
| Domain | Indicators (Tracer items) | Measurement | Percent score (%) | Percent score (%).1 |
| --- | --- | --- | --- | --- |
| Domain | Indicators (Tracer items) | Measurement | Indicator | Domain |
| ANC service readiness index | ANC service readiness index | ANC service readiness index | ANC service readiness index | ANC service readiness index |
| Staff and guidelines | Guidelines for diagnosis and treatment of ANC | Yes | 12.50 | 25.00 |
| Staff and guidelines | Guidelines for diagnosis and treatment of ANC | No | 0.00 | 25.00 |
| Staff and guidelines | At least one staff member trained in ANC | Yes | 12.50 | 25.00 |
| Staff and guidelines | At least one staff member trained in ANC | No | 0.00 | 25.00 |
| Equipment | Blood pressure (BP) apparatus | Yes | 25.00 | 25.00 |
| Equipment | Blood pressure (BP) apparatus | No | 0.00 | 25.00 |
| Diagnostics | Hemoglobin (Hb) | Yes | 12.50 | 25.00 |
| Diagnostics | Hemoglobin (Hb) | No | 0.00 | 25.00 |
| Diagnostics | Urine dipstick- protein | Yes | 12.50 | 25.00 |
| Diagnostics | Urine dipstick- protein | No | 0.00 | 25.00 |
| Medicines and commodities | Iron and folic acid combined tablets | Yes | 8.33 | 25.00 |
| Medicines and commodities | Iron and folic acid combined tablets | No | 0.00 | 25.00 |
| Medicines and commodities | Tetanus diphtheria vaccine | Yes | 8.33 | 25.00 |
| Medicines and commodities | Tetanus diphtheria vaccine | No | 0.00 | 25.00 |
| Medicines and commodities | Albendazole | Yes | 8.33 | 25.00 |
| Medicines and commodities | Albendazole | No | 0.00 | 25.00 |
| Total ANC readiness index score | Total ANC readiness index score | Total ANC readiness index score | Total ANC readiness index score | 100.00 |
| NCD service readiness index | NCD service readiness index | NCD service readiness index | NCD service readiness index | NCD service readiness index |
| Staff and guidelines | Guidelines for diabetes, CVD and CRD diagnosis and treatment | Yes | 12.50 | 25.00 |
| Staff and guidelines | Guidelines for diabetes, CVD and CRD diagnosis and treatment | No | 0.00 | 25.00 |
| Staff and guidelines | At least one staff member trained in diabetes, CVD, and CRD diagnosis and treatment | Yes | 12.50 | 25.00 |
| Staff and guidelines | At least one staff member trained in diabetes, CVD, and CRD diagnosis and treatment | No | 0.00 | 25.00 |
| Equipment | Stethoscope | Yes | 3.57 | 25.00 |
| Equipment | Stethoscope | No | 0.00 | 25.00 |
| Equipment | Blood pressure apparatus | Yes | 3.57 | 25.00 |
| Equipment | Blood pressure apparatus | No | 0.00 | 25.00 |
| Equipment | Adult scale | Yes | 3.57 | 25.00 |
| Equipment | Adult scale | No | 0.00 | 25.00 |
| Equipment | Measuring tape (height board/stadiometer) | Yes | 3.57 | 25.00 |
| Equipment | Measuring tape (height board/stadiometer) | No | 0.00 | 25.00 |
| Equipment | Oxygen | Yes | 3.57 | 25.00 |
| Equipment | Oxygen | No | 0.00 | 25.00 |
| Equipment | Peak flow meter apparatus | Yes | 3.57 | 25.00 |
| Equipment | Peak flow meter apparatus | No | 0.00 | 25.00 |
| Equipment | Spacers for inhalers | Yes | 3.57 | 25.00 |
| Equipment | Spacers for inhalers | No | 0.00 | 25.00 |
| Diagnostics | Blood glucose | Yes | 8.33 | 25.00 |
| Diagnostics | Blood glucose | No | 0.00 | 25.00 |
| Diagnostics | Urine protein | Yes | 8.33 | 25.00 |
| Diagnostics | Urine protein | No | 0.00 | 25.00 |
| Diagnostics | Urine glucose | Yes | 8.33 | 25.00 |
| Diagnostics | Urine glucose | No | 0.00 | 25.00 |
| Medicines and commodities | Metformin cap/tab | Yes | 1.67 | 25.00 |
| Medicines and commodities | Metformin cap/tab | No | 0.00 | 25.00 |
| Medicines and commodities | Glibenclamide cap/tab | Yes | 1.67 | 25.00 |
| Medicines and commodities | Glibenclamide cap/tab | No | 0.00 | 25.00 |
| Medicines and commodities | Insulin regular injectable | Yes | 1.67 | 25.00 |
| Medicines and commodities | Insulin regular injectable | No | 0.00 | 25.00 |
| Medicines and commodities | Glucose 50% injectable | Yes | 1.67 | 25.00 |
| Medicines and commodities | Glucose 50% injectable | No | 0.00 | 25.00 |
| Medicines and commodities | Gliclazide tablet or glipizide tablet (only collected in Bangladesh HFS) | Yes | 1.67 | 25.00 |
| Medicines and commodities | Gliclazide tablet or glipizide tablet (only collected in Bangladesh HFS) | No | 0.00 | 25.00 |
| Medicines and commodities | ACE inhibitor (e.g., enalapril, lisinopril, ramipril, perindopril) | Yes | 1.67 | 25.00 |
| Medicines and commodities | ACE inhibitor (e.g., enalapril, lisinopril, ramipril, perindopril) | No | 0.00 | 25.00 |
| Medicines and commodities | Hydrochlorothiazide tablet or other thiazide diuretic tablet | Yes | 1.67 | 25.00 |
| Medicines and commodities | Hydrochlorothiazide tablet or other thiazide diuretic tablet | No | 0.00 | 25.00 |
| Medicines and commodities | Beta blocker (e.g., bisoprolol, metoprolol, carvedilol, atenolol) | Yes | 1.67 | 25.00 |
| Medicines and commodities | Beta blocker (e.g., bisoprolol, metoprolol, carvedilol, atenolol) | No | 0.00 | 25.00 |
| Medicines and commodities | Calcium channel blockers (e.g., amlodipine) | Yes | 1.67 | 25.00 |
| Medicines and commodities | Calcium channel blockers (e.g., amlodipine) | No | 0.00 | 25.00 |
| Medicines and commodities | Aspirin cap/tab | Yes | 1.67 | 25.00 |
| Medicines and commodities | Aspirin cap/tab | No | 0.00 | 25.00 |
| Medicines and commodities | Salbutamol inhaler | Yes | 1.67 | 25.00 |
| Medicines and commodities | Salbutamol inhaler | No | 0.00 | 25.00 |
| Medicines and commodities | Beclomethasone inhaler | Yes | 1.67 | 25.00 |
| Medicines and commodities | Beclomethasone inhaler | No | 0.00 | 25.00 |
| Medicines and commodities | Prednisolone cap/tab | Yes | 1.67 | 25.00 |
| Medicines and commodities | Prednisolone cap/tab | No | 0.00 | 25.00 |
| Medicines and commodities | Hydrocortisone injection | Yes | 1.67 | 25.00 |
| Medicines and commodities | Hydrocortisone injection | No | 0.00 | 25.00 |
| Medicines and commodities | Epinephrine injectable | Yes | 1.67 | 25.00 |
| Medicines and commodities | Epinephrine injectable | No | 0.00 | 25.00 |
| Total NCD readiness index score | Total NCD readiness index score | Total NCD readiness index score | Total NCD readiness index score | 100.00 |
Service readiness scores for ANC and NCD were calculated by adding the presence of domain-specific indicators, providing equal weight to each domain and each indicator within the domain. As there were four domains in both ANC and NCD, each domain accounted for $25\%$ of the readiness score. The weighting for each indicator within the domain was equal to $25\%$ divided by the number of indicators in the specific domain. The details of the score calculation is provided in Table 2. The scores for ANC and NCD joint service readiness for each facility were calculated by summing the percentages. Health facilities with scores of $50\%$ and above for both ANC and NCD services were considered ready for providing both ANC and NCD services, while those scoring less than $50\%$ for any of the two services were not considered ready [23, 26]. Thus, a binary variable of readiness to provide ANC and NCD services (Yes/No) was considered as the outcome variable for this study.
## Independent variables
The independent variables for the study were facility managing authority (public or private/NGO), location (rural/urban), routine quality assurance activities (performed/not performed), system to obtain client feedback (No/Yes), external supervision (occurred/ did not occur in previous 4 months), and regular monthly managing meetings (No/Yes). Location of the facilities (rural and urban) were classified as the rural areas (rural municipality) and urban areas (metropolitan/sub metropolitan city and municipality). Routine quality assurance activity was categorized as “Performed” for facilities routinely conducting quality assurance activities as documented by report of quality assurance activities, and “Not performed” for those without quality assurance activities. External supervision measured whether health facilities had received external supervision in the past three months or not. The management level independent variable was categorized as “Yes’’ for facilities that performed monthly management meetings and “No” for facilities which did not conduct such meetings at least monthly. Regular management meetings are not reported in the BHFS, and is not included in the current analysis for Bangladesh. The selection of independent variables was informed by the literature on facility readiness [26–29].
## Data analysis
Most variables reported in this study were categorical, and are summarized using proportions and then presented in a table for each country. The relationship of the outcome variable–readiness for integrating ANC and NCD services–with the defined independent variables was analyzed using binary logistic regression models. Unadjusted and adjusted odds ratio (OR) was used, and the p-value and $95\%$ confidence interval (CI) for the odds ratios (OR) was used to measure the significance level. A p-value of 0.05 or lower was considered a statistically significant association. Before fitting the model, correlations between independent variables and the outcome variable were checked, with no significant correlations observed, hence all independent variables were included. Data were analyzed using STATA 15.0. The complex sample design used in the SPA surveys was accounted for by using the “svy” command in the STATA software. Sampling weights were used to correct for non-responses and disproportionate sampling.
## Ethical considerations
This study analyzed existing SPA survey data sets that are freely available upon request. SPA survey protocols undergo ethical review by the United States ICF’s institutional review board. These surveys also undergo ethical review in their respective countries.
## Background characteristics of selected health facilities
Table 3 shows the characteristics of health facilities according to the distribution of covariates used in this study, which included 1565 health facilities in Nepal, and 512 in Bangladesh. In Nepal, most health facilities ($92.6\%$) were managed by the public authority, while in Bangladesh $79.2\%$ were managed by public authority. About $79\%$ of health facilities in Bangladesh were located in urban areas, whereas in Nepal, health facilities were evenly split between rural and urban. Around one fifth of health facilities in Nepal and Bangladesh reported undertaking quality assurance activities. About half of the facilities in Nepal ($54.1\%$) and Bangladesh ($46.4\%$) had systems to obtain client feedback. In the past four months, a higher proportion of facilities were supervised externally; $87.9\%$ in Bangladesh, and $66.2\%$ in Nepal. About two thirds ($64.0\%$) of the health facilities in Nepal reported regular monthly management meetings (Table 3).
**Table 3**
| Variable | Nepal n (%) | Bangladesh n (%) |
| --- | --- | --- |
| Managing Authority | | |
| Public | 1448 (92.6) | 402 (79.2) |
| Private/NGO | 116 (7.4) | 106 (20.8) |
| Location | | |
| Rural | 730 (46.7) | 404 (78.9) |
| Urban | 834 (53.3) | 108 (21.1) |
| Routine quality assurance | | |
| Not performed | 1201 (76.7) | 409 (80.0) |
| Performed | 364 (23.3) | 102 (20.0) |
| System to obtain client feedback | | |
| No | 718 (45.9) | 274 (53.6) |
| Yes | 847 (54.1) | 237 (46.4) |
| External supervision in the last 4 months | | |
| Did not occur | 529 (33.8) | 62 (12.0) |
| Occurred | 1036 (66.2) | 450 (87.9) |
| Regular monthly management meetings | | |
| No | 564 (36.0) | - |
| Yes | 1001 (64.0) | - |
| Total | 1565 | 512 |
## Availability and readiness for ANC, NCD and joint readiness for ANC and NCD services
Table 4 presents the distribution of the availability of, and readiness for providing, ANC and NCD services among study health facilities. Most health facilities in Nepal ($98\%$) and Bangladesh ($97\%$) reported offering ANC services, while $72.0\%$ of health facilities in Nepal and $35.1\%$ in Bangladesh reported offering NCD services. After combining these services, $70.9\%$ in Nepal, and $34.1\%$ in Bangladesh reported offering both ANC and NCD services.
**Table 4**
| Indicators | Nepal (N = 1565) | Nepal (N = 1565).1 | Bangladesh (N = 512) | Bangladesh (N = 512).1 |
| --- | --- | --- | --- | --- |
| Service availability | n | % (95% CI) | n | % (95% CI) |
| ANC | 1538 | 98.4 (97.7–98.8) | 494 | 96.5 (94.7–97.7) |
| NCD | 1127 | 72.0 (68.7–75.1) | 180 | 35.1 (32.1–38.1) |
| Both ANC and NCD | 1109 | 70.9 (67.6–74.0) | 174 | 34.1 (31.2–37.2) |
| Domains of ANC readiness | | | | |
| Staff and guidelines | 293 | 18.7 (16.6–20.8) | 178 | 34.8 (32.4–37.2) |
| Equipment | 1498 | 95.7 (94.3–97.1) | 459 | 89.6 (87.0–92.1) |
| Diagnostics | 423 | 27.0 (24.3–29.7) | 172 | 33.6 (30.8–36.5) |
| Medicine and commodities | 1166 | 74.5 (73.2–75.7) | 306 | 59.7 (58.1–61.3) |
| Domains of NCD readiness | | | | |
| Staff and guidelines | 174 | 11.1 (9.4–12.7) | 29 | 5.7 (5.0–6.5) |
| Equipment | 806 | 51.5 (50.3–52.7) | 200 | 39.0 (37.0–41.0) |
| Diagnostics | 338 | 21.6 (19.4–23.9) | 105 | 20.5 (18.0–22.9) |
| Medicines and commodities | 457 | 29.2 (28.2–30.2) | 38 | 7.5 (6.5–8.5) |
| Overall facility readiness | | | | |
| ANC readiness | 836 | 53.4 (52.4–54.5) | 278 | 54.4 (53.0–55.9) |
| NCD readiness | 444 | 28.4 (27.4–29.3) | 93 | 18.2 (17.1–19.3) |
| Readiness for both ANC and NCD | 379 | 24.2 (21.4–27.2) | 83 | 16.3 (14.1–18.7) |
There was substantial variation in the readiness for providing ANC and NCD services across the study countries. Health facilities in both countries showed lower scores in staff and guidelines for ANC service ($34.8\%$ in Bangladesh, and $18.7\%$ in Nepal). Compared to Bangladesh ($33.6\%$), the readiness of the diagnostics components of the ANC services was lower in Nepal ($27.0\%$). In terms of ANC service readiness, health facilities were in general strong in the equipment domains. A higher proportion of facilities in Nepal were ready in terms of medicines/commodities ($70.9\%$) compared to Bangladesh ($59.7\%$) (Table 4).
Regarding NCD service readiness, fewer study facilities had readiness in the staff and guidelines domains ($5.7\%$ in Bangladesh and $11.1\%$ in Nepal). Around half of the facilities in Nepal ($51.5\%$) and $39.0\%$ in Bangladesh had readiness in the equipment domain. The proportion of facilities showing NCD service readiness in the diagnostics domain was $21.6\%$ in Nepal, and $20.5\%$ in Bangladesh. A higher proportion of facilities in Nepal ($29.2\%$) compared to Bangladesh ($7.5\%$) had readiness in terms of medicines and commodities (Table 4).
Table 4 also shows the overall facility readiness for ANC, NCD and joint readiness for both ANC and NCD services. The overall readiness to offer specific services for ANC and NCD among the study facilities was $53.4\%$ and $28.4\%$ respectively in Nepal, and $54.4\%$ and $18.2\%$ respectively in Bangladesh. In terms of readiness for providing both ANC and NCD services, $24.2\%$ of the study facilities in Nepal, and $16.3\%$ in Bangladesh were ready to provide both services.
Availability and readiness of services were compared across the four groups: only ANC, only NCD, both ANC and NCD, and neither ANC nor NCD (Table 5). Facilities having availability and readiness for only ANC was $27.4\%$ and $44.2\%$ respectively in Nepal, while $62.4\%$ and $55.3\%$ facilities in Bangladesh had only ANC service availability and readiness respectively. Few facilities were providing only NCD service in both countries. The availability and readiness for both ANC and NCD was higher in Nepal, compared to Bangladesh. Nearly one-third of the facilities in Nepal ($31.1\%$) and Bangladesh ($28.4\%$) were not ready to provide ANC or NCD.
**Table 5**
| Unnamed: 0 | Nepal (N = 1565) | Nepal (N = 1565).1 | Bangladesh (N = 512) | Bangladesh (N = 512).1 |
| --- | --- | --- | --- | --- |
| | Availability (%) | Readiness† (%) | Availability (%) | Readiness† (%) |
| Only ANC | 27.4 | 44.2 | 62.4 | 55.3 |
| Only NCD | 1.1 | 0.5 | 1.0 | 0.0 |
| Both ANC and NCD | 70.9 | 24.2 | 34.1 | 16.3 |
| Neither ANC nor NCD | 0.6 | 31.1 | 2.6 | 28.4 |
Fig 1 shows that NCD service availability and readiness among the facilities offering ANC services in Nepal ($$n = 1538$$) were $72.1\%$ and $28.3\%$ respectively, while in the case of Bangladesh ($$n = 494$$), NCD service availability and readiness within ANC facilities was $35.4\%$ and $18.5\%$ respectively.
**Fig 1:** *NCD service availability and readiness among facilities providing ANC service.*
## Factors associated with facility readiness
Table 6 presents the unadjusted and adjusted ORs using binary logistic regression to identify factors associated with facility readiness for both ANC and NCD services. Similar patterns were observed in both unadjusted and adjusted analyses. In bivariate logistic regression analyses, managing authority type, routine quality assurance, availability of system to obtain client feedback, external supervision conducted, and regular monthly management meetings were positively associated with readiness for ANC and NCD services in Nepal. For Bangladesh, all the covariates measured were associated with readiness for providing both services.
**Table 6**
| Variables | Nepal (N = 1565) | Nepal (N = 1565).1 | Bangladesh (N = 512) | Bangladesh (N = 512).1 |
| --- | --- | --- | --- | --- |
| Variables | Unadjusted OR (95%CI) | Adjusted OR (95%CI) | Unadjusted OR (95%CI) | Adjusted OR (95%CI) |
| Managing authority | | | | |
| Public | Ref. | Ref. | Ref. | Ref. |
| Private/NGO | 8.9*** (5.9–13.2) | 10.1***(6.5–15.7) | 12.6***(8.1–19.6) | 3.0***(1.7–5.4) |
| Location | | | | |
| Rural | Ref. | Ref. | Ref. | Ref. |
| Urban | 1.3 (0.9–1.8) | 0.9 (0.6–1.3) | 15.4***(9.5–24.9) | 4.7*** (2.6–8.3) |
| Routine quality assurance | | | | |
| Not Performed | Ref. | Ref. | Ref. | Ref. |
| Performed | 1.8** (1.3–2.7) | 1.9** (1.2–2.8) | 3.7***(2.5–5.5) | 2.3** (1.3–4.0) |
| System to obtain client feedback | | | | |
| No | Ref. | Ref. | Ref. | Ref. |
| Yes | 1.7**(1.2–2.5) | 1.3*(1.0–2.0) | 7.2***(4.5–11.5) | 2.7** (1.4–5.0) |
| External supervision in the last 4 months | | | | |
| Did Not Occur | Ref. | Ref. | Ref. | Ref. |
| Occurred | 1.4* (1.0–2.0) | 1.5 (0.9–2.2) | 0.5**(0.3–0.8) | 0.6 (0.3–1.0) |
| Regular monthly management meetings | | | | |
| No | Ref. | Ref. | - | - |
| Yes | 1.8**(1.3–2.7) | 1.5* (1.0–2.2) | - | - |
In the final adjusted models, some variations in covariates in terms of level of significance and the strength of association were observed across the study countries. Nepal showed that health facilities managed by the private sector or an NGO (aOR = 10.1; $95\%$ CI:6.5–15.7) compared to the public sector; facilities performing routine quality assurance (aOR = 1.9; $95\%$ CI: 1.2–2.8); having a system to obtain client feedback (aOR = 1.3; $95\%$ CI:1.1–2.0); and regular monthly management meetings (aOR = 1.8; $95\%$ CI: 1.0–2.2) were significantly associated with readiness of the integrated services.
In Bangladesh, facilities being managed by the private sector or an NGO (aOR = 3.0; $95\%$ CI:1.7–5.4) compared to the public sector, facilities located in urban locations (aOR = 4.7; $95\%$ CI:2.6–8.3) compared to rural locations, facilities performing routine quality assurance (aOR = 2.3; $95\%$ CI: 1.3–4.0), and having a system to obtain client feedback (aOR = 2.7; $95\%$ CI:1.4–5.0) were positively associated with readiness for providing ANC and NDC services (see Table 6).
In addition, we assessed whether the facilities that are ready for providing both ANC and NCD services differ from facilities that are ready for ANC only in each country. Most of the factors predicting readiness for only ANC services were similar to those predicting joint readiness in both countries (S1 Table). Health facilities managed by the private sector or an NGO, facilities undertaking routine quality assurance, and having a system to obtain client feedback were positively associated with service readiness for providing only ANC services. Differences were observed in external supervision and regular monthly management meetings. Facilities supervised in the previous 4 months had higher odds for ANC only readiness index, while regular monthly management meetings were not associated with ANC only service readiness in Nepal.
## Discussion
Considering the present and future burden of NCDs, it is imperative that the health care systems in LMICs prioritize strategies to prevent and control NCDs. Women being in close contact with primary health care for maternal health care services during pregnancy can become a bigger part of the solution. This study described service readiness and associated factors for both ANC and NCD services in two LMICs using recent survey data from SPAs.
The findings showed that $71\%$ facilities in Nepal, and $34\%$ in Bangladesh had service availability for both ANC and NCD. Similarly, $24\%$ of facilities in Nepal, and $16\%$ in Bangladesh showed readiness for providing integrated ANC and NCD services. Among the facilities providing ANC services, NDC service availability was observed in $72\%$ and $35\%$ of facilities in Nepal and Bangladesh respectively, while NCD service readiness was found among $28\%$ of facilities in Nepal and $19\%$ of facilities in Bangladesh. There was a higher proportion of facilities reporting NCD service availability and readiness in Nepal compared to Bangladesh and this may be related to the recent implementation of the Package of Essential Non-communicable Diseases (PEN) at the primary health care level as recommended by the WHO. Additionally, as the survey year was more recent in Nepal (2021 in Nepal and 2017 in Bangladesh), improvement in NCD services might have occurred in more recent times in Bangladesh.
In both countries, a smaller proportion of the health facilities reported having both ANC and NCD guidelines in place, having at least one health provider trained in both ANC and NCD services, and having a laboratory for ANC and NCD services. In terms of domains of ANC readiness, a large proportion of health facilities in both study countries lacked trained staff and guidelines, and diagnostics. In terms of NCD readiness, few facilities had adequate staff and guidelines in place ($11.1\%$ in Nepal, and $5.7\%$ in Bangladesh). Further, NCD service readiness of facilities in Nepal and Bangladesh was low in the domains of diagnostics, and medicine and commodities.
There is a substantial gap in the capacity of health facilities to provide both ANC and NCD services in the study countries. *In* general, gaps were particularly notable in the availability of adequately trained staff and guidelines, basic equipment, diagnostics, and medicines. The presence of guidelines and standard operating procedures coupled with trained staff is essential to providing quality health services. Facilities with clear guidelines are more likely to provide integrated health care services in LMICs [26, 30]. This study suggests that the proportion of health facilities with trained staff and service guidelines for both ANC and NCD was low across the two study countries. These results are consistent with previous research reporting poor service readiness for NCD in LMICs [29, 31–35].
There was variation in factors associated with the readiness for ANC and NCD services. *In* general, health facilities managed by the private sector or an NGO, facilities undertaking routine quality assurance, and having a system to obtain client feedback (in both Nepal and Bangladesh) were positively associated with service readiness for providing integrated services. Facilities located in urban areas (in Bangladesh), and facilities undertaking regular management meetings (in Nepal) also had higher odds of readiness index. The findings suggesting poorer readiness of public facilities supported previous research showing better provision of NCD services in private facilities [36–38]. Previous studies have also reported deficits in the training and development of staff, and in equipment and medicine supplies in rural areas [39, 40]. In this study, the analysis of the association between external supervision and joint service readiness yielded somewhat unexpected results: there was no significant association in both Nepal and Bangladesh. Factors predicting readiness for only ANC services are similar to those predicting joint ANC and NCD readiness, with the exception of external supervision (associated with ANC only service readiness) and regular monthly management meetings (did not predict ANC only service readiness).
## Policy implications
Results from this study highlight the challenges for LMICs in providing an integrated ANC and NCD service. Consistent with the literature [10, 41], there is opportunity for LMICs to routinely screen women for common health conditions and NCDs that contribute to pregnancy-related complications, to identify and engage women requiring treatment and preventive care, and to provide ongoing support to both mother and child in the postpartum period and beyond to adopt a healthy lifestyle. This is crucial to prevention of intergenerational disease, and to addressing its long-term health and economic impacts.
Building innovative partnerships and multisectoral collaboration between services to support an integrated approach to better health care is also essential to mothers’ and newborns’ health, and to their subsequent long-term health outcomes. The analysis of readiness for both ANC and NCD services provides a basis that facilities in LMICs have in providing integrated services. For successful integration of ANC and NCD services, health planners and policymakers should ensure adequate availability of skilled personnel, policy, guidelines and standards, and diagnostics, medicines, and commodities. Evidence provided in this study highlights the gap in joint readiness for integrating ANC and NCD services and the need to strengthen capacity in both Nepal and Bangladesh. There is also a need for policymakers, and service providers to ensure that the health staff are suitably trained to provide person-centred, evidence-based, and culturally competent care. In addition, communities have a vital role to play, and strategies can include training and mobilization of community leaders, community-based organizations, traditional birth attendants, and volunteers, as well as health campaigns, school-based health promotion and home-based care [42].
## Strengths and limitations
To our knowledge, this is the first study to assess the availability and readiness for integration of ANC and NCD services in LMICs dealing with two countries. The study used data from the Demographic and Health Survey Program’s SPA, which is a representative and comprehensive nationwide sample survey of health facilities. The outcome variable “readiness for integration” was created based on indicators suggested in the WHO’s SARA framework. The provided estimates were adjusted and weighted to account for cluster sampling, non-response, and disproportionate sampling.
However, the study also had limitations, which should be considered. First, the SARA framework is designed to assess the underlying prerequisites of service quality [34] and the availability and readiness of integrated services; although these are the preconditions for quality care, they do not necessarily indicate that quality, competent care is actually being provided [43]. Second, this study considered only three NCDs–diabetes, cardiovascular diseases, and chronic respiratory diseases. The availability and readiness for services for other NCDs, such as cancer, mental health conditions, and kidney diseases, which are also prevalent in LMICs, may differ. Third, the nationally representative surveys from the two LMICs used in this study may limit the generalizability of the results to other settings. Fourth, although several important health facility- and management-related factors were included in the multivariate logistic regression models, important variables, such as the type of facility and insurance, were not included, as these were not uniformly measured or reported in the included surveys. Further, the lack of similar previous studies assessing facility readiness for ANC and NCD services limits the comparability of these findings with other similar studies.
## Conclusions
Service readiness for integration of ANC and NCD was weak in Nepal and Bangladesh, largely due to shortages in their trained health care workforces, an absence of guidelines and policy, and limited availability of diagnostics, medicines, and commodities. Strengthening the health care system is a priority to facilitate the integration of ANC and NCD services, and as such the above identified issues should be addressed to enable health services to provide integrated care at an acceptable level of quality.
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|
---
title: 'Body mass index and obesity-related behaviors in African American church-based
networks: A social network analysis'
authors:
- Soohyun Nam
- Sunyoung Jung
- David Vlahov
- Carl Latkin
- Trace Kershaw
- Robin Whittemore
journal: PLOS ONE
year: 2023
pmcid: PMC10010537
doi: 10.1371/journal.pone.0281145
license: CC BY 4.0
---
# Body mass index and obesity-related behaviors in African American church-based networks: A social network analysis
## Abstract
A growing body of research suggests that obesity can be understood as a complex and biobehavioral condition influenced by social relationships ─social networks. Social network analysis allows us to examine how an individual’s network characteristics (e.g., popularity) are associated with obesity and obesity-related behaviors. The objectives of the study were to (a) examine whether network members in African American churches are similar in body mass index (BMI) and obesity-related behaviors (physical activity, eating, alcohol consumption) and (b) examine whether an individual’s network characteristics, such as popularity (i.e., receiving nominations from peers) and expansiveness (i.e., sending nominations to peers) are associated with BMI and obesity-related behaviors. We used a cross-sectional study design and conducted social network analysis using Exponential random graph models with three African American church-based social networks (network A, B, and C, $$n = 281$$). There were no significant network members’ similarities on BMI in the three church-based networks. One out of three networks showed similarities in fruit and vegetable consumption (network B), fast food consumption (network C), physical activity, sedentary behaviors, and alcohol consumption (network A). African Americans with a high BMI were more popular, as were individuals with greater fat intake and alcohol consumption. Our findings support the perspective that we need to improve obesity-related behaviors by targeting influential individuals and existing ties and to develop obesity interventions using social networks. The degree to which our findings varied across churches also suggests that the relationship among an individual’s obesity-related behaviors and network characteristics should be understood in the unique social context.
## Introduction
Obesity and obesity-related conditions such as type 2 diabetes, cardiovascular disease, and some types of cancers disproportionately affect African Americans’ health and well-being [1, 2]. Compared to non-Hispanic Whites, African Americans also reported higher rates of physical inactivity and calorie dense food consumption, which are well known obesity-related behaviors [1, 3, 4]. The effects of alcohol consumption on obesity have been mixed and more nuanced depending on amount of alcohol consumption, age, gender and racial groups. However, in recent reviews, alcohol consumption has been linked to obesity and weight gain in cross-sectional and longitudinal studies, and was associated with other obesity-related behaviors such as physical activity [4–6].
Despite substantial public health efforts to reduce obesity, current behavioral interventions to address obesity in African Americans, a high-risk, underserved population have not been successful. A growing body of research has shown that obesity is a complex and biobehavioral condition that can best be understood in a social context going beyond an individual level, and a social (sociocentric) network approach may be a promising method [7, 8]. There is evidence that obesity-related behaviors such as patterns of physical activity, eating, and alcohol consumption may be shared through social networks [7, 9–11]. Behavioral interventions informed by an understanding of social networks associated with obesity-related behaviors may have potential to reduce obesity in African Americans [12].
In social network research, social networks include egocentric networks with an individual at the center −from the perspective of the individual − or sociocentric networks with entire networks within the boundary (i.e., ideally interviewing all connected individuals within the boundary). Egocentric network data are collected from respondents (index persons) about their network members without interviewing those network members. The egocentric network data are analyzed using conventional statistical methods (e.g., regression analysis). In this paper, we will present and discuss social network analyses and findings of the studies that used a sociocentric network approach. Table 1 shows key terms and definitions used in the paper.
**Table 1**
| Term | Definition |
| --- | --- |
| Egocentric | Network data collected from respondents about their contacts without interviewing those contacts. |
| Sociocentric (sociometric) | Network data collected from the boundary community. |
| Actor | A respondent in one of the African American church networks. |
| Node | An object that may or may not be connected to other objects in a network. In this study, nodes represent respondents who participated in the study (= actor). |
| Tie | A connection (link) between two nodes. |
| Density | The number of connections in the networks (ties present divided by number of possible ties) |
| Ego | The person whose network and behavior are being analyzed. |
| Alter | A person who is named as a friend by the ego. In other words, an actor who is connected to the ego who may influence the behavior of the ego. |
| Centrality | Centrality is the extent to which a person inhabits a critical position in the network. Centrality is a node or person-level measure (vs. “centralization” is the extent to which the network is focused on one or a few people and refers to a network-level measure) |
| Degree | The number of links to and from a person. |
| In-degree | Number of ties received. This a measure of the number of friendship tie nominations one receives and reflects a dimension of popularity. |
| Out-degree | The number of ties sent. This is an indicator of the general tendency to send friendship nominations, and reflects the actor’s expansiveness or sociality in a network. |
| Reciprocity | The tendency to have mutually reciprocated friendships among any two people (i.e., ties to go in both directions: from A to B and B to A). |
| Transitivity | The tendency to choose a friend of a friend as a friend (i.e., Friends of friends are friends) |
Social network analysis provides distinct measures and tools to understand the structure of networks (network-level analysis) and health behaviors of individual network members (individual-level analysis). First, network-level analysis examines structures of networks (e.g., density) and has potential application as a planning, diagnostic and evaluation tool in group or community-based interventions [8]. For example, if the network is sparse (i.e., low density) and not well connected, building networks may be necessary to increase group cohesion and effectively spread the effect of health interventions in the community. Second, while network-level analysis characterizes network structures to understand connectedness among people, and overall properties of the network, individual-level analysis may address unique research questions such as whether individual’s network characteristics (e.g., individual’s position in networks: central, periphery, bridge, isolate) are associated with his/her health outcomes including health behaviors [8].
Physical activity, eating, and alcohol consumption are “social behaviors” that people often share and are influenced by social norms, social learning, and social support—social influence [9, 13]. Through observing others’ behaviors and comparing to one’s own behaviors, people convey social influence by defining social norms about which behaviors are appropriate for a given social environment. Often the most important reference group for an individual is his/her social networks [14]. Members of social networks such as peers or family members learn not only from their own experience but also by modeling or imitating other’s behavior—social learning [14]. Another prominent feature of social network influences is social support. Network members provide one another social support ─ emotional, information, financial and material support ─which is highly associated with health outcomes [14].
Social selection (homophily: ‘birds of a feather flock together’) is another potential social process that may be associated with obesity or obesity-related behaviors [15]. This conception is that people tend to cluster together based on shared outcomes or beliefs. For example, people may select friends based on the similarity (e.g., race) or similar behaviors (e.g., smoking). In a recent systematic review of social network studies, individuals with similar body weight status or weight-related behaviors were more likely to share a network tie (social relationship) than individuals with dissimilar traits [16].
ERGMs are relatively new statistical models for expressing structural properties of social networks and are used to identify whether the particular configuration of ties that occur more or less than would be expected at chance, given the number of nodes and density of the network by generating simulated networks [17]. The most frequently used centrality measure of social networks is degree (Table 1). In-degree counts the number of times a person is nominated by others in the network. In-degree identifies opinion leaders in a network and in friendship networks it indicates popularity [12]. Identifying opinion leaders or popular people is often important to promote behavior change in a group setting. Out-degree is the number of names a person provides in response to a network question (i.e., the number of close friends, the number of sexual partners). Out-degree is a useful indicator for personal attributes such as person’s socialness or sociality and often referred to as expansiveness [12]. Although little is known about how an individual’s popularity or expansiveness is associated with obesity-related behaviors in African American adults, social network studies of health behaviors in other populations have shown the relationship between popularity/expansiveness and health behaviors. In a longitudinal study of 5104 adolescents, those not connected to the rest of the network (neither popular nor sociable, i.e., isolates) were the most likely to use substances [18]. In a review of social networks in dietary behavior in youth, more popular adolescents tended to consume more unhealthy foods [19]. Youth with greater popularity or expansiveness reported engaging in more physical activity than their more isolated counterparts [11, 20].
With a growing interest in social networks and health research, a few studies have been conducted to examine body size norms and weight loss of Black and Hispanic adults in the context of social networks using an egocentric network approach [21, 22]. Also, several reviews have identified gaps in research regarding how social network properties were associated with obesity-related behaviors in adults [23, 24]. Among African Americans, the church has been a central community resource and a key setting for health intervention recruitment and participation [25]. In a recent study of African American church-based social networks, the importance of understanding social network structures for developing group-based health promotion programs at the network-level was demonstrated [26]. To date, however, no published study is available that examines how African American individual’s network characteristics (i.e., individual-level network analysis) are associated with their body mass index (BMI) and obesity-related behaviors using a sociocentric network approach. Individual-level network analysis may provide potential tools to identify and target key players in the networks to enhance the effect of future group- or community-based obesity interventions. Most studies of social networks and health outcomes have reported findings from predominantly white populations, including children.
The purposes of the study were twofold: (a) to examine whether network members in African American churches are similar in BMI and obesity-related behaviors (physical activity, eating, alcohol consumption) and (b) to examine whether an individual’s network characteristics such as popularity and expansiveness (i.e., sociality) are associated with BMI and obesity-related behaviors. Our hypothesis is: (a) network members in African American churches will be similar in their BMI and obesity-related behaviors. Exploratory hypotheses are: (b) popularity or expansiveness will be positively associated with higher BMI, more physical activity, unhealthy eating, and alcohol consumptions.
## Design, participants and procedures
We used a cross-sectional study design to examine the network structure and individuals’ network characteristics and their relationships with BMI and obesity-related behaviors within three African American church congregations in New England urban area. Eligibility criteria for participants included the following: (a) men or women over 21 years of age, (b) self-reported Black or African American, and (c) able to speak and read English. We excluded individuals who reported disabilities or acute/terminal conditions (e.g., terminal cancer, dialysis) that affect daily physical activity, or active psychiatric illnesses such as thought disorders. Informed consent was obtained from all individual participants included in the study. Eligible and consenting participants completed self-administered surveys (30–40 minutes) and anthropometric measurements and received a $30 gift card. All study protocols were reviewed by the Yale University Institutional Review Board prior to study implementation.
We collaborated with Yale University Center for Clinical Investigation (National Institute of Health, Clinical and Translational Science Award), “Community Partnership” working group, which comprised local ministers and church leadership groups. Black community leaders from the local chapter of the National Association for the Advancement of Colored People also provided guidance for the recruitment plan. We presented the study purpose, described study-related activities, and discussed effective recruitment strategies in small group meetings in the churches such as bible studies, prayer meetings, and choir group practices. Because church rosters were not available to us, we visited each church every Sunday consecutively for 4–6 months per church to obtain optimally complete sociocentric network data by identifying people who either had already participated or declined to participate. Pastors and the study’s principal investigator announced the study at a Sunday service to initiate recruitment and then every 3–4 weeks to encourage participation. We enrolled and collected data every Sunday and some weekdays at the church from May 2015 to June 2016. The seating capacity for Sunday worship of the three churches (network A, B, & C) was approximately 150, 170, and 230 people. The range of Sunday worship attendance was from 80 to 170 per church including children. Approximately $75\%$ of church congregations from each church participated in the study based on the mean Sunday worship attendance rates (97.3, 126, and 151.3 people).
## Statement regarding ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
## Sociodemographic characteristics
Sociodemographic data included age, gender, educational level, and annual household income.
## Anthropometric characteristics
We weighed each participant on a calibrated digital scale and measured height using a portable stadiometer in a private space at the church. BMI was calculated as weight (kg)/height squared (m2). Percent body fat was estimated using the same digital scale that measures foot-to-foot bioelectric impedance. Waist circumference was taken at the narrowest part of the torso, at the end of a normal expiration. Hip circumference was taken around the buttocks in a horizontal plane at the level of maximal extension of the buttocks. The average of three measurements was calculated, and inter- and intra-observer reliability were checked [27].
## Obesity-related behaviors
Diet behaviors were measured by following validated instruments. The All-Day Screener of the Eating at America’s Table Study was used to assess daily fruit and vegetable intake [28]. The All-day Screener consisted of 19 items on the frequency and portion size of fruits and vegetables consumed over the past month (“Never = 0” to “≥5 items/day = 9”). The portion size was rated on a 4-point scale depending on each fruit or vegetable type (e.g., from < $\frac{3}{4}$ cup [= 1] to > 2 cups [= 4]). The daily fruit and vegetable intake were calculated by the scoring method of the National Cancer Institute (NCI) using the times/day and cup equivalents of the 2005 MyPyramid for each portion size category; a higher score indicates more daily fruit and vegetable intake. Percentage energy from fat intake was measured by the 13-item NCI Fat Screener [29]. Participants chose the frequency of a particular food or food group, ranging from 1 (never) to 8 (≥2 /day). The mean fat daily intake was estimated by a scoring algorithm, using the values for the age-gender specific portion sizes and the regression coefficients developed by NCI; a higher score indicates greater percentage energy from fat. Fast food consumption was assessed by asking how many times food was purchased at a restaurant where food was ordered at a counter or at a drive-through window in the past month (from never or rarely = 1 to 3 ≥/day = 9), with a higher score indicating higher fast food consumption.
The 8-item Paffenbarger Physical Activity Questionnaire (PPAQ) was used to capture habitual physical activities, structured exercise, and sedentary activities [30]. A physical activity index (PAI) provides an estimate of weekly energy expenditure by taking the sum across all items after multiplying the kcal/day score for each activity [31]; a higher score indicates more energy expenditure. Total weekly activity (TWA) was measured by the number of hours in a typical weekday and weekend day subjects spent sleeping, engaged in quiet sitting activity, in light activity, moderate activity, or vigorous activity. Then a metabolic equivalents (MET) score was assigned by the intensity of each activity: vigorous = 7 MET, moderate = 4.5 MET, light = 3 MET, sitting = 1 MET, and sleeping = 0.8 MET. Thus, the TWA scale is expressed in units of MET*h per week. Overall scores for TWA were calculated by taking a sum across all items of weekday and weekend; a higher score indicates more total weekly activities. Sedentary activity was measured by the number of hours spent on eating, reading, desk work, watching TV/movies, listening to a radio, and playing video/computer games in a typical weekday and weekend. An overall score for sedentary activity was calculated by taking a mean of the weekday and weekend scores. The validity and reliability of the PPAQ has been supported in many studies [31, 32].
Alcohol consumption was asked by “How many days did you drink more than a glass of alcohol during the last month?” ( A glass of alcoholic beverage means one can of beer, a glass of wine, a glass of cocktail, etc) and “How often do you have five or more drinks on one occasion?”
## Social networks
A name generator survey was used to elicit actors’ social networks within the church [12]. A name generator survey asked about actors’ network members who provided emotional, financial, and informational support within the church; network members with whom the individual gets together to socialize or have fun doing things such as shopping, going to the movies or clubs, or just hanging out; network members who the individual sought advice from; type and frequency of shared activities per week or month. Participants were then asked to list the names and relationships of their network members within the church. At the end of the name generator survey, we provided an example table with five rows as a guide. However, if participants wanted to report more or less than five people (their network members), they were instructed to list as appropriate. Based on the name generator answers, an N by N adjacency matrix for each church (= each network) was created, where N is the number of participants in the network. If participant i named participant j as a network member, then the i,j entry in the matrix was a “one”, and all other entries were “zero.” Thus, each row of the matrix corresponds to a particular participant i, called an ‘‘ego,” and each ego is surrounded by his or her local ‘‘alters”: other actors in the network with their own attributes, network characteristics, and behaviors, indexed by the subscript j, corresponding to the columns in the adjacency matrix.
## Statistical analysis
Descriptive statistics were conducted to summarize sample characteristics and study variables. Differences among participants of the three churches regarding demographics, BMI, diet behaviors, physical activity, and alcohol consumption were examined with one-way analysis of variance (ANOVA) for continuous variables and with χ2 tests for categorical variables. For all analyses,.05 was used as the significance level (two-tailed). To determine whether there was the propensity of an individual’s attributes (i.e., BMI, obesity-related behaviors) associated with the formation of network ties, we conducted ERGMs with Markov Chain Monte Carlo Maximum Likelihood estimates using PNet software, based on a fixed number of nodes and graph density [33]. By fixing the graph density, the number of arcs/edges will not change during estimation and facilitate the convergence for parameter estimation [17, 34].
## ERGMs
ERGM parameters represent a range of different tie configuration; for example, reciprocity (the extent to which ties are reciprocated) and transitivity (shared friendship; ‘friends of my friends are also my friends’)—each of which relates to specific structural processes between network ties and node level attributes [17]. That is, the formation of ties—the network structure—is assumed to be based on a structural (‘endogenous’) process such as reciprocity or transitivity, as well as on an ‘exogenous’ process (node [actor or individual] level attributes), including social selection [33]. In an ERGM, networks are treated as an endogenous process and actor attributes are treated as exogenous or explanatory variables that affect the presence of social ties [33]. Therefore, ERGMs can be used to describe a network structure and determine if individual attributes (node level characteristics) are associated with network structural properties [12]. The statistical models based on stochasticity assumptions estimate which effects, such as network structural properties and health behaviors, significantly explain the network structure to determine whether particular configurations of ties occur more or less than expected by chance, given the fixed number of nodes and density of the network and given other effects in the network model. A positive parameter suggests the effect is more prevalent and a negative parameter indicates that the effect is less prevalent than chance, given the other effects in the model [34].
“Actor-relation effects,” which refer to “the association of a particular attribute with a social relationship tie,” were analyzed with parameters of three types of effects: homophily, sender, and receiver effect [33]. Fig 1 shows the configurations and descriptions for the actor-relation effects used in this study [33].
**Fig 1:** *Tie configurations and description of individual-relation effects.Note: Based on Lusher, Koskinen, & Robins [33]; Snijders, Pattison, & Robins [46].*
To assess the homophily effect by demographic characteristics of linked ties, a matching (for categorical attributes) or interaction (for binary attributes) parameter was included in the model specification. The homophily effect of BMI and obesity-related behaviors were estimated by an absolute difference for an attribute between individuals who shared a directed tie; a significant negative estimate indicates that linked network members have a similarity on BMI or each obesity-related behavior (i.e., have less of a difference than expected by chance). The parameter for sender effects, which represents an association between out-degree (the number of social contacts named by an individual) and each obesity-related behavior were included for each continuous attribute; a significant positive estimate indicates that a high value on this attribute is associated with sending more ties (i.e., nominating a great number of network members). In other words, if the parameter estimate is positive and large, the parameter associated with expansiveness are more probable in the model. Similarly, receiver effects indicating an association between each continuous attribute and in-degree were also estimated; a significant positive value indicating a high value on this attribute is associated with receiving more ties. We also computed odds ratios (OR) for the ERGM parameter estimates to help in interpreting the magnitudes of associations relative to the other effects included in each model [34, 35].
In all the actor-relation effect models, we controlled the structural network parameters (i.e., reciprocity, popularity, and transitivity) [33] and significant actor-relation effects of demographics (i.e., age and gender) were also controlled.
The standard errors test the significance of the result by calculating a t-statistic. A parameter estimate greater than twice its standard error was considered statistically significant (commonly expressed as $p \leq 0.05$) [33]. Convergence of the estimation algorithm was assessed by a t-ratio (parameter observation-sample mean/standard error) [17]. Goodness-of-fit (GOF) was assessed by examining simulations of the observed networks generated from the estimated parameters using PNet [33]. The GOF t-ratios for the estimated parameters (e.g., reciprocity, transitivity) should be smaller than 0.1 in absolute value. For statistics of the graph features that were not specifically included in our models, the GOF statistics with a t-ratio smaller than the absolute value of 2.0 are considered as a reasonable model fit [33].
## Sociodemographic characteristics and obesity-related behaviors
Table 2 shows the sociodemographic characteristics and obesity-related behaviors from three church-based social networks (Network A, B, & C) with 281 African American men and women. The sample included $100\%$ self-identified African American and $32.4\%$ were currently married. About $89\%$ were either overweight (BMI 25–29.99 kg/m2) or obese (BMI ≥30 kg/m2). There was no significant difference in obesity-related behaviors among the three churches. However, significant differences were found in gender, income, mean BMI, and mean body fat percentage among the three churches.
**Table 2**
| Variables | Mean (SD) or N (%) | Mean (SD) or N (%).1 | Mean (SD) or N (%).2 | Mean (SD) or N (%).3 | F or χ2 | p |
| --- | --- | --- | --- | --- | --- | --- |
| Variables | Total (n = 281) | Network A (n = 113) | Network B (n = 95) | Network C (n = 73) | F or χ2 | p |
| Agea (years) | 52.8 (14.8) | 53.7 (16.8) | 52.9 (12.2) | 51.3 (14.8) | 0.58 | .56 |
| Genderb | | | | | 9.17 | .01* |
| Woman | 216 (76.9) | 94 (83.2) | 75 (78.9) | 47 (64.4) | | |
| Education levelb | | | | | 7.81 | .10 |
| High school graduate or below | 157 (55.9) | 59 (52.2) | 51 (53.7) | 47 (64.4) | | |
| College graduate | 62 (22.1) | 21 (18.6) | 25 (26.3) | 16 (21.9) | | |
| Graduate school or higher | 62 (22.1) | 33 (29.2) | 19 (20.0) | 10 (13.7) | | |
| Annual household incomeb | | | | | 15.07 | .02* |
| 0-$39,999 | 108 (38.4) | 35 (31.0) | 38 (40.0) | 35 (47.9) | | |
| $40,000-$79,999 | 76 (27.0) | 37 (32.7) | 24 (25.3) | 15 (20.5) | | |
| $80,000 or higher | 62 (22.1) | 33 (29.2) | 18 (18.9) | 11 (15.1) | | |
| Refused to answer | 35 (12.5) | 8 (7.1) | 15 (15.8) | 12 (16.4) | | |
| Anthropometrics characteristics | | | | | | |
| BMI (kg/m2) a | 32.0 (7.0) | 32.3 (7.2) | 33.1 (7.5) | 30.0 (5.5) | 4.36 | .01* |
| <25 | 32 (11.4) | 9 (8.0) | 11 (11.6) | 12 (16.4) | | |
| 25–29 | 76 (27.0) | 30 (26.5) | 21 (22.1) | 25 (34.2) | | |
| 30–39 | 136 (48.4) | 60 (53.1) | 44 (46.3) | 32 (43.8) | | |
| 40 or higher | 37 (13.2) | 14 (12.4) | 19 (20.0) | 4 (5.5) | | |
| Waist-hip ratio a | 0.9 (0.1) | 0.9 (0.1) | 0.9 (0.1) | 0.9 (0.1) | 0.95 | .39 |
| Body fat (%)a | 40.5 (10.2) | 41.1 (8.7) | 42.4 (10.2) | 37.1 (11.5) | 6.32 | .00* |
| Diet | | | | | | |
| Fruit/vegetable intake (servings/day) a | 3.3 (4.8) | 2.8 (2.3) | 4.2 (7.1) | 2.7 (3.5) | 2.87 | .06 |
| % energy from fat a | 33.4 (5.0) | 33.3 (4.4) | 33.7 (6.6) | 33.2 (3.3) | 0.21 | .81 |
| Fast-food consumption b | | | | | 2.75 | .07 |
| 2–3 times or less/month | 175 (62.3) | 66 (58.4) | 67 (70.5) | 42 (57.6) | | |
| 1 or more times/week | 89 (31.7) | 35 (31.0) | 25 (26.3) | 29 (39.7) | | |
| 1 or more times/day | 17 (6.0) | 12 (10.6) | 3 (3.2) | 2 (2.7) | | |
| Physical activity | | | | | | |
| PAI (kcal/week) a | 11,988.9 (2,917.2) | 11,593.8 (1,734.3) | 2,255.6 (3,299.3) | 22,253.5 (3,714.0) | 1.74 | .18 |
| TWA (MET*h/week) a | 406.6 (95.7) | 402.4 (92.0) | 402.7 (101.9 | 418.2 (93.3) | 0.72 | .49 |
| Sedentary activity (h/week) a | 41.1 (17.9) | 41.0 (19.2) | 41.6 (17.5) | 40.8 (16.6) | 0.44 | .96 |
| Alcohol (days/month) a | 2.3 (5.1) | 2.5 (5.7) | 1.8 (4.6) | 2.6 (4.8) | 0.67 | .52 |
## Network structural properties in African American church-based social networks
The sizes of three networks were 113, 95 and 73. Density (0.01, 0.01, 0.02), average degree (1.40, 0.60, 1.16), reciprocity (0.21, 0.14, 0.23), transitivity (0.08, 0.08, 0.14), clustering coefficient (0.21, 0.17, 0.20) and centralization (0.13, 0.05, 0.05) were similar among the three churches. The detailed network level findings have been published elsewhere [26]. Overall, $61\%$ of participants among the three churches reported that their network members were friends; $3\%$ were household members; and $17\%$ were non-household family members such as siblings and relatives. Significant reciprocity effects were shown in all three networks, indicating that the ties tended to be reciprocated between dyad. There were significant popularity effects in all three networks, meaning that nodes with high in-degrees (actors who received many nominations) tended to exist. All three networks also had significant transitivity effect, indicating that there was a tendency for ‘friends of my friends are also my friends’ (shared friendship). The tendencies that social networks form within similar age and gender groups after controlling for the aforementioned, significant structural effects, such as reciprocity, popularity, and transitivity were shown Fig 2.
**Fig 2:** *Effects of network structures and sociodemographic attributes on the church-based social networks by exponential random graph models (n = 281).Note: * Significant effect (i.e., parameter estimate is greater than two times the standard error in absolute value). SE; standard error. The parameter is the weight applied to the statistics (just as in a logistic regression with predictor variables and regression coefficient). An Exponential Random Graph Model (ERGM) assigns a probability to a graph by a sum of statistics weighed by parameters. For example, if the estimate of reciprocity parameter is large and positive, then graphs with many reciprocities are more probable in the graph distribution for that model. However, if the estimate of reciprocity parameter is large and negative, then graphs with fewer reciprocity become more probable under the model. Convergence of the estimation algorithm assessed by a t-ratio (parameter observation-sample mean/standard error): the absolute value should be < 0.1.*
## Effects of absolute difference, sender, and receiver on BMI and obesity-related behaviors
We controlled for the structural (‘endogenous’) effects, age, and gender found in the observed networks from the previous analyses (Fig 2). The GOF statistics for each network indicated that the models had a good fit with the t-ratios of an absolute value of less than 0.1 for the estimated parameters (the selected GOF details were provided in the supplementary material).
Table 3 shows estimates of actor-relation effects (absolute difference, sender, receiver) on BMI and obesity-related behaviors, controlling for significant structural effects (i.e., reciprocity, popularity, transitivity) and significant demographic homophily effects (i.e., age, gender).
**Table 3**
| Actor-relation effects | Network A | Network A.1 | Network A.2 | Network A.3 | Network A.4 | Network A.5 | Network B | Network B.1 | Network B.2 | Network B.3 | Network B.4 | Network B.5 | Network C | Network C.1 | Network C.2 | Network C.3 | Network C.4 | Network C.5 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Actor-relation effects | Estimate | SE | t-ratio | Odds ratio | 95% CI | 95% CI | Estimate | SE | t-ratio | Odds ratio | 95% CI | 95% CI | Estimate | SE | t-ratio | Odds ratio | 95% CI | 95% CI |
| Actor-relation effects | Estimate | SE | t-ratio | Odds ratio | Lower | Upper | Estimate | SE | t-ratio | Odds ratio | Lower | Upper | Estimate | SE | t-ratio | Odds ratio | Lower | Upper |
| Absolute difference effect | Absolute difference effect | Absolute difference effect | Absolute difference effect | Absolute difference effect | Absolute difference effect | Absolute difference effect | Absolute difference effect | Absolute difference effect | Absolute difference effect | Absolute difference effect | Absolute difference effect | Absolute difference effect | Absolute difference effect | Absolute difference effect | Absolute difference effect | Absolute difference effect | Absolute difference effect | Absolute difference effect |
| Body mass index | 0.05 | 0.06 | 0.09 | 1.05 | 0.94 | 1.18 | -0.06 | 0.14 | -0.01 | 0.94 | 0.72 | 1.24 | -0.07 | 0.12 | -0.03 | 0.93 | 0.74 | 1.18 |
| Diet behaviors | | | | | | | | | | | | | | | | | | |
| Fruit/vegetable intake | -0.18 | 0.12 | -0.07 | 0.84 | 0.66 | 1.06 | -1.14* | 0.47 | -0.04 | 0.32 | 0.13 | 0.80 | -0.16 | 0.11 | -0.01 | 0.85 | 0.69 | 1.06 |
| % energy from fat | -0.05 | 0.07 | -0.05 | 0.95 | 0.83 | 1.09 | -0.17 | 0.17 | 0 | 0.84 | 0.61 | 1.18 | 0 | 0.09 | 0.07 | 1.00 | 0.84 | 1.19 |
| Fast-food consumption | -0.11 | 0.07 | 0 | 0.90 | 0.78 | 1.03 | 0.13 | 0.1 | -0.03 | 1.14 | 0.94 | 1.39 | -0.25* | 0.12 | 0 | 0.78 | 0.62 | 0.99 |
| Physical activity | | | | | | | | | | | | | | | | | | |
| PAI (physical activity index) | -0.19* | 0.09 | 0.05 | 0.83 | 0.69 | 0.99 | 0.26 | 0.31 | -0.01 | 1.30 | 0.71 | 2.38 | -0.08 | 0.18 | 0.02 | 0.92 | 0.65 | 1.31 |
| TWA (total weekly activity) | -0.08 | 0.07 | 0.04 | 0.92 | 0.81 | 1.06 | 0 | 0.13 | -0.02 | 1.00 | 0.78 | 1.29 | -0.12 | 0.12 | 0.06 | 0.89 | 0.70 | 1.12 |
| Sedentary activity | -0.17* | 0.08 | 0.04 | 0.84 | 0.72 | 0.99 | -0.09 | 0.15 | -0.03 | 0.91 | 0.68 | 1.23 | -0.1 | 0.13 | -0.02 | 0.91 | 0.70 | 1.17 |
| Alcohol consumption | -0.32* | 0.09 | -0.02 | 0.73 | 0.61 | 0.87 | 0.23 | 0.51 | 0.02 | 1.26 | 0.46 | 3.42 | -0.39* | 0.17 | 0.07 | 0.68 | 0.49 | 0.95 |
| Sender effect | | | | | | | | | | | | | | | | | | |
| Body mass index | 0.07 | 0.08 | 0.05 | 1.07 | 0.92 | 1.26 | 0.06 | 0.14 | 0.01 | 1.06 | 0.81 | 1.40 | -0.17 | 0.12 | 0.02 | 0.84 | 0.67 | 1.07 |
| Diet behaviors | | | | | | | | | | | | | | | | | | |
| Fruit/vegetable intake | 0.15 | 0.11 | -0.05 | 1.16 | 0.94 | 1.44 | 1.26* | 0.45 | -0.05 | 3.53 | 1.46 | 8.52 | 0.30* | 0.11 | -0.02 | 1.35 | 1.09 | 1.68 |
| % energy from fat | -0.09 | 0.08 | -0.07 | 0.91 | 0.78 | 1.07 | 0.19 | 0.17 | 0.01 | 1.21 | 0.87 | 1.69 | -0.07 | 0.12 | -0.06 | 0.93 | 0.74 | 1.18 |
| Fast-food consumption | 0.05 | 0.09 | 0 | 1.05 | 0.88 | 1.25 | 0.04 | 0.13 | -0.08 | 1.04 | 0.81 | 1.34 | 0.21 | 0.14 | 0.03 | 1.23 | 0.94 | 1.62 |
| Physical activity | | | | | | | | | | | | | | | | | | |
| PAI (physical activity index) | 0.1 | 0.08 | 0.03 | 1.11 | 0.95 | 1.29 | -0.64* | 0.3 | 0.02 | 0.53 | 0.29 | 0.95 | 0.22 | 0.18 | 0.03 | 1.25 | 0.88 | 1.77 |
| TWA (total weekly activity) | -0.18* | 0.09 | 0.03 | 0.84 | 0.70 | 0.99 | -0.04 | 0.13 | -0.06 | 0.96 | 0.75 | 1.24 | 0.24 | 0.13 | 0 | 1.27 | 0.99 | 1.64 |
| Sedentary activity | 0.24* | 0.09 | -0.02 | 1.27 | 1.07 | 1.52 | -0.21 | 0.16 | 0.01 | 0.81 | 0.59 | 1.11 | 0.02 | 0.13 | -0.03 | 1.02 | 0.79 | 1.32 |
| Alcohol consumption | 0.21* | 0.09 | -0.06 | 1.23 | 1.03 | 1.47 | -0.11 | 0.5 | 0.03 | 0.90 | 0.34 | 2.39 | 0.18 | 0.16 | 0.08 | 1.20 | 0.88 | 1.64 |
| Receiver effect | | | | | | | | | | | | | | | | | | |
| Body mass index | -0.09 | 0.07 | 0.04 | 0.91 | 0.80 | 1.05 | 0.26* | 0.12 | 0.01 | 1.30 | 1.03 | 1.64 | 0.09 | 0.11 | 0 | 1.09 | 0.88 | 1.36 |
| Diet behaviors | | | | | | | | | | | | | | | | | | |
| Fruit/vegetable intake | -0.15 | 0.09 | 0 | 0.86 | 0.72 | 1.03 | -0.49 | 0.4 | -0.03 | 0.61 | 0.28 | 1.34 | 0.02 | 0.1 | -0.05 | 1.02 | 0.84 | 1.24 |
| % energy from fat | 0.14* | 0.07 | -0.05 | 1.15 | 1.10 | 1.32 | -0.05 | 0.14 | -0.06 | 0.95 | 0.72 | 1.25 | 0 | 0.12 | -0.02 | 1.00 | 0.79 | 1.27 |
| Fast-food consumption | 0.07 | 0.07 | 0.02 | 1.07 | 0.94 | 1.23 | -0.11 | 0.1 | -0.05 | 0.90 | 0.74 | 1.09 | -0.09 | 0.13 | 0.01 | 0.91 | 0.71 | 1.18 |
| Physical activity | | | | | | | | | | | | | | | | | | |
| PAI (physical activity index) | 0.06 | 0.07 | 0.04 | 1.06 | 0.93 | 1.22 | -0.09 | 0.29 | -0.02 | 0.91 | 0.52 | 1.61 | -0.06 | 0.16 | 0.02 | 0.94 | 0.69 | 1.29 |
| TWA (total weekly activity) | 0.06 | 0.07 | 0 | 1.06 | 0.93 | 1.22 | 0.02 | 0.11 | -0.02 | 1.02 | 0.82 | 1.27 | -0.14 | 0.12 | -0.01 | 0.87 | 0.69 | 1.10 |
| Sedentary activity | -0.1 | 0.07 | 0.09 | 0.91 | 0.79 | 1.04 | -0.08 | 0.13 | -0.02 | 0.92 | 0.72 | 1.19 | -0.15 | 0.12 | 0.01 | 0.86 | 0.68 | 1.09 |
| Alcohol consumption | 0.19* | 0.08 | -0.06 | 1.21 | 1.03 | 1.42 | -0.58 | 0.47 | -0.04 | 0.56 | 0.22 | 1.41 | 0.17 | 0.15 | 0.09 | 1.19 | 0.88 | 1.59 |
Our hypothesis that there would be similarities on BMI and obesity-related behaviors in African American church-based networks was tested by network parameters for absolute difference effects. Significant negative estimates in absolute differences (denoted by an asterisk [*] in Table 3) indicate that there were similarities on obesity-related behaviors. There were no significant BMI similarities among network members in all three churches. In Network B and Network C, network members tended to be alike on some of their diet behaviors; however, in Network A, there was no evidence that network members were alike on diet behaviors. Instead, in Network A, network members were found to engage in similar amounts of physical activity and sedentary activities. Similarities on alcohol consumption among network members were found in Network A (OR = 0.73, $95\%$ CI [0.61,0.87]) and C (OR = 0.68, $95\%$ CI [0.49,0.95]), indicating that social relationships were more likely to form when they had similar alcohol consumption status.
For sender effects (expansiveness) on an individual’s obesity-related behaviors, both Network B (OR = 3.53, $95\%$ CI [1.46,8.52]) and C (OR = 1.35, $95\%$ CI [1.09,1.68]) showed that individuals with higher fruit/vegetable intake were more likely to nominate a larger number of network members. Also, for Network A (OR = 0.84, $95\%$ CI [0.70,0.99]) and B (OR = 0.53, $95\%$ CI [0.29,0.95]), the negative and significant sender effects for physical activity indicated that individuals who were less engaged in physical activity tended to send more ties. In Network A, individuals with more sedentary behaviors (OR = 1.27, $95\%$ CI [1.07,1.52]) and higher alcohol consumptions (OR = 1.23, $95\%$ CI [1.03,1.47]) tended to send more ties.
We examined receiver effects (popularity) on an individual’s BMI and obesity-related behaviors. In Network A, individuals with a high percentage of energy from fat intake (OR = 1.15, $95\%$ CI [1.10,1.32]) and alcohol consumption (OR = 1.21, $95\%$ CI [1.03,1.42]) tended to be popular. For Network B, the positive and significant receiver effect for BMI indicated that individuals with higher BMI tended to receive more ties (popularity) (OR = 1.30, $95\%$ CI [1.03,1.64]). However, the receiver effect for BMI was not found in Network A and C. Also, there was no receiver effect on physical activity and sedentary behaviors in all three networks.
## Discussion
Substantial research of social and behavioral factors and health has focused on dyadic ties and used an egocentric network approach, obtaining data from the index person’s perception [36]. While these studies are valuable, a growing number of studies also suggest that understanding the role of social connections on health behaviors needs to take into account the complex structures of the social relationship ties beyond the examination of dyads and perceptions of index individuals in small groups [24, 37]. We conducted sociocentric networks analyses to examine whether social connections among African Americans were associated with similar BMI and health behaviors in church-based networks. To date, no published research has examined similarities on BMI and obesity-related behaviors among African Americans in church settings, the central hub of social life and community-based health research for African Americans [25].
In our African American church-based social networks, we did not find similarities in BMI among network members. African American adults in church-based networks were found to be similar in some obesity-related behaviors. One out of the three church-based networks showed similarities in fruit and vegetable consumption, fast food consumption, physical activity, and sedentary behaviors. Two of our three church-based networks showed that network members were alike in their alcohol consumption. To date, findings of homophily effect on BMI, health beliefs, and health behavior in social network studies are mixed by types (e.g., sorority, school) and sizes of networks and populations (e.g., race, gender, age) [38–40].
As shown in another study that compared multiple social networks within the study [41], our observed social networks were different from one another. This suggests that it cannot be taken for granted that adult social networks are based around universal behavioral similarities. The varied findings from the small number of networks sampled in our study should be interpreted in the social context, including church level differences in health-related social norms and church membership characteristics. All three churches were African-American Methodist denominations in New England area. Also, these three churches had community outreach groups within the congregation for health promotion. Although we controlled for significant individual’s attributes such as age and gender that may influence network tie formation within the network, there were significant differences in gender, income, mean BMI, and mean body fat across the three churches that may have influenced the degree to which our findings varied across the churches. With our currently available data, the relationship among behavioral homophily, social-environmental influences such as social inequalities (e.g., income, education), and network tie formation cannot be answered [42]. More information on social inequalities within the church and participants’ perceptions about how they build social relationships and adopt health behaviors is needed. The findings across our observed networks warrant mixed methods studies integrating qualitative perspectives. Also, findings suggest that future group-based obesity interventions—where social interaction among participants may change group dynamics and the effect of interventions—may need to be modified to address the local social circumstances.
Social norms about what is socially acceptable influence an individual’s health behaviors within a social network [12]. Although a single observation of social networks with our cross-sectional study design may not disentangle the mechanisms of social processes—social influence and social selection, some findings on BMI or obesity-related behaviors may reflect social cultural norms around body image, food (e.g., soul food) and health beliefs in the African American community [43, 44]. Our participants, African men and women with a high BMI, received popularity in one network. Participants with a high percentage of fat intake or alcohol consumptions were the most popular in one network. While heterogeneity within African American culture is also recognized [45], some of our findings are consistent with salient social norms and beliefs about large body size, social pressure around physical activity, and eating among African Americans [43, 44]. Future longitudinal studies are warranted to understand the underlying social processes that may be unique to local networks and culture in African Americans.
The current study benefited from methodological strengths, including objectively measured weight and height for BMI and comparing multiple church-based social networks using a sociocentric network approach. The study, however, is not without limitations. Limitations of this study, as shown in other social networks studies, are whether localized social process and network structures from observed networks are sufficient to explain global network properties. It may be difficult to investigate such questions without a case of global model resulting from combinations of many small-scale structures [17]. We investigated church-based social networks because churches are often considered to be hubs for providing culturally tailored group interventions and community-based health programs for African Americans [25]. Working with the African American community partners and church leadership groups, we respected their confidentiality concerns using rosters of congregations. Using a free-recall name generator rather than a roster method, which helps as a memory aid in nominating network members and for setting the boundary of networks, could have influenced our findings. Particularly, low average degree observed among the church networks may have been affected by this methodological limitation using a name generator. The small proportion of household family members ($3\%$) and various non-household familial relationships connected by blood vs. marriage made it difficult to analyze with a dyad-level variable to further explore genetic and environmental effects on obesity and obesity-related behaviors. The high prevalence of overweight and obesity among our participants, which reflect the national prevalence of overweight/obesity in African Americans, may not have had variability in BMI and influenced our findings on the relationships with BMI compared to social networks in other populations. We used self-reported, obesity-related behaviors; some of the health behaviors may be over- or underestimated resulting from social desirability [37].
Longitudinal research is also needed to tease apart network dynamics and social influence. Our findings may be influenced by both social selection where individuals adopt behaviors that are similar to those of their network members and other processes of social influence such as social norms, and social support. In any event, our findings support that we need to improve obesity-related behaviors and social norms around obesity by harnessing influential individuals and existing ties (e.g., reciprocated ties) and developing obesity programs with an understanding of social networks [45].
Based on substantial variations found in studies of social networks and health including the current study, it is also important to note that social context such as types, functions and structures of networks is a fundamental precursor to any observed relationship between social relational ties and health behaviors [42]. Studying social contexts within networks using mixed methods research and developing measures of social context relevant to social network and behavioral theories would facilitate future research to improve understanding of social networks and health.
In conclusion, emerging studies continue to demonstrate the notable implications of social networks on health. Our study is the first study that applied ERGMs to examine BMI and obesity-related behaviors among African Americans in church-based networks. The degree to which our findings varied across churches also suggests that the relationship among an individual’s obesity-related behaviors and social network characteristics should be understood in the unique social context and that the development of future obesity intervention may need to address local network circumstances.
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|
---
title: Non-invasive and objective tear film breakup detection on interference color
images using convolutional neural networks
authors:
- Yasushi Kikukawa
- Shin Tanaka
- Takuya Kosugi
- Stephen C. Pflugfelder
journal: PLOS ONE
year: 2023
pmcid: PMC10010540
doi: 10.1371/journal.pone.0282973
license: CC BY 4.0
---
# Non-invasive and objective tear film breakup detection on interference color images using convolutional neural networks
## Abstract
### Purpose
Dry eye disease affects hundreds of millions of people worldwide and is one of the most common causes for visits to eye care practitioners. The fluorescein tear breakup time test is currently widely used to diagnose dry eye disease, but it is an invasive and subjective method, thus resulting in variability in diagnostic results. This study aimed to develop an objective method to detect tear breakup using the convolutional neural networks on the tear film images taken by the non-invasive device KOWA DR-1α.
### Methods
The image classification models for detecting characteristics of tear film images were constructed using transfer learning of the pre-trained ResNet50 model. The models were trained using a total of 9,089 image patches extracted from video data of 350 eyes of 178 subjects taken by the KOWA DR-1α. The trained models were evaluated based on the classification results for each class and overall accuracy of the test data in the six-fold cross validation. The performance of the tear breakup detection method using the models was evaluated by calculating the area under curve (AUC) of receiver operating characteristic, sensitivity, and specificity using the detection results of 13,471 frame images with breakup presence/absence labels.
### Results
The performance of the trained models was $92.3\%$, $83.4\%$, and $95.2\%$ for accuracy, sensitivity, and specificity, respectively in classifying the test data into the tear breakup or non-breakup group. Our method using the trained models achieved an AUC of 0.898, a sensitivity of $84.3\%$, and a specificity of $83.3\%$ in detecting tear breakup for a frame image.
### Conclusions
We were able to develop a method to detect tear breakup on images taken by the KOWA DR-1α. This method could be applied to the clinical use of non-invasive and objective tear breakup time test.
## Introduction
Dry eye disease (DED) affects hundreds of millions of people worldwide and is one of the most common causes of visits to eye care practitioners. It is defined as a multifactorial disease of the ocular surface characterized by a loss of homeostasis of the tear film, and accompanied by ocular symptoms, in which tear film instability and hyperosmolarity, ocular surface inflammation and damage, and neurosensory abnormalities play etiological roles [1].
Age, gender, computer use, and contact lens wear are considered risk factors for DED. The prevalence of DED varies depending on the condition and region of the study, but it is generally high, with a reported prevalence of $6.8\%$ in adults of the United States, affecting approximately 16.4 million people [2, 3]. The effects of DED, such as reduced vision, quality of life, and work productivity, are considered to be an economic burden to society.
It is important to evaluate the stability of the tear film for DED diagnosis, and the fluorescein breakup time (FBUT) test has been widely used in clinical practice [4–6]. FBUT is measured as the time elapsed between a complete blink and the appearance of the first breakup in the tear film after sodium fluorescein is instilled into the test eye [7]. When performing FBUT instillation of fluorescein dye has been found to decrease the stability of the tear film [8, 9]. The quantity and concentration of fluorescein instilled during the test can also affect the FBUT measurement [7]. In addition, since the test is basically a manual and subjective measurement, it is difficult to obtain reproducible results and there is a tendency for inter-examiner variability. There are two issues with the FBUT test: it is subjective and invasive.
In recent years, there has been a lot of research on the use of artificial intelligence (AI) in the medical field, and an automated AI diagnostic system for diabetic retinopathy was approved by the United States Food and Drug Administration (USFDA) in 2018 [10]. Among AI technologies, convolutional neural networks (CNNs), which are one of the deep learning methods, have been attracting attention in image classification and identification, and many studies using CNNs have been reported in the field of eye care [11–14]. Su et al. developed an automatic method to measure FBUT using a CNN model that learned visual features of fluorescein stained images of the ocular surface to detect tear breakup [15].
The Tear Film and Ocular Surface Society (TFOS) international Dry Eye Workshop II (DEWS II) report recommends the measurement of non-invasive tear breakup time (NIBUT) as the test for tear film stability [7, 16]. There are several commercially available non-invasive test devices based on topographic or videokeratographic methods. These devices measure NIBUT by analyzing changes in the reflected placido mires projected on the ocular surface [7, 17].
The KOWA DR-1α video interferometer (Kowa Company, Ltd., Tokyo, Japan) is a device that allows non-invasive observation of the tear film dynamics of the entire cornea by projecting white light onto the tear film and using the optical interference color image created by the difference between the light reflected from the front surface of the lipid layer and the light reflected from the back surface (Fig 1) [18]. Since the KOWA DR-1α enables to observe tear breakup patterns equivalent to those observed with fluorescein staining from the images taken, it is possible to measure the TBUT subjectively by visual inspection [19]. The NIBUT measurement of commercially available devices detect tear breakup indirectly, whereas the KOWA DR-1α can directly observe tear breakup. Various studies have been conducted on the KOWA DR-1α, including tear lipid layer grading system, measurement of lipid layer thickness (LLT), measurement of tear meniscus height (TMH), and classification of dry eye subtypes, but no study has been reported on objective and automatic detection of tear breakup yet [7, 17, 18]. Several elements such as interference fringes and oil particles appear in the tear interference color images, which may require training and experience to subjectively detect tear breakup by visual inspection. Therefore, an image classification model that identifies the characteristics of tear interference color images was constructed using CNN.
**Fig 1:** *Overview of the KOWA DR-1α.(A) Appearance of the device. (B) Optical path diagram. (C) Principle of interference. (D) Example of interference color image. (E) Example of interference color image with tear breakup.*
We have developed a method to detect tear breakup on the tear film images taken by the KOWA DR-1α using our image classification model toward realizing a non-invasive and objective tear film stability test.
## Data collection
Retrospective review and analysis of study data was approved by the Institutional Review Board of Baylor College of Medicine (IRB No. H-51925). The need for informed consent was waived by the IRB because of the retrospective design of the study. It adhered to the tenets of the Declaration of Helsinki for clinical research.
The examinations for this study were conducted at Alkek Eye Center (Houston, Texas, USA) from September 30, 2019 to March 8, 2021. The exam was performed on both dry eye patients and healthy subjects and a total of 183 participants were included in the study. The diagnostic criteria for DED were a Symptom Assessment in Dry Eye (SANDE) score of >80 and a FBUT of <10 seconds. We used 350 eye data (female 272 and male 78, and dry eye 303 and healthy 47) from 178 participants (mean age 59.98±15.09 years, female 138 and male 40, and dry eye 153 and healthy 25), excluding 5 participants for insufficient data.
The KOWA DR-1α test was performed according to the NIBUT measurement on the DEWS II report, with the instructions to blink naturally three times and then keep the eye open as long as possible [7]. The tear interference color video was taken for 30 seconds by the KOWA DR-1α’s built-in camera. The video was recorded with a resolution of 640 x 480 pixels at 30 frames per second.
## Construction and evaluation of CNN model
We used the ResNet50 model which was pre-trained on the ImageNet dataset to build a CNN model for detecting characteristics of tear interference color images by performing transfer learning on images extracted from videos recorded by the KOWA DR-1α [20–22]. The ResNet50 model consisted of 49 convolutional layers and one fully-connected layer, and had over 23 million trainable parameters, so that this model architecture demonstrated successful performance when applied to image classification [20]. The model used in this study was pre-trained on the ImageNet including about 1,000 categories, and was a model from the machine learning library Keras [21]. For transfer learning, the final fully-connected layer was removed, the other layers were frozen and adopted as fixed feature extractor, and the new fully-connected layer was trained with the dataset prepared in this study using the softmax activation function so that the output was nine classes described below.
The image classification performed by our CNN model was defined to classify into nine classes, three breakup related classes and six non-breakup related classes by analyzing the characteristic elements in the tear interference color images. Although the shape and size of tear breakup vary from case to case, the image size to perform the image classification was defined as 96 x 96 pixels so that each class can be distinguished. The training data used to train the CNN model was created by using our dedicated software to select any frame image of the video and extracting a 96x96 pixels image patch of the area where the desired classification is displayed.
The breakup category was classified into three classes: Area pattern, Spot pattern, and Line pattern. In studies of tear breakup patterns (BUPs) observed by fluorescein staining, various BUPs have been reported based on the timing of occurrence, location and shape of tear breakup [23], but when BUPs are classified by shape, they can be summarized into three types. Therefore, since the purpose of this study is to detect tear breakup, we classified the tear breakup category into three classes. The Area pattern class was defined as an image with tear breakup where the corneal surface was appeared to be exposed due to thinning of the tear film. The Spot pattern class was defined as an image with circular breakup, and the Line pattern class was defined as an image with linear breakup. Example image patches of each tear breakup class are shown in Fig 2A. In addition, examples of the three tear breakup types observed in the tear interference color images taken with the KOWA DR-1α are shown in S1 Fig.
**Fig 2:** *Examples of image patches for each class of training data.(A) Tear breakup classes. (B) Non-breakup classes.*
The non-breakup category was classified into six classes: Uniform, Interference Fringe, Bright Reflection, Particle, Eyelash, and Eyelid. The Uniform class was defined as an interference image of the tear film in which the interference color was one color and there was no stripe pattern or it was too thin to be seen clearly, and the Interference Fringe class was defined as an interference image in which the interference stripe pattern could be seen clearly. The Bright Reflection class was defined as an image containing areas of higher brightness than the surrounding area, as seen in eyes with intraocular lenses, and the Particle class was defined as an image containing black objects such as oil particles or debris. The Eyelash class was defined as an image containing the upper or lower eyelashes, and the Eyelid class was defined as an image containing a part of the upper or lower eyelids or the circular camera mask. Example image patches of each non-breakup class are shown in Fig 2B.
Strong interference fringes were sometimes difficult to distinguish from the Line pattern, so the Interference Fringe class was established as one independent class in our image classification. In addition, the strong reflections observed in eyes with intraocular lenses could be misclassified as the Spot pattern, so the Bright Reflection class was established as an independent class.
The training data was created by three engineers who had previously trained the tear breakup detection criteria with a specialist. A total of 9,089 image data of nine classes was prepared as shown in Table 1.
**Table 1**
| category | breakup | breakup.1 | breakup.2 | non-breakup | non-breakup.1 | non-breakup.2 | non-breakup.3 | non-breakup.4 | non-breakup.5 | total |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| class | Area pattern | Spot pattern | Line pattern | Uniform | Interference Fringe | Bright Reflection | Particle | Eyelash | Eyelid | total |
| number of images | 643 | 684 | 877 | 1099 | 1201 | 1046 | 1230 | 1119 | 1190 | 9089 |
The training of our CNN model was performed in a six-fold cross-validation. We divided the training data into six groups so that images extracted from the same video belonged to the same group, and that there was no class bias among the groups (Tables 2 and 3).
As a preprocessing for training, data augmentation was performed to increase the diversity of the training data [24]. Specifically, scaling, horizontal and vertical shifting, and horizontal flipping were applied to the images of the training data. Vertical flipping was not applied because some of the breakup patterns have vertical shape characteristics. The images of the training data were rescaled to a resolution 224 x 224 pixels to match the input size of the CNN model. The training process was performed as follows. The optimization function was adam, the error function was categorical crossentropy, the activation function of the output layer was softmax, the batch size was 20, and the number of cycles was 1,000 epochs. The model when the accuracy of the validation data was maximized in the training process was adopted as the training result. The training process was conducted using two workstation PCs, one with an NVIDIA Quadro P200 GPU, and the other with an NVIDIA Quadro P2200 GPU. The ResNet50 model used in this study was implemented using Keras version 2.0.8 and Tensorflow-1.10.0-gpu. Our training program was written in the Python programming language (Python 3.5 Python Software Foundation).
The trained models were evaluated for image classification performance using the results of the test data in the six-fold cross validation. Accuracy, recall, precision, and F1-score were used as performance metrics.
## Method and evaluation of tear breakup detection using CNN model
The detection procedure for tear breakup on a frame image of the KOWA DR-1α video using the trained CNN model was as follows. A 384 x 384 pixels region near the center of a frame image, where almost the entire cornea was captured, was used for detection. The target region was divided into 96 x 96 pixels segmented regions with a stride length of 48 pixels. Adjacent segmented regions were overlapped by half a region to prevent missing detection (Fig 3). Each segmented region was classified by our CNN model. If the number of segmented regions classified as breakup (Area, Spot, or Line pattern) was equal to or greater than a certain number, the frame image was determined to have tear breakup.
**Fig 3:** *Method for tear breakup detection in a frame image.The target region for detection (384 x 384 pixels) is divided into 96 x 96 pixels segments with a stride length of 48 pixels, and each segment region is classified by the trained model. When the number of segments classified as breakup is equal to or greater than a certain number, the frame image is determined to have tear breakup.*
We prepared 13,471 frame images labeled with the presence or absence of tear breakup from 350 eye videos to evaluate our detection method. The frame images for evaluation were created by extracting up to 40 frame images from a single video and labeling the extracted frame images with the presence or absence of tear breakup using our dedicated software. The work was carried out by three engineers who were trained in the tear breakup detection criteria.
The program for the detection procedure was written in Python 3.5. The detection process of the prepared frame images was conducted on a workstation PC with an NVIDIA Quadro P2000 GPU. A receiver operating characteristic (ROC) analysis was performed to evaluate the detection method. The performance of the method was expressed as the area under curve (AUC) of ROC curve with $95\%$ confidence interval ($95\%$ CI), sensitivity, and specificity. The statistical programming language R (Version 4.0.3, The R Foundation for Statistical Computing) was used for the statistical analysis.
## Image classification results of CNN models
The computation time (mean ± standard deviation) per epoch for the training process was 109.30±9.88 seconds with an NVIDIA Quadro P2000 GPU and 81.50±1.45 seconds with an NVIDIA Quadro P2200 GPU. The test results of the six CNN models trained by six-fold cross validation are shown in the confusion matrix in Fig 4A. The overall accuracy was $81.3\%$. The recalls of Area pattern, Spot pattern, and Line pattern related to breakup category were $89.7\%$, $71.5\%$, and $62.9\%$, respectively. The recalls of Uniform, Interference Fringe, Bright Reflection, Particle, Eyelash, and Eyelid related to non-breakup category were $80.7\%$, $71.5\%$, $91.3\%$, $82.3\%$, $83.5\%$, and $94.5\%$, respectively. The precisions of Area pattern, Spot pattern, and Line pattern were $91.7\%$, $69.2\%$, and $66.1\%$, respectively. The precisions of Uniform, Interference Fringe, Bright Reflection, Particle, Eyelash, and Eyelid were $83.2\%$, $76.6\%$, $92.9\%$, $79.3\%$$82.8\%$, and $86.7\%$, respectively. The F1-scores of Area pattern, Spot pattern, and Line pattern were 0.907, 0.703, and 0.645, respectively. The F1-scores of Uniform, Interference Fringe, Bright Reflection, Particle, Eyelash, and Eyelid were 0.819, 0.740, 0.921, 0.808, 0.831, and 0.904, respectively. The results of six-fold cross-validation, expressed as mean ± standard deviation, were $81.3\%$±2.53, $81.0\%$±2.24, $81.5\%$±2.49, and 0.810±0.022 for accuracy, recall, precision, and F1-score, respectively.
**Fig 4:** *Confusion matrices.(A) Confusion matrix for image classification of test data in six-fold cross validation. (B) Confusion matrix aggregated for breakup and non-breakup.*
Fig 4B shows the confusion matrix aggregated into two groups: breakup and non-breakup. The ability of our CNN model to classify into the tear breakup or non-breakup group was $92.3\%$, $83.4\%$, and $95.2\%$ for accuracy, sensitivity, and specificity, respectively. The results of six-fold cross-validation, expressed as mean ± standard deviation, were $92.3\%$±0.91, $83.7\%$±5.44, and $95.1\%$±2.80 for accuracy, sensitivity, and specificity, respectively.
## Evaluation results of tear breakup detection method
The detection process of 13,471 frame images took 4 hours and 5 minutes. Thus, the computation time per frame image was approximately 1.1 seconds.
The evaluation results of the method for detecting tear breakup for a frame image are shown in the receiver operating characteristic (ROC) curve in Fig 5. Our method achieved to detect tear breakup with sensitivity and specificity of $84.3\%$ and $83.3\%$, respectively. The area under the curve (AUC) was 0.898 ($95\%$ CI: 0.891 to 0.905).
**Fig 5:** *Receiver operating characteristic (ROC) curve.This curve shows that our method achieved an area under curve (AUC) of 0.898, a sensitivity of 84.3%, and a specificity of 83.3% in detecting tear breakup for a frame image.*
## Discussion
To discuss the details of the image classification performance of the trained CNN models, the confusion matrix which is expressed as the ratio of the number of predicted data to the number of actual data is shown in Fig 6. The trained CNN models were able to classify Area pattern, Bright Reflection, and Eyelid with high recalls of approximately $90\%$ or better. Uniform, Particle, and Eyelash were also classified with more than $80\%$ recalls. On the other hand, the recalls for Spot pattern, Line pattern, and Interference Fringe were lower than those for the other classes. The Spot pattern and Line pattern tended to be misclassified into each other. This misclassification may be due to the following reasons. The Line pattern at the time of occurrence could be similar in shape to the Spot pattern due to its shorter length (Fig 7A). Also, the Spot pattern at a few seconds after occurrence could be similar in shape to the Line pattern due to the shape change caused by the upward movement of tear fluid (Fig 7B).
**Fig 6:** *Heatmap confusion matrix.Each cell shows the ratio of the number of predicted data to the number of actual data.* **Fig 7:** *Examples of misclassified images.(A) Line pattern misclassified to Spot pattern. (B) Spot pattern misclassified to Line pattern. (C) Breakup patterns misclassified to Particle: (i) Spot pattern and (ii) Line pattern.*
The Spot pattern and Line pattern tended to be misclassified as the Particle class. When there were the Spot or Line pattern breakup and black particles in the same region, our CNN model sometimes classified them into the Particle class (Fig 7C).
In order to reduce the misclassification of the Line pattern breakup and interference fringes, the Interference Fringe class was established and trained, but there was still a tendency for them to misclassify each other. *In* general, the performance of AI depends on the quality and quantity of the training data, so if we can use a lot of good data for training, we can improve the accuracy of our CNN model further.
Since the method developed in this study for detecting tear breakup for a frame image was based on the results of image classification of the CNN model, the trend of the results basically matched the trend of the results of image classification of the CNN model. The detection of tear breakup in a frame image had a sensitivity of $84.3\%$ and a specificity of $83.3\%$, and the presence or absence of tear breakup occurrence could be determined with satisfactory accuracy (Fig 8).
**Fig 8:** *Examples of a frame image where breakup was detected correctly.The colored regions are where tear breakup was detected: (A) Area pattern (purple), (B) Spot pattern (blue), and (C) Line pattern (green).*
Considering the cases where the detection of tear breakup failed, we found that it was sometimes determined that no tear breakup occurred when the area of tear breakup was small (Fig 9). Therefore, it may be possible that tear breakup can be detected by optimizing the conditions for judging the occurrence of tear breakup depending on the type of breakup pattern.
**Fig 9:** *Examples of a frame image where detection of breakup failed.In (A) and (B), the breakup detection area was too small to determine the presence of breakup: (A) Spot pattern and (B) Line pattern. In (C), the high intensity reflections along the lower eyelid were incorrectly detected as Line pattern breakup.*
High intensity reflections observed along the edge of the eyelid were sometimes mistakenly detected as the Line pattern class (Fig 9). Also, blurred tear breakup, which is difficult to determine even for experts, was sometimes classified as a non-breakup class. These are expected to be improved by improving the accuracy of the CNN model.
## Conclusions
We were able to develop a method for detecting tear breakup by using a CNN model trained on the characteristics of tear film interference color images of the KOWA DR-1α video interferometer. This method could be practical for non-invasive and objective TBUT measurement, which would be useful for dry eye diagnosis in clinical practice.
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|
---
title: 'Relationship between fall history and toe grip strength in older adults with
knee osteoarthritis in Japan: A cross-sectional study'
authors:
- Yuya Mawarikado
- Yusuke Inagaki
- Tadashi Fujii
- Takanari Kubo
- Akira Kido
- Yasuhito Tanaka
journal: PLOS ONE
year: 2023
pmcid: PMC10010548
doi: 10.1371/journal.pone.0282944
license: CC BY 4.0
---
# Relationship between fall history and toe grip strength in older adults with knee osteoarthritis in Japan: A cross-sectional study
## Abstract
### Background
Knee osteoarthritis (KOA), one of the most common musculoskeletal diseases in older adults, is associated with a high incidence of falls. Similarly, toe grip strength (TGS) is associated with a history of falls in older adults; however, the relationship between TGS and falls in older adults with KOA who are at risk of falling is not known. Therefore, this study aimed to determine if TGS is associated with a history of falls in older adults with KOA.
### Methods
The study participants, older adults with KOA scheduled to undergo unilateral total knee arthroplasty (TKA), were divided into two groups: non-fall ($$n = 256$$) and fall groups ($$n = 74$$). Descriptive data, fall-related assessments, modified Fall Efficacy Scale (mFES), radiographic data, pain, and physical function including TGS were evaluated. The assessment was conducted on the day before performing TKA. Mann–Whitney and chi-squared tests were performed to compare the two groups. Multiple logistic regression analysis was performed to determine the association of each outcome with the presence or absence of falls.
### Results
Mann-Whitney U test revealed that the fall group had statistically significantly lower height, TGS on the affected and unaffected sides, and mFES. Multiple logistic regression analysis revealed that the incidence of fall history is associated with TGS on the affected side; the weaker the affected TGS of the KOA, the more likely the individual is to fall.
### Conclusions
Our results indicate that TGS on the affected side is related to a history of falls in older adults with KOA. The significance of evaluating TGS among patients with KOA in routine clinical practice was demonstrated.
## Introduction
Falls in older adults can lead to injuries, such as bone fractures, and to significant deterioration of physical function. Over $30\%$ of people aged 65 or older experience approximately one fall every year [1]. Knee osteoarthritis (KOA) is one of the most common musculoskeletal diseases in older adults. It has been reported that an increased history of falls and hip fractures is associated with an increase in knee pain, and the incidence of non-vertebral fractures is 1.6 times higher in older adults with KOA than in those without [2]. The incidence of falls in older adults with KOA is approximately $30\%$ higher than that in healthy older adults, and approximately half of individuals aged 60 years or older fall at least once annually [3, 4]. Older adults with KOA and a fall history are more likely to fall after total knee arthroplasty (TKA) than a cohort of older adults with KOA who do not have a history of falls [5, 6].
As mentioned, older adults with KOA have a high incidence of falls; however, related risk factors remain undetermined in this population. Knee pain, impaired balance, lower muscle weakness, and decreased walking ability have been reported as risk factors for falls in individuals with KOA [7, 8]. Previous studies on older adults without KOA indicated that clinical benchmarks, such as Timed Up and Go (TUG) [9, 10], fall-related self-efficacy [11], knee extension strength [12], and toe grip strength (TGS) [13–15] are associated with the risk of a fall. These outcomes may be even stronger predictors in individuals with KOA, who generally have impaired mobility and muscle strength.
Among the aforementioned risk factors for falls, particular attention has been paid to TGS. Many studies reported an association between TGS and falls in older adults. With increasing age, TGS in older adults deteriorates [16–18], resulting in decreased walking speed and static balance ability [19–21]. TGS training to nursing home residents led to significant improvements in the fall risk index (-1.4 points). A significant increase in TGS was noted in the intervention group compared to that in the non-intervention group (Intervention group increased 1.9 kg, non-intervention group decreased 0.2 points) [22]. Therefore, it is clinically important to evaluate and strengthen TGS to prevent falls in older adults.
To date, no study has investigated the relationship between falls and TGS in older adults with KOA who are at risk of falling owing to lower limb muscle weakness and impaired balance. We hypothesized that the history and frequency of falls in older adults with KOA would be associated with TGS. Therefore, this study aimed to determine whether TGS is associated with a history of falls in older adults with KOA.
## Ethical issues
This study complied with the Declaration of Helsinki and was approved by the Research Ethics Committee of Kashiba Asahigaoka hospital [2019-04-21-007]. Details of the study protocol and aim were explained to all participants, both verbally and in writing. All study participants then signed a written consent prior to participating in the study.
## Participants
This study used a descriptive cross-sectional design to identify the association between falls and clinical evaluation factors including TGS. We recruited 407 participants with KOA, scheduled to undergo unilateral TKA at a single hospital in Japan between May 2019 and September 2021. The inclusion criteria were: 1. diagnosis of KOA, 2. ability to ambulate independently or with a T-cane at the time of pre-operative evaluation, 3. individuals who were scheduled for primary TKA, 4. individuals between 60 and 84 years of age, and 5. informed consent to participate in the study was obtained. The exclusion criteria were: 1. diagnosis of rheumatoid arthritis, idiopathic osteonecrosis, or foot and ankle disorders; 2. individuals with bilateral toe flexion problems, neurologic diseases, or other musculoskeletal diseases that significantly impair basic movements, such as walking; and 3. Those with severe depression or dementia, which would hinder evaluation. The number of falls over one-year period was obtained retrospectively within the inclusion time by nurses. The group with one or more falls was defined as the fall group, and the group with no falls was defined as the non-fall group.
## Experimental procedure
We assessed the participants one day prior to TKA. Descriptive data, fall-related assessments (presence of falls and fear of falling), radiographs, and physical function data were collected from electronic medical records. Physical function measurements were measured in the rehabilitation room, and were performed by 14 randomly assigned physical therapists for all participants to reduce bias as much as possible. The surgical side was considered as the affected side, whereas the non-surgical side was considered as the unaffected side.
## Fall definition
A fall was defined as “an event that results in a person coming to rest unintentionally on the ground or other lower level, not as a result of a major intrinsic event of overwhelming hazard” [23]. Falls were excluded if they were not related to gait, standing and transfer, for example a fall with a bicycle and ladder.
## Primary outcome
A toe grip dynamometer (T.K.K.3362; Takei Scientific Instruments, Niigata, Japan) was used to measure TGS (Fig 1) in a sitting position with 90° hip and knee joint flexion and the ankle in a neutral position. Under vertical loading on the foot, the plantar aponeurosis was extended with the foot truss structure [24]. The participants were instructed to place their test foot within the heel stopper and to grasp the dynamometer grip bar with their toes. They first performed a few test contractions with maximum effort to familiarize themselves with the measurement process and then performed as many voluntary isometric contractions as possible. Maximum TGS was measured twice, and the mean value (kg) was calculated. Participants performed maximum-effort contractions after the "warm-up" repetitions. An almost perfect inter- and intra-rater reliability of this measurement protocol using the toe grip dynamometer has been observed previously in people aged 60–79 years [25].
**Fig 1:** *Toe grip strength assessment.a. Toe grip dynamometer employed to measure toe grip strength. b. The grip bar of the instrument was adjusted to the first metatarsophalangeal joint of the participant. The participants sat on the edge of their seats keeping their trunks in a vertical position and the hip and knee joints bent to approximately 90°.*
## Secondary outcomes
Descriptive data, such as gender, age, height, weight, and body mass index were collected by nurses during the evaluation. The patients self-reported whether and how many times they experienced falls in the one past year. The severity of KOA was determined using the Kellgren–Lawrence (K-L) grading system [26]. Four orthopedic surgeons evaluated all medical records and determined the K-L grade severity. Isometric knee extension strength (IKES) was measured using a hand-held dynamometer (μ-tas F1, ANIMA, Tokyo, Japan) with participants in a seated position and the knee in 90° flexion [27]. The reliability and validity of this measurement method have been previously demonstrated in patients with KOA [27, 28]. The participants were instructed to increase the intensity of knee extension against the dynamometer gradually and for approximately 2 seconds to avoid explosive contraction, and to maintain their maximal force output for approximately 3 seconds. Maximum IKES was measured twice, and the mean value (kg) was calculated. Pain levels at rest and during walking were determined using a visual analog scale ranging from 0 (no pain) to 100 mm (worst pain) [29]. This measurement method has been reported to be reliable and valid for the assessment of individuals with KOA [30]. The TUG was used as a behavioral measure of knee function using standard test methods [31]. The reliability and validity of this measurement method have been demonstrated [32, 33]. The participants stood up from an armless chair upon the assessor’s signal, walked to the 3-m point, and returned to sit on the same chair. The TUG measurements were recorded twice and a mean value between the two measurements was calculated and used for statistical analysis. Fear of falling was assessed using the Japanese version of the modified Fall Efficacy Scale (mFES), developed by Hill et al [34]. The mFES is a 10-grade scale comprising 14 items (score range: 0–140 points), and a modified version of the Falls Efficacy Scale developed by Tinetti et al. [ 35]. The mFES is used to determine the level of confidence in performing specific movements and actions without falling, with higher scores indicating higher levels of self-efficacy in fall prevention and less fear of falls. The reliability and validity of this measurement method have been demonstrated [34].
## Data analysis
Descriptive data were presented as the number of cases, mean with standard deviations (SD), and percentages. For all analyses, the significance level was set at $5\%$. All statistical analyses were performed using SPSS Statistics for Windows, version 26.0 (IBM Corp, Tokyo, Japan). The sample size was calculated by G-power Post-hoc. The sample sizes for both the fall and non-fall groups were calculated based on effect size = 0.5 and α = 0.05.
Before comparing the difference between the two groups, a Kolmogorov–Smirnov test was performed as a homogeneity test. The results confirmed that the p-values for all factors were less than 0.05 for both groups. Therefore, Mann–Whitney- and chi-squared tests were used to test the difference between the two groups. The factors subjected to Mann–Whitney U test were age, height, weight, BMI, TGS on both sides, IKES on both sides, pain at rest on both sides, pain while walking on both sides, TUG, and mFES. Factors subjected to the chi-squared test were sex and K-L grade.
Multiple logistic regression was performed to examine the association of each factor to the dependent variable (i.e., fall or no fall history). The independent variables were height, weight, TGS on both sides, and mFES. Hosmer-Lemeshow test was performed to determine whether the analysis result was significantly fitted to the actual data. The significance of the regression equation was confirmed by rate of accurate discrimination. The variance inflation factor (VIF) was calculated to account for the degree of multicollinearity among the related factors.
## Results
We excluded 77 participants, while 330 met the inclusion criteria (Fig 2). The 330 participants were divided into two groups: fall and non-fall. The participants’ descriptive characteristics and results of Mann–Whitney- and chi-squared tests are summarized in Table 1. Of the 330 participants enrolled in the study, 74 ($22.4\%$) were in the fall group and 256 ($77.6\%$) were in the non-fall group. The percentage of fall was calculated from the 330 participants who met the inclusion criteria of the study. Of the participants with a fall history, 42 reported 1 fall, 14 reported 2 falls, 10 reported 3 falls, 4 reported 4 falls, and 4 reported 5 falls. A history of 6 or more falls was never recorded. The mean number of falls per participant was 1.85. In addition, the sample size was calculated with 74 patients in the fall group and 256 in the non-fall group, the power was 0.97.
**Fig 2:** *Flow diagram of the study participants’ inclusion process.* TABLE_PLACEHOLDER:Table 1 Factors that differed significantly between the fall and non-fall groups were height ($$p \leq .014$$), TGS on the affected side ($$p \leq .003$$), TGS on the unaffected side ($$p \leq .007$$), and mFES ($$p \leq .001$$).
The results of multiple logistic regression analysis are presented in Table 2. The model chi-squared test revealed significant results, indicating association with TGS on the affected side (β = -.081, $$p \leq .024$$, Odds ratio [OR] =.922), and mFES (β = -.013, $$p \leq .002$$, OR =.987). The model χ2 was significant at $p \leq .01.$ The result of the Hosmer–Lemeshow test was not significant at $$p \leq .56$$, and the fit of regression equation was good. The rate of accurate discrimination was $77.6\%$. VIF for mFES and TGS on the affected side were 1.023 in both cases, and no multicollinearity was observed. VIF calculated from the related factors were height: 1.165; weight: 1.059; and TGS on the unaffected side: 2.320.
**Table 2**
| Unnamed: 0 | B | Standard Error | Wald | p-value | Odds ratios |
| --- | --- | --- | --- | --- | --- |
| | B | Standard Error | Wald | p-value | (95% confidence interval) |
| TGS on the affected side | -0.081 | 0.036 | 5.069 | 0.024 | 0.922 (0.86–0.99) |
| mFES | -0.013 | 0.004 | 9.673 | 0.002 | 0.987 (0.98–0.99) |
| Constant | 0.760 | 0.494 | 2.370 | 0.124 | |
## Discussion
We analyzed a number of factors that potentially contributed to a history of falls in patients with KOA, including TGS, and clarified which of those had an impact. Only a few studies reported the incidence of falls in individuals with KOA in Japan [36]. To the best of our knowledge, this is the first study to investigate the relationship between history of falls and TGS in older adults with KOA. Our results indicated that falls in older adults with KOA were related to TGS. In short, falls in older adults with KOA are associated with lower TGS.
## The percentage of fall rate in this study
In a study of 5,062 frail older adults in Japan, approximately $30\%$ experienced a fall at least once in a year [37]. Compared to older adults with KOA in Australia ($48\%$ fell within 12 months prior to TKA) [4] and in the United Kingdom ($24\%$ fell within 3 months prior to TKA) [5], the fall rate 12 months prior to TKA in our study was lower ($22.4\%$). However, according to the publication of vital statistics in Japan, accidental deaths from falls among older adults are on the rise. The overall mortality rates per 100000 persons in the older population increased from 19.5 in 1997 to 20.5 in 2016 [38]. A previous study has reported that fall history before performing TKA increased the risk of post-operative falls [5, 6]. This insight might lead to a better understanding of prevention of injuries from post-operative falls.
## Toe grip strength and fall history
A previous study has reported that TGS declines with age [39], resulting in diminished walking ability and static balance, which may be risk factors for falls. Tsuyuguchi et al. recruited middle-aged adults and, whose average age was 62.02, divide them into high and low risk of falls. They found TGS to be an independent risk factor for fall occurrence [40]. However, there is no report on whether reduced TGS in individuals with KOA is associated with the fall itself. Based on the results of our study, we believe that TGS contributes to the challenges faced by older adults with KOA and a fall history. Although a detailed causal relationship is unknown, multiple regression analysis has identified TGS as an independent factor associated with KOA [41]. Conversely, abnormal loading of the knee joint can be caused by changes in the kinematic relationship between the foot and knee [42, 43]. Compared to healthy older adults, those with KOA have lower TGS, and the measured pressure decreases during walking [44, 45]. It is possible that the progression of KOA leads to decreased TGS; conversely, decreased TGS may contribute to KOA progression. However, this causal relationship is unclear; therefore, further studies are required to investigate it. Regardless of the causal pathway, there is interdependence between TGS and KOA, which increases the risk of falls. In the future, studies should approach the causal relationship between KOA and TGS from the perspectives of kinesiology and biomechanics, in addition to seeking strategies to prevent falls.
## Self-efficacy for falls and fall history
Adults with KOA with more frequent falls may have a more pronounced fear of falling than those with fewer falls. Tinetti et al. [ 46] defined fear of falling as "anxiety about falling that causes one to avoid activities of daily living, even though one is capable of performing them”. In their study, fear of falling depended on the history of falls, ranging from 12–$65\%$ among community-dwelling older adults without a fall history, and from 29–$92\%$ among those with [47]. Therefore, fall history is associated with fear of falling [48]. Our results support those of previous studies [48]. In addition, participants with KOA and a history of falls experienced knee pain, knee instability, and muscle weakness in the lower extremities, which could have also contributed to a greater fear of falling. Post-fall syndrome, in which a loss of self-confidence after a fall leads to a decreased activity level, in turn amplifies the fear of falling.
## Limitations
This study had three major limitations. First, TGS was the only assessment performed on the foot. The degree of flatfoot and range of motion of the foot, which are common problems in individuals with KOA, were not measured. The abnormal foot posture of KOA has excessive first medial tibiofemoral contact force during walking [49]. As these factors are also associated with gait, which is involved in half of all fall scenarios [50], they are likely to contribute to falls. Second, previous studies have reported decreased physical activity [51] as well as hip [52] and ankle [53] weakness as risk factors for falls. Because we did not evaluate these factors and did not include these results in our regression analysis, we could not determine the relative contribution of TGS to physical activity and muscle strength for the above factors in older adults with KOA. Finally, we conducted a cross-sectional observational study; therefore, subsequent cohort and intervention studies should be conducted to better clarify the causal relationships between foot function and falls in older adults with KOA.
## Conclusions
In this study, we investigated the factors leading to falls in older adults with KOA in Japan. Our results indicated that falls in older adults with KOA were related to TGS. In short, falls in older adults with KOA are associated with lower TGS. Physical therapy interventions to enhance TGS could be one solution to help prevent falls in individuals with KOA. In the future, cohort and interventional studies evaluating the relationship between TGS and falls should be performed.
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---
title: 'Distinctive origin and evolution of endemic thistle of Korean volcanic island:
Structural organization and phylogenetic relationships with complete chloroplast
genome'
authors:
- Bongsang Kim
- Yujung Lee
- Bomin Koh
- So Yun Jhang
- Chul Hee Lee
- Soonok Kim
- Won-Jae Chi
- Seoae Cho
- Heebal Kim
- Jaewoong Yu
journal: PLOS ONE
year: 2023
pmcid: PMC10010555
doi: 10.1371/journal.pone.0277471
license: CC BY 4.0
---
# Distinctive origin and evolution of endemic thistle of Korean volcanic island: Structural organization and phylogenetic relationships with complete chloroplast genome
## Abstract
Unlike other Cirsium in Korea, *Cirsium nipponicum* (Island thistle) is distributed only on Ulleung Island, a volcanic island off the east coast of the Korean Peninsula, and a unique thistle with none or very small thorns. Although many researchers have questioned the origin and evolution of C. nipponicum, there is not much genomic information to estimate it. We thus assembled the complete chloroplast of C. nipponicum and reconstructed the phylogenetic relationships within the genus Cirsium. The chloroplast genome was 152,586 bp, encoding 133 genes consisting of 8 rRNA genes, 37 tRNA genes, and 88 protein-coding genes. We found 833 polymorphic sites and eight highly variable regions in chloroplast genomes of six Cirsium species by calculating nucleotide diversity, as well as 18 specific variable regions distinguished C. nipponicum from other Cirsium. As a result of phylogenetic analysis, C. nipponicum was closer to C. arvense and C. vulgare than native Cirsium in Korea: C. rhinoceros and C. japonicum. These results indicate that C. nipponicum is likely introduced through the north Eurasian root, not the mainland, and evolved independently in Ulleung Island. This study contributes to further understanding the evolutionary process and the biodiversity conservation of C. nipponicum on Ulleung Island.
## Introduction
Cirsium nipponicum (Maxim.) *Makino is* a perennial flowering plant that can be found near the seashore and belongs to the Carduoideae subfamily in Asteraceae. Among eight Cirsium species that grow naturally in Korea [1], C. nipponicum, also known as island thistle, is predominantly found only on Ulleung Island, an oceanic volcanic island on the east coast of the Korean Peninsula, and has no or very small thorns on its leaves. Like other Cirsium species traditionally used as a medicinal plant in East Asia for their bioactivities, including hepatoprotective, antioxidant, and antidiabetic activities [2–7], dried C. nipponicum has also been used as a medicinal source. It is an abundant producer of polyphenols and flavonoids such as cirsimarin and pectolinarin with antioxidant and anti-inflammatory activity [3, 8, 9]. In addition, the leaves known to be different from other Cirsium are also used as a resource for vegetables. Based on the fact that other Caruoideae species like milk thistle, were studied to investigate medicinal effects [10–12], studies were also conducted on C. nipponicum [3, 8, 13, 14].
Although several Cirsium species are distributed in Korea and neighboring countries (Fig 1A), the origin of the Korean C. nipponicum, which is distributed only on Ulleung Island, is not yet clear. Previous studies on phylogenetic relationships have shown that C. nipponicum is distinct from other endemic Cirsium [1, 15]. However, there is a limitation to understanding the biological differences based on genomic studies among Cirsium species, as few studies have been conducted using the DNA of C. nipponicum in recent decades. Furthermore, despite the presence of other comparative analyses with C. nipponicum, the phylogenetic analyses have also been performed in a limited way using combinations of morphological characteristics and only small portions of genomic DNA, such as DNA barcode regions, which are problematic even in the evolutionary process [16, 17].
**Fig 1:** *Cirsium species distribution map and chloroplast genetic map.(a) Geographical distribution of Cirsium species around Korea (source: Natural Earth). (b) Genetic map of the C. nipponicum. Genes drawn outside the circle are transcribed counterclockwise, and others inside are transcribed clockwise. (c) C. vulgare distributed near Ulleung Island, provided by Bio Resource Information Service (BRIS). (d) C. rhinoceros distributed near Ulleung Island, provided. (e) C. japonicum distributed near Ulleung Island, provided by National Institute of Biological Resources (NIBR). (f) C. arvense distributed near Ulleung Island, provided by NIBR.*
Islands are considered a prosperous region in terms of plant species diversity, and Ulleung *Island is* one of the biodiversity hot spots in Korea [18, 19]. Nonetheless, the current biological species in islands are under threat from the loss of native habitats and climate change, such that many plants in Ulleung Island are suffering from various forms of development [20–22]. Under these circumstances, conservation work on endemic species of Ulleung Island, including C. nipponicum, has just begun, and at the same time, genome construction of these species is required. Since the development of next-generation sequencing [23] technology has enabled researchers to study and understand the genome from a broader and deeper perspective, the acquisition of genetic resources has been activated and the quality has also improved. In addition, many projects involving genomic data, such as genome skimming or DNA barcoding, have been accompanied. Therefore, we aimed to present the chloroplast genomic data of C. nipponicum based on future studies, as genomic data can complement small remaining challenges and provide an accurate method for the biological understanding and biodiversity of Ulleung Island.
Plastid genomes were sequenced before the nuclear genome in most plant organisms because of their conserved traits, such as gene contents, low recombination, self-replication, genome structure, small compact size, maternal inheritance, and moderate substitution rates for comparative analysis within related species [24–26]. For those reasons, the study of the chloroplast genome is regarded as a valuable resource for investigating phylogenetic analysis, population genetics, or plant systematics. For example, previous studies using the chloroplast genome have inferred phylogenetic relationships in traditionally intricate groups of tribe Cardueae [27, 28]. Moreover, variable regions such as repeat sequences or intergenic spacer (IGS) in chloroplast genomes of many species have been explored as helpful information for effective strategies to conserve endangered species [29]. Hence, constructing the chloroplast genome of C. nipponicum will be of great help in studying the evolutionary process of Cirsium and its adaptation to specific environments.
In this study, we assembled a complete chloroplast genome of C. nipponicum for the first time through NGS paired-end data and compared its chloroplast genome with other previously published chloroplast genomes. Then, we identified the genetic structure of the C. nipponicum chloroplast genome and performed comparative analyses with other Cirsium species. As a result, repeat elements and highly variable regions within Cirsium species were detected to distinguish C. nipponicum from others and constructed phylogenetic trees to observe the evolutionary relationship among Carduoideae.
## Plant material, DNA extraction, and sequencing
Fresh leaves of C. nipponicum were collected from a conservation garden in Ulleunggun Agriculture Technology Center, Ulleung-gun, Gyeonsangbuk-do, Korea (37°27’37.0"N 130°52’29.9"E) under guide of Chul Hee Lee (research officer of Ulleunggun Agriculture Technology Center). The plant materials produced and used in this study comply with Korean guidelines and legislation. All the experiments were carried in accordance with national and international guidelines. Genomic DNA of C. nipponicum was extracted from leaf tissues using a cetyl trimethylammonium bromide (CTAB)-based protocol [30]. A paired-end library with a 2 x 151 paired-end (PE) was constructed following the manufacturer’s instructions (Illumina, USA) and sequenced using HiSeq platform.
## Read data processing and chloroplast genome assembly
Quality control of removing low-quality reads and adaptor sequences was performed using fastQC and Trimmomatic programs [31, 32]. The adapter sequences were removed, and the end of reads with Phred score less than 20 was trimmed. Afterward, high-quality reads were assembled using GetOrganelle-1.7.1 [33], and then annotated using PGA v3 and GeSeq based on the four reference chloroplast genomes: C. rhinoceros (NC_044423.1), C. eriophorum (NC_036966.1), C. vulgarae (NC_036967.1), and C. arvense (NC_036965.1) [34, 35]. The tRNA genes were verified with tRNAscan-SE v2.0.5 program, and further manual adjustment was performed with BLATN and BLATX [36, 37]. The annotated chloroplast genome of C. nipponicum was submitted to GenBank under accession number MW248139. The genome map was illustrated by Organellar Genome DRAW (OGDRAW) [38]. The irScan and IRscope identified inverted repeat regions [39] for genomes with no information about IR annotations [40, 41]. Sequences of all protein-coding genes were used to analyze codon preference. Relative synonymous codon usage (RSCU) was calculated based on the following equation [42]: RSCUij=Xij∑$j = 1$njXijni *Xij is* the number of occurrences of the jth codon for the ith amino acid, and ni is the number of alternative codons for the ith amino acid.
## Repeat sequence identification
Simple-sequence repeats (SSRs) in the C. nipponicum chloroplast genome were determined using MISA with the minimal repeat numbers set to 10, 5, 4, 3, 3, and 3 for mono-, di-, tri-, tetra-, penta-, and hexa-nucleotide, respectively [43]. REPuter was used to identify dispersed repeats, including forward, reverse, complement, and palindromic kinds of repeat sequences with a minimum size of 30 bp and hamming distance of 3 [44].
## Divergent hotspot identification
The MAFFT alignment [45], followed by DNASP [46] was performed to compare the chloroplast genome of C. nipponicum with following five Cirsium species: C. japonicum, C. rhinoceros, C. eriophorum, C. vulgare, and C. arvense. In order to identify variant divergence regions, the multiple sequence alignments were analyzed to calculate nucleotide diversity with window length 600 and step size 200 options.
## Phylogenetic analysis
Phylogenetic analyses were conducted using the Cirsium species with Cardueae tribe species and one *Gerbera jamesonii* as an outgroup. The multiple sequence alignment for 20 sequences listed in S1 Table was performed using MAFFT with 1.53 gap penalty and FFT-NS-2 default method [45]. PAUP and Modeltest were used for Bayesian inference [47, 48]. MrBayes [49] was implemented with 1,000,000 generations and 250,000 generations burn-in, as well as the maximum likelihood analysis to construct phylogenetic trees. IQ-tree was performed to estimate maximum likelihood with 1000 bootstrap replications [50].
## Chloroplast genome of C. nipponicum
We sequenced whole genomic paired-end data of C. nipponicum in 16,415,067,154 bp size. By trimming adapters and low-quality sequences, a total of 3,739,051,830 high-quality reads were used as GetOrganelle-1.7.1 [33] input for chloroplast genome assembly. Based on the seed reads identified from GetOrganelle with 88,093,650 bp in length and 577x in sequencing depth, chloroplast genomic DNA was assembled into a circular form (Fig 1B). The length of the assembled genome of C. nipponicum was 152,586 bp with quadripartite structures, consisting of a large single-copy (LSC) region of 83,520 bp and a small single-copy (SSC) region of 18,701 separated by two inverted repeats (IRa, IRb) of 25,191 bp each. The GC content of the C. nipponicum chloroplast genome was $37.69\%$, and that of LSC, SSC, and IRs regions were $35.83\%$, $37.49\%$, and $43.11\%$, respectively. LSC exhibited the lowest value of GC contents among the four regions of the chloroplast genome, and IR regions had the highest value.
Using PGA [35] and GeSeq [34] annotation tools, the chloroplast genome of C. nipponicum annotated 133 genes consisting of 8 rRNA genes, 37 tRNA genes, and 88 protein-coding genes (Table 1). In total of 133 genes, 18 genes including 7 tRNA genes (trnI-CAU, trnL-CAA, trnV-GAC, trnI-GAU, trnA-UGC, trnR-ACG, trnN-GUU), 4 rRNA genes (rrn4.5, rrn5, rrn16, rrn23), and 7 protein-coding genes (rpl2, rpl23, rps7, rps12, ycf2, ycf15, ndhB) were duplicated in IR regions. Also, 11 protein-coding genes (rpl2, rpl16, rps12, rps16, rpoC1, atpF, ycf3, clpP, petB, petD, ndhA, and ndhB) contained exons and introns. The small subunit ribosomal protein 12 (rps12) gene was trans-spliced, where the first exon was located in the LSC region and others in the IR regions.
**Table 1**
| Classification of Genes | Classification of Genes.1 | Names of Genes | Number |
| --- | --- | --- | --- |
| RNA genes | Ribosomal RNAs | rrn4.5 (x 2), rrn5 (x 2), rrn16 (x 2), rrn23 (x 2) | 8 |
| RNA genes | Transfer RNAs | trnA-UGC (x 2), trnC-GCA, trnD-GUC, trnE-UUC, trnF-GAA, trnM-CAU, trnG-GCC, trnG-UCC, trnH-GUG, trnI-CAU (x 2), trnI-GAU (x 2), trnK-UUU, trnL-CAA (x 2), trnL-UAA, trnL-UAG, trnM-CAU, trnN-GUU (x 2), trnP-UGG, trnQ-UUG, trnR-ACG (x 2), trnR-UCU, trnS-GCU, trnS-GGA, trnS-UGA, trnT-GGU, trnT-UGU, trnV-GAC (x 2), trnV-UAC, trnW-CCA, trnY-GUA | 37 |
| Protein Coding genes | Ribosomal proteins, large subunits | rpl14, rpl16, rpl2 (x 2), rpl20, rpl22, rpl23 (x 2), rpl32, rpl33, rpl36 | 11 |
| Protein Coding genes | Ribosomal proteins, small subunit | rps11, rps12 (x 2), rps14, rps15, rps16, rps18, rps19, rps2, rps3, rps4, rps7 (x 2), rps8 | 14 |
| Protein Coding genes | RNA polymerases | rpoA, rpoB, rpoC1, rpoC2 | 4 |
| Protein Coding genes | Photosystem 1 | psaA, psaB, psaC, psaI, psaJ | 5 |
| Protein Coding genes | Photosystem 2 | psbA, psbB, psbC, psbD, psbE, psbF, psbH, psbI, psbJ, psbK, psbL, psbM, psbT, psbZ | 14 |
| Protein Coding genes | Cytochrome b6/f complex | petA, petB, petD, petG, petL, petN | 6 |
| Protein Coding genes | ATP synthase | atpA, atpB, atpE, atpF, atpH, atpI | 6 |
| Protein Coding genes | NADH dehydrogenase | ndhA, ndhB (x 2), ndhC, ndhD, ndhE, ndhF, ndhG, ndhH, ndhI, ndhJ, ndhK | 12 |
| Protein Coding genes | Rubisco | rbcL | 1 |
| Protein Coding genes | clpP, matK | clpP, matK | 2 |
| Protein Coding genes | Hypothetical chloroplast reading frames (ycf) | ycf1 (x 2), ycf15 (x 2), ycf2 (x 2), ycf3, ycf4 | 8 |
| Protein Coding genes | Other genes | accD, ccsA, cemA, infA, pbf1 | 5 |
| Total | Total | Total | 133 |
To distinguish the C. nipponicum chloroplast genome within other Cirsium species, we compared five well-known chloroplast genomes from NCBI RefSeq Sequences and reassigned quadripartite structures: C. arvense (NC_03695.1), C. vulgare (NC_036967.1), C. eriophorum (NC_036966.1), C. rhinoceros (NC_044423.1), and C. japonicum var. spinosissimum (NC_050046.1). All of these species, except for C. eriophorum, were found in Korea. C. vulgare and C. arvense were exotic species distributed worldwide including Russia, China, and Japan, and the remaining two, C. rhinoceros and C. japonicum, were endemic to Korea. As a result of basic statistics from comparing each chloroplast genome, the C. nipponicum chloroplast genome showed the lowest GC content in the whole chloroplast genome among six Cirsium species, whereas the GC content in the SSC region showed the highest value (Table 2).
**Table 2**
| Species | C. nipponicum | C. japonicum | C. rhinoceros | C. eriophorum | C. vulgare | C. arvense |
| --- | --- | --- | --- | --- | --- | --- |
| Total length (bp) | 152586 | 152342 | 152576 | 152557 | 152567 | 152855 |
| IR length (bp) | 25191 | 25191 | 25806 | 25176 | 25076 | 25182 |
| LSC length (bp) | 83502 | 83254 | 83662 | 83486 | 83738 | 83859 |
| SSC length (bp) | 18701 | 18706 | 18742 | 18719 | 18677 | 18632 |
| Total gene number | 133 | 127 | 133 | 133 | 133 | 133 |
| CDS number | 88 | 83 | 88 | 88 | 88 | 88 |
| rRNA number | 8 | 8 | 8 | 8 | 8 | 8 |
| tRNA number | 37 | 36 | 37 | 38 | 37 | 37 |
| GC % | 37.69 | 37.72 | 37.71 | 37.70 | 37.70 | 37.71 |
| LSC GC % | 35.83 | 35.88 | 35.84 | 35.85 | 35.81 | 35.87 |
| SSC GC % | 37.49 | 31.34 | 31.37 | 31.38 | 31.39 | 31.37 |
| IR GC % | 43.11 | 43.11 | 43.20 | 43.11 | 43.20 | 43.11 |
| GenBank accession | . | NC_050046.1 | NC_044423.1 | NC_036966.1 | NC_036967.1 | NC_036965.1 |
## Expansion and contraction of IR regions
Many studies have identified variations in the length of chloroplast genomes when comparing IR regions, including boundary junctions within the same genus species. Considering that the chloroplast genome is regarded as the most conserved region, the appearance of expansion and contraction in IR regions could be a part of the genome evolution. Thus, we performed IRscope [41] with six Cirsium species to investigate the differences in IR regions (Fig 2). As a result, the rps19 gene showed across a junction between LSC and IR regions in C. nipponicum, C. arvense, C. eriophorum, and C. japonicum. On the other hand, the rpl2 gene was across the junction in C. vulgare and C. rhinoceros. *The* gene pattern around the IR junction of C. nipponicum was similar to that of C. arvense and C. eriophorum. Subsequently, multiple sequence alignment using chloroplast genome based on C. nipponicum IR regions revealed four deletions-two in ycf2 gene, one in trnI-GAU gene, and one in the intergenic region between rrn5 and trnR-ACG-and two insertions in ycf2 gene and the same intergenic region as deletion (S2 Table).
**Fig 2:** *Comparison of the IR regions and the junctions of LSC, IR, and SSC regions among chloroplast genomes of six Cirsium.C. arvnes, C. vulgare, C. eriophorum, C. rhinoceros, C. japonicum have NC_036965.1, NC_036967.1, NC_036966.1, NC_044423.1, NC_050046 accession numbers respectively.*
## Codon preference analysis
We analyzed the frequency of codon usage using the protein-coding genes of C. nipponicum, including the other five Cirsium species. As a result, isoleucine and leucine were the most abundant amino acids (10. $86\%$, $10.63\%$), while cysteine was the least encoded ($1.12\%$) in C. nipponicum (S3 Table). The percentage of the amino acids in the other five Cirsium species showed the same pattern as C. nipponicum (S1 Fig). Furthermore, all amino acids were found in the six Cirsium chloroplast genomes and exhibited codon preference except methionine and tryptophan. As we calculate the relative synonymous codon usage (RSCU) of C. nipponicum to measure the extent of codon bias, there were 30 codons with high preference (RSCU > 1) and 32 codons with low preference (RSCU < 1) out of 64 codons encoded 20 amino acids. The highest value of the RSCU codon was UUA (1.80–1.83), and the lowest codon was AGC (0.35–0.38) in all chloroplast. The patterns of RSCU values were similar to C. vulgare (Fig 3). Twenty-nine codons with RSCU values greater than 1 were codons ending with A or U, whereas 29 out of 32 codons with RSCU values less than 1 were codons ending with G or C.
**Fig 3:** *Heat map of relative synonymous codon usage values of chloroplast protein coding genes among six Cirsium species.*
## Repeat sequence analysis
Repeat elements have essential roles in characterizing genomes with particular perspectives. Especially in terms of conservativeness in the chloroplast genome, it can be helpful in species identifications. We identified dispersed repeats in six Cirsium species using REPuter (Kurtz et al., 2001) software (Fig 4A and 4B). The dispersed repeats were detected in three types of repeats (forward, reverse, palindromic) and ranged from 30 to 58 bp in length. Among these species, C. nipponicum contained the largest number of repeats and only carried a reverse type of repeats. The total number of dispersed repeats in C. nipponicum was 49, consisting of 28 forward, two reverse, and 19 palindromic repeat sequences. These repeats were located in various regions: three non-coding genes, 24 coding genes, 18 intergenic, and four intergenic spacers (IGSs) (S4 Table).
**Fig 4:** *The number and type of repeats in six Cirsium species.a. Frequency of three types dispersed repeats; b. Frequency of dispersed repeats by length; c. Frequency of simple sequence repeats (SSRs) motifs in different types; d. Frequency of four SSRs types.*
In addition to dispersed repeats, simple sequence repeats (SSRs), also known as microsatellites, were investigated using the MISA program [43]. There were 40 to 54 SSRs in Cirsium species, and mono-, di-, tri-, and tetra-nucleotide were detected in all Cirsium chloroplast genomes (Fig 4C and 4D). Most SSRs consisted of mono-nucleotide with the A/T motif, but the C/G motif was presented only in C. arvense and C. japonicuum. Moreover, C. nipponicum showed 43 SSRs with 26 mono-, 4 di-, 4 tri-, and 9 tetra-nucleotides. They were located in LSC regions ($74\%$) and SSC regions ($21\%$), and only a few in IR regions ($5\%$) (S5 Table).
## Divergence of hotspot regions
Highly variable regions in chloroplast genomes have been widely used in species identification studies. Since only morphological characteristics in Cirsium limit the distinction between each species, we performed multiple sequence alignment using six Cirsium species to find highly variable regions. As a result, there were 833 polymorphic sites, and the nucleotide diversity was calculated over the whole chloroplast genome (Fig 5). Among six Cirsium species, Pi values ranged from 0 to 0.01367 with an average of 0.00195. The highly variable regions that contain polymorphic sites were considered when Pi values were greater than 0.00743. The number of regions exceeding a given threshold was eight, with highly variable sites only in LSC and SSC regions (S6 Table). Three of the eight highly variable regions were located in coding sequences (trnD-GUC, ndhF, and ycf1), and the remaining five regions were spanned intergenic regions. Moreover, 18 specific variations were identified, mainly focusing on distinguishing C. nipponicum from other species (S7 Table). The regions that contained these specific substitutions were also in LSC and SSC regions.
**Fig 5:** *Sliding window of nucleotide diversity from the alignment of six Cirsium plastomes.*
## Phylogenetic analysis and species resolution
For a better understanding of the phylogenetic relationship among C. nipponicum and other species across tribes, phylogenetic analysis with two methods, Bayesian inference (BI) and maximum likelihood (ML), was conducted with *Gerbera jamesonii* as an outgroup. First, we achieved 20 complete chloroplast genomes from NCBI and then estimated the substitution model, known as the DNA sequence evolution model. Based on the best substitution models, TVM+F+I+G4 in the ML method and GTR+I+G in the BI method were applied to construct phylogenetic trees, and both results showed the same topology structure (Fig 6A). In the subtribe level, three Carlininae species and 17 Carduinae species were separated into each clade, and C. vulgare was the closest to C. nipponicum. In addition, we used matK and rbcL sequences to get more information about relationships between species and the resolution of speciation (Figs 6B and S2 and S3). Phylogenetic trees constructed by matK gene sequences showed similar results to complete chloroplast genome trees. The matK gene trees based on BI and ML methods had the same patterns of topology structure, and species were clustered by subtribe and genus levels. However, Cirsium species were split with low bootstrap values in the ML tree. When using rbcL gene sequences, we obtained six more sequences, 4 Sanger and 2 Illumina sequencing platforms, from the NCBI database to find the relationship of C. nipponicum sampled from Ulleung Island with others, especially with other Korean Cirsium and C. nipponicum KC589829.1, distributed in Japan. As a result, two of the rbcL trees had similar but low bootstrap values, especially low posterior probabilities around Cirsium species (S3 Fig). Moreover, Japanese C. nipponicum KNC589829.1 was close to Japanese C. tanakae and Korean C. japonicum, not to Ulleung Island C. nipponicum; however, C. nipponicum from Ulleung Island was still close to C. vulgare and C. arvense. The trees made by three sequence types revealed that C. nipponicum was far from C. japonicum and C. rhinoceros compared to C. vulgare and C. arvense in phylogenetic relationships.
**Fig 6:** *Phylogenetic trees based on the whole chloroplast genomes and the rbcL.(a) Phylogenetic relationship based on whole chloroplast genomes inferred by maximum likelihood (ML) with numbers beside the nodes representing the ML bootstrap values and Bayesian inference posterior probabilities; (b) Phylogenetic relationship based on the rbcL inferred by ML with numbers besides the nodes representing the ML bootstrap values.*
## Discussion
Although the advances in high-throughput sequencing technologies has facilitated rapid progress in the field of genomics as well as chloroplast genetics [23], limited chloroplast genomes of Cirsium species were available. Herein, we present the complete chloroplast genome of C. nipponicum for the first time and provide convincing evidence for the distinctive origin and evolution of C. nipponicum by analyzing genome structure and phylogenetic relationships among Cirsium species. As a result, GC contents in the IR regions of six Cirsium species were higher than both LSC and SSC regions, indicating the presence of rRNA [51, 52]. Besides, when considering that the GC content of the SSC region in C. nipponicum is relatively higher than others, GC-biased gene conversion (gBGC) related to intraplastomic recombination could be proposed as another cause of GC content pattern [53–55]. These GC content patterns and repeat elements are helpful in identifying speciation because of their polymorphism [56]. Identifying speciation based on a molecular marker such as a barcode system is important to the efficiency of species protection and management [29]. For DNA primer candidates, we found some repeats in several genes, including ndhA, ycf1, and near rRNA and IGS (S5 and S6 Tables). Furthermore, as these SSRs and dispersed repeats affect the genetic investigations such as population or phylogenetic relationship [15, 57], this study suggests its applicability to the evolution mechanism of Cirsium, especially in genetic structures of chloroplast genomes.
The codon usage bias is commonly observed in genomes of all organisms, including plants, such that understanding the evolutionary significance of its phenomenon was a common interest among biologists. The usage of synonymous codons for amino acids is not random, but it has bias [58], which is related to highly expressed genes and even plays a role in the evolution of chloroplast genomes [59, 60]. Since the chloroplast genome of plants is well-known to have the codon usage bias, the analysis of RSCU in the chloroplast of C. nipponicum can help understand genetic features and evolutionary process [61, 62]. Our results showed that the patterns of RSCU in C. nipponicum were more similar to C. vulgare than C. rhinoceros and C. japonicum (Fig 3). Hence, the preference for synonymous codons may imply a part of chloroplast genome evolution in Cirsium species.
We used five whole chloroplast genomes of Cirsium species available in the NCBI RefSeq database, considering the data validation and updates to reflect current knowledge, to perform comparative analyses. Compared with a previous study of three Carduus species that belong to the same subtribe as Cirsium, which reported nucleotide diversity with an average of 0.003442 and a peak of 0.0171 [63], our study showed that Cirsium species are more stable and conservative than Carduus species. Furthermore, the variation analysis results were consistent with the general feature, such that IR regions in the chloroplast of angiosperm were the most conserved region (S6 Table). Interestingly, when comparing the IR regions, C. niponnicum was close to C. japonicum, whereas the whole chloroplast genome was close to C. vulgare. Despite that expansion and contraction in IR regions are essential to the evolutionary process in chloroplast genome size [64, 65], variation in whole regions was more related to speciation within Cirsium species than in IR region. Recently, many researchers have used barcode systems for species separation using meta-barcode or universal mini-barcodes called matK and rbcL [66]. However, our constructed phylogenetic trees with matK and rbcL genes separately presented a low bootstrap value of ML and probability of BI, which indicate an unreliable topology, especially in matK (S2A Fig). Thus, we believe that phylogenetic trees using mini-barcodes could not be an appropriate method for speciation within Cirsium species.
As C. nipponicum is predominantly located on Ulleung Island, we initially thought it could be evolutionary similar to those close to the mainland or Japan, just like other plants growing on Ulleung Island. Ulleung *Island is* located about 137 km off the east coast of the *Korean peninsula* and was formed approximately 2 million years ago (Mya) [67, 68]. It is known to have about 600 taxa of vascular plants on Ulleung Island and is suggested to be derived and evolved from a founder population from the land close to the island, a mode of speciation known as anagenetic speciation [69]. However, our results showed that C. nipponicum was not grouped with two Korean species, C. rhinoceros and C. japonicum, or two Japanese species, C. nipponicum and C. tanakae (Figs 6 and S3). Moreover, C. nipponicum from Ulleung Island was more closely related to C. vulgare than others. The patterns of morphological characters in C. nipponicum are also distinct from other Cirsium species, such as C. japonicum and C. rhinoceros [1]. Additionally, the leaf shape of C. nipponicum is morphologically most similar to that of C. vulgare among the other Cirsium species around Ulleung Island (Fig 1C–1F). Therefore, C. nipponicum in Ulleung Island may not be originated from endemic species of Japan or Korea, but it may instead be derived from Russia [70], given the distribution of C. vulgare that is not distributed in Korea.
Based on the fact that the Cirsium species is known as a cosmopolitan [71], the probability of its dispersal to Ulleung Island can be inferred in several ways. One of the most effective methods to disperse the seeds of the family Asteraceae has been suggested as wind [72]. Although westerly winds are the dominant winds in Ulleung Island, dispersing by wind may be limited considering that there is no C. vulgare in the Korean peninsula, which is registered as invasive species by the Korean government [73]. Ocean currents are another possibility of dispersing, suggesting that dispersal of *Fangus via* floating masses from the north and south to Ulleung *Island is* possible [69]. Lastly, the dispersal of migratory birds traveling to Ulleung *Island is* another possibility. It has been reported that transporting seeds by birds may occur in Northeast China, Far East Russia, and Southern Korea and Japan [69] to the extent that reports of waterfowls passing through Ulleung Island were identified [74]. Some of these waterfowls were regarded as important vectors of exotic plant species [75]. Thus, endozoochory by waterfowls can be suggested as a factor explaining the dispersal of C. nipponicum on Ulleung Island. This study suggested that C. nipponicum of Ulleung Island originated from Cirsum other than Korean or Japansese endemic Cirsium, and has been adapted to the Ulleung Island environment.
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|
---
title: Combined non-psychoactive Cannabis components cannabidiol and β-caryophyllene
reduce chronic pain via CB1 interaction in a rat spinal cord injury model
authors:
- Anjalika Eeswara
- Amanda Pacheco-Spiewak
- Stanislava Jergova
- Jacqueline Sagen
journal: PLOS ONE
year: 2023
pmcid: PMC10010563
doi: 10.1371/journal.pone.0282920
license: CC BY 4.0
---
# Combined non-psychoactive Cannabis components cannabidiol and β-caryophyllene reduce chronic pain via CB1 interaction in a rat spinal cord injury model
## Abstract
The most frequently reported use of medical marijuana is for pain relief. However, its psychoactive component Δ9-tetrahydrocannabinol (THC) causes significant side effects. Cannabidiol (CBD) and β-caryophyllene (BCP), two other cannabis constituents, possess more benign side effect profiles and are also reported to reduce neuropathic and inflammatory pain. We evaluated the analgesic potential of CBD and BCP individually and in combination in a rat spinal cord injury (SCI) clip compression chronic pain model. Individually, both phytocannabinoids produced dose-dependent reduction in tactile and cold hypersensitivity in male and female rats with SCI. When co-administered at fixed ratios based on individual A50s, CBD and BCP produced enhanced dose-dependent reduction in allodynic responses with synergistic effects observed for cold hypersensitivity in both sexes and additive effects for tactile hypersensitivity in males. Antinociceptive effects of both individual and combined treatment were generally less robust in females than males. CBD:BCP co-administration also partially reduced morphine-seeking behavior in a conditioned place preference (CPP) test. Minimal cannabinoidergic side effects were observed with high doses of the combination. The antinociceptive effects of the CBD:BCP co-administration were not altered by either CB2 or μ-opioid receptor antagonist pretreatment but, were nearly completely blocked by CB1 antagonist AM251. Since neither CBD or BCP are thought to mediate antinociception via CB1 activity, these findings suggest a novel CB1 interactive mechanism between these two phytocannabinoids in the SCI pain state. Together, these findings suggest that CBD:BCP co-administration may provide a safe and effective treatment option for the management of chronic SCI pain.
## Introduction
Neuropathic pain often becomes a chronic debilitating condition that results from spinal cord injury (SCI), significantly reducing a patient’s quality of life [1–3]. To date, standard therapies for SCI pain such as opioids have low efficacy and are encumbered with undesirable side effects including tolerance formation and addiction [4–7]. Therefore, many novel approaches are currently being investigated to mitigate chronic neuropathic pain. Recently, there has been increased interest in constituents of the *Cannabis sativa* plant as an alternative treatment.
One of the barriers for widespread therapeutic use of cannabis are the effects caused by the major psychoactive component, delta-9-tetrahydrocannabinol (THC), which has led to differing views on its clinical efficacy and safety [8–10]. However, *Cannabis sativa* contains a multitude of other phytocannabinoids such as cannabidiol (CBD) and β-caryophyllene (BCP) which have been shown to have analgesic potential in reducing chronic pain and lack the psychotropic effects associated with THC [11–16]. Further, the use of CBD and BCP are not hindered by the same stringent federal regulations as THC and can both be purchased over the counter (OTC) thereby increasing accessibility to the general public. CBD oils derived from hemp, with undetectable THC, are now widely available. BCP is a sesquiterpene and major component (up to $35\%$) in the essential oils of Cannabis sativa, found in many other plant species, and available for use as a flavor-enhancing food additive. Taken together, this highlights the importance of exploring other phytocannabinoids that have safer therapeutic profiles and are easier to procure.
Numerous preclinical animal studies have assessed CBD and BCP’s analgesic potential in a variety of pain models. These studies have demonstrated that both CBD and BCP significantly reduce hypersensitivity in chronic pain states without any overt side effects [17–19]. Past research has demonstrated the therapeutic potential of individually administered CBD and BCP in peripheral neuropathic pain models such as chronic constriction injury (CCI), spared nerve injury (SNI), and diabetic neuropathy [11,14,20–23]. For example, administration of either CBD or BCP was effective in attenuating or reversing the development of mechanical hypersensitivity in the CCI or SNI model, respectively [14,20]. Since CBD and BCP are thought to act via distinct mechanisms, their combination could provide additive or enhanced antinociception. BCP has been hypothesized to act as a CB2-receptor-selective agonist [24–26] to produce pain-reducing effects in several rodent models, including chronic inflammatory and peripheral neuropathic pain [12,26,27]. Further, BCP has been shown to reduce inflammatory cytokines including tumor necrosis factor-α, interleukin-1β, and interleukin-6 [12,28,29]. In contrast, CBD shows little binding to CB1 and CB2 receptors; thus its mechanism is not well understood, but may involve activation of transient receptor potential channels of both vanilloid type 1 (TRPV1) or ankyrin type 1 (TRPA1) [14,30–32], indirect action via inhibition of endocannabinoid degradation [33,34], or serotonergic system activation via 5-HT1A receptors [14,35,36]. The goal of this study was to assess the potential use of these cannabis components to reduce SCI neuropathic pain. In addition, while tested individually, no studies have analyzed the pain-relieving efficacy of these two phytocannabinoids in combination. To address this, we first evaluated the individual analgesic efficacy of CBD and BCP in a rat chronic SCI pain model. We then explored whether the analgesic potential could be improved through co-administration of these cannabis constituents. Using an isobolographic approach we sought to determine whether additive/synergistic interactions between CBD and BCP result, and whether this combination strategy can provide increased attenuation of SCI-related neuropathic pain. Preliminary findings from this work have been reported previously [37].
## Animals
Male and female Sprague-Dawley rats (approx. 140–200 g, Envigo, MN) were used for the experiments. Animals were housed two per cage with corncob bedding and allowed free access to food and water in a 12-h light/dark cycle. Experimental procedures were reviewed and approved by the University of Miami Animal Care and Use Committee and followed the recommendations of the ‘Guide for the Care and Use of Laboratory Animals’ (National Research Council).
## Spinal cord injury
The method to induce SCI via clip compression [38] has been used successfully by our laboratory for pharmacologic antinociceptive evaluations and hypersensitivity evaluations over the past several years [39–46]. For all surgeries, aseptic surgical techniques were used. Rats were anesthetized with 4–$5\%$ isoflurane in O2 and maintained on 2–$3\%$ isoflurane/O2. The back of the rats, from lumbar to cervical vertebrae, were shaved and the skin was swabbed with antiseptic solution. Following incision of the skin, 2–3 thoracic vertebrae were removed and a laminectomy was performed to expose spinal cord segments T6-T8. An aneurysm clip 1 mm wide (20 g compression force; Harvard Apparatus) was oriented in a vertical position on an exposed spinal cord segment between T6-T7. The dura and spinal nerve roots were not disturbed and the clip was left in place for 60 s. The clip was removed, and the surgical area closed. Rats recovered in their home cages and were given free access to food and water. Following spinal compression, bladders were expressed a minimum of three times daily for 7–10 days or until voiding was regained. All behavioral testing began 4 weeks post-SCI once pain behaviors were fully expressed.
## Drugs administration
In order to utilize a readily available OTC source of CBD, Broad Spectrum CBD Gold Oil (Koodegras, Millcreek, UT) was used. BCP was obtained from Sigma-Aldrich (St. Louis, MO). The CB1 and CB2 receptor antagonists AM251 and AM630, respectively, the mu-opioid receptor antagonist naloxone, and mixed CB1/CB2 synthetic agonist WIN 55,212–2 were obtained from Sigma-Aldrich (St. Louis, MO). On each day of the experiment CBD was prepared in a 3:1:16 ethanol/Tween 80/$0.9\%$ NaCl plus $2\%$ Tween vehicle and BCP was prepared in a $5\%$ Tween in saline vehicle. CBD or vehicle were administered as an i.p. injection (volume = 0.3ml). BCP or vehicle was administered by oral gavage (18 oral feeding needle, volume = 0.3ml). For antagonist experiments, AM251, AM630, naloxone or vehicle were administered as a s.c. injection 0.5h prior to cannabinoid delivery. In the side effects assessment, positive control WIN 55,212–2 was administered s.c. 0.5h prior to testing. Morphine sulfate (Sigma) was prepared in saline and administered s.c. 30 min prior to CPP training. Additionally, although the focus of the study was assessment of OTC sources, since CBD oils contain trace amounts of minor cannabinoids, a small pilot comparison was done with known CBD (NIDA Drug Supply Program).
## Experimental design
All behavioral measurements were taken prior to SCI and immediately before drug administration at 4 weeks post SCI when animals demonstrated stable pain-related behavior, and then over a 3 week period with at least 72 hr wash out period between dosing as described in individual experiments below. The number of animals per group was determined at the beginning of the study by SigmaStat Power Analysis with the input data based on our previous studies and desired power set at 0.8 with alpha 0.050. For all experiments, animals were randomly assigned to the experimental groups and the experimenter was blind to all drugs or dose combinations being tested.
## Experiment 1—time course and dose response profiles
To determine time course and dose response profiles, CBD and BCP were individually administered at various doses. Animals were randomly assigned a dose of either CBD or BCP (or vehicle), tested, then allowed a 72 hr washout period before another dose administration and testing, until sufficient data was attained for each drug/dose. Tactile paw withdrawal threshold (PWT) measurements and acetone cold responses were taken at 0.5, 1, 1.5, 2, and 5 hr post CBD administration. PWT and cold responses were tested similarly but only up to 2 hr post BCP since all significant antinociceptive effects of BCP were resolved by that time point. A50 values, a concentration of a drug needed for half-maximal effect, for individual drugs were calculated (JFlash).
## Experiment 2—fixed-ratio combinations
To determine the analgesic potential of CBD/BCP coadministration compared to individual administration and to determine the optimal combination, various fixed-ratio combination doses. based on Experiment 1, were tested. Animals were randomly assigned a dose of CBD/BCP (or vehicles), tested, and then allowed a 72 hr washout period before another dose administration and testing, until sufficient data was attained for each dose combination. Since the A50 doses for both CBD and BCP, obtained from Experiment 1, differed for cold and tactile hypersensitivity, the cold and tactile combination evaluations were performed on different days in the combination studies, with a 72 hr washout period between each administration. Tactile PWT measurements and acetone responses were taken at 0.5, 1, 1.5, 2, and 5 hr post combination drug administration. An additional measure for tactile PWT was taken at 24 hr post administration in case of some residual antinociceptive effects at 5 hr.
## Experiment 3—side effects
To determine if CBD and BCP produce adverse cannabinoidergic effects, CBD and BCP were in combination at the highest therapeutic dose combination to maximize detection of any potential adverse side effects. Animals were tested with either CBD/BCP or synthetic CB agonist WIN 55,212–2 as a positive control, with 72 hr washout between treatments. To fully evaluate these cannabinoidergic side effects, intact non-SCI animals were needed due to physical limitations following SCI. However, a subgroup of SCI animals was also used to test some side effects when feasible. Rotarod latency was measured at 0.5, 1, 1.5, 2 and 24 hrs. Body temperature and catalepsy were measured at 0.5, 2, 5, and 24 hrs.
## Experiment 4 –antagonists
To determine potential antinociceptive mechanisms of the CBD/BCP combination, antagonists were administered 30 min prior to CBD/BCP coadministration at their combination A50 dose determined for each test from Experiment 2. Animals were randomly assigned an antagonist, with a 72 hr washout between antagonists until sufficient data was attained. Tactile PWT and acetone response measurements were taken at 0.5, 1, 1.5 2, 5 and 24 hr post drug administration.
## Experiment 5—conditioned place preference
To determine if repeated CBD/BCP administration can potentially decrease opioid seeking behavior, a conditioned place preference (CPP) test was used with 2x combined A50 dose. This dose was used in order to assure maximum antinociceptive benefits were achieved. Morphine or saline was administered 30 minutes prior to placing the animal in a place preference box and CBD/BCP was administered 1.5 hours prior to coincide with the time of peak antinociceptive effects.
## Behavioral analysis
Two standard sensory tests for tactile hypersensitivity using von Frey filaments and cold hypersensitivity with hindpaw acetone droplets were used. Behavioral testing for tactile and cold hypersensitivity was carried out by individuals blinded to the experimental groups. All testing was done during the light cycle (6am-6pm) in a designated animal testing room.
## Tactile hypersensitivity
For assessment of mechanical hypersensitivity, calibrated von Frey filaments ranging from 0.4 to 15 g were used [47]. Animals were placed in a clear plastic cage on an elevated wire mesh surface and allowed 15 min to acclimate. The Dixon up-down method was used and filaments were applied to the right plantar hind paw and kept in place for 6 s [48]. If a response was evoked the next lower filament was tested; if there was a negative response the next higher filament was tested. This process was repeated until a total of six responses were recorded. A positive response was recorded as a brisk withdrawal in conjunction with a supraspinal response to reduce potential confounding SCI spinal hyperreflexia. This requires the animal to also vocalize, orient their head towards the stimulus or groom the tested paw. Generally, uninjured rats do not respond to a force of <15 g. As such, an upper limit of 15 g was used in experiments since a greater force may lift the hind paw itself.
CBD was tested at 0.1–5.0 mg/kg i.p., dose range based on previous reports in other rodent pain models [14,23,58]. CBD reduced mechanical hypersensitivity in males in a dose- and time-dependent manner compared to vehicle (overall F[4, 25] = 24.21, $p \leq 0.0001$) with maximal reduction at 60 min post injection for 3 mg/kg ($p \leq 0.0001$) and 5 mg/kg doses ($p \leq 0.0001$) compared to vehicle (Fig 1A). Dose 5 mg/kg was significantly more potent compared to other doses at 60–120 mins post injection, with at least $p \leq 0.05.$ Dose 3 mg/kg was significantly more potent than other lower doses at 60 min post injection, with at least $p \leq 0.01.$ The CBD effects tended to return towards pre-drug baseline by 2–5 hours after administration. However, the highest dose 5 mg/kg showed more prolonged effects in reducing mechanical hypersensitivity, still apparent at 2–5 hrs post-injection with at least $p \leq 0.05$ compared with vehicle. CBD produced significant reduction in mechanical hypersensitivity compared to vehicle in females starting at 30 min post injection for the 5 mg/kg dose ($$p \leq 0.0034$$) and peaking at 60 min for the 5 mg/kg ($p \leq 0.0001$) and 3 mg/kg ($$p \leq 0.0363$$) doses. Dose 5 mg/kg was also significantly more potent at 60 min post injection compared to other doses, with at least $p \leq 0.05.$ The CBD effects appeared to be shorter lasting in females, returning towards pre-drug baseline by at approximately 90–120 min after administration. Overall F[4,20] = 2.404, $$p \leq 0.0449$$ (Fig 1B).
**Fig 1:** *Time course of CBD antinociceptive effects in male and female rats.Time plots of the effects of CBD (0.1, 1, 3 or 5 mg/kg) and matched vehicle on mechanical PWT in A) males and B) females, and acetone responses in C) males and D) females (n = 5–6 per treatment group). Animals were tested starting at 4 weeks post-SCI; pre-SCI baseline responses are also displayed as indicated by the star. *, **, ***, **** denote p < 0.05, 0.01, 0.001 and 0.0001 compared to vehicle for each treatment group. # denote p at least <0.05 between 5mg/kg (red #) and lower concentrations of CBD or 3mg/kg (blue #) and lower concentrations of CBD.*
BCP administration in males (Fig 3A) lead to maximum attenuation of tactile hypersensitivity at 60 min post injection at the 20 mg/kg and 50 mg/kg doses ($p \leq 0.0001$) compared to vehicle (overall F[3,20] = 13.08, $p \leq 0.0001$). Both doses were also more effective than the 10 mg/kg dose ($p \leq 0.0001$). Dose 50 mg/kg was also effective at 30 min ($$p \leq 0.0247$$) and 90 min post injection ($p \leq 0.0001$), dose 20 mg/kg was effective at 90 min ($$p \leq 0.0112$$). In females (Fig 3B) a dose of 50 mg/kg showed maximum effect at 60–90 mins post injection ($p \leq 0.0001$) compared to vehicle. The 20 mg/kg also produced effect at 60 min post injection ($$p \leq 0.0051$$). The 50 mg/kg dose was more potent than all lower doses at 60–90 mins post injection (at least $p \leq 0.05$). Overall F[3,17] = 7.316, $$p \leq 0.0023.$$
**Fig 3:** *Time course of BCP antinociceptive in male and female rats.Time plots of the effect of BCP (10, 20, 50 mg/kg) and matched vehicle on mechanical PWT in A) males and B) females and acetone responses in C) males and D) females (n = 5–6 per treatment group). Animals received a single oral administration at time 0h, 4 weeks post SCI surgery; pre-SCI data is also displayed (indicated by star). *, **, **** denote p < 0.05, 0.01, and 0.0001 compared to vehicle for each treatment group. A) ####p<0.001 for 50mg/kg vs 10mg/kg and 20mg/kg vs 10mg/kg B) #p at least <0.05 for 50mg/kg vs lower BCP doses.*
In males (Fig 5A), the highest concentration of CBD/BCP using the respective A50 doses in combination was 3 mg/kg CBD and 22 mg/kg BCP (dose ratio 3:22). The dose 3:22 was effective at 30–120 mins post injections, with maximum effects observed at 60 min and 90 min (all $p \leq 0.0001$ compared with vehicle). A strong effect was also observed for the 1.5:11 dose, peaking at 60 min post injection ($p \leq 0.0001$), with $$p \leq 0.0005$$ at 30 min and $$p \leq 0.0462$$ and $$p \leq 0.0164$$ at 90 and 120 mins post injection, respectively, compared to vehicle. The lowest dose combination of 0.8:5.5 was also effective compared to vehicle at 60 min ($p \leq 0.0001$) and 90 min post injection ($$p \leq 0.0001$$). Overall F[3,16] = 34.10, $p \leq 0.0001.$ For context and comparison with clinically utilized agents, gabapentin, which is widely used as a first line treatment for neuropathic pain including SCI, results in dose-related attenuation of tactile hypersensitivity in this SCI model, with effects of the highest dose (100 mg/kg) comparable to the combined CBD/BCP 3:22 dose in male rats [41]. Morphine is also comparatively effective at 3 mg/kg, but develops rapid tolerance and has high misuse and side effects risks [43].
**Fig 5:** *Time course of CBD and BCP in combination on SCI pain responses in males and female rats.Time plots of the effect of (A) CBD plus BCP on mechanical PWT in males; (B) CBD plus BCP on PWT in females; (C) CBD plus BCP on acetone responses in males; (D) CBD plus BCP on acetone responses in females (n = 5 per treatment group). All doses and dose ratios were determined from previous individual dose-response analysis as described in the text. *, **, ***, **** denote p < 0.05, 0.01, 0.001 and 0.0001 compared to vehicle for each treatment group.*
In females (Fig 5B), a similar trend was observed. Using the calculated A50s of the individual drugs in females, the highest starting concentration for the combination was 7 mg/kg CBD and 35 mg/kg BCP (7:35 dose ratio). This dose combination significantly reduced mechanical hypersensitivity at 30–120 mins post injection (at least $p \leq 0.01$ compared with vehicle during that time). The tested lower combination doses were also effective at 30–120 mins (at least $p \leq 0.05$ compared with vehicle). Overall F[3,16] = 11.58, $$p \leq 0.0003.$$
## Cold hypersensitivity
For assessment of sensitivity to a non-noxious cooling stimulus, responses to acetone droplets on the hind paw were measured [49]. A blunted 22 g needle was used to apply 100 μl of acetone onto the lateral margin of the hind paw. Acetone was applied for a total of 5 times with 2 min between applications. Response frequency (%) was calculated by the number of positive responses out of the five trials. In uninjured rats, acetone does not evoke a withdrawal response. Responses were marked positive only when a supraspinal response was observed in addition to paw withdrawal, such as a head turning towards the stimulus, paw licking, or shaking.
In males, 0.11 mg/kg dose showed no difference from vehicle throughout the test period. Doses 3 mg/kg and 5 mg/kg were significantly more potent compared to vehicle at 30–120 mins post injection, with at least $p \leq 0.05.$ Dose 1 mg/kg produced significant effects at 120 min post injection compared to vehicle ($$p \leq 0.0447$$). Overall F[4,20] = 14.41, $p \leq 0.0001$ (Fig 1C). Reduction in cold hypersensitivity was seen in females for the 5mg/kg dose from 60–120 mins post injection (at least $p \leq 0.05$). Dose 3mg/kg was effective at 60–90 mins (at least $p \leq 0.05$), dose 1 mg/kg was effective only at 90 min post injection ($$p \leq 0015$$). Overall F[4,20] = 6.043, $$p \leq 0.0023$$ (Fig 1D).
In males (Fig 3C), 50 mg/kg induced significant decrease in acetone responses at 60 min ($$p \leq 0.0068$$) and 90 min post injection ($$p \leq 0.0448$$) compared to vehicle; 20mg/kg was effective at 60 min post injection ($$p \leq 0.0403$$). In females (Fig 3D), 50 mg/kg reduced acetone responses at 60 min post injection compared to vehicle ($$p \leq 0.0107$$).
In males (Fig 5C), the starting concentration was determined as 1 mg/kg CBD and 20 mg/kg BCP (1:20 dose ratio) based on Experiment 1 results. All tested combination doses, including the ¼ dose combination induced significant reduction in responses to acetone stimulation starting at 30 min post injection and lasting up to 120 mins (at least $p \leq 0.001$ compared with vehicle). The most potent effects were observed for the 1:20 and 0.5:10 doses at 30–90 mins post injection with $p \leq 0.0001.$ Overall F[3,16] = 41.51, $p \leq 0.0001.$ In females (Fig 5D), the strongest effect on reducing cold hypersensitivity was observed for their highest combination doses (2 mg/kg CBD: 20 mg/kg BCP) at 30 min ($$p \leq 0.0067$$) and 60 min ($$p \leq 0.0002$$) post injection. The 1:10 dose was also effective at 30–60 mins ($$p \leq 0.0325$$ and 0.0126 respectively). Overall F[3,16] = 2.531, $$p \leq 0.0334.$$
## Conditioned Place Preference (CPP)
For assessment of ongoing pain, a subgroup of male rats with SCI and uninjured animals underwent CPP. The place preference apparatus is a two-chambered box with distinct walls. One chamber has black walls and the other chamber has black and white striped walls. Access to either chamber can be blocked by a removable divider. Animals were acclimated to the open two-chambered box for 30min/day for 2 days before training and testing. Morphine (0.3ml, 2 mg/kg, s.c.) was used as a reinforcing analgesic agent, in conjunction with or without the 2xA50 CBD/BCP dose (2.0 mg/kg i.p. and 16 mg/kg oral gavage, respectively) in order to evaluate potential effects of concomitant CBD/BCP administration on morphine-seeking behavior. This low dose of morphine was chosen because it has been shown in other models to produce a CPP in animals with chronic pain but not intact animals [50,51]. Saline (0.3ml, i.p.) was used as a CPP control treatment. Prior to CPP conditioning, SCI animals were divided into one of three treatment groups: morphine, morphine and CBD/BCP or saline. Uninjured animals were used to assess the extent to which this low dose morphine produced CPP in intact controls. For all groups, on day 0, the animal’s preferred side was determined by recording the time spent in each chamber. On conditioning days 1–5, morphine was paired with the rat’s non-preferred chamber in the morning and saline with their preferred chamber in the afternoon with the divider closed. On day 6, CPP was evaluated by recording time spent in each chamber with the divider removed and CPP scores calculated by subtracting the time spent in the non-preferred chamber prior to training from time spent in that chamber following morphine pairing.
## Side effects
To assess potential side effects common to cannabis, the highest antinociceptive dose combination CBD/BCP was evaluated for common side effects of the cannabinoid “tetrad” test, including locomotor dysfunction, catalepsy, and hypothermia, in comparison with saline controls.
Body temperature. Body temperature was measured by infrared touchless thermometer positioned to the left side of the trunk. Rotarod test: The effect of cannabis constituents on motor function was assessed with an accelerating rotarod apparatus (Harvard Apparatus) [52]. One day prior to testing, rats were briefly trained on the apparatus to get accustomed to it. The rotarod was accelerated 5–25 rpm over 60 s. Rats that did not fall off the rotarod prior to the 60s cut-off were assigned a latency of 60s. At each time point evaluated, the latency to fall (s) off the rotarod was recorded. Catalepsy test: Using the bar test [53], the forepaws of the rat were placed on a metal bar located 10 cm above a Plexiglass surface. At each time point evaluated, the total amount of time spent immobile was determined by a stopwatch.
## HEK293 CB1 cells
The HEK293 cell line expressing N-terminus hemagglutinin tagged-CB1 receptor was a gift from Prof. Ken Mackie, Indiana University (Bloomington, IN) for initial studies, and purchased from Kerafast, North Carolina for more recent analyses [54,55]. Cells were plated and maintained in Dulbecco’s Modified Eagle Medium: Nutrient Mixture F-12 (Gibco, Thermo Fisher, USA) supplemented with $10\%$ fetal bovine serum (Sigma-Aldrich, MO, USA) and antibiotics ($1\%$ penicillin, $1\%$ kanamycin, $0.5\%$ gentamycin; Gibco). Cells were kept in an incubator at 37°C and $5\%$ CO2. After reaching near to $100\%$ confluence, cells were split; media was removed and cells rinsed in Hank’s Balanced Salt Solution (Sigma-Aldrich). Cells were then treated with $0.25\%$ trypsin for 3 minutes for complete removal of cells from the flask and then neutralized with media. All cells were transferred to a 15ml tube and spun at 3500 rpm for 5 min at 4°C to pellet the cells. Afterwards, supernatant was discarded, and cells were resuspended in media. Cells were counted using $0.4\%$ trypan blue and transferred either to a 12well plate (50.000 cells per well), or 25ml flask (5–10 million cells) depending on the treatment.
## CB1 redistribution assay
Cells were plated in 12-well plates at 50,000 cells/well and cultured overnight. Wells were treated with vehicle (negative control), 3μM solution of WIN 55,212–2 (Sigma-Aldrich), CBD (0.5mg/ml), BCP (4mg/ml), CBD/BCP (0.5mg/ml:4mg/ml) and AM251 (1mg/ml, Sigma Aldrich) for 20 minutes. Cultures were rinsed with Hyclone phosphate buffered solution (PBS; Sigma-Aldrich), fixed with $4\%$ paraformaldehyde overnight and washed with Hyclone PBS. Fixed cells were immunostained overnight with Alexa 594 conjugated anti-hemagglutinin 1:500 (2μL/mL, Invitrogen, ThermoFisher, USA) to visualize the CB1 receptor [54,56,57]. Excess antibody was removed and cells were washed with PBS and counter-stained with 4’, 6-diamidino-2-phenylindole (DAPI) to reveal the nuclei.
## Statistical analysis
All statistical analyses were done in Graph Pad Prism 8.4.2. Data are expressed as mean±SEM with statistical significance taken at $p \leq 0.05.$ Dose–effect curves were obtained by converting the withdrawal thresholds to a percent maximum possible effect (MPE): ((drug threshold-baseline threshold)/(pre-injury–baseline))x100. A50 values and isobolographic analysis were determined using JFlashCalc software (University of Arizona). All behavioral tests were compared using two-way RM ANOVA with Tukey post-hoc test. Conditioned place preference data were analyzed by one-sample t-test with zero hypothesis and Wilcoxon test and one-way ANOVA for group comparisons.
## Results
We first evaluated the time course and dose responses of CBD and BCP individually to determine A50s. At least 3 doses of each agent were tested to generate time course and dose-response curves and calculate antinociceptive A50. Behavioral testing began at 4 weeks post SCI and showed mechanical and cold hypersensitivity responses differed over time after administration of CBD or BCP.
## Dose response
The MPE for each dose was calculated and used to create dose response plots and to determine A50 for each test (Fig 2). For tactile hypersensitivity, CBD A50 was 3.02 mg/kg in males (Fig 2A) and 7.5 mg/kg in females (Fig 2B). The tactile MPE calculated at 60 mins (approximate time of peak effect) for 5 mg/kg CBD (highest dose used) was $71.36\%$ (males) and $59.44\%$ (females). For cold hypersensitivity, CBD A50s were 1.23 mg/kg (males, Fig 2C) and 1.8 mg/kg (females, Fig 2D), respectively. The cold MPE at 60 mins for 5 mg/kg CBD was $83.33\%$ (males) and $73.33\%$ (females).
**Fig 2:** *Dose response curves for CBD antinociceptive effects in male and female rats.Dose response curves for the effect of CBD on mechanical paw withdrawal threshold in A) males and B) females and acetone responses in C) males and D) females. A50 for each treatment group is displayed. MPE for the highest dose of CBD is indicated as well. Data are shown as % maximal possible effect (% MPE) ± SEM.*
Experiment 1: Dose-response effect of BCP reduces tactile and cold hypersensitivity BCP was administered via feeding tube (oral gavage) at initial dose ranges of 10–50 mg/kg p.o. [ 27,59].
MPEs for each dose was calculated and used to determine A50 for each test (Fig 4). For tactile hypersensitivity, BCP A50s were 22.61 mg/kg in males (Fig 4A) and 35.22 mg/kg in females (Fig 4B). MPE at 60 mins for 50 mg/kg BCP (highest dose used) was $72.29\%$ (males) and $61.32\%$ (females). For cold hypersensitivity, BCP A50s were 19.03 mg/kg in males (Fig 4C) and 20.70 mg/kg in females (Fig 4D). MPE at 60 mins for 50 mg/kg BCP was $60.03\%$ (males) and $68.67\%$ (females).
**Fig 4:** *Dose response curves for BCP antinociceptive effects in male and female rats.Dose response curves for the effect of BCP on mechanical paw withdrawal threshold in A) males and B) females and acetone responses in C) males and D) females. A50 for each treatment group is displayed. MPE for the highest dose of BCP is indicated as well. Data are shown as % maximal possible effect (% MPE) ± SEM.*
In order to determine the A50s for the drug combinations, the MPEs for each drug/test/sex were determined (Fig 6). A50 values for males were 1.06 mg/kg and 7.91 mg/kg for CBD and BCP respectively for tactile hypersensitivity (Fig 6A), and 0.26 mg/kg and 5.12 mg/kg for CBD and BCP respectively for cold hypersensitivity (Fig 6C), when used in combination at the selected dose ratios as determined from Experiment 1. In females, A50 values were 5.60 mg/kg and 28.32 mg/kg for CBD and BCP respectively for tactile hypersensitivity (Fig 6B), and 0.69 mg/kg and 6.90 mg/kg for CBD and BCP respectively for cold hypersensitivity (Fig 6D). MPEs for the highest CBD and BCP combination doses were over $80\%$ in each test/sex, except for tactile hypersensitivity in females (MPE = $56.37\%$).
**Fig 6:** *Dose response curves for antinociceptive effects of CBD and BCP in combination in males and females.Dose response curves for the effect of CBD:BCP on mechanical paw withdrawal threshold in A) males and B) females and acetone responses in C) males and D) females. A50 for each drug in combination is displayed for each test. %MPE for the maximal doses of both drugs in combination is shown for each test. Data are shown as % maximal possible effect (% MPE) ± SEM.*
## Experiment 2: Co-injection of CBD and BCP enhances reduction in tactile and cold hypersensitivity
Next, we evaluated the effect of co-administration of CBD and BCP using their respective A50 doses for each test to assess for possible synergistic effects. The dose ratios for CBD and BCP were calculated from the approximate individual CBD:BCP A50s. Dose combinations tested were the initial A50 doses of CBD and BCP, ½ the A50 doses, and ¼ the A50 doses.
## Experiment 2: Synergistic effect of CBD/BCP for cold hypersensitivity
The individual A50 values of CBD and BCP were used to plot the theoretical line of additivity of the combined drug administration for each of the sets (Fig 7). The experimental A50 values obtained from the combinations were plotted on these to determine additivity or potential synergism or antagonism. This analysis showed that, for tactile hypersensitivity, the effects of the CBD/BCP combinations were additive in both males ($$p \leq 0.083$$) (Fig 7A) and females ($$p \leq 0.236$$) (Fig 7B). For reducing cold hypersensitivity, the effects of the CBD and BCP co-administration were synergistic (males: $$p \leq 0.024$$, Fig 7C; females: $$p \leq 0.021$$, Fig 7D).
**Fig 7:** *Isobolographs of CBD and BCP antinociceptive effects in combination in males and female rats.Isobolographic analyses for combination CBD and BCP treatment on mechanical paw withdrawal threshold in A) males and B) female rats and acetone responses in C) males and D) female rats. P values and the effect are indicated.*
## Experiment 3: Lack of significant side effects of CBD and BCP
To test for adverse effects of the antinociceptive CBD/BCP combination, we examined the traditional cannabinoid side effects profile. Intact rats were used for this, due to locomotor limitations of rats with SCI precluding full side effects analyses in rotarod and bar tests. A subset of SCI rats were evaluated using limited outcome tests. For all side effects testing, animals were administered the highest therapeutic doses 7:35 mg/kg CBD:BCP in order to detect any adverse effects of the combination. As a positive control, we also compared the side effects profiles of the combination to the mixed CB1/CB2 receptor agonist WIN 55212–2.
In both males and females, no significant differences were observed for rotarod latency or catalepsy in animals that received the CBD/BCP combination compared to baseline. In intact males, the CBD/BCP combination did not evoke any apparent detrimental effects in the rotarod test, with all latency values unchanged throughout the test. In contrast, WIN 55,212–2 injection led to decreased fall latency at 30–120 mins post injection compared to pre-injection baseline (p at least <0.01 throughout this time course). There were significant differences between treatments with overall F[1,10] = 23.25, $$p \leq 0.0007$$ (Fig 8A). In intact females, fall latencies in the rotarod test were overall comparable between treatment groups (Fig 8B). There was a transient non-significant drop in fall latency in the WIN 55212–2 group at 30 min post injection. Overall F [1,10] = 3.60, $$p \leq 0.0870.$$ We observed a transient increase in body temperature at 2 hours post CBD/BCP injection ($$p \leq 0.0151$$vs baseline) in intact males (Fig 8C); overall F [1,10] = 5.669, $$p \leq 0.0385.$$ A similar trend was observed in intact females, but this was not statistically significant (Fig 8D); overall F [1,10] = 0.3320. No effect of CBD/BCP was observed in the catalepsy bar test in either male or female non-injured rats. In contrast, WIN 55,212–2 induced significant catalepsy starting 30 min post-injection through 5 hours in male rats ($$p \leq 0.0099$$, 0.0049 and $p \leq 0.0001$ respectively vs baseline), and strong differences between groups were observed with overall F[1,9] = 63.23, $p \leq 0.0001$ (Fig 8E). In females, WIN 55,212–2 injection also caused significant catalepsy 30 min-5 hours post injection (with p value at least <0.05 compared with pre-injection baselines, and significant differences between the drug treatments (overall F[1,10] = 31.09, $$p \leq 0.0002$$; Fig 8F).
**Fig 8:** *Side effects profiles for CBD and BCP combination in male and female rats.Time course showing the effect of maximum utilized antinociceptive dose combination of CBD (7 mg/kg) and BCP (35 mg/kg) compared with WIN 55212–2 (3 mg/kg) on rotarod latency in A) males and B) females, body temperature in C) males and D) females, respectively, and catalepsy bar latency in E) males and F) females (n = 6 per treatment group). Animals received a subcutaneous injection of WIN 55212–2 or an intraperitoneal injection and single oral administration of CBD:BCP following baseline determinations. *, **, ***, **** denote p < 0.05, 0.01, 0.001 and 0.0001 compared to baseline for each treatment group.*
In SCI animals, no significant effects of the CBD/BCP combination were observed in body temperature or bar tests ($$p \leq 0.8650$$ compared with pre-injection baseline; S1 Fig). However, significant hypothermic effects of WIN 55212–2 were observed in this group from 30 min– 2 hrs post-injection in both males and females (p at least <0.01). There was also a transient (at 30 min) significant enhanced catalepsy behavior in male SCI rats ($$p \leq 0.0418$$).
## Experiment 4: Attenuation of antinociceptive effects of the CBD/BCP combination by selective antagonists suggests interaction at the CB1 receptor
We examined the potential contributing role of CB1, CB2 or opioid receptor interactions on the antinociceptive actions of the combined CBD and BCP by pre-treating animals with selective receptor antagonists AM251, AM630, or naloxone 30 minutes prior to co-administering CBD/BCP at their determined A50 combination doses.
The CB1 antagonist AM251 strongly attenuated the antinociceptive effects of CBD/BCP on tactile hypersensitivity in males (Fig 9A) at 60–120 mins post injection (at least $p \leq 0.01$; overall F[3,17] = 10.37, $$p \leq 0.0004$$). In females (Fig 9B), AM251 also strongly attenuated the antinociceptive effects of CBD/BCP at 60–120 mins post injection (at least $p \leq 0.05$, overall F[3,16] = 23.46, $p \leq 0.0001$). AM251 also strongly attenuated the antinociceptive effects of CBD/BCP on cold hypersensitivity in males (Fig 9C) at 30–120 mins post injection (at least $p \leq 0.05$; overall F [3,17] = 9.879, $$p \leq 0.0005$$). This however was not observed for cold hypersensitivity in females (overall F[3,16] = 1.151, $$p \leq 0.3588$$). The other antagonists tested only showed transient and modest effects in some of the groups (e.g. AM630 at 60 min on tactile hypersensitivity in males; naloxone at 60 min on cold hypersensitivity in females), but were overall ineffective in reducing the robust antinociceptive effects of the CBD/BCP combination in SCI rats.
**Fig 9:** *Effect of selective antagonists on the antinociceptive effects of CBD and BCP combination.Time plots of the antinociceptive effects of combined CBD (0.5, 1.5 mg/kg) and BCP (10, 15 mg/kg) following antagonist administration: AM251 (3 mg/kg), AM630 (1 mg/kg) or mu-opiod receptor antagonist naloxone (5 mg/kg) on mechanical PWT in A) males and B)females, and acetone responses in C) males and D) females, respectively (n = 5 per treatment group). Animals received a subcutaneous injection of the antagonist or saline 30 min prior to CBD/BCP administration in SCI rats. *, **, ***, **** denote p < 0.05, 0.01, 0.001 and 0.0001 (color coded), compared to vehicle for each treatment group.*
Since the apparent significant contribution of CB1 receptor-mediated effects of CBD/BCP combination was somewhat surprising, as neither of these agents have been thought to act via CB1, an additional comparison was done in retrospect with purified CBD in case of trace additional cannabinoids in the OTC CBD oil contributing to its antinociceptive effects. Results from this pilot comparison are shown in S2 Fig. Findings suggest potential modestly increased antinociceptive effects of CBD oil over pure CBD in males, but this was not significant likely due to the high degree of variability in the purified CBD groups.
## Experiment 5: Morphine seeking behavior is attenuated by CBD/BCP
To examine whether repeated CBD/BCP administration may reduce ongoing basal pain and consequently reduce opioid-seeking behavior, we used a low dose of morphine (2 mg/kg) as an analgesic reinforcing agent. 2x A50 doses were used for this part of the study to assure maximal antinociceptive benefits from the CBD/BCP in rat males SCI prior to morphine exposure. In non-injured intact animals, this low dose of morphine was not reinforcing and morphine CPP did not develop (Fig 10). In SCI animals not being treated with CBD/BCP, significant CPP to morphine developed, in comparison with SCI animals paired with saline only ($$p \leq 0.0078$$). In contrast, this was attenuated, with no significant morphine CPP observed in rats that had received CBD/BCP administration during their morphine conditioning. In addition, there were no significant differences between the CBD/BCP SCI group and the uninjured morphine group.
**Fig 10:** *Conditioned place preference measurements.Data are shown as differences in time spent on the non-preferred side after conditioning minus pre-conditioning. Morphine (2 mg/ kg) was used as the conditioning analgesic agent in SCI or non-injured male rats; saline vehicle was used as conditioning control in SCI animals. Animals in the CBD/BCP group received CBD/BCP 1 hour prior to morphine during CPP training. ** denotes p < 0.01 compared to saline-paired SCI group.*
## Discussion
This study demonstrated that both CBD and BCP individually reduce hypersensitivity in males and females in a rat spinal cord injury pain model. Further, the co-administration of CBD:BCP synergistically attenuated cold hypersensitivity in both males and females with additive effects seen for reducing tactile hypersensitivity in males. Minimal cannabinoidergic-like side effects were observed by the combination. Together, findings from this study suggest that these non-psychoactive cannabis components may be an effective and readily attainable option for managing challenging neuropathic pain resulting from spinal cord injury.
Chronic pain following SCI is estimated to occur in up to $70\%$ of patients, with at least one-third of patients rating it as so severe that it is their primary impediment to participation in daily activities and social well-being [1,60–63]. Although rigorously controlled studies have not been conducted for SCI pain, anecdotal reports from SCI patients with chronic pain have reported substantial pain relief from marijuana and whole plant medicinal extracts, suggesting the possibility that cannabinoids may be of particular value as a treatment option for this indication [64–68]. Cannabinoids have been shown to be effective in attenuation of pain-related behaviors in a wide variety of rodent inflammatory and peripheral neuropathy models, primarily via interaction with peripheral or spinal nociceptors [14,16,23,58,69–74]. However, there have thus far been limited studies evaluating the effects of naturally-derived cannabis components in preclinical SCI pain models [75,76] and only a few investigating synthetic CB$\frac{1}{2}$ agonists for this indication in preclinical models, primarily from work on our laboratory [41,43,77]. Further, strong CB1 agonists can additionally mediate undesirable CNS effects with systemic administration. Since the cannabis plant produces a wealth of cannabinoid compounds and terpenes acting via distinct mechanisms, beneficial analgesic effects in the absence of undesired side effects may be possible to achieve due to additive or synergistic contribution of several complementary components. Thus the goal of the current study was to explore the combination of two predominant and readily available OTC cannabis components with good safety and purportedly distinct mechanistic profiles to target SCI pain.
Results from this study showed that systemic administration of CBD or BCP individually can dose-dependently reduce SCI-related hypersensitivity in both male and female rats. Systemically administered CBD has shown dose-related moderate effectiveness in other rodent neuropathic pain models [69,70,78]. In the SCI model, CBD appeared more effective in reducing cold hypersensitivity than tactile hypersensitivity in both sexes, with more robust and prolonged attenuation of SCI-induced acetone responses, particularly at the higher doses (3–5 mg/kg). Effects on tactile hypersensitivity were more modest and short-lived, with only partial attenuation at the highest dose. Systemic BCP alone also reduced SCI tactile and cold hypersensitivity, consistent with studies in other neuropathic pain models [12,20,26]. Following BCP administration we observed dose-dependent antinociceptive effects for both sexes. Although anti-allodynic effects were observed with BCP, this agent alone showed overall lower efficacy and shorter duration compared to CBD in reducing hypersensitivity in both sexes, even at the highest doses.
Upon coadministration of CBD and BCP, we observed dose-dependent reduction in SCI mechanical and cold hypersensitivity. Using isobolographic analysis, findings revealed that there was an additive effect in reducing mechanical hypersensitivity in males and a synergistic effect in reducing cold hypersensitivity across both sexes. Changes in MPE indicated that the coadministration of these two agents can improve the potency and efficacy of both CBD and BCP. In females, we observed an increase in the efficacy of CBD in reducing cold hypersensitivity (from %MPE = $73.33\%$ individually to $88.33\%$ in combination at highest doses), as well as an increase in CBD potency from A50 1.8 mg/kg to A50 0.69 mg/kg and increased BCP potency from A50 20.7 mg/kg to 6.9 mg/kg. In males, CBD maintained similar high efficacy in reducing cold hypersensitivity both when individually administered and in combination with BCP, but at substantially lower doses of both agents (from A50 1.23 mg/kg CBD and 19.03 mg/kg BCP with individual administration to A50 0.26 mg/kg CBD and 5.1 mg/kg BCP in combination). The combination also increased both efficacy and potency in reducing tactile hypersensitivity in males (CBD A50 from 3.02 mg/kg to 1.06 mg/kg; BCP A50 from 22.6 mg/kg to 7.9 mg/kg), and reaching nearly full reversal of SCI induced tactile hypersensitivity with the highest dose combination.
Interestingly, the effects of both compounds and the combination appeared less effective in females compared to males notably in reducing SCI induced tactile hypersensitivity. Numerous differences were observed throughout the study, including the duration of anti-allodynic effects of CBD individually and in combination with BCP, the potency and efficacy of CBD and BCP alone, and especially in the CBD/BCP combination. While SCI tactile hypersensitivity could be nearly completely reversed in males receiving the combination treatment, only partial attenuation was achieved in females despite the higher doses of both CBD and BCP used in the latter. These data suggest that there is some reduced effectiveness of cannabinoids in reducing SCI tactile hypersensitivity in females, both in response to CBD or BCP individually, in particular in response to the CBD/BCP combination, and underscore the importance of considering potential sex differences when developing cannabinoid-pain reducing strategies. Sex differences in responses to cannabinoids, including antinociceptive and locomotor effects in rodent models have been observed in numerous previous reports [27,79–83]. This has been hypothesized to result from differences in cannabinoid absorption, distribution, and metabolism, expression and contribution of CB1 and CB2 receptors and the endocannabinoid system, and/or interactions with gonadal hormones. However, findings have been inconsistent, with some reporting greater, lower, or equivalent antinociceptive effects in females vs males, depending on the etiology, outcome measure, and phenotype. Interestingly, in preclinical rodent acute and inflammatory pain models, females have been reported to show fairly consistently higher sensitivity than males to cannabinoid agonists such as THC and synthetic CB agonist CP55,940 [80,82,84], which seems in contrast with observations from the current study suggesting that the potential CB1-mediated antinoceptive effects are stronger in males than females. However, similar to our current findings, a CB1 agonist was found to require 30-fold higher dose in females than males in reducing mechanical hypersensitivity in a rat myositis model [81]. There have been fewer comparative studies with CBD alone, although a recent report showed no effect on females compared with males in a formalin test [85]. In addition, the stabilized CBD precursor cannabinoid, CBDA-ME (cannabidiolic acid methyl ester) was shown to produce significant anti-hyperalgesic effects in males with peripheral neuropathic cuff injury, but had no effect on females [86]. With regard to BCP, more pronounced reduction in persistent inflammatory responses (formalin test) have been reported in males than females [27]. Thus, the current findings following these individual treatments are consistent with the limited preclinical literature. There have also been some reports using combination approaches, albeit not the current CBD/BCP combination. For example, using other *Cannabis sativa* terpenes in combination with synthetic CB agonist WIN55,212, boosted cannabinoid activity in acute pain responses (tail flick) was equivalentally observed in all cases in males and females except for terpene linalool, which produced the same effects alone in both sexes, but greater potentiation of antinociceptive effects in males when in combination with the WIN55,212 [87]. There have been no comparative studies comparing sexually dimorphic effects of cannabinoids on SCI pain. However, spinal cord injury has been shown to produce dramatic increases in the spinal endocannabinoid system early after SCI, including increased levels of endocannabinoids and CB1 and CB2 receptors which may be involved in early neuroprotective effects, followed by later reduced expression [88–90].
Changes in CB receptors in higher brain processing regions have also been observed following SCI [91]. There are also reported increases in spinal endocannabinoid and CB1 receptors in peripheral neuropathic pain models in male rats [92,93]. In a recent evaluation of sex-related differences in a chemotherapy model of neuropathic pain, numerous differences were found in the DRG endocannabinoid components [94]. Similarly, in an orofacial myositis model, significant upregulation of CB1 receptor mRNA levels were found in trigeminal ganglia of male but not female rats, in parallel with a markedly reduced mechanical hypersensitivity by a selective CB1 agonist in males compared with females [81]. The latter was attributed to a testosterone role in the upregulation of CB1 receptors following myositis. Thus, the current observation of potential sex-related distinctions in responsiveness to cannabinoids may result from differences in endogenous cannabinoid system regulation following SCI. There are also emerging interesting sexually dimorphic T-cell differences in males but not females in parallel with improved neuropathic mechanical hypersensitivity following CBD and THC [95]. Further study of these potential differences will be critical moving forward towards clinical application.
To determine potential cannabinoidergic pharmacologic mechanisms mediating the effects of combined CBD and BCP, we pretreated animals with either CB1-selective antagonist AM251 or CB2-selective antagonist AM630 prior to administration of antinociceptive CBD/BCP combination. The opioid antagonist naloxone was also tested to assess for contribution of host opioid-mediated effects. Unexpectedly, the antinociceptive effects of CBD/BCP combination were nearly completely blocked by the CB1 antagonist (in both males and females for tactile hypersensitivity and males for cold hypersensitivity). Neither CB2 receptor antagonist nor opioid receptor antagonist resulted in substantial attenuation of effects. This finding suggests that, when used in combination, antinociceptive mechanisms involves CB1 receptor pathways. While our current study and previous literature showed that CBD and BCP can individually reduce hypersensitivity, their combined antinociceptive mechanism may differ from their purported individual mechanisms. Neither of the components of the CBD/BCP combination are thought to individually produce their antinociceptive effects via CB1 receptors according to the existing literature. In particular, BCP pharmacologic effects are nearly always attributed to CB2 receptor agonist activity, both in pain and other inflammatory tissue injury models [12,24–27,96–99]. There are additional potential neuropathic pain targets suggested for this terpene in addition to CB2 activation, but does not appear to involve CB1 when individually administered [100].
The current SCI model appears to involve both neuropathic and inflammatory components, as anti-inflammatory mediators in spinal cord tissue and surrounding CSF are markedly increased following this injury as reported previously in our lab [101,102]. The current SCI model appears to involve both neuropathic and inflammatory components, as anti-inflammatory mediators in spinal cord tissue and surrounding CSF are markedly increased following this injury as reported previously in our lab [101,102]. We have recently also found a reduction in a phantom limb pain model by CBD/BCP combined administration, along with reduced spinal inflammatory markers [103]. However, CB2 mechanisms are not likely to be the primary contributor to this chronic SCI pain, as previous findings in our lab have shown that selective CB1 antagonists, but not selective CB2 antagonists, block the analgesic effects of synthetic mixed CB1/CB2 agonists WIN 55,212–2 or CP 55,940 in this model [42,104]. In addition, anti-inflammatory agents have only modest beneficial effects in reducing clinical chronic SCI pain.
Thus, there is likely an additional contributing mechanism in the current observed robust effects of the combined CBD-BCP treatment. These results suggest the possibility that there are spinal cord injury-induced changes in cannabinoidergic pain processing leading to increased sensitivity to dorsal horn CB1 mediated effects. Previous findings in our lab have shown that SCI pain is particularly and uniquely sensitive to synthetic cannabinoid treatment in comparison with other pain models [42,43,104]. Thus, the current effective CBD/BCP combination may, directly or indirectly via downstream effects, activate novel upregulated antinociceptive CB1 sites or induce changes in endocannabinoid levels acting at CB1 receptors as described above. This interesting observation will be pursued in future studies. As a caveat, the original plan of the study did not include antagonist evaluations of the separate CBD and BCP components, as their likely mechanisms had already been reported in the literature and the CB1 dependence of the combined CBD/BCP had not been anticipated; thus, possible individual interactions with CB1 receptors cannot be ruled out. However, in light of this unexpected finding suggesting a CB1 receptor role in the combined CBD/BCP effect, we initiated pilot in vitro internalization assays to indicate whether this may involve a direct effect on CB1 receptors. Using CB1 receptor expressing cells, results suggested that CBD alone produces little effect, and BCP alone produces marginal CB1-activated receptor internalization, while the combination of CBD and BCP appears to enhance CB1 internalization, and this is blocked by CB1 antagonist AM251 (S3 Fig). Thus, it is possible that these drugs can allosterically facilitate one another’s effect on CB1 receptors, and further supports a contribution of CB1 receptor activation, at least in part, to the observed SCI antinociceptive effects.
Another possibility that may account for the apparent key role of CB1 receptors is the presence of trace amounts of other cannabinoids found in OTC CBD oils that may act via CB1 receptors. The CBD Gold Oil used in the current study contains $0.04\%$ CBD-V, $0.01\%$ cannabigerol, $0.03\%$ cannabinol, and $0.07\%$ cannabichromene in addition to $5.45\%$ CBD (undetectable delta-9 THC, delta-8 THC, THC-V, THC-A, cannabigerol-A, and CBD-A), according to the certificate of analysis (Koodegras; Millcreek, UT). While the aim of the study was to evaluate readily accessible CBD oil, a pilot comparison with purified CBD provided via the NIDA Drug Supply Program, showed essentially comparable albeit marginally higher antinociceptive effects of the same doses of CBD oil. Although unlikely to account for the robust observed CB1 effects of the CBD/BCP combination, this possibility will be pursued further in future studies.
In conclusion, the current findings indicate that the combination of readily accessible non-psychoactive cannabis components CBD oil and BCP may be particularly effective in reducing neuropathic pain resulting from spinal cord injury. In addition, cannabinoid-like side effects were minimal using this combination. Further, the observed decrease in opioid-seeking behavior suggest that this treatment may be useful as a supplemental therapeutic to reduce opioid needed for effective pain management. Together, these findings are supportive of the beneficial effects of combining cannabis components in the armamentarium for chronic pain management.
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|
---
title: 'Determinants of Treatment Adherence and Health Outcomes in Patients With Type
2 Diabetes and Hypertension in a Low-Income Urban Agglomerate in Delhi, India: A
Qualitative Study'
journal: Cureus
year: 2023
pmcid: PMC10010632
doi: 10.7759/cureus.34826
license: CC BY 3.0
---
# Determinants of Treatment Adherence and Health Outcomes in Patients With Type 2 Diabetes and Hypertension in a Low-Income Urban Agglomerate in Delhi, India: A Qualitative Study
## Abstract
Background Diabetes and hypertension (HTN) are increasing threats to global public health. Despite evidence of effective management of diabetes and HTN by medications that help in the prevention and reducing mortality of the disease, a large proportion of people either remain undiagnosed or untreated, especially in low-resource countries. This study was conducted to explore the patient treatment pathway and their health-seeking behavior in a low-income urban area.
Methodology We conducted 45 in-depth interviews of adult patients affected by type 2 diabetes mellitus (DM) and/or HTN on treatment for at least two years and attended the weekly clinic catering to an urban resettlement colony in the Northeast district of Delhi. Interviews were conducted and transcribed into Hindi and translated into English. Data analysis was done using Microsoft Excel. The patient treatment pathways were mapped, and their health-seeking behavior, treatment adherence, and experiences were described.
Results Most patients reported taking treatment from the government primary health facilities due to optimal healthcare accessibility as the prescribed drugs for DM/HTN control were available free of cost at these healthcare facilities. Those who visited private facilities thought of shorter waiting times and the quality of drugs. Patients also had little knowledge of complications of diabetes and hypertensive disorders. Nearly $25\%$ of patients had poor adherence to the medications, and lifestyle modification was rarely practiced by patients although they were aware of the same.
Conclusions Expanding the role of community health workers or volunteers in providing information on noncommunicable diseases might help improve patient treatment pathways to care.
## Introduction
Noncommunicable diseases (NCDs) after an ongoing epidemiological and demographic transition globally account for an estimated two-thirds of the global disease burden in 2019 compared to one-third in 1990 [1]. NCDs being incurable, the failure to achieve optimal control due to nonadherence to prescribed treatment and lifestyle modifications accelerates the premature incidence of complications, reduced quality of life, and excess mortality, which is disproportionately concentrated in the low- and middle-income countries (LMICs) [2].
Appropriate care-seeking behaviors in the context of chronic diseases reflect adherence to self-care practices and a continuum of care with licensed healthcare providers to enable successful disease management and achieve improved health outcomes [3]. However, both restricted healthcare system accessibility and adverse social determinants of health such as low socioeconomic status (SES), female gender, illiteracy, and low education contribute to inappropriate care-seeking, which correlates with poor health outcomes [4,5]. Elderly populations have a significantly higher prevalence of NCDs but are also at increased risk of deficient care-seeking, particularly in the absence of familial and social support [6,7].
Public primary healthcare (PHC) services in developing countries are considered the cornerstone in the provision of free and equitable delivery of healthcare services for preventive, promotive, and curative health [8]. PHC services through qualified general-duty medical officers are adequate for most patients with DM and HTN in achieving their recommended health targets and specialist consultation is required in only a fraction of the cases [9,10]. Urban healthcare services have, however, disproportionately focused on secondary and tertiary-based curative services, which can deprive the most vulnerable urban poor populations living in urban slums and resettlement colonies, of maintaining continuum of care for chronic disease management [11,12].
India has globally the second-highest diabetes burden in the world accounting for over 69 million patients [13]. Hypertension (HTN) is estimated to directly account for $57\%$ of all stroke deaths and $24\%$ of all coronary heart disease (CHD) deaths in India [14]. PHC services in urban areas of the country at urban primary health centers (UPHCs) that are staffed mostly by allopathy medical officers who at least have an MBBS degree with or without an additional specialist (MD/MS) degree. All services are provided free of cost without any user charges inclusive of consultation, generic drugs from an essential drug list, and select laboratory investigations. However, a referral from UPHCs is not mandatory for availing specialist outpatient consultation services from higher facilities consisting of secondary and tertiary care government hospitals. A major reason for high out-of-pocket expenses incurred in Indian outpatient health settings among patients with NCDs is the lack of assured and sustained availability of diagnostics and drugs and the absence of public health insurance coverage in outpatient health departments [15,16].
There is a paucity of information on the patterns of health and treatment-seeking behavior of patients with DM and HTN in low-income urban agglomerates of Delhi. Thus, this study was done to identify treatment-seeking behavior pathways of patients with diabetes and HTN in a low-income urban area of Delhi, India.
## Materials and methods
Study design and setting This was a qualitative study conducted in the outpatient settings of a Delhi Government Dispensary (DGD), also the primary health facility catering to an urban resettlement and slum population in the Northeast district of Delhi. The site was purposively selected as it is the field practice of the Department of Community Medicine of a major government hospital in Delhi.
Adult patients with type 2 diabetes mellitus (DM) and/or HTN on treatment for at least two years and attending the weekly NCD clinic at the DGD were eligible to participate in this study. Patients with preexisting serious comorbidities such as cancer, advanced renal failure requiring dialysis, advanced COPD, and advanced cardiovascular disease were excluded.
Data collection Data were collected through in-depth interviews, with the participants each lasting nearly 20-25 minutes. A semistructured interview guide adapted from a previous study in low-resource settings was used to assess patients' perspectives on treatment adherence and diabetes and HTN-related health outcomes [17,18]. A maximum of five participants were interviewed in a single day from August 2021 to November 2021. A short, structured patient interview schedule was used to collect sociodemographic and clinical information from the participants. The SES was measured with the modified Kuppuswamy scale updated for the 2021 consumer price index [19].
The study involved in-depth interviews conducted by trained field investigators (one male and one female). Interviews were conducted in the local language, Hindi, and audio-recorded following written informed consent. All interviews were transcribed in Hindi by the interviewers themselves, translated into English, reviewed line by line, and double-checked by two investigators.
Ethical considerations The study was approved by the Institutional EethicsCcommittee, Maulana Azad Medical College, and Associated Hospitals, New Delhi (F.1/IEC/MAMC/($\frac{84}{02}$/2021/No383). All the study participants provided written and informed consent. The study team made efforts to ensure interviews were undertaken while maintaining the privacy and confidentiality of the participants.
Data analysis To ensure the validity and reliability of analyses, the interview transcripts were separately and independently coded by two of the investigators and any differences were resolved by discussion. Manual coding was done using an inductive approach using Microsoft Excel including line-by-line analysis. Thematic analysis was used for the identification of recurrent themes by applying the standardized Erlingsson and Brysiewicz framework [20].
Participants' recruitment ceased following thematic saturation. Interviews were recorded and transcribed in full. Each transcript contains the assigned participant number and age, sex, health condition, and comorbidity status of the participant.
## Results
The study participants included 21 diabetes and HTN comorbidity patients, nine diabetes patients without HTN, and 15 hypertensive patients without diabetes. The mean (±SD) age of the study participants was 54.7 (±10.9) years. There were 32 women and 13 men participants (Table 1).
**Table 1**
| Characteristics | Number | Percentage |
| --- | --- | --- |
| Age | Age | Age |
| Mean (SD) | 54.9 (10.7) | |
| Median (IQR) | 56 (45-63) | |
| 30-39 years | 5 | 11.1 |
| 40-49 years | 7 | 15.6 |
| 50-59 years | 13 | 28.9 |
| >= 60 years | 20 | 44.4 |
| Gender | Gender | Gender |
| Female | 32 | 71 |
| Male | 13 | 29 |
| Education status | Education status | Education status |
| Illiterate | 28 | 62 |
| Literate | 17 | 38 |
| Occupation | Occupation | Occupation |
| Homemaker | 28 | 62.2 |
| Unemployed | 2 | 4.4 |
| Semiskilled worker | 6 | 13.3 |
| Unskilled worker | 9 | 20.0 |
| Socioeconomic status | Socioeconomic status | Socioeconomic status |
| Lower | 11 | 24.4 |
| Upper lower | 23 | 51.1 |
| Lower middle | 9 | 20.0 |
| Upper middle | 2 | 4.4 |
| Upper | 0 | 0 |
Nearly three in four participants belonged to the lower SES and a majority were illiterate and none had any health insurance meeting outpatient expenses. None of the patients with DM were currently on insulin therapy.
A majority ($53.6\%$) of patients with DM had poor glycemic control ($$n = 30$$). Routine follow-up investigations for monitoring the health of patients with DM/HTN were delayed in most of the participants (Table 2).
**Table 2**
| Characteristics | Number | Percentage | Unnamed: 3 |
| --- | --- | --- | --- |
| Glycemic control (n = 30) | Glycemic control (n = 30) | Glycemic control (n = 30) | Glycemic control (n = 30) |
| Good | 14 | 46.7 | |
| Poor | 16 | 53.3 | |
| BP control (n = 36) | BP control (n = 36) | BP control (n = 36) | BP control (n = 36) |
| Good | 21 | 58.3 | |
| Poor | 15 | 41.7 | |
| Adherence to investigation | Adherence to investigation | Adherence to investigation | |
| Plasma glucose | Plasma glucose | Plasma glucose | |
| Yes (within 90 days) | 20 | 44.4 | |
| Delayed | 11 | 22.4 | |
| Not done within one year | 7 | 15.6 | |
| Never done | 7 | 15.6 | |
| SMBG | SMBG | SMBG | |
| Yes (within seven days) | 4 | 8.9 | |
| Delayed | 3 | 6.7 | |
| Not done within one year | 2 | 4.4 | |
| Never done | 36 | 80.0 | |
| HbA1c | HbA1c | HbA1c | |
| Yes (within 180 days) | 2 | 4.4 | |
| Delayed | 7 | 15.6 | |
| Not done within one year | 8 | 17.8 | |
| Never done | 28 | 62.2 | |
| Lipid profile | Lipid profile | Lipid profile | |
| Yes (within one year) | 3 | 6.7 | |
| Delayed | 6 | 13.3 | |
| Not done within one year | 14 | 31.1 | |
| Never done | 22 | 48.9 | |
| KFT | KFT | KFT | |
| Yes (within one year) | 4 | 8.9 | |
| Delayed | 4 | 8.9 | |
| Not done within one year | 17 | 37.8 | |
| Never done | 20 | 44.4 | |
| Adherence to medications | Adherence to medications | Adherence to medications | |
| Do you ever forget to take your medicine? | Do you ever forget to take your medicine? | Do you ever forget to take your medicine? | |
| Yes | 16 | 35.6 | |
| No | 29 | 64.4 | |
| Are you careless at times about taking your medicine? | Are you careless at times about taking your medicine? | Are you careless at times about taking your medicine? | |
| Yes | 11 | 24.4 | |
| No | 34 | 75.6 | |
| When you feel better, do you sometimes stop taking your medicine? | When you feel better, do you sometimes stop taking your medicine? | When you feel better, do you sometimes stop taking your medicine? | |
| Yes | 9 | 20.0 | |
| No | 36 | 80.0 | |
| Sometimes you feel worse, when you take the medicine, do you stop taking it? | Sometimes you feel worse, when you take the medicine, do you stop taking it? | Sometimes you feel worse, when you take the medicine, do you stop taking it? | |
| Yes | 3 | 6.7 | |
| No | 42 | 93.3 | |
| Health-seeking behavior for medication* | Health-seeking behavior for medication* | Health-seeking behavior for medication* | |
| Government Health Facility | 41 | 91.1 | |
| Private Health Facility | 13 | 28.9 | |
| Jan Aushadhi Store | 2 | 4.4 | |
Most patients reported a common treatment pathway from the government primary health facilities (DGD or Aam Aadmi Mohalla Clinic [AAMC]) due to optimal healthcare accessibility as the prescribed drugs for DM/HTN control were available free of cost at these health facilities (Figure 1). Private pharmacies were utilized by a few patients on rare occasions when the prescribed drugs were unavailable at government healthcare facilities. Treatment preference from private practitioners was observed in very few participants due to shorter waiting times and perception of better efficacy of the prescription in controlling DM/HTN: “At private, the work (physician consultation) is done with less effort and quickly” (P12, F).
**Figure 1:** *Overview of the patient treatment pathways.Figure credits: All authors.*
Patients' knowledge of DM and self-care Overall, most of the patients had some awareness of DM. Diabetes was perceived as a disease causing weakness or/and body aches with symptoms such as excessive thirst and hunger. “ My legs start feeling weak and my body aches a lot” (P12, F). Another female mentioned, “I stay hungry for food. Also, I encounter dizziness quite often” (P37, F). However, most of the patients lacked an understanding of the etiology of DM in terms of absolute or relative insulin deficiency. “ Sugar (DM) is caused by eating sweets…always feeling kind of weak and tired…. sugar can cause complications such as all kinds of ailments like fever, weakness, headache….” ( P11, F). Awareness of DM-related complications in the patients was mixed and ranged from no awareness, non-specific (weakness and sickness) to substantial awareness (end organ damage, delayed wound healing, death). “...... there may be loss of eyesight and kidneys (also) get affected” (P4, F). The majority of the patients also felt that DM had a significant negative impact on their life and health, leading to frequent bouts of sickness, weakness, hunger, thirst, body pains, and dizziness. “ My health has corroded” (P9, F). Control of DM was considered possible apart from medication through a combination of a healthy diet and exercise although their relative importance varied among the patients. “ Go for a walk every day, stop eating sweets or sweetened products” (P27, M). A healthy diet was mostly linked to avoiding sweets, and none of the patients reported the importance of concepts of calorie restriction, food exchange, high-fiber diet, etc. “ I stopped consuming sweetened food products. I eat them sometimes only” (P42, F). Nonadherence to a healthy diet was frequently attributed to poor glycemic control. “ I might not be taking the correct diet and cannot always follow the restrictions” (P17, M). A few patients did know why they were unable to control DM. “ I don’t know” (P5, M).
Patients' knowledge of HTN and self-care HTN was understood by the patients as a complex symptom characterized by headache, weakness, restlessness, vomiting, nervousness, stress, sweating, and an increase in heartbeat. “ All I know is that I have a lot of headaches, vomiting and my hands and legs stop working” (P2, F). The etiology of HTN was attributed to several factors such as stress, overthinking, a salty diet, being emotional, and having a family history of the disease. “ Well, if you overthink too much your BP level will increase” (P3, M). “... there are many problems in life that cause tension….I feel very tired and sleepy” (P8, F). However, some patients had misconceptions related to the disease's etiology. “ I used to take medicines for cough, that could be the only reason (for developing HTN)” (P33, M). Some patients perceived the disease had a negative impact on health and caused serious complications of the kidney, heart, rupturing of vessels (stroke), etc. “ The problem is that BP has damaged my eyes, caused painful feet, and headache - if I think of something a little, if there is any tension in the house, there is a fight, then I have a headache” (P7, F). However, occasionally HTN was not construed as a serious disease. “… I do not know much about it… These diseases cannot do anything to the poor...” (P21, F); “Nothing much” (P1, F). Most patients were aware that apart from medication, salt restriction, and physical activity were useful for controlling HTN and its complications. However, none of the patients could differentiate exercise from physical activity, brisk walking from a stroll, and moderate exercise requirement of a minimum of 150 minutes per week. “ By taking medicines, also by eating less oily food; also doing regular exercise. Also, less salty food” (P5, M). “ When BP rises one must reduce salt consumption” (P13, M).
Medication adherence in DM and HTN: facilitators and barriers A total of 33 ($73\%$) patients were adherent and 12 ($27\%$) patients were nonadherent to their prescribed medications. All the patients were on treatment and had trust in the efficacy of the medications in controlling DM and HTN. “ Only change is now I am surviving because of the medicines. Without these medicines, I do not know what would happen” (P18, F). “ if one has to live then he/she has to take medicines…” (P3, M). Forgetfulness was the most common reason attributed to medication nonadherence in nearly one in three patients (Table 2). Family support through reminders and less common payment for medications helped improved their adherence. A majority of patients were unaware of the names of their prescribed anti-DM and anti-HTN medications, but they could identify medicines by physical (shape/color/size) characteristics and correctly reported the frequency of dosing and timing of the medicines. “ Small tablet before breakfast. one tablet in the afternoon before lunch, and one in the evening after snacks” (P3, M). Very few patients reported side effects related to anti-DM and anti-HTN medications that were mostly mild (constipation, flatulence, and restlessness) and did not cause any disruption of medication intake. Majority of patients reported never using any alternative medicine to treat DM or HTN. A few patients reported using ayurvedic medicines (an ancient Indian system of medicine) and homemade remedies such as almonds, bitter gourd juice, and neem leaves. Among alternative medicine users, there was consensus on their lack of side effects, but their perceived efficacy for blood sugar or blood pressure control was contested. “ Yes, they are pretty effective (for other conditions). But I could not feel their effect when my blood sugar increased. That is why I switched to allopathy” (P22, M). “ When the medicines (allopathy) run out of stock, then I (sometimes) take the (ayurvedic) mixture in the morning... It controls my sugar, doesn’t let it increase” (P18, F). None of the patients reported substance abuse or perceived stress as factors that reduce adherence.
## Discussion
This study conducted among patients with DM and HTN attending a primary health facility in a low-income urban area in Delhi assessed patients' perspectives on adherence to self-care practices and their barriers and facilitators. Nearly one in four patients reported medication nonadherence, which is comparable to that observed in other studies conducted in North Indian hospital settings. Most patients could not read medicine labels in the English language due to illiteracy and had to overcome this barrier by memorizing the shape and color of the pills and the associated frequency of dosing.
However, the continuum of care from government PHC facilities in the area, including the DGD, was good due to the regular availability of free-of-cost medications with the absence of significant crowding or queuing, with only a few patients reporting utilizing private pharmacies. Previous studies in LMICs have observed higher utilization of private pharmacies to meet the medicinal needs of patients with NCDs probably due to the difficulty in the accessibility of drugs from the public healthcare system [21,22]. The use of alternative medicines for the management of DM or HTN in the patients was also low due to their lack of perceived usefulness, a finding that corroborates evidence from a large-scale, cross-sectional survey in India.
However, follow-up blood investigations for monitoring glycated hemoglobin, renal function tests, and lipid profile in the patients were poor due to lack of regular prescription by healthcare providers, unavailability of these tests at the PHC facility (DGD), and lack of affordability to spend out of pocket in private laboratories. Similarly, the rates of self-monitoring of blood glucose were low, partly explained by the additional cost of a glucometer and strips that are not provided by public health facilities.
The aforementioned factors are also known to contribute to the phenomenon of therapeutic inertia due to the failure of intensification of medical therapy despite patients not meeting their recommended health targets, a phenomenon also observed in this study. Healthcare providers with limited resources can frequently attribute poor glycemic control to poor medication adherence and avoid treatment intensification, particularly using insulin. The risk of therapeutic inertia is increased in patients lacking up-to-date blood sugar profiles, especially glycated hemoglobin, and in socioeconomically vulnerable populations.
Evidence-based research suggests that lifestyle changes such as a healthy diet and physical activity improve glycemic and blood pressure, reduce the risk of complications, and enhance the quality of life of patients with DM and HTN [21,22]. In this study, awareness of the harmful effects of DM and HTN leading to end-organ damage was inadequate. Similarly, most patients had poor knowledge and understanding of the concept of healthy diet and exercise, findings that are suggestive of the suboptimal patient-provider interaction and deficient counseling in the health facility, reported in both facility- and community-based studies.
However, none of the patients reported abstinence from tobacco and alcohol as a requirement for the effective management of diabetes and HTN and for safeguarding their health. On the other hand, similar studies conducted in India and Bangladesh have reported on patients' awareness of the need for cessation of tobacco and alcohol to control NCDs [23,24].
The strengths of the study are that it was conducted in a vulnerable urban population in a primary health facility setting. Study limitations include the lack of representativeness of the study sample and its lack of generalizability to other urban or rural settings wherein the predominant treatment-seeking behavior is from the private sector or secondary or tertiary hospital settings. Nevertheless, this study identifies the major barriers and challenges in achieving optimal adherence and health targets in socioeconomically disadvantaged patients undergoing treatment in PHC facilities within resource-limited settings.
## Conclusions
Diabetes and HTN are increasing threats to global public health and are responsible for premature deaths and loss of disability-adjusted life years (DALYs) and quality-adjusted life years (QALYs). Expanding the role of community health workers or volunteers in the prevention and treatment of NCDs and including information regarding nonpharmacological interventions in health promotion packages might help to improve treatment outcomes, adherence, and patient treatment pathways to care.
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|
---
title: 'Behçet’s Disease in Saudi Arabia: Clinical and Demographic Characteristics'
journal: Cureus
year: 2023
pmcid: PMC10010633
doi: 10.7759/cureus.34867
license: CC BY 3.0
---
# Behçet’s Disease in Saudi Arabia: Clinical and Demographic Characteristics
## Abstract
Objective This study aimed to analyze and determine the clinical features and characteristics of patients diagnosed with Behçet’s disease (BD) in Saudi Arabia.
Methods This single-center study was conducted in a tertiary care center in the western region of Saudi Arabia. Electronic medical records of patients with BD aged 14 years and older were reviewed and their demographic and clinical data were collected by trained rheumatologists. Between comparisons, Fisher’s exact test, independent t-test, or Mann-Whitney U test was applied. The normality of variables was tested using the Kolmogorov-Smirnov and Shapiro-Wilk tests.
Results The mean age of symptom onset was 29.6 ± 11.4 years, and mean age at the time of diagnosis was 31.1 ± 11.9 years. Most patients were overweight (mean body mass index 26.7 ± 5.60 kg/m2). The most associated medical comorbidities were diabetes mellitus and hypertension. The most common clinical manifestations were oral ulcers ($91.2\%$), genital ulcers ($81.3\%$), arthritis ($41.8\%$), and pseudofolliculitis ($34.1\%$). Colchicine was the most prescribed treatment ($95.6\%$), followed by prednisolone ($72.5\%$), and azathioprine ($36.3\%$). Male patients were significantly more likely to have pseudofolliculitis ($$p \leq 0.011$$) and take a tumor necrosis factor alpha (TNF-α) inhibitor ($$p \leq 0.045$$). Female patients were more likely to have neurological involvement ($$p \leq 0.029$$).
Conclusion Awareness of BD symptoms and early recognition can help provide timely and effective treatment to avoid disease complications.
## Introduction
Behçet’s disease (BD) is a chronic variable vessel vasculitis characterized by a multisystem inflammatory disorder that manifests with a wide range of mucocutaneous, articular, ocular, central nervous system (CNS), gastrointestinal, pulmonary, and cardiovascular presentations. Oral and genital ulcers have been reported to be the commonest clinical presentation and were previously considered a mandatory criterion for classification [1]. The disease has been reported worldwide with a specific geographic predilection [2]. For example, Turkey has the highest reported prevalence rate, with approximately 400 cases per 100,000 adults [3]. Also, it is most commonly encountered in the Mediterranean and Far-East countries. Meanwhile, it is low in countries away from the Silk Road. For instance, the prevalence in the United Kingdom, Colombia, Spain, and Japan ranges from 0.64, 1.1, and 6.4 to 16 per 100,000 persons, respectively [4-6]. The etiology and pathogenesis of this disease remain unclear. The mean age at disease presentation is 30-40 years, and it is rarely diagnosed in early and late ages. Moreover, it seems to be underestimated in Black populations [7]. The phenotypic expression also varies among ethnic groups and countries. For example, ocular involvement has been reported to be as high as $60\%$ worldwide; however, it is rarely found in Australian populations [8]. German patients with BD tend to have fewer ocular lesions than Turkish patients [9]. Fewer ocular and genital ulcers have been observed in Chinese patients [10]. In addition, a higher frequency of ocular and neurological manifestations has been reported in Brazil [11]. Gender variations were observed, as genital ulcers and erythema nodosum were found more frequently in women [12]. Two previous cohorts from Saudi Arabia found no significant difference in clinical manifestation or prognosis between male and female patients with BD [13,14]. However, despite decades of disease acknowledgment, large studies addressing BD incidence and prevalence have not been performed. Therefore, this study aimed to analyze and determine the clinical features and characteristics of patients diagnosed with BD in western Saudi Arabia.
## Materials and methods
This single-center study was conducted in a tertiary care center in the western region of Saudi Arabia.
Patients We included all adult patients aged 14 years or older who were diagnosed with BD according to the International Criteria for Behçet’s Disease (ICBD) at the Department of Rheumatology in National Guards Hospital, western region, Saudi Arabia, from January 2015 to December 2021. The patient’s electronic medical records were reviewed, and their demographic and clinical data were collected by trained rheumatologists. Patients younger than 14 years were excluded from the study.
Data collection *Our data* collection sheet included patients’ demographic data, clinical features of BD, and treatment data. The patients’ demographic data included age at the time of symptom onset and diagnosis, sex, BMI, history of smoking, and medical comorbidities. In addition, details of the clinical features of BD were collected, including oral or genital ulcers, skin manifestations, erythema nodosum, uveitis, arthritis, neurological involvement, vascular manifestations, intestinal involvement, and the Pathergy test. We also collected data on the treatment received throughout the disease course.
Statistical analysis Descriptive statistics are presented as numbers, percentages, means, SDs, and medians (min-max) whenever appropriate. Between comparisons, Fisher’s exact test, independent t-test, or Mann-Whitney U test was applied, and a p-value <0.05 was considered statistically significant. The normality of variables was tested using the Kolmogorov-Smirnov and Shapiro-Wilk tests. Data were analyzed using SPSS version 26 (IBM Corp, Armonk, NY, USA).
Ethics This study was conducted according to the ethical principles of the Declaration of Helsinki. The study was approved by the Research and Ethics Committee at King Abdullah International Medical Research Center (reference number RJ$\frac{14}{012}$/J). Data were secured on a safe database. No direct contact with patients was required; therefore, no consent was needed.
## Results
The present study included 91 patients who were diagnosed with BD as per ICBD, with 67 male ($73.6\%$) and 24 female patients ($26.4\%$). The mean age of symptom onset was 29.6 ± 11.4 years; meanwhile, at the time of diagnosis was 31.1 ± 11.9 years. Most patients were overweight, with a mean BMI of 26.7 ± 5.60 kg/m2. The most associated medical comorbidities were diabetes mellitus and hypertension ($6.6\%$ for both). The remaining baseline characteristics are presented in Table 1.
**Table 1**
| Study variables | N (%) |
| --- | --- |
| Gender | |
| Male | 67 (73.6%) |
| Female | 24 (26.4%) |
| Smoking | |
| Yes | 16 (17.6%) |
| No | 75 (82.4%) |
| Associated comorbidities | |
| | 69 (75.8%) |
| Diabetes mellitus | 6 (6.6%) |
| Hypertension | 6 (6.6%) |
| Osteoporosis | 3 (3.3%) |
| Fibromyalgia | 3 (3.3%) |
| Chronic lung disease | 2 (2.2%) |
| Cardiomyopathy | 2 (2.2%) |
| Depression | 2 (2.2%) |
| Seizure disorder | 2 (2.2%) |
| Rheumatoid arthritis | 1 (1.1%) |
| Chronic kidney disease | 1 (1.1%) |
| | Mean ± SD |
| Age at symptoms (years) | 29.6 ± 11.4 |
| Age at diagnosis (years) | 31.1 ± 11.9 |
| BMI (kg/m2) | 26.7 ± 5.60 |
The most common clinical manifestations in our cohort were oral ulcers ($91.2\%$), followed by genital ulcers ($81.3\%$), arthritis ($41.8\%$), and pseudofolliculitis ($34.1\%$). Erythema nodosum was the least commonly reported clinical feature ($3.3\%$) (Figure 1).
**Figure 1:** *Patients clinical features.*
Colchicine was the most prescribed treatment ($95.6\%$), followed by prednisolone ($72.5\%$) and azathioprine ($36.3\%$) (Figure 2).
**Figure 2:** *Received medications.TNF-a inhibitor: Tumor necrosis factor-alpha inhibitor.*
When measuring the differences in the clinical manifestation of BD between male and female patients, it was found that male patients were significantly more likely to be smokers ($$p \leq 0.005$$), have pseudofolliculitis ($$p \leq 0.011$$), and take a tumor necrosis factor-alpha (TNF-α) inhibitor ($$p \leq 0.045$$). Female patients were more likely to have neurological involvement ($$p \leq 0.029$$). Additionally, female patients had a significantly higher BMI ($p \leq 0.001$) (Table 2).
**Table 2**
| Factor | Male N (%) (n=67) | Female N (%) (n=24) | P-value |
| --- | --- | --- | --- |
| Smoking | | | |
| Yes | 16 (23.9%) | 0 | 0.005 ** |
| No | 51 (76.1%) | 24 (10%) | 0.005 ** |
| Associated comorbidities | | | |
| Yes | 12 (17.9%) | 10 (41.7%) | 0.027 ** |
| No | 55 (82.1%) | 14 (58.3%) | 0.027 ** |
| Clinical features * | | | |
| Oral ulcers | 59 (88.1%) | 24 (100%) | 0.104 |
| Genital ulcers | 55 (82.1%) | 19 (79.2%) | 0.765 |
| Pseudofolliculitis | 28 (41.8%) | 3 (12.5%) | 0.011 ** |
| Uveitis | 19 (28.4%) | 4 (16.7%) | 0.412 |
| Arthritis | 23 (34.3%) | 15 (62.5%) | 0.029 ** |
| Neurologic involvement | 19 (28.4%) | 8 (33.3%) | 0.795 |
| Deep venous thrombosis | 10 (14.9%) | 2 (08.3%) | 0.506 |
| Arterial thrombosis | 4 (06.0%) | 0 | 0.570 |
| Arterial aneurysm | 4 (06.0%) | 0 | 0.570 |
| Erythema nodosum | 2 (03.0%) | 1 (04.2%) | 1.000 |
| Intestinal involvement | 2 (03.0%) | 2 (08.3%) | 0.283 |
| Treatment used * | | | |
| Prednisolone | 48 (71.6%) | 18 (75.0%) | 1.000 |
| Colchicine | 63 (94.0%) | 24 (100%) | 0.570 |
| Azathioprine | 27 (40.3%) | 06 (25.0%) | 0.222 |
| Tumor Necrosis Factor-alpha inhibitor | 10 (14.9%) | 0 | 0.045 ** |
| Mycophenolate mofetil | 5 (07.5%) | 1 (04.2%) | 0.577 |
| Cyclosporine | 2 (03.0%) | 0 | 0.392 |
| Warfarin | 10 (14.9%) | 2 (08.3%) | 0.506 |
| Plasma exchange | 1 (01.5%) | 0 | 1.000 |
| | Mean ± SD | Mean ± SD | P-value |
| Age at symptoms onset (years) | 28.4 ± 11.1 | 33.2 ± 11.9 | 0.072 |
| Age at diagnosis (years) | 29.9 ± 11.7 | 34.2 ± 12.2 | 0.132 |
| BMI (kg/m2) | 25.4 ± 4.73 | 30.5 ± 6.19 | <0.001 ** |
## Discussion
The current cohort study was conducted in a tertiary care hospital and presented the baseline characteristics and demographics of BD in Saudi Arabia. The mean age of symptoms onset and age of diagnosis was 29.6 ± 11.4 and 31.1 ± 11.9 years, respectively, which is consistent with the first published paper in the region by al-Dalaan AN et al. [ 13]. A similar finding was observed in data from the Iran Registry, with the mean age of patients being 28.3 ± 8.7 years [15]. However, a later age of onset and diagnosis has been reported in the literature from different geographical areas. For instance, an older paper from the southwestern region of Saudi Arabia showed that the mean age of patients was 37.11 ± 11.90 years [14]. In addition, a multicenter Korean study found that the median age of onset and age at diagnosis was 33 and 41 years, respectively [16]. The mean interval between the time of symptoms onset and diagnosis was approximately two years. Although contrary to other global reports with a low prevalence of disease, this finding may be supported by the awareness of BD among treating physicians in the region. For instance, an eight-year delay between symptom onset and the diagnosis was reported in Switzerland [17].
Interestingly, the prevalence of neurological involvement in BD (neuro-Behçet’s disease) is high in our cohort, reaching $30\%$, with a statistically significant difference between sexes; however, this finding is less reported in the literature, which could raise the possibility of underdiagnosis of milder forms of BD that do not require medical attention until more severe presentations develop. In the literature, the reported prevalence of neuro-BD is as low as $5.3\%$, as found by Serdaroglu P et al.; [ 18] however, higher rates of neuro-Behçet’s presentation were reported within the region in previously mentioned papers as high as $44\%$ and $36.2\%$, respectively [13,14]. A similar rate was found in a Tunisian study, with a prevalence of $28\%$ [19]. One possible theory explaining the higher prevalence of neuro-BD is that research in a tertiary care center receives referrals from different regional hospitals for further management of more complex cases. With regards to treatment received, colchicine and prednisolone were the most commonly used medications ($95.6\%$ and $72.5\%$, respectively), which was consistent with another study conducted in Tunisia by Daoud F et al. [ 20]. One patient in our cohort received plasma exchange for refractory neuro-BD with ocular involvement and had a good initial response; however, the loss of follow-up made it impossible to determine the efficacy of such an intervention for such an indication. However, in some case reports, plasma exchange was successfully applied to patients with ocular manifestations. For example, one case describes a 50-year-old lady who presented with bilateral optic neuritis due to BD responding moderately after five sessions of therapeutic plasma exchange [21]. Nevertheless, this is the third-largest cohort of patients with BD in Saudi Arabia. However, the high rate of neurological involvement needs to be addressed in a multicenter study to ascertain the exact prevalence and other clinical manifestations. The study was limited by the loss of outcome assessment, as some patients did not follow-up regularly and were lost to follow-up.
## Conclusions
BD is commonly seen in the western region of Saudi Arabia with different clinical manifestations that align with global reports; however, the prevalence of neuro-BD was high in our study, mainly due to the research being conducted in a high referral tertiary care center. High BMI in females was statistically significantly associated with neuro-BD. Awareness of BD symptoms and early recognition can help provide timely and effective treatment to avoid disease complications.
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|
---
title: A viscoelastic alginate-based hydrogel network coordinated with spermidine
for periodontal ligament regeneration
authors:
- Songbai Zhang
- Yuanbo Jia
- Jingyi Liu
- Fan Feng
- Zhao Wei
- Min Zhang
- Feng Xu
journal: Regenerative Biomaterials
year: 2023
pmcid: PMC10010660
doi: 10.1093/rb/rbad009
license: CC BY 4.0
---
# A viscoelastic alginate-based hydrogel network coordinated with spermidine for periodontal ligament regeneration
## Abstract
Periodontitis can cause irreversible defects in the periodontal ligament (PDL), the regeneration of which is the major obstacle to the clinical treatment of periodontitis. Implanting hydrogel for releasing anti-inflammatory drugs is a promising treatment to promote PDL regeneration. However, existing hydrogel systems fail to mimic the typical viscoelastic feature of native periodontium, which may have been shown as an important role in tissue regeneration. Meanwhile, the synergistic benefits of mechanical cues and biochemical agents for PDL regeneration remain elusive. In this study, we developed a bi-crosslinking viscoelastic hydrogel (Alg-PBA/Spd) by integrating phenylboronic acid-modified alginate with anti-inflammatory agent (spermidine) through borate ester and B–N coordination bonds, where spermidine will be released with the degradation of the hydrogel. Alg-PBA/Spd hydrogel is biocompatible, injectable and can quickly adapt to complex periodontal structures due to the dynamic crosslinking. We demonstrated in rat models that the viscoelastic Alg-PBA/Spd hydrogel significantly promotes the deposition of periodontal collagen and accelerates the repair of periodontal damage. Our results suggest that the viscoelastic Alg-PBA/Spd hydrogel would be a promising mechano-biochemically synergistic treatment for periodontal regeneration.
## Graphical Abstract
## Introduction
Periodontitis is an inflammatory disease caused by infection of periodontal tissue [1], with the typical feature of irreversible destruction of periodontal ligament (PDL) and alveolar bone [2]. Traditional periodontitis therapies [3] (e.g. periodontal scaling treatment [4], periodontal flap treatment [5], inducing osteogenesis [6]) aim to slow disease progression by eliminating the source of infection and reducing inflammation, but fail to achieve the regeneration of PDL [7, 8]. PDL is a structure composed of mainly collagen fibers, with an amorphous matrix [9, 10]. Such a compositional basis determines the typical viscoelasticity of PDL [11], which can structurally modulate the response of the tooth–PDL–bone complex pair to dynamic loading [12], in turn mediating bone remodeling through dynamic changes of fibers and extracellular matrix [13]. Although PDL regeneration is critical to periodontitis treatment, the complex inflammatory microenvironment of periodontitis [14] and the complex structural [15] and mechanical properties [16, 17] of the periodontium set a huge obstacle to PDL regeneration [18]. Therefore, it is of great importance to develop more effective treatments.
In clinics, various drugs (e.g. tetracycline, chlorhexidine and metronidazole) have been used in the treatment of periodontitis, which can achieve anti-inflammatory function through antibacterial activities [19]. In recent years, traditional anti-inflammatory drugs (e.g. acetylsalicylic acid) are also loaded in implanted repair materials to treat periodontitis [20]. However, the above drugs can only treat a certain pathogenic factor of periodontitis [21]. Therefore, there is still an unmet need to find a multi-functional drug for the treatment of periodontitis. Spermidine is an emerging anti-inflammation agent that inhibits the expression of interleukin 1β (IL-1β) [22] and exerts anti-inflammatory effects [23], which can keep cellular functions and help maintain cellular homeostasis [24]. Spermidine also has many regenerative benefits such as immunomodulation [25], antioxidant functions [26] and inhibition of the formation of osteoclasts [27], which has not been explored yet in PDL regeneration. Besides, spermidine is usually delivered orally [28] or directly encapsulated in hydrogel [29], which results in too rapid drug release to cover the treatment period of periodontitis (∼7 days) [30].
Hydrogels are considered to be a promising regenerative material [31], with the capabilities to provide controlled drug release and mechanical support to tissues [32, 33]. Existing hydrogel-based strategies for PDL regeneration are limited to the delivery of biochemical factors (e.g. drugs and cells) [34], with limited functional recovery of PDL [35]. Accumulating evidence shows that the mechanical properties of hydrogels (e.g. stiffness, viscoelasticity) play important roles in tissue regeneration [36]. In view of the critical role of PDL viscoelasticity in maintaining periodontal function [37], we hypothesize that integrating spermidine therapy with viscoelastic hydrogel may be a promising approach for PDL treatment. However, the mechanical benefits of viscoelastic hydrogels for PDL regeneration and the mechano-biochemically synergetic effect remain elusive.
Many approaches have been developed to construct viscoelastic hydrogels [33], including host–guest interaction, ionic cross-linking and dynamic covalent bonding [38–40]. Among them, borate ester dynamic interaction-based viscoelastic hydrogels have attracted much attention because of their good injectability and viscoelasticity compatible with biological tissues [41]. The structural basis of the borate ester bond is the dynamic ester bond formed by the boronic acid and the cis-diol [42, 43], which was widely used in the regenerative medicine and drug delivery [44–46]. Particularly, since primary and secondary amines in spermidine can form a B–N coordination bond with phenylboronic acid [47]. The B–N coordination bond that formed between the secondary amine and the B atom can improve the hydrolytic stability of the entire system [48]. The introduction of spermidine into the borate hydrogel may integrate mechanical viscoelasticity and controlled release of spermidine.
Herein, we report a bi-crosslinking viscoelastic hydrogel (Alg-PBA/Spd) by integrating phenylboronic acid-modified alginate (Alg-PBA) with spermidine (Spd). Alg-PBA can self-gel by binding to cis-diols on the alginate backbone, which provides an ideal viscoelastic hydrogel template to study the effect of hydrogel viscoelasticity on PDL regeneration. Spermidine is introduced to the hydrogel network through the B–N coordination bond which exerts anti-inflammatory effects after release from the network with the degradation of the hydrogel. We demonstrated the synergistically therapeutic potential of viscoelastic Alg-PBA/Spd hydrogel in a rat PDL injury model.
## Materials
Alginate (Alg, Mw = 20–50 kDa) was purchased from Macklin (Shanghai, China). 3-Aminophenylboronic acid (PBA), N-(3-dimethylaminopropyl)-N′-ethyl carbodiimide hydrochloride (EDC), N-hydroxy succinimide (NHS), MES monohydrate (MES), Methacrylic anhydride (MA) and deuterium oxide were purchased from Aladdin (Shanghai, China). α-MEM, fetal bovine serum (FBS) and Penicillin–Streptomycin (PS) were from Gibco/Thermo Fisher Scientific (USA). Spermidine (Spd), phosphate-buffered saline (PBS), cell counting kit-8 (CCK-8) and Calcein-AM/PI Live-Dead Cell Staining Kit were purchased from Solarbio (Beijing, China). Six-week-old male rats were purchased from the Laboratory Animal Center of the Fourth Military Medical University.
## Synthesis of Alg-PBA
Alg-PBA was synthesized through the esterification of Alg with PBA [42]. Briefly, 1 g Alg was first dissolved in 0.1 M MES buffer to form a $1\%$ (w/v) solution. Then, 500 mg PBA dissolved in 5 ml methyl sulfoxide was added to the Alg solution followed by adding 0.5 g EDC and 0.7 g NHS. The pH of the mixed solution was kept to 4.5–5 for 24 h using 1M NaOH at 25°C (Supplementary Fig. S1A). When the reaction finished, the solution was centrifuged at 7500 rpm/min for 0.5 h to remove unreacted PBA. Finally, the supernatant was dialyzed (MWCO 3500) against deionized water for 5 days and lyophilized. The successful modification of alginate was confirmed by 1H NMR spectroscopy (400 MHz JEOL). The degree of modification of phenylboronic acid on alginic acid is calculated by using the molar ratio of the phenylboronic acid unit to the alginic acid group, that is, the ratio of the integral value of the phenylboronic acid unit to the integral value of the alginic acid group.
## Preparation and characterization of Alg-PBA/Spd hydrogel
To construct the hydrogel, solutions of Alg-PBA ($4\%$, $3\%$, $2\%$ w/v in PBS) and Spd ($1\%$ w/v in PBS) were mixed by the ruhr lock at a volume ratio of 20:1 in a 1-ml injection syringe at room temperature to obtain hydrogel with different concentrations of Alg-PBA. At the same time, hydrogel with different final concentrations of Spd ($0.05\%$, $0.15\%$, $0.25\%$, w/v) were obtained by changing the concentration ratio of Alg-PBA ($4\%$, w/v) to Spd ($1\%$/$3\%$/$5\%$, w/v). The microstructure of hydrogel was observed by scanning electron microscope (SEM) (Hitachi SU3500).
## Rheological tests of hydrogel
Rheological tests were carried out using a rotational rheometer (Anton Paar MCR 302) at 37°C. After the hydrogel was uniformly mixed in the syringe, 0.2 ml was injected on the plate of the rheometer. The surrounding hydrogel was removed after lowering the rotor height to a distance of 1 mm from the plate. The stiffness of different Alg-PBA concentration hydrogel (Alg-PBA:Spd 20:1 w/v) was investigated by testing the storage (G′) and loss (G″) moduli at a range of 10–0.1 rad/s and $1\%$ strain amplitude over time by frequency sweep. The loss tangent of different Spd concentration hydrogel was measured by the same method as stiffness with constant Alg-PBA concentration ($4\%$ w/v). The stress relaxation was assessed by relaxation tests at different constant strains ($1\%$, $5\%$ and $10\%$) for 300 s. The self-healing properties were tested by storge (G′) and loss (G″) moduli alternating strain cycles of the $5\%$ and $500\%$ strains. The self-healing properties were evaluated by shear-thinning testing with shear rate varying in the range of 0.01–5 Hz.
## Characterization of injectability, self-healing, tensile properties, remodeling and degradability properties of hydrogel
The injectability of hydrogel was characterized by squeezing the hydrogel out through a 12# needle. The self-healing properties of the hydrogel were characterized by cutting-healing experiments. For the swelling and degradation tests, 1 ml of $4\%$ Alg-PBA/Spd hydrogel and a 5-ml centrifuge tube were weighed, respectively. Then the hydrogel and 1 ml of PBS were added to the 5 ml centrifuge tube, and PBS was drained completely every 4 h before weighing. The PBS was carefully aspirated with a pipette, and the remaining mass was weighed; when the mass no longer increased, it was weighed every 24 h until it was completely degraded.
## Drug release characterization of hydrogels
We prepared Alg-PBA/Spd hydrogels and Alg/Ca2+ hydrogels containing the same concentration of Spd. For Alg/Ca2+/Spd hydrogels, spermidine is directly mixed in the Alg solution and Ca2+ is added to form Alg/Ca2+ hydrogel. We added 1 ml PBS and 5 mM H2O2 to 100 µl Alg-PBA/Spd hydrogel and Alg/Ca2+/Spd hydrogels and the hydrogels were soaked for 6 days. We collected 500 µl supernate every day, and then added the same amount of PBS or H2O2. We used high performance liquid chromatography to detect the concentration of spermidine in the supernate and calculated the cumulative release of spermidine. Column: C18 column (4.6 mm × 250 mm, 5 µm), liquid phase: Phase A is ultrapure water, Phase B is acetonitrile, gradient elution (the elution procedure is: 0 min, $40\%$ water, $60\%$ acetonitrile; 5 min, $20\%$ water, $80\%$ acetonitrile; 14 min, $5\%$ water, $95\%$ acetonitrile; 18 min, $40\%$ water, $60\%$ acetonitrile). The detection wavelength is 254 nm, and the column temperature is 30°C.
## Degradation test of the hydrogel in vivo
In vivo fluorescence images of cy7-labeled hydrogels were taken by an animal imaging system (AniView600, Guangzhou Biolight Biotechnology, China). Alg-PBA was replaced by Alg-PBA with cy7-labeled to prepare a Cy7-Alg-PBA hydrogel. Cy7-amine (Duofluor Inc, China) was grafted with Alg-PBA to synthesize Cy7-Alg-PBA. In short, 200 mg of Alg-PBA was dissolved in 20 ml water, then 10 mg Cy7-amine was added to the Alg-PBA solution with 140 mg of EDC and 100 mg of NHS. One molar of NaOH was used to maintain the pH value of the solution from 4.5 to 5.5 for 24 h. After the reaction was over, it was purified by dialysis with deionized water for 3 days, and then freeze-dried. 0.1 ml Cy7-Alg-PBA/Spd hydrogel was injected under the skin of rats for 9 days to test the degradation of hydrogel in vivo.
## Cell culture and biocompatibility of hydrogel
Four percent of Alg-PBA/$1\%$ Spd hydrogel was dissolved in the medium and the PDL fibroblast cells (PDLFs) were seeded on 96-well plates at the same cell density for 24 h. From the next day, the PDLFs were cultured for 3 days with different concentrations of hydrogel solutions (0.025–$10\%$). All culture medium was added with $10\%$ FBS and $1\%$ PS at 37°C in $5\%$ CO2. The biocompatibility of the hydrogel was characterized by cell death staining and cell proliferation assays. The effect of materials on cell proliferation at 1, 3, 5 days was detected by CCK-8 assay. Dead and live cells were stained using Calcein-AM/PI live/dead cell double staining kit. To further characterize the biocompatibility of the Alg-PBA/Spd hydrogel, PDLFs were seeded in a 6-well plate. After 24 h, 100 µl of Alg-PBA/Spd hydrogel and Alg/Ca2+ hydrogel were added to the medium separately. $4\%$Alg-PBA/Spd hydrogel was evenly spread on the bottom of the 6-well plate to form a hydrogel substrate (thickness: 1 mm). The PDLFs were also seeded in the hydrogel and cultured in 3D for 1 and 3 days. The cells were stained with Calcein-AM/PI dye to detect the state of the cells. Images were taken using a confocal microscope (Olympus FV 3000), and the cell viability was calculated as (number of living cells/number total cells) ×$100\%$.
## Anti-inflammatory properties of the hydrogel
PDLFs were seeded in six-well plates and cultured for 24 h; 10 µg/ml lipopolysaccharide, 10 µg/ml concanavalin (final concentration) and different concentrations of spermidine solutions (final concentration: $\frac{0}{1}$/10 µM) were added into the six-well plates with periodontal membrane fibroblasts adherence. After 24 h, we collected the cell culture solution. We used enzyme-linked immunosorbent assays to detect IL-1β and IL-6 in the supernatant.
## Synthesis and characterization of Alg-MA-PBA/Spd hydrogels
One gram of Alg was dissolved into a $1\%$ solution and the solution was pre-cooled to 4°C. 1.2 ml MA (Aladdin, China) was slowly added to the $1\%$ Alg solution at unlight and low temperature. The pH of the reaction was maintained between 8 and 9 for 8 h, which was adjusted by 1M NaOH. Alg-MA was obtained after 3 days of dialysis and lyophilized. Alg-MA-PBA was synthesized by the amidation reaction of Alg-MA and 3-PBA (the same method as the synthesis of Alg-PBA by Alg and 3-PBA) (Supplementary Fig. S1B). Alg-MA-PBA/Spd hydrogel was obtained by mixing with 10 mg/ml Spd in a volume ratio of 20:1, and a photoinitiator of 50 mg/ml LAP (Sigma-Aldrich, USA) was added in a volume ratio of 100:1. The hydrogel was cross-linked under 490 nm blue light for 30 s. At 0.01–10 Hz and $1\%$ strain, the storage modulus and loss modulus of the Alg-MA-PBA/Spd hydrogel were measured by frequency sweep, and the stiffness was compared at a frequency of 1 Hz. The stress relaxation of Alg-MA-PBA/Spd hydrogel and Alg-PBA/Spd hydrogel was measured within 200 s under $1\%$ strain, and the half stress relaxation time was compared.
## Measurement of viscoelasticity properties of PDL in vivo
Fresh pig maxillary bones were from the slaughterhouse. The maxillary bone was cut by an electric motor saw to obtain the tooth–PDL–bone complex. The complex was trimmed to ensure a flat shape. The stress relaxation of the pig periodontal membrane was measured by a moving-magnetic biomaterial testing system (BOSE3200, USA) (the compression strain is $10\%$). The stress relaxation data of pig periodontal membrane were compared with the stress relaxation level of the hydrogel.
## Establishment of the animal model
The rat PDL models were established according to the literature [49]. In brief, we separated the maxillary first molar gingiva with a probe in SD rats (male, 300–350 g) and incised the gingiva along the mesial direction to fully expose the root. Then we used a probe to penetrate into the PDL space to destroy the buccal, lingual, and mesial PDLs. Four percent of Alg-PBA/$1\%$ Spd hydrogel was injected into the PDL space to fill the periodontal defect, and the gingiva was sutured. Rats were randomly divided into the following groups: Blank (no treatment), Alg-PBA (viscoelastic hydrogel), Alg-MA-PBA/Spd (elastic drug-load hydrogel), Alg-PBA/Spd (viscoelastic drug-load hydrogel). We euthanized the rats at different time points and harvested periodontal tissue to assess healing. All animal procedures performed in this study were reviewed and approved by the Animal Experimental Ethical Inspection of Fourth Military Medical University [2019-035] and were performed in accordance with the guidelines of the International Association for the Study of Pain.
## Histological evaluation
Rats were sacrificed at 1, 2 and 3 weeks, and the maxilla including the first molars and surrounding soft tissues were completely removed. After being fixed in $4\%$ paraformaldehyde for 3 days, it was transferred to EDTA decalcification solution (neutral) for decalcification. The samples were placed in a shaker at 37°C, the decalcification solution was changed every day, and decalcified for 2 weeks. The decalcified samples were sectioned longitudinally along the long axis of the teeth. H&E staining and Masson staining were used to evaluate the effect of hydrogel on PDL repair, and the arrangement of fibers and collagen volume fraction were observed or calculated.
## Statistical analysis
Porosity, viable cell number, inflammatory area and collagen volume were counted using ImageJ (NIH, Bethesda, MD, USA). Significance analysis was performed using GraphPad Prism, using one-way ANOVA and two-way ANOVA and t-test, and the significance level was determined at $P \leq 0.05$ (*).
## Synthesis of phenylboronic acid modified sodium alginate and preparation of Alg-PBA/spd hydrogel
To synthesize a dynamic double-crosslinked network hydrogel, we prepared phenylboronic acid modified sodium alginate (Fig. 1A) and Spd (Fig. 1B). The successful grafting of PBA on Alginate was verified by nuclear magnetic resonance hydrogen spectroscopy (NMR) (Supplementary Fig. S1C). By comparing the characteristic peak of the phenyl signal of 7.6 ppm and the reference peak of the alginate skeleton of 4.97 ppm, we calculated that the degree of modification is $21.6\%$. The hydrogel network is crosslinked by dynamic boronic esters between boronic acid and cis-diol in the alginate backbone and further crosslinked by dynamic B–N coordination bonds between boronic acid and amino groups of Spd (Fig. 1C and E). To verify the gelation of hydrogel, we mixed $4\%$ Alg-PBA solution and $1\%$ Spd solution in a ratio of 20:1. Due to the rapid cross-linking and dissociation of the borate bond and the B–N coordination bond, the precursors gel immediately after mixing and the hydrogel does not flow when placed horizontally or upside down (Fig. 1F).
**Figure 1.:** *Schematic illustration of Alg-PBA/spd hydrogel. The chemical structure of (A) Alg-PBA, (B) Spermidine, (C) boronic bond and (D) B–N coordination. (E) Scheme of the preparation process of forming Alginate-PBA/spermidine hydrogel. (F) Optical photographs of the Alginate-PBA/spermidine mixture solution and the formed Alg-PBA/spd hydrogel.*
## Characterization of mechanical properties and structure of Alg-PBA/spd hydrogel
To evaluate the effect of Alg-PBA concentration on the storage modulus (G′) and loss modulus (G″) of Alg-PBA/Spd hydrogel, we mixed different concentrations ($2\%$, $3\%$, $4\%$) of Alg-PBA with $1\%$ Spd solution at a ratio of 20:1. We observed that both G′ and G″ increase with the increase of Alg-PBA concentration, but decrease with decreasing angular frequency (Fig. 2A). This is because Alg-PBA/Spd hydrogel composed of dynamic bonds is viscoelastic and there are no frequency bins where the modulus is stable. Therefore, to assess the stiffness of hydrogel with different concentrations, we chose the storage modulus at an angular frequency of 1 rad/s as a reference, with the storage modulus of hydrogel with different concentrations being significantly different (Fig. 2B). To compare the effects of different concentrations of Alg-PBA on the hydrogel porosity, we characterized the microstructure of the hydrogel using SEM and quantified the porosity. We observed that all concentrations of hydrogel exhibit irregular porous microstructures with pore sizes greater than 200 μm and porosity between $50\%$ and $80\%$ (Fig. 2C and D). The porous structure of these hydrogel ensure the transportation of nutrients and oxygen, which is conducive to cell penetration and proliferation, thereby promoting the formation of new tissues. To maximize the drug loading of the hydrogel and improve the stiffness of the hydrogel, we chose $4\%$ Alg-PBA/Spd as the material concentration for the subsequent experiments.
**Figure 2.:** *Mechanical properties and structure of Alg-PBA/spd hydrogel. (A) Variations of storage modulus and loss modulus (G′ and G″) of Alg-PBA/spd hydrogel with different Alg-PBA concentrations versus angular frequency (0.1–10 rad/s) and 1% strain. (B) Storage modulus of Alg-PBA/spd hydrogel with different Alg-PBA concentrations at 1 rad/s frequency and 1% strain. Values are exhibited as mean ± SD. ***P < 0.001. (C) Representative SEM images of the Alg-PBA/spd hydrogel. Scale bar = 50 μm. (D) Porosity of Alg-PBA/spd hydrogel with different Alg-PBA concentrations. Values are exhibited as mean ± SD. *P < 0.05, ***P < 0.001. (E) Loss tangent of the Alg-PBA/spd hydrogel with different spermidine concentrations versus angular frequency (0.1–10 rad/s) and 1% strain. (F) Stress relaxation curves of Alg-PBA/spd hydrogel at different strains.*
To prove the interaction of spermidine and Alg-PBA, we changed the concentration ratio of $4\%$ Alg-PBA and $1\%$/$3\%$/$5\%$ Spd to check the gel point (loss tangent = 1) of the hydrogel when the loss tangent varies with the angular frequency. We observed that different ratios cause the curves to have different trends, proving the intermolecular interaction between Spd and Alg-PBA (Fig. 2E). According to the results, the gel point of the hydrogel was changed with the different concentration of Spd. In addition, there was a significant difference in the loss modulus of hydrogels with different concentrations at low shear frequency, demonstrating that the concentration of Spd changed the dynamic crosslinking network of the hydrogel. Therefore, these results indicated the formation of the B–N coordination bond between Spd and Alg-PBA.
To characterize the viscoelastic properties of the Alg-PBA/Spd hydrogel, we tested the stress relaxation of the hydrogel under different strains (Fig. 2F). The hydrogel showed a short stress relaxation time, with complete stress relaxation within 100 s under different strains ($1\%$, $5\%$, $10\%$). These results indicated that Alg-PBA/Spd hydrogel possessed good viscoelasticity and might be effective in cushioning periodontal stress through rapid relaxation.
## Characterization of injectability, self-healing, tensile properties, remodeling and degradability of Alg-PBA/spd hydrogel
To characterize the self-healing properties of hydrogel, we performed the alternating cyclic strain measurements, and macro- and micro-hydrogel segmentation-self-healing experiments. We observed that Alg-PBA/Spd hydrogel exhibits yielding behavior at the high strain level of $500\%$ (G″ > G′), but the mechanical properties recover rapidly at the low strain level of $5\%$ (G′ > G″) (Fig. 3A). This indicated that the self-healing process can also be repeated many times without compromising the mechanics of the hydrogel. From a macroscopic point of view, we touched two separate hydrogels of different colors and observed that the two hydrogels quickly fuse into a whole, which can be easily lifted resisting its own weight (Fig. 3B). We further incised a piece of hydrogel from the middle and observed that the incision decreases rapidly with complete self-healing within ∼120 s (Fig. 3C). These results demonstrated the self-healing ability and stability of the double dynamic cross-linked network of boronate bond and B–N coordination bond. The self-healing properties of Alg-PBA/Spd hydrogel can greatly improve their application scenarios. When the hydrogel was filled in the defect area of the periodontal tissue, the external force may cause the hydrogel to rupture while the rapid self-healing ability will help to improve the adaptation of the hydrogel to the complex defect patterns.
**Figure 3.:** *Injectability, self-healing, tensile properties, remodeling, degradability and drug release of Alg-PBA/spd hydrogel. (A) Self-healing capacity of Alg-PBA/spd hydrogel by testing the G′ and G″ at alternating strain cycles of 5% and 500%. (B) Two pieces of cracked Alg-PBA/spd hydrogels are in contact with each other and the healed hydrogel can support its own weight. (C) Monitoring of the self-healing process of a scratch made on an Alg-PBA/spd hydrogel film by optical microscopy. Scale bar = 500 μm. (D) Viscosity of Alg-PBA/spd hydrogel with the shear rate from 0.1 to 5 1/s. (E) Photographs of the continuous injection of Alg-PBA/spd hydrogel through the 12# needle into any custom-designed shape. (F) Photographs of Alg-PBA/spd hydrogel with good tensile properties. (G) Photographs of Alg-PBA/spd hydrogel that can be remolded into various shapes. (H) Swelling and degradation curves of Alg-PBA/spd hydrogel in PBS. (I) The curve of cumulative spd release in PBS and 5 mM H2O2.*
Considering the narrow and complex spatial structure of periodontal defects, injection is a very important modality for delivering drugs to periodontal tissues. To assess the injectability of the Alg-PBA/Spd hydrogel, we carried out a frequency–viscosity test. The viscosity of the hydrogel decreased significantly with increasing shear rate (Fig. 3D). Besides, the hydrogel can be injected continuously without interruption using a 12# needle into any shape (Fig. 3E). These results suggested that dynamic B–N coordination bonds and borate ester bonds can endow hydrogels with good shear-thinning properties.
To characterize the tensile properties and remodeling of the hydrogel, we stretched the Alg-PBA/Spd hydrogel and observed that the hydrogel can resist a wide range of tensile strain (Fig. 3F) and reshape into different shapes (Fig. 3G). These results indicated that the hydrogel can be adapted to irregularly shaped periodontal defects to achieve perfect filling of the defects, and can remain shape stable under the extrusion of surrounding soft tissues.
To test the degradability of the Alg-PBA/Spd hydrogel, we soaked the hydrogel in PBS solution. We observed that the hydrogel rapidly swells to the maximum volume within 4 h and begins to degrade after 4 h until completely degraded after 5 days (Fig. 3H), which indicated that Alg-PBA/Spd hydrogel can be degraded due to the existence of dual dynamic cross-linking. Since the spermidine was crosslinked with a dynamic bond in the hydrogel, the dynamic bond would be destroyed during the degradation of the hydrogel, providing the synchronous and slow release of spermidine.
To evaluate the spermidine release of Alg-PBA/Spd hydrogel under normal conditions and in the presence of ROS, we tested the drug release of Alg-PBA/Spd hydrogel in PBS and H2O2 (Fig. 3I). We observed that spermidine is released ∼$60\%$ in the first 2 days and ∼$80\%$ in the fourth day under normal conditions in vitro, indicating the slow and uniform drug release. Compared with normal conditions, H2O2 can significantly accelerate the release of spermidine, with Spermidine completely released on Day 3. At the same time, we also observed the complete degradation of the hydrogel on Day 3. Our results showed that in the presence of reactive oxygen species in tissues (such as inflammation), Alg-PBA/Spd drugs can accelerate the release and act on inflammatory tissues, thereby performing anti-inflammatory functions faster.
## Comparison of hydrogels between borate bond and ionic bond
Compared with conventional metal ion (Ca2+) hydrogels, Alg-PBA hydrogels have advantages in several ways. Conventional metal ions (Ca2+) induced gelation through the formation of ionic bonds between Ca2+ and alginate can achieve rapid gelation, but the process is physical interactions, which may make hydrogel heterogeneous. The improved calcium ion penetration gelation method will greatly extend the gelation time. As a result, boronate bond can perform fast and achieve well-formed hydrogel.
To compare the mechanical properties of Alg-PBA/Spd with traditional Alg/Ca2+ hydrogels, we prepared $4\%$ Alg-PBA/Spd hydrogels and $4\%$ Alg/Ca2+ hydrogels ($4\%$ Alg was soaked with 0.1M calcium chloride for 30 min). We used frequency sweep to characterize the stiffness of Alg-PBA/Spd hydrogel and Alg/Ca2+ hydrogel (Supplementary Fig. S2A). The results showed that the average stiffness of Alg-PBA/Spd hydrogel is lower than that of Alg/Ca2+ hydrogel at 1 Hz (Supplementary Fig. S2B). The Alg-PBA/Spd hydrogel has a faster stress relaxation compared with the Alg/Ca2+ hydrogel under $10\%$ strain (Supplementary Fig. S2C and D). The ionic bond crosslinking formed between calcium ions and alginate is relatively stable, so it has higher stiffness and worse dynamics. On the contrary, borate bonds have better dynamics, and the hydrogel network is easily re-crosslinked after breaking. So, the stiffness of the hydrogel is lower, and at the same time it has an extremely fast stress relaxation rate.
To compare the in vitro biocompatibility of Alg-PBA/Spd hydrogel and Alg/Ca2+ hydrogel, we detected the live/dead quantity of the cells on Days 1 and 3 by Calcein-AM/PI dye. We observed that both Alg-PBA/Spd hydrogel and Alg/Ca2+ hydrogel have good biocompatibility (Supplementary Fig. S2E).
The spermidine and calcium ions in Alg-PBA/Spd hydrogels and Alg/Ca2 hydrogels are distributed in the body. Spermidine in the human body is mainly derived from food and microorganisms in the intestine, and Ca2 is an important component of the human body. So both Spd and Ca++2+ in hydrogels have good biocompatibility. In addition, alginate and its derivatives are macromolecular polysaccharides that are widely distributed in nature, so the hydrogels containing alginate also have good biocompatibility.
To compare the drug release of Alg-PBA/Spd hydrogels and Alg/Ca2+ hydrogels, we characterized the spermidine release of Alg-PBA/Spd hydrogels and Alg/Ca2+ hydrogels loaded with spermidine (Supplementary Fig. S2F). We observed that Alg/Ca2+ releases spermidine faster, with more than $80\%$ released within 2 days. In comparison, the spermidine release of Alg-PBA/Spd hydrogel is significantly slower than that of Alg/Ca2+. Alg-PBA can form a B–N covalent crosslinking with Spd. With the slow degradation of the Alg-PBA/Spd hydrogel, the B–N coordination bond is broken and the spermidine is slowly released. Alg/Ca2+ and Spd are physically mixed to achieve drug delivery, so spermidine is directly released.
## Characterization of the biocompatibility and anti-inflammation
To assess the cytotoxicity of the hydrogel, we characterized the cytotoxicity of $4\%$ Alg-PBA/Spd hydrogel for the PDLFs using CCK-8 assay. We observed that when the hydrogel solution concentration is lower than $1\%$ (volume fraction, v/v), the cell viability remained stable (cell viability >$100\%$), while cell viability decreased rapidly with increasing concentrations higher than $1\%$ (Fig. 4A). According to this result, we selected the concentration of hydrogel solution (lower than $1\%$) to further detect the level of cell proliferation. PDLFs maintain proliferation under different concentrations of hydrogel solutions for 5 days (Fig. 4B).
**Figure 4.:** *Biocompatibility and anti-inflammation of Alg-PBA/spd hydrogel. (A) Cytotoxicity of Alg-PBA/Spd through CCK-8 assay of PDLFs viability after co-culture with different hydrogels mass fraction at specific time points (Day 3). (B) CCK-8 assay of PDLFs proliferation after co-culture with different hydrogel mass fraction for Days 1, 3, 5. PDLFs, periodontal ligament fibroblast cells; CCK-8, cell counting kit-8. (C) Live/dead staining of PDLFs at specific time points (Days 1, 3, 5) in 1 week of co-culture. Calcein AM staining (green) shows the high viability of cells. Scale bar =100 μm; 0.50%, 0.25%, 0, the mass fraction of Alg-PBA/spd hydrogel. (D) Live cell counts of the live/dead staining of PDLFs in (C). (E) ELISA analysis of IL-1β secretion in different spd concentrations. (F) ELISA analysis of IL-6 secretion in different spd concentrations. Data are shown as mean ± SD and compared using one-way ANOVA followed by Bonferroni’s post hoc test. ns, *, ** and *** indicate P > 0.05, P < 0.05, P < 0.01 and P < 0.001, respectively.*
To further verify the biocompatibility of the hydrogel, we also performed Live/Dead staining experiments. We observed that PDLFs in the experimental groups ($0.5\%$ and $0.25\%$) and the control group kept mostly live (green) with normal spindle-like morphology. After 5 days of culture, the proliferation process was not affected (Fig. 4C). No significant difference was observed among the $0.5\%$, $0.25\%$ and $0\%$ group at the same time point (Fig. 4D), which remained consistent with the results of the CCK-8. We further evaluated the viability when PDLFs were cultured on Alg-PBA/Spd hydrogel (2D) and in Alg-PBA/Spd hydrogel (3D) for 1 and 3 days (Supplementary Fig. S3A). The cell viability in 2D culture is over $90\%$, and the viability in 3D is over $75\%$. The above results all confirmed good biocompatibility of the Alg-PBA/Spd hydrogel.
To evaluate the anti-inflammatory ability of spermidine in vitro, we used different concentrations of spermidine to treat PDLFs in the inflammatory state and detected the IL-1β and IL-6 content in the cell culture solution using enzyme-linked immunosorbent experiments (Fig. 4E and F). When the spermidine concentration is 1 and 10 µM, the IL-1β concentration is 82.9 and 50.5 pg/ml, respectively. When the spermidine concentration is 1 and 10 µM, the concentration of IL-6 was 63.9 and 41.6 pg/ml, respectively. Spermidine can significantly reduce the production of IL-1β and IL-6. This result proves that spermidine has good anti-inflammatory ability, which may be an important reason why spermidine can promote periodontal tissue regeneration.
## The degradation of the hydrogel in vivo and mucoadhesive property
To test the degradation of the hydrogel in vivo, we implanted Cy7-Alg-PBA/Spd hydrogel under the skin of rats, and evaluated the degradation of the hydrogel through quantitative fluorescence intensity for up to 9 days (Supplementary Fig. S4A). The results showed that the hydrogel remained about $40\%$ after 5 days of implantation and ∼$20\%$ after 9 days of implantation (Supplementary Fig. S4B). This suggested that the hydrogel in the body can cover the rapid regeneration stage of PDL before degradation completely.
To evaluate the adhesion formation after PBA modification, we designed the related adhesion experiments. We pasted two pieces of fresh pork on the slide and evenly applied Alg-PBA/Spd hydrogel on the surface. We observed that the Alg-PBA/Spd hydrogel pastes two pieces of pork into a whole. When we separated the two pieces of pork, we observed the filamentous hydrogel produced by adhesion between the pork tissues (Supplementary Fig. S5A). The alginate hydrogel modified by PBA can form a cis-diol structure, which is similar to mussel adhesion, providing good adhesion properties for alginate. At the same time, the positively charged amino groups in spermidine can be adsorbed on the tissue surface by electrostatic action. The primary amino group can covalently bind to the carboxyl group on the tissue surface, which may enhance the adhesion of the hydrogel.
## Synthesis and characterization of hydrogels of Alg-MA-PBA/spd
To evaluate the mechanical properties of Alg-MA-PBA/Spd hydrogels and Alg-PBA/Spd hydrogels, we used frequency sweep to test their G′ and G″ and the stiffness was compared at 1 Hz (Supplementary Fig. S6A). The results showed that there is no significant difference in their stiffness at 1 Hz (Supplementary Fig. S6B). To suggest the viscoelastic differences between Alg-MA-PBA/Spd hydrogels and Alg-PBA/Spd hydrogels, we measured the stress relaxation of the two hydrogels (Supplementary Fig. S6C). We found that the stress relaxation of Alg-MA-PBA/Spd hydrogels is significantly slower than that of Alg-PBA/Spd hydrogels (Supplementary Fig. S6D). The half stress relaxation time is 1385 s for Alg-MA-PBA/Spd hydrogel, and 38 s for Alg-PBA/Spd hydrogel. We chose Alg-MA-PBA/Spd hydrogel as the elastic control group for viscoelastic Alg-PBA/Spd hydrogel.
## Measurement and simulation of viscoelasticity of PDL in vivo
To evaluate the similarity of viscoelasticity between our hydrogels and native PDL tissues, we got a fresh pig tooth-periodontal membrane–bone complex and measured the viscoelasticity of the PDL tissues. The stress relaxation curve of Alg-PBA/Spd hydrogel and PDL tissue has a similar trend, while the stress relaxation curve of Alg-MA-PBA/Spd hydrogel and PDL tissue is quite different (Supplementary Fig. S7A). We further compared their half stress relaxation time, and observed there is no significant difference between Alg-PBA/Spd hydrogel and PDL tissue, but a significant difference between Alg-MA-PBA/Spd hydrogel and PDL tissue (Supplementary Fig. S7B). Alg-PBA/Spd hydrogel can completely cover the stress relaxation range of native PDL tissue. In addition, Alg-MA-PBA/Spd hydrogel can be used as an elastic control group.
## Evaluation of in vivo therapeutic effect of Alg-PBA/spd hydrogel in animal models of periodontal defects
To evaluate in vivo therapeutic effect of Alg-PBA/Spd hydrogel, we established the periodontal defect with a width of 1.5 mm and a depth of 2 mm was formed around the maxillary first molar of the rat, and the defect was completely filled with hydrogel (Fig. 5A and B). To assess the histology and regeneration of PDL in each group, we performed Masson trichrome staining and hematoxylin and eosin (H&E) staining. The collagen fibers in the PDL space of the four groups show an increasing trend with the prolongation of tissue recovery time (Fig. 5C). In the first week, the total amount of PDL and gingival tissue in the groups of Alg-PBA and Alg-PBA/Spd hydrogel showed greater than those in the groups of blank and Alg-MA-PBA/Spd hydrogel. There were a large number of red-stained muscle fibers in the PDL space of the blank group, but only a small amount of collagen fibers arranged in bundles in the Alg-PBA/Spd group and no similar neatly arranged fiber structures in the other three groups. At the second week, the PDL spaces of all four groups were all filled with new periodontal collagen fibers. The fibers of the Alg-PBA and Alg-PBA/Spd groups arranged regularly with dense fiber bundles, while the fibers in the blank and Alg-MA-PBA/Spd group showed irregularly arranged, even with a lot of muscle fibers in the blank group. At the third week, the proportion of collagen fibers in all four groups increased. The enlarged image in the blank group showed that there were irregular muscle fibers in the local area, with the collagen fibers in the remaining three groups being arranged in a neat fiber bundle structure.
**Figure 5.:** *Alg-PBA/Spd hydrogel promotes PDL tissue regeneration in vivo. (A) Schematic diagram of periodontal defect modeling in rats. (B) The mucoperiosteal flap was elevated to expose the alveolar bone on the lingual side of the first maxillary molar. The alveolar bone covering the root surface is removed, creating a periodontal window defect. The Alg-PBA/spd hydrogel is injected at the defect site. After the hydrogel completely filled the defect, the gums were sutured. (C) Masson staining images of the periodontal tissues at specific time points (Weeks 1, 2, 3) after different treatments. Scale bar = 200 and 50 μm. PDL, periodontal ligament; B, alveolar bone; C, cementum. (D) The collagen volume fraction of the Masson staining images (a). *P < 0.05, **P < 0.01, ***P < 0.001. (E) H&E staining images of the periodontal tissues at specific time points (Week 3) after different treatments. Scale bar = 100 and 50 μm.*
Next, we quantified the volume fraction of collagen and the amount of collagen fibers generated (Fig. 5D). In the early stage of tissue recovery, the volume of collagen in the experimental groups treated with hydrogel showed higher than that in the blank control group, with the highest volume fraction in the Alg-PBA/Spd group. In the middle and late stages of tissue recovery, there was no significant difference in the volume of collagen produced between the Alg-PBA/Spd and Alg-MA-PBA/Spd groups, with those of both groups being significantly higher than the Alg-PBA and blank groups.
Further, we assessed the arrangement of PDL cells by H&E staining (Fig. 5E). As indicated by the arrows, the cells in the blank group were arranged in disorder with clear alveolar bone tissue resorption. The cells in Alg-PBA group were mostly concentrated near the alveolar bone, with dense and regularly arranged fibrous structures. The cells in Alg-MA-PBA/Spd and Alg-PBA/Spd groups were evenly distributed along the fibers, and the fibers were arranged neatly.
Based on the analysis of the direction distribution of collagen fibers in the HE staining results (Supplementary Fig. S5), we found that the direction of fiber distribution in the viscoelastic group (Alg-PBA and Alg-PBA/Spd) is concentrated in the horizontal direction and arranged orderly. The fibers of the elastic group and the blank group (Alg-MA-PBA/Spd and Blank) are distributed in the vertical direction, and the distribution of the elastic group in all directions is relatively small.
We believed that the viscoelastic group can match the viscoelasticity of native PDL. In the process of PDL fiber regeneration, similar viscoelasticity causes the hydrogel to have a smaller external force on the PDL fibers, which helps the fibers to be arranged more orderly. The elastic group hinders the regeneration of PDL fibers, resulting in the disordered arrangement of regenerative PDL fibers. The PDL fibers of the blank group are not affected by external forces and can regenerate in a vertical direction. These results demonstrated that viscoelastic hydrogel might facilitate the generation of PDL fibers in the early stage of repair, and the synergistical treatment with spermidine could even promote the production of collagen by PDLFs and maximize the therapeutic effect.
## Conclusion
In this study, we constructed a double cross-linked hydrogel (Alg-PBA/Spd hydrogel) for PDL repair and regeneration based on the formation of borate ester bonds and B-N ligand bonds. We demonstrated that the mechanical viscoelasticity of Alg-PBA/Spd hydrogel could synergize with biochemical factors (spermidine) to achieve optimal PDL repair. These results highlight the important role of viscoelastic implants in tissue repair, especially in PDL, a tissue subjected to dynamic mechanical stimulation. Based on this work, the coupling between dynamic periodontal stresses and the dynamic viscoelasticity of hydrogels can be explored in the future, which may provide more interesting insights into the mechanisms of PDL repairing using viscoelastic materials. Besides, the role of mechano-biochemically synergistic treatment for tissue regeneration and functional recovery has attracted increasing attention and has been applied in a variety of scenarios (e.g. myocardial infarction, myasthenia gravis, traumatic brain injury). The present study suggests mechanical factors as important design parameters and functional indicators in the field of regenerative medicine. On the other hand, drug (spermidine) loading through dynamic bonding could be insightful, which represents a simple, inexpensive, but effective approach to the sustained release of drugs and integration with hydrogel viscoelasticity. These could inspire more material designs that combine dynamic crosslinking chemistry with other factors (e.g. drug loading, material degradation, cell culture) to create biomaterials with emerging properties for regenerative medicine.
## Supplementary data
Supplementary data are available at Regenerative Biomaterials online.
## Funding
This work was supported by the National Natural Science Foundation of China (31971248, 12225208 and 12002263), Science and Technology Innovation Team Project, Shaanxi Province (2021TD-46) and the Young Talent Support Plan of Xi’an Jiaotong University, and supported by the Fundamental Research Funds for the Central Universities (xzy012020079, xzd012021037).
Conflicts of interest statement. 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: 'The Effect of Female Sex on Short-Term Outcomes of Patients
Undergoing Off-Pump Versus On-Pump Coronary Artery Bypass
Grafting'
authors:
- Jun Fan
- Shao-ling Luo
- Yi-chao Pan
- Tian-yuan Wu
- Yu Chen
- Wei-jie Li
journal: Brazilian Journal of Cardiovascular Surgery
year: 2023
pmcid: PMC10010711
doi: 10.21470/1678-9741-2021-0301
license: CC BY 4.0
---
# The Effect of Female Sex on Short-Term Outcomes of Patients
Undergoing Off-Pump Versus On-Pump Coronary Artery Bypass
Grafting
## Abstract
### Introduction
According to the American Heart Association guideline for coronary artery bypass grafting (CABG), female patients undergoing on-pump CABG (ONCAB) are at higher risk of short-term adverse outcomes than male patients. However, whether off-pump CABG (OPCAB) can improve the short-term outcome of female patients compared to ONCAB remains unclear.
### Methods
We conducted a meta-analysis to study the effect of the female sex on short-term outcomes of OPCAB vs. ONCAB. A total of 31,115 patients were enrolled in 12 studies, including 20,245 females who underwent ONCAB and 10,910 females who underwent OPCAB.
### Results
The in-hospital mortality in female patients who underwent OPCAB was significantly lower than in those in the ONCAB group with ($2.7\%$ vs. $3.4\%$; odds ratio [OR] 0.76; $95\%$ confidence interval [CI] 0.65-0.89) and without (OR 0.68; $95\%$ CI 0.52-0.89) adjustment for cardiovascular risk factor. The incidence of postoperative stroke in female patients who underwent OPCAB was lower than in those in the ONCAB group ($1.2\%$ vs. $2.1\%$; OR 0.59; $95\%$ CI 0.48-0.73) before cardiovascular risk factor adjustment but was not significant (OR 0.87; $95\%$ CI 0,66-1.16) after adjustment. There was no significant difference in the incidence of postoperative myocardial infarction between women who underwent OPCAB and those in the ONCAB group ($1.3\%$ vs. $2.3\%$; OR 0.88; $95\%$ CI 0.54-1.43).
### Conclusion
In contrast to the American Heart Association CABG guideline, female patients who had OPCAB don’t have unfavorable outcomes compared with the ONCAB group.
## INTRODUCTION
Coronary artery disease (CAD) is the leading cause of death in both developed and developing countries[1]. The mortality and quality of life of CAD patients have been significantly improved by the effective application of primary[2] and secondary prevention[3]. Clinical trials have shown that improving the management of hypertension[4], diabetes mellitus[5], and hyperlipidemia[6] promoted better clinical outcomes in CAD patients. However, several risks factors affecting the outcomes of CAD patients remain unclear[7].
Coronary artery bypass grafting (CABG) is a treatment strategy for coronary artery revascularization. According to the American Heart Association CABG guideline, the female sex is a risk factor for adverse outcomes[8]. In Kim et al.[9] meta-analysis involving 23 studies, early mortality and complications were higher among females after CABG than among males. However, this conclusion was based on the studies of on-pump CABG (ONCAB) or studies not stratified based on the cardiopulmonary bypass technique used.
In the CABG Off or On Pump Revascularization Study[10] (CORONARY) and the Randomized On/Off Bypass trial[11] (ROOBY), there was no significant difference between off-pump CABG (OPCAB) and ONCAB in the 30-day mortality rate. In addition, there was no significant difference in the occurrence of myocardial infarction (MI), stroke, or renal failure requiring dialysis between OPCAB and ONCAB groups in the CORONARY study[10]. However, to this date, there are no reports concerning the influence of sex difference on the outcomes of OPCAB vs. ONCAB clinically.
The study by Attaran et al.[12] was the first meta-analysis that compared the short-term outcomes between off-pump vs. on-pump revascularization among female patients. In this study, no statistically significant difference was observed in the 30-day mortality rate and other morbidity outcomes between the OPCAB and ONCAB groups, except for perioperative MI. Recently, several new studies in this field, including the propensity score matching (PSM) study[13] and studies that were adjusted for cardiovascular risk factors[13,14], have been published. This study aims to investigate the latest research to study the effect of the female sex on short-term outcomes in OPCAB vs. ONCAB patients.
## METHODS
Since this study is a systematic review and meta-analysis based on previous articles, ethics committee approval was not required; it was conducted in accordance with the Helsinki Declaration of 1975 (revised in the year 2000). This is an observational meta-analysis that followed the guidelines for the Meta-analysis of Observational Studies in Epidemiology. This study has been registered on PROSPERO (CRD42021250888). We searched literature databases including PubMed®, Web of Science™, Embase®, Scopus™, Ovid, the China National Knowledge Infrastructure (or CNKI), the Chinese Biomedical Literature service system (or SinoMed), and the Wanfang Data Knowledge Service Platform with the keywords “coronary artery bypass”, “female”, “women”, “woman”, “gender”, and “sex”. We did not limit the start time of the studies, but we limited their end time to 2021-8-1, when retrieving the literature. After this strategy, 4,358 pieces of literature were retrieved. LSL and PYC carefully read and analyzed all the retrieved studies, and the publications were further screened according to the flow chart shown in Figure 1. Finally, 12 retrospective observational studies were included in our meta-analysis. Of the 12 studies, two were PSM studies.
Fig. 1Flow diagram describing study selection in our meta-analysis. CABG=coronary artery bypass grafting; CNKI=China National Knowledge Infrastructure; SinoMed=Chinese biomedical literature service system We included two main types of studies in our meta-analysis: 1) studies which only included female patients grouped by OPCAB and ONCAB and 2) studies which included male and female patients undergoing CABG (OPCAB and ONCAB), but containing a clear delineation between OPCAB and ONCAB subgroups. Both types of studies must also possess documented primary and secondary endpoints.
Primary endpoints included in-hospital death, 30-day death rate after surgery, postoperative MI, and stroke. Secondary endpoints included postoperative acute renal failure (ARF), renal replacement therapy, blood transfusion, reoperation for bleeding, sternal wound infection, atrial fibrillation, and postoperative lower cardiac output.
The selected literature was not restricted by language. Abstracts, conference abstracts, and supplementary issues were also included. Patients who underwent concomitant surgical procedures such as valvular repair or replacement, correction of congenital malformation, and ascending aortic aneurysm repair, to name a few, were excluded from this study. FJ and PYC analyzed the data extracted from these studies. A consensus was reached through discussion in cases of disagreements.
## Extraction of Data
LWJ and FJ extracted data from the selected literature, including the first author’s name, the year when the study was published, the type of research, and the country where the study was conducted. General characteristics such as age, race, body mass index, and smoking status were recorded. Preoperative diseases including hypertension, diabetes, hyperlipidemia, heart failure, stroke, and peripheral vascular disease were included. Patients’ echocardiographic measurement parameters, such as left ventricular ejection fraction, were also collected. Primary and secondary endpoints were collected for investigation. The quality of the studies was evaluated according to the Newcastle-Ottawa Scale (or NOS).
## Statistical Analysis
RevMan 5.4 (Nordic Cochrane Center) statistical software was employed for meta-analysis. A P-value of < 0.05 was considered statistically significant. Publication bias was assessed using visual inspection of funnel plots. All included studies were retrospective in nature. A random-effects model was adopted in this study to avoid the impact of inter-study heterogeneity on the results.
## Literature Retrieval
We searched the literature database as abovementioned; 4,358 scientific works were retrieved after preliminary screening. We then further screened the literature according to the strategy in Figure 1. Ultimately, 12 studies were included in our meta-analysis. All studies were retrospective observational studies. Primary and secondary endpoints were extracted for analysis.
## Characteristics of the Included Studies
The 12 studies included were observational, three reports were from the United States of America[13,15-18], and the remaining were clinical studies from Germany[19-21], Netherlands[14,22], Portugal[23], Poland[24-27], and Canada[28,29]. The detailed characteristics of the included patients and quality assessment are shown in Table 1. We investigated in-hospital mortality rate (Figure 2), 30-day hospital mortality rate (Figure 3), myocardial infarction incidence (Figure 4), stroke incidence (Figure 5), incidence of red blood cell transfusion and re-exploration for bleeding (Supplementary Figure 1), acute renal failure and renal replacement therapy (Supplementary Figure 2), deep wound infection (Supplementary Figure 3A), atrial fibrillation (Supplementary Figure 3B), and postoperative lower cardiac output (Supplementary Figure 3C) among female patients received ONCAB or OPCAB. A funnel plot is shown in Supplementary Figures 4-8.
**Table 1**
| Source | Region | Design | Total of women, nº | OPCAB, nº | ONCAB, nº | Study quality* |
| --- | --- | --- | --- | --- | --- | --- |
| Woorst | Netherlands | Observational | 3684 | 414 | 337 | 6 |
| Rieß | Germany | Observational | 660 | 259 | 401 | 4 |
| Sá | Portugal | Observational | 941 | 549 | 392 | 4 |
| Eifert | Germany | Observational | 733 | 252 | 481 | 7 |
| Maganti | Canada | Observational | 296 | 148 | 148 | 8 |
| Czech | Poland | Observational | 677 | 275 | 402 | 4 |
| Bucerius | Canada | Observational | 2182 | 152 | 203 | 4 |
| Mack | United States of America | Observational | 7376 | 3688 | 3688 | 4 |
| Perek | Poland | Observational | 301 | 31 | 270 | 4 |
| Petro | United States of America | Observational | 1831 | 304 | 1527 | 6 |
| Puskas | United States of America | Observational | 3248 | 1381 | 1867 | 6 |
| Woś | Poland | Observational | 689 | 31 | 658 | 4 |
Fig. 2Forest plots demonstrating in-hospital mortality of off-pump coronary artery bypass grafting (OPCAB) vs. on-pump coronary artery bypass grafting (ONCAB) for (a) original data without adjustment, (b) in-hospital mortality with cardiovascular risk factor adjustment, (c) in-hospital mortality of propensity score matching studies. Chi=Chi-squared; CI=confidence interval; df=degree of freedom; IV=inverse variance; M-H=Mantel-Haenszel; SE=standard error; Tau=Tau-squared Fig. 3Forest plot demonstrating the 30-day hospital mortality rate of off-pump coronary artery bypass grafting (OPCAB) vs. on-pump coronary artery bypass grafting (ONCAB). Chi=Chi-squared; CI=confidence interval; df=degree of freedom; M-H=Mantel-Haenszel; Tau=Tau-squared Fig. 4Forest plot demonstrating postoperative myocardial infarction incidence of off-pump coronary artery bypass grafting (OPCAB) vs. on-pump coronary artery bypass grafting (ONCAB). Chi=Chi-squared; CI=confidence interval; df=degree of freedom; M-H=Mantel-Haenszel; Tau=Tau-squared Fig. 5Forest plots demonstrating postoperative stroke incidence of off-pump coronary artery bypass grafting (OPCAB) vs. on-pump coronary artery bypass grafting (ONCAB) for (a) original data without adjustment, (b) stroke with cardiovascular risk factor adjustment, (c) stroke of propensity score matching studies. Chi=Chi-squared; CI=confidence interval; df=degree of freedom; IV=inverse variance; M-H=Maentel-Haenszel; SE=standard error; Tau=Tau-squared
## Clinical Characteristics of the Included Patients
A total of 31,115 patients were enrolled in the 12 studies, including 20,245 women who underwent ONCAB and 10,910 women who underwent OPCAB. The clinical characteristics and differences between female patients who underwent OPCAB or ONCAB, including age, hypertension, diabetes, smoking status, ejection fraction, chronic obstructive pulmonary disease, peripheral vascular disease, and previous MI, are shown in Table 2.
**Table 2**
| Unnamed: 0 | Age, mean, years | Age, mean, years.1 | Diabetes, % | Diabetes, %.1 | Hypertension, % | Hypertension, %.1 | Dyslipidemia, % | Dyslipidemia, %.1 | Smoking, % | Smoking, %.1 | LVEF, % | LVEF, %.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Source | OPCAB | ONCAB | OPCAB | ONCAB | OPCAB | ONCAB | OPCAB | ONCAB | OPCAB | ONCAB | OPCAB | ONCAB |
| Woorst | 67.6 a | 68.8 | 24.4 a | 29.5 | 62.6 | 59.0 | NR | NR | NR | NR | NR | NR |
| Rieß | 71.6 | 70.4 | 28.2 | 30.2 | NR | NR | NR | NR | 25.1 | 24.7 | NR | NR |
| Sá | 68.6 | 69.2 | 45.9 a | 35.9 | 68.7 a | 72.1 | NR | NR | 14.2 | 12.7 | NR | NR |
| Eifert | 66.2 | 65.5 | 13.5 | 15 | 59.7 | 63.7 | NR | NR | 41.6 | 44.3 | 64.3 | 58.9 |
| Maganti | 65 | 64 | 41 | 41 | 70 | 68 | 74 | 73 | NR | NR | NR | NR |
| Czech | NR | NR | NR | NR | NR | NR | NR | NR | NR | NR | NR | NR |
| Bucerius | 67.9 | 68.1 | 45.4 | 44.6 | 82.2 | 78.3 | 55.3 | 55.9 | NR | NR | NR | NR |
| Mack | 68.6 | 68.9 | 34.6 | 34.2 | 69.5 a | 66.6 | NR | NR | 13.7 | 12.6 | NR | NR |
| Perek | NR | NR | NR | NR | NR | NR | NR | NR | NR | NR | NR | NR |
| Petro | 67 | 66 | 36 a | 46 | NR | NR | NR | NR | NR | NR | NR | NR |
| Puskas | 65.1 | 64.8 | 42 | 42 | 84 | 79 | NR | NR | 33 | 26 | NR | NR |
| Woś | 57 | 62 | NR | NR | NR | NR | NR | NR | NR | NR | NR | NR |
## Effect of OPCAB on In-Hospital Mortality Rate Among Female Patients
We included eight studies to investigate the effect of OPCAB on in-hospital mortality in women. Female coronary heart disease patients undergoing ONCAB were the control group. A total of 23,896 women were enrolled in these eight studies, including 9,833 women who underwent OPCAB and 14,063 women who underwent ONCAB. The number of deaths in OPCAB and ONCAB patients was 264 and 483, respectively. The in-hospital mortality rate in female patients who underwent OPCAB was significantly lower than in those in the ONCAB group ($2.7\%$ vs. $3.4\%$; odds ratio [OR] 0.76; $95\%$ confidence interval [CI] 0.65-0.89) (Figure 2A).
In two of the eight studies, OR values were corrected for cardiovascular risk factors. Consistently with the meta-analysis results of these eight studies, the mortality rate of female patients who underwent OPCAB was lower than of those in the ONCAB group (OR 0.68; $95\%$ CI 0.52-0.89) (Figure 2B).
Among these eight studies, a PSM method was employed in two of them. A total of 3,836 female patients who underwent OPCAB and 3,836 female patients who underwent ONCAB were enrolled in these two studies. The number of deaths in OPCAB and ONCAB patients was 118 and 146, respectively. In contrast to the abovementioned results, there was no significant difference in in-hospital mortality rate between female patients who underwent OPCAB or ONCAB ($3.1\%$ vs. $3.8\%$; OR 0.80; $95\%$ CI 0.63-1.03) (Figure 2C).
## Effect of OPCAB on 30-Day Hospital Mortality Rate Among Female
Patients
We selected four studies to investigate the effect of OPCAB on the 30-day postoperative mortality rate in female patients. Female patients who underwent ONCAB were employed as the control group. A total of 7,529 women were enrolled in these four studies, including 1,077 OPCAB patients and 6,182 ONCAB patients. The female 30-day death rate of OPCAB and ONCAB were nine and 222, respectively. Patients who underwent OPCAB had a lower 30-day mortality rate than those in the ONCAB group ($0.8\%$ vs. $3.6\%$; OR 0.28; $95\%$ CI 0.15-0.55) (Figure 3).
## Effect of OPCAB on Myocardial Infarction Incidence Among Female
Patients
We included six studies to investigate the effect of OPCAB on postoperative MI in female patients. There was no significant difference in the incidence of postoperative MI in women who underwent OPCAB compared with those that underwent ONCAB ($1.3\%$ vs. $2.3\%$; OR 0.88; $95\%$ CI 0.54-1.43). Of the 12 studies included in this study, the PSM method was employed in one, and the result of this study was consistent with previous results (Figure 4).
## Effect of OPCAB on Stroke Incidence Among Female Patients
We included eight studies to investigate the effect of OPCAB on stroke in female patients. Female patients who underwent ONCAB were used as the control group. A total of 27,657 women were enrolled in these eight studies, including 10,269 women who underwent OPCAB and 17,388 women who underwent ONCAB. The number of postoperative strokes in OPCAB and ONCAB female patients was 123 and 359, respectively. The incidence of postoperative stroke in OPCAB female patients was lower than in those in the ONCAB group ($1.2\%$ vs. $2.1\%$; OR 0.59; $95\%$ CI 0.48 - 0.73) (Figure 5A).
Among these eight studies, two used the PSM method. Consistently with previous results, the incidence of stroke in the OPCAB group was lower than in the ONCAB group ($1.0\%$ vs. $1.8\%$; OR 0.56; $95\%$ CI 0.37-0.83) (Figure 5B). In two of the eight studies, postoperative stroke OR values were adjusted for cardiovascular risk factors. In contrast to previous results, there was no significant difference in the incidence of postoperative stroke between the OPCAB and ONCAB groups (OR 0.87; $95\%$ CI 0.66-1.16) (Figure 5C).
## Effect of OPCAB on the Incidence of Red Blood Cell Transfusion and
Re-exploration for Bleeding
We included three studies to investigate the effect of OPCAB on blood transfusion occurrence in female patients. The incidence of blood transfusion in female patients who received OPCAB was lower than in those in the ONCAB group ($31.1\%$ vs. $61.4\%$; OR 0.27; $95\%$ CI 0.16-0.46) (Supplementary Figure 1A).
Seven studies were included to investigate the effect of OPCAB on re-exploration for bleeding among female patients. Postoperative re-exploration bleeding was lower in female OPCAB patients than in those in the ONCAB group ($4.2\%$ vs. $4.8\%$; OR 0.70; $95\%$ CI 0.50-0.97) (Supplementary Figure 1B). However, in the meta-analysis of PSM studies, there was no significant difference in re-exploration for bleeding incidence in female ONCAB patients compared with the control group (Supplementary Figure 1C).
## Effect of OPCAB on Acute Renal Failure and Renal Replacement Therapy in
Female Patients
We included seven studies to investigate the effect of OPCAB on postoperative ARF among female patients. A total of 25,508 women were enrolled, including 10,011 women who underwent OPCAB and 15,497 women who underwent ONCAB. The incidence of ARF in OPCAB female patients was lower than in those in the ONCAB group ($1.9\%$ vs. $3.6\%$; OR 0.62; $95\%$ CI 0.42-0.91) (Supplementary Figure 2A). Two studies that investigated OR adjusted by cardiovascular risk factors also showed a lower risk of postoperative ARF in women who underwent OPCAB (OR 0.69; $95\%$ CI 0.56-0.84) (Supplementary Figure 2B). We also found that the incidence of female patients receiving renal replacement therapy after surgery was lower in the OPCAB than in the ONCAB group ($1.02\%$ vs. $2.57\%$; OR 0.51; $95\%$ CI 0.28-0.91) (Supplementary Figure 2C).
## Effect of OPCAB on Deep Wound Infection in Female Patients
Six studies were included to investigate the impact of OPCAB on deep wound infection among female patients. A total of 9,707 OPCAB female patients and 13,970 ONCAB female patients were included in these studies. We found that the incidence of deep wound infection in OPCAB patients was lower than in ONCAB patients ($0.3\%$ vs. $0.7\%$; OR 0.58; $95\%$ CI 0.37-0.90) (Supplementary Figure 3A).
## Effect of OPCAB on Atrial Fibrillation and Postoperative Lower Cardiac
Output
We included six studies to investigate the effect of OPCAB on postoperative atrial fibrillation in women. A total of 6,319 female patients who underwent OPCAB and 9,927 female patients who underwent ONCAB were included in these studies. The incidence of postoperative atrial fibrillation showed no statistical difference in OPCAB patients compared with ONCAB patients ($20.2\%$ vs. $23.4\%$; OR 0.85; $95\%$ CI 0.68-1.06) (Supplementary Figure 3B). Three studies were included to investigate the effect of OPCAB on postoperative lower cardiac output in women. The results showed no significant difference in postoperative lower cardiac output incidence in OPCAB patients compared with ONCAB patients ($5.3\%$ vs. $6.5\%$; OR 0.88; $95\%$ CI 0.52-1.51) (Supplementary Figure 3C).
## DISCUSSION
In this study, we included 12 retrospective observational studies regarding the influence of the female sex on the short-term clinical outcomes following OPCAB and ONCAB. A total of 31,115 patients were included, which consisted of 20,245 males and 10,910 females. We observed that the incidence of adverse events in female patients who underwent OPCAB was lower or not significant, but not higher, than in those in the ONCAB group.
According to the American Heart Association guidelines for CABG, women are at a higher risk for adverse clinical outcomes, including postoperative mortality and stroke[8]. However, most of these studies are based on ONCAB. Previous research from Risum et al.[29] confirmed that the risk of early mortality and low-output syndrome needing intra-aortic balloon support was higher in women than in men. In addition, a meta-analysis from Wognsen et al.[30] found that females run an increased risk of early death and the development of postoperative complications after CABG compared with males. These results were mainly caused by the increased complexity of the procedure due to women’s smaller body surface area[30].
OPCAB is performed on a beating heart without extracorporeal bypass compared with traditional extracorporeal bypass surgery. OPCAB has many advantages, such as shorter operation time, reduced hospitalization and intensive care unit length of stay, lower medical costs, and fewer surgery-related complications[31].
Large randomized controlled clinical trials, including CORONARY[10] and ROOBY[11], found no significant difference between OPCAB and ONCAB regarding the 30-day death rate, MI, stroke, or renal failure requiring dialysis. However, these clinical studies did not investigate whether female patients who underwent OPCAB had a better short-term outcome compared to female patients who underwent ONCAB.
A meta-analysis from Attaran et al.[12] investigated short-term outcomes among OPCAB vs. ONCAB female patients. In this study, no statistically significant difference was observed in the 30-day mortality rate and other morbidity outcomes between the OPCAB and ONCAB groups, except for perioperative MI. This study’s results are limited because both 30-day mortality and not in-hospital mortality rates were considered primary endpoints after CABG, but most of the included research investigated in-hospital mortality rates. Although 30-day mortality and in-hospital mortality rates are both short-term effects, failure to delineate them may give incorrect conclusions. Furthermore, Attaran et al. ’s study did not investigate the OR adjusted by cardiovascular risk factors, leading to confounding factors affecting the results[12].
In contrast to Attaran’s study, we found that the in-hospital and 30-day mortality rates in female patients who underwent OPCAB were significantly lower than in those in the ONCAB group in studies with and without cardiovascular risk factor adjustment. The in-hospital mortality rate of OPCAB female patients was not significantly different from ONCAB female patients in PSM studies. In the primary meta-analysis, the incidence of postoperative stroke in female patients who underwent OPCAB was lower than in those in the ONCAB group, while the difference in postoperative stroke between OPCAB and ONCAB in PSM studies and post-MI was insignificant. The incidence of unfavorable outcomes in female patients who underwent OPCAB was not higher than in those in the ONCAB group. In summary, the short-term clinical outcomes of women who underwent OPCAB were not worse than of those in the ONCAB group. Notably, the in-hospital mortality and postoperative 30-day mortality rates of OPCAB patients were lower than of ONCAB patients. We surmise that this may be related to the fact that OPCAB causes less trauma and minimally affects patients’ circulation compared to ONCAB.
## Limitations
Our study has the following shortcomings: 1) we had not retrieved random control studies, so the studies we included were all retrospective case-control observational studies that might attenuate our research’s strength; 2) our study did not further explore the effect of sex difference on long-term prognosis after CABG due to the lack of relevant literature.
## CONCLUSION
Compared to the American Heart Association CABG guideline, the incidence of adverse events in female patients who underwent OPCAB was lower or not significant, but not higher, than in those in the ONCAB group. Our findings should nevertheless be treated with caution due to the limitations attributed to observational studies. Randomized controlled trials are warranted to further substantiate our conclusion in the future.
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|
---
title: 'm6A RNA Methylation Decreases Atherosclerotic Vulnerable
Plaque Through Inducing T Cells'
authors:
- Chunmei Qi
- Haoran Li
- Yongshu Yu
- Ji Hao
- Hao Zhang
- Lele Wang
- Jingjing Jin
- Qiang Zhou
- Ya Hu
- Chengmeng Zhang
- Qingdui Zhang
journal: Brazilian Journal of Cardiovascular Surgery
year: 2023
pmcid: PMC10010713
doi: 10.21470/1678-9741-2021-0039
license: CC BY 4.0
---
# m6A RNA Methylation Decreases Atherosclerotic Vulnerable
Plaque Through Inducing T Cells
## Abstract
### Introduction
Knockdown of fat mass and obesity-associated gene (FTO) can induce N6-methyladenosine (m6A) ribonucleic acid (RNA) methylation. The objective of this study was to explore the effect of m6A RNA methylation on atherosclerotic vulnerable plaque by FTO knockdown.
### Methods
A total of 50 New Zealand white rabbits were randomly divided into pure high-fat group, sham operation group, vulnerable plaque group, empty load group, and FTO knockdown group (10 rabbits/group).
### Results
Flow cytometry showed that helper T (Th) cells in the FTO knockdown group accounted for a significantly higher proportion of lymphocytes than in the vulnerable plaque group and empty load group ($P \leq 0.05$). Th cells were screened by cell flow. The level of m6A RNA methylation in the FTO knockdown group was significantly higher than in the vulnerable plaque group and empty load group ($P \leq 0.05$). The levels of total cholesterol, triglyceride, and low-density lipoprotein C were higher at the 12th week than at the 1st week, but the high-density lipoprotein C level was lower at the 12th week than at the 1st week. At the 12th week, the interleukin-7 level was significantly lower in the adeno-associated virus-9 (AVV9)-FTO short hairpin RNA group than in the control and AVV9-green fluorescent protein groups ($P \leq 0.001$).
### Conclusion
After successfully establishing a vascular parkinsonism rabbit model, m6A RNA methylation can decrease Th cells and vulnerable atherosclerotic plaques.
## INTRODUCTION
In the modern society, with the improvement of living standards, the incidence of cardiovascular diseases is rising rapidly. The epidemiological investigation has showed that the mortality rate of cardiovascular diseases ranks first in all major cities, even in the whole China[1]. The main pathological basis of cardiovascular disease is the formation and evolution of atherosclerotic plaque. Current studies have shown that vascular parkinsonism (VP) is the comprehensive mechanism for atherosclerotic plaque, mainly including inflammatory immune response, oxidative stress response, apoptosis, autophagy, vascular remodeling, lymphatic neovascularization, plaque stress, and shear force[2]. The inflammatory immune response throughout the formation of VP is the core mechanism. Batista et al.[3] first elucidated the biological role of N6-methyladenosine (m6A) modification in T cell-mediated pathogenesis in 2017, and they confirmed that m6A ribonucleic acid (RNA) demethylation controlled T cell homeostasis by targeting interleukin (IL)-7/signal transducer and activator of transcription 5 (STAT5)/suppressors of cytokine signalling (SOCS) to inhibit the occurrence of enteritis. It provides a new way for m6A RNA demethylation to regulate cellular immunity.
As an important intermediate medium in the immune response, helper T (Th) cells play a role in stabilizing other immune cells. Through self-proliferation, they indirectly activate other types of immune cells to directly act on inflammatory responses[4]. It has been reported that IL-7 is the main regulator of T cell homeostasis, and its main function is to promote the adhesion of white fine cells to endothelial cells during inflammation[5]. Batista et al.[3] have demonstrated that m6A RNA demethylation can control T cell homeostasis by targeting IL-7/STAT5/SOCS, and IL-7 is closely related to homeostasis, proliferation, and differentiation of Th cells. However, whether Th cells undergo m6A RNA methylation by themselves has not been clearly studied.
In our previous study, high-fat feeding, balloon damage, and p53 gene transfection techniques have been applied to successfully establish the VP model of atherosclerosis[6]. It has been confirmed that statin drugs can achieve partial stabilization of plaque by inhibiting inflammatory factors. Therefore, the purpose of this study was to explore the effect of m6A RNA methylation on atherosclerotic vulnerable plaque by knockdown of fat mass and obesity-associated gene (FTO), so as to provide the experimental basis for gene therapy to stabilize VP through the immune mechanism in the future.
## Animal Model
The three-month-old New Zealand white rabbits were purchased from the experimental animal center of Xuzhou Medical University (Xuzhou, China). The experimental design was approved by the laboratory animal ethics committee of Xuzhou Medical University (license number: SYXK (Su) 2015-0029, certificate number: 201904540).
A total of 50 New Zealand white rabbits were randomly divided into pure high-fat group (high-fat feeding), sham operation group (sham operation + high-fat feeding), vulnerable plaque group (operation + high-fat feeding + adenovirus mediated p53 gene [Ad-p53] transfection), empty load group (operation + high-fat feeding + Ad-p53 transfection + adeno-associated virus-9 [AVV9] empty load), and FTO knockdown group (operation + high-fat feeding + Ad-p53 transfection + AVV9-FTO knockdown), with 10 rabbits in each group. The rabbits were weighed and placed in a fixed cage, and they were anesthetized with $1\%$ sodium pentobarbital (10 mg/ml, Sigma Chemical Co., St. Louis, Missouri, United States of America) by intravenously injection through the ear margin. After successful anesthesia, they were fixated on the animal operating table. The upper side of the right femoral artery (the strongest pulsation point) was selected as the surgical site, the hairs were shaved, and the site was disinfected and dissected. Subcutaneous tissues and muscles were separated layer by layer, with a blunt separation of about 3 cm from the femoral artery. The femoral artery was clipped with a hemostatic clip, and a small orifice was punctured with a no. 7 needle. A guiding needle was inserted. The hemostatic clamp and observation guide needle arterial bleeding obvious were loosened and placed into the 0.014-inch thread. After pulled the guided needle, 15-mm diameter balloon catheter (diluted with 1:15 heparin saline infiltration) was placed into the abdominal aorta. Then a balloon catheter with a diameter of 3.5 mm and a length of 15 mm (infiltrated with 1:15 diluted sodium heparin and normal saline) is fed into the abdominal aorta about 10 cm along the guide wire. The distilled water was pushed up until the balloon was filled with eight atmospheric pressure, then the balloon was pulled back to the common iliac artery pressure. We repeatedly did this three times to ensure the abdominal aorta endothelial damage. The balloon catheter was taken out after local wound bleeding. Tie thin lines on both ends of the femoral artery. Penicillin (Huabei Pharmaceutical Co., Ltd., Shijiazhuang, China) was given locally to the muscles to prevent infection.
## Collection of Blood Samples
The blood samples were collected at the 1st week and the 12th week. After all rabbits fasted for 12 hours or more, their fasting blood was extracted through the ear vein with scalp needle for biochemical examination. After centrifugation (1000 ×g) for 20 min, the supernatant serum was collected for further detection.
Detection of the levels of total cholesterol (TC), triglyceride (TG), high-density lipoprotein C (HDL-C), and low-density lipoprotein C (LDL-C) The levels of TC, TG, LDL-C, and HDL-C in serum were detected by using Erba XL-600 automatic biochemical analyzer and commercially available diagnostic kits (Nanjing Jiangcheng Bioengineering Institute, Nanjing, China) in accordance with the instructions.
## Histopathology
The rabbits were killed by cervical dislocation. The corresponding arteries were taken from the rabbits in the high-fat group and sham group. The plaque tissues in the vulnerable plaque group, empty load group, and FTO knockdown group were taken from the marker. After washed with normal saline, the tissues were frozen at -80°C. After rinsed with normal saline, the vessels were immersed in $10\%$ formalin (Hubei Xingyinhe Chemical Co., Ltd., Wuhan, China) and fixed for at least 24 hours. After routine histological treatment, 5-µm sections were cut and stained with hematoxylin and eosin. Histological analysis was performed under an optical microscope (OLYMPUS BX41, OLYMPUS, Tokyo, Japan) and photographed at 100× magnification.
## Detection of the IL-7 Level
At the end of the 12th week, the corresponding arteries in the five groups were taken to measure IL-7 levels. The IL-7 level was detected by enzyme linked immunosorbent assay (or ELISA) with commercially available diagnostic kit (Nanjing Jiangcheng Bioengineering Institute, Nanjing, China) in accordance with the instructions.
## Detection of Th Cells
Annexin V-fluorescein isothiocyanate/propidium iodide (V-FITC/PI) kit (Takara company, Japan) was used to detect Th cells in each group. The isolated tissues were digested with trypsin. Cells were collected by centrifugation (1000 ×g, 10 min) and washed with pre-cooled phosphate buffer. 1× binding buffer was added to resuspend 100 µl cells. 5-µl Annexin V-FITC and 5-µl PI were added to the cell suspension. After gently mixed and incubated in dark for 15 min at room temperature, the ratio of Th cells in each group was determined by flow cytometry.
## Detection of m6A RNA Methylation
Total RNA of Th cells was extracted using a tissue/cell total RNA isolation kit (Tiangen Biotech Co., Ltd., Beijing, China) under the conditions recommended by the manufacturer. Reaction buffer and 5 µl supernatant were added into 96 well plate. The operation was performed according to the instructions of commercially available diagnostic kit (Bradford). The absorbance was detected at 405 nm.
## Detection of FTO Protein Expression
The tissues of each group were lysed on ice with lysis buffer (Beyotime Institute of Biotechnology, Shanghai, China) for 10 min. The supernatant was obtained by centrifugation (1000 ×g) at 4°C for 20 min. After sodium dodecyl sulfate-polyacrylamide gel electrophoresis (BIO-RAD Co., California, United States of America), the protein was transferred to nitrocellulose membrane (Pall Co., New York, United States of America). After being blocked with nonfat dried milk (Sangon Biotech Inc., Shanghai, China), the membrane was incubated with anti-FTO antibody anti-β-actin antibody (Abcam Technology, Cambridge, United Kingdom) overnight at 4°C. The membrane was then incubated with secondary antibody (ZSGB Biotech Co., Ltd., Beijing, China) for one hour at 25°C. Enhanced chemiluminescence solution was added to the darkroom for development, exposure, and photography by gel imager. With β-actin as internal reference, the data were analyzed by QuantityOne image analysis software.
## Statistical Analysis
Statistical analyses were performed by MATLAB 2016b software. The measurement data were expressed as means ± standard deviation. t-test and Chi-square test were used for the comparison of differences between two groups. One-way analysis of variance was used for the comparison among multiple groups. $P \leq 0.05$ was considered as statistical difference.
## Successful Establishment of the Animal Model of VP
During the whole experiment, eight New Zealand rabbits died, including two in the sham group (died from infection or anesthesia), two in the vulnerable plaque group (died from anesthesia), two in the empty load group (died from infection) and two in the FTO knockdown group (died from anesthesia or infection). Finally, 42 rabbits survived.
## Biochemical Indexes
Blood samples were taken at the end of the 1st week and the end of the 12th week to analyze the levels of TG, TC, LDL-C, and HDL-C. At the end of the 12th week, six rabbits were randomly selected from the five groups. There were no statistical significances in the levels of TG, TC, LDL-C, and HDL-C among the five groups ($P \leq 0.05$) (Table 1).
**Table 1**
| Items | Pure high-fat group | Sham operation group | Vulnerable plaque group | Empty load group | FTO knockdown group |
| --- | --- | --- | --- | --- | --- |
| TC | TC | TC | TC | TC | TC |
| Week 1 | 1.295±0.061 | 1.298±0.057 | 1.300±0.064 | 1.306±0.056 | 1.291±0.062 |
| Week 12 | 10.24±0.657 | 10.58±0.924 | 10.82±0.911 | 10.68±0.907 | 10.28±0.807 |
| LDL-C | LDL-C | LDL-C | LDL-C | LDL-C | LDL-C |
| Week 1 | 0.519±0.062 | 0.525±0.053 | 0.522±0.067 | 0.520±0.061 | 0.531±0.055 |
| Week 12 | 7.162±0.275 | 7.684±0.249 | 7.531±0.285 | 7.272±0.278 | 7.472±0.328 |
| HDL-C | HDL-C | HDL-C | HDL-C | HDL-C | HDL-C |
| Week 1 | 0.958±0.145 | 1.105±0.136 | 1.079±0.119 | 1.109±0.124 | 1.089±0.112 |
| Week 12 | 0.657±0.093 | 0.652±0.051 | 0.650±0.108 | 0.655±0.103 | 0.635±0.111 |
| TG | TG | TG | TG | TG | TG |
| Week 1 | 1.151±0.111 | 1.135±0.327 | 1.094±0.162 | 1.097±0.163 | 1.091±0.153 |
| Week 12 | 4.604±0.355 | 4.886±0.221 | 4.754±0.375 | 4.734±0.365 | 4.712±0.402 |
## Histopathological Results of Abdominal Aorta
The histopathological changes in the five groups are shown in Figure 1. The inner membrane of cells in the pure high-fat group and the sham group were smooth and uniform, but no foam cells were observed. A large number of foam cells were observed in the vulnerable plaque group, the empty load group, and the FTO knockdown group, with thinner blood vessel walls, exfoliated endothelium, and significantly narrowed lumen. Compared with the vulnerable plaque group and empty load group, platelet aggregation was more obvious in the FTO knockdown group.
Fig. 1Histopathological changes in the five groups. A, B, C, D, and E represent the pure high-fat group, the sham operation group, the vulnerable plaque group, the empty load group, and the fat mass and obesity-associated gene knockdown group, respectively. From left to right, the magnification was 10× and 40×.
## Il-7 Levels
There was no significant difference in the IL-7 level between the pure high-fat group and the sham group ($$P \leq 0.251$$), and between the vulnerable plaque group and the empty load group ($$P \leq 0.471$$). The IL-7 levels in the vulnerable plaque group, FTO knockdown group, and empty load group were statistically higher than in the pure high-fat group ($P \leq 0.01$), while that in the FTO knockdown group was statistically higher than in the vulnerable plaque group ($P \leq 0.01$). The results are shown in Table 2.
**Table 2**
| Groups | Pure high-fat group | Sham operation group | Vulnerable plaque group | Empty load group | FTO knockdown group |
| --- | --- | --- | --- | --- | --- |
| Survive number | 10 | 8 | 8 | 8 | 8 |
| Average | 7.78±1.60 | 8.72±1.63 | 12.88±1.98* | 11.41±0.73* | 16.42±1.3*# |
## Western Blot Analysis of FTO Knockdown
At the end of the 12th week, three rabbits were taken from the vulnerable plaque group, empty load group, and FTO knockdown group. The vulnerable plaque group was set as control group, the empty load group as AVV9-green fluorescent protein group, and the FTO knockdown group as AVV9-FTO short hairpin RNA group. Figure 2 shows that there was no significant difference in FTO protein expression between the vulnerable plaque group and the empty load group ($P \leq 0.01$). The FTO protein expression level in the FTO knockdown group was significantly lower than those in the vulnerable plaque group and the empty load group ($$P \leq 0.001$$).
Fig. 2Western blot results of fat mass and obesity-associated gene (FTO) protein expression in the vulnerable plaque group, empty load group, and FTO knockdown group. *** $P \leq 0.001$ vs. control group; ###$P \leq 0.001$ vs. AVV9-GFP group. AVV9=adeno-associated virus-9; GFP=green fluorescent protein; shRNA=short hairpin ribonucleic acid.
## m6A RNA Methylation Results
The Th cells in the vulnerable plaque group, empty load group, and FTO knockdown group were screened by flow cytometry (Figure 3). After screening, RNA was extracted from the Th cells and m6A methylation was measured by colorimetry. The results showed that there was no significant difference between the vulnerable plaque group and empty load group ($$P \leq 0.2198$$). As shown in Figure 4, the m6A methylation in the FTO knockdown group was significantly higher than in the vulnerable plaque group and the empty load group ($$P \leq 0.001$$). This indicated that after FTO knockdown, m6A methylation value in the Th cells was higher than in the non-knockdown group.
Fig. 3T helper cells in the five groups detected by flow cytometry. CD3=anti-CD3; CD4=anti-CD4; FITC-A=fluorescein isothiocyanate-area; FSC-A=forward scatter-area; SSC-A=side scatter-area.
Fig. 4Correlation diagram of N6-methyladenosine (m6A) ribonucleic acid methylation results. ANOVA=analysis of variance. df=degrees of freedom; F=F-ratio; MS=mean square; SS=sum of squares.
## DISCUSSION
The pathological mechanism of atherosclerotic plaque formation is complicated. In this experiment, the VP rabbit model was involved in the balloon from the femoral artery to strain the endothelium, and the inflammatory reaction was formed locally. The atheromatous plaque was successfully formed through high-fat feeding. The stability of the plaque was reduced through local injection of Ad-p53. Finally, VP was formed, which well simulated the whole process of VP formation.
Our results showed that the expression of histone in FTO knockdown was significantly decreased, indicating that FTO knockdown was successful. The methylation level in the Th cells in the FTO knockdown group was significantly higher than that in the vulnerable plaque group and empty load group, indicating that the regulation of m6A RNA could affect the methylation level of Th cells. The IL-7 level, an inflammatory marker, and the proportion of Th cells in lymphocytes were also significantly increased in the FTO knockdown group. We considered the following three possibilities. m6A RNA increases the IL-7 level, and IL-7 plays a role in the proliferation of Th cells. The proliferating Th cells indirectly activate other types of immune cells, making them directly affect the inflammatory response and indirectly affect VP. Moreover, this is consistent with the results of Batista et al.[3] Methylated Th cells can activate other types of immune cells indirectly through self-proliferation. Furthermore, methylated Th cells limit self-proliferation, contrary to the proliferation effect of IL-7 on Th cells. However, it mainly be the proliferation effect of IL-7 on Th cells.
The research on RNA methylation is at the forefront. At present, the RNA methylation modification has been defined to two types, namely m6A and 5-methylcytosine (or m5C), with m6A as the main mode. m6A is the most abundant internal modification in micro RNA, mainly occurring on the consistent moduli of G[G>A]m6AC[U>A>C][7-10]. Although m6A was first discovered in the 1970s[11,12], the lack of technology to study RNA modification limits m6A research, and the field has not advanced for decades. In 2012, a full transcriptome method for immunoprecipitation of m6A RNA was reported, followed by the next generation of sequencing (m6A-seq or merip-seq), which detected m6A peaks in over 7,000 messenger RNA (mRNA) transcripts and hundreds of long non-coding RNAs in human and mouse cells, many of which were conserved between humans and mice[13,14]. The next research has showed that mRNA or non-coding RNA decorated m6A played a key role in organization development, stem cell self-renewal and differentiation, heat shock response and circadian rhythm control, and RNA fate and function, such as mRNA stability, splicing, transport, positioning and translation, interaction between proteins and RNA - primary micro RNA processing[15-24] -, tumor stem cell growth, self-renewal and tumorigenesis[25-28], RNA metabolism, including tiny deoxyribonucleic acid/RNA/low nuclear acid RNA biology, processing, and export[29-31].
In 2016, He Chuan published a review on m6A RNA in Nature Reviews Genetics, which fully analyzed the mechanism of m6A[32]. As the most common post-transcriptional modification on eukaryotic mRNA, over $80\%$ of RNA bases are associated with methylation. It was found that m6A accelerated mRNA metabolism and translation modification in cell. It plays an important physiological role in cell differentiation, embryo development, and immune response. Li Huabing, Batista, and other Chinese and American co-researchers published a study in Nature in 2017. They first elucidated the biological role of m6A modification in the pathogenesis mediated by T cells, confirming that m6A mRNA demethylation controls T cell homeostasis by targeting IL-7/STAT5/SOCS pathway, thus inhibiting the occurrence of colitis. A large number of studies have shown that m6A plays an extensive and important role in the regulation of mRNA. The selection of m6A in this study was based on these studies to explore the effect of m6A on VP.
In the past, we treated atherosclerotic plaque more from the perspective of etiology to control the risk factors. As one of the risk factors, immune factors can be controlled by few methods. It is assumed that by regulating the level of relevant factors, local inflammatory responses can be controlled, and the evolution of plaque to VP of atherosclerosis can be inhibited, which will greatly slow down the formation of VP of atherosclerosis and provide a new idea for the treatment of atherosclerosis.
Limitations *In previous* studies, our group has successfully established a rabbit VP model, which is based on local transfection of Ad-p53 and high-fat feeding on the basis of arterial endothelial balloon strain. In this study, based on the amount of tissue required for the plaque model (the amount of inflammatory cells in atherosclerotic VP is not large, and it is difficult to extract Th cells), we chose the atherosclerotic VP rabbit that was successful in the previous experiment model. There are currently no METL3 and METL14 knockdown types in this model rabbit. We chose to inject AVV-FTO knockdown to achieve local methylation. According to theory, the effect of overall injection of AVV9-FTO is more obvious, but based on the funding and the weight of the rabbit, this operation is impractical, so we choose local injection of AVV9-FTO to knockdown to achieve the effect of methylation.
## CONCLUSION
In conclusion, our study showed that we successfully established a VP rabbit model. m6A RNA methylation can decrease Th cells and vulnerable atherosclerotic plaques.
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|
---
title: 'Deep Sternal Wound Infection Following OPCAB: Delving Deeper into the
Predisposition!'
authors:
- Jes Jose
- Rohan Magoon
- Jasvinder Kaur Kohli
- Ramesh Kashav
journal: Brazilian Journal of Cardiovascular Surgery
year: 2023
pmcid: PMC10010731
doi: 10.21470/1678-9741-2021-0547
license: CC BY 4.0
---
# Deep Sternal Wound Infection Following OPCAB: Delving Deeper into the
Predisposition!
## Body
Dear Editor, Predisposition to deep sternal wound infection (DSWI) following off-pump coronary artery bypass (OPCAB) grafting surgery classifies as an area of particular research interest. Given the fact that a sound evaluation of the risk factors for DSWI mandates a comprehensive approach, we wish to highlight a few important concerns pertaining to the Enginoev et al.[1] study recently published in the Brazilian Journal of Cardiovascular Surgery.
Interestingly, the index analysis does not outline diabetes mellitus as a preoperative risk factor for DSWI ($30.2\%$ in DSWI and $26.2\%$ in non DSWI group, $$P \leq 0.5$$)[1] albeit the lack of data on perioperative glycaemic control deserves attention. Appropriate to the context, the Mayo Clinic research group delineate as high as $30\%$ increase in adverse outcomes, including infective complications for every 20 mg/dL rise in mean intraoperative glucose levels[2]. Moreover, specific literature linking glycaemic fluctuations with infective complications continues to accumulate over the past decade[3,4]. Järvelä et al.[3] found a significantly heightened rate of postoperative infections in their cardiac surgical cohort manifesting repeated hyperglycaemia ($39.7\%$ incidence) as opposed to normoglycaemic or those with single hyperglycaemic episode ($12.1\%$ and $8.2\%$, respectively, $$P \leq 0.019$$).
Furnary et al.[4] reveal the independent DSWI predictive ability of post-cardiac surgery hyperglycaemia in the Portland Diabetic Project, wherein the subset with 48-hour mean blood glucose levels >200 mg/dL demonstrated a 2.2 times elevated risk of DSWI. Concomitantly, there is convincing evidence to suggest that perioperative glucose control with insulin infusion management protocols considerably attenuate the DSWI incidence[4-6]. Alongside the absence of perioperative glucose data, Enginoev et al.[1] fail to describe the glucose management regime employed in their retrospective study[1].
In addition, the authors could have elaborated whether or not any of the study participants were receiving preoperative corticosteroids[1,7]. Herein, a comparative account of the preoperative leucocytic counts of the DSWI and non-DSWI groups could also have added incremental value[1,8]. As much as we laud the endeavours of Enginoev et al.[1], the points of perioperative relevance elucidated by us and the authors’ explanation would probably assist readers to comprehend this dynamic research area in a more holistic manner.
## References
1. Enginoev S, Rad AA, Ekimov S, Kondrat’ev D, Magomedov G. **Risk Factors for Deep Sternal Wound Infection after Off-Pump Coronary Artery Bypass Grafting: a Case-Control Study [ahead of print]**. *Braz J Cardiovasc Surg* (2021). DOI: 10.21470/1678-9741-2020-0444
2. Gandhi GY, Nuttall GA, Abel MD, Mullany CJ, Schaff HV. **Intraoperative hyperglycemia and perioperative outcomes in cardiac surgery patients**. *Mayo Clin Proc* (2005) **80** 862-866. PMID: 16007890
3. Järvelä KM, Khan NK, Loisa EL, Sutinen JA, Laurikka JO, Khan JA.. **Hyperglycemic Episodes Are Associated With Postoperative Infections After Cardiac Surgery**. *Scand J Surg* (2018) **107** 138-144. PMID: 28934890
4. Furnary AP, Wu Y, Bookin SO. **Effect of hyperglycemia and continuous intravenous insulin infusions on outcomes of cardiac surgical procedures: the Portland Diabetic Project**. *Endocr Pract* (2004) **10** 21-33. PMID: 15251637
5. Kramer R, Groom R, Weldner D, Gallant P, Heyl B. **Glycemic control and reduction of deep sternal wound infection rates: a multidisciplinary approach**. *Arch Surg* (2008) **143** 451-456. PMID: 18490552
6. Cotogni P, Barbero C, Rinaldi M. **Deep sternal wound infection after cardiac surgery: Evidences and controversies**. *World J Crit Care Med* (2015) **4** 265-273. PMID: 26557476
7. Magoon R, Choudhury A, Sahoo S, Malik V. **Steroids for adult cardiac surgery: The debate echoes on**. *J Anaesthesiol Clin Pharmacol* (2019) **35** 560-562. PMID: 31920249
8. Dey S, Kashav R, Kohli JK, Magoon R. **Systemic Immune-Inflammation Index Predicts Poor Outcome After Elective Off-Pump CABG: A Retrospective, Single-Center Study**. *J Cardiothorac Vasc Anesth* (2021) **35** 2397-2404. PMID: 33046365
|
---
title: 'Ischemic Postconditioning Attenuates Myocardial
Ischemia-Reperfusion-Induced Acute Lung Injury by Regulating Endoplasmic
Reticulum Stress-Mediated Apoptosis'
authors:
- Aimei Li
- Siyu Chen
- Jianjiang Wu
- Jiaxin Li
- Jiang Wang
journal: Brazilian Journal of Cardiovascular Surgery
year: 2023
pmcid: PMC10010732
doi: 10.21470/1678-9741-2021-0043
license: CC BY 4.0
---
# Ischemic Postconditioning Attenuates Myocardial
Ischemia-Reperfusion-Induced Acute Lung Injury by Regulating Endoplasmic
Reticulum Stress-Mediated Apoptosis
## Abstract
### Objective
To explore the effect of ischemic postconditioning on myocardial ischemia-reperfusion-induced acute lung injury (ALI).
### Methods
Forty adult male C57BL/6 mice were randomly divided into sham operation group (SO group), myocardial ischemia-reperfusion group (IR group), ischemic preconditioning group (IPRE group) and ischemic postconditioning group (IPOST group) (10 mice in each group). Anterior descending coronary artery was blocked for 60 min and then reperfused for 15 min to induce myocardial IR. For the IPRE group, 3 consecutive cycles of 5 min of occlusion and 5 minutes of reperfusion of the coronary arteries were performed before ischemia. For the IPOST group, 3 consecutive cycles of 5 min reperfusion and 5 minutes of occlusion of the coronary arteries were performed before reperfusion. Pathological changes of lung tissue, lung wet-to-dry (W/D) weight ratio, inflammatory factors, oxidative stress indicators, apoptosis of lung cells and endoplasmic reticulum stress (ERS) protein were used to evaluate lung injury.
### Results
After myocardial IR, lung injury worsened significantly, manifested by alveolar congestion, hemorrhage, structural destruction of alveolar septal thickening, and interstitial neutrophil infiltration. In addition, lung W/D ratio was increased, plasma inflammatory factors, including interleukin (IL)-6, tumor necrosis factor (TNF)-α, and IL-17A, were increased, malondialdehyde (MDA) activity of lung tissue was increased, and superoxide dismutase (SOD) activity was decreased after myocardial IR. It was accompanied by the increased protein expression levels of ERS-related protein glucose regulatory protein 78 (GRP78), CCAAT/enhancer-binding protein (C/EBP) homologous protein (CHOP), and caspase-12, and the increased apoptotic indices of lung tissues.
### Conclusion
IPOST can effectively improve myocardial IR-induced ALI by inhibiting ERS-induced apoptosis of alveolar epithelial cells.
## INTRODUCTION
Under normal physiological and many pathological conditions, there is a high degree of interaction between heart and lung. Acute lung injury (ALI) is a serious complication of heart surgery and acute myocardial infarction[1,2]. Acute respiratory distress syndrome is the most serious form of lung injury, which significantly increases mortality, medical expenses and length of hospital stay[3]. Myocardial ischemia-reperfusion (IR) injury occurs during heart surgery and revascularization treatment of acute myocardial infarction[4]. The heart is one of the most vulnerable organs to ischemic injury. Reactive oxygen free radicals, white blood cell activation and systemic inflammatory response during myocardial IR can cause ischemic microcirculation damage and distal organ damage. Lung is the earliest and most obvious organ with distal organ damage after reperfusion. Myocardial IR will cause ALI to increase morbidity and mortality in patients undergoing heart surgery[4,5]. However, there is no specific and effective treatment for ALI after myocardial IR. Therefore, clarifying the mechanism of ALI after myocardial IR is of great significance for searching effective therapies to prevent or treat lung injury.
Endoplasmic reticulum (ER) is an important organelle in eukaryotes, which is also the main site for intracellular protein synthesis, processing, folding, transportation, and intracellular calcium storage. ER can perceive the changes in the intracellular environment over time and maintain its balance[6]. When subjected to external stimuli, misfolded and unfolded proteins accumulate in the ER cavity, which activates the unfolded protein response (UPR) to induce endoplasmic reticulum stress (ERS). ERS can lead to pathological imbalance of ER homeostasis and physiological dysfunction[7]. Infection, hypoxia, starvation, oxidative stress, calcium disturbance and other stimuli can induce ERS activation, which in turn activates related signaling pathways to induce cell death, inflammation and apoptosis[8]. There is much evidence that ERS is involved in lung injury caused by various factors and plays an important pathophysiological role in the occurrence and development of ALI[9,10]. Moreover, inhibition of ERS can effectively reduce lipopolysaccharide-induced ALI[11]. ALI after myocardial IR is related to inflammation, oxidative stress and autophagy. However, it is not clear whether ERS is involved.
Nonfatal transient ischemia-induced IPRE can produce ischemic tolerance and protect cardiomyocytes from damage after subsequent fatal transient ischemia. However, IPRE occurs before myocardial ischemia, and its clinical application is limited[12]. As a new endogenous myocardial protection strategy, IPOST has become a research hotspot due to its organ adaptation to combat IR injury[13]. It is a series of transient protective treatments implemented during reperfusion. Its myocardial protection is equivalent to IPRE with clinical feasibility. IPOST can be used as an alternative strategy to IPRE. The biological protective effect of IPOST on the myocardium has been well confirmed in experimental models of a variety of major human diseases and multiple clinical trials[14,15]. It can exert a myocardial protective effect by inhibiting inflammation, apoptosis, oxidative stress, and cell pathways[15,16].
In terms of lung diseases, the latest research confirms that IPOST can also protect against lung injury after myocardial IR[17]. However, its specific mechanism has not yet been elucidated. Therefore, the purpose of this study was to explore the effect of ischemic postconditioning on ALI after myocardial IR.
## Animals
This study was approved by our hospital's animal experimental medicine ethics committee. All experimental procedures were carried out in accordance with the guidelines for the care and use of laboratory animals. Forty nonpathogenic C57BL/6 male mice, weighing 20-25 g, were purchased from Beijing Weitong Lihua Experimental Animal Technology Co., Ltd. (Beijing, China), No SCXK (Jing) 2016-0010. The mice were maintained in SPF Laboratory of Experimental Animal Center of Xinjiang Medical University at (22±2) °C, relative humidity of [50-60]% and a 12-h light-dark cycle.
## Protocol
Forty adult male C57BL/6 mice were randomly divided into a sham operation group (SO group), myocardial ischemia-reperfusion group (IR group), ischemic preconditioning group (IPRE group) and ischemic postconditioning group (IPOST group) (10 mice in each group) (Supplementary Figure 1). Anterior descending coronary artery was blocked for 60 minutes and then reperfused for 15 minutes to induce myocardial IR. For the IPRE group, 3 consecutive cycles of 5 minutes of occlusion and 5 minutes of reperfusion of the coronary arteries were performed before ischemia. For the IPOST group, 3 consecutive cycles of 5 minutes of reperfusion and 5 minutes of occlusion of the coronary arteries were performed before reperfusion.
During anesthesia, a heating pad was used to keep the body temperature between 36.5°C and 37°C. Ketamine (8 mg/100 g), methylthiazide (2 mg/100 g) and atropine (0.12 mg/100 g) (i.e., KXA mixture) were injected intraperitoneally to anesthetize mice, and the injection dose was 0.1-0.2 ml/10g. The electrocardiographic electrodes were connected subcutaneously to the limbs, and 20# venous indwelling needle was used for tracheal intubation under direct oral vision, with a depth of 1.5-2 cm. The tracheal intubation was connected to the small animal ventilator, with a tidal volume of 0.8-1.0 ml and a respiratory rate of 90-110 times/min.
After fixation, the pleura was cut into along the left 3rd and 4th intercostal spaces to enter the thoracic cavity. The intersection point between the pulmonary conus and the right edge of the left atrial appendage and the line between the apices of the heart were used as markers of the anterior descending branch of the coronary artery in mice. The left anterior descending branch was searched under a microscope. The needle is inserted 1-2 mm below the root of the left atrial appendage with a $\frac{6}{0}$ suture and the root of the vessel is ligated with the needle from the left edge of the pulmonary artery cone. The sign of successful ligation includes that the movement of the myocardial tissue around the anterior wall of the left ventricle and the apex is weakened, the ST segment of the electrocardiogram (ECG) is elevated by more than 0.2 mv, the T wave is high and the QRS wave is increased and widened. After 60 min, the ligation line was released, and the reperfusion time was 15 min. ECG showed ST-segment depression and redness of the apex.
## Lung Wet-to-Dry Weight Ratio
At the end of the experiment, the left lung of 5 mice in each group was weighed. The wet weight was taken. After dried in an oven at 60°C for 48 h for dehydration, it was weighed again. The dry weight was taken. The lung wet-to-dry (W/D) weight ratio was calculated. The body weight ratio was calculated twice as an index of pulmonary edema.
## Pathological Examination of Lung Tissues
At the end of the experiment, the right upper lobe of the lung of 5 mice from each group was fixed in $10\%$ paraformaldehyde and embedded in paraffin. After cutting into 5 µM slides, hematoxylin-eosin (HE) staining was used to detect the degree of tissue damage. Each animal was randomly divided into 5 sections (3 areas per section). Histopathological evaluation was performed by the blind method. Experienced laboratory pathologists comprehensively evaluated according to alveolar congestion, hemorrhage, infiltration or aggregation of neutrophils in the alveolar or vascular wall, and alveolar wall/hyaline membrane thickness. The four-point system score was used to assess[1]: 0, no change; 1, light damage; 2, moderate damage; 3, severe damage. The total score was obtained by adding the characteristic values of each mouse in the group.
## Determination of Interleukin-6, Tumor Necrosis Factor-α, and
Interleukin-17A in Plasma
After reperfusion, blood of each group of mice was taken to measure levels of inflammatory factors. Microsample multi-index flow cytometry protein quantification technology and cytometric bead array (CBA) mouse kit (BD company, 560485, USA) were used to determine plasma levels of interleukin-6, tumor necrosis factor-α (TNF-α), and interleukin (IL)-17A. All steps involved were followed the manufacturer's instructions.
## Determination of Malondialdehyde and Superoxide Dismutase Activities in Lung
Tissues
Right lung specimens from 10 mice in each group were taken. The microplate reader was used to measure MDA and SOD values in lung tissues. The lung tissue samples were crushed and ground in a mortar and weighed. Phosphate buffer solution (PBS) (4°C) was added according to a mass-to-volume ratio of 1:9 and the mixture was homogenized on ice. The homogenate was centrifuged to collect the supernatant and used as a sample for subsequent experiments. According to the manufacturer's instructions (Nanjing Jiancheng, A003-1), the prepared samples were mixed with vortex mixer and soaked in water at 95°C for 40 min. After cooled with running water, it was centrifuged for 10 min (2000 rpm/min). The absorbance of 300 µL of supernatant was measured at 532 nm for malondialdehyde (MDA) values. According to the manufacturer's instructions (Nanjing Jiancheng, A003-1), the prepared samples were evenly mixed, incubated at 37°C for 20 min, and the absorbance values of each sample were measured at 450 nm for SOD values.
## In Situ Detection of Apoptotic Cells in Lung Tissues
Formalin was used to fix, and paraffin was used to embed the lung tissue sections. Then they were used to detect the apoptotic cells in lung tissues that were detected by in situ apoptosis detection kit (MK1020, Boster Bioengineering Co., Ltd., Wuhan, China). According to the manufacturer's instructions, terminal deoxynucleotidyl transferase (TdT)-mediated nick-end labeling (TUNEL) method was used to detect and quantify apoptosis. The TUNEL positive cells showed brown stained nuclei under light microscope. Ten lung sections of each mouse were randomly counted by the blind method. At least 100 cells were observed in each field under 200× microscope. The apoptosis index (AI) was calculated as the percentage of stained cells, i.e. AI = number of apoptotic cells / total number of nucleated cells × $100\%$. Apoptosis index was used to measure the degree of apoptosis.
## Western Blotting
After the experiment, the right lung of 3 mice in each group was removed. After rapid freezing in liquid nitrogen, it was placed in the refrigerator at -80°C for standby. About 100 mg of lung tissue sample was taken, and 400 µl of RIPA lysate (Beyotime Institute of Biotechnology, Shanghai, China) was added. After grinded, the mixture was homogenized and centrifuged at 4°C for 15 min at 12,000 rpm. The supernatant was collected to determine the protein concentration by bicinchoninic acid (BCA) protein concentration assay kit (Beyotime Institute of Biotechnology, Shanghai, China). An appropriate amount of 5 × sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) buffer was added and heated with boiling water at 100°C for 5 min. The equivalent protein samples were separated by $15\%$ SDS-PAGE and transferred to the polyvinylidene fluoride (PVDF) membrane. The membrane was blocked with $5\%$ skimmed milk powder and incubated with anti-GRP78 (1:500, Abcam, ab21685), anti-C/EBP homologous protein (CHOP) (1:200, Abcam, ab11419), anti-caspase-12 (1:500, Abcam, ab62484) or anti-β-actin antibody (1:800, Shanghai Shenggong, d110001), respectively. After washing with TBST, horseradish peroxidase (HRP)-labeled secondary antibody (ZSGB Biotech Co., Ltd. Beijing, China) was added and incubated at 37°C for 1 h. Finally, the membrane was detected and photographed with Chemiscope minichemiluminescence instrument. Semiquantitative analysis was performed.
## Statistical Analysis
All experiments were repeated three times with the same sample. Statistical analysis was performed using SPSS 22.0 software (IBM Corp., Armonk, NY, USA). Significant differences between groups were assessed by one-way analysis of variance (ANOVA). All data were expressed as mean±standard deviation (SD). Differences were considered statistically significant when $P \leq 0.05.$
## IPOST Improved the Morphological Characteristics of Lung Tissues
Standard HE staining was used to detect the pathological changes in lung tissues under a light microscope. As shown in Figure 1A, there was no obvious pathological change in the SO group. Under the microscope, the alveolar structure of the mice was normal, and there was no narrowing, congestion, hemorrhage, neutrophil infiltration and alveolar septum thickening. In the IR group, there were obvious alveolar congestion, hemorrhage, alveolar septum thickening, structural damage and interstitial neutrophil infiltration. Pathological damages in the IPRE and IPOST groups were significantly reduced. As shown in Figure 1B, the lung injury score of IR group was significantly higher than that of SO group ($P \leq 0.05$), the lung injury scores of IPRE and IPOST groups were significantly lower than that of IR group ($P \leq 0.05$), and the lung injury score of IPOST group was lower than that of IPRE group ($P \leq 0.05$). These results indicated that IPOST had a protective effect on myocardial IR-induced lung injury in mice.
Fig. 1Effect of IPOST on the pathological changes and W/D ratio of lung tissue after myocardial IR. ( A) Representative photomicrographs of HE staining of lung sections (200× magnification). ( B) Histological score of lung injury. ( C) Analysis of lung W/D ratio. Data were expressed as mean±standard deviation (SD). △ compared with SO group, $P \leq 0.05$; ▲ compared with IR group, $P \leq 0.05$; ▽ compared with IPRE group, $P \leq 0.05.$
## IPOST Decreased the W/D Ratio of Lung Tissues
At the end of the experiment, the left lung of mice was taken to measure wet weight and dry weight. The effects of IPRE and IPOST on W/D ratio are shown in Figure 1C. Compared with SO group, myocardial IR significantly increased the W/D ratio of lung tissues (4.420±0.119 vs. 3.414±0.081, $P \leq 0.05$). Compared with IR group, IPRE and IPOST significantly limited the increase of lung W/D ratio (4.089±0.076 and 3.833±0.154, $P \leq 0.05$). The W/D ratio in the IPOST group was lower than that in the IPRE group ($P \leq 0.05$). These results suggested that IPOST reduced the degree of pulmonary edema after myocardial IR.
## IPOST Decreased Plasma Levels of Inflammatory Factors
At the end of the experiment, the levels of TNF-α, interleukin-6 and IL-17A in the plasma of mice were detected to evaluate the degree of histopathological inflammatory reaction after myocardial IR. As shown in Figure 2, myocardial IR significantly increased the levels of TNF-α, IL-6 and IL-17A in plasma in comparison with SO group ($P \leq 0.05$). Compared with IR group, IPRE and IPOST could significantly limit the increase of TNF-α, IL-6 and IL-17A in plasma ($P \leq 0.05$). Compared with IPRE group, the plasma levels of TNF-α, IL-6 and interleukin-17A in IPOST group were notably lower ($P \leq 0.05$). The results showed that IPOST reduced the inflammatory reaction after myocardial IR.
Fig. 2Effect of IPOST on the plasma pro-inflammatory cytokine content after myocardial IR in mice. ( A) *Tumor necrosis* factor alpha (TNF-α) levels; (B) Interleukin 6 (IL-6) levels; (C) Interleukin-17A (IL-17A) levels. Data were expressed as mean±standard deviation (SD). △ compared with SO group, $P \leq 0.05$; ▲ compared with IR group, $P \leq 0.05$; ▽ compared with IPRE group, $P \leq 0.05.$
## IPOST Decreased Lipid Peroxidation and Increased Production of Superoxide
Free Radicals in Lung Tissues
To detect the production of lipid peroxidation and superoxide radical, we measured MDA content of and SOD activity in lung tissues. As shown in Figure 3, compared with SO group, MDA content in lung tissues of IR group increased significantly, while SOD activity decreased significantly ($P \leq 0.05$). Compared with IR group, MDA content decreased and SOD activity notably increased in IPRE and IPOST groups ($P \leq 0.05$). Compared with IPRE group, the MDA content decreased and the SOD activity increased in IPOST group ($P \leq 0.05$). These results suggested that IPOST can reduce lipid peroxidation in lung tissues after myocardial IR but increased the production of superoxide free radicals.
Fig. 3Effects of IPOST on MDA content and SOD activity in lung tissues. ( A) MDA level; (B) SOD level. Data were expressed as mean±standard deviation (SD). △ compared with SO group, $P \leq 0.05$; ▲ compared with IR group, $P \leq 0.05$; ▽ compared with IPRE group, $P \leq 0.05.$
## IPOST Decreased the Apoptosis Rate of Lung Cells
To detect the apoptosis rate of lung cells, we performed TUNEL staining. As shown in Figure 4A, the apoptosis in the SO group was lower than in the IR group. In contrast, the number of apoptotic cells in IPRE and IPOST groups was lower than in IR group. As shown in Figure 4B, the apoptotic index of IPRE and IPOST groups were significantly lower than those of IR group ($P \leq 0.05$), while the apoptotic index of IPOST group was lower than that of IPRE group ($P \leq 0.05$). These results indicated that myocardial IR can induce apoptosis of a large number of lung cells, and IPOST can more effectively reduce the apoptosis rate of lung cells.
Fig. 4Effect of IPOST on apoptosis of lung tissue after myocardial IR (magnification 200×). ( A) Typical micrographs of lung stained with TUNEL. The number of apoptotic cells in lung tissue was significantly increased in IR group and decreased in IPRE and IPOST groups. The number of apoptotic cells in IPOST group was significantly lower than that in IPRE group. ( B) *The apoptosis* index (AI) of lung tissue. Data were expressed as mean±standard deviation (SD). △ compared with SO group, $P \leq 0.05$; ▲ compared with IR group, $P \leq 0.05$; ▽ compared with IPRE group, $P \leq 0.05.$
## IPOST-Inhibited Endoplasmic Reticulum Stress in Lung Tissues
To elucidate the role of IPOST in endoplasmic reticulum stress (ERS), the protein expression levels of (ERS)-related proteins GRP78, CHOP and caspase-12 were analyzed by Western blotting. It showed that compared with SO group, myocardial IR significantly increased the protein expression levels of GRP78, CHOP and caspase-12 in the lung of mice ($P \leq 0.05$). Compared with IR group, IPRE and IPOST limited the increased expressions of GRP78, CHOP and caspase-12 in lung tissue ($P \leq 0.05$). Compared with IPRE group, the protein expression levels of GRP78, CHOP and caspase-12 in lung tissues of IPOST group were significantly lower ($P \leq 0.05$).
## DISCUSSION
Our results suggested that myocardial IR induced ALI by increasing inflammatory response, oxidative stress and ERS-mediated apoptosis, which can be inhibited by IPRE and IPOST. We provided evidence that, compared with IPRE, the postconditioning of three cycles of 5 minutes of reperfusion and 5 minutes of occlusion can provide more effective lung protection, which may be related to the duration of preconditioning and postconditioning and the number of cycles.
ALI is usually caused by a variety of factors, which can increase vascular permeability and inflammatory response. ALI seriously affects the postoperative recovery of patients undergoing cardiac surgery. The mechanism of ALI after myocardial IR is complex and has not been elucidated. Studies have shown that reactive oxygen species, cytokines and prostaglandins can be released out of control after myocardial IR injury. The migration and accumulation of inflammatory factors and apoptotic factors can produce a large number of oxygen free radicals and proteases, leading to injury of pulmonary capillary endothelial cells and alveolar epithelial cells to induce ALI[18]. Many pro-inflammatory cytokines, including TNF-α, IL-6 and IL-1β, can be produced after myocardial IR[19]. They have been reported to mediate the development of ALI[20]. Consistent with previous studies, in this study we provided additional evidence that myocardial IR can cause an increase of circulating pro-inflammatory cytokines TNF-α, IL-6, IL-17A, aggravate lung histopathological injury, increase the lung wet-to-dry weight ratio, lead to increased lung inflammation, and cause ALI.
During the process of reperfusion after myocardial ischemia, a large amount of O2 influx leads to the increase of electron leakage of mitochondrial respiratory electron transport chain and neutrophil respiratory burst. These two processes lead to a large number of oxygen free radicals. ROS released from ischemic myocardium may lead to injury of many distal organs[21]. Oxidative stress and lipid peroxidation play an important role in distal organ injury after IR[22]. The lung is in a hyperoxic environment with a large area and rich blood supply, which is prone to oxidative stress-mediated tissue damage. Effective assessment of oxidative stress can help to more accurately assess the severity of lung injury and predict treatment response and prognosis[23]. MDA is one of the end products of lipid peroxidation, considered a marker of cell peroxidation. SOD is an endogenous oxygen free radical scavenger, and its activity can reflect the body's antioxidant capacity[24,25]. The results showed that myocardial IR could induce oxidative stress in lung tissues, which showed that MDA levels were increased and SOD levels were decreased in lung tissues. This is consistent with the results of Kip et al.[22].
More and more evidence shows that ERS plays a key role in ALI-induced cell dysfunction[23,26]. Because lung epithelial cells secrete a large number of surfactant and other proteins, these cells are prone to ERS[27]. ALI can induce ERS-related apoptosis, which is different from the classical apoptosis pathways (exogenous/death receptor and endogenous/mitochondrial cell death pathway). ERS-induced cell death signal can occur through a variety of pathways[28]. GRP78 is a central regulator of ER function, a major ER chaperone protein, and is related to the activation of ERS transmembrane sensor. It is a marker protein of UPR and ERS response[29,30]. CHOP is an apoptotic transcription factor induced by ERS. It is also a common molecular marker for evaluating ERS. It can hardly be detected under normal physiological conditions. CHOP is significantly induced in ERS and participates in regulating the expression of apoptosis-related genes[8]. The recycling of misfolded protein between ER and Golgi complex can enhance the CHOP expression[31]. CHOP expression is positively correlated with the severity of ERS, which is the key mediator of ERS leading to cell death[32]. Previous studies have shown that CHOP can be activated by UPR signaling pathway, inhibit anti-apoptotic protein, promote the activation of apoptotic protein caspase-3, and induce apoptosis[33]. In addition, the typical caspase family apoptosis pathway is unique to ERS. Caspase cascade-related proteins have also been reported as the markers of ERS-related apoptosis. Caspase-12, in particular, is associated with ER membrane and plays a proximal regulatory role in ERS-induced caspase activated apoptosis. Activated caspase-12 can further induce caspase-3 activation, start caspase cascade reaction and lead to apoptosis[34].
Bi et al. reported that ERS proteins CHOP, GRP78 and caspase-12 were significantly increased in lipopolysaccharide (LPS)-induced acute lung injury model, and helix B surface peptide could significantly limit the increase of ERS-related proteins and reduce lung tissue-related apoptosis[35]. Consistent with other reports, we found that the expression of ERS-related proteins GRP78, CHOP and caspase-12 in lung tissues were increased and the apoptosis indexes in lung tissues were increased in ALI model after myocardial IR.
IPRE and IPOST, as an endogenous protective pathway, have a significant protective effect on myocardial IR injury[36]. However, the application time of IPRE is difficult to control, which limits its clinical application. IPOST overcomes the above shortcomings and is easier to operate and accurately control the time, so it has great potential in clinical application[37-39]. Studies have shown that pulmonary ischemic postconditioning is a repetitive injury to the inferior pulmonary vessels, which may aggravate endothelial dysfunction[40]. Myocardial ischemic postconditioning can be used as an alternative strategy to protect the lung from IR injury during cardiac surgery without affecting the inferior pulmonary vessels[17]. Our study confirmed that myocardial ischemic postconditioning can effectively reduce lung injury, which indicated that myocardial ischemic postconditioning not only protects the heart from the invasion of IR injury, but also protects the distal organs during myocardial IR. This is consistent with the results of Gao et al.[17]. Our results showed that ischemic preconditioning and postconditioning could reduce the levels of TNF-α, IL-6 and IL-17A, limit the increase of MDA and decrease the level of SOD in lung tissues after myocardial IR. It could significantly inhibit the activation of ERS-related molecules such as GRP78, CHOP and caspase-12 in lung tissues, and reduce the apoptosis of lung cells. Liu et al.[41] and other studies showed that IPOST and IPRE had the same effect in reducing lung injury in ALI model after intestinal IR. The results showed that the effect of IPOST was more significant than that of IPRE in ALI model after myocardial IR in mice. This may be related to the number of cycles, operating time and model making.
## CONCLUSION
In conclusion, this study confirmed that IPOST and IPRE can reduce myocardial IR-induced lung injury, and its potential mechanism may be related to the inhibition of ERS-mediated apoptosis. In addition, compared with pretreatment, post-treatment not only has advantages in operation and clinical application, but also has a more significant lung protection effect. Our results better elucidated the protective effect and mechanism of IPOST and may provide a theoretical basis for the prevention of lung injury in cardiac surgery and myocardial infarction. Myocardial ischemic postconditioning, as a protective measure superior to preconditioning and pulmonary ischemic postconditioning, has great potential in clinical cardiovascular surgery. However, the specific molecular mechanism still needs further study.
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|
---
title: Prevalence and Correlates of Sleep Disorders Among Pediatric Inpatients in
a Tertiary Pediatric Hospital
journal: Cureus
year: 2023
pmcid: PMC10010750
doi: 10.7759/cureus.34871
license: CC BY 3.0
---
# Prevalence and Correlates of Sleep Disorders Among Pediatric Inpatients in a Tertiary Pediatric Hospital
## Abstract
Background *It is* possible to define sleep disorders as any disturbance in sleep timing, quality, or quantity that results in daytime distress and impairment in functioning that, in turn, affects the baseline functional status of an individual. Our study aimed to describe how sleep disorders might affect pediatric inpatients at King Abdulaziz University Hospital (KAUH) as well as estimate their prevalence [2021-2022]. We assessed the sleep habits using questionnaires and analyzed and combined these data to create rankings to compare the different issues affecting sleep habits in pediatric patients.
Methodology Two scoring systems were used in this study, namely (a) the Children’s Sleep Habits Questionnaire (CSHQ) and (b) the Pediatric Sleep Questionnaire. Analyses of the data were conducted using SPSS version 23 (IBM Corp., Armonk, NY, USA) and GraphPad Prism version 8 (GraphPad Software, Inc., San Diego, CA, USA).
Results The prevalence of sleep disorders and their correlations were evaluated among 98 pediatric inpatients at KAUH, Saudi Arabia, between 2021 and 2022. The average duration of hospital stay was 11.97 ± 11.0 days ($$n = 78$$), and the average number of previous admissions was 2.85 ± 3.7 ($$n = 93$$).
Conclusions According to the sleep behavior domain of the CSHQ, most children woke up sweating, screaming, and inconsolable during the night. Furthermore, bedtime resistance and sleep anxiety were the most prevalent sleep disturbances observed in the study population.
## Introduction
Sleep disorders are defined as any disturbance in the sleep timing, quality, or quantity that affects the baseline functional status of an individual [1,2]. According to the third edition of the International Classification of Sleep Disorders (ICSD-3), there are seven major categories of sleep disorders, namely, sleep-related breathing disorders, insomnia, parasomnia, central disorders of hypersomnolence, circadian rhythm sleep-wake disorders, sleep-related movement disorders, and other sleep disorders [3]. Although epidemiological studies have shown variations in the prevalence of pediatric sleep disorders, sleep problems are prevalent in $50\%$ of children [4-6].
The essentiality of sleep in maintaining body health is well known and studied, especially in the pediatric population, as their physical, psychological, and mental functions are still developing [7,8] Chronic childhood sleep deprivation is considered a risk factor for impaired mental health, cognition, emotional regulation, immunity, and the development of chronic diseases in adulthood [8-11].
Decreased sleep during the hospitalization period is associated with abnormal vital signs such as blood pressure and blood glucose, in addition to increased recovery time [12-14]. Pediatric inpatients are vulnerable to poor sleep quality and less sleep than recommended during the hospitalization period [15].
According to a study conducted in Chicago, several factors in hospital environments disturb sleep in pediatric patients. These include medical interventions such as vital sign monitors, continuous staff assessment, medication administration, and noise during the night [16]. Similarly, a study conducted in Southampton, UK, on pediatric inpatients and their co-sleeping parents to objectively measure their sleep quality in the hospital and compare it with their sleep at home, found that both participants experienced a decrease in sleep quality in the hospital. Moreover, residents in the pediatric ward were exposed to a higher noise level than recommended by the World Health Organization [17]. A cohort study conducted on pediatric inpatients in Mexico found that hospitalization improved sleep in patients with previous sleep problems (PSPs). However, the hospital environment caused sleep disturbances in patients without PSPs [18].
A study in Egypt concluded that sleep disturbance, excessive daytime sleepiness, and restless leg syndrome are prevalent in pediatric patients with chronic kidney disease. However, this study did not investigate these findings in pediatric inpatients [19]. Nevertheless, there are no data on the screening prevalence of sleep disorders in pediatric inpatients and the effect of these disorders on the recovery duration and length of hospital stay [20,21].
Although many studies have investigated sleep quality in hospitals and published recommendations to control the environmental factors that decrease sleep quality among pediatric inpatients, there is an overlap between sleep disorders and sleep problems, and the published recommendations are for improving sleep quality overall, but not specifically for inpatients with sleep disorders [8,9,16,22].
As there are no previous studies in this field that have focused on the effects and prevalence of sleep disorders, specifically in pediatric inpatients, this study aimed to provide new data to pediatric, psychiatric, and sleep medicine to help develop recommendations that can improve patient care and well-being.
Our study aimed to describe how sleep disorders might affect pediatric inpatients at King Abdulaziz University Hospital (KAUH) as well as estimate their prevalence [2021-2022].
## Materials and methods
In this cross-sectional study, identical inclusion/exclusion criteria were used in two scoring systems, namely, (A) the Children’s Sleep Habits Questionnaire (CSHQ). The CSHQ has been utilized in many studies to investigate sleep habits in young children as it is a retrospective questionnaire composed of 45 elements distributed among eight domains [23]. For the purpose of the study, an abbreviated form of the CSHQ was used. It included 22 elements distributed among four major domains (bedtime, sleep behavior, waking during the night, and morning wake-up) [23]. ( B) The Pediatric Sleep Questionnaire (PSQ). The PSQ contains 22 items categorized into three symptoms (hyperactive or inattentive behavior, snoring, and excessive daytime sleepiness) [24].
The PSQ and CSHQ questionnaires were translated into the Arabic language and then given to the caregivers of 98 pediatric male and female patients aged 4-14 years to be completed. The translation of the questionnaire to the Arabic language was validated by previously published studies [21].
We excluded patients who were admitted to the hospital due to sleep disorders and non-Arabic-speaking patients and caregivers.
Our study was conducted in the Department of Medicine, Psychiatry Division in KAUH, Saudi Arabia, between 2021 and 2022. Ethical approval was obtained from the Research Ethics Committee, Unit of Biomedical Ethics, King Abdulaziz University, Jeddah, and was approved by the Institutional Review Board of King Abdulaziz University (reference number: 419-21).
Statistical analysis Data obtained in this study were analyzed using SPSS version 23 (IBM Corp., Armonk, NY, USA) and GraphPad Prism version 8 (GraphPad Software, Inc., San Diego, CA, USA).
Simple descriptive statistics were used to define the sociodemographic characteristics through counts and percentages for the categorical variables, while continuous variables were presented as means and standard deviations.
Reliability analysis was then used with a model of alpha (Cronbach) to study the properties of the measurement scales, the items that comprise the scales, and the average interitem correlation.
The chi-squared test was used to establish the relationships between categorical variables. Variables represented as means were correlated using Pearson’s correlation coefficient. Independent t-tests and one-way analysis of variance tests were employed to compare the means of two or more groups. The variables correlated were CSQH scores, PSQ scores, and bedtime and wake-up time routines of the children. These tests were performed under the assumption of a normal distribution. A p-value of <0.05 was the criterion used to discard the null hypothesis.
## Results
The prevalence of sleep disorders and their correlations were evaluated among 98 pediatric inpatients at KAUH, Saudi Arabia, between 2021 and 2022. Of these, 51 ($53.7\%$) were males, and 44 ($46.3\%$) were females. Their age ranges were toddlers (2-5 years), school-aged (6-12 years), and adolescents (13-18 years), with percentages of 30.5, 58.9, and $10.5\%$, respectively. Of these, $58.2\%$ were Saudi, and $41.8\%$ were non-Saudi. The average duration of hospital stay was 11.97 ± 11.0 days ($$n = 78$$), and the average number of previous admissions was 2.85 ± 3.7 times ($$n = 93$$).The average scores obtained by the patients for each item within each domain of the CSHQ are shown in Figure 1. The highest mean score of 3.53 ± 0.9 ($$n = 95$$) was observed for the child awakens during the night and is sweating, screaming, and inconsolable item under the sleep behavior domain, while the lowest score of 1.23 ± 1.6 ($$n = 95$$) was observed in the child falls asleep in parent’s or sibling’s bed item in the bedtime domain. In addition, reliability statistics were calculated for each domain of the CSHQ.
**Figure 1:** *Distribution of mean scores per domain of the Children’s Sleep Habits Questionnaire of the studied patients (N = 98).*
The frequency of answers to each item of the PSQ was evaluated (Table 1). The results showed that most patients did not respond favorably to each item. The mean scores obtained by patients for each item of the PSQ were also measured. The highest mean score of 0.37 ± 0.5 ($$n = 95$$) was observed for the Child often interrupts or intrudes on others item while the lowest score of 0.03 ± 0.2 ($$n = 95$$) was observed for the Seeing the child stop breathing during the night item.
**Table 1**
| Pediatric Sleep Questionnaire (N = 98) | Pediatric Sleep Questionnaire (N = 98).1 | Yes | No | Don’t know |
| --- | --- | --- | --- | --- |
| While sleeping, does your child | Snore more than half the time? | 19 (20.0) | 73 (76.8) | 3 (3.2) |
| While sleeping, does your child | Always snore? | 13 (13.7) | 81 (85.3) | 1 (1.1) |
| While sleeping, does your child | Snore loudly? | 8 (8.4) | 85 (89.5) | 2 (2.1) |
| While sleeping, does your child | Have “heavy” or loud breathing? | 16 (16.8) | 77 (81.1) | 2 (2.1) |
| While sleeping, does your child | Have trouble breathing, or struggle to breathe? | 15 (15.8) | 79 (83.2) | 1 (1.1) |
| Have you ever seen your child stop breathing during the night? | Have you ever seen your child stop breathing during the night? | 3 (3.2) | 91 (95.8) | 1 (1.1) |
| Does your child | Tend to breathe through the mouth during the day? | 16 (16.8) | 68 (71.6) | 11 (11.6) |
| Does your child | Have a dry mouth when waking up in the morning? | 22 (23.2) | 62 (65.3) | 11 (11.6) |
| Does your child | Occasionally wet in the bed? | 21 (22.1) | 73 (76.8) | 1 (1.1) |
| Does your child: | Wake up feeling unrefreshed in the morning? | 17 (17.9) | 77 (81.1) | 1 (1.1) |
| Does your child: | Have a problem with sleepiness during the day? | 17 (17.9) | 77 (81.1) | 1 (1.1) |
| Has a teacher or other supervisor commented that your child appears sleepy during the day? | Has a teacher or other supervisor commented that your child appears sleepy during the day? | 10 (10.5) | 79 (83.2) | 6 (6.3) |
| Is it hard to wake your child up in the morning? | Is it hard to wake your child up in the morning? | 16 (16.8) | 79 (83.2) | 0 (0.0) |
| Does your child wake up with headaches in the morning? | Does your child wake up with headaches in the morning? | 10 (10.5) | 82 (86.3) | 3 (3.2) |
| Did your child stop growing at a normal rate at any time since birth? | Did your child stop growing at a normal rate at any time since birth? | 24 (25.3) | 64 (67.4) | 7 (7.4) |
| Is your child overweight? | Is your child overweight? | 14 (14.7) | 81 (85.3) | 0 (0.0) |
| This child often | Doesn’t seem to listen when spoken to directly | 24 (25.3) | 68 (71.6) | 3 (3.2) |
| This child often | Has difficulty organizing tasks and activities | 19 (20.0) | 66 (69.5) | 10 (10.5) |
| This child often | Is easily distracted by extraneous stimuli | 29 (30.5) | 62 (65.3) | 4 (4.2) |
| This child often | Fidgets with hands or feet, or squirms in seat | 31 (32.6) | 57 (60.0) | 7 (7.4) |
| This child often | Is “on the go” or often acts as if “driven by a motor” | 22 (23.2) | 73 (76.8) | 0 (0.0) |
| This child often | Interrupts or intrudes on others (e.g., butts into conversations or games) | 35 (36.8) | 58 (61.1) | 2 (2.1) |
The association between the mean PSQ results and the sociodemographic characteristics of the patients was then determined (Table 2). The results revealed no significant association ($p \leq 0.05$) between the PSQ results of having no sleep problems or having sleep problems (breathing disorders) and age, gender, and nationality. Furthermore, the association between the mean PSQ results and CSHQ domains was measured (Table 3). The mean PSQ results were significantly associated with the sleep behavior domain of the CSHQ, as well as waking during the night and morning wake-up domains, with p-values of <0.001, 0.017, and 0.043, respectively.
## Discussion
To our knowledge, no study has reported the prevalence of sleep disorders among pediatric inpatients children in Saudi Arabia. Therefore, this study was designed to estimate the prevalence of sleep disorders among pediatric inpatients at KAUH.
Regarding the research objective, we determined that most children woke up sweating, screaming, and inconsolable during the night according to the sleep behavior domain of the CSHQ, contrary to the results of Robyn et al., who found no association between CSHQ scores and risk of awakening in hospitals [10]. While the least mentioned sleep habit among pediatric inpatients was falling asleep in their parent’s or sibling’s bed.
Furthermore, we found that Bedtime resistance and Sleep anxiety were the most prevalent sleep disturbances when comparing our results with those of Liu et al., who used the CSHQ to examine Chinese kindergarteners’ sleep patterns and disorders [25]. Additionally, van Litsenburg et al. examined the sleep habits and problems of healthy Dutch children. They found that sleep onset delay was more common in older than in younger children, children while bedtime resistance was more commonly observed in younger children and was more common than in older children [26].
Consequently, inpatients might have more difficulty maintaining their sleep cycle than the general pediatric population experiencing difficulty falling asleep. This finding is consistent with that of Linder and Christian who investigated the effects of the hospital care environment on the sleep quality of pediatric cancer inpatients [22]. It found that sleep was significantly affected by the disruption of the sleep cycle during the night, for example, by frequent awakening and excessive noise.
Based on the PSQ results of this study, 12 patients reported more than eight positive responses, suggesting sleep problems related to breathing disorders. Despite this, the PSQ item of seeing the child stop breathing during the night was associated with the least prevalent sleep problems among pediatric inpatients. These results differ from a study conducted in Saudi Arabia in 2019, which investigated the prevalence of sleep-disordered breathing in primary school children using the PSQ. The study showed that the overall risk of sleep-disordered breathing was $21\%$ [27]. The variation between our results and this study might be attributed to the pediatric populations studied, as we investigated sleep problems faced by pediatric inpatients rather than those of the general pediatric population.
Interestingly, the PSQ results were found to be significantly associated with the CSHQ domains, including sleep behavior, waking during the night ($$p \leq 0.017$$), and morning wake-up ($$p \leq 0.043$$) according to the independent t-test at $p \leq 0.05$ level. One implication is the possibility of benefiting from integrating both scales to develop an all-inclusive scale.
This study has a few limitations that need to be acknowledged. First, the sample size is small. We excluded non-Arabic-speaking parents, although $85\%$ of the admissions were Arabic speaking. Second, this study was limited to inpatients at KAUH rather than multicenter patients, which limits its generalizability. Another limitation of this study is the use of a cross-sectional design. Objective measurements of sleep disturbances, such as polysomnography, may be more accurate than subjective measurements. Using questionnaires such as the PSQ and CSHQ is another limitation, as they do not replace the clinical diagnosis of an experienced physician.
The ages of the participants varied between two and 18 years, with younger children who sleep with their parents having their parents observing them during sleep. For older children who usually sleep alone, parents may be less aware of their sleeping habits.
In future investigations, we recommend comparing the sleeping habits of inpatient and outpatient children to determine whether they are affected by their surrounding environment, as well as taking into account the admitting diagnosis of the inpatients.
## Conclusions
This study estimated the prevalence of sleep disorders among pediatric inpatients using two scoring systems, namely, the CSHQ and the PSQ at KAUH, Jeddah, Saudi Arabia. In conclusion, according to the sleep behavior domain of the CSHQ, the majority of children woke up sweating, screaming, and inconsolable during the night. Furthermore, bedtime resistance and sleep anxiety were the most prevalent sleep disturbances among the chosen population.
Finally, the results revealed that age, sex, and nationality did not significantly influence the PSQ scores for having no sleep problems or having a sleep problem (breathing disorder). Other PSQ results, such as sleep behavior, waking during the night, and morning wake-up, were significantly associated with CSHQ domains.
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|
---
title: 'Physical function, ADL, and depressive symptoms in Chinese elderly: Evidence
from the CHARLS'
authors:
- Yumeng Yan
- Yiqian Du
- Xue Li
- Weiwei Ping
- Yunqi Chang
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10010774
doi: 10.3389/fpubh.2023.1017689
license: CC BY 4.0
---
# Physical function, ADL, and depressive symptoms in Chinese elderly: Evidence from the CHARLS
## Abstract
### Background
Depressive symptoms are a serious public health problem that affects the mental health of older adults. However, current knowledge of the association between ADL disability and physical dysfunction and depressive symptoms in Chinese adults is insufficient. We intend to analyze the association between physical function, ADL, and depressive symptoms in older Chinese adults.
### Methods
The data obtained from the China Health and Retirement Longitudinal Survey (2015 and 2018) (CHARLS). This includes 3,431 in 2015 and 3,258 in 2018 over the age of 60. Comparing 2015 and 2018 data, multivariate logistic regression models were used to explore the relationship between physical function, ADL, and depressive symptoms in urban and rural older adults, adjusting for sociodemographic factors associated with depression in older adults.
### Results
The prevalence of depressive symptoms among older adults in China was 33.8 percent in 2015 and 50.6 percent in 2018. In baseline data from 2015 and 2018, residence, gender, marital status, drinking, physical function, ADL, and self-rated health were all found to be significantly associated with depressive symptoms in older adults. The differences in physical function, ADL and depressive symptoms among older adults in 2015 and 2018 were further analyzed based on urban and rural stratification. Both physical dysfunction and ADL disability were significantly associated with depressive symptoms in rural older adults in 2015 and 2018. And in urban areas, ADL was found to be significantly associated with depressive symptoms in urban older adults. Multivariate logistic regression analysis demonstrated that ADL disability was significantly associated with depressive symptoms among older adults in both urban and rural areas. Physical dysfunction was only significant in rural areas with depressive symptoms. The alpha level was instead set to 0.05 for all statistical tests.
### Conclusion
Rural, female, 60–70 years of age, primary school or below, married, non-smoking, non-drinking, physical dysfunction, ADL disability and self-rated poor health make-up a higher proportion of depressed older adults. ADL disability and physical dysfunction were more likely to be associated with depressive symptoms in rural Chinese older adults. Therefore, the physical and mental health of rural elderly should be of concern. The rural older adults should receive additional support from the government and society.
## 1. Introduction
As the number of older adults continues to increase, increasing attention was paid to the health problems of older adults [1]. Depressive symptoms are common in older adults [1]. There is evidence that depressive symptoms could be difficult to treat later in life [2]. Besides, it might lead to reduced physical activities, lower the quality of life, and generate self-grief and even suicide [3]. Studies showed that prevalence of depressive symptoms in Asian elderly was 7.8–$46\%$ [4]. The overall prevalence of depressive symptoms was higher in Brazilian older adults ($30.2\%$) than Chilean older adults ($26.3\%$) [5]. Furthermore, studies on older adults in China revealed that the prevalence differed between 13 and $41\%$ [6]. The disease burden of depressive symptoms in China had been on the rise and would continue to increase in the coming decades [7].
Researchers identified some as risk factors of depressive symptoms including female gender, somatic illness, cognitive impairment, functional disability, and history of depressive symptoms [8]. Studies showed that reduced physical function in older adults was the main risk factor for developing depressive symptoms [9]. A longitudinal community-based study reported that physical function independently predicts depressive morbidity in late-life [10]. A study showed physical symptoms and poorer physical function reported increased depressive symptoms [11]. At the same time, a decline in physical function leads to a loss of independence and consequent depressive symptoms. These studies demonstrated that those with the lowest levels of physical function carry the largest risk of onset of both depressive symptoms and anxiety over time [12]. There was evidence that ADL disability may be a risk factor for depressive symptoms in previous studies [13, 14]. ADL disability was associated with depressive symptoms and expanded psychological burden in older adults [15]. An article on the level of depressive symptoms among elderly Turkish people, the findings indicated that ADL anticipated depressive symptoms among older adults [16]. A study in South Korea reported that restriction of ADL, which means restriction of physical function, was also associated with early depressive symptoms. Lack of physical function leads to diminished social relations and depressive symptoms [17]. In addition, economic, political, cultural, and other factors affect depressive symptoms differently. Depressive symptoms a financial burden on older adults and families. Studies demonstrated the medical expenses on depressive symptoms were 1.86 times that of non-depressed patients [18].
Currently, there was limited knowledge about the relationship between ADL disability and physical dysfunction and depressive symptoms in the Chinese older adults. In a prospective study of 2,713 Chinese older adults who completed interviews with the Chicago Chinese Aged Population Study, a significant relationship was discovered between depressive symptoms and the occurrence of functional disability [19]. ADL disability was found to be a high-risk group for depressive symptoms in older adults in a study on changes in depressive symptoms levels in older Chinese [20]. In a community-based study in Beijing, it was indicated that older adults with disabilities were more likely to experience depressive symptoms [21]. Similarly, community-based research has linked ADL disability with increased risk of depressive symptoms in middle-aged and older Chinese adults [22]. Data from one study showed that physical dysfunction in older silicosis patients was significantly associated with the prevalence of depressive symptoms [23]. In addition, an analysis of factors influencing mental health in older Chinese adults showed that physical function and ADL were strongly associated with depressive symptoms in older adults [24].
In previous studies, depressive symptoms in older adults have mainly been studied in terms of ADL disability in a particular region or community. Our study was based on the China Health and Retirement Longitudinal Study (CHARLS), which was collected from respondents across the country. The sample of over-60 s used for the study was broader and more representative. Correspondingly, based on the above data, we mainly explore the relationship between physical function, ADL, and depressive symptoms. The purposes were the following: [1] To compare depressive symptoms prevalence in 2015 and 2018; [2] To study the influencing factors of depressive symptoms in older adults; [3] To evaluate the association between physical function, ADL, and depressive symptoms among urban and rural older Chinese adults.
## 2.1. Data
The China Health and Retirement Longitudinal Study (CHARLS) is a large-scale interdisciplinary survey project hosted by the National Development Institute of Peking University and carried out by the China Social Science Survey Center of Peking University. It is high-quality microdata representing the households and individuals of middle-aged and older Chinese adults over the age of 45. CHARLS conducted surveys and interviews in 150 counties and 450 communities (villages) of 28 provinces (autonomous regions and municipalities) in 2011, 2013, 2015, and 2018, respectively. The CHARLS National Baseline Survey was launched in 2011 and followed for two years, with 23,000 respondents in 12,400 households. Data from 2015 and 2018 are used in this study. Seniors aged 60 and over were selected for the study. A total of 3,431 subjects were screened in 2015 and 3258 in 2018.
Ethical approval for data collection in CHARLS is obtained from the Biomedical Ethics Review Committee of Peking University. Peking University Public Data Management Agency agreed to our use of the data.
## 2.2. Depression
The Center for Epidemiological Studies Depression Scale (CES-D-10) was used to measure depressive symptoms in the CHARLS questionnaire. CES-D-10 was highly reliable and effective in successfully measuring depressive symptoms in middle-aged and older adults [25]. Previous studies demonstrated that a score of 10 on the CES-D had reasonable levels of sensitivity (0.85) and specificity (0.80) in Chinese adults [26]. The simplified scale consists of 10 questions with options as “rarely or none of the time (< 1 day), some or few times (1–2 days), occasionally or a moderate number of times (3–4 days), most of the time (5–7 days), the assignment value range was 0–3 points, total score was calculated. A higher score indicates greater symptoms of depression. A score of 10 and below was “no depressive symptoms” and assigned a value of 0; the score above 10 was “depressive symptoms” and assigned a value of 1 [24].
## 2.3. State of health
In CHARLS, self-rated health (SRH) was obtained by asking participants, “How do you feel about your health status?” SRH was transformed into two categories of variables, respectively, self-rated good health and self-rated poor health.
## 2.4. Physical function
The CHARLS questionnaire sets some physical function related questions, including: running or jogging 1 km, wandering 1 km, walking 100 meters, sitting in a chair for a long time and then standing up, ascending several floors continuously, bending over, bending knees or squat, stretch arms up along your shoulders, walk 100 meters to run or jog 1 km, pick up a tiny coin from the table, each answer for questions was divided into four responses as follows: [1] No difficulty; [2] Difficulty but still can be completed; [3] Difficulty and need help; [4] Unable to complete. If a subject reported difficulty with any of the 9 items, they were defined as having a physical dysfunction [24].
## 2.5. ADL
In CHARLS, the ADL scale was used to determine the disability of older adults. The ADL scale had good reliability and validity and was generally used in China and abroad [27]. The ADL scale consists of 12 items: dressing, bathing, eating, getting into or out of bed, using the bathroom, controlling urination and defecation, doing household chores, cooking, shopping, making phone calls, taking medication, managing money. Each answer for questions was divided into 4 reactions as follows: [1] No, I do not have any difficulty; [2] I have difficulty but still can do it; [3] Yes, I have difficulty and need help; [4] I cannot do it [15]. If a subject report having difficulty with any of the 12 items, then they were defined as having an ADL disability [24].
## 2.6. General demographic information
Covariates included gender, age, education level, marital status, address, smoking, drinking, physical exercise, and social activity. Gender included both males and females. Age was divided into 60–70, 71–80, 80 and above. Education levels were divided into primary school or below, middle school, high school or secondary school, and college or above. Marital status was classified as married or unmarried. Smoking, drinking, physical exercise, and social activity were divided into two groups: yes and no.
## 2.7. Statistical method
Excel 2019 was used to store and filter the data. IBM SPSS (version 22.0) was used for statistical analysis. Descriptive statistics were assigned to describe the demographic information of the participants. Continuous variables were presented as means and standard deviations. The categorical variables were presented as frequencies and percentages. The chi-squared test was used to compare categorical variables. Logistic regression was used when multiple variables were considered simultaneously. Multivariate logistic regression models were performed to compute the relationship between physical function, ADL, and depressive symptoms based on urban and rural stratification. Multivariate logistic regression analysis adjusted for sociodemographic confounding factors associated with depression in older adults. The statistical significance level was set at 0.05. Results were presented as odds ratios (ORs) and $95\%$ confidence intervals (CIs).
## 3.1. Study population
In 2015 and 2018, 3,431 and 3,258 older adults were included, respectively. The mean age of older adults was 66 [Standard Deviation (SD) = 7.041], and $63.1\%$ of the participants were female, $27.7\%$ resident in urban areas in 2015. The mean age of older adults was 68 [Standard Deviation (SD) = 6.563] years, and $68.4\%$ of the participants were female, $31.2\%$ resident in urban areas in 2018. The baseline characteristics were presented in Table 1.
**Table 1**
| Variables | Unnamed: 1 | 2015 (n = 3,431) | 2015 (n = 3,431).1 | 2015 (n = 3,431).2 | Unnamed: 5 | 2018 (n = 3,258) | 2018 (n = 3,258).1 | 2018 (n = 3,258).2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Total | No depressive symptoms (n = 2,271) | Depressive symptoms (n = 1,160) | P | Total | No depressive symptom (n = 1,608) | Depressive symptoms (n = 1,650) | P |
| Residence | Residence | Residence | Residence | Residence | Residence | Residence | Residence | Residence |
| Urban | 945 (27.7) | 685 (30.2) | 260 (22.4) | < 0.001 | 1,016 (31.2) | 567 (35.3) | 449 (27.2) | < 0.001 |
| Rural | 2,486 (72.5) | 1,586 (69.8) | 900 (77.6) | | 2,242 (68.8) | 1,041 (64.7) | 1,201 (72.8) | |
| Gender | Gender | Gender | Gender | Gender | Gender | Gender | Gender | Gender |
| Female | 2,166 (63.1) | 1,308 (57.6) | 858 (74.0) | < 0.001 | 2,230 (68.4) | 929 (59.6) | 1,271 (77.0) | < 0.001 |
| Male | 1,265 (36.9) | 963 (42.4) | 302 (26.0) | | 1,028 (31.6) | 649 (40.4) | 379 (23.0) | |
| Age (year) | Age (year) | Age (year) | Age (year) | Age (year) | Age (year) | Age (year) | Age (year) | Age (year) |
| 60–70 | 2,249 (65.5) | 1,450 (63.8) | 799 (68.9) | 0.001 | 1,817 (55.8) | 884 (55.0) | 933 (56.5) | 0.440 |
| 71–80 | 923 (26.9) | 624 (27.5) | 299 (25.8) | | 1,162 (35.7) | 577 (35.9) | 585 (35.5) | |
| >80 | 259 (7.5) | 197 (8.7) | 62 (5.3) | | 279 (8.6) | 147 (9.1) | 132 (8.0) | |
| Education level | Education level | Education level | Education level | Education level | Education level | Education level | Education level | Education level |
| Primary school or below | 3,077 (89.7) | 2,034 (89.6) | 1,043 (89.9) | 0.315 | 2,541 (78.0) | 1,180 (73.4) | 1,361 (82.5) | < 0.001 |
| Middle school | 253 (7.4) | 163 (7.2) | 90 (7.8) | | 420 (12.9) | 250 (15.5) | 170 (10.3) | |
| High school | 79 (2.3) | 56 (2.5) | 23 (2.0) | | 231 (7.1) | 134 (8.3) | 97 (5.9) | |
| College or above | 22 (0.6) | 18 (0.8) | 4 (0.3) | | 66 (2.0) | 44 (2.7) | 22 (1.3) | |
| Marital status | Marital status | Marital status | Marital status | Marital status | Marital status | Marital status | Marital status | Marital status |
| Unmarried | 795 (23.2) | 491 (21.6) | 304 (26.2) | < 0.001 | 776 (23.8) | 332 (20.6) | 444 (26.9) | < 0.001 |
| Married | 2,636 (76.8) | 1,780 (78.4) | 856 (73.8) | | 2,482 (76.2) | 1,276 (79.4) | 1,206 (73.1) | |
| Smoking | Smoking | Smoking | Smoking | Smoking | Smoking | Smoking | Smoking | Smoking |
| No | 2,485 (72.4) | 1,554 (68.4) | 931 (80.3) | < 0.001 | 2,949 (90.5) | 1,450 (90.2) | 1,499 (90.8) | 0.511 |
| Yes | 946 (27.6) | 717 (31.6) | 229 (19.7) | | 309 (9.5) | 158 (9.8) | 151 (9.2) | |
| Drinking | Drinking | Drinking | Drinking | Drinking | Drinking | Drinking | Drinking | Drinking |
| No | 2,468 (71.9) | 1,578 (69.5) | 890 (76.7) | < 0.001 | 2,522 (77.4) | 1,203 (74.8) | 1,319 (79.9) | < 0.001 |
| Yes | 963 (28.1) | 693 (30.5) | 270 (23.3) | | 736 (22.6) | 405 (25.2) | 331 (20.1) | |
| Physical exercise | Physical exercise | Physical exercise | Physical exercise | Physical exercise | Physical exercise | Physical exercise | Physical exercise | Physical exercise |
| No | 360 (10.5) | 226 (10.0) | 134 (11.6) | 0.148 | 272 (8.3) | 112 (7.0) | 160 (9.7) | 0.005 |
| Yes | 3,071 (89.5) | 2,045 (90.0) | 1,026 (88.4) | | 2,986 (91.7) | 1,496 (93.0) | 1,490 (90.3) | |
| Social activity | Social activity | Social activity | Social activity | Social activity | Social activity | Social activity | Social activity | Social activity |
| No | 1,841 (53.7) | 1,204 (53.0) | 637 (54.9) | 0.292 | 1,646 (50.5) | 815 (50.7) | 831 (50.4) | 0.855 |
| Yes | 1,590 (46.3) | 1,067 (47.0) | 523 (45.1) | | 1,612 (49.5) | 793 (49.3) | 819 (49.6) | |
| Physical function | Physical function | Physical function | Physical function | Physical function | Physical function | Physical function | Physical function | Physical function |
| Normal | 526 (15.0) | 381 (16.8) | 135 (11.6) | < 0.001 | 1,016 (31.2) | 646 (40.2) | 370 (22.4) | < 0.001 |
| Dysfunction | 2,915 (85.0) | 1,890 (83.2) | 1,025 (88.4) | | 2,242 (68.8) | 962 (59.8) | 1,280 (77.6) | |
| ADL | ADL | ADL | ADL | ADL | ADL | ADL | ADL | ADL |
| Normal | 1,563 (45.6) | 1,157 (50.9) | 406 (35.0) | < 0.001 | 1,607 (49.3) | 891 (55.4) | 716 (43.4) | < 0.001 |
| Disability | 1,868 (54.4) | 1,114 (49.1) | 754 (65.0) | | 16,51 (50.7) | 717 (44.6) | 934 (56.6) | |
| Self-rated health | Self-rated health | Self-rated health | Self-rated health | Self-rated health | Self-rated health | Self-rated health | Self-rated health | Self-rated health |
| Poor | 1,956 (57.0) | 1,172 (51.6) | 784 (67.6) | < 0.001 | 2,677 (82.2) | 1,220 (75.9) | 1,457 (88.3) | < 0.001 |
| Good | 1,726 (45.4) | 1,099 (48.4) | 376 (32.4) | | 581 (17.8) | 388 (24.1) | 193 (11.7) | |
## 3.2. Depressive symptoms in older adults
In 2015, $33.8\%$ of older adults had depressive symptoms, which increased to $50.6\%$ in 2018. In 2015, the proportion of older people with depressive symptoms in urban and rural areas was 27.5 and $36.2\%$, respectively, and will increase to 44.2 and $53.6\%$ in 2018. Those who were unmarried, residence in rural, younger, lower education level, physical dysfunction, ADL disability, self-rated poor health was more likely to suffer from depressive symptoms in 2015 and in 2018 (Table 1).
## 3.3. Depressive symptoms in urban and rural
The prevalence of depressive symptoms in older adults was assessed in 2015 and 2018 respectively, and stratified by urban and rural areas at baseline. Based on 2015 data, physical dysfunction and ADL disability were all substantially related to depressive symptoms in rural older adults (Table 2). Based on data in 2018, physical dysfunction and ADL disability were all significantly related to depressive symptoms in urban and rural older adults (Table 3).
In 2015, the older adults with depressive symptoms had higher ADL disability ($51.5\%$) than those without depressive symptoms ($40.9\%$) in urban areas (Table 2). In 2018, the proportion of ADL disability with depressive symptoms (51.2) was higher than for older adults without depressive symptoms ($35.8\%$) (Table 3). The percentage of older adults with physical dysfunction who were depressed was $69.5\%$ in 2018 compared to $81.5\%$ in 2015.
In rural areas, older adults who had trouble taking care of themselves were more likely to be depressed. In 2015, $90.3\%$ of rural older adults with physical dysfunction had elevated depressive symptoms, and in 2018, $80.6\%$ had elevated depressive symptoms. The percentage of depressed older adults with ADL disability was $68.9\%$ in 2015 compared to $58.6\%$ in 2018. In both 2015 and 2018, older adults with ADL disability had higher rates of depressive symptoms than those without depressive symptoms (Tables 2, 3).
## 3.4. Association between physical function, ADL, and depressive symptoms
Table 4 depicts the relationship between physical function and ADL and depressive symptoms in urban and rural older adults in 2015. Table 5 describes the relationship between physical function and ADL and depressive symptoms in 2018 urban and rural populations of older adults. In 2015 and 2018, we found that ADL disability was significantly associated with depressive symptoms among older adults in both urban and rural areas.
In urban areas, ADL disability was associated with a higher risk of depressive symptoms in 2015 (OR = 1.50) and in 2018 (OR = 1.79). In rural areas, ADL disability (OR = 1.69) and physical dysfunction (OR = 1.51) were associated with a higher risk of depressive symptoms in 2015. Similarly, in 2018, ADL disability (OR = 1.34) and physical dysfunction (OR = 1.61) were significant (Tables 4, 5).
In summary, both ADL disability and physical dysfunction were more likely to be associated with depressive symptoms in rural older adults.
## 4. Discussion
Based on data from the China Longitudinal Survey of Health and Retirement (CHARLS) in 2015 and 2018, we compared the characteristic differences among populations of depressive symptoms in older adults. In addition, multivariate logistic regression models were designed to identify urban-rural differences in physical function, ADL, and depressive symptoms in the Chinese adults, and to adjust for confounding factors. Key findings of the present study were [1] the prevalence of depressive symptoms among older adults in China was higher in 2015 than in 2018, and [2] residence, gender, marital status, drinking, physical function, ADL, and self-rated health were linked to depressive symptoms, and [3] among rural older adults with ADL disability and physical dysfunction, the likelihood of depressive symptoms was higher.
In the current report, the prevalence of depressive symptoms among older adults in China varied from $33.8\%$ in 2015 to $50.6\%$ in 2018, indicating a high level of depressive symptoms. The results were like previous research, depressive symptoms burden had been and would be progressively enhancing in China [7, 27]. One study demonstrated that depressive symptoms were over $41\%$ among older adults in China [28]. A study in Bangladesh surveyed 168 healthy retired residents aged 60-80 years and found a $36.9\%$ rate of depressive symptoms in older adults [29]. While in a cross-sectional study abroad, the rate of depressive symptoms was $66.9\%$ in 229 older adults in Hanoi, Vietnam [30].
Depressive symptoms were higher among rural older adults in our survey than among urban older adults. With rapid social and economic development, the gap between urban and rural areas has become more pronounced. Young and middle-aged workers work in municipalities, while older people and children live in rural areas. More attention should be paid to the mental health of older adults [31]. A previous study identified that older adults who live alone in rural areas have a higher risk of depressive symptoms [31]. “ Empty nesters” tendency might be to account for the increased prevalence of depressive symptoms among older adults in rural China [32]. The value of family was very important to the Chinese adults. Children of older adults in rural areas went out to work and were separated from their parents, reducing contact with the elderly, and increasing loneliness [33]. At the same time, the responsibility for caring for infants among older adults in rural areas has expanded. As a result, older adults in rural China need additional social assistance. The health of older adults was considerably affected by the social environment. Urban older adults had higher quality medical resources and financial assistance than rural older adults [34]. Older adults in urban areas can enjoy social activities and find spiritual comfort in their spare time. These findings denote that the government and society should pay more attention to the psychological problems of older adults in rural areas, allocate resources more effectively, expand public service provision, and reduce the gap between urban and rural areas [35].
In addition, the results demonstrated that residence, gender, marital status, drinking, physical function, ADL, and self-rated health were linked to depressive symptoms. Females were more likely to experience depressive symptoms. According to a Chinese study on the relationship between fat and depressive symptoms, $19.9\%$ of males and $33.2\%$ of females had depressive symptoms. Females were more likely to be depressed as a result of hormonal variations [36]. According to a longitudinal study of aging in Ireland, females had a greater fear of tumbling and activity restrictions. This fear could affect the psychology of older adults. Our findings agreed with previous studies. In our study, older adults who were married had lower rates of depressive symptoms. Previous research on older adults has revealed that marital status was a strong predictor of depressive symptoms, with unmarried older adults being more likely to be depressed [6, 37]. Single or split older adults had higher levels of depressive symptoms [37]. Older adults could be psychologically affected by these events. Alcohol use and self-rated health were shown to be strongly linked with depressive symptoms in older adults in a poll of community-dwelling older adults [38]. According to the survey results, self-rated health was highly correlated with depressive symptoms in older adults, which was consistent with earlier studies. A prospective study conducted in Spanish uncovered that moderate alcohol use protects older adults from developing depressive symptoms [39]. Furthermore, Dao A et al. indicated that elderly people who drank alcohol had 3.4 times fewer depressive symptoms than no-drinkers [30]. The second most important factor in determining depressive symptoms was self-rated health. Evidence was mounting that the older adults who self-rate their health as poor had higher levels of depressive symptoms [6]. In the 2015 findings, older adults aged 60-70 and non-smokers were more likely to be depressed. While not significant in the 2018 study results. The two-year sample size varied, as did the study's findings. Further investigation was needed in the future to reveal the relationship between smoking and age and depressive symptoms.
Ultimately, this study focused on the relationship between physical function, ADL, and depressive symptoms in older Chinese adults in urban and rural areas. Physical function declines with age and numerous daily activities are difficult to perform independently. Physical decline was a key challenge to self-care ability of older adults [40]. Limitations in daily activities and physical function cause older adults to lose their independence, leading to depressive symptoms and grief. These conditions could lead to psychosocial and financial difficulties. Substantial evidence suggested that ADL disability were at a higher risk of depressive symptoms [6], physical dysfunction associated with depressive symptoms in Chinese adults aged 55 and older [41]. Older adults with elevated levels of functional restriction might have depressive symptoms [40], ADL disability might promote the development of depressive symptoms [22]. This study demonstrates previous research by analyzing the association between physical function, ADL, and depressive symptoms. Depressed older adults were more likely to have physical dysfunction and ADL disability. Other studies have identified a strong association between ADL disability and physical dysfunction and risk of depressive symptoms in rural older adults. Therefore, the Chinese government and society must pay attention to the physical health of the elderly, especially those in rural areas. The government and society should give additional help to older adults with ADL disability and physical dysfunction. For individuals, sedentary lifestyle led to a decline in the capacity to conduct ADL [42]. Older adults would require frequent physical activity to enhance their functional capacity and mental health [43].
Several limitations of the present study should be mentioned. First, the cross-sectional study was unable to draw causal inferences. Second, the CES-D-10 might exhibit recall bias and could only be used to screen for depressive symptoms, not to diagnose depressive symptoms [22]. Third, the older adults included in this paper were screened from a database containing 23,000 respondents and may differ from the original data. Finally, self-aggregated data might overestimate the association between variables and depressive symptoms.
## 5. Conclusion
In summary, this study provides evidence of an association between physical function, ADL, and depressive symptoms in older Chinese adults. It showed that rural, female, 60–70 years old, primary school or below, married, non-smoking, non-drinking, physical dysfunction, ADL disability and self-rated poor health make-up a higher proportion of depressed older adults. Multivariate logistic regression models suggest that ADL disability and physical dysfunction were more likely to be associated with depressive symptoms in rural Chinese older adults. Older adults should be encouraged to participate in moderate physical and social activities to prevent physical dysfunction. The government and society should pay attention to the mental health of older adults in rural areas.
## 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
YY and YD: design of the study and interpretation of data. XL: data processing. WP: data processing, article design, and revision. YC: article modification. 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: Outcome of topiramate in migraine prophylaxis
authors:
- Etedal Ahmed A. Ibrahim
- Wadia Abdalla Balla Elhardallo
- Khabab Abbasher Hussien Mohamed Ahmed
- Mohammed E. Abdalla Omer
- Mohammed Mahmmoud Fadelallah Eljack
journal: Annals of Medicine and Surgery
year: 2023
pmcid: PMC10010782
doi: 10.1097/MS9.0000000000000093
license: CC BY 4.0
---
# Outcome of topiramate in migraine prophylaxis
## Abstract
### Background:
Topiramate is an antiepileptic medication originally and one of the first-line drugs for migraine prophylaxis. Herein, we aimed to assess the outcome of topiramate in migraine prophylaxis by evaluating the reduction in frequency and/or severity of attacks and addressing the most common adverse effects associated with it.
### Methods:
A descriptive, prospective hospital-based study was conducted at Ibrahim Malik Hospital, National Center of Neurological Disease and Sciences from October 2018 to May 2019. A comprehensive, structural, close-ended questionnaire was used to collect data on demographics, clinical, risk factors, treatment, side effects, and outcome.
### Results:
This study covered 32 study participants; the mean age was 33±10 years, with a female predominance of 27 ($84\%$). Nearly, half of them 15 ($47\%$) migraine triggered by weather changes, and 13 ($41\%$) had menstruation. About 17 ($53\%$) was suffering from headache more than 24 months and most of them 26 ($81\%$) used over-the-counter medications for acute pain headache. The mean frequency of attacks per month was reduced from 6.1 baselines to 3.2, in the severity means was 6.9 turns to 5. Reduction in frequency of attacks there was significant in both number and severity ($P \leq 0.001$) with no significant difference in 50 and 100 mg doses. Concerning adverse effects, 5 ($15.6\%$) did not complain of any, more than a third 12 ($38\%$) experienced weight loss, 7 ($22\%$) both abdominal/gastrointestinal symptoms and dizziness, 5 ($16\%$) mood changes, 4 ($13\%$) both paresthesia and decreased memory, 3 ($9\%$) both anorexia and sleepiness.
### Conclusion:
Topiramate is effective in reducing headache frequency and reasonably well tolerated in adult Sudanese patients with episodic migraine. This may provide good evidence to support its use in routine clinical management.
## Introduction
Headache is one of the most common diseases of the nervous system. It is estimated that nearly half of the adult population has had at least one headache in the past year. As well as social burdens such as pain, disability, poor quality of life and financial costs. For a few people, a headache itself is a symptom of pain and weakness. It may be a primary disease, namely migraine, tension headache, and cluster headache. Headaches can also be caused by a long list of other diseases or secondary to other diseases, the most common of which is drug abuse headache1.
Headache is one of the most common reasons for neurological presentation in sub-Saharan Africa being responsible for $31.9\%$ of visits to Outpatient Neurology Clinics. According to the WHO, 1.7–$4\%$ of the adult population of the world has headaches on 15 or more days every month suggesting that up to 170 million adults worldwide have headaches fitting chronic tension-type criteria, according to the International Classification of Headache Disorders. Headache disease brings a heavy burden to patients, and its severity ranges from low quality of life and economic costs to serious comorbidities such as depression. Migraine or migraine patients are three times more likely to develop depression than healthy people2.
Globally, the top three causes of consultation for headaches, in both primary and specialist care, are migraine, tension-type headaches, and a combination of these. Tension-type headache is the most prevalent disorder worldwide, but consultation frequencies overall for migraine and tension-type headache only partially reflect this difference. Migraine is associated with a higher probability (per person affected) than the tension-type headache of consultation for headache, and more so in specialists than in primary care. Primary care physicians, nearly universally, are consulted extra frequently for tension-kind headaches, which nearly without a doubt displays its extra prevalence. Specialists on the other hand see more migraine, probably a reflection of their relative severity (greater individual burden)3.
In recent years, there have been advances in the treatment of migraine. New therapies for acute and preventive migraine treatment are being prepared. Migraine can have a major impact on the quality of life and daily life. A moderate reduction in the frequency or severity of migraines can bring significant benefits to individuals. Therefore, a need to evaluate the existing treatments thus this study aims to evaluate the outcome of topiramate in migraine prophylaxis in Sudanese patients.
Our aims of this study were to evaluate the outcome of topiramate in migraine prophylaxis among Sudanese patients at Ibrahim Malik Neurological Center–Khartoum State 2018–19, to address factors that trigger a migraine headache, to assess the efficacy of topiramate in migraine prophylaxis and to determine the most common side effects related to topiramate.
## Study design
This is a descriptive, prospective hospital-based study conducted at Ibrahim Malik Hospital, National Center of Neurological Disease and Sciences within the period between October 2018 and May 2019. This hospital is located in Khartoum State. It is considered one of the important secondary referral hospitals in Sudan where education opportunities are provided for medical students, house officers, and registrars. This hospital provides medical care for neurological disease patients referred from all over the surrounding residential and rural areas.
This study was conducted in a line with STROCCS criteria 20214.
## Study population
Patients diagnosed with migraine either male or female over 18 years of age, attending the National Center for Neurological Science (NCNS) outpatient clinics, and receiving topiramate for migraine prophylaxis and who fulfill the inclusion criteria. Those who refused to participate and those who did not complete 3 months of topiramate by the end of the study duration were excluded from the study.
## Sample size
The total coverage method was applied as the study participants’ recruitment technique, about 32 participants were the number of patients who met the criteria and agreed to be involved in the study.
## Data collection
Participants were interviewed before and after using topiramate. Data was collected using a comprehensive, structural, close-ended data collection form composed of the following variables:*Sociodemographic data* including age, sex, occupation, and marital status. Trigger factors including weather changes, stress, food (cheese, chocolate, and caffeine), menstruation, missing meals, exercise, and computer use. Characteristics of the migraine including duration of attack per year, effect on daily activities such as sleep, relationships, work, eating, and analgesics use. Family history of migraine. Topiramate: dose, duration of use, outcome (assessed through number of attacks+pain severity), side effects.
## Data analysis and interpretation
Data were entered, cleaned, and analyzed using SPSS, version 25.0. Descriptive statistics in terms of frequency tables with percentages and graphs. Means and SDs are presented with relevant graphical representations for quantitative data. A P-value of 0.05 or less is considered statistically significant. Data represented after analysis in form of univariable tables, cross-tabulation (bivariable tables), figures, and narrative illustration.
## Ethical consideration
Written ethical clearance and approval for conducting this research were obtained from Sudan Medical Specialization Board Ethical Committee. Written permission was obtained from the administrative authority of the NCNS, Khartoum. Study data/information used for research purposes only. Privacy issues are intentionally considered. Participation is voluntary. Any participants have his/her right to stop at any stage. Written informed consent was obtained from all participants.
## Results
This study covered 32 study participants; three-quarters of them 24 ($75\%$) were below 40 years in age; the mean age was 33±10 years, with female predominance by 27 ($84\%$). Nearly a quarter of the participants were housewives 10 ($31\%$), and 6 ($19\%$) were an employee. While nearly half of them 15 ($47\%$) were married. Concerning the factors that trigger their headache, the study found that nearly half of them 15 ($47\%$) triggered by weather changes, 13 ($41\%$) menstruation, 12 ($38\%$) missing meals, 10 ($31\%$) stress, 6 ($19\%$) exercise, 3 ($9\%$) computer use. While food content factors such as 8 ($25\%$) cheese, 5 ($16\%$) caffeine, and 4 ($13\%$) chocolate.
Regarding the family history of migraine headaches among the participants; only 13 ($41\%$) of them have a positive family history. In more detailed history about the characteristics of headache; our study found more than half of the participants 17 ($53\%$) was suffering from headache more than 24 months, 7 ($22\%$) 12–24 months, 6 ($19\%$) 6–12 months, and the rest 2 ($6\%$) less than 6 months. Most of them 26 ($81\%$) used over-the-counter medications for acute pain headaches. While there are many daily activities inhibited by migraine pain, the most common was affected in almost three-quarters of them 23 ($72\%$) sleep, followed by 17 ($53\%$) relationships, 16 ($50\%$) both work and eating.
Regarding details of topiramate dosage, the study found that 25 ($78\%$) were on 50 mg daily, and 7 ($22\%$) were on 100 mg daily. While their period of topiramate usage was distributed as follows: 10 ($31\%$) 3 months, 9 ($28\%$) 6 months, 7 ($22\%$) 4 months, and 6 ($19\%$) 5 months.
The overall reduction rate in frequency was $48\%$, while the mean frequency of attacks per month was 6.1 reduced to 3.2, in the severity mean was 6.9 turned to 5. Reduction in attacks was as follows a reduction of at least $50\%$ in 17 ($53\%$), frequency reduction of 25–$49\%$ in 13 ($41\%$), and only in 2 ($6\%$) the reduction was less than $25\%$. Paired samples test used for the difference in frequency of attacks was P-value less than 0.000 and for severity before and after topiramate also ($P \leq 0.000$) indicating that topiramate is effective in migraine prophylaxis.
In this study, cross-tabulation was done to assess the possible association between the reduction in the frequency of migraine attacks and both topiramate dose and duration of use and the ($P \leq 0.05$).
Concerning adverse effects by study participants due to topiramate; more than a third, 12 ($38\%$) experienced weight loss, 7 ($22\%$) both abdominal/gastrointestinal symptoms and dizziness, 5 ($16\%$) mood changes, 4 ($13\%$) both paresthesia and decreased memory, 3 ($9\%$) both anorexia and sleepiness and only 1 ($3\%$) taste changes. In adverse effect frequency, our study found that 5 ($15.6\%$) did not complain of any adverse effect; while the biggest share 14 ($43.8\%$) have only one adverse effect. Cross-tabulation was done to assess the possible association between the adverse effects of frequency and topiramate dose and duration of use and the ($P \leq 0.05$), detailed in Tables 1 and 2.
## Discussion
This study aimed to assess the outcome of topiramate in migraine prophylaxis in adult Sudanese and covered 32 study participants; three-quarters of them 24 ($75\%$) were below 40 years of age and none above 55, with female predominance by 27 ($84\%$). Similarly, previously described by Vetvik and Macgregor that migraine prevalence initially increases with age, with peak prevalence between 30 and 39 years, followed by a gradual decline over subsequent years in both sexes. After puberty, the sex ratio increases, and women are two to three times more likely to have migraines than men5. And by also similar to Martins et al. 6 migraineurs were mainly women ($81.6\%$). Just $18.4\%$ were men. Nearly a third of the participants were housewives 10 ($31\%$) and 6 ($19\%$) were an employee. And almost half of them 15 ($47\%$) were married. Corresponding to Ali’s7 study finding in a total of 40 Sudanese patients with migraine, $75\%$ as females and $25\%$ as males, the majority of them 18 ($45\%$) were found in the age group 30–39 years and ($62\%$) were married.
Concerning the factors that trigger their headache, the study found that nearly half of them 15 ($47\%$) triggered by weather changes, 13 ($41\%$) menstruation, 12 ($38\%$) missing meals, 10 ($31\%$) stress, 6 ($19\%$) exercise, 3 ($9\%$) computer use. While food content factors such as 8 ($25\%$) cheese, 5 ($16\%$) caffeine, and 4 ($13\%$) chocolate. Similar results were seen in the study by Holzhammer et al. 8: The most common trigger factors experienced by the patients were weather ($82.5\%$), stress ($66.7\%$), menstruation ($51.4\%$), and relaxation after stress ($50\%$) As was mentioned by Fukui et al. the most common triggers for patients are stress/tension, premature eating, fatigue and lack of sleep in addition to weather, smell, smoke, and light9. And while variable results were in a study of triggers factors of migraine which list: at least one dietary trigger, fasting was the most frequent one, followed by alcohol and chocolate. Hormonal factors accounted for $53\%$, of which the premenstrual period was the most common trigger, $13\%$ of physical activity caused migraine, $2.5\%$ of sexual activity, and $64\%$ of emotional stress as triggers. Overall, $81\%$ cited some sleep problems as triggers. Regarding environmental factors, smells were reported by $36.5\%$10. Regarding the family history of migraine headache among the participants; 13 ($41\%$) of them has a positive family history. The role of heredity in migraine has been a major focus of clinical and research interest for years. Because minimal data are available and contradictory findings as well as methodological inadequacies characterize most of the genetic studies, the genetic assumption is tentative at best11.
Migraine without aura seems to be caused by a combination of genetic and environmental factors, while migraine without aura may be primarily or entirely genetic12. In more detailed history about the characteristics of headache; our study found more than half of the participants 17 ($53\%$) was suffer from headache more than 24 months, 7 ($22\%$) 12–24 months, 6 ($19\%$) 6–12 months, and the rest 2 ($6\%$) less than 6 months. most of them 26 ($81\%$) used over-the-counter medications for acute pain headaches. While there are many daily activities inhibited by migraine pain, the most commonly affected in almost three-quarters of them is sleep 23 ($72\%$), followed by relationships 17 ($53\%$), and both work and eating 16 ($50\%$). Similarly, a survey has shown that among the millions of Canadians suffering from headaches, adverse effects on relationships with family, friends, and colleagues are common. Headaches also result in significant limitations in activity and lead to avoidance behaviors13. And worldwide studies have shown that more than $50\%$ of patients report a disability severe enough to lead to decreased productivity in some of the most important aspects of life–work, school, and home14.
Regarding details of topiramate dosage, the study found that 25 ($78\%$) were on 50 mg daily, and 7 ($22\%$) were on 100 mg daily. While their period of topiramate usage was distributed as follows: 10 ($31\%$) 3 months, 9 ($28\%$) 6 months, 7 ($22\%$) 4 months, and 6 ($19\%$) 5 months. According to pharmacological management of migraine guidelines issued in February 2018, topiramate (50–100 mg daily) is recommended as a prophylactic treatment for patients with episodic or chronic migraine and should be used for at least 3 months at the maximum tolerated dose before deciding if it is effective or not15.
Prophylaxis aims to reduce the frequency, severity, and duration of migraine attacks and to prevent the development of medication overuse16. In our study, the overall reduction rate in frequency was ($48\%$), while the mean frequency of attacks was 6.1 at baseline to 3.2 per month, in the severity mean was 6.9 turn to 5. The result is very close to what Capuano et al. were given at the dose of 100 mg/d, in the prophylactic treatment of recorded a significant reduction in the frequency of migraine crises (from 5.26 at baseline to 2.60) in 4 weeks duration17. Reduction in attacks was as follow the reduction of at least $50\%$ was in 17 ($53\%$), frequency reduction 25–$49\%$ 13 ($41\%$), and only in 2 ($6\%$) reduction was less than $25\%$.
As migraine can have a considerable impact on quality of life and daily function. Modest improvements in the frequency or severity of migraine headaches may provide considerable benefits. Within trials, a reduction in migraine headache severity and/or frequency of 30–$50\%$ is regarded as a successful outcome15. Paired samples test used for the difference in frequency of attacks was ($P \leq 0.000$) and for severity before and after topiramate was also ($P \leq 0.000$) indicating that topiramate is effective in migraine prophylaxis. similar to Von Seggern et al. 18, a study in which the frequency of headaches at the start of topiramate (baseline) and all subsequent visits up to 24 weeks, declined significantly from baseline to end of treatment (10.684–7.477, respectively; $$P \leq 0.0004$$).
In adverse effect frequency, our study found that $15.6\%$ did not complain of any adverse effect, while the biggest shares ($43.8\%$) have only one adverse effect. While concerning adverse effects by study participants due to topiramate; more than a third ($38\%$) experienced weight loss, $22\%$ both abdominal/gastrointestinal symptoms and dizziness, $16\%$ mood changes, 4 ($13\%$) both paresthesia and decreased memory, 3 ($9\%$) both anorexia and sleepiness and only 1 ($3\%$) taste changes. Linde and colleagues conducted a systematic review in trials of topiramate against a placebo, seven adverse events (AEs) were reported by at least three studies. These were usually mild and of a nonserious nature. Except for dysgeusia and weight loss, there were no significant differences in the total AE or the incidence of seven specific AEs between placebo and topiramate 50 mg. The frequency of AEs in general and all specific AEs except nausea is significantly higher on Topiramate 10019. Moreover, another study revealed that topiramate 100 mg daily was associated with a higher rate of AEs than placebo, although these were mild to moderate.
Side effects include nausea, paresthesia, anorexia, and weight loss. Cognitive side effects are common, vary in severity, are usually dose-dependent, and usually determine drug tolerability. Depression is also a common side effect15. In a study done by James Adelman et al. in which two groups where included (one group was given placebo and the other one was given topiramate the following were detected: Fatigue ($5\%$), nausea ($2\%$), and difficulty concentrating ($2\%$). Compared with the placebo, the average body weight of patients treated with topiramate was significantly lower20.
The study had some limitations; the relatively limited number of study participants (32 study participants from one study area only) may affect negatively the probability of finding more significant outcomes among patients receiving topiramate for migraine in other Sudanese hospitals. Another limitation is follow-up. Some outcomes – such as a long-term outcome or the presence of long-term effects and/or adverse effects – may need to be followed over time for a longer period. So, a long-term prospective cohort follow-up design may be useful for a more detailed description of the confirmatory practices.
## Conclusions
Migraine affects more young females. Migraine pain interferes with many daily activities, including sleep, relationships, work, and eating. Topiramate reduces the frequency and severity of migraine attacks by nearly half, indicating that it is effective in migraine prevention in terms of both frequency and severity.
In terms of side effects, the most common were weight loss, followed by weight loss, abdominal/gastrointestinal symptoms, dizziness, and mood changes, with the least percentage reporting none.
## Recommendations
Increase the awareness of the primary care doctors and general practitioners by the International Health *Society criteria* and the diagnostic criteria of the International Health Society for a migraine headache to avoid misdiagnosis. Physicians and neurologists should take into their account the established efficacy and safety record topiramate has in the prophylaxis of migraine. Further cohort follow-up – research is highly recommended to assess to long-term outcome of topiramate in migraine, and comparing with other prophylactic medications.
## Sources of funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
## Authors’ contribution
All authors participated in planning the study, data collection, results, and discussion sections.
## Conflicts of interest disclosure
The authors declare that they have no financial conflict of interest with regard to the content of this report.
## Research registration unique identifying number (UIN)
Not applicable.
## Guarantor
Mohammed Mahmmoud Fadelallah Eljack.
## Provenance and peer review
Not commissioned, externally peer-reviewed.
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World Health Organisation (WHO). Atlas of headache disorders and resources in the world 2011. WHO Library Cataloguing-in-Publication Data. 2011. pp. 10-12.
4. Mathew G, Agha R. **STROCSS 2021: Strengthening the Reporting of cohort, cross-sectional and case-control studies in Surgery**. *Int J Surg* (2021.0) **96** 106165. PMID: 34774726
5. Vetvik KG, Macgregor EA. **Sex differences in the epidemiology, clinical features, and pathophysiology of migraine**. *Lancet Neurol* (2016.0) **16** 76-87. PMID: 27836433
6. Martins KM, Bordini CA, Bigal ME. **Migraine in the elderly: a comparison with migraine in young adults**. *Headache* (2006.0) **46** 312-316. PMID: 16492241
7. Ali DT
8. Holzhammer J, Zeitlhofer J, Wessely P. **Trigger factors of migraine and tension-type headache: experience and knowledge of the patients**. *J Headache Pain* (2006.0) **7** 188-195. PMID: 16897622
9. Spierings ELH, Ranke AH, Honkoop PC. **Precipitating and aggravating factors of migraine versus tension-type**. *Headache* (2001.0) **41** 554-558. PMID: 11437890
10. Fukui PT, Rachel T, Gonçalves T. **Trigger factors in migraine patients**. *Arq Neuropsiquiatr* (2008.0) **66** 494-499. PMID: 18813707
11. Adams HE, Feuerstein M, Fowler JL. **The migraine headache: a review of parameters, theories, and interventions**. *Psychol Bull* (1980.0) **87** 217-237. PMID: 7375599
12. Russell Michael Bjorn OJ. **Increased familial risk and evidence of genetic factor in migraine**. *BMJ* (1995.0) **311** 541. PMID: 7663209
13. Edmeads J, Findlay H, Tugwell P. **Impact of migraine and tension-type headache on life-style, consulting behaviour, and medication use: a Canadian population survey**. *Can J Neurol Sci* (1993.0) **20** 131-7. PMID: 8334575
14. Brandes JL. **The migraine cycle: patient burden of migraine during and between migraine attacks**. *Headache* (2008.0) **48** 430-41. PMID: 18179565
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Scottish Intercollegiate Guidelines Network. Pharmacological management of migraine. SIGN155. 2018;155 (February).
16. Diener H. **Topiramate in migraine prevention topiramate in migraine prevention**. *Eur Neurol Dis* (2006.0) **26** 375-87
17. Capuano DMA, Evangelista CVM, Tonali DFP. **Topiramate in migraine prophylaxis: a randomised double-blind versus placebo study**. *Neurol Sci* (2004.0) **25** 245-50. PMID: 15624081
18. Von Seggern RL, Mannix LK, Adelman JU. **Efficacy of topiramate in migraine prophylaxis: a retrospective chart analysis**. *Headache* (2002.0) **42** 804-9. PMID: 12390645
19. Linde M, Wm M, Ep C. **Topiramate for the prophylaxis of episodic migraine in adults (review)**. *Cochrane Database Syst Rev* (2016.0) 11-12
20. Shi Y, Ascher S, Mao L. **Analysis of safety and tolerability data obtained from over**. *Pain Med* (2008.0) **9** 175-185. PMID: 18298700
|
---
title: 'Clinicodemographic profile of chronic liver disease patients at a tertiary
care hospital: a retrospective analysis'
authors:
- Anish K. Shrestha
- Anisha Shrestha
- Sangam Shah
- Aashna Bhandari
journal: Annals of Medicine and Surgery
year: 2023
pmcid: PMC10010787
doi: 10.1097/MS9.0000000000000248
license: CC BY 4.0
---
# Clinicodemographic profile of chronic liver disease patients at a tertiary care hospital: a retrospective analysis
## Body
The global burden of chronic liver disease (CLD) is on the rise. Based on data from the Global Burden of Disease study, the age-standardized incidence rate of CLD and cirrhosis was 20.7 per 100 000 in 2015, a $13\%$ increase from 20001. Cirrhosis is among the top 20 causes of disability-adjusted life years2. As such, the health and economic burden of CLD with and without cirrhosis is significant globally. Mishra et al. ,3 had shown alcoholic liver disease (ALD) to be the most common cause of CLD in a tertiary care center in Nepal. Our study is aimed at delineating the clinicodemographic profile and assessing the common causes of admission among CLD patients admitted to the inpatient ward of the Department of Gastroenterology at Tribhuvan University Teaching Hospital (TUTH).
## Abstract
### Introduction:
With the global burden of chronic liver disease (CLD) on the rise, especially due to the rise in obesity and metabolic syndrome, a third-world country like Nepal faces a different problem. With alcohol intake being rooted in Nepalese culture, alcoholic liver disease (ALD) is the most common cause of CLD in our society.
### Methods:
This is a retrospective observational study conducted in the inpatient ward of the Department of Gastroenterology at the University in Nepal. Ethical approval was taken from the Institutional Review Committee, and a structured questionnaire format was used to record the data retrospectively using admission log books and admission sheets. Demographic data regarding age, sex, and address were collected, while the form of decompensation during presentation was used as a source of clinical data. For statistical analysis, see SPSS 21 (IBM Corp., Released 2012. IBM SPSS Statistics for Windows, Version 21.0; IBM Corp.) was used.
### Results:
A male-to-female ratio of 2:1 was found, with ALD the most common cause of CLD in admitted patients. Similarly, the majority of patients were admitted due to ascites, which was compounded by spontaneous bacterial peritonitis. $93.60\%$ of patients admitted with CLD had a deranged prothrombin time, while only about a third of patients had elevated aspartate aminotransferase and/or alanine aminotransferase.
### Conclusion:
The large burden of ALD highlights the importance of awareness programs at the community level, which have been neglected till date.
## Study design and setting
This study was conducted retrospectively at TUTH, located in Maharajgunj, Kathmandu. This center was chosen for study because of its high patient flow. Ethical approval for conducting the study was taken from the Institutional Review Board (IRB) of TUTH, IOM [approval number: 164 [6-11] E2 $\frac{077}{078}$].
## Inclusion criteria
Patients with a diagnosis of CLD with or without cirrhosis were admitted to the inpatient ward in the Department of Gastroenterology at TUTH.
## Exclusion criteria
Patients with acute liver disease.
## Sampling
Nonprobability sampling.
## Study tools and techniques
A structured proforma was used to record the data retrospectively for the admitted patients.
## Study variables
The variables were categorized under the headings of demographic and clinical factors. Age, sex, and address of the patient were included under demographic factors. Similarly, significant alcohol consumption, as defined by greater than 80 g/day for more than 5 years4 and the form of decompensation during their presentation were recorded under clinical factors.
## Statistical analysis
Data were compiled, edited, and checked daily to maintain consistency. The data was collected in Microsoft Excel (Ver. 2013). For statistical analysis, SPSS 21 (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0; IBM Corp.) was used. A descriptive analysis was done to identify the clinicodemographic characteristics of patients.
## Results
A total of 469 patients were admitted from January 2018 to December 2019 to the inpatient ward of the Department of Gastroenterology, TUTH, consisting of 303 male ($64.60\%$) and 116 female ($35.40\%$) patients (Table 1 and Figs 1, 2).
The majority of patients admitted were from the age group above 60 years ($37.74\%$) and Province 3 ($51.81\%$) of Nepal (Tables 2, 3).
## Discussion
A total of 469 admitted CLD patients were analyzed, with a male-to-female ratio of 2: 1. Patients aged over 60 years ($37.74\%$) were the ones most commonly admitted for CLD. Since this center lies in Province 3 of Nepal, most patients admitted here were from Province 3 ($51.81\%$), although, being a tertiary care center, a significant number of cases were from other provinces ($48.19\%$) as well.
The absolute number of CLD cases (inclusive of any stage of disease severity) is estimated at 1.5 billion worldwide1 with the most common cause being NAFLD ($59\%$), followed by hepatitis B virus ($29\%$), hepatitis C virus ($9\%$), and ALD ($2\%$)2,5. However, our analysis shows that a major proportion of patients admitted to our center with CLD were due to ALD ($82.30\%$), with other causes contributing to only a minority of cases. The major reason for this is our cultural acceptance of alcohol.
Typical presenting clinical features include jaundice, ascites, hepatic encephalopathy, hepatorenal syndrome, or variceal hemorrhage, which are also the various forms of decompensation in CLD6. Among the CLD patients admitted, $65.45\%$ had ascites, $12.79\%$ had upper gastrointestinal bleeding, $19.18\%$ had hepatic encephalopathy, $10.87\%$ had hepatorenal syndrome or acute kidney injury, and $73.56\%$ had jaundice. Similarly, many patients admitted due to ascites as a cause of decompensation had spontaneous bacterial peritonitis ($45.14\%$). This higher percentage of SPB in ascitic patients is due to the rigorous criteria for their admission to the inpatient ward.
In patients with ALD, the aspartate aminotransferase (AST): alanine aminotransferase (ALT) ratio is greater than 1 in $92\%$ of patients and greater than 2 in $70\%$7. In our study, however, $36.79\%$ of the patients with ALD had an AST/ALT ratio greater than 2, while $53.11\%$ of patients had an AST/ALT ratio greater than 1.5. This could be because of the shorter t$\frac{1}{2}$ (half-life) of AST (18 h) as compared to ALT (36 h)8. Since about $48.19\%$ of patients come to our center from a different province, it is likely that they would have already been treated for a number of days in a different center before their referral here. Therefore, while the AST and ALT levels may be elevated, their ratio may not be greater than or equal to 2:1 over time. Further, although elevated levels of AST and ALT often signify ongoing hepatic inflammation, many patients with CLD may have normal values due to burnout9. In such cases, prothrombin time serves as a marker of hepatic function, which still remains elevated.
Anemia is a common finding in CLD patients, with a prevalence ranging from 50 to $87\%$. The causes of anemia are varied in CLD, ranging from upper gastrointestinal bleeding to anemia of chronic disease, hypersplenism, and malnutrition10. About $77.4\%$ of patients in our study had anemia.
This study shows that ALD is the most common cause of CLD in our community reflecting upon the sociocultural acceptance of alcohol in our community. While infectious causes may contribute to a major proportion of cases of acute liver disease, they form only a minority of cases of CLD. Effective vaccination programs and therapeutic options might have contributed to this outcome, though the major contributing factor still seems to be the rampant use of alcohol in our community. The major limitation of the study is the inclusion of only the inpatient population within a single center. Since this a the retrospective study, all the data were not available.
## Conclusion
This study shows the demographic and clinical profile of patients with CLD admitted to the inpatient ward of the Department of Gastroenterology, TUTH. The huge burden of ALD as a contribution to CLD highlights the importance of harm reduction programs that need to be implemented at a community level.
## Ethical approval
Ethical approval was obtained from the research ethics committee of the Institutional Review Committee (IRC) of Institute of Medicine (IOM) [Ref: 164 [6-11] E2 $\frac{077}{078}$].
## Consent
Written informed consent was obtained from the patient for the publication of this case report and accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal on request.
## Sources of funding
No funding was received for the study.
## Conflicts of interest disclosure
Authors have no conflict of interest to declare.
## Author contribution
A.K.S., A.S., and S.S. wrote the original manuscript, reviewed, and edited the original manuscript. A.K.S., A.S., S.S., and A.B. reviewed and edited the original manuscript.
## Research registration unique identifying number (UIN)
1. Name of the registry: Research Registry.
2. Unique identifying number or registration ID: researchregistry8369.
3. Hyperlink to your specific registration (must be publicly accessible and will be checked): Register Now – Research Registry
## Guarantor
Dr Anish Kumar Shrestha.
## Provenance and peer review
Not commissioned, externally peer-reviewed.
## References
1. Moon AM, Singal AG, Tapper EB. **Contemporary epidemiology of chronic liver disease and cirrhosis**. *Clin Gastroenterol Hepatol* (2020) **18** 2650-66. PMID: 31401364
2. Asrani SK, Devarbhavi H, Eaton J. **Burden of liver diseases in the world**. *J Hepatol* (2019) **70** 151-71. PMID: 30266282
3. Mishra A, Shrestha P, Bista N. **Pattern of liver diseases**. *J Nepal Health Res Counc* (2009) **7** 14-18
4. Davidson SS. **Davidson’s medicine**. *Davidson’s Princ Pract Med* (2010) **21** 498-9
5. Sepanlou SG, Safiri S, Bisignano C. **The global, regional, and national burden of cirrhosis by cause in 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017**. *Lancet* (2020) **5** 245-66
6. Moreau R, Jalan R, Gines P. **Acute-on-chronic liver failure is a distinct syndrome that develops in patients with acute decompensation of cirrhosis**. *Gastroenterology* (2013) **144** 1426-37. PMID: 23474284
7. Cohen JA, Kaplan MM. **The SGOT/SGPT ratio – an indicator of alcoholic liver disease**. *Dig Dis Sci* (1979) **24** 835-38. PMID: 520102
8. Botros M, Sikaris KA. **The De Ritis ratio: the test of time**. *Clin Biochem Rev* (2013) **34** 117. PMID: 24353357
9. Lominadze Z, Kallwitz ER. **Misconception: you can’t have liver disease with normal liver chemistries**. *Clin Liver Dis* (2018) **12** 96-99
10. Scheiner B, Semmler G, Maurer F. **Prevalence of and risk factors for anaemia in patients with advanced chronic liver disease**. *Liver Int* (2020) **40** 194-204. PMID: 31444993
|
---
title: 'Risk factors for fall among the elderly with diabetes mellitus type 2 in Jeddah,
Saudi Arabia, 2022: a cross-sectional study'
authors:
- Rami S. Alasmari
- Hattan A. Hassani
- Nawwaf A. Almalky
- Abdullah F. Bokhari
- Abdullah Al Zahrani
- Alwalied A. Hafez
journal: Annals of Medicine and Surgery
year: 2023
pmcid: PMC10010846
doi: 10.1097/MS9.0000000000000269
license: CC BY 4.0
---
# Risk factors for fall among the elderly with diabetes mellitus type 2 in Jeddah, Saudi Arabia, 2022: a cross-sectional study
## Abstract
### Background:
Diabetes mellitus type 2 is a major chronic condition that is considered common among elderly people, with multiple potential complications that could contribute to falls. However, this concept is not well understood; thus, the aim of this study is to estimate the prevalence of falls among diabetes patients.
### Methods:
In this observational cross-sectional study, 309 diabetic patients aged 60 years or more who visited the primary healthcare centers of the Ministry of National Guard – Health Affairs in Jeddah were chosen via convenience sampling method. To collect the data, a structured Fall Risk Assessment questionnaire and Fall Efficacy Score scale were used.
### Results:
The mean age of the participants was estimated to be 68.5 (SD: 7.4) years. Among the participants, $48.2\%$ have fallen before, and $63.1\%$ of them suffered falls in the past 12 months. The results showed that gait problems were independently associated with a higher likelihood of falls among elderly patients [odds ratio (OR)=1.98; $95\%$ CI: 1.08–3.62; $$P \leq 0.026$$]. Based on the linear regression analysis, we identified the following risk factors for lower falls efficacy: having gait problems (β=12.50; $95\%$ CI: 7.38–17.6; $P \leq 0.001$), balance difficulties (β=6.58; $95\%$ CI: 1.35–11.8; $$P \leq 0.014$$), and neurological/cognitive impairments (β=9.62; $95\%$ CI: 3.89–15.4; $$P \leq 0.001$$), as well as having poor sleep quality (β=8.11, $95\%$ CI: 3.32–12.9; $P \leq 0.001$).
### Conclusion:
This paper suggests that diabetes mellitus is an independent fall risk factor among the elderly. Therefore, identifying such patients as being at higher risk and prompt referral to a specialist falls clinic is recommended.
## Introduction
According to the WHO, a fall is defined as ‘an event which results in a person coming to rest inadvertently on the ground, floor or other lower level’1. In the United States of America, falls are a major cause of morbidity and mortality among the elderly2. Pursuing this further falls among the elderly can result in various adverse consequences, including higher incidence of hospitalization, health service use, loss of independence, decreased daily functioning, and greater fear of falling3,4. For someone to be described as elderly, they should have an age of 60 and above, according to the United Nations5. Unfortunately, Saudi Arabia does not have sufficient literature surrounding the topic of falls among the elderly. One study conducted in Riyadh calculated the annual prevalence rate of falling in the elderly group to be $49.9\%$, and linked risk factors were polypharmacy, walking aids, cerebrovascular accidents, retinopathy, and low back pain6. In addition, Alshammari et al.7 reported that age, female gender, impaired health, and environmental hazards were significantly associated with falls in $57.7\%$ of the elderly participants of Riyadh. Moreover, a study in Unaizah city, Saudi Arabia, related to falling risk factors in the elderly, reported that the prevalence of obesity (with BMI>30) was $30.9\%$; furthermore, diabetes was estimated to be found to $18.2\%$, polypharmacy was found in $58\%$, and visual impairment, gait problems, memory loss, incontinence, and chronic pain were reported in $15.6\%$ of elderly with falls8. Globally, ∼462 million individuals of all ages are diagnosed with diabetes mellitus type 2 (DMT2), corresponding to $6.28\%$ of the world population. Moreover, in 2017 alone, there were more than 1 million deaths as a result of this condition ranking it as the ninth leading cause of mortality9. Saudi Arabia ranks the second highest in the Middle East and is seventh in the world for the rate of diabetes. Pursuing this further, it is estimated that around 7 million of the population are diabetic, and almost around 3 million have prediabetes10. It is well established that diabetes mellitus is associated with high mortality and multiple long-standing health complications such as microvascular complication including retinopathy and nephropathy, and macrovascular complications, such as peripheral vascular diseases resulting in injuries, nonhealing ulcers and gait disturbances ultimately resulting in lower limb amputation11. Nevertheless, the relationship between diabetes mellitus and falls in the elderly has not been studied in Saudi Arabia. Previous studies have demonstrated defined risk factors for falls (which included poor vision, balance difficulties, poor gait, motor weakness, sedatives, orthostatic hypotension, and others) among frail elderly3,12. Various potential complications from diabetes mellitus, such as diabetic retinopathy and peripheral neuropathy exhibiting as diabetic foot ulcer or inadequate glycemic control resulting in hypoglycemia, could be the cause of falls5,13,14. This is supported by many studies pointing out that diabetes mellitus might associate with falls among the elderly5,13–15. However, there is a knowledge gap regarding the risk factors for falls, particularly among the elderly with diabetes, since there is no such study that has been carried out in Saudi Arabia. Therefore, the aim of the present study is to identify the risk factors and to determine the relationship between falls and diabetes mellitus among the elderly.
## Study participants, area, and period
A total of 309 diabetic patients aged 60 or more visited the primary healthcare centers of the Ministry of National Guard – Health Affairs in Jeddah, Saudi Arabia from May to June 2022. The inclusion criteria included 60 years of age or older Saudi patients with DMT@, and these participants were interviewed by five medical students for ∼10–30 min, and they answered questionnaires about the frequency of falls, the risk of falls, the activities of daily living, and cognition. In accordance with the Declaration of Helsinki, this study was registered in Research Registry with a unique identifying number which is researchregistry8607, and the study was approved by Institutional Review Board at King Abdullah International Medical Research Center #NRJ22J/$\frac{097}{04.}$
## Study design
This study employed a cross-sectional design through a population-based survey which was performed among elderly patients with diagnosed DMT2 in agreement with the STROCSS (Strengthening The Reporting Of Cohort Studies in Surgery) 2021 guidelines16.
## Instruments
Based on the proposed target population, who are type 2 diabetic patients at National Guard Hospital, we recruited an approximate number of 300 participants to be $95\%$ confident with a $5\%$ margin of error which is calculated using Raosoft.
The data were collected by a self-administered questionnaire that targets elderly Saudi patients with DMT2 at King Abdulaziz Medical City (KAMC), Jeddah, Saudi Arabia. The questionnaire was based on a validated questionnaire survey from previous studies15,17. Few modifications were made. First, the instruments used to collect data were a form addressing sociodemographic variables. Second, a structured Fall Risk questionnaire included a history of falls in the previous 1 year, medications, medical history, and environmental hazards. Lastly, the falls efficacy score (FES) scale in which its responses were recorded on a five-point Likert scale, ranging from strongly confident=1 to not confident at all=5. A raw FES was computed by summing up the values of the individual items ($$n = 10$$). Therefore, the raw score ranged between 10 and 50. A percent FES was calculated to facilitate the interpretation of the results (range 20–100). Based on the responses of patients, higher FESs indicated lower fall self-efficacy.
## Statistical analysis
BMI was computed based on patients’ weights and heights, and the BMI values were then categorized into underweight (<18.5 kg/m2), healthy weight (18.5 to <25 kg/m2), overweight (25 to <30 kg/m2) and obese (≥30 kg/m2). The prevalence of falls was assessed using a one-sample proportions test, and the outcome was expressed as a proportion and $95\%$ CI. Categorical data were presented as frequencies and percentages. Numerical data were expressed as mean±standard deviation (SD). To assess the association between the history of falls and sociodemographic and clinical variables, we used Fisher’s exact test or Pearson’s χ 2 test for categorical data and Wilcoxon rank sum test for numerical data. The significantly associated variables from the univariate analysis were subsequently used as independent variables in a multivariate logistic regression model to assess the risk factors for falls. The outcomes of the regression model were expressed as odds ratio (OR) and the respective $95\%$ CI. To identify the risk factors for higher FES (lower self-efficacy), we carried out a multiple linear regression analysis using the backward stepwise method (FES was the dependent variable). This was performed by including all the potential independent variables at once (sociodemographic and clinical variables) and eliminating the least contributive variables until retaining the predictors that best fit the model. The results of the regression model were expressed as β-coefficient and $95\%$ CIs. Statistical significance was considered at P less than 0.05. The analysis was performed using RStudio (version 4.1.1).
## Sociodemographic characteristics and the characteristics of fall
In the current study, data of 309 patients were analyzed. More than half of them were males ($53.1\%$) and nonsmokers ($68.6\%$). The majority of them were married ($86.4\%$) and have at least one offspring ($97.7\%$). Being overweight and obesity were prevalent among $35.3\%$ and $47.9\%$ of patients, respectively. A total of 149 patients ($48.2\%$; $95\%$ CI: 42.5–$53.9\%$) indicated that they had never fallen before (Table 1). Focusing on patients who have fallen before, $36.9\%$ of them have not fallen in the last year, $36.9\%$ of patients have fallen once, $12.1\%$ of them have fallen twice, and $13.4\%$ have fallen three times, or more (Fig. 1). The mean±SD age of patients was 68.5±7.4 years, and patients who have a history of fall were significantly older than those who have never fallen (70.1±7.7 vs. 66.8±6.8 years; $P \leq 0.001$). Other sociodemographic variables were not associated with the history of falls (Table 1).
## The association between clinical factors of patients and fall
Significantly higher proportions of patients who have ever fallen had gait problems ($64.8\%$ vs. $28.7\%$ among those who had no gait problems; $P \leq 0.0001$), difficulties in the balance ($69.8\%$ vs. $35.4\%$ among those who had no difficulties in the balance; $P \leq 0.0001$), visual problems ($52.6\%$ vs. $31.1\%$ among those who had no visual problems; $$P \leq 0.003$$), musculoskeletal disorders ($58.9\%$ vs. $31.1\%$ among those who had no musculoskeletal disorders; $P \leq 0.0001$), and cardiovascular disease ($51.0\%$ vs. $35.1\%$ among those who had no cardiovascular disease; $$P \leq 0.034$$). A positive fall history was also associated with self-perceptions of an inappropriate surrounding environment ($80.0\%$ as inappropriate vs. $46.4\%$ as appropriate; $$P \leq 0.011$$) and a poor quality of sleep ($66.7\%$ vs. $34.3\%$% with good sleep quality; $P \leq 0.0001$). Furthermore, a history of falls was associated with receiving less than 3 drugs ($40.2\%$) and at least 3 drugs ($54.0\%$) compared to patients who did not receive drugs ($0.0\%$; $$P \leq 0.002$$; Table 2). Upon incorporating the significantly associated variables into a regression model, results showed that only gait problems were independently associated with a higher likelihood of fall among the elderly patients (OR=1.98; $95\%$ CI: 1.08–3.62; $$P \leq 0.026$$; Table 3).
## Falls efficacy scale
The responses to the FES scale showed excellent internal consistency (Cronbach’s α=0.957); therefore, the results were reliable. The highest mean scores were related to getting on and off the toilet without falling (2.29±1.52) and preparing meals not requiring carrying heavy or hot objects (2.21±1.53), while the lowest scores were reported for personal grooming (1.63±1.16) and getting dressed and undressed (1.79±1.30; Table 4). Based on the linear regression analysis, we identified the following risk factors for lower falls efficacy: having gait problems (β=12.50; $95\%$ CI: 7.38–17.6; $P \leq 0.0001$), balance difficulties (β=6.58; $95\%$ CI: 1.35–11.8; $$P \leq 0.014$$) and neurological/cognitive impairments (β=9.62, $95\%$ CI: 3.89–15.4; $$P \leq 0.001$$), as well as having poor sleep quality (β=8.11; $95\%$ CI: 3.32–12.9; $P \leq 0.0001$; Table 5).
## Discussion
The aim of the present study was designed to determine the risk factors of falls in elderly diagnosed with diabetes mellitus and its association. The significant finding of this study was found to be that a total of 149 patients representing ∼$48.2\%$ have experienced falling before. Among this population, there is nearly $63.1\%$ who have fallen in the past 1 year. As previously indicated, fall incidents among the elderly are a major health problem. About 36 million falls are reported among the elderly each year, resulting in more than 32 000 deaths18. Consistent with findings by Montero-Odasso et al.19, we found that the elderly with gait problems are at higher risk of developing mobility decline and falls. A continuous gait observation that provides fall risk assessment would permit timely interventions aiming at preventing falls20. Step training, therefore, is a key component of fall prevention interventions21. Difficulties in balance was one of the findings suggesting risk factor for falls. Multiple studies done in the United States, Brazil, and Canada showed an agreement with this study results and pointed out that decreased fear of falling, balance and strength promote the elderly quality of life and independence22–24. In the current paper, the purpose of using FES scale is to determine the extent to which fear of falling exhibits an independent effect on the functional decline among the elderly. The responses to the FES scale showed excellent internal consistency (Cronbach’s α=0.957); therefore, the results were reliable. Based on the linear regression analysis, having gait problems, balance difficulties, neurological/cognitive impairments, as well as having poor sleep quality were determined to be significant risk factors for lowering fall efficacy, thus increasing fear of falling. If the fear of falling confirms to be an independent factor in functional decline, and if the person at risk of developing a fear of falling can be recognized, then the fear of falling efficacy should be a specific target of clinical intervention17. In fact, Bandura et al.25 and Soh et al.26 state that self-efficacy has been shown to be amenable to behavioral modification. Moreover, physical and occupational therapy could be targeted at promoting confidence in mobility and performance of daily activities27,28. Our study reveals that elderly diabetic patients are more prominent to experience falling (Fig. 1). Moreover, the prevalence of falls increased as the patients complain of gait problems (Table 3). Unfortunately, there are a few studies that have been carried out to discuss the relationship between elderly diabetic patients and falls. However, a prospective cohort study held in the United States showed agreement with our result by indicating that the fall incidence rate for elderly residents of a long-term care facility with and without diabetes mellitus was $78\%$ and $30\%$, respectively, considering gait and balance as an independent predictor of falls29. Another study done in the United Kingdom displays that $39\%$ of elderly diabetic patients report falling each year30. Roman de Mettelinge et al.31 and Schwartz et al.32 demonstrate that diabetes mellitus might be an independent risk factor for falls in elderly diabetic patients. The prevalence of falls in elderly diabetic patients increased as the frequency of hypoglycemia increased, according to a study held in Japan33. Finally, according to the literature and the present study, the authors believe that elderly diabetic patients are more prone to experience falling, and the probability of that would increase if the patients complained of an associated gait problem, balance difficulties, and hypoglycemic attacks. There are few limitations in our study that need to be addressed. First, the current study was an observational cross-sectional study. A prospective study will be indispensable to illustrate the relationship between balance difficulties, gait problems, and hypoglycemic attacks with risk of falls. Furthermore, our sample size was limited and collected conveniently. Hence, a large-scale multicenter study with larger sample size will be needed. Additionally, medications were restricted to only numerals. The medications need to be specified and classified as they might be causative for hypoglycemia, insulin, for instance.
## Conclusion
To sum up, this paper demonstrated that gait problems and balance difficulties, to some extent, are independent risk factors for falls in elderly diabetic patients. Not only having gait problems and balance difficulties but also neurological/cognitive impairments as well as poor sleep quality were confirmed to lower fall efficacy, thus increasing fear of falling. In conclusion, diabetes mellitus, defined by the use of hypoglycemic agents, is an independent risk factor for falls among the elderly. Pursuing this further, it should be considered a risk factor for falls in this population. In fact, recognizing such patients as being at higher risk could assist some preventive measures targeted at reducing the risk of falls.
## Ethical approval
Ethical approval (reference number: NRJ22J/$\frac{097}{04}$) was granted by the Institutional Review Board of King Abdullah International Medical Research Center, Jeddah, Saudi Arabia. This study was conducted in accordance with the principles of the Declaration of Helsinki.
## Patient consent
Informed written consent was obtained at the start of the questionnaire.
## Sources of funding
There were no sources of funding received for the current study.
## Author contribution
A.A.Z.: contributed to the proposal and manuscript writing and editing; R.S.A.: was involved in proposal and manuscript writing in addition to data collection and analysis; N.A.A., H.A.H., and A.F.B.: were involved in proposal and manuscript writing in addition to data collection; A.A.H.: contributed in revising the questionnaire and the manuscript. All authors granted approval of the final version of the manuscript.
## Conflicts of interest disclosure
The authors declare no conflicts of interest.
## Research registration unique identifying number (UIN)
Name of the registry: Research Registry http://www.researchregistry.comUnique identifying number or registration ID: researchregistry8607.Hyperlink to your specific registration (must be publicly accessible and will be checked): https://www.researchregistry.com/browse-the-registry#home/registrationdetails/63aef7688eaf0a0011ee3d5d/
## Provenance and peer review
Not commissioned, externally peer-reviewed.
## Guarantor
Rami S. Alasmari, King Saud bin Abdulaziz University for Health Sciences, College of Medicine, Jeddah, Saudi Arabia. Tel: +966 593 908 385. E-mail: [email protected], [email protected]. Address: Jeddah, Alssamer District, Mendad Alkoukhi Street, Postcode 23462.
## References
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|
---
title: 'Tagging Efficiency of
99m
Tc-SC Radiolabeled Alternative Gastric Emptying Meals: A Quantitative Study'
authors:
- Deepak Kumar Pal
- Dhananjay Kumar Singh
- Satyawati Deswal
- Anurag Pathak
journal: World Journal of Nuclear Medicine
year: 2022
pmcid: PMC10010854
doi: 10.1055/s-0042-1757255
license: CC BY 4.0
---
# Tagging Efficiency of
99m
Tc-SC Radiolabeled Alternative Gastric Emptying Meals: A Quantitative Study
## Abstract
Objective The aim of this study was to know the tagging efficiencies of technetium-99m labeled sulfur colloid (99mTc-SC) with different meals.
Materials and Methods Egg white sandwiches are the gold standard for gastric-emptying scan (GES); thus, an egg white omelet labeled with 99m Tc-SC is taken as a standard meal. For evaluation, we included four meals, bread and butter, instant oatmeal, idli, and chapatti, and all meals were prepared by labeling them with 99m Tc-SC. After preparation, food articles were chopped with the help of a metal fork and mixed in simulated gastric fluid. Four samples were taken simultaneously from each food article and analyzed for 1 to 4hours after agitation within the centrifuge. The samples were filtered and separated from the sediments and supernatants. We analyzed the activity in each sample before and after filtration.
Results The mean values of labeling efficiency in per cent of various meals were obtained. There was no significant difference in labeling stability for egg whites, chapatti, and idli meals labeled with 99m Tc-SC from 1 to 4hours as their p -value ($p \leq 0.05$) was insignificant.
Conclusion Radiolabeled chapatti and idli with 99m Tc-SC show higher labeling stability, while oatmeal and bread and butter samples show relatively low stability. Thus, for GES, chapatti and idli labeled with 99m Tc-SC can be used as alternatives to eggs for vegetarian people or for those allergic to eggs.
## Introduction
A gastric emptying scan (GES) is a scintigraphy test that is done in patients having gastroparesis, a syndrome characterized by delayed GE with symptoms such as vomiting, belching, bloating, distensions, fullness, and early satiety. In the case of early emptying, also known as dumping syndrome, GES are preferred by gastroenterologists. This GES is done with the help of radiolabeled test meal, which is currently used an egg white sandwich (as gold standard), but the problem arises in vegetarian patients, who are allergic to eggs or uncomfortable in ingesting eggs; thus, in these patients alternative meals are required and in our study, we are trying to find out the suitable alternative of egg meal so that we can use them in the vegetarian patient for GES. 1 Earlier, the chicken liver is used for the GES. However, the procedure of labeling chicken liver with technetium-99m labeled sulfur colloid (99m Tc-SC) is too burdensome and could not be possible in the current working environment in any nuclear medicine department. Thus, alternatives of nonvegetarian meals were tried by many researchers, but the task is not yet fulfilled up to the optimum levels. Various pieces of literature show the use of a variety of food articles for GES, such as milk, peanut butter sandwiches, burritos, and muffins. Despite the assortments of various meals, variations in the cooking method for labeling with 99m Tc-SC were also observed, such as adding 99m Tc-SC before and after cooking in various meals. 2 To overcome the variations, lack of consistency, and standardization, a panel of expert gastroenterologists from the American Neurogastroentrology and Motility Society and nuclear medicine physicians from the Society of Nuclear Medicine and Molecular Imaging (SNMMI) has published a consensus guideline for solid meal GES in the year 2008. 3 *The consensus* recommendation standardized the following parameters for the procedure: the frequency of imaging, test meal used, duration of the procedure, and normative control values of GE. 3 The regulation of technique is required to allow the referring physician to interpret the results of GES from different centers confidently. But the presence of single standardized meal results in reduced utilization of this diagnostic procedure in current situations.
The reliable results of GES are dependent upon how efficiently the test meal is labeled with the 99m Tc-SC. Unstable labeling will result in the false and poor interpretation of GES. Thus, we have the requirement of finding a radiolabeled test meal with equal or optimum labeling stability of that of liquid egg whites.
Thus, in this study, we include four different test meals other than liquid eggs white omelet, which is taken as standard as recommended by SNMMI for GES; these are chapatti, idli, bread and butter, and oatmeal. The choice of different meals was dependent upon the ease of preparation of the meal and easy availability of them.
## Materials and Methods
This prospective study compared the assessed alternative meals with the standard egg meal used for GES for gastroparesis and other gastric-related pathologies. This in vitro study was performed in the nuclear medicine department at DRRMLIMS, Lucknow, from August 2019 to September 2021.
## The Inclusion Criteria
The following materials are required for the experiment purpose:
## Labeling Method
99m Tc-SC 925 MBq (25mCi) was added to different meals for preparing sample meals. Sample meals included were chapatti, idli meal, bread and butter, and oatmeal. Cooked egg whites labeled with 99m Tc-SC served as control. The required radioactivity was mixed in liquid egg whites taken and blends well for uniform distribution. A pan was taken, and after that, one teaspoon of oil was sprinkled in a pan and heated the pan. After 1minute, the blended mixture was poured into the pan and waited until it gained the solid-state, cooked on both sides, and removed on a plate and the test meal was ready to use. Packed instant oatmeal and idli batter were prepared by adding water containing the required radioactivity. Packed instant oatmeal was prepared by cooking them in a pan on flame for 3minutes with continuous string. Idli meal was prepared by brushing the mold with refined vegetable oil for easy lodging of them after cooking and then the batter was poured into the mold with the help of a spoon, the mold was placed into the steamer after 15 to 20minutes and then it was removed from the steamer and allowed to cool for 5minutes and removed from the mold with the help of spoon and the idli meal was ready for use. Chapatti of wheat flour was prepared by adding the radioactivity at the time of preparation of dough and further the dough was rolled, and chapatti was prepared by baking them on flame. Bread and butter sample was prepared by adding the required radioactivity into the butter at melting. When the butter regained the semisolid state, it spread upon the bread, and a sandwich of bread and butter was prepared. The cooking of every meal was ensured before using them further in the experiment.
## Stability Test Method
Each meal is allowed to cool at room temperature after its preparation. The food articles were chopped into small pieces with the help of a metal fork to simulate chewing and for gastric stimulation, the food articles were kept under simulated gastric fluid prepared by 1N hydrochloric acid (HCL) solution and pepsin enzyme solution.
At the same time, four samples each of 2g were taken from two food articles, assessed their activity inside the dose calibrator (CAPINTEC, INC. CRC-ULTRA), and placed inside the centrifuge vials in the acidic medium and homogenized with the simulated gastric fluid. The contents of the centrifuge vial were 3mL of water, 2mL of 1N HCl, and 2mL of pepsin enzyme solution to the test tube. Similarly, four samples were agitated for the period of 1 to 4hours and subsequently centrifuge at 2,000rpm for 2minutes before the assessment of readings inside the dose calibrator at 1-hour interval, and first test tube of both samples was removed from the centrifuge machine filtered them with the help of Whatman filter paper using Y type funnel in test tube stand. We separated the supernatant and sediments and assessed their activity before and after filtration; we washed the sediments with 2mL of saline during filtration. Similarly, the experiment was repeated until we covered all the samples and repeated the testing method for four consecutive experiments.
Calculated labeling efficiency (LE) of different samples as:
## Results
After finding the results for the percentage of LE of various meals concerning time for experiment first, second, third, and fourth are given below in the tables. As we compare the results, we can see that percentage binding of eggs white; chapatti and idli meals are good concerning other two taken alternatives (Table 1).
**Table 1**
| Sl. no. | Meals labeled with 99m Tc sulfur colloid | Labelingstability% at 1 hour | Labeling stability % at 2 hours | Labeling stability % at 3 hours | Labelingstability % at 4 hours |
| --- | --- | --- | --- | --- | --- |
| 1.0 | Egg white omelet | 97.62±2.18 | 96.45±3.85 | 93.67±3.40 | 91.56±3.80 |
| 2.0 | Chapatti | 92.01±1.51 | 89.39±3.79 | 86.73±3.91 | 84.47±5.28 |
| 3.0 | Idli meal | 89.44±5.26 | 86.79±6.10 | 82.63±7.38 | 77.75±9.11 |
| 4.0 | Instant oatmeal | 71.13±8.38 | 66.97±11.00 | 63.65±11.53 | 58.46±11.03 |
| 5.0 | Bread and butter | 71.70±7.12 | 68.66±6.28 | 65.87±6.24 | 61.62±5.03 |
The results after the assessment of LE are plotted in Fig. 1. The standard taken is egg white omelet, radiolabeled chapatti gives excellent result, and idli meal has also been used as an alternative vegan meal as its result is also up to the optimum level. The percentage of activity bound to the solid phase of eggs white, chapatti, and idli was 97.62±2.18, 92.01±1.51, and 89.44±5.26, respectively, at 1 hour; 96.45±3.85, 89.39±3.79, 86.79±6.10, respectively, at 2hours; 93.67±3.40, 86.73±3.91, and 82.63±7.38, respectively, at 3hours; and 91.56±3.80, 84.47±5.28, and 77.75±9.11, respectively, at 4hours. The other alternatives, oatmeal and bread and butter, were not given the desired results, so both could not suggest the percentage bound in the solid phase of oatmeal and bread and butter was 71.13±8.38, 71.70±7.12, respectively, at 1 hour; 66.97±11.00, 68.66±6.28 at 2hours, 63.65±11.53, 65.87±6.24 at 3hours; and 58.46±11.03, 61.62±5.03, respectively, at 4hours. We found the percentage activity bound in solid food to be less than $80\%$, but the variability between these two samples was significant between 1 and 4hours.
**Fig. 1:** *Percentage of solid binding at different time points.*
## Discussion
GES is mainly performed in nuclear medicine to detect gastroparesis or any type of gastrointestinal disorder. GES is accomplished by radiolabeling the solid or liquid component of a meal and then measuring the radioactivity in the stomach with time. It has been considered the gold standard test for measuring the rate of GE due to its noninvasive, physiologic, and quantifiable properties. 5 After getting the results by applying the statistical tests (ANOVA and Tukey multiple comparison tests) on the experimental data, we learned that there is no significant difference between the egg white and chapatti as well as between chapatti and idli meal as in both cases ($p \leq 0.05$) and all three meals gave us good LE percentage suggesting that chapatti and idli meal could be an excellent alternative to egg white meal. We were further looking at the data, and we observed that there is no significant difference between bread butter meal and oatmeal ($p \leq 0.05$). However, both showed relatively poor LE compared with other meals.
Various factors are involved in the efficient binding of the radiotracer, which in our case was 99m Tc-SC. Those factors are in the following text.
## Uniformity of Radiotracer Inside Meal
It is essential that the molecules of radiolabel should be uniformly distributed and remains associated with the particles of solid during exposure to gastric fluid. 2 In our results, the radiotracer molecules could get the spaces with uniform distribution in the egg meal, chapatti, and idli because of the excellent 99m Tc-SC mixing during meal preparation. Even the prior study of idli meal done by Somasundaram et al 6 indicates that uniform distribution of radiotracer in meal gives us better binding and efficiency results. Their study of radiolabeling stability showed that the mean percentage activity remaining bound to the idli meal at 1, 2, 3, and 4hours of treatment with SGF was 98.2, 98.2, 97.8, and $96.8\%$, respectively, thus providing a strong binding of the 99m Tc-SC with food particles. Moreover, only a part of the meal was radiolabeled in the egg white sandwich meal and the chicken liver meal (egg white in egg white sandwich and chicken liver in the latter). In contrast, the rest of the meal was unlabeled (bread slices and jam in egg white sandwich as well as broth in chicken liver meal). 7 8 This means the radiotracer molecules were not uniformly distributed. In our experiment also bread and butter is a case in which the bread slices are not radiolabeled, and the butter has the radiotracer in it, and it has shown poor radiolabeling efficiency in comparison to other meals, which reads out to be 78.68, 76.66, 74.28, and $68.89\%$ at the time interval of 1, 2, 3, and 4hours, respectively (mean values of all four experiment). Similar is the case with oatmeal in which hot water had radiotracer molecules bound to it and oats were not having a uniform distribution of 99m Tc-SC within it, leading to relatively poor results of radiolabeling efficiency, which are found to be 78.75, 75.54, 70.22, and $67.81\%$ at the time interval of 1, 2, 3, and 4hours, respectively.
## State of Meal
The state of the meal also plays a specific role in determining radiolabeling efficiency of a particular meal. If it is a solid meal, there are much more chances to get better efficiency. We get relatively lower efficiency in the case of liquid meals because atoms in solid are closely packed, or we can say are compact and make efficient bonds within them, contrary to liquids where the higher movement of atoms leads to the relatively weak binding. Even few studies have stated that solid GES are more delicate for detecting gastroparesis than liquid GE, which is the rare requirement of GE liquid studies. 9 In our results, oatmeal, the only semi-solid meal taken, showed comparatively poor radiolabeling efficiency than other solid meals. However, in some studies, liquid nutrient meals have been suggested as an alternative to standard egg white based on the results found on gastric retention. 10 11
## Method of Preparation
In a study done by Knight et al, 12 it was concluded that the stability of a radiolabeled solid test meal must be performed for GES according to its constituents and the preparation method. The addition of 99m Tc-SC to a selected meal should be chosen correctly, which means whether radiotracer is to be added before, during, or after cooking. An experiment showed that radiolabeling by adding 99m Tc-SC to whole eggs before microwave cooking resulted in a significantly higher LE than radiolabeling when the 99m Tc-SC was squirted on eggs after microwave cooking. 4 Here, we can also say that when radiolabeling was done before cooking, radiotracer molecules got uniformly distributed, resulting in higher radiolabeling efficiency. When 99m Tc-SC was spilled over the cooked meal, it could not spread uniformly. Earlier, we discussed the uniform distribution of radiolabeled particles in a meal on the radiolabeling efficiency. It can be interpreted that it is the method of preparation that has a connection with the distribution of radiotracer molecules in a meal. Whenever radiotracer is added to a meal before its cooking, there would be the chances of uniformity in which radiotracer would get in meal, and the radionuclide 99m Tc-SC after cooking would result in nonuniformity in the meal. Our results also show that egg white, chapatti, and idli meal have shown us better results. The radiolabeling efficiency percentage for egg white was found to be 99.07, 98.85, 95.34, and 90.04 at 1, 2, 3, and 4hours, respectively. In the case of chapatti, it was found to be 89.84, 83.70, 81.36, and $76.92\%$, respectively. From the point of the interval of 1 hour each from the instant, it was prepared. Moreover, the solid-bound activity percentage with idli meal was 83.38, 81.07, 77.33, and $75.46\%$ in similar intervals. This meal showed better results because of the good uniformity of 99m Tc-SC inside solid particles of meal for which the meal preparation method has to credit which allowed the molecules to get proper spaces.
## Medium of Testing
Another factor responsible for radiolabeling efficiency is the medium in which the radiolabeling stability test is performed. It is also suggested in the experiment done by Knight 2 that stability testing in a gastric fluid with HCl only, without pepsin, may be incorrect such that GES meal testing in just acid might not be as stable as in simulated gastric fluid with pepsin.
So, it is to be kept in mind that HCl is not the proper substitute for gastric fluids. 12 A few studies' oatmeal showed good radiolabeling efficiency. However, in a study done by Laura A. Drubach et al, 13 it was observed that the radiolabeling efficiency of oatmeal in gastric fluids was not suitable where the percentage of activity bound to solid phase was 62.1±1.7 and 77.2±6.8 at 1 and 4hours, respectively. Similarly, in our results, we found relatively poor radiolabeling efficiency in the case of oatmeal that is found to be 78.75, 75.54, 70.22, and $67.81\%$ at the time interval of 1, 2, 3, and 4hours, respectively, performed under the simulated gastric fluids that are HCl and pepsin.
## Type of Radionuclide Used
The radionuclide is chosen to prepare the radiolabeled meal that also alters the radiolabeling efficiency of the meal. In a study performed to find out the effect of radionuclide used in meal preparation, it was concluded that the LE of the particulate agents (99m Tc-SC, 99m Tc tin colloid, 99m Tc Nano colloid, and 99m Tc MAA) was between 90 and $100\%$ and LE of 99m Tc pertechnetate and 99m Tc DTPA was between 60 and $80\%$ at 90minutes. 14 In an experiment done by Tseng et al using 99m Tc DTPA as a radionuclide, the traditional values of oatmeal-based GES were established and observed good correlation with cardinal gastroparesis symptoms within the Chinese population. 14 In our experiment, we used the 99m Tc-SC as radionuclide in meal preparation as it is approved by the U.S. Food and Drug Administration for oral administration. In contrast, most other radiopharmaceuticals, including 111In radiopharmaceuticals, are not. 2 Considering the factors including uniformity of the radiotracer, state of the meal, method of preparation, which are responsible for the efficient binding of radionuclide to the food particles or the meal and clearly observing the results after application of required statistical tests on the data we got after experimenting with the defined methodology earlier, it was highlighted that egg white meal had shown us brilliant binding that was expected and along with that chapatti labeled with 99m Tc-SC has proved that it can be worthy of being a better substitute to the egg white as it showed the radiolabeling binding efficiency of 92.01, 89.39, 86.73, and $84.47\%$ at 1, 2, 3, and 4hours, respectively. The result of the idli meal in our experiment indicates that it can be a substitute to the standard egg white but lags behind the chapatti in terms of the effective binding with the radionuclide. Bread and butter showed relatively poor radiolabeling because there is a lack of uniformity in the meal. Oatmeal has gained popularity in the race of being a substitute to the standard egg white because of the easy preparation. It has again shown the comparatively inefficient binding compared with the other meals because of the nonuniformity of 99m Tc-SC the components of the meal and being a semisolid meal also justifies the less binding efficiency in comparison to other meals that are all found to be in solid state.
The medium of testing and the type of radionuclide used in our experiment, which were simulated gastric fluid (pepsin and HCl) and 99m Tc-SC, respectively, were the same for the all-sample meals, and that is the reason that the binding efficiency of the radionuclide we got for each meal can be easily compared.
In addition to that, after applying the correlation test on the experimental data, we learned that there was a strong correlation between all the radiolabeled meals prepared for our quantitative analysis.
The radiolabeled chapatti has emerged as an alternative solution for our dependency on the egg whites for conducting the GES. Our experiment has also opened up a new horizon to easily use a meal with rich nutritional value for GES. However, at the same time, it has further raised a question that despite showing better results in vitro where simulated gastric fluids were used, would be able to match the conditions inside the human stomach or not and what the patient's digestive responses would be of the patient for radiolabeled chapatti.
Moreover, after knowing how the type of radionuclide can alter the binding efficiency, there is a requirement of a study related to efficient binding of different types of radionuclide with the chapatti and idli meal as well. We know that egg whites bind better with 99m Tc-SC but testing the radiolabeling efficiency on the substitutes such as chapatti and idli meal with the radionuclide other than 99m Tc-SC such as tin colloid, nanocolloid, MAA, as well as 99m Tc DTPA can further give us new dimension as it may or may not predict the much more efficient binding.
Earlier studies done at various institutes and our study have provided suggestions over the different alternatives, which can be a roadmap for establishing a list of meals that can be used in GES. It can benefit the patients as they would undergo the GES after eating the radiolabeled meal as per their desire and get treatment later. However, further studies are required before clearly opting out meals that can bind to a radionuclide well and on this aspect, attention is needed toward the various factors we have discussed above, and new characteristics must be explored.
## Conclusion
After analyzing the results from experiments, we can easily conclude that the LE of chapatti meal and idli meal is very much near eggs white's omelet and could be used as alternative to egg whites in vegetarian patients or patients who have an allergy to eggs. The other meals of bread along with butter and instant oatmeal meals did not show the desired results and cannot be used as an alternative to egg white as their LE is too low compared with standard egg whites.
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